Search Results (1,339)

Background/Objectives: Visual impairment is an underrecognized but functionally disabling complication of stroke that adversely affects rehabilitation potential, autonomy, and quality of life. Clinical, anatomical, and ophthalmologic determinants of post-stroke visual recovery remain incompletely defined, particularly in Eastern European tertiary stroke units where structured visual follow-up is not standardized. This study aimed to identify clinical, imaging, and ophthalmologic predictors of favorable visual recovery and to evaluate whether integrating these domains improves early prognostic stratification beyond standard neurological assessment. Methods: We conducted a retrospective single-center cohort study of 71 consecutive adult patients admitted with acute stroke and a documented visual complication between January 2022 and September 2025 at Pius Brinzeu Emergency County Hospital and Victor Babes University of Medicine and Pharmacy Timisoara. Favorable recovery was defined as ≥50% improvement in visual field index (VFI) at 6 months. Group comparisons used Student’s t-test, Mann–Whitney U test, chi-square test, and Fisher’s exact test. Multivariable logistic regression, Cox proportional hazards modeling, and unsupervised k-means clustering were performed. Results: Twenty-nine patients (40.8%) achieved favorable recovery, while 42 (59.2%) had persistent impairment. Responders were younger (62.8 ± 10.7 vs. 70.4 ± 10.8 years, p = 0.005) and had lower admission National Institutes of Health Stroke Scale (NIHSS) (6.4 ± 2.9 vs. 10.3 ± 4.4, p < 0.001), smaller lesion volumes (18.7 ± 11.4 vs. 33.2 ± 18.7 mL, p < 0.001), thicker peripapillary retinal nerve fiber layer (89.3 ± 7.6 vs. 78.2 ± 9.4 μm, p < 0.001), and earlier rehabilitation initiation (11.4 ± 5.3 vs. 21.7 ± 9.8 days, p < 0.001). NIHSS, time to rehabilitation, and optical coherence tomography (OCT) pRNFL thickness remained independent predictors. The full integrated model achieved an area under the receiver operating characteristic curve (AUC) of 0.87. Cluster analysis identified three distinct phenotypes with favorable recovery rates of 79.2%, 34.8%, and 8.3%. Conclusions: Combined clinical, neuroimaging, and ophthalmologic profiling—particularly OCT pRNFL—meaningfully refines early prediction of post-stroke visual recovery and supports phenotype-driven rehabilitation planning. Full article

High-efficiency industrial motors are increasingly important for reducing energy consumption, operating costs, and indirect carbon emissions. This study presents a comparative evaluation of IE2 and IE4 efficiency class induction motors with the same rated power and frame size through finite element analysis and prototype testing. Two-dimensional transient electromagnetic models were developed in ANSYS Maxwell to investigate magnetic flux distribution, torque behavior, losses, and steady-state performance, and the numerical results were experimentally validated according to IEC 60034-2-1 procedures. The results show that the IE4 motor provides a more balanced magnetic flux distribution, lower local saturation tendency, reduced torque ripple, and lower total losses than the IE2 motor. Experimental measurements confirmed the numerical predictions with good agreement, particularly at the rated operating point. In addition to higher efficiency, the IE4 motor exhibited stronger starting and breakdown torque characteristics, indicating superior load-handling capability. An economic assessment based on a representative duty cycle showed that the relative additional cost of the IE4 motor can be recovered within approximately 0.81 years, while lower annual electricity consumption also reduces indirect CO₂ emissions. Furthermore, the IE4 prototype operated at a lower thermal steady-state temperature, supporting longer insulation life and improved long-term reliability. Overall, the findings demonstrate that replacing conventional IE2 motors with IE4 alternatives is not merely an efficiency upgrade, but also a technically robust, economically justified, and environmentally effective strategy for sustainable industrial systems. Full article

