Reactions of quinones with compounds containing an amino group can produce a wide variety of addition or substitution products that depend on reactivity of both quinone and amino derivative. 6,7-Dichloropyrido[1,2-a]benzimidazole-8,9-diones undergo selective nucleophilic substitution reaction with different benzohydrazides and α-hydroxy-p-quinone imine derivatives stabilized by strong intramolecular hydrogen bond were isolated. Synthesized compounds represent a combination of several structural motifs: benzimidazole core fused with α-hydroxy-p-quinone imine which contains a benzamido fragment. The protonation/deprotonation processes were investigated in a solution using UV-Vis spectroscopy and 1H NMR titration experiment. X-ray crystallography analysis revealed a set of weak non-covalent interactions such as intra- and intermolecular hydrogen bonds and π-π stacking. Additionally, the redox behavior of 6,7-dichloropyrido[1,2-a]benzimidazole-8,9-dione and its p-imino derivative was investigated in acidic and neutral environment using cyclic voltammetry measurements. Cathode material based on 6,7-dichloropyrido[1,2-a]benzimidazole-8,9-dione could act as potential effective active electrode in aqueous electrolyte batteries, however further optimization is required.

Relative to several model bacteria, the ethanologenic bacterium Zymomonas mobilis is shown here to have an elevated resistance to exogenous antimicrobial peptides (AMPs) – both, with regard to peptide bulk concentration in the medium, and the numbers of peptide molecules per cell. By monitoring integration of the AMPs in the bacterial cell membrane and observing the resulting effect on membrane energy-coupling, it is concluded that the membranotropic effects of the tested AMPs in Z. mobilis and in Escherichia coli are comparable. The advantage of Z. mobilis over E. coli apparently results from its uncoupled mode of energy metabolism that, in contrast to E. coli, does not rely on oxidative phosphorylation, and hence, is less vulnerable to disruption of its energy-coupling membrane by AMPs. It is concluded that the high resistance to antimicrobial peptides (AMPs) observed in Z. mobilis not only proves crucial for its survival in its natural environment but also offers a promising platform for AMP production and sheds light on potential strategies for novel resistance development in clinical settings.

Some management practices, such as maintaining birds under high group density, used in the poultry industry may cause birds stress, leading to injurious behaviors, such as injurious pecking, aggression, and cannibalism. In addition, some management practices used to prevent severe injuries in birds may cause pain. Beak trimming (BT), removal of 1/3 to 1/2 of a beak, is a routine husbandry procedure practiced in laying hens to prevent or reduce injurious behaviors. However, BT causes tissue damage, which may increase somatosensory sensitization of the damaged nerve tissues, resulting in pain (acute, chronic or both) in the treated birds because the beak is a complex, functional organ with an extensive nerve supply. BT has already been heavily regulated or prohibited in several European countries and, in time, this trend will impact the practice used in the United States poultry industry. With the growing public concern for poultry welfare there is a pressing need to identify and develop alternatives to BT. Probiotics defined “as a source of live (viable) naturally occurring microorganisms (direct-fed microbials)” have been used as dietary supplements or functional foods to target gut microbiota (microbiome) for prevention or therapeutic treatment of mental diseases including social stress-induced psychiatric disorders in humans and various experimental animals. In our studies, chickens were used as an animal model to assess if dietary supplementation of probiotic, Bacillus subtilis, reduces injurious behaviors following social challenge. Hens of Dekalb XL strain, an aggressive line, were used in the studies. Our results indicate that dietary supplementation of the Bacillus subtilis based probiotic reduces aggressive behaviors in chickens. These results suggest dietary probiotics could be a suitable strategy for increasing hosts’ health status and welfare conditions.

Impressively increasing availability of the mechanical circulatory/cardiac support systems (MCS) worldwide, together with the deepening of the knowledge of critical care medical practitioners, have inevitably led to the discussion about further improvements of intensive care associated to MCS. An appealing topic of the left ventricle (LV) overload related to VA ECMO support endangering myocardial recovery is being widely discussed within the scientific community. Unloading of LV leads to the reduction of LV end-diastolic pressure, reduction of pressure in the left atrium and decrease of the LV thrombus formation risk. Consequently, better conditions for myocardial recovery, with comfortable filling pressures and better oxygen delivery/demand ratio are achieved. The combination of VA ECMO and Impella device, also called ECPELLA seems to be a promising strategy which may bring the improvement of CS mortality rates. The series of presented trials and meta-analyses clearly showed potential benefits of this strategy. However, the ongoing research has brought a series of new questions, as whether Impella itself is the only appropriate unloading modality, or any other approach to unload LV would be beneficial in the same way? Benefits and potential risks of LV unloading and its timing are being discussed in current review.

This study addresses the enhancement of material efficiency and reduction of brittleness in timber-to-concrete adhesive connections for beam-type timber and timber-concrete composite panels. The research explores the potential benefits of adding longitudinal timber ribs to Cross-Laminated Timber (CLT) beam-type panels. Three groups of flexure-tested specimens were analysed: (1) timber panels (1400 mm × 400 mm) with two 100 mm thick CLT panels and two 60 mm thick CLT panels reinforced with 150x80 mm timber ribs; (2) eight specimens (600 mm × 100 mm × 150 mm) with CLT members (600 mm × 100 mm × 100 mm) connected to a 50 mm concrete layer using granite chips and Sikadur-31 (AB) epoxy adhesive; (3) six CLT panels (1400 mm × 400 mm × 50 mm) bonded to a 50 mm concrete layer, with two panels containing polypropylene microfibers and two panels incorporating polyethene dowels for mechanical connection. Specimens were subjected to three-point bending tests and analysed using the transformed section method, γ-method, and finite element method with ANSYS 2023R2 software. Results indicated a 53% increase in load-carrying capacity for ribbed CLT panels with no additional material consumption, a 24.8%-41.1% increase for CLT panels strengthened with a concrete layer, and improved ductility and prevention of disintegration in timber-concrete composites with polypropylene microfibers.

An outbreak caused by coronavirus disease 2019 (COVID-19) occurred in Wuhan City, Hubei Province, China, in December 2019. Up to March 2, 2020, at least 80180 cases have been reported. Most of the patients had a history of visiting Hubei Province or contacting with people who had ever stayed in or passed by Hubei Province, or exposed to symptoms. Some patients got infected only from asymptomatic contacts. This study aimed to report the epidemic features and lab identification of a patient confirmed with COVID-19 infection only from asymptomatic contact.

The present study aimed to investigate the effect of solution of ethanol extractions of different microalgae species: Spirulina sp., Dunaliella sp. and Chlorella sp. on the accumulation of biologically active compounds in tomato fruits. Ethanol solution as well as treatment with drinking water were used as a control. Tomato cultivar ‘Belle’ F1 (Enza Zaden) was grown in 25 L pots, in peat substrate (pHKCl 5.5, producer Laflora LTd), polycarbonate greenhouse. Chemical composition – total carotenoids, lycopene and β-carotene content, total phenolics and anthocyanins, organic acids, vitamin C as well as dry matter, pH and soluble solids content – was determined in tomato fruits at the stage of full ripen. The influence of tested extracts on biologically active compounds on quality parameters of tomatoes was different, but no negative effect was observed. As only pH values as well as total phenol content and total soluble solids had statistically significant differences, results can be described with genetical stability of the cultivar.

Since the integration of electronic devices and intelligent electronic devices into the power grid, power quality (PQ) has consistently remained a significant concern for system operators and experts. Maintaining high standards of power quality is crucial to prevent malfunctions and faults in electric assets and in connected loads. Recently, PQ studies have shifted their focus to a specific frequency range, previously not considered problematic, the supraharmonic 2kHz–150kHz range. This range is not populated by the harmonic components of the 50-60 Hz mains fundamental, but a combination of intentional emissions, switching and non-linearity by-products and various types of resonances. This paper aims to provide a detailed analysis of the impact of SH on power network operation and assets, focusing on the most relevant documented negative effects, namely power loss and heating of grid elements, aging of dielectric materials, failure of medium voltage (MV) cable terminations, and interference to equipment and to power line communication (PLC) technology in particular. Under some shareable assumptions limits are derived and compared to existing ones for harmonic phenomena, providing a clear identification of the primary issues associated with supraharmonics and suggestions for the standardization process. Strictly related is the problem of grid monitoring and assessment of SH distortion, discussing the suitability of normative requirements for instrument transformers (ITs) with specific focus on their accuracy.

Hybrid reinforcement in a composite material (CM), in many situations, allows better govern material mechanical properties. Polymer matrix CM plate is reinforced with small particles and macro-fibers. Particles are oil shale ash (OSA) powder, macro-fibers – basalt fiber threads im-pregnated by matrix material. With the goal to find out elastic properties of the matrix- polymer with OSA, samples with its different concentrations were experimentally fabricated and their elastic properties were experimentally measured. Obtained data were used in numerical model-ling. Was observed polymer matrix composite slab, reinforced with OSA and three-dimensional (3D) basalt fiber fabric. Fabric are forming threads impregnated by matrix (polymer resin mixed with OSA particles). Damage accumulation in the stretched slab was modelled. Impregnated thread, in the modelling, is observed as a macro-fiber (MF) with averaged elastic properties. With the load increase MF in the material began to break. At first, appeared isolated single breaks. A probabilistic sequential MF rupture accumulation model was accepted and numerical-ly realized. The geometry of the reinforcing knitted fabric was modeled using the Leaf and Glaskin approach. In the knitted fabric highly, stressed MF cross-sections are observed as po-tential location where thread breakage can occur. If one thread breaks, the cross-sections of the adjacent threads experience the overload caused by this rupture. More overloaded crossection in the adjacent overloaded thread breaks and we obtain two adjacent broken MF designated as a defect with the size j=2. The probabilities of obtaining defect consisting of at least 2, 3, … j adjacent broken MF in the plate were calculated. The calculations continued until the rupture process becomes unstable (j→∞). The overstresses on adjacent threads in the CM were calculat-ed using the FEM three-dimensional numerical approach. Analytical and stochastic methods were used for the load-carrying capacity determination and different size defects accumulation in the hybrid composite material (HCM) plate. Also, was calculated the probability of the plate rupture.

To accurately determine the refractive error of the eye, sufficient relaxation of eye accommodation is essential, either through the fogging technique or pharmacological cycloplegia. This study aimed to evaluate the clinical information revealed after cycloplegia and assess how age and non-cycloplegic refractive status influence the classification of eye refractive error. We analyzed the records of 472 non-population-based subjects aged 3–28 years (mean ± SD: 9.1 ± 4.6) who attended a private ophthalmology practice from January 2018 to December 2020. Cycloplegia was induced with one drop of cyclopentolate 1% in each eye, and refraction was measured 30 ± 5 min later using an objective autorefractometer. The average spherical equivalent (SE) difference (mean ± SD) induced by cycloplegia was 0.72 ± 0.73 D, with the highest difference observed in the 3-5 years age group (1.18 ± 0.85 D). Gender had no significant effect on the SE values before or after cycloplegia (p > 0.05). Cycloplegia caused a hyperopic refractive shift in 83% of participants, no shift in 12%, and a myopic refractive shift in 6%. This study highlights the importance of cycloplegia use in children and young adults.

The main sources of intrinsic noise in excitable cells at the microcircuit and network levels are the stochastic characteristics of ion channel gating and activation of the synaptic conductance. Studies using in vivo, in vitro, and in silico methods to examine the effects of synaptic background activity were not adequately investigated in non-neuronal excitatory cells, where neurotransmitter-based innervation also occurs. We created a mathematical model to replicate the background synaptic noise dynamics in a non-neuronal cell. We utilized the stochastic Ornstein-Uhlenbeck process to represent excitatory synaptic conductance, which was incorporated into a whole-cell model to produce spontaneous and evoked cellular electrical activities. The single-cell model includes many biophysically detailed ion channels represented by a set of ordinary differential equations in Hodgkin-Huxley and Markov formalisms. This paradigm effectively induced irregular spontaneous depolarizations (SDs) and spontaneous action potential (sAP) resembling in vitro-like electrical activity in the cells. The input resistance decreased by multiple factors, and the spontaneous action potential firing rate elevated. The potential to reach the action potential threshold is altered. Background synaptic activity can alter the input/output characteristics of non-neuronal excitatory cells. Suppressing these baseline activities would facilitate the discovery of new pharmaceutical targets for different clinical diseases.

This work compared the performance of three methods for constructing a regional ensemble prediction system (EPS) for wind speed forecasts: dynamical downscaling, breeding of growth modes (BGM), and blending method. The Weather Research and Forecasting (WRF) model was used to downscale the European Centre for Medium-range Weather Forecast (ECMWF) EPS. In addition, as the BGM method needs observation data for generating scaling factors, an alternative method for generating scaling factors was proposed to eliminate dependence on observation data. One-month tests between October 1st and October 30th, 2020, were implemented to evaluate the performance of three methods in the Gansu province of China. The results demonstrate that the blending method outperforms the other two methods. Furthermore, the difference in performance is evident mainly in early forecast lead time and becomes negligible as forecast time increases.

