Search Results (2,083)

This paper presents a lightweight method for assessing the resilience of power distribution systems that integrates climate and infrastructure data through impact chains and a probabilistic approach, while minimizing data integration and implementation complexity. The method is demonstrated for heatwave hazards by combining network characteristics, failure probabilities of heat-sensitive components (e.g., medium-voltage cable joints), and location-specific climate projections to generate spatial maps of failure risk and network resilience. These maps support the identification and prioritization of critical components requiring intervention. Critical segments are then further analyzed using probabilistic resilience metrics to compare alternative adaptation strategies. Overall, this work contributes a practically applicable, low-complexity methodology for identifying the weakest portions of distribution networks, along with a more in-depth probabilistic approach for assessing their climate resilience. The com-prehensive framework is illustrated through a case study of a representative portion of the Italian electricity distribution system in the urban area of Rome. It is implemented in a test environment that reflects realistic distribution network data structures and automatically integrates climate data from established online repositories. Full article

Thermal accumulation is a critical constraint in robotic grinding of ZGMn13 high-manganese steel, whereas the variables that can be prescribed or monitored reliably online are often limited to the normal load F_z, spindle speed n, and feed speed ν_w. Most existing studies focus on single-pass conditions or scalar thermal indicators, while full-process near-surface transient temperature histories in multi-layer robotic grinding remains insufficiently addressed. This study presents a full-process near-surface transient temperature histories framework for multi-layer robotic grinding under fixed wheel–workpiece conditions and limited online sensing. Multi-channel near-surface thermal measurements were first reorganized into layer-resolved time-series data. A process-driven thermal surrogate was then constructed from the deployable inputs (F_z, n, ν_w), and a recursive temperature-evolution model was developed by incorporating intra-layer thermal retention and interlayer residual-heat inheritance. The proposed formulation predicts the near-surface transient temperature history over successive grinding layers. Experimental results showed clear layer-wise transience and progressive thermal accumulation during multi-layer grinding. Under representative conditions, the proposed framework reproduced the dominant transient structure of the measured full-process near-surface temperature histories, and grouped validation further showed that the recursive formulation preserved more useful history-level information than the reduced baselines within the tested domain. Within the tested operating domain, the predicted histories were further reduced to derived thermal indicators and planning-oriented peak-temperature maps. Full article

This study examines how high-speed rail (HSR) hubs shape public space vitality in central-city station areas, using Shapingba Station (Chongqing, China) as a representative case of station–city integration. We delineated pedestrian catchments using Baidu Map walking isochrones (300–1200 s) and integrated multi-source data, including Public Space Public Life (PSPL) field observations (eight monitoring points, 07:00–24:00), Baidu heat maps, point-of-interest (POI) records, streetscape semantic segmentation, and a perception questionnaire. Indicators were synthesized via entropy weighting, and multivariate associations between perceived vitality and environmental variables were examined using Mantel tests. Pedestrian flow exhibits a clear double-peak pattern (09:00–11:00 and 15:00–16:00), averaging 42,248 pedestrians per day (2347 per hour) and showing strong spatial heterogeneity across monitoring points. POIs show a pronounced core–periphery structure: totals increase from 803 (300 s) to 4365 (600 s) and 7539 (1200 s), while overall density declines from 7477 to 2492 POIs/km², highlighting a 600 s core where accessibility and functional agglomeration are most strongly coupled. Overall, this study contributes a replicable multi-source evaluation framework and quantitative evidence on accessibility–function coupling and micro-scale design effects in HSR station areas, enabling theory-informed comparisons across station typologies and urban contexts. Full article

