Search Results (4,140)

Kalanchoe pinnata, commonly known as the “miracle plant” or “life plant”, is a succulent species traditionally used for various health conditions. Recent research investigations have intensified interest in this species due to its diverse repertoire of bioactive constituents, including flavonoids, alkaloids, triterpenes, and glycosides. These compounds have been associated with multiple therapeutic effects, notably antioxidant, anti-inflammatory, and antidiabetic activities. Although several studies have highