Search Results (45,637)

Aiming at the problems of the large prediction error of model-driven algorithms and poor interpretability (even potential violation of physical laws) of pure data-driven algorithms in the prediction of aerospace vehicle plume characteristics, a physics mechanism-guided prediction algorithm for aerospace vehicle plume characteristics was proposed. Taking the long short-term memory (LSTM) network as the backbone, this algorithm constructed a hybrid physics–data model by embedding the prior knowledge of physical laws and empirical rules into the neural network, and designed a loss function combined with physical mechanisms to guide network training. The aerospace vehicle plume dataset was preprocessed through characteristic parameter extraction, extended physical parameter calculation, data splicing and sliding window operation, and the LSTM network structure was optimized by adjusting hyperparameters such as the number of hidden layers and neurons. Experimental results show that the proposed algorithm achieves a Mean Absolute Error (MAE) of 31.89 and a Physical Inconsistency of 0.1723 on the test set, with MAE reduced by 14% and Physical Inconsistency reduced by 7.5% compared with traditional machine learning models such as Random Forest. Ablation experiments verify that the introduction of physical mechanisms can improve the prediction accuracy of the model by about 25%. This algorithm makes up for the defects of traditional prediction algorithms, has good generalization ability and physical consistency, and provides an effective method for the prediction of engine exhaust plume temperature distribution. Full article

Metallurgical batching—governing raw material proportioning across sintering, blast furnace ironmaking, converter steelmaking, and non-ferrous smelting—critically determines product quality, energy consumption, and production cost throughout the full process chain. Its inherent complexity, characterized by strong nonlinear physicochemical coupling, measurement delays of up to 1.5 h, and multi-source raw material disturbances, renders conventional linear programming and empirical methods inadequate for dynamic, multi-objective industrial environments. This review systematically examines 98 representative studies (2020–2026) on intelligent algorithms applied to metallurgical batching optimization. A two-dimensional analysis framework of the fusion algorithm function and metallurgical scene is established. All kinds of methods are divided into three categories: prediction-oriented, optimization-oriented and decision-oriented, covering four typical scenes of sintering burdening, blast furnace ironmaking, converter steelmaking and non-ferrous metal smelting. Traditional machine learning models achieve sintering burn-through point prediction with R² ≈ 0.85 and offer superior interpretability via SHAP analysis. Deep learning architectures deliver blast furnace silicon content prediction with RMSE ≈ 0.04%, while multi-objective evolutionary algorithms provide mature Pareto optimization for batching cost and carbon objectives. Reinforcement learning holds long-term potential for closed-loop adaptive control but remains constrained by Sim-to-Real safety barriers. Converter steelmaking and non-ferrous smelting are identified as underexplored domains. Three priority directions are proposed: domain-adaptive predictive modeling for cross-plant generalization, real-time re-optimization embedding mechanism constraints, and safe reinforcement learning transfer via high-fidelity digital twins. Full article

Amid accelerating urbanization, tensions between economic growth and environmental protection have become increasingly salient. Improving Total Factor Carbon Productivity (TFCP) is crucial for achieving sustainable urban development. Drawing on panel data for 282 Chinese prefecture-level cities from 2009 to 2021, this paper examines the effects and underlying mechanisms of the Low-Carbon City Pilot (LCCP) policy on urban TFCP. The results suggest that the LCCP policy noticeably contributes to higher TFCP, and the finding remains valid after robustness checks and endogeneity corrections. The impact of the policy exhibits marked variation, yielding stronger gains in western regions of China, in small- and medium-sized cities, in cities not dependent on resource extraction, and in major transportation nodes. Technological progress, the optimization of industrial structure, and advances in economic development serve as key intermediary mechanisms. Moreover, the LCCP policy exhibits positive spatial spillover effects that help lift TFCP in neighboring cities. These findings provide empirical support for differentiated low-carbon policy design and regional coordinated low-carbon development, and carry considerable practical and strategic significance for balancing high-quality economic development and ecological protection. Full article

