Search Results (688)

The high proportion of renewable energy connected to the grid has resulted in insufficient consumption capacity in distribution networks, while the construction of new-type power distribution systems has imposed higher reliability requirements. With its flexible power synchronization control capabilities, grid-forming energy storage systems possess the ability to both promote the consumption of distributed energy resources in new-type distribution networks and enhance their reliability. However, current control methods are still hindered by drawbacks such as high computational complexity and a singular optimization objective. To address this, this paper proposes an optimized strategy for unified active–reactive power coordinated control in high-proportion renewable energy distribution networks with the participation of multiple grid-forming energy storage systems. Firstly, to optimize the parameters of grid-forming energy storage systems more accurately, this paper employs an improved iterative self-organizing data analysis technique algorithm to generate typical scenarios consistent with the scheduling time scale. Quantile regression (QR) and Gaussian mixture model (GMM) clustering are utilized to generate typical scenarios for renewable energy output. Subsequently, considering operational constraints and equipment state constraints, a unified active–reactive power coordinated control model for the distribution network is established. Meanwhile, to ensure the optimality of the results, this paper adopts an improved northern goshawk optimization (NGO) algorithm to solve the model. Finally, the effectiveness and feasibility of the proposed method are validated and illustrated through an improved IEEE-33 bus test system tested on MATLAB 2024B. Through analysis, the proposed method can reduce the average voltage fluctuation by 6.72% and increase the renewable energy accommodation rate by up to 8.64%. Full article

Accurate forecasting of cellular traffic in non-stationary environments remains a formidable challenge, as real-world traffic patterns dynamically evolve, emerge, and vanish over time. To tackle this, we propose a novel meta-learning framework, GMM-SCM-DCM, which features a Dynamic Component Management (DCM) mechanism. This framework employs a Gaussian Mixture Model (GMM) for probabilistic meta-feature representation. The core innovation, the DCM mechanism, enables online structural evolution of the meta-learner by dynamically splitting, merging, or pruning Gaussian components based on a bimodal similarity metric, ensuring sustained alignment with shifting data distributions. A Single-Component Mechanism (SCM) is utilized for precise base learner initialisation. To ensure a rigorous and realistic validation, we reconstructed the Telecom Italia Milan dataset by applying unsupervised clustering and meta-feature engineering to identify and label four distinct functional zones: residential, commercial, mixed use, and crucially, non-stationary areas. This curated dataset provides a critical testbed for non-stationary forecasting. Comprehensive experiments demonstrate that our model significantly outperforms traditional methods and meta-learning baselines, achieving a 9.3% reduction in MAE and approximately 70% faster convergence. The model’s superiority is further confirmed through extensive ablation studies, robustness tests across base learners and data scales, and successful cross-dataset validation on the Shanghai Telecom dataset, showcasing its exceptional generalization capability and practical utility for real-world network management. Full article

Non-nutritive artificial sweeteners (NASs) are xenobiotics widely used in the food industry as sugar substitutes, since they provide few to no calories compared to sucrose. While NASs are considered safe at the acceptable daily intake (ADI) established by regulatory agencies, there is increasing controversy regarding their potential ability to promote metabolic derangements, especially to disrupt the gut microbiome balance. In this study, we analyzed a large cohort of the most commonly consumed beverages in Spain, categorizing them by the type of soda to determine the composition and content of the most frequently used NASs in the food industry. All commercial NAS formulations analyzed contained mixtures of different NASs. The NAS contents were always within regulated limits, although some samples yielded values close to these thresholds. Most soda samples analyzed contained NASs, even though the majority were not labeled as “zero sugars”, “no sugar added”, or “reduced calories”, which may mislead consumers. A preliminary statistical evaluation of the obtained results (cluster analysis) suggests that beverages can be grouped into three distinct clusters based on the total amount of NAS present in the samples. Differences in the total NAS content were significant among the three groups, with one cluster showing two- and four-fold higher levels than the others. Full article

