Search Results (33,944)

The adoption of Interior Permanent Magnet Synchronous Motor (IPMSM) in Battery Electric Vehicle (BEV) and Plug-in Hybrid Electric Vehicle (PHEV) drives the need for innovative approaches to improve control performance and power conversion efficiency. This paper aims at evaluating advanced Model Predictive Control (MPC) strategies for IPMSM drives in a methodic comparison with the most widespread Field Oriented Control (FOC). Different extensions of direct Finite Control Set MPC (FCS-MPC) and indirect Continuous Control Set MPC (CCS-MPC) MPCs are considered and evaluated in terms of reference tracking performance, robustness, power efficiency, and complexity based on Matlab, Simulink™ simulations. Results confirm the inherent better control quality of MPCs over FOC in general and allow us to further identify some possible directions for improvement. Moreover, indirect MPCs perform better, but complexity may prevent them from supporting real-time implementation in some cases. On the other hand, direct MPCs are less complex and reduce inverter losses but at the cost of increased Total Harmonic Distortion (THD) and decreased robustness to parameters deviations. These results also highlight various trade-offs between different predictive control strategies and their feasibility for high-performance automotive applications. Full article

This work presents a generalization and analysis of the physical properties of rocks and ores from the Zhezkazgan ore district. Studies were carried out to identify general patterns in variations in the magnetic, density, velocity, and electrical parameters of the rocks that make up the geological section of the region. Based on the physical parameter measurements of the rock samples and drill cores collected in large quantities evenly throughout the region, a spatial analysis and quantitative assessment were conducted for the magnetic susceptibility, density, specific electrical resistivity, polarizability, and seismic velocity of the rocks. These properties were systematized at the level of formations, individual suites, and lithological heterogeneities. Correlations between the physical properties of the rocks, their composition, and the conditions of their formation were established. This study demonstrated the potential of using petrophysical characteristics in tectonic studies, geological mapping, and the identification of the exploration and ore-controlling factors in copper mineralization. It was found that the deposits of the productive horizons of the Zhezkazgan and Taskuduk suites are characterized by consistent physical parameters across the entire area, due to their relative homogeneity in lithological, structural–textural, and other features. The physical parameters of the rocks are influenced by several factors associated with mineralization processes, including changes in the total porosity, structure, and texture of the host rocks, alteration of the original mineral composition of the ores, fragmentation, fracturing, fissuring, and others. The obtained results significantly improve the reliability of geologically interpreting geophysical anomalies, especially in areas covered by loose sediments and where productive horizons are deeply buried. The detailed petrophysical analysis of the region has made it possible to provide recommendations for selecting an optimal set of geophysical methods for further successful work at the prospecting-evaluation and exploration stages in the Zhezkazgan ore district. Full article

Immersion deep ultraviolet (DUV) lithography remains an indispensable core technology in advanced integrated circuit manufacturing, particularly when combined with multiple patterning techniques to achieve sub-10 nm feature patterning. However, at advanced technology nodes, dynamic instabilities of DUV light sources—including spectral characteristics (bandwidth fluctuations, and center wavelength drift), coherence variations, and pulse-to-pulse energy instability—can adversely affect imaging contrast, normalized image log-slope (NILS), and critical dimension (CD) uniformity. To quantitatively assess the impact of laser parameter fluctuations on NILS and CD, this work establishes systematic physical models for imaging perturbations caused by multi-parameter laser output instabilities under immersion DUV lithography. Through simulations, we evaluate the influence of laser parameter variations on the imaging fidelity of representative line/space (L/S) and tip-to-line (T2L) structures, thereby validating the proposed perturbation model. Research demonstrates that the spectral attributes (bandwidth fluctuation and center wavelength drift), coherence variations, and pulse energy instability collectively induce non-uniform electric field intensity distribution within photoresist, degrading NILS, and amplifying CD variation, which ultimately compromise pattern fidelity and chip yield. Notably, at advanced nodes, pulse energy fluctuation exerts a significantly greater influence on imaging errors compared to bandwidth and wavelength variations. To satisfy the 10% process window requirement for 45 nm linewidths, pulse energy fluctuations should be rigorously confined within 1%. This research provides theoretical foundations and practical insights for the design of dynamic stability control of light source and process optimization of next-generation DUV light sources. Full article

