Search Results (2,088)

Background/Objectives: Survival prediction in non-small cell lung cancer (NSCLC) remains challenging, particularly in limited-sample settings where end-to-end deep learning models may suffer from limited generalization. This study aimed to develop a data-efficient, multimodal, and explainable framework integrating computed tomography (CT)-derived imaging information with clinical variables for NSCLC survival prediction. Methods: CT images, tumor segmentations, and clinical data from the publicly available NSCLC Radiomics (LUNG1) dataset (377 patients) were used. Tumor-focused regions were extracted using segmentation masks, and pretrained RadImageNet-InceptionV3 embeddings were obtained from the largest tumor-containing slice and neighboring-slice summaries. Deep imaging embeddings, engineered imaging features, and clinical variables were fused into a unified tabular representation. To improve robustness under limited-sample conditions, feature blocks were compressed using principal component analysis. CatBoost, XGBoost, and LightGBM models were trained on a development set and evaluated on a strictly held-out final validation set. Results: In three-class survival stratification, assigning censored/non-event patients to the upper survival group produced the strongest ordinal prognostic performance. Under the EX_PLUS_NON_EX_TOP setting, CatBoost achieved the best holdout score-based class C-index of 0.655. In continuous survival regression, LightGBM achieved the best holdout event-patient C-index of 0.576. Clinical variables provided the dominant prognostic signal, while compact deep image embeddings contributed complementary information, particularly in separating short- and long-survival groups. SHAP analysis confirmed contributions from both clinical and image-derived features. Conclusions: The proposed framework provides a proof-of-concept demonstration of a data-efficient and explainable image-to-tabular approach for NSCLC survival prediction under strict internal holdout validation. The results suggest that pretrained CT embeddings, clinical variables, gradient-boosted trees, and SHAP-based interpretation can be combined in a feasible, limited-sample survival modeling pipeline, while external validation remains necessary before clinical translation. Full article

Mountain photovoltaic (PV) stations are increasingly deployed in complex terrain, where generation is jointly controlled by solar-resource variability, near-surface meteorology, and local topography. However, the quantitative contribution of topographic factors to regional-scale PV generation remains insufficiently evaluated, and many prediction studies rely on single-station or short-term records. In this study, monthly measured generation from 118 standardized village-level mountain PV stations in Badong County, western Hubei Province, China (2019–2021), was integrated with Solargis Global Horizontal Irradiance (GHI)-related solar-resource data, high-resolution gridded meteorological data, a 25 m digital elevation model, seasonal-cycle variables, and historical-generation features. After seasonally grouped median-absolute-deviation (MAD) outlier screening, GIS-based spatial matching, terrain extraction, and viewshed-derived shading analysis, regression models and climatology baselines were compared under both chronological validation and station-exclusion spatial cross-validation. Under the strict chronological validation, CatBoost achieved the best temporal performance among the tested models (R² = 0.3119, MAE = 2719.7 kWh, RMSE = 3245.6 kWh), slightly outperforming the monthly climatology baseline. In the station-exclusion spatial cross-validation, XGBoost achieved the highest mean R² (0.8659), indicating good spatial transferability to unseen stations. Correlation and partial-correlation analyses showed that the temperature-related variable group and monthly radiation were the dominant meteorological controls, whereas elevation, slope, and terrain shading showed weak direct correlations with monthly generation for already-sited stations. Annual 90% prediction intervals were further estimated using residual bootstrapping, with an empirical coverage of 94.9%. The proposed framework provides a practical basis for monthly generation forecasting and operational assessment of already-built distributed PV stations in mountainous regions, while its application to greenfield site selection requires additional site engineering and near-field obstruction information. Full article

Wave overtopping is one of the most complex coastal hazards to characterize in field conditions due to its high non-linearity and the interaction between unsteady hydrodynamics and wave–structure processes. To get insights into the underlying occurrence and persistence of overtopping, this study proposes an integration of numerical and data-driven models. Multi-month field observations made at a breakwater are used to investigate the hydro-meteorological parameters causing overtopping initiation and persistence. High-frequency video-derived overtopping detections are combined with coupled ADCIRC–UnSWAN (ADvanced CIRCulation–Unstructured Simulating WAves Nearshore) hindcasts to construct near-structure hydro-meteorological conditions. The results reveal a clear dynamical asymmetry showing that overtopping initiation corresponds to exceedance of crest elevation at individual wave-scale associated with elevated wave height, water level, wave steepness, and wind characteristics, whereas overtopping persistence depends on short-term temporal effects associated with wave energy, direction, and sustained water levels. Gradient-boosted decision trees, temporal convolutional networks, and Transformer models are employed, demonstrating that persistence cannot be inferred from instantaneous sea-states alone, indicating a separation of timescales between triggering and sustained overtopping dynamics. These findings provide field-scale evidence of distinct hydrodynamic regimes governing overtopping processes, highlighting the importance of temporal characteristics for understanding overtopping dynamics and developing predictive coastal hazard frameworks. Full article