To advance the prediction accuracy of remaining useful life (RUL) for rolling element bearings, this study proposes a novel hybrid prognostic framework that synergistically integrates digital twin (DT) technology with the Time-series Mixer (TSMixer) architecture. A high-fidelity virtual bearing digital prototype is constructed via multi-physics fusion modeling of DT, enabling real-time state perception and dynamic evolution simulation of the physical bearing system, which further generates abundant, high-reliability augmented operational data to compensate for the scarcity of measured full-life degradation samples. By fusing multi-domain features extracted from both historical monitoring datasets and DT-augmented simulation sequences, a comprehensive multi-source feature space covering the full degradation trajectory of bearings is established. Subsequently, the TSMixer model, which excels in parallel capturing of local temporal patterns and global long-range dependencies, is employed to learn the implicit time-varying degradation characteristics from the constructed time-series datasets, so as to achieve accurate RUL estimation of the tested bearings. Experimental validation on the publicly available benchmark dataset demonstrates that the proposed framework yields superior prognostic performance, with a prediction accuracy improvement of approximately 9.23% compared with several state-of-the-art baseline models. Furthermore, the proposed hybrid paradigm exhibits remarkable robustness and generalization capability under variable working conditions, which provides a promising technical solution for intelligent predictive maintenance and active fault prevention of high-end mechanical equipment. Full article

Lycopene is a highly significant carotenoid in daily life, exhibiting potent antioxidant properties and recognized as one of the most powerful natural antioxidants identified in plants to date. Its functionality originates from electronic and vibrational states that exhibit a high sensitivity to environmental perturbations. Nevertheless, exclusively experimental methodologies face challenges in delivering a comprehensive molecular-level comprehension of the influence exerted by particular environmental factors on the vibronic characteristics. This deficiency in understanding hinders the accurate prediction of its behavior and functional performance within complex systems. The first principle computational investigation enables a precise elucidation of the coupling mechanisms between electronic excitations and vibrational modes under diverse solvation and interaction environments. The results indicate that the local environment significantly influences the charge distribution and orbital energies of lycopene, altering its vibrational and electronic state properties. This provides a fundamental theoretical framework for predicting their photophysical behavior and biological functions within complex matrices. Full article

To address the strong variability, complex cyclic characteristics, and difficulty in characterizing the main shaft torque load of vertical ring-die biomass briquetting machines, this study proposes a random load spectrum generation method based on kernel density estimation (KDE) and a Copula function. The measured torque time-history signal was processed using wavelet-threshold denoising and rainflow counting to extract cycle mean and cycle amplitude samples. KDE was used to estimate their marginal distributions, and a Copula function was introduced to construct the joint distribution model. The random load spectrum was then reconstructed through two-dimensional probability integration based on the fitted joint density function. The results show that the Frank Copula best describes the dependence structure between the cycle mean and the cycle amplitude. The reconstructed load spectrum agrees well with the measured load spectrum in terms of marginal frequency distribution and main peak intervals, with an RMSE of 6.3161 and an NRMSE of 6.94%. Compared with the KDE-independent baseline model, the proposed KDE–Frank Copula model reduces the RMSE by 12.74%. These results indicate that the proposed method can effectively characterize the statistical features of the random torque load of the main shaft and provide methodological support for load spectrum generation, fatigue life prediction, and reliability design of vertical ring-die biomass briquetting machines. Full article

Road infrastructure managers are increasingly required to ensure adequate pavement performance under constrained financial resources. This has led to the widespread adoption of asset management principles, where infrastructure is evaluated not only from a technical perspective but also in terms of its economic value and the level of service provided to users. Pavement asset value can be understood as a function of two principal components: structural condition, reflecting the load-bearing capacity of the pavement, and user-related performance, primarily influenced by surface characteristics such as roughness. A key limitation of current approaches lies in the simplified representation of deterioration processes, which often fail to capture the full-life-cycle progression of degradation and may lead to inaccurate predictions of pavement condition, user costs, and optimal intervention timing. This paper proposes an integrated framework that links full-life-cycle pavement deterioration modeling with asset value assessment and decision-making processes. The methodology is based on experimentally validated and empirically supported deterioration models derived from accelerated pavement testing and long-term pavement performance monitoring of real road sections. This approach is demonstrated through a case study, illustrating the interaction between structural condition and user-related performance. The results demonstrate how a deterioration model derived from full-life-cycle observations can be incorporated into economic evaluation and resource-allocation processes in pavement management systems. Full article

