Search Results (27,655)

With the intensification of global climate change, frequent extreme temperature events pose increasing challenges to agricultural production. The aim of this study is to characterize the spatiotemporal evolution of county-level agricultural total factor productivity (ATFP) under extreme temperature events, reveal key driving factors and crop-specific heterogeneity, and predict potential high-risk areas, which is crucial for providing scientific basis for risk management and adaptive policy formulation in globally climate-sensitive agricultural regions. This paper selects Jiangsu Province as a typical case study, uses the DEA-Malmquist model to measure agricultural total factor productivity (ATFP), systematically analyzes the spatiotemporal dynamic evolution characteristics of ATFP at the county scale, and selects the random forest and XGBoost ensemble models with optimal accuracy through model comparison for prediction, assessing the evolution trends of ATFP under different climate scenarios. The results showed that: (1) From 1993 to 2022, the average ATFP increased from 0.7460 to 1.1063 in the province, though development showed uneven distribution across counties, exhibiting a “high in the south, low in the north” gradient pattern. (2) Mechanization, agricultural film and land inputs are the core elements driving the overall ATFP increase but there are obvious crop differences: mechanization has a more prominent role in promoting the productivity of wheat and maize, while labor inputs have a greater impact on the ATFP of rice. (3) The negative impacts of extreme climate events on agricultural production will be significantly amplified under high-emission scenarios, while moderate climate change may have a promotional effect on certain crops in some regions. Full article

Based on the collected mobile phone location data, this paper analyzes changes in four mobile location-based indicators and their spatiotemporal distribution characteristics before and after the earthquake, summarizing crowd movement patterns and communication behaviors after the M_S6.8 Dingri earthquake. By comparing natural neighbor interpolation and Thiessen polygon interpolation methods, we explore novel rapid assessment approaches for earthquake perception ranges, combined with actual seismic intensity maps. The results indicate an uneven distribution of population and differing dynamics in mobile phone signal activity. This reflects different behavioral patterns and the potential perceived extent of the earthquake. Within 50 km of the epicenter, all four indicators showed varying degrees of decline post-earthquake, while areas beyond 100 km exhibited short-term surges, reflecting differentiated behavioral responses based on seismic impact severity. In areas experiencing strong shaking, risk avoidance behavior predominated, while in areas where shaking was noticeable but less severe, communication behavior was more prominent. Mobile data decline zones showed high spatial correlation with intensity VIII+ regions, proving their effectiveness as rapid indicators for identifying strongly affected areas. Notably, mobile location data enabled accurate identification of strongly affected zones within 30 min post-earthquake. Full article

Predicting internal quality parameters, such as Brix and water content, of apples, is essential for quality control. Existing near-infrared (NIR) and hyperspectral imaging (HSI)-based techniques have limited applicability due to their dependence on equipment and environmental sensitivity. In this study, a transportable quality assessment system was proposed using spatiotemporal domain analysis with long-wave infrared (LWIR)-based thermal diffusion phenomics, enabling non-destructive prediction of the internal Brix of apples during transport. After cooling, the thermal gradient of the apple surface during the cooling-to-equilibrium interval was extracted. This gradient was used as an input variable for multiple linear regression, Ridge, and Lasso models, and the prediction performance was assessed. Overall, 492 specimens of 5 cultivars of apple (Hongro, Arisoo, Sinano Gold, Stored Fuji, and Fuji) were included in the experiment. The thermal diffusion response of each specimen was imaged at a sampling frequency of 8.9 Hz using LWIR-based thermal imaging, and the temperature changes over time were compared. In cross-validation of the integrated model for all cultivars, the coefficient of determination (R²_cv) was 0.80, and the RMSEcv was 0.86 °Brix, demonstrating stable prediction accuracy within ±1 °Brix. In terms of cultivar, Arisoo (Cultivar 2) and Fuji (Cultivar 5) showed high prediction reliability (R²_cv = 0.74–0.77), while Hongro (Cultivar 1) and Stored Fuji (Cultivar 4) showed relatively weak correlations. This is thought to be due to differences in thermal diffusion characteristics between cultivars, depending on their tissue density and water content. The LWIR-based thermal diffusion analysis presented in this study is less sensitive to changes in reflectance and illuminance compared to conventional NIR and visible light spectrophotometry, as it enables real-time measurements during transport without requiring a separate light source. Surface heat distribution phenomics due to external heat sources serves as an index that proximally reflects changes in the internal Brix of apples. Later, this could be developed into a reliable commercial screening system to obtain extensive data accounting for diversity between cultivars and to elucidate the effects of interference using external environmental factors. Full article

