Search Results (3,168)

The increasing integration of decentralised photovoltaic (PV) systems and electric vehicles into low-voltage (LV) distribution networks introduces significant variability in net-load patterns, exemplified by the “duck curve” (daytime overgeneration) and sharp peak demands. Addressing these challenges is critical to maintaining network reliability and minimising infrastructure costs. This study presents a comparative analysis of mixed integer linear programming (MILP) and heuristic control strategies within a model predictive control (MPC) framework for load levelling using a battery energy storage system (BESS), evaluated under perfect and LightGBM-based net-load forecast scenarios. These forecasts serve as input to the MPC framework, which optimally schedules MPC charging and discharging actions of MPC over a 24 h horizon to minimise the net-load deviations from predefined thresholds. Under perfect forecasts, MILP achieves superior load levelling performance through predictive optimisation, while the heuristic provides a robust, low-complexity baseline. With LightGBM forecasts, the performance gap between MILP and heuristic approaches widens due to forecast uncertainties, underscoring the critical role of robust control strategies in handling imperfect net-load predictions for BESS operation. Full article

Object detection models can experience substantial performance degradation when the input representation at deployment differs from that used during fine-tuning. In this study, we investigate a specific grayscale-to-RGB distribution shift in which detectors fine-tuned on grayscale-derived images are subsequently evaluated on RGB inputs. Experimental observations reveal a pronounced asymmetry in this setting: RGB-trained models generally retain stable performance on grayscale inputs, whereas grayscale-trained models exhibit severe degradation and substantial run-to-run variability on RGB inputs. We analyze the chromatic information processing mechanism in the first layer of multiple YOLO-family detectors and show that grayscale fine-tuning induces chromatic variance collapse in specific channels, which is closely associated with RGB detection mismatch. Based on this analysis, we introduce ColorScore to quantify kernel chromatic sensitivity and propose a kernel-aware regularization method that controls the chromatic–luminance sensitivity allocation of the first input layer, thereby mitigating variance collapse without modifying the inference architecture. Across four YOLO backbones, the proposed method reduces the mean absolute mAP50 gap between grayscale and RGB evaluation from 0.140 to 0.014. The method also alleviates performance degradation in RT-DETR-L and Faster R-CNN under the same grayscale-to-RGB evaluation setting. Full article

Background/Objectives: Efficient resource allocation is essential for the sustainability of public hospitals operating under financial and capacity constraints. This study evaluated the volume-based technical efficiency and productivity of public hospitals within a Greek Health Region and explored associations between efficiency and selected contextual characteristics. Methods: Of 15 public hospitals in the study setting, two were excluded on production-comparability grounds, leaving 13 hospitals analyzed over 2015–2021 using input-oriented Data Envelopment Analysis under constant and variable returns to scale. Bootstrap bias correction was applied to CRS efficiency estimates, while scale efficiency was derived from CRS and VRS scores. Exploratory associations between 2021 bias-corrected efficiency and population ageing, population density, and unemployment were examined using Simar–Wilson double-bootstrap truncated regression. Productivity change was assessed using the Malmquist Productivity Index. Results: Mean bias-corrected CRS efficiency ranged from 0.7486 to 0.8556, and no hospital achieved a bias-corrected score of 1. Mean scale efficiency ranged from 0.8981 to 0.9416. Population ageing, population density, and unemployment were negatively associated with efficiency in the primary second-stage specification, with coefficient directions remaining broadly consistent across sensitivity models. Malmquist analysis indicated an overall decline in productivity, driven predominantly by shifts in the estimated production frontier, with substantial year-to-year variation. Conclusions: Hospital efficiency and productivity varied substantially across hospitals and over time. DEA can support regional performance review, but results should be interpreted alongside case mix, quality, accessibility, population need, and local service responsibilities rather than as stand-alone performance rankings. Full article

