Search Results (500)

Sustainable entrepreneurship is essential for promoting the integrated development of economic, environmental, and social systems, particularly in emerging economies such as China. Drawing on social identity theory and resource bricolage theory, this study examines how founder identity influences sustainable entrepreneurial behavior and also explores the mediating role of entrepreneurial bricolage and the moderating effect of perceived uncertainty. Using survey data from 210 Chinese new ventures, the hypotheses were tested by structural equation modeling and moderated mediation analysis. The empirical results indicate that founder identity positively influences sustainable entrepreneurship, with entrepreneurial bricolage partially mediating this relationship. Moreover, perceived uncertainty weakens the positive relationship between founder identity and bricolage. It also reduces the indirect effect of bricolage on sustainable entrepreneurship, indicating that higher uncertain environments constrain entrepreneurs’ willingness to rely on bricolage as a resource acquisition strategy. By elucidating the underlying mechanisms and boundary conditions through which founder identity influences sustainable entrepreneurial behavior, this study enriches micro-level research on sustainable entrepreneurship. It also provides practical insights for entrepreneurs and policymakers in strengthening strategic resilience and fostering the development of sustainable entrepreneurship. Full article

Wetlands are a critical component of the global biogeochemical cycle and have great potential for carbon sequestration under the changing climate. However, previous studies have mainly focused on the dynamics of soil organic carbon while paying little attention to the vegetation carbon storage in wetlands. Poyang Lake is the largest freshwater lake in China, where intra-annual and inter-annual variations in water levels significantly affect the vegetation carbon storage in the floodplain wetland. Therefore, we assessed the seasonal distribution and carbon storage of six typical plant communities (Arundinella hirta, Carex cinerascens, Miscanthus lutarioriparius, Persicaria hydropiper, Phalaris arundinacea, and Phragmites australis) in Poyang Lake wetlands from 2019 to 2024 based on field surveys, the literature, and remote sensing data. Then, we used 16 preseason meteorological and hydrological variables for two growing seasons to investigate the impacts of environmental factors on vegetation carbon storage based on four correlation and regression methods (including Pearson and partial correlation, ridge, and elastic net regression). The results show that the C. cinerascens community was the most dominant contributor to vegetation carbon storage, occupying 12.68% to 44.22% of the Poyang Lake wetland area. The vegetation carbon storage in the Poyang Lake wetland was significantly (p < 0.01) higher in spring (87.75 × 10⁴ t to 239.10 × 10⁴ t) than in autumn (77.32 × 10⁴ t to 154.78 × 10⁴ t). Water body area emerged as a key explanatory factor, as it directly constrains the spatial extent available for vegetation colonization and growth by alternating inundation and exposure. In addition, an earlier start or end to floods could both enhance vegetation carbon storage in spring or autumn. However, preseason precipitation and temperature are negative to carbon storage in spring but exhibited opposite effects in autumn. These results assessed the seasonal dynamics of dominant vegetation communities and helped understand the response of the wetland carbon cycle under the changing climate. Full article

This study investigates the weak-form efficiency of South Africa’s summer grain spot markets, focusing on white maize, yellow maize, sunflower, and soybean. Using daily log return data from 2007 to 2025, we apply autocorrelation, Portmanteau (Q), and heteroskedasticity-robust Lo–MacKinlay variance ratio tests, along with Bai–Perron structural break analysis, Pesaran–Timmermann directional accuracy tests, and mean return per trade calculations. Results reveal significant short-term serial dependence and mean-reverting behaviour across all commodities, indicating partial predictability and deviations from weak-form efficiency. Structural break analysis identifies multiple regimes within the price series, showing that market dynamics are not constant over time. Directional accuracy and MRP results indicate that while some predictability exists, the economic gains from exploiting past prices are small and likely insufficient to overcome trading frictions. These findings suggest that price adjustments are gradual rather than instantaneous, reflecting structural and operational market frictions such as limited liquidity, low adoption of electronic trading, and constrained transparency. Enhancing digital trading platforms, improving real-time price reporting, and investing in storage and logistics could strengthen price discovery and reduce transaction costs. The study provides insights into emerging agricultural markets and highlights the importance of considering market structure when evaluating efficiency. Full article

