Search Results (13,325)

Phosphate tailings (PTs), a solid waste generated from phosphate flotation, are a low-grade phosphate resource rich in quartz and dolomite. Their long-term accumulation leads to both resource loss and environmental risks, making valorization increasingly important for the sustainable development of the phosphorus chemical industry. In this study, calcareous–magnesian PTs were used as raw materials, and selective hydrothermal leaching with weakly acidic AlCl₃ solution was employed to separate the dolomite phase and directly construct a Ca-Mg-Al precursor solution for layered double hydroxides (LDHs). The LDHs were subsequently synthesized by co-precipitation and evaluated for phosphate removal from wastewater. The results showed that the precipitation pH markedly affected the phase composition and platelet morphology of the LDHs, while appropriate aging conditions further improved their adsorption performance. Under the optimal conditions of pH 12, aging at 40 °C for 2 h, the obtained LDHs exhibited the best phosphate uptake. Adsorption kinetics followed the pseudo-second-order model, and the maximum adsorption capacity calculated from the Langmuir model reached 38.61 mg-P/g. Characterization by XRD, FTIR, TG-DTA, point of zero charge, and XPS indicated that phosphate removal was dominated by surface complexation, accompanied by anion exchange, ionic precipitation, and electrostatic attraction. Full article

Rainfall variability strongly influences both flood and drought hazards, especially in climatic transition zones where precipitation is highly seasonal and spatially heterogeneous. This study assessed long-term changes in rainfall patterns and compound flood–drought hazard in the Huaihe River Basin, China, using ERA5-Land-derived daily precipitation series at 174 spatial sampling locations during 1950–2025. Rainfall pattern indicators, flood-related rainfall extremes, and SPI-3-based drought indicators were calculated to characterize rainfall amount, frequency, intensity, dry–wet persistence, heavy rainfall events, and meteorological drought conditions. The Mann–Kendall test and Sen’s slope estimator were used to detect long-term trends, and a compound flood–drought hazard classification framework was developed based on a flood-related rainfall hazard index (FHI) and a drought-related hazard index (DHI). The results showed that annual total precipitation, wet days, and consecutive wet days decreased significantly, indicating reduced rainfall occurrence and wet spell persistence. Flood-related rainfall indicators generally showed decreasing tendencies, with more evident declines in persistent multi-day extremes than in single-day rainfall. In contrast, mean SPI-3 showed a significant drying tendency, although drought frequency, severe drought frequency, and drought intensity did not exhibit significant monotonic trends. Spatially, rainfall pattern, flood-related, and drought-related indicators showed clear heterogeneity across the basin. The compound hazard classification identified flood-dominated and drought-dominated areas as the two major hazard types, each accounting for 31.03% of the spatial sampling locations, while low compound hazard and compound flood–drought hazard areas each accounted for 18.97%. These findings indicate that flood- and drought-related hazards coexist but vary spatially across the Huaihe River Basin. The proposed framework provides preliminary rainfall-based information for differentiated flood–drought hazard assessment, climate-adaptive water resources planning, and the sustainable management of water resources in regions facing spatially heterogeneous hydroclimatic hazards. Full article

Small-scale vertical-axis wind turbines (VAWTs) are increasingly essential for the “blue economy,” providing autonomous power to remote coastal communities, offshore platforms, and marine industries. However, the design of efficient Darrieus-type rotors is complicated by complex unsteady aerodynamics, particularly the phenomenon of dynamic stall. This study aims to establish and validate a cost-effective yet accurate mathematical modeling approach for simulating unsteady turbulent flow around a Darrieus H-rotor to support practical engineering applications. The research methodology integrates computational fluid dynamics (CFD) with physical experiments in a hydrodynamic channel. The numerical model utilizes the unsteady Reynolds-averaged Navier–Stokes (URANS) equations closed with the Strain-Adaptive Linear Spalart–Allmaras (SALSA) turbulence model, chosen for its efficiency in capturing flow separation. The system of initial equations was being devised relatively to an arbitrary curvilinear coordinate system. The pressure and velocity fields have been coordinated using the artificial compressibility method adapted to calculate non-stationary problems. Experimental verification was conducted in the GT-400 hydrodynamic tube using a three-bladed H-rotor model, where flow structures were visualized via the colored jet method at tip speed ratios λ ranging from 2 to 5 and Reynolds number 1470. The findings reveal that dynamic stall occurs over a significant portion of the blade trajectory, characterized by vortex generation at the leading edge and subsequent advection along the chord. Qualitative comparison demonstrates a high degree of correlation between the calculated vortex dynamics and physical flow spectra. These results confirm that the URANS-SALSA approach provides a rational compromise between computational cost and physical accuracy. Full article