The proposed methodology combines analytical control laws with adaptive mechanisms and machine-learning-assisted modules based on regression trees, random forests, and extreme gradient boosting (XGBoost). Machine learning models are employed to approximate unknown nonlinear dynamics, compensate disturbances, and adjust controller parameters, while the overall control structure is constrained by Lyapunov stability conditions. This ensures that the inclusion of data-driven components does not violate the fundamental requirement of system stability. The effectiveness of the proposed approach is evaluated through simulation experiments across three operating modes with varying degrees of nonlinearity and dynamic complexity. The results show that hybrid models incorporating ensemble machine learning methods improved performance compared with the analytical and adaptive baselines examined. XGBoost-based control achieves the lowest error values and the highest level of Lyapunov stability compliance (up to 99.3%). The main contribution of this study lies in the development of a unified synthesis framework in which machine learning is not used as a standalone control strategy but as a machine-learning-assisted support mechanism integrated into a theoretically grounded control architecture. The proposed approach provides a balance between adaptability, accuracy, and rigorous stability guarantees, suggesting potential applicability to simulation-based and offline-assisted control design tasks, while real-time embedded implementation requires additional computational optimization and validation. Full article

Alzheimer’s disease is a progressive neurodegenerative disorder in which early detection remains limited by the cost and invasiveness of positron emission tomography and cerebrospinal fluid testing. We evaluated whether plasma proteomic profiles could distinguish amyloid PET-positive from amyloid PET-negative individuals using the Bio-Hermes cohort. After quality control and missing-data filtering, 988 participants and 295 proteins were analysed; 31 proteins showing group differences were used for supervised classification. Random Forest, Gradient Boosting, and Neural Network models were trained across four train/test splits with repeated cross-validation and class downsampling. Amyloid-positive and amyloid-negative groups differed across a subset of proteins, with five remaining significant after false discovery rate correction. Tree-based models performed most consistently, with Random Forest and Gradient Boosting achieving AUC values of 0.79–0.81 and balanced accuracy of 0.68–0.73. Eight proteins (SERPINA1, C3, CRP, APOE4, CFH, VTN, C1QTNF5, and PON1) emerged as recurring high-importance features. These findings indicate that discovery-driven plasma proteomics can identify multi-protein signatures associated with amyloid status and can complement established single-analyte blood biomarkers by adding pathway-level information. Full article

Drilling optimization seeks to enhance the efficiency of drilling operations by fine-tuning adjustable factors like weight on bit (WOB); the goal is to boost the rate of penetration during drilling and decrease overall well expenses. It is crucial to efficiently and precisely manage weight on bit (WOB) to fine-tune drilling parameters promptly. Drilling optimization focuses on adjusting controllable variables, such as weight on the bit and bit rotation speed, to achieve the highest possible drilling rate during operations. Consequently, it is necessary to conduct a comparative analysis of ML models to evaluate practitioners in picking the appropriate predictive model. This research employs four machine learning methods to forecast weight on bit: Random Forest (RF), K-Nearest Neighbors (KNNs), Gradient Boosting Regression (GBR), and Decision Tree (DT). Machine learning techniques are being evaluated using datasets sourced from well drilling data in Western Sudan, marking the first instance of such data being utilized for this purpose. The key accomplishment of this study is the automation of predicting weight on bit by utilizing machine learning techniques tailored to our datasets. The findings indicated that among the algorithms tested, Random Forest stood out as the most dependable, displaying a prediction accuracy of 98% and a lower RMSE value of 1.015. In contrast, KNN, GBR, and DT achieved accuracies of 91.40%, 80.66%, and 100.00% respectively, with RMSE values of 2.008, 3.011, and 6.27 on the testing dataset, correspondingly. At last, this research is acknowledged as a groundbreaking effort in the field, utilizing machine learning techniques to predict weight on bit occurrences. Consequently, this study presents a publicly available dataset containing details about drilled wells in the Sudanese oil and gas sector. This dataset is meant to be used for upcoming experiments, validating algorithms, and for analytical purposes. Full article