Background: Coronavirus disease (COVID-19) related pneumonia is leaded by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), which infects host cells through receptors named Angiotensin-converting enzyme 2 (ACE2). Smoking is thought to be related to poor disease prognosis, as a large amount of evidence highlights the negative effects of smoking on lung health and its causal relationship with various respiratory diseases. Methods: We first evaluated the role of ACE2 expression level with tobacco effect. And correlation analyses were used to identify the related genes of ACE2 both in smoker trait and human normal multi-tissues. After intersections, hub genes were later used to further GSEA and co-regulated mechanisms were explored by GeneMANIA. We used UALCAN to perform survival curves of pan-cancers included 33 types of cancers. New clinical model of top co-occurrence cancer type was constructed and validated. Results: Effected by cigarette smoking, the expression level of ACE2 expressed statistically upwards in current smokers compared with never smokers, upwards in groups after acute smoke exposed compared with normal control. But no strong evidence detected in third-hand smoke, as poor amounts of samples, only 4. A little trend of expressing upwards became in groups after third-hand smoke compared with groups after clean air exposing. 4 genes included PIR, ADH7, AKR1C2 and AKR1C3 were identified as ACE2 related genes in smoker trait and human normal multi-tissues. Then, we made the survival curves of pan-cancers in 4 genes. Brain lower grade glioma was the co-occurrence type as both ACE2, PIR and AKR1C3 had the significantly prognostic situation. Later, we made the new clinical prediction model as the C-index was 0.827 and the Area Under Curves (AUCs) of 1-year survival, 2-year survival and 3-year survival were 0.921(95%CI, 0.882-0.961), 0.911(95%CI, 0.860-0.962) and 0.878(95%CI, 0.818-0.939). In inner validation, the AUCs of 1-year survival, 2-year survival and 3-year survival were 0.777(95%CI, 0.640-0.914), 0.916(95%CI, 0.842-0.990) and 0.888(95%CI, 0.768-1.000). In outer validation one, the AUCs of 1-year survival, 2-year survival and 3-year survival were 0.764(95%CI, 0.672-0.856), 0.848(95%CI, 0.783-0.913) and 0.748(95%CI, 0.656-0.841). And in outer validation two the AUCs of 1-year survival, 2-year survival and 3-year survival were 0.740(95%CI, 0.654-0.826), 0.766(95%CI, 0.688-0.843) and 0.794(95%CI, 0.721-0.866). Conclusions: In our study, we compared the ACE2 expression level between smokers and non-smokers crowds and identified associated hub genes. Then we explored the further role of hub genes in tumor fields to identify the correlation and influence between the avoidable host factors such as smoking on COVID-19 contamination and tumor. ACE2 and AKR1C3 are potential prognostic genes for brain lower grade glioma, and we created the web dynamic nomogram and encapsulation app through validations.

Purpose of research was to determine changes in optical quality for monthly disposable soft contact lenses (SCL) wearers with new and monthly worn SCL. Retinal straylight was assessed in SCL wearers using a C-Quant straylight meter. The study included 33 participants (age 22 ± 1.4 years) who wore monthly disposable silicone hydrogel contact lenses. Measurements were taken with month-old SCL that had been worn for no more than 4 hours. Participants then replaced the old lenses with new ones. After a 15-minute adaptation period, straylight measurements were taken with the new SCL. A questionnaire was administered to all participants, inquiring about vision complaints and the type of contact lens material used. The decimal logarithm of the stray-light parameter, log(s), was notably higher with month-old SCL (log(s) = 0.97±0.17) than with brand-new SCL (log(s) = 0.86±0.15), p < 0.001. The average increase in retinal straylight after a month of SCL use was log(s) = 0.11 ± 0.08. The questionnaire revealed that 35% of participants observed a reduction in visual quality after wearing the contact lenses for a month. Results showed that monthly disposable soft contact lenses could reduce visual quality when scheduled replacement time has been reached.

Rice production in the Tonle Sap basin is one of the main drivers for economic and social development in Cambodia. The Tonle Sap basin has experienced many different forms of disasters while more attention has been drawn to drought disaster. The objective of this study is to assess the impacts of drought on agriculture and food security through a case study of the Baribo basin, a sub-basin of Tonle Sap basin, Cambodia. Ground observations and satellite-based products were used for drought assessment from 1985 to 2008 which was the period with relatively good data quality. The Standardized Precipitation Index (SPI) and Standard Vegetation Index (SVI) were selected for meteorological and agricultural droughts assessment, correspondingly. Both SPI and SVI consistently suggested that drought is a major natural hazard causing food insecurity in the target basin. The highest drought intensity (DI) and severity (DS) occurred between 1993-1994 and the longest drought duration (DD) occurred between 2002 and 2006. The most severe damage to rice production was in 2004, affecting about 46% of the total cultivated area. The analysis showed that drought duration had a strong relationship with the affected area growing rice as well as food insecurity in the Tonle Sap basin.

Surface soil moisture (SSM) is a significant factor affecting crop growth. This paper presents a method for retrieving SSM over wheat-covered areas using synergy dual-polarization C-band Sentinel-1 synthetic aperture radar and Sentinel-2 optical data. Firstly, a modified water cloud model (WCM) was proposed to remove the influence of vegetation from the backscattering coefficient of the radar data. The vegetation fraction was then introduced in this WCM, and the vegetation water content (VWC) was calculated using multiple linear regression model. Subsequently, the support vector regression technique was used to retrieve the SSM. This approach was validated using in-situ measurements of the wheat field in Hebi, in the north of Henan Province. The key findings of this study are as follows: (1) Based on vegetation indices obtained from Sentinel-2; the proposed VWC estimation model can effectively eliminate the influence of vegetation; (2) compared with vertical transmit and horizontal receive polarization, vertical transmit and vertical receive polarization is better for detecting changes in SSM at different growth stages of wheat; and, (3) the validation results indicated that the proposed approach, based on Sentinel-1 and Sentinel-2 data, successfully retrieved SSM in the study area.

Cambodia is a predominantly agrarian society, with a majority of the population residing in rural areas and relying on agriculture for food and income. The primary sources of food for the Khmer society are rice and fish. However, the country faces vulnerabilities due to climate change, natural disasters, impacts of hydropower and infrastructure development, and land grabs, which disrupt food sources and the livelihoods of local communities. Since 2015, the Royal Government of Cambodia (RGC) has been actively promoting rice production for export, resulting in increased rice farming seasons and annual rice production. Despite these efforts, a significant portion of the population still experiences food insecurity. Therefore, this study aims to explore the reasons why food security issues persist despite the government program to increase rice production. The study involves a review of existing literature and an examination of food security in three provinces in the Cambodian Mekong Delta. It concludes that, on the one hand, the increased rice production has increased food availability, but access to and utilization of food have been challenged by indebtedness, increased cost of production and migration, and on the other hand, the rights to foods have been challenged by land crabs, water conflicts, and lack of sectoral integrations of food systems; and compounded by climate change and environmental degradation, which all contribute to food security.

With the rapid evolution of SARS-CoV-2, the emergence of new strains is an intriguing question. This paper presents an evolutionary theory to analyze the mutations of the virus and identify the conditions that lead to the generation of new strains. We represent the virus variants using a 4-letter sequence based on amino acid mutations on the spike protein and employ an n-distance algorithm to derive a variant phylogenetic tree. We show that the theoretically-derived tree aligns with experimental data on virus evolution. Additionally, we propose an A-X model, utilizing the set of existing mutation sites (A) and a set of randomly generated sites (X), to calculate the emergence of new strains. Our findings demonstrate that a sufficient number of random iterations can predict the generation of new macro-lineages when the number of sites in X is large enough. These results provide a crucial theoretical basis for understanding the evolution of SARS-CoV-2.

The three-pronged sliding universal coupling is a new kind of three-pronged coupling. To study its kinematic and dynamic characteristics, directional cosine matrices are utilized as tools to analyze the coordinate systems by establishing simplified geometric models and corresponding motions. Through the analysis of the motions of the input and output shafts and employing the method of single-force element, a set of equilibrium equations for the forces acting on the output shaft and the input shaft are formulated for solution analysis. The research indicates that during the rotation process, there exists a small angular difference between the input shaft and the intermediate shaft, demonstrating the quasi-constant angular velocity characteristics of the new tripod sliding universal coupling. The curves of the forces and torques acting on each component of the coupling approximate sinusoidal curves. The optimal operating angle range for the new tripod universal coupling is at angle , during which the system exhibits good transmission and mechanical performance.

One of the most important applications in the wireless sensor networks (WSN) is to classify mobile targets in the monitoring area. In this paper, a multi-DBN weighted voting classification algorithm is proposed on the basis of the Deep Belief Network (DBN) classifier and combined with the idea of voting method, which is implemented on the nodes of the WSN monitoring system by means of "upper training, lower transplantation" appraoch. The performance of the algorithm is verified by using real-world experimental data, and the results show that the proposed method has a higher accuracy in classifying the target signal features, achieving an average classification accuracy of 84.63% across four different types of moving targets. The experiment reveals that the multi-DBN weighted voting algorithm enhances the target classification accuracy by approximately 5% in comparison to the single DBN classifier, but the memory and computation time required for the algorithm to run are also increased at the same time. Compared to the FFNN classifier, which exhibited the highest classification accuracy among the four selected methods, the algorithm achieves an improvement of approximately 8.8% in classification accuracy. However, it incurs greater time overhead to run.

Traveling salesman problems (TSPs) are well-known combinatorial optimization problems, and most existing algorithms are challenging for solving TSPs when its scale is large. To improve the efficiency of solving large-scale TSPs, this work presents a novel adaptive layered clustering framework with improved genetic algorithm (ALC\_IGA). The primary idea behind ALC\_IGA is to break down a large-scale problem into a series of small-scale problems. First, the $k$-means and improved genetic algorithm are used to segment the large-scale TSPs layer by layer and generate the initial solution. Then, the developed two phases simplified $2$-opt algorithm is applied to further improve the quality of the initial solution. The analysis reveals that the computational complexity of the ALC\_IGA is between $O(n\log n)$ and $O(n^2)$. The results of numerical experiments on various TSP instances indicate that, in most situations, the ALC\_IGA surpasses the state-of-the-art algorithms in convergence speed, stability, and solution quality. Specifically, the ALC\_IGA can solve instances with $2 \times 10^5$ nodes within 0.15h, $1.4 \times 10^6$ nodes within 1h, and $2 \times 10^6$ nodes in three dimensions within 1.5h.

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.

It is expected that scholars will continue to strengthen the depth and breadth of theoretical research on RIS, so as to provide a higher theoretical upper bound for the engineering application of RIS. While making breakthroughs in academic research, great progress has been made in engineering application research and industrialization promotion. This article will provide an overview of RIS engineering applications, mainly including the typical features, typical classifications, and typical deployment scenarios of RIS. Then the challenges and candidate solutions of the RIS are systematically and deeply analyzed, involving the cascade channel decoupling for solving the RIS beamforming, the influences and solutions of RIS regulation constraints, RIS system architecture of network controlled mode, the integrated channel regulation and information modulation, TDD mechanism used for RIS. Future trends and challenges are also provided.

Enhancing energy efficiency within the building sector is imperative to curbing energy losses, given that this sector alone contributes to over 34% of global energy consumption. Employing a building management system, along with its regular updates, presents a strategic avenue to decrease energy usage, enhance building energy efficiency, and more. Tailored control strategies, aligned with the unique characteristics and usage patterns of each building, are essential for achieving energy savings. This article presents an evaluation of HVAC system efficiency in office buildings, utilizing a data-driven approach coupled with simulations conducted in IDA ICE software. The research explores the control strategy of an office building equipped with a constant air volume HVAC system, featuring a regularly controlled air handling unit. The objective is to boost energy efficiency while striking a balance between occupant comfort and energy consumption. Findings indicate that by analyzing measured data and adjusting configurable parameters, building energy consumption can be significantly reduced. Close monitoring of indoor parameters by building operators and corresponding adjustments to the HVAC system can yield energy savings of up to 16%. Leveraging these insights, the paper suggests integrating data-driven and dynamic simulation methods into building management system models to optimize HVAC systems, enhance energy efficiency, and advance ambitious carbon neutrality objectives.

3,4-Dihydroquinolin-2(1H)-ones represent a class of valuable bioactive compounds with six-membered nitrogen-containing heterocyclic structures. The development of simple, mild and efficient synthetic methods has been widely concerned by synthetic chemists. In this review, we have summarized a series of different synthetic strategies for the synthesis of dihydroquinoline-2(1H)-ones via catalytic annulation of α,β-unsaturated N-arylamides in the past decade, including covering electrophilic cyclization, radical initiated cyclization and photochemical cyclization reactions. Besides, the substrate scope and mechanistic details are also discussed. This paper will provide a useful reference for the development of diverse synthesis methodologies of 3,4-dihydroquinolin-2(1H)-ones.