The production of high-purity hydrogen from chemical hydrogen storage materials such as sodium borohydride (NaBH₄) has been identified as a particularly promising candidate due to its high hydrogen storage capacity and environmentally benign hydrolysis products. The incorporation of tungsten (W), thereby developing W-promoted NiMo catalytic systems, results in the enhance activity toward NaBH₄ hydrolysis, thereby developing ternary NiMoW catalytic systems. The synthesis of NiMoW-coated copper catalysts (NiMoW/Cu) containing 3–11 wt.% of W was accomplished using a cost-effective and efficient electroless deposition method from citrate-based plating baths containing Ni²⁺, Mo⁶⁺, and W⁶⁺ ions. Morpholine borane was utilized as the reducing agent in this process. The catalytic activity of the prepared coatings toward alkaline NaBH₄ hydrolysis increased as the tungsten content decreased within the investigated range of 3–11 wt.%. The highest hydrogen generation rate, reaching 9.87 L min⁻¹ gcat⁻¹, was achieved using the NiMoW/Cu catalyst containing 3 wt.% of W at 343 K. The corresponding apparent activation energy was calculated to be 52 kJ mol⁻¹. In addition, the catalyst demonstrated notable 89.1% stability, maintaining a high degree of catalytic activity after undergoing five successive hydrolysis cycles. The enhanced catalytic performance was attributed to synergistic interactions between Ni, Mo, and W and to the favorable surface morphology of the multicomponent coating, which promoted the hydrogen generation reaction. Full article

Power cycling reliability is one of the most widely used frameworks to evaluate the lifetimes of power semiconductor switching devices from a thermal stress perspective. Experimental tests can be used to predict their lifetimes under operating conditions. An estimation of the number of cycles to failure $N_f$ can also be given by several lifetime models, which express the number of cycles to end of life as a function of empirical coefficients. In the existing literature, these empirical coefficients are generally estimated using the classical least squares method (to find the best-fitting line through data points), where outliers are removed using the Random Sample Consensus algorithm. The aim of this paper is to present a general strategy for the calculation of empirical coefficients for different lifetime models, such as Coffin–Manson, Coffin–Manson–Arrhenius, Norris–Landzberg, and simplified Bayerer, aiming at minimizing the number of required experimental tests. The results show that the number of experimental trials required varies between two and four, depending on the number of empirical coefficients to be determined, which is specific to the lifetime model used. Furthermore, a limited number of experimental data points are selected to avoid any degradation in accuracy. The accuracy of coefficient estimation is significantly improved by excluding outliers: some relative errors decrease by 25%. Additionally, each empirical coefficient is determined under specific thermal stress conditions, such as a constant junction temperature swing $\mathsf{\Delta }{T}_j$, constant current per bond wire I, constant cycling frequency f, or constant mean junction temperature $T_{jm}$. Furthermore, a limited number of experimental data are selected to avoid any degradation in accuracy due to outliers. Moreover, this general method can be applied to all power devices, such as IGBTs or MOSFETs. Finally, the limitations of the analytical solution for the Scheuermann lifetime model are discussed. Full article

