Search Results (22,427)

Supply chains are increasingly exposed to sudden disruptive events (SDEs) such as natural disasters and trade wars. We develop a multi-stage game-theoretical model to investigate a novel coping mechanism: when a firm is forced to exit the market because of SDEs, the firm can regain profits by licensing its proprietary production tech to a competitor. We find that, compared with the scenario before SDEs, such events can even increase the profit of each manufacturer under certain conditions. Under certain conditions, the cooperative strategy (i.e., supply chain reshaping) yields a higher supply chain system profit than the non-cooperative strategy. After SDEs, the common manufacturer may either accept or reject cooperation, depending on the customer transfer rate and the cooperation cost. Notably, under the cooperation strategy, the high-tech manufacturer extracts part of the common manufacturer’s profit through patent licensing, and the existence of cooperation cost further contributes to a misalignment between the common manufacturer’s optimal decision and the supply chain system optimum. These findings contribute to the literature by identifying a novel supply chain reshaping mechanism driven by patent licensing and offer strategic guidance for firms and policymakers navigating SDE-induced market exits. Full article

Atomic magnetometers (AMs), recognized for their ultra-high magnetic sensitivity, demand highly uniform pump light fields to maximize measurement accuracy. In this paper, a phase modulation-based method using convolutional neural networks (CNN) and the Gerchberg–Saxton (GS) algorithm is proposed to generate the pumping light field, and the model was trained using a supervised learning approach with a custom dataset. The specific training settings are as follows: the backpropagation algorithm was adopted as the training algorithm, and the Adam optimization method was used for network training, with a learning rate of 0.001 and a total of 100 training epochs, utilizing a liquid crystal spatial light modulator (LCSLM) to regulate the light field phase distribution dynamically. By transforming Gaussian beams into flat-top beams, the method significantly enhances polarization uniformity within vapor cells, leading to improved magnetometric sensitivity. The proposed hybrid algorithm reduces the mean square error from 35% to 19% and peak non-uniformity from 21% to 7.6%. A reflective LCSLM-based optical setup is implemented to produce circular and square flat-top beams with a measured non-uniformity of 5.1%, resulting in an enhancement of magnetic sensitivity from $14.04\mathrm{fT}/{\mathrm{Hz}}^{1/2}$ to $7.80\mathrm{fT}/{\mathrm{Hz}}^{1/2}$. Full article

A series of 6:2 fluorotelomer ethoxylates (FTEOs) has been recently detected in human serum. Whether it has the potential to disrupt lipid metabolism in human populations remains largely unexplored. This study quantified serum concentrations of 6:2 FTEOs in 237 healthy Chinese adults, examined the gender- and age-specific differences in serum levels of 6:2 FTEOs, and investigated the associations between serum levels of 6:2 FTEOs and lipid profiles for the first time. Nine 6:2 FTEO homologues were detected in collected human serum, with detection frequencies of 22–81%. 6:2 FTEO8 and 6:2 FTEO9 were the more abundant 6:2 FTEO homologues in human serum, displaying the mean levels of 0.69 ng/mL (range < LOD–7.36 ng/mL) and 0.71 ng/mL (<LOD–8.12 ng/mL), respectively. Male participants had much higher (p < 0.05) mean serum levels of 6:2 FTEO6 (0.61 vs. 0.31 ng/mL), 6:2 FTEO7 (0.44 vs. 0.21 ng/mL), 6:2 FTEO8 (0.91 vs. 0.38 ng/mL), and 6:2 FTEO11 (0.35 vs. 0.18 ng/mL) than female subjects. Correlation analysis revealed a significantly positive relationship (p < 0.01) between the age of participants and human serum concentrations of 6:2 FTEO6–6:2 FTEO11. Multivariate linear regression identified significant positive associations between specific 6:2 FTEO homologues (e.g., 6:2 FTEO6, 6:2 FTEO8–6:2 FTEO10) and elevated total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and triglyceride levels. Full article