Special economic zones (SEZs) are widely used to stimulate investment, employment, and industrial growth. Yet their contribution to sustainable regional development remains poorly measured. This is especially true in Kazakhstan, where zone-level assessment is largely absent from regional planning frameworks. This study addresses that gap. We construct a Regional Sustainable Development Index (RSDI) that integrates economic, social, and environmental indicators across nine Kazakhstani regions hosting active SEZs. Economic performance alone gives an incomplete picture. Omitting social and environmental dimensions distorts policy conclusions and masks structural imbalances. Our results reveal sharp differentiation across regions. In the Atyrau region, high investment volumes correspond closely with sustainability gains. This suggests structural coherence between zone operations and broader regional outcomes. The Pavlodar region presents a contrasting case. There, the leading driver of sustainability performance is not investment volume but the reduction of environmental pollution. This finding underscores why disaggregating sustainability components matters—the composite index alone is not sufficient. A comparison against official target indicators identifies both achievements and systematic shortfalls. Investment and employment targets are frequently decoupled: capital attraction does not reliably generate proportional job creation. The social dimension remains the weakest across most zones. Environmental governance shows formal recognition but limited implementation. The RSDI framework offers a practical diagnostic tool for public authorities. It makes imbalances visible before they become entrenched. Beyond Kazakhstan, the index provides a transferable instrument for resource-dependent emerging economies seeking to embed sustainability criteria into SEZ governance and regional planning. Full article

As a critical component of urban ecological infrastructure, urban forests play a pivotal role in regulating regional climate and mitigating the urban heat island (UHI) effect. However, existing studies have predominantly focused on single temporal snapshots or aggregate spatial scales, with limited attention to the seasonal dynamics of urban forest landscape patterns and a lack of systematic quantification of their nonlinear regulatory mechanisms. Empirical evidence from subtropical cities remains particularly scarce. In this study, Hangzhou was selected as the study area. Land Surface Temperature (LST) was retrieved using the Google Earth Engine (GEE) platform, and the Thermal Field Variance Index was employed to classify UHI intensity. Six representative forest landscape indices were selected to construct an evaluation framework. Pearson correlation analysis and the XGBoost model were further applied to quantify the relationships between landscape patterns and seasonal LST variations. The results reveal that: (1) LST in Hangzhou exhibits pronounced seasonal variability, following the order of summer > spring > autumn > winter. Areas without UHI effects dominate in spring, summer, and autumn, whereas the extent of strong UHI zones increases markedly in winter. (2) All landscape indices are significantly correlated with seasonal LST; forest ratio and forest largest patch index show negative correlations, while forest patch density, forest landscape shape index, number of patches, and landscape division index (DIVISION) are positively correlated. (3) The XGBoost model indicates that DIVISION consistently exhibits high contribution across all seasons, identifying it as a key determinant of LST variation. These findings provide a scientific basis for optimizing urban forest landscape configuration and developing effective UHI mitigation strategies. Full article

This study investigates the soundscape of Taoist temples and its influence on visitors’ worship experiences, integrating auditory perception, visual landscape evaluation, and emotional and experiential responses into a comprehensive analytical framework. Based on questionnaire surveys conducted in multiple Taoist temples, the study examines how different sound sources affect soundscape evaluation and how this evaluation shapes perceptual and experiential outcomes. The results indicate that Taoist ritual sounds (e.g., ritual music and chanting) play a significant positive role in shaping visitors’ soundscape evaluation, whereas artificial sounds related to general human activities show a negative effect. Soundscape evaluation is found to significantly influence visual landscape evaluation and emotional perception, and further contributes to visitors’ overall temple experience. Visual landscape evaluation is found to partially mediate the relationship between soundscape evaluation and emotional perception, while emotional perception further mediates the relationship between soundscape evaluation and temple experience. A comparison across sensory dimensions suggests that soundscape evaluation exerts a relatively stronger influence on temple experience than visual landscape evaluation, highlighting the important role of auditory experience in religious and cultural environments. The study also identifies a synergistic interaction between auditory and visual evaluation, indicating that multisensory integration can enhance the overall experiential quality of Taoist temples. Overall, this research provides empirical insights into the role of soundscapes in religious spaces and offers practical implications for the design, management, and optimization of multisensory environments in Taoist temples. Full article