Intensified water consumption has driven rapid groundwater depletion globally, threatening economic and environmental sustainability. Understanding large-scale groundwater dynamics has been constrained by the scarcity of long-term, high-resolution records. This study uses multi-decadal, high-density groundwater level monitoring data from the Southern Hebei Plain (SHP) to analyze the evolution of the groundwater flow field and depression cones from 1959 to 2020. We quantitatively characterize trends over six decades and assess the impact of the South-to-North Water Diversion Project (SNWD). The regional flow field shifted from a natural topographic-driven pattern (foothills to coast) in the 1960s to localized systems centered on depression cones by the 1980s. Subsequent management policies and the SNWD have progressively reduced the extent of these cones, facilitating a partial recovery of the regional flow pattern towards its original direction. Shallow aquifer levels declined steeply from the 1980s until 2016, particularly along the Taihang Mountains’ alluvial fan margins, with cumulative drawdown of 20–60 m. After SNWD implementation, levels stabilized and began recovering in piedmont urban areas. Deep aquifer levels generally declined from the 1980s to 2016, with the most significant drawdown (40–90 m) occurring in the central–eastern plain. The recovery of deep aquifers lagged behind shallow ones. These results provide critical insights for supporting sustainable groundwater management and depression cone recovery in the Hebei Plain. Full article

Wheat (Triticum aestivum L.) sustains global caloric intake, but its productivity in Andean highlands is constrained by soil fertility and input reliance. This study represents one of the first field-based evaluations of biofertilizers under high-altitude, rainfed Andean conditions, addressing a major knowledge gap in low-input mountain agroecosystems. This study evaluated three seed-applied biofertilizers—Azospirillum brasilense, Trichoderma viride (Trichomax), and an anchovy (Engraulis ringens) based liquid biofertilizer, compared with an untreated control and a soil-test mineral fertilization benchmark in rainfed wheat (Triticum aestivum L.) cv. INIA 405 in the central Andes of Peru. A 5 × 5 Latin square design (25 plots) was established under farmer-realistic conditions. At physiological maturity (Zadoks 9.5), plant height, spike length, grains per spike, thousand-grain weight, test weight, root dry mass, and grain yield were recorded. Mineral fertilization achieved the highest yield (1.20 ± 0.79 t ha⁻¹), nearly doubling the control (0.60 ± 0.47 t ha⁻¹). Notably, A. brasilense delivered an intermediate yield of 0.90 ± 0.64 t ha⁻¹, representing a 50% increase over the control—accompanied by a marked rise in root dry mass. T. viride and the anchovy-based input yielded 0.85 ± 0.59 and 0.81 ± 0.59 t ha⁻¹, respectively. Grain physical quality remained stable across treatments (thousand-grain weight ≈ 42 g; test weight 68–75 kg hL⁻¹). Trait responses were complementary: root dry mass increased with mineral fertilization and A. brasilense, whereas spike length increased with mineral fertilization and the anchovy-based input. Overall, the evidence supports biofertilizers, particularly A. brasilense, as effective complements that enable partial fertilizer substitution within integrated nutrient-management strategies for sustainable wheat production in Andean rainfed systems. Full article

Swarm coverage by unmanned underwater vehicles (UUVs) is essential for inspection, environmental monitoring, and search operations, but remains challenging in three-dimensional domains under limited sensing and communication. Pheromone-based stigmergic coordination provides a low-bandwidth alternative to explicit communication, yet conventional single-field models are susceptible to depth-dependent sensing inconsistencies and multi-source signal interference. This paper introduces a dual-trail stigmergic coordination framework in which a virtual pheromone field encodes short-term motion cues while an auxiliary coverage trail records the accumulated exploration effort. UUV motion is guided by the combined gradients of these two fields, enabling more consistent behavior across depth layers and mitigating ambiguities caused by overlapping pheromone sources. At the macroscopic level, swarm evolution is modeled by a coupled system of partial differential equations (PDEs) describing vehicle density, pheromone concentration, and coverage trail. A Lyapunov functional is constructed to derive sufficient conditions under which perturbations around the uniform coverage equilibrium decay exponentially. Numerical simulations in three-dimensional underwater domains demonstrate that the proposed framework reduces coverage holes, limits redundant overlap, and improves robustness with respect to a single-pheromone baseline and a potential-field-based controller. These results indicate that dual-field stigmergic control is a promising and scalable approach for UUV coverage in constrained underwater environments. Full article

