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Background: Accurate deformity classification and fusion-level planning are essential in adolescent idiopathic scoliosis (AIS) surgery and are traditionally guided by Cobb angle measurement and the Lenke system. Multimodal large language models (LLMs) (e.g., ChatGPT-4.0; Claude 3.7 Sonnet, Gemini 2.5 Pro, DeepSeek-R1-0528 Chat) are increasingly used for image interpretation despite limited validation for radiographic decision-making. This study evaluated the agreement and reproducibility of contemporary multimodal LLMs for AIS assessment compared with expert spine surgeons. Methods: This single-center retrospective study included 125 AIS patients (94 females, 31 males; mean age 14.8 ± 1.9 years) who underwent posterior instrumentation (2020–2024). Two experienced spine surgeons independently performed Lenke classification (including lumbar and sagittal modifiers) and selected fusion levels (UIV–LIV) on standing AP, lateral, and side-bending radiographs; discrepancies were resolved by consensus to establish the reference standard. The same radiographs were analyzed by four paid multimodal LLMs using standardized zero-shot prompts. Because LLMs showed inconsistent end-vertebra selection, LLM-derived Cobb angles lacked a common anatomical reference frame and were excluded from quantitative analysis. Agreement with expert consensus and test–retest reproducibility (repeat analyses one week apart) were assessed using Cohen’s κ. Evaluation times were recorded. Results: Surgeon agreement was high for Lenke classification (92.0%, κ = 0.913) and fusion-level selection (88.8%, κ = 0.879). All LLMs demonstrated chance-level test–retest reproducibility and very low agreement with expert consensus (Lenke: 1.6–10.2%, κ = 0.001–0.036; fusion: 0.8–12.0%, κ = 0.003–0.053). Claude produced missing outputs in 17 Lenke and 29 fusion-level cases. Although LLMs completed assessments far faster than surgeons (seconds vs. ~11–12 min), speed did not translate into clinically acceptable reliability. Conclusions: Current general-purpose multimodal LLMs do not provide reliable Lenke classification or fusion-level planning in AIS. Their poor agreement with expert surgeons and marked internal inconsistency indicate that LLM-generated interpretations should not be used for surgical decision-making or patient self-assessment without task-specific validation. Full article

The spatiotemporal evolution of ecosystem services has a profound influence on the fragile eco-environment in Inner Mongolia and the arid/semi-arid and the ecological barrier regions of Northern China; in particular, the small-scale and high-value land variables may lead to large eco-environment effects through altering the ecosystem services, which is still unclear in this vulnerable area. The differential driving mechanism of both human activities and natural factors on ecosystem services also needs to be revealed. To solve this scientific issue, the synergistic methodology of spatial analysis technology, the improved ecosystem service assessment method, flow gain/loss model, global/local Moran’s I approach, and the Geographically and Temporally Weighted Regression (GTWR) model were applied. Our main results are as follows: remote sensing monitoring showed that the land changes featured a persistent expansion of cropland and built-up areas, with a decline in grassland and wetland, along the east–west gradient from forests, grasslands, and unused-lands, to become the dominant cover type. According to our improved model, the ecosystem services considering the internal structure of build-up lands were first investigated in this ecologically fragile area of China, and the evaluated ecosystem service value (ESV) reduced from CNY 5515.316 billion to CNY 5425.188 billion, with an average annual decrease of CNY 3.004 billion from 1990 to 2020. Another finding was that the small-scale land variables with large ecological service impacts were quantified; namely, the proportion of grassland, woodland, wetland, and water body decreased from 62.71% to 61.34%, with only a relatively minor fluctuation of −1.37%, but this decline resulted in a large ESV loss of CNY 116.141 billion from 1990 to 2020. From the driving perspective, the temperature, digital elevation model (DEM), and slope exhibited negative effects on ESV changes, whereas a positive association was analyzed in terms of the precipitation and human footprint during the studied period. This study provides important support for optimizing land resource allocation, guiding the development of agriculture and animal husbandry, and protecting the ecological environment in arid/semi-arid and ecological barrier regions. Full article