Accurate load profile identification is crucial for effective and sustainable power system planning. This study proposes a characterization methodology based on clustering techniques applied to consumption data from medium- and low-voltage users, as well as distribution transformers from an electric utility. Three algorithms—K-means, DBSCAN (Density-Based Spatial Clustering of Applications with Noise), and Gaussian Mixture Models (GMM)—were implemented and compared in terms of their ability to form representative strata using variables such as observation count, projected energy, load factor ($LF$), and characteristic power levels. The methodology includes data cleaning, normalization, dimensionality reduction, and quality metric analysis to ensure cluster consistency. Results were benchmarked against a prior study conducted by Empresa Eléctrica Regional Centro Sur C.A. (EERCS). Among the evaluated algorithms, GMM demonstrated superior performance in modeling irregular consumption patterns and probabilistically assigning observations, resulting in more coherent and representative segmentations. The resulting clusters exhibited an average $LF$ of 58.82%, indicating balanced demand distribution and operational consistency across the groups. Compared to alternative clustering techniques, GMM demonstrated advantages in capturing heterogeneous consumption patterns, adapting to irregular load behaviors, and identifying emerging user segments such as induction-cooking households. These characteristics arise from its probabilistic nature, which provides greater flexibility in cluster formation and robustness in the presence of variability. Therefore, the findings highlight the suitability of GMM for real-world applications where representativeness, efficiency, and cluster stability are essential. The proposed methodology supports improved transformer sizing, more precise technical loss assessments, and better demand forecasting. Periodic application and integration with predictive models and smart grid technologies are recommended to enhance strategic and operational decision-making, ultimately supporting the transition toward smarter and more resilient power distribution systems. Full article

In this study, 74 bacterial isolates were obtained from sediments of Šibenik Bay, which has historically been impacted by heavy metal pollution. Isolates were tested for tolerance to cadmium (Cd), cobalt (Co), chromium (Cr), copper (Cu), mercury (Hg), nickel (Ni), lead (Pb), tin (Sn), and zinc (Zn), both individually and in mixtures, and for their biofilm-forming ability. Toxicity followed the trend Hg > Sn > Zn/Cd/Cr > Co/Ni > Pb, with Cu showing resistance across different concentrations. Resistance traits were observed against all tested metals, with some isolates displaying multi-metal resistance to as many as seven metals, reflecting long-term selective pressure in the Bay. The Bacillus species dominated the community (48 isolates across five clusters), confirming this genus as the principal group in metal-polluted sediments. Several less-explored genera, including Ruegeria/Cribrihabitans, Bhargavaea, Pseudoalteromonas, and Lysinibacillus/Sporosarcina, also exhibited notable resistance traits, underscoring their potential as novel candidates for bioremediation. Eleven isolates from Bacillus/Mesobacillus/Cytobacillus, Bacillus/Peribacillus/Rossellomorea, Bacillus/Pseudoalkalibacillus/Alkalibacillus, Lysinibacillus/Sporosarcina, and Ruegeria/Cribrihabitans clusters showed resistance and robust growth under metal mixtures. Among all isolates, 11, 32, 81, and 82 (Bacillus/Mesobacillus/Cytobacillus and Bacillus/Peribacillus/Rossellomorea) combined broad multi-metal tolerance with strong biofilm formation, positioning them as candidates for site-specific, nature-based bioremediation of heavy-metal-impacted coastal ecosystems such as Šibenik Bay. Full article

This paper presents a real-time terrain mapping method for aircraft’s navigation, combining probabilistic terrain modeling with adaptive azimuth scan command adjustment. The method refines a preloaded DTED in real time using radar scan data, enabling aircraft to update and utilize terrain elevation information during flight. The terrain is represented using a Gaussian Mixture Model (GMM), where radar scan data are evaluated based on their posterior responsibilities. A conditional nested GMM refinement is selectively applied in structurally ambiguous regions to capture multi-modal elevation patterns. The azimuth scan command is adaptively adjusted based on posterior responsibilities by increasing the step size in well-mapped regions and decreasing it in areas with low responsibility. This lightweight and adaptive strategy supports real-time operation with low computational cost. Simulations across diverse terrain types demonstrate accurate grid updates and adaptive scan control, with the proposed method achieving max error 29 m compared to grid-based averaging of 43 m and K-means clustering of 81 m. As the total number of updates is comparable to the existing methods, the proposed approach offers an advantage for real-time applications with enhanced grid accuracy. Full article

