In this paper, we consider a reconfigurable intelligent surface (RIS)-assisted integrated satellite-high altitude platform-terrestrial networks (IS-HAP-TNs) that can improve network performance by exploiting HAP's stability and RIS's reflection. Specifically, the reflector RIS is installed on the side of HAP to reflect signals from the multiple ground user equipments (UEs) to the satellite. To aim at maximising system sum rate, we jointly optimize the transmit beamforming matrix at the ground UEs and RIS phase shift matrix. Due to the limitation of the unit modulus of the RIS reflective elements constraint, the combinatorial optimization problem is difficult to tackle it effectively by traditional solving methods. Based on this, this paper studies deep reinforcement learning (DRL) algorithm to achieve online decision making for this joint optimization problem. In addition, it is verified through simulation experiments that the proposed DRL algorithm outperforms the standard scheme in terms of system performance and execution time, and higher computing speed, making real-time decision making truly feasible。

Epidermal growth factor receptor (EGFR) is a receptor tyrosine kinase that instigates several signaling cascades, including the NF-kB signaling pathway, to induce cell differentiation and proliferation. Overexpression and mutations of EGFR are found in up to 30% of solid tumors and correlate with a poor prognosis. Although it is known that EGFR-mediated NF-kB activation is involved in tumor development, the signaling axis is not well elucidated. Here, we found that PKP2 and the LUBAC complex were required for EGFR-mediated NF-kB activation. Upon EGF stimulation, EGFR recruited PKP2 to the plasma membrane, and PKP2 bridged HOIP, the catalytic E3 ubiquitin ligase in the LUBAC, to the EGFR complex. The recruitment activated the LUBAC complex and the linear ubiquitination of NEMO, leading to IkB phosphorylation and subsequent NF-kB activation. Furthermore, EGF-induced linear ubiquitination was critical for tumor cell proliferation and tumor development. Knockout of HOIP impaired EGF-induced NF-kB activity and reduced cell proliferation. HOIP knockout also abrogated the growth of A431 epidermal xenograft tumors in nude mice by more than 70%. More importantly, the HOIP inhibitor, HOIPIN-8, inhibited EGFR-mediated NF-kB activation and cell proliferation of A431, MCF-7, and MDA-MB-231 cancer cells. Overall, our study reveals a novel linear ubiquitination signaling axis of EGFR, and perturbation of HOIP E3 ubiquitin ligase activity is potential targeted cancer therapy.

Hyperspectral small target detection (HSTD) is a promising pixel-level detection task. However, due to the low contrast and imbalance number between the target and background spatially and high dimensions spectrally, it is a challenging one. To address these issues, this work proposes a representation learning-based graph and generative network for hyperspectral small target detection. The model builds a fusion network through frequency representation for HSTD, where the novel architecture incorporates irregular topological data and the spatial-spectral feature to improve its representation ability. Firstly, a graph convolutional network (GCN) module better models the non-local topological relationship between samples to represent the hyperspectral scene’s underlying data structure. The mini-batch-training pattern of the GCN decreases the high computational cost of building an adjacency matrix for high-dimensional data sets. In parallel, the generative model enhances the differentiation reconstruction and the deep feature representation ability with respect to the target spectral signature. Finally, a fusion module compensates for the extracted different types of HS features and integrates their complementary merits for hyperspectral data interpretation while increasing detection and background suppression capabilities. Experiments on different hyperspectral data sets demonstrate the advantages of the proposed architecture.

Land cover segmentation, a fundamental task within the domain of remote sensing, boasts a broad spectrum of application potential. In this article, we focus on the land cover segmentation tasks and complete the following research work: Firstly, to address the issues of uneven distribution of foreground and background and significant differences in target scales in remote sensing images, we propose a decoder called MDCFD based on multi-dilation rate convolution fusion. The decoder utilizes dilated convolution to expand the receptive field, enhancing the model's ability to capture global features and thus improving the model's ability to distinguish between foreground and background. Meanwhile, we design a multi-dilation rate convolution fusion module (MDCFM), which fuses the outputs of different dilation rate convolution layers. Secondly, aiming at the problems of diverse scenes, significant differences between categories, and many background interferences in remote sensing images, we propose a hybrid attention module called LKSHAM based on large kernel convolution. This module combines spatial attention and channel attention mechanisms and combines the two attention modules in series. By introducing large kernel convolution, the model's ability to extract contextual information is improved. At the same time, we adopt a strategy of decomposing large kernel convolution into multiple depthwise convolutions to reduce computational complexity. The improved network models designed by this paper can achieve an improvement of over 1.1% in the mIoU metric on segmentation tasks on the Potsdam and Vaihingen datasets.

In dentistry, maxillofacial surgery, traumatology, and orthopedics, there is a need to use osteoplastic materials that have not only osteoinductive and osteoconductive properties but are also convenient for use. In the study, compositions based on collagen hydrogel were developed. Polylactide granules (PLA) or a traditional bone graft, a mixture of hydroxyapatite and β-tricalcium phosphate (HAP/β-TCP), were used for gel filling to improve mechanical osteoconductive properties of compositions. The mechanical tests showed that collagen hydrogels filled with 12 wt% highly porous PLA granules (elastic modulus 373 ± 55 kPa) or 35 wt% HAP/β-TCP granules (elastic modulus 451 ± 32 kPa) had optimal manipulative properties. All composite components were cytocompatible. The cell’s viability was above 90%, and the components’ structure facilitated the cell’s surface adhesion. The bone morphogenetic protein-2 (BMP-2) provided osteoinductive composition properties. It was impregnated directly into the collagen hydrogel with the addition of fibronectin or inside porous PLA granules. The implantation of a collagen hydrogel with BMP-2 and PLA granules into a critical-size calvarial defect in rats led to the formation of the most significant volume of bone tissue: 61 ± 15%. It was almost 2.5 times more than in the groups where a collagen-fibronectin hydrogel with a mixture of HAP/β-TCP (25 ± 7%) or a fibronectin-free composition with porous PLA granules impregnated with BMP-2 (23 ± 8%) were used. Subcutaneous implantation of the compositions also showed their high biocompatibility and osteogenic potential in the absence of a bone environment. Thus, the collagen-fibronectin hydrogel with BMP-2 and PLA granules has optimal biocompatibility, osteogenic, and manipulative properties.

Background: Old age and illnesses can limit physical activity (PA). Objectives: To assess the level of PA and its limitations. Design: Cross-sectional study, January and June 2024. Setting: Diabetes Center. Subjects: Patients (N=207) >64 years with diabetes (DM). Methods: Surveying using: IPAQ, Accompanying Survey (AS). The significance was assessed by: Student’s t-test, Mann-Whitney U test, Pearson’s test. The relationships between the IPAQ and the AS results were analyzed based on a meta-analysis model for variable effects, OR with a 95% CI. ROC curve was used to determine the threshold values for age, BMI. Correlations between selected key variables were evaluated using the PCA. Results: The median age: 72 years (65–87), BMI: 28.70 kg/m2 (18.61–49.69). The median PA level: 1,837 MET-min/week (0–9,891). The individuals who obtained insufficient (n=28), sufficient (n=75), and high levels of PA (n=53) were 17.95%, 48.08%, and 33.97%, respectively. Diseases were the main factor limiting PA (53,54%) in patient’s opinion, followed by the lack of a companion or motivation. The meta-analysis found no diseases linked to insufficient PA, but those with BMI > 33.3 kg/m² and over 75 years old more often had insufficient PA. PCA revealed several characteristics that predispose individuals to insufficient PA. Conclusions: People over 75 years of age tend to avoid PA more than younger seniors, leading to its insufficient level, similarly like higher BMI. The individual with DM who has an insufficient level of PA is typically a single, woman, over 75, BMI>33.

Ulcerative colitis (UC) is a chronic inflammatory disease, the incidence of which is increasing worldwide. However, the etiology and pathogenesis of UC remains unclear. The n-butanol extract of pulsatilla decoction (BEPD) of the traditional Chinese medicine herbal formula has been shown to be effective in treating UC. We aimed to explore the molecular mechanism underlying the effects of BEPD on UC, in particular its effects on neutrophil extracellular trap (NET) formation by neutrophils. High-performance liquid chromatography was used to determine the principal compounds of BEPD. UC was induced in mice using dextran sodium sulfate, and mice were treated with 20, 40, or 80 mg/kg BEPD daily for seven days. Colonic inflammation was determined by assessing the disease activity index, histopathology, colonic mucosal damage index, colonic mucosal permeability, and pro- and anti-inflammatory cytokine levels. The infiltration and activation status of neutrophils in the colon were determined by analyzing the levels of chemokine (C-X-C motif) ligand (CXCL)1 and CXCL2, reactive oxygen species, Ly6G, and numerous NET proteins. Our results showed that BEPD improved the disease activity index, histopathology, and colonic mucosal damage index scores of mice with UC, and restored colonic mucosal permeability compared with untreated mice. The expression levels of the pro-inflammatory cytokines interleukin-1β, interleukin-6, and tumor necrosis factor-α in colon tissues were significantly decreased, while the expression levels of anti-inflammatory cytokines in colon tissues were significantly increased, exceeding those of control mice. In addition, BEPD reduce the expression of the neutrophil chemokines CXCL1 and CXCL2 in the colon tissue of mice with UC, reduced neutrophil infiltration, reduced reactive oxygen species levels, and significantly reduced the expression of NET proteins. BEPD also significantly reduced NET formation. The results of this study suggest that BEPD exerts therapeutic effects in a murine model of UC by inhibiting neutrophil infiltration and activation in the colon, as well as by inhibiting the expression of key proteins involved in NET formation and reducing NET formation, thereby alleviating local tissue damage and disease manifestations.

Satellite clock bias (SCB) prediction is a crucial technology for satellite navigation systems, holding significant importance for Global Navigation Satellite System. This paper proposes a deep learning model for SCB prediction based on the fusion of the Beluga Whale Optimization (BWO), Convolutional Neural Network (CNN), Bidirectional Gated Recurrent Unit (BiGRU), and an attention mechanism. The CNN is utilized to extract the spatiotemporal characteristic information from the clock bias sequence, while the BiGRU fully extracts relevant features through forward and backward propagation. The introduction of an attention mechanism aims to preserve essential features within the clock bias sequence to enhance feature extraction by both CNN and BiGRU networks. Additionally, the BWO is employed to optimize parameter selection in order to improve model accuracy. Experimental verification demonstrates that for the BeiDou Navigation Satellite System's (BDS) hydrogen-maser atomic clocks, the predicted clock bias for 6 hours, 3 days, and 15 days are 0.078 ns, 0.475 ns, and 2.130 ns respectively - representing improvements of 31%, 45%, and 66% over CNN-BiGRU-Attention; 6%, 51%, and 56% over CNN-BiGRU; and 32%, 35%, and 73% over BiGRU, respectively.

A smart precipitation estimation method based on FY-4B meteorological satellite data (FY-4B_AI) is proposed in this paper. This method spatiotemporally matches 125 features derived from multi-temporal and multi-channels of FY-4B satellite data with the precipitation at stations. Then a precipitation model using the Light Gradient Boosting Machine algorithm is constructed. Comparative results between FY-4B_AI and GPM/IMERG-L products for over 450 million station cases throughout 2023 shows that: 1) FY-4B_AI is superior to GPM IMERG-L in the average absolute error, root mean square error, relative error, correlation coefficient, probability of detection and critical success index. While in the mean error and false alarm rate, FY-4B_AI is slightly inferior to GPM/IMERG-L. 2) Evaluation of strong weather event applications reveals that both FY-4B_AI and GPM/IMERG-L can accurately represent the spatial distribution characteristics of precipitation, no matter in the southeast humid region or the northwest dry region. Notably, FY-4B_AI, due to its higher spatiotemporal resolution, provides a more detailed distribution of precipitation.

Background: Carbapenem-resistant A. baumannii (CRAB) hospital-acquired pneumonia (HAP)/ventilator-associated pneumonia (VAP) is becoming a globally therapeutic problem. Method: The open-label, randomized, superiority, single-blind trial was conducted in Rajavithi Hospital, a super-tertiary care in Bangkok, Thailand. We included CRAB HAP/VAP patients randomly assigned to receive sitafloxacin-colistin-meropenem compared to colistin-meropenem to assess mortality, clinical response, and adverse effects. Result: April 2021-2022, 77 patients were randomized and received combination treatment sitafloxacin-colistin-meropenem (n = 40) or colistin-meropenem (n = 37). Among two groups, the all-cause mortality rate was no significant difference at 7-days and 14-days (7.5% vs 2.7%; p = 0.616 and 10% vs 10%; p = 1, respectively). Patients received sitafloxacin-colistin-meropenem showed improved clinical response, compared patients receiving colistin-meropenem with both intentions to treat (87.5% vs 62.2%; p = 0.016) and per-protocol analysis (87.2% vs 67.7%; p = 0.049). The adverse effects were not different among 2 groups. Conclusion: Adding sitafloxacin as third agent in meropenem plus colistin could improve clinical in CRAB HAP/VAP with comparable adverse effects. Sitafloxacin-meropenem-colistin could be another option for combatting CRAB HAP/VAP

Over the years, the need for autonomous systems such as drones, also known as unmanned aerial vehicles (UAVs), has significantly increased in popularity across several industries, including the military. The Internet of Battlefield Things (IoBT) represents a modern technological advancement that significantly enhances the operational efficacy of defense systems. It establishes an integrated military force by connecting individuals to intelligent technology in drones, radios, equipment, antennas and ground stations. This is achieved through the utilization of cloud and edge computing, sensors, mobile devices, embedded systems, and IoT-based systems. However, IoBT security is significantly compromised by enemies that exploit the vulnerabilities and malicious intrusions that specifically target the external environment or to obtain sensitive data. In addition, a comprehensive understanding of cyberattacks and countermeasures is essential for assuring the security of IoBT. On the other hand, as cyber threats get more complicated, trust becomes increasingly important for the security of digital systems. Within this context, the zero trust architecture contends that the security of a complex network is inherently vulnerable to internal and external threats. In defense, UAV security system applications, for example, the primary purpose of the zero trust architecture is to limit the vulnerability of military UAV systems to cyberattacks, hence reducing the possibility of data breaches and illegal entry. This study conducts a systematic review aimed at identifying different aspects of cybersecurity strategies for protecting UAV systems within the IoBT domain. Furthermore, zero trust architecture is presented in the study as an effective prospective solution to the security issues that develop in defense UAV systems.

The month of May 2024 was characterized by solar energetic particles events directed towards the Earth, especially the big event causing a strong terrestrial geomagnetic storm during the night from 10 to 11 May 2024, with auroras observed everywhere in Europe. This was the strongest storm for the last 20 years with a Dst < -400 nT. In the present work, we show with observations of GOES, PROBA-V/EPT and MetOP/MEPED that this exceptional event was associated with the injection of energetic protons in the proton radiation belt, with important consequences for the South part of the South Atlantic Anomaly (SAA). In addition, the geomagnetic storm caused by the solar eruption has had tremendous impacts on the electron radiation belts. Indeed, we show that for 0.3 to 1 MeV electrons, the storm led to a long lasting four belts configuration which was not observed before with EPT launched in 2013, until a smaller geomagnetic storm on June 15 took place. Moreover, for the first time since its launch, observations of the EPT show that ultra-relativistic electrons with E>2 MeV have been injected into the inner belt until L=2.4, violating the impenetrable barrier previously estimated to be located at L=2.8.

