Government support plays an important role in Chinese economy. New energy industries, concerning innovation-driven source and environmental protection, are also supported by government. This paper aims to study the effects of the traditional government support at supply side on firms’ innovation and development. In this paper, we propose enterprise behavior model including characteristics of new energy industries, and study the innovation reaction of firms to government support in different situation. We further use propensity score matching to verify the results in theoretical model, and conduct robustness analysis. Our main conclusions include: (1) In the normal years government support can only promote the innovation output of firms which have innovated, however, can not promote the innovation probability of firms which have not innovated. That is to say, government support can only enhance the intensive margin of innovation, but can not enhance the extensive margin of innovation with less competition. (2) In the situation of bad economic environment and intense competition, firms’ innovation probability rises as the government support increase. Therefore, government should provide more R&D special subsidies and implement strict financial supervision to make the effectiveness of support policies especially in the normal years.

Precipitates are the primary source of strength for the Al-Mg-Si alloy. Aluminum alloy in the peak-aged state mainly contain β'' and β' precipitates. In the past, most of the literature only considered the strengthening effect of β''. Here we develop a single crystal intensity model including both precipitate enhancement effects for the first time. This model was subsequently implemented into a crystal plastic finite element method to model the uniaxial tensile process of a polycrystalline aggregate model of Al-Mg-Si alloy. The simulation results for uniaxial stretching are in good agreement with the experimental results, confirming that the constitutive parameters used for the single crystal strength model with two precipitates are based on realistic physical implications. Furthermore, by comparing the uniaxial tensile simulation results of a peak-aged alloy considering the actual precipitated phase composition of the alloy with those assuming that the precipitated phase is only the β'' phase, the predicted tensile strength of the former is around 5.65% lower than that of the latter, suggesting that the two kinds of precipitation should be considered separately when simulating the mechanical response of Al-Mg-Si alloy. It is highly expected that the present simulation strategy is not limited to the Al-Mg-Si alloys, and it can be equally applied to the other age-enhanced alloys.

In the increasingly complex environment of downhole working conditions such as frequent opening and closing of oil and gas wells, acidification/multi-stage fracturing, steam huff and puff, and strong injection and production, the traditional casing string design method standards based on strength design have gradually highlighted their limitations. It is necessary to formulate string design method standards that consider the full life cycle of wells such as drilling and completion, fracturing, and production operations. Starting from the analysis of the advantages and disadvantages of traditional casing string design methods, this paper presents the characteristics of the strain design methods and seal design methods that have been formulated for the life cycle of the string. The strain design method breaks through the traditional design concept and allows the design concept of controllable deformation of the pipe string. The sealing design method is currently the only standard method for the design of tubing strings. At the same time, it further proposes to consider the trend of pipe strength deterioration and establish a time dimension-based life cycle pipe string design method standard, which effectively solves the safety problem of pipe string design in production and operation.

Vibrio parahaemolyticus carrying a plasmid encoding toxins PirA and PirB is one of the causative agents leading to the severe disease of acute hepatopancreatic necrosis disease (AHPND) in shrimp aquaculture. However, there is a lack of deep understanding of the host resistant characteristics against V. parahaemolyticus infection. Here, we performed comparative transcriptome analysis on the expression profiles at the background level of hemocytes from shrimp with different resistant abilities against V. parahaemolyticus infection in two independent populations. Principal component analysis and sample clustering results showed that samples from the same population had a closer relationship than that from shrimp with similar disease-resistant abilities. Differentially expressed genes (DEGs) analysis revealed that the number of DEGs between two populations was much more than that between V. parahaemolyticus¬-resistant and susceptible shrimp. A total of 31 DEGs and 5 DEGs were identified from the comparison between V. parahaemolyticus¬-resistant and susceptible shrimp from population 1 and 2, respectively. There was no DEGs overlap between two comparisons. DEGs from population 1 were mainly cytoskeleton related genes, metabolic related genes, and immune related genes. DEGs from population 2 included tubulin alpha-3, dihydropyrimidinase-like gene, diacylglycerol kinase 1, prohibitin and iroquois-class homeodomain protein IRX-2-like. The data suggest that shrimp from different populations might adopt distinct disease-resistant mechanisms, which provided new insights into understanding disease-resistant mechanisms at population level against pathogen infection. A new strategy for screening disease resistant genes and disease resistant breeding was proposed based on the present study.

As the largest energy consumer and carbon emitter, China has made substantial efforts to improve energy efficiency for decrease energy consumption, while the energy rebound effect determines its effectiveness. The embodied energy consumption of construction projects accounted for nearly one-sixth of the total economy's energy consumption in China. This paper is based on the logical relationship among capital input, technological progress, economic growth, and energy consumption, adapting an alternative estimation model to estimate the energy rebound effect for the construction industry in China for the first time. Empirical results in our paper reveal that the energy rebound effect for the construction industry in China is about 59.5% for the period of 1990–2014. The results indicate that the energy rebound effect does exist in China’s construction industry and it presented a fluctuating declining trend. This implies that half of the energy savings by technological progress is achieved. In addition, China’s government should implement proper energy pricing reforms and energy taxes to promote the sustainable development of China’s construction industry.

The cast defects strongly degrade the mechanical properties in cast alloys. The effect of Sn addition on Al-Si alloys was investigated by 3D computed tomography, SEM and TEM. Amorphous Sn oxides are found near the alumina film, cause more shrinkage pores, initiate cracks and deteriorate mechanical properties. This work suggests not adding Sn in various Al alloys used in cast state.

This paper presents an optimized energy management strategy for Li-ion power batteries used on electric vehicles (EVs) at low temperatures. Under low-temperature environments, EVs suffer a sharp driving range loss resulted from the energy and power capability reduction of the battery. Simultaneously, because of Li plating, battery degradation becomes an increasing concern as temperature drops. All these factors could greatly increase the total vehicle operation cost. Prior to battery charging and vehicle operating, preheating battery to a battery-friendly temperature is an approach to promote energy utilization and reduce total cost. Based on the proposed LiFePO4 battery model, the total vehicle operation cost under certain driving cycles is quantified in the present paper. Then given a certain ambient temperature, a target temperature of preheating is optimized under the principle of minimizing total cost. As for the preheating method, a liquid heating system is also implemented on an electric bus. Simulation results show that the preheating process becomes increasingly necessary with a decreasing ambient temperature; however, the preheating demand declines as driving range grows. Vehicle tests verify that the preheating management strategy proposed in this paper is able to save total vehicle operation cost.

Globally,coastal regions are vital areas of human activities and, as such, are centres of population growth and urban and economic development. Long-term human development has had a major impact on the ecological environment of coastal zones. Therefore, exploring the distribution and provenance of marine sediment types in coastal areas heavily influenced by human activities can provide scientific evidence and references for the current and future ecological management of these sensitive environments. For this reason, we have conducted an analysis on the sediment grain size, Endmember, organic matter content and geochemical elements in the Lu’erhuan River‒Malan Island‒Sandun Island area in the eastern part of Qinzhou Bay, a region heavily influenced by human activities. Sediment grain size clearly differs throughout the study site and the material provenances and hydrodynamic conditions are also varied, likely due to the local environmental conditions and the significant impact human activities have had on the area. The finest-grained sediment is imported from either inland or coastal areas via rivers and weak tidal currents, the next finest component is input from coastal areas through weak tidal currents, and the moderately coarse component mainly originates from nearby beaches. The two coarsest-grained sediment components are influenced by the combination of human activities, tidal currents and waves and enter the water via erosion. Organic matter provenance resembles that of the sediment components, exhibiting varied characteristics. This is due to the combination of natural and human activities in the bay. The organic matter in the upper reaches of the Lu’erhuan River originates from the river and coastal paddy fields, with obvious terrigenous characteristics; the organic matter in northern Malan Island mainly comes from external sources related to oyster farming; while organic matter in eastern Sandun Island is mainly produced endogenously by marine plankton. Al, Ti, Fe, Mg, K, Ga and other elements indicate that terrestrial sediments are significantly disturbed by human activities. However，Mn reflects the marine distribution of terrestrial sediments from the Lu’erhuan River to Jishuimen. Ca and Sr, indicators of marine sediments, are distributed in the eastern offshore area of Sandun Island, which is connected to the open waters. Due to the influence of human activities, As and Cd are highly enriched in the study area, while Cu is less affected by human activities.

A low-cost bio-mass-derived carbon substrate has been employed to synthesize MoS₂@carbon composites through a hydrothermal method. Carbon fibers derived from natural cotton provide a three-dimensional and open framework for the uniform growth of MoS₂ nanosheets, thus constructing hierarchically coaxial architecture. The unique structure could synergistically benefit fast Li-ion and electron transport from the conductive carbon scaffold and porous MoS₂ nanostructures. As a result, the MoS₂@carbon composites, when served as anodes for Li-ion batteries, exhibit a high reversible specific capacity of 820 mAh g^-1, high-rate capability (457 mAh g^-1 at 2 A g^-1), and excellent cycling stability. The superior electrochemical performance makes the MoS₂@carbon composites to be low-cost and promising anode materials for Li-ion batteries.

Diabetes is a major health concern that the next-generation of on-demand insulin releasing implants may overcome for personalized therapy. Therein, 3D-printed phenylboronic acid-containing subcutaneous implants with on-demand glucose-triggered drug release abilities are produced using high resolution stereolithography technology. To that end, the methacrylation of phenylboronic acid is targeted following a two-step reaction. The resulting photocurable phenylboronic acid derivative is accordingly incorporated within bioinert polyhydroxyethyl methacrylate-based hydrogels at varying loadings. The end result are sub-centimeter scaled 3D-printed bionert implants that can be remotely activated with 1,2-diols and 1,3-diols such as glucose for on-demand drug administration such as insulin. As a proof of concept, varying glucose concentration from hypoglycemia to hyperglycemia levels readily allow the release of pinacol, i.e. a model molecule with similar release properties than insulin, at respectively low and high rates. In addition, the results demonstrated that adjusting the geometry and size of the 3D-printed part is a simple and suitable method for tailoring the release behavior and dosage.

Research on the development of new lightweight Al-Li alloys using selective laser melting pro-cess has great potential for industrial applications. This paper reports on the development of novel aluminum-lithium alloys using selective laser melting technology. Al-Cu-Li-Mg-Ag-Sc-Zr pre-alloyed powders with lithium contents of 1 wt.%, 2 wt.% and 3 wt.%, respectively, were prepared by inert gas atomization. After SLM process optimization, the microstructure and me-chanical properties of the as-formed specimens were investigated. The densification of the three newly developed alloys are 99.51 %, 98.96 % and 92.01 %, respectively. They all have good form-ability, with the lithium loss rate about 15 %. The as-formed alloy with 1% Li content presents good comprehensive properties with a yield strength of 412.6 ± 16.0 MPa, an ultimate tensile strength of 461.1 ± 11.7 MPa, and an elongation of 13.6 ± 1.1%. The three alloys exhibit a layered molten pool stacking morphology and have a typical heterostructure. The columnar crystals and equiaxed fine grains are alternately arranged, and most of the precipitated phases are enriched at the grain boundaries. The change of Li content mainly affects the precipitation of Cu-containing phase. When the Li content is 1 wt.%, there are θ phase, T1 phase and TB phase. When Li increased to 2 wt.%, T1 and T2 phases precipitated together. When Li reaches 3 wt.%, δ' phase precipitates with T2 phase. This study provides useful guidance for the future SLM form-ing of new crack-free and high-strength Al-Li alloys.

Addressing real-time aircraft target detection in microsatellite-based visible light remote sensing video imaging requires considering the limitations of imaging payload resolution, complex ground backgrounds, and the relative positional changes between the platform and aircraft. These factors lead to multi-scale variations in aircraft targets, making high-precision real-time detection of small targets in complex backgrounds a significant challenge for the detection algorithms. Hence, this paper introduces a real-time aircraft target detection algorithm for remote sensing imaging using an improved lightweight attention mechanism that relies on the YOLOv7 framework (SE-CBAM-YOLOv7). The proposed algorithm replaces the standard convolution (Conv) with a lightweight convolutional SEConv to reduce the computational parameters and accelerate the detection process of small aircraft targets, thus enhancing real-time onboard processing capabilities. Besides, the SEConv-based SPPCSPC (Spatial Pyramid Pooling and Connected Spatial Pyramid Convolution) module extracts image features. It improves detection accuracy while the feature fusion section integrates the CBAM hybrid attention network, forming the CBAMCAT module. Furthermore, it optimizes small aircraft target features in channel and spatial dimensions, improving the model's feature fusion capabilities. Experiments on public remote sensing datasets reveal the proposed SE-CBAM-YOLOv7 improves detection accuracy by 3% and mAP value by 4.2% compared to YOLOv7, significantly enhancing the detection capability for small-sized aircraft targets in satellite remote sensing imaging.

Low-dimensional carbon-based (LDC) materials have attracted extensive research attentions in electrocatalysis because of their unique advantages such as structural diversity, low cost, and chemical tolerance. They have been widely used in a broad range of electrochemical reactions to relief environmental pollution and energy crisis. Typical examples include hydrogen evolution reaction (HER), oxygen evolution reaction (OER), oxygen reduction reaction (ORR), carbon dioxide reduction reaction (CO₂RR), and nitrogen reduction reaction (NRR). Traditional “trial and error” strategies seriously slowed down the rational design of electrocatalysts for these important applications. Recent studies show that the combination of density functional theory (DFT) calculations and experimental research is capable of accurately predicting the structures of electrocatalysts, thus could reveal the catalytic mechanisms. Herein, current well-recognized collaboration methods of theory and practice are reviewed. The history of modern DFT, commonly used calculation methods, and basic functionals are briefly summarized. Special attention is paid to descriptors that are widely accepted as a bridge links the structure and activity, and the breakthroughs for high-volume accurate prediction of electrocatalysts. Importantly, correlating multiple descriptors are used to systematically describe the complicated interfacial electrocatalytic processes of LDC catalysts. In addition, machine learning and high-throughput simulations are crucial in assisting the discovery of new multiple descriptors and reaction mechanisms. This review will guide the further development of LDC electrocatalysts for extended applications from the aspect of DFT computations.