Skeleton is an extreme sliding sport in the Winter Olympics, where formulating targeted sliding strategies, based on training videos to navigate complex tracks, is particularly important. To make in-depth use of training video records, this study proposes an analytical method based on Mixture of Gaussians (MoG) and K-means clustering to extract and analyze trajectories from recorded videos for sliding performance evaluation and strategy development. A case study was conducted using data from the Chinese national skeleton team at the Yanqing Sliding Center, obtaining 741, 834, and 726 sliding trajectories from three representative curves. These trajectories were divided into groups based on sliding completion time (fast, medium, and slow groups). The consistency of trajectories within each group was calculated to evaluate sliding stability, while trajectory patterns in the fast group were clustered and described based on the average values of multiple features (starting position, ending position, and apex orthogonal offset). The results showed that more skilled athletes exhibited greater sliding stability (lower ${\rho }_C$-values), and on each curve, there were sliding patterns that performed significantly better than others. This research quantifies the characteristics of athletes’ sliding trajectories on curves, facilitating the visual tracking of training effects and the development of personalized strategies. It provides coaches and athletes with scientific decision-making support and clear directions for improvement, ultimately enabling precise enhancements in training efficiency and competitive performance, while also laying a technical foundation for the future development of intelligent training systems. Full article

This study compiled a dataset of published works relating to fatigue testing in asphalt mixes, covering 2020–2025. The dataset was subjected to bibliometric and textual analyses, including a systematic review, to explore emerging trends and patterns in experimental protocols. Bibliometrix, VOSviewer, and IRaMuTeQ were employed to map the scientific landscape of 368 articles. Following PRISMA guidelines, the 100 most-cited articles were reviewed to identify prevailing test setups and parameters. The results showed a growing scientific production (9.1% per year), concentrated in a few high-impact journals and dominated by China, with emphasis on sustainability. A comparison between scientific output and a road quality index revealed a disconnect between academic research and field implementation. Five thematic clusters emerged: sustainable pavement management, mechanical characterization, binder modification, performance modeling, and evaluation of innovative materials. Indirect tensile and four-point bending tests were the most common loading modes. Considerable variability in protocols, frequent omissions of methodological details, and limited statistical treatment were also observed. The study highlighted the importance of standardized reporting and robust analysis, offering a reproducible framework to understand fatigue behavior and support future research. Full article

It was previously demonstrated that timolol (TIM), naphazoline (NAPH), and diflunisal (DIF) are susceptible to degradation when exposed to extreme pH conditions and UV/Vis light. However, their stability in the presence of pharmaceutical excipients remains largely unexplored. Thus, their binary mixtures (1:1 ratio, w/w) with five excipients, hydroxyethyl cellulose (HCA), mannitol (MAN), poly(vinyl alcohol) (PVA), poly(vinylpyrrolidone) (PVP), and Tris HCl (TRIS), were subjected to forced degradation (70 °C/80% RH and UV/Vis light in the dose 94.510 kJ/m²). Forced degradation was designed to accelerate potential interactions between these compounds, allowing the earlier identification of degradation risk compared to formal stability studies. FT-IR/ATR and NIR spectroscopy, along with chemometric evaluation using Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA), was applied to assess changes in the spectra, compared to individual compounds and the non-stressed mixtures. A hybrid approach, combining visual assessment with chemometric evaluation of the spectral data, enabled the detection of changes that were not clearly observable using a single analytical method. In particular, interactions of TIM, NAPH, and DIF with MAN and TRIS were clearly identified, while the mixtures of NAPH with excipients proved to be the least sensitive to forced degradation. Full article

