Search Results (24,618)

This paper presents research on a Predictive and Uncertainty Assessment Framework (PUAF), providing a comparative analysis of two prominent methods, Monte Carlo (MC) Dropout and Bootstrap-based models, used in uncertainty estimation techniques of Neural Network predictions of human activity recognition using accelerometer data. Unlike traditional studies that optimize classification accuracy, this work emphasizes uncertainty quantification to enhance model reliability, particularly for critical health-related activities. Among the five activity classes of Sit, Sleep, Walk, Cough and Fall, this work concentrates on the Cough and Fall cases. The study exploits acceleration data from a wearable device positioned on the user’s chest, with features derived from three-axis motion measurements. Synthetic datasets are generated by systematically introducing noise variations, added to the original dataset across all axes, to assess robustness under real-world conditions. Each uncertainty estimation method estimates the probabilities for the five different classes along with the corresponding 95% confidence intervals to quantify the prediction uncertainty. A detailed evaluation is conducted by analyzing the average width of these confidence intervals across different noise levels, identifying the most reliable feature and model combination. Both the MC Dropout and Bootstrap enhance model robustness and uncertainty awareness under noisy sensor conditions. The MC Dropout provides sharper and more sensitive uncertainty estimates, while the Bootstrap yields more stable and better-calibrated predictions. The evaluation using the proposed PUAF demonstrates that each method offers distinct advantages, highlighting the importance of uncertainty quantification for reliable wearable-based HAR systems. Full article

Background and Objectives: Identifying regulatory genes that integrate epigenetic, transcriptional, immune, and non-coding RNA networks may improve prognostic stratification in hepatocellular carcinoma (HCC). ZBTB40 is a poorly characterized transcription factor whose clinical relevance and multi-layered regulatory role in HCC remain unclear. This study systematically investigated the prognostic significance, molecular regulatory networks, and toxicogenomic interactions of ZBTB40 in HCC. Materials and Methods: Comprehensive multi-omics analyses were conducted utilizing TCGA-HCC datasets and various public bioinformatics platforms. We systematically evaluated ZBTB40 expression patterns, survival outcomes, clinicopathological associations, DNA methylation status, immune cell infiltration, and competing endogenous RNA (ceRNA) networks. Additionally, chemical–gene interactions were analyzed using the Comparative Toxicogenomics Database (CTD). Results: ZBTB40 was significantly overexpressed in HCC, closely correlating with advanced clinicopathological features and poor survival outcomes. This upregulation was significantly associated with promoter hypomethylation. Furthermore, ZBTB40 expression was associated with specific immune infiltration patterns. A ZBTB40-centered ceRNA network identified key regulatory miRNAs, including miR-24-3p, miR-34a-5p, miR-132-3p, and miR-222-3p, along with prognostically relevant lncRNAs and circRNAs. CTD analysis identified 39 key chemical modulators of ZBTB40 (e.g., sorafenib, aflatoxin B1) and revealed RNF13 and CHD3 as functionally related genes sharing substantial chemical interaction profiles. Functional analyses suggested ZBTB40’s involvement in chromatin remodeling, the cell cycle, and immune-related pathways. Conclusions: ZBTB40 expression is associated with multi-layered molecular features involving epigenetic, post-transcriptional, immune-related, and toxicogenomic signatures in HCC. Full article

Surfactant adsorption onto reservoir rocks remains a critical challenge in chemical enhanced oil recovery (cEOR), as it directly impacts flooding efficiency and chemical costs. This study presents a comprehensive scientometric analysis of research on surfactant adsorption for EOR applications over the period 2005–2025. Based on the Scopus database, 877 publications accounting for more than 22,100 citations were retrieved and analyzed to map the intellectual and conceptual structure of this research field. VOSviewer 1.6.20 software was employed to generate keyword co-occurrence networks, author bibliographic coupling, and country-level contributions. The results reveal a strong growth in scientific output after 2016, with annual publications increasing from fewer than 30 papers per year before 2010 to more than 100 papers per year after 2021. “Enhanced Oil Recovery” (165 occurrences), “Adsorption” (101 occurrences), and “Surfactant” (88 occurrences) emerged as the most frequent and highly interconnected keywords. At the geographical level, China (29.4%), the United States (22.3%), and Iran (9.6%) were identified as the leading contributors, together accounting for more than 60% of the global research output. Bibliographic coupling analysis highlighted a core group of highly influential authors shaping the field through strong collaborative networks. Emerging themes such as nanoparticle-assisted EOR, wettability alteration, and low-salinity surfactant systems were identified as rapidly growing research frontiers. This scientometric analysis provides the first quantitative mapping dedicated specifically to adsorption phenomena in cEOR, while highlighting future opportunities for optimizing adsorption control strategies and improving reservoir performance. Full article