Pipeline conflicts arising from data dependencies are a common issue encountered in the hardware implementation of layered QC-LDPC decoders. This paper proposes an efficient layered decoding architecture to reduce pipeline conflicts without introducing stall cycles, by reordering the processing order offline and using the patch method based on variable-to-check message. The experimental results on the LDPC code of the IEEE802.16 standard in the OFDM-PON system demonstrate that the proposed architecture has a sensitivity improvement of 0.125 dBm and 0.375 dBm, respectively, compared with our previous work and the method described in the other work.

Abstract: Although the pathophysiology of fibromyalgia syndrome has been better understood in recent decades, a unified model of its pathogenesis and an effective therapeutic approach are still far from being realized. The main aim of this article will be to delve into the fundamental mechanisms of the pathophysiology of fibromyalgia conceptualized as stress intolerance syndrome. Using the biopsychosocial model of chronic pain syndromes, we will describe the potential role of the attachment system, C tactile fibers, and oxytocinergic system dysfunction in the pathophysiology of fibromyalgia syndrome and other central sensitivity syndromes. At the end of the article, the therapeutic implications of this new global and translational pathophysiological model will be briefly discussed.

This study investigates the long-term stability of the Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) moderate-resolution Thermal Emissive Bands (M TEBs; M12 – M16) covering a period from February 2012 to August 2020. It also assesses inter-sensor consistency of the VIIRS M TEBs among three satellites-S-NPP, NOAA-20, and NOAA-21 over eight months spanning from March 18 to November 30, 2023. The field of interesting is limited to the ocean surface between 60°S and 60°N, specifically under clear-sky conditions. Taking radiative transfer modeling (RTM) as the transfer reference, we employed the Community Radiative Transfer Model (CRTM) to simulate VIIRS TEB brightness temperature (BTs), incorporating European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis data as inputs. Our results reveal two key findings. Firstly, the reprocessed S-NPP VIIRS TEBs exhibit a robust long-term stability, as demonstrated through analyses of the observation minus background BT differences (O-B ∆BTs) between VIIRS measurements (O) and CRTM simulations (B). The drifts of the O-B BT differences are consistently less than 0.105 K/Decade across all S-NPP VIIRS M TEB bands. Notably, observations from VIIRS M14 and M16 stand out with drifts well within 0.04 K/Decade, reinforcing their exceptional reliability for climate change studies. Secondly, excellent inter-sensor consistency among these three VIIRS instruments is confirmed through the double-difference analysis method (O-O). This method relies on the O-B BT differences obtained from daily data. The mean inter-VIIRS O-O BT differences remain within 0.08 K for all M TEBs, except for M13. Even in the case of M13, the O-O BT differences between NOAA-21 and NOAA-20/S-NPP have values of 0.312 K and 0.234 K, respectively, which are comparable to the 0.2 K difference between some TEBs among VIIRS and MODIS. These disparities are primarily attributed to the significant differences in the Spectral Response Function (SRF) of NOAA-21 compared to NOAA-20 and S-NPP. Our study confirms the RTM-based TEB quality evaluation method’s versatility and effectiveness in assessing long-term sensor stability and inter-sensor consistency. The double-difference approach effectively mitigates uncertainties and biases inherent to CRTM simulations, establishing itself a robust mechanism for assessing inter-sensor consistency. In addition, we’ve observed that, except for M13, M12 always exhibits larger spatial variations of O-O BT differences with greater uncertainties compared to the other M TEBs. The influence of solar contribution through sea surface reflection on the Top of Atmosphere (TOA) radiance measurements during daytime in M12 should not be underestimated.

Traditional population-based metaheuristic algorithms are effective in solving complex real-world problems but require careful strategy selection and parameter tuning. Parameter-less population-based optimization algorithms have gained importance due to their simplicity and efficiency. However, research on their applicability for solving large systems of nonlinear equations is still incipient. In this paper, a systematic review and detailed description of metaphor-less optimization algorithms is made, including the Jaya algorithm, the main Jaya variants for continuous optimization problems, with a focus on the Enhanced Jaya (EJAYA) algorithm, the three Rao algorithms, the best-worst-play (BWP) algorithm, and the very recent max-min-greedy-interaction (MaGI) algorithm. This paper also discusses a few previous parallelizations of the Jaya optimization algorithm, as well as recent GPU-based massively parallel implementations of the Jaya, Enhanced Jaya (EJAYA), Rao, and BWP algorithms developed by the authors. In addition, a novel GPU-accelerated version of the MaGI algorithm is proposed. The GPU-accelerated versions of the metaphor-less algorithms developed by the authors were implemented using the Julia programming language and tested primarily on GeForce RTX 3090 and NVIDIA A100 GPUs, as well as on Tesla V100S and Tesla T4 GPUs, using a set of difficult, large-scale nonlinear equation system problems. The computational experiments carried out produced quite significant speedups, which highlights the efficiency of the GPU-based versions of the metaphor-less algorithms developed for solving large-scale systems of nonlinear equations.

The critical systems, which the failure and malfunction may result in human, environment and financial severe damages, are essential components in various sectors and particularly in energy domains. Although unwished, integration error problems in supervision of critical systems occurs, incurring significant expenses because of operator’s subjective analysis and on hardware topology failures. In this work, a malleable model design approach is proposed to formulate and solve the integration error problem in the critical systems supervision in terms of reliability. A real Hybrid Power Plant (HPP) case is considered as a requirement for a study case with simulated data. A method framework with informal approach (C4 diagram) and formal approach (Hierarchical Coloured Petri Nets) in radial spectrum is applied to the HPP supervision design. Using formal methods, a formulation and solution to this problem through structured, scalable and compact mathematical representations are possible. This malleable model intends to guarantee functional correctness and then reliability of the plant supervision system based on system software architecture. Outcomes suggest corroboration that the malleable model is appropriate for energy domain, and can be used for other types of critical systems, bringing all the benefits of this methodology to the context in which it will be applied.

The Hindu Kush Himalaya (HKH) is one of the most flood-prone regions in the world, yet heavy cloud cover and limited in-situ observations have hampered efforts to monitor the impact of heavy rainfall, flooding, and inundation during severe weather events. This paper introduces HydroSAR, a Sentinel-1 SAR-based hazard monitoring service that was co-developed with in-region partners to provide year-round, low-latency weather-hazards information across the HKH. The paper describes the end-user focused concept and overall design of the HydroSAR service. It introduces the main processing algorithms behind HydroSAR’s broad product portfolio, which includes qualitative visual layers as well as quantitative products measuring surface water extent and water depth. We summarize the cloud-based implementation of the developed service, which is providing capability to scale automatically with event size. A performance assessment of our quantitative algorithms is described, demonstrating capabilities to map flood extent and water depth at an accuracy of >90% and

Atopic dermatitis (AD) is a chronic inflammatory disorder presenting a dysregulated immune response and derangement of the epidermal barrier lipids. The Th2-type cytokines interleukin IL-4 and IL-13 play a prominent role in AD by activating the Janus Kinase/Signal Transduction and Activator of Transcription (JAK/STAT) intracellular signal axis. This study aimed to investigate the role of JAK/STAT in the lipid perturbations induced by Th2 signaling in 3D-epidermal equivalents. We used tofacitinib, a low-molecular-mass JAK inhibitor, to screen for the JAK/STAT-mediated deregulation of lipid metabolism. Th2 cytokines decreased the expression of elongases 1, 3, and 4, and serine-palmitoyl-transferase and increased that of sphingolipid delta(4)-desaturase, and carbonic anhydrase 2. Th2 cytokines inhibited the synthesis of palmitate and caused a depletion of triglycerides. This depletion was associated with altered PC profiles and fatty acid (FA) metabolism. Overall, the ceramide profiles were minimally affected. Except for most sphingolipids and very long chain FAs, the effects of Th2 on lipid pathways were reversed by co-treatment with tofacitinib. An increase in the mRNA levels of CPT1A and ACAT1, reduced by tofacitinib, suggests that Th2 cytokines promote FA beta-oxidation. In conclusion, pharmacological inhibition of JAK/STAT activation prevents the lipid disruption caused by halted homeostasis of FA metabolism.

Crop productivity is directly dependent to soil fertility. High soil organic carbon (SOC) content in soil is vital as it leads to improved soil quality, increased productivity, and stable soil-aggregates. In addition, with the signing of the climate agreement, there is growing interest in carbon sequestration in landscapes. This paper looks at how SOC can be increased so that it not only contributes to reduction of CO₂, but also translates to increased food production thereby enhancing food security. This synergy between mitigation and enhancing food security is even more relevant for mountain landscapes of the Hindu Kush Himalayan (HKH) region where there remains huge potential to increase CO₂ sequestration and simultaneously address food security in the chronic food deficit villages. Soil samples were collected from seven transects each in Bajhang and Mustang and from 4 land use types in each transect. Samples of soils were taken from two depths in each plot; 0-15 cm below the soil surface and 15-30 cm below the soil surface to compare the top soil and subsoil dynamics of the soil nutrients. The lab analysis was performed to assess the soil texture, soil color, soil acidity in 'power of hydrogen' (pH), macro-nutrients as soil fertility. Secondary data was used to analyze the level of food deficit in the villages. The result shows that most of the sample soils from Mustang were clay (82.1%) which is 46 samples out of 56. The pH value of soil from Bajhang ranged from 5.29 to 9.09. The pH value of soil ranged from 5.65 to 8.81 in Mustang. SOC contents of sampled soils from Bajhang ranged from 0.20% to 7.69% with mean amount of 2.47% ± 0.17. SOC contents of sampled soils from Mustang ranged from 0.51% to 8.56% with mean amount of 2.60% ± 0.25. By land use type, forest land had the highest carbon (C) content of 53.61 t ha^-1 in Bajhang whereas in Mustang, agricultural land had the highest C content of 52.02 tons ha-1. Based on these data, we can say that there is potential for increasing SOC through improved soil health and crop production and soil. Sustainable soil management should be practiced for higher productivity. Livestock may also provide farmyard manure, which can be used to fertilize cultivated soils, which increases soil productivity. Increasing productivity would aid in increasing the access and availability of food in these mountain villages.

In this work, we propose simple and robust technique for the retrieval of underlying surface spectral albedo using spaceborne observations. It can be used to process both multispectral moderate resolution satellite data and also multi - zone high spatial resolution data. The technique can work automatically for different types of land surfaces without using huge databases accumulated in advance. The new cloud discrimination and retrieval of the water vapor content in atmosphere procedures are presented. The key point of the proposed atmospheric correction technique is the suggested single-wavelength method for determining the atmospheric aerosol optical thickness without reference to a specific type of underlying surface spectrum. The retrievals of spectral albedo for various land surfaces with developed technique, performed using computer simulation and experimental data, have demonstrated a high retrieval accuracy.

Central Nervous System (CNS) cancers pose a formidable challenge in the medical world, characterized by aggressive behaviors and limited therapeutic options. The emergence of nanomedicine has offered new hope in CNS cancer treatment. The COVID-19 pandemic has further accelerated the need for innovative therapeutic strategies, emphasizing the significance of nanomedicine in this era. This mini review explores the current status of nanomedicine in CNS cancer therapy, with a particular focus on the post-COVID-19 landscape. We discuss the unique challenges presented by CNS cancers, the potential of nanomedicine in overcoming these challenges, and the recent developments in the field. Additionally, we highlight the lessons learned from the pandemic and how they can be applied to improve CNS cancer therapeutics. We also shed light on the evolving regulatory landscape and the prospects of personalized nanomedicine in CNS cancer treatment. The integration of nanomedicine in CNS cancer therapy is becoming increasingly promising, and its role in the post-COVID-19 era is poised to shape the future of neuro-oncology.

Fire is a serious security threat that can lead to casualties, property damage, and environmental damage. However, despite the availability of object detection algorithms, challenges persist in detecting fires and smoke. These challenges include slow convergence speed, poor performance in detecting small targets, and high computational cost limiting deployments. In this paper, Fire smoke and human detection based on ConvNeXt and Mixed encoder (FCM-DETR), an end-to-end object detection algorithm based on Deformable DETR, is proposed. Firstly, we introduce ConvNeXt to take the place of Resnet, which greatly reduces the amount of computation and improves the ability to extract irregular flame features. Secondly, to effectively process multi-scale features, the original encoder is decoupled into two modules. Then Mixed encoder, an innovative encoder structure, is proposed, resulting in excellent performance of multi-scale fire and smoke features fusion. What can’t be overlooked is that the encoder block is compatible with any DETR-based models. Finally, the convergence speed is accelerated and the mean of average precision is improved by applying a novel loss function called Powerful IoU v2. The experimental results indicate that our model achieves the best detection accuracy compared to other models, with mAP reaching 66.7%, mAPs and Accuracyfire achieving impressive 50.2% and 98.05%, respectively.

: In this paper we present an optimization of the planar manufacturing scheme for stretch-free, shape induced metal interconnects to simplify the fabrication with the aim of maximizing the flexibility in the structure regarding to stress and strain. The Formation of trenches between silicon islands are actively used in the lithographic process to create arc shape structures by spin coating resist into the trenches. The resulting resist form is used as a template for the metal lines, which are structured on top. Because this arc shape is beneficial for the flexibility of these bridges. The trench depth as a key parameter for the stress distribution is investigated by applying numerical simulations. The simulated results show that the increase in penetration depth of the metal bridge into the trench increases the tensile load which is converted into a shear Force Q(x), that usually leads to increased strains the structure can generate. For the fabrication the filling of the trenches with resist is optimized by varying the spin speed. Compared to the theoretical resistance, the current-voltage measurements of the metal bridges show a similar behavior and almost every structural variation is capable of functioning as a flexible electrical interconnect in a complete island-bridge array.

The statistical analysis of impact of the top 50 solar flares of X-class (1997-2023) on the global seismic activity, as well as on the earthquake preparation zones located in illuminated part of the globe and in the area of 5000 km radius around the subsolar point was carried out. It is shown by a method of epoch superposition that for all cases the increase of seismicity is observed, especially in the region around the subsolar point (up to 38%) during 10 days after the solar flare in comparison with preceding 10 days. The case study of aftershock sequence of strong M=9.1 earthquake (Su-matra-Andaman Islands, 26.12.2004) after the solar flare of X7.2 class (20.01.2005) demonstrated that the number of aftershocks with magnitude M≥2.5 increases more than 20 times after the solar flare with a delay of 7 days. For the case of the Darfield earthquake (M=7.1, 04.09.2010, New Zealand) it was shown that strong solar flares of class X and M probably triggered two strong aftershocks (M>6) with the same delay of 6 days on the Port Hills fault, which is the most sensitive to external electromagnetic impact from point of view of the fault electrical conductivity and orientation. Based on the obtained results the possible application of natural electromagnetic triggering of earthquakes is discussed for a short-term earthquake prediction using confidently recorded strong external electromagnetic triggering impacts on the specific earthquake preparation zones, as well as ionospheric perturbations due to aerosol emission from the earthquake sources recorded by satellites.