Population stratification based on gut microbiota composition has revealed several enterotypes in humans and animals, providing valuable tools for studying the gut microbiota landscape, which is crucial for animal health and production. However, knowledge about rumen enterotype identification in sheep, its influencing factors, and its association with growth performance and host genetics remains limited. Here, we investigated host genetic effects and phenotypic landscapes of rumen bacterial enterotypes in a large sheep population. Ruminal contents from 1150 healthy sheep were analyzed using 16S rRNA gene sequencing and genus-level clustering, complemented by extensive phenotypic data covering 47 traits spanning growth, feed efficiency, meat yield, and ruminal fermentation, along with whole-genome resequencing data. We identified two distinct enterotypes: Enterotype 1 (E1), a mixture of multiple genera, and Enterotype 2 (E2), dominated by Prevotella. E2 sheep exhibit superior growth and meat production performance, but lower feed efficiency and increased fat deposition. Two-part beta-regression models and co-occurrence network analyses revealed the extensive impact of enterotypes on microbial community structure, with E1 displaying a higher frequency of unique bacterial interactions. The estimated heritability of the enterotype was 0.47, and a GWAS identified five key genetic markers associated with rumen enterotype, localized to two candidate genes: CHODL and ENPP6. These markers significantly influence 58 ruminal bacterial genera, including key taxa and driving genus. Overall, our data provide new insights into sheep rumen-enterotype characteristics, contributing to a better understanding of microbial interactions that are crucial for improving ruminant growth performance. Full article

The present study aimed to determine whether enriching the finishing ration of lambs with incremental doses of a protected fatty acid (FA) blend would result in noticeable differences in the eating experience of the resulting meat. Three isonitrogenous diets containing 0, 50, or 100 g day⁻¹ of the FA mixture were formulated, and the lambs were fed these diets until slaughter under otherwise identical management conditions. After postmortem aging, boneless loin samples from each treatment were submitted to a descriptive sensory evaluation by a rigorously trained panel that followed international guidelines. Multivariate techniques—principal component analysis combined with hierarchical clustering—were applied to integrate the panel’s quantitative scores and visualize how the treatments segregated in sensory space. The lamb meat presented a level of acceptance dependent on the proportion of fatty acids. In general, this study suggests that adding an extra 50 or 100 g of fat supplement to a lamb’s diet towards the end of its growth can significantly improve the enjoyment consumers get from eating the meat. Sensory analysis of lamb meat enriched with fatty acids indicated that the most important attributes determining the acceptance of lamb meat were color, flavor, odor, and toughness. Consequently, it can be recommended that dietary fatty acids be strategically increased during the finishing phase as a practical approach to enhancing the sensory appeal of sheep meat without compromising panel consensus. Full article

Fault identification in Printed Circuit Board Assembly (PCBA) testing is essential for assuring product quality; nevertheless, conventional methods still have difficulties due to the lack of labeled faulty data and the “black box” nature of advanced models. This study introduces a label-free, interpretable self-supervised framework that uses two pretext tasks: (i) an autoencoder (reconstruction error and two latent features) and (ii) isolation forest (faulty score) to form a four-dimensional representation of each test sequence. A two-component Gaussian Mixture Model is used, and the samples are clustered into normal and fault groups. The decision is explained with cluster mean differences, SHAP (LinearSHAP or LinearExplainer on a logistic-regression surrogate), and a shallow decision tree that generated if–then rules. On real PCBA data, internal indices showed compact and well-separated clusters (Silhouette 0.85, Calinski–Harabasz 50,344.19, Davies–Bouldin 0.39), external metrics were high (ARI 0.72; NMI 0.59; Fowlkes–Mallows 0.98), and the clustered result used as a fault predictor reached 0.98 accuracy, 0.98 precision, and 0.99 recall. Explanations show that the IForest score and reconstruction error drive most decisions, causing simple thresholds that can guide inspection. An ablation without the self-supervised tasks results in degraded clustering quality. The proposed approach offers accurate, label-free fault prediction with transparent reasoning and is suitable for deployment in industrial test lines. Full article

