Search Results (5,845)

Detecting and identifying faults in industrial chemical plants is essential for safe and stable operation, and modern monitoring systems increasingly rely on deep learning to classify faults from multivariate sensor data. A practical obstacle to adoption is trust: most deep-learning diagnosers reach their decisions through a classification layer that operators cannot inspect, making it hard to see how the model maps process signals to a particular fault. This study targets fault diagnosis on the Tennessee Eastman (TE) process, a standard benchmark of simulated chemical-plant sensor data, and asks whether this final decision stage can be made directly inspectable without sacrificing accuracy. We propose CNN–Transformer–KAN (CTKAN), a hybrid model that learns local temporal patterns with a one-dimensional convolutional encoder, captures global inter-time-step dependencies with a Transformer encoder, and classifies faults with a Kolmogorov–Arnold Network (KAN) head whose learnable B-spline activations can be plotted and examined individually, in place of a conventional multi-layer perceptron (MLP). On the TE benchmark, CTKAN attains a Macro-F1 of 91.38 ± 0.26% over ten independent runs, comparable to a CNN + Transformer + MLP ablation (91.21 ± 0.32%) and a capacity-matched MLP-head variant (91.43 ± 0.37%) within seed-to-seed variability. The main finding is therefore not a higher score: at matched capacity the KAN and MLP heads are statistically indistinguishable in accuracy, so the KAN head’s value is to add a directly inspectable view of the classification stage at no measurable accuracy cost, helping process engineers sanity-check how the diagnoser separates faults in safety-critical settings. Full article

The sources of potentially toxic elements (PTEs) in the lower reaches of the Yellow River (LYR) remain poorly understood due to intensive human activities in this region. To elucidate the spatiotemporal distribution characteristics and sources of PTEs, water samples were collected from both mainstream and tributary sites during the dry season (DS) and flood season (FS). Concentrations of eight PTEs (Fe, Mn, Cu, Zn, Pb, As, Cr, and Hg) were determined. The single-factor pollution index, Nemerow comprehensive pollution index, statistical techniques, and the positive matrix factorization (PMF) receptor model were jointly employed to evaluate PTEs pollution levels and quantitatively apportion its sources. The results showed that PTEs concentrations in the mainstream were significantly higher than those in the tributaries, with Fe and Mn being the primary contaminants exceeding standards. During the DS, the mean concentrations of Fe and Mn were 1.33 mg/L and 0.34 mg/L, with exceedance rates of 100% and 84.2%, respectively. In contrast, both concentrations declined markedly in the FS (Fe: 0.27 mg/L; Mn: 0.112 mg/L). The PMF model identified three sources in the DS, with contribution rates of 42.1% (geogenic background and domestic sewage), 32.4% (industrial wastewater), and 25.5% (agricultural sources). In the FS, two sources were resolved, namely a mixture of non-point source pollution and domestic sewage (64.3%) and a mixture of geogenic background and industrial wastewater (35.7%). The pronounced increase in non-point source contribution during the FS highlights the role of rainfall runoff in driving pollutant input. This study provides a scientific basis for PTEs pollution control in the LYR. Full article

To clarify the internal mechanism through which the scale of higher education influences regional economic development, this work constructed an operational framework of education, talents, science and technology, and industry. Based on the 2023 data of 31 provincial administrative regions in China, covering 178 national high-tech industrial development zones, an empirical analysis was conducted using descriptive statistics and the Bootstrap mediating-effect test. The findings indicate that the expansion of higher education scale can enhance the level of talent supply, promote the agglomeration of scientific and technological innovation resources, drive the development of industrial scale, and thereby significantly boost economic growth. Among these pathways, the scale of the undergraduate and postgraduate student population exerts a complete mediating effect, while research and development investment and the number of enterprises in high-tech zones demonstrate a partial mediating effect. Notably, a striking contrast emerges between regular undergraduate institutions and double-first-class universities. The former exhibit significant positive mediating effects, whereas the latter’s economic driving effect remains largely unrealized. Furthermore, the uneven distribution of high-quality educational resources, particularly the spatial polarization of double-first-class universities, coupled with a mismatch between talent cultivation and industrial demands, and the “spatial isolation” of achievements, all restricted the radiating effect of higher education on regional economies. Therefore, it is necessary to implement a regionally differentiated layout of higher education, optimize the allocation mechanism of scientific and technological innovation resources, strengthen industry–university–research collaboration, and give full play to the effect of industrial agglomeration. Full article

