Search Results (377)

Polysaccharide-based organic-inorganic hybrid carriers combine renewable polymer matrices with inorganic phases that can modify mechanical integrity, swelling, barrier performance, payload retention, and release behavior. This review critically evaluates alginate as a reference matrix together with chitosan, cellulose/nanocellulose, starch/maltodextrin, pectin, carrageenan, and related polysaccharides, focusing on how matrix chemistry, inorganic-phase properties, interfacial interactions, and fabrication route govern encapsulation efficiency, loading, structural stability, swelling, mechanical and barrier properties, storage retention, and release kinetics. Silica and mesoporous silica, clays and halloysite, layered double hydroxides (LDHs), metal oxides, hydroxyapatite, magnetic particles, and metal-organic frameworks are compared according to their reservoir, reinforcing, diffusion-controlling, responsive, and safety-related functions. Representative quantitative findings illustrate the importance of hybrid architecture; for example, incorporation of LDHs into an alginate matrix reduced erythropoietin release after 108 h from 86% to 24% while increasing mechanical performance by approximately 5–30-fold. In this review, particular attention is given to volatile and bioactive compounds, for which storage retention, oxidation stability, headspace behavior, and application-relevant release are as important as initial encapsulation efficiency. Key challenges, such as long-term stability, standardization of release studies, scalability, safety assessment, and performance in real formulations, are also discussed, together with future directions for sustainable, application-specific hybrid carrier systems. Overall, the review provides a structure-property-application framework for selecting matrix-filler-processing combinations for controlled-release systems. Full article

With the rapid expansion of the global lithium-battery industry, efficient and sustainable lithium recovery from low-grade lithium resources, such as lithium precipitation mother liquor characterized by a high sodium-to-lithium ratio, has become a critical research challenge. Among the emerging technologies, the adsorption method, recognized for its operational simplicity, high selectivity, and process flexibility, has garnered significant attention. This review systematically summarizes recent advancements in two primary categories of adsorbents for selective lithium recovery: organic adsorbents (crown ether-based materials) and inorganic adsorbents (aluminum-based layered double hydroxides (LiAl-LDHs), titanium-based ion sieves (H₂TiO₃, H₄Ti₅O₁₂), and manganese-based ion sieves (HMn₂O₄, H₁.₆Mn₁.₆O₄, H₄Mn₅O₁₂). For each class, the synthesis methods, adsorption mechanisms, performance (capacity, selectivity, kinetics, and cycling stability), and key influencing factors are thoroughly discussed and compared. Titanium-based sieves demonstrate high capacity and stability, manganese-based materials show excellent kinetics, aluminum-based adsorbents offer industrial scalability, and crown ether-based materials exhibit superior ion size selectivity. The review also identifies limitations, such as the slow kinetics of H₂TiO₃, manganese dissolution in manganese-based ion sieves, and the cost of functionalized organics. Finally, future research directions are proposed, focusing on enhancing adsorption kinetics and stability via material design (e.g., morphology control, doping, hybridization), developing scalable and cost-effective synthesis routes, and exploring the integration of adsorption with other separation technologies to create efficient hybrid processes for the sustainable exploitation of low-grade lithium. Full article

Vessel segmentation is a fundamental task in medical image analysis and plays an important role in disease diagnosis and treatment assessment. However, existing segmentation methods often show limited adaptability to feature extraction from single-channel X-ray coronary angiograms, which restricts their performance in segmenting small vessels and low-contrast vascular regions. To address these limitations, this study proposes a Channel-Attention U-Net, termed CA-UNet, which integrates residual connections and a channel attention mechanism. Based on the conventional encoder–decoder architecture of U-Net, the proposed method introduces a residual-enhanced double-convolution block to alleviate gradient vanishing in deeper networks. In addition, a dual-pooling channel attention module is incorporated to enhance the selection of discriminative vascular features. Furthermore, the data loading, normalization, and augmentation strategies are optimized to improve the adaptability of the network to single-channel PGM grayscale images. Under three-fold out-of-fold evaluation, CA-UNet achieved the highest mean Dice coefficient (0.7450) and IoU (0.5972) among the evaluated models, while maintaining real-time-rate inference at 41.7 frames per second. These results indicate that CA-UNet provides an effective balance of segmentation accuracy, stability, and computational efficiency for vessel segmentation. Full article

