Search Results (3,091)

The day-ahead scheduling of multi-stack Power-to-X (PtX) plants must simultaneously cope with stack degradation under variable loading and with compound uncertainty in renewable generation and electricity prices. Existing scheduling frameworks address these two challenges in isolation, since degradation-aware models remain deterministic and stochastic models treat the electrolyser as a constant-efficiency device. This work develops a degradation-aware two-stage stochastic mixed-integer linear programming (MILP) framework that closes this gap. First-stage binaries fix the commitment and startup decisions of every stack, while second-stage scenario-indexed variables capture the dispatch, the hydrogen output, the shortfall, and the load-dependent and start–stop cycling degradation cost monetised at the stack level through a piecewise linear epigraph. Joint wind price uncertainty is represented by a Gaussian copula fitted on empirical CDF marginals and reduced to twenty representative scenarios via k-medoids clustering. The framework is fully implemented in MATLAB R2024a with the Optimization Toolbox, using the built-in intlinprog and linprog solvers. On a 100 MW reference plant with ten heterogeneous PEM stacks, out-of-sample evaluation against four formal benchmarks demonstrates the lowest LCOH at EUR 24/kg, the highest demand reliability at 85.0%, the highest hydrogen delivery at 7.68 t/day, and up to 50% total cost reduction over deterministic baselines, with end-to-end runtime under two minutes on standard workstation hardware. Full article

The target price for naval equipment orders is driven by the coupling of multidimensional technical and economic factors, exhibiting typical characteristics such as high dimensionality, strong nonlinearity, multicollinearity, and small-sample fluctuations. Traditional cost estimation methods struggle to achieve high-precision fitting and interpretable decision support. To address these issues, this paper constructs an integrated prediction model that combines Boruta–Lasso two-stage feature selection, grid search-optimized CatBoost, and SHAP interpretability analysis. First, the Boruta algorithm is used for rough screening of feature significance, then Lasso regression is applied for sparse fine screening, effectively eliminating redundant features and significantly mitigating multicollinearity; grid search and five-fold repeated cross-validation are employed to optimize CatBoost hyperparameters, while 10 repeated experiments with random seeds are conducted to verify model generalization robustness. SHAP is used to quantify the marginal contribution of features, revealing nonlinear associations and statistical response transition points between core features and price. This study is based on 33 publicly available real data from main combat vessels, from which 198 modeling samples were generated through interpolation-based small-sample data augmentation. The interpolated samples were only used for data augmentation and were not considered independent empirical samples. All core conclusions were validated on the 33 original real samples, and there are no missing values in the dataset. Experimental results show that the proposed model achieved the best individual results on the test set, with a coefficient of determination of R² = 0.8949, root mean square error RMSE = 0.0554, and mean absolute error MAE = 0.0476. Across 10 repeated robustness experiments, the average results were R² = 0.8828, RMSE = 0.0586, and MAE = 0.0529, with overall performance better than comparison models such as XGBoost, random forest, and standard CatBoost. Ablation experiments validated the effectiveness of the two-stage Boruta–Lasso selection strategy in improving model accuracy and stability. SHAP attribution analysis shows that full-load displacement, number of vertical missile launch cells, number of phased array radars, and combat capability are core features highly correlated with price, all showing significant nonlinear positive correlations and clear statistical response transition points. The dataset in this study has no missing values, is entirely constructed based on publicly traceable data, and does not include confidential information such as internal shipyard costs. The findings reflect statistical associations rather than causal effects. However, the sample size and ship-type coverage are limited, so the model’s applicability is somewhat constrained, and its generalization ability needs to be further verified on larger-scale, multi-ship-type independent datasets. This model combines high prediction accuracy, strong robustness, and good interpretability, providing reliable technical support for ship equipment procurement pricing demonstration, full lifecycle cost management, and scientific procurement decision-making. Full article

In scientific research, the optimization of organic light-emitting diodes (OLEDs) is generally achieved through a lengthy and expensive experimental process as new ideas and configurations are tested on real devices. Electro-optical simulation allows for the rapid evaluation of key performance parameters of device structures, thus reducing manufacturing time and costs. This paper presents an original contribution to the electro-optical modeling and optimization of multilayer OLED devices using the Non-dominated Sorting Genetic Algorithm II (NSGA-II). This optimization explicitly incorporates defect states within the ITO/NPB/Alq₃:C545T/Alq₃/LiF-Al structure. The simulated model is calibrated using experimental data by fitting the trap state distribution. The Pareto front resulting from the multi-objective optimization identifies a set of non-dominated configurations, including an optimal intermediate structure defined by an electron transport layer (ETL) thickness of approximately 42 nm and a hole transport layer (HTL) thickness of approximately 53 nm. This configuration leads to a limited reduction of 1.75–2% in current efficiency (${\eta }_c$) while offering a remarkable improvement of 23–30% in power efficiency (${\eta }_p$) compared to the extreme configurations of the optimal Pareto set. Thus, this solution represents an optimal Pareto trade-off between high current efficiency and improved power efficiency. This paper shows that combining defect modeling and thickness optimization provides a reliable framework for the electro-optical optimization of OLED devices. Future work will extend this approach to spectral and colorimetric analysis. Full article