Non-small cell lung cancer (NSCLC) exhibits genomic heterogeneity that affects tumor immunogenicity and PD-L1 expression. Patient clustering based on shared mutational profiles using social network analysis (SNA) has been narrowly explored. The study aimed to identify subgroups of NSCLC patients with similar somatic mutation profiles using network-based modularity clustering, and to compare these groups with respect to PD-L1 expression, Tumor mutation burden (TMB), and clinical variables. Data of patients with stage 4 (metastatic) NSCLC, whose tumor tissue samples were collected between 2022 and 2024, were analyzed. This retrospective study included NSCLC patients harboring actionable driver mutations in genes such as EGFR, KRAS, ALK, BRAF, MET. A social network of 129 patients was constructed. Two distinct genomic clusters were identified. Cluster 2 (n = 55) showed a higher prevalence of KRAS, TP53, BRAF, STK11 and additional mutations, while cluster 1 (n = 74) displayed a limited number of driver mutations. Cluster 2 had significantly higher PD-L1 expression (29.8% vs. 13.7%, p = 0.001) and higher TMB (7.8 vs. 5.8, p = 0.021). In multivariate logistic regression, both PD-L1 and TMB were associated with cluster assignment (p < 0.05). Mutation-based SNA clustering delineated two biologically distinct subgroups of NSCLC patients. The highly mutated cluster displayed higher PD-L1 expression and TMB, a profile consistent with a more immunogenic phenotype. This method offers a novel integrative approach that requires prospective validation before clinical implementation. Full article

Modeling thermal environments across scales is crucial for climate-adaptive design and energy management. Traditional numerical methods (e.g., CFD) offer high accuracy and physical consistency, but they are computationally expensive. In contrast, purely data-driven models, though efficient, lack physical consistency and generalization capability. This review systematically examines Physics-Informed Neural Networks (PINNs), a hybrid paradigm in which physical prior knowledge is embedded directly into the neural network training process. A structured keyword search of the Web of Science Core Collection was performed, and 94 peer-reviewed journal articles were analyzed. The evolution from numerical simulations and data-driven surrogate models to PINNs is outlined. PINN methods are classified according to the stage at which physical prior information is integrated (i.e., dataset development, model construction, or loss function formulation). Current research remains heavily focused on loss function constraints, whereas systematic integration into data augmentation and model construction remains limited. Application domains span indoor environments, outdoor environments, and building systems, with each domain exhibiting unique prior integration strategies tailored to specific problems. Future PINN modeling should evolve toward multi-physics coupling, adaptive loss balancing, cross-scenario transfer learning, and unified evaluation benchmarks. PINNs in this field are promising but remain at an early stage, especially for complex urban-scale deployment. This review synthesizes existing research around the three stages of dataset development, model construction, and loss function formulation, summarizes the prior integration strategies adopted in the domain of building thermal environments, and provides a practical workflow for embedding physical prior knowledge at different stages of model development. Full article

Commensalism, a frequent type of interaction among freshwater invertebrates, remains poorly studied. Some turbellarians (Platyhelminthes: Temnocephalidae) are specialized obligate commensals of crustaceans, mollusks, insects, and turtles. In the Neotropics, Temnocephala iheringi inhabits the mantle cavity of snails (Mollusca: Gastropoda) from Pantanal (Brazil) to the Pampean region of Argentina, where several species serve as hosts. This study aimed to molecularly characterize several populations of T. iheringi and to analyze their genetic and morphological variability across different host species and geographic areas. Using the mitochondrial COI marker, we assessed populations associated with five of its seven known host species through phylogenetic reconstructions, species delimitation approaches, and haplotype network analyses. Combined with morphological data, results support COI as an effective identification tool for Temnocephalidae. Several genetic lineages were identified and were largely congruent with collection localities. However, specimens associated with hosts displaying high dispersal capabilities (i.e., Pomacea canaliculata and P. maculata) showed low mitochondrial genetic differentiation and minimal phylogenetic structure across large distances, which may be consistent with recent dispersal and/or ongoing connectivity among populations. These findings provide new insights into the evolutionary dynamics of this specific temnocephalid–snail association. Given that some hosts are highly invasive and even considered pests in several countries, the data and genetic sequences generated in this study may prove valuable for future research on symbiont diversity and dispersal. Full article