Taiwan cobra (Naja atra) bites account for approximately 20% of all venomous snakebites in Taiwan. In Taiwan, the rates of wound necrosis and secondary infection from Taiwan cobra bites are higher than those associated with other venomous snakebites. Clinical tools to evaluate the infection risk after Taiwan cobra bites are lacking. Therefore, in this study, we developed a useful clinical tool to evaluate the infection risk after Taiwan cobra bites. Moreover, we investigated wound infection bacteriology. We analyzed the data of patients bitten by N. atra who had undergone freeze-dried neurotoxic antivenin treatment in emergency rooms of the Chang Gung Memorial Hospital network, which comprises seven hospitals and the largest medical system in Taiwan, from January 2001 to May 2017. Because patients with wound necrosis required antibiotics for infection treatment, we included only patients with wound infection but without tissue necrosis in the development of our Cobra Bacteriology of Infections in Taiwanese snake Envenomation (Cobra BITE) score by using univariate and multiple logistic regression. In total, 8,295,497 emergency department visits occurred from January 2001 to May 2017, and 195 patients were diagnosed has having cobra bites. Among them, 23 and 30 patients had wound necrosis and wound infection, respectively. The wound infection rate was 27.2% (53/195). Regardless of whether the patients had necrosis, Enterococcus faecalis and Morganella morganii were the main bacteria identified in the culture report. Gentamicin, ceftriaxone, ciprofloxacin, and levofloxacin are the ideal first-line antibiotics for treating N. atra bite wounds in Taiwan. As per our Cobra BITE score, the three factors predicting secondary wound infection after cobra bites are hospital admission, a white blood cell count (in 103/µL) × by neutrophil–lymphocyte ratio value of ≥114.23, and the use of antivenin medication. The area under the receiver operating characteristic curve for the Cobra BITE score system was 0.88. The ideal sensitivity and specificity were 0.89 and 0.76, respectively, and the optimal cutoff point for Cobra BITE score was 7. The Hosmer–Lemeshow p value was 0.4. In conclusion, our Cobra BITE study established a new practical clinical tool for clinicians to evaluate infection risk after N. atra bites. This score system enables the assessment of wound infections after N. atra bites, and it could be modified and improved in future for other Naja spp. bites.

The surging electricity demand driven by a growing global population, coupled with the increasing electrification of industries, transportation, and power-intensive appliances, poses a substantial challenge to electricity systems worldwide. This challenge is further compounded by the pressing issue of climate change. To address climate change and mitigate the environmental consequences of fossil fuel use, renewable energy sources are expected to play a pivotal role. However, certain renewable sources, such as wind and solar, exhibit stochastic behavior, resulting in unpredictable power output and significant fluctuations. This intermittency presents a challenge in maintaining a stable and reliable power supply. To overcome this challenge and ensure a balanced electricity grid, the strategic deployment of energy storage devices throughout the power system becomes critically important. Energy storage technologies play a vital role in mitigating the mismatch between renewable power generation and consumption. Additionally, they enable the storage of surplus energy generated from renewable sources during periods of high production for later use during periods of low or no generation. Each technology possesses unique characteristics and applications, providing flexibility and diversity in energy storage solutions. This work provides an overview of different energy storage technologies and explores their role in establishing a sustainable power system.

Power spectra always play an important role in the theory of inflation. In particular, the ability to reproduce the galaxy matter power spectrum $ P(k) $ and the CMB temperature angular power spectrum $ C_l $’s to high accuracy is often considered a triumph of inflation. In our previous work, we presented an alternative explanation for the matter power spectrum based on nonperturbative quantum field-theoretical methods applied to Einstein’s gravity, instead of inflation models based on scalar fields. In this work, we review the basic concepts and provide further in-depth investigations. We first update the analysis with more recent data sets and error analysis, and then extend our predictions to the CMB angular spectrum coefficients $ C_l $, which we did not consider previously. Then we investigate further the potential freedoms and uncertainties associated with the fundamental parameters that are part of this picture, and show how recent cosmological data provides significant constraints on these quantities. Overall, we find good general consistency between theory and data, even potentially favoring the gravitationally-motivated picture at the largest scales. We summarize our results by outlining how this picture can be tested in the near future with increasingly accurate astrophysical measurements.

Agriculture is an industry that heavily relies on the environment. The increasing frequency of ex-treme disasters attributable to climate change has adversely affected agricultural land and dis-rupted the stability of the supply of agricultural products. Therefore, we implemented a research program focused on current strategies for adapting to agricultural meteorological disasters. The primary goal was to actively promote disaster prevention information and techniques through information platforms and applications and to provide refined forecasting and an early warning system to enhance preparedness for and recovery from agricultural disasters. This initiative can transform passive mitigation into dynamic action by disseminating crucial information to farmers. We integrated meteorological data and crop characteristics to develop disaster prevention strate-gies for the predisaster, during-disaster, and postdisaster stages to empower farmers to implement autonomous disaster prevention, ensuring their profitability and minimizing the impact of disas-ters. This paper presents an agricultural disaster prevention system involving cross-disciplinary collaboration to establish an early warning system for crop disasters. However, the effectiveness of the early warning system relies on the availability of complete meteorological and crop infor-mation, necessitating high-density agricultural meteorological stations, refined weather forecast services, historical crop damage data, and ongoing research and development on disaster preven-tion technology. In the system, a diversified social media interface is used to provide local farmers with weather forecast information. When impending disasters are identified, the platform acti-vates disaster prevention measures to reduce agricultural losses. This article paper introduces Taiwan’s agricultural disaster prevention system as a complete disaster prevention strategy.

In view of the problem of charging and hydrogen filling facilities construction in the transition from fuel vehicles to electric vehicles and hydrogen fuel cell vehicles, in order to meet the electric energy demand of electric vehicles and hydrogen energy demand of hydrogen fuel cell vehicles at the same time, this paper puts forward a method of siting and capacity determination of electric-hydrogen refueling integrated station (EHRIS) based on Voronoi diagram and particle swarm algorithm based on calculating the demand of charging and filling of hydrogen in vehicles. Firstly, OD pair(Origin-Destination) is used to represent the starting point and end point of the car to portray the travel demand of the car, and on the basis of the traffic network model, the shortest driving path of the new energy car is determined by Dijkstra algorithm, and Monte Carlo simulation is used to get the electric-hydrogen energy demand of the car; secondly, the Voronoi diagram is introduced to classify the service scope of the electric-hydrogen charging station to determine the equipment capacity of the electric-hydrogen charging station, while taking into account the electric-hydrogen charging and refueling capacity. Secondly, a Voronoi diagram is introduced to divide the service scope of the EHRIS, determine the capacity of the equipment in the EHRIS, and consider the distance constraints between the sites of EHRIS, so as to make the division of the site and service scope more reasonable. Finally, a dynamic optimal current model framework for distribution networks based on second-order cone relaxation is established, and each element of the active distribution network is planned, so that the distribution network can operate safely and stably after being connected to the EHRIS. With the objective of minimizing the total social cost of EHRIS and considering the constraints of the charging equipment and hydrogen production and injection equipment of the EHRIS, a siting and capacity model to meet the electric-hydrogen energy demand of electric and hydrogen fuel cell vehicles is developed, and solved by a particle swarm algorithm. Finally, the simulation planning is carried out with Sioux Falls city and IEEE33 network, which ensures the stable operation of the power grid while meeting the energy demand of automobiles, and the results verify the effectiveness and feasibility of the proposed method.

The multi-modal data fusion method based on IA-net and CHMM technical proposed is designed to solve the problem that the incompleteness of target behavior information in complex family environment leads to the low accuracy of human behavior recognition.The two improved neural networks(STA-ResNet50、STA-GoogleNet)are combined with LSTM to form two IA-Nets respectively to extract RGB and skeleton modal behavior features in video. The two modal feature sequences are input CHMM to construct the probability fusion model of multi-modal behavior recognition.The experimental results show that the human behavior recognition model proposed in this paper has higher accuracy than the previous fusion methods on HMDB51 and UCF101 datasets. New contributions: attention mechanism is introduced to improve the efficiency of video target feature extraction and utilization. A skeleton based feature extraction framework is proposed, which can be used for human behavior recognition in complex environment. In the field of human behavior recognition, probability theory and neural network are cleverly combined and applied, which provides a new method for multi-modal information fusion.

Although instructional feedback plays an essential role in regulating learning and improving performance, few studies have systematically investigated the needs of teachers and students for instructional feedback system or developed designs and experiments, especially at the high school level. To address this research need, the present study investigated the needs of selected students and teachers in a high school in Hubei Province, China, and designed and developed a diagnostic visual feedback system for an experimental study with 125 students from a 10th-grade biology class in the same high school. The results showed that this diagnostic visual feedback report improved student performance (ES=0.37) and that functions such as misconception location, knowledge diagnosis, and knowledge alert were well received by students. These findings have multiple implications for facilitating the design and development of diagnostic visual feedback system.

Bacteria and archaea participate in and are influenced by processes of substance circulation and energy exchanges in natural environment. Generally, the community changes of bacteria and archaea in sediment are mainly driven by seasonality in mid-latitude regions. But in our study, water diversion to Fen river played a more important role on OTU number, diversity and community structure of bacteria and archaea in sediment than seasonal variation, which was found by 16S rRNA high-throughput sequencing technology. This phenomenon might be caused by external transferred water on the physicochemical water environment and accelerated release of positive nitrogen from sediment caused by rise of water level. Changes of carbon-nitrogen cycle and increase of electrical conductivity (EC) value induced more diversion-responders than season-responders both for bacteria and archaea. Seasonal changes have been influencing bacteria and archaea mildly throughout the whole study reach. After water diversion, the environment indicators relating to bacteria community obviously changed from nutrients to salinity while that for archaea almost disappeared. Our research showed the effects of human activities on the communities of bacteria and archaea outweigh the forcing from natural seasonal changes in mid-latitude regions and revealed the mechanism, highlighting different responses of bacteria and archaea to environmental changes.

With the development of deep learning, several Graph Neural Networks (GNN)-based approaches have been utilized for text classification. However, GNNs encounter challenges in capturing contextual text information within a document sequence. To address this, a novel text classification model RB-GAT is proposed by combining RoBERTa-BiGRU embedding and a multi-head Graph ATtention Network (GAT). First, the pre-trained RoBERTa model is exploited to learn word and text embeddings in different contexts. Second, the Bidirectional Gated Recurrent Unit (BiGRU) is employed to capture long-term dependencies and bidirectional sentence information from the text context. Next, the multi-head graph attention network is applied to analyze this information, which serves as a node feature for the document. Finally, the classification results are generated through a Softmax layer. Experimental results on three benchmark datasets demonstrate that our method can achieve an accuracy of 71.48%, 98.45%, and 80.32% on Ohsumed, R8, and MR, which is superior to the existing nine text classification approaches.

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.

Currently, drug screening is primarily based on human cell culture for initial high-throughput screening, and subsequently, rodent model to confirm the biological effects. However, the mammalian system is known for time-consuming and highly-cost to be difficult to perform high-throughput drug screening, which exists a critical gap between in vitro cell-based models and the in vivo mammalian models. Therefore, the zebrafish could bridge this gap in preclinical toxicity screening along the drug development pipeline because of its efficiency. We aimed to develop an in vivo zebrafish platform for rapid drug screening. Zebrafish, due to its high genomic conservation with mammals and rapid development and differentiation, it has many advantages, such as short life span, large number of offspring and low cost, easy manipulation for generating transgenic species, to serve as animal model for disease-based research. In 96-well microplates, zebrafish embryos were incubated with small molecular compounds that affected bone mineralization. The level of osteogenic mineralization was evaluated by fluorescent dye staining and quantified by image analysis software. Quantitative real time-PCR (qRT-PCR) was performed to evaluate the biological pathways involved in bone metabolism at the molecular level. The system was validated by demonstrating that response to alendronate and Dorsomorphin in zebrafish. In our study, we screened for 24 compounds within the CYCU-1120~1152 chemical library and identified 3 compounds, pentamidine (CYCU-1140), BML-267 (CYCU-1147), and alendronate (CYCU-1152), increased embryonic mineralization; while 6 compounds, RWJ-60475 (CYCU-1126), levamisole HCL (CYCU-1128), tetramisole HCL (CYCU-1129), fenvalerate (CYCU-1132), NSC-663284 (CYCU-1138), and BML-267ester (CYCU-1148), were inhibitory to bone mineralization. We also found that alendronate enhanced the level of bone mineralization by inhibiting osteoclast-related genes. To sum up, our research showed that zebrafish may have potential to be a drug-screening and mechanism-analysis platform for bone mineralization.

Osteoarthritis (OA) has become one of the most common disabilities among elders, especially in female. Obesity and mechanical injury causing OA are attributed to joint loading, cartilage disintegration, bone loss and inflammation as well. Several strategies used for treatment OA including non-pharmacological and pharmacological. Fucoidan possesses several bioactivities such as antitumor, antiviral, anticoagulation, anti-obesity, and immunomodulation. This study aims to investigate the effect of fucoidan in surgery-induced OA on diet-induced obesity rats. OA was induced by anterior cruciate ligament transection and partial medial meniscectomy (ACLT+MMx). Male SD rats were fed high-fat diet (HFD) for 4 weeks to induce obesity before ACLT+MMx to induce OA. OA rats were administered with intragastric water or fucoidan in three different concentrations (32 mg/kg, 64 mg/kg, and 320 mg/kg) after the surgeries for 40 days with HFD. We observed that the swelling in knee joint was alleviated and hind paw weight distribution was rectified after feeding fucoidan, with no significant effect on weight gain and feed intake. Fucoidan administration indicated no significant variation on HDL-Cholesterol level, but reduced plasma triglycerides and LDL-Cholesterol level. In addition, weight-bearing tests showed improvement in the fucoidan-treated group. Our results suggested that fucoidan may improve meniscal/ligamentous injury and obesity-induced OA.

