Search Results (2,626)

Hyperspectral image classification is a core task in remote sensing image analysis and understanding. Existing Transformer-based methods have achieved excellent performance but are limited by the quadratic computational complexity of the self-attention mechanism, while the high-dimensional redundancy of hyperspectral data and the difficulty in deeply integrating spatial–spectral features also restrict further performance improvement. To address these issues, we introduce the Mamba architecture based on state-space models into hyperspectral image classification and propose the DFMamba model. The main innovations include (1) constructing a Hyperspectral Spatial Attention Embed (HSAE) to achieve efficient channel compression and feature extraction via adaptive grouped convolution, depth-wise separable convolution, and spatial attention; (2) proposing a spatial–spectral dual-branch collaborative modeling mechanism, EnhancedBothMamba, which separately models global dependencies in the spatial and spectral branches and integrates their outputs through softmax-normalized learnable global weights together with a learnable residual scaling factor; and (3) building an improved classification head, ClsHead, with a multi-scale branch fusion strategy to fully exploit local and global feature information. The experimental results on four standard hyperspectral datasets demonstrate that DFMamba achieves overall accuracy (OA) of 97.41% on the Pavia University dataset, 92.25% on the HanChuan dataset, 95.12% on the HongHu dataset, and 94.98% on the Houston dataset. Under the adopted evaluation protocol, DFMamba obtains higher mean OA than MambaHSI and the other compared methods while retaining favorable computational efficiency. Full article

Against the backdrop of the “Rural Revitalization” strategy, contract farming has emerged as a new mechanism and a growing research focus. This study employs differential game theory to systematically analyze the dynamic decision-making and operational performance differences among supply chain members by comparing scenarios with and without research institution participation, as well as different government subsidy policies. The innovative contribution lies in introducing a dual-effect index to capture consumers’ quality utility from agricultural products and psychological utility derived from supporting farmers. Furthermore, we explore the synergistic effects of research institutions’ technological empowerment and government subsidy strategies on supply chain efficiency. Through the above research and analysis, the study draws the following conclusions: (1) The collaborative agricultural supporting model significantly enhances both farmer yields and overall supply chain profits through technology diffusion and brand premium effects. However, revenue distribution conflicts may lead enterprises to limit their cooperation depth. (2) Government subsidies can alleviate benefit allocation conflicts. Subsidizing a research institution proves more effective at stimulating long-term technological dividends, while subsidizing an enterprise primarily ensures short-term market stability. (3) Brand advantage, technological leadership, and consumer preferences exhibit nonlinear driving effects on supply chain efficiency. Specifically, the positive impact progressively strengthens with wider brand advantages, greater technological leadership, and more intense consumer preferences. The study provides a theoretical foundation for tripartite collaboration, revealing the pivotal role of policy precision and dynamic benefit allocation equilibrium in agricultural supply chain upgrading. Full article

Propelled by accelerated technological innovation, the advancement of new material development has become imperative for emerging industries. The finite element method, leveraging multi-scale modeling and interdisciplinary integration, has established itself as one of the pivotal computational tools for significantly boosting research and development efficiency in material science. This study employed a combined approach of ABAQUS simulation and experimentation to efficiently design and synthesize an Al/W energetic composite characterized by concentrated energy release, high reaction enthalpy, and a unique discrete embedded structure. The finite element analysis focused on the stress distribution, equivalent strain, embedding depth, and energy conversion phenomena of heterogeneous particles under varying impact velocities. The results demonstrated that at a collision velocity of 500 m/s, embedded collisions between Al and W particles were achieved, providing effective guidance for synthesizing Al/W energetic composites with embedded structural features via high-speed impacts. TG-DSC analysis revealed that the Al/W energetic composites exhibited a reaction enthalpy change of 8806.0 ± 152 J/g and a maximum heat flow rate of 101.5 ± 2.8 W/g, which were 3.5-fold and 3.1-fold higher than those of pure Al with identical dimensions, respectively, and significantly surpassed the values of the mechanically mixed Al/W energetic composites. This integrated finite element simulation and experimentation provides a new pathway towards the efficient design and synthesis of novel materials with analogous components and structures. Full article