As the traditional sand fixation measures have many limitations, new sand fixing technologies and methods are urgently needed to be developed. This study demonstrated a new sand fixation method using palm mat geotextile to fix the sand dunes and plant grass. We experimented the physical property of the palm mat geotextile in the laboratory, and observed the vegetation growth in the Hobq Desert. The results showed that (1) The palm mat geotextile is more lighter and tougher than the common straw mat geotextile, the average weight, thickness and tensile strength of palm mat geotextile were 2023 g/m², 20.14 mm, and 842–860 N/m, after a year of field observation, the tensile strength decreased by only 2%. (2) The palm mat geotextile had excellent water retention capacity and scouring resistance, the maximum water content reached 227%, and the substrate lost 2.9% after the simulation of heavy rainfall for 3 hours with rainfall intensity of 30 mm/h in laboratory. (3) The palm mat geotextile significantly decreased the soil temperature and increase the humidity in summer, the observed results shown that the palm mat geotextile had the greatest influence on 5cm depth of the soil temperature and 10cm depth of soil humidity. (4) The field experiment results showed that the vegetation coverage and the biomass of the palm mat geotextile with 2 m × 2 m were 35% and 11.21 g (3.9 times and 4.1 times than that of control group, 1.7 times and 1.8 times than that of high-banded salix psammophila sand-barriers group which is widely used in the Hobq Desert).

In order to coordinate the damping performance and energy regenerative performance of energy regenerative suspension, this paper proposes a structure of vehicle semi-active energy regenerative suspension with electro-hydraulic actuator (EHA). In light of the proposed concept, a specific energy regenerative scheme is designed and the mechanical properties test is carried out. Based on the test results, the parameter identification for the system model is conducted using recursive least squares algorithm. On the basis of system principle, the nonlinear model of the semi-active energy regenerative suspension with EHA is built. Meanwhile, LQG control strategy of the system is designed. And then the influence of the main parameters of EHA on the damping performance and energy regenerative performance of suspension is analyzed. Finally, the main parameters of EHA actuator are optimized via genetic algorithm. The test results show that when sinusoidal is input at the frequency of 2Hz and the amplitude of 30mm, the spring mass acceleration RMS value of optimized EHA semi-active energy regenerative suspension is reduced by 22.23% and energy regenerative power RMS value is increased by 40.51%, which means while meeting the requirements of certain vehicle ride comfort and driving safety, energy regenerative performance is improved significantly.

Lithium rich layered oxides (LRLO) are a wide class of innovative active materials for positive electrodes in lithium-ion (LIB) and lithium-metal secondary batteries (LMB). LRLOs are over-stoichiometric layered oxides rich in lithium and manganese, with general formula Li1+xTM1-xO2, where TM is a blend of transition metals comprising Mn (main constituent), Ni, Co, Fe and others. Due to the very variable composition and the extended defectivity their structural identity is still debated among researchers being likely an unresolved hybrid between a monoclinic (mC24) and a hexagonal lattice (hR12). Once casted in composite positive electrode films and assembled in LIBs or LMBs, LRLOs can delivery reversible specific capacities above 220-240 mAhg-1, thus beyond any other available intercalation cathode material for LIBs, with mean working potential above 3.3-3.4 V vs Li for hundreds of cycles in liquid aprotic commercial electrodes. In this review we critically outline the recent advancements in the fundamental understanding of the physico-chemical properties of LRLO as well as the most exciting innovations in their battery performance. We focus in particular on the elusive structural identity of these phases, on the complexity of the reaction mechanism in batteries as well as on practical strategies to minimize or remove cobalt from the lattice while preserving the outstanding performance upon cycling.

Background: The life courses of sexually and gender diverse individuals are shaped by a series of events that include acceptance of one’s own sexual orientation or gender identity, the coming out process, socialization in the LGBTQ+ environment. Generally experienced in a cis-heteronormative context, it is marked by stigma and discrimination and the social harms they can cause, including a higher-than-average prevalence of alcohol use. Objectives: To profile the alcohol use of SGD youth from a life course perspective; and to explore individual perceptions regarding the personal, social and cultural factors (including stigmatization and its consequences) modulating their consumption. Methodology: This qualitative descriptive study is grounded in symbolic interactionism. Semi-structured interviews lasting approximately 90 minutes were conducted with LGBTQ+ youth aged 18 to 30 using alcohol at least once a week. A thematic analysis was performed. Results: 31 individuals aged 18–29 (average age: 25) were interviewed. The average score regarding alcohol use was 14.25 (SD: 4–31), which corresponds to moderate risk and indicates the need for a brief intervention. Our study document how higher alcohol use among LGBTQ+ youth is shaped by individual, community and cultural factors at different points in the queer life course. Among the factors influencing drinking are the emotions experienced when questioning sexual orientation and/or gender identity as well as the feelings resulting from stigma and discrimination. Our findings also indicate the influence of socializing in the queer community and meeting peers and partners, as well as that of LGBTQ+ cultural practices. Discussion: Our study indicates the need for grassroots-level interventions that work to mitigate social pressures in queer socialization contexts. Accordingly, any intervention, whether preventive or therapeutic, must consider the interplay of personal, social, community and cultural factors. Interventions regarding alcohol use must build on the strengths of community and the sense of belonging.

The rock strata movement deformation generated by the mining process of the mine will inevitably lead to surface subsidence and a series of environmental hazards, so it is particularly important to monitor the rock strata movement deformation and surface subsidence in the goaf area. With the development of surface subsidence monitoring technology, InSAR technology has been widely used in the research of surface subsidence in mining areas with the advantages of high precision and wide monitoring. In this paper, by obtaining the image data of 59 Sentinel-1A scenes in the mining area, the obtained PS points were analyzed and processed by ArcGIS difference analysis by PS-InSAR technology, and the surface deformation cloud map and settlement curve of the mining area in various periods were obtained, and the rock strata movement deformation and surface subsidence law of the goaf before treatment and after different treatment methods were studied by influencing factors such as stope layout and mining depth. The results show that the rock strata movement deformation and surface subsidence degree of the goaf under different treatment methods are different due to different influencing factors, and their positions also change dynamically with time. Applying InSAR technology to study rock strata movement deformation and surface subsidence can provide an important basis for mine safety mining, goaf treatment, rock strata movement deformation and surface subsidence monitoring.

Aiming at the classification identification problem of aeroengines, this paper adopts telemetry Fourier Transform Infrared Spectrometer to collect aeroengine hot jet infrared spectrum data, and proposes an aeroengine classification identification method based on spectral feature vectors. First, aeroengine hot jet infrared spectrum data are acquired and measured, meanwhile, the spectral feature vectors based on CO2 are constructed. Then, the feature vectors are combined with the seven mainstream classification algorithms to complete the training and prediction of the classification model. In the experiment, two Fourier transform infrared spectrometers, EM27 developed by Bruker and a self-developed telemetry FTIR, were used to telemeter the hot jet of three aeroengines to obtain infrared spectral data. The training data set and test data set were randomly divided in a ratio of 3:1. The model training of the training data set and the label prediction of the test data set were carried out by combining spectral feature vectors and classification algorithms. The classification evaluation indicators were accuracy, precision, recall, confusion matrix, and F1-score. The classification recognition accuracy of the algorithm was approximately 98%. This paper has great significance for the fault diagnosis of aeroengines and classification recognition of aircrafts.

Abstract: To reduce the indiscriminate use of pesticides and extend the postharvest shelf life of peach fruit (cv. Baihua) from southeast China, microbial antagonism of indigenous yeasts was mainly studied and applied in construction of composite film. After isolation, purification, cultivation and identification, a total of 14 yeast strains from 9 genera were screened out from the surface of peaches. By experimental analysis of in vitro inhibition zone and in vivo colonizing capacity, Candida oleophila sp-ELPY12B and Cryptococcus laurentii sp-ELPY15A, which have conservative structure of D1/D2 domain sequences and were considered as new species by phylogenetic analysis, were finally chosen as fungicides against the major pathogens. In combination of Na-alginate film (0.4 % glycerin as plasticizer and 0.1 % Tween-80 as emulsifier), the preservative effects of composite-treated groups (1 × 108 CFU mL−1 of Candida oleophila sp-ELPY12B and Cryptococcus laurentii sp-ELPY15A) showed best antifungal effects, which significantly delayed the postharvest preservation period about 6 - 7 d under ambient temperature of 25 ± 3°C and relative humidity of 50 - 70%.

Most existing data synthesis methods are designed to tackle problems such as dataset imbalance, data anonymization and insufficient sample size. There is a lack of effective synthesis methods for the limited number of datasets which contain a large of features and unknown noise to expand the size of the dataset. We propose a data synthesis method, named Adaptive Subspace Interpolation for Sample Optimization (ASISO). The idea is to divide the original feature space into several subspaces with an equal number of samples, and then perform interpolation for the samples in the adjacent subspaces. This method can adaptively adjust the size of the dataset containing unknown noise, and the expanded data typically contain minimal error with actual. Moreover, it adjusts the structure of the samples, which can significantly reduce the proportion of samples with large errors. In addition, the hyperparameters of this method have an intuitive explanation and usually require little calibration. Experimental results on artificial data and benchmark data sets demonstrate that ASISO is a robust and stable method to optimize samples.

Cost-efficient catalyst for hydrogen evolution reaction (HER) and methanol oxidization reaction (MOR) is of great importance for industrial application. The alloying of platinum with cheap transition metal offers a great opportunity to reduce cost and enhance the electro-catalytic performance. Herein, we report a simple and green route to synthesize porous Pt-Cu catalysts, where Pt-Cu solid particles were first produced by using cuprous chloride (CuCl) nanoparticles as the template, followed by a dealloying treatment to obtain final porous product. The porous alloy nanoparticles show higher catalytic activity and superior stability for HER and methanol oxidation reaction, which is beneficial from the porous structure and synergetic effect between Pt and Cu.

To conduct a precise impact assessment of marine diesel engines, a 200t floating impact platform was utilized to simulate realistic testing conditions. The testing generated the acceleration time curve and the impact response spectrum for the diesel engine. According to the applicable standards, the spectral velocity was chosen as the evaluation index, and an evaluation of the longitudinal, transverse, and vertical impact environment of the diesel engine was conducted. The shock factor interpolation method was corrected using the confidence interval based on normal distribution, and the interpolated confidence interval of the shock factor was determined. The findings reveal that: The 200t-class floating shock platform is capable of providing a reliable shock environment for diesel engine tests. The shock wave serves as the primary external force during the experiment, and the impact of bubble pulsation can be disregarded when assessing using the floating shock platform. To more accurately describe the experimental shock intensity when presented with dispersed data, the confidence interval can be utilized, and the resulting prediction formula provides a higher level of impact safety. The obtained conclusions can serve as a reference for predicting the shock environment of large shipboard equipment using the shock platform.

We report on the role of synergistic polarization of Ba0.9Sr0.1TiO3 (BST) nanoparticles in sulfur cathodes to enhance the redox kinetics of polysulfides for high-capacity Li-S batteries. Ferroelectric nanoparticles are known to significantly improve the electrochemical performance of Li-S batteries due to their inherent self-polarization and high adsorption capacity towards polysulfides. X-ray diffraction spectra confirmed the tetragonal symmetry (c/a=1.0073), while Raman spectroscopic analysis validated the presence of tetragonal orientation Raman modes in BST-modified composites. Scanning electron microscope (SEM) images showed a homogeneous distribution of BST in the sulfur cathode system, with grain sizes ranging from 1 to 1.5 μm. Notably, the BST-coupled S50BST30CB10PVDF10 composite cathode achieved a capacity of approximately 820 mAh/g at 100 mA/g, maintaining stability over 100 cycles, demonstrating improved electrochemical performance. Two distinct plateaus between 2.3 V to 2.0 V and 2.0 V to 1.5 V further underscore the superior performance of BST ferroelectric nanoparticles in enhancing the redox kinetics of Li-S batteries. By leveraging the favourable affinity of polar substances towards polysulfides, we aimed to create a more stable reactive environment within the cathodic site, effectively trapping polysulfide intermediates through the synergistic polarization of BST nanoparticles. The synergistic polarization induced by the asymmetric crystal structure of ferroelectrics is anticipated to generate internal electric fields, enhancing chemisorption with heteropolar reactive. The observed high cyclic stability further validates the efficacy of these composite cathodes in mitigating the polysulfide shuttle effect, offering promising prospects for advancing Li-S battery technology.

Cervical cancer is the most common female reproductive tract tumor and is currently the only cancer that can be prevented by vaccination. Early detection and treatment of cervical cancer can be achieved through regular screening for human papillomavirus (HPV). Therefore, in this paper, an electrochemical sensor was designed to detect HPV 16 using a dual signal amplification technique. APTES modified glassy carbon electrode to improve stability. Combining gold nanoparticles and chain amplification reactions for signal amplification purposes. The limit of detection (LOD) was 1.731×10-16 mol/L, and the detection range of this electrochemical sensor was increased in comparison to previous research, with a linear response range from 1.0×10-13 mol/L to 1.0×10-5 mol/L (R2 = 0.99232) for the target detector.In addition, the sensor showed equally good recovery in sophisticated human serum samples. It shows that it has good interference resistance and the designed biosensor can be a powerful and reliable tool for clinical disease diagnosis.

