Search Results (429)

Visible–near-infrared spectroscopy enables rapid, non-destructive soil organic matter (SOM) detection, yet its prediction accuracy relies heavily on the effectiveness of the chosen algorithmic models. Weighted Averaging Ensemble Models (WAEM) are robust but face a key challenge: their performance depends on optimal base learner weight allocation, which standard evaluation indices often fail to achieve, risking biased weights and local optima. This study significantly enhances WAEM by determining optimal weights using information extracted from the model training process via seven methods, including reinforcement learning and a self-attention mechanism (Sam). Experiments on 704 soil samples from China’s Tarim River Basin employed a dynamic data structure for real-time weight updating. Results show that six WAEM methods utilizing training process information outperformed conventional evaluation index approaches. Improvements reduced WAEM root mean square error (RMSE) by 0.028–1.279 g kg⁻¹ and increased the correlation coefficient (R²) by up to 0.06. Sam achieved the highest performance, with R² and RMSE reaching 0.927 and 2.325 g kg⁻¹, respectively. Furthermore, model R² began converging at 26 base learners, indicating diminishing returns from adding more. This research confirms that dynamic WAEM weight allocation via Sam significantly boosts SOM prediction accuracy, providing a scientific foundation for infrared-based soil monitoring. Full article

Background: Life for women drastically altered after the 2021 US-NATO military withdrawal from Afghanistan. Methods: Applying a gendered general strain theory (GGST) model, this paper presents mixed-method findings from a 2023 semi-structured digital survey of 29 Afghan women, identifying key shared hardships concerning the daily lives of Afghan women (ages 18–65) and psychosocial stress. Results: A thematic analysis of their responses indicates that support for the Taliban’s return to power ranges among women; however, respondents experience diminished quality-of-life (DQOL) factors like persistent food insecurity that affect their views of the current government and affect their psychosocial health. Furthermore, most struggle with financial insecurity and growing governmental restrictions, particularly gender discrimination policies (GDP), further increasing their stress as they try to acclimate to the new political environment. Additionally, we triangulate the key qualitative findings with a statistical analysis to help illustrate emerging patterns between DQOL factors, GDP experiences, and psychosocial stress (PSS). Conclusions: This study is one of the first known semi-structured surveys conducted within the country of Afghanistan after the Taliban reseized control, offering crucial insights into life of Afghan women through their own intimate experiences and perspectives. Full article

Atmospheric turbulence, quantified by the refractive index structure parameter (${C_n}^2$), degrades the performance of optical systems. Reliable ${C_n}^2$ estimation is critical for free-space optical communication, remote sensing, and astronomy. This study compares frame-based and event-based approaches to turbulence strength estimation. A high-speed CMOS camera (180/90/30 frames per second (FPS)) and an event camera were deployed along a 300 m outdoor path, with a scintillometer providing ground truth. Event streams were segmented into 5 s windows, features were extracted, and predictions were made using an Extreme Gradient Boosting regressor (XGBoost). A hybrid model was also tested, combining CMOS-based predictions with event features. Results show that CMOS accuracy is strongly dependent on frame rate, with diminishing returns beyond 90 FPS under weak turbulence. Event-based models achieved higher correlation with ground truth in strong turbulence but produced larger errors in weak regimes. The hybrid approach yielded the best overall performance in moderate-to-strong turbulence, reducing mean estimation error by ~35% compared to CMOS-only at 180 FPS. These findings demonstrate the complementary strengths of frame and event modalities. Frame cameras provide stability in weak turbulence, while event sensors capture fast fluctuations under stronger conditions. Together, they enable more robust ${C_n}^2$ estimation and motivate further research into advanced hybrid sensing strategies for operational turbulence monitoring. Full article