Regional water systems in rapidly urbanizing hilly basin cities are affected by hydrological variability, population concentration, industrial water demand, and water-use efficiency. This study evaluated the water resources carrying capacity (WRCC) of Jinhua City, southeastern China, from 2011 to 2023 using an integrated 15-indicator system covering water resources support, water-use and population pressure, economic structure and water-use efficiency, and ecological and environmental support. Indicator definitions, units, directions, and data sources were harmonized using official water resources bulletins and statistical records. A combined weighting method integrating the modified Analytic Hierarchy Process and the entropy weight method was coupled with a fuzzy matter-element model and the Hamming closeness measure. WRCC grades were assigned using standard-derived Hamming closeness thresholds based on pooled-reference membership transformation. Obstacle degree, leave-one-indicator-out sensitivity, and redundancy diagnostics were further used for interpretation and robustness assessment. The combined weights were mainly concentrated in water-use and population pressure (35.85%), water resources support (26.77%), and economic structure and water-use efficiency (26.10%). Industrial water use, per capita comprehensive water use, population density, water consumption per 10,000 yuan industrial value added, and water consumption per 10,000 yuan GDP had the highest indicator weights. Annual Hamming closeness ranged from 0.2621 to 0.6391. Jinhua’s WRCC reached Grade II in 2015, 2019, 2020, and 2021, while the remaining years were classified as Grade III. The highest closeness occurred in 2019, whereas 2022 and 2023 declined to Grade III and were close to the II/III threshold. Obstacle diagnosis showed that water-use and population pressure were the dominant subsystem obstacles. Sensitivity analysis showed that the peak year and the lowest year remained unchanged across all leave-one-indicator-out scenarios, whereas the boundary years showed grade sensitivity. The results provide a transparent annual assessment and diagnostic evidence for WRCC management. Full article

Tourism development potential and observed development performance do not necessarily evolve synchronously, particularly in old industrial and restructuring regions where attraction supply, market linkage, and visitor experience may be spatially uneven. This study develops a multi-source diagnostic framework for identifying tourism potential–performance mismatch across the 14 prefecture-level cities of Liaoning Province, China. Drawing on Ctrip review texts, rating scores, timestamps, platform-displayed reviewer-origin labels, A-level scenic-spot point data, and annual official city-level tourism statistics, the study constructs three dimension-specific sub-indices—the Scenic Experience Index (ESI), the Market Linkage Index (MLI), and the Attraction Foundation Index (AFI)—and synthesizes them into a Comprehensive Potential Index (CPI). The CPI is then compared with an Observed Performance Index (OPI) constructed from domestic tourist arrivals and domestic tourism revenue for 2016–2022. The results show that attraction foundation contributes most strongly to composite tourism potential, while market linkage and scenic experience condition how this structural basis is associated with observed outcomes. The CPI–OPI comparison identifies three relationship types: matched, potential-leading, and performance-leading cities. Dalian and Shenyang are high-level matched cities, Benxi and Jinzhou are high-potential but under-converted cities, and Anshan and Dandong are performance-leading cities. These findings demonstrate that favorable structural tourism conditions are not automatically transformed into realized market performance. The study contributes a multidimensional, gap-analysis-based diagnostic architecture that can support tourism-related spatial planning and territorial governance in transitional regions. Full article

Red blood cells (RBCs) are sensitive biomarkers of human health, influenced by urbanization and agricultural exposures. Using scanning electron microscopy (SEM) within an exposome framework, we examined RBC morphology in residents of an industrialized area of Thessaloniki, Greece, and in a rural population primarily exposed to agricultural stressors. Blood samples and questionnaires covering demographics, lifestyle, and environmental factors were statistically analyzed. SEM revealed moderate morphological alterations without significant differences between groups. Observed features were associated with longer residence duration and suboptimal nutrition, suggesting subclinical cellular stress. Integrating these findings into exposome research may clarify cumulative industrial and agricultural impacts on RBC morphology. Full article