To investigate the responses and mechanisms of slash pine under low orthophosphate (Pi) stress and to identify Pi-efficient lines, we analyzed 12 indices related to biomass, root traits, and tissue Pi concentration across 13 slash pine lines subjected to varying Pi treatments. The composite assessment value of low-phosphorus tolerance (D) was calculated by evaluating these 12 response indicators through principal component analysis, in conjunction with the fuzzy membership function method. Nine low-phosphorus tolerance factors (LPTFs)—including above-ground fresh weight (0.69), below-ground fresh weight (0.52), total root length (0.56), root surface area (0.63), root volume (0.67), above-ground Pi concentration (0.78), below-ground Pi concentration (0.52), bioconcentration factor (0.77), and P utilization efficiency (−0.76)—showed significant correlations with D (p < 0.05). Utilizing these nine LPTFs, cluster analysis classified the 13 lines into the following three groups according to their low-phosphorus (P) tolerance: high-P-efficient, medium-P-efficient, and low-P-efficient lines. Under low Pi and Pi-deficiency treatments, line 27 was identified as a high-P-efficient line, while lines 1, 6, and 9 were classified as low-P-efficient lines. Notably, eight genes (SPX1, SPX3, SPX4, PHT1;1, PAP23, SQD1, SQD2, NPC4) and five genes (SPX1, SPX3, SPX4, PAP23, SQD1) were significantly up-regulated in the roots and leaves of both line 27 and line 9 under low-phosphorus stress, respectively. However, the high-P-efficient line 27 exhibited a stronger regulatory capacity with a higher expression of two genes (SPX4, SQD2) in the roots and nine genes (SPX1, SPX3, SPX4, PHT1;1, PAP10, PAP23, SQD1, SQD2, NPC4) in the leaves under low Pi stress. These findings reveal differential responses to low Pi stress among slash pine lines, with line 27 displaying superior low-P tolerance, enabling better adaptation to low Pi environments and the maintenance of normal growth, development, and physiological activities. Full article

Metro foundation pits are important components of urban infrastructure projects. Dewatering and excavation are essential stages in foundation pit construction; however, this process can significantly induce groundwater drawdown, as well as diaphragm wall deformation and surface settlement. Based on a subway station foundation pit project, in this study, we employ three-dimensional numerical software to simulate the process of dewatering and excavation. A refined model is used to investigate groundwater seepage, the deformation of the retaining structure, and surface settlement under spatial effects. The finite element model accounts for stratified excavation and applied prestress conditions for the support system within the foundation pit. Its accuracy is validated through a comparison and analysis with measured data from the actual foundation pit. The results indicate that foundation pit excavation leads to significant groundwater drawdown around the pit and the formation of a characteristic “funnel-shaped” drawdown curve. Moreover, extending the diaphragm wall length contributes to maintaining a higher external groundwater level surrounding the foundation pit. The horizontal displacement of the diaphragm wall increases progressively during dewatering and excavation, and the bending moment of the diaphragm wall exhibits a trend consistent with its horizontal displacement. Surface settlement decreases as the length of the diaphragm wall increases. Full article

To enhance regional flood control capacity, this study focused on the DX River section in Zhejiang Province. Unmanned vessel bathymetry was employed to obtain precise river cross-section data. A hydrodynamic model was established to simulate flood propagation processes and conduct flood routing analyses. Flood scenarios under 5-year, 10-year, and 20-year return periods were simulated to assess water level variations and overflow risks. The results indicate that under a 5-year flood, 19.5% of the right bank fails to meet flood control standards. This risk intensifies significantly with increasing return periods. Building on these findings, a flood optimal operation model was developed. The resulting coordinated strategy, which lowers the peak water level by 1.2 m during a 20-year flood, is sufficient to prevent overflow at the critical section and enhances regional flood control capacity. This is followed by dynamic gate regulation to match the outflow to the inflow. Dynamic regulation of spillway gates should then be implemented to achieve outflow rates commensurate with the incoming flood magnitude. This study demonstrates a robust workflow from high-resolution data acquisition to actionable operational rules, providing a transferable framework for mitigating flood risks in complex, regulated river systems. Full article

Accurate prediction of chlorophyll content in Brassica napus L. (rapeseed) is essential for monitoring plant nutritional status and precision agricultural management. The current study focuses on single cultivars, limiting general applicability. This study used unmanned aerial vehicle (UAV)-based RGB and multispectral imagery to evaluate six rapeseed cultivars chlorophyll content across mixed-growth stages, including seedling, bolting, and initial flowering stages. The ExG-ExR threshold segmentation was applied to remove background interference. Subsequently, color and spectral indices were extracted from segmented images and ranked according to their correlations with measured chlorophyll content. Partial Least Squares Regression (PLSR), Multiple Linear Regression (MLR), and Support Vector Regression (SVR) models were independently established using subsets of the top-ranked features. Model performance was assessed by comparing prediction accuracy (R² and RMSE). Results demonstrated significant accuracy improvements following background removal, especially for the SVR model. Compared to data without background removal, accuracy increased notably with background removal by 8.0% (R²p improved from 0.683 to 0.763) for color indices and 3.1% (R²p from 0.835 to 0.866) for spectral indices. Additionally, stepwise fusion of spectral and color indices further improved prediction accuracy. Optimal results were obtained by fusing the top seven color features ranked by correlation with chlorophyll content, achieving an R²p of 0.878 and an RMSE of 52.187 μg/g. These findings highlight the effectiveness of background removal and feature fusion in enhancing chlorophyll prediction accuracy. Full article