Fiber-reinforced polymers such as basalt fiber-reinforced polymers (BFRP) can be used in structural parts, which often require assembly operations. Thus, the surface quality after drilling operations is especially important. BFRP laminates have been drilled with three different tools, and their profile roughness and surface roughness have been evaluated by analyzing the following variables: average roughness (Ra), maximum height of profile (Rz), arithmetic mean height (Sa) and maximum height (Sz), by means of an optical system. The optical measurement of surface roughness has been hampered by fiber breakage. A statistical analysis has allowed for developing a general linear model that predicts the values of variables. The fitted model for Ra and Rz has a variation coefficient of 97.00% and 95.58% respectively, while that 91.74% and 68.02% for Sa at the inlet hole and outlet hole respectively; and 86.08% and 82.22% for Sz at the inlet hole and outlet hole respectively. Additionally, different machine learning regression algorithms have been applied using different configurations to establish prediction models of the main rugosity parameters. In this way, linear methods, Gaussian regression methods, Support Vector Machines, and fine trees have been applied using the rotation speed, feed rates, and tool as features. Also, a neural network has been optimized and applied for the same goal. The methods yielded satisfactory prediction results within the tested experimental domain for some roughness parameters. Although the behavior of all variables is similar across all drill bit types, drill bits with a point angle of 120° provided better results. Full article

To investigate the effects of coagulation methods on the structure and properties of sheet rubber, this study prepared natural rubber using different coagulation systems, including acetic acid, formic acid, biological coagulants, and pineapple juice, and systematically analyzed their non-rubber components, gel content, molecular weight distribution, rheological behavior, and mechanical properties of the vulcanized rubber. The results indicate that the type of coagulant significantly affects the protein, phospholipid, and gel content. Among these, the pineapple juice gel exhibited the lowest residual protein content, suggesting that the proteases, organic acids, and active components it contains promote the degradation and removal of non-rubber components. GPC and rheological results show that pineapple juice gel and bio-gel samples possess a broad molecular weight distribution and exhibit a more pronounced viscoelastic response at high temperatures. After uniform vulcanization, the differences in hardness, tensile strength, and tear resistance among the various samples were minimal, indicating that the vulcanized network determines the final mechanical properties, while the coagulation method primarily regulates microstructure and processing behavior. This study provides a theoretical basis for the application of bio-coagulants in the processing of green shikigai gum. Full article

To understand how habitat heterogeneity drives fungal community assembly in different habitats of the pied avocet (Recurvirostra avosetta), we analyzed four habitat types (water bodies, aquatic plants, soil, and nest sediments) using high-throughput sequencing. A total of 9980 ASVs (Amplicon Sequence Variants) were detected, with only 68 shared across all habitats, indicating strong community differentiation. Ascomycota and Basidiomycota dominated (50–60% relative abundance), reflecting fungal adaptability to wetlands. Water bodies showed significantly higher alpha diversity than aquatic plants and nest sediments. Beta diversity and principal coordinates analysis (PCoA) revealed closer similarity in fungal composition between water and aquatic plant communities, whereas soil and nest sediments formed distinct clusters. PERMANOVA based on binary Jaccard distances further confirmed that habitat type explained 10.9% of the variation in fungal community structure (R² = 0.109, p = 0.001). LEfSe (LDA Effect Size) identified habitat-specific indicator taxa, supporting niche filtering and competitive exclusion as selection mechanisms. The co-occurrence network was dominated by positive correlations, suggesting metabolic complementarity that maintains ecosystem stability. Unclassified fungi accounted for 18–22% of communities, representing untapped fungal resources. These findings support that habitat heterogeneity governs multi-media fungal assembly, revealing how microhabitat conditions regulate fungal composition, diversity, and interactions. This study provides a theoretical basis for biodiversity conservation and ecological restoration in avocet habitats. Full article