Socio-ecological systems (SESs) exhibit nonlinear feedback across environmental, social, and economic processes, requiring integrative analytical tools capable of representing such coupled dynamics. This study presents a quantitative framework that integrates a compartmental model of a global human–ecosystem with two complementary optimization approaches (Fisher Information (FI) and Multi-Objective Optimization (MOO)) to evaluate policy strategies for sustainability. The model represents biophysical and socio-economic interactions across 15 compartments, incorporating feedback loops between greenhouse gas (GHG) accumulation, temperature anomalies, and trophic–economic dynamics. Six policy-relevant decision variables were selected (wild plant mortality, sectoral prices (agriculture, livestock, and industry), base wages, and resource productivity) and optimized under temporal (25-year) and magnitude (±10%) constraints to ensure policy realism. FI-based optimization enhances system stability, whereas the MOO framework balances environmental, social, and economic objectives using the Ideal Point Method. Both approaches prevent the systemic collapse observed in the baseline scenario. The FI and MOO strategies reduce terminal global temperature by 11.4% and 15.0%, respectively, relative to the baseline (35 °C → 31.0 °C under FI; 35 °C → 29.7 °C under MOO). Resource-use efficiency, measured through the resource requirement coefficient (λ), improves by 8–10% under MOO (0.6767 → 0.6090) and by 6–7% under FI (0.6668 → 0.6262). These outcomes offer actionable guidance for long-term climate policy at national and international scales. The MOO framework provided the most balanced outcomes, enhancing environmental and social performance while maintaining economic viability. Overall, the integration of optimization and information-theoretic approaches within SES models can support evidence-based public policy design, offering actionable pathways toward resilient, efficient, and equitable sustainability transitions. Full article

Decarbonizing heavy-duty freight transport is essential for achieving climate neutrality targets. Although internal combustion engine (ICE) trucks currently dominate logistics, they contribute substantially to greenhouse gas emissions. Zero-emission alternatives, such as battery electric vehicles (BEVs) and hydrogen fuel cell vehicles (H2), provide different decarbonization pathways; however, their relative roles remain contested, particularly in small economies. While BEVs benefit from technological maturity and declining costs, hydrogen offers advantages for high-payload, long-haul operations, especially within energy-intensive cold supply chains. The aim of this paper is to examine the gradual transition from ICE trucks to hydrogen-powered vehicles with a specific focus on cold-chain logistics, where reliability and energy intensity are critical. The hypothesis is that applying a system dynamics forecasting approach, incorporating investment costs, infrastructure coverage, government support, and technological progress, can more effectively guide transition planning than traditional linear methods. To address this, the study develops a system dynamics economic model tailored to the structural characteristics of a small economy, using a European case context. Small markets face distinct constraints: limited fleet sizes reduce economies of scale, infrastructure deployment is disproportionately costly, and fiscal capacity to support subsidies is restricted. These conditions increase the risk of technology lock-in and emphasize the need for coordinated, adaptive policy design. The model integrates acquisition and maintenance costs, fuel consumption, infrastructure rollout, subsidy schemes, industrial hydrogen demand, and technology learning rates. It incorporates subsystems for fleet renewal, hydrogen refueling network expansion, operating costs, industrial demand linkages, and attractiveness functions weighted by operator decision preferences. Reinforcing and balancing feedback loops capture the dynamic interactions between fleet adoption and infrastructure availability. Inputs combine fixed baseline parameters with variable policy levers such as subsidies, elasticity values, and hydrogen cost reduction rates. Results indicate that BEVs are structurally more favorable in small economies due to lower entry costs and simpler infrastructure requirements. Hydrogen adoption becomes viable only under scenarios with strong, sustained subsidies, accelerated station deployment, and sufficient cross-sectoral demand. Under favorable conditions, hydrogen can approach cost and attractiveness parity with BEVs. Overall, market forces alone are insufficient to ensure a balanced zero-emission transition in small markets; proactive and continuous government intervention is required for hydrogen to complement rather than remain secondary to BEV uptake. The novelty of this study lies in the development of a system dynamics model specifically designed for small-economy conditions, integrating industrial hydrogen demand, policy elasticity, and infrastructure coverage limitations, factors largely absent from the existing literature. Unlike models focused on large markets or single-sector applications, this approach captures cross-sector synergies, small-scale cost dynamics, and subsidy-driven points, offering a more realistic framework for hydrogen truck deployment in small-country environments. The model highlights key leverage points for policymakers and provides a transferable tool for guiding freight decarbonization strategies in comparable small-market contexts. Full article

