Search Results (4,589)

Aiming at the multi-objective solution problem of the deployment optimization of the hybrid communication network based on PLC, wireless and dual-mode collaborative networking, this paper proposes an algorithm design based on comprehensive performance optimization with business benefits as the orientation. Firstly, according to the non-ideal channel conditions and the low latency service requirements, the cross-layer modeling of the physical layer and MAC layer is adopted. Then, a dynamic weighting mechanism based on different service levels is defined, and a hybrid communication network adaptive access model considering the constraints of business benefits, network performance, and networking costs is designed. The hybrid communication network deployment and access algorithm design based on K-mean clustering and the improved NSGA-II are realized. Finally, the algorithm performance simulation and comparative analysis are carried out. The simulation results show that the proposed algorithm design can effectively balance the two objectives of network benefits and deployment costs under various network constraints and provide diversified deployment strategies in a targeted manner. Full article

The proliferation of cloud-based data outsourcing has intensified the need for efficient semantic retrieval over encrypted data. Existing searchable encryption schemes often face a coupled bottleneck: (i) semantic index can be unstable or overly coarse, yielding loose pruning bounds and high query cost, and (ii) semantic query expansion can easily introduce noise, forcing an unfavorable accuracy–efficiency trade-off. To address these issues, we propose SES-HI, a Semantically Enhanced Searchable Encryption scheme with a stability-oriented hierarchical index for efficient ranked semantic search over encrypted cloud data. SES-HI contains three core innovations. First, it constructs a balanced $\omega$-ary hierarchical index using a two-stage clustering pipeline (Ward → k-means) to produce semantically compact groups and more representative node vectors, enabling tighter pruning bounds. Second, it performs topic-guided query expansion using LDA and applies Word2Vec-based similarity filtering to enrich semantic coverage while suppressing expansion noise. Third, it introduces a dual-pruning strategy that couples a global threshold with top-k competitive pruning to reduce traversal and ranking overhead without sacrificing recall. We formally prove that SES-HI is secure against adaptive chosen-keyword attacks under an explicit leakage profile. Extensive experiments on the TREC dataset demonstrate that SES-HI consistently improves the accuracy–latency trade-off compared with state-of-the-art baselines, supporting practical semantic search for privacy-sensitive cloud applications. Full article

The issue of water resources in a semi-arid country such as Morocco has been present for many years and is becoming increasingly critical. The droughts experienced over recent decades have demonstrated the country’s extreme vulnerability to any water deficit. In this context, the Berrechid plain represents a relevant case study illustrating both the practical and theoretical challenges of groundwater governance. The aquifer is heavily exploited to satisfy agricultural, industrial, and domestic needs. This study develops a hybrid “grey-box” modeling approach for predicting groundwater depth (GWD) fluctuations under climate change (CC). Unlike conventional black-box machine learning models, our framework combines a deterministic physical engine with a stochastic machine learning corrector. The physical component simulates aquifer mass balance using the Hargreaves method for evapotranspiration, linear drainage, climate memory via exponential decay, and an anthropogenic trend parameter (xi). The machine learning component—XGBoost with quantile regression—is trained exclusively on physical model residuals and predicts the 5th, 50th, and 95th percentiles, providing explicit 90% confidence intervals. Hydrological states (dry, normal, wet) are identified via K-means clustering for context-aware correction. The model is calibrated using historical data (1972–2019) and validated using blocked time-series cross-validation. Climate projections under the RCP 4.5 and RCP 8.5 scenarios were used to forecast GWD up to 2100. At piezometer 3933/20, the best performance was achieved, with an RMSE of 0.347 m and a KGE of 0.742 during the validation period. The proposed approach is suitable for seasonal GWD forecasting and offers practical value for water managers and decision-makers in the Berrechid region. Full article

Urban heat stress is intensifying under climate change, particularly in outdoor public spaces where conventional mechanical cooling is impractical. This study develops a climate-driven, system-level numerical framework to evaluate the pre-deployment feasibility of modular, solar-powered spray cooling canopies across 110 cities in Türkiye. Hourly Typical Meteorological Year (TMYx) weather files, representing a single typical year constructed from 2009 to 2023 source data, are used to estimate photovoltaic (PV) energy yield, electrical load, feasible misting duration, water demand, and PV-to-load autonomy under summer daytime conditions. The misting operation is governed by a rule-based adaptive control strategy based on air temperature, relative humidity, and plane-of-array irradiance. To support transferable comparison, the cities are classified into six summer climate-archetype zones using k-means clustering of standardized climate variables, including temperature, humidity, irradiance, wind speed, and summer precipitation. Results show that evaporative cooling feasibility is governed primarily by humidity rather than temperature alone. Hot–Dry Inland cities exhibit the longest mean misting duration (501.90 h) and highest water demand (30,152 L per module), but the lowest PV-to-load autonomy ratio (1.55) because of high pump-driven electrical demand. In contrast, Humid Black Sea cities show minimal misting duration (11.43 h) and water use (465 L per module), but the highest autonomy ratio (39.68) due to very limited system activation. Thus, high autonomy does not necessarily indicate high cooling usefulness. The proposed framework provides a reproducible screening tool for identifying where PV-powered spray cooling canopies are climatically suitable, where water and PV sizing become limiting, and where alternative outdoor heat-mitigation strategies may be more appropriate. Full article

