Search Results (1,368)

The Northeast Black Soil Region is an important commercial grain production base in China. However, ecological issues such as black soil degradation and soil erosion pose direct threats to food security. Previous studies have mainly examined individual factors of black soil degradation. Few have integrated spatial thickness distribution with multi-dimensional ecological sensitivity. To address this gap, this study establishes an ecological sensitivity evaluation index system for Bayan County, located in the eastern Songnen High Plain. Based on a review of relevant literature, the system includes four dimensions: topography, climate, natural resources, and human activities. A combined Analytic Hierarchy Process (AHP) and Entropy Weight Method (EWM) was used to determine indicator weights. Compared with single-weighting methods, this approach balances expert judgment with data-driven variation. The results are as follows. (1) The thickness of the black soil layer in Bayan County ranges from 18 to 77 cm. Medium, thin, and thick layers account for 78.81%, 16.32%, and 4.87% of the area, respectively. The total black soil reserve is estimated at about 1.267 billion m³. (2) Among the primary indicators, natural resources have the highest weight (0.53). The five most important secondary indicators are the river buffer zone (0.14), NDVI (0.13), soil type (0.12), land use type (0.12), and road buffer zone (0.09). (3) The overall ecological sensitivity of the county is moderate, with a composite index ranging from 1.45 to 4.45. The proportions of extremely sensitive, highly sensitive, moderately sensitive, mildly sensitive, and insensitive areas are 10.79%, 25.51%, 28.95%, 24.23%, and 10.52%, respectively. These findings provide a scientific basis for ecological protection and black soil conservation. They also support the development of targeted, zone-specific management strategies in Bayan County. Full article

Grain and cultivated land resources constitute the most fundamental means of human subsistence, and their spatial mismatch can directly reveal issues related to the rationality of regional resource utilization and urban–rural development patterns. The downstream region of the Yangtze River Economic Belt, as a major grain-producing area in China, holds significant importance for optimizing regional arable land utilization patterns, achieving sustainable use of cultivated land resources, and ensuring national food security through the investigation of the spatiotemporal mismatch characteristics between grain production and arable land resources and their influencing factors. This study focuses on the downstream region of the Yangtze River Economic Belt, employing the Center of Gravity Transfer Model, Spatial Mismatch Model, and Geographical and Temporal Weighted Regression Model to analyze the spatiotemporal variation characteristics of grain production and cultivated land area, as well as their mismatch patterns. It further investigates the factors that influence such mismatches and their spatial heterogeneity. The research findings indicate that, in terms of temporal characteristics, grain production in the downstream region of the Yangtze River Economic Belt exhibited an upward, fluctuating trend from 2000 to 2023. The cultivated land area initially decreased, then gradually increased, while the overall quantity showed a net reduction. From the perspective of spatial changes, the migration rate of grain production was significantly higher than that of cultivated land. The center of gravity of grain production shifted 78.85 km northwestward, while the center of gravity of cultivated land moved 4.16 km in the same direction. The overall mismatch between grain production and cultivated land shows fluctuating changes, while its spatial characteristics show an increasing trend toward polarization. The average intensity order of influencing factors is as follows: effective irrigated area > fertilizer’s equivalent weight > the proportion of agricultural output value > total power of agricultural machinery > urbanization rate > the proportion of people employed in the primary industry. Meanwhile, these influencing factors exhibit significant spatial heterogeneity characteristics, with their impact directions and intensities varying across different development stages in distinct regions. From a spatiotemporal perspective, the research findings provide differentiated policy recommendations for the efficient utilization of cultivated land resources and grain production in the downstream region of the Yangtze River Economic Belt. Full article