In the paper, a fully discrete compact difference scheme with $O(\tau^{2}+h^{4})$ precision is established by considering the numerical approximation of the one-dimensional Allen-Cahn equation. The numerical solutions satisfy discrete maximum principle under reasonable step ratio and time step constraint is proved. And the energy stability for the fully discrete scheme is investigated. An example is finally presented to show the effectiveness of scheme.

To ensure consumers can purchase high-quality rice, accurate identification of rice varieties is particularly important. Methods: This article conducts research based on machine learning algorithms from the perspectives of image, spectrum, and spectrogram fusion. Six types of hyperspectral image data of rice were preprocessed using convolutional smoothing (SG) and multiple scatter correction (MSC). Texture information of the images was extracted using gray-level co-occurrence matrix. Spectra, texture, and spectrogram data were fused into a new matrix. Spectral, texture, and spectrogram fusion data were used as inputs for the model, and support vector machines (SVM), logistic regression (LR), and K-nearest neighbors (KNN) classification models were constructed and compared. Results: From the classification results, the spectrogram fusion classification performance was better than classification models using only spectra or texture. Conclusion: The research results showed that the accuracy of SVM and LR classification models exceeded 90%, and the LR model performed the best, effectively classifying rice varieties.

Drone and aerial remote sensing images are widely used, but their imaging environment is complex and prone to image blurring. Existing CNN deblurring algorithms usually use multi-scale fusion to extract features in order to make full use of aerial remote sensing blurred image information, but images with different degrees of blurring use the same weights, leading to increasing errors in the feature fusion process layer by layer. Based on the physical properties of image blurring, this paper proposes an adaptive multi-scale fusion blind deblurred generative adversarial network (AMD-GAN), which innovatively applies the degree of image blurring to guide the adjustment of the weights of multi-scale fusion, effectively suppressing the errors in the multi-scale fusion process and enhancing the interpretability of the feature layer. The research work in this paper reveals the necessity and effectiveness of a priori information on image blurring levels in image deblurring tasks. By studying and exploring the image blurring levels, the network model focuses more on the basic physical features of image blurring. Meanwhile, this paper proposes an image blurring degree description model, which can effectively represent the blurring degree of aerial remote sensing images. The comparison experiments show that the algorithm in this paper can effectively recover images with different degrees of blur, obtain high-quality images with clear texture details, outperform the comparison algorithm in both qualitative and quantitative evaluation, and can effectively improve the object detection performance of aerial remote sensing blurred images. Moreover, the average PSNR of this paper's algorithm tested on the publicly available dataset RealBlur-R reached 41.02dB, surpassing the latest SOTA algorithm.

With the rapid development of the Internet of Things, massive amounts of data will be generated at the network edge, resulting in increased latency of the traditional cloud computing model. Cloud service providers deploy edge servers near the users to improve the quality of service. Due to the large scale of networks, the inefficiency of edge server deployment and task scheduling will result in excessive latency and severe workload imbalance among edge servers. In this paper, we propose a Server Deployment and Task Scheduling (SDTS) framework in multi-edge collaborative computing, which can effectively reduce latency and ensure workload balancing. First, a metropolitan area network (MAN) is divided into dense candidate regions (DCRs) based on the density characteristics of mobile end devices, and then servers are deployed independently in each sub-region. Then, a two-step clustering algorithm is designed to deploy servers as close as possible to the mobile devices. Finally, a task scheduling algorithm is presented based on an edge scheduling layer in DCRs, which can leverage collaboration between edge servers to improve the performance of the entire edge computing system. The experimental results based on the Cellular Base Station (CBS) dataset of Shanghai Telecom indicate that the proposed approach outperforms several representative strategies in terms of latency and workload balancing.

A mathematical model is proposed to analyze the spreading dynamics of COVID-19. By using the parameters of the model, namely the basic reproduction number (R0) and the attenuation constant (k), the daily number of infections (DNI) and the cumulative number of infections (CNI) are deduced and shown to be in good agreement with experimental data. This model effectively addresses three key issues: explaining the waveform pattern of DNI, predicting the occurrence of the second wave of infection, and understanding the competitive spread of two viruses in a region. The findings demonstrate that these significant challenges can be comprehensively tackled using a simple mathematical framework. The theoretical insights derived from this model hold potential in guiding the estimation of the severity of an infection wave and formulating effective strategies for the control and mitigation of epidemic outbreaks.

Based on the 2014-2015 China Education Panel Survey (CEPS) and using the propensity score matching method, we studied the causal relationship between physical exercise and prosocial behavior of junior middle school students in China. Ordinary least squares regression and propensity score matching estimation results showed that participation in physical exercise significantly increases students’ prosocial behavior by more than 0.2 standard points. The results of this study were tested and found to be robust.

This paper considers the Hermitian solutions of a new system of commutative quaternion matrix equations, we establish both necessary and sufficient conditions for the existence of solutions. Furthermore, we derive an explicit general expression when it is solvable. To illustrate our main findings, we present two numerical algorithms and examples in this paper.

The safe and efficient mining of coal seams with low porosity, low permeability and high inhomogeneity under complex geological conditions is a major challenge, among which the permeability of coal seams is crucial for coal mine gas extraction. The development of coal seam permeability enhancement technology can help coal mines to produce safely and efficiently, and the extracted coal bed methane can be utilized as a green energy. In order to study the effect of freezing and thawing on the evolution of mechanical and permeability properties of coal, triaxial permeability tests were carried out on low-permeability coal under two kinds of surrounding pressures. At the same time, dry, saturated and freeze-thaw coal were set up for comparison, and the effects of water and freeze-thaw were separated from each other. The triaxial mechanics and percolation laws of dry, saturated and freeze-thaw coal rocks were obtained, and the mechanism of the influence of freeze-thaw on the permeability evolution law of coal was revealed. The research results can provide theoretical guidance for the gas extraction technology of low permeability coal seams with increased permeability.

In present work, the effects of bioflavonoids (ginkgetin and sciadopitysin) on stimulus-induced superoxide generation, tyrosyl and serine/threonine phosphorylation of proteins in human neutrophils, and the translocation of cytosolic compounds (p47phox, p67phox and Rac) to cell membrane were studied, which were isolated from the needles of Taxus media var. Hicksii. Meanwhile, three normal flavonoids (apigenin, quercetin and isoquercetin) were involved as contrasts. The results indicated that ginkgetin and sciadopitysin were capable of concentration-dependently inhibitory effects on the superoxide generation induced by N-formyl-methionyl-leucyl-phenylalanine (fMLP), arachidonic acid (AA) and phorbol-12-myristate 13-acetate (PMA). And they also suppressed fMLP- and AA- induced tyrosyl or PMA-induced serine/threonine phosphorylation and the translocation of cytosolic compounds (p47phox, p67phox and Rac) to cell membrane, which were in parallel with the suppression of the stimulus-induced superoxide generation. The effect of these compounds on the radical-scavenging was also investigated. Ginkgetin and sciadopitysin did not show remarkable effect on DPPH radical-scavenging activity, and they didn’t display the radical-scavenging activity on superoxide anion generated by phenagine methoxysulfate (PMS)-NADH system. Apparently, ginkgetin and sciadopitysin had great performance in pharmacological value and they are worthy of in-depth study.

A two-step, one-pot synthesis of 3-substituted 1H-dibenzo[e,g]indazoles in good to high yields via a LiOtBu-promoted intramolecular cyclization of 2’-alkynyl-biaryl-2-aldehyde N-tosylhydrazones, formed in situ by the reactions of 2’-alkynyl-biaryl-2-aldehydes with p-methylbenzenesulfonohydrazide was developed. Two kinds of hydrogen bonds forming in several products were observed in DMSO-d6 solution in 1H NMR spectroscopic data, which were assigned to the formation of solvated products and dimers of products, supported by the studies of density functional theory (DFT).

The COVID-19 pandemic resulted in government restrictions that altered the lifestyle of people worldwide. Studying the impact of these restrictions on exercise behaviors will improve our understanding of environmental factors that influence individuals’ PA. We conducted a retrospective analysis using an index of government pandemic stringency developed by Oxford and a wearable device for runners to compare strictness of lockdowns and exercise habits, using digitally-logged PA data from more than 7,000 runners on a global scale. Additionally, time-of-day of PA globally and levels of PA in 14 countries are compared between the pre-pandemic year of 2019 and the first pandemic year of 2020. We found that during the pandemic the time-of-day that people exercised experienced a major shift, with significantly more activities logged during standard working hours on workdays (p<0.001) and fewer during the same time frame on weekends (p<0.001). Of the countries examined, Italy and Spain had among the most strict lockdowns and suffered the largest decreases in activity counts, whereas France experienced a minimal decrease in activity counts despite enacting a similarly strict lockdown. This study suggests that there are several factors affecting PA, including government policy, workplace policy, and cultural norms.

The current urban environment is faced with the potential threat of frequent natural disasters, and the sustainable development of post-disaster community has become a global issue. As an intrinsic motivation influencing the social interaction and capital operation of community, social network is an important mechanism promoting such sustainable development. However, the difference in social network caused by different member structure, spatial arrangement and management mechanism of post-disaster communities in different reconstruction modes has influenced such sustainable development process. Therefore, reasonable selection of reconstruction mode is crucial. This paper applied analytic hierarchy process to comprehensively measure and compare the social network strength in post-disaster communities in the four reconstruction modes adopted by the government of China, i.e. unified planning and unified construction, unified planning and independent construction, in situ reconstruction and relocation resettlement, with communities after the 2008 Sichuan earthquake in Chengdu, China as study objects from the perspective of three social relations (the relations between residents and residents, residents and managers, and residents and servers). The results showed that strong connections are generally presented in the social network of post-disaster communities in unified planning modes, that the strength is significantly higher than that of those in non-unified planning modes, and that the strength of UPIC communities is the highest. Meanwhile, government intervention, residents’ free participation and market operation are positively correlated to government trust, community interaction and community service respectively. The positive impact of government intervention is the most significant, but it has a peak value. No government management and excessive government intervention will exert negative impacts. The coordination of government, society and market is the key contents of post-disaster community reconstruction. The reconstruction modes based on “government leadership, resident participation and market operation” may become a feasible path for such sustainable development.

We investigate and discuss in detail the structure of the restricted singular value decomposition for a tensor triplet under t-product (T-RSVD). The algorithm is provided with a numerical example illustrating the main result. For applications, we consider color image watermarking processing with T-RSVD.

Wobbling motion as an exotic collective mode in nuclei without axial symmetry, was intensively discussed during the last few years. The observation of the newly proposed transverse wobbling, first reported in $^{135}$Pr and soon after in nuclei from other mass regions, was considered as a significant discovery in low-spin nuclear structure. However, both the reported experimental results and the proposed theoretical models were lively questioned in a series of works devoted to the investigation of the low-spin wobbling mode in the same nucleus. The electromagnetic properties of the $\Delta I=1$ transitions connecting the one- to zero-phonon and the two- to one-phonon wobbling bands in $^{135}$Pr were recently remeasured, demonstrating their predominant $M1$ magnetic character which is in contradiction with the wobbling motion interpretation. These new experimental observables being well reproduced by both quasiparticle-plus-triaxial-rotor model and interacting boson-fermion model calculations, are against the previously proposed wobbling characteristics of the low-spin bands in $^{135}$Pr. On the other hand, we obtained conclusive experimental evidence for the theoretically suggested transverse wobbling bands at medium spin in $^{136}$Nd. The comparison of the experimental data with calculations utilizing the triaxial projected shell model and a new developed particle-rotor model with frozen orthogonal geometry of the active nucleons, supports the description in terms of transverse wobbling of medium-spin bands in triaxial even-even nuclei.

Artificial intelligence (AI) has found extensive applications in the sustainability domain. The municipal solid waste incineration (MSWI) process can achieve waste-to-energy (WTE) goals through the assistance of automation and information technologies. In theory, AI can facilitate the intelligent operation of the MSWI process, simultaneously achieving low pollution emissions and high production efficiency. This study provides an overview of AI applications in the MSWI process. Addressing fundamental challenges, the optimal control of the MSWI process is categorized into four aspects: modeling, control, optimization, and maintenance. Subsequently, this study conducts a detailed analysis and discussion of AI applications in each of these technological domains. In conclusion, this paper outlines the future research directions and provides an outlook on the fundamental issues pertaining to AI applications in the optimal control of the MSWI process.