High-temperature corrosion severely impairs the service life of boiler heating tubes and threatens the safe and economical operation of thermal power units. With diversified fuels (coal, biomass and refuse-derived fuels) and continuously elevated operating parameters (steam temperature exceeding 620 °C for ultra-supercritical units), boiler heating surfaces are exposed to increasingly complex corrosive environments. High-temperature oxidation, sulfidation, chlorination, molten salt hot corrosion and deposit-induced multi-factor coupled corrosion coexist and exacerbate each other. This paper adopts a four-dimensional analytical framework of “mechanisms–technologies–materials–evaluation” to systematically summarize relevant research progress. From the perspective of corrosion mechanisms, the evolution of understandings from single high-temperature oxidation to multi-factor coupled corrosion is reviewed. In terms of surface coating technologies, seven mainstream processes including HVOF/HVAF spraying, plasma spraying, cold spraying, laser cladding and weld overlay are compared in terms of preparation characteristics and engineering applicability. For coating materials, twelve material systems such as NiCr alloys, MCrAlY, cermets, Fe-based amorphous/nanocrystalline alloys and high-entropy alloys are evaluated for their corrosion resistance under diverse service conditions. As for monitoring and evaluation, this work introduces full-range corrosion management technologies covering electrochemical monitoring, non-destructive testing, numerical simulation and life assessment. Finally, the paper discusses the application prospects of gradient coating design, AI-assisted material screening and digital twin technology, and points out key research gaps including long-term service reliability verification of coatings and quantitative prediction models for multi-factor coupled corrosion. Full article

Progressive stamping is a high-efficiency sheet metal forming method in the automotive and mass production industries, where material characteristics significantly influence process stability, cutting force, tool life, and final part quality. Herein, we report the effects of the chemical composition and mechanical properties of DD11 low-carbon steel on punching force during progressive stamping. Ten DD11 material batches with varying chemical compositions and mechanical properties were subjected to experimental investigation. Material characterization involved spectroscopic chemical analysis, tensile testing in accordance with ISO 6892-1, and hardness measurement. Punching tests were performed with a Zwick BZ2-MMAG100.SH01 universal testing machine that incorporates a punch–die assembly to study force–displacement behavior under controlled conditions. The cutting curves of these materials were analyzed to determine the maximum cutting and fracture loads, which were then statistically correlated with the materials’ chemical and mechanical parameters. The results indicated that tensile strength and yield strength are the strongest statistically significant contributors to the maximum cutting load and the fracture point, and that the correlation coefficients for these measurements were +0.866 and +0.869, respectively. Carbon, chromium, and silicon showed the most positive effect on cutting resistance; whereas, titanium was negatively associated with each of the tested responses among chemical composition measures. But none of the chemical factors were statistically significant. The analysis also showed that material hardness yields the highest predictive performance for cutting force behavior (Pearson correlation coefficients up to 0.935 and a regression coefficient of R² = 0.875). Results of this study show that DD11 cutting behavior at progressive stamping is controlled primarily by strength-dependent mechanical characteristics rather than chemical composition variations. Full article