The prehistoric and historic Greek populations have a long and glorious history and could teach us significant lessons relevant to water resources and their management. Most Greek civilizations lived in harmony with the environment, with a profound understanding of environmental sustainability. The Minoan era, considered as Pax Minoica (or Minoan peace), was a time when palaces and other living places did not have defensive walls; in that time, human rights and power without a military aristocracy developed. During that time, hydro-structures with a high degree of security, which remained in operation for millennia, were developed, most of them established in predominantly arid areas for reasons of security, protection, and public health. The study presents important elements of the development and progress of these technological achievements provided by ancient civilizations throughout the prehistoric to modern period, in the context of revealing and highlighting potential lessons to understand and address current critical issues in the management of water resources. Furthermore, the methodology used and the technological structural advancement of water works, their infrastructure durability, and early water law principles are considered. Many modern systems are designed for operational lifespans of 50–100 years, whereas several ancient Greek hydraulic structures remained functional for centuries by relying on renewable natural resources—reflecting a fundamentally different design philosophy centered on longevity and robustness. Thus, terms such as “sustainability” and “water security/safety”, first taught by ancient civilizations, need to be reconsidered and adopted again nowadays to inspire policies, strategies, and actions against the increasing challenges. Full article

Patients leave millions of medical reviews annually, providing critical data for quality management. However, manual processing is infeasible, and existing systems fail to distinguish medical from organizational problems—a distinction essential for complaint routing. The consequences of misrouting are significant: clinical issues may go unaddressed when medical complaints reach administrative staff, while systemic service problems remain unresolved when organizational complaints reach medical directors. We developed a hybrid approach combining expert annotation with Large Language Models (LLMs). Fifteen prompt iterations on 1500 reviews with expert validation (modified Cohen’s kappa (κ_mod), which weights errors hierarchically, reached 0.745) preceded the LLM annotation of 15,000 mixed-sentiment and positive reviews. These were combined with 7417 expert-annotated negative reviews to form a corpus of 22,417 reviews. Eight architectures, ranging from Logistic Regression to a BERT + TF-IDF + LightGBM ensemble, were compared using both standard metrics and domain-specific practical metrics tailored to complaint routing. The best model, scaled to 4.3 million Russian-language reviews from the Prodoctorov.ru platform, achieved 92.9% Practical Accuracy—the proportion of reviews classified without critical medical–organizational misclassification errors (M ↔ O)—compared to 68.0% standard accuracy, which treats all errors equally. Critical errors were reduced to 1.4%, yielding 144,000 more correctly processed complaints than traditional methods (TF-IDF + Logistic Regression). Analysis of the scaled data revealed the following: 46.1% M (medical), 21.0% O (organizational), and 32.9% C (combined) reviews; medical ratings were highest (4.75 vs. 4.59 for organizational, p < 0.001); combined reviews were longest (802 characters); zero-star reviews comprised 3.8% of feedback, with organizational complaints dominating (38.2%) among extreme negatives; and average ratings rose by 1.24 points over 14 years. This hybrid approach yields expert-comparable corpora, automates 93% of feedback processing, ensures correct complaint routing, and contributes to healthcare sustainability by reducing administrative burden, accelerating resolution, and enabling data-driven quality management without proportional increases in human resources. All analyses were conducted on Russian-language patient reviews. Full article

Organizational change is a complex process often accompanied by intense human reactions and increased uncertainty. Resistance to change (RtC) can cause critical performance declines during the organizational change period, which can delay implementation. The evolution of information systems and digital infrastructures provides immediate access to operational data and analytical tools, making it possible to continuously monitor performance and timely adjust decisions during change. Although recent approaches attempt to minimize these impacts through continuous monitoring and resource reallocation, they typically view human resource allocation as a single-level problem. In hierarchical structures where work and decision-making are distributed across levels, RtC can increase backlogs, place an excessive amount of work on managers, and result in operational issues or the failure of the change. From an algorithmic perspective, the proposed method formulates a hierarchical dynamic optimization problem with two coupled assignment layers, in which the operational output of Level 1 dynamically determines the workload processed at Level 2. Both assignment problems are solved at each time step using the Hungarian algorithm, while RtC is modelled as a time-dependent stochastic process aligned with a reference change curve, allowing employee and managerial performance to be updated dynamically over the planning horizon. In contrast to static Classical Change Management Model (CCMM), large-scale experimental results demonstrate that the new approach increases total processed workload by approximately 20%, while at the peak of resistance, the improvement reaches 56.8%. At the same time, it substantially reduces backlog accumulation, maintaining very low backlog levels (18 versus 16,424 units) within the tested setting. Finally, by applying a 50% reallocation threshold, the organization maintains 98.5% of maximum performance while avoiding 45% of the reallocations. Overall, the proposed method provides a dynamic optimization framework that combines hierarchical organizational modeling with stochastic performance updates across organizational levels. Full article