Background: Fresh ovarian cancer tissue is a demanding starting material for cellular workflows, particularly when the aim is to obtain tumor-derived suspensions suitable for further TIL-oriented procedures. In this study, we evaluated post-isolation cell concentration and viability in ovarian cancer specimens processed according to a standardized mechanical–enzymatic protocol. Methods: Biological material was collected from 24 patients undergoing routine surgery for ovarian cancer or suspected advanced ovarian malignancy. After macroscopic assessment and exclusion of extensively necrotic tissue, 19 specimens were included in the final analysis. The cohort was dominated by high-grade serous ovarian carcinoma, which accounted for 15 of 19 analyzed cases. Tissue specimens varied markedly in mass, with a median processed tissue weight of 2.05 g and a range from 0.11 to 14.93 g. Results: The median final cell concentration was 8.10 × 10⁶ cells/mL, with values ranging from 6.40 × 10³ to 9.26 × 10⁸ cells/mL. Median viability was 80.8%, although individual results ranged widely from 2.3% to 98.7%. The calculated median concentration of viable cells was 6.17 × 10⁶ viable cells/mL. Tissue mass showed a moderate positive association with final cell concentration (Spearman’s rho = 0.50, p = 0.028), but not with viability. Conclusions: These findings show that standardized processing of ovarian cancer surgical specimens can generate cell suspensions suitable for post-isolation quality assessment, even when the input material is highly variable. The present study focused on early post-isolation quality-control readouts obtained after tumor dissociation and did not evaluate TIL expansion capacity, detailed TIL phenotype, or antitumor function. The wide range of final cell concentrations underlines the biological and practical heterogeneity of ovarian cancer tissue and supports the need for careful reporting of early tissue-processing parameters in TIL-oriented workflows. Full article

Purpose: Early detection of glottic cancer is essential for better outcomes, but current diagnostic methods remain largely invasive, highlighting the need for reliable non-invasive alternatives. Acoustic voice signals offer a non-invasive screening input, but their clinical value depends on whether signal-processing features and learned models can distinguish malignancy from acoustically similar benign lesions rather than merely separate pathological from healthy voices. Despite growing research on acoustic analysis combined with machine learning (ML) techniques, no systematic synthesis of this evidence currently exists. Therefore, the present review aimed to identify acoustic features that differentiate early glottic malignancies from both benign vocal fold lesions and normal vocal function using various ML techniques. Method: Following PRISMA 2020 guidelines, a comprehensive literature search was conducted across Scopus, EBSCO, Ovid (Embase), and PubMed databases through November 2025. Eighteen studies met inclusion criteria, and PROBAST was used for quality appraisal of included studies. Data extraction focused on acoustic feature categories, ML techniques, classification performance metrics, and validation strategies. Results: Nine acoustic feature categories emerged, with cepstral coefficients (particularly MFCCs) most prevalent across 55.6% of studies. Binary pathological-versus-normal classification achieved consistently high accuracy (96–98.3%) across both traditional machine learning and deep learning approaches. However, malignant-versus-benign discrimination demonstrated substantially lower performance (81–87.88% accuracy, AUC 0.631–0.91), with voice-only models proving insufficient. Multimodal models incorporating clinical variables or laryngoscopic images achieved higher performance, but their results cannot be attributed to voice features alone. All 17 prediction studies were judged at high overall risk of bias, predominantly because of small effective sample sizes, model-selection optimism, possible data leakage, and limited independent validation. Conclusions: ML can detect broad voice pathology, but current evidence is insufficient to support stand-alone acoustic screening for early glottic cancer. Future studies require clearly defined cancer-specific targets, patient-level analysis, standardized acquisition, nested model development, calibration, and independent multicentervalidation. Full article