Background/Objectives: Breast cancer remains one of the leading causes of cancer-related mortality among women worldwide. Early and accurate characterization of suspicious mammographic microcalcifications is essential for improving diagnostic decision-making and reducing unnecessary invasive procedures. Microcalcifications classified as BI-RADS 4 and BI-RADS 5 are clinically important radiological findings; however, differentiating benign from malignant lesions remains challenging because of overlapping morphological and distribution patterns. This study aimed to develop a structured feature-based machine learning model for predicting the pathological diagnosis of breast microcalcifications by integrating mammographic descriptors, patient age, and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) contrast enhancement findings. Methods: The dataset included 53 biopsy-confirmed cases and consisted of clinical and radiological variables, including patient age, calcification morphology, calcification size, distribution pattern, DCE-MRI contrast enhancement status, and histopathological outcome. Several conventional machine learning algorithms were evaluated, including Logistic Regression, Support Vector Machine with radial basis function kernel, K-Nearest Neighbors, Decision Tree, Random Forest, Extra Trees, Gradient Boosting, AdaBoost, and CatBoost. Hyperparameter optimization was performed using grid search with five-fold cross-validation. Model performance was assessed using accuracy, precision, recall, F1-score, ROC-AUC, and log loss. Results: Logistic Regression achieved the highest overall performance, with an accuracy of 0.909 and an F1-score of 0.889, while AdaBoost achieved a recall of 1.000 in the internal evaluation. However, given the limited sample size and lack of external validation, these findings should be interpreted as preliminary. Conclusions: The results suggest that structured radiological descriptors combined with DCE-MRI enhancement information may support malignancy risk stratification of BI-RADS 4–5 microcalcifications, although larger multicenter studies are required before clinical implementation. Full article

As High-Performance Computing (HPC) architectures evolve towards the Exascale, storage infrastructure reliability has emerged as a critical operational challenge, with traditional reactive and static preventive maintenance strategies proving increasingly insufficient. This study addresses this gap by proposing a comprehensive methodological framework for the transition from predictive to predictive-prescriptive maintenance in large-scale storage environments. By integrating the CRISP-DM industry standard with a multi-layered eXplainable Artificial Intelligence (XAI) suite, we develop a system capable of isolating hardware degradation signals amidst massive volumes of routine telemetry. To validate our approach, we leveraged a publicly available disk failure dataset to evaluate multiple Machine Learning configurations, addressing the challenge of severe class imbalance through optimized oversampling and Gradient Boosting algorithms. The methodology employs global and local XAI techniques, including Permutation Feature Importance, SHAP, and surrogate decision trees, to translate probabilistic risk assessments into auditable hardware engineering rules. Our results demonstrate that this hybridization of robust predictive modeling with multi-layered explainability provides a transparent, evidence-based decision support system. Ultimately, we conclude that converting opaque risk predictions into technical justifications enables infrastructure managers to optimize hardware lifecycle management and minimize system downtime in mission-critical environments, establishing a viable pathway toward more resilient and auditable storage management. Full article

Background/Objectives: Acute care use (ACU)—emergency department visits, inpatient hospitalizations, and observation stays—drives morbidity and costs among adults with diabetes. We developed and evaluated machine-learning models to predict 1-year ACU risk using electronic health record (EHR) data and neighborhood-level data. Methods: We performed a retrospective cohort study using de-identified EHR data from a large urban academic health center, including adults (≥18 years) with diabetes (N = 23,052). The index date was defined as one year before each patient’s last encounter, and ACU was assessed during the subsequent year. We modeled 180 predictors spanning demographics, Area Deprivation Index (ADI), prior healthcare utilization, vitals/BMI, comorbidities, medications, and laboratory results. Decision tree and gradient-boosted models (XGBoost, LightGBM, CatBoost) were tuned with Optuna using 8-fold stratified cross-validation, optimizing area under the receiver operating characteristic curve (AUC). To improve class-balanced classification performance under outcome imbalance, we selected post hoc probability thresholds that maximized Macro F1 and quantified interpretability with permutation feature importance. Results: ACU occurred in 30.53% of patients (7039/23,052). Boosted models achieved AUC ≈ 0.78, with LightGBM performing best (AUC = 0.7839). Macro F1–optimized thresholds (<0.5; typically 0.375–0.40) improved class-balanced performance versus a 0.5 cutoff. Across boosted models, prior utilization features dominated, followed by discharge-related factors and neighborhood deprivation; comorbidities and laboratory results contributed. Conclusions: In this single urban academic health-system cohort of adults with diabetes, EHRbased boosted models demonstrated moderate discrimination for predicting 1-year ACU and identified interpretable predictive signals. Threshold optimization improved class-balanced statistical performance. Prior utilization, care transitions, and neighborhood deprivation emerged as dominant predictive features. External and temporal validation are needed before broader application. Full article