Software quality assessment in agile projects remains fragmented. Technical, process-related, and team-related indicators are often evaluated through separate models, tools, and reports. This fragmentation limits cross-project comparability and weakens evidence-based decisions for software quality improvement. To address this problem, this study proposes the Overall Software Quality Index (OSQI), a multidimensional decision-support framework for software quality assessment in agile projects. OSQI integrates code quality, process quality, and team quality into a single project-level assessment model. The framework was initially grounded in ISO/IEC 25010:2011 and is discussed in relation to the ISO/IEC 25010:2023 revision, particularly its explicit inclusion of Safety as a product quality characteristic. Since the industrial datasets used in this study were not collected from safety-critical systems, Safety was not modeled as a separate OSQI dimension in the current version; instead, it is addressed as a scope limitation and future extension. The measurement structure was defined using the Goal–Question–Metric (GQM) approach. An initial set of 49 candidate metrics was reduced to 15 core indicators. This reduction was performed using dimension-specific strategies: Random Forest-based feature importance for code quality, Delphi and Analytic Hierarchy Process (AHP) for process quality, and thematic consolidation for team quality. The selected indicators were normalized and integrated through entropy-based weighting. This process generates an interpretable composite quality score. The main contribution of OSQI is not the isolated use of these methods, but their integration into a reproducible and tool-supported framework. The framework converts heterogeneous software engineering signals into a unified decision-support index. OSQI was evaluated using industrial agile project data. The data included static code analysis outputs, issue-tracking records, team assessment results, and product outcome indicators. In an exploratory validation across five industrial projects, OSQI showed a strong positive association with Net Promoter Score ($r=0.97, p=0.0076$) and a strong negative association with churn rate ($r=-0.97, p=0.0061$). A supporting software tool was also developed to automate data integration, score calculation, visualization, and project-level comparison. The findings suggest that OSQI can support quality monitoring, project benchmarking, and evidence-based improvement decisions in agile software engineering contexts. Full article

Diabetes mellitus remains a major metabolic disorder requiring the development of new and effective α-glucosidase inhibitors. The present study aimed to identify, design, and optimize novel 3-amino-2,4-diarylbenzo[4,5]imidazo[1,2-α]pyrimidine derivatives with promising inhibitory activity against the α-glucosidase enzyme using a comprehensive in silico strategy. Approximately 300 molecular descriptors were calculated to characterize a dataset of 32 compounds (Peytam et al.) and to investigate the structural factors governing their biological activity. Based on these descriptors, a multiple linear regression model was developed to predict the inhibitory activities of the compounds against alpha-glucosidase. The developed model demonstrated satisfactory predictive performance and was internally and externally validated to ensure its accuracy, robustness, and reproducibility. In addition, the applicability domain analysis confirmed the reliability of the predictions. Using the validated QSAR model, seven new derivatives were designed with predicted pIC₅₀ values exceeding the maximum activity of the parent compounds. The leverage analysis demonstrated that all newly designed compounds were located within the applicability domain of the model, supporting the reliability of the predictions. To further evaluate their inhibitory potential, molecular docking studies were performed to investigate the interactions between the designed compounds and the α-glucosidase active site. The docking results revealed favorable binding interactions comparable to those reported for known α-glucosidase inhibitors. Furthermore, ADMET analysis indicated generally favorable pharmacokinetic properties, although potential CYP3A4 inhibition-related pharmacokinetic risks were identified and discussed. Molecular dynamics simulations, including replicated runs and MM/GBSA binding free energy calculations, confirmed the stability of the most promising protein–ligand complexes throughout the simulation period. In conclusion, this study proposes a robust and integrated computational workflow combining descriptor generation, QSAR modeling, applicability domain analysis, molecular docking, ADMET prediction, and molecular dynamics simulations for the rational design of potential α-glucosidase inhibitors. The findings highlight the therapeutic potential of the designed derivatives and provide a valuable in silico framework for the future development of antidiabetic agents. Full article

