Search Results (10,603)

Bank telemarketing campaigns often experience low subscription rates due to customer heterogeneity and severe class imbalance, which pose challenges for reliable predictive modeling. This study investigates a data-driven approach that integrates synthetic minority oversampling and cost-sensitive learning to improve the prediction of telemarketing outcomes. Experiments are conducted using the Portuguese Bank Marketing dataset, comprising 41,188 instances with a positive response rate of 11.3%. Eight machine learning models are evaluated under a unified preprocessing pipeline and five-fold stratified cross-validation, including Logistic Regression, Decision Tree, Random Forest, and Ensemble methods. The results show that Ensemble models, particularly CatBoost, XGBoost, and LightGBM, achieve improved performance compared with traditional baselines, with notable gains in minority-class recall and overall discrimination ability. The best-performing model attains an F1-score of 0.540, a recall of 0.812 for the positive class, and a ROC–AUC of 0.908. To enhance interpretability, SHAP-based analysis is applied to quantify feature contributions, identifying campaign duration, previous contact outcomes, and selected macroeconomic indicators as key predictors. These findings indicate that combining resampling strategies with cost-sensitive optimization provides a robust and transparent approach for learning from imbalanced telemarketing data, thereby supporting reproducible and data-driven financial decision-making by explicitly addressing difficulty in minority-class identification under imbalance and class imbalance under cross-entropy training in imbalanced banking data. Full article

Wheel–rail coupled excitation introduces strong low-frequency modulation, random impact interference, and broadband background noise into the vibration system of locomotive gearboxes, causing early weak bearing fault features to become submerged and making traditional deconvolution methods insufficient for effective enhancement. To address this challenge, this study proposes an adaptive parameter optimization method for MCKD based on the weighted envelope spectrum factor (WESF). WESF integrates the Hoyer index, kurtosis, and envelope spectrum energy to jointly characterize sparsity, impulsiveness, and periodicity of signal components. By using WESF as the fitness function, the sparrow search algorithm (SSA) is employed to simultaneously optimize the key MCKD parameters L, T, and M, enabling optimal enhancement of weak periodic impacts. To further mitigate modal aliasing caused by wheel–rail excitation, the original signal is first adaptively decomposed using successive variational mode decomposition (SVMD), and modes with WESF values above the average are selected for signal reconstruction. The reconstructed signal is subsequently enhanced via SSA–MCKD, and fault characteristic frequencies are extracted using envelope spectrum analysis. Experimental validation using gearbox bearing data collected under 40, 50, and 60 Hz operating conditions shows that the proposed method achieves fault feature coefficient (FFC) values of 12.8%, 7.5%, and 7.2%, respectively—representing an average improvement of approximately 156% compared with traditional methods (average FFC of 3.6%). These results demonstrate that the proposed SVMD–WESF–SSA–MCKD approach can significantly enhance weak periodic impact features under strong background noise and wheel–rail excitation, exhibiting strong practical applicability for engineering implementation. Full article

Photovoltaic (PV) power forecasting is strongly influenced by volatility, randomness, and changing meteorological conditions, while conventional point forecasting provides limited uncertainty information for engineering use. This study proposes a hybrid interval forecasting framework for PV prediction. Similar-day clustering first segments weather data into distinct scenarios (sunny, cloudy and overcast) to reduce noise and redundant information within sequences, enhancing stability and thereby providing a more refined feature space for deep learning. A BiLSTM–Transformer model is then used as the core forecaster, taking multiple meteorological variables as multi-feature time-series inputs. BiLSTM captures bidirectional temporal dependencies, and the Transformer enhances long-range feature extraction via attention. To improve robustness and stability, the Alpha Evolution (AE) algorithm is applied for hyperparameter optimization, balancing global exploration and local refinement. For probabilistic forecasting, Adaptive Bandwidth Kernel Density Estimation (ABKDE) is employed to construct prediction intervals, where the local bandwidth is determined by minimizing a local error function to adapt to data density and error distribution. Case studies utilizing a full-year, 5 min high-resolution dataset from the DKASC station demonstrate that the proposed AE-BiLSTM–Transformer achieves highly accurate point forecasts across diverse weather conditions, reducing the RMSE by 81.85%, 76.99%, and 72.26% under sunny, cloudy, and overcast scenarios, respectively, compared to the baseline LSTM. ABKDE further produces reliable and compact intervals; at the 90% confidence level on sunny days, it achieves PICP = 0.921 with PINAW = 0.0378, reducing PINAW by 75.16% relative to conventional KDE while maintaining comparable coverage. Full article