Multi-laser powder bed fusion (PBF-ML) enables efficient fabrication of large components but introduces spatially heterogeneous thermal histories near inter-laser overlap zones. However, how global thermal-input variables and local thermal-path variations jointly affect defect evolution, microstructure, and mechanical response in geometrically constrained Ti-6.5Al-3.5Mo-1.5Zr-0.3Si (TC11) structures remains unclear. Here, TC11 arch structures were fabricated by varying laser power, scanning speed, overlap-zone position, and interlayer rotation and characterized by tensile testing, optical metallography, Scanning Electron Microscope (SEM), and fractography. Increasing laser power from 170 to 290 W increased Yield Strength (YS) and Ultimate Tensile Strength (UTS) from 955 and 1228 MPa to 1259 and 1463 MPa, respectively, while Elongation (EL) decreased from 14.7% to 3.1%. Increasing the scanning speed from 1050 to 1450 mm/s produced the opposite strength–ductility trend, whereas overlap-zone position had limited influence on strength and 67° interlayer rotation produced moderate improvements. Defect count and area fraction did not always vary synchronously, while semi-quantitative SEM analysis revealed microstructural coarsening with increasing laser power and refinement with increasing scanning speed. These results show that global thermal-input variables primarily govern the overall strength–ductility and microstructural-scale responses, whereas local thermal-path variables mainly regulate spatial defects and microstructural heterogeneity. Full article

Agricultural cooperatives play a key role in improving the competitiveness and sustainability of the agri-food sector. This study evaluates the productivity dynamics and operational performance of twenty-two Greek fruit and vegetable cooperatives during 2019–2023, using Data Envelopment Analysis (DEA) and the Malmquist Productivity Index to assess technical efficiency, scale efficiency, and productivity change over time. Total assets, operating expenses, financial expenses, and equity capital were used as inputs, while sales, operating surplus, and return on assets (ROA) were used as outputs. Mean technical efficiency was 0.718 under Constant Returns to Scale (CRS) and 0.820 under Variable Returns to Scale (VRS), with sixteen of the twenty-two cooperatives operating under increasing returns to scale. Total factor productivity declined on average by approximately 6.4% per year (TFPCH = 0.936), a cumulative decline of approximately 23% over 2019–2023, driven by technological regress (TECHCH = 0.863) that more than offset gains in technical efficiency (EFFCH = 1.085), mainly attributable to pure technical efficiency (PECH = 1.109) rather than scale efficiency. These results provide useful evidence for managers and policymakers, highlighting the importance of organizational improvement, technological modernization, and economies of scale in strengthening the long-term competitiveness and economic sustainability of Greek fruit and vegetable cooperatives. Full article

Shoreline erosion is a stress to coastal communities, and this is particularly true under climate change and sea level rise conditions. Understanding the mechanisms controlling shoreline erosion and the ability for future projections are critical for mitigation and planning. A parameterization has been developed to model shoreline erosion based on wave power and shoreline characteristics. The present study couples the shoreline erosion model with the Semi-implicit Cross-scale Hydroscience Integrated System Model (SCHISM) and the third-generation Wind Wave Model (WWMIII) and conducts a long-term (1991–2014) simulation constrained by observed long-term shoreline erosion rates. The model revealed that wind waves inside the Bay are mostly locally formed and influence from offshore wave propagation is limited. Shoreline erosion quantity is comparable to sediment loads from the watershed but composed of more coarse material like sand. Interannual variability of shoreline erosion from weather variation was up to 58 percent from 1991 through 2014. Shoreline erosion has a significant influence on sediment distribution away from riverine inputs, but estuarine turbidity maxima dominate the transitional zones between fresh and salt waters in the tributaries as well as in the main stem Bay. A sensitivity analysis run with 53 cm sea level rise resulted in a 28 percent increase in shoreline erosion due to increases in water depth and wave power. Full article