This paper examines whether a residual structural state extracted from cross-asset downside-risk dependence contains incremental information for forecasting next-day market downside risk beyond a strong heterogeneous autoregressive (HAR) benchmark. The empirical analysis uses Binance intraday data from September 2019 to December 2025 and a fixed sample of 24 liquid cryptocurrencies obtained through explicit data-quality screening and sample diagnostics. The forecasting target is the log of an equal-weight cross-sectional downside-risk index constructed from strictly valid asset-level realized downside semivariance measures. The empirical design is deliberately conservative: the market sample is fixed ex ante, the target is evaluated against Bitcoin (BTC) and Ethereum (ETH) dominance diagnostics, and asset-level HAR-type models are estimated recursively to generate ex-ante one-step-ahead residuals, from which rolling residual-dependence matrices and structural signatures are constructed. The selected residual state contains four components: average residual correlation, Frobenius-type deformation, influence concentration, and influential-set turnover. The evidence supports three qualified conclusions. First, the full residual state attains the lowest average QLIKE loss relative to the HAR benchmark, although the corresponding Diebold–Mariano test under the primary QLIKE loss does not reject equal predictive accuracy at conventional levels. Complementary Clark–West evidence on the nested log-scale comparison supports incremental predictive content for the level-state and full-state augmentations. Second, the strongest forecasting evidence comes from the full state rather than from deformation-only specifications. Third, event-window diagnostics show that structural reorganization is most pronounced around stress-entry and extreme-risk episodes, supporting an onset-sensitive rather than a long-lead early-warning interpretation. Overall, the evidence supports a cautious and statistically qualified predictive conclusion: residual market structure may contain incremental information for short-horizon downside-risk forecasting in cryptocurrency markets, especially around stress onset, but the result should not be interpreted as a decisive primary-loss improvement or as evidence that deformation alone dominates a strong benchmark. Full article

This study examined the critical sustainability challenge of regional demographic decline in South Korea by analyzing how young people’s mobility decisions are intricately influenced by structurally and socially constructed meaning systems. Countering strictly economic deterministic views, this research posited that youth out-migration is a complex socio-cognitive process mediated by social representations of place—collectively constructed and circulated meanings attached to regions. Applying a secondary analysis of Q-sort data from 24 undergraduate students at a regional national university, the study integrated Q methodology with Social Representation Theory to systematically identify youth typologies regarding regional identity, territorial stigma, and local extinction. Participants sorted 44 statements encompassing place attachment, local consumption, cultural experiences, and policy effectiveness. Rigorous factor analysis revealed four distinct perception typologies: identity-based strategic mobility, conditional leaving based on internalized success norms, re-anchoring toward alternative lifestyles, and skeptical leaving rooted in profound institutional distrust. The findings empirically demonstrated that identical structural constraints can produce highly divergent mobility trajectories—ranging from active retention to complete resignation—depending entirely on the region’s socio-cognitive representation. This study demonstrates that local extinction is not merely a demographic condition, but a socially constructed framework of meaning and an object of social representation that shapes youth perception typologies and mobility judgments. Accordingly, moving beyond conventional technical interventions, meaning governance, and strategic communication are needed to help reimagine regional futures. Full article

Urbanization increases pluvial flood risk by expanding impermeable surfaces, which is a trend likely to intensify with climate change. Permeable pavement (PePav) made from industrial byproducts, in accordance with circular economy principles, may improve soil permeability. Public acceptance remains a critical barrier to its implementation. Existing measures of willingness to accept (WtA) new technologies are inconsistent, limiting interdisciplinary collaboration. Therefore, a concise WtA scale was adapted from the Bogardus Social Distance Scale to assess acceptance of PePav at varying levels of proximity in residential contexts, from public flood-prone roads to private yards. The scale was evaluated across three studies: Study 1 (N = 195) and Study 2 (N = 187) utilized mixed student samples, while Study 3 (N = 625) involved a non-student sample. The 5-item solution, identified through factor analysis in Study 1, consistently demonstrated a unidimensional and cumulative structure and satisfactory reliability, even after the proposed PePav ingredient modification in subsequent studies. The scale correlated with recycling experience and professional background, indicating convergent validity, but not with flooding or informal construction experience, across all samples. Study 3 provided evidence of external validity by incorporating empirically well-established Theory of Planned Behavior (TPB) constructs and showing that WtA predicted PePav use beyond TPB variables and demographics. The scale also showed measurement invariance across sample type (student vs. general population) and different levels of construction experience. The constructed WtA scale is suitable for efficiently assessing professional and public acceptance of circular building materials and may have broad cross-disciplinary relevance. This enables timely, targeted interventions and informed policy decisions to advance sustainable technologies in the built environment, with substantial implications for education, professional policy, and sustainable engineering. Nevertheless, further validation is required. Full article