Lithium-ion batteries (LIBs) are essential for current technological infrastructure, driving the development of portable electronics, electric vehicles or grid-scale energy storage. The performance and sustainability of LIBs are critically dependent on their anode materials. This comprehensive review analyzes the evolution and characteristics of key anode materials, highlighting the specific properties they confer to the final battery products. Beyond material properties, the synthesis methods employed for these materials, from conventional techniques (such as solid-state reactions, sol–gel, hydrothermal/solvothermal, co-precipitation, etc.) to innovative and greener approaches (like electrospinning and a novel induction furnace-oxidation hybrid method for complex oxides), are a crucial part in the development of sustainable materials. While these methods offer different advantages, the challenges in achieving optimal electrochemical performance, including issues related to material stability, capacity retention and scalability, remain significant for both research and manufacturing industries. Furthermore, a significant focus is placed on strategies for mitigating the environmental impact associated with anode material production, emphasizing the importance of unconventional and sustainable synthesis routes. Ultimately, the sustainable evolution of LIB technology to achieve future energy demands hinges on overcoming existing limitations. This necessitates integrated research combining advanced material modeling and design, scalable and environmentally conscious synthesis techniques and in-depth electrochemical characterization. Full article

Pediatric oncology nursing is among the highest-intensity care specialties, exposing nurses to repeated child death and sustained emotional investment in family relationships. This occupational burden is a recognized psychosocial risk factor and a driver of compassion fatigue, burnout, and reported intention to leave, yet the organizational and educational determinants of this risk remain poorly captured by existing assessment approaches. This study investigated the lived experiences of nurses caring for terminally ill children in a pediatric onco-hematology unit and hospice ward in central Italy (N = 15), using a qualitative descriptive design, informed by a phenomenological sensibility to lived experience, supported by computer-assisted textual analysis. Structured face-to-face interviews were analyzed through thematic content analysis with stepwise replication, while quantitative textual analysis was performed using T-Lab software to map co-occurrence patterns among key lemmas, providing a frequency-based, semantically grounded picture of nurses’ representations of occupational risk. Seven themes emerged, including quality of nursing care, parental relationships, coping with a child’s death, and impact on personal and professional life. Findings showed that reported intention to leave was associated, in participants’ accounts, with absent psychological debriefing, cumulative emotional burden, inadequate end-of-life training, and a near-total absence of organizational vocabulary for peer-level support—a gap directly visible in the co-occurrence structure of nurses’ language. As a secondary aim, by combining qualitative depth with complementary lexical analysis of thematic patterns, this study also offers a methodological example of how psychosocial risk in high-intensity care settings can be assessed and translated into actionable indicators for organizational climate and workforce well-being. The findings inform concrete prevention strategies and policy recommendations for nursing education and occupational health, consistent with this Special Issue’s focus on methodological innovation for psychosocial risk assessment and public health policy design. Full article

Mechanized harvesting of subsurface crops has long been confronted with the critical engineering dilemmas of high damage rates and high impurity rates. Traditional taxonomic classification methods based on botanical families and genera fail to provide effective guidance for the engineering research and development of harvesting machinery. From an engineering perspective, this paper proposes a novel classification logic that categorizes subsurface crops into three major types based on their soil burial depth and physical distribution characteristics: shallow-soil clustered growth type (0–20 cm), mid-soil scattered growth type (20–40 cm), and deep-soil vertically rooted type (>40 cm). The harvesting bottlenecks of representative crops within these strata, including potato, onion, peanut, sweet potato, cassava, and yam, are systematically elucidated. Furthermore, this review provides an in-depth analysis of the current state of frontier core technologies, such as bionic drag reduction excavation, flexible multi-stage separation, microscopic discrete element method (DEM) simulation, kinematic optimization, and AI-based visual perception. This paper aims to reveal the common bottlenecks in subsurface crop harvesting and prospect future developmental trends centered on the deep integration of machinery and agronomy as well as intelligent perception and adaptation, thereby providing a solid theoretical foundation and engineering reference for the innovation of global agricultural machinery. Full article