Forest carbon sinks (FCS)—referring specifically to ecosystem-based carbon sequestration provided by forest ecosystems—are being increasingly recognized as a strategic form of natural capital under China’s “dual carbon” goals. While the ecological value of FCS is being translated into economic benefits through carbon markets, eco-compensation, and green finance, the extent to which ecosystem carbon sinks can continuously drive regional economic growth—and how such effects differ across regions—remains insufficiently understood. Using panel data for 294 Chinese prefecture-level cities from 2010 to 2022, this study employs dynamic panel methods to examine the dynamic, nonlinear, and heterogeneous impacts of ecosystem-based FCS on economic growth. The results show that (1) FCS significantly promote economic growth but follow an inverted U-shaped pattern, indicating diminishing marginal returns; (2) notable regional heterogeneity exists, with the strongest effects in central and western regions, while eastern cities exhibit weaker responses due to structural and spatial constraints; and (3) clear threshold effects are present, suggesting that industrial upgrading, urbanization, and moderate government intervention can amplify the economic contribution of FCS. These findings clarify the mechanism through which FCS transitions from ecological assets to economic capital, providing theoretical and empirical support for sustainable forest management, ecological-industrial integration, and carbon market optimization in the pursuit of carbon neutrality. Full article

As an active flow-control technology, the counterflowing jet can reduce drag by reconstructing the flow field structure during the re-entry of a vehicle, thereby mitigating the adverse effects of high overload on personnel. However, variations in the angle of attack (AoA) and nozzle mass flow rate tend to induce transitions in its flow field modes and fluctuations in drag reduction performance. To further investigate the aerodynamic interference characteristics of the counterflowing jet during the re-entry process, this study focused on a 2.6% subscale model of the Apollo return capsule. The Reynolds-averaged Navier–Stokes (RANS) equations turbulence model was employed to numerically analyze the effects of different mass flow rates and freestream AoAs on the flow field modes and the drag behavior. The results indicate that with an increase in AoA, the flow field structure of the long penetration mode (LPM) is likely to be destroyed, and the shock wave shape exhibits significant asymmetric distortion. In contrast, the flow field structure of the short penetration mode (SPM) remains relatively stable; however, the bow shock and Mach disk exhibit two typical offset patterns, whose offset characteristics are jointly regulated by the mass flow rate and AoA. In terms of drag characteristics, the AoA significantly weakens the drag reduction effect of the LPM. In contrast, the SPM can maintain a stable drag reduction efficiency of approximately 50% within a certain AoA range. Nevertheless, as the AoA further increases, the drag reduction effect of the SPM gradually diminishes. Full article

Intangible cultural heritage (ICH) is deeply embedded in everyday social life, yet its officially recognized spatial distribution reflects both the independent influences of cultural traditions, development trajectories, and governance practices, and the complex interactions among them. Focusing on 494 national-level ICH items across ten categories in Jiangsu(J), Zhejiang(Z), and Shanghai(H), this study adopts a social-geographical perspective to examine both the spatio-temporal evolution and the driving mechanisms of ICH recognition in one of China’s most developed regions. After rigorous verification of point-based ICH locations, we combine kernel density estimation and the average nearest neighbor index to trace changes across five batches of national designation, and then employ the univariate and interaction detectors of the Geodetector model to assess the effects of 28 natural, socioeconomic, and cultural-institutional variables. The results show, first, that ICH exhibits significant clustering along river corridors and historical cultural belts, with a persistent high-density core in the Shanghai–southern Jiangsu–northern Zhejiang zone and a clear shift over time from highly concentrated to more dispersed and territorially balanced recognition. Second, human-environment factors—especially factors such as urban and rural income and consumption; residents’ education and cultural expenditures; and public education and cultural facilities—have far greater explanatory power than natural conditions, while different ICH categories embed distinctively in urban and rural socio-economic contexts. Third, bivariate interactions reveal that natural and macroeconomic “background” variables are strongly amplified when combined with demographic and cultural factors, whereas interactions among strong human variables show bivariate enhancement with diminishing marginal returns. In summary, these findings enrich international debates on the geography of ICH by clarifying how recognition processes align with regional development and social equity agendas, and they provide a quantitative basis for category-sensitive, place-based strategies that coordinate income policies, public cultural services, and the joint safeguarding of tangible and intangible heritage in both urban renewal and rural revitalization planning. Full article