The airborne corner reflector (ACR), a novel radar passive jamming device, has attracted increasing attention from researchers worldwide due to its enhanced interference coverage when suspended by a parachute. However, the directional nature of ACRs renders their effectiveness highly sensitive to in-flight attitude dynamics. By analyzing the parachute body and the corner reflector separately, we propose an improved dynamic model to describe the parachute–payload system. Key innovations include the following: (i) by introducing an 11-degree-of-freedom model for motion analysis of the parachute-mounted double-pyramid octahedron structure, the issue of imprecise analysis in previous methods has been overcome; (ii) explicit modeling of tether tension and geometric constraints is undertaken to capture the parachute–payload coupling mechanism. Numerical simulations of the steady-descent phase demonstrate convergence of the payload’s angular rates and Euler angles, and the results show good agreement with full-scale flight test data. The model strikes a favorable balance between computational efficiency and physical fidelity, and is particularly suited for dynamic analysis of non-axisymmetric payloads in parachute descent systems. Full article

Symbolic digital transformation, whereby firms overstate digital initiatives through digital narratives without substantive upgrading, may undermine the developmental value of industrial digitalization. This study examines whether China’s Smart Manufacturing Pilot Policy (SMPP) curbs such behavior. Using panel data on Chinese A-share listed manufacturing firms from 2011 to 2024, we treat the staggered implementation of the SMPP as a quasi-natural experiment and estimate policy effects within a double machine learning framework. The baseline results show that the SMPP significantly reduces firms’ symbolic digital transformation (SDT), and this finding remains robust to alternative specifications and endogeneity tests. Dynamic effect analysis indicates that the policy generates a persistent restraining effect, although its marginal effect gradually declines as governance becomes more normalized over time. Mechanism analysis shows that the policy mainly works by easing financing constraints and reducing information asymmetry, while increased media attention creates a countervailing reputational incentive that may encourage SDT. Threshold analysis further reveals that the policy effect is stronger among firms with higher managerial myopia and is most pronounced when corporate opacity is moderate, but becomes insignificant once opacity exceeds a critical level. Overall, the SMPP promotes a shift from symbolic to substantive digital transformation and may provide indirect implications for sustainable manufacturing development. Full article

Reactive oxygen species (ROS) are central regulators of cancer biology and represent a double-edged target in oncology. At physiological levels, ROS support signal transduction, proliferation, differentiation, and immune responses, whereas sustained ROS imbalance promotes DNA damage, genomic instability, metabolic reprogramming, and remodeling of the tumor microenvironment, thereby contributing to tumor initiation, progression, metastasis, and therapy resistance. Conversely, because many cancer cells operate close to the limit of tolerable oxidative stress, further ROS elevation can trigger apoptosis, ferroptosis, immunogenic cell death, and other cytotoxic programs. This review summarizes the major intracellular and microenvironmental sources of ROS, the mechanisms by which redox signaling shapes malignant transformation and tumor adaptation, and the antioxidant systems that buffer oxidative stress in cancer cells. We further discuss current therapeutic approaches based on both ROS suppression and ROS amplification, including redox-modulating small molecules, radiotherapy, photodynamic and sonodynamic therapy, catalytic nanomaterials, and ROS-responsive prodrugs and drug delivery systems. Particular attention is given to the context-dependent effects of ROS, the antioxidant paradox, tumor heterogeneity, hypoxia, off-target toxicity, and the need for robust redox biomarkers. A deeper understanding of tumor-specific redox vulnerabilities will be essential for developing precise and clinically effective ROS-oriented cancer therapies. Full article