In this study, we combine experimental and computational approaches to elucidate a density functional theory (DFT)-derived mechanism for superoxide scavenging by resveratrol, piceatannol, and oxyresveratrol. Using rotating ring–disk electrode (RRDE) hydrodynamic voltammetry, the superoxide radicals are generated in situ, allowing direct measurement of antioxidant activity. Data show that the catechol-containing piceatannol is approximately four times more active than resveratrol, while resveratrol and oxyresveratrol exhibit similar efficiencies, indicating that the additional 2′-OH group in oxyresveratrol has minimal impact. Vitamin C (ascorbic acid) facilitates scavenging by acting as a proton donor for resveratrol, piceatannol, and 4′-OH oxyresveratrol, but it is unable to deprotonate the 2′OH group of oxyresveratrol. The experimental results suggest a superoxide dismutase (SOD)-like mechanism, obtained from energetically feasible DFT calculations, in which these stilbenes convert two superoxide anions into H₂O₂ and O₂, helped by vitamin C. Mechanistically, the first superoxide is reduced by abstracting a hydroxyl-group hydrogen atom, while the second undergoes oxidation via π–π interaction with the aromatic system, releasing O₂. Notably, resveratrol can be regenerated through a catalytic cycle involving vitamin C. These data underscore the SOD-mimicking properties of dietary polyphenols and suggest a need to reevaluate resveratrol’s clinical utility regardless of its low bioavailability. Full article

Background. Accurate prediction of pathologic complete response (pCR) to neoadjuvant chemotherapy (NAC) in muscle-invasive urothelial bladder cancer (MIBC) remains an unmet clinical need. The neutrophil-to-platelet/hemoglobin-to-lymphocyte (NPLH) ratio, a composite hematologic index that reflects both systemic inflammation and nutritional oxygen-carrying capacity, has not been previously evaluated as a predictor of NAC response in this setting. Methods. We retrospectively analyzed 114 consecutive patients with MIBC (cT2–T4, N0–N3) who received accelerated MVAC (aMVAC) NAC followed by radical cystectomy at a single academic center. Pretreatment NPLH (calculated as [neutrophils × platelets]/[hemoglobin × lymphocytes]) was assessed as a predictor of pCR (ypT0N0) and tumor regression grade (TRG). Receiver operating characteristic (ROC) curve analysis, Mann–Whitney U test, and logistic regression were used. NPLH performance was compared to NLR and PLR. Results. pCR was achieved in 35 patients (30.7%). Median NPLH was significantly lower in pCR vs. non-pCR patients (33.9 [IQR 23.1–42.4] vs. 47.6 [IQR 30.7–90.4]; p = 0.0007). NPLH yielded an AUC of 0.700 (bootstrap 95% CI 0.596–0.794) for pCR prediction, numerically superior to NLR (AUC 0.645 [0.542–0.741]) and PLR (AUC 0.643 [0.533–0.747]); DeLong test: NPLH vs. NLR p = 0.079, NPLH vs. PLR p = 0.090. At the optimal cut-off of 44.5, NPLH demonstrated 80.0% sensitivity and 57.0% specificity. pCR rates declined progressively across NPLH quartiles: 48.3% (Q1) to 10.3% (Q4). On multivariate logistic regression, log-transformed NPLH was the only independent predictor of pCR (parsimonious model, OR 0.292, 95% CI 0.131–0.652; p = 0.003; EPV = 17.5). A positive correlation was observed between NPLH and TRG score (Spearman r = 0.284; p = 0.0022), with significant differences between TRG 1 and TRG 3 subgroups (p = 0.0036). Conclusions. Pretreatment NPLH is an independent predictor of pCR to aMVAC in MIBC and is numerically superior to NLR and PLR (DeLong p = 0.079). Consisting exclusively of standard complete blood count parameters, NPLH is readily available and inexpensive. This single-center exploratory study is hypothesis-generating and requires prospective external validation before clinical implementation. Full article

Among a broad range of promising applications, the use of cellular solids as lightweight structural components is an important field of research that requires reliable predictions of their stiffness and strength. Predictive and general models should not depend on extensive parameter-fitting experiments and should not rely on computationally intensive numerical calculations for each new set of geometric parameters and loading conditions. An overview of models for 2D, 2.5D, and three-dimensional structures will be presented. Most 2D and 2.5D models neglect out-of-plane behaviour and the face sheets used in sandwich panels. 3D studies, mainly by finite element models (FEMs), are often limited to a narrow set of geometries and simple loading conditions. Elastic anisotropy is well covered, but calculating yield surfaces remains a challenge. Simplified models based on structural mechanics are rare and often limited in scope. They offer a flexible, computationally efficient approach for simulating truss-based materials. For more advanced designs, parameter-based FEMs must be developed for any loading condition to facilitate the generalised incorporation of 3D cellular solids in mechanical design. Artificial intelligence and machine learning are promising approaches for making optimal use of experimental and FEM results across multidimensional parameter spaces. Full article