Covering water demand for secondary uses with resources of inferior quality is already an established practice. In coastal aquifers, saline groundwater can serve as an alternative source. In this paper, we examine concurrent optimization of freshwater and seawater pumping from a coastal aquifer, which may lead to more efficient overall solutions. The particular objective is to determine well locations and pumping rates that meet specified freshwater and saline water demands while preventing seawater intrusion into freshwater wells. A genetic algorithm code is used as an optimization tool, combined with a groundwater flow simulation model based on the Boundary Element Method (BEM). The BEM scheme has a relatively low computational cost and can be efficiently incorporated into the genetic algorithm’s fitness evaluation. Validity of the resulting optimal solutions is further investigated using two, more detailed, groundwater flow and mass transport models: (a) A combination of BEM with a particle-tracking (moving point) technique to simulate seawater movement from the coast towards the wells, and (b) the MODFLOW 6 computational package, including the Groundwater Transport (GWT) model for solute transport. The procedure is illustrated through its application to a synthetic coastal aquifer. Full article

At present, human activity is the main source of water pollution. The tanning industry is a primary source of water contamination with Cr(III), which can cause various diseases if ingested. A circular economy approach proposes an effective, low-cost solution. The utilization of waste from the food industry is used for the removal of Cr(III) through biosorption. This study evaluated the adsorption capacity of orange peel (OP) and pineapple crown (PC) pretreated with H₂O₂ and NaOH was evaluated under different operating conditions. The physicochemical properties of the biosorbents were characterized using techniques such as Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS) and X-ray diffraction (XRD). The results show that treatment with NaOH at 60 °C obtained an adsorption capacity of 61.63 mg/g and 64.19 mg/g for OP and PC, respectively. The combined biosorbents resulted in an approximately 50% increase in the adsorption capacity of Cr(III) compared to individual biosorbents. The isotherms that best fit the experimental data were Sips and Redlich–Peterson (RP) models, suggesting heterogeneous adsorption behavior in biosorbents. Thermodynamic parameters indicated that biosorption process was spontaneous and endothermic. Full article

The increasing adoption of alternative fuels such as hydrogen and ammonia in energy systems has created a growing need for advanced diagnostic techniques capable of monitoring combustion products with high specificity and flexibility. In this context, Raman spectroscopy represents a promising optical approach for gas analysis, as it enables the simultaneous detection of multiple species without requiring sample preparation. In this work, the performance of a cost-effective Raman-based system on quantitative detection of nitrogen oxides (NO and NO₂) and nitrous oxide (N₂O) is presented. The experimental setup is based on a multi-pass optical configuration designed to enhance the Raman signal and employs off-the-shelf components, including an uncooled CMOS detector. Calibration measurements were carried out using gas mixtures at known partial pressures, and gas concentrations were retrieved through a nonlinear least-squares fitting procedure applied to the measured spectra. The results show that the system provides linear and repeatable responses for NO and N₂O over the investigated pressure ranges, with low mean errors and limited data dispersion, while NO₂ performance could not be fully quantified in a comparable manner due to the high reactivity of the species under the tested conditions. Overall, the proposed system represents a viable and cost-effective solution for multi-species gas analysis in emerging combustion applications. This work aims to extend the industrial applicability of Raman spectroscopy to NO_x and NO₂ diagnostics. Full article