This case study evaluates the economic and energy efficiency of retrofitting a hospital heating system in Krakow, Poland, by transitioning from a district-heating-only model to a bivalent hybrid system. The analyzed configuration integrates air-to-water heat pumps (HP), a 180 kWp photovoltaic (PV) installation, and a 120 kWh battery energy storage (ES) unit, while retaining the municipal district heating network as a peak load and backup source. Utilizing high-resolution quasi-steady-state simulations in Ebsilon Professional (10 min time step) and projected 2025 market data, the study compares three modernization scenarios differing in heat pump capacity (20, 40, and 60 kW). The assessment focuses on key performance indicators, including Net Present Value (NPV), Levelized Cost of Heating (LCOH), and Simple Payback Time (SPBT). The results identify the bivalent system with 40 kW thermal capacity (Variant 2) as the economic optimum, delivering the highest NPV (EUR 121,021), the lowest LCOH (0.0908 EUR/kWh), and a payback period of 11.94 years. Furthermore, the study quantitatively demonstrates the law of diminishing returns in the oversized scenario (60 kW), confirming that optimal sizing is critical for maximizing the efficiency of bivalent systems in public healthcare facilities. This work provides a detailed methodology and data that can form a basis for making investment decisions in similar public utility buildings in Central and Eastern Europe. Full article

In recent years, honeycomb sandwich structures have seen continuous development due to their excellent structural performance and design flexibility in heat dissipation. However, their complex heat transfer mechanisms and diverse modes of thermal exchange necessitate research on the air flow behavior and temperature distribution characteristics of micro-channels and lattice pores. This study investigates the internal flow field within a ventilated honeycomb sandwich structure through numerical simulation. The spatial flow characteristics and temperature distribution are analyzed, with a focus on the effects of turbulent kinetic energy, heat flux distribution on the heated surface, and varying pressure drop conditions on the thermal performance. The results indicate that the micro-channels inside the honeycomb core lead to a strong correlation between temperature distribution, flow velocity, and turbulence intensity. Regions with higher flow velocity and turbulent kinetic energy exhibit lower temperatures, confirming the critical role of flow motion in heat transfer. Heat flux analysis further verifies that heat is primarily removed by airflow, with superior heat exchange occurring inside the honeycomb cells compared to the solid regions. The intensive mixing induced by highly turbulent flow within the small cells enhances contact with the solid surface, thereby improving heat conduction from the solid to the flow. Moreover, as the inlet pressure increases, the overall temperature gradually decreases but exhibits a saturation trend. This indicates that beyond a certain pressure level, further increasing the inlet pressure yields diminishing returns in heat dissipation enhancement. Full article

Blockchain systems have been widely adopted in today’s society, with consensus algorithms serving as their core component to ensure all participants in the network agree on a specific data state. Existing consensus algorithms such as Proof of Work (PoW), Proof of Stake (PoS), and the Practical Byzantine Fault-Tolerant Algorithm (PBFT) exhibit certain limitations in terms of scalability, security, and efficiency. To address these limitations, this paper proposes a novel Network-based Reputation Consensus (NRC) algorithm. The main research contributions of this work include the following: (1) An intelligent grouping mechanism that dynamically groups nodes based on network awareness, forming consensus groups with low internal latency and high bandwidth utilization, significantly reducing intra-group communication overhead. (2) A dynamic reputation system incorporating a “diminishing returns” reward function and a “multiplicative penalty” mechanism, effectively incentivizing honest node participation while preventing power monopoly. (3) A two-phase model of “intra-group BFT consensus + global communication committee ordering” that decomposes complex global consensus into parallel intra-group processing and coordination among a small set of elite nodes, thereby drastically improving efficiency. (4) Comprehensive simulations comparing the NRC algorithm with mainstream consensus algorithms, demonstrating its superior performance in communication overhead, throughput, latency, and tolerance to malicious nodes, thereby laying the foundation for large-scale applications. Full article