Staphylococcus aureus has been recognized as an important foodborne pathogen and methicillin-resistant S. aureus (MRSA) can cause fatal infections worldwide. Of great concern is that MRSA have been found in animals and non-healthcare settings; however, knowledge about the prevalence and genetic characteristics of S. aureus, especially MRSA from animals, retail meats and market shared shopping vehicles in the same district, is limited. In this study, we collected 423 samples including handrail swabs (n = 226) of shopping trolleys and baskets from 18 supermarkets, retail meats (n = 137) and swine nasal swabs (n = 60) between 2018 and 2020 in China. S. aureus isolates were isolated and identified by PCR, and then the mecA was used to confirm the MRSA. The antibiotic resistance and virulence genes among S. aureus were also analyzed, followed by whole genome sequencing (WGS). S. aureus isolates were widely distributed in shared shopping vehicles (8.0%, 18/226), retail meats (14.6%, 20/137) and swine (18.3%, 11/60). In total, 49 S. aureus were obtained and 20 of the 49 isolates were MRSA. We firstly reported a high prevalence of MRSA in shared shopping vehicles (7.5%, 17/226), followed by raw meats (2.2%, 3/137), and 44.4% (8/18) of the 18 supermarkets possessed MRSA-positive shopping vehicles. All 20 MRSA isolates were SCCmec IVa MRSA clones. Enterotoxin genes (sea/seb) associated with S. aureus food poisoning were present in 45.0% of the 20 S. aureus isolates from retail meats and 25.0% of the 20 MRSA isolates carried enterotoxin genes. Retail meats in this study carried ST6-MRSA, a common ST type of S. aureus from food-poisoning outbreaks in China. WGS showed that the MRSA from meats harbored enterotoxin gene sea and immune evasion genes (sak and scn) associated with human infections, and were clustered with previously reported MRSA isolates from animals and humans. The MRSA isolates carrying multiple virulence genes from shopping vehicles were also clustered with previously reported MRSA isolates from humans and animals, suggesting that the exchange of MRSA isolates might occur among different niches. Our results highlighted the risk of retail meats and shared shopping vehicles in spreading antimicrobial-resistant pathogens including MRSA. To our knowledge, this is the first report of the wide spread of MRSA in shared shopping vehicles in China. Full article

Pulse current-assisted flexible granular medium forming is a promising approach for manufacturing complex thin-walled components from difficult-to-deform Ti₂AlNb-based alloys. In this study, the electro-thermo-mechanical deformation behavior of Ti₂AlNb sheets is investigated through pulse current-assisted uniaxial tensile tests, microstructural characterization, and finite element simulations. The influences of pulse current intensity and strain rate on flow behavior, fracture characteristics, and phase evolution are clarified, and an effective forming window is identified. Numerical simulations are employed to analyze the role of granular medium friction in material flow and wall thickness distribution, providing guidance for forming box-shaped components. The results demonstrate that forming at approximately 950 °C with a strain rate of 0.001 s⁻¹ reduces deformation resistance, while enhanced tangential interaction between the granular medium and the sheet improves wall thickness uniformity. This study provides a feasible processing route and practical guidelines for the fabrication of complex Ti₂AlNb sheet components. Full article