Drones have been used in a variety of scenarios such as atmospheric monitoring, fire rescue, agricultural irrigation, etc., in which accurate environmental perception is of crucial importance for both decision-making and control. Among the drone sensors, the RGB camera is indispensable for capturing rich visual information for vehicle navigation but encounters a grand challenge in high-dynamic-range scenes that occur frequently in real applications. Specifically, the recorded frames suffer from under-exposure and over-exposure simultaneously and degenerate the successive vision tasks. To solve the problem, we take object tracking as an example and leverage the superior response of event cameras over a large intensity range to propose an event-assisted object tracking algorithm that can achieve reliable tracking under large intensity variations. Specifically, we propose to pursue feature matching from dense event signals, and based on which to (i) design a UNet-based image enhancement algorithm to balance the RGB intensity with the help of neighboring frames in the time domain, and then (ii) construct a dual-input tracking model to track the moving objects from intensity balanced RGB video and event sequence. The proposed approach is comprehensively validated in both simulation and real experiments.

Doubly-fed induction generators (DFIG) find extensive application in variable-speed wind power plants, providing notable advantages such as cost-effectiveness, operational flexibility across varying speeds, and enhanced power quality. This research focuses on the control of DFIGs employed in variable-speed wind turbine configurations. A suitable mathematical model is chosen for representative systems following a comprehensive review of contemporary research. Subsequent analysis reveals the instability of the open-loop time response of the system. To address this instability, the initial approach involves the implementation of the conventional Model Predictive Controller (MPC). However, the outcomes indicate that this controller falls short of delivering satisfactory performance despite the enhanced stability. In the subsequent phase, efforts are made to mitigate the impact of wind input variability by utilizing the Kalman filter, given its effectiveness in handling high variability. Following this, a novel methodology is introduced, which combines nonlinear MPC with the Lyapunov function. This method is based on the nonlinear model of the system. By using the Lyapunov function in the nonlinear MPC structure, the stability of the designed controller is guaranteed. The simulation results conducted using MATLAB software show that the output variables of the modeled DFIG system achieve stability within a reasonable timeframe applying the input.

A new approach is presented for dynamic modeling of chemical and isotopic evolution of C1-3 during the hydrocarbon generation process. Based on systematic data obtained from published papers for pyrolysis of various hydrocarbon sources(type I kerogen/source rock, type II kerogen/source rock, type III kerogen/source rock, crude oil, asphalt, etc), the empirical evolution framework of chemical and isotopic composition of C1-3 during the hydrocarbon generation process was built. Although the empirical framework was built only by fitting large amount of pyrolysis data, the chemical and isotopic composition of C1-3 derived from the pyrolysis experiments all follow the evolution laws, convincing us that it is applicable to the thermal evolution process of various hydrocarbon sources. Based on the simplified formula of the isotopic composition of mixed natural gas at different maturities (δ13Cmixed) δ13Cmixed=(X〖*n〗_iA 〖*δ〗^13 C_iA+Y〖*n〗_iB 〖*δ〗^13 C_iB)/(X〖*n〗_iA+Y〖*n〗_iB ), it can be derived that the cumulative isotopic composition of alkane generated in certain maturity interval can be expressed by the integral of the product of instantaneous isotopic composition and instantaneous yield at certain maturity point, and then divided by the cumulative yield of alkane generated in corresponding maturity interval. Thus, the cumulative isotopic composition(A(X)), cumulative yield(B(X)), instantaneous isotope(C(X)),and instantaneous yield(D(x)) in the dynamic model comply with the following formula during the maturity interval of (X0-X) A(X)=(∫_(X_0)^X▒〖C(X)*D(X)dx〗)/(B(X)), where A(X) and B(X) can be obtained by fitting of pyrolysis data, and D(x) can also be obtained from the derivation of B(X). The dynamic model was applied on the pyrolysis data of Pingliang Shale to illustrate the quantitative evolution of cumulative yield ,instantaneous yield, cumulative isotope and instantaneous isotope of C1-3 with increasing maturity.The dynamic model can quantify the yield of methane, ethane and propane as well as δ13C1, δ13C2 and δ13C3 respectively during the hydrocarbon generation process, which is of great significance to evaluate the natural gas resources of hydrocarbon source rock with different maturity and to identify the origin and evolutionary process of hydrocarbons by chemical and isotopic data. Moreover, this model provides an approach to study the dynamic evolution of isotope series of C1-3 (including reversed isotopic series), which is hopeful to reveal the mechanism responsible for isotopic reversal when combined with post-generation studies.

Therapeutics antibodies have increasingly shifted the paradigm of disease treatments, from small molecules to biologics, especially in cancer therapy. Despite the increasing number of antibody candidates, much remains unknown about the antibody and how its various regions interact. In fact, the constant region can govern effects that might be useful in reducing the unwanted consequences resulted from systemic circulation. For this reason, apart from the commonly used IgG isotypes, IgA antibodies are promising therapeutics drugs, given its localized mucosal effects. While the antibody Fc effector cell activity has been well explored, recent research has shown evidences that the constant region of the antibody can also influence antigen binding, challenging the conventional idea of region-specific antibody functions. To further investigate this, we analyzed the IgA antibody constant and its allosteric effects onto the antigen binding regions, using recombinant Pertuzumab IgA1 and IgA2 variants. We found mutations in the C-region to reduce Her2 binding, and our computational structural analysis showed that such allosteric communications were highly dependent on the antibody hinge, providing the evidence to consider antibodies as a whole protein rather than a sum of functional regions.

The digital orthophoto is an image with geometric accuracy and no distortion. It is acquired through a top view of the scene and finds widespread applications in map creation, planning, and related fields.This paper classifies the algorithms for digital orthophoto generation into two groups: explicit methods and implicit methods. Explicit methods rely on traditional geometric methods, obtaining geometric structure presented by explicit parameters with Multi-View Stereo (MVS) theories, as seen in our proposed Top view constrained Dense Matching (TDM). Implicit methods rely on neural rendering, obtaining implicit neural representation of scenes through the training of neural networks, as exemplified by Neural Radiance Fields (NeRF). Both of them obtain digital orthophoto by rendering from a top view perspective. In addition, this paper conducts an in-depth comparative study between explicit and implicit methods. The experiments demonstrate that both algorithms meet the measurement accuracy requirements and exhibit a similar level of quality in terms of generated results. Importantly, the explicit method shows a significant advantage in terms of efficiency, with a time consumption reduction of two orders of magnitude under our latest CUDA version TDM algorithm. Although explicit and implicit methods differ significantly in their representation forms, they share commonalities in the implementation across algorithmic stages. These findings highlight the potential advantages of explicit methods in orthophoto generation, while also providing beneficial references and practical guidance for the fast digital orthophoto generation using implicit methods.

An active power dispatch method for a microgrid (MG) with multi-type loads, renewable energy sources (RESs) and distributed energy storage devices (DESDs) is the focus of this paper. The MG operates in a grid-connected model, and distributed power sources contribute to the service for load demands. The outputs of multiple DESDs are controlled to optimize the active power dispatch. Our goal with optimization is to reduce the economic cost under time-of-use (TOU) price, and to adjust the excessively high or low load rate of distributed transformers (DTs) caused by the peak-valley demand and load uncertainties. To simulate a practical environment, the stochastic characteristics of multi-type loads are formulated. The transition matrix of system state is provided. Then, a finite-horizon Markov decision process (FHMDP) model is established to describe the dispatch optimization problem. A learning-based technique is adopted to search the optimal joint control policy of multiple DESDs. Finally, simulation experiments are performed to validate the effectiveness of the proposed method, and the fuzzification analysis of the method is presented.

The last decade has witnessed rapid development of immersive virtual reality (IVR) and its application in various contexts. However, its application in supporting real-time virtual collaboration has been quite rare due to technical barriers and the lack of validated design principles. To address this research gap, this study designed and developed an IVR space to enable multi-user synchronous co-located collaboration. An evaluation study (N = 95) was conducted to explore its useful design considerations and the influencing factors for collaboration experience. The IVR space was enabled by the simultaneous localization and mapping (SLAM)-based inside-out tracking technique and was informed by four essential design considerations for promoting effective collaboration in IVR, namely, the role script, learning task, collaboration mechanism, and communication design. The study results revealed that students in general were satisfied with their collaboration experience in IVR, with social presence and collaboration competency as significant predictors of collective efficacy and social experience. Based on both quantitative and qualitative results, this study proposes four validated principles for designing effective IVR spaces to support synchronous co-located collaboration.

An ultra-low anti-reflection optical coating on both surfaces of a plastic cover slip was studied for use in confocal image measurement. The optical reflectance at a wavelength of 632.8 nm was less than 0.1% when the coated sample was placed in a liquid having a refractive index of 1.34 close to the aqueous solution of the biomaterial. The high- and low-index coating films, Substance-2 (PrTiO3) and silicon dioxide (SiO2), were measured by an ellipsometer to determine their optical refraction indices and extinction coefficients. Theoretically, when the two layer thicknesses are designed using the optical admittance diagram of the cover slip to approach the equivalent index of 1.34, a reflectance of 1.6×10-5% in the liquid could be obtained. Experimentally, the reflectance of the sample deposited on the two faces of the cover slip was 4.223±0.145% as measured in the air; and 0.050±0.002% as measured by a He-Ne laser in the liquid.

Aim: Aging is companied by deteriorations of insulin resistance (IR) and insulin secretion. However, little is known about the roles of aging on different phases of insulin secretion (ISEC); i.e., the first- and second-phase of insulin secretion (FPIS, SPIS) and glucose effectiveness (GE). Methods: Totally 169 individuals (43 men and 126 women) with the similar fasting plasma glucose (FPG: 90 mg/dL) and BMI (men: 23 kg/m2, women 22 kg/m2) were enrolled. IR, FPIS, SPIS, and GE were estimated using our previously developed equations shown below. Pearson correlation analysis was conducted to assess the correlations between age and four diabetes factors (DFs: IR, FPIS, SPIS and GE). The equations that are used to calculate the DF in the present study were built and published by our group. Results: The age of the participants ranged from 18- to 78-year-old. Men had higher FPIS but lower HDL-C levels than women (2.067±0.159, 1.950±0.186 μU/min and 1.130±0.306, 1.348±0.357 mmol/dl, accordingly). The results of Pearson correlation revealed that age was negatively related to IR and GE in both genders (IR: r = -0.39, p < 0.001 for men, r = -0.24, p < 0.003 for women; GE: r = 0.66, p < 0.001 for men, r = 0.78, p < 0.001 for women). In the meanwhile, FPIS was also only found to be negatively correlated with age in female (r = -0.238, p = 0.003). But there was no different with SPIS and age in both of genders. Conclusions: We have found that in Chinese with normal FPG level (90 mg/dl) and body mass index (men: 23 kg/m2, women: 22: kg/m2), age is negatively related to IR and GE in both genders. At the same time, only FPIS was found to be negatively related to age in women. The tightness of their relationships, from the highest to the lowest are, GE, FPIS and IR accordingly. The importance of GE should be re-evaluated. These results should be interpreted with caution because of the small sample size.

Cloud Manufacturing (CMfg) has attracted lots of attention from scholars and practitioners. The purpose of quality of service (QoS)-aware manufacturing cloud service composition (MCSC), as one of the key issues in CMfg, is to combine different available manufacturing cloud services(MCSs) to generate an optimized MCSC that can meet diverse requirements of customers. However, many available MCS, deployed in the CMfg platform, have same function but different QoS attributes. It is a great challenge to achieve optimal MCSC with high QoS. In order to obtain better optimization results efficiently for the QoS-MCSC problems, a whale optimization algorithm(WOA) with adaptive weight, Lévy flight, and adaptive crossover strategies (ASWOA) is proposed. In the proposed ASWOA, the adaptive crossover inspired by the genetic algorithm is developed to balance the exploration and exploitation. The Lévy flight is designed to expand the search space of WOA and accelerate the convergence of WOA with adaptive crossover. The adaptive weight is developed to extend the search scale of the exploitation. Simulation and comparison experiments are conducted on different scale QoS-MCSC problems. The experimental results demonstrate that the proposed ASWOA outperforms other compared cutting-edge algorithms.

This paper provides the numerical investigation of two-dimensional stochastic Navier-Stokes equation driven by additive white noises. The vorticity-stream function method is presented to calculate the incompressible fluid flow. In order to solve the formulation of steam function equation, we propose a Crank-Nicolson Fourier pseudo-spectral method. Numerical results obviously show that the presence of stochastic forcing can have an impact upon the shapes of vortex in fluid flow. To a certain extent, the phenomenon of eddy shedding and the formation of some new vortex in a random medium can be observed, which make it possible for us to conduct optimal control experiments in the development of vortex structures.

Traditional graphene-based films normally possess high thermal conductivity (TC) only along single direction, which is not suitable for thermal interface materials (TIMs). Here, a graphene film with excellent bidirectional TC and mechanical properties was prepared by hot-pressing super-elastic graphene aerogel (SEGA). Thermal annealing at 1800℃ improves the further restacking of graphene sheets, bring the SEGA high structure stability for enduring the hot-pressing process. The junctions and nodes between the graphene layers in the hot-pressed SEGA (HPSEGA) film provide bidirectional heat transport paths. The in-plane TC and through-plane TC of HPSEGA film with thickness of 101μm reach 740 Wm-1K-1 and 42.5 Wm-1K-1, respectively. In addition, HPSEGA film with higher thickness still maintains excellent thermal transport properties due to the interconnected structure reducing the effect of the defects. The infrared thermal images visually manifest the excellent thermal-transfer capability and thermal dissipation efficiency of the HPSEGA films, indicating the great potential as advanced bidirectional TIMs.