The study is based on Land Surface Temperature (LST) and Air Temperature data and Nonparametric Data Envelopment Analysis (DEA) technique to evaluate heat efficiency and detect anomalies in the thermal regime in the Eastern Black Sea Region, particularly in Hopa and Artvin, during the period 2000–2024. The regulating role of the Black Sea has resulted in Hopa having the warmest and most stable temperature patterns, with daytime temperatures 1.8 to 3.7 °C higher than Artvin. Previous DEA analysis of daytime temperatures has shown that the 2018–2020 period had the highest daily temperatures, while the 2001–2010 decade was characterized by the highest nighttime temperatures. A future heat map based on Monte Carlo simulation using six climate change scenarios indicates that in the most optimistic case, assuming a temperature increase of +0.8 °C, efficiency scores could increase as high as 0.995. On the other hand, if global warming leads to a sudden temperature increase above +7.2 °C, there is a 21.7% climate efficiency loss. Sensitivity analysis showed that technological innovation and good governance are the main positive factors affecting climate efficiency. Random Forest (RF) and SHapley Additive Explanations (SHAP) analyses were applied to determine the impact of climate factors on DEA scores and also indicated areas requiring risk assessment. The findings highlight the importance of considering location-specific climate adaptation strategies. Based on the observed thermal contrasts between coastal and inland environments, potential adaptation considerations may include urban heat management and agricultural water stress in coastal areas such as Hopa, and cold-climate resilience and energy-efficient infrastructure in inland locations such as Artvin. Full article

For irregularly shaped 820CrMnTi carburizing and nitriding parts, the challenges of high reflectivity-induced overexposure, low surface contrast, and interference from minute burrs in industrial online inspection are addressed in this paper. An innovative precision detection method integrating adaptive imaging and a dual-drive heterogeneous coupling model (RGFCN) is proposed. Such parts, due to surface photovoltaic characteristic changes caused by carburizing and nitriding heat treatment and the complex on-site lighting environment, are prone to local overexposure and “false out-of-tolerance” measurements caused by outlier sensitivity in traditional inspections. First, an innovative programmatic adaptive exposure control algorithm based on grayscale histogram feedback is introduced, which dynamically adjusts imaging parameters in real time to effectively suppress high-brightness overexposure under specific working conditions. Second, a novel adaptive main-axis scanning strategy is designed to construct a dynamic follow-up coordinate system, eliminating projection errors introduced by random positioning from a geometric perspective. Additionally, Gaussian gradient energy fields are combined with the Huber M-estimation robust fitting mechanism to suppress thermal noise while automatically reducing the weight of burrs and oil stains, achieving “immunity” to non-functional defects. Meanwhile, a data-driven innovative compensation approach is introduced. Based on sample training, gradient boosting decision trees (GBDTs) are integrated to explore the nonlinear mapping relationship between multidimensional feature spaces and system residuals, achieving implicit calibration of lens distortion and environmental coupling errors. By simulating factory conditions with drastic 24 h day–night lighting fluctuations and strong oil stain interference, statistical analysis of over 1000 mass-produced parts shows that this method exhibits excellent robustness in complex environments. It reduces the false out-of-tolerance rate caused by burrs by over 90%, and the standard deviation of repeated measurements converges to the micrometer level. This effectively addresses the visual inspection challenges of irregular, highly reflective parts on dynamic production lines. Full article

Polyphenols have attracted considerable scientific interest over recent years due to their broad spectrum of biological activities, including antioxidant, cardioprotective, anti-inflammatory, antidiabetic, and anticancer properties. However, their practical application is often limited by unfavorable physicochemical characteristics, particularly low aqueous solubility. Consequently, amorphous solid dispersions (ASDs) have been extensively investigated as a formulation strategy to overcome these limitations. This article represents the first part of a two-part review and presents the current state of the art in amorphous solid dispersions of polyphenols. The available literature is systematically summarized with respect to the investigated polyphenolic compounds, the employed carriers (with particular emphasis on polymeric systems), the preparation methods, and the solid-state characterization techniques used to confirm amorphization. Both single-component systems and binary combinations of polyphenols reported in the literature are considered. The collected data are presented in tabular form and complemented by a heat map illustrating the frequency of reported polyphenol–carrier combinations. The aim of this review is to organize the available knowledge, identify the most extensively studied systems, and highlight research areas that remain underexplored. A detailed discussion of the pharmaceutical benefits and mechanistic aspects of polyphenols in ASD systems will be provided in Part II. Full article