Crop residue burning produces a lot of polluting gases and fine particles, endangering human health, damaging soil structure, and causing fire accidents. In addition to the impact of residue burning on the local environment, pollutants can spread with the wind to more distant areas and impact the air quality of surrounding areas, especially cities. Nevertheless, a comparative analysis of the impact of two common residue burning parameters, the number of residue fire points and residue burned area, on urban air quality indicators has not been reported. In this study, the correlation between these two different residue burning parameters on air quality in Daqing City was investigated comparatively using the Visible Infrared Imaging Radiometer Suite (VIIRS) fire point product, the Moderate-resolution Imaging Spectroradiometer (MODIS) burned area product, and buffer zone analysis. The association between MODIS burned area products and air quality index (AQI) was found to be around 0.8, with a maximum of 0.816 at a buffer zone radius of 50 km. Meanwhile, it was found that the correlation between the number of residue fire points extracted from the VIIRS active fire products and air quality was above 0.6, again with a maximum of 0.745 at a buffer radius of 50 km. Within other levels of buffer zones, the correlation between residue burned area and AQI was consistently higher than that between residue fire points and AQI. By comparing the correlation between VIIRS fire points, MODIS burned area, and AQI with the concentration of each pollutant, we found that the correlation between residue burned area and AQI and the concentration of each pollutant is higher. MODIS burned area monitoring, on the other hand, detects changes in the time series of images taken by satellite at two transit moments to obtain the new burned area and cumulative burned area during this period, allowing the monitoring of fire traces caused by fire points at non-transit moments. From analyzing the correlation between residue fire points, residue burned area, and the concentration of each pollutant (PM2.5, PM10, CO, NO2, SO2, O3), we found significant correlations between residue burning and PM2.5, PM10, CO, and NO2 concentrations, with the highest correlation (R2) of 0.81 for PM2.5. Moreover, the correlation between residue burned area and PM2.5, PM10, CO, and NO2 concentrations was significantly higher than that between the number of residue fire points and their concentrations.

This paper aims to explore the relationship between airfoil deformation and airfoil aerial-aquatic adaptation. The form of “flexible skin and rigid drive structura” holds immense potential for various applications. In this study, we utilize the B-spline method to parametrically describe the airfoil shape. An airfoil deformation parameterization method is proposed based on the isogeometric method. This method allows us to depict airfoil deformation as the position and displacement of the driving point, enabling an expression of the flexible skin's deformation driven by the internal structure. We utilize the Xfoil application and Gaussian regression method to elucidate the relationship between driving point position and displacement and the airfoil's dynamic performance. Additionally, we employ the Sobol method to assess the sensitivity of the driving point variables to the airfoil's dynamic performance. Our findings indicate that the attack angle of the airfoil exhibits the highest sensitivity index to the airfoil's dynamic performance. Furthermore, variables of the driving point situated at the leading and trailing edges of the lower surface significantly impact the airfoil lift coefficient, those located at the mid-section of the upper surface have a considerable effect on the airfoil drag coefficient.

Speech emotion recognition is a critical component for achieving natural human-robot interaction. The modulation-filtered cochleagram is a feature based on auditory modulation perception, which contains multi-dimensional spectral-temporal modulation representation. In this study, we propose an emotion recognition framework that utilizes a multi-level attention network to recognize emotions from the modulation-filtered cochleagram. The channel-level attention and spatial-level attention modules are used to capture emotional saliency maps of channel and spatial feature representations from the 3D convolution feature maps, respectively. Furthermore, the temporal-level attention module captures significant emotional regions from the concatenated feature sequence of the emotional saliency maps. Our experiments on the IEMOCAP dataset demonstrate that the modulation-filtered cochleagram significantly improves the prediction performance of categorical emotion compared to other evaluated features. Moreover, our emotion recognition framework achieves a better unweighted accuracy of 71% in categorical emotion recognition than several existing approaches in the experiments. In summary, our study demonstrates the effectiveness of the modulation-filtered cochleagram in speech emotion recognition, and our proposed multi-level attention framework provides a promising direction for future research in this field.

With the advancement of next generation sequencing in past several years, a rising number of non-coding RNAs have been found as new actors to regulate gene expression. Non-coding RNAs not only play important roles in carcinogenesis, but also affect the clinical treatment strategies. They have been proved to be deeply correlated with chemoresistance in several cancers. Ovarian cancer is the leading cause of death in gynecological malignancy, with low 5-year survival rate. Most patients are identified when they have late-stage disease. This review mainly makes a compilation of the most relevant research literature in this field with the purpose of shedding light on the relation between ncRNAs and chemoresistance in ovarian cancer.

Flux-switching permanent magnet (FSPM) motors have gained increasing attention in the electric vehicles (EVs) applications due to the advantages of high power density, high efficiency. However, the heat sources of both permanent magnet (PM) and armature winding are located on the limited stator space in the FSPM motors, which may result in the PM overheated and irreversible demagnetization caused by temperature rise and it is often ignored in the conventional thermal analysis. In this paper, a new electrical-thermal two-way coupling design method is proposed to analyze the electromagnetic performances, where the change of PM material characteristics under different temperatures is taken into consideration. Firstly, the motor topology and design equations are introduced. Secondly, the demagnetization curves of PM materials under different temperatures are modeled due to PM materials are sensitive to the temperature. And based on the electrical-thermal two-way coupling method, the motor performances are evaluated in details, such as the load PM flux linkage and output torque. Then, the motor is optimized, and the electromagnetic performances between initial and improved motors are compared. Finally, a prototype motor is manufactured, and the results are validated by experimental measurements.

Fungal endophytes are the most ubiquitous plant symbionts on earth and are phylogenetically diverse. Studies on the fungal endophytes in tobacco have shown that they are widely distributed in the leaves, stems, and roots, and play important roles in the composition of the microbial ecosystem of tobacco. Herein, we analyzed and quantified the endophytic fungi of healthy tobacco leaves at the seedling (SS), resettling growth (RGS), fast-growing (FGS), and maturing (MS) stages at three altitudes [600 (L), 1000 (M), and 1300 m (H)]. We sequenced the ITS region of fungal samples to delimit operational taxonomic units (OTUs) and phylogenetically characterize the communities. The result showed the number of clustering OTUs at SS, RGS, FGS and MS were greater than 170, 245, 140, and 164, respectively. At the phylum level, species in Ascomycota and Basidiomycota had absolute predominance, representing 97.8% and 2.0 % of the total number of species. We also found the number of unique fungi at the RGS and FGS stages were higher than those in the other two stages. Additionally, OTU richness was determined by calculating the Observed Species, Shannon, Simpson, Chao1, ACE, Goods coverage and PD_whole_tree indices based on the total number of species. Our results showed RGS samples had the highest diversity indices. Furthermore, we found that the diversity of fungal communities tended to decrease with increasing tobacco growth altitude. The results from this study indicated that the tobacco harbors an abundant and diverse endophytic fungal population, which provides new opportunities for exploration their potential utilization.

This study develops a progressive optimal fault-tolerant control method based on insufficient fault information. By combining passive and active fault-tolerant control manners during the process of fault diagnosis, insufficient fault information is fully used, and optimal fault-tolerant control effect is achieved. In addition, the fault-tolerant control method based on guaranteed robust cost control is introduced. The proposed progressive optimal fault-tolerant control method considers two aspects. First, as the amount of fault information continually increases, the performance index of the progressive optimal fault-tolerant controller improves. Second, at each moment, based on the corresponding insufficient fault information and prior knowledge, optimal fault-tolerant control is achieved according to current fault information. The process of progressive optimal fault-tolerant control converges to active fault-tolerant control when the fault is completely identified and the optimal fault-tolerant controller is no longer reconfigured until no more useful fault information can be provided. Furthermore, a progressive optimal fault-tolerant control algorithm based on the grid segmentation in the parameter uncertainty domain and the selection of different auxiliary center points is introduced. The simulation results verify the feasibility of the proposed algorithm and the validity of the proposed theory.

Co-free Li-rich oxide is a promising cathode for low cost high energy density Li-ion battery but presents poor cyclic stability. To address this issue, CeO2 is coated on Co-free Li-rich oxide through a low-temperature aged process, resulting in a novel CeO2-coated Li-rich oxide compo-site. Due to the low-temperature aged process, a uniform CeO2 coating layer of 6 nm in thickness is successfully coated on Co-free Li-rich oxide. With this uniform coating, the resulting CeO2-coated Li-rich oxide composite presents improved cyclic stability as well as rate capability as the cathode of Li-ion battery. The capacity retention of the resulting CeO2-coated Li-rich oxide is enhanced from 67% to 85% after 100 cycles and its capacity retention of 5C/0.05C is enhanced from 10% to 23%, compared with the uncoated sample. Such significant improvements enable this low-temperature aged process to be helpful for the practical application of Li-rich oxide materials, not limited to Co-free Li-rich oxide.

The geographical location of Yunnan province is at the upstream area of water vapor transportation from the Bay of Bengal and the South China Sea to inland China. Understanding the spatiotemporal variations of water vapor over this region holds significant importance. We utilized the GNSS data collected from 12 stations situated in Yunnan, which are part of Crustal Movement Observation Network of China, to retrieve hourly precipitable water vapor (PWV) from 2011 to 2022. The retrieved PWV at Station KMIN were evaluated by the nearby radiosonde data, and the result shows that the mean bias and RMS of the differences between the two datasets are 0.08 and 1.78 mm respectively. Average PWV values at these stations are in the range of 11.77 to 33.53 mm, which decrease from the southwest to the north of Yunnan and are negatively correlated with the station heights and latitudes. Differences between average PWV in wet season and dry season range from 12 to 27 mm. These differences tend to increase as the average PWV increases. The yearly rates of PWV variations, averaging 0.18 mm/year, are all positive for the stations, indicating a year-by-year increase in water vapor. Amplitudes of the PWV annual cycles are 9.75-20.94 mm. The spatial variation of these amplitudes is similar to that of average PWV over the region. Generally, monthly average PWV values increase from January to July and decrease from July to December, and the growth rate is less than the decline rate. Average diurnal PWV variations show unimodal PWV distributions over the course of the day at the stations except Station YNRL, where bimodal PWV distribution was observed.

The kinase pathway plays a crucial role in blood vessel function. Particular attention is paid to VEGFR-type 2 angiogenesis and vascular morphogenesis as the tyrosin kinase pathway is preferentially activated. In silico studies were performed on several peptides that affect VEGFR2 in both stimulating and inhibitory ways. This investigation aims to examine the molecular properties of VEGFR2, a molecule primarily involved in the processes of vasculogenesis and angiogenesis. These relationships were defined by the interactions between vascular endothelial growth factor receptor 2 (VEGFR2) and the structural features of the systems. The chemical space of the inhibitory peptides and stimulators was described using topological and energetic properties. Furthermore, chimeric models of stimulating and inhibitory proteins (for VEGFR2) were computed using the protein system structures. The interaction between the chimeric proteins and VEGFR was computed. The chemical space was further characterized using complex manifolds and high-dimensional data visualization. The results show that a slightly similar chemical area is shared by VEGFR2 and is shared by stimulating and inhibitory proteins. On the other hand, the stimulator peptides and the inhibitors have distinct chemical spaces.

Various wood fibres, including pine and flax, are used in wood–plastic composites (WPCs). This paper studies the effect of the lignin content on the morphological and thermal degradation and damage due to the ultraviolet (UV) aging of polypropylene/flax (PP-flax) and polypropylene/pine (PP-pine) fibres composites. Flax and pine fibres exhibited distinct densities of 1.51 and 1.47 g/cm³, respectively, potentially influenced by growth factors and varying compositions of light substances, such as hemicellulose, lignin, and impurities. The thermal decomposition mass loss increased proportionally with the percentage of lignin in the fibre. From 238°C to 390°C, the pine fibre exhibited a 72% mass loss compared to flax fibre, which showed a 54% mass loss, owing to the higher lignin content in pine fibres. Based on differential scanning calorimetry (DSC), the fibre composition affected the material melting temperature. Moreover, DSC curves revealed a higher degree of crystallinity in the case of the PP-flax biocomposite (12%) compared to pure polypropylene (9%) and PP-pine (6%). This result can be attributed to the high content of crystalline components in flax fibres, such as cellulose. Acoustic emissions analysis confirmed that the high lignin content delays degradation and mitigates the appearance of microcracks on the surface of the PP-pine biocomposite. Overall, the study provides valuable data for understanding the UV degradation phenomenon in biocomposites and highlights the influence of fibre composition on material performance.

The effects of simulated acid rain (SAR) on the photosynthetic performance of subtropical coniferous species have not been thoroughly investigated. In this study, we treated two coniferous species, Pinus massoniana (PM) and Cunninghamia lanceolata (CL), with SAR and then analyzed photosynthetic activities via the simultaneous measurement of prompt fluorescence(PF), delayed fluorescence(DF), and modulated reflection at 820 nm(MR820). Following the low-pH SAR treatment, the I–P phase of the OJIP curves of PM and CL decreased significantly and then the K-band and L-band were detectable. The delayed fluorescence for both PM and CL decreased as the pH of the SAR solution decreased, with a more significant decrease in the I1 peak than in the I2 peak. According to the MR820 curve, the exposure to SAR stress inhibited the rapid decrease and slow increase in the MR820 transients of PM and CL. The gradients of the decreasing and increasing phases were lower for CL than for PM at pH 2.5. In addition, SAR decreased the light energy captured per unit area (ABS/CSm) and the quantum yield of electron transfer per unit area (ETO/CSm), but it increased the accumulation of QA−, thereby decreasing primary photochemical efficiency (TRO/CSm, φPO). These changes in photosystem II (PSII) resulted in a decrease in the maximum quantum yield of PSII (Fv/Fm) and the performance index (PIABS). Moreover, PSII was protected from thylakoid membrane photodamages through the increase in heat dissipation per unit area (DIO/CSm) in response to SAR. The increase in δRO as the pH of the SAR solution decreased was observed for PM, but not for CL. Considered together, the study findings suggest that SAR treatments can decrease chlorophyll contents and damage the OEC in PM and CL, which obstructs the transfer of electrons on the donor and acceptor sides of PSII. However, PM is better able to withstand SAR stress than CL, likely because of the activation of a protective mechanism (reflected by the change in δRO). Therefore, in coniferous forest areas severely affected by acid rain, CL may need to be protected more than PM.

Generator automatic voltage control device (AVC) is an automatic voltage and reactive power optimization control terminal on the power plant side. Aiming at improving the performance of generator automatic voltage control device, this paper introduces a detection method of AVC substation performance based on virtual dispatching terminal environment. During the testing, the upper computer of redundant power plant is connected to the virtual dispatching terminal environment to simulate the sending instructions of AVC master station, the behavior of AVC sub stations and the excitation system of generators. The performance of AVC substation is evaluated according to the feedback adjustment results, which can effectively detect the abnormal behavior of AVC substation in power plant.