Root plasticity is vital for crop survival amid global warming. Yet, the molecular mechanisms governing wheat root thermotolerance remain largely unknown. In this study, we combined phenomics, transcriptomics, and metabolomics with machine learning to analyze the performance of heat-tolerant cultivar YM158 and heat-sensitive cultivar YM15 under varying heat stress. While high temperatures (35 °C) severely inhibited root growth and caused oxidative damage in YM15, YM158 maintained robust root architecture and redox balance. Using weighted gene co-expression network analysis (WGCNA) alongside the random forest feature selection algorithm, we identified the flavonoid biosynthesis pathway as central to thermotolerance. Protein–protein interaction network analysis revealed that wheat root adaptability to high temperatures involves maintaining protein homeostasis via the endoplasmic reticulum protein processing system, specifically activating the flavonoid biosynthesis pathway and enhancing the antioxidant enzyme system. Furthermore, we identified a potential regulatory hub involving the cell wall sensor FERONIA (FER) and heat shock factors (HSFs), highlighting a complex interaction between hormonal signaling and secondary metabolism. Our study offers a detailed map of root heat adaptation and positions the flavonoid-mediated antioxidant system as a promising target for breeding climate-resilient crops. Full article

Background: Diabetic foot ulcers (DFUs) are considered a prevalent complication of diabetes mellitus, frequently accompanied with compromised peripheral circulation, slower healing, as well as high risk of infection in addition to risk of amputation. Additional treatments that enhance microvascular perfusion and lessen plantar pressure may accelerate the healing process. This study was carried out to examine the impact of pulsed electromagnetic field (EMF) therapy as well as customized silicone gel insoles in terms of peripheral circulation in addition to vascular indices in patients with DFUs. Methods: A randomized, controlled clinical trial, including sixty-six adults diagnosed with type II diabetes as well as plantar DFUs (Wagner grade I–II) were divided into three groups (n = 22 each): Group A was given low-frequency electromagnetic field therapy (15–50 Hz, 2–5 mT, 30 min, three times per week for 8 weeks), Group B was given a customized silicone gel insoles produced for ulcer offloading, and Group C (control) was given conventional physiotherapy along with wound care. Peripheral microcirculation as well as tissue perfusion were the primary outcomes, and they were measured using Laser Doppler Flowmetry (LDF), Photoplethysmography (PPG), in addition to the Toe–Brachial Index (TBI). The secondary outcome included the Ankle–Brachial Pressure Index (ABPI). A blinded assessor measured the outcomes at the beginning of the study, after the intervention (week 8), and again after the follow-up (week 16). Results: EMF therapy significantly improved LDF (baseline: 45.2 ± 6.5 PU; week 8: 62.5 ± 7.2 PU), PPG (0.42 ± 0.08 mV to 0.68 ± 0.10 mV), TBI (0.64 ± 0.07 to 0.82 ± 0.08), and ABPI (0.88 ± 0.06 to 0.97 ± 0.05) compared with insoles and controls (p < 0.001, partial η² 0.25–0.37). The insole group exhibited moderate enhancements, whereas the control group demonstrated minor changes. Between-group analyses showed substantial differences in favor of EMF therapy across all measured variables (F = 13.5–19.9, p < 0.001). Improvements continued at the 8-week follow-up. Conclusions: Patients with DFUs who receive EMF therapy experience a significant improvement in their peripheral microcirculation, tissue perfusion, as well as vascular indices. This is more effective than just mechanical offloading, and custom insoles offer extra benefits by redistributing pressure. Combining EMF therapy with regular DFU care may speed up healing and lower the risk of problems. Additional research should investigate the efficacy of combined EMF as well as off-loading interventions and their long-term outcomes. Full article

