Search Results (3,647)

Fault concealment in complex software programs and the difficulty of generating test cases to detect such faults present significant challenges in software testing. To resolve these challenges, this paper suggests a novel method that integrates mutation testing, fuzzy clustering, convolutional neural networks (CNN), and particle swarm optimization (PSO) to efficiently generate test cases that cover multiple paths with numerous faults (mutant branches). Initially, mutation-based paths are classified using fuzzy clustering based on their coverage difficulty and similarity. A multi-feature CNN model (MF_CNNpro) is then constructed and trained on the paths of each cluster. Finally, the predicted particles from the MF_CNNpro model are used as the initial population for PSO, which evolves to generate the test cases. The proposed method is evaluated on six test programs, and the results demonstrate that it significantly improves clustering separation and reduces clustering compactness. By selecting only the cluster center paths to construct the MF_CNNpro model, training and prediction costs are effectively reduced. Moreover, the use of MF_CNNpro and PSO to select representative individuals as the initial population greatly enhances the evolutionary efficiency of PSO. The proposed method outperforms traditional approaches in clustering, prediction, and test data generation. Specifically, the SC clustering method improves cluster separation (SP) by 0.021, reduces compactness (CP) by 0.054, and decreases clustering rate (CR) by 4.97%, thereby enhancing clustering precision. The MF_CNNpro model improves the IA metric by 38.2% and reduces the U-Statistic and MSE by 83.0% and 97.9%, respectively, optimizing prediction performance. The MF_CNNpro+PPSOpro method increases the path coverage success rate from 47.9% to 97.4% (a 103.3% improvement), reduces the number of iterations by 84.1%, and decreases execution time by 95.6%, significantly improving generation efficiency. Full article

Endometriosis has a significant impact on the social, psychological, psychosomatic, and physical aspects of women’s lives. There is increasing evidence that endometriosis has to be seen as a systemic and complex disorder with a multifactorial etiology, accompanied by numerous other pathologies, such as mental disorders and even cancer. Herein, we analyzed Disability-Adjusted Life Years (DALYs) and Years Lived with Disability (YLDs) generated from the Global Burden of Disease Study (GBD 2021), which are key metrics used to measure the worldwide impact of diseases. Besides, differential gene expression data generated from the Turku Endomet Database were calculated. Briefly, log2-transformed gene expression counts were investigated using linear modeling with the function expression ~ condition to generate log2 fold changes and p-values for each gene. This enabled a precise comparative analysis of mRNA expression levels between control endometrium and various endometriosis-affected tissues, including ovarian endometrioma, peritoneal lesions, and deep endometriosis. Expression patterns of specific genes related to pain and malignant turnover within endometriosis samples and controls have been analyzed. The identification of upregulated genes like FOS, DES, SIRT1, SBDS, SRF, SPN, P2RX1, TEAD3, and SLITRK3, alongside downregulated genes such as KIF22, KIF25, GAS2L2, and HINT3, highlights a broad transcriptional reprogramming within endometriotic tissues. The clustering analysis, which reveals pain-related genes (SRP14/BMF, GDAP1, MLLT10, BSN, and NGF), further solidifies the genetic basis for the chronic and often debilitating pain experienced by patients with endometriosis. In 2021, women with endometriosis experienced the highest rates of total YLDs at 19.98%, with anxiety contributing 17.21% and major depression 8.12%, equating to mean YLDs of 15–24 years. In conclusion, our findings reinforce the need for adopting a holistic, psychosomatic approach to managing endometriosis. The identified genetic markers related to pain provide a biological basis for the profound physical suffering. At the same time, the robust DALYs and YLDs data quantify the devastating impact on mental health, particularly highlighting the significant burden of depression and anxiety. Full article