Neighbourhood-scale sustainable urban mobility assessment requires analytical tools that evaluate walking, cycling, and public transport together rather than as separate modes. Existing studies often rely on single-mode indicators or aggregated urban-scale measures, which limit their ability to reveal micro-scale spatial inequalities and multimodal performance imbalances. This study addresses this gap by developing an integrated Geographic Information Systems (GIS)–Analytic Hierarchy Process (AHP)–correlation–clustering framework for six sample areas in Kayseri, Türkiye. The framework evaluates three main criteria—walkability, bikeability, and public transport accessibility—through ten sub-criteria. In addition, seven land-use and urban design variables are used to examine built environment relationships. A 100 × 100 m grid-based spatial database was created; criteria weights were determined using AHP; mobility scores were examined through correlation analysis; and spatial mobility typologies were identified using K-means clustering. The findings indicate that development density and land-use diversity support walkability. However, similar density patterns do not automatically improve cycling performance or public transport integration. The clustering results reveal persistent modal imbalances, even in areas with medium-to-high overall performance. The study demonstrates that density alone is insufficient for multimodal sustainability and offers an adaptable decision-support framework for context-sensitive neighbourhood planning. Full article

Background: Age-related macular degeneration (AMD) is the leading cause of irreversible vision loss in older adults. An important challenge is the recognition of its early asymptomatic stages and the monitoring of its progression, which requires reliable biomarkers. Growing evidence indicates that AMD-related biochemical changes are reflected in the proteome of tear fluid (TF). Although TF is a non-invasive and easily collectable diagnostic material, its proteomic analysis is complex and costly and therefore has limited clinical value. Methods: In this pilot single-center retrospective cross-sectional study, we developed a new method for dry AMD screening based on analysis of nano-differential scanning fluorimetry (nanoDSF) tear protein denaturation profiles (TDPs) within 15 min. The TDPs were recorded in representative groups of dry AMD patients (37% early, 48% intermediate, 15% geographic atrophy), and in control groups, including patients with refractive abnormalities (basic control), other retinal degenerative diseases (diabetic retinopathy, peripheral retinal dystrophy), or TF-affecting conditions (dry eye syndrome). High-dimensional TDP data were processed using unsupervised machine learning followed by k-means cluster analysis. Results: The presented pipeline distinguished AMD from the basic control with 74% accuracy and a sensitivity of 0.81 without relying on prior labels. The specificity of AMD detection was confirmed by its effective differentiation from diabetic retinopathy (72%; 0.74), peripheral retinal dystrophy (79%; 0.76) and dry eye disease (76%; 0.81). Classifying the AMD group from the entire population of other patients yielded an accuracy of 71% and a sensitivity of 85%, with a false-negative rate of only 15%. Conclusions: This study is a proof of concept for the nanoDSF-based approach, which can be considered a fast, cost-effective, and convenient tool for population screening for dry AMD, suitable for use in preventive medicine and public health. Full article

Coarsely gridded Land Surface Models (LSMs) often smooth over sub-grid spatial heterogeneity and non-linear surface soil moisture dynamics during extreme-precipitation events. This study introduces a clustering-based Soil Moisture Active Passive (SMAP) residual framework, evaluating the spatiotemporal discrepancies between 3 km SMAP Level 2 (SMAP-L2) retrievals and 9 km SMAP Level 4 (SMAP-L4) data-assimilation products within the Yanco study region during the extreme March 2021 floods in New South Wales, Australia. By applying k-means clustering to the residual time series, we partitioned the landscape into three distinct hydrological response patterns: a Low-Residual Baseline ($64.5\%$), a Persistent Positive Anomaly ($20.7\%$) indicative of unmodeled inundation, and a Transient Negative Anomaly ($14.8\%$) representing rapid drainage. Consequently, $35.5\%$ of the usable analysis area exhibited temporal trajectories that diverged significantly from model expectations, highlighting profound geographic heterogeneity in surface wetting and retention that cannot be captured by uniform precipitation inputs alone. Benchmarking the satellite-derived time series against the Yanco in situ network provided critical context for cross-scale variations, illustrating general agreement in overarching temporal trends despite the inherent scale mismatch. Ultimately, this approach leverages residual dynamics as a scalable spatial diagnostic, offering a robust, data-driven method to map localized flood responses that are typically obscured by broad-scale model parameters. Full article