This study asks whether Ethereum’s proof-of-stake (PoS) incentives not only make economic sense on paper but also feel attractive to real validators who may be loss-averse and sensitive to risk. We take a canonical Eth2 slot-level model of rewards, penalties, costs, and proposer-conditional maximal extractable value (MEV) and overlay a prospect-theoretic valuation that captures reference dependence, loss aversion, diminishing sensitivity, and probability weighting. This Prospect-Theoretic Incentive Mechanism (PT-IM) separates the “money edge” (expected accounting return) from the “felt edge” (behavioral value) by mapping monetary outcomes through a prospect value function and comparing the two across parameter ranges. The mechanism is parametric and modular, allowing different MEV, cost, and penalty profiles to plug in without altering the base PoS model. Using stylized numerical examples, we identify regions where cooperation that pays in expectation can remain unattractive under plausible loss-averse preferences, especially when penalties are salient or MEV is volatile. We discuss how these distortions may affect validator participation, economic security, and the tuning of rewards and penalties in Ethereum’s PoS. Integrating behavioral valuation into crypto-economic design thus provides a practical diagnostic for adjusting protocol parameters when economics and perception diverge. Full article

Background: Understanding how Regional Innovation Systems (RISs) drive innovation outputs remains a central question in innovation studies. Most existing empirical research relies on linear or single-indicator models, which fail to capture nonlinear interactions among the key RIS dimensions—Firms, Knowledge, Government, and Economy. Methodology: This study proposes an integrated analytical framework that combines Confirmatory Factor Analysis (CFA), CatBoost machine learning, and SHAP-based explainability to bridge theory-driven modeling with data-driven prediction. Using provincial panel data from China spanning 2011–2023, CFA is first employed to construct and validate four latent RIS dimensions. These latent constructs are then used as inputs in a CatBoost model to predict regional patent outputs, followed by SHAP analysis to quantify the marginal and interactive contributions of each dimension. Results: The CFA results confirm the reliability and validity of the four latent dimensions, establishing a robust structural foundation for the RIS. The CatBoost model achieves high predictive accuracy (log-transformed $R^2$ = 0.975, RMSE = 0.206), substantially outperforming traditional linear benchmarks. SHAP analysis indicates that the Firm dimension is the primary driver of innovation output, while Knowledge, Government, and Economy dimensions exhibit context-dependent moderating effects characterized by diminishing returns, threshold effects, and nonlinear synergies. Conclusions: By integrating latent-variable modeling with interpretable machine learning, this study develops a “CFA-CatBoost-SHAP” closed-loop paradigm for transparent and high-precision analysis of innovation mechanisms. This approach advances RIS theory by empirically validating its multidimensional structure, enriches the methodological toolkit for innovation research, and provides actionable insights for the design of targeted R&D and innovation policies. Full article

While a number of studies have analyzed the determinants of economic growth in Asia, the research on the synergistic interplay of the quality of governance and the investments in research and development have not received nuanced attention in the scholarly research. This study fills the research gap by looking at the joint effect of governance and R&D investment on economic growth in Asian nations with varying levels of development. Using the fixed-effects model and the generalized method of moments (GMM) model, this study investigated the individual and combined effect of governance and R&D investment in driving economic growth in the static as well as dynamic panel of 34 Asian nations for the period 2000–2024. The study further undertakes a comparative assessment of the lower-middle-income, upper-middle-income, and high-income economies on the continent. The findings reveal that the interaction between R&D and governance is negative and significant in lower-middle-income countries such as India, Indonesia, Philippines, and Tajikistan, showing that weak institutions hinder R&D effectiveness. It turns strongly positive in upper-middle-income economies such as China, Kazakhstan, Malaysia, and Thailand, as governance strengthens, but becomes insignificant, in high-income nations such as Israel, Korea, Singapore, and Qatar, suggesting diminishing returns. The results under dynamic panel estimation show positive and significant effects of the interaction between R&D and governance upon per capita GDP in all countries’ panels. The findings suggest that the diverse and nonlinear progression from technological adoption to creation in Asian nations requires sustained investments in R&D and deliberate policy alignment with national innovation systems. Full article