Background/Objectives: Advanced hormone receptor-positive (HR+), epidermal growth factor 2-negative (HER2−) breast carcinoma (BC) patients receive frontline therapy with cyclin-dependent tyrosine kinase 4/6 inhibitors + endocrine therapy (ET). At progression, the best management includes mutational analysis for ESR-1, allowing second-line therapy with elacestrant. The aim of this study was to evaluate the efficacy and safety of elacestrant in an Italian real-world setting. Methods: A multicenter, observational study with a mixed retrospective and prospective design was conducted in 13 medical oncology units across Italy. The study population included adult patients with HR+/HER2− locally advanced or metastatic breast cancer with an activating ESR1 mutation documented by liquid biopsy and progressing after at least one line of endocrine therapy containing a CDK4/6 inhibitor. Mutational analysis of plasma was performed using next-generation sequencing with a multigene panel that included ESR1, PIK3CA, AKT, and PTEN. The sample size was calculated according to the two-stage Simon design. Toxicity was classified according to CTCAE version 5.0 criteria. Survival analyses were conducted using the Kaplan–Meier method. Results: At the time of analysis, 39 evaluable patients were enrolled, all female and Caucasian, with a median age of 67 years (range 41–89). The efficacy analysis documented an overall ORR of 28% and a disease control rate of 56%. The median duration of response was 6+ months (95% CL: 3.5–10.6 m). Median overall survival was not reached with a median follow-up of 10 months. The toxicity profile was overall favorable: grade ≥2 asthenia was the most frequent adverse event (23%), followed by gastrointestinal toxicity, which was generally mild. No treatment-related toxicity was reported in 64% of patients. Dose reductions were necessary in 15% of cases, while permanent treatment discontinuation due to toxicity occurred in only 4%. Conclusions: The results of this Italian multicenter observational study confirm the efficacy and tolerability of elacestrant in HR+/HER2− metastatic breast cancer with ESR1 mutation, in a real-world context consistent with the data from the pivotal EMERALD study and with real-world data present in the literature. Full article

Background: The research question explored in this study revolves around the quantitative evaluation of the carbon footprint of cranioplasty surgery, a neurosurgical intervention meant to reconstruct skull defects. Methods: Following a calculation of the emissions pertaining to Scope 1 to 3 of the Greenhouse Gas (GHG) Protocol, the authors engaged with various stakeholders to identify possible interventions meant to drive the carbon efficiency of a cranioplasty pathway. The service improvement project (SIP) that ensued was aimed at reducing the volume and weight of the packaging materials for cranioplasty shipping boxes, and decreasing the paper consumption relative to the preparation of user manuals without compromising patients’ safety. Results: Our analysis indicates a cumulative carbon footprint of 104.35 kg CO₂e for a single unilateral cranioplasty operation, where packaging corresponds to 6.4% of Scope 3 emissions and 1.41% of its total emissions. Of note, our SIP led to an overall 76.53% decrease in the number of emissions generated by the packaging equivalent required for a unilateral titanium implant. Conclusions: This study demonstrates the effectiveness of a partnership between public institutions and medtech companies in driving carbon net efficiency of a cranioplasty pathway, and we suggest that such approach is scalable to other surgical specialties. Full article