With the adoption of more ambitious emission reduction strategies in the shipping industry by the International Maritime Organization and the resulting stricter greenhouse gas emission reduction requirements, it is particularly important for all stakeholders in the global maritime shipping industry to assess the energy efficiency of shipping vessels. Forming predictive capabilities for ship fuel consumption and Carbon Intensity Indicator (CII) annual ratings, for example, are two important works. This article adopted 14 different algorithms in three categories of data-driven approaches, i.e., statistics, machine learning and deep learning, including polynomial regression, ridge regression, adaptive boosting, categorical boosting, elastic net, etc., and built the ship fuel consumption prediction model using ship noon report as the data source. The prediction accuracy and computational efficiency of model training were compared based on metrics of coefficient of determination, mean absolute percentage error and floating-point operations per amount of training data. Cross-validations were performed for all 14 algorithms to analyze their sensitivities to their respective tuned parameters. Comparisons indicated that algorithms of the statistics approach were sensitive to the quality of the data source, compared with the machine learning and the deep learning approaches. The accuracy of the elastic net algorithm was sensitive to the tuned parameters. Two algorithms, light gradient boosting machine and random forest, were selected based on their performances of prediction accuracy and computational efficiency of model training. Then, the selected algorithms were separately combined with long short-term memory as the time-series prediction algorithm to form their respective coupled framework. Both of the coupled frameworks achieved successful prediction of the CII annual discriminant and rating of the studied ships. The prediction accuracy was validated to be sufficient. Full article

The Dead Sea, the Earth’s lowest major surface water body, serves as the terminal basin for surface and groundwater flow in its surrounding region. However, anthropogenic activities and natural processes contribute to significant alterations in the lake’s area. The scope and implications of these changes remain insufficiently documented, necessitating further investigation. The CA-Markov model was used to project the Dead Sea’s surface area for 2034 and 2050. Time series of observed and future climate data, especially temperature data, under Representative Concentration Pathways (RCPs) 4.5 and 8.5, were analyzed to track climate change. Statistical analyses of the Kendall correlation matrix were performed on the observed and predicted surface areas, water levels, and temperatures. This study revealed that the Dead Sea decreased by 41.8% from 1971 to 2022, and the sea level is expected to decrease by 12.63 m and 33 m by 2034 and 2050, respectively. In addition, there were significant inverse relationships between surface area, water level, and temperature, with correlations of r = −0.79 (p = 0.001) and r = −0.82 (p = 0.001), respectively. Notably, from 2022 to 2050, the mean annual temperature is expected to increase by at least 1 °C. The long-term strategic vision for stabilizing Dead Sea water levels involves a twofold approach: (1) augmenting natural inflow by introducing 300–400 million cubic meters (MCM) from manufactured sources and channeling them into the Jordan River, and (2) reducing water extraction by Dead Sea industries by a maximum of 330 MCM. Full article

Post-industrial waterfront regeneration can improve environmental quality and public space, but it may also produce green gentrification and unequal access to regeneration benefits. To support socially responsive planning evaluation, this study examines how green gentrification is spatially manifested and how residents perceive and support waterfront green space development in Shanghai’s Yangpu Riverside. A sequential mixed-methods design combines census, housing price, and green space data from 2000 to 2020 with 317 resident questionnaires. The study identifies socio-spatial changes associated with green gentrification, cross-culturally adapts and validates the Gentrification Worldview Instrument (GWI), and examines the associations among gentrification worldviews, place attachment, and support for green space development. Results show no statistically significant relative acceleration in housing price growth in near-waterfront neighborhoods during the regeneration period, but reveal an expanding housing price premium, educational upgrading, and population decline. These patterns are consistent with a spatially differentiated tendency toward green gentrification embedded in the broader state-led waterfront regeneration process, rather than demonstrating an independent effect of greenbelt construction. The Chinese-adapted GWI retains the three dimensions of neighborhood preservation, development support, and social integration. Among surveyed residents, development support and place identity are positively associated with support for waterfront green space development, whereas neighborhood preservation is negatively associated with support. The results further indicate a statistical mediation pattern in which place identity forms a significant indirect association between development support and support for green space development. The findings provide an evidence-based framework for evaluating inclusive waterfront regeneration and suggest that planning and design should integrate green space accessibility, local memory, residents’ perceptions, and social equity. Full article

Predictive quality systems are central to Industry 4.0 manufacturing, yet detecting rare defects that pass established quality gates remains an open problem. This study addresses post-quality-gate failure detection in automotive brake manufacturing, where 310 faulty units (1.20%) among 25,756 production records create a naturally occurring extreme class imbalance of 1:82. Fault labels are derived from warranty reports and linked to multi-station production line measurements, while negative samples may include latent failures, motivating a recall-focused evaluation. We propose a Quality 4.0 machine learning framework that compares five resampling methods (ADASYN, SMOTE-Tomek, KMeans-SMOTE, CTGAN, and TVAE) plus a no-resampling baseline across 24 classifiers and stacking ensembles. In total, 504 configurations are tested on a held-out test set. The proposed SVM-RBF model trained on ADASYN-augmented data achieves recall of 0.871, specificity of 0.982, balanced accuracy of 0.926, and ROC-AUC of 0.952, producing only 93 false positives (FPR = 1.8%). Stacking ensembles provide alternative operating points maximizing the detection rate (93.5%) and a separate operating point with the highest discrimination capacity (ROC-AUC = 0.975). Feature importance analysis through Permutation Importance and SHAP identifies Force Increment as the leading feature under both attribution methods. Friedman and Wilcoxon tests confirm statistically significant differences among strategies. The framework offers a practical way to add predictive capability to existing quality control systems. Full article