Multi-layer low Earth orbit constellations (ML-LEOs) have become a pivotal trend in the development of satellite network systems, where their layered orbital architecture improves system performance by strategically deploying satellites in distinct orbital layers. However, two critical issues remain open: how does the configuration of ML-LEO affect its performance, and how many layers are required to achieve optimal performance? This paper first investigates the impact of the number of layers L on the capacity of ML-LEOs. By analyzing the distribution of inter-layer inter-satellite links (ISLs) and the flow count on bottleneck links, we derive a closed-form mathematical expression for ML-LEO capacity under different values of L. In particular, we show that when each layer adopts an identical constellation topology and the number of satellites per orbit equals the number of orbits, the capacity of the ML-LEO is $\sqrt{L}$ times that of a single-layer low Earth orbit constellation (SL-LEO). Furthermore, we present the optimal parameter configuration for ML-LEOs: the number of orbits per layer should equal the number of satellites per orbit, the number of layers should be half the number of satellites per orbit, and the optimal number of inter-layer ISLs is twice the product of the number of orbits per layer and the number of layers. Finally, extensive simulations are carried out to thoroughly verify the accuracy of the analytical results. Our analysis reveals the performance benefits of multi-layer topology and establishes a theoretical framework for parameter optimization in ML-LEO. Full article

Machine learning prediction of turbulent bursting in near- and far-wake flow zones past two horizontal cylinders was studied in the present article. Based on the bursting dataset, two predictive models were constructed using Artificial Neural Networks (ANNs) and Support Vector Regression (SVR) with stress ratios as target values for each bursting event. After analyzing a number of plots, it was observed that the ANN and SVR models achieved satisfactory estimation accuracy, with minor overfitting specifically in the case of ANN models. By using deep learning for quadrant analysis and highlighting the adaptability of machine learning methods in open-channel turbulence, the current work should strengthen the understanding of bursting occurrences in bluff-body hydrodynamics. Full article

Agriophyllum squarrosum (L.) Moq. is a desert-adapted pseudocereal that has recently attracted attention as a climate-resilient crop and source of valuable phytochemicals and nutritionally relevant metabolites. Despite their ecological and nutritional importance, comprehensive studies combining lipid and phenolic profiles across natural populations remain limited. In the present study, five populations of A. squarrosum from ecologically contrasting regions of Kazakhstan were analyzed to evaluate biochemical diversity and potential for functional food applications. Total lipid content was determined using near-infrared spectroscopy, fatty acid composition was assessed by GC-MS, and phenolic compounds were quantified by HPLC. Multivariate approaches, including PCA, MANOVA, PLS analysis, correlation networks, and TOPSIS ranking, were applied to evaluate population differentiation and relationships between biochemical traits and environmental conditions. Total lipid content in seeds ranged from 7.71% to 15.40%, linoleic acid represented 50.20–57.67% of total fatty acids, and oleic acid ranged from 24.80% to 40.10%. Isorhamnetin was the dominant phenolic compound in leaves, with concentrations between 0.24 and 0.65 mg/g. Populations from Aktobe showed higher lipid and oleic acid contents, whereas Almaty populations accumulated greater flavonoid levels, including isorhamnetin, quercetin, and kaempferol. These findings reveal substantial metabolic differentiation among populations and suggest possible associations with ecological conditions. The observed accumulation of unsaturated fatty acids and phenolic compounds, including isorhamnetin, quercetin, and kaempferol, identifies promising germplasm resources for future studies on functional food development and biological activity evaluation. The results further support the potential utilization of A. squarrosum in sustainable agriculture in arid regions. Full article