Understanding the spatial and temporal structure of crustal deformation is essential for identifying tectonic blocks, assessing seismic hazard, and detecting precursory deformation associated with major megathrust earthquakes. In this study, we analyze twenty years of continuous GNSS observations from the Japanese Islands to identify coherent deformation domains and anomalous regions using an integrated time-dependent clustering framework. The workflow combines six machine learning algorithms (Hierarchical Agglomerative Clustering, K-means, Gaussian Mixture Models, Spectral Clustering, HDBSCAN and consensus clustering) and constructs a set of deformation-related features including steady-state velocities, strain rates, co-seismic and post-seismic displacements, and spatial distance metrics. Optimal cluster numbers are determined by validity metrics, and the most robust segmentation is obtained using a consensus approach. The resulting spatiotemporal domains reveal clear segmentation associated with major geological structures such as the Fossa Magna graben, the Median Tectonic Line, and deformation belts related to Pacific Plate subduction. The method also highlights deformation patterns potentially associated with the preparation stages of megathrust earthquakes. Our results demonstrate that machine learning-based clustering of long-term GNSS time series provides a powerful data-driven tool for quantifying deformation heterogeneity and improving the understanding of active geodynamic processes in subduction zones. Full article

Timely and accurate urban change detection is vital for sustainable urban development, infrastructure management, and disaster response. Traditional two-dimensional approaches often overlook vertical and structural variations in dense urban areas. This study proposes a three-dimensional (3D) change detection framework that integrates high-resolution optical imagery and Digital Surface Models (DSMs) from two time points to capture both horizontal and vertical transformations. The method is based on a deep learning architecture combining a ResNet34 encoder with a UNet++ decoder, enabling the joint learning of spectral and elevation features. The research was carried out in two stages. First, a binary classification model was trained to detect areas of change and no-change, allowing direct comparison with conventional methods such as Principal Component Analysis (PCA), Change Vector Analysis (CVA) with thresholding, K-Means clustering, and Random Forest classification. In the second stage, a multi-class model was developed to categorize the types of structural changes, including new building construction, complete destruction, building height increase, and height decrease. Experiments conducted on a high-resolution urban dataset demonstrated that the proposed CNN-based framework significantly outperformed traditional methods, achieving an overall accuracy of 96.58%, an F1-score of 96.58%, and a recall of 96.7%. Incorporating DSM data notably improved sensitivity to elevation-related changes. Overall, the ResNet34–UNet++ architecture offers a robust and accurate solution for 3D urban change detection, supporting more effective urban monitoring and planning. Full article

Generating air-conditioning system load data is crucial for tasks such as power grid scheduling and intelligent energy management. Air-conditioning load data exhibit strong non-stationarity. Their load curves are influenced by seasonal variations and highly correlated with outdoor meteorological conditions, indoor activity patterns, and equipment operational strategies. These characteristics lead to pronounced periodicity, sudden shifts, and diverse data patterns. Existing load generation models tend to produce averaged distributions, which often leads to the loss of specific temporal patterns inherent in air-conditioning loads. Moreover, as purely data-driven models, they lack explicit physical constraints, resulting in generated data with limited physical interpretability. To address these issues, this paper proposes a hybrid generation framework that integrates the DTW clustering algorithm, a physically-constrained TimeGAN model, and an LSTM-based model selection mechanism. Specifically, DTW clustering is first employed to achieve structured data partitioning, thereby enhancing the model’s ability to recognize and model diverse temporal patterns. Subsequently, to overcome the dependency on detailed building parameters and extensive sensor networks, a parameter-free physical constraint mechanism based on intrinsic temperature-load correlations is incorporated into the TimeGAN supervised loss. This design ensures thermodynamic consistency even in sensor-scarce environments where only basic operational data is available. Finally, to address adaptability challenges in long-term sequence generation, an LSTM-based selection mechanism is designed to evaluate and select from clustered submodels dynamically. This approach facilitates adaptive temporal fusion within the generation strategy. Extensive experiments on air-conditioning load datasets from Southeast China demonstrate that the framework achieves a local similarity score of 0.98, outperforming the state-of-the-art model and the original TimeGAN by 11.4% and 13.3%, respectively. Full article

This paper proposes a coordinated planning method for distribution network lines considering geographical constraints and load distribution, aiming to improve the economy and engineering feasibility of distribution network planning. First, a hierarchical system of geographical constraints based on the Interval Analytic Hierarchy Process (IAHP) is established to systematically quantify the influence weights of spatial factors such as terrain undulation, ecological protection zones, and construction obstacles. Second, the density peak clustering algorithm and load complementarity coefficient are introduced to generate equivalent load nodes, and a spatially continuous load density grid model is constructed to accurately characterize the distribution and complementary characteristics of the load. Third, an improved A-star algorithm is adopted, which integrates a heuristic function guided by geographical weights and load density to dynamically avoid high-cost areas and approach high-load areas. Additionally, Bézier curves are used to optimize the path, reducing crossings and obstacle interference, thus enhancing the implementability of line layout. Verification via a real distribution network case study in a certain area of Guangdong Province shows that the proposed method outperforms traditional planning strategies. It significantly improves the economy, safety, and engineering feasibility of the path, providing effective decision support for distribution network line planning in complex environments. Full article