A biogenic ZnO/Ag photoelectrode treated with atmospheric-pressure plasma was evaluated as an anode in a photo-assisted electroflotation system for the transformation of chromophoric pollutants in real industrial wastewater. ZnO was synthesized from Azadirachta indica leaf extract and plasma-treated for 10 min (M2) and 15 min (M3), with an untreated reference (M1). XRD, SEM-EDS, Raman, FTIR, EPR, and XPS analyses showed that the plasma preserved the wurtzite structure, relaxed the bulk, and modified the surface by removing residues, deoxygenating it, and activating oxygen vacancies (V_O). Although M3 reached the highest deoxygenation, M2 showed the most favorable response; thus, the performance did not depend only on the total amount of V_O. Under dark conditions, M2 showed a 14.86 percent decrease in COD compared to the control in a single batch and had the most negative ORP value. However, only ORP came close to statistical significance after multiplicity correction, with p_adj = 0.055. Under illumination, it showed the strongest photoinduced changes in conductivity and total suspended solids. The light–dark differences (ΔL−O) showed sign reversals in COD, conductivity, and pH, which identified three functional regimes and indicated that the electronic coupling of the surface V_O, rather than its amount, controlled the performance. ΔL−O was proposed as an operational test to distinguish these regimes, with the plasma exposure time as a key control variable. Because the effluent responses were single determinations, they are considered exploratory; the mechanism is primarily based on structural and spectroscopic characterization and supported by photoelectrochemical tests. Full article

Automotive Noise, Vibration, and Harshness (NVH) has emerged as a critical interdisciplinary field influencing vehicle performance, passenger comfort, brand perception, and regulatory compliance. This thematic literature review synthesizes key research trends, methodological approaches, and technological developments shaping contemporary NVH studies. Drawing on 255 scholarly and industry sources, the review identifies five dominant themes: (1) sources and characterization of noise and vibration in internal combustion, hybrid, and electric vehicles; (2) advanced modeling and simulation techniques—including finite element analysis, statistical energy analysis, and machine learning–based prediction models; (3) materials, components, and structural optimization strategies for NVH mitigation; (4) the rapidly evolving landscape of electric and autonomous vehicle NVH; and (5) emerging active noise and vibration control technologies and data-driven diagnostics. The analysis highlights a definite shift toward holistic, data-driven, and multi-physics approaches, driven by lightweighting imperatives, widespread electrification, and increasingly stringent occupant comfort expectations. Key gaps in current research—including the need for unified evaluation metrics, real-time in-vehicle NVH monitoring, closer integration of subjective psychoacoustic perception with objective physical measurement, and validated simulation workflows for novel EV architectures—are identified and discussed. This review provides a consolidated and expanded framework for understanding contemporary NVH research directions and articulates opportunities for transformative innovation in next-generation vehicle development. Full article

The valorization of agri-food by-products represents a key strategy for improving sustainability and promoting circular economy principles in food systems. Pumpkin seed press cake is a protein-rich by-product with potential application in bakery products. The aim of this study was to evaluate the feasibility of using defatted pumpkin seed press cake flour (PPSF) as a major ingredient in cookie formulations and to optimize its incorporation in order to maximize nutritional quality and sensory acceptability. Chemical characterization showed that PPSF has a superior nutritional profile compared to wheat flour, containing 55.75% protein, 8.78% minerals, and 6.15% total dietary fiber, along with significantly higher levels of total phenolics, total carotenoids, and β-carotene (0.26 mg/100 g). Formulation optimization using response surface methodology (RSM) enabled a high inclusion level of 69.61% PPSF, with 41.32% sugar and a baking time of 9 min and 29 s. The developed predictive models for diameter, thickness, overall acceptability, and bending stiffness were highly significant (p < 0.05) with a non-significant lack of fit (p > 0.05), confirming their statistical reliability for exploring the design space. The optimized C-PPSF (defatted pumpkin seed press cake flour) cookies showed a significant nutritional improvement, with protein content increasing from 13.05% to 30.17% and antioxidant capacity (DPPH) rising from 2.90% to 7.10%. While the enriched cookies had a darker color (L* 51.98) and reduced snapping force (39.7 N) due to gluten dilution, they maintained stable geometric parameters and achieved higher sensory scores for aroma, taste, and overall acceptability compared to the control. The main finding of this study is that PPSF can replace a substantial proportion of wheat flour in cookies while maintaining consumer acceptability and significantly improving nutritional quality. The optimized formulation with approximately 70% PPSF shows that this by-product has the potential to serve as a major ingredient in bakery products rather than only as a nutritional supplement. These results confirm that PPSF is a powerful functional ingredient that supports zero-waste manufacturing and provides a foundation for its broader use in bakery formulations within circular economy approaches. Future research should focus on shelf-life stability, bioaccessibility of bioactive compounds, volatile aroma profiling (e.g., GC–MS analysis), and industrial-scale validation of PPSF-based formulations. Full article