In this paper thermo-mechanical behavior of 4D printed Polylactic Acid (PLA) was investigated, focusing on its response to varying strain rates and temperatures below the glass transition temperature. Dynamic mechanical analysis and uniaxial tensile tests confirmed PLA's dependency on strain rate, showcasing its sensitivity to external stimuli. Stress-strain curves exhibited typical thermoplastic behavior, with yield stresses varying with strain rates, underscoring PLA's responsiveness to different deformation speeds. Clear strain rate dependence was observed, particularly at quasi-static rates, with temperature and strain rate fluctuations significantly influencing PLA's mechanical properties, including yield stress and deformation behavior. Isothermal compression tests demonstrated predictable stress-strain curves with distinct yield points, while adiabatic tests reveal additional complexities such as heat accumulation leading to further softening. Thermal imaging revealed temperature increase during deformation, highlighting the material's thermo-sensitive nature. These findings provide the basis for future research with the focus on advanced modeling techniques and mitigation strategies for self-heating effects, aiming to enhance PLA-based product reliability and performance in applications with deformations at higher strain rates, while also developing models to simulate shape recovery in 4D printed PLA structures at both cold and hot programming temperatures.

Dry intrusion is an important mid-latitude atmosphere phenomenon within the upper troposphere and lower stratosphere. It is always found to be related to the cyclogenesis, rainstorm, as well as convection generation and precipitation enhancement. Since the atmosphere environment for any of these above-mentioned weather is terribly complicated, those preexisting popular schemes which takes no account of water vapor may not suitable for detecting the dry intrusion related to these weathers. With regard to the merits and demerits of the current preexisting schemes, a new scheme based on Fengyun-2E geo-stationary satellite data is presented in this study to detect the atmospheric dry intrusion. The scheme is set up based on the statistical relationship between water vapor at high level troposphere, the general moist potential vorticity, ozone concentration and upper-level jet. After using the total amount of ozone and ozone profile operational products retrieved by Fengyun-3 Polar Orbiting Meteorological Satellites and the potential vorticity calculated by ECMWF Interim data for validation, this scheme is applied to analyze two typical middle-latitude weather processes. One is the famous Beijing extreme rainfall of 21 July 2012 and the other is a hailstorm occurred on the eastern China during March 19, 2014. A good application effect in both cases suggests that our new method of detecting dry intrusion is feasible and can be helpful in middle-latitude disastrous weather monitoring and forecasting.

Introduction: In Traditional Chinese Medicine (TCM), tongue diagnosis plays an important role. Besides shape, colour and textural attributes of tongue regions have significance due to their association with different organs (viz. heart, lung, kidney-bladder, liver-gallbladder and stomach-spleen) in health and disease. Although numerous quantitative methods have been proposed to reduce inter and intra observer variability in tongue diagnosis, almost all of them considered only global colour/ textural features i.e. from entire body of tongue. In this work, regional colour and texture information of tongue have been analysed for healthy subjects (NOM) and patients suffering from chronic acidity-indigestion (ACD). Methods: Tongue boundaries are marked manually in each image and then tongue bodies are automatically partitioned into five regions linked with said internal organs. Rough assessments of regions’ boundaries are obtained from literature and then they are fine-tuned according to experts’ advice. As boundaries are not well defined, regions near the common boundary of two or more organs, are not assigned to any organ. Several first and second order statistical features based on grey scale intensities and a* values of LAB colour space (indicative of redness) have been extracted from five different regions of tongue images. Intragroup analysis using Wilcoxon signed rank test compared different regions within individual study group. Additionally, intergroup analysis using Wilcoxon rank sum test compared tongues of NOM with ACD for each region separately. Results: Intragroup analysis reveals a* values are statistically different (p&lt;0.01) between stomach-spleen and liver-gallbladder regions for ACD while no significant change of a* are noted between these regions of normal tongue. It was found from intergroup analysis that correlation (one of the second order texture features extracted from grey scale images) of heart region has significantly decreased while correlation of kidney, liver-gallbladder and stomach-spleen regions significantly increased from NOM to ACD. Conclusion: This study sheds light on importance of inclusion of regional textural analysis of tongue in quantitative studies and can be applied to automated tongue diagnosis for other diseases.

Soil potentially toxic elements (PTEs) pollution in black soil is of significant concern in China. However, research is scarce regarding the current status, ecological risk, and human health risk of PTEs in the black soil area of northeast China. In our study, 304 soil samples (0-20 cm) were collected in Gonghe Town, Hailun City. The pollution degree and spatial distribution patterns of soil PTEs were analyzed by using the single-factor pollution index (PI) and the Nemerow pollution index (NPI). The source apportionment of PTEs was carried out by combining correlation analysis, principal component analysis (PCA), and positive matrix factorization (PMF) methods. Meanwhile, the potential ecological risk (RI) and the human health risk (HI) associated with soil contamination from various sources were evaluated through the RI and the HI. The results demonstrated that the average content (mean ± standard deviation) of As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn were 11.16 ± 1.32 mg/kg, 0.11 ± 0.04 mg/kg, 65.29 ± 3.46 mg/kg, 22.56 ± 1.32 mg/kg, 0.03 ± 0.01 mg/kg, 27.07 ± 1.46 mg/kg, 26.09 ± 2.84 mg/kg, and 66.01 ± 4.52 mg/kg, respectively. The overall PTEs in the study area exhibited slight pollution levels. The PCA and PMF model source apportionment was validated against each other to yield four sources, which were natural source (33.2%), irrigation source (29.5%), traffic source (23.4%), and fertilizer source (13.2%). The overall RI in the study area was determined to be at the level of slight ecological risk. The non-carcinogenic risk of PTEs to children and adults was ignored, and the carcinogenic risk was at an acceptable level. The comprehensive analysis of PTEs, pollutant sources, RI, and HI concluded that the fertilizer source should be the primary control source, with Cd identified as the first control PTE. The irrigation source and the traffic source were identified as the secondary control sources, with As, Pb, and Hg identified as the secondary control PTEs. This study revealed the status, risks, and sources of PTEs in black soils and provided a scientific basis of PTEs control for policymakers.

Urban tourism has witnessed significant growth in recent years, driven by various factors that foster the transformation of urban areas into tourist hotspots. These factors include a rising interest in culture and heritage, ongoing urban redevelopment and conversion processes, and the introduction of new experiences and activities. Two particularly influential factors contributing to this growth are low-cost travel, which facilitates more accessible access to urban destinations, and the emergence of new types of accommodation, such as short-term rental (also known as local accommodation or Airbnb-type accommodation). This sudden and continuous growth of this type of tourist accommodation brings new challenges to cities and local communities. On the positive side, it contributes to job opportunities, the dynamism of the local economy, and urban revitalization. On the other hand, it also represents more difficulty in retaining the local population in city centers, as more and more buildings are converted into hostels and tourist apartments. In this context, it becomes essential to conduct studies evaluating the impacts of these dynamics in urban areas and how they influence residents' and visitors' perspectives. Therefore, this study aims to identify the main transformations resulting from short-term rental (STR) development, focusing on the impacts felt by tourists and residents in Porto. A questionnaire was distributed to tourists and residents in Porto's city center in June 2023. The results reveal differences in opinions between tourists and residents, particularly concerning historical heritage preservation, local population relocation, and reduced housing availability for residents. Nevertheless, there is a similarity in their opinions regarding the stimulus that local accommodation brings to the economy and the rehabilitation of buildings and urban areas. This study reinforces the need to pay more attention to the future development of tourist accommodation in city centers, especially regarding local policies and territorial planning, to balance better tourism development and local communities' quality of life.

This study employs the reflection symmetry decomposition (RSD) method to extract polarization scattering features from ground object images, aiming to determine the optimal data input scheme for deep learning networks in polarimetric synthetic aperture radar classification. Eight distinct polarizing feature combinations are designed, and the classification accuracy of various approaches is evaluated using the classic convolutional neural networks(CNNs) AlexNet and VGG16. The findings reveal that the commonly employed 6-parameter input scheme, favored by many researchers, lacks the comprehensive utilization of polarization information and warrants attention. Intriguingly, leveraging the complete 9-parameter input scheme based on the polarization coherence matrix results in improved classification accuracy. Furthermore, the input scheme incorporating all 21 parameters from the RSD and polarization coherence matrix notably enhances overall accuracy and the Kappa coefficient compared to the other 7 schemes. This comprehensive approach maximizes the utilization of polarization scattering information from ground objects, emerging as the most effective CNN input data scheme in this study. Additionally, the classification performance using the second and third component total power values (P2 and P3) from the RSD surpasses the approach utilizing surface scattering power value (PS) and secondary scattering power value (PD) from the same decomposition.

Fast convergence routing is an important issue for LEO constellation network, due to its dynamical topology changing and time varying transmission requests. Most of existing research focus on the OSPF routing algorithm, which cannot handle the frequently links state changing of network. In this paper, we propose a Fast-Convergence Reinforcement Learning Satellite Routing Algorithm (FRL-SR) for LEO satellite networks, in which the satellite gets the network links status fast and adjusts its routing strategy. In FRL-SR, each satellite node is regarded as an agent. The agent selects the port for packet forwarding according to its own routing policy. When the satellite network state changes, agent would send ’hello’ packets to the neighbor node to update the neighbor node’s routing policy. Compared with traditional reinforcement learning, FRL-SR can perceive network information faster, and then converge faster. Also, FRL-SR can mask the dynamics of satellite network topology and adaptively adjust the forwarding strategy according to the link state. Various simulation is constructed, the results show that the proposed FRL-SR algorithm out performance the Dijkstra algorithm in performance of average delay, packet arriving ratio, network load balance.

The concept of geostationary VHTS (Very High Throughput Satellites) is based on multibeam coverage with intensive frequency and polarization reuse in addition to the use of larger bandwidths in the feeder links, in order to provide high capacity satellite links at a reduced cost per Gbps in orbit. The dimensioning and design of satellite networks based on VHTS imposes the analysis of multiple trade-offs to achieve an optimal solution in terms of cost, capacity and figure of merit of the user terminal. In this paper, we propose a new method for sizing VHTS satellite networks based on an analytical expression of the forward link CINR (Carrier-to-Interference-plus-Noise Ratio) that is used to evaluate the trade-off of different combinations of system parameters. The proposed method considers both technical and commercial requirements as inputs including the constraints to achieve the optimum solution in terms of the user G/T, the number of beams and the system cost. The cost model includes both satellite and ground segments. Exemplary results are presented with feeder links using Q/V bands, DVB-S2X and transmission methods based on CCM and VCM (Constant and Variable Coding and Modulation, respectively) in two scenarios with different service areas.

Abstract: With the growing integration of the Internet of Things (IoT) in smart buildings, it is crucial to ensure the precise implementation and operation of wireless sensor networks (WSNs). This paper aims to study the implementation aspect of Wireless Sensor Networks (WSNs) in a commercial multi-story building, specifically addressing the difficulty of dealing with variable environmental conditions on each floor. This research addresses the disparity between simulated situations and actual deployments, offering valuable insights that have the potential to signifi-cantly improve the efficiency and responsiveness of building management systems. We obtain real-time sensor data to analyze and evaluate the system’s performance. Our investigation is grounded in the growing importance of incorporating WSNs into buildings to create intelligent environments. By leveraging the capabilities of Contiki operating system, this study employs a specialized simulator to generate simulated data, allowing for a comparison between real-world and simulated scenarios. We provide an in-depth analysis to scrutinize the disparities and commonalities between the datasets obtained from real-world deployments and simulated en-vironments. We highlight the necessity of precise simulation to validate real-life sensor data, shedding light on the challenges and opportunities associated with implementing WSNs in in-telligent buildings. Results obtained show the significance of accurate simulation models for re-liable data representation, providing a roadmap for further developments in the integration of WSNs into intelligent building scenarios. The findings highlight the potential for optimizing living and working conditions based on the real-time monitoring of critical environmental pa-rameters. This includes insights into temperature, humidity, and light intensity, offering oppor-tunities for enhanced comfort and efficiency in intelligent environments.

Atmospheric turbulence characteristics are essential in determining the quality of astronomical images and implementing adaptive optics systems. In this study, the vertical distributions of optical turbulence at the Peak Terskol observatory (43.27472 ° N 42.50083 ° E, 3127 m a.s.l.) using the Era-5 re-analysis and scintillation measurements are investigated. For the closest reanalysis grid node to the observatory, vertical profiles of the structural constant of the air refractive index turbulent fluctuations Cn2 were obtained. The calculated Cn2(z) vertical profiles are compared with the vertical distribution of turbulence intensity obtained from tomographic measurements with Shack-Hartmann sensor. The atmospheric coherence length at the location of Terskol Peak was estimated. Using combination of atmospheric models and scheme paramaterization of turbulence, Cn2(z) profiles at Mt. Kurapdag were obtained. The values of atmospheric coherence length at the Peak Terskol are compared with estimated values of this length at the ten astronomical sites including Ali, Lenghu and Daocheng.

Substance use is a growing problem among young people in sub-Saharan Africa; tobacco use is the dominant one among these practices. The aim was to analyse prevalence rates and determinants for cigarette smoking of school-attending adolescents in the capital, Bissau, Guinea-Bissau. In June 2017, a survey questionnaire was implemented in 16 schools among adolescents aged 14–19 (52% girls). The Chi-square (p&lt;0.05) and odds ratios (OR) were calculated with 95% confidence intervals (CI). Potential explanatory variables for the dependent variables were introduced into a multinomial logistic regression model, and p-values were transformed to LogWorth values. The prevalence of lifetime smoking experience among 1,845 participants was 14.8%; 6.5% were current users, and 2.2% were daily smokers. The most significant determinants for cigarette smoking included having friends who smoke, being a boy, attending a private school, smoking initiation at a young age, other substance use and violent behaviour. It is imperative to initiate preventive actions against smoking and other use of tobacco in Guinea-Bissau. Considering the significant impact of the peer group, policymakers need to engage young people in developing primary prevention strategies to curb the use of tobacco and other substances.

The Greenland ice sheet is a major contributor to sea level rise, adding an estimated 0.47 +/− 0.23 mm/yr to global mean sea level between 1991 and 2015 (van den Broeke et al., 2016). Making sea level rise projections for the future and understanding the processes controlling current observed rates of sea level rise are crucially dependent on understanding the present-day state of the ice sheet. Here, we provide an overview of the current state of the mass budget of Greenland based on satellite gravimetry and remote sensing observations of surface elevation change, ice sheet velocity and calving front positions. We also combine these essential climate variables with a regional climate model (RCM) output from an ice sheet model (ISM) to gain insight into poorly understood ice sheet dynamical and surface mass processes. On average from 1992 to 2017 the ice sheet in some locations has lost up −2.65 m/yr in elevation based on ESA Radar altimetry analysis. Calving fronts have retreated all around Greenland since the 1990s and in only two out of 28 study locations have they remained stable. The locations of grounding lines at 5 key glaciers with floating ice tongues have remained stable over the observation period. However a detailed case study at Petermann glacier with an ice fracture model shows the sensitivity of these floating ice shelves to future climate change. GRACE gravimetric mass balance (GMB) data allows us to tie together disparate lines of evidence showing that Greenland has lost about 265 +/− 25 Gt/yr of ice over the period 2002 to 2015. RCM and ISM simulations show that surface mass processes dominate the overall Greenland ice sheet mass budget except for areas of fast ice sheet flow but marked differences between models and between models and observations indicate that not all processes are captured accurately, indicating areas of greater uncertainty and directions of future research for future sea level rise projections.