Background/Objectives: Attention-Deficit/Hyperactivity Disorder (ADHD) is associated with working memory deficits linked to frontoparietal alterations. Non-invasive brain stimulation (NIBS) is a potential intervention to modulate neuroplasticity and improve this executive function. In this study, we aimed to evaluate the clinical efficacy of non-invasive brain stimulation techniques (tDCS/rTMS) for strengthening working memory in children and adolescents with ADHD. Methods: This systematic review adhered to the PRISMA 2020 guidelines, with a search of Scopus and Web of Science conducted to identify relevant studies published between 2011 and 2026. Eligibility criteria, defined a priori, included original empirical studies (RCTs and quasi-experimental designs) focusing on pediatric populations (≤18 years) diagnosed with ADHD. Eligible interventions involved tDCS or rTMS with explicit working memory outcomes. Only peer-reviewed articles published in English or Spanish were included. Reviews, case reports, and studies exclusive to adults were excluded. Data on application parameters, durability, and safety were extracted for narrative synthesis. Results: Six studies met the criteria. Both tDCS and rTMS targeting the left dorsolateral prefrontal cortex showed improvements in working memory, particularly in executive components measured using digit span backward and N-back tasks. High-frequency rTMS (10 Hz) with repeated sessions showed more consistent effects, while tDCS showed modest and variable improvements. Evidence regarding long-term effects was limited. Both techniques were well-tolerated, with mild and transient adverse events. Conclusions: NIBS shows promise as a complementary intervention to improve working memory in pediatric ADHD; however, current evidence is limited. Larger, standardized, longitudinal trials are required to confirm its efficacy and clinical utility. Full article

The shift to 15 min market time units (MTUs) in single-day-ahead coupling (SDAC) increases temporal granularity, but complicates the interpretation of intra-hour electricity price spikes and rapid ramps. This paper examines whether architectural decomposition improves the reliability of large language model (LLM)-based diagnostics in price-only settings, rather than causal market analytics, under severe information constraints. We compare a proposed agentic workflow featuring structured context extraction, spike/ramp detection, hypothesis generation, consistency checks, and explicit uncertainty calibration against non-agentic baselines. The paper contributes: (i) a reproducible benchmark for 15 min diagnostic question answering in day-ahead markets, (ii) an agentic architecture tailored to structured time-series reasoning with explicit uncertainty handling, and (iii) empirical evidence that decomposition and verification improve evidence grounding and trustworthiness in market analytics. The evaluation includes 360 price-only cases sampled across autumn 2025, winter 2025–2026, and early spring 2026, balanced by bidding zone, temporal period, event type, and impact tier, comprising 180 spike and 180 ramp cases from six Central and Eastern European bidding zones (Bulgaria, Czechia, Hungary, Poland, Romania, and Slovakia). Using identical inputs, we assess automatic reliability metrics and human ratings. The agentic workflow improves reliability (∆ = +0.067, 95% CI [+0.049, +0.085]) and significantly increases calibrated price-only disclaimers (∆ = +0.500) relative to the monolithic LLM baseline. Human evaluation confirms higher overall quality (+0.74), helpfulness (+1.06), and correctness (+0.94), with a 65.5% pairwise win rate. Overall, the results support a narrower conclusion: structured decomposition and verification improve calibration and perceived explanation quality relative to a simple monolithic LLM baseline, but their advantages are not uniform across stronger non-agentic baselines and remain limited by the absence of exogenous market data. Full article

The paper presents the results of the optimization of the paper production process as a function of paper grade, basis weight and key operating parameters of paper machines, wire speed V_s and reel speed V_n, using computer simulation. Based on empirical data from 2015 to 2020, the analysis accounts for web breaks, downtimes, and grade changes, all of which affect production continuity and the operation of the plant’s combined heat and power (CHP) system supplying thermal and electrical energy. Production interruptions reduce CHP stability; therefore, the optimization criterion was profit maximization, including energy-related effects associated with forecasting the availability of surplus energy for sale when participating in the capacity market. The results were compared with those obtained using numerical taxonomy methods, which confirmed the effectiveness of their application to the problem under consideration. Full article