CO2 flooding stands as a pivotal technique for significantly enhancing oil recovery in low-permeability reservoirs. The movement and sweeping rules at the front of CO2 flooding play a critical role in its oil recovery, yet a comprehensive quantitative analysis remains an area in need of refinement. In this study, we have developed 1D and 2D numerical simulation models to explore the sweeping behavior of miscible, immiscible, and partly-miscible CO2 flooding patterns. The front position and movement rules of the three CO2 flooding patterns are determined. A novel approach of the contour area calculation method is introduced to quantitatively characterize the sweep coefficients, and the sweeping rules are discussed regarding geological parameters, oil viscosity, and injection-production parameters. Furthermore, the Random Forest (RF) algorithm is employed to identify the controlling factor of the sweep coefficient, as determined by out-of-bag (OOB) data displacement analysis. The results show that the miscible front is located at the point of maximum CO2 content in the oil phase. The immiscible front occurs at the point of maximum interfacial tension near the production well. Remarkably, the immiscible front moves at a faster rate compared to the miscible front. Geological parameters, including porosity, permeability, and reservoir thickness, significantly impact the gravity segregation effect, thereby influencing the CO2 sweep coefficient. Immiscible flooding exhibits the highest degree of gravity segregation, with a maximum gravity segregation degree (GSD) reaching 78.1. The permeability ratio is a crucial factor, with a lower limit of approximately 5.0 for reservoirs suitable for CO2 flooding. Injection-production parameters also play a pivotal role in sweep coefficient. Decreased well spacing and increased gas injection rates are found to enhance sweep coefficients by suppressing gravity segregation. Additionally, higher gas injection rates can improve the miscibility degree of partly-miscible flooding from 0.69 to 1.0. Oil viscosity proves to be a significant factor influencing the sweep coefficients, with high seepage resistance due to increasing oil viscosity dominating the miscible and partly-miscible flooding patterns. Conversely, gravity segregation primarily governs the sweep coefficient in immiscible flooding. In terms of controlling factors, the permeability ratio emerges as a paramount influence, with a factor importance value (FI) reaching 1.04. these results provide a theoretical foundation for the application of CO2 flooding, enhancing the understanding of the critical factors governing its success.

Amidst the backdrop of continually declining total fertility rates and a population structure trending towards super-aging in China, can simple adjustments to birth policies effectively address the challenges of low fertility and aging? This study constructs a three-phase overlapping-generation model that incorporates fertility rates and delayed retirement. It investigates the economic impact of adjustments in birth policies and delayed retirement, and utilizes simulations and analyses based on real-world data from China. The research yields several key findings:1.Although theoretically, the impact of population aging on economic growth rates is uncertain, empirical data simulations indicate that the current level of population aging in China has already exerted a certain adverse impact on economic growth.2.Given the current population structure, delayed retirement can, to some extent, promote economic growth. However, due to potential vulnerabilities of older individuals who choose delayed retirement, such as reduced labor efficiency and learning abilities, the implementation of delayed retirement policies should be flexible and context-specific.3.Increasing fertility rates can mitigate the economic impact of the current demographic challenges. Thus, relaxing birth policies remains a crucial strategy that China must implement. A coordinated adjustment of delayed retirement and fertility rates is more likely to enhance economic growth.

PEDOT:PSS (polyethylene dioxythiophene:polystyrenesulfonate) is a commonly used hole injec-tion layer (HIL) in optoelectronic devices due to its high conductive properties and work function. However, the acidic and hygroscopic nature of PEDOT:PSS can be problematic for device stability over time. To address this issue, in this study, we have demonstrated the potential of an organic-inorganic hybrid HIL, by incorporating solution-processed WOx nanoparticles (WOx NPs) into the PEDOT:PSS mixture. This hybrid solution was found to have a superior hole transport ability and low Ohmic contact resistance, contributing to higher brightness (~62 000 cd m-2) and current efficiency (13.1 cd A-1) in the manufactured quantum-dot based light-emitting diodes (QLEDs). In addition, the resulting devices achieved a relative operational lifetime of 7,071 hours, or approxi-mately twice that of traditional QLEDs with PEDOT:PSS HILs. The proposed method is an un-complicated, reliable and low-cost way to achieve long operational lifetimes without sacrificing efficiency in optoelectronic devices.

An on-site, ultra-sensitive, and quantitative sensing method was developed based on quantum dot nanobeads (QDNBs) and test strip for the determination of total aflatoxins (AFTs) in rice and peanut. The monoclonal antibody against AFT (mAbAFT) was home-made and labeled with QDNB. After the pre-coating of the AFT antigen on the test line (T line), the competitive immunoreactions were conducted between AFT and AFT antigen on the T line with QDNBs-mAbAFT. Under optimal conditions, this approach allowed a rapid response towards AFT with a considerable sensitivity of 1.4 pg/mL and 2.9 pg/mL in rice and peanut matrices, respectively. The put-in and put-out duration were within 10 min. The recoveries for AFT in rice and peanut samples matrices were recorded from 86.25–118.0% with the relative deviations (RSD) below 12%. The assay was further validated via the comparison between this QDNB strip and the conventional HPLC method using spiked samples. Thus, the design provided a potential alternative for on-site, ultra-sensitive, and quantitative sensing of AFT that could also be expanded to other chemical contaminants for food safety.

In this paper, we introduce a new causal formula to distinguish Gliosarcoma (GSM) from Glioblastoma (GBM). Our formula combines Probabilistic Easy Variational Causal Effect (PEACE) with XGBoost, or eXtreme Gradient Boosting algorithm. Unlike prior research, which often relied on statistical models to reduce dataset dimensions before causal analysis, our approach uses the complete dataset with PEACE and XGBoost algorithm. PEACE provides a comprehensive measurement of direct causal effects, applicable to both continuous and discrete variables. It offers a spectrum of both positive and negative causal effects of the events causal effect values based on the degree ?, reflecting the rarity and frequency of the events. By using PEACE with XGBoost, we achieve a detailed and nuanced understanding of the causal relationships within the dataset features, facilitating accurate differentiation between GSM and GBM.

(1) Background: Sentinel lymph node (SLN) mapping represents an accurate and feasible technique for the surgical staging of endometrial and cervical cancer. This is commonly performed by conventional laparoscopy or robotic-assisted laparoscopy, but in recent years, a new retroperitoneal transvaginal natural orifice transluminal endoscopic surgery (vNOTES) approach has been described. This technique provides easy visualization of lymphatic afferent vessels and pelvic lymph nodes, early SLNs assessment, and a coherent mapping methodology following the lymphatic flow from caudal to cranial. However, only a few publications have reported it. Following the IDEAL (Idea Development Exploration Assessment Long-term follow-up) framework, research concerning this technique is at stage 2a, with only small case series as evidence of its feasibility. Its standardized description appears necessary to provide the surgical homogeneity required to move further. (2) Methods: description of a standardized approach for retroperitoneal pelvic SLN mapping by vNOTES. (3) Results: we described a 10-step approach to successfully perform retroperitoneal vNOTES SLN mapping, including pre-, intra-, and postoperative management. (4) Conclusions: This IDEAL stage 2a study could help other surgeons approach this new technique, and it proposes a common methodology necessary to evolve through future IDEAL stage 2b (multi-center studies) and stage 3 (randomized controlled trials) studies.

This study investigated the effectiveness of virtual reality (VR) technology in table tennis education compared to traditional training methods. A 12-week randomized controlled trial was conducted with 120 participants divided equally between VR and traditional training groups. Performance metrics, learning motivation, and satisfaction were assessed at regular intervals. Results demonstrated significant advantages of VR training, with the VR group showing superior improvements in serve accuracy (23.5% vs. 15.8%, p < 0.001), rally endurance (increase of 8.2 vs. 5.7 shots, p < 0.01), and overall skill scores (18.7 vs. 13.2 points improvement, p < 0.001). The VR group also exhibited higher increases in learning motivation (23.5% vs. 12.8%, p < 0.001) and satisfaction (31.5% vs. 18.7%, p < 0.001). Subgroup analysis revealed particular benefits for novice players and younger participants. These findings suggest that VR technology offers a promising approach to enhance table tennis education, potentially revolutionizing sports training methodologies. Future research should focus on long-term skill retention and the optimization of VR training protocols.

Billions of euros are invested every year by professional football clubs for the recruitment of players. This article presents the latest advances in the statistical modelling of the factors that market actors take into consideration to determine the transfer prices of professional football players. It extends to a global scale the econometric approach previously developed by the authors to evaluate the transfer prices of players under contract with clubs from the five major European leagues. The statistical technique used to build the model is multiple linear regression (MLR), with fees paid by clubs as an independent variable. The sample comprises over 8000 transactions of players transferred for money from clubs worldwide during the period stretching from July 2014 to March 2024. This paper shows that a statistical model can explain almost 85% of the differences in the transfer fees paid for players. Despite the specific cases and other possible distortions mentioned in the discussion, the use of a statistical model to determine player transfer prices seems highly relevant.

The soil microbial community serves as a crucial indicator for assessing soil fertility and health. The composition and activity of soil microorganisms reflect the soil's well-being and its ability to provide proper nutrients for crops. Fertilization plays a prominent role in enhancing soil fertility, and novel types of fertilizers, such as stabilized fertilizers with nitrification/urease inhibitors and coated fertilizers, have gained popularity in rice and maize cultivation. However, the long-term effects of these stabilized and coated urea fertilizers on soil chemical properties, microbial di-versity and community structure, as well as nitrogen cycling functional genes, remain unclear. Therefore, it is essential to investigate the impact of extended use of stabilized and coated urea fertilizers on soil fertility and microbiota in rice paddy fields. This research will provide scientific and theoretical support for the development and promotion of stabilized and coated urea ferti-lizers. To examine the effects of long-term application of these stabilized and coated urea fertilizers on soil chemical properties and microorganisms in rice paddy fields, soil samples were collected from brown soil rice paddy fields treated with various urease and nitrification inhibitors, stable urea, sulfur-coated urea (SCU), and resin-coated urea (PCU). The study revealed that 16 years of long-term use of conventional urea nitrogen fertilizer led to a considerable reduction in soil TP. On the other hand, NBPT and conventional urea fertilizers, when applied for an extended period, significantly increased soil organic matter (SOM). Moreover, except for HQ and NBPT+DMPP, the prolonged application of new urea fertilizers also signifi-cantly enhanced soil total potassium (TK). Notably, among all the treatments, PCU treatment had higher values for various soil chemical properties. In the case of SCU fertilizer used in brown soil rice paddy fields, it resulted in a significant decrease in soil pH over time. However, this change in pH did not affect the population of ammonia-oxidizing bacteria (AOB), as it was primarily in-fluenced by soil available nitrogen. DMPP, HQ+DCD, NBPT+DMPP, SCU, and PCU significantly reduced the copy number of bacterial 16S rRNA genes in the soil, with the coated urea fertilizers (SCU and PCU) having a greater impact. The long-term use of stabilized urea fertilizers containing HQ significantly reduced the bacterial community in rice paddy soil. Conversely, HQ+DCD stable urea fertilizer significantly increased the population structure and abundance of Basidiomycota fungi while decreasing the population structure and abundance of Rozellomycota fungi. DMPP stabilized urea fertilizer notably increased the population structure and abundance of Ascomycota fungi while decreasing the population structure and abundance of Rozellmycota and Chytridiomycota fungi. Further-more, HQ stabilized urea fertilizer significantly reduced the population structure and abundance of Chytridiomycota fungi. It is important to note that SCU fertilizer is not suitable for long-term application in neutral to slightly acidic meadow brown soil with a background pH. On the other hand, stable urea fertilizers and resin-coated urea fertilizers containing HQ, NBPT, DCD, DMPP, and their combinations are suitable for long-term application in neutral to slightly acidic meadow brown soil.

In this study, the effects of various beryllium (Be) addition (Be-free, 0.1, 0.25, 0.50 wt.%) on the microstructure, mechanical properties, and corrosion corrosion of the Al-Mg-Li alloys were systematically investigated. The best performance was obtained in alloy with 0.1 wt.% Be addition. Trace Be addition promotes the grain refinement of the as-cast alloy ingot and contributes positive effects to the recrystallization, bringing improvement of tensile property. Meanwhile, the best anti-corrosion behavior is also presented at 0.1 wt.% Be addition, due to its potential to reduce the width of precipitates free zone (PFZ). As the Be content increase to an excessive level, the comprehensive performance decreases. Therefore, it is strongly recommended that adding trace Be element into Al-Mg-Li alloys has a positive effect on the comprehensive service performance.

Accurate lithium battery diagnosis and prognosis is critical to increase penetration of electric vehicles and grid-tied storage systems. They are both complex due to the intricate, nonlinear, and path-dependent nature of battery degradation. Data-driven models are anticipated to play a significant role in the behavioral prediction of dynamical systems such as batteries. However, they are often limited by the amount of training data available. In this work, we generated the first big data comprehensive synthetic datasets to train diagnosis and prognosis algorithms. The proof-of-concept datasets are over three orders of magnitude larger than what is currently available in the literature. With benchmark datasets, results from different studies could be easily equated, and the performance of different algorithms can be compared, enhanced, and analyzed extensively. This will expend critical capabilities of current AI algorithms, tools, and techniques to predict scientific data.