In order to further reduce the emissivity of the hydrophobic low infrared emissivity composite coating and improve the mechanical properties of the coating, the dispersant, adhesion enhancer, and defoamer were used to improve the dispersion state of the fillers, the interface structure, and the surface state of the hydrogen silicone oil modified polyurethane/Al composite coating. The effects of dispersant, adhesion enhancer, and defoamer on the micro-structure, emissivity, glossiness, hydrophobic property, and mechanical properties of the coating were systematically studied. The results show that the polycarboxylate anionic dispersant can obviously improve the dispersion state of flake Al powder and nano-SiO2 in the coating, so that the infrared emissivity of the coating can be reduced, and the coating can have higher hydrophobic and mechanical properties. The bonding strength between the resin matrix and the metal substrate of the coating can be significantly improved by the adhesion enhancer through the bridging action, so that the adhesion strength and impact strength of the coating can be significantly improved. The defoamer can significantly reduce the pores in the coating, so that the surface state of the coating can be significantly improved, and the mechanical properties of the coating can be significantly improved. The coating has the best emissivity (0.527), glossiness (4.3), adhesion strength (grade 1), impact strength (40 kg.cm), and hydrophobic property (water contact angle (WCA) is 140o) when the amount of dispersant, adhesion enhancer, and defoamer is 5 wt%, 4 wt%, and 1 wt%, respectively.

Singapore, being a land-scarce country, is heavily dependent on imported food, which poses a challenge in ensuring food security. In addition, the substantial amount of food waste generated in Singapore further deepens food insecurity challenges, imposing enormous pressure on the country's food and land resources. In the recent years, valorising food waste streams into higher value products has been an increasing trend in tackling food wastage due to its economical and environmental benefits. This paper provides a thorough review on food waste valorisation in Singapore and summarizes critical information from the current food landscape to the food waste management strategies adopted. The review highlights the prototypes developed from food waste streams by the local research and development capabilities and the technological processing and techniques used for versatile applications ranging from food and nutraceuticals to urban agriculture. The challenges associated with the valorisation of food waste are also addressed. Two ambitious Initiatives by the Singapore government Zero Waste Plan and the 30-by-30 food security goal to encourage food waste valorisation are explored and presented. The review serves as a reference for other countries in ensuring food security and achieving sustainable development goals.

Based on the sensitive sum frequency generation cross-correlation frequency-resolved optical gating (SFG-XFROG) measurement setup, besides the pulse broadening, blue shift, red shift and obvious pulse acceleration, we observed the soliton evolution when the low energy soliton pulse with wavelength of 1555nm transmit through the Si photonic crystal waveguide. The measurements were nicely matched with the simulation results, which are achieved with an optimized nonlinear Schrödinger equation (NLSE) modeling. The effects of various parameters of the silicon photonic crystal waveguides and the incident pulses on the pulse transmission were also analyzed, including the nonlinear effects and dispersion such as the self-phase modulation (SPM), self-steepening (SS) and intra-pulse Raman scattering(IRS). The results help us understand further the ultra-fast nonlinear dynamics of soliton in silicon-based waveguides, and even open a novel way for soliton-based functional elements in CMOS-compatible platforms.

This paper addresses the problem of assessing and optimizing acoustic positioning system for underwater target localization with range measurements only. We present a new three-dimensional assessment model to assess the optimal geometric beacon formation whether meet user needs. For the sake of mathematical tractability, it is assumed that the measurements of the range between the target and beacons are corrupted with white Gaussian noise with variance is distance-dependent. Then by adopting dilution of precision (DOP) parameters in the assessment model, the relationship between DOP parameters and positioning accuracy is derived. In addition, the optimal geometric beacon formation that will yield the best performance is achieved by minimizing the values of geometric dilution of precision (GDOP) on condition that the position of target is known and fixed. Next, in order to make sure whether the estimate positioning accuracy over interesting region satisfy the precision needed by the users, geometric positioning accuracy (GPA), horizonal positioning accuracy (HPA) and vertical positioning accuracy (VPA) are utilized to assess the optimal geometric beacon formation. Simulation examples are designed to illustrate the exactness of the conclusion. Unlike other work which only use GDOP to optimize the formation and cannot assess the performance of the specified dimensions, this new three-dimensional assessment model can assess the optimal geometric beacon formation in each dimension for any point in three-dimensional space, which can provide users with guidance advices to optimize performance of every specified dimension.

Dual quaternions have wide applications in automatic differentiation, computer graphics, mechanics and others. Due to its application in control theory, the matrix equation AXB=C has been extensively studied. However, there is currently limited information on matrix equation AXB=C over the dual quaternion algebra. In this paper, we provide the necessary and sufficient conditions for the solvability of the dual quaternion matrix equation AXB=C, and present the expression for the general solution when it is solvable. As an application, we derive the ϕ-Hermitian solutions for the dual quaternion matrix equation AXAϕ=C, where the ϕ-Hermitian extends the concepts of Hermiticity and η-Hermiticity. Lastly, we present a numerical example to verify the main research results of this paper.

In this work,the wave behaviour propagation of single-walled fluid conveying carbon nanotubes (SWCNT) under longitudinal magnetic fields and elastic foundations is studied, based on the nonlocal strain gradients theory(NSGT). With considertion of surface effect, the governing differential equation are obtained utilising Timoshenko beam theory and Hamilton's variational principle. The influence of small-scale parameters, fluid density, magnetic flux, surrounding elastic medium and surface effects on wave behaviour characteristics of carbon nanotubes are discussed in detail. The numerical results illustrated that with the magnetic flux increase，the phase velocities of the carbon nanotube will increase. When the fluid effects inside the carbon nanotube, the wave frequencies of the system reduces with increase in the non-local coefficient, while promotes with increase in the strain gradient coefficient. In addition, fluid density, surrounding elastic medium and surface effects have meaningful influence on the phase velocity of the system.

Up-conversion nanoparticles (UCNP) under near-infrared (NIR) light irradiation have been well investigated in the field of bio-imaging. However, the low up-conversion luminescence (UCL) intensity limits applications. Plasmatic modulation has been proposed as an effective tool to adjust the luminescence intensity and lifetime. In this study discrete dipole approximation (DDA) was explored concerning guiding the design of UCNP@mSiO2-Au structures with enhanced UCL intensity. The extinction effects of gold shells could be changed by adjusting the distance between the UCNPs and the Au NPs by synthesized tunable mesoporous silica (mSiO2) spacers. Enhanced UCL was obtained under 808 nm irradiation. Theoretical predictions could not be demonstrated to full extend by experimental data, indicating that better models for simulation need to better take into account inhomogeneities in particle morphologies.

Clarifying the response of plant phenology to drought duration is helpful for accurately interpreting and predicting carbon sinks in ecosystems. Based on the response of different phenological periods of the dominant species Stipa krylovii to monthly, seasonal and semiannual time scale drought in the typical steppe of Inner Mongolia from 1983 to 2018, the results revealed that (1) the start of the growing season (SOS) was characterized by an advance-delay-advance pattern, and the heading stage (HOS), flowering stage (FOS) and end of the growing season (EOS) all showed consistent advanced trends, which provided additional insight into the conclusions of previous studies that found the SOS was advanced in arid and semiarid regions. (2) The response mechanism of the SOS to the timing of drought was not consistent. Among the response mechanism, the SOS was delayed because of January-February drought at different time scales, but advanced because of April drought at different time scales. The HOS/FOS was delayed by June-July drought at different time scales, and the EOS was advanced by August-September drought at different time scales. (3) More importantly, the SOS, HOS, FOS and EOS were affected by predrought and its persistence, and the effects were greater the closer to the phenological periods that drought occurred. (4) At the monthly scale, droughts in January, June and August were the critical drought periods affecting the SOS, HOS/FOS and EOS, while seasonal and semiannual scale droughts in February, June-July and September were the critical periods affecting the SOS, HOS/FOS and EOS. The results of this study enrich our understanding of how drought at different time scales affects different phenological periods, providing a basis for improving plant phenological models.

Currently, an interactive environmental planning analysis system platform based on " Three Lines One Permit " (TLOP) is being developed to support environmental planning, construction project approval, and the application of TLOP outcome data in Guangzhou. The main objective is to provide governments, businesses and the public with environmental planning analysis tools to determine the site of construction projects. The platform is using the system architecture of the browser and server. Its core functions are interactive environmental planning analysis tool for construction project and the results display tool supporting map viewing. It provides users with a large number of detailed geospatial data and TLOP results data access and environmental planning analysis functions. This article describes the system architecture and implementation of the system platform and has a case study illustrating the system functionality. At present, the platform has been deployed and trial-operated. The content of the analysis framework is constantly expanding. This promotes the matching of environmental planning and analysis with local conditions. This will implement the application of TLOP and improve the efficiency of project construction and the level of ecological environment planning and management.

Electric energy has become essential nowadays not only for the daily life of each of us but also for the economy of different countries. The dissemination of geographic information plays an important role in national development as it facilitates communication between managers, investors, and consumers in this sector. Since the management of electricity network data was previously done in Tunisia based on paper maps and plans, the purpose of this article is to present a case of planning based on GIS, Web, and 3D Web GIS, which would have significant positive consequences on this sector from a technical and financial sides with an improvement in customer satisfaction and the creation of an intelligent electricity network which will be a real decision-making tool. This work draws up an inventory of the network MV (Medium Voltage)/LV (Low Voltage) of the region of Medjez El Bab which routes electricity to the big centers of consumption with access to MV/LV subscribers. The analysis of the network's impedance allowed carrying out different scenarios to optimize performance and obtain more realistic routes. Many thematic maps were produced as part of this project (Slope map, Land use map, map of the MV voltage domains, map of the MV/LV transformer stations power, etc.). A three-dimensional virtual city has been developed to visualize the graphical and attribute data for the study area. A Web and 3D Web GIS applications that allows the publication of the interactive maps on the Web as well as the database information have been developed to offer users the possibility of consulting the produced products by internet. Finally, a website related to the study was developed.

Background: Pro-golfers are increasingly trained scientifically, but swing technique characteristics have not yet been conclusively determined for golf performance. We investigated the relationship between the player's thorax, pelvis and club based on a wireless biofeedback system. We also determined whether thoracic and pelvic changes during different swing phases have different effects on ball striking. Methods: Eight male professionals performed 10 successful full-swing shots using a 5-iron and applied the K-vest wireless biofeedback system for analysis. Results: Peak angular velocities between the pelvis, torso, and clubs were all significantly different (p&lt;0.05) in the men's professional players. All players deviated more from the PGA players range metrics during the preparation stance. Excessive lateral pelvic bending angle, over-rotation, and hyperextension of the trunk were prevalent at the top of the backswing. Conclusions: Most players have an irrational sequence of pelvic, trunk and club rotation times, which is not conducive to improving club head speed and distance. Excessive pelvic rotation in the closed direction at the apex of the backswing is not conducive to maximizing the X-factor. At the moment of striking the ball, maintain a moderate forward trunk lean and full trunk rotation to avoid sports injuries.

Electric tractors have many advantages, including high torque, excellent controllability, energy efficiency, simple structure, and electric interface for expansion. However, a significant limitation lies in their endurance. This study presents the design of an extended-range power supply system to ensure continuous endurance of electric tractor. The objective is to provide a continuous power source for our self-developed electric tractor while preserving the benefits of electric propulsion. Extended-range power systems utilize a primary mover, typically an oil-fueled internal combustion engine, to drive the generator for electricity generation, and the generated AC form electricity is subsequently converted into stable DC bus voltage by a power electronic converter. The hardware and control design of the extended-range power supply system have been finalized and validated through experimental trials. Results demonstrate the system’s capability to sustain a stable DC bus voltage amidst disruptions such as sudden load shifts and fluctuations in prime mover speed. Even with 50% sudden load change, the voltage drop is within 12% and could recover to ±3% within 4 seconds. The range extender can be used alone without battery to power the electric tractor, or it can used in parallel with other extended-range extenders or battery thanks with the droop control ability for power sharing.

Vein segmentation and projection correction constitute the core algorithms of auxiliary venipuncture device, responding to the accurate venous positioning to assist puncture and reduce the number of punctures and pain of patients. This paper proposes an improved U-Net for segmenting vein and a coaxial correction for image alignment in the self-build vein projection system. The proposed U-Net is embedded by Gabor convolution kernels in the shallow layers to enhance segmentation accuracy. Additionally, to mitigate the semantic information loss caused by channel reduction, the network model is lightweighted by the mean of replacing conventional convolutions with inverted residual blocks. During the visualization process, a method that combining coaxial correction and homography matrix is proposed to address the non-planarity of the dorsal hand in this paper. First, use a hot mirror to adjust the light paths of both projector and camera to be coaxial, and then align the projected image with dorsal hand using a homography matrix. Using this approach, the device requires only a single calibration before use. With the implementation of the improved segmentation method, an accuracy rate of 95.12% is achieved by the dataset. The intersection over union ratio between the segmented and original images is reached at 90.07%. The entire segmentation process is completed in a 0.09 second, and the largest distance error of vein projection onto dorsal hand is 0.53mm. Experiments show that the device has reached practical accuracy and has the value of research and application.

