Search Results (43,160)

Background/Objectives: Milk consumption is inversely associated with stroke risk. However, the average dairy consumption in Japan is below recommended guidelines. Therefore, we aimed to evaluate potential health and economic impacts of increased milk intake to achieve the recommended daily dairy intake for stroke prevention. Methods: A Markov model stratified by sex and age group simulated the effects of achieving the recommended dairy intake—by increasing milk consumption to 180 g/day—on stroke incidence, stroke-related deaths, and national healthcare expenditures among Japanese adults aged 30–79 years over 10 years. Two scenarios were defined; an immediate increase (Scenario 1) and a constant annual growth rate (Scenario 2) in milk intake, whereas the average dairy product consumption in 2023 was maintained in the base-case scenario. Results: Compared with the base-case scenario, increasing milk consumption to 180 g/day was projected to reduce stroke incidence and stroke-related deaths by 7.0% in Scenario 1 and by 3.2% in Scenario 2. National healthcare expenditures for stroke were decreased by 5.1% in Scenario 1 and 2.2% in Scenario 2. Conclusions: Achieving the recommended dairy intake may contribute to reductions in healthcare costs by preventing stroke in Japan. Full article

Timely and accurate soil property information is critical for sustainable agriculture and precision nutrient management. Conventional laboratory methods are accurate but costly and labor-intensive, restricting their feasibility for high-density soil mapping. Low-cost, portable near-infrared (NIR) spectroscopy presents a promising alternative for rapid, on-site, and non-destructive soil analysis. This study aimed to evaluate the potential of a low-cost, portable NIR sensor (NeoSpectra) for the quantitative prediction of key soil properties in paddy fields from Southeastern China. The target properties were soil organic matter (SOM), total nitrogen (TN), pH, and particle size fractions (clay, silt, and sand). A total of 995 soil samples were collected from representative paddy fields in the region and spectra measurements were conducted in the laboratory on air-dried samples. We developed and compared the performance of multiple machine learning algorithms, including partial least squares regression (PLSR), Cubist, random forest (RF) and memory-based learning (MBL), to build robust calibration models. The predictive models showed substantial performance for SOM and TN, indicating high accuracy (R² > 0.75, LCCC > 0.85, RPD > 2) for quantitative prediction. Predictions for pH, silt, sand, and clay were less accurate (R² of 0.48–0.53, LCCC of 0.67–0.71, RPD of 1.39–1.49), suggesting the sensor’s utility is limited to indicating general trends for these properties. Among the tested algorithms, MBL consistently provided the most accurate and robust predictions across the majority of soil properties. Our findings demonstrate that the low-cost portable NIR sensor, when coupled with appropriate machine learning algorithms, is a powerful and viable tool for the rapid and reliable estimation of critical paddy soil fertility properties (SOM and TN). This technology has significant potential to support field-level soil health monitoring, precision fertilization strategies, and sustainable land management in the agricultural systems of Southeastern China. Full article

Remote sensing (RS) images, characterized by their large size and rich texture, require algorithms capable of effectively integrating both global and local features for compression. However, existing Learned Image Compression (LIC) approaches face distinct bottlenecks. While Transformer-based architectures typically suffer from heavy computational loads, standard State Space Models (SSMs) often incur prohibitive memory costs when processing high-resolution inputs. To address these limitations, we propose MambaLIC, a novel RS image compression network that integrates the efficient long-range modeling of SSMs with the local modeling ability of CNNs. In this paper, we introduce an innovative Remote Sensing State Space Model (RS-SSM) module, which combines visual SSM with dynamic convolution for remote sensing image compression. This integration facilitates effective interaction between local and global information, thereby enhancing the performance of RS image compression. Furthermore, we propose an SSM attention-based (SSA-based) spatial-channel context model for better entropy modeling. Compared to Transformer-CNN mixed architectures, MambaLIC reduces computational complexity by 63.9% and achieves superior rate-distortion (RD) performance. Consequently, compared to the latest SS2D-based method MambaIC, MambaLIC achieves substantial efficiency gains, saving 78.8% in memory usage. Experimental results demonstrate that MambaLIC achieves state-of-the-art (SOTA) performance, outperforming VVC (VTM-17.0) by 14.22%, 18.48%, and 17.47% in BD-rate on UC-Merced, LoveDA, and xView datasets, respectively. Full article