Background and Objectives: Effective palliative care relies on accurate identification and management of symptoms, especially in patients referred for palliative radiotherapy (PRT). This study aimed to identify symptom clusters (SCs)—defined as ≥2 interrelated symptoms—in patients evaluated at a multidisciplinary Radiotherapy and Palliative Care (RaP) outpatient clinic, using the Edmonton Symptom Assessment System (ESAS). Materials and Methods: We retrospectively analyzed data from patients referred to the RaP clinic between February 2017 and April 2020. Demographic and clinical characteristics, including ESAS scores at first visit, were collected. SCs were identified with principal component analysis (PCA) and unsupervised k-means clustering (KMC), determining the number of SCs based on the maximum gap statistic and interpretability. Associations with ECOG performance status (PS), primary tumor and metastases site, and PRT administration were analyzed. Exploratory survival analyses were performed. Results: Among 215 patients (median age = 71 years; 53% male), the mean total ESAS score was 24.03 (SD = 15.28). PCA identified four SCs: SC_PCA1 (tiredness, drowsiness, dyspnea, malaise), SC_PCA2 (depression, anxiety), SC_PCA3 (nausea, loss of appetite) and SC_PCA4 (pain). KMC revealed three SCs: SC_KMC1 (pain, tiredness, drowsiness, malaise), SC_KMC2 (nausea, loss of appetite, dyspnea), and SC_KMC3 (depression, anxiety). Worse ECOG PS correlated with physical SCs (p < 0.05). Psychological SCs were associated with lower likelihood of receiving PRT (OR_PCA2 = 0.26, CI: 0.07–0.80, OR_kmc3 = 0.19, CI: 0.02–0.85, p < 0.05), but when associated with pain/systemic clusters correlated with greater PRT use. A trend toward shorter survival was seen in SC_KMC2. Conclusions: SC analysis could improve personalized symptom management and clinical decision-making in the PRT setting. 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

Background: It is rare in clinical practice to encounter a common arterial trunk when the ventricular septum is intact. In this setting, other clinical diagnoses, such as hypoplastic left heart syndrome with aortic atresia, may be mistaken for a common arterial trunk. Data for this combination is largely limited to case reports and small case series. We have conducted a systematic review of reported cases, performing cluster analyses to provide an objective grouping of the cases. Methods: A systematic review of the literature was performed to identify cases of a common arterial trunk with an intact ventricular septum. Cases for which individual data were available were included in the final analyses. Cluster analysis using K-means clustering was conducted to provide an objective grouping of the hearts based on morphologic findings. Results: K-means clustering identified three distinct groups among hearts with a common arterial trunk with intact ventricular septum. The commitment of the common ventriculo-arterial junction to the left, right, or both ventricles was the defining feature of each group. Hearts with a common trunk committed to one of the ventricles demonstrated significant hypoplasia or atresia of structures related to the other ventricle. Conclusions: Distinct patterns can be identified when a common arterial trunk is found with an intact ventricular septum. They depend on the ventricle or ventricles, which support the common ventriculo-arterial junction. Full article

Underground mine production scheduling under uncertainty is a complex and multi-field coupling system project. In this study, underground mine production scheduling seeks to determine the optimal start time of extraction-related projects, with the objectives of maximizing net present value, minimizing makespan, and maximizing resource utilization rate. The Copula function is adopted to formulate the correlation between uncertain project duration and cost and generate a set of stochastic scenarios. Then, the K-means algorithm classifies the scenarios into multiple scenario families, and the SBR algorithm is adopted to perform scenario reduction. Moreover, a rank choice function-based hyper-heuristic algorithm is extended to solve the multi-objective optimization model, which makes an excellent balance among the three objective functions. For determining the optimal scheduling plan, the cross-efficiency DEA algorithm is used to evaluate the archive set, sort the optimal solution, and guide the next iteration. The computational case verifies the effectiveness and efficiency of the multi-objective underground mine scheduling model, stochastic scenario and technical and hyper-heuristic algorithm. Full article