This paper proposes an encryption scheme for high pixel density images. Based on the application of the quantum random walk algorithm, the Long short-term memory (LSTM) can effectively solve the problem of low efficiency of the quantum random walk algorithm in generating large-scale pseudorandom matrices, and further improve the statistical properties of the pseudorandom matrices required for encryption. The LSTM is then divided into columns and fed into the LSTM in order for training. Due to the randomness of the input matrix, the LSTM cannot be trained effectively, so the output matrix is predicted to be highly random. The LSTM prediction matrix of the same size as the key matrix is generated based on the pixel density of the image to be encrypted, which can effectively complete the encryption of the image. In the statistical performance test, the proposed encryption scheme achieves an average information entropy of 7.9992, an average number of pixels changed rate (NPCR) of 99.6231%, an average uniform average change intensity (UACI) of 33.6029% and an average correlation of 0.0032. Finally, various noise simulation tests are also conducted to verify its robustness in real-world applications where common noise and attack interference are encountered.

Remote sensing image registration plays an important role in military and civilian fields, such as natural disaster damage assessment, military damage assessment and ground targets identification, etc. However, due to the ground relief variations and imaging viewpoint changes, non-rigid geometric distortion occurs between remote sensing images with different viewpoint, which further increases the difficulty of remote sensing image registration. To address the problem, we propose a multi-viewpoint remote sensing image registration method which contains the following contributions. (i) A multiple features based finite mixture model is constructed for dealing with different types of image features. (ii) Three features are combined and substituted into the mixture model to form a feature complementation, i.e., the Euclidean distance and shape context are used to measure the similarity of geometric structure, and the SIFT (scale-invariant feature transform) distance which is endowed with the intensity information is used to measure the scale space extrema. (iii) To prevent the ill-posed problem, a geometric constraint term is introduced into the L2E-based energy function for better behaving the non-rigid transformation. We evaluated the performances of the proposed method by three series of remote sensing images obtained from the unmanned aerial vehicle (UAV) and Google Earth, and compared with five state-of-the-art methods where our method shows the best alignments in most cases.

Abstract: The Micro-Electro-Mechanical System (MEMS) LiDAR point cloud in autonomous vehicles has a large deflection range, which results in slow registration speed and poor applicability. To maximize speed, an improved NDT (Normal Distribution Transform) method that integrates point cloud density features has been proposed. First, the point cloud is reduced using a modified voxel filter and a pass-through filter. Next, the ISS (Intrinsic Shape Signature) algorithm is utilized to analyze the point cloud features and extract keypoints, the 4PCS (4-Point Congruent Sets) algorithm is then employed to calculate the initial pose under the constraints of the keypoint set to complete the coarse registration. Finally, the self-adaptive segmentation model is constructed by using KD-tree to obtain the density features of keypoints, and the NDT algorithm is combined with this model to form an SSM-NDT algorithm, which is used for fine registration. Each algorithm was compared on the autonomous vehicle dataset PandaSet and actual collected data sets, the results show that the novel method increases the speed by at least 60% and takes into account good registration accuracy and strong anti-interference.

Cambodia faces the challenge of managing excess water during the wet season and insufficient water during the dry season. This harms human life and endangers aquatic and natural resources, agricultural practices, and food security. In order to ensure the well-being of both people and food security, water governance is crucial. However, Cambodia's water governance is hindered by various obstacles, including sectoral and centralized influences, top-down and large-scale strategies, a lack of coordination among relevant agencies, and limited involvement of local communities. This study delves into water governance across different sectors, from centralized to community-based natural resources management to tackle these challenges. Through analyzing literature and case studies of farmer water user communities (FWUC), community fisheries (CFis), and community fish refuges (CFRs) in three Mekong Delta provinces in Cambodia, the study concludes that although water governance has improved, it has resulted in a decline in fishery resources from rivers and water bodies and an increase in water conflicts among farmers and sectors in the face of climate change. To enhance water governance in Cambodia, it is critical to integrate it at the district level. This will promote sustainable water use and management across the country and pave the way for a brighter future.

Detecting cracks in structural health monitoring is crucial for ensuring infrastructure safety and longevity. Using drones to obtain crack images and automate processing can improve the efficiency of crack detection. To address the challenges posed by the limited computing resources of edge devices in practical applications, we propose CrackScopeNet, a lightweight segmentation network model that simultaneously considers local and global crack features, suitable for deployment on drone platforms with limited computational power and memory. CrackScopeNet features a multi-scale branch to improve sensitivity to cracks of varying sizes without substantial computational overhead and a stripe-wise context attention mechanism to enhance the capture of long-range contextual information, mitigating the interference from complex backgrounds. Experimental results on CrackSeg9k dataset demonstrate that CrackScopeNet leads to a significant improvement in prediction performance, with the highest mean intersection over union (mIoU) scores reaching 82.12%, while maintaining a lightweight architecture with only 1.05M parameters and 1.58G floating point operations (FLOPs). Besides, CrackScopeNet excels in inference speed on edge devices without GPU because of its low FLOPs. CrackScopeNet contributes to the development of efficient and effective crack segmentation networks, suitable for practical structural health monitoring applications using drone platforms.

This paper develops a novel dynamic correction method for the reliability assessment of large oil-immersed power transformers. First, with the transformer oil-paper insulation system (TOPIS) as the target of evaluation and the winding hot spot temperature (HST) as the core point, an HST-based static ageing failure model is built according to the Weibull distribution and Arrhenius reaction law, in order to describe the transformer ageing process and calculate the winding HST for obtaining the failure rate and life expectancy of TOPIS. A grey target theory based dynamic correction model is then developed, combined with the data of Dissolved Gas Analysis (DGA) in power transformer oil, in order to dynamically modify the life expectancy calculated by the built static model, such that the corresponding relationship between the state grade and life expectancy correction coefficient of TOPIS can be built. Furthermore, the life expectancy loss recovery factor is introduced to correct the life expectancy of TOPIS again. Lastly, a practical case study of an operating transformer has been undertaken, in which the failure rate curve after introducing dynamic corrections can be obtained for the reliability assessment of this transformer. The curve shows a better ability of tracking the actual reliability level of transformer, thus verifying the validity of the proposed method and providing a new way for transformer reliability assessment. This contribution presents a novel model for the reliability assessment of TOPIS, in which the DGA data, as a source of information for the dynamic correction, is processed based on the grey target theory, thus the internal faults of power transformer can be diagnosed accurately as well as its life expectancy updated in time, ensuring that the dynamic assessment values can commendably track and reflect the actual operation state of the power transformers.

The rampant misuse of drones poses a serious threat to national security and human life. Currently, the CNN method has been widely used in drone detection. However, there is a challenge that traditional CNN cannot cope with, which is small drone targets often have reduced amplitude or even lost features in infrared images. This paper proposes a Progressive Feature Fusion Network (PFFNet) that gradually increases the response amplitude of the target in the deep network. The Feature Selection Model (FSM) is designed to improve the utilisation of the output coding graph and enhance the feature representation of the target in the network. A lightweight segmentation head is also designed to achieve progressive feature fusion with multi-layer outputs. Experimental results show that the proposed algorithm can achieve low duration and high accuracy in drone target detection. On the public dataset, the IoU is improved by 2.53% and the detection time is reduced by 81.03%.

This study examined the use of high dosages of ultraviolet germicidal irradiation (UVGI) (253.7 nm) to deal with various concentrations of air pollutants, such as formaldehyde (HCHO), total volatile organic compounds (TVOC), under various conditions of humidity. We also estimated the emission of ozone as a secondary pollutant of UVGI as treatment. A number of irradiation methods were applied for various durations in field studies to examine the efficiency of removing HCHO, TVOC, bacteria, and fungi. The removal efficiency of air pollutants (HCHO and bacteria) through long-term exposure to UVGI appears to increase with time. The effects on TVOC and fungi concentration were insignificant in the first week; however, improvements were observed in the second week. No differences were observed among the various irradiation methods in this study regarding the removal of HCHO and TVOC; however significant differences were observed in the removal of bacteria and fungi.

Research has shown that the consumption of herbal teas is linked with positive effects on the regulation of postprandial blood glucose level. In this study, a comparative analysis of aqueous and ethanol extractions is performed on four herbal teas (Mallotus, Cyclocarya, Rubus, Vine) to evaluate their phytochemical profiles and functional activities. Investigations of phytochemical contents, antioxidant activities, α-glucosidase inhibitory activities and chemical compositions are carried out through colorimetric analyses and UPLC-Q-Orbitrap HRMS/MS, respectively. Results showed that among the studied herbal teas, Vine exhibited the highest glucose re-gulating abilities under both aqueous and ethanol extractions. Overall, ethanol extracted samples yielded higher phytochemicals content, but the compositions of the phytochemicals varied. Meanwhile, aqueous extracts showed surprisingly strong antioxidant activities and comparable α-glucosidase inhibitory activities with ethanol extracts. Phytochemicals contents presented a positive correlation with antioxidant activities and α-glucosidase inhibitory activities. Nevertheless, antioxidant activities displayed a weak positive correlation with α-glucosidase inhibitory activities. Results from this study provided evidence that aqueous extracts from herbal teas possessed valuable phytochemicals composition for antioxidants and the hyperglycemic populations, and are promising candidates for potential applications as functional ingredients with health promoting effects and as natural additives to boost the nutritional value of herbal food products.

Roadway accidents significantly contribute to intermittent congestion and increased CO2 emissions on freeways. This research introduces a statistical approach designed to predict the rise in CO2 emissions resulting from traffic disturbances or jams triggered by such incidents. It also assesses the influence of varying levels of accident management effectiveness in different situations. To construct these scenarios, the study employs VISSIM, a traffic modeling software, incorporating diverse factors such as traffic volume, vehicle types, incident durations, and freeway lane counts. It then produces traffic flow characteristics in the form of vehicle paths. The emission estimates are derived by correlating these simulated vehicle paths with emission rates from the MOVES model. The study then applies a regression analysis to examine the connection between the increase in emissions and various influencing factors. The findings indicate that this approach efficiently reflects the impact of variables like accident duration, vehicle mix, and traffic volume on CO2 emissions across different lane configurations. The accuracy of these predictions is also confirmed. The outcomes suggest the model's potential usage in guiding efforts to lower emissions and determining the optimal duration of incident management, particularly in terms of lane closure, to mitigate emission impacts. This paper also discusses the limitations of the model and the future improvement direction.

Change detection with heterogeneous remote sensing images (Hete-CD) plays an important role in practical applications, especially when homogenous remote sensing images are unavailable. However, bitemporal heterogeneous remote sensing images (HRSIs) cannot compare directly to measure change magnitude, and many deep learning methods require large amounts of samples to train the module. Moreover, labeling many samples for land cover change detection with HRSIs is time-consuming and labor-intensive. Therefore, acquiring satisfactory performance of Hete-CD remains a challenge for deep learning networks with very small training samples. In this study, we promote a novel deep-learning framework for Hete-CD to obtain satisfactory performance while the initial samples are very small. We initially design a multiscale network with select kernel-attention module to focus on capturing different change targets with various sizes and shapes. Then, a simple yet effective non-parametric sample-enhanced algorithm based on the Pearson correlation coeffi-cient is promoted to explore potential samples around each initial sample. Finally, the proposed network and sample-enhanced algorithm are fused into one iterative framework to improve the change detection performance with very small samples. Experimental results conducted on four pairs of actual HRSIs indicated that the proposed framework can achieve competitive accuracies with very small samples for initialization when compared with some state-of-the-art methods. For example, the improvement is approximately 3.38% and 1.99% when compared with the selected traditional methods and deep learning methods, respectively.

This paper presents a calibration and thermal compensation method that aims to minimize cost and processing time while maintaining high accuracy. First, the number of positions to perform the calibration procedure is optimized, and then, based on this optimized calibration number, thermal compensation is performed, which is necessary for environments with large temperature variations, since calibration parameters change at different temperatures. The calibration procedures and algorithms were experimentally validated on marketed accelerometers. Based on the optimized calibration method, the calibrated results have been obtained nearly 100 times improvement. Thermal drift calibration experiments on the triaxial accelerometer show that the thermal compensation scheme in this paper can effectively reduce drift in the temperature range of -40°C to 60°C. The temperature drifts of x- and y-axes are reduced from -13.2 and 11.8 mg to -0.9 and -1.1 mg, respectively. The z-axis temperature drift is reduced from -17.9 to 1.8 mg. We have conducted various experiments on the proposed calibration method and demonstrated its capacity to calibrate the sensor frame error model (SFEM) parameters. Therefore, the MEMS sensors calibrated in this paper have certain engineering application values for tilt measurement.