An increase in penetration of photovoltaic (PV) systems in a distribution system causes voltage regulation issues that create serious problems for the On-Load Tap Changer (OLTC) of the power transformer, leading to higher tap-changing frequency and reduced transformer life. Traditional voltage control methods are ineffective when PV penetration exceeds load demand, and more sophisticated control methods are needed. This paper combines a systematic literature review conducted in accordance with the PRISMA 2020 guidelines with a case study on operational issues of OLTC transformers under both normal and non-normal operating conditions. It entails a detailed examination of the effect of PV integration on the operating characteristics of OLTC in a systematic approach and also dwells upon coordination processes between OLTC and Flexible AC Transmission Systems (FACTS) devices, such as Distribution Static Synchronous Compensator (D-STATCOM) or Static VAR Compensator (SVC), which are highly effective in reducing tap operations. The future directions covered in the review include the operation of hybrid systems, cost-effective implementations, weather effects, predictive analytics, adaptive control techniques, etc. The case study included online monitoring of OLTC performance in two scenarios at the cement factory. First, under supply changes and load changes. Second, including PV penetration. The results show that OLTC increases the average daily tapping frequency (90 taps/day) by about 60%, with full PV penetration. It is concluded that this can’t be applied without coordinated control among OLTC, D-STATCOM, and PV inverters to maintain transformer life, improve reliability, and provide stable voltage profiles even under highly variable PV generation conditions. These results aim to provide a comprehensive resource for academics and practitioners, facilitating the advancement of advanced voltage control methods to support the transition to sustainable energy systems. Full article

To address post-acidification, microbial contamination, and quality deterioration in fermented pickled mustard greens after fermentation, this study systematically evaluated the effects of thermal treatment on quality preservation, selected a preferred thermal processing condition from three kinetically designed treatments, and predicted product shelf life. Based on heat penetration curves and the thermal death kinetics of the target heat-resistant microorganism, Bacillus subtilis, three thermal processing conditions were established: 75 °C for 64 min, 85 °C for 19 min, and 95 °C for 17 min. The D-value of B. subtilis spores at 85 °C was 1.37 min, and the corresponding thermal treatments were designed according to a 2D reduction principle. HS-SPME-GC-MS analysis identified 84 volatile compounds, with isothiocyanates representing key contributors to the characteristic pungent aroma of mustard-based pickles. Sensory evaluation showed that the 85 °C treatment group achieved the best observed balance among pungency, refreshing aroma, mellow flavor, color, texture, and overall acceptability, whereas excessive heating at 95 °C promoted isothiocyanate loss and texture deterioration. During accelerated storage, the selected treatment inhibited post-acidification, maintained nitrite at a low level (<1 mg/kg), and delayed microbial and sensory deterioration. Integrating physicochemical indices, microbial populations, and sensory scores, the theoretical shelf life under refrigeration at 4 °C was predicted to be 170 days using the Q10 model. These findings provide practical guidance for selecting thermal processing conditions that balance microbial safety, flavor retention, and shelf-life extension in industrial fermented pickled mustard greens. Full article

Background: Sleep quality plays an essential role in cognitive performance, memory consolidation, learning efficiency, and overall wellbeing. University students are particularly vulnerable to sleep disturbances because of academic stress, irregular sleep schedules, and lifestyle-related factors. Poor sleep quality has been associated with impaired academic performance and reduced cognitive functioning. Objective: This study aimed to evaluate the relationship between sleep patterns, sleep quality, and academic performance among female university students. Methods: A cross-sectional study was conducted among 201 female university students at Qassim University, Saudi Arabia. Data were collected using an electronic self-administered questionnaire that included demographic characteristics, sleep-related behaviors, and the Pittsburgh Sleep Quality Index (PSQI). Academic performance was assessed using self-reported Grade Point Average (GPA). Statistical analysis included descriptive statistics, Spearman correlation analysis, one-way ANOVA with Tukey post hoc analysis, Chi-square tests, and multiple linear regression analysis. Results: The mean GPA of the participants was 4.13 ± 0.60 (on a 5-point scale), while the mean PSQI score was 8.81 ± 3.26, indicating generally poor sleep quality. A significant negative correlation was observed between PSQI score and GPA (rho = −0.200, p = 0.0047). Students with good sleep quality demonstrated significantly higher GPA scores compared with students with poor sleep quality (F(2,194) = 6.31, p = 0.0022). Significant associations were also identified between sleep quality and both bedtime (p = 0.0009) and sleep duration category (p = 0.0002). However, after adjustment for other variables, the independent effect of PSQI on GPA was attenuated and did not reach statistical significance (p = 0.138). This discrepancy between the significant bivariate correlation (rho = −0.200, p = 0.005) and the non-significant multivariate result represents the most important finding of this study, suggesting that sleep quality alone does not independently predict GPA when other academic and behavioral factors are considered. Conclusion: Poor sleep quality was highly prevalent among female university students and showed a significant bivariate association with lower academic performance, though this relationship was attenuated in the multivariate model. Promoting healthy sleep behaviors may support student wellbeing and academic functioning, cognitive wellbeing, quality of life, and the advancement of Sustainable Development Goal 3 (Good Health and Well-Being). Full article