Li₂MnO₃ is critical component in the well-studied Li-excess cathode materials xLi₂MnO₃•(1- x)LiMO₂ for achieving high lithium storage capacity. In this article, the diffusion of Li ions in Li₂MnO₃ is studied using molecular dynamics (MD) simulation with well-behaved empirical force fields obtained by fitting against the crystal structure from experiment and phonons calculated using Density Function Theory (DFT). We have found two possible tetrahedral hopping channels, 0-TM and 1-TM(Mn⁴⁺) channel, which are differentiated by the face sharing octahedral cations. Simulation results show that the 0-TM channel is active for Li hopping, while 1-TM(Mn4+) channel is inactive. During the delithiation process, the Li ions in the TM layered are firstly removed, then those in the Li layer. However, the Li ions will be trapped in the tetrahedral 0-TM channels as long as the four face sharing octahedral sites are cleared. Up to x=1.0 for Li₂-xMnO₃, almost all the Li ions are located at the tetrahedral sites, forming a regular array along a axis. The de-intercalation of tetrahedral Li ions requires a high voltage (>5.2 V vs. Li/Li+), limiting the practical capacities measured in lab. The diffusion of Mn ions into the Li layers is observed in a deeper delithiated structure (x=1.2 for Li₂-xMnO₃), indicating an initial phase transformation to a spinel-like structure. However, the Mn ions are mainly trapped in the tetrahedral sites in the Li layer, instead of the octahedral sites fin spinel-like structure. A few of Mn ions diffusing into the octahedral sites in Li layers have no face sharing tetrahedral Li ions, revealing a further Li de-intercalation is imperative for the complete phase transformation. Our model is not stable for x≥1.4 in Li₂-xMnO₃. Other charge compensation mechanism should be considered in this high delithiation stage, eg. oxygen release.

To understand the molecular mechanism associated with growth variability in bivalves, the Solexa/Illumina technology was employed to analyze the transcriptomic profiles of extreme growth rate differences (fast- VS. slow-growing individuals) in one full-sib family of the ark shell Scapharca subcrenata. De novo assembly of S. subcrenata transcriptome yielded 276,082,016 raw reads, which were assembled into 98,502 unique transcripts by Trinity strategy. A total of 6,357 differentially expressed genes (DEGs) were obtained between fast- and slow-growing individuals, with 580 up-regulated expression and 5777 down-regulated expression. Functional annotation revealed that the largest proportion of DEGs were classified to the large or small subunit ribosomal protein, all of which showed significantly lower expression levels in fast-growing group than those in slow-growing group. GO enrichment analysis identified the maximum of DEGs to biological process, followed by molecular function and cellular component. Most of the top enriched KEGG pathways were related to energy metabolism, protein synthesis and degradation. These findings reveal the link between gene expression and contrasting phenotypes in ark shells, which support that fast-growing individuals may be resulted from decreased energy requirements for metabolism maintenance, accompanying with greater efficiency of protein synthesis and degradation in bivalves.

Significant amounts of anthropogenic radionuclides were introduced in ocean waters following nuclear atmospheric tests and development of the nuclear industry. Dispersion of artificial dissolved radionuclides has been extensively measured for decades over the European continental shelf. The radionuclide measurement and release fluxes databases provided here represent an exceptional opportunity to validate dispersion hydrodynamic models. MARS hydrodynamic model have been applied at different scales to reproduce in realistic conditions the measured dispersion. Specific methods have been developed to obtain qualitative and quantitative results and perform model/measurement comparisons. Model validation concerns short to large scales with dedicated surveys following the dispersion: it was performed within two and three dimensions framework and from minutes and hours following a release up to several years. Results are presented concerning the dispersion of radionuclides in marine systems deduced from standalone measurements, or according to model comparisons. It allows characterising dispersion over the continental shelf, pathways, transit times, budgets and source terms. This review exhibits the main features retained from the point of view of radiotracers, hydrodynamic models and model/measurement methods with perspectives of applications in other areas or oceanographic domains.

In this study, we use descriptive statistics and data visualization techniques to analyze datasets and identify patterns and relationships between bikeshare and other transportation modes. We also use regression analysis to quantify the impact of various factors on bikeshare usage. Our results indicate that bikeshare trips during the pandemic were much longer compared to pre-pandemic times. Moreover, usage of bikeshare differed by user group, with casual users showing a greater increase in usage compared to subscribers. We plotted bivariate choropleth maps to visualize the spatial distribution of changes in bikeshare usage. Our findings suggest that an increase in bikeshare travel was associated with a reduction in travel by ride-hailing and public transit, especially in the northern part of the city, indicating a potential replacement of other modes of transportation. On the western and southern boundary of the city, bikeshare had a complementary effect with other modes. Overall, the data acquired for this study provides a valuable opportunity to understand the behavior of bikeshare users in Chicago, the intercorrelation of bikeshare with other transportation modes, and the impact of infrastructure on bikeshare usage. The findings of this study can inform the design and implementation of bikeshare systems in other cities and contribute to the development of a more sustainable transportation system.

The International Maritime Organization (IMO) is enforcing increasingly stringent regulations on ship carbon emissions, The International Maritime Organization (IMO) has been implementing progressively strict regulations on ship carbon emissions, leading to the adoption of the virtual arrival (VA) method by many vessels to reduce their carbon footprint. However, the effectiveness of the traditional VA method often varies in busy ports with complex traffic organization scenarios. To address this, our study presents a novel, comprehensive model that integrates vessel scheduling with the VA approach. This model is designed to achieve a dual objective: reducing carbon emissions through Virtual Arrival and simultaneously minimizing vessel waiting times. In addition to these goals, it incorporates essential aspects of safety, efficiency, and fairness in port management, utilizing the NSGA-2 algorithm to find optimal solutions. This model has been tested and validated through a case study at Ningbo-Zhoushan port, employing its dataset. The results demonstrate that our innovative model and algorithm significantly outperform traditional scheduling methods, such as First-Come-First-Serve (FCFS) and Virtual-Arrival Last-Serve (VALS), particularly in terms of operational efficiency and reduction in vessel carbon emissions.

Numerous studies have demonstrated that multiple intrinsic and extrinsic factors shape the structure and composition of gut microbiota in a host. The disorder of gut microbiota may trigger various host diseases. Here, we collected fecal samples from wild-caught Japanese geckos (Gekko japonicus) and captive conspecifics fed with mealworms (mealworm-fed geckos) and fruit flies (fly-fed geckos), aiming to examine dietary and sexual correlates of gut microbiota. We used the 16S rRNA gene sequencing technology to determine the composition of gut microbiota. The dominant phyla with a mean relative abundance higher than 10% were Verrucomicrobiota, Bacteroidota and Firmicutes. Gut microbial community richness was higher in mealworm-fed geckos than in fly-fed and wild geckos, and community diversity was higher in mealworm-fed geckos than in wild geckos. Neither alpha nor beta diversity of gut microbiota differed among wild, mealworm-fed and fly-fed geckos. The beta rather than alpha diversity of gut microbiota was sex-dependent. Based on the relative abundance of gut bacteria and its gene functions, we concluded that gut microbiota contributed more significantly to the host’s metabolic and immune functions. Higher diversity of gut microbiota in mealworm-fed geckos could result from higher chitin contents of insects of the order Coleoptera. This study not only provides basic information about the gut microbiota of G. japonicus, but also shows that gut microbiota correlates with dietary habit and sex in the species.

In the present work, an innovative range of foams, based on flax gum-filled epoxy resin was developed, reinforced or not by flax fibers. Foams and composites with different gum and epoxy resin contents were produced and characterized for mechanical and thermal performances. To enhance the organic flax gum filler's cross-linking, we exploited the oxidized components' reactivity with the amine hardener (isophorone diamine). We compared the materials obtained with those derived from the native components. Flax gum and fibers were primarily characterized by chemical analysis, NMR, and FTIR to evaluate the mild oxidation of native materials. The formation of chemical bonds between oxidized polymer chains, epoxy resin, and hardener has been evidenced by FTIR, and the materials were then studied by SEM, X-ray computed micro-tomography (CT), and submitted to mechanical and thermal tests. The relevance of oxidation treatment was highlighted, through a significant increase in density and mechanical performances (+36% and +81%, respectively for the 100% flax gum material). The positive effect of flax fibers on homogeneity evidenced through micro-CT analysis was also clearly addressed. This set of promising results paves the way for future development of fully flax-based insulation composite materials.

Health of migrant is a widely studied topic. It has been argued that migrant health may deteriorate over time. Though migrants are ‘a hard to reach’ population in survey data, this paper builds on a unique dataset provided by Médecins du Monde from five countries. We study self perceived health (SPH) in connection with socio-economic and demographic factors and length of stay. Results show different results for men and women. Asylum seekers compared to other documented migrants have a worse health. Migrants with better living conditions tend to be in better health. Employment and stable accommodation has a positive effect on SPH. Women from poorer countries have a better physical SPH after 3 months of residing in the host country. This paper contributes widely to knowledge of health of migrants. Contrarily to other evidence, health of migrants tends to improve for some migrants.

Plants have developed unique mechanisms to cope with the harsh environmental conditions to compensate for their lack of mobility. A key part of their coping mechanisms is the synthesis of secondary metabolites. In addition to their role in plants’ defense against pathogens, they also possess therapeutic properties against diseases, and their use by humans predates written history. Viruses are a unique class of submicroscopic agents, incapable of independent existence outside a living host. Pathogenic viruses continue to pose a significant threat on global health, leading to unprecedented fatalities on a yearly basis. The use of medicinal plants as a natural source for an-tiviral agents has been widely reported in literature in the past decades. Metabolomics is a powerful research tool for the identification of plant metabolites with antiviral potentials and can be used to isolate compounds with antiviral activities in plants and study the biosynthetic pathways involved in viral disease progression. This review discusses the use of medicinal plants as antiviral agents, with a special focus on the metabolomics evidence supporting their efficacy. Additionally, suggestions are made for the optimization of various metabolomics methods for characterizing the bio-active compounds in plants and subsequent understanding of the mechanism of their operation.

Aiming at solving difficulties related to aero-engine classification and identification, the infrared spectra of aero-engine hot jets are measured by telemetry Fourier transform infrared spectrometer, and a six types aero-engine hot jet spectrum data set is generated. The spectrum data set were randomly sampled at a ratio of 8:1:1 to generate training set, validation set and prediction set. In this paper, a Peak Finding Siamese Convolutional Neural Network (PF-SCNN) is designed to match and classify spectrum data. In the first place, the matching pairs of training set and validation set are made independently with the labels of positive and negative samples creating. The spectrum feature extraction and distance similarity calculation are carried out by the Siamese convolutional neural network (SCNN). When entering the prediction phase, the prediction set and training set are integrated to predict in the trained model. The prediction set labels are ultimately determined by matching them with the training set labels having the highest similarities to ensure accurate predictions. To improve the operation efficiency of the SCNN, a peak finding method is introduced to extract the spectrum peaks. Peak positions of high frequency are counted, while the peak data are extracted. The peak data are used to take the place of the original data for training and prediction. The performance measures are defined as Accuracy, Precision, Recall, Confusion matrix and F1-score, and the prediction accuracy was up to 99%. The experimental results indicate that the proposed PF-SCNN is suitable for our spectral dataset and can complete the task of infrared spectrum classification of aero-engine hot jets.

Focusing on the problem of identifying and classifying aero-engine models, this paper measures the infrared spectrum data of aero-engine hot jets using a telemetry Fourier transform infrared spectrometer. Simultaneously, infrared spectral data sets with the six different types of aero-engines are created. For the purpose of classifying and identifying infrared spectral data, a CNN architecture based on the continuous wavelet transform peak seeking attention mechanism (CWT-AM-CNN) is suggested. This method calculates the peak value of middle wave band by continuous wavelet transform, and the peak data is extracted by the statistics of the wave number locations with high frequency. Attention mechanism is used for the peak data, and the attention mechanism is weighted to the feature map of the feature extraction block. The training set, vali-dation set and prediction set are divided in the ratio of 8:1:1 for the infrared spectral data sets. For three different data sets, CWT-AM-CNN proposed in this paper is compared with the classical classifier algorithm based on CO2 feature vector and the popular AE, RNN and LSTM spectral processing networks. The prediction accuracy of the proposed algorithm in the three data sets is as high as 97%, and the lightweight network structure design not only guarantees high precision, but also has a fast running speed, which can realize the rapid and high-precision classification of the infrared spectral data of the aero-engine hot jets.

Chickens have extensive phenotypic variation. The Wuhua yellow chicken (WHYC) is an important traditional yellow-feathered chicken in China, characterized by white tail feathers, white flight feathers, and strong disease resistance. However, the genomic basis of traits associated with WHYC is still poorly understood. In this study, whole genome resequencing was performed with an average coverage of 20.77-fold to investigate heritable variation and identify selection signals in WHYC. Reads were mapped onto the chicken reference genome (Galgal5) with a coverage of 85.95%. After quality control, 11,953,471 SNPs and 1,069,574 InDels were obtained. In addition, 41,408 structural variants and 33,278 copy number variants were found. A comparative genomic analysis of WHYC and other yellow-feathered chicken showed that selected regions were enriched in genes involved in transport and catabolism, immune system, infectious diseases, signal transduction, and signaling molecules and interaction. Several genes associated with disease resistance were identified, including IFNA, IFNB, CD86, IL18, IL11RA, VEGFC, and ATG10. Furthermore, PMEL and TYRP1 may contribute to the coloring of white feathers in WHYC. These findings improve our understanding of the genetic characteristics of WHYC and may contribute to future breed improvement.

Gastrodia elata Bl., one of the well-known edible plants in China, belongs to the family of Orchidaceae. In recent years, the consumption rate of this plant as food supplements or health products is gradually rising in China. However, the traditional industrialization processing procedures (TP) for this plant was found to be too complicated and not friendly to the environment with time and energy consuming. Hence, we investigated whether the processing procedures could be simplified without affecting its overall chemical profile and anti-oxidation activity. In this experiment, a new processing procedure (NP) was designed for processing this plant, and the chemical compositions of the extract for Gastrodia elata produced by two different processing procedures were identified by UPLC-Q-TOF/MS. It was found that phenols and parisins were the most abundant chemical components for this extract. Among all the identified components, the contents of gastrodin and its citric acid derivatives were obviously higher than that of other components. Therefore, the contents of six gastrodin derivatives and ethanol soluble extract rate for Gastrodia elata produced by two different processing procedures, traditional and newly designed processing procedures, were further comparatively investigated for the validation of the applicability. The DPPH radical scavenging activities of the extract from Gastrodia elata produced by two different processing procedures were also investigated. The results demonstrated that the contents of gastrodin and p-hydroxybenzyl alcohol in all samples were slightly decreased in NP samples compared with that of TP samples (except batches of S3 and S6 samples), but no significant difference was observed between these two groups (p = 0.8273 for gastrodin and p= 0.2320 for p-hydroxybenzyl alcohol). Additionally, the total contents of parisins were also in a decreased trend in NP samples but with no significant difference when comparing with that in TP samples (p = 0.1721). The results for ethanol soluble extract rate also had the same situation (p = 0.1094). Taken together, it was suggested that the chemical profiles and contents of gastrodin derivatives and ethanol soluble extract rate for NP samples was not significant different to that of TP samples. The DPPH radical scavenging activity of the extract of Gastrodia elata produced with NP method was not significant different from that of TP samples. As a result, the industrial processing procedures for the preservation of this material plant could be simplified to a more time and energy saving procedure without changing the quality of the products, which is also consistent with the concept of green and sustainable industrial production.