Background/Objectives: Ulcerative colitis (UC) involves inflammatory response, oxidative stress, changes in metabolites, and the gut microbiota. Jingangteng capsule (JGTC) has been utilized clinically for the treatment of inflammatory diseases for many years. However, the efficacy of JGTC in ameliorating UC remains unclear, and the underlying mechanisms have not yet been elucidated. This study aims to investigate the effect and mechanism of JGTC on UC. Methods: The chemical compositions of JGTC were examined using ultra-high-performance liquid chromatography with quadrupole time-of-fight mass spectrometry. The anti-UC effect of JGTC was evaluated by assessing the disease activity index (DAI), colon length, intestinal barrier recovery, and inflammatory factors in a dextran sulfate sodium (DSS)-induced colitis model. Mechanisms were investigated through fecal 16S rDNA sequencing, metabolomics analysis, enzyme-linked immunosorbent assay (ELISA), Western blotting, and network pharmacology analysis. Results: JGTC significantly reduced the DAI scores in UC mice, increased their body weight and colon length (p < 0.001), repairing damaged intestinal tissue. It decreased the levels of inflammatory cytokines TNF-α, IL-6, IL-1β, and LPS (p < 0.01, p < 0.001), alleviating intestinal inflammation. It also raised the expression of tight junction proteins ZO-1, Claudin-1, and Occludin (p < 0.05, p < 0.001), thereby enhancing intestinal barrier function. Fecal metabolomic analysis revealed that the favorable alterations in amino acid and lipid metabolites were more pronounced. Heat maps showed strong correlations between pharmacological indicators and gut microbiota, as well as between the main differential metabolites and gut microbial communities. UPLC-QTOF-MS detection yielded 33 components of JGTC, and network pharmacology analysis based on these components predicted pathways of action of JGTC in UC. Functional pathways closely associated with significantly differential metabolites and metabolic pathways were also investigated. The PI3K-AKT-mTOR pathway was one of them, which is consistent with the conclusions drawn from network pharmacology. JGTC significantly modulated key factors in this pathway, inhibiting the expression of PI3K, Akt, PDK1, and mTOR, while augmenting the expression of PTEN (p < 0.05, p < 0.01, p < 0.001). It also mitigated the levels of related oxidative stress factors MDA, MPO, and D-LA, and raised SOD levels (p < 0.01, p < 0.001). Conclusions: JGTC improved the excessive inflammatory response in UC by regulating intestinal flora and metabolic disorders, affecting the PI3K-AKT-mTOR signaling pathway, restoring intestinal tissue damage and intestinal barrier, and inhibiting inflammatory and oxidative stress factors. Full article

Historic buildings often exhibit high energy use intensity (EUI), while conservation constraints limit envelope retrofits, making it difficult to identify robust and actionable operational predictors. Using four in-use historic buildings in Shenyang, China, this study presents a pilot methodological demonstration with a controlled-comparability workflow consisting of two linked layers: (i) a Driver layer of intervenable operational variables and (ii) a Pathway layer of calibrated EnergyPlus heat-balance terms for physics-informed interpretation. Three importance approaches (Spearman, wrapper RFE with XGBoost, and Random Forest) are compared; rankings are fused via reciprocal rank fusion, and stability is tested using cross-period rolling validation across Top-K feature sets. After similarity screening, EUI variation is better explained by operational predictors and the corresponding simulated loss channels than by macro-scale structural heterogeneity. Infiltration-related indicators and envelope/infiltration loss components remain consistently prominent, while Spearman importance is less stable in the Pathway layer under seasonal switching and nonlinear coupling. A Top-10 subset provides a favorable accuracy–stability trade-off. The proposed Driver–Pathway mapping supports conservation-compatible prioritization hypotheses within a simulation-consistent interpretive framework; findings are associational and context dependent and should be validated through field measurements and experimental or quasi-experimental studies before prescriptive claims are made. Full article

Laser powder bed fusion (LPBF) is susceptible to defects arising from melt pool instabilities, spatter, heat accumulation, and powder spreading anomalies. In situ infrared (IR) monitoring can detect these issues; however, it typically generates large volumes of data that are costly to store and analyze. This work proposes a projection-based framework that directly maps in situ thermal measurements onto a three-dimensional (3D) voxelized part geometry, substantially reducing storage requirements while preserving spatial fidelity. In addition, several IR derived features are incorporated into a practical workflow for defect detection and process model calibration, including laser scan order, local pre-deposition temperature, maximum pre-scan temperature, and spatter generation and landing locations. For completeness, commonly used metrics such as interpass temperature, heat intensity, cooling rate, and relative melt pool area are extracted within the same unified processing pipeline. All features are computed using a consistent, reproducible Python-based implementation to streamline integration into routine monitoring and analysis tasks. Multiple parts are fabricated, monitored, and characterized to evaluate the proposed framework, demonstrating that the extracted features reliably identify process anomalies and correlate with observed defects. Full article