In recent years, bidirectionally reinforced composite foundations have been widely used in highway, railway, and bridge engineering with notable results. The key mechanism is the soil-arching effect, which arises from the self-adjustment of the soil and directly affects the bearing capacity of the foundation. In this study, numerical simulation was employed to analyze the vertical stress in the subgrade soil and the transfer of particle contact forces from the macro and micro perspectives. The existence of the soil-arching effect was confirmed, and its variation under loading was revealed. To quantify the degree of the soil-arching effect, the stress transfer efficiency of the soil between piles was introduced. Subsequently, a bidimensional theoretical model was established based on the coordinated deformation among the embankment, the horizontally reinforced cushion, the vertical piles, and the soil. In this model, the combined effects of the embankment soil-arching, the reinforcement of cushion net, and the stress diffusion were incorporated. A method for the calculating of the pile–soil stress ratio of bidirectionally reinforced composite foundation was proposed, and the influence of various factors on this ratio was explored. The results indicate that the soil-arching effect can be divided into three stages according to the height of the subgrade fill: no-arch stage, transition stage, and soil-arching stage. Reducing pile spacing or increasing cushion thickness can improve the stress transfer efficiency. When the pile length is appropriate, the stress in the foundation soil at 0.55 times the pile depth was contoured, enhancing stability. The pile–soil stress ratio decreases with the increase in filling weight and pile spacing, increased first and then decreased with increasing internal friction angle of filling materials, and increased with the increasing height of embankment, the number of geogrid layers, and the cohesion of filling materials. Full article

Lipid-based nanomedicines are already widely used in antitumor therapy and gene delivery. However, their complex structural features demand advanced mesoscopic structural characterization tools for effective research and development (R&D) and quality control. Synchrotron small-angle X-ray scattering (SAXS) is a powerful, non-invasive technique for probing nanoscale membrane organizations, monitoring in situ dynamic membrane assembly, and exploring the interactions of components in lipid-based drug delivery systems, including liposomes, lipoplexes, lipid nanoparticles (LNPs), and lyotropic liquid crystals (LLCs). Recent advances in high-flux synchrotron facilities, high-frequency detectors, and automated SAXS data processing pipelines permit a detailed structural characterization of lamellarity, bilayer spacing, internal phases, core–shell morphology, as well as “pump-probe” dynamic process studies for lipid nanomedicines. Though major challenges remain in sample polydispersity and model fitting, the advances in time-resolved synchrotron SAXS, high-throughput automation, and artificial intelligence (AI)-assisted modeling are rapidly reducing this barrier. This review summarizes SAXS methodology and introduces representative case studies in the field of lipid nanomedicines. The performance of BioSAXS beamline BL19U2 in the Shanghai synchrotron radiation facility (SSRF) and prospects of AI-guided drug screening at BL19U2 are highlighted to advance intelligent R&D and quality control for lipid nanomedicines. Full article

The selective catalytic reduction (SCR) system is a key component for addressing NOx emissions from internal combustion engines. To resolve the issues of modeling distortion in SCR systems and the difficulty in characterizing the local reaction mechanism, a multi-dimensional SCR reaction model based on the coupling of Eley-Rideal (E-R) and Langmuir-Hinshelwood (L-H) dual mechanisms was established and conducted by experiment. The SCR catalytic characteristics and the dual-mechanism reaction process were systematically investigated. Additionally, based on the combined analysis of species concentration distribution coupled with temperature characteristics, a calculation method for the synergy of concentration-temperature fields was developed, and the synergistic characteristics of the concentration-temperature fields were explored. The results showed that high load accelerated the light-off speed, but this effect was counteracted by the negative impact of high flow rate. A strong negative correlation was maintained between temperature and NOx concentration across the full load range, and the axial consistency increased with load increasing. The results provide important theoretical support for the mechanism analysis of diesel engine SCR reactions and the optimization of thermal management. 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