Global navigation satellite system (GNSS) array antenna receivers are widely used to suppress wideband interference in navigation countermeasures. However, existing array antenna receivers all adopt digital array structure and digital beamforming technique, and it has limited analog-front-end (AFE) dynamic range. In strong interference scenarios, AFE saturation will occur, which limits the maximum interference suppression ability of the array receiver. Aiming at this issue, this paper proposes a robust wideband interference suppression method for GNSS array antenna receivers based on hybrid beamforming technique. Firstly, a novel fully connected hybrid array receiver structure is proposed; Secondly, the corresponding hybrid beamforming method is proposed at the same time, and it realizes the complete elimination of strong interference by jointly suppression in analog domain and digital domain. After mathematical simulation experiments, it is verified that compared to the digital beamforming-based anti-jamming technique, the proposed method can effectively suppress strong wideband interference, and the maximum interference suppression ability is improved by 36 dB.

A speculative argument is presented which suggests the possible existence of a global metasystem that would be characterized as an emerging from the interaction of the units that make up the planetary system. The metasystem´s units would be the different physical, chemical and biological processes occurring in the subsystems that form the metasystem: magnetosphere, atmosphere, geosphere, hydrosphere, and biosphere. The revised global metasystem is broader than that considered in the Gaia theory or in Earth System Science, where the Earth's crust and upper atmosphere, i.e., the volume where the presence of life occurs, are considered as the limits of the system. The maintenance of the dynamic state of the global metasystem it is achieved by dissipating the free energy derived from the electromagnetic radiation of the Sun, the obtained from the Earth-Moon gravitational interaction and the energy resulting from the dynamics of the Earth core and mantle, which produces the magnetic field and much of tectonic activity. For the human species, the importance of a greater understanding of global metasystem is based on the fact that natural resources and the climate system are products of the subsystems of the global metasystem. It is possible therefore that human activities that modify the atmosphere, hydrosphere, and biosphere, change the dynamics of global metasystem.

Rapid increase in digital data coupled with advances in deep learning algorithms is opening unprecedented opportunities for incorporating multiple data sources for modeling spatial dynamics of human infectious diseases. We used Convolutional Neural Networks (CNN) in conjunction with satellite imagery-based urban housing and socio-economic data to predict disease density in a developing country setting. We explored both single (uni) and multiple input (multimodality) network architectures for this purpose. We achieved maximum test set accuracy of 81.6 per cent using a single input CNN model built with one convolutional layer and trained using housing image data. However, this fairly good performance was biased in favor of specific disease density classes due to an unbalanced data set despite our use of methods to address the problem. These results suggest CNN are promising for modeling spatial dynamics of human infectious diseases, especially in a developing country setting. Urban housing signals extracted from satellite imagery seem suitable for this purpose, under the same context.

Hydrodynamic modeling and hazard mapping are essential tools for urban flood mitigation and spatial planning. Leveraging software like HEC-RAS (Hydrologic Engineering Center - River Analysis System) and ArcGIS, this study aimed to map flood-prone areas along the Jijia River in the Ungheni administrative unit of Iasi County. Human-induced environmental changes and global climate shifts have heightened flood risks worldwide, particularly in urbanized areas. Hydrological and hydraulic modeling anticipate and assess flood events, while hydrodynamic modeling accurately simulates flow propagation, in-forming flood extent, timing, and volume. Flood hazard maps offer essential insights into flooded areas, water depths, and velocities, assisting in risk assessment and mitigation strategies. Through the use of different scenarios, river modeling aims to accurately replicate real-world conditions. This study focuses on Ungheni commune in Iasi County, Romania, situated along the Prut River's eastern bank. Utilizing a combination of fixed-grid and irregular mesh elements in modeling al-lows for fine-tuning of flow details and optimization of simulation times. Model accuracy, particularly regarding peak river levels, hinges on hydrograph duration. While a 30-minute error is typically accepted, greater tolerances may apply to river basins with longer response times. The extraction and processing of model results, including maximum water depths, flood extents, and velocity maps, are facilitated by ArcGIS, providing valuable data for effective flood management strategies.

In data-scarce regions, prudent planning and precise decision-making for sustainable development especially in agriculture remains a challenging task due to the lack of correct information. Remotely sensed satellite images provide a powerful source for assessing land use land cover (LULC) classes and crop identification. Applying Remote Sensing (RS) in conjunction with the Geographical Information System (GIS) and modern tools/ algorithms of artificial intelligence (AI) and deep learning has been proven effective for strategic planning and integrated resources management. The (this)study was conducted in the canal command area of the Lower Chenab Canal system in Punjab, Pakistan. Crop features/ classes were assessed using the Normalized Difference Vegetation Index (NDVI) algorithm. The MODIS (Moderate Resolution Imaging Spectroradiometer) 250 m and Landsat 5 TM (thematic mapper) images were deployed for NDVI time-series analysis with unsupervised classification technique to get LULC classes that helped to discern cropping pattern, crop rotation, and the area of specific crops, which were then used as a key input for agricultural mechanization planning and resources management. The accuracy of the LULC map was 78%, assessed by the error matrix approach. Limitations of high-resolution RS data availability and the accuracy of the results are the concerns observed in this study that could be managed by the availability of good quality local sources and advanced techniques of processing, which will make it more useful and applicable for regional agriculture and environmental management.

Atta capiguara is a grass-cutting ant species frequently found in Cerrado biome. However, little is known about the giant nest architecture of this ant. In this study, we investigated the architecture of three A. capiguara nests from the fragment of cerrado in Botucatu, São Paulo, Brazil. Casts were made of the nests by filling them with cement to permit better visualization of internal structures such as chambers and tunnels. After excavation, the depth and dimensions (length, width, and height) of the chambers were measured. The results showed the typical shape of Atta capiguara nests consisting of mounds of loose soil with unique features resembling a conic section. The fungus chambers were found outside the apparent main part of the nest and were spaced apart and distributed laterally at ground level. The waste chambers were located beneath the largest mound of loose soil. Both the fungus and waste chambers exhibited a sectoral distribution. Our study contributes to a better understanding of the so far unknown nest architecture of the grass-cutting ant A. capiguara.

Formaldehyde (HCHO) is one of the most important carcinogenic air contaminants. However, the lack of global surface concentration of HCHO monitoring is currently hindering research on outdoor HCHO pollution. Traditional methods are either restricted to small areas or data- demanding for a global scale of research. To alleviate this issue, we adopted neural networks to estimate surface HCHO concentration with confidence intervals in 2019, where HCHO vertical column density data from TROPOMI, in-situ data from HAPs (harmful air pollutants) monitoring network and ATom mission are utilized. Our result shows that the global surface HCHO average concentration is 2.30 μg/m3. Furthermore, in terms of regions, the concentration in Amazon Basin, Northern China, South-east Asia, Bay of Bengal, Central and Western Africa are among the highest. The results from our study provides a first dataset of the global surface HCHO concentration. In addition, the derived confidence interval of surface HCHO concentration adds an extra layer for the confidence to our results. As a pioneer work in adopting confidence interval estimation into AI-driven atmospheric pollutant research and the first global HCHO surface distribution dataset, our paper will pave the way for the rigorous study on global ambient HCHO health risk and economic loss, thus providing a basis for pollutant controlling policies worldwide.

Natural disasters ravage the world's cities, valleys, and shores on a monthly basis. Having precise and efficient mechanisms for assessing infrastructure damage is essential to channel resources and minimize the loss of life. Using a dataset that includes labeled pre- and post- disaster satellite imagery, we train multiple convolutional neural networks to assess building damage on a per-building basis. In order to investigate how to best classify building damage, we present a highly interpretable deep-learning methodology that seeks to explicitly convey the most useful information required to train an accurate classification model. We also delve into which loss functions best optimize these models. Our findings include that ordinal-cross entropy loss is the most optimal loss function to use and that including the type of disaster that caused the damage in combination with a pre- and post-disaster image best predicts the level of damage caused. Our research seeks to computationally contribute to aiding in this ongoing and growing humanitarian crisis, heightened by climate change.

The small satellite market continues to grow year after year. A compound annual growth rate of 17% is estimated during the period between 2020 and 2025. Low-cost satellites can send a vast amount of images to be post-processed at the ground to improve the quality and extract detailed information. In this domain lies the resolution enhancement task, where a low-resolution image is converted to a higher resolution automatically. Deep learning approaches to Super-Resolution (SR) reached the state-of-the-art in multiple benchmarks; however, most of them were studied in a single-frame fashion. With satellite imagery, multi-frame images can be obtained at different conditions giving the possibility to add more information per image and improve the final analysis. In this context, we developed and applied to the PROBA-V dataset of multi-frame satellite images a model that recently topped the European Space Agency’s Multi-frame Super Resolution (MFSR) competition. The model is based on proven methods that worked on 2D images tweaked to work on 3D: the Wide Activation Super Resolution (WDSR) family. We show that with a simple 3D CNN residual architecture with WDSR blocks and a frame permutation technique as data augmentation better scores can be achieved than with more complex models. Moreover, the model requires few hardware resources, both for training and evaluation, so it can be applied directly from a personal laptop.

Abstract: Conventional antibiotic and multidrug treatments become less and less effective and a discovery of new effective and safe antibacterial agents got a global priority. The returning to the natural antibacterial product is relatively new current trend. Terrestrial biota is a rich source of biologically active substances whose antibacterial potential was not fully utilized. Aim of this review is to present the current state-of-the art of terrestrial biota-derived antibacterial agents and inspired by nature treatments. It is a summary of the most important sources and new identified or modified antibacterial agents and treatments during the last 5 years. It focuses on the significance of plants- animals- and bacteria-derived biologically active agents as powerful alternatives to antibiotics as well as on the advantages of utilizing natural antibacterial molecules alone or in combinations with antibiotics. The main conclusion is that the terrestrial biota derived antibacterial products and substances open variety of new ways to modern improved therapeutic strategies. New terrestrial sources of known antibacterial agents and new antibacterial agents from terrestrial biota were discovered during the last 5 years, which are under investigation together with some long ago known but now surviving their renaissance for the development of new medical treatments. Use of natural antibacterial peptides as well as combinational therapy by commercial antibiotics and natural products outlines as the most promising for treating of bacterial infections. In vivo testing and clinical trials are necessary to reach clinical application.

Catastrophic mass flows originating from the high mountain cryosphere often cause hazard cascades. With increasing human activities in the alpine region and the sensitivity of glaciers, snow and permafrost to climate warming, the cryospheric hazard risks tend to be more active in the Tibetan Plateau(TP). The remoteness and inaccessibility of high mountain regions make the remote sensing technology the most effective approach for monitoring and investigating the cryospheric hazards. Exploring the evolution process and impact of ice avalanches and subsequent hazards are crucial to understand drivers and nature of mass flow process chain in order to prevent and mitigate potential hazard consequences. In this study, the glacier and ice-rock avalanches occurred in Arutso and Sedongpu basins in Tibet were investigated on the basis of Sentinel-2 time-series images and in-situ investigation, the evolution process of these events were reconstructed, and impacts and driving factors were analyzed. The massive Chamoli rock-ice avalanche occurred in February 2021 in the western Himalayas was also reviewed using using same methods. The study shows that Arutso glacier No. 53 (Arutso-53) avalanche completely melted away in July 2018 after two years from occurrence to final disappearance, while Arutso glacier No. 50 (Arutso-50) avalanche completely melted by the end of August 2023 after lasting for seven years. Four large-scale ice-rock avalanche and debris flow events in the Sedongpu basin in 2017 and 2018 not only had significant impacts on the river flow, landscape and geomorphologic shape in the basin, but also caused serious disasters in the basin and downstream. In March 2021, a small-scale ice-rock avalanche and debris flow event occurred again in the Sedongpu basin. The glacier and ice-rock avalanches occurred in Tibet area were mainly induced by climate warming, heavy precipitation and seismic activity, which acting on specific topographic and geomorphic structure of glacier properties in the high mountain regions. Catastrophic hazard events originating from the high-elevation glacier and ice-rock collapses in the mountain cryosphere and subsequent hazard cascades in the TP and Himalayas highlight the importance of mountain hazard monitoring, early warning systems and sustainable mountain development.

Satellite communication is expected to play a vital role in realizing Internet of Remote Things (IoRT) applications. This article considers an intelligent reflecting surface (IRS)-assisted downlink low Earth orbit (LEO) satellite communication network, where IRS provides additional reflective links to enhance the intended signal power. We aim to maximize the sum-rate of all the terrestrial users by jointly optimizing the satellite’s precoding matrix and IRS’s phase shifts. However, it is difficult to directly acquire the instantaneous channel state information (CSI) and optimal phase shifts of IRS due to the high mobility of LEO and the passive nature of reflective elements. Moreover, most conventional solution algorithms suffer from high computational complexity and are not applicable to these dynamic scenarios. A robust beamforming design based on graph attention networks (RBF-GAT) is proposed to establish a direct mapping from the received pilots and dynamic network topology to the satellite and IRS’s beamforming, which is trained offline using the unsupervised learning approach. The simulation results corroborate that the proposed RBF-GAT can achieve approximate performance compared to the upper bound with low complexity.

In orthopedics, bone drilling is a crucial part of a surgical method commonly carried out for internal fixation in bone fracture treatment. The primary purpose of bone drilling is the creation of holes for screw insertion to immobilize fractured parts. The bone drilling task depends on the orthopedist and surgeon’s high level of skill and experience. This paper aimed to provide a summary of previously published review studies in the field of bone drilling. This review paper also presents a comprehensive review of the application of machine learning for bone drilling and as a future direction for the automation system. This review can also help medical surgeons and bone drillers understand the latest improvements through parameter selection and optimization strategies to reduce bone damage in bone drilling procedures. Apart from the review study, bone drilling vibration data collected in the University laboratory experiment is also presented in this study. The vibration data consist of three different layers of femur cow bone which are processed and classified using several machine learning methods. LSTM is, used in the bone drilling classification study to prove that the layers of the bone drilling are associated with the vibration signal and can be classified and predicted using the machine learning method.