Commodity and carbon markets are central to natural resource allocation, energy security, and the effectiveness of carbon-pricing policies, yet their risk linkages can intensify sharply during crises. This study examines nonlinear, tail-dependent volatility spillovers across strategically important resource markets using a Quantile-on-Quantile connectedness framework. We employ weekly observed data from 3 January 2010 to 27 April 2025 for eleven futures markets spanning metals (copper, silver, gold), energy (WTI crude oil, heating oil, natural gas, gasoline), agricultural commodities (sugar, coffee, corn), and carbon emissions. Volatility is measured using GARCH-based estimates and embedded in quantile VAR dynamics to map state-contingent shock transmission across the distribution. The results indicate strong asymmetries: connectedness rises markedly in tail regimes and attains its highest levels during the COVID-19 pandemic and the Russia–Ukraine war, relative to the 2015–2016 energy market adjustment. Heating oil, gold, and natural gas frequently act as key volatility transmitters, while the carbon market shifts from a peripheral receiver to a more integrated and sometimes systemic node within the broader commodity risk network. The findings indicate that carbon-price risk propagates through resource markets in a regime-dependent manner, with implications for stress testing, tail-sensitive hedging, and the coordination of resource and climate policy under turbulent market states. Full article

This paper investigates how climate shelter initiatives implemented in European cities interact with National Adaptation Strategies (NAS) and National Adaptation Plans (NAP), assessing the degree of vertical integration between local practices and national climate adaptation frameworks. As urban heat increasingly threatens public health and exacerbates socio-spatial inequalities, climate shelters, conceived as networks of safe, accessible public spaces providing thermal comfort and social support, have emerged as innovative adaptation tools; however, their recognition within national policy architectures remains uneven across the EU. This study adopts a qualitative–comparative design structured in three phases: (i) a systematic review of NAS and NAP in the 27 EU Member States through keyword screening and classification of references as explicit, implicit, or absent; (ii) a mapping of climate shelter initiatives across 244 NUTS-2 capital cities; and (iii) an integrative cross-analysis of national frameworks and local implementation patterns. According to our results, only 4 Member States explicitly refer to climate shelters, 11 include implicit references, and 12 show no recognition, while 88 cities implement 97 initiatives, predominantly based on Nature-based Solutions and schoolyard transformations; 5 recurring governance configurations reveal bottom-up, top-down, and hybrid dynamics, demonstrating that local experimentation can anticipate, complement, and potentially reshape national adaptation policies. Full article

This study establishes experimentally grounded circumferential thermal criteria for heat-shrinkable crosslinked polyethylene (PEX) joint casings by coupling DSC-defined thermal activation with through-thickness thermal lag measured under trench-constrained irradiation. The activation temperature was identified as 140 °C from DSC, while an upper bound of the allowable outer-surface temperature was set to avoid thermal damage during installation. Full-scale temperature mapping revealed persistent circumferential non-uniformity caused by geometric line-of-sight limitations and inter-module gap regions, where the outer-surface temperature remained approximately 10–15 °C lower than directly irradiated locations, and the inner surface exhibited a delayed response due to the low thermal conductivity of PEX. Based on these observations, a two-stage heating sequence—an initial high-power stage followed by a reduced-power soaking stage—was experimentally derived to satisfy dual constraints: achieving inner-surface activation (≥140 °C) while maintaining the outer surface below the conservative outer-surface upper bound (~280 °C) and reducing circumferential temperature differences without surface overheating. Comparative joint tests confirmed that the proposed thermal criteria and sequence promote stable interfacial bonding and cohesive failure in the mastic layer, yielding higher repeatability and smaller strength scatter than conventional manual torch heating. The proposed framework provides experimentally grounded thermal criteria and a transferable procedure for designing heating conditions for heat-shrinkable polymer casing systems under constrained field environments. Full article