As ESG considerations increasingly influence access to finance, participation in global supply chains, and long-term competitiveness, understanding which factors experts perceive as most likely to influence future ESG adoption among small and medium-sized enterprises (SMEs) has become an important issue in sustainable finance and corporate management research. While existing studies largely focus on ESG performance, disclosure, and implementation outcomes, limited attention has been paid to the relative importance of competing ESG-related adoption factors in SMEs in Kazakhstan, an emerging-market context, particularly in contexts where ESG adoption remains in its early stages. This study investigates expert perceptions of the factors most likely to shape ESG implementation decisions among SMEs in Kazakhstan. Drawing on Institutional Theory, the Resource-Based View (RBV), and the concept of double materiality, the study applies the Analytic Hierarchy Process (AHP) to evaluate six ESG-related criteria: coercive institutional pressure, normative pressure, organizational capabilities, financial materiality, impact materiality, and internal governance and processes. Expert judgments were collected through pairwise comparisons and aggregated using the geometric mean within a group AHP framework. The aggregated matrix demonstrated acceptable group-level consistency (CR = 0.0193), providing a basis for exploratory interpretation of the aggregated priority structure, while not validating the consistency of all individual judgments. The findings indicate a structured priority pattern among the expert-perceived ESG-related decision factors. The financial dimension received the highest priority weight, followed by coercive and normative pressure. Internal governance occupied an intermediate position, whereas impact materiality and organizational capabilities received nearly identical lower weights. These results suggest that experts expect future ESG adoption among Kazakhstani SMEs to be influenced primarily by financial relevance and regulatory compliance rather than by impact-oriented sustainability objectives or internally developed sustainability capabilities. This study contributes to the literature in three ways. First, it advances understanding of ESG adoption readiness and prioritization mechanisms among SMEs in Kazakhstan, as an emerging-market context, by integrating institutional, resource-based, and materiality-oriented perspectives. Second, it extends the debate on double materiality by suggesting that financially relevant ESG considerations may play a particularly important role in ESG-related decision-making under conditions of resource scarcity and institutional uncertainty. Third, it provides evidence relevant to ESG regulation, sustainable finance, and SME support policies in developing economies. More broadly, the findings suggest that within the Kazakhstani SME context, ESG is expected to become financially relevant before it becomes fully internalized as a strategic sustainability practice. Full article

Global climate challenges and regulatory pressures have strengthened the link between carbon risk and corporate financial distress. We examine the impact of carbon risk on corporate financial distress and its underlying mechanisms using China’s accession to the Paris Agreement as an exogenous shock, employing a combination of difference-in-differences and double machine learning approaches. We find that high-carbon firms are significantly less likely to experience financial distress compared to low-carbon firms in China. Mechanism analysis indicates that the relationship between carbon risk and corporate financial distress is positively moderated by the green innovation effect, ESG performance, and media attention. The heterogeneity analysis indicates that carbon risk mitigates financial distress more pronouncedly in high-tech industries, high-pollution industries, competitive markets, and firms with strong environmental governance practices. Furthermore, we investigate whether carbon risk is an effective predictor of financial distress. Based on XGBoost (eXtreme Gradient Boosting, version Python 3.13) and SHAP (Shapley Additive Explanations) value analysis, we find that carbon risk significantly enhances the accuracy and explanatory power of financial distress prediction models. The results provide important guidance for policymakers in creating low-carbon strategies, businesses in improving financial management, and investors in assessing carbon risk. Full article

Despite extensive research on knowledge spillovers, existing literature has paid limited attention to the formation mechanism of innovation chains. To address this gap, this paper constructs a multi-party evolutionary game model including two innovators and one supporter, incorporating knowledge spillover effects, and empirically tests the theoretical predictions using data from Chinese listed companies (2016–2025). The results show that the final equilibrium is (cooperation, cooperation, support). Empirically, we find a nonlinear relationship between knowledge spillover and the formation mechanism of an innovation chain with a double threshold effect of absorptive capacity: the positive impact of spillover increases as capacity moves from low to moderate, but diminishes-while remaining positive-when capacity becomes excessively high, revealing an S-shaped pattern. The originality lies in two aspects. Theoretically, our multi-player model extends the conventional bilateral framework to better reflect real-world parallel collaboration. Empirically, our firm-level analysis of Chinese listed companies is among the first to identify a double threshold effect, moving beyond regional-level or single-threshold studies. Practically, our findings suggest that governments should tailor subsidy intensities to firms’ absorptive capacity levels—allocating more to moderate-capacity firms where spillover gains are maximized—offering a more precise strategy for promoting sustainable innovation chain development. Full article