Swirl-stabilized combustors are central to gas turbine technology, where the swirl number critically determines flow structure and combustion stability. This work systematically investigates the isothermal flow in a dual-swirl combustor, focusing on two primary objectives: evaluating advanced turbulence models and quantifying the impact of geometric-induced swirl number variations. Large Eddy Simulation (LES), Detached Eddy Simulation (DES), Scale-Adaptive Simulation (SAS), and the k-$\omega$ SST RANS model are compared against experimental data. The results suggest that while all models capture the mean recirculation zones, the scale-resolving approaches (LES, DES, SAS) more accurately predict the unsteady dynamics, such as shear layer fluctuations and the precessing vortex core, which are challenging for the RANS model. Furthermore, a parametric study of vane angles (60° to 70°) reveals a non-monotonic relationship between geometry and the resulting swirl number, attributed to internal flow separation. An intermediate swirl number range (S ≈ 0.79) was found to promote stable and coherent recirculation zones, whereas higher swirl numbers led to more intermittent flow structures. These findings may provide practical guidance for selecting turbulence models and optimizing swirler geometry in the design of modern combustors. Full article

Background: Monitoring training load and recovery is essential for performance optimization and injury prevention in youth football. However, predicting subjective recovery in preadolescent athletes remains challenging due to biological variability and the multidimensional nature of training responses. This exploratory study examined whether supervised machine learning (ML) models could predict Total Quality of Recovery (TQR) using integrated external load, internal load, anthropometric and maturational variables collected over one competitive microcycle. Methods: Forty male sub-elite U11 and U13 football players (age 10.3 ± 0.7 years; height 1.43 ± 0.08 m; body mass 38.6 ± 6.2 kg; BMI 18.7 ± 2.1 kg/m²) completed a microcycle comprising four training sessions (MD-4 to MD-1) and one official match (MD). A total of 158 performance-related variables were extracted, including external load (GPS-derived metrics), internal load (RPE and sRPE), heart rate indicators (U13 only), anthropometric and maturational measures, and tactical–cognitive indices (FUT-SAT). After preprocessing and aggregation at the player level, five supervised ML algorithms—K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Gradient Boosting (GB)—were trained using a 70/30 train–test split and 5-fold cross-validation to classify TQR into Low, Moderate, and High categories. Results: Tree-based models (DT, GB) demonstrated the highest predictive performance, whereas linear and distance-based approaches (SVM, KNN) showed lower discriminative ability. Anthropometric and maturational factors emerged as the most influential predictors of TQR, with external and internal load contributing modestly. Predictive accuracy was moderate, reflecting the developmental variability characteristics of this age group. Conclusions: Using combined physiological, mechanical, and maturational data, these ML-based monitoring systems can simulate subjective recovery in young football players, offering potential as decision-support tools in youth sub-elite football and encouraging a more holistic and individualized approach to training and recovery management. Full article

Achieving stable contact on uneven terrain remains a key challenge in humanoid robotics, as most feet rely on rigid or passively compliant structures with fixed stiffness. This work presents the design, fabrication, and analytical modeling of a compact soft pneumatic submodule capable of tunable longitudinal stiffness, developed as a proof-of-concept unit for adaptive humanoid feet. The submodule features a tri-layer architecture with two antagonistic pneumatic chambers separated by an inextensible layer and reinforced by rigid inserts. A single-step wax-core casting process integrates all materials into a monolithic soft–rigid structure, ensuring precise geometry and repeatable performance. An analytical model relating internal pressure to equivalent stiffness was derived and experimentally validated, showing a linear stiffness–pressure relation with mean error below 10% across 0–30 kPa. Static and dynamic tests confirmed tunable stiffness between 0.18 and 0.43 N·m/rad, a rapid symmetric response (2.9–3.4 ms), and stable stiffness under cyclic loading at gait-relevant frequencies. These results demonstrate the submodule’s suitability as a scalable building block for distributed, real-time stiffness modulation in next-generation humanoid feet. Full article