Gelatin methacrylate (GelMA) microgels serve as promising bioscaffolds for tissue engineering and drug screening. However, conventional solid GelMA microgels often exhibit limited mass transfer efficiency and provide insufficient protection for embedded cells. In this study, we developed a droplet-based microfluidic platform to fabricate core–shell structured GelMA microgels. This system enabled precise control over microgel size and core-to-shell ratio by modulating flow rates. Encapsulation of A549 cells within these core–shell microgels preserved cellular viability and facilitated the formation of three-dimensional tumor spheroids. These outcomes confirmed both the protective function of the core–shell architecture during encapsulation and the overall biocompatibility of the microgels. The developed GelMA core–shell microgel system presents considerable applicability in research domains such as organoid modeling and high-throughput pharmacological screening. Full article

Accurate State of Charge (SOC) estimation for retired power batteries remains a critical challenge due to their degraded electrochemical properties and heterogeneous aging mechanisms. Traditional methods relying solely on electrical parameters (e.g., voltage and current) exhibit significant errors, as aged batteries experience altered internal resistance, capacity fade, and uneven heat generation, which distort the relationship between electrical signals and actual SOC. To address these limitations, this study proposes a surface temperature-assisted SOC estimation method, leveraging the distinct thermal characteristics of retired batteries. By employing infrared thermal imaging, key temperature feature regions—the positive/negative tabs and central area—are identified, which exhibit strong correlations with SOC dynamics under varying operational conditions. A Gated Recurrent Unit (GRU) neural network is developed to integrate multi-region temperature data with electrical parameters, capturing spatial–temporal thermal–electrical interactions unique to retired batteries. The model is trained and validated using experimental data collected under constant current discharge conditions, demonstrating superior accuracy compared to conventional methods. Specifically, our method achieves 64.3–68.1% lower RMSE than traditional electrical-parameter-only approaches (V-I inputs) across 0.5 C–2 C discharge rates. Results show that the proposed method reduces SOC estimation errors compared to traditional voltage-based models, achieving RMSE values below 1.04 across all tested rates. This improvement stems from the model’s ability to decode localized heating patterns and their hysteresis effects, which are particularly pronounced in aged batteries. The method’s robustness under high-rate operations highlights its potential for enhancing the reliability of retired battery management systems in secondary applications such as energy storage. Full article

Atmospheric aerosols significantly impact solar photovoltaic (PV) energy generation through their effects on surface solar radiation. This study quantifies the impact of PM_2.5 pollution on PV power output using observational data from 10 stations across Hebei Province, China (2018–2019). Our analysis reveals that elevated PM_2.5 concentrations substantially attenuate solar irradiance, resulting in PV power losses reaching up to a 48.2% reduction in PV power output during severe pollution episodes. To capture these complex aerosol–radiation–PV interactions, we developed and compared the following six machine learning models: Support Vector Regression, Random Forest, Decision Tree, K-Nearest Neighbors, AdaBoost, and Backpropagation Neural Network. The inclusion of PM_2.5 as a predictor variable systematically enhanced model performance across all algorithms. To further optimize prediction accuracy, we implemented a stacking ensemble framework that integrates multiple base learners through meta-learning. The optimal stacking configuration achieved superior performance (MAE = 0.479 MW, indicating an average prediction error of 479 kilowatts; R² = 0.967, reflecting that 96.7% of the variance in power output is explained by the model), demonstrating robust predictive capability under diverse atmospheric conditions. These findings underscore the importance of aerosol–radiation interactions in PV forecasting and provide crucial insights for grid management in pollution-affected regions. Full article

Ginger powder (GP) has antioxidant properties and can be a suitable alternative to antibiotics in laying hen diets; however, research on its effects remains limited. Therefore, our study aimed to evaluate the impact of dietary GP supplementation on production performance during the pre-peak production stage. A total of 270 hens, 18 weeks old and averaging 1.83 ± 0.03 kg, were divided into three groups: control (CN, basal diet), CN + 5 g/kg GP (T1), and CN + 10 g/kg GP (T2), with six replicates of 15 hens each, in a 10-week feeding trial. Dietary GP had no significant effect on feed intake (p > 0.05), but it dose-dependently improved laying rate, egg mass, and feed conversion ratio (p < 0.05). Egg quality parameters, including albumen height, Haugh unit, eggshell thickness, and strength, were also linearly improved with GP supplementation (p < 0.05). Dietary GP linearly enhanced the antioxidant status of hens (p < 0.01) and reduced malondialdehyde activity (p < 0.0001). Furthermore, 10 g/kg GP supplementation slightly improved gizzard index and liver morphology, and it linearly enhanced intestinal morphology (p < 0.01). These findings suggest that 10 g/kg GP supplementation can improve the productivity and health of laying hens. Full article