Evaluation ecosystem service value (ESV) is critical for “lucid waters and lush mountains are invaluable assets”. However, the ESV estimation and spatial-temporal analysis for mountainous soil erosion area is rare, e.g., Changting county, China. In this study, we developed a remote sensing driven mountainous equivalent factor (RS-MEF) method to estimate the ESV of soil erosion area in subtropical mountain. It is a hybrid of a conventional equivalent factor framework and the remote sensing techniques for mountains, and achieves several highlights of spatial adjustment using carbon sink with vegetation canopy, improvement spatial resolution, and removal topographic effect. Using the RS-MEF method, we estimated that the ESV of Changting county was about 15.80 billion CNY in 2010 and 34.83 billion CNY in 2022. Specifically, the ESV per unit area of major soil erosion area (MSEA) in the county was less than that of non-major soil erosion area (n-MSEA), however, the ESV growth rate of MSEA from 2010 to 2022 was faster than that of n-MSEA. Therefore, the ESV gap between two areas was shortened from 28.99% in 2010 to 15.83% in 2022. Topographic gradient effect analysis illustrates that the area with high elevation and small slope is one control focus in the next step. Our study indicates that the ecological restoration in the county has made great achievements in the ESV perspective, with low ESV area shrank evidently in MSEA; and the achieved knowledge of the ESV growth and inherent factors is valuable and instructive for the next soil and water conservation.

Floods, recognized for their erratic behavior and devastating effects, are a significant threat as a natural disaster. In Brunei Darussalam, the impact of climate change is becoming increasingly evident through higher temperatures and an expected increase in annual rainfall (5.0mm per year from 2021 to 2050), highlighting the critical need for updated flood risk management techniques. In line with the climate resilience and adaptation objectives of WAWASAN 2035, this paper introduces an innovative approach—a cutting-edge flood risk management framework that utilizes the capabilities of big data analytics, sophisticated machine learning algorithms, and smart Internet of Things (IoT) sensors. Traditional methods, which rely on numerical and physical data modeling, often do not provide precise short-term flood forecasts. Our suggested framework uses machine learning to analyze real-time flood forecasting, sensor data and enhance warning systems, addressing the shortcomings of traditional approaches. The key advantage of this approach is its potential to drastically decrease flood-related damages, protect lives, and boost infrastructure resilience. This proposal is not only in aligns with Brunei's vision for a safe and climate-adaptable future but also marks a significant step forward in adopting innovative and sustainable flood risk management solutions amidst an evolving climate.

The digital model of airborne frequency selective surface radomes (AFSSRs) is the basis of design, simulation analysis, manufacturing, and other related research of AFSSRs. This paper proposes a rapid modeling method of AFSSRs based on dynamic customizable primitives. Firstly, a layered digital model construction scheme of AFSSR is presented based on the typical radome wall structure. Then, according to the characteristics of various surface configurations and complex wireframe information of AFSSRs, the dynamic primitives are raised to express the boundary and contour information of all kinds of radomes. Focusing on the undevelopable characteristics of the aero-dynamic shape surface of the AFSSR, the arrangement solution and mapping method for frequency selective elements on undevelopable surfaces are proposed. Furthermore, the imple-mentation logic of this method on the creation of each layer model and the assembly of the whole machine model is introduced. Finally, according to this method, a rapid modeling system (RMS) is established, and the modeling effect and efficiency of the RMS are demonstrated and compared, which verifies the feasibility and effectiveness of this method.

Breast cancer (BC) is a prevalent malignancy with various subtypes, each presenting unique diagnostic and therapeutic challenges. Triple-negative breast cancer (TNBC), which comprises 12-17% of breast cancers, is particularly aggressive due to its lack of estrogen receptor (ER), progesterone receptor (PR), and HER2 expression. This case report details the management of a 64-year-old woman with TNBC. Initially presenting with a left breast nodule, she underwent a biopsy that confirmed triple-negative infiltrating ductal carcinoma. Despite negative BRCA genetic testing, her significant family history of cancer and a newly detected right breast lesion led to a bilateral mastectomy. Post-surgery imaging revealed multiple hypodense nodules and a spiculated pulmonary nodule, prompting further investigation. An endoscopic lung biopsy identified a primary pulmonary carcinoma with histological features similar to the breast cancer, indicating the lung as the primary source. This case underscores the complexity of TNBC, the importance of comprehensive diagnostic evaluations, and the need for considering extramammary origins in metastatic cases. The findings emphasize the role of multidisciplinary teams in managing such rare and challenging cases and highlight the necessity for thorough and repeated evaluations in atypical BC presentations.

Aerial image classification has become popular and has attracted extensive research efforts in recent decades. The main challenge lies in its very high spatial resolution but relatively insufficient spectral information. To this end, spatial-spectral feature extraction is a popular strategy for classification. However, parameter determination for that feature extraction is usually time-consuming and depends excessively on experience. In this paper, an automatic spatial feature extraction approach based on image raster and segmental vector data cross-analysis is proposed for the classification of very high spatial resolution (VHSR) aerial imagery. First, multi-resolution segmentation is used to generate strongly homogeneous image objects and extract corresponding vectors. Then, to automatically explore the region of a ground target, two rules, which are derived from Tobler’s First Law of Geography (TFL) and a topological relationship of vector data, are integrated to constrain the extension of a region around a central object. Third, the shape and size of the extended region are described. A final classification map is achieved through a supervised classifier using shape, size, and spectral features. Experiments on three real aerial images of VHSR (0.1 to 0.32 m) are done to evaluate effectiveness and robustness of the proposed approach. Comparisons to state-of-the-art methods demonstrate the superiority of the proposed method in VHSR image classification.

Polar mesocyclones (MCs) are small marine atmospheric vortices. The class of intense MCs, called polar lows, are accompanied by extremely strong surface winds and heat fluxes and thus largely influencing deep ocean water formation in the polar regions. Accurate detection of polar mesocyclones in high-resolution satellite data, while challenging, is a time-consuming task, when performed manually. Existing algorithms for the automatic detection of polar mesocyclones are based on the conventional analysis of patterns of cloudiness and involve different empirically defined thresholds of geophysical variables. As a result, various detection methods typically reveal very different results when applied to a single dataset. We develop a conceptually novel approach for the detection of MCs based on the use of deep convolutional neural networks (DCNNs). As a first step, we demonstrate that DCNN model is capable of performing binary classification of 500x500km patches of satellite images regarding MC patterns presence in it. The training dataset is based on the reference database of MCs manually tracked in the Southern Hemisphere from satellite mosaics. We use a subset of this database with MC diameters falling in the range of 200-400 km. This dataset is further used for testing several different DCNN setups, specifically, DCNN built “from scratch”, DCNN based on VGG16 pre-trained weights also engaging the Transfer Learning technique, and DCNN based on VGG16 with Fine Tuning technique. Each of these networks is further applied to both infrared (IR) and a combination of infrared and water vapor (IR+WV) satellite imagery. The best skills (97% in terms of the binary classification accuracy score) is achieved with the model that averages the estimates of the ensemble of different DCNNs. The algorithm can be further extended to the automatic identification and tracking numerical scheme and applied to other atmospheric phenomena characterized by a distinct signature in satellite imagery.

Is there a qualitative difference between apes’ and human Theory-of-Mind (or ability to estimate others’ mental states)? After opting for the idea that expectations are empty profiles that recognize a particular content when it arrives, I apply the same description to ‘vicarious expectations’ –very probably present in apes. Thus, (empty) vicarious expectations and one’s own (full) contents are distinguished without need of meta-representation. Then, three proposals are made. First, vicarious expectations are enough to support apes’ Theory-of-Mind (including ‘spontaneous altruism’). Second, since vicarious expectations require a profile previously built in the subject that activates them, this subject cannot activate any vicarious expectation of mental states that are intrinsically impossible for him. Third, your mental states that think of me as a distal individual are intrinsically impossible states for me, and therefore, to estimate them, I must estimate your mental contents. This ability (the original nucleus of human Theory-of-Mind) is essential in human lifestyle. It is involved in unpleasant and pleasant self-conscious emotions, which respectively contribute to ‘social order’ and to cultural innovations. More basically, it makes possible the human (prelinguistic or linguistic) communication, since it originally made possible the understanding of others’ mental states as states that are addressed to me, and that are therefore impossible for me.

Generative models have recently gained popularity in Remote Sensing, offering substantial benefits for interpreting and utilizing satellite imagery across diverse applications such as climate monitoring, urban planning, and wildfire detection. These models are particularly adept at addressing the challenges posed by satellite images, which often exhibit domain variability due to seasonal changes, sensor characteristics, and especially variations in spectral bands. Such variability can significantly impact model performance across various tasks. In response to these challenges, our work introduces a novel approach that harnesses the capabilities of Generative Adversarial Networks (GANs), augmented with Contrastive Learning, to generate target domain images that account for multi-spectral band variations effectively. By maximizing mutual information between corresponding patches and leveraging the power of GANs, our model aims to generate realistic-looking images across different multi-spectral domains. We present a comparative analysis of our model against other well-established generative models, demonstrating its efficacy in generating high-quality satellite images while effectively managing domain variations inherent to multi-spectral diversity.

Accurate and consistent rainfall observations are vital for climatological studies in support of better planning and decision making. However, estimation of accurate spatial rainfall is limited by sparse rain gauge distributions. Satellite rainfall products can thus potentially play a role in spatial rainfall estimation but their skill and uncertainties need to be under-stood across spatial-time scales. This study aimed at assessing the temporal and spatial performance of seven satellite products (TARCAT (Tropical Applications of Meteorology using SATellite and ground-based observations (TAMSAT) African Rainfall Climatology And Time series), Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), Tropical Rainfall Measuring Mission (TRMM-3B43), Climate Prediction Center (CPC) Morphing (CMORPH), the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks- Climate Data Record (PERSIANN-CDR), CPC Merged Analysis of Precipitation (CMAP) and Global Precipitation Climatology Project (GPCP) using gridded (0.05o) rainfall data over East Africa for 15 years(1998-2012). The products’ error distributions were qualitatively compared with large scale horizontal winds (850 mb) and elevation patterns with respect to corresponding rain gauge data for each month during the ‘long’ (March-May) and ‘short’ (October-December) rainfall seasons. For validation only rainfall means extracted from 284 rain gauge stations were used, from which qualitative analysis using continuous statistics of Root Mean Squared Difference, Standard deviations, Correlations, coefficient of determinations (from scatter plots) were used to evaluate the products’ performance. Results revealed rainfall variability dependence on wind flows and modulated by topographic influences. The products’ errors showed seasonality and dependent on rainfall intensity and topography. Single sensor and coarse resolution products showed lowest performance on high ground areas. All the products showed low skills in retrieving rainfall during ‘short’ rainfall season when orographic processes were dominant. CHIRPS, CMORPH and TRMM performed well, with TRMM showing the best performance in both seasons. There is need to reduce products’ errors before applications.

This study explores the use of Bayesian Neural Networks (BNNs) for estimating chlorophyll-a concentration ([CHL-a]) from remotely sensed data. The BNN model enables uncertainty quantification, offering additional layers of information compared to traditional ocean colour models. An extensive in situ bio-optical dataset is utilized, generated by merging 27 data sources across the world’s oceans. The BNN model demonstrates remarkable capability in capturing mesoscale features and ocean circulation patterns, providing comprehensive insights into spatial and temporal variations in [CHL-a] across diverse marine ecosystems. In comparison to established ocean colour algorithms, such as Ocean Colour 4 (OC4), the BNN shows comparable performance in terms of correlation coefficients, errors, and biases when compared with the in situ data. The BNN, however, further provides critical information about the distribution of [CHL-a], which can be used to assess uncertainties in the prediction. Moreover, the BNN model’s ability to provide more information for coastal waters, especially with higher spatial resolution imagery, offers valuable advantages for marine research and management. By quantifying uncertainty, the model builds confidence in [CHL-a] predictions, even in regions with limited data coverage. This is reflected by the spread in the model predictions, where the BNN can detect a range of uncertainty bounds that reflect confidence in the retrievals. Introducing BNNs as probabilistic ocean colour models represents a significant advancement, enabling more accurate and reliable predictions of [CHL-a] in the global oceans.

The use of didactic and interactive materials is essential in generating interest among students and teachers in utilizing mathematical models and digital technologies for the educational process. These materials create a dynamic and engaging learning environment that promotes a deeper understanding of complex concepts and fosters a passion for learning. Additionally, integrating interactive materials into pilot courses can assist teachers in better assessing student learning and adjusting the teaching strategies accordingly and the teachers can receive valuable insights into students' strengths and weaknesses, allowing them to provide targeted support and intervention when needed. In this context, the purpose of this paper is to introduce an approach for solving mathematics problems embedded in technical scenarios within the defense and security fields with the aid of digital technology. In this way, students can visualize abstract concepts, experiment with different scenarios, and receive instant feedback on their understanding. This paper helps to reinforce key concepts and enhance problem-solving skills, sparking curiosity, creativity, and encouraging active participation and collaboration. Throughout the development of this proposal, good practices are adopted, and advice for improvement is collected while having a wide range of users, giving feedback, discussions, and testing (pilot) short-term learning/teaching/training activities.

This study analyses regional development in one of the poorest provinces in China, Guizhou Province, between 2000 and 2012 using a multiscale and multi-mechanism framework. In general, regional inequality has been declining since 2000. In addition, economic development in Guizhou Province presented spatial agglomeration and club convergence, which shows how the development pattern of one core area, two-wing areas and a contiguous area at the edge of the province have been developed between 2006 and 2012. Multilevel regression analysis revealed that industrialization and investment level were the primary driving forces of regional economic disparity in Guizhou Province. The influences of marketization and decentralization on regional economic disparity were relatively weak. Investment level reinforced regional economic disparity and the development of core-periphery structure in the province. However, investment level actually weakened the regional economic disparity in Guizhou Province when the variable of time was considered. In addition, both the topography and urban–rural differentiation were the two main reasons for forming a core-periphery structure in Guizhou Province.

Leakages in the water distribution networks (WDNs) are real problems for utilities and other governmental agencies. Timely leak detection and location identification has been a challenge. In this paper, an integrated approach to geospatial and infrared image processing method was used for robust leak detection. The method combines drops in flow, pressure, and chlorine residuals to determine potential water leakage locations in the WDN using Geographic Information System (GIS) techniques. GIS layers were created from the hourly values of these three parameters for the city of Sharjah provided by Sharjah Electricity, Water and Gas Authority (SEWA). These layers are then analyzed for locations with dropped values of each of the parameters and are overlaid with each other. In the case where there were no overlaying locations between flow and pressure, further water quality analysis was avoided, assuming no potential leak. In the case where there are locations with drops in flow and pressure layers, these overlaying locations are then examined for drops in chlorine values. If overlaying locations are found, then these regions are considered potential leak locations. Once potential leak locations are identified, a specialized remote sensing technique can be used for precise leak location. This study also demonstrated the suitability of using an infrared camera for leak detection in a laboratory-based setup. This paper concludes that the following methodology can help water utility companies in the timely detection of leaks, saving money, time, and effort.