Thin twin-roll cast strips from a model Al-Cu-Mg-Li-Zr alloy with a small addition of Sc were prepared. A combination of a fast solidification rate and a favorable effect of Sc microalloying impacts the size and distribution of primary phase particles and the dimension of eutectic cells. Small interdendritic spacing in the range of 10-15 um enables substitution of the initial long-term homogenization annealing by a two-step annealing at 300 and 450 °C and a short solution treatment at 530 °C before quenching and the final age-hardening procedure. A dense dispersion of small Al3(Sc,Zr) precipitates intensively inhibiting recrystallization forms during this treatment. Further refining and fragmentation of the structure were performed using constrained groove pressing before the solution treatment. Small pre-straining of quenched specimens and subsequent age-hardening simulating T8 temper were used to promote the formation of a homogeneous distribution of strengthening particles and to suppress preferential heterogeneous precipitation on subgrain and grain boundaries. Microstructural features and microhardness evolution during annealing were critically examined and confronted with microstructural characteristics observed on reference mold cast alloy or alloys without Sc alloying. The proposed treatment represents a model near net shape procedure for manufacturing thicker sheets for the aerospace industry.

Molecular dynamics (MD) simulations present a sophisticated nano-scale computational approach that can play a critical role in material design for next generation batteries. One critical piece of information needed for MD simulations is the non-bonded potential parameters, which can be obtained through quantum mechanics (QM) calculations or experimental methods. However, experimental data is not available for exploratory and novel materials to derive the potential parameters. Also, the QM approach needs significant computational power and is too time consuming for large systems. On the other hand, current Artificial Intelligence techniques are faster, but they cannot be generalized across molecules and properties. As a result, there is a significant barrier to discovering and exploring new materials. Overcoming this barrier, in this work, we propose a Machine Learning (ML) based technique that can learn inter-atomic potential parameters for various particle-particle interactions employing QM calculations. This ML model can be used as an alternative for QM calculations for predicting non-bonded interactions in a computationally efficient manner. Using these parameters as input to MD simulations, we can predict a diverse range of properties, enabling researchers to design new and novel materials suitable for various applications in the absence of experimental data. We employ our ML-based technique to learn the Buckingham potential, a non-bonded interatomic potential. Subsequently, we utilize these predicted values to compute the densities of four distinct molecules, achieving an accuracy exceeding 93%. This serves as a strong demonstration of the efficacy of our proposed approach.

The increasing frequency of urban flooding, driven by global climate change, poses significant threats to the safety and resilience of urban rail transit systems. This study systematically examines the cascading failure processes and resilience of these networks under flood conditions, with a specific focus on the Shanghai Metro. A comprehensive resilience evaluation model was developed by integrating geographic information, static network characteristics, and dynamic passenger flow indicators. This study employs an improved Coupled Map Lattice (CML) model to simulate cascading failures, considering the coupling effects of station centrality, geographic elevation, and passenger flow dynamics. Results indicate that stations with higher degree centrality are more likely to trigger rapid cascading failures across the network. However, incorporating dynamic passenger flow and geographic elevation data helps mitigate these effects, emphasizing the need for multi-dimensional resilience strategies. The findings provide valuable insights for urban transit management, offering a scientific foundation for developing targeted disaster response strategies to enhance network resilience against floods. This study advances the understanding of the vulnerability of urban rail transit systems and offers practical guidance for improving disaster preparedness in urban transportation infrastructure.

Graphene-based nanoparticles possess remarkable physiochemical properties, making them promising for diverse applications in biomedicine, agriculture, food, and industrial applications. These nanoparticles have also been used in the fight against COVID-19. Human and environmental exposure to graphene-based nanomaterials is increasing at an unprecedented rate. However, there is still a huge knowledge gap regarding its safety in clinical applications. The topic remains controversial; although several routes of degradation exist, the cytotoxicity of graphene-based nanoparticles has been demonstrated. Various factors that can influence the cytotoxicity of graphene-based materials are discussed. This review summarizes the physiochemical properties of graphene-based materials and critically examines the possible effects of graphene-based nanoparticles on the molecular level and adverse health outcomes. While oxidative stress-mediated cell damage has been proposed as a primary cytotoxicity mechanism for graphene-based materials, various in vivo biodistribution and cytotoxicity mechanisms are also highlighted. Therefore, this review of the literature provides an overview of the cytotoxicity of GBMs and raises concerns about their widespread application with potential hazardous consequences on the environment and human health.

Salinity acts as a critical environmental filter on microbial communities in natural systems, negatively affecting microbial diversity. However, how salinity affects the community assembly remains unclear. This study used Wendeng multi-pond saltern as a model to evaluate the prokaryotic community composition and diversity and quantify the relative importance of ecological processes across salinity gradients. Results showed that low saline salterns (45-80 g/L) exhibited higher bacterial diversity than those in high saline salterns (175-265 g/L). The relative abundance of taxa assigned to Halanaerobiaceae, Haloferacaceae, Desulfohalobiaceae, Phormidiaceae, Rohodobacteraceae, and Nitrococcaceae was higher with increasing salinity. Salinity and pH were the primary environmental factors that directly or indirectly determined the composition and diversity of prokaryotic communities. Microbial co-occurrence network dynamics were more complex in the sediment than in water of salterns. An infer Community Assembly Mechanisms by Phylogenetic-bin-based null model analysis (iCAMP) showed that microbial community assembly in sediment and water differed. Our findings provide more information about microbial community structure and the importance of various ecological processes in controlling microbial community diversity and succession along salinity gradients in water and sediment environments.

Smart City has emerged as the mainstream paradigm for urban governance innovation, sustainable development, and strategy upgrades, which is drawing attention from scholars worldwide. However, current frameworks for Smart City assessment remain incomplete and simplistic. In is paper, 30 national or provincial capitals in China were selected and we designed a tri-dimensional SPI model—Social, Physical, and Information Space—for smart city spatial development assessment. Utilizing methods such as entropy weighting, coupled coordination degree models, and the Dagum Gini coefficient, this study assesses the spatial development and coupled coordination of 30 cities from 2011 to 2019. Finally, by means of BP neural networks, the study examines the contribution of each indicator to the spatial coupled coordination. The results indicated that with a narrowing disparity in development speeds among different regions, the spatial coupled coordination development level of smart capitals in China has steadily increased, presenting a pattern of staggered distribution. Moreover, the IS subsystem plays the most significant role in coupled coordination. The significance of this research lies in its tri-dimensional spatial perspective of the spatial development and coupled coordination differences of the Smart City, providing evidence-based support for the regional layout and optimization in China.

Phytochromobilin (PΦB) participates in the regulation of plant growth and development as an important synthetase of photoreceptor phytochromes (phy). And Arabidopsis Long Hy-pocotyl 2 (HY2) appropriately works as a key PΦB synthetase. However, whether HY2 takes part in plant stress response signal network remains unknown. Here, we described the func-tion of the HY2 in NaCl signaling. The hy2 mutant was NaCl-insensitive, whereas HY2-overexpressing lines showed NaCl-hypersensitive phenotypes during seed germination. The exogenous NaCl induced the transcription and the protein level of HY2 which positively mediated the expression of downstream stress-related genes of RD29A, RD29B and DREB2A. Further quantitative proteomics showed the patterns of 7,391 proteins under salt stress. HY2 was then found to specifically regulate 215 differentially regulated proteins (DRPs) which, according to GO enrichment analysis, were mainly involved in ion homeostasis, flavonoid biosynthetic & metabolic, hormone response (SA, JA, ABA, ethylene), reactive oxygen spe-cies (ROS) metabolic, photosynthesis and detoxification pathway to respond to salt stress. More importantly, ANNAT1-ANNAT2-ANNAT3-ANNAT4 and GSTU19-GSTF10-RPL5A-RPL5B-AT2G32060, two protein interaction networks specifi-cally-regulated by HY2, jointly participated in the salt stress response. These results direct the pathway of HY2 participating in salt stress, and provide new insights for the plant to re-sist salt stress.

In response to the growing need for efficient energy conversion technologies, this pa-per introduces an innovative Tubular Thermoelectric Generator (TTEG), specifically de-signed for high-efficiency conversion of waste heat into electrical energy. Unlike conven-tional flat plate thermoelectric generators (FTEGs), the TTEG incorporates full-ring ther-moelectric elements within a tubular layout, offering a novel approach to thermal energy recovery from fluid-based systems. This design significantly enhances the heat transfer capabilities, thereby improving the efficiency of energy conversion. Comprehensive simulations and analyses form the core of this study, where the per-formance of the TTEG is rigorously compared with traditional FTEGs. These comparisons highlight the advantages of the tubular design in terms of energy conversion efficiency and practical applicability in various scenarios. The findings reveal that the TTEG not only outperforms its flat plate counterparts but also presents a scalable and adaptable so-lution for waste heat recovery in a wide range of industrial and automotive applications. This research contributes to the field of energy science and technology by presenting a detailed experimental setup, complete with reproducible procedures and results. It opens new pathways for the development of more sustainable and efficient energy conversion systems, aligning with the goal of harnessing renewable and sustainable energy sources. The potential applications of this technology in enhancing energy efficiency and reducing environmental impact are vast, making it a significant step forward in the quest for clean and sustainable energy solutions.

The gut microbiota plays a crucial role in childhood obesity, and diet is a dominating driver. The effects of fructo-oligosaccharides (FOS), as a dietary fiber, on the composition and metabolism of gut microbiota in healthy children was investigated by vitro fermentation system with a reformative YCFA medium (rich in tryptic hydrolysates of meat). The 16S rRNA sequencing technology was utilized to analyze the varieties of gut microbiota. Measurement of short chain fatty acids (SCFAs) and gases were used by the gas chromatograph. Majorbio Cloud Platform and MetOrigin, as the interactive cloud server, perform the microbiota analysis, the metabolic pathway enrichment analysis, the statistical correlations, and biological relationships using network visualization. We found that the FOS group significantly regulated the composition and metabolism of gut microbiota. The co-metabolism network showed that 3 metabolites were related to 6 differential bacteria and 8 metabolism pathways. These findings suggest that dietary fiber could regulate the composition of gut microbiota and its metabolites in a better direction, but when dietary fiber participates in precision nutrition formula, it may be relevant for precision obesity, may help identify windows of opportunity for the dietary intervention of childhood obesity.

This paper provides a study on the development of solar energy, its prospects, and its impacts in Africa. Although the main focus is on technologies adopted for different uses (solar farms, mini-grids, off-grid, solar houses, irrigation, lighting, etc.), we also consider some aspects that increase the adoption of solar energy, such as legislation and business models. Finally, we will examine current and future solar projects managed by NGOs and international companies and how they benefit to leveraging social and economic standards, quality of life as well as capacity building in Africa. This study shows the plethoric interest of institutions outside Africa in the market of solar energy in the continent and that there is no African strategy to increase its independency toward a sustainable development particularly through energy transition.

This study discusses the application of deep learning technology in network intrusion detection systems (IDS) and focuses on a new model named CNN-Focal. First, through the review of traditional IDS technology, it analyzes its limitations in dealing with complex network traffic. Then, the design principle of the CNN-Focal model is described in detail, which uses threshold convolution and SoftMax multi-class classification technology to effectively improve abnormal traffic detection's accuracy and efficiency. The experimental results show that CNN-Focal performs well on the open data set, demonstrating the potential and advantages of its application in the natural network environment and providing a new perspective and method for further research of deep learning in the field of network security in the future.

In recent times, there has been significant enthusiasm for the development of all solid state batteries. This interest stems from a dual focus on safety—addressing concerns related to toxic and flammable organic liquid electrolytes—and the pursuit of high energy density. \cite{SCHNELL2018160,Zheng2018}. While liquid electrolyte batteries constitute for now the vast majority of commercial cells, solid electrolyte batteries show great promise. In parallel with experimental research, computational models \cite{GrazioliEtAlCM2016} clarify the several fundamental physics \cite{LiMonroeARCBM2020} that take place throughout battery operations. We review some classical models, emphasizing the conceptual advancements documented in the most recent works.

The possibility of using graphane monolayer crystals as the electrode material is becoming popular. Graphane is stable at room temperature and has large surface area, but its chemical inertness hinders its direct interactions with Li ions. In this study, we performed density functional theory calculations to study the energetic stability, structural and electronic properties of Li on graphane with various CH divacancy configurations (v12, v13 and v14 ). The results show that adsorption of Li atom reduces the formation energy of the CH divacancy configurations. The Li-v12 is most stable with the highest binding energy of 3.25 eV/Li and relaxes to in-plane with other C atoms. Altering the Li charge state to have Li−1-v12 or Li+1 -v12 affects the energetic stability and electronic characters of Li-v12 . The Li−1-v12 (Li +1-v 12 ) enhances (reduces) the binding force between Li and v 12 configuration, and furthermore it improves (deteriorates) the conductivity of the structure. Further investigation of graphane with vacancies is encouraged due to these intriguing observations, as it holds promise for potential utilization as an electrode material.