Ulaanbaatar, the capital of Mongolia, operates one of the world’s largest district heating (DH) systems in the coldest national capital (heating degree-days ~5800). Despite serving over 60% of the city’s 1.6 million residents, the current 3rd generation DH system suffers from high thermal losses (~17–18%) and relies on coal-fired combined heat and power plants. Transitioning to 4th generation district heating (4GDH) with lower supply temperatures could reduce these losses while enabling future low-temperature renewable energy integration. A geographic information system (GIS)-based spatial heat load density (HLD) analysis uses operational data from the Ulaanbaatar District Heating Company, encompassing 13,500 buildings with a total connected capacity of 3924 MW. Grid-based spatial analysis was performed at two resolutions (1 km² and 2 km²). Threshold sensitivity analysis was conducted across HLD criteria of 1–5 MW/km². Results indicate that median HLD values exceed the European reference threshold of 3 MW/km², with log-normal distributions confirmed by Shapiro–Wilk tests. Three candidate pilot zones were identified. A hybrid temperature strategy (65/35 °C above −25 °C; 90/60 °C below) further contextualizes the findings. These results suggest spatially favorable conditions for 4GDH development, providing a quantitative foundation for subsequent techno-economic feasibility studies. Full article

Background/Objectives: Hemodynamic monitoring is essential for guiding appropriate treatment by assessing cardiac output and volume status, as well as for preventing complications associated with excessive fluid administration. The EdgeFlow CW10 Plus is a device that extends conventional hemodynamic monitoring by incorporating pulmonary abnormality surveillance through B-line detection. This study aimed to evaluate whether the hemodynamic monitoring and pulmonary monitoring functions are well integrated, and verify the usability and efficiency of the system. Methods: A usability test was conducted with a panel of 15 medical professionals from diverse specialties and varying levels of clinical experience. Data from satisfaction surveys, heat maps, the System Usability Scale (SUS), and the NASA-TLX were analyzed to determine whether usability differences existed based on the duration of clinical experience. Results: The device demonstrated a high overall task success rate, averaging 93.2%. Regarding eye-tracking analysis based on clinical experience, it was observed that participants with more years of experience either failed to direct their gaze toward task-relevant user interface (UI) elements as effectively as those with fewer years of experience or showed similar patterns. Conclusions: The usability evaluation confirmed that the hemodynamic and pulmonary monitoring functions of the EdgeFlow CW 10 PLUS are well integrated, with the device demonstrating high usability and satisfaction. This integration is expected to support medical professionals in monitoring cardiac output and fluid status, facilitating timely therapeutic interventions while preventing complications related to fluid overload. Full article

Effective water resource management requires understanding the causal mechanisms driving water quality dynamics, yet extracting actionable insights from complex multivariate monitoring data remains a persistent challenge. This study presents CEcBaN (CCM-ECCM-Bayesian Networks), a decision-support tool that integrates Convergent Cross Mapping (CCM) for detecting dynamical coupling, Extended CCM (ECCM) for identifying temporal lags and causal directionality, and Bayesian network (BN) modeling for probabilistic scenario-based inference. The tool was designed to enable managers and researchers without programming expertise to reconstruct causal networks from routine monitoring data, distinguish direct from indirect effects, and evaluate intervention scenarios. CEcBaN was validated using four synthetic datasets with known causal structures, achieving superior specificity (0.83) and edge count accuracy (25% error) compared to Transfer Entropy (0.47 specificity, 139% error), Granger causality (0.82, 39% error), and the PC algorithm (0.83, 46% error). Application to Lake Kinneret, Israel, demonstrated the tool’s utility across three water quality challenges: (1) nitrogen cycling, where the nitrification pathway was reconstructed and seasonal stratification was identified as a key modulator (accuracy 0.931); (2) thermal dynamics, where a transition from atmosphere-driven to internally regulated heat transfer during stratification was revealed (2.1-fold increase in coupling strength); and (3) cyanobacterial bloom prediction, where prior phytoplankton community composition provided a 4–6-week early warning window (accuracy 0.846). CEcBaN advances causal inference in water resource management by making these analytical methods accessible through an intuitive interface. Full article