Millimeter-wave radar has gradually gained popularity as a sensor mode for Human Activity Recognition (HAR) in recent years because it preserves the privacy of individuals and is resistant to environmental conditions. Nevertheless, the fast inference of high-dimensional and sparse 4D radar data is still difficult to perform on low-resource edge devices. Current models, including 3D Convolutional Neural Networks and Transformer-based models, are frequently plagued by extensive parameter overhead or quadratic computational complexity, which restricts their applicability to edge applications. The present paper attempts to resolve these issues by introducing RadarSSM as a lightweight spatiotemporal hybrid network in the context of radar-based HAR. The explicit separation of spatial feature extraction and temporal dependency modeling helps RadarSSM decrease the overall complexity of computation significantly. Specifically, a spatial encoder based on depthwise separable 3D convolutions is designed to efficiently capture fine-grained geometric and motion features from voxelized radar data. For temporal modeling, a bidirectional State Space Model is introduced to capture long-range temporal dependencies with linear time complexity $O\left(T\right)$, thereby avoiding the quadratic cost associated with self-attention mechanisms. Extensive experiments conducted on public radar HAR datasets demonstrate that RadarSSM achieves accuracy competitive with state-of-the-art methods while substantially reducing parameter count and computational cost relative to representative convolutional baselines. These results validate the effectiveness of RadarSSM and highlight its suitability for efficient radar sensing on edge hardware. Full article

College dropout is a global challenge due to its high prevalence and its consequences for individuals, institutions, and society, particularly in terms of institutional sustainability, inefficient use of public resources, and human capital loss. This issue is especially salient in engineering, where first-year dropout rates remain high. This study examines factors associated with first-year dropout among engineering students at a Chilean public university, framing dropout as a sustainability challenge for higher education systems. The analysis combines administrative records ($n=825$) with survey data on psychosocial variables ($n=417$). Results show that admission to a first-choice program and early performance are strongly associated with persistence, highlighting admission alignment and early university experience as factors contributing to the sustainable use of institutional resources. Despite equivalent academic performance across genders, a marked discrepancy emerged between students’ high self-reported confidence and limited implementation of learning strategies. Cluster analysis identified a clear performance gradient across socio-educational profiles, with students combining high academic capital, low socioeconomic vulnerability, and first-choice admission showing the most favorable outcomes. These findings underscore the relevance of admission preference, trajectories, and socio-educational context for first-year persistence, with implications for institutional sustainability and the consolidation of human capital in engineering education. Full article

This study explored Entities’ Performance as an explained function of Human Resource Costs (HRC) to further justify recognition of the Labour Costs proxies in the Statement of Financial Position (SOFP). This has been investigated to provide robust empirical evidence from the Nigerian Exchange Group (NGX) to spur the International Accounting Standard Board (IASB) to release an Exposure Draft (ED) for public discussion and have a standard to recognize proxies of HRC as assets in the SOFP. To provide grounds for inclusion of HRC in the SOFP by the IASB, unlike most other empirical studies reviewed, which deployed limited methods and years of time series data, this study expanded the scope and methods using Pooled Cross-Sectional (PCS) time series data of 27 quoted companies from 1992 to 2023 in the NGX. While most studies employed inefficient Ordinary Least Squares (OLS), this current study progressed from Descriptive Statistics to OLS, Pooled OLS, and Rodman’s Xtabond2 Generalized Method of Moments (GMM) to resolve the conundrums of endogeneity, reversed causality, and stationarity common to unbalanced PCS time series data. The results revealed from the GMM showed that LSW (18.40), positive, and LTD (−22.63), inverse, and Wald ^2 = 66.35 with p-value (0.002), obviously validated the strong joint significance of the regressors on ROA (performance) of 27 sampled firms in the NGX. It is recommended that IASB align with the momentum from the output of research from academia by issuing standards to recognize HRC as assets in the SOFP. Full article