Given the lack of clinical data supporting the effects of a novel guava jelly drink on cognition and mood regulation, we aimed to explore these effects and their possible mechanisms in working-age volunteers. In an 8-week, three-arm double-blind, placebo-controlled trial, healthy males and females aged 20–40 years old (N = 25/arm) were randomly assigned to consume 86 g per day of either a placebo or guava jelly drink containing either a low (36.6%) or high (73.2%) dose of guava juice with mint syrup. N100 and P300 brain waves, working memory, Perceived Stress Scale (PSS), Hospital Anxiety and Depression Scale (HADS), and biomarkers related to oxidative stress, inflammation, neurotransmitters, and gut microbiota were assessed at baseline and after 4 and 8 weeks of consumption. The low-dose group showed improvements in N100 amplitude, P300 latency, and HADS anxiety score, whereas the high-dose group exhibited improved N100 amplitude, working memory, PSS score, and total HADS score. In addition, the high-dose group also exhibited increased GPx activity without a reduction in MDA. Overall, these results suggest that guava jelly drink positively modulates perceived stress, anxiety, selective attention, and working memory. However, the precise underlying mechanism requires further study. Full article

Short-term power load forecasting is fundamental to the secure operation and optimal dispatch of modern power systems. This study proposes an Improved Pied Kingfisher Optimization–Temporal Convolutional Network–Bidirectional Gated Recurrent Unit (IPKO-TCN-BiGRU) model to address the challenges of strong non-stationarity, high randomness, and multi-factor coupling in load time series. The model employs a multi-scale TCN for simultaneous extraction of local and global temporal features, a BiGRU enhanced with an Improved Self-Attention (ISA) mechanism for bidirectional dependency modeling, and an Autoregressive (AR) module combined with an election mechanism to jointly capture linear and nonlinear load components. The Improved Pied Kingfisher Optimization (IPKO) algorithm—incorporating SPM chaotic initialization, a planetary optimization strategy, and adaptive t-distribution perturbation—is applied to globally optimize key hyperparameters, demonstrating superior convergence accuracy and global search capability over the original PKO and other benchmark optimizers. To ensure evaluation integrity, dataset splitting precedes all normalization operations, with StandardScaler fitted exclusively on the training set and applied to the test set without leakage. Validation is conducted on two benchmark datasets: a U.S. residential electricity load dataset (hourly, 2012, 13-dimensional features including HVAC and lighting systems) and a China Electrical Engineering Mathematical Modeling Competition dataset (15 min intervals, three years, enriched with five meteorological variables). The U.S. dataset exhibits a clear annual double-peak seasonal pattern, while the Chinese dataset shows strong intraday fluctuations significantly coupled with temperature and humidity, both posing substantial forecasting challenges. On the U.S. dataset, the proposed model achieves MAE = 0.0190 kW, RMSE = 0.0301 kW, MAPE = 1.7673%, and R² = 0.9947; on the China dataset, MAE = 79.8125 MW, RMSE = 109.4154 MW, MAPE = 1.1124%, and R² = 0.9955. The proposed model consistently outperforms six mainstream baseline models—including Transformer, Autoformer, and FEDformer—reducing RMSE by up to 34.4% and 18.9% on the two datasets, respectively, while maintaining a compact architecture of 15.2 MB and 74.6–78.9 MFLOPs. Ablation experiments confirm the significant and synergistic contribution of each module, and the direct comparison between PKO-TCN-BiGRU and IPKO-TCN-BiGRU validates that the algorithmic improvements translate into measurable forecasting gains beyond benchmark function optimization. The proposed model is most suitable for ultra-short-term to short-term single-step-ahead forecasting within a horizon of 15 min to 24 h, with an inference latency of 2.3–2.7 ms per sample, fully meeting the real-time requirements of practical power dispatching systems. Full article

Nanoglasses (NGs) have received much attention due to their superior ductility and well-retained strength compared to their metallic glass counterparts. However, few studies have examined how notch geometry affects the mechanical behavior and deformation mode of NGs. In this work, molecular dynamics simulations are performed on un-notched and symmetric double-edge notched Mg₃₃Cu₆₇ NGs under tensile loading, with focus on the roles of notch depth, height, and sharpness in determining their mechanical properties and failure modes. Our simulation results show that symmetric double-notched specimens exhibit higher strength and plasticity than un-notched counterparts. The improved plasticity is attributed to a transition in the deformation mode. Furthermore, the deformation mode and strength of notched specimens strongly depend on the notch depth and sharpness. The strengthening effect is enhanced with increasing notch depth or sharpness. This enhancement is likely related to the constrained growth of the plastic zone, which requires a higher stress for continued propagation. In addition, by altering notch depth and sharpness, the deformation mode is observed to change from shear banding-dominated to mixed-mode and then to necking-governed behavior. The mixed mode, characterized by the intersection of V-shaped shear bands, can accommodate substantial additional plastic deformation. Our key finding is that the mixed deformation mode, enabled by proper notch geometry, leads to a remarkable enhancement in both strength and plasticity. This work aims to provide significant insights into the deformation and failure mechanisms of notched NGs, offering an effective design strategy for optimizing their strength and ductility. Full article