The long-term effects of water immersion on the physicochemical and functional properties of ethylene-propylene-diene monomer (EPDM) elastomer, widely used as insulation in medium-voltage electrical cables, were investigated over a period of 140 days at room temperature. A multi-scale experimental approach combining complementary characterization techniques was employed to establish quantitative correlations between moisture-induced physicochemical changes and the resulting evolution of functional performance. Water uptake, governed by Fickian diffusion kinetics, remained limited to 0.30 wt%. At the surface, progressive roughening was observed alongside the formation of microcavities and microcracks. Leaching of mineral fillers and an increase in surface polarity were found to enhance wettability. These combined physicochemical alterations translated into measurable degradation of functional properties, with two distinct kinetic regimes identified. Shore hardness, volume resistivity, and dielectric strength underwent rapid deterioration within the first few days of immersion, whereas tensile strength, elongation at break, dielectric permittivity, and dielectric loss factor evolved more gradually over timescales of several tens of days. Temporal profiles for each property were fitted to appropriate models, and characteristic degradation timescales were estimated. These findings provide a structured, physically grounded picture of EPDM degradation under water exposure and offer quantitative data to support the development of service-life prediction models for cable insulation systems. Full article

Background: Cervicogenic headache (CEH) is a secondary headache disorder originating from dysfunction in the cervical spine. In addition to pain, individuals with CEH frequently experience disturbances in postural control and sensorimotor integration, which may compromise functional capacity and quality of life. Conventional clinical assessments typically focus on pain intensity and cervical range of motion; however, these measures often fail to capture the multifactorial mechanisms underlying balance impairments in this population. Machine learning (ML) methods offer the ability to integrate multidimensional clinical data and may provide a more comprehensive approach for identifying patterns of postural stability and the factors influencing balance regulation in CEH. Methods: A secondary analysis was conducted using baseline data pooled from three registered randomized controlled trials, comprising 68 independent participants diagnosed by a neurologist according to the International Classification of Headache Disorders, 3rd edition (ICHD-3). Postural Stability Class served as the primary outcome and was derived from quantitative stability scores categorized as High, Moderate, or Low. Predictor variables included demographic characteristics (age, gender), clinical measures (pain intensity, headache frequency, symptom duration, cervical range of motion), and sensorimotor parameters (center-of-pressure sway and gaze accuracy). Five machine learning algorithms—Random Forest, XGBoost, Support Vector Machine, Logistic Regression, and Gradient Boosting—were trained and evaluated using 10-fold cross-validation with procedures implemented to reduce overfitting. Results: The Gradient Boosting classifier demonstrated the best performance, achieving an accuracy of 0.857 and an F1 score of 0.857, with a cross-validated accuracy of 0.802 ± 0.063. Random Forest and XGBoost achieved accuracies of 0.786. Feature importance analysis identified center-of-pressure sway and pain intensity as the most influential predictors of stability classification, followed by cervical flexion range of motion and gaze accuracy. Demographic variables showed minimal contribution to model performance. Conclusions: Machine learning models were able to distinguish different levels of postural stability in individuals with CEH. The findings highlight the central role of pain and sensorimotor control in balance regulation and suggest that predictive analytics may support precision physiotherapy by enabling rehabilitation strategies tailored to individual sensorimotor profiles. Full article