Petroleum refinery wastewaters are highly recalcitrant and recognized as one of the most challenging industrial effluents requiring advanced treatment strategies. This study aims to investigate the synergistic performance of a sequential electrocoagulation (EC) and electro-Fenton (EF) process for the mineralization of this complex effluent. The EC pretreatment was optimized using response surface methodology via Doehlert design, establishing optimal conditions at pH 6.0, 0.8 A, and a 75 min electrolysis time. Under these conditions, 39% of total organic carbon (TOC) and 56% of chemical oxygen demand (COD) were removed. The quadratic polynomial model developed for the EC stage presented an excellent fit with the experimental data (R² = 0.99, R²_adj = 0.97, p < 0.05), confirming its strong predictive robustness. In order to degrade the remaining recalcitrant organic pollutants, the pretreated effluent underwent EF oxidation (0.01 M ferrous ion, 0.8 A, pH 3), leading to TOC and COD removal rates of 68% and 76%, respectively, after a 360 min electrolysis time. The integrated EC-EF process achieved an overall mineralization of 81% and an oxidation efficiency of 89%. Finally, a comprehensive evaluation of the system’s energy consumption and economic viability established a solid techno-economic baseline for this sequential approach, indicating a competitive total operating cost of USD 0.036 per gram of TOC removed. Full article

The increasing integration of renewable energy resources into modern microgrids requires reliable scheduling methods capable of managing uncertainty, seasonal variability, operating cost, and environmental impact. This study proposes a stochastic day-ahead scheduling approach for a representative grid-connected multi-energy microgrid comprising photovoltaic generation, wind generation, a microturbine, a fuel cell, an energy storage system, and utility-grid exchange. The proposed model was implemented and simulated in a MATLAB (2024b) environment. The Birds of Prey-Based Optimization algorithm is applied to determine the optimal 24 h dispatch schedule by minimizing a weighted objective function that combines operating and emission costs. Uncertainties in solar irradiance, wind speed, electrical load, ambient temperature, and electricity prices are modeled using probabilistic distributions and Monte Carlo simulations. To improve computational efficiency, 1000 generated scenarios are reduced to 10 representative scenarios using Fast Forward Selection based on Kantorovich distance. Seasonal case studies for winter, spring, summer, and autumn are used to evaluate the proposed method. Compared with five metaheuristic algorithms, the proposed approach achieves the lowest fitness value in all seasons, with reductions of 15.2%, 26.5%, 6.8%, and 23.9%, respectively. The results confirm improved economic and environmental microgrid operation under seasonal renewable uncertainty. Full article

Accurate crop row detection is identified as a critical prerequisite for autonomous agricultural navigation, yet it remains challenging in complex field environments. To achieve a balance between segmentation accuracy, robustness, and real-time performance, an improved crop row segmentation and navigation method based on the DeepLabV3+ framework was developed. MobileNetV2 was adopted as the backbone to minimize computational costs, while feature representation was enhanced through integrated attention mechanisms and multi-scale fusion. Specifically, split-attention convolution was integrated into the backbone, a DenseASPP + SP module was employed for multi-scale contextual capture, and a Convolutional Block Attention Module (CBAM) was added to refine feature responses. Experimental results demonstrated that the proposed method outperformed mainstream models, achieving a mean Intersection over Union (mIoU) of 93.42% and an f1-score of 96.8%. The model maintained a lightweight architecture with 8.35 M parameters and a real-time speed of 32 FPS. Furthermore, crop row anchor points were extracted and processed via DBSCAN clustering and RANSAC fitting to generate high-precision navigation lines. Validation showed that the middle crop row yielded the highest fitting accuracy with minimal angular and lateral errors. This study provides an efficient visual perception solution for intelligent field operations. Full article

This study develops a ternary Fe-based geopolymer system composed of metakaolin (MK), red mud (RM), and fly ash (FA) for the preparation of sustainable water-retaining subgrade materials for sponge-city roadbed applications. Unlike conventional formulations primarily designed for structural strength or rapid permeability, the proposed MK–FA–RM system was designed to improve water-storage capacity while maintaining adequate mechanical support and environmental compatibility. In this ternary system, MK provides highly reactive aluminosilicate species for geopolymer network formation, RM introduces Fe-bearing phases and enhances industrial solid-waste utilization, and FA contributes to particle packing, workability, and resource efficiency. A constrained ternary mixture design implemented using Design-Expert software was adopted to optimize precursor proportions. Within the investigated compositional range, the fitted first-order mixture model showed acceptable statistical adequacy for preliminary composition screening (R² = 0.86). The optimal blend (60% MK, 30% RM, and 10% FA) achieved a 7-day compressive strength of 8.37 MPa and a water retention rate of 35.3% under ambient curing conditions, satisfying the strength requirement considered for the target subgrade/base-layer application. Microstructural and phase analyses suggest that the synergistic interaction of the three precursors promoted Fe-modified aluminosilicate gel formation together with conventional geopolymer gel products, while improving matrix continuity and preserving interconnected pore space for water storage. This multiscale structural effect helps explain how the material achieved a balance between water retention capacity and mechanical support. Under the tested conditions, the material maintained acceptable residual strength after short-term exposure to water, acid, and sulfate-containing solutions. Life-cycle assessment indicated a 70% reduction in CO₂ emissions compared with ordinary Portland cement, while pilot-scale cost analysis showed a 39% lower production cost than MetaMax-based geopolymer materials. Pilot-scale application further demonstrated the constructability and water-regulation potential of the material in practical environments. Overall, the proposed ternary Fe-based geopolymer demonstrates that Fe-rich industrial wastes can be engineered into low-carbon and economically viable water-retaining subgrade materials that balance hydraulic regulation, structural adequacy, and sustainability. Nevertheless, long-term durability, cyclic loading performance, and direct nanoscale characterization of Fe-bearing gel evolution still require further investigation. Full article