Food security is being experienced particularly deeply in vulnerable regions that are impacted by climate change. Therefore, this study aims to examine the impact of climate change and gross national income on food security in the Gulf Cooperation Council (GCC) countries. The study utilized cross-country panel data for GCC countries from 2000 to 2024, with food access acting as the dependent variable for food security. The annual meteorological temperature, energy-related carbon emissions, and gross national income are involved as independent variables representing the factors of climate change and economic growth, respectively. The Pedroni and Johansen–Fisher panel cointegration tests were implemented. Furthermore, the study employs Bayesian random-effects (BRE) and Bayesian mixed-effects (BME) models, estimated through Markov Chain Monte Carlo (MCMC) methods, for achieving posterior distributions of the model’s parameters. The results confirm the existence of a long-term cointegrating relationship among the selected variables. Gross national income has a positive impact on food security, whereas carbon emissions exert a negative effect. The findings reveal that food security is shaped by interconnected economic and climate factors, with notable differences between countries. These results underline the importance of regional cooperation and country-specific policies that focus on enhancing income, mitigating emissions, and investing in food systems. Full article

Data-driven approaches are increasingly required to optimize biofertilization strategies in forage systems. Machine learning (ML) provides an efficient tool for identifying functional drivers in complex plant–soil–microbe systems, offering important perspectives for precision data-driven agriculture. However, despite its potential, ML remains data-driven in studies involving diazotrophic inoculation using biochar as a pelletizing material, particularly in forage grasses. This study applied ML to predict the key drivers controlling Brachiaria brizantha performance and soil quality under biochar-pelletized diazotrophic bacteria (DB). Five isolates were inoculated with or without biochar, and plant traits and soil attributes, including pH, potassium, phosphorus, sodium, and urease activity were evaluated. These data were integrated into multivariate analyses and ML algorithms, including Linear Discriminant Analysis, Random Forest, and Support Vector Machine, to identify the functional drivers that best discriminate treatment performance and uncover mechanistic functional drivers. All isolates increased soil potassium content, with the highest values in the biochar amended treatments, and a 39% increase. Soil pH and urease activity were significantly modulated by isolate identity, while biomass allocation patterns differed among treatments. Overall, the results highlight that biochar pelletization can enhance the effectiveness of DB inoculants. ML revealed that dry foliar biomass, soil pH, and fresh root weight were the most predictive variables, highlighting consistent signatures explaining plant–soil responses to biochar-pelletized DB. These findings demonstrate that interpretable ML can disentangle complex plant–soil–microbe interactions, support precision biofertilization design, and serve as an efficient decision-support tool for sustainable pasture management. Beyond the present system, this study establishes a transferable and scalable analytical framework for precision biofertilization strategies in forage systems and other biochar-mediated agroecosystems, advancing predictive and data-driven approaches in sustainable agricultural engineering. Full article