Road safety assessment is essential for reducing traffic fatalities, with road infrastructure contributing to a substantial proportion of crashes worldwide. International frameworks such as the International Road Assessment Program (iRAP) define standardized attributes for infrastructure auditing; however, many of these attributes remain challenging to automate using imagery alone. This study evaluates V-RoAst (visual question answering for road assessment), a public dataset of road images that are annotated with iRAP-style attributes, using state-of-the-art multimodal large language models (MLLMs), specifically Gemini 2.0 and Gemini 2.5. The analysis focuses on how prompt design influences the accuracy and stability of single image iRAP inference. A token-efficient reduced prompt is developed that preserves the iRAP schema while removing single-class constants, hard-coded administrative fields, and derived or non-visual codes, retaining only visually interpretable attributes. Performance is compared with the original full multi-attribute prompt and single attribute prompts using a fixed evaluation protocol incorporating majority voting, bootstrap 95% confidence intervals, and per-code sample-size checks. Results indicate only minor performance differences between Gemini 2.0 and Gemini 2.5, while prompt optimization produces the most consistent gains, improving macro-F1 scores and tightening confidence intervals for visually grounded attributes such as roadside severity, intersection channelization, and service-road presence. Token analysis shows an approximate 30% reduction in prompt length, reducing computational cost and truncation risk. Overall, the findings demonstrate that prompt scope has a greater impact than model version in image-only iRAP coding, offering practical guidance for scalable infrastructure assessment. Full article

The rapid incorporation of solar energy (PV) systems into electrical grids has increased the demand for accurate short-term forecasts to ensure stability and improve processes. Although hybrid artificial intelligence (AI) models are increasingly being suggested to address this challenge, there is a lack of systematic compilation of their structures, effectiveness, and readiness for use in real-world applications. This paper provides a detailed analysis of 58 peer-reviewed articles (2020–2025) focused on hybrid models for short-term (1–24 h) solar photovoltaic power forecasting. We propose an innovative classification that groups hybrids into four categories: AI-AI (28%), AI with optimization (21%), decomposition-based (17%), and image-based (7%). Our research indicates that weather conditions (34%) and historical photovoltaic energy records (32%) are the most frequent inputs, and that optimized hybrids and those using decomposition achieve the best balance between effectiveness and computational efficiency. From a geographical perspective, the study focuses mainly on the United States (29%) and China (22%), suggesting that more extensive climate validation is crucial. Essentially, we have identified ongoing obstacles to implementation, such as high computational costs, data quality issues, and gaps in interpretation. In addition, we present a plan for future research focusing on hybrid architectures that are lightweight, understandable, and interactive with the grid. This analysis provides a thorough assessment of the current landscape and a strategic framework to guide the creation of operational forecasting systems capable of supporting highly solar-integrated grids. Full article

To address the challenges commonly encountered in civil aircraft operating under multi-mode, strongly coupled closed-loop control—namely scarce fault samples, pronounced distribution shift, and root-cause explanations that are easily confounded by covariates—this paper proposes a root-cause analysis method that integrates ensemble learning with constraint-guided reasoning by large language models (LLMs). First, for Full Authority Digital Engine Control (FADEC) monitoring sequences, a feature system comprising environment-normalized ratios, mechanism-informed mixing indices, and multi-scale temporal statistics is constructed, thereby improving cross-mode comparability and enhancing engineering-semantic expressiveness. Second, in the anomaly detection stage, a cost-sensitive LightGBM model is adopted and a validation-set-based adaptive thresholding strategy is introduced to achieve robust identification under highly imbalanced fault conditions. Furthermore, for Root Cause Analysis (RCA), a “computation–reasoning decoupling” framework is developed: Shapley Additive exPlanations (SHAP) are used to generate segment-level contribution evidence, while causal chains, engineering prohibitions, and structured output templates are injected into prompts to constrain the LLM, enabling it to infer root-cause candidates and produce structured explanations under mechanism-consistency constraints. Experiments on real flight data demonstrate that our method yields an anomaly detection F1-score of 0.9577 and improves overall RCA accuracy to 97.1% (versus 62.3% for a pure SHAP baseline). Practically, by translating complex high-dimensional data into actionable natural language diagnostic reports, the proposed method provides reliable and interpretable decision support for rapid RCA. Full article