Interferometric synthetic aperture radar (InSAR) and tomographic SAR measurement techniques are commonly used for the three-dimensional (3D) reconstruction of complex areas, while the effectiveness of these methods relies on the interferometric coherence among SAR images with minimal angular disparities. Radargrammetry exploits stereo image matching to determine the spatial coordinates of corresponding points in two SAR images, and acquire their 3D properties. The performance of the image matching process directly impacts the quality of the resulting digital surface model (DSM). However, the presence of speckle noise, along with dissimilar geometric and radiometric distortions, poses considerable challenges to achieve accurate stereo SAR image matching. To address the aforementioned challenges, this paper proposes a radargrammetric method based on composite registration of multi-aspect SAR images. The proposed method combines coarse registration using scale invariant feature transform (SIFT) with precise registration using normalized cross-correlation (NCC) to achieve accurate registration between multi-aspect SAR images with large disparities. Furthermore, the multi-aspect 3D point clouds are merged by the proposed radargrammetric 3D imaging method, resulting in 3D imaging of target scenes based on multi-aspect SAR images. For validation purpose, this paper presents a comprehensive 3D reconstruction of the Five-hundred-meter Aperture Spherical radio Telescope (FAST) by using Ka-band airborne SAR images. It does not necessitate prior knowledge of the target and is applicable to the detailed 3D imaging of large-scale areas with complex structures. In comparison to other SAR 3D imaging techniques, it reduces the requirements for orbit control and radar system parameters. To sum up, the proposed 3D imaging method with composite registration guarantees imaging efficiency while enhances the imaging accuracy of crucial areas with limited data.

An all-dielectric metasurface composed of orthogonal-slit silicon disks is proposed in this study. By modifying the unit structure of the metasurface with the bound states in the continuum (BIC) theory, a sharp Fano resonance can be generated. The resonance properties of the metasurface are investigated by analyzing the effects of the structural parameters on the resonance using the eigenmode analysis method. The Q factor and the resonance wavelength can be adjusted by varying the slit width, the disk thickness, and the disk radius. The electromagnetic characteristics and mechanism of the toroidal dipole-BIC (TD-BIC) are explored in depth through an analysis of the multipole expansion of the scattered power, along with the electromagnetic field and the current distribution at resonance. This research provides a novel approach for excitation of a strong TD-BIC resonance and proposes potential applications in optical switches, high-sensitivity optical sensors, and related areas.

Solute carrier (SLC) proteins transport a wide range of substrates across biological membranes and more than 400 proteins are considered as SLCs based on its sequence. Despite it has important potential applications in drug development, the SLC superfamily is still understudied. Drosophila blot, an orphan SLC protein, regulates F-actin organization in the syncytial blastoderm and the Malpighian tubules. However, the molecular mechanism was unknown. In this study, we found Blot was localized at the furrow canal, the front of the invaginating membrane during cellularization process. Blot is required for maintaining proper morphology of furrow canal and cellularization process. By affinity purification and mass spectrometry we identified RhoGEF ELMO/DOCK complex was required for Blot-mediated F-actin organization and cellularization. Further analysis revealed that ELMO localization depends on Blot. Taken together, we propose Blot plays a role in F-actin organization as an upstream factor of ELMO by recruiting ELMO to the target membrane compartments.

The early Paleozoic is a crucial period in the formation and evolution of the Eastern Kunlun Orogenic Belt (EKOB), and is of great significance for understanding the evolutionary history of the Proto-Tethyan Ocean. This paper presents new petrography, geochemistry, zircon U–Pb dating, and Lu–Hf isotopic research on the Yuejingshan gabbro from the eastern segment of the EKOB. Zircon U–Pb data suggests that the gabbro formed in the Early Silurian (435 ± 2 Ma). All samples have relatively low TiO₂ contents (0.45–2.97%), widely varying MgO (6.58–8.41%) and Mg^# (58–65) contents, and are rich in large ion lithophile elements (LILE such as Rb, Ba, Th, and U) and light rare earth elements (LREE). This indicates that it has a similar geochemical composition to island arc basalt. The major element features indicate that the formation of this gabbro underwent fractional crystallization of clinopyroxene, olivine, and plagioclase. The depletion of high field strength elements (HFSE, such as Nb, Ta, and Ti), and a slightly positive Hf isotope (with εHf_(t) ranging from 1.13 to 2.45) may be related to the partial melting of spinel-bearing peridotite, led by slab fluid metasomatism. The gabbro likely represents magmatic records of the latest period of the early Paleozoic oceanic crust subduction in the Eastern Kunlun. Therefore, the final closure of the Proto-Tethyan Ocean and the beginning of collisional orogeny occurred before the Early Silurian.

The design, facilities and conditions inside a classroom play a significant role in the teaching and learning experience for both students and lecturers. Prior studies of primary schoolchildren indicate three design principles affecting student learning, namely: naturalness, individualisation and stimulation. The current study extends these investigations to Higher Education through a survey of undergraduate students and university lecturers aimed at determining the most critical factors in undergraduate classroom design. One-to-one interviews were conducted with students and lecturers (n. 31) at the University of Nottingham, Malaysia Campus. Interviewees were encouraged to express their opinions, comments, concerns and suggestions through open-ended questions. The interviews were recorded and then transcribed and coded using NVivo10. Results show a strong desire among lecturers and students for improved classroom equipment, greater flexibility in classroom arrangement, more attractive decoration and for the addition of natural elements to the classroom environment. Of the three design principles, individualisation and naturalness emerged most strongly from the interviews and appear to be more important factors for undergraduates than stimulation. These findings could make a novel and significant contribution to the physical aspects of classroom design in Higher Education settings. Educational institutions are increasingly employing non-traditional classroom designs, which are expected to provide for more flexible, collaborative, and active learning and teaching experiences. Taking into consideration the environmental psychology of teaching and learning, several of the reported design attributes can serve as benchmarks for upgrading current classroom design and facilities in the future, as institutions look to upgrade their physical infrastructures to meet the changing demands of teachers and learners arising from technological innovations and shifts in our understanding of the methods and purposes of Higher Education.

Percutaneous kyphoplasty (PKP) is a highly successful treatment option for Kümmell's disease (KD). Due to the obstruction of the fibrocartilaginous membrane at the periphery of the intervertebral vacuum cleft (IVC) and inadequate osteointegration of conventional bone cement, there is insufficient locking strength between trabecular bone and bone cement in Kümmell's disease (KD), leading to postoperative displacement of the bone cement and sub-sequent early surgical failure. However, there is no standard strategy managing this complica-tion at present. We reported a rare case of premature bone cement displacement resulting in adjacent vertebra fracture. An 80-year-old male with Kümmell's disease at T10 received per-cutaneous kyphoplasty (PKP) with bone cement leaking into the upper disc. Only 3 days after the surgery, the patient complained of severe back pain again. X-ray and MRI showed bone cement displacement at T10, adjacent vertebra fracture at T9. In consideration of poor toler-ance of open surgery for the old patient, we successfully revised by using percutaneous ky-phoplasty with multi-point anchoring technique (A-PKP). During a 10-month postoperative follow-up, the patient reported pain relief. X-ray and three-dimensional CT scan revealed no obvious progress of bone cement displacement. Although the exact mechanism underlying this rare complication remains unclear, we observed that the premature bone cement dis-placement can result in adjacent vertebra fracture especially when the pattern of bone cement is blocky. Percutaneous kyphoplasty with multi-point anchoring technique (A-PKP) appears effective in managing this complication.

Ganoderic Acid (GA), a major bioactive compound isolated from an oriental medicinal mushroom, i.e. Ganoderma tsugae, has traditionally been found to have significant medicinal properties. As a result of GA's poor solubility in water, formulation poses several challenges. Additionally, the traditional method of administering it via alcohol always results in unfavorable patient side effects. In order to overcome these problems, Ganoderma Tsugae extracts obtained by ethanol extraction were encapsulated in nanodispersions by ultrasonic cavitation and solvent evaporation to maximise bioavailability. Resultant nanodispersions contained Ganoderic acid in a hydrophobic core with a mean particle size of no more than 200 nm and a very narrow particle distribution. An analysis of the interactions between the control variables and the process optimisation was conducted using the Response Surface Methodology (RSM). Based on the results of the experiment, it was found that the particle size of nanodispersions was influenced by the Hydrophilic-Lipophilic Balance (HLB) number and the evaporation temperature. Moreover, the physical stability of micellar systems is generally determined by the organic phase within them. Regarding formulation stability, the zeta average of the nanodispersions remained virtually unchanged in solutions with different pH levels and short-term storage (14 days) at room temperature. Overall, it has been demonstrated that Ganoderic acid nanodispersions can be efficiently generated by combining ultrasound cavitation and solvent evaporation.

Over the last ten years, the integrated pond aquaculture (IPA) has been developing rapidly as a new aquaculture mode, and mechanical power oxygenator is a common oxygenation device in this aquaculture mode. However, with the gradual expansion of the application range of this aquaculture mode, the problems of low efficiency, poor oxygenation uniformity, and low applicability of mechanical power oxygenators, which can produce water reflux and suction pollution accumulation, have gradually emerged. Therefore, we designed a new type of push-water aerator device (NPWAD) applicable to the integrated recirculating water aquaculture mode of ponds. Mainly, the specific parameters of the key components were clarified through the selection calculation and structural design, and the experiments were carried out in the self-constructed pond integrated aquaculture system at the experimental base. The experiments were carried out on the water in the tank for grass carp culture, and the oxygenation and water pushing effects of the type of push-water aerator device under 15 groups of different operating parameters were carried out by using multiple types of high-precision water quality monitoring sensors, with the rotational speed of the device, the operating time (0-30 min) and the height of the water (0.5-1.5 m) as the independent variables, and the oxygenation amount R, the oxygenation power efficiency E_s and the surface layer of the water as the evaluation indexes, average flow velocity V_f were used as its evaluation indexes of oxygenation and water pushing. The results show that the optimal parameters of the NPWAD are impeller water lifting component speed n_1=1000r/min, small-hole aeration component speed n_2=2280r/min. At this time, the average oxygen increase of this device is R=1.96mg/L, the average oxygenation power efficiency is E_s=3.8 kg·(h·kW)-1, the DO uniformity of the water M_1=95.3%, and the average flow velocity V_f=190.8mm/s, which can effectively promote the water directional flow and achieve the water circulation and layer exchange, and it is in line with the preferred swimming speed of grass carp at various growth stages. The new type of push-water aerator device is able to enhance the DO concentration of the water faster, but also to achieve the purpose of promoting the directional flow of the water and renewing the water in the tank, as well as in a short period of time to improve the uniformity of the DO of the water faster, for the improvement of the pond aquaculture production efficiency has practical significance.

Rational delineation of urban–rural boundaries is a foundational prerequisite for holistic urban and rural development planning and rational resource allocation. However, the results of division of urban–rural boundaries extracted using a single data source are non-comprehensive. To address this problem, the present study proposes a method for using multiple sources such as population data, nighttime light data, land use, and points of interest (POI) data to extract urban–rural boundaries. Considering Guizhou Province for a case study, we here present a two-step method for identifying urban–rural boundaries. First, the random forest model was combined with the dasymetric mapping method to obtain the population spatialization data with a 30-m resolution in the studied province. Second, using the breaking point method, we extracted the urban–rural boundary for Guizhou Province in 2020 based on the spatialized population. This method fully integrated the benefits of various data and judiciously extracted the boundaries of the main urban areas and small- and medium-sized towns of each city in the study province at the same spatial scale. The stratified random sampling method revealed that the average overall accuracy was 88.05%. The method proposed has certain universality and application value and allows identifying the urban–rural boundaries more accurately and practically.

Eimeria tenella rhoptry protein has the properties of a protective antigen. EtROP27 is a pathogenic related gene detected by transcriptome, but its expression pattern, immunogenicity and potency have been unknown. Therefore, a gene segment of EtROP27 was amplified and transplanted to pET28a prokaryotic vector for recombinant protein expression and purified to generate polyclonal antibody. Then, RT-PCR, Western blotting were performed to know the expression pattern of EtROP27. Subsequently, animal experiments were conducted to evaluate the immunoprotective effect of the recombinant protein using different immunizing doses (50, 100, 150 μg). The results showed that the expression of EtROP27 gradually increased with the prolongation of infection time, reaching the highest level after 96 hours and then decreased. And EtROP27 was a natural antigen of coccidia which can stimulate the body to produce high levels of IgY. As recombinant protein vaccines, the immune protection evaluation tests of EtROP27 showed that the average weight gain rate of the immune challenge groups were significantly higher than that of the infection control group. The average lesion scores were significantly lower than that of the control group. The oocyst excretion decreased by 81.25%, 86.21%, 80.01% and the anticoccidial index were 159.45, 171.47, 166.75. EtROP27 is a promising candidate antigen gene for development of coccidiosis vaccine.

Temporal downscaling of gridded geophysical data is essential for improving climate models, weather forecasting, and environmental assessments. However, existing methods often could not accurately capture multi-scale temporal features, affecting their accuracy and reliability. To address this issue, we introduce an Enhanced Residual U-Net architecture for temporal downscaling. The architecture, which incorporates residual blocks, allows for deeper network structures without the risk of overfitting or vanishing gradients, thus capturing more complex temporal dependencies. The U-Net design inherently could capture multi-scale features, making it ideal for simulating various temporal dynamics. Moreover, we implement a flow regularization technique with advection loss to ensure that the model adheres to physical laws governing geophysical fields. Our experimental results across various variables within the ERA5 dataset demonstrate an improvement in downscaling accuracy, outperforming other methods.