With the increasing constraints of resource and environmental factors and the prominent issues of regional development imbalance, how to scientifically measure the level of agricultural sustainable development and reveal its spatial-temporal differentiation patterns has become a key scientific question that urgently needs to be addressed in optimizing land use layout and promoting rural revitalization. This study takes the human-land spatial systems coupling theory as the core framework and constructs an evaluation index system for agricultural sustainable development covering five dimensions: economy, society, resources, ecology, and technology. Based on provincial panel data in China from 2001 to 2024, the entropy method is employed to measure agricultural sustainable development, while Dagum’s Gini coefficient, kernel density estimation, and convergence models are applied to analyze its spatial–temporal evolution. Furthermore, the fuzzy-set qualitative comparative analysis (fsQCA) method is introduced to identify multi-factor configurational driving pathways. The results indicate that the overall level of agricultural sustainable development in China shows a steady upward trend, exhibiting a regional gradient pattern characterized by “central region leading, eastern region steadily advancing, and western region gradually catching up”. The overall disparity presents a weak convergence trend, with inter-regional differences as the primary source, although their contribution is gradually declining. The development structure has evolved from regional fragmentation to a more complex spatial interaction pattern. The overall distribution shifts rightward with evident stage-based differentiation, accompanied by significant positive spatial dependence, with “high–high” and “low–low” clustering coexisting over the long term. Convergence analysis shows that σ-convergence exists at the national level. After accounting for spatial effects, significant absolute β-convergence is observed in the eastern and western regions, while the central region does not exhibit significant convergence. Conditional β-convergence further confirms the existence of regional convergence trends, although the convergence speeds vary. The fsQCA results indicate that agricultural sustainable development is not driven by a single factor but by multiple configurational pathways formed through the interaction of various conditions. These findings provide empirical evidence for optimizing agricultural spatial layout, strengthening land factor support, and promoting regionally coordinated agricultural sustainable development. Full article

In ecologically fragile and geomorphologically complex mountainous regions, ensuring a smooth transition from poverty alleviation to multidimensional sustainable rural development remains a key issue in regional governance. Focusing on the Qinba Mountains, a typical former contiguous poverty-stricken region in China covering 18 prefecture-level cities in six provinces, this study uses 2009–2023 prefecture-level panel data to examine the spatiotemporal evolution and driving mechanisms of coordinated rural revitalization. An integrated framework of “multi-dimensional evaluation–spatiotemporal tracking–attribution diagnosis” is developed by combining the improved AHP–entropy-weight TOPSIS method, the Coupling Coordination Degree (CCD) model, spatial Markov chains, spatial autocorrelation, and the Geodetector. The results show pronounced subsystem asynchrony. Livelihood and Well-being Security (U5) improves steadily, while Level of Industrial Development (U1), Civic Virtues and Cultural Vibrancy (U3), and Rural Governance (U4) also rise but with clear spatial differentiation; by contrast, Quality of Human Settlements (U2) fluctuates in stages under ecological fragility. Overall, the coupling coordination level advances from the Verge of Imbalance to Intermediate Coordination, yet the regional pattern remains uneven, with eastern basin cities leading and western deep mountainous cities lagging. State transitions display both policy responsiveness and path dependence: the probability of retaining the original state ranges from 50.0% to 90.5%; low-level neighborhoods reduce the upward transition probability to 25%, whereas medium-to-high-level neighborhoods raise the upward transition probability of low-level cities from 36.36% to 53.33%. Spatial dependence is also evident, with Global Moran’s I increasing, with fluctuations, from 0.331 in 2009 to 0.536 in 2023; high-value clusters extend along the Guanzhong Plain–Han River Valley corridor, while low-value clusters remain relatively locked in mountainous border areas. Driving mechanisms show clear stage-wise succession. At the single-factor level, the explanatory power of Road Network Density (F6) declines from 0.639 to 0.287, whereas Terrain Relief Amplitude (F1) becomes the dominant background constraint in the later stage (q = 0.772). Multi-factor interactions are generally enhanced. In particular, the traditional infrastructure-led pathway weakens markedly, with F1 ∩ F6 = 0.055 in 2023, while the interaction between terrain and consumer market vitality becomes dominant, with F1 ∩ F7 = 0.987 in 2023. On this basis, three major pathways are identified: government fiscal intervention and transportation accessibility improvement, capital agglomeration and market demand stimulation, and human–earth system adaptation and ecological value realization. These findings provide quantitative evidence for breaking spatial lock-in and improving cross-regional resource allocation in ecologically constrained mountainous regions. Full article