Purpose: The paper deals with short-horizon foreign exchange (FX) predictability through predictive directional bias and how these are intertwined with the choice of features in weak-signal trading systems. Although FX markets are generally considered extremely efficient, temporal predictability at very short horizons might exist, but is exaggerated by feature selection, causing structural directional imbalance. This paper is intended to address the question of whether explicit bias-corrected feature selection can enhance tradable next-day FX performance under realistic cost constraints. Method: The approach of the study is the bias-corrected feature selection with Annealing (BFSA) and a fixed-penalty variant (BFSA-Fixed) built into a rolling walk-forward trading model. The process of feature selection and model estimation is repeated and re-estimated again in a time-respecting fashion, and forecasts are converted to directional trading decisions. The analysis takes into consideration transaction costs and puts emphasis on the net risk-adjusted performance, but not the sole predictive accuracy. Data: Daily information is provided in the empirical analysis of 14 liquid FX pairs, which include seven major and seven minor currencies. The motivation behind the choice of this universe is that it creates realistic conditions for execution, and it does not conflate the effects of extreme liquidity predictive performance with those of extreme liquidity. Results: Economic and statistically significant gains of performance with BFSA-Fixed at one day horizon (H = 1), as well as pair-level Sharpe ratios of 1 to 2 and above, annualized returns of 15 to 30, win rates of 55 to 60, and contained draws. These returns are constructively added together to a portfolio Sharpe of over 2. Conversely, performance reduces quickly in longer horizons (H = 2 and H = 3), with Sharpe ratios becoming negative and cumulative returns become flatten and negative, which are in line with rapid information decay and FX markets’ efficiency. Implications: The article shows that bias-corrected feature selection can significantly increase tradable next-day FX strategies with no leaning on persistent directional exposure or overfitting. Conclusion: The results justify the short-term use of bias-aware feature selection and highlight the inability of the FX to be predictable on a long-term basis. Full article

China is experiencing rapid population aging and is actively promoting smart home–based eldercare. Smart homes offer a promising means of supporting older adults in aging in place. However, low adoption and limited sustained use constrain their potential benefits, thereby exacerbating social, economic, and healthcare burdens. This study examined factors influencing older adults’ continuance intention to use smart homes in Shandong Province, China, by integrating the Expectation–Confirmation Model of Information Systems and the Technology Acceptance Model and incorporating China-specific contextual antecedents, including government policy, intergenerational technical support, compatibility, and cost. Data were collected using an online questionnaire survey of older adults aged 60 years and older with prior smart home experience (n = 421) and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Results showed that perceived usefulness, perceived ease of use, satisfaction, and cost directly affected continuance intention, whereas government policy, compatibility, and intergenerational technical support influenced continuance intention through perceived usefulness, perceived ease of use, and confirmation. Based on these results, this study proposes a conceptual framework for understanding older adults’ continuance intention toward smart homes. The findings provide implications for inclusive policy, user-centered design, and family-supported digital aging in rapidly aging societies. Full article

The fracturing development of low-permeability and ultra-low-permeability oil and gas reservoirs urgently requires a fracturing fluid that combines high performance and low damage. To overcome this challenge, this study synthesized a novel polyamine boron crosslinker (PBC) suitable for 0.2% guar gum. The molecular structure was characterized by Fourier transform infrared spectroscopy (FT-IR) and nuclear magnetic resonance hydrogen spectroscopy (¹H NMR). Meanwhile, this study introduced the response surface methodology and established a second-order regression model to determine the optimal synthesis conditions (polyetheramine 10.8 g, n-butanol 7.4 g, and ethylene glycol 20.7 g) with a model prediction error of only 0.7%. The results indicated that PBC exhibited excellent performance in 0.2% guar gum. The viscosity of crosslinked gel fracturing fluid remained stable at approximately 100 mPa·s under 60 °C and 100 s⁻¹ shear. The wall forming filtration coefficient was 2.30 × 10⁻⁴ m/s^1/2, and the initial filtration was 1.30 × 10⁻³ m³/m². The static settling rate was 2.4 cm·min⁻¹, demonstrating good suspended sand capacity. Furthermore, the synergistic interaction between borate ester bond and polyetheramine in the PBC conferred dynamic reversible crosslinking and uniform network formation. This enabled high-strength, low-damage crosslinking effects at low concentrations. This study provides an efficient crosslinker solution for 0.2% guar gum, holding both theoretical and engineering significance for advancing the low-cost development of fracturing fluid. Full article