The increasing emergence of multidrug resistant (MDR) pathogens causes difficult-to-treat infections, with long-term hospitalizations and high incidence of death, thus representing a global public health problem. To manage MDR bacteria bugs, new antimicrobial strategies are necessary, and their introduction in practice is a daily challenge for scientists in the field. An extensively studied approach to treat MDR infections consists in inducing high levels of reactive oxygen species (ROS) by several methods. Although further clinical investigations are mandatory on the possible toxic effects of ROS to mammalian cells, clinical evaluations are extremely promising, and their topical use to treat infected wounds and ulcers, also in presence of biofilm, is already clinically approved. Biochar (BC) is a carbonaceous material obtained by pyrolysis of different vegetable and animal biomass feedstocks, at 200−1000 °C in limited presence of O2. Recently, it has been demonstrated that BC capability of removing organic and inorganic xenobiotics is mainly due to the presence of persistent free radicals (PFRs), which can activate oxygen, H2O2 or persulfate in the presence or not of transition metals by electron transfer thus generating ROS, which in turn degrade pollutants by advanced oxidation processes (AOPs). In this context, the antibacterial effects of BC containing PFRs have been demonstrated by some authors against Escherichia coli and Staphylococcus aureus, thus giving birth to our idea of the possible use of BC-derived PFRs as a novel method capable of inducing ROS generation for antimicrobial oxidative therapy. Here, the general aspects concerning ROS physiological and pathological production and regulation, and the mechanism by which they could exert antimicrobial effects have been reviewed. The methods currently adopted to induce ROS production for antimicrobial oxidative therapy have been discussed. Finally, for the first time, BC-related PFRs have been proposed as a new source of ROS for antimicrobial therapy via AOPs.

Rhizosphere microorganisms can utilize nutrient resources in the rhizosphere efficiently, while rhizosphere growth-promoting bacteria play a crucial role in regulating soil fertility and promoting plant health. In this study, we employed a combination of isolation, purification, identification of dominant microorganisms, and high-throughput sequencing technology to analyze the main microbial groups and diversity in the rhizosphere soil of Helichrysum arenarium（L.）Moench. in Altay, Xinjiang，China. By isolating bacterial strains from the rhizosphere soil using a dilution coating method, we successfully obtained 43 strains. Subsequently, selective medium was utilized to screen for growth-promoting characteristics among these isolated strains from the rhizosphere soil of H. arenarium（L.）Moench.. The results obtained through high-throughput amplification sequencing revealed that the rhizosphere soil harbored diverse bacterial communities belonging to 35 phyla, 93 orders, 215 families, 324 genera and 231 species associated with H. arenarium（L.）Moench., as well as fungal communities consisting of 14 phyla ,47 orders ,96 families ,204 genera and 571 species. Among these bacterial communities identified, eight phyla exhibited relative abundances exceeding 1%, with Actinobacteriota being most predominant at an abundance rate of 41.30%. Additionally,five bacterial genera were found to have relative abundances exceeding 1% ,with Arenimicrobium_luteum being most abundant at 6.11% . Furthermore,four fungal phyla displayed relative abundances surpassing1 % ,with Ascomycota accounting for 67.21% of total fungal community composition.Finally,four fungal genera demonstrated relative abundances higher than 1% ,with Knufia representing 3.80% of total fungal community composition.The analysis on culturable bacteria's promotion activity within the rhizospheric soil indicated that two strains,S16and S29, exhibited the highest solubility index for inorganic phosphorus. However, it lacked the ability to dissolve organophosphorus and potassium. On the other hand, strains S7 and S10, which were screened, demonstrated nitrogen-fixing capabilities. Nine strains with excellent iron-bearing capacity were identified, with strain S16 exhibiting the highest D/d value and strongest iron-bearing capacity among them. Our findings revealed that 43 rhizosphere soil bacteria belonged to three genera. The ten growth-promoting bacteria were identified as Kocuria rosea, Priestia megaterium, Bacillus mobilis, B. bataviensis, B. mycoides (two strains), B. paramobilis, and Alcaligenes faecalis. This study provides a particular research basis for the influence of H. arenarium（L.）Moench. rhizosphere microorganisms on soil nutrient release and depicts a solution for improving the yield and quality cultivation of H. arenarium（L.）Moench. by isolating, screening, and identifying rhizosphere soil growth-promoting bacteria, including screening strains with growth-promoting functions and analyzing the population structure of rhizosphere and habitat soil in combination with high-throughput sequencing technology.

Satellite Image Time Series (SITS) is a data set which includes satellite images across several years with a high acquisition rate. Radiometric normalization is a fundamental and important preprocessing method for remote sensing applications using SITS due to the radiometric distortion caused by noise between images. Normalizing the subject image based on the reference image is a general strategy when using traditional radiometric normalization methods to normalize multi-temporal imagery (usually two scenes or three scenes in different time phases). However, these methods are not suitable for calibrating SITS because they cannot minimize the radiometric distortion between any pair of images in SITS. The existing relative radiometric normalization methods for SITS are based on linear assumptions which cannot effectively reduce nonlinear radiometric distortion caused by continuously changing noise in SITS. To overcome this problem and obtain a more accurate SITS, this study proposed a Nonlinear Radiometric Normalization Model (named NMAG) for SITS based on Artificial Neural Networks (ANN) and Greedy Algroithm (GA) . In this method, GA was used to determine the correction order of SITS and calculate the error between the image to be corrected and normalized images, which avoided the selection of a single reference image. ANN was used to obtain the optimal solution of error function, which minimized the radiometric distortion between different images in SITS. SITS composed of 21 Landsat-8 images in Tianjin City from October 2017 to January 2019 were selected to test the method. We compared NMAG with other two contrasting method (refered as CM1 and CM2), and found that the average of root mean square error $(\mu_{RMSE})$ of NMAG $(497.22)$ was significantly smaller than those of CM1 $(641.39)$ and CM2 $(543.47)$, and the accuracy of normalized SITS obtained by using NMAG has increased by 22.4\% and 8.5\% toward CM1 and CM2, respectively. These experimental results confirmed the effectiveness of NMAG in the reduction of radiometric distortion caused by continuously changing noise between images in SITS.

On February 28th, 2023, Spanish Royal Decree 150/2023 as published, approving the plans for the management of the maritime space of the five Spanish marine demarcations. This Royal Decree Law inaugurates the Planning of the Maritime Space in Spain. Terrestrial Space Planning is very advanced, both in the legislation and in the instruments that are enabled to organize this space, from the national scale to the municipal scale, passing through the regional, provincial, and supra-municipal scales. Naturally, both Planning must converge and thus speak of an Integrated Space Planning. In some of the countries that we can take as global references, this convergence in the form of integration is taking place. On the other hand, and within the context of climate change, coastal cities will be the most affected, both by the potential rise in sea level and by the increasingly continuous and greater extreme events. Integrated Space Planning is postulated as the most effective tool to carry out adequate territorial and urban planning of our coastal cities, as well as to analyse the influence and possible corrective measures to apply against the effects produced by climate change.

During choriogenesis in insects, chorion (eggshell) is formed by surrounding follicular epithelial cells in ovarioles. However, the regulatory endocrine factor(s) activating choriogenesis and the chemical components on eggshell deserve further exploration. In two representative Coleopterans, a Coccinellid Henosepilachna vigintioctopunctata and a Chrysomelid Leptinotarsa decemlineata, genes encoding the 20-hydroxyecdysone (20E) receptor heterodimer, ecdysone receptor (EcR) and ultraspiracle (USP), and two chitin biosynthesis enzymes UDP-N-acetylglucosamine pyrophosphorylase (UAP) and chitin synthase (ChS1), were highly expressed in ovaries of the young females. RNA interference (RNAi)-aided knockdown of either HvEcR or Hvusp in H. vigintioctopunctata inhibited oviposition, suppressed the expression of HvChS1, and lessened the positive signal of Calcofluor staining on the chorions, which suggests the reduction of a chitin-like substance (CLS) deposited on eggshells. Similarly, RNAi of LdEcR or Ldusp in L. decemlineata constrained oviposition, decreased the expression of LdUAP1 and LdChS1, and declined CLS contents in the resultant ovaries. Knockdown of LdUAP1 or LdChS1 caused similar defective phenotypes, i.e., reduced oviposition and CLS contents in the L. decemlineata ovaries. These results, for the first time, indicate that 20E signaling activates choriogenesis in two Coleopteran species. Moreover, our findings propose the deposition of a CLS on the chorions.

Earth observation satellites can capture images of specific areas in Earth's orbit based on user requirements. Despite the high demand for satellite imagery, satellites remain a scarce resource. When there are many observation tasks that are large and dense, they may interfere with each other due to satellite mobility. To address this issue, a multi-satellite scheduling algorithm based on a task merging mechanism has been proposed. This paper proposes a two-stage approach to the problem: task merging phase and merging task scheduling phase. The task merging phase introduces two methods based on Mean Shift and Complete Graph to generate the merging task set. In the merging task scheduling phase, a multi-satellite scheduling algorithm based on merging tasks is employed to solve the problem. Finally, certain measurement experiments suggest the effectiveness of the algorithm.

This study explores the near-surface dispersion mechanisms of contaminants in coastal waters, leveraging a comprehensive method that includes using dye and drifters as tracers, coupled with diverse observational platforms like drones, satellites, in-situ sampling, and HF radar. The aim is to deepen our understanding of surface currents' impact on contaminant dispersion, thereby improving predictive models for environmental incident management, including pollutant releases. Rhodamine WT dye, chosen for its significant fluorescent properties and detectability, along with drifter data, allowed us to investigate the dynamics of near-surface physical phenomena such as the Ekman current, Stokes drift, and wind-driven currents. Our research emphasizes the importance of integrating scalar tracers and Lagrangian markers in experimental designs, revealing differential dispersion behaviors due to near-surface vertical shear caused by the Ekman current and Stokes drift. The elongation direction of the dye patch aligns with the Ekman current direction during slow current conditions. Analytical calculations of vertical shear, based on the Ekman current and Stokes drift, closely matched those derived from tracer observations. Over a 7-hour experiment, the vertical diffusivity of near-surface water was observed at 2×10-4 m2/s, and the horizontal eddy diffusivity of the dye patch and drifters reached the order of 1 m2/s and at a 1000m length scale. Particle tracking models demonstrate that while HF radar currents can effectively predict the trajectories of tracers near the surface, incorporating near-surface currents, including Ekman current, Stokes drift, and windage, is essential for a more accurate prediction of the fate of surface floats.

In bacteria, enzymes usually comprise of enzymatic domains only. In eukaryotes/mammals, and here particularly in context of signal transduction, proteins with enzymatic functions contain additional modules promoting interactions with cooperating signaling proteins. Often such protein-protein interaction domains, such as SH2- and SH3-domains, help to localize the enzymatic function (e.g. kinase, phosphatase) to another protein, cellular compartment, or membrane nanodomain. In such cases, the interaction domain(s) serve(s) the enzymatic function. However, there are molecular situations in which an interaction domain fulfills its task completely independent of the protein´s enzymatic function and in this review, we call such function the adapter function of an enzyme. Though many stories can be told, we will concentrate on several proteins playing crucial roles in cells of the immune system, such as BTK, PI3K, and SHIP1, as well as in cancer cells, such as proteins of the RAS/ERK MAPK pathway. We will also discuss how these adaptor functions of enzymes determine or even undermine the efficacy of targeted therapy compounds, such as ATP-competitive kinase inhibitors, thereby highlighting the need to develop pharmacological approaches, such as PROTACs, that eliminate the entire protein. We also review how genetic kock-out and knock-in approaches can be leveraged to identify adaptor functions of signaling proteins.

The constant increase in volume and wide variety of available Internet of Things (IoT) devices leads to highly diverse software and hardware stacks, which opens new avenues for exploiting previously unknown vulnerabilities. The ensuing risks are amplified by the inherent IoT resource constraints both in terms of performance and energy expenditure. At the same time, IoT devices often times generate or collect sensitive, real-time data used in critical application scenarios (e.g. health monitoring, transportation, smart energy, etc.). All these factors combined make IoT networks a primary target and potential victims for malicious actors. In this paper, we present a brief overview of existing attacks and defense strategies against urban IoT networks. The goal of this work is twofold: First, it presents a summary of some of the common attack vectors and the corresponding solutions available in the research literature. Then, the paper lays out a theoretical plan and a corresponding pipeline of steps (i.e. development and implementation process) for the design and application of the solutions encountered throughout the course of the research efforts. The end goal of following this plan is the deployment of the proposed IoT security measures in a real-world urban IoT infrastructure.

This study extends the CGAN-based MODIS cloud vertical-profile (64×64-scene, about 70km width×15km height) retrieval technique developed by Leinonen et al. (2019) [1] to construct seamless 3D cloud fields for the MODIS granules [2]. Firstly, the accuracy and spatial continuity of the Leinonen et al. [1] retrievals are statistically evaluated. Then, according to the characteristics of the retrieval error, a spatially overlapping-scene ensemble generation method and a bi-directional Ensemble Binning Probability Fusion (CGAN-BEBPF) technique are developed, which improves the CGAN retrieval accuracy and support to construct seamless 3D clouds. The CGAN-BEBPF technique involves three steps: cloud masking, intensity scaling, and optimal value selection. It ensures adequate coverage of the low reflectivity areas while preserving the high reflectivity cloud cores. The technique is applied to retrieve the 3D cloud fields of Typhoon Chaba and a multi-cell convective system. The cloud structures of the CGAN-BEBPF results are highly consistent with the ground-based radar observations. It can retrieve weak clouds at the top levels of convective storms and hurricanes that were missed by ground-based radars and fill the gaps of the ground-based radar lower-level PPI scan. Overall, CGAN-BEBPF can be automated to retrieve the rich informative 3D structures of typhoons and severe convective systems over the oceans along the MODIS swath, which certainly presents great value for improving maritime and near-shore typhoons and convection prediction and other cloud-sensitive applications.

Traditional photodynamic therapy (PDT) is often limited in its efficacy by insufficient light penetration, inefficient photosensitizer energy transfer, and inadequate target tissue oxygenation. X-ray activated PDT (XPDT), using nanoparticle (NPs) photosensitizers comprised of europium-doped yttrium oxide cores encased within silica shells (Y₂O₃:Eu@SiO₂), offers the potential of overcoming most of these limitations. Aimed at maximizing the generation of reactive oxygen species (ROS), this study reports methodologies for investigation of the effects of synthesis conditions on the characteristics of Y₂O₃:Eu@SiO₂ NP. Transmission electronic microscope (TEM) revealed that the NP core diameter was linearly, positively correlated with condensation time (R2 = 0.7357, p < 0.05) and linearly, negatively correlated with urea concentration (R2 = 0.958, p < 0.05). The addition of cetyltrimethylammonium bromide (CTAB) during core synthesis demonstrated enhanced particle dispersion. Silica coating via APTES-1 and TEOS-3 conditions resulted in Y₂O₃:Eu@SiO₂ NPs that generated the highest ROS, with 8.7-fold and 8.9-fold increase respectively in relative fluorescence intensity at 8 Gy irradiation compared to a 5.2-fold increase in PBS under the same condition. In vivo evaluation using 18F-fluorothymidine positron emission tomography (18F-FLT PET) of mouse xenografts of a human prototypical cancer (ovarian) revealed a 55% reduction in tumor proliferation of radiation-sensitive CAOV3 tumors treated with Y₂O₃:Eu@SiO₂ NPs compared to a 19% reduction in tumor proliferation of radiation-resistant SKOV3 tumors on the fourth day post-treatment. Taken together, these findings suggest Y₂O₃:Eu@SiO₂ NPs can serve as effective photosensitizers for XPDT and their performance characteristics can be optimized by selecting synthesis conditions and parameters to deliver the desirable therapeutic effects, offering novel approaches to circumventing many of the limitations of conventional PDT in the treatment of cancer.