Diabetic foot ulcers (DFUs) are among the most serious and costly complications of diabetes, characterised by delayed healing, frequent infections, and a high risk of recurrence. Despite advances in wound care, many current therapies fail to address the multifactorial pathophysiology of diabetic wounds, including vascular dysfunction, immune dysregulation, chronic inflammation, and microbial imbalance. In this context, topical probiotics have emerged as a promising microbiome-based strategy aimed at restoring microbial balance while promoting tissue repair. This review summarises current evidence on the use of topical probiotics in diabetic wound healing, with a particular focus on DFUs, outlining key pathophysiological barriers to healing and examining how probiotic therapies may counteract these processes through antimicrobial, antibiofilm, immunomodulatory, and pro-angiogenic mechanisms. Preclinical studies suggest that topical probiotics may promote accelerated wound closure, reduce bacterial burden, modulate inflammatory responses, and enhance collagen deposition and angiogenesis following topical probiotic application. Early clinical studies investigations remain limited to small pilot studies and case series but have reported preliminary signals of enhanced healing and acceptable short-term tolerability in small exploratory cohorts. In addition, recent advances in probiotic delivery, such as bioengineered dressings, postbiotic formulations, and nano-enabled systems designed to improve stability and therapeutic performance, are also discussed. While existing data indicate biological plausibility and early clinical feasibility, larger, well-designed randomized controlled trials and deeper mechanistic investigations are still required to confirm efficacy, clarify safety in high-risk populations, and enable responsible clinical translation. Full article

This study evaluates how different alert conditions influence driver attention transitions under conditions of visual distraction using sequence analysis. Employing a within-subject experimental design, 13 participants underwent trials in a driving simulator, experiencing three distinct alert conditions: face-tracking auditory alerts, steering wheel auditory torque alerts, and a control scenario without alerts. An eye-tracking system was used to capture drivers’ gaze durations and sequences across three key areas of interest: road, dashboard, and tablet-based infotainment system. Analysis involved computation of transition probabilities, Markov chain modeling for long-term attentional distributions, and entropy analyses to quantify the randomness of gaze transitions. Results showed that face-tracking alerts significantly increased the likelihood of gaze redirection to the road compared to the other conditions, enhancing both immediate and sustained attention. Steering wheel torque alerts demonstrated minimal effectiveness, sometimes performing worse than the no-alert condition due to their passive nature, allowing drivers to bypass attention redirection. Steady-state analyses confirmed that face alerts notably improved sustained driver focus on the road by approximately 3.6%, reinforcing their utility for prolonged attentional control. Entropy analyses further revealed that face alerts provided an optimal balance between structured attention shifts and behavioral flexibility, enhancing attentional predictability. Findings are consistent with previous literature, emphasizing the superior effectiveness of active, gaze-based interventions over passive mechanisms. This research underscores the importance of designing proactive alert systems in vehicle safety technology to effectively mitigate visual distraction-related risks. Full article

Background/Objectives: The recent discovery of the importance of gut microbiota has enhanced our understanding of several issues related to energy metabolism, immune systems, and post-exercise recovery, which could have an impact on sports performance. Probiotics are used as sports supplements and have recently been proposed to be effective in reducing the incidence of gastrointestinal and respiratory infections during training and competition. This scoping review aimed to evaluate the gut microbiota composition of competitive cyclists and investigate the effect of probiotic administration in this sports population. Methods: A literature review was conducted using the following databases: PubMed/Medline, Web of Science, and Scopus, and all studies until 1 November 2025 were considered. After dual-reviewer screening, data were charted to identify the composition of gut microbiota and the effects of probiotics on these types of athletes. Results: After all the study identification phases, eleven studies were selected. Seven studies evaluated the composition of the gut microbiota, while four randomized controlled trials evaluated probiotic intake. The results indicate an abundance of Prevotella distinct for this type of athlete, which could facilitate the metabolism of glucose and short-chain fatty acids. Among the four main areas of improvement identified in relation to probiotics, a 16-week multi-strain supplementation protocol showed improved aerobic performance and exertion rate in amateur cyclists. Conclusions: Despite the limited number of studies, certain microbiota traits could be identified in competitive cyclists, which may correspond to their high metabolic rate. Although further strain standardized studies are needed on professional cyclists, the data could indicate that certain probiotic supplementation may be an effective addition for competitive cyclists. Full article