To address the increasing complexity and diversity of user requirements in underwater rescue equipment, this study proposes a Digital Twin (DT)-enhanced KJ-Kano conceptual design framework. It systematically closes the feedback loop between requirement prioritization and experiential validation. Unlike traditional approaches, this framework orchestrates KJ clustering, Kano analysis, and mission-aware DT simulation in a domain-adapted, iterative workflow, enabling dynamic validation of user needs under high-risk, simulated rescue scenarios. Functional expectations and preferences were clustered and prioritized, then instantiated in a modular DT prototype for navigation, manipulation, and perception tasks. To evaluate design effectiveness, 55 participants operated the robot DT model and its control interfaces in virtual rescue missions. User satisfaction across functionality, interactivity, intelligence, and appearance was assessed with a five-point Likert scale, and the results showed high reliability (Cronbach’s $\alpha$ = 0.86) and positive evaluations (overall mean = 3.83). Intelligent experience scored highest (3.95), while ease of operation was lowest (3.60), suggesting potential for interface optimization. The framework effectively transforms heterogeneous, context-specific user requirements into validated design solutions, offering a replicable, data-driven methodology for early-stage conceptual design of underwater rescue robots and other safety-critical human–machine systems, bridging the gap between generic design methods and high-risk domain application. Full article

Gut microbiota play a central role in shaping bile acid (BA) metabolism through community-specific capacities for deconjugation, dehydroxylation, and other transformation reactions. Distinct microbiome compositional patterns—often referred to as enterotype-like clusters—correspond to reproducible functional profiles that generate unique BA metabolic signatures with relevance for metabolic and gastrointestinal health. This narrative review synthesizes current evidence describing the interplay between microbial composition, BA metabolism, and metabolic dysfunction. A structured literature search was conducted in PubMed, Web of Science, EMBASE, and Scopus using predefined keywords related to bile acids, microbiome composition, metabolic disorders, and enterotypes. Studies were screened for human clinical relevance and mechanistic insights into BA–microbiome interactions. Across the evidence base, Bacteroides-, Prevotella-, and Ruminococcus-associated community types consistently demonstrate different BA transformation capacities that influence secondary BA production and downstream host signaling through FXR and TGR5. These differences are linked to variation in metabolic dysfunction-associated steatotic liver disease, obesity, type 2 diabetes, inflammatory bowel disease, and colorectal cancer. Host genetic variations in BA synthesis, transport, and signaling further modify these microbiome–BA interactions, contributing to the heterogeneity of dietary intervention responses. Overall, the literature supports a model in which microbiome-derived BA profiles act as metabolic phenotypes that shape host lipid and glucose homeostasis, inflammation, and gut–liver axis integrity. Emerging clinical applications include microbiome-stratified dietary strategies, targeted probiotics with defined BA-modifying functions, and therapeutic approaches that align BA-modulating interventions with an individual’s microbial metabolic capacity. Establishing integrated biomarker platforms combining microbiome clustering with BA profiling will be essential for advancing precision nutrition and personalized management of metabolic and gastrointestinal diseases. Full article

Real-world decision-making often involves uncertainty, incomplete data, and the need to evaluate alternatives based on both quantitative and qualitative criteria. To address these challenges, this study presents FAS-XAI, a unified methodological framework that integrates fuzzy clustering and explainable artificial intelligence (XAI). FAS-XAI supports interpretable, data-driven decision-making by combining three key components: fuzzy clustering to uncover latent behavioral profiles under ambiguity, supervised prediction models to estimate decision outcomes, and expert-guided interpretation to contextualize results and enhance transparency. The framework ensures both global and local interpretability through SHAP, LIME, and ELI5, placing human reasoning and transparency at the center of intelligent decision systems. To demonstrate its applicability, FAS-XAI is applied to a real-world B2B customer service dataset from a global ERP software distributor. Customer engagement is modeled using the RFID approach (Recency, Frequency, Importance, Duration), with Fuzzy C-Means employed to identify overlapping customer profiles and XGBoost models predicting attrition risk with explainable outputs. This case study illustrates the coherence, interpretability, and operational value of the FAS-XAI methodology in managing customer relationships and supporting strategic decision-making. Finally, the study reflects additional applications across education, physics, and industry, positioning FAS-XAI as a general-purpose, human-centered framework for transparent, explainable, and adaptive decision-making across domains. Full article