Conventional pavement design methods are generally intended for highways and are not suited to the extreme loading conditions experienced by mining haul roads. This study presents an innovative pavement design for a heavily trafficked phosphate mine haul road (≈22.35 kT·day⁻¹) constructed entirely from phosphate mine waste rock (PMWR), offering a sustainable alternative to conventional aggregates. The proposed structure comprises a 0.35 m sub-base (0–100 mm), a 0.25 m base (0–63 mm), and a 0.07 m semi-granular asphalt concrete (BBSG 0–20 mm) wearing course designed to combine high mechanical performance with effective dust control. The design was validated through an integrated experimental program that included repeated load triaxial testing (RLTT), asphalt stiffness, fatigue and rutting tests, thermogravimetric analysis (TGA), and full-scale field trials involving EV₂ plate-load testing, dust monitoring, and emergency braking tests using a Komatsu 730E haul truck. The results demonstrate that the proposed pavement provides excellent structural performance. The asphalt mixture achieved a stiffness modulus of 9160 MPa, a fatigue resistance of 139.6 µε, and a proportional rut depth (PRD) of only 2.2%. In the field, the compacted sub-base and base reached average EV₂ values of 153 MPa and 181 MPa, respectively, confirming their high load-bearing capacity. The paved haul road reduced airborne dust emissions by approximately 91%, surpassing the mine’s target of 80%, while also enabling haul-truck operating speeds to double, with associated reductions in tire wear and maintenance. Despite these performance gains, the proposed solution remains economically attractive, with a construction cost of approximately 23.97 €/m². Overall, the study demonstrates that phosphate mine waste rock can be successfully transformed into a durable, cost-effective, and environmentally sustainable pavement solution for heavy-haul mining roads, providing a practical example of circular economy principles in mining infrastructure. Full article

Green and resilient agriculture can strike a balance between agricultural production and ecological conservation, ensuring a continuous and stable supply of food and agricultural products. Based on provincial panel data from 31 provinces in China during 2014–2023, this study constructs an evaluation system for agricultural green resilience and employs the fixed-effects model, mediation effect model, and spatial Durbin model to empirically examine the impact mechanism and spatial effects of digital inclusive finance on agricultural green resilience. The main findings are as follows: (1) Digital inclusive finance significantly enhances agricultural green resilience. (2) Agricultural technological innovation plays a mediating role in the relationship between digital inclusive finance and agricultural green resilience. (3) Digital inclusive finance generates significant positive spatial spillover effects on agricultural green resilience, not only improving local agricultural green resilience but also promoting coordinated development in neighboring regions. (4) Regional heterogeneity analysis indicates that the promoting effect is strongest in the central region, followed by the western region, while no significant effect is observed in the eastern region. From the perspective of different dimensions, the effect intensity follows the order of coverage breadth, depth of use, and degree of digitalization. These findings suggest that digital inclusive finance serves as an important driver of agricultural green resilience and sustainable agricultural transformation in China. Therefore, policymakers should further expand digital financial inclusion, strengthen support for agricultural technological innovation, and promote coordinated regional development to enhance agricultural green resilience. Full article

This study investigates how community engagement and outreach programs can serve as a vehicle for knowledge co-production between higher educational institutions (HEIs) and industry in developing economies. Despite growing calls for collaboration between HEIs and industry, the existing literature remains policy-driven and largely instrumental, offering a limited understanding of how engagement practices are socially constructed, negotiated, and experienced by diverse stakeholders. There is limited qualitative research on how community voices are integrated into co-creation processes and how these interactions shape sustainable development in emerging economies. The research methodology for the study was developed using the research onion, and an interpretivist research philosophy was adopted. The study employed an inductive research approach to explore actors’ lived experiences and the meanings attached to engagement practices and collected data through in-depth interviews. Participants in the study included representatives from HEIs, industry, and community stakeholders across selected sectors of the economy, and the data were analysed thematically to capture patterns of interactions, power relations, and knowledge and exchange processes underpinning co-production. The study revealed that effective community engagement goes beyond formal partnerships to encompass building lasting relationships which are characterised by trust, mutual learning, and context adaptation. Further, the study also showed that power asymmetries, institutional pressures, and resource constraints have restricted real co-production, resulting in symbolic rather than real engagement. Nevertheless, the use of inclusive and reflective approaches has been shown to foster locally acceptable and essential innovative interventions that can improve sustainable development. The study therefore recommends embedding participatory frameworks within HEIs and industry collaborations to strengthen institutional support for inclusive engagement and to reorient policies towards community-centred knowledge systems. This study, therefore, contributes to the body of knowledge by enhancing theoretical and practical understanding of sustainable higher education–industry collaboration in developing economies by promoting an understanding of engagement as a co-production process. Full article