The main purpose of this study was to create a prototype of an unmanned aerial system equipped with intelligent hardware and software technologies necessary for monitoring the health and growth of crops in orchards. Another important objective was to use low-cost sensors that accurately measure ultraviolet solar radiation. The device, which needs to be attached to the commercial DJI Mini 4 Pro drone, should be small in size, portable, and have very low energy consumption. For this purpose, the widely used Vishay VEML6075 digital optical sensor was selected and implemented in a prototype, alongside a Raspberry Pi Zero 2W minicomputer. To collect data from these sensors, a program written in Python was used, containing specific blocks for data acquisition from each sensor, to facilitate the monitoring of ultraviolet (UV) radiation, or battery current. By analyzing the data obtained from the sensors, several important conclusions are drawn that may provide valuable pathways for the further development of mobile or modular equipment. Furthermore, the results of the plant condition analysis with proposed models in the Geographic Information System (GIS) environment were also presented. The visualization of maps indicating variations in vegetation condition led to the identification of problem areas like hydric stress.

In this paper, we studied the problem of early detection of natural phenomena associated with the formation of severe floods and the possibility of constant monitoring of their occurrence. The test object for the study was severe floods on the Crimean Peninsula, which occurred due to heavy rainfall in the summer of 2021. Based on the selected satellite and other data related to this period, an analysis carried out and key atmospheric objects identified, according to the proposed algorithm for their identification and grouping. Based on the results of the work, a positive conclusion made on the issue of their use to identify the likelihood of natural disasters that could lead to floods. In addition, the authors proposed to develop a portal for collecting and processing a large amount of satellite data related to flood prevention and monitoring the development of such processes, and stressed the need to create it.

The Sentinel-2 satellite mission offers high resolution multispectral time series image data, enabling the production of detailed land cover maps globally. At this scale, the trade-off between processing time and result quality is a central design decision. Currently, this machine learning task is usually performed using pixelwise classification methods. The radical shift of the computer vision field away from hand engineered image features and towards more automation by representation learning comes with many promises, including higher quality results and less engineering effort. In this paper we assess fully convolutional neural networks architectures as replacements for a Random Forest classifier in an operational context for the production of high resolution land cover maps with Sentinel-2 time series at the country scale. Our contributions include a framework for working with Sentinel-2 L2A time series image data, an adaptation of the U-Net model for dealing with sparse annotation data while maintaining high resolution output, and an analysis of those results in the context of operational production of land cover maps.

A model of a single-server queuing-inventory system (QIS) with catastrophes and a finite waiting room for consumer customers (c-customers) has been developed. When a catastrophe occurs, all inventory in the system's warehouse is destroyed, but c-customers in the system are still waiting for restocking. In addition to c-customers, negative customers (n-customers) are also taken into account, which displaces one c-customers (if any). The policy (?, ?) is used to replenish stocks. If upon arrival of the c-customer the inventory level is zero, then according to Bernoulli’s scheme this c-customer is either lost or joining the queue. The mathematical model of the studied QIS is constructed in the form of a two-dimensional continuous-time Markov chain (2D CTMC). Exact and approximate methods for calculating the steady-state probabilities of constructed 2D CTMCs are developed and closed-form expressions are derived for calculating the performance measures. The results of numerical experiments are presented, demonstrating the high accuracy of the developed approximate formulas, as well as the behavior of performance indicators depending on the reorder point. In addition, an optimization problem is solved to obtain the optimal reorder point to minimize the expected total cost.

In recent decades, as eutrophication in inland and coastal waters (ICWs) has increased due to anthropogenic activities and global warming, so has the need for timely monitoring. Compared with traditional sampling and laboratory analysis methods, satellite remote sensing technology can provide macro-scale, low-cost, and near real-time water quality monitoring services. The Geostationary Ocean Color Imager (GOCI), integrated onboard the Communication Ocean and Meteorological Satellite (COMS) from the Republic of Korea, was the first geostationary ocean color observation satellite and was operational from April 1, 2011 to March 31, 2021. Over ten years, GOCI has observed oceans, coastal waters, and inland waters within its 2,500 km×2,500 km target area centered on the Korean Peninsula. The most attractive feature of GOCI, compared with other commonly used watercolor sensors, was its high temporal resolution (1h, eight times daily from 0 UTC to 7 UTC), providing the opportunity to monitor the quality of ICWs, where optical properties can change rapidly throughout the day. This systematic review aims to comprehensively review GOCI features and applications in ICWs, analyzing progress in atmospheric correction algorithms and water quality monitoring. Analyzing 123 articles from the Web of Science and China National Knowledge Infrastructure (CNKI) through a bibliometric quantitative approach, we examined GOCI’s strength and performance with different processing methods. These articles reveal that GOCI played an essential role in monitoring the ecological health of ICWs in its observation area in East Asia. GOCI has led the way to a new era of geostationary ocean satellites, providing new technical means for monitoring water quality in oceans, coastal zones, and inland lakes. We also discuss the challenges encountered by geostationary satellites in monitoring water quality and provide suggestions for improvements.

Recent omics technologies have revolutionized the study of molecular plant responses to air pollution, overcoming previous limitations. This review synthesizes the latest advancements in molecular plant responses to major air pollutants, emphasizing ozone (O3), nitrogen oxides (NOX) and particulate matter (PM) research. These pollutants induce stress responses common to other abiotic and biotic stresses, including the activation of reactive oxygen species (ROS)-scavenging enzymes and hormone signaling pathways. New evidence showed the central role of antioxidant phenolic compound biosynthesis via phenylpropanoids pathway in air pollution stress responses. Transcription factors like WRKY, AP2/ERF, and MYB, which connect hormones signaling to antioxidant biosynthesis, were also affected. To date, research has predominantly focused on laboratory studies analyzing individual pollutants. This review highlights the need for comprehensive field studies and the identification of molecular tolerance traits, which are crucial for the identification of tolerant plant species aimed to the development of sustainable Nature Based Solutions (NBS) to mitigate urban air pollution.

The aim of this research is to expand recent developments in the mapping of pasture yield with remotely piloted aircraft systems to that of satellite-borne imagery. Up to date, spatially explicit and accurate information of the pasture resource base is needed for improved climate-adapted livestock rangeland grazing. This study developed deep learning predictive models of pasture yield, as total standing dry matter in tonnes per hectare (TSDM(tha−1)), from field measurements and both remotely piloted aircraft systems and satellite imagery. Repeated remotely piloted aircraft system structure measurements derived from structure from motion photogrammetry, provided measures of pasture biomass from many overlapping high-resolution images. Repeated remotely piloted aircraft system measures throughout a growing season, were modelled with persistent photosynthetic pasture responses from various Planet Dove high spatial resolution satellite image-derived vegetation indices. Pasture height modelling as an input to the modelling of yield was assessed against terrestrial laser scanning and reported correlation coefficients (R2) from 0.3 to 0.8 for both a coastal grassland and inland woodland pasture. Accuracy of the predictive modelling from both the remotely piloted aircraft system and the Planet Dove satellite image estimates of pasture yield ranged from 0.8 to 1.8 TSDM(tha−1). These results indicated that the practical application of repeated remotely piloted aircraft system derived measures of pasture yield can, with some limitations, be scaled-up to satellite-borne imagery to provide more temporally and spatially explicit measures of the pasture resource base.

In order to make full use of the complementarity of existing navigation systems to improve the navigation and positioning accuracy, this paper investigates the visual/INS integrated navigation system based on the factor graph optimization (FGO) algorithm. In the traditional visual/INS integrated navigation system, there is often the problem of image blurring due to the rapid change of light and shadow captured by the vision sensor and the attitude change of the carrier, which leads to the decrease of the positioning accuracy of the combined navigation system. Therefore, this paper firstly introduces Retinex algorithm and BID algorithm to carry out the design of image de-blurring method; then implements the visual/INS integrated navigation algorithm based on the factor graph optimization method; finally, the experimental scheme is designed, comparative experiments are carried out, and the researched method is verified by using the public dataset. The experimental results show that the combined navigation algorithm realized in this paper has high accuracy, and its positioning accuracy is improved by 40% compared with visual positioning.

The Geostationary Ocean Color Imager (GOCI) of the Communication, Ocean, and Meteorological Satellite (COMS) increases the chance of acquiring images with greater clarity at eight times a day and is equipped with spectral bands suitable for monitoring crop yield with a moderate spatial resolution. The objectives of this study were to classify nationwide paddy fields and to project rice (Oryza sativa) yield and production using the grid-based GRAMI-rice model and GOCI satellite products over South Korea from 2011 to 2014. Solar insolation and temperatures were obtained from COMS and the Korea local analysis and prediction system for model inputs, respectively. The paddy fields and transplanting dates were estimated using Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance and land cover products. The crop model was calibrated using observed yield data in 11 counties and was applied to 62 counties in South Korea. The overall accuracies of the detected paddy fields ranged from 89.5 to 90.2%. The simulated rice yields statistically agreed with observed yields, with root-mean-square errors of 0.219 to 0.451 ton ha-1 and Nash–Sutcliffe efficiencies of 0.241 to 0.733 in four years, respectively. According to paired t-tests (α = 0.05), the simulated and observed rice yields were not significantly different. These results demonstrate the possible development of a crop information delivery system that can classify land cover, simulate crop yield, and monitor regional crop production on a national scale.

Space–air–ground integrated network (SAGIN) is considered as an enabler for the sixth-generation (6G) networks. By integrating terrestrial and non-terrestrial (satellite, aerial) networks, SAGIN seem to be a quite promising solution to provide reliable connectivity everywhere and all the time. Their availability can be further enhanced if hybrid free space optical (FSO)/radio frequency (RF) links are adopted. In this paper, the performance of a hybrid FSO/RF communication system operating in SAGIN has been analytically evaluated. In the considered system, a high altitude platform station (HAPS) is used to forward the satellite signal to the ground station. Moreover, the FSO channel model assumed takes into account the turbulence, pointing errors and path losses, while for the RF links, a relatively new composite fading model has been considered. In this context, a new link selection scheme has been proposed that is designed to reduced the signaling overhead required for the switching operations between the RF and FSO links. The analytical framework that has been developed is based on the Markov chain theory. Capitalizing on this framework, the performance of the system has been investigated using the criteria of outage probability and the average number of link estimations. The numerical results presented reveal that the new selection scheme offers a good compromise between performance and complexity.

High-wind structures were identified in the Cross-Calibrated Multi-Platform (CCMP) ocean wind vector reanalysis for comparison with winds measured by satellite radiometers and synthetical aperture radar (SAR) instruments from February to October 2023. The comparison aims to evaluate bias, uncertainty, and spatial correlations with the goal of enhancing the accuracy of ocean wind datasets during tropical cyclones (TCs). In 10°longitude×10°latitude blocks, each containing a TC, Soil Moisture Active Passive (SMAP) and Advanced Microwave Scanning Radiometer 2 (AMSR2) winds are 6.5% and 4.8% higher than CCMP, while Advanced Scatterometer (ASCATB) is 0.8% lower. For extratropical cyclones, AMSR2 and SMAP also show stronger winds with a 5% difference, and ASCATB is about 0.3% weaker compared to CCMP. The comparison between SAR and CCMP for TC winds, sampled at the locations and time frames of SAR tiles, indicates that SAR winds around TCs are about 9% higher than CCMP winds. Using empirically defined TC structural indices, we find that the TCs observed by CCMP are shifted in locations and lack a compact core region. A Random Forest (RF) regressor was applied to TCs in CCMP with corresponding SAR observations, nearly correcting the full magnitude of low bias in CCMP statistically, with a 15m/s correction in the core region. The hierarchy of importance among the predictors is as follows: CCMP wind speed (62%), distance of SAR pixels to the eye region (21%) and eye center (7%), and distance of CCMP pixels to the eye region (5%) and eye center.

Abstract: Giant Cell Arteritis (GCA) is an autoimmune/autoinflammatory disease affecting large vessels in patients over 50 years old. The disease presents as an acute inflammatory response with two phenotypes, cranial-GCA and Large Vessel Vasculitis (LV)-GCA, involving the thoracic aor-ta and its branches. 18F-fluorodeoxyglucose positron emission tomography-computed tomogra-phy (18F-FDG PET-CT) is among the imaging techniques contributing to diagnosis of patients with systemic disease. However, its association with soluble inflammatory markers is still elu-sive. This proof-of-concept study aims to identify novel soluble serum biomarkers in PET/CT pos-itive patients with LV-GCA and associate them with active (0 months) and inactive disease (6 months following treatment), in sequential samples. The Most-Disease-Segment Target-to-Background Ratio (TBRMDS) was calculated for 9 LV-GCA patients, while 12 cranial-GCA and 7 Polymyalgia Rheumatica patients with negative initial PET/CT served as disease controls. Serum macrophage-related cytokines were evaluated by Cytometric Bead Array (CBA). Finally, previously published NMR-metabolomics data acquired at the same blood sampling were associated with PET/CT findings. TBRMDS was significantly increased in active versus inactive disease (3.45 vs. 2.55, p=0.008). The analysis identified 6 serum metabolites, as more sensitive to change from the active to inactive state. Among them, choline levels were exclusively altered in the LV-GCA group, but not the disease controls. Cytokine levels were not associated with PET/CT activity. Combining with CRP, ESR, and TBRMDS a composite index was generated to depict well the differences between active and inactive systemic LV-GCA (25.45 vs 11.45, p=0.0039). These preliminary results could pave the way for more extensive studies integrating serum metabolomic parameters with PET/CT imaging data to extract sensitive composite disease indexes useful for the everyday clinical practice.

The signal-to-noise ratio (SNR) data is part of the global navigation satellite systems (GNSS) observables. In a marine environment, the oscillation of the SNR data can be used to derive reflector heights. Since the attenuation of the SNR oscillation is related to the roughness of the sea surface, the significant wave height (SWH) of the water surface can be calculated from the analysis of the attenuation. The attenuation depends additionally on the relation between the coherent and the incoherent part of the scattered power. The latter is a function of the correlation length of the surface waves. Because the correlation length changes with respect to the direction of the line of sight relative to the wave direction, the attenuation must show an anisotropic characteristic. In this work, we present a method to derive the wave direction from the anisotropy of the attenuation of the SNR data. The method is investigated based on simulated data as well by the analysis of experimental data from a GNSS station in the North Sea.