Demand-side management (DSM) is a security-critical function in residential smart grids. The same communication and sensing infrastructure that enables fine-grained load flexibility also exposes schedulers to corrupted measurements, price manipulation, and delayed control signals. Conventional DSM formulations generally treat cyber and communication impairments as external disturbances, which are addressed only after the schedule has already been calculated. This study proposes and evaluates Cyber-Resilient and QoS-Aware Demand-Side Management (CQ-DSM) as a hierarchical optimization framework that embeds cyber-risk likelihood and communication quality-of-service (QoS) directly into the scheduling objective. Local home energy management systems (HEMSs) solve mixed-integer linear programs at the appliance level, and central aggregators broadcast compact coordination signals based on real-time prices, measured QoS, and a sliding-window GRU-feature MLP risk estimator. The key intuition is to convert uncertainty about trust and actuation reliability into scheduling prices: high cyber risk discourages exposed loads during vulnerable periods, whereas poor QoS increases the value of locally preserving thermal flexibility. Under the simulation conditions (NYISO August pricing, P = 50 prosumers, Seed 42), CQ-DSM reduces overall system costs by 5.75% and imbalance procurement costs relative to an attack-unaware baseline under normal operation, limits the FDI-induced cost increase to 0.46% versus 0.83% (44% reduction in cost overrun), and reduces thermal-violation penalties by 81% under degraded QoS. The ablation results are consistent with cyber-risk pricing and QoS-aware fallback being complementary rather than redundant under the scenarios tested. Full article

With the large-scale integration of renewable energy and the deepening of electricity market reform, uncertainty in power system operation has increased significantly. This creates new challenges for multiple generation companies when they work together to develop generation schedules that balance economic efficiency and low-carbon goals. Most existing studies assume fixed loads and ignore the active regulation capability of the demand side under price signals and incentive signals. To address this gap, this paper proposes a low-carbon generation schedule optimization method for multiple generation companies. The method considers heterogeneous flexible loads. First, the paper decomposes flexible load adjustability into two components: price elasticity-based load shifting and incentive-based adjustable capacity. Using the price elasticity matrix method, the market clearing price serves as a known input. The load shifting amount under price elasticity regulation is pre-calculated for each park and treated as an exogenous parameter in the generation schedule model. This allows generation companies to directly use demand-side flexibility information during the planning stage. Second, the paper uses the proportion of residential and industrial loads as a core parameter. It characterizes the heterogeneity of four parks along two dimensions: elasticity coefficients and upper limits of adjustable capacity. Parks with a higher proportion of industrial loads have stronger flexible regulation capability. This result is consistent with real physical characteristics. It also provides a quantitative basis for generation companies to utilize flexible resources differently across parks and optimize their output arrangements. Finally, the paper uses the upward and downward adjustable capacity of each park as decision variables. It builds a multi-generator low-carbon generation schedule optimization model with heterogeneous flexible loads. Generator output constraints, power balance constraints, flexible load adjustable capacity constraints, and carbon quota constraints are all integrated into a single-level mixed-integer linear programming framework. This framework can be solved efficiently using commercial solvers. It helps generation companies develop optimal generation schedules that balance economic efficiency and low-carbon targets. Case study results show that combining price elasticity regulation with incentive-based adjustable capacity can effectively improve both the economic performance and low-carbon performance of generation schedules. Full article

The deployment of offshore wind farms (OWFs) has increased impressively over the last decade. While a group of frontrunner countries has led early deployment, the offshore wind sector is expanding to new regions; the Thracian Sea represents a promising area for OWFs deployment due to its favorable wind and wave climate. The successful implementation of OWFs projects depends on a comprehensive understanding of local environmental conditions, with particular emphasis on complex wind–wave interactions quantification, as well as on robust and representative power performance evaluation. In the present paper, hourly environmental data spanning 29 years (1993–2021), including wind and wave parameters, are utilized to quantify joint probability distributions at selected four locations in the Thracian Sea. Corresponding environmental contours are derived and presented using a probabilistic model for given return period. The joint probability distributions of wind and wave conditions are estimated and the environmental contour surfaces for 50- and 100-year return periods are calculated and presented for generic use. Furthermore, the power production of an OWF comprising nine IEA 15 MW turbine units arranged in an orthogonal grid layout is assessed through a numerical model developed in an open access computational tool. The model accounts for key physical processes influencing OWF capacity performance, including wake interactions, atmospheric conditions, turbine control strategies, and layout effects. The results indicate a substantial value of annual energy production and capacity factor for different zones within Thracian Sea achieving a value of 526 GWh and 44%, respectively. The presented results provide practical guidance for OWFs development in the Thracian Sea and contributes to reducing uncertainty in early-stage project planning and future engineering studies. Full article