Campaign-level assessments of human lunar exploration architectures are commonly performed over finite horizons, which can conceal systematic buffer drawdown and asset drift beyond the simulated window. This paper presents a reproducible methodology in which sustainability is formalized as a periodic (steady-state) property of the coupled inventory–asset system, defined with respect to the modeled state variables and enforced as a binary feasibility gate on all performance evaluation. The architecture is represented as an event-driven multi-commodity flow over a reduced, SpaceNet-compatible network, equivalent to a time-expanded formulation, with propellant demand coupled to transported mass through the rocket equation. Standard measures of effectiveness are augmented with three dimensionless indicators and a composite index whose alignment with the dominant principal component of the trade space is tested a posteriori. On an Artemis III-derived case study the reference cycle closes ($z=1$, 32.3-day margin), yet 18 of 81 nominally feasible design points lose periodic sustainability under launch-delay perturbations, all 18 classifications being statistically significant at the 95% level. Periodic-state closure thus provides a formal criterion for cycle-to-cycle depletion that requires no arbitrary horizon choice. Applied unchanged to three architectures spanning nearly seven-fold in launch mass, the analysis shows an expendable direct-descent concept leading every mass-normalized indicator at a fixed two-crew objective, an ordering that is stable under $\pm 30\%$ dry-mass and $\pm 10\%$ specific-impulse perturbations, while a Gateway hub becomes preferable once orbital infrastructure and extensibility are valued. This study regenerates deterministically from a single input dataset. Full article

Pressure-dependent pore-volume deformation is a critical but poorly constrained variable in dynamic reserve assessment for ultra-deep fractured-vuggy carbonate reservoirs, where fractures, dissolution pores, and vugs respond differently to effective-stress loading. In this work, a pore-structure-aware evaluation strategy was developed by integrating high-temperature and high-pressure volumetric measurements with data-driven regression. Twelve carbonate core plugs from the Ordovician Yijianfang and Yingshan formations of the Fuman Oilfield were selected to represent matrix-pore, dissolution-pore, fracture-vug, and fracture-dominated pore systems. Stepwise net-pressure experiments were performed under simulated reservoir conditions, and pore-volume compressibility (Cp) was calculated from corrected pore-volume changes. Measured C_p values reveal a distinct stress-sensitive response: C_p declines sharply during the low-net-pressure stage and then tends toward a quasi-stable level as net pressure increases, indicating progressive closure of mechanically compliant fractures, narrow throats, and weakly supported dissolution pores. Although porosity is positively associated with Cp, samples with comparable porosity display markedly different compressibility values, confirming that pore-space geometry and fracture-related compliance must be considered. Eight representative regression algorithms were then compared, using net pressure, porosity, permeability, initial pore volume, surface porosity, temperature, and a pore-structure index as model inputs. To further assess model generalization to completely unseen core plugs, additional core-ID-based leave-one-core-out (LOCO) validation was performed for k-nearest neighbors and AdaBoost. Under this grouped validation, k-nearest neighbors yielded an RMSE of 13.5978 × 10⁻⁴ MPa⁻¹ and an R² of 0.8408, whereas AdaBoost achieved an RMSE of 10.0160 × 10⁻⁴ MPa⁻¹ and an R² of 0.9136, indicating greater cross-core robustness of AdaBoost. Permutation-importance analysis of the split-specific KNN model indicated that net pressure, porosity, surface porosity, and pore-structure index made the largest predictive contributions within that model. Moreover, the predicted normalized Cp values reproduced the experimentally observed decreasing trend with increasing net pressure, supporting the physical consistency of the k-nearest neighbors predictions. The proposed experimental–machine learning framework offers a pressure-dependent method for estimating pore-volume compressibility within the geological and petrophysical domain represented by the investigated Fuman Oilfield cores, and provides more representative inputs for material-balance analysis, dynamic reserve evaluation, and production adjustment. Full article