This study examines how Green Human Resource Management (GHRM) is linked to sustainable organizational performance, encompassing environmental, economic, and social outcomes through the capability-building mechanisms of green innovation (GI) and green organizational culture (GOCL) in emerging manufacturing systems. Drawing on the Resource-Based View and capability-based sustainability perspectives, GHRM is conceptualized as a strategic organizational capability that enables firms in developing economies to beyond short-term regulatory compliance toward measurable and integrated sustainability performance outcomes. Survey data were collected from 446 managerial and technical respondents in Ethiopia’s garment and textile industrial parks, one of Africa’s fastest-growing industrial sectors facing significant sustainability challenges. Using Partial Least Squares Structural Equation Modeling (PLS-SEM) with bootstrapping-based mediation analysis, the results show that GHRM is positively associated with sustainable organizational performance, with GI and GOCL operating as key mediating mechanisms that translate HR-related practices into measurable sustainability outcomes. The findings highlight the role of GHRM in strengthening firms’ adaptive and developmental sustainability capabilities by fostering pro-sustainability mindsets and innovation-oriented behaviors, which are particularly critical in resource-constrained and weak-institutional contexts. The study contributes to sustainability and management literature by explicitly linking Green HRM to triple-bottom-line performance through a capability-building framework and by providing rare firm-level empirical evidence from a low-income emerging economy. Practically, the results provide guidance for managers and policy makers to design, monitor, and evaluate HRM systems that intentionally cultivate human, cultural, and innovative capabilities to support long-term organizational sustainability transitions. Full article

Terrestrial ecosystems in Hunan Province have undergone marked yet spatially heterogeneous vegetation changes under concurrent climate change and intensifying human activities. The aim of this study is to resolve how vegetation responses vary among land-use types by quantifying kernel Normalized Difference Vegetation Index (kNDVI) dynamics during 2000–2023 using precipitation, temperature, and solar radiation, coupled with trend analysis and a partial-derivative-based attribution. Mean kNDVI increased overall at 0.0016 yr⁻¹; vegetation improved over 76.30% of the area, whereas 5.72% of the area experienced degradation. Built-up land exhibited the largest degraded fraction (35.04%). Human activities and temperature emerged as the dominant drivers of kNDVI change, contributing 62.25% and 27.92%, respectively, while precipitation (3.08%) and solar radiation (6.77%) played comparatively minor roles. Spatially, human activities primarily controlled vegetation dynamics in plains and urban clusters (~78% of the area), whereas temperature constrained vegetation in high-elevation mountain ranges. Analysis along the human footprint (HFP) gradient reveals that driver composition remains steady in resilient ecosystems (farmland and forest), despite increasing anthropogenic pressure, whereas fragile ecosystems (grassland and bareland) exhibited pronounced volatility and heightened sensitivity to environmental constraints. These findings provide a quantitative basis for developing sustainable ecological security strategies, incorporating region-specific measures such as adaptive afforestation, sustainable agricultural management, and strict ecological protection, to enhance ecosystem resilience by prioritizing the climate resilience of mountain forests and the stability of fragile grassland systems. Full article

To address the challenging requirements of citrus detection in complex orchard environments, this paper proposes ACDNet (Adaptive Citrus Detection Network), a novel deep learning framework specifically designed for automated citrus harvesting. The proposed method introduces three key innovations: (1) Citrus-Adaptive Feature Extraction (CAFE) module that combines fruit-aware partial convolution with illumination-adaptive attention mechanisms to enhance feature representation with improved efficiency; (2) Dynamic Multi-Scale Sampling (DMS) operator that adaptively focuses sampling points on fruit regions while suppressing background interference through content-aware offset generation; and (3) Fruit-Shape Aware IoU (FSA-IoU) loss function that incorporates citrus morphological priors and occlusion patterns to improve localization accuracy. Extensive experiments on our newly constructed CitrusSet dataset, which comprises 2887 images capturing diverse lighting conditions, occlusion levels, and fruit overlapping scenarios, demonstrate that ACDNet achieves superior performance with mAP@0.5 of 97.5%, precision of 92.1%, and recall of 92.8%, while maintaining real-time inference at 55.6 FPS. Compared to the baseline YOLOv8n model, ACDNet achieves improvements of 1.7%, 3.4%, and 3.6% in mAP@0.5, precision, and recall, respectively, while reducing model parameters by 11% (to 2.67 M) and computational cost by 20% (to 6.5 G FLOPs), making it highly suitable for deployment in resource-constrained robotic harvesting systems. However, the current study is primarily validated on citrus fruits, and future work will focus on extending ACDNet to other spherical fruits and exploring its generalization under extreme weather conditions. Full article