Li-rich manganese-based cathode materials (LRMs) are considered one of the most promising cathode materials for the next generation lithium-ion batteries (LIBs) because of their high energy density. However, there are problems such as capacity decay, poor rate performance, and continuous voltage drop, which seriously limit their large-scale commercial applications. In this work, Li1.2Mn0.54Co0.13Ni0.13O2 coated with Li2MoO4 with unique spinel structure was prepared by wet chemistry method and the subsequent calcination process. The Li2MoO4 coating layer with spinel structure can provide a 3D Li+ transport channel, which is beneficial for improving rate performance, while protecting LRMs from electrolyte corrosion, suppressing interface side reactions, and improving cycling stability. The capacity retention rate of LRMs coated with 3wt% Li2MoO4 increased from 69.25% to 81.85% after 100 cycles at 1C, and the voltage attenuation decreased from 7.06 to 4.98 mV per cycle. The lower Rct also exhibits improved magnification performance. The results indicate that the Li2MoO4 coating effectively improves the cyclic stability and electrochemical performance of LRMs.

The compositions of metalliferous sediments which is associated with hydrothermal vents can provide reliable geochemical indicators for the exploration of seafloor sulfides. In this study, surface sediments from the Xunmei hydrothermal field (HF) on the South Mid-Atlantic Ridge (SMAR) were selected, and their major and trace elements were analysed. The results showed that the sediments are composed of pelagic material (calcium biogenic components), basaltic debris, iron-manganese oxides, and a mixture of hydrothermal components. Additionally, the sediments were found to be significantly enriched in Cu, Zn, Fe and Co derived from hydrothermal sources, as well as Mn, V, Mo, U and P, which are primarily scavenged from seawater. The Cu and Zn concentrations are highest in the northeastern part of Xunmei, while the northeastern, northern, and southern parts are characterized by great inputs of hydrothermal Fe. Manganese and Mo are mainly enriched in the western and southern parts and show a strong positive correlation, indicating that Mo is mainly scavenged by Mn oxides. There are high positive correlations among U, P, and Fe, indicating their coprecipitation with Fe from hydrothermal plumes. Vanadium and Co are introduced into sediments in two ways, i.e., V is scavenged and coprecipitates from hydrothermal plumes, and Co is derived from sulfide debris. Based on the variations in Cu and Zn contents and in the Cu/Fe (0.159), Zn/Fe (0.158), and Fe/Mn (1440) ratios, it can be inferred that a high-temperature hydrothermal vent existed in northeastern Xunmei. In combination with the distribution patterns of the above elements, the hydrothermal vents in the southern part ceased erupting after a short period of activity. In addition, the high Mn anomaly and the high U/Fe ratios at the boundaries of the investigated area indicate the presence of a relatively oxidized environment in southwestern Xunmei.

The effect of H2S with high concentrations on catalytic dry reforming of methane (DRM) process has seldom been focused on previously. Herein, a thermodynamic analysis of the DRM reactions in the presence of H2S with the concentration varying in 0-20 vol% was conducted firstly. Several typical types of catalysts including MgO, NiO/MgO and LaNiO3 in different states were prepared for executing DRM at 800 C, 0.1 MPa in the feed of 20 vol% CO2 and 20 vol% CH4 balanced with N2. The catalytic performance of each catalyst for DRM process under conditions of absence and presence of H2S was compared. A promotion effect of increasing H2S concentration on both the conversions of CO2 and CH4 and the molar yields of CO and H2 was observed on all the catalysts especially the MgO and the pristine NiO/MgO. While a significant decline in catalytic activity of either the reduced NiO/MgO or the reduced LaNiO3 catalyst after adding H2S, moderate reactant conversions still sustained. The results of process analysis and catalyst structure characterization suggest that H2S participation can contribute to the increment in CO2 and CH4 conversion, and active S-adsorbed species may play the key role of catalysis in the reactions involving H2S.

The reduction in rheological parameters and dissolution rate of precursors in geopolymer coatings during early hydration significantly contributes to sagging. This study aims to improve the sag resistance of these coatings by incorporating diatomite filler. Rheological testing was conducted to assess the impact of diatomite and its concentration on yield stress, plastic viscosity, and thixotropy of the geopolymer coatings. The results indicated that diatomite's large specific surface area and high reactivity have a significant influence on the rheological parameters and early dissolution rate of precursors. With a diatomite concentration of 1.1%, the coating exhibited a yield stress of 2.749 Pa and a plastic viscosity of 0.921 Pa·s, maintaining stability, homogeneity, and no sagging at a thickness of 600 μm. Furthermore, the highly active SiO2 in diatomite participates in the secondary hydration reaction of the geopolymer materials, leading to the formation of substantial C-(A)-S-H gel. This gel enhances internal interconnectivity within the coating, thereby improving its rheological and mechanical properties.

The working environment of mining electric locomotives is wet and muddy coal mine 1 roadway. There may be idling or slipping between the wheels and rails of mining electric locomotives 2 due to low friction between the wheel and rail and insufficient utilization of creep rate. Therefore, it 3 is necessary to control the creep rate within a reasonable range. In this paper, the autonomous control 4 algorithm for mining electric locomotives based on improved ε-greedy is theoretically proven to be 5 convergent and effective firstly. Secondly, after analyzing the contact state between the wheel and rail 6 under wet and slippery road conditions, it was concluded that the value of creep rate is an important 7 factor affecting the autonomous driving of mining electric locomotives. Therefore, the autonomous 8 control method for mining electric locomotives based on creep control is proposed in this paper. 9 Finally, the effectiveness of the proposed method was verified through simulation. The problem of 10 wheel slipping and idling caused by insufficient friction of mining electric locomotives in coal mining 11 environments is effectively suppressed. Autonomous operation of vehicles with optimal driving 12 efficiency can be achieved through quantitative control and utilization of the creep rate between 13 wheels and rails

Postpartum haemorrhage remains a significant contributor to maternal mortality and morbidity worldwide, particularly in low- and middle-income countries. PPH continues to pose challenges in both developed and developing nations despite being preventable. This review examines the current state of knowledge regarding PPH within the context of Vietnam, a country with its unique healthcare landscape. This review identifies critical areas for improvement and future research directions based on a comprehensive literature analysis, including epidemiological insights, risk factors, prevention strategies, and management approaches. Discrepancies in PPH definitions and prevalence rates across regions highlight the need for tailored interventions and policies. Understanding the specific challenges and opportunities related to PPH in Vietnam is crucial for informing evidence-based interventions and policies to improve maternal health outcomes. This review contributes to the broader global effort to reduce maternal mortality by addressing gaps in knowledge and proposing strategies to enhance PPH prevention and management in Vietnam.

Recent studies have proven that synthetic aperture radar (SAR) automatic target recognition (ATR) models based on deep neural networks (DNN) are vulnerable to adversarial examples. However, existing attacks are easily failed in the case where adversarial perturbations cannot be fully fed to victim models. We call this situation perturbation offset. Moreover, since background clutter takes up most of the areas in SAR images and has low relevance to recognition results, fooling models with global perturbations is quite inefficient. This paper proposes a semi-whitebox attack network, called Universal Local Adversarial Network (ULAN), to generate universal adversarial perturbations (UAP) for the target regions of SAR images. In the proposed network, we calculate the model’s attention heatmaps through layer-wise relevance propagation (LRP), which is used to locate the target regions of SAR images that have high relevance to recognition results. In particular, we utilize a generator based on the U-Net to learn the mapping from noise to UAPs and craft adversarial examples by adding the generated local perturbations to target regions. Experiments indicate that the proposed method fundamentally prevents perturbation offset and achieves comparable attack performance to conventional global UAPs by perturbing only a quarter or less of SAR image areas.

To investigate the differences in cadium (Cd) uptake and translocation among edible rapes and select suitable rape varieties for safe production, a field experiment was carried out with 25 varieties of edible rapes on a farmland polluted with Cd in the central souothern part of Hunan Province. The growing characteristics and Cd contents in rape tissues were measured, and the amount of Cd removed was calculated. The results showed that the growth of 25 rape varieties was not inhibited without withering or inconsistent changes in leaf and so on. The Cd contents in stem-leaf and root of rapes were in the range 0.05-0.26mg·kg-1 and 0.04-0.26mg·kg-1, respectively. Basing on BCF, the stem-leaf showed a greater capacity for Cd transport than root. And the total Cd removed by rape were 0.002-0.016 mg·plant-1. Except for plant height, BCF of soil available Cd from soil to stem-leaf, TCF of Cd from root to stem-leaf and Cd taken away by rape, there was no significant difference among edible rape varieties. Although Cd contents in stem-leaf of 25 rape varieties does not exceed the national safety standards, planting “Lvjin 1”, “Guanyou Qingjing” and “Guanyou brassica” not only reduced soil pollution but also allowed the production of safe leafy rape.

Peach (L.) is a kind of fruit tree with considerable economic and nutritional significance. However, it shows a high sensitivity to drought stress, which has a profound impact on its growth. L-methionine (MET) and L-proline (PRO) are two amino acids synthesized by plants, which play an important role in abiotic stress response. However, their exact mechanism of action remains elusive. Through physiological analysis, MET (500ppm) and PRO (500ppm) treatments can effectively reduce the adverse effects of drought stress on the growth of peach seedlings. The results show that MET and PRO can effectively inhibit the stomatal opening of plant leaves under drought conditions and reduce the water evaporation of leaves under drought conditions. In addition, the application of MET or PRO promoted the accumulation of carbohydrates in the leaf tissues of peach seedlings under drought conditions, increased the intracellular relative water content (24%, 24%), decreased the leaf electrical conductivity (21%, 24%), and alleviated the membrane damage caused by drought. Experimental studies have confirmed that the alleviation of drought stress is due to the removal of reactive oxygen species (ROS) in vivo, that is, the downregulation of malondialdehyde (18%, 19%)，peroxidase (50%, 5%) and superoxide dismutase (12%, 14%) activities. In addition, the application of exogenous amino acids also improved the root morphological structure and root vitality of peach seedlings，which was conducive to absorbing external buffer substances and alleviating drought stress. In summary, the application of exogenous amino acids can effectively alleviate the effects of drought stress on plants and reduce the harm.

A study was carried out in a Waste Sorting Plant (WSP) located in France and treating dry recyclable household waste (DRHW) as well as dry recyclable commercial and industrial waste (DRCIW). The aim was to investigate the exposure to bioaerosol and dust. The aim of the study was to assess the extent to which the measurement of concentration, species composition and particle size distribution can contribute to a better assessment of the biological risks associated with exposure. Both stationary and personal bioaerosol and dust samples were collect in the (WSP). The results confirm that waste and waste sorting activities are sources of airborne fungi. Indeed, ambient concentrations ranged from 7.3×103 to 8.5×105 CFU/m3 for culturable moulds and up to up to 4 mg/m3 for dust. Personal exposure to inhalable dust was found up to 3 mg/m3 for dust and ranged from 8.6×103 to 1.5×106 CFU/m3 for fungi. Airborne fungal communities were found to be dominated by the Penicillium genera in both bioaerosols and settled dust samples, followed by the Aspergillus, Cladosporium, Wallemia, Mucor, and Rhizopus genera. Fungi were carried by particles of aerodynamic diameters mainly between around 2.0 and 10.0 µm. The findings dealing with size distribution and biodiversity of bioaerosols suggest that employees are exposed to complex bioaerosols during their work and helps to make a finer diagnosis of the risks involved, which is often difficult in the absence of any OEL for bioaerosols in general.

Native infective endocarditis is a global phenomenon, defined by infection of a native heart valve and involving the endocardial surface. The causes and epidemiology of the disease have evolved in recent decades, with a doubling of the average patient age. A higher incidence was observed in patients with implanted cardiac devices that can result in right-sided infection of the tricuspid valve. The microbiology of the disease has also changed. Previously, staphylococci, which are most often associated with health-care contact and invasive procedures, were the most common cause of the disease. Nevertheless, this has now been superseded by streptococci. While innovative diagnostic and therapeutic strategies have emerged, mortality rates have not improved over the past year and remain at 30%, which is higher than for many cancer diagnoses. The lack of randomized trials and logistical constraints impede clinical management, and long-standing controversies such as the use of antibiotic prophylaxis persist. This state of the art review addresses clinical practice, controversies, and strategies to combat this potentially devastating disease.

Leaf photosynthetic pigments play a crucial role in evaluating nutritional elements and physiological states. In facility agriculture, it is vital to obtain rapidly and accurately the pigment content and distribution of leaves to ensure precise water and fertilizer management. In our research, we utilized chlorophyll a (Chla), chlorophyll b (Chlb), chlorophyll (Chll), and carotenoid (Caro) as indicators to study the variations in leaf position of Lycopersicon esculentum Mill. Under 10 nitrogen concentration applications, a total of 2610 leaves (435 samples) were collected using visible-near infrared hyperspectral imaging (VNIR-HSI). In this study, a "coarse-fine" screening strategy was proposed by using competitive adaptive reweighted sampling (CARS) and iteratively retained informative variable (IRIV) algorithm to extract characteristic wavelengths. Finally, simultaneous and quantitative models were established using partial least squares regression (PLSR). The CARS-IRIV-PLSR was used to create models to achieve a better prediction effect. The coefficient determination (R2), root mean square error (RMSE), and ratio performance deviation (RPD) were predicted to be 0.8240, 1.43, 2.38 for Chla, 0.8391, 0.53, 2.49 for Chlb, 0.7899, 2.24, 2.18 for Chll, and 0.7577, 0.27, 2.03 for Caro, respectively. The combination of these models with the pseudo-color image allowed for a visual inversion of the content and distribution of pigment. These findings have important implications for guiding pigment distribution, nutrient diagnosis, and fertilization decisions in plant growth management.

Amid escalating urbanization, devising rational commercial space layouts is a critical challenge. Leveraging machine learning, this study uses a Back-propagation (BP) neural network to optimize commercial spaces in Weinan City's central urban area. The results indicate an increased number of commercial facilities with a trend of multi-centered agglomeration and outward expansion. Based on these findings, we propose a strategic framework for rational commercial space development emphasizing aggregation centers, development axes, and spatial guidelines. This strategy provides valuable insights for urban planners in small and medium-sized cities in the Yellow River Basin and metropolitan areas, ultimately showcasing the power of machine learning in enhancing urban planning.