Groundwater quality in semi-arid regions is influenced by interacting geological, climatic, and human factors. This study integrated hydrochemical analysis, ionic relationships, multivariate statistics, Water Quality Index (WQI), and GIS-based spatial analysis to evaluate groundwater quality in Fars Province, southern Iran. A total of 171 groundwater wells were sampled during each of the 2020 and 2021 monitoring campaigns. Hydrochemical diagrams and ionic relationships indicated the predominance of Na–Cl facies and showed that groundwater chemistry is mainly controlled by carbonate and silicate weathering, evaporite dissolution, cation exchange, water–rock interaction, and evaporation–crystallization. Chloro-alkaline indices indicated a mixed cation-exchange system, with positive CAI values predominating regionally and negative values occurring locally. Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA) consistently identified groundwater mineralization as the dominant source of hydrochemical variability, characterized by EC, TDS, major ions, and hardness, while bicarbonate and nitrate represented a secondary source of variability reflecting both carbonate-related processes and localized nutrient inputs. Based on WQI, about 48% and 42.7% of the sampled wells were classified as excellent or good in 2020 and 2021, respectively, whereas 26% and about 30% were unsuitable for drinking. Spatial analysis revealed widespread mineralization and enrichment of Na⁺, Cl⁻, and SO₄²⁻, although interpolation results for EC and TDS should be interpreted cautiously and primarily for exploratory visualization of spatial patterns because of their lower predictive performance. In general, regional groundwater quality is governed primarily by natural hydrogeochemical evolution, while localized anthropogenic influences may contribute to nutrient variability. The results provide a basis for targeted monitoring and sustainable groundwater management in semi-arid aquifers. Full article

Cavitation bubble collapse generates impact loads that can initiate surface damage and accelerate material degradation in hydraulic and fluid-handling systems. Predicting these loads remains challenging because the collapse response depends on nonlinear interactions among bubble dynamics, stand-off distance, and polymer mechanical, acoustic, and viscoelastic properties. This study developed machine-learning regression models to predict impact loads from laser-induced single-bubble collapse on polyethylene, polytetrafluoroethylene (PTFE), polyamide, and antistatic polyethylene terephthalate (antistatic PET). Experiments were performed using a pulsed Nd:YAG laser at energies of 12.5, 25, and 50 mJ and normalized stand-off distances of $\gamma$ = 1–5, producing 180 measurements. Impact loads were measured using a force-calibrated PVDF sensor with material-specific calibration equations. Bubble radius was obtained from high-speed imaging, whereas collapse time was determined from the PVDF voltage-time response. Thirteen input variables were used to train linear, ridge, LASSO, multilayer perceptron, and random forest regressors with Bayesian optimization and grouped five-fold cross-validation. Random forest regression achieved the best performance, with $\mathrm{RMSE}=0.6820\mathrm{N}$, $\mathrm{MAE}=0.5339\mathrm{N}$, $R^2=0.9168$, and $\mathrm{MAPE}=12.44\%$. SHAP analysis identified collapse time, acoustic impedance, and loss modulus as dominant predictors. The model is therefore suitable for trend prediction within the tested experimental domain. Full article

Advance prediction and management of ground subsidence are crucial, as its occurrence can lead to human casualties and property damage, particularly in densely populated metropolitan areas. This study developed artificial intelligence (AI)-based models to predict the daily occurrence of urban ground subsidence using meteorological factors. Focusing on selected areas within South Korea, a daily time-series dataset spanning 2010–2015, the primary analysis period selected for record consistency, was constructed using daily precipitation, temperature, and ground subsidence occurrence records. The predictive performance of seasonality-based baselines, conventional machine-learning models (random forest, extreme gradient boosting (XGBoost)) and deep-learning models (long short-term memory (LSTM), LSTM-Transformer (LT)) was evaluated under a strictly chronological, leakage-free protocol with multi-seed repetition and bootstrap confidence intervals. Antecedent meteorological conditions provided predictive skill significantly beyond seasonal climatology; notably, this skill was captured most effectively by a logistic regression on a compact summary of the preceding day’s conditions (macro F1 = 0.608, ROC-AUC = 0.655), which the deep sequence models matched but did not exceed. Analyses of input sequence length showed that short windows outperformed longer ones, and temperature variables emerged as the dominant predictors, indicating that recent antecedent conditions—rather than extended meteorological sequences or model complexity—carry most of the predictive information. This study confirms the feasibility of meteorologically informed daily screening of ground subsidence risk at a prototype level. These findings are expected to facilitate the development of a more robust ground subsidence prediction system through future integration with station-level meteorological inputs and data on subsurface infrastructure and geological conditions. Full article