Current machine learning-based gross primary productivity (GPP) upscaling error correction approaches exhibit two critical limitations: (1) failure to account for nonuniform density distributions of sub-pixel heterogeneity factors during upscaling and (2) dependence on subjective classification thresholds for characterizing factor variations. These shortcomings reduce accuracy and limit transferability. To address these issues, we propose an equidistant sampling method with optimized step size and intervals that precisely quantifies nonuniform density distributions and enhances correction precision. We validate our approach by applying it to correct 480 m resolution GPP simulations generated from an eco-hydrological model, with performance evaluation against 30 m resolution benchmarks using determination coefficient (R²) and root mean square error (RMSE). The proposed method demonstrates a significant improvement over previous elevation-based correction research (baseline R² = 0.48, RMSE = 285 gCm⁻²yr⁻¹), achieving a 0.27 increase in R² and 91.22 gCm⁻²yr⁻¹ reduction in RMSE. For comparative analysis, we implement k-means clustering as an alternative geostatistical method, which shows lesser improvements (ΔR² = 0.21, ΔRMSE = −63.54 gCm⁻²yr⁻¹). Crucially, when using identical statistical interval counts, our optimized-step equidistant sampling method consistently surpasses k-means clustering in performance metrics. The optimal-step equidistant sampling method, paired with appropriate interval selection, offers an efficient solution that maintains high correction accuracy while minimizing computational costs. Controlled variable experiments further revealed that the most significant factors affecting GPP upscaling error correction are land cover, altitude, slope, and TNI, trailed by LAI, whereas slope orientation, SVF, and TWI hold equal relevance. Full article

Extreme autumn rainfall has become frequent due to climate change, making disaster prevention increasingly difficult. We combined a retrieval-augmented generation (RAG) framework with a multimodal large language model (multimodal LLM) to automatically cluster and explain weather patterns. The multimodal LLM assists in selecting an appropriate clustering method, such as hierarchical clustering, to determine the optimal number of clusters. To enhance weather map interpretation and reduce hallucinations or uncertainty, 13 specialized prompt roles are designed to guide the model’s reasoning process. The method is applied to autumn-season data from 1960 to 2019, using weather records from the Taiwan Climate Change Projection and Information Platform and the ERA5 reanalysis dataset by the European Center for Medium-Range Weather Forecasts. The results show that three dominant weather types were identified. The identified types are typhoon with companion system (TC_NE, 51%), northeasterly pattern (NE, 30%), and tropical cyclone (TC, 19%). The developed method in this study provides a new approach for interpreting extreme weather events under changing climate conditions. Full article

This study aims to examine the acoustic characteristics of Mandarin vowels produced by children with cochlear implants and to explore the differences in their speech production compared with those of children with normal hearing. We propose a multiview model-based method for vowel feature analysis. This approach involves extracting and fusing formant features, Mel-frequency cepstral coefficients (MFCCs), and linear predictive coding coefficients (LPCCs) to comprehensively represent vowel articulation. We conducted k-means clustering on individual features and applied multiview clustering to the fused features. The results showed that children with cochlear implants formed discernible vowel clusters in the formant space, though with lower compactness than those of normal-hearing children. Furthermore, the MFCCs and LPCCs features revealed significant inter-group differences. Most importantly, the multiview model, utilizing fused features, achieved superior clustering performance compared to any single feature. These findings demonstrated that effective fusion of frequency domain features provided a more comprehensive representation of phonetic characteristics, offering potential value for clinical assessment and targeted speech intervention in children with hearing impairment. Full article