As one of the critical state parameters of the battery management system, lithium battery state of charge (SOC) can provide an essential reference for battery safety management, charge/discharge control, and energy management of electric vehicles. To analyze the application of deep learning in electric vehicle power battery SOC estimation, this study reviewed the technical process, common public datasets, and the neural networks used, structural characteristics, advantages and disadvantages of lithium battery SOC estimation in deep learning method. First, the specific technical processes of the deep learning method for SOC estimation were analyzed, including data collection, data preprocessing, feature engineering, model training, and model evaluation. Secondly, the current commonly and publicly used lithium battery dataset was summarized. Then, the input variables, data sets, errors, and advantages and disadvantages of four types of deep learning methods, were concluded using the structure of neural network used for training as the classification criterion. Finally, the challenges and future development directions of lithium battery SOC estimation in deep learning method were explained.

We have proposed a data-driven approach for designing mesoscale porous structures of Li-ion battery electrode with three-dimensional virtual structures and machine learning techniques. Over 2,000 artificial 3D structures assuming positive electrode composed of random packed spheres as active material particles are generated, and charge/discharge resistance has been evaluated using simplified Physico-chemical model. In this model, resistance from Li diffusion in active material particles (diffusion resistance), transfer resistance of Li+ in electrolyte (electrolyte resistance) and reaction resistance on the interface between active material and electrolyte are simulated based on mass balance of Li, Ohm’s law in and linearized Butler-Volmer equation, respectively. Using these simulation results, regression models via Artificial Neural Network (ANN) have been created in order to predict charge/discharge resistance from porous structure features. In this study, porosity, active material particle size and volume fraction, pressure in the compaction process, electrolyte conductivity, and binder volume fraction are adopted as features, associated with controllable process parameters for manufacturing battery electrode. As results, the predicted electrode resistance by ANN regression model is good agreement with the simulated values. Furthermore, sensitivity analysis and optimization of the process parameters have been carried out. The proposed data-driven approach could be a solution as a guiding principle for manufacturing battery electrode.

In this paper the application of recycled Li-ion batteries spent cathode (LIB-SC) combined with NaHCO₃/H₂O₂ system is presented for the first time in the literature as an alternative for degradation of potentially toxic organic molecules. The model pollutant choice was methylene blue molecule. The spent cathode composition corresponds to LiCoO₂, which was proved by the XRD and EDX. Regarding the decolorization of methylene blue solution, the addition of NaHCO₃ in comparison with only H₂O₂ reduces the complete decolorization time in 96%. This reduction occurs because the radical CO₃ is more stable than OH. In this way, the application the system proposed in this article is aimed at solving two major global problems: the disposal of cell phone batteries and the pollution of liquid effluents.

Measuring sound pressure is a complex and tedious task in industrial environments. Classical methods can be costly, inaccurate, and potentially dangerous for workers. Integrating robotics and computer vision into acoustic measurement processes offers an innovative solution to these problems. This paper uses a Doosan Robotic Vision System to investigate an innovative automatic acoustic sound pressure measurement solution. The Doosan robot, equipped with a scanning camera and microphone, captures acoustic data and produces a map of the machine’s acoustic radiation using computer vision algorithms. The measurement technique is accurate, efficient, and safe for workers. It can also be used for continuous monitoring of the industrial acoustic environment. The developed measurement method offers a promising way to automate acoustic measurement, which can be performed faster and with better accuracy to help industries improve their product sound quality and reduce noise levels. The method was compared to analytical predictions. A 3D acoustic mapping was conducted to visualize the spatial distribution of sound sources within the environment. The experimental results show a good correlation with the theory.

This is an exercise to explore the concentration of Lithium, Lithium-7 isotope and the possible presence of black dirty ice in the lunar surface using spectral data obtained from the Clementine mission. The main interest in tracing the lithium and presence of dark ice in the lunar surface are closely related to future human settlement missions in the Moon. We investigate the distribution of Lithium and 7 Li isotope on the Lunar surface, employing spectral data from the Clementine images. We utilized Visible (VIS-NIR) imagery at wavelengths of 450, 750, 900, 950, and 1000 nm, along with Near-Infrared (NIR-SWIR) at 1100, 1250, 1500, 2000, 2600, and 2780 nm, encompassing 11 bands in total. This dataset offers a comprehensive coverage of about 80% of the lunar surface, with resolutions ranging from 100 to 500 meters, spanning latitudes from 80°S to 80°N. In order to extract quantitative abundance of Lithium, ground truth sites were used to calibrate the Clementine images. Samples (12045, 15058, 15475, 15555, 62255, 70035, 74220 and 75075) returned from Apollo missions 12, 15, 16 and 17 have been correlated to the Clementine VIS-NIR bands and five spectral ratios. The 5 spectral ratios calculated synthesize the main spectral features of sample spectra that were grouped by their Lithium and 7 Li content using Principal Component Analysis. The ratios spectrally characterize substrates of anorthosite, silica-rich basalts, olivine-rich basalts and High-T mare basalts and Orange soils. Our findings reveal a strong linear correlation between the spectral parameters and the lithium content in the 8 Apollo samples. With the values of the 11 Clementine bands and the 5 spectral ratios for each of the 8 lunar soils we performed linear regression models to estimate the concentration of Lithium and 7 Li. Also, we calculated Digital Terrain Models (DTMs; Altitude, Slope, Aspect, DirectInsolation and WindExposition) from LOLA-DTM to discover relations between relief and spatial distribution of the extended models of lithium and 7 Li. The analysis was conducted in a mask polygon extracted from Lunar Orbiter Laser Altimeter (LOLA) and located around the Apollo 15 landing site. This analysis seeks to uncover potential 7 Li enrichment through spallation processes, influenced by varying exposure to solar wind. To explore the possibility of find ice mixed with regolithe (black ice) we used a spectral library (350-2500nm) form ices dirty with different concentration of volcanic particles.

In tobacco plants, symbiont endophytic fungi are widely distributed in all tissues where they play important roles. It is therefore important to determine the species distribution and characteristics of endophytic fungi in tobacco. Here, two parasitic fungi Leptosphaerulina chartarum and Curvularia trifolii were isolated and identified from normal tobacco tissue. We sequenced the mitogenomes of these two species and analysed their features, gene content, and evolutionary histories. The L. chartarum and C. trifolii mitochondrial genomes were 68,926 bp and 59,100 bp long circular molecules with average GC contents of 28.60% and 29.31%, respectively. The L. chartarum mitogenome contained 36 protein coding genes, 26 tRNA genes, and 2 rRNA genes (rrnL and rrnS), which were located on both strands. The C. trifolii mitogenome contained 26 protein coding genes, 29 tRNA genes, and 2 rRNA genes (rrnL and rrnS). The L. chartarum 26 tRNAs ranged from 70 bp to 84 bp in length, whereas the 29 tRNAs in C. trifolii ranged from 71 bp to 85 bp. L. chartarum and C. trifolii mtDNAs had an identical mitochondrial gene order and orientation and were phylogenetically identified as sisters. These data therefore provide an understanding of the gene content and evolutionary history of species within Pleosporales.

Employing remote sensing techniques to evaluate crop residue cover (CRC) enables rapid mapping of tillage practices over large cropland areas, which has significant practical implications for monitoring the implementation of conservation tillage practices. This research utilized Sentinel-2 MSI and Landsat-8 OLI remotely sensed data, various spectral indices were calculated and the datasets were correlated with the field-measured CRC data. Selected spectral indices, highly significantly correlated, were chosen to build a correlation model between the indices and the CRC. Considering Sentinel-2 MSI and Landsat 8 OLI remotely sensed data variations in spectral and spatial scales, the accuracy of CRC models was evaluated using R2 and RMSE values. From the results, it's evident that all six spectral indices, including STI, NDTI, SRNDR, NDI5, NDI7 and NDSVI, registered correlation coefficients with the CRC exceeded 0.3 in the study area. STI and NDTI showed a stronger correlation to CRC, with coefficients of 0.810 and 0.803, respectively. For the Landsat 8 data, the lowest correlation obtained was for NDSVI with the Landsat 8 data with a correlation coefficient of 0.358. Applying the linear regression technique, STI and NDTI obtained higher accuracy compared to other indices in the Sentinel-2 MSI data. For NDTI and STI, The R2 values were 0.650 and 0.665, respectively, while their corresponding RMSE values were 3.96% and 4.03%. The highest model accuracy was obtained for the STI and NDTI constructed from the Landsat 8 OLI data (R2 values 0.41 and 0.40; RMSE values 5.25% and 5.27%, respectively). The CRC model built using partial least squares regression (PLSR) exhibited greater precision when applied to the Sentinel-2 MSI data (R2 value 0.894; RMSE value 2.18%) in comparison to the Landsat 8 OLI data (R2 value 0.695; RMSE value 3.70%). PLSR has the potential to enhance the precision of the model. In addition, the study area shows that the estimation of the CRC is better in the one-dimensional linear regression model and the PLSR model created with the Sentinel-2 MSI optical data compared to the Landsat 8 OLI optical remotely sensed data. The validation suggests that the CRC estimation in the study area is more precise when using the model relying on Sentinel-2 satellite imagery rather than the Landsat 8 OLI imagery.

In the construction of shield crossing existing mined tunnel without load, it is imperative to develop corresponding design standards that reflect actual engineering force characteristics to ensure the successful completion of tunnel construction. This research uses The MIDAS-GTS finite element software to facilitate the creation of a numerical model of the shield structure for an air-push-over mine tunnel project in Changsha, China, investigating the stress field’s evolution during shield construction, and calculating the maximum positive and negative bending moments and maximum axial forces for different structures and other force states under various construction conditions. This study's findings informed the design and construction optimisation of the shield tunnelling empty-push method. The outcomes of this numerical simulation led to several key findings: (1) The soil density exerted a significantly greater impact on the internal forces of the initial support structure than both the tunnel depth and soil Poisson's ratio. Additionally, a sudden shift in internal forces occurred within the 300-350 mm range when the lining thickness was altered. (2) Factors such as the tunnel depth, soil density, soil Poisson's ratio μ, and lining thickness have a similarly influenced internal forces of the segment and the initial support. Notably, the backfill layer thickness significantly affected the segment’s maximum axial force causing an abrupt change of approximately 300 mm. (3) Under the shield machine equipment’s weight constraint, it is essential to control the guide rail’s thickness, to prevent it from becoming overly large.

The robust fusion estimation problem of a multi-sensor system with communication constraints, random packet drops, d-step state delays, and deterministic control inputs is investigated. The state augmentation method is used to transform the time delay system into a non-time delay system, and then a robust fusion estimation algorithm based on sensitivity penalty is proposed and the pseudo-cross-covariance matrix is updated. An event-triggered robust fusion estimator with state delay and deterministic control inputs is considered to make the model closer to reality while effectively reducing network congestion and lowering communication costs. Conditions are given to ensure that the estimation errors of the robust fusion estimator are uniform boundedness. Two sets of numerical simulations are provided to illustrate the effectiveness of the derived fusion estimator.

Background: The liver is involved in disseminated tuberculosis in more than 80% of the cases while primary liver involvement is rare, representing < 1% of all cases. Hepatic tuberculosis (TB) can be treated by conventional anti-TB therapy, however, diagnosing this disease still remains a challenge. The diagnosis might be particularly difficult in patients with a single liver lesion that could be misdiagnosed as a tumor or other focal liver lesions. While computed tomography and magnetic resonance imaging findings have been described, there is a paucity of literature on contrast-enhanced ultrasound (CEUS) features of hepatic TB. Case Summary: herein, we describe a case of a patient with tuberculous lymphadenopathy and chronic HCV-related liver disease who developed a single macronodular hepatic TB lesion. Due to the finding of a hepatocellular carcinoma (HCC) highly suggestive CEUS pattern, specifically a LR5 category according to the Liver Imaging Reporting and Data System (LI-RADS), and a good response to antitubercular therapy, a non-invasive diagnosis of HCC was made, and the patient underwent liver resection. We also review the published literature on imaging features of hepatic TB and discuss the diagnostic challenge represented by hepatic TB when occurs as a single focal liver lesion. Conclusions: this report shows for the first time that CEUS pattern of hepatic TB might be misinterpreted as HCC and specific imaging features are lacking. Personal history and epidemiological data are mandatory in interpreting CEUS findings of a focal liver lesion even when the imaging pattern is highly suggestive of HCC.

Receptor-interacting protein kinase 1 (RIPK1) plays a crucial role in controlling inflammation and cell death. Its function is tightly controlled through post-translational modifications, enabling its dynamic switch between promoting cell survival and triggering cell death. Phosphorylation of RIPK1 at various sites serves as a critical mechanism for regulating its activity, exerting either activating or inhibitory effects. Perturbations in RIPK1 phosphorylation status have profound implications for the development of severe inflammatory diseases in humans. This review explores the intricate regulation of RIPK1 phosphorylation and dephosphorylation and highlights the potential of targeting RIPK1 phosphorylation as a promising therapeutic strategy for mitigating human diseases.

Recently, attention mechanisms have developed into an important tool for performance improvement of deep neural networks. In computer vision, attention mechanisms are generally divided into two main branches: spatial and channel attention. Both attention categories have their own advantages. The fusion of both attentions achieves higher performance, on the cost of the computational load. This paper introduces an innovative and lighter n-shifted sigmoid channel and spatial attention (CSA) module to reduce the computational cost and to improve the 3D scene relevant features selection. To validate the proposed attention module, 3D scene object detection in the deep Hough voting point sets is considered as the testing application. The proposed attention module with its piecewise n-shifted sigmoid activation function improves the network’s learning and generalization capacity which effectively predict bounding box parameters directly from 3D scenes and detect objects more accurately. This advantage is achieved by selectively attending to more relevant features of the input data. When used in the deep Hough voting point sets, the proposed attention module outperforms state-of-the-art 3D detection methods on the sizable SUNRGBD dataset. Experiments conducted showed an increase of 12.02 mean accuracy precision (mAP) when compared to the celebrated VoteNet (without attention). It also got 9.92 mAP higher compared to the MLVCNet, and 10.32 mAP higher than the Point Transformer. The proposed model not only decreases the sigmoid vanishing gradient problem but also brings out valuable features by fusing channel-wise and spatial information while improving accuracy results in 3D object detection.