Machine learning (ML)-assisted design of epsilon-negative polymer nanocomposites requires a clear connection between experimentally controllable synthesis parameters, core–shell nanoparticle geometry, and the resulting effective optical response. The targeted optical response is unusual because the polymer film is predicted to exhibit near-zero or negative real effective permittivity in selected visible spectrum regions, arising from Ag core plasmonic polarizability, SiO₂-mediated dielectric spacing, nanoparticle filling factor, and effective medium coupling rather than from the intrinsic polymer matrix. In this study, a two-stage ML-assisted synthesis-to-optics framework is developed for Ag@SiO₂ core–shell nanoparticle/polymer composite films intended for visible spectrum effective permittivity screening. In the first stage, Stöber synthesis parameters, including water volume, ethanol volume, TEOS content, catalyst volume, reaction time, Ag nanoparticle size, and Ag nanoparticle concentration, were used to predict SiO₂ shell thickness. In the second stage, Ag core size, SiO₂ shell thickness, wavelength, and nanoparticle filling factor were used to screen the real effective permittivity of Ag@SiO₂/polymer nanocomposites within an effective medium design space. Using a duplicate-aware validation workflow, Gradient Boosting provided the strongest held-out test performance for shell thickness prediction, with a test R² of 0.8997, MAE of 7.1822 nm, RMSE of 8.8344 nm, and cross-validation R² of 0.5371 ± 0.4648. The relatively large cross-validation variability indicates that the model is useful for interpolation-based synthesis screening but should not be interpreted as fully robust across heterogeneous literature-derived data. For the optical response task, the highest held-out test performance was obtained by a Decision Tree model (test R² = 0.7586), but cross-validation results were unstable, indicating that the epsilon model should be interpreted as a design space screening tool rather than a generalizable predictor. Design window analysis identified candidate negative effective permittivity regions primarily at 400 nm and high nanoparticle filling factor, with predicted $\mathrm{R}\mathrm{e}\left({\epsilon }_{\mathrm{e}\mathrm{f}\mathrm{f}}\right)$ values ranging from −5.4229 to −0.2086 across selected windows. The main contribution of this work is the treatment of SiO₂ shell thickness as a bridge variable between Stöber-derived synthesis control and effective permittivity screening. Experimental validation remains necessary to confirm the predicted design windows, particularly because shell uniformity, Ag core polydispersity, nanoparticle aggregation, polymer dispersion, high-filling-factor feasibility, and effective medium validity can strongly influence the measured optical response. Full article

Deterministic radio wave propagation models provide high accuracy in complex outdoor environments but remain computationally impractical for large-scale network planning and spectrum management. In contrast, empirical and hybrid models offer low complexity at the expense of reduced accuracy, systematic bias, and limited terrain sensitivity. This paper proposes a unified delta learning framework that enhances fast baseline propagation models by learning a data-driven correction toward a deterministic Parabolic Equation Modeling (PEM) reference. A key novelty lies in a compact, physics-informed feature representation that replaces the full terrain profile with an 18-dimensional vector combining local geometric descriptors, global terrain characteristics, and baseline responses, enabling accurate correction with low-dimensional input. The study also provides the first systematic investigation of delta-based correction across multiple widely used propagation models. The framework is evaluated for free-space propagation, ITU-R P.1546, ITU-R P.1812, and ITU-R P.452 using ridge regression, kernel ridge regression, gradient boosting regression trees, and a neural network model. Model performance is assessed in terms of error reduction, bias mitigation, robustness across learning algorithms, and profile-level generalization to previously unseen propagation paths within the considered terrain categories. Results show substantial error reduction, with up to twofold improvement for simpler baseline models and consistent gains for hybrid models, while preserving computational efficiency. Full article

Rapid urbanization reshapes urban land systems and intensifies surface thermal heterogeneity, yet nonlinear day–night land surface temperature (LST) responses to grey–green spatial organization and building morphology remain insufficiently understood, particularly in thermally stressed areas across the urban–rural gradient. Using Xi’an, China, as a case study, this study develops a priority-area-based land–climate interaction framework. Priority areas were defined as grid cells where elevated LST coincided with relatively strong local explanatory relationships between LST and land-cover or morphological variables. Multiscale geographically weighted regression (MGWR), gradient boosting decision trees (GBDTs), SHAP-based interpretation, and threshold sensitivity analysis were combined to identify dominant drivers, nonlinear response patterns, and interaction structures of daytime and nighttime LST. The results show pronounced day–night differentiation: daytime hotspots were concentrated in the built-up core, whereas nighttime hotspots extended toward the urban–rural fringe. Daytime LST was mainly associated with building coverage and grey-space organization, while nighttime LST was more strongly related to mean building height and the cooling contribution of green-space coverage. The analysis further identified localized empirical response ranges for built-up intensity, grey-space connectivity, building height, and green-space coverage within the priority areas. These findings clarify how land-cover configuration and building morphology jointly shape day–night surface thermal responses and provide context-specific evidence for land-use planning and targeted urban heat mitigation. Full article