Artificial intelligence is increasingly integrated into physical products, industrial scenarios, and innovation ecosystems, yet management research has focused mainly on AI adoption rather than the depth of organizational integration. This study examines how AI embedding affects enterprise competitiveness in China’s embodied intelligence industry and whether competitive structure reconfiguration mediates this relationship. Using survey data from 266 firms and partial least squares structural equation modeling, the analysis shows that AI embedding positively affects both enterprise competitiveness and competitive structure reconfiguration. Competitive structure reconfiguration also improves enterprise competitiveness and partially mediates the focal relationship, with a variance accounted for value of 43.7%. By contrast, the moderating effects of data–computing foundation and scenario openness are not supported. These findings indicate that the competitive value of AI depends not only on adoption but also on its integration into R&D, decision-making, organizational coordination, and scenario development, as well as on the structural changes that follow. The study contributes by distinguishing AI embedding from AI adoption and by identifying competitive structure reconfiguration as a process mechanism linking embedded AI to technological, ecosystem, and rule-based competitiveness. Full article

The transition toward Agriculture 4.0 aims to improve farm performance and sustainability; however, existing macroeconomic indicators do not fully capture the depth of farm-level digital adoption. This study proposes the Digital Farm Index (DFI) as a tool for assessing digital maturity and regional disparities in European agriculture. The research combines bibliometric mapping of the scientific literature with an empirical DFI assessment for 18 European Union Member States using Eurostat data. The empirical assessment utilizes multiple linear regression, log-linear scale modeling, and hierarchical clustering to analyze adoption patterns and structural determinants. The index integrates four dimensions: connectivity, precision agriculture, robotics, and farm management information systems (FMIS). Results indicate that adoption is concentrated in larger farms, as area-weighted digital maturity (DFI-Hectares) consistently exceeds farm-level adoption (DFI-Farms). CAPEX-based cost modeling suggests the existence of a technological indivisibility threshold, whereby digitalization may become an entry barrier for fragmented farms with limited economies of scale. Multiple linear regression suggests an East–West structural trend: while farm size influences the territorial diffusion of technology, a more consolidated regional innovation ecosystem appears more relevant for farm-level adoption. Findings also highlight a hardware–software imbalance and limited use of data-driven managerial tools. Support policies should therefore move beyond equipment subsidies and include technology transfer networks, digital skills, and managerial data-integration tools. Full article

With the continuous digital transformation of the construction industry, differences in the depth and effectiveness of BIM application during the construction phase of building projects have become increasingly evident. To systematically evaluate BIM application maturity during this stage, this study develops a construction phase-specific maturity assessment framework. First, based on the characteristics of BIM application during the construction phase and the logic of maturity assessment, the concept of BIM application maturity is defined. An evaluation indicator system is then constructed using the Balanced Scorecard as an organising framework, covering four dimensions: financial and cost-effectiveness, customer and delivery value, internal processes and efficiency, and learning and innovation capability. Second, grey relational analysis is used to screen and optimise the initial indicators, while the AHP-CRITIC combined weighting method is adopted to integrate subjective expert judgement and objective data characteristics. On this basis, a cloud model-based evaluation method is introduced to transform qualitative maturity assessment into quantitative evaluation while considering fuzziness and randomness. Finally, the proposed framework is applied to Project C as an empirical case application. The results show that the overall BIM application maturity of Project C during the construction phase is classified as the Integration Level. Among the four first-level dimensions, the customer and delivery value dimension performs relatively strongly, while the learning and innovation capability dimension shows the lowest expectation value. The proposed framework can help identify the maturity level and relative weaknesses of BIM applications during the construction phase, and can provide a reference for targeted BIM improvement and construction management decision-making. Full article