Accurate remaining useful life (RUL) prediction is critical for the reliable operation of lithium-ion batteries. Traditional data-driven methods often suffer from parameter redundancy and error accumulation in state prediction. This paper proposes a hybrid data-driven RUL prediction framework based on Gaussian process regression (GPR) optimized by the lightning search algorithm (LSA). First, both local and global indirect health features (HFs) are extracted from the external characteristic parameter curves and the incremental capacity curves during battery charging/discharging. Second, the Pearson correlation coefficient is applied to select highly relevant features, forming a compact feature set. Third, a GPR model is developed, and the LSA is introduced to optimize its hyperparameters, overcoming the tendency of the conjugate gradient method to fall into local optima or fail to converge. Experimental results under identical conditions show that the proposed LSA–GPR model achieves a prediction error of 3% or less. Full article

Background: Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection (Sepsis-3 definition), associated with high mortality in intensive care unit (ICU) patients. Composite immune–nutritional indices derived from routine laboratory data have emerged as accessible prognostic tools; however, their comparative value in critically ill septic patients remains insufficiently characterised. This study aimed to compare the prognostic performance of the haemoglobin–albumin–lymphocyte–platelet (HALP) score, pan-immune-inflammation value (PIV), and Naples Prognostic Score (NPS) for predicting in-hospital mortality in ICU patients with sepsis as the primary outcome, and to assess their incremental predictive value as the secondary objective. Methods: In this retrospective, two-centre cohort study, 1020 consecutive eligible adult patients fulfilling Sepsis-3 criteria (suspected or confirmed infection with an acute increase in SOFA score ≥ 2 points) admitted to the ICUs of Necmettin Erbakan University Hospital and Beyhekim Training and Research Hospital between January 2016 and June 2025 were included. HALP was calculated as haemoglobin (g/L) × albumin (g/L) × lymphocyte count (×10⁹/L) ÷ platelet count (×10⁹/L); PIV as (neutrophil × platelet × monocyte) ÷ lymphocyte (all ×10⁹/L). NPS was computed from serum albumin, neutrophil-to-lymphocyte ratio, and lymphocyte-to-monocyte ratio, with the total-cholesterol component imputed due to availability in only 31.7% of patients. Discriminative performance was evaluated by receiver operating characteristic (ROC) analysis, pairwise DeLong tests, bootstrap resampling (1000 iterations), Hosmer–Lemeshow calibration, and net reclassification improvement (NRI)/integrated discrimination improvement (IDI) analyses. Five pre-specified nested multivariable logistic regression models were constructed. Results: Of 1020 patients (median age 76 years, IQR 67–83; 59.8% male), 521 (51.1%) died during hospitalisation. HALP showed the highest discriminative ability among individual indices (AUC 0.626, 95% CI 0.594–0.658), while PIV was non-discriminatory (AUC 0.504, p = 0.78) and NPS showed limited performance (AUC 0.563, 95% CI 0.531–0.595). HALP remained an independent predictor of mortality after multivariable adjustment (OR 0.98, 95% CI 0.97–0.99, p = 0.002). NRI and IDI analyses showed no incremental value with NPS addition. Conclusions: HALP demonstrated modest but independently consistent discrimination for in-hospital mortality in ICU patients with sepsis, outperforming PIV and NPS. However, an AUC of 0.626 does not support standalone clinical use; external validation and comparison with established severity models are required before integration into risk stratification frameworks. Full article