Ensuring effective governance of local budgets is critical for public service delivery and sustainable development. Public audit institutions—including internal auditors and independent supreme audit bodies—are hypothesized to enhance local budget effectiveness by promoting transparency, accountability, and efficiency in the use of public funds. The main purpose of this article is to test the hypothesis that stronger and more independent public audit institutions are associated with more effective local budget governance and to answer three research questions concerning (i) how different audit models are organized, (ii) how audit strength is quantitatively related to governance outcomes, and (iii) how these relationships manifest in transfer-dependent settings such as Kazakhstan. Drawing on cross-country indicators and a case study of Kazakhstan, the empirical analysis focuses on the period 2021–2023, when the most recent and comparable data on audit oversight and budget transparency became available. This study reviews international best practices and experiences, analyzes relevant global indices, and conducts a comparative examination of advanced economies and Central Asian countries to assess how audit bodies influence local budget outcomes. Correlation analysis using cross-country data and case studies is employed to quantify and illustrate these relationships. Best-performing countries adopt performance auditing approaches that focus not only on compliance but also on evaluating value-for-money and socio-economic impact. However, gaps remain; globally, while supreme audit institutions often meet standards, legislative oversight and public participation in budgeting are frequently insufficient, and many governments fail to act on audit findings. This study underscores the need for holistic reforms—especially in transfer-dependent regions—combining empowered audit institutions with policy changes to incentivize local revenue generation and responsible financial management. Effective public audit oversight emerges as a cornerstone of good local governance, helping to safeguard public funds and improve trust in government. Full article

This study and theoretical analysis of local relaxation processes and their physicomechanical and physicochemical characteristics in uncured epoxy oligomers DER-330, ED-20, ED-16 and ED-8 were carried out in the dynamic mode of freely damped torsional oscillations excited in specimens of the investigated systems. Internal friction spectra and temperature dependences of the frequency of free damped oscillations were obtained within the temperature range covering both the solid and liquid states of the epoxy oligomers. Based on the phenomenological models of a standard linear solid and the Maxwell model, the energetic and relaxation characteristics for each local dissipative process, as well as the temperature changes in strength properties (considering the defects of the shear modulus of the relaxation process) of the system as a whole, were calculated. Full article

The Trump administration signalled a shift toward protectionism in U.S. trade policy, imposing tariffs on imports from both strategic partners and competitors, which generated renewed uncertainty in international trade relations and the future of existing frameworks such as the African Growth and Opportunity Act (AGOA) and the Generalised System of Preferences (GSP). Earlier analysis has shown that a Free Trade Agreement (FTA) between the Southern African Customs Union (SACU) and the United States can be trade-creating and lead to improved macroeconomic outcomes in SACU countries. However, these positive effects decline over time, with varying impacts across different industries, influenced by initial tariff levels and export orientation relative to the US. This paper examines whether there are economic and strategic incentives for SACU to negotiate a more beneficial agreement than a simple across-the-board elimination of ad valorem import tariffs. Using a dynamic computable general equilibrium (CGE) model, the paper examines the outcomes if cereals, poultry, dairy products, red meat, and sugar products—often classified as sensitive due to their labour intensity, food security implications, and exposure to import competition—were to retain some level of protection under a SACU–US Free Trade Agreement. The results suggest that while the FTA boosts key macroeconomic indicators in the short run, gains taper off over time. Crucially, real wages and employment remain stagnant, and terms of trade deteriorate, raising questions about the inclusivity and sustainability of such a deal. Shielding vulnerable sectors initially enhances SACU’s exports and supports some industry growth, particularly in agriculture. However, without broader reforms and export diversification, long-term competitiveness remains weak. A nuanced FTA design, combined with structural support policies, is essential to unlock lasting and inclusive trade benefits. Full article

Modern romantic relationships face increasing internal and external pressures that may leave partners emotionally depleted and overwhelmed, yet empirical tools for assessing relationship burnout remain limited, mononormative, and psychometrically underdeveloped. Across two studies, we developed and validated the Antecedents of Relationship Burnout Scale (ARBS), a multidimensional measure grounded in the Job Demands–Resources (JD–R) model and designed to capture the relational demands and resource deficits that precipitate burnout. Study 1 generated and evaluated an initial 51-item pool using a sample of 175 partnered adults. Exploratory factor analysis revealed a clear and robust two-factor structure: Relationship Depletion and Exhaustion (e.g., emotional detachment, diminished appreciation, unmet emotional/sexual needs) and Relational Overload (e.g., external stressors, partner demands, role strain). Study 2 sought to confirm this structure and establish the ARBS’s psychometric validity via a sample of 288 adults. A confirmatory factor analysis supported a 36-item two-factor model with strong internal consistency, full measurement invariance across gender, and theory-consistent associations with relationship satisfaction, therapy participation, and infidelity urge, demonstrating both convergent and predictive validity. Together, these studies introduce the ARBS as the first comprehensive, theoretically grounded measure of the antecedents of relationship burnout, offering a rigorous foundation for future research, assessment, and intervention. Full article