This study focuses on forecasting and optimizing the performance of a real-world object modelled by a multi-server queueing system that processes two types of users: primary (new) users and repeating users. The repeating users are those who succeeded in entering processing upon arrival and then decided to repeat it. These users have privilege and can enter processing when they wish once at least one device is idle. The primary user is admitted to the system only if the number of occupied devices is less than some threshold value and the quantity of repeating users residing in the system does not exceed certain thresholds. Repeating users are impatient and non-persistent. Arrivals of primary users are described by the Markovian arrival process. Processing times of primary and repeating users have distinct phase-type distributions. Utilizing the concept of the generalized phase–time distributions, the dynamics of this queueing system are formally characterized by the multidimensional Markov chain, which is examined in this paper. The ergodicity condition is derived. The relation of the key performance characteristics of the system and the thresholds defining the policy of the primary user’s admission is numerically highlighted. Optimal threshold selection is demonstrated numerically. Full article

Grinding is an essential process in mineral processing. Hydrogen-based mineral phase transformation, used to efficiently process refractory iron ores, can alter the physical and chemical properties of the ore, affecting its grinding characteristics. This paper uses iron ore from Baoshan, Shanxi Province, as the raw material for laboratory-scale hydrogen-based mineral phase transformation (HMPT) experiments and grinding tests. It examines the impact of four cooling methods on the ore’s grinding characteristics. The results show that samples cooled in a reducing atmosphere to 200 °C and then water-quenched exhibit the best relative grindability. For the same grinding time, the content of coarse-sized particles (+0.074 mm) in the product is lowest, while the fine-sized particles (−0.030 mm) is highest. The grinding kinetic parameters of the samples with this cooling method are the highest. After 2 min of grinding, the value of n is 1.3363, and the particle size distribution of the product is the most uniform. The BET and SEM test results indicate that samples with this cooling method have more internal pores, the largest pore size, and the most surface cracks and pores. This paper clarifies the effects of the HMPT cooling methods on grinding characteristics, providing a theoretical foundation for the efficient separation of iron ores. Full article

This paper establishes a comprehensive statistical framework for analyzing quantum teleportation protocols under realistic noisy conditions. We develop novel mathematical tools to characterize the complete statistical distribution of teleportation fidelity, including both mean and variance, for systems experiencing decoherence and channel imperfections. Our analysis demonstrates that the teleportation fidelity follows a characteristic distribution $F\sim \mathcal{P}(F_{\mathrm{avg}},\Delta F^2)$ where the variance $\Delta F^2$ depends crucially on entanglement quality and channel noise. We derive the optimal resource allocation condition $\partial E_{\mathrm{ent}}/\partial F/\partial C_{\mathrm{classical}}/\partial F=\beta /\alpha$ that minimizes total resource consumption while achieving target fidelity. Furthermore, we introduce a Bayesian adaptive protocol that enhances robustness against noise through real-time statistical estimation. The theoretical framework is validated through numerical simulations and provides practical guidance for experimental implementations in quantum communication networks. Full article

Surface charge accumulation and trap distribution are the core factors affecting the surface flashover characteristics of insulating materials. Considering the low-temperature gradient environment of superconducting energy pipeline terminations, this paper systematically studies the surface charge dynamic characteristics and trap distribution law of epoxy glass fiber (GFRP) by using the isothermal surface potential decay (ISPD) method combined with finite element simulation. A temperature-controlled ISPD test platform of −30~20 °C (193~293 K) was built to measure the surface potential decay curves at different temperatures and calculate the trap energy level and density; a charge migration model considering temperature gradient was established to analyze the influence of trapped charges on surface potential and electric field distribution. The results show that low temperature significantly reduces the surface potential decay rate (the residual potential after 5000 s is 92.91% of the initial value at 193 K, and only 3.51% at 293 K); the traps of GFRP at 193 K are dominated by deep traps (central energy level 0.68 eV, density 1.63 × 10²⁰ m⁻³·eV), while there is a bimodal distribution of shallow traps (0.92 eV) and deep traps (0.98 eV) at 293 K; under temperature gradient, the accumulation of deep trap charges in the low-temperature region leads to a surface electric field distortion rate of 12.60, which is the key microscopic mechanism for the decrease of flashover voltage. Full article