Shallow groundwater systems of rural municipalities are highly vulnerable to long-term contamination from former on-site sanitation systems, while the hydrochemical response of the aquifer after sewerage network development may be delayed by several factors. In the present study, a total of 147 shallow groundwater samples collected during the summer sampling campaigns of 2018, 2019, 2023, and 2024 were analyzed for general water-quality parameters including pH, EC, NH₄⁺, NO₂⁻, NO₃⁻, PO₄⁻, Cl⁻, SO₄²⁻, microelements, and potentially toxic elements, including As, Pb, Cd, Ni, Cu, Zn, Fe, and Mn. The dataset was evaluated using descriptive statistics, Piper, Wilcox, and Gibbs diagrams, hierarchical cluster analysis, principal component analysis, and GIS-based spatial interpolation. The results indicate that, more than ten years after sewerage network development (2014), shallow groundwater in the study area still shows considerable contamination, primarily characterized by elevated mean concentrations of ammonium (0.836 mg/L), nitrate (177.43 mg/L), and chloride (313.26 mg/L), accompanied by high electrical conductivity (3115 µS/cm) and sodium enrichment (378.12 mg/L). Spatial and boxplot analyses of SAR further indicated increasing sodium-related heterogeneity after 2018, with higher local SAR values in 2023–2024. Hydrochemical diagrams revealed a shift towards Ca-Cl type to Na–Cl types, while multivariate analyses confirmed that salinity enrichment, nitrate contamination, water–rock interaction and redox-sensitive trace element mobilization act as overlapping but partly separable controls. The nitrate–chloride source plot indicated mixed contamination origins, dominated by residual sewage influence and manure-related inputs, with diffuse agricultural nitrogen leaching. Arsenic was used as a supporting indicator of mixing with wastewater; however, As was no longer detectable in most of the investigated wells, suggesting a marked reduction in the former wastewater leakage. These results support the slow attenuation of contamination in the shallow groundwater system affected by former wastewater infiltration and highlight the need for continuous monitoring. Full article

In this article, we propose a novel real-time terrain recognition and slip estimation method for quadruped robots using proprioceptive sensors and temporal convolutional networks (TCNs). As quadruped robots are increasingly deployed in complex environments, accurate terrain understanding is crucial. External sensors can be affected by lighting variations, occlusion, reflective surfaces, and others. To overcome these challenges, we propose a proprioceptive sensing-based complementary perception module with a TCN, enabling reliable real-time terrain recognition while reducing dependence on external perception. The TCN model effectively captures temporal dependencies in sensor signals, enabling precise and robust detection. The framework is validated through extensive real-world experiments and deployed on an embedded edge computing platform for real-time operation. Results show that the proposed TCN method achieves 98.8% recognition accuracy, outperforming the baseline models compared in this study. In addition, this study analyzes how locomotion speed and environmental conditions affect slip in quadruped robots. These findings confirm that quadruped robots can not only recognize terrain types but also detect surface states, enabling safer and more adaptive locomotion. Therefore, the proposed system is a cost-effective, robust, and low-latency solution for real-time terrain recognition, providing a strong foundation for future deployment across more diverse terrains. Full article

Voltage source converters (VSCs), together with their inner current and outer power/voltage control loops, are fundamental building blocks in the modern, converter-dominated power systems, particularly within high-voltage DC (HVDC) frameworks. Selecting effective control methods for VSCs is essential to ensure the stability, power quality, and dynamic performance of HVDC grids. This paper seeks to advance the current body of research by delivering an in-depth, consistent, unified framework and systematic examination of VSC control architectures within HVDC networks. It thoroughly explores various control strategies for VSCs interfacing with HVDC grids, such as grid-following and grid-forming strategies, with particular emphasis on both stationary ($\alpha \beta$) and synchronous (dq) reference frames. Moreover, the paper provides a comprehensive analysis of the theoretical underpinnings and decoupled control strategies, like the feedforward decoupling of the d- and q-axis currents in the dq frame and the inherently decoupled structure of the αβ frame. Additionally, advanced filtering techniques, including Moving Average Filter (MAF), Cascaded Delayed Signal Cancellation (DSC), and LCL filters, are analyzed. In addition, harmonic mitigation strategies, like parallel/multiple resonant (PR) terms in the αβ frame and cascaded notch filters in the dq frame, are presented. Furthermore, precise power control approaches and synchronization methods are discussed in detail. Also, this paper presents a detailed comparison of the performance characteristics of phase-locked loop (PLL) and frequency-locked loop (FLL) in response to grid frequency variations. Moreover, this paper proposes circuit representations and VSC models in both synchronous and stationary reference frames. The simulation results corroborate the theoretical insights discussed in the paper under various operational conditions, including initial responses, grid disturbances, three-phase-to-ground temporary fault scenarios, harmonic distortions, and load imbalances, in terms of overshoot, settling time, active- and reactive-power fluctuation reduction, voltage unbalance factor, total harmonic distortion, and post-fault convergence time, all evaluated in accordance with the limits defined in EN-50160. This comprehensive comparison of the presented control strategies facilitates researchers in identifying the most appropriate controller depending on their specific application requirements. Full article