The escalating energy consumption in China’s rural residences necessitates the adoption of targeted energy-efficient design strategies. However, existing studies have mainly focused on urban buildings or cold-climate rural residences, and insufficient attention has been given to form-based energy optimization for rural housing in hot summer and cold winter regions. Hangzhou was selected because it is a representative city in this climate zone, where rural residences face both summer cooling and winter heating demands. This study systematically investigates passive design pathways for rural residential buildings by optimizing architectural forms. We conducted in-depth field surveys and data analysis on 76 diverse samples, including both self-built and unified construction types, to establish three representative typical residential models (rectangular, L-shaped, U-shaped) for the Hangzhou region. DesignBuilder was employed to simulate the impacts of eight morphological elements—Shape Coefficient, building area, aspect ratio, orientation, number of floors, floor height, floor height ratio, and roof slope—on building energy consumption. The Gradient Boosting Decision Tree (GBDT) method was then used to quantify the nonlinear effects and relative importance of these elements. The results indicate clear nonlinear relationships between elements and the energy-saving rate. Floor height is identified as the most critical factor affecting energy consumption, followed by roof slope, with building area and other elements also showing significant influence. Based on the quantitative analysis, this study proposes energy-efficient design optimization strategies for rural housing in Hangzhou, offering a validated methodological framework and practical design references for the sustainable development of rural residences in hot summer and cold winter regions. Full article

Feature prioritization in early-stage software startups is a critical yet poorly structured challenge, as prevailing frameworks rely predominantly on expert intuition and fail to exploit patterns latent in historical delivery data and labor market dynamics. This study proposes a deep learning framework that explores labor-market signals as a reproducible proxy for market-driven feature prioritization. The framework encodes two complementary information sources: internal sprint delivery history processed by a Bidirectional Long Short-Term Memory network with attention, and external market signals from LinkedIn job postings processed by a Convolutional Neural Network encoder; the resulting representations are fused via a cross-modal layer to classify job postings as proxies for High or Low feature-priority market demand. The model is evaluated on the publicly accessible LinkedIn Job Postings dataset (2023–2024, approximately 124,000 records) and achieves an Area Under the Receiver Operating Characteristic Curve of 0.961 on the proxy classification task, outperforming classical baselines including gradient Boosted Trees, Random Forest, Support Vector Machine, and Logistic Regression. SHapley Additive exPlanations analysis identifies industry sector and geographic location as the two most influential market-signal predictors. These results suggest that jointly encoding internal delivery dynamics and external market signals offers a promising, scalable decision-support tool to assist startup product teams in data-driven roadmap prioritization, subject to further validation against direct expert priority labels. Full article

Above-ground biomass (AGB) is a critical indicator for evaluating crop growth, with its large-scale monitoring being fundamental to precision agriculture. To improve the efficiency and reduce the cost of large-scale farmland monitoring, this study developed an unmanned aerial vehicle (UAV)–satellite collaborative inversion framework. The data, including rice AGB, UAV imagery, and satellite imagery, were collected in 2024. The proposed Distance-Correlation–Correlation-Feature-Selection (DC-CFS) algorithm was employed to select compact feature subsets for each growth stage. Subsequently, six machine learning models were compared to identify the optimal UAV-scale inversion model for each specific stage. Then, the AGB values simulated by the UAV-scale model were used to train the satellite-scale inversion model. A paddy field mask covering the entire district was generated using Segment Anything Model (SAM) and the temporal spectral variation pattern of rice, enabling county-scale AGB mapping. Research results indicate that the DC-CFS algorithm can effectively select a small number of low-redundancy features for each growth stage. The optimal UAV scale model type varies dynamically with growth stages, with ExtraTrees demonstrating overall superior performance. Except for the heading stage, the R² of the models remained above 0.69. Furthermore, the BayesianRidge algorithm also presents a viable and competitive alternative when computational efficiency is a consideration. At the satellite scale, eXtreme Gradient Boosting (XGBoost) and Extremely Randomized Trees (ExtraTrees) were identified as the optimal models for rice AGB estimation due to their stable performance across all stages, with R² values consistently above 0.74. Finally, rice growth classification maps and corresponding fertilization recommendations were generated based on the satellite-scale inversion results, providing technical support for precision agriculture practices. Full article