Mining-induced seismicity poses significant safety risks in deep coal mining operations, necessitating advanced monitoring and accurate hazard assessment. Based on 15,584 microseismic events from a coal mine in Gansu, China, in 2024, this study investigates the spatiotemporal characteristics of mining-induced seismicity and its quantitative relationship with excavation disturbances. The methodology integrates Gaussian Mixture Model (GMM) clustering analysis with maximum likelihood estimation of b-value. Key findings include: (1) GMM clustering effectively identifies distinct seismic zones under different stress states, with significant variations in b-values (0.64–0.70). Low b-value zones correspond to high stress concentration and potential for strong events, enabling refined hazard assessment; (2) The time-sliding window analysis reveals the dynamic evolution of the b-value, which exhibits a clear negative correlation with high-energy seismic activity. When the b-value drops sharply to 0.6 or below, the likelihood of high-energy events increases markedly. Notably, 7 out of 8 high-energy seismic events occurred below this threshold. (3) Seismicity migrates with working face advancement, with monthly excavation length positively correlating with seismic energy release, confirming excavation as the primary trigger. This b-value spatiotemporal analysis framework provides scientific basis for early warning and mining optimization in deep coal mines. Full article

To elucidate the multi-performance evolution mechanisms of basalt fiber-reinforced lightweight expanded polystyrene geopolymer concrete (LEGC), a two-tiered investigation was conducted. In the first part, a series of LEGC mixtures with varying volume fractions of EPS (10–40%) and basalt fiber (BF) (0.4–0.8%) were designed. Experimental tests were carried out to evaluate density, flowability, compressive strength, flexural strength, and splitting tensile strength. Crack propagation behavior was monitored using DIC-3D speckle imaging. Additionally, X-ray CT scanning revealed the internal clustering of EPS particles, porosity distribution, and crack connectivity within LEGC specimens, while SEM analysis confirmed the bridging effect of basalt fibers and the presence of dense matrix regions. These microstructural observations verified the consistency between the synergistic effects of EPS weakening and fiber reinforcement at the microscale and the macroscopic failure behavior. The results indicated that increasing EPS content led to reduced mechanical strength, whereas the reinforcing effect of basalt fiber followed a rising-then-falling trend. Among all specimens, LEGC20BF06 exhibited the best comprehensive performance, achieving a compressive strength of 40.87 MPa and a density of 1747.6 kg/m³, thus meeting the criteria for structural lightweight concrete. In the second part, based on the experimental data, predictive models were developed for splitting tensile and flexural strengths using compressive strength as a reference, as well as a dual-factor model incorporating EPS and fiber contents. Both models were validated and demonstrated high predictive accuracy. Furthermore, a splitting tensile elasto-plastic damage constitutive model was proposed based on composite mechanics and energy dissipation theory. The model showed excellent agreement with experimental stress–strain curves, with all fitting coefficients of determination (R²) exceeding 0.95. These findings offer robust theoretical support for the performance optimization of LEGC and its application in green construction and prefabricated structural systems. Full article

The characterization and mapping of urban landscape spatial form are critical for advancing sustainable planning and informed environmental management. From a morphometric perspective, this study introduces a novel, data-driven framework for typo-morphological analysis. First, morphological cells (MCs) are defined as objectively and universally applicable spatial units for morphometric investigation. Second, by integrating a multi-dimensional cognition of full-scale morphological and associated landscape elements, we construct a set of 48 spatial form indicators and attach them to morphological cells, enabling a precise description of each unit. Third, a Gaussian mixture model (GMM) is employed to cluster the metrical information within the spatially lagged context derived from the topological structure of the morphological cells, resulting in the delineation of distinct typo-morphological zones (TMZs). We then adopt Ward’s algorithm to establish a hierarchical relationship among identified urban landscape types. Using Wuxi City, China, as a case study, our results demonstrate the effectiveness of the proposed framework in capturing the heterogeneity and underlying connotation of urban landscape spatial characteristics. Building upon the unsupervised clustering results, we further apply the classification and regression tree (CART) to provide a supervised interpretation of the key spatial form conditions driving typological decisions. It facilitates the systematic identification of the components and formative mechanisms of spatial form. The findings contribute a scalable, reproducible, and interpretable typo-morphometric approach for analyzing urban landscape spatial characteristics, thereby providing a robust quantitative foundation for integrated decision-making in landscape planning, socio-ecological assessment, and urban design practices. More broadly, the study carries both applied and theoretical significance for advancing refined urban governance and fostering interdisciplinary research related to urban sustainable development. Full article