The agricultural industry is at a critical juncture, experiencing global pressures in the form of climate volatility, a shortage of labor, and an increase in production costs. Although artificial intelligence (AI) has the potential for revolution due to its predictive analytics and self-controlled machinery, it has not achieved widespread and even distribution for use, especially among small-to-medium-sized farms in the Midwestern United States. This study formulates and empirically examines a comprehensive socio-technical model to determine the drivers and barriers to the adoption of AI in this agricultural region. Based on a synthesized framework of the “Unified Theory of Acceptance and Use of Technology” (UTAUT) and “Task–Technology Fit” (TTF), the study incorporates agriculture-specific contextual factors such as “environmental risk, access to broadband, economic constraints, and policy support”. The analyses of the 489 farmers in the U.S. Midwest were conducted through the “partial least squares structural equation modeling” (PLS-SEM) “SmartPLS v.3.9”. The findings provide full empirical evidence of the proposed model, which supports 11 hypothesized relationships. The key results show that the strongest positive predictors of adoption intention are “performance expectancy, effort expectancy, and trust”. On the other hand, data security concerns and financial restrictions are strong deterrents. The paper also outlines the significant facilitating functions of the broadband infrastructure and policy support in building farmer perceptions of technology’s ease-of-use and facilitating conditions. These lessons can provide policymakers, ag-tech developers, and extension agencies with a roadmap on how to create more equitable and contextual interventions that overcome the rural digital divide and create resilient data-driven farming systems. Full article

Background/Objectives: Early identification of breast cancer patients who are likely or unlikely to benefit from neoadjuvant chemotherapy (NAC) remains clinically important because ineffective treatment may delay definitive surgery and expose patients to unnecessary toxicity. Quantitative ultrasound (QUS) radiomics offers a contrast-free and repeatable method for extracting tissue-sensitive imaging biomarkers from raw ultrasound data. This study aimed to evaluate whether baseline QUS radiomic features and early treatment-induced changes could predict a pathologic response to NAC in a real-world single-center cohort. Methods: We designed a prospective observational study including 96 consecutive women with biopsy-proven stage II–III breast cancer treated with NAC at Victor Babes University of Medicine and Pharmacy Timisoara. All patients underwent standardized QUS examinations before treatment and again at week 2. The response was defined pathologically at surgery as residual cancer burden class 0/I versus II/III. Clinical, histopathologic, and QUS variables were compared between responders and non-responders. Group comparisons used Student’s t test, Mann–Whitney U test, chi-square testing, and Fisher’s exact test where appropriate. Multivariable logistic regression was used to identify independent predictors of response. Model discrimination was summarized using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy. Results: Forty-three patients (44.8%) were classified as responders and 53 (55.2%) as non-responders. Responders had higher baseline Ki-67 values (47.8 ± 13.1% vs. 41.9 ± 13.0%, p = 0.033), lower baseline homogeneity (0.3 ± 0.1 vs. 0.4 ± 0.1, p = 0.010), and higher peritumoral heterogeneity (0.9 ± 0.1 vs. 0.8 ± 0.2, p = 0.027). At week 2, responders showed larger increases in mid-band fit (3.0 ± 0.8 vs. 1.2 ± 0.8 dB, p < 0.001), greater entropy change (0.7 ± 0.2 vs. 0.2 ± 0.2, p < 0.001), more pronounced spectral intercept reduction (−3.5 ± 1.4 vs. −1.2 ± 1.3, p < 0.001), and greater tumor shrinkage (−24.3 ± 7.0% vs. −11.1 ± 5.7%, p < 0.001). In multivariable analysis, Δ MBF and Δ entropy remained independent predictors of pathologic response. The combined clinical-plus-QUS model achieved an AUC of 0.89. Conclusions: Baseline microstructural heterogeneity and very early QUS-derived treatment changes were strongly associated with the pathologic response to NAC. These findings support the potential role of QUS radiomics as a low-cost, repeatable early-response biomarker in breast cancer. Full article