Objectives: The recovery of arm function after macroreplantation is influenced by various factors. The aim of this study was to present the results of functional rehabilitation outcome after replantation of an upper extremity. Moreover, we assessed nerve conduction validity in the process of monitoring the return of manual functions. Methods: The study was performed in a group of seven patients after upper extremity macroreplantation and rehabilitation. Assessments included measuring hand/arm function loss using Swanson’s method, range of motion, muscle strength, sensation, and manipulation dexterity through the NHPT (Nine-Hole Peg Test). The nerve conduction study measured response amplitude, conduction speed, and distal latency. Results: The average loss of function of the hand diminished from 63.6% to 49.18%. Significant improvement in global pressure was achieved. In the functional capacity test (NHPT), the average time of the test was improved. The final nerve conduction study demonstrated improvements in motor and sensory conduction parameters. A correlation between improvement in conduction parameters in sensory fibers and sensation in the two-point discrimination test was found. Increased potential amplitude in motor fibers of the examined nerves correlated with a decrease in loss of function of the arm. Conclusions: Functional assessment and tailored rehabilitation strategies would maximize recovery potential after macroreplantation. Nerve conduction remains a crucial tool in monitoring the progress of manual skills after months of rehabilitation. Our findings highlight the importance of long-term follow-up of these patients. Full article

Global fishery resources are under increasing pressure from environmental change and institutional constraints. China’s seasonal fishing moratorium has contributed to resource recovery but has also created income and employment challenges for small-scale fishers. This study examines how livelihood capital structures shape annual livelihood portfolios under predictable closure constraints, using three representative fishing communities in Guangdong Province as case studies. A combination of data augmentation, regression analysis, and agent-based simulation was applied to analyze the relationships between capital endowments and behavioral responses. Results show that environmental and financial capital significantly increase the likelihood of maintaining capture as the primary livelihood, while psychological capital stabilizes decisions under uncertainty. Physical capital and social networks exhibit more variable effects, reflecting differentiated adaptive capacities. Simulations further reveal threshold effects and diminishing marginal returns in capital accumulation, with heterogeneous temporal impacts across capital types. Theoretically, the study extends the Sustainable Livelihoods Approach by incorporating environmental and psychological capital, thereby enriching the understanding of capital mechanisms in fisheries. Overall, the findings advance knowledge of how small-scale fishers adapt under institutional constraints and provide practical insights for policies aimed at aligning livelihood security with the sustainable use of marine resources. Full article

Analyzing the spatial patterns of vitality in historic urban areas and their influencing elements is essential for improving the vitality of historic and cultural cities and fostering sustainable urban development. This research investigated the historic urban area of Kaifeng City. Employing Baidu Huiyan population location data, it assessed the spatial distribution of vitality on weekdays and weekends. A built environment indicator system was developed using multi-source data, and the CatBoost-SHAP model was applied to examine the nonlinear relationship between the built environment and the vitality of a historic urban area, along with the interactions among different factors. The study systematically explored the spatiotemporal dynamics of vitality and the influence mechanisms of the built environment. The results showed the following: (1) The vitality of Kaifeng’s historic urban area demonstrated significant spatiotemporal heterogeneity, exhibiting an “inner-hot, outer-cold” spatial pattern. Overall vitality levels were higher on weekends than on weekdays, with a progressive decline from morning to night. (2) Built environment factors dynamically influenced vitality across time periods. The impacts of POIM and BD shifted markedly, indicating temporal variations in vitality-driving mechanisms. (3) Synergistic interactions among built environment factors exerted nonlinear effects on urban vitality. Within reasonable threshold ranges, BSD, POID, and BD promoted vitality but exhibited diminishing marginal returns under high-density conditions. Notably, BSD played a core moderating role in multi-factor interactions. These findings reveal the complex and dynamic relationship between the built environment and historic urban vitality. They indicate that spatial governance should prioritize the synergistic integration of transportation, functions, ecology, and culture to achieve dual improvements in urban vitality and environmental quality, thereby providing important theoretical support and practical guidance for planning and spatial optimization in historic urban areas. Full article