As Battery Management Systems (BMSs) continue to expand in both scale and capacity, conventional state-of-charge (SOC) estimation methods—such as Coulomb counting and model-based observers—face increasing challenges in meeting the requirements for cell-level precision, scalability, and adaptability under aging and operating variability. To address these limitations, this study integrates a Deep Neural Network (DNN)–based estimation framework into a node-level BMS architecture, enabling edge-side computation at each individual battery cell. The proposed architecture adopts a decentralized node-level structure with distributed parameter synchronization, in which each BMS node independently performs SOC estimation using shared model parameters. Global battery characteristics are learned through offline training and subsequently synchronized to all nodes, ensuring estimation consistency across large battery arrays while avoiding centralized online computation. This design enhances system scalability and deployment flexibility, particularly in high-voltage battery strings with isolated measurement requirements. The proposed DNN framework consists of two identical functional modules: an offline training module and a real-time estimation module. The training module operates on high-performance computing platforms—such as in-vehicle microcontrollers during idle periods or charging-station servers—using historical charge–discharge data to extract and update battery characteristic parameters. These parameters are then transferred to the real-time estimation chip for adaptive SOC inference. The decentralized BMS node chip integrates preprocessing circuits, a momentum-based optimizer, a first-derivative sigmoid unit, and a weight update module. The design is implemented using the TSMC 40 nm CMOS process and verified on a Xilinx Virtex-5 FPGA. Experimental results using real BMW i3 battery data demonstrate a Root Mean Square Error (RMSE) of 1.853%, with an estimation error range of [4.324%, −4.346%]. Full article

Rainfall infiltration is one of the main factors inducing slope instability, while the spatial heterogeneity and uncertainty of soil parameters have profound impacts on slope response characteristics and stability evolution. Traditional deterministic analysis methods struggle to reveal the dynamic risk evolution process of the system under heavy rainfall. Therefore, this paper proposes an uncertainty analysis framework combining Karhunen–Loève Expansion (KLE) random field theory, Polynomial Chaos Kriging (PCK) surrogate modeling, and Monte Carlo simulation to efficiently quantify the probabilistic characteristics and spatial risks of rainfall-induced slope instability. First, for key strength parameters such as cohesion and internal friction angle, a two-dimensional random field with spatial correlation is constructed to realistically depict the regional variability of soil mechanical properties. Second, a PCK surrogate model optimized by the LARS algorithm is developed to achieve high-precision replacement of finite element calculation results. Then, large-scale Monte Carlo simulations are conducted based on the surrogate model to obtain the probability distribution characteristics of slope safety factors and potential instability areas at different times. The research results show that the slope enters the most unstable stage during the middle of rainfall (36–54 h), with severe system response fluctuations and highly concentrated instability risks. Deterministic analysis generally overestimates slope safety and ignores extreme responses in tail samples. The proposed method can effectively identify the multi-source uncertainty effects of slope systems, providing theoretical support and technical pathways for risk early warning, zoning design, and protection optimization of slope engineering during rainfall periods. Full article

The demand for clean-label functional foods has increased interest in natural polysaccharides with health benefits. Acemannan, an O-acetylated glucomannan from Aloe vera, possesses antioxidant, immunomodulatory, and prebiotic activities, but its performance in fat-based systems is not well understood. This study examined the incorporation of acemannan into dark chocolate at 1% and 5% (w/w) and its effects on physicochemical, rheological, antioxidant, and sensory properties. Particle size distribution remained within acceptable limits, though the 5% sample showed a larger mean size and broader span. Rheological tests confirmed shear-thinning behavior, with the higher concentration increasing viscosity at low shear and reducing it at high shear. Antioxidant activity measured by the DPPH assay showed modest improvement in enriched samples. Consumer tests with 30 panelists indicated a strong preference (89%) for the 1% formulation, which maintained a smooth mouthfeel and balanced sensory characteristics, while the 5% sample displayed more fruity and earthy notes with lower acceptance. GC–MS analysis revealed altered volatile profiles, and FTIR spectroscopy confirmed acemannan stability in the chocolate matrix. These findings demonstrate that acemannan can be incorporated into dark chocolate up to 1% as a multifunctional, structurally stable polysaccharide ingredient without compromising product quality. Full article