Colombian transitory crop production exhibits marked structural heterogeneity across department–crop combinations, yet empirical characterizations of productive scale at the subnational level remain scarce. This study presents a descriptive analysis and clustering-based productive scale segmentation of Colombian transitory crops at the departmental level for the period 2007–2024. Data from the Evaluaciones Agropecuarias Municipales(EVA) were processed through a structured CRISP-DM pipeline comprising preprocessing of 347,141 records, departmental aggregation, and engineering of five clustering features: average production, average planted area, number of active periods, and temporal and spatial Herfindahl–Hirschman indices. K-Means clustering ($k=3$)was applied to a final dataset of 490 department–crop pairs and validated based on a global silhouette coefficient of 0.888. The segmentation reveals a markedly asymmetric productive structure: 93.7% small scale (459 pairs), 5.3% medium scale (26 pairs), and 1.0% large scale (5 pairs), with natural breakpoints at approximately 35,386 t and 275,959 t. Large-scale production is concentrated in papa (Cundinamarca, Boyacá, Nariño) and arroz (Casanare, Tolima). Clustering demonstrated quantitative superiority over quartile-based classification, reducing the within-group coefficient of variation from 223.9% to 30.6% for the upper segment. The methodology is replicable across national agricultural statistics systems, and the processed dataset is publicly available under CC BY 4.0. Full article

The low-carbon transition (LCT) of manufacturing clusters is a critical pathway to addressing bottlenecks in global climate governance and promoting sustainable economic development in developing countries. Accurately measuring the level of this transition and clarifying its dynamic trends are of great significance. Drawing on the economic rationale of a low-carbon economy, this study constructs a comprehensive evaluation indicator system and employs the entropy-weighted CRITIC-grey relational TOPSIS method to measure the LCT levels of China’s four major industrial bases from 2013 to 2023. Combined with convergence analysis, the Theil index, mechanism analysis, and policy scenario simulation, it systematically analyzes the characteristics of disparities and the underlying mechanisms. The study’s results show that low-carbon technology is the core driver of the LCT of the four major industrial bases. The LCT levels of the four major industrial bases have generally increased, with some bases exhibiting a catch-up effect internally. The overall disparity among the four major industrial bases has widened, primarily driven by intra-base differences. Specifically, the Beijing–Tianjin–Tangshan industrial base displays polarization characteristics, while the Central-Southern Liaoning industrial base shows a relatively low-level equilibrium. The transition of resource-based cities lags, mainly constrained by rigid industrial structures and insufficient investment in technology. Industrial structure optimization plays a certain role in improving resource-based regions, whereas technological innovation has a more pronounced effect in developed regions. This study constructs a comprehensive analytical framework of “measurement–evolution–mechanism–simulation,” which refines the quantitative evaluation system for the LCT of manufacturing clusters. The findings provide empirical support for formulating differentiated low-carbon policies for manufacturing clusters and optimizing coordinated emission reduction pathways, while also offering a reference paradigm for similar research in other developing countries. Full article

Upper Northern Thailand continues to face a protracted structural crisis from fine-particulate matter (PM_2.5), primarily driven by biomass burning and wildfires. Conventional mechanical capture systems, such as cyclones, often suffer a drastic efficiency drop when treating sub-micron particles. This study introduces an innovative Evaporative Condensation Growth Scrubber (ECGS) designed to bridge this technological gap by promoting the growth of fine particles through heterogeneous nucleation. Experimental testing across 10 different nozzle configurations was conducted to optimize the system’s performance. The results revealed that the ECGS system significantly outperformed the dry cyclone (Baseline) across all nine testing configurations. While the Baseline showed inherent limitations in capturing sub-micron particles, the ECGS demonstrated relative efficiency improvements ranging from 39.53% to 83.23% for PM_2.5, and 26.10% to 61.50% for PM₁₀ compared to the baseline. Optimal performance was achieved using a 90-degree injection angle and a 10 cm distance, which created a complete spray curtain and maximized collision probability. Under these conditions, the outlet PM_2.5 concentration stabilized at 11.81 µg/m³ within 180 s of water injection. Crucially, despite sensor interference caused by high relative humidity, the system’s effectiveness was confirmed by a significant difference in performance in PM₁₀ and PM_2.5 removal. The PM₁₀ collection efficiency outperformed that of PM_2.5 by 28.82%, providing empirical evidence that PM_2.5 particles successfully acted as nuclei for condensation and grew into the larger PM₁₀ size range. This particle growth enabled more effective centrifugal separation, demonstrating that the ECGS system offers a viable and efficient solution for fine particle removal in highly polluted environments. Full article