Islands often suffer from relatively limited freshwater resources, and the effective utilization and distribution of water resources are a key issues for the sustainable development of island-based economies and societies. While island water security has been widely discussed, few studies trace the socio-technical construction of island water-supply systems across the stages of planning, construction, and operation. Integrating Actor-Network Theory with political ecology, this study investigates the water-supply infrastructure of Kinmen. Drawing on official archives, participant observation, and in-depth interviews, this research analyzes the collective actions mobilized to address Kinmen’s water scarcity following the lifting of martial law in 1992. These efforts jointly reshaped both water-supply practices and the infrastructural network. Over the past three decades, Kinmen’s water-supply system has transformed into a sophisticated technological network, integrating reservoirs, desalination plants, and advanced sewage infrastructure. The introduction of these technologies, which function as critical non-human actors within the system, marks a clear shift in how water is managed and distributed. However, the rapid expansion of water-intensive industries, especially tourism, liquor distilling, and cattle farming, has outpaced local ecological limits, precipitating the current water crisis. The study concludes that this shortage has been mitigated through the strategic integration of water sources, most notably the cross-strait pipeline from mainland China, which now provides more than 80 percent of the island’s water. This transition marks a profound shift in the island’s socio-technical and geopolitical network. Full article

Facial emotion recognition (FER) plays an important role in healthcare, human–computer interaction, and intelligent security systems. However, despite recent advances, many state-of-the-art FER methods depend on computationally intensive CNN or transformer backbones and large-scale annotated datasets while suffering noticeable performance degradation under cross-dataset evaluation because of domain shift. These limitations hinder practical usage in resource-constrained and real-world environments. To address this issue, we propose Cross-Adaptive Gated Fusion Network (CA-GFNet), a lightweight dual-stream FER framework that explicitly combines shallow structural features with deep semantic representations. The proposed architecture integrates domain-robust gradient-based descriptors with compact deep features extracted from a VGG-based backbone. After face detection and normalization, the structural stream captures fine-grained local appearance cues, whereas the semantic stream encodes high-level facial configurations. The two feature streams are projected into a shared latent space and adaptively fused using a gated fusion mechanism that learns sample-specific weights, allowing the model to prioritize the more reliable feature source under dataset shift. Extensive experiments on KDEF along with zero-shot cross-dataset evaluation on CK+ using a strict train-on-KDEF/test-on-CK+ protocol with subject-independent splits demonstrate the effectiveness of the proposed method. CA-GFNet achieves 99.30% accuracy on KDEF and 98.98% on CK+ while requiring significantly fewer parameters than conventional deep FER models. These results confirm that adaptive gated fusion of shallow and deep features can deliver both high recognition accuracy and strong cross-dataset robustness. Full article