With the rapid adoption of automated driving systems, ensuring safe and efficient driver takeover has become a crucial challenge for road safety. This study introduces a novel psychological framework for understanding and predicting takeover behavior in conditionally automated vehicles, leveraging an extended Theory of Planned Behavior (TPB) model enriched by real-world driver experience. Drawing on survey data from 385 automated driving system users recruited in Shaoguan City, China, through face-to-face questionnaire administration covering various ADS types (ACC, lane-keeping, automatic parking), we demonstrate that driver attitudes, perceived behavioral control, and subjective norms are significant determinants of takeover intention, collectively explaining nearly half of its variance (R² = 48.7%). Importantly, our analysis uncovers that both intention and perceived behavioral control have robust, direct effects on actual takeover behavior. Crucially, this work is among the first to reveal that individual user characteristics—such as driving experience and ADS (automated driving system) usage frequency—substantially moderate these psychological pathways: experienced or frequent users rely more on perceived control and attitude, while less experienced drivers are more susceptible to social influences. By advancing a multi-dimensional psychological model that integrates personal, social, and experiential moderators, our findings deliver actionable insights for the design of adaptive human–machine interfaces, tailored driver training, and targeted safety interventions in the context of automated driving. Using structural equation modeling with maximum likelihood estimation (χ²/df = 2.25, CFI = 0.941, RMSEA = 0.057), this psychological approach complements traditional engineering models by revealing that takeover behavior variance is explained at 58.3%. Full article

Visual–inertial odometry (VIO) is the primary supporting technology for autonomous systems, but it faces three major challenges: initialization sensitivity, dynamic illumination, and multi-sensor fusion. In order to overcome these challenges, this paper proposes stereo direct sparse visual–inertial odometry with efficient second-order minimization. It is entirely implemented using the direct method, which includes a depth initialization module based on visual–inertial alignment, a stereo image tracking module, and a marginalization module. Inertial measurement unit (IMU) data is first aligned with a stereo image to initialize the system effectively. Then, based on the efficient second-order minimization (ESM) algorithm, the photometric error and the inertial error are minimized to jointly optimize camera poses and sparse scene geometry. IMU information is accumulated between several frames using measurement preintegration and is inserted into the optimization as an additional constraint between keyframes. A marginalization module is added to reduce the computation complexity of the optimization and maintain the information about the previous states. The proposed system is evaluated on the KITTI visual odometry benchmark and the EuRoC dataset. The experimental results demonstrate that the proposed system achieves state-of-the-art performance in terms of accuracy and robustness. Full article

Fibrillins (FBNs) are indispensable for plant growth and development, orchestrating multiple physiological processes. However, the precise functional role of FBNs in cotton fiber development remains uncharacterized. This study reports a genome-wide characterization of the FBN gene family in cotton. A total of 28 GhFBN genes were identified in upland cotton, with systematic analyses of their phylogenetic relationships, protein motifs, gene structures, and hormone-responsive cis-regulatory elements. Expression profiling of GhFBN1A during fiber development revealed stage-specific activity across the developmental continuum. Transcriptomic analyses following hormone treatments demonstrated upregulation of GhFBN family members, implicating their involvement in hormone-mediated regulatory networks governing fiber cell development. Collectively, this work presents a detailed molecular characterization of cotton GhFBNs and establishes a theoretical foundation for exploring their potential applications in cotton breeding programs aimed at improving fiber quality. Full article

Surface functionalization is key for tuning the electronic and magnetic properties essential in spintronics, yet its impact on chromium-based MXenes (Cr₃C₂T₂) is not fully understood. Using spin-polarized DFT+U, this study investigates how O, F, and OH groups modify the magnetic state, electronic structure, and Curie temperature. Functionalization dramatically changes magnetism: O termination gives ferromagnetism, while F and OH yield ferrimagnetism. Our results show surface functionalization effectively adjusts the Curie temperature, critical for spintronic materials. The electronic character is highly functional group dependent: pristine Cr₃C₂ is half-metallic, Cr₃C₂O₂ metallic, and Cr₃C₂F₂/Cr₃C₂(OH)₂ semiconducting with narrow gaps. Structures with dynamic stability are analyzed through phonon spectroscopy. These findings provide fundamental insights into controlling MXene properties via surface functionalization, guiding the design of next-generation spintronic materials. Full article