Accurate prediction of the ultimate bearing capacity (UBC) of single piles is essential for safe and economical foundation design, as it directly impacts construction safety and resource efficiency. This study aims to develop a hybrid prediction framework integrating Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) to optimize a Backpropagation Neural Network (BPNN). GA performs global exploration to generate diverse initial solutions, while PSO accelerates convergence through adaptive parameter updates, balancing exploration and exploitation. The primary objective of this study is to enhance the accuracy and reliability of UBC prediction, which is crucial for informed decision-making in geotechnical engineering. A dataset consisting of 282 high-strain dynamic load tests was employed to assess the performance of the proposed GA-PSO-BPNN model in comparison with CNN, XGBoost, and traditional dynamic formulas (Hiley, Danish, and Winkler). The GA-PSO-BPNN achieved an R² of 0.951 and an RMSE of 660.13, outperforming other AI models and traditional approaches. Furthermore, SHAP (SHapley Additive exPlanations) analysis was conducted to evaluate the relative importance of input variables, where SHAP values were used to explain the contribution of each feature to the model’s predictions. The findings indicate that the GA-PSO-BPNN model provides a robust, cost-efficient, and interpretable approach for UBC prediction, which aligns with current sustainability goals by optimizing resource usage in foundation design. This model shows significant potential for practical use across various geotechnical settings, contributing to safer, more sustainable infrastructure projects. Full article

Corn (Zea mays L.) ranks among the most important cereal crops globally, extensively cultivated for food, animal feed, and industrial applications. Its large-scale production generates substantial amounts of agricultural residues such as cobs, husks, stalks, leaves and other, which are often underutilized, leading to environmental concerns. Due to their high carbon content, lignocellulosic structure, and abundant availability, these residues represent a sustainable and low-cost raw material for the synthesis of activated carbon. Corn waste-derived activated carbon has emerged as a promising material for the efficient removal of heavy metals from aqueous solutions. Its high surface area, well-developed porosity, and adjustable surface chemistry, referring to the functional groups on the adsorbent surface that can be modified to enhance affinity toward metal ions, facilitate effective adsorption. This review provides a comprehensive overview of (1) the potential of corn waste biomass as a precursor for activated carbon production, (2) methods of carbonization and activation that influence the textural and chemical properties of the resulting adsorbents, (3) adsorption performance for heavy metal removal under varying experimental parameters such as pH, initial concentration, contact time, and adsorbent dosage, (4) adsorption mechanisms responsible for heavy metal uptake. Reported maximum adsorption capacities vary for different metals, ranging from 2.814–206 mg/g for lead, 0.21–87.72 mg/g for cadmium, 9.6246–175.44 mg/g for chromium, and 0.724–643.92 mg/g for copper. Utilizing corn waste not only provides an eco-friendly approach for managing agricultural residues but also supports the development of efficient adsorbents. Nevertheless, challenges such as scaling up production and evaluating adsorbent performance in real wastewater samples remain and require further investigation. Finally, the review highlights key challenges and knowledge gaps in current research and offers recommendations for future studies aimed at advancing the practical application of corn waste–based activated carbons in water treatment. Full article

Background/Objectives: Traditional medicinal plants are a rich source of phytochemicals with diverse biological effects, yet their safety and mechanistic impact on reproductive health remain underexplored. In this study, we investigated the effects of Ruscus hyrcanus, Juniperus oblonga, and Stachys lavandulifolia extracts on survival, fertility, and germline integrity in Caenorhabditis elegans. Methods: Synchronized young adult worms were exposed to each extract, and survival and reproductive parameters were statistically analyzed using two-tailed Mann–Whitney tests. Results: Through LC–MS analysis, we identified that all three extracts shared 78 compounds, mainly including phenolic acids, flavonoids, and terpenoids. Our findings indicate that reactive oxygen species generation is a major driver of nematocidal and fertility-reducing effects, while modulation of DNA damage response pathways further contributes to germline defects. Conclusions: Taken together, these results demonstrate that exposure to the extracts significantly (p < 0.05) reduces survival, impairs larval development, elevates the High Incidence of Males phenotype, and disrupts germline integrity in a dose-dependent manner. Full article