The five-phase fault-tolerant consequent-pole permanent-magnet Vernier machine (FTCP-PMVM) has attracted extensive research attention owing to its excellent permanent-magnet utilization while maintaining competitive electromagnetic performance. However, the double-salient structure poses considerable challenges for analysis. The torque generation and power factor characteristics of the FTCP-PMVM are analyzed from a magnetic field modulation perspective in this work. Initially, based on the air gap field modulation effect, the modulation processes of both the permanent-magnet field and the armature field are analyzed. Subsequently, the torque generation mechanism is explained through harmonic matching resulting from the field modulation process, the results demonstrate that the 23rd air gap harmonic dominates the generation of average electromagnetic torque and accounts for the majority of output torque. Furthermore, the power factor is examined in depth by analyzing the reactive power contributed by the machine inductive components (e.g., self-inductance, mutual inductance and leakage inductance) and the active power generated by the permanent-magnet portion, all from the standpoint of field modulation. Finally, a prototype is fabricated to test the machine’s torque, power factor and efficiency. Experimental data confirms the reliability of the theoretical analysis. Full article

Climate-related risks and low-carbon transition pressures are increasingly reshaping firms’ strategic decisions, but their internal distributional consequences remain underexplored. This question matters because changes in within-firm income inequality may influence organizational incentives, cohesion, and long-term firm sustainability. Against this background, we examine the relationship between managerial climate attention and within-firm income inequality using a sample of Chinese listed companies from 2001 to 2023. We employ a double/debiased machine learning approach to estimate this relationship. We find that greater managerial climate attention is significantly associated with higher within-firm income inequality between senior executives and ordinary employees, and that this result remains robust across a series of robustness checks. We further explore three potential channels—operational stability, internal control quality, and green innovation. The results show that managerial climate attention is significantly associated with all three channel variables, suggesting that it may be related to within-firm income distribution through firms’ operating conditions, governance arrangements, and innovation-driven organizational change. Further analysis indicates that this inequality-widening pattern is more pronounced among firms in low-pollution industries and firms with lower ownership concentration, with more suggestive evidence among firms with higher risk. This study highlights that, beyond environmental outcomes, corporate climate strategies require deliberate attention to their internal income distribution consequences and corresponding governance safeguards. Full article

Post-harvest losses of mango (Mangifera indica L.) in developing economies are estimated at 5% to 30%, largely due to manual management practices that depend on subjective visual assessments. This paper proposes a lightweight deep learning architecture, termed SE-DBIRNet, for real-time classification of mangoes into five shelf-life stages: unripe, semi-ripe, fully ripe, overripe, and perished. The model incorporates three key design strategies: (i) depthwise separable convolutions, achieving an 88.5% reduction in parameters when integrated into the ResNet50 backbone; (ii) a double-branch inverted residual (DBIR) module designed to enhance feature diversity and richness; and (iii) a squeeze-and-excitation (SE) attention mechanism for adaptive channel-wise recalibration. Using a public benchmark dataset of 4428 RGB images (Mendeley Data) under 10-fold cross-validation, SE-DBIRNet achieved 98.24% accuracy. Among lightweight CNN architectures (EfficientNetB0, MobileNetV2, ResNet50), SE-DBIRNet outperformed the best lightweight baseline (EfficientNetB0: 96.57%) by 1.67 percentage points. While dedicated attention-based DenseNet variants (e.g., DSA-DenseNet: 99.20%) achieved higher accuracy, SE-DBIRNet offers a superior trade-off among accuracy, inference speed (56.9 ± 1.8 FPS), and memory efficiency (8871 ± 45 MB CPU memory). EigenCAM activation visualizations revealed that the model focuses on biologically relevant and stage-discriminative features, including surface color gradients, texture uniformity, lenticel patterns, and decay boundaries. Overall, SE-DBIRNet achieves a Pareto-optimal balance among accuracy, speed, and memory efficiency, making it a strong candidate for real-time, edge-deployable post-harvest mango quality-monitoring systems, particularly when computational resources are limited. Full article