External defects considerably reduce the quality, consumer acceptance, and market value of citrus fruits. Therefore, a rapid and reliable, non-destructive inspection method is necessary for postharvest processing. In this study, a non-destructive approach for external defect classification of citrus fruits is developed by combining visible–near infrared hyperspectral imaging (HSI) with effective wavelength selection (EWS) algorithms. First, 1702 spectral samples of normal and defective regions on citrus fruit surfaces were collected. A partial least squares discriminant analysis (PLS-DA) model was developed using the full wavelength range (400–1000 nm), which achieved 99.02% prediction accuracy. Four EWS algorithms—weighted regression coefficients, variable importance in projection, sequential forward selection (SFS(5, 10, 15)), and random frog—were evaluated for optimal spectral dimensionality and computational efficiency. The SFS15-PLS-DA model, which selected 15 optimal variables out of the initial 300 and used maximum normalization preprocessing, achieved the highest prediction accuracy of 99.80%. This model demonstrated near-perfect classification while reducing the total number of wavelengths by 95.0% (from 300 to 15 wavelengths). Further, a pixel-wise image classification algorithm was implemented using the optimal model, which effectively detected physical damage, pest infestation, and fungal decay. These results demonstrate that combining HSI with EWS enables compact, interpretable, and high-performance models suitable for real-time postharvest sorting. This approach has strong potential to enhance automation, speed, and reliability in commercial citrus quality assessment. Full article

Hyaluronic acid (HA) is a biologically active glycosaminoglycan with recognized roles in wound healing and inflammation modulation, and its adjunctive use in dental and periodontal therapy has gained interest, particularly in medically compromised patients. This narrative review critically evaluated preclinical and clinical evidence on locally applied HA in periodontal, oral surgical, peri-implant, and oral medicine treatments in patients with systemic conditions. A literature search of PubMed/MEDLINE, Scopus, and Web of Science (January 2015–December 2025) identified in vivo translational studies, randomized and controlled clinical trials, and selected systematic reviews involving medically compromised populations. Qualitative synthesis focused on biological plausibility, clinical outcomes, and safety. Nine core studies were included, comprising two preclinical in vivo investigations and seven clinical trials. In diabetic models, cross-linked high-molecular-weight HA reduced macrophage infiltration and delayed collagen membrane degradation without impairing angiogenesis. Clinically, adjunctive HA use in patients with type 2 diabetes mellitus was associated with modest but statistically significant short-term improvements in clinical attachment level (CAL) and enhanced early soft tissue healing following tooth extraction. In peri-implantitis therapy and oncology-related oral complications, HA application was linked to reduced inflammatory markers, decreased lesion severity, and improved patient-reported symptoms. No systemic adverse effects were reported. Overall, HA appears to be a locally safe adjunct that may support early healing and inflammation control in medically compromised patients, although its effects are primarily short-term and do not indicate disease-modifying potential. Full article

Growing regulatory, investor, and societal pressures have heightened the importance of environmental, social, and governance (ESG) disclosure as a key mechanism for corporate transparency and accountability, particularly in emerging markets. This study examines the relationship between ethical leadership and ESG disclosure among publicly listed companies in Saudi Arabia within the context of the Vision 2030 reforms. Drawing on ethical leadership theory and stakeholder theory, ethical leadership is conceptualized as an internal behavioral governance mechanism shaping firms’ sustainability reporting practices. The empirical analysis is based on panel data of 147 non-financial firms listed on the Saudi Stock Exchange (Tadawul) from 2020 to 2024, yielding 735 firm-year observations. ESG disclosure was measured using Refinitiv ESG scores, whereas ethical leadership was captured using the CSRHub Ethical Leadership Index. Employing a random-effects panel regression model with firm-level clustered robust standard errors, the results reveal a positive and statistically significant association between ethical leadership and ESG disclosure. These findings indicate that leadership ethics play an important role in enhancing transparency and accountability in sustainability reporting, and offer relevant implications for corporate governance and ESG policy development in Saudi Arabia. Full article