Background/Objectives: Stevia rebaudiosides represent a class of compounds extracted from the Stevia rebaudiana Bertoni plant or produced via yeast fermentation, which provide a sweet taste with little to no calories. These compounds are commercially referred to as stevia and are used in the food industry to reduce sugar in foods and beverages. Stevia is a non-nutritive sweetener (NNS), which is a class of ingredients which represent both artificial and plant-based sweeteners. NNSs are widely used and have been well studied. However, their effects on efficacy for weight management as a sugar reduction tool and overall metabolic effects are inconsistent. Of the approved NNSs for use, stevia is relatively new and one of the least studied. However, recent human clinical research has provided insights into stevia’s metabolic effects, effects on the gut microbiome and effects on weight management when used to replace sugar. The objective of this narrative review of human clinical studies is to provide an overview of the effects of stevia rebaudiosides (largely rebaudioside A) on glucoregulatory and cardiometabolic functions, as well as their effects on gut microbiome and weight management. These studies were typically short term (acute to three months) and heterogeneous by design, and they contained stevia rebaudiosides as lone sweeteners and as part of a binary blend with other NNSs. The majority of metabolic studies on stevia rebaudiosides have evaluated the effects on glucose homeostasis and, to a lesser extent, the effects on cardiometabolic function, the gut microbiome, and weight management. These studies suggest that stevia rebaudiosides have no statistically significant effects on glycemia, insulinemia, blood lipids, appetite hormones, or the gut microbiome. Limited studies suggest that, particularly when compared to sucrose, stevia produces very modest body weight and BMI changes, while studies on subjective appetite and food intake have had inconsistent results. Conclusions: Longer-term studies are needed, with more consistent and rigorous design protocols across various populations. However, current human clinical studies suggest that stevia rebaudiosides have a limited impact on metabolic functions, and the observed effects on gut microbiome and changes in body weight, particularly when used to replace sugar, warrant further study. Full article

Vernacular housing in the Andean region embodies long-standing building knowledge, environmental adaptation, and forms of social organization rooted in rural life. Over recent decades, these dwellings have undergone visible transformations linked to migration, changing aspirations, and the growing presence of industrialized construction materials. Rather than disappearing, vernacular forms have increasingly merged with contemporary solutions, producing hybrid architectural landscapes whose local dynamics are still insufficiently documented. This study analyzes the material, typological, and functional transformation of rural housing in Las Lagunas and Quisquinchir, two Indigenous communities located in Saraguro, Loja, Ecuador. A total of 192 houses were recorded through field observation and a structured digital survey implemented with KoBoCollect. The information was processed in R using descriptive statistics, contingency tables, chi-square tests, Cramér’s V, and standardized residual analysis. The findings show that architectural change in both communities does not occur through a simple replacement of traditional housing by modern models. Instead, vernacular, hybrid, and modern/eclectic typologies coexist within the same rural setting, revealing uneven and locally specific processes of transformation. The clearest differences emerge in construction materiality. Las Lagunas preserves a stronger presence of traditional wall systems, especially adobe and bahareque, while Quisquinchir shows a broader incorporation of industrialized materials, particularly concrete block. Statistical analysis confirmed significant associations between community and wall material, as well as between typology and wall material, whereas the relationship between community and architectural typology was comparatively weaker. Functional changes were also identified through the reduction or reconfiguration of intermediate spaces such as portals, patios, and corridors, suggesting a gradual shift toward more enclosed and specialized domestic environments. These results contribute empirical evidence for understanding architectural hybridization in Indigenous rural territories and support conservation and planning approaches capable of recognizing continuity, adaptation, and change within evolving Andean built landscapes. Full article