To address the insufficient accuracy of short-term load forecasting caused by the strong randomness of distributed energy output, variable electricity consumption patterns, and complex meteorological factors, this study proposes a load forecasting method that integrates K-means scene clustering and a Transformer–BiGRU–Attention (CTBA) hybrid deep learning architecture. Different from conventional Transformer–BiGRU hybrid forecasters that train a single global predictor across all operating conditions, the proposed CTBA framework first partitions daily load curves into representative scenes and then routes each sample to a scene-specific Transformer–BiGRU–Attention predictor, thereby reducing distributional heterogeneity before temporal modeling. First, the K-means algorithm is used to perform scene clustering on historical daily load curves, and the optimal number of clusters is selected according to the silhouette coefficient and downstream prediction performance. Subsequently, the CTBA model is trained separately for each clustering subset. The Transformer encoder captures the long-range global dependencies of load sequences through the self-attention mechanism, the BiGRU module extracts local bidirectional temporal fluctuation features, and the Attention mechanism further focuses on key time nodes such as morning and evening peaks while fusing multi-source data including historical load, day-ahead electricity price, and multi-dimensional meteorological factors. Experimental results based on the German ENTSO-E power dataset show that the coefficient of determination R² of the proposed model reaches 0.9893, with MAE, RMSE, and MAPE as low as 0.0141, 0.0187, and 3.92%, respectively, which are significantly improved compared to benchmark models such as SVR, LSTM, CNN, and TCN-BiGRU. Ablation experiments further demonstrate that removing the clustering, Transformer, BiGRU, or attention layer will degrade performance, thus verifying the effectiveness and superiority of the method in short-term load forecasting and providing an accurate solution for the short-term load forecasting of power systems. Full article

Operational landslide susceptibility mapping (LSM) remains challenging in regions with pronounced geo-environmental heterogeneity, where single global models often overlook spatially variable landslide-environment relationships. Northern Guangdong, China, is a typical humid mountainous region where steep terrain, diverse lithology, and highly variable rainfall produce non-stationary landslide controls. To address this challenge, we develop a cluster-informed LSM framework that integrates unsupervised consensus K-means sub-zoning with localized Random Forest (RF) models and SHapley Additive exPlanations (SHAP). We use a harmonized inventory of 1510 landslides (2011–2022), together with twelve 30 m conditioning factors, for model training and validation. Compared with logistic regression, Support Vector Machines (SVM), and Light Gradient Boosting Machine (LightGBM), RF consistently achieves higher accuracy across clusters, and the cluster-wise RF ensemble attains pooled ACC = 0.8212, F1 = 0.8176, and AUC = 0.8956. SHAP highlights both regionally consistent predictors (e.g., NDVI, distance to road) and distinct cluster-specific controls linked to geomorphic and hydrologic settings. The proposed framework enhances predictive accuracy, produces finer susceptibility gradients, and yields better-calibrated probability estimates than a single global model. These results demonstrate that explicitly accounting for geo-environmental heterogeneity can generate interpretable, spatially adaptive susceptibility outputs. By identifying high-risk zones for priority monitoring, land-use regulation, infrastructure protection, and mitigation planning, the proposed framework provides a practical decision-support tool for sustainable mountain development and disaster risk reduction in heterogeneous mountainous regions. Full article

Reducing carbon emissions from daily travel has become an important goal of 15-minute living-circle planning, yet it remains unclear which facility configurations are most supportive of green travel. Using 634 living circles and 20 million mobile-phone travel records and point-of-interest (POI) data, this study examines how facility layout within a 15-minute cycling circle influences residents’ walking and cycling travel behavior. Extreme Gradient Boosting (XGBoost) models and Shapley Additive Explanations (SHAP) suggest that low accessibility is generally associated with lower green travel shares, while moderate facility density promotes green travel, yet for some facility types, high density may show diminishing marginal benefits. Vegetable markets and primary schools emerge as key facilities, with education facilities driven mainly by accessibility, entertainment facilities by density, and commercial and healthcare facilities by both. K-means clustering identifies three types of low-green-travel-performing living circles—characterized by low density and poor accessibility—concentrated in peripheral and newly developed areas. The methodology is transferable, and the derived numerical ranges and living-circle typologies offer context-specific implications for Tangshan, and identified differences in facility importance and diminishing marginal benefits enrich 15-minute city theory. Full article

Historic blue bricks are fundamental to Beijing’s architectural heritage, yet cross-site compositional data for guiding material-compatible restoration remain scarce. This study applies WD-XRF, XRD, SEM, thermal expansion measurement, and physical property testing to 21 blue brick specimens from four Beijing-area sites spanning the Tang through Qing dynasties, with PCA and K-means clustering used to explore compositional grouping structures. Within this exploratory dataset, a compositional distinction separates the Ming and Qing Great Wall bricks: CaO falls from 7.7 to 1.5 wt.% as anorthite gives way to albite, while Qing specimens are denser (1.79 vs. 1.65 g·cm⁻³) with lower water absorption (15.9% vs. 20.9%). Two Wanping City bricks are strongly sulfate-enriched (SO₃ up to 9.8%), and WP-SE3 additionally carries a heavy chloride load (Cl 2.1%), masking their original clay signatures and illustrating how unrecognized weathering can distort compositional grouping and source-related interpretation from bulk chemistry. K-means clustering yields compositional types that overlap only partially with site boundaries, capturing raw material variation rather than site-specific manufacturing fingerprints. Despite constraints in sample size and physical property coverage, the integrated dataset offers preliminary compositional benchmarks and limited performance data to inform period-specific brick replacement at these heritage sites. Full article