To address the issue that the current research on TBM disc cutter rock breaking insufficiently considers actual stratified rock masses, this study constructs numerical models of stratified rock masses with different bedding dip angles and bedding spacings based on the discrete element method (DEM). The whole process of TBM disc cutter rock breaking is numerically simulated through the displacement loading mode. The research results show that the bedding dip angle has a significant impact on the crack propagation mode. When α = 45°, the bedding intersects with the contact point of the disc cutter, and cracks penetrate directly along the bedding without an obvious “crushed zone”, resulting in the minimum number of cracks. The bedding spacing regulates the rock-breaking effect in stages. When d = 45°, the “crushed zone” interacts with two beddings to form three branch cracks, reaching the peak number of cracks and achieving the optimal rock-breaking efficiency. The cracks generated by disc cutter rock breaking exhibit the characteristic of “slow initial growth and rapid later surge” with the increase in time steps, which is highly consistent with the actual mechanical process of rock breaking. This study reveals the influence mechanism of bedding properties on TBM disc cutter rock breaking, verifies the reliability of the DEM combined with PB and SJ models in the simulation of stratified rock mass breaking, and provides theoretical support and data references for the parameter optimization of TBM disc cutters and efficient tunneling under complex stratified geological conditions. Full article

This study introduces a novel Interleaved Fusion Learning (IFL) methodology leveraging transfer learning to generate a family of models optimized for specific datasets while maintaining superior generalization performance across others. The approach is demonstrated in cervical cancer screening, where cytology image datasets present challenges of heterogeneity and imbalance. By interleaving transfer steps across dataset partitions and regulating adaptation through a dynamic learning parameter, IFL promotes both domain-specific accuracy and cross-domain robustness. To evaluate its effectiveness, complementary metrics are used to capture not only predictive accuracy but also fairness in performance distribution across datasets. Results highlight the potential of IFL to deliver reliable and unbiased models in clinical decision support. Beyond cervical cytology, the methodology is designed to be scalable to other medical imaging tasks and, more broadly, to domains requiring equitable AI solutions across multiple heterogeneous datasets. Full article

Renewable energy sources offer a promising future, backed by mature technologies and a viable pathway toward sustainable energy systems. However, careful planning is necessary to efficiently utilize these resources, especially during site selection. Many rural areas lack access to grid electricity, making off-grid hybrid wind-solar power an attractive solution. In the Tigray region of Ethiopia, no such research has been conducted before. This study aims to identify suitable sites for hybrid wind-solar power for rural electrification using Geographic Information System (GIS), Analytic Hierarchy Process, and Monte Carlo simulation. The criteria fall into three categories: Climate, Topography, and Infrastructure, prioritized through pairwise comparisons by thirteen experts from five organizations engaged in renewable energy research, planning, and operations. Monte Carlo simulation was used for sensitivity analysis to address uncertainties in expert judgments and validate the rankings. The spatial analysis reveals 6470 km² as highly suitable for off-grid solar, 76 km² for off-grid wind with predominant easterly winds, and 177 km² as most favorable for hybrid generation. Areas of good suitability measure 447 km² for wind, 44,128 km² for solar, and 16,695 km² for hybrid systems. Based on this assessment, techno-economic analysis quantified the Levelized Cost of Energy (LCOE) under varying solar–wind shares and battery autonomy days. The analysis shows a minimum LCOE of $0.23/kWh with one-day storage and $0.58/kWh with three-day storage, indicating shorter autonomy is more cost-effective while longer autonomy enhances reliability. Sensitivity analysis shows financial parameters, particularly discount rate and battery capital cost, dominate system economics. Full article