The use of zirconia as a material in the base of modern restorative dentistry is due to its high strength, biocompatibility, and improved aesthetic performance. The aim of this review is to provide an integrated and coherent overview of the recent developments in zirconia crowns by focusing on the development of materials, microstructure, digital fabrication processes, optical capabilities, and clinical performance. A survey of literature in the form of a narrative literature review was conducted in the most significant databases, such as PubMed, Scopus, Web of Science, and Google Scholar, including publications published since 2000, with a focus on systematic reviews, meta-analyses, clinical studies, and materials science studies. The results show that zirconia materials have developed beyond traditional 3Y-TZP systems, characterized by high strength and fracture toughness to high-translucency and multilayer zirconia (4Y 6Y-PSZ) systems, which provide better aesthetics at the cost of lower mechanical reliability. The implementation of CAD/CAM technologies has enhanced the accuracy of fabrication, marginal fit and reproducibility and the development of sintering, surface modification and bonding protocols has enhanced clinical performance. Recent clinical results have shown high survival rates (around 85–95 percent over 5–10 years), and the results depend on the design of the restoration, the zirconia generation, and the functional loading circumstances. Despite these developments, there are still concerns about the durability of bonding, trade-offs between translucency and strength, and long-term performance of high-translucency zirconia. The development of new technologies, such as additive manufacturing, design-aided artificial intelligence, and bioactive surface modification, is a promising avenue toward improving clinical reliability and performance. Full article

Background: Prolonged hospitalization after endoscopic endonasal transsphenoidal surgery for non-functioning pituitary adenomas increases costs and complications. Early identification of high-risk patients is crucial for optimizing perioperative management. Methods: In this dual-center retrospective study of 368 patients, a predictive model was developed using a training cohort (n = 268). Prolonged length of stay was defined as ≥75th percentile (≥16 days). LASSO regression selected features from clinical, radiological, and perioperative variables. Independent predictors from multivariable logistic regression were dichotomized via ROC analysis and integrated into a nomogram. Model performance was assessed internally and validated externally (n = 100). Results: Six independent predictors were identified: age > 50 years, vertical tumor diameter > 17.8 mm, anteroposterior diameter > 20.5 mm, transverse diameter > 17.8 mm, anesthesia duration > 194 min, and systolic blood pressure > 119 mmHg. The nomogram showed moderate but reproducible discrimination (AUC = 0.762 in training; 0.750 in validation). Calibration and decision curve analysis confirmed good fit and clinical utility. Conclusion: We developed and validated a practical nomogram predicting prolonged hospitalization risk using readily available perioperative variables. This tool may assist individualized risk stratification and perioperative planning in comparable clinical settings, with potential implications for patient flow and resource utilization. Full article

Monitoring center-of-mass is crucial for assessing postural control, but field measurements are often impractical or cost-prohibitive. This study investigates the feasibility of predicting center-of-mass kinematics from the motion of an unstable base—the Sensopro Luna—using deep learning, eliminating the need for wearable sensors. We conducted a cross-sectional study in which 64 participants were recorded performing three coordination exercises (Single-Leg Stance, Stepping, and Waves). Marker-based motion capture and auxiliary inertial sensors were used to record reference and tape kinematics. The model inputs consisted of IMU- and motion-capture-derived tape segment orientations, IMU accelerations and angular velocities, and algorithmic estimates of the lowest tape positions. Nine axis-specific exercise models were developed using a hybrid Encoder–LSTM–Decoder architecture and compared against linear regression baselines. Our results indicate that the deep learning models successfully predicted horizontal center-of-mass displacements (DNN Mean Absolute Errors of 16.1–23.7 mm for X-axis and 4.4–31.3 mm for Y-axis) and exhibited descriptively lower errors than linear models in mean absolute error and signal morphology. However, vertical predictions were less reliable, likely due to the physical constraints inherent to the kinematics of the unstable base. Error analysis revealed that prediction accuracy was highest within common postural ranges, but decreased for extreme displacements. These findings provide a proof-of-concept for wearable-free postural monitoring, particularly for movement along the mediolateral and sagittal axes. Such a system could facilitate automated, cost-effective postural feedback and performance tracking in rehabilitation and fitness environments, supporting autonomous coordination training without the practical constraints of traditional measurement systems. Full article