Probabilistic models of glued laminated timber are a required input to structural reliability analysis, and the JCSS Probabilistic Model Code is the reference most analyses adopt without independent verification. This paper reports an experimental programme of 61 small clear-wood specimens of GL24h glulam produced in Croatia, cut from three parent elements from one producer, delivered together as a single lot, covering bending, tension and compression parallel to grain, shear of wood and of the adhesive line, compression perpendicular to grain, and finger joints in tension and bending. All eight strength properties are tested against the reference model with confidence intervals and a family-wise error correction across the eight comparisons. Exactly one deviation survives: the coefficient of variation (COV) of tensile strength parallel to grain is 0.339 against the reference 0.180, with a Holm-adjusted p = 0.0064; no other property is distinguishable from the reference. The measured density COV of 0.026, against a reference value of 0.10, shows directly that the specimens are a cluster sample from three parent elements rather than independent draws from the GL24h population, which makes the elevated tensile scatter the more notable, since clustering suppresses observed variability rather than inflating it. The reliability consequence is then shown to depend on an assumption usually left implicit. Holding the characteristic value at the declared 19.2 MPa, as grading enforces, the reliability index of a tension-critical element falls from 3.30 to 3.09; holding the mean constant, it falls to 2.41. The absolute values depend on the load-duration class assumed for snow, but the contrast between the two treatments does not. Carrying the sampling uncertainty of the COV estimate through the calculation lowers these to predictive values of 2.88 and 1.82. The conclusion is correspondingly narrow: elevated tensile variability matters for the assessment of existing structures, where the mean is measured rather than declared, and for reliability studies that adopt generic parameters and vary only dispersion; it matters little where grading against a fractile is active. Full article

Heritage buildings sit outside most national energy codes, yet Palestine alone hosts over 3000 historic structures whose 80–120 cm two-leaf stone construction is absent from any published, measurement-grounded retrofit dataset. A 19th-century three-storey heritage building (Qaser Morcos, Bethlehem; net floor area 406 m², volume 2100 m³) was investigated through (i) in situ measurements (heat-flux meter to ISO 9869-1; infrared thermography; illuminance survey); (ii) a Heritage Building Information Model (H-BIM) built in DesignBuilder v7 with EnergyPlus 25.1 and the Jerusalem-centre typical meteorological year weather file, with end-uses other than heating, ventilation and air conditioning (HVAC) calibrated to three years of measured electricity records and the HVAC baseline comfort-normalised; (iii) a Heritage Impact Assessment per EN 16883:2017; and (iv) a 5000-run Monte Carlo uncertainty analysis on five envelope and system inputs. Two retrofit stages were evaluated: Stage 1 (passive and renewable measures—roof insulation, shading elements, LED lighting with motion sensors, solar water heating, and a 20 kWp photovoltaic system) and Stage 2 (deep envelope and system upgrade—Stage 1 plus triple-glazing, Variable Refrigerant Flow system, and a 17.5 kWp photovoltaic system). Predicted final (delivered) electricity intensity falls from 94.5 to 71.2 kWh/m²·y under Stage 1 (−24.7%) and to 63.3 kWh/m²·y under Stage 2 (−33.0%; 90% Monte Carlo uncertainty interval 59.0–70.4 kWh/m²·y), both well below the Palestinian Energy Building Code limit of 120 kWh/m²·y. On-site generation exceeds post-retrofit electrical demand under both stages, yielding a net-positive electrical balance. Direct carbon dioxide emissions fall by 6.84 t/y (−33%) at Stage 2. Discounted paybacks are 4.6 y (Stage 1) and 10.4 y (Stage 2), with internal rates of return of 24.7% and 11.7%. All proposed interventions received project-team consensus scores of ≥3/5 against EN 16883:2017 heritage-impact criteria. This study delivers a replicable simulation-based workflow for the energy assessment and heritage-compatible retrofit of thick-walled Palestinian stone buildings and one of the first field-informed heritage-retrofit datasets from the Levant. Full article