Liver graft shortage remains a major limiting factor in contemporary liver transplantation, particularly in the setting of increasing waiting list pressure and constrained donor availability. While the biological quality of donor organs cannot be modified surgically, several operative strategies have been developed to optimize liver utilization and compensate for insufficient graft volume. These include split liver transplantation (SLT), dual-graft living donor liver transplantation (DGLT), auxiliary procedures, and selected multi-graft or hybrid configurations. This review provides an updated and structured overview of surgical concepts aimed at maximizing effective liver mass for transplantation. We discuss indications, technical considerations, and reported outcomes of split, dual, and combined graft approaches, with particular emphasis on graft-to-recipient weight ratio (GRWR), portal inflow modulation, and prevention of small-for-size syndrome. The role of machine perfusion technologies—including normothermic and hypothermic approaches—as enabling tools for graft assessment and safer utilization of partial grafts is also examined. Finally, we address ethical and logistical challenges associated with complex graft strategies and outline future directions in which advances in perfusion, graft assessment, and staged transplantation concepts may further refine patient selection and procedural safety. Collectively, these strategies represent complementary solutions for extending liver transplantation beyond conventional single-graft paradigms in highly selected settings. Full article

Fault-tolerant control in safety-critical industrial systems demands adaptive responses to equipment degradation, parameter drift, and sensor failures while maintaining strict operational constraints. Traditional model-based controllers struggle under these conditions, requiring extensive retuning and dense instrumentation. Recent safe multi-agent reinforcement learning (MARL) frameworks with control barrier functions (CBFs) achieve real-time constraint satisfaction in robotics and power systems, yet assume comprehensive state observability—incompatible with sensor-hostile industrial environments where instrumentation degradation and contamination risks dominate design constraints. This work presents a safety-aware multi-agent deep reinforcement learning framework for adaptive fault-tolerant control in sensor-lean industrial environments, achieving formal safety through learned implicit barriers under partial observability. The framework integrates four synergistic mechanisms: (1) multi-layer safety architecture combining constrained action projection, prioritized experience replay, conservative training margins, and curriculum-embedded verification achieving zero constraint violations; (2) multi-agent coordination via decentralized execution with learned complementary policies. Additional components include (3) curriculum-driven sim-to-real transfer through progressive four-stage learning achieving 85–92% performance retention without fine-tuning; (4) offline extended Kalman filter validation enabling 70% instrumentation reduction (91–96% reconstruction accuracy) for regulatory auditing without real-time estimation dependencies. Validated through sustained deployment in commercial beverage manufacturing clean-in-place (CIP) systems—a representative safety-critical testbed with hard flow constraints (≥1.5 L/s), harsh chemical environments, and zero-tolerance contamination requirements—the framework demonstrates superior control precision (coefficient of variation: 2.9–5.3% versus 10% industrial standard) across three hydraulic configurations spanning complexity range 2.1–8.2/10. Comprehensive validation comprising 37+ controlled stress-test campaigns and hundreds of production cycles (accumulated over 6 months) confirms zero safety violations, high reproducibility (CV variation < 0.3% across replicates), predictable complexity–performance scaling ($R^2=0.89$), and zero-retuning cross-topology transferability. The system has operated autonomously in active production for over 6 months, establishing reproducible methodology for safe MARL deployment in partially-observable, sensor-hostile manufacturing environments where analytical CBF approaches are structurally infeasible. Full article

This study explores how digital and traditional marketing tools influence higher education students’ career decision-making, satisfaction, and career commitment during students’ educational trajectories in Iraq’s rapidly expanding university sector. Using an explanatory sequential mixed-methods design, a survey of 622 students was analysed with partial least squares structural equation modelling (PLS-SEM), followed by 24 semi-structured interviews with marketing and recruitment professionals. The quantitative findings show that students’ first-choice preferences, demographic factors, and engagement with LinkedIn, WeChat, blogs, and university webpages significantly shaped their career choices and satisfaction levels. Qualitative insights reveal that authenticity, transparent communication, and alignment between institutional messaging and lived experiences were key to sustaining trust. Traditional channels such as brochures and fairs remained important for credibility, supporting a hybrid marketing approach. The study contributes to management theory and practice in universities by linking digital communication strategies to student engagement and institutional performance. It also highlights the need for inclusive, transparent, and culturally adaptive marketing that reflects local and global contexts. These findings provide actionable guidance for higher education administrators seeking to build sustainable student trust, enhance recruitment effectiveness, and strengthen institutional reputation in competitive and resource-constrained systems. Full article