The workplace, as a promising location for Electric Vehicle Supply Equipment (EVSE), presents a particular challenge, as different user requirements (e.g., parking and charging durations) meet a spatially and quantitatively limited offer of EVSE. However, integrating electric vehicles synergistically into the energy system of the employer can increase the profitability of the system and, correspondingly, increase the number of EVSE. For this, a deep understanding of employees’ charging behavior is key. For providing some evidence of empirical charging patterns at the workplace, this work examined a dataset of 23.9 million observations on empirical charging processes at workplaces in 2023. To identify user groups, a probabilistic model (Gaussian Mixture Model) and a K-Means clustering approach were applied and the results compared. Eight groups were identified, including full-time and part-time employees, pool vehicle users, and opportunists. The group-specific probability distributions are used to publish a synthetic dataset of parking and charging patterns at workplaces. The openly provided dataset helps to identify the right composition of EVSE in the employee context and to optimize potential fields of action. Full article

Evidence increasingly indicates that synaptic vesicle dysfunction emerges early in Parkinson’s disease (PD), preceding overt dopaminergic neuron loss rather than arising solely as a downstream consequence of neurodegeneration. α-Synuclein (αSyn), a presynaptic protein that regulates vesicle clustering, trafficking, and neurotransmitter release under physiological conditions, exhibits dose-, conformation-, and context-dependent actions that distinguish its normal regulatory roles from pathological effects observed in disease models. This narrative review synthesizes findings from a structured search of PubMed and Scopus, with emphasis on α-syn-knockout (αSynKO) and BAC transgenic (αSynBAC) mouse models, which do not recapitulate the full human PD trajectory but provide complementary insights into αSyn physiological function and dosage-sensitive vulnerability. Priority was given to studies integrating ultrastructural approaches—such as cryo-electron tomography, high-pressure freezing/freeze-substitution TEM, and super-resolution microscopy—with proteomic and lipidomic analyses. Across these methodologies, several convergent presynaptic alterations are consistently observed. In vivo and ex vivo studies associate αSyn perturbation with impaired vesicle acidification, consistent with altered expression or composition of vacuolar-type H⁺-ATPase subunits. Lipidomic analyses reveal age- and genotype-dependent remodeling of vesicle membrane lipids, particularly curvature- and charge-sensitive phospholipids, which may destabilize αSyn–membrane interactions. Complementary biochemical and cell-based studies support disruption of SNARE complex assembly and nanoscale release-site organization, while ultrastructural analyses demonstrate reduced vesicle docking, altered active zone geometry, and vesicle pool disorganization, collectively indicating compromised presynaptic efficiency. These findings support a synapse-centered framework in which presynaptic dysfunction represents an early and mechanistically relevant feature of PD. Rather than advocating αSyn elimination, emerging therapeutic concepts emphasize preservation of physiological vesicle function—through modulation of vesicle acidification, SNARE interactions, or membrane lipid homeostasis. Although such strategies remain exploratory, they identify the presynaptic terminal as a potential window for early intervention aimed at maintaining synaptic resilience and delaying functional decline in PD. Full article

Accurate group target association is essential for multi-sensor multi-target tracking, particularly in heterogeneous radar systems where systematic biases, asynchronous observations, and dense formations frequently cause ambiguous or incorrect associations. Existing approaches often rely on strict spatial assumptions or pre-trained models, limiting their robustness when measurement distortions and sensor-specific deviations are present. To address these challenges, this work proposes a robust association framework that integrates deep feature embedding, density-adaptive clustering, and global graph-theoretic matching. The method first applies an autoencoder–HDBSCAN clustering scheme to extract stable latent representations and obtain adaptive group structures under nonlinear distortions and non-uniform target densities. A weighted bipartite graph is then constructed, and a global optimal matching strategy is employed to compensate for heterogeneous systematic errors while preserving inter-group structural consistency. A mutual-support verification mechanism further enhances robustness against random disturbances. Monte Carlo experiments show that the proposed method maintains over 90% association accuracy even in dense scenarios with a target spacing of 1.4 km. Under various systematic bias conditions, it outperforms representative baselines such as Deep Association and JPDA by more than 20%. These results demonstrate the method’s robustness, adaptability, and suitability for practical multi-radar applications. The framework is training-free and easily deployable, offering a reliable solution for group target association in real-world multi-sensor fusion systems. Full article