Operational weather and also flood forecasting has been performed successfully for decades and is of great socioeconomic importance. Up to now, forecast products focus on atmospheric variables, such as precipitation, air temperature and, in hydrology, on river discharge. Considering the full terrestrial system from groundwater across the land surface into the atmosphere, a number of important hydrologic variables are missing especially with regard to the shallow and deeper subsurface (e.g. groundwater), which are gaining considerable attention in the context of global change. In this study, we propose a terrestrial monitoring/forecasting system using the Terrestrial Systems Modeling Platform (TSMP) that predicts all essential states and fluxes of the terrestrial hydrologic and energy cycles from groundwater into the atmosphere. Closure of the terrestrial cycles provides a physically consistent picture of the terrestrial system in TSMP. TSMP has been implemented over a regional domain over North Rhine-Westphalia and a continental domain over European in a real-time forecast/monitoring workflow. Applying a real-time forecasting/monitoring workflow over both domains, experimental forecasts are being produced with different lead times since the beginning of 2016. Real-time forecast/monitoring products encompass all compartments of the terrestrial system including additional hydrologic variables, such as plant available soil water, groundwater table depth, and groundwater recharge and storage.

The demand for improved service in mobile networks is always increasing. That means signal transmission with higher capacity is required. To achieve that goal a combined optical and wireless link using double polarization multiplexing has been developed. A high quality signal transmission is achieved by applying high polarization extinction ratio. The new concept is validated by experiments varying the bit rate and fiber length. A signal with 12 Gbit/s bit rate was transmitted over a 25 km long combined link with about 1.10-8 bit error rate. That result is much better than the already published data which were measured on links transmitting information with 2.5 Gbit/s bit rate. The polarization multiplexing technique is very practical for capacity enhancement in a specific section of an existing optical fiber by inserting a new link into it without deploying a new fiber.

Phenology is one of the ubiquitous fingerprints of climate change on our ecosystems. Monitoring the spatiotemporal patterns of vegetation phenology is thus critical. A wide range of sensors have been used to monitor vegetation phenology. Sensor point of view and resolution can potentially impact estimates of phenology. We compared three different sensors from three different remote sensing platforms—a UAV mounted RGB camera, an under canopy, upward facing hemispherical camera with R, G and NIR capabilities, and a tower mounted RGB PhenoCam—to estimate spring phenological transition in a mixed-species temperate forest in central Virginia, USA. Our study had two objectives: 1) to compare the above- and below- canopy inference of canopy greenness (green chromatic coordinate and normalized difference vegetation index) and canopy structural attributes (leaf area and gap fraction) by matching under-canopy hemispherical photos with high spatial resolution (0.03 m) drone imagery to find the appropriate spatial coverage and resolution for comparison; 2) to compare how each sensor performed in estimating the temporality of the spring phenological transition. We find that a spatial buffer of 20 m radius for UAV imagery is most closely comparable to under-canopy imagery in this system. Sensors and platforms agree within +/- 5 days of when canopy greenness stabilizes from the spring phenophase into the growing season. This work has implications for paring UAV imagery with both tower-based observation platforms, as well as plot-based studies (e.g. long-term monitoring, existing research networks, permanent plots).

This paper presents a comprehensive and adaptive framework for orchid conservation programs illustrated with data from published and unpublished case studies. There is a specific focus on West Australian terrestrial orchids, but many of the approaches have universal relevance. Aspects of the framework include (1) setting appropriate objectives, (2) establishing effective collaborations between scientists, volunteers and regulators to fill knowledge and funding gaps, (3) use of survey and demographics data to determine extinction risks and management requirements for species, (4) effective habitat management to overcome threats such as grazing, (5) finding potential new habitats by modelling climate and site data, (6) investigating the effectiveness of pollinators and (7) using seed baiting to detect mycorrhizal fungi. The relative cost and effectiveness of different methods used to propagate orchids for translocation are compared. Methods known to be successful, in order of complexity, include placement of seed in situ, vegetative propagation, symbiotic germination in non-sterile organic matter, symbiotic germination in sterile culture, asymbiotic sterile germination and clonal division in tissue culture. These form a continuum of complexity, cost, time required, faculties needed, as well as the capacity to maintain genetic diversity and produce seedlings preadapted to survive in situ. They all start with seed collection and lead to seed storage, living collections used as tuber banks and seed orchards, as well as translocation for conservation. They could also lead to commercial availability and sustainable ecotourism, both of which are needed to reduce pressure on wild plants. Overall, there has been a strong preference to use relatively complex, expensive and time-consuming methods for orchid conservation, despite evidence that simpler approaches have also been successful. These simpler methods, which include in situ seed placement and non-sterile germination on inorganic substrates, should be trialled in combination with more complex orchid propagation methods as part of an adaptive management framework. It is essential that orchid conservation projects harness the unique biological features of orchids, such as abundant seed production and mycorrhizal fungi which are far more widespread than their hosts. This is necessary to increase the efficiency and coverage of recovery actions for the largest and most threatened plant family.

Structural diversity is a key feature of forest ecosystems that influences ecosystem functions from local to macroscales. The ability to measure structural diversity in forests with varying ecological composition and management history can improve the understanding of linkages between forest structure and ecosystem functioning. Terrestrial LiDAR has often been used to provide a detailed characterization of structural diversity at local scales, but it is largely unknown whether these same structural features are detectable using aerial LiDAR data that are available across larger spatial scales. We used univariate and multivariate analyses to quantify cross-compatibility of structural diversity metrics from terrestrial versus aerial LiDAR in seven National Ecological Observatory Network sites across the eastern USA. We found strong univariate agreement between terrestrial and aerial LiDAR metrics of canopy height, openness, internal heterogeneity, and leaf area, but found marginal agreement between metrics that describe heterogeneity of the outer most layer of the canopy. Terrestrial and aerial LiDAR both demonstrated the ability to distinguish forest sites from structural diversity metrics in multivariate space, but terrestrial LiDAR was able to resolve finer-scale detail within sites. Our findings indicate that aerial LiDAR can be of use in quantifying broad-scale variation in structural diversity across macroscales.

Aiming at the problems of inaccurate segmentation of long object and information loss of small object in real-time semantic segmentation algorithm, this paper proposes a lightweight multi-branch real-time semantic segmentation network based on BiseNetV2. The new auxiliary branch makes full use of spatial details and context information to cover the long object in the field of view. Meanwhile, in order to ensure the inference speed of the model, the asymmetric convolution is used in each stage of the auxiliary branch to design a structure with low computational complexity. In the multi-branch fusion stage, the alignment and fusion module is designed to provide guidance information for deep and shallow feature mapping, so as to make up for the problem of feature misalignment in the fusion of information at different scales, and thus reduce the loss of small target information. In order to further improve the model’s awareness of key information, a global context module is designed to capture the most important features in the input data. The proposed network uses NVIDIA GeForce RTX 3080 Laptop GPU experiment, on the road street view data set Cityscapes and CamVid average occurring simultaneously ratio reached 77.1% and 78.4% respectively, with running speed of 127 frames/s respectively and 112 frames/s. The experimental results show that the proposed algorithm can achieve real-time segmentation and improve the accuracy significantly, showing good semantic segmentation performance

Childhood nutrition is a cornerstone of long-term health, yet many children exhibit reluctance to consume healthy foods such as vegetables. This aversion can be influenced by various factors, including food neophobia and the sensory and visual appeal of the foods presented. Understanding how visual cues affect children's willingness behaviors can provide insights into effective strategies to enhance their dietary habits. This study aims to investigate the liking and emotional responses on the relationships between intrinsic and extrinsic cues and children's willingness to eat and to ask parents to buy. To understand how these factors influence children with food neophobia food-related behaviors and to identify effective strategies to promote healthier eating habits. A sample size consisting of 420 children recruited was needed in this cross-sectional study. A validated and reliable self-administered questionnaire was employed. Independent t-test and ANOVA were used to test the differences between gender and food neophobia means. Spearman correlations were used for correlation analysis. Hierarchical regression analyses were conducted to explore the relationships among intrinsic cues, extrinsic cues, and the mediating effect of liking and emotional responses. Attributes such as plate color and shape significantly influence children's food-related behaviors and their emotions. Liking and emotional responses significantly mediates the relationship between intrinsic/extrinsic cues and willingness. Positive visual cues experiences are crucial for increasing children's inclination to purchase and consume. Positive emotional responses enhance acceptance and purchase. There are significant differences between boys and girls. Boys prefer blue and triangular plates, while girls prefer pink plates and generate more positive emotions. Children with food neophobia will initially experience a disorder, but its negative effects can be mitigated by increasing sensory appeal and emotional engagement. By employing strategies designed to enhance visual cues and liking and emotions, children’s willingness to choose healthy eating habits can be effectively increased.

Wounding and exogenous auxin are needed to induce adventitious roots in Chestnut microshoots. However, the specific inductive role of wounding has not been characterized in this species. In the present work two main goals were established: First, we prompted to optimize exogenous auxin treatments to improve overall health status of the shoots at the end of the rooting cycle. Second, we developed a time-series transcriptomic analysis to compare gene expression in response to wounding alone and wounding plus auxin, focusing in the early events within the first days after treatments. Results suggest that the expression of many genes involved in the rooting process is under direct or indirect control of both stimuli. However, specific levels of expression of relevant genes are only attained when both treatments are applied simultaneously, leading to the successful development of roots. In this sense, we have identified four transcription factors upregulated by auxin (CsLBD16, CsERF113, Cs22D, CsIAA6), with some of them being also induced by wounding. The highest expression levels of these genes occurred when wounding and auxin treatments were applied simultaneously, correlating with the rooting response of the shoots. Results in this work clarify the genetic nature of the wounding response in chestnut, its relation to adventitious rooting and might be helpful in the development of more specific protocols for the vegetative propagation of this species.

Technological developments within the maritime sector are resulting in rapid progress that will see the commercial use of autonomous vessels, known as Maritime Autonomous Surface Ships (MASS). Such ships are equipped with a range of advanced technologies, such as IoT devices, artificial intelligence (AI) systems, machine learning (ML) based algorithms and augmented reality (AR) tools. Through such technologies, the autonomous vessels can be remotely controlled from Shore Control Centres (SCC) by using real-time data to optimise their operations, enhance safety and reduce the possibility of human error. Apart from the regulatory aspects, which are under definition by the International Maritime Organisation (IMO), cybersecurity aspects must be considered and properly addressed to prevent such complex systems from being tampered with. In this paper, we propose an approach to secure communication channels between the SCCs and the vessels using SECOM standard and the underlying digital infrastructure in charge of data collection, storage and processing by relying on a set of machine learning (ML) algorithms for anomaly detection. The proposed approach is validated against a real implementation of the SCC deployed at Livorno seaport premises. Finally, the experimental results and the performance evaluation are provided to assess its effectiveness accordingly.

As an emerging network paradigm, Space-Air-Ground integrated networks (SAGIN) has attracted the attentions from academia and industry. That is because SAGIN can implement seamless global coverage and connections among electronic devices in space, air, and ground spaces. Additionally, the shortages of computing and storage resources in mobile devices greatly affect the quality of experiences for intelligent applications. Hence, we devise to integrate SAGIN as an abundant resource pool into mobile edge computing environments (MEC). To facilitate efficient processing, we need to solve the optimal task offloading decisions. Different from the existing MEC task offloading solutions, we have to face some new challenges, such as the fluctuation of processing capability for an edge computing node, the uncertainty of the transmission latency caused by the heterogeneous network protocols, the uncertain amount of uploaded tasks during a period, and so on. In this paper, we firstly describe a task offloading decision problem in new challenge environments. But, we cannot use the standard robust optimization and stochastic optimization methods to obtain the optimal result under the uncertain network environments. In this paper, we propose the condition value at risk-aware distributionally robust optimization algorithm, named as CVAR-DRO, to solve the task offloading decision problem. The proposed CVAR-DRO method combines the distributionally robust optimization and the condition value at risk model for solving the optimal result. And then, We have evaluated our approach in simulation SAGIN environments with the confidence interval, the number of mobile task-offloading and the various parameters. We compare our proposed CVAR-DRO algorithm with the state-of-the-art algorithms, such as the standard robust optimization algorithm, the stochastic optimization algorithm, the DRO algorithm, and the brute algorithm. The experimental results show that CVAR-DRO can get a sub-optimal mobile task-offloading decision. Overall, CVAR-DRO is more robust than others to the new challenges mentioned above in SAGIN.

In this paper, the results of a high-precision control survey carried out in the underground mining of the Oyu Tolgoi project using terrestrial laser scanning technology are presented. The purpose of the measurements was to ensure that the direction, total length and volume of the penetration and excavation works were being implemented in accordance with the planned and designed dimensions. The coordinates of the geodetic traverse checkpoints were also assessed. In addition, we checked a traverse network of 18 checkpoints in the tunnel of the selected underground level. These checkpoints were repeatedly measured four times by two using readings of the Trimble S7 1” total station and were processed using the Compass/Bowditch method with Trimble Business Center software. All points except for one were within the tolerance limits. Based on these points, the excavation length and volumes were determined using the terrestrial laser scanners (TLSs) Trimble X7 and Trimble SX12 [25]. The difference between the planned work quantity provided by the Oyu Tolgoi project and the actual values measured using laser scanner was calculated with the comparative method. The TLS partial control measurements in the excavations made through the ore bodies revealed a length of 20285.72m and a volume of 441128.80m3 for the seven underground mine levels, while their project values were 16158.26m length and 422837.50m3 volume.

We consider fractional integral operators $(I-T)^d, d \in (-1,1)$ acting on functions $g: \mathbb{Z}^{\nu} \to \mathbb{R}, \nu \ge 1 $, where $T $ is the transition operator of a random walk on $\mathbb{Z}^{\nu}$. We obtain sufficient and necessary conditions for the existence, invertibility and square summability of kernels $\tau (\mathbf{s}; d), \mathmb{s} \in \mathbb{Z}^{\nu}$ of $(I-T)^d $. Asymptotic behavior of $\tau (\mathbf{s}; d)$ as $|\mathbf{s}| \to \infty$ is identified following local limit theorem for random walk. A class of fractionally integrated random fields $X$ on $\mathbb{Z}^{\nu}$ solving the difference equation $(I-T)^d X = \varepsilon$ with white noise on the right-hand side is discussed, and their scaling limits. Several examples including fractional lattice Laplace and heat operators are studied in detail.

The modern supply chain (SC) is growing in terms of data, devices, users, and stakeholders, which introduced new security challenges and threats, especially with the reliance on centralized servers or cloud platforms. In addition, increased trust among system participants exposes the SC to a higher risk of vulnerabilities which require strong security measures. This article proposes a hybrid security framework for SC systems, BC-DRLzSC, that integrates Blockchain (BC) and Deep Reinforcement Learning (DRL) designed to operate in a zero trust (ZT) environment. In particular, we propose a decentralized BC-based approach integrated with smart contracts to manage system participant registration and authentication and to control access to system resources. BC-DRLzSC adopts a ZT architecture to reinforce SC security, which can be achieved with an advocate to verify each entity’s trustworthiness before granting or retaining access to system resources. Incorporating the ZT architecture, with BC and DRL, can potentially and significantly bolster SC system security. DRL is employed to develop a proactive attack detection model that continuously monitors the incoming traffic from authenticated nodes within the network and predicts any malicious actions. Finally, we evaluate the performance of our proposed DRL solution using the NSL-KDD dataset.