Optimizing public investment in urban green infrastructure under water scarcity is a core challenge in resource economics. Against the backdrop of global climate change—characterized by rising temperatures, increased frequency and intensity of droughts, and altered precipitation patterns—this study addresses the critical knowledge gap in quantifying the economic returns on the physiological adaptations of urban trees, which are central to their value as natural capital. We integrated dual-water isotope (δ²H, δ¹⁸O) and leaf carbon isotope (δ¹³C) analyses to mechanistically decode the water use strategy of Machilus yunnanensis (M. yunnanensis) in drought-prone Kunming, China. The results show strategic seasonal plasticity: a shift from shallow soil water (10–50 cm) in the wet season to deeper soil sources (50–90 cm) and stem reserves in the dry season, coupled with a dynamic, diurnally variable water use efficiency (WUE_13C). We then constructed a transparent economic valuation model translating these traits into three quantifiable benefit streams: (1) operational cost savings (EV₁) from reduced irrigation demand; (2) enhanced marginal productivity of water (EV₂) in ecosystem service generation; and (3) climate resilience value (EV₃) via mitigated mortality risk. Our “Water–Carbon–Economy” nexus framework provides a generalizable methodology for assessing the cost-effectiveness and risk-adjusted returns of urban forest species, demonstrating that tree selection based on such eco-efficient traits is not merely an ecological choice but a sound economic investment, offering direct implications for budget-constrained municipalities seeking to maximize green infrastructure benefits under climate uncertainty. Full article

Within a spatial-economics framework, this paper extends a general-equilibrium model to examine how high-speed rail (HSR) openings reduce migration costs and thereby alleviate regional factor misallocation. The model predicts that improved connectivity lowers labor mobility frictions, facilitates cross-regional reallocation of productive factors, and reduces misallocation. Using a panel of China’s prefecture-level cities from 2006 to 2016 and a difference-in-differences design, we estimate the causal effects of HSR on the misallocation of labor and capital. The results show that HSR openings significantly improve both labor and capital allocation, and the findings remain robust to a range of endogeneity checks and alternative specifications. Heterogeneity analyses indicate that the improvement is concentrated in eastern cities, while the effects are statistically insignificant in central and western regions. We also find that the reduction in misallocation occurs in both provincial capital and non-capital cities. These results imply that HSR can enhance resource-use efficiency and support sustainable regional development by reducing spatial frictions and promoting more balanced factor allocation. From a policy perspective, accelerating HSR network expansion can lower cross-regional mobility costs and enable freer flows of labor and capital, thereby improving allocative efficiency and fostering inclusive and sustainable growth. Full article

With the rapid development of the offshore wind power industry, the construction process of offshore wind power jackets faces numerous quality risks, particularly in welding, coating, and assembly operations. This paper aims to investigate the identification, assessment, and management of quality risks during the construction of offshore wind turbine foundation structures. By establishing a multidimensional quality risk assessment framework, key risk factors affecting quality were identified through expert interviews and brainstorming sessions. Comprehensive evaluations of these risk factors were conducted using the Entropy Weight Method (EWM), the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and Grey Relational Analysis (GRA). The findings indicate that welding and coating processes pose the highest risks during construction. Based on these assessments, corresponding risk mitigation measures are proposed, including process optimization, automation enhancement, environmental control, and management system refinement. This study provides theoretical foundations and practical guidance for improving construction quality and reducing costs in offshore wind turbine foundation manufacturing. It advances quality risk management by introducing an integrated evaluation model that addresses the limitations of single-method approaches in complex construction scenarios. Full article