Stroke-surviving patients may present a wide range of neurological deficits affecting both sensory and motor functions as well as the cognitive and the emotional domains, with an impact on independence on daily activities and quality of life in general. Assessment scales are essential tools for evaluating all these aspects of a patient’s condition and for monitoring their evolution in time, attempting to provide a quantitative index to complex and sometimes indirectly observable parameters. In fact, the use of these scales entails methodological and interpretative challenges that can limit their applicability and effectiveness. This narrative review explores the current state and limitations of assessment scales used in the rehabilitative evaluation of post-stroke patients. Common neurorehabilitation techniques and traditionally used assessment scales for measuring patient progress are reviewed, highlighting their main limitations. As an alternative to the observational approach, direct assessment of the effect of the ongoing rehabilitative process on the functional recovery of the damaged neurological network, based on the recording of their electric signaling or on the modification in regional cerebral blood flow, have been recently proposed. Innovative rehabilitation assessment methods based on quantitative data are reviewed, with a special focus on ultrasound-based techniques, aiming to improve accuracy and sensitivity in clinical assessment. Full article

The Lomb–Scargle method (LSM) constitutes a robust method for frequency and amplitude estimation in cases where data exhibit irregular or sparse sampling. Conventional spectral analysis techniques, such as the discrete Fourier transform (FT) and wavelet transform, rely on orthogonal mode decomposition and are inherently constrained when applied to non-equidistant or fragmented datasets, leading to significant estimation biases. The classical LSM, originally formulated for univariate time series, provides a statistical estimator that does not assume a Fourier series representation. In this work, we extend the LSM to multivariate datasets by redefining the shifting parameter $\tau$ to preserve the orthogonality of trigonometric basis functions in ${\mathbb{R}}^n$. This generalization enables simultaneous estimation of the frequency, phase, and amplitude vectors while maintaining the statistical advantages of the LSM, including consistency and noise robustness. We demonstrate its application to solar activity data, where sunspots serve as intrinsic markers of the solar dynamo process. These observations constitute a randomly sampled two-dimensional binary dataset, whose characteristic frequencies are identified and compared with the results of solar research. Additionally, the proposed method is applied to an ultrasound velocity profile measurement setup, yielding a three-dimensional velocity dataset with correlated missing values and significant temporal jitter. We derive confidence intervals for parameter estimation and conduct a comparative analysis with FT-based approaches. Full article

The Yellow River Basin faces severe challenges in water security and ecological protection: at the basin scale, complex hydrological processes and fragile ecosystems undermine the water security pattern; at the local scale, waterlogging risks have intensified in Zhengzhou—a core city in the lower reaches—impacting the city itself and also exerting negative effects on the basin’s water security. To address this, mapping the scientific layout of green infrastructure (GI) is urgent. However, existing studies on GI layout at the basin-urban scale have certain limitations: neglect of underlying surface spatial heterogeneity, insufficient integration of natural, hydrological and social factors’ synergies, and lack of research on large-scale basins and cities, especially ecologically sensitive areas with complex hydrological processes. To fill these gaps, this study proposes an integrated framework (SCS–GIS–MCDM) combining the SCS hydrological model, GIS spatial analysis, and multi-criteria decision making (MCDM). The SCS hydrological model is refined via localized parameter calibration for better accuracy; indicator weights are determined through the MCDM framework; and green infrastructure (GI) suitability maps are generated by integrating ArcGIS spatial analysis with fuzzy logic. Results show that (1) 6.8% of Zhengzhou is highly suitable for GI, mainly in riparian areas and the Yellow River alluvial plain; (2) sensitivity analysis confirms flooded areas and runoff corridors as key drivers; (3) spatial validation against government-issued ecological control zone plans demonstrates the model’s value in balancing flood safety and socio-economy. This framework provides a replicable application model for GI construction in cities along the Yellow River Basin, thereby supporting urban planners in making evidence-based decisions for sustainable blue–green space planning. Full article