Background: Positive and existential psychiatry are approaches to mental health that emphasize the promotion of well-being, resilience, and optimal functioning alongside the conventional management of mental illness. Research suggests that the development of self-regulatory metacompetencies is associated with positive mental health and well-being outcomes. Artificial intelligence (AI) technologies are increasingly being used as assistive tools in psychiatry. However, the integration of AI in therapeutic interventions remains underexplored. Objectives: Thus, this systematic review aimed to synthesize evidence from randomized controlled trials evaluating whether AI-based positive and existential psychiatry interventions contribute to improvements in mental and emotional health. A second objective was to examine whether the therapeutic components and psychological processes implemented in these interventions conceptually relate to self-regulatory metacompetencies that underpin sustainable mental health and human flourishing. Methods: The review was conducted according to PRISMA 2020 guidelines. Only experimental studies including randomized controlled trials (RCTs) published from 2015 to 2025 were included. Twenty-four studies met the inclusion criteria. Results: Across interventions using conversational AI chatbots, generative AI and AI-augmented reflective systems, embodied conversational agents, social and humanoid AI robots, consistent improvements were observed in depression, anxiety, negative affect, and loneliness. The interventions enhanced various metacompetencies such as emotional regulation, emotional awareness, self-reflection, and cognitive reappraisal. Conclusions: The findings suggest that AI-based positive and existential psychiatry interventions can support mental and emotional health, especially when fostering key metacompetencies. Although promising, further high-quality trials are needed to clarify long-term effects. The findings of this study can contribute to the discussion about the ways AI-supported interventions may promote sustainable mental health. Full article

Background/Objectives: Perioperative malnutrition, sarcopenia, and reduced functional reserve are frequent in adults undergoing elective gastrointestinal (GI) surgery and are associated with higher postoperative morbidity and delayed recovery. Enhanced Recovery After Surgery (ERAS) pathways incorporate nutrition-focused elements, but reported effects vary across procedures, protocols, and baseline risk. This review aims to summarize and critically appraise current evidence on perioperative nutritional strategies within ERAS-focused elective GI care, including risk identification, nutritional prehabilitation (oral nutritional supplements and immunonutrition), preoperative carbohydrate loading, early postoperative feeding, and selected microbiome-directed adjuncts. Methods: This narrative literature review was informed by a focused search of PubMed/MEDLINE and Scopus (2010–early 2026), supplemented by targeted screening of relevant clinical practice guidelines and consensus statements (e.g., ESPEN). Evidence was interpreted by hierarchy (guidelines/meta-analyses, randomized trials, observational studies) and discussed with attention to heterogeneity in surgical populations, intervention definitions (composition, timing, duration), and endpoint reporting. Results: Early nutritional risk screening is consistently supported to identify malnutrition and sarcopenia and to trigger tailored optimization plans. Perioperative oral nutritional supplementation, particularly when started preoperatively and continued postoperatively, is frequently associated with improved intake and reduced infectious morbidity in malnourished or at-risk patients, though effect sizes vary. Immunonutrition shows potential benefit in selected high-risk settings but remains formulation- and timing-dependent. Carbohydrate loading is generally endorsed within ERAS and may reduce insulin resistance and improve patient comfort, while impacts on major clinical outcomes are context-dependent. Early oral/enteral feeding is feasible in many elective GI procedures and may accelerate gastrointestinal recovery without increasing major complications when implemented with structured advancement and appropriate patient selection. Probiotics/synbiotics show the most consistent signals in colorectal surgery, with strain-specific effects and important safety boundaries in immunocompromised or critically ill patients. Conclusions: Perioperative nutritional optimization is a core component of elective GI surgical care within ERAS pathways. Benefits are most reproducible in higher-risk patients and when interventions are integrated into high-compliance multidisciplinary programs. Future research should prioritize procedure-specific, risk-stratified trials with standardized interventions and clinically meaningful endpoints. Full article