Despite rapid global growth in renewable energy capacity, fossil fuels still dominate the energy mix. Renewable energy firms often face limited access to bank credit because their asset-light, technology-intensive business models provide little collateral, constraining investment in clean energy deployment. This study examines whether China’s Fintech Reform Pilot Zones, which introduce digital technology-based credit evaluation, can alleviate these financing constraints and accelerate corporate energy transition. Using a staggered difference-in-differences design on a panel of Chinese listed renewable energy firms, we find that pilot zone designation significantly improves firms’ access to external financing and increases Energy Transition Depth (ETD) by approximately 3.6 percentage points, equivalent to 24.7% of the sample mean, indicating economically meaningful improvements in corporate energy transition. The strongest effects are observed in solar photovoltaic deployment and battery storage penetration. Greater energy transition is also associated with lower firm-level greenhouse gas emission intensity, suggesting potential environmental benefits. Mediation analysis identifies two complementary pathways: an innovation-accumulation route which advances renewable energy technology, and a capital-deployment route which supports renewable energy capacity expansion by relaxing firms’ general financing constraints. Regions with more developed renewable energy industries also exhibit lower fossil energy consumption and carbon emissions, suggesting potential regional spillover effects. These findings demonstrate that Fintech-enabled financial reform can facilitate renewable energy deployment and support broader energy transition and decarbonization, with important implications for emerging economies. Full article

Specialized firms face a strategic tension between extending established capabilities into new applications and preserving the coherence that underpins their competitive advantage. Integrating dynamic capability theory with research on managerial time orientation, this study examines whether disclosed action-based managerial long-term orientation is associated with subsequent product-application extension and whether such extension remains related to the firm’s existing capability base. The analysis uses an unbalanced retrospective cohort of 1272 firm-year observations from 325 listed Chinese firms that subsequently attained specialized, refined, distinctive, and innovative (SRDI) Little Giant status in the first three national batches. A change from the 10th to the 90th percentile of the raw long-term-orientation score (2 to 3) is associated with a 0.050 increase in the extension score (95% confidence interval: 0.021–0.080), equivalent to 1.25% of the full coding range and 10.9% of the outcome standard deviation. Firm fixed-effects and Mundlak estimates locate this modest association primarily in persistent differences across firms. A direct coefficient comparison favors related product-application extension, although the rarity of unrelated-expansion events limits the precision of this contrast. Digital-depth interactions vary across specifications, and extension is not significantly associated with sales growth or return on assets over the available horizons. These findings refine dynamic-capability theory by locating capability redeployment within persistent firm-level configurations and extend research on strategic time orientation by linking managerial horizons to the direction of product-application extension. Full article

Wide-scale, spatio-temporal quantification of soil CO₂ efflux is essential for understanding terrestrial carbon dynamics, predicting climate change, and evaluating the carbon balance in managed and natural ecosystems. Rising global temperatures, changing land use patterns, and other activities aimed at boosting crop productivity have resulted in an increase in microbial activity, increasing the impact of soil on gas exchange. Therefore, it is important to measure ${\mathrm{CO}}_2$ gas exchange in situ, over wide areas and extended periods without manual intervention. However, current approaches such as remote sensing lacks sufficient spatial and depth resolution, while other direct measurements such as eddy covariance demand expensive infrastructure, limiting wide-scale deployment. In this work, we propose a low-cost, battery-operated ${\mathrm{CO}}_2$ sensing system that provides long-term and scalable monitoring of soil respiration and carbon flux, with the promise for high-resolution measurements. Our innovative design features a PVC-based gas chamber that periodically opens and closes to allow for gas exchange, and a sensor module with low-cost temperature, moisture, pressure, and ${\mathrm{CO}}_2$ sensors, with a low-power wireless LoRa network for real-time monitoring. Our system was rigorously validated through multiple outdoor deployments, over long periods to demonstrate its practicality. We observe that temperature, air pressure, and humidity trends show responsiveness to the environment. We also observe that ${\mathrm{CO}}_2$ emission flux rate vary significantly across different modules, underscoring the need for fine-grained spatial and temporal resolution in monitoring. Full article