Purpose: To describe the indication spectrum for high-dose 8 mg aflibercept for neovascular age-related macular degeneration (nAMD) in a real-world cohort in a tertiary referral center. Methods: The database of the University Eye Hospital Munich, Ludwig Maximilians-University was screened for eyes with nAMD treated with 8 mg aflibercept. Demographic data, multimodal imaging and treatment parameters were recorded. Reasons for treatment with 8 mg aflibercept were analyzed. Results: Thirty-four consecutive eyes of 31 patients (mean age 78.6 ± 8.9 years) were identified. There were 22 women (70.1%) and 9 men (29.9%). In all eyes (100%), 8 mg Aflibercept was applied as switching therapy. Prior to switching, the mean anti-vascular endothelial growth factor (VEGF) treatment duration for nAMD was 3.9 ± 2.9 years, pretreatment amounted to a mean of 34.5 ± 26.3 injections, equaling 9.2 ± 2.4 injections/year, and the mean visual acuity (VA) was 0.4 ± 0.4 logMAR. The last treatment before switching was 2 mg aflibercept in 76%, faricimab in 18%, ranibizumab in 3% and bevacizumab in 3% of cases. Reasons for switching included (A) recalcitrant nAMD with persistent fluid despite q4w dosing (17 eyes, 50%), (B) the wish for interval extension (15 eyes, 44%) and (C) macular hemorrhage (2 eyes, 6%). In group B, two-thirds of eyes (10/15, 66.7%) were maintained at ≤q6w prior to switching. Conclusions: In this study, high-dose 8 mg aflibercept was exclusively used as a switch therapy. Most eyes (76%) switched were from pretreatment with 2 mg aflibercept. The main reasons for switching were recalcitrant nAMD with persistent fluid despite q4w dosing (50%) or the wish for treatment extension beyond 6 weeks (32%). In the future, these data will aid in the design of prospective real-world studies comparing the efficacy of high-dose 8 mg aflibercept with older generation treatment options, especially 2 mg aflibercept. Full article

Objective: To evaluate the psychometric properties of the short version of the Token Test (SVTT) and the Rey–Osterrieth Complex Figure (ROCF) using an item response theory (IRT) framework and to establish normative data for Colombian children and adolescents based on ability scores. Methods: A total of 668 healthy participants aged 6–17 years took part in this study. Factorial structure was assessed through confirmatory factor analysis (CFA). Item parameters were estimated using a two-parameter logistic (2PL) model for the SVTT, which accounts for both item difficulty and discrimination in dichotomous responses, and a graded response model (GRM) for the ROCF, suitable for items scored on ordered categories reflecting increasing levels of performance accuracy and Differential Item Functioning (DIF) analysis was conducted to assess potential bias related to sex. Reliability was examined using the Test Information Function (TIF), internal consistency throughout Cronbach’s alpha, and the influence of sociodemographic variables was analyzed through regression models. Results: CFA confirmed unidimensionality for all measures. For most items, moderate-to-low ability was sufficient to achieve the highest scores in the ROCF, and low ability in the SVTT. DIF analysis indicated no meaningful sex-related bias in any of the subtests. Both tests showed excellent reliability and internal consistency. Copy scores were influenced by polynomial age and parents’ mean years of education (MPE), while both immediate recall in the ROCF and SVTT were affected by MPE and the interaction of logarithmic age. Conclusions: This study provides strong psychometric evidence and, together with the integration of digital tools for generating normative data, represents a meaningful advancement in neuropsychological assessment. Full article

This paper presents a comprehensive study on the estimation of multicomponent stress–strength reliability under progressively censored data, assuming the inverse Rayleigh distribution. Both maximum likelihood estimation and Bayesian estimation methods are considered. The loss function and prior distribution play crucial roles in Bayesian inference. Therefore, Bayes estimators of the unknown model parameters are obtained under symmetric (squared error loss function) and asymmetric (linear exponential and general entropy) loss functions using gamma priors. Lindley and MCMC approximation methods are used for Bayesian calculations. Additionally, asymptotic confidence intervals based on maximum likelihood estimators and Bayesian credible intervals constructed via Markov Chain Monte Carlo methods are presented. An extensive Monte Carlo simulation study compares the efficiencies of classical and Bayesian estimators, revealing that Bayesian estimators outperform classical ones. Finally, a real-life data example is provided to illustrate the practical applicability of the proposed methods. Full article