The decarbonization of the building stock by 2050, as set by the European Green Deal, calls for an unprecedented wave of energy renovations. Yet, reliable evidence on the real costs and performance of retrofit interventions remains scarce. This paper presents the results of a large-scale technical and economic analysis conducted on 34 residential buildings, all renovated under a national Italian programme supporting energy efficiency improvements. For each building, pre- and post-renovation energy performances were assessed using standardised procedures, while detailed investment cost data were collected for all implemented measures, including envelope insulation, HVAC system upgrades, and renewable integrations. By combining these datasets, the study evaluates the actual cost-effectiveness of different retrofit strategies, revealing the true financial effort required to achieve substantial energy improvements. The results highlight both the opportunities and limitations of current approaches, showing a significant gap between theoretical models and real outcomes. The findings contribute to the European debate on the economic sustainability of deep renovation policies. Full article

Deploying Large Language Models (LLMs) on Maritime Autonomous Surface Ships (MASS) entails a critical trade-off between reasoning depth, inference latency, and hardware constraints. To fill the existing gap, we introduce MARTIAN (Maritime Agent for Real-time Tactical Inference And Navigation), a 14B-parameter decision support agent engineered for edge deployment on standard vessel hardware (e.g., the NVIDIA Jetson AGX Orin). Central to our approach is the Cognitive Core architecture, which utilizes a verified dataset of 21,800 Chain-of-Thought (CoT) instruction–response pairs to align general linguistic capabilities with maritime procedural logic. Empirical evaluations demonstrate that MARTIAN achieves an overall accuracy of 73.23% (SFT only) and 81.16% (SFT + RAG) on the Bilingual Maritime Multiple-Choice Questionnaire (BM-MCQ), a standardized assessment dataset constructed based on Officer of the Watch (OOW) competencies. Notably, the SFT-only configuration attains 78.53% on pure-logic-intensive COLREG tasks—surpassing the 72B-parameter Qwen-2.5 foundation model in this domain—while maintaining a real-time inference latency of 22.4 ms/token. Crucially, our ablation studies support a nuanced Interference Hypothesis: while RAG significantly enhances factual recall in knowledge-intensive domains (boosting total accuracy from 73.23% to 81.16%), it concurrently introduces semantic noise that degrades performance in pure logic reasoning tasks (e.g., COLREG maneuvering accuracy decreases from 78.53% to 77.36%). On the basis of this finding, we identify and empirically motivate a decoupled cognitive design principle that separates procedural reflexes (via SFT) from declarative knowledge (via RAG). While the full implementation of an adaptive routing mechanism is deferred to future work, the ablation results presented herein offer a validated, cost-effective reference architecture for deploying transparent and regulation-compliant AI on resource-constrained merchant vessels. Full article

Low-cost air quality sensors, such as PurpleAir monitors, have rapidly expanded fine particulate matter (PM2.5) monitoring across the United States, providing dense, hyper-local measurements. While prior research has focused largely on sensor accuracy and calibration, less is known about where these sensors are deployed and whether they persist long enough to support multi-year analyses relevant to exposure assessment and policy. Using publicly available PurpleAir data, we characterized the geographic distribution, deployment longevity, and persistence of outdoor sensors across the United States from 2016 to 2025. We quantified deployment duration as the time between first and last publicly available observations and summarized patterns nationally, by U.S. Census region, and by state. Most publicly shared sensors remained deployed for more than three years, indicating substantial potential for multi-year applications, particularly in the western United States, where sensor density and longevity were highest. As an illustrative component, we present descriptive summaries of PM2.5 concentrations in four high-coverage states (California, Minnesota, Pennsylvania, and Texas) by deployment duration and urban–rural classification to demonstrate the types of analyses enabled by these networks. These results establish a national baseline of sensor availability and temporal continuity. By focusing on deployment patterns, this study provides foundational context for future exposure modeling, epidemiologic studies, and targeted expansion of community air quality monitoring networks. Full article