Obesity-associated inflammation underlies much of cardiometabolic pathology, reflecting the convergence of chronic, low-grade systemic immune activation with region-specific maladaptation of adipose depots. Among these, epicardial adipose tissue (EAT)—a visceral fat layer contiguous with the myocardium and sharing its microvasculature—functions as a cardio-proximal immunometabolic interface that influences atrial fibrillation, heart failure with preserved ejection fraction, and coronary atherogenesis through paracrine crosstalk. These relationships extend beyond crude measures of adiposity, emphasizing the primacy of local inflammatory signaling, adipokine flux, and fibro-inflammatory remodeling at the EAT–myocardium interface. Of importance, substantial weight reduction only partially reverses obesity-imprinted transcriptional and epigenetic programs across subcutaneous, visceral, and epicardial depots, supporting the concept of an enduring adipose memory that sustains cardiovascular (CV) risk despite metabolic improvement. Accordingly, therapeutic strategies should move beyond weight-centric management toward mechanism-guided interventions. Resolution pharmacology—leveraging specialized pro-resolving mediators and their cognate G-protein-coupled receptors—offers a biologically plausible means to terminate inflammation and reprogram immune–stromal interactions within adipose and CV tissues. Although preclinical studies report favorable effects on vascular remodeling, myocardial injury, and arrhythmic vulnerability, clinical translation is constrained by pharmacokinetic liabilities of native mediators and by incomplete validation of biomarkers for target engagement. This review integrates mechanistic, depot-resolved, and therapeutic evidence to inform the design of next-generation anti-inflammatory strategies for obesity-related CV disease. Full article

Freshwater scarcity increasingly constrains agricultural sustainability and global food security, particularly where crop production and trade shape national water balances. This study quantifies Ecuador’s green (soil moisture/rainfall) and blue (surface and groundwater) virtual water flows associated with seven strategic crops (banana, cocoa, pineapple, maize, rice, barley and potato) from 2000 to 2023 using the Hoekstra–Mekonnen accounting framework, and FAOSTAT production and bilateral trade data. Furthermore, Logarithmic Mean Divisia Index (LMDI) decomposition analysis was applied to identify the key drivers influencing virtual water trade, including economic growth, population, product structure, and water intensity. Results reveal that Ecuador operates as a persistent net exporter of virtual water, with export flows dominated by green water, reflecting the country’s reliance on rainfall-supported production. Virtual water exports increased from 3000 to >15,000 Mm³·yr⁻¹ over the study period, while imports remained substantially smaller, confirming Ecuador’s structurally export-oriented agricultural economy. The LMDI outcomes show that export growth is driven primarily by economic expansion (8.28 × 10⁸ m³) and shifts in the crop export mix, partially offset by improvements in water intensity. These findings highlight Ecuador’s vulnerability to trade-related water pressures and demonstrate the value of virtual water indicators for guiding water governance and SDG-aligned trade strategies, thereby promoting the decoupling of economic growth from water resource consumption and connecting virtual water trade to domestic water scarcity. Full article

Various TinyML models face a constantly challenging environment when running on emerging sixth-generation (6G) edge networks, with volatile wireless environments, limited computing power, and highly constrained energy use. This paper introduces DRL-TinyEdge, a latency- and energy-sensitive deep reinforcement learning (DRL) platform optimised for the 6G edge of adaptive TinyML. The suggested on-device DRL controller autonomously decides on the execution venue (local, partial, or cloud) and model configuration (depth, quantization, and frequency) in real time to trade off accuracy, latency, and power savings. To assure safety during adaptation to changing conditions, the multi-objective reward will be a combination of p95 latency, per-inference energy, preservation of accuracy and policy stability. The system is tested under two workloads representative of classical applications, including image classification (CIFAR-10) and sensor analytics in an industrial IoT system, on a low-power platform (ESP32, Jetson Nano) connected to a simulated 6G mmWave testbed. Findings indicate uniform improvements, with up to a 28 per cent decrease in p95 latency and a 43 per cent decrease in energy per inference, and with accuracy differences of less than 1 per cent compared to baseline models. DRL-TinyEdge offers better adaptability, stability, and scalability when using a CPU < 5 and a decision latency < 10 ms, compared to Static-Offload, Heuristic-QoS, or TinyNAS/QAT. Code, hyperparameter settings, and measurement programmes will also be published at the time of acceptance to enable reproducibility and open benchmarking. Full article