Against the backdrop of global climate change, enhancing urban adaptive capacity to climate shocks has become a critical issue for sustainable urban development. Based on this, this study treats the Climate-Adaptive City Pilot (CACP) policy in China as a quasi-natural experiment and employs a difference-in-differences (DID) approach to empirically evaluate its impact on urban ecological resilience, using panel data from Chinese prefecture-level cities from 2010 to 2023. Heterogeneity and mechanism analyses are further conducted to explore differential policy effects and underlying transmission channels. The results indicate that the Climate-Adaptive City Pilot policy significantly enhances urban ecological resilience, and this finding remains robust after a series of robustness checks, including winsorized regressions, propensity score matching, time placebo tests, and individual placebo tests. Further analysis reveals that the policy effects are more pronounced in cities with lower or higher levels of human capital development, as well as in cities with low to medium water resource endowments. Mechanism analysis suggests that resilient infrastructure investment and green technological innovation constitute the key pathways through which the pilot policy improves urban ecological resilience. From the perspective of urban ecological resilience, this study provides empirical evidence on the effectiveness of climate-adaptive city pilot policies and offers important policy implications for deepening the implementation of climate-adaptive city initiatives, designing context-sensitive adaptation strategies, and improving urban climate adaptation governance mechanisms. Full article

As artificial intelligence (AI) systems are increasingly adopted in recruitment practices, applicants’ responses to AI-mediated interviews have become an important issue for organizations. Understanding how applicants interpret these systems is relevant for organizational attractiveness and employer branding. Drawing on social exchange theory and signaling theory, this study examines the role of AI interview explainability in shaping applicants’ evaluations of organizations. It proposes that explainability influences organizational attractiveness through two parallel mechanisms: perceived organizational support and perceived innovativeness. Survey data were collected from 196 job applicants with experience in AI-based interviews. The results show that higher perceived explainability of AI interviews is associated with stronger perceptions of organizational support and organizational innovativeness. Both perceptions are positively related to organizational attractiveness. These findings support a dual-mediation model and suggest that explainable AI interview systems communicate both supportive intentions and technological capability to applicants. By focusing on applicants’ perceptions, this study contributes to the growing literature on AI use in human resource management. It highlights the importance of explainable system design in shaping early applicant reactions. The findings also provide practical implications for organizations seeking to implement AI-based recruitment tools that are transparent, credible, and attractive to potential applicants. Full article

Background/Objectives: The ethical climate in a healthcare institution encompasses the shared perceptions of how ethical issues are managed in everyday practice. Our prior survey at the University Hospital of Split, Croatia, showed a simultaneous predominance of the “Rules” and “Laws and professional codes” ethical climates. Building on these findings, we explored how these climates manifest in everyday practice, how they align with staff values and guide their ethical decision-making, and how they are shaped by external factors. Methods: We conducted seven focus groups with 31 participants: nurses, residents, specialists, and members of the Hospital Ethics Committee (HEC). We identified patterns in the data using Graneheim and Lundman’s qualitative content analysis. Results: Three themes emerged from our analysis. We observed that the ethical climate was shaped predominantly by healthcare professionals themselves based on shared professional values and informal norms, rather than explicit institutional rules. Nurses, positioned as frontline workers, felt particularly exposed to ethical dilemmas, reporting perceived subordination to physicians, increased pressures from patients, and vulnerability in ethically ambiguous situations. The participants generally believed that institutional leadership insufficiently utilised existing tools, bodies, and mechanisms to support ethical behaviour and sanction misdemeanors, resulting in gaps in human resource management, a lack of practical protocols, and a weak HEC. Conclusions: To strengthen the ethical climate, institutional leadership should provide clear and practical guidelines, effectively utilise regulating bodies and support services, establish dedicated mechanisms to support nurses, and consistently enforce sanctions for unethical behaviour. Full article