This study proposes a novel four-pointed-star-shaped honeycomb structure having zero Poisson’s ratio, designed to overcome the stress concentration inherent in traditional point-to-point connected star-shaped honeycombs.By introducing a horizontal connecting wall at cell junctions, the new configuration achieves a more uniform stress distribution and enhanced structural stability. An analytical model for the in-plane equivalent elastic modulus was derived using homogenization theory and the energy method. The model, along with the structure’s zero Poisson’s ratio characteristic, was validated through finite element simulations and experimental compression tests. The simulations predicted an equivalent elastic modulus of 51.71 MPa (Y-direction) and 74.67 MPa (X-direction), which aligned closely with the experimental measurements of 56.61 MPa and 60.50 MPa, respectively. The experimental Poisson’s ratio was maintained near zero (v = 0.02). Parametric analysis further revealed that the in-plane equivalent elastic modulus decreases with increases in the wall angle, horizontal wall length, and wall thickness. This work demonstrates a successful structural optimization strategy that improves both mechanical performance and manufacturability for zero Poisson’s ratio honeycomb applications. Full article

This study analyzed the spatial distribution of large green algae (LGA) in the northern coastal waters of China, including the Yellow Sea and Bohai Sea, using Coastal Zone Imager (CZI) data from the HY-1C/D satellites. An inversion model (coastal zone imager model) of LGA was established, based on which the distribution details of large green algae in the Yellow Sea and Bohai Sea were investigated. The results indicated the following: (1) LGA exhibits a clearly seasonal pattern from May to August. Initially occurrences are detected in May in the southern Yellow Sea (32–34° N), followed by a rapid expansion and intensification from June to mid-July, with peak distribution around 35° N near the Shandong Peninsula. The affected area subsequently decreases in late August. (2) High LGA coverage is mainly concentrated along the Subei Shoal and the Shandong Peninsula in the Yellow Sea, as well as the coastal regions of Yantai, Qinhuangdao, and Yingkou in the Bohai Sea. (3) The LGA-M inversion model demonstrates stable performance in nearshore waters with similar optical characteristics and is applicable to LGA extraction in adjacent coastal seas, highlighting the potential of HY-1C/D satellite data in marine environmental monitoring and protection. Full article

Artificial Intelligence (AI) has increasingly contributed to advancements in pharmaceutical practice, particularly by enhancing the pharmacist–patient relationship and improving medication adherence. This quantitative, descriptive, cross-sectional study investigated Eastern Romanian pharmacists’ perception of AI-based applications as effective optimization tools, correlating it with disruptive communication factors. An anonymous and online questionnaire was distributed to community pharmacists, examining sociodemographic characteristics, awareness of disruptive factors, and the perceived usefulness of AI. The sample included 437 respondents: pharmacists (55.6%), mostly female (83.8%), and aged between 25 and 44 (52.6%). Data analysis involved descriptive statistics and independent t-tests. The statistical analysis revealed a significantly positive perception (p < 0.001) of AI on pharmacist–patient communication. Respondents viewed AI as a valuable tool for reducing medication errors and optimizing counseling time, though they maintain a strong emphasis on genuine human interaction. Significant correlations were found between disruptive factors—such as noise and high patient volume—and the quality of communication. Participants also expressed an increased interest in applications like automatic prescription scheduling and the use of chatbots. The study concludes that a balanced implementation of AI technologies is necessary, one that runs parallel with the continuous development of pharmacists’ communication skills. Future research should focus on validating AI’s impact on clinical outcomes and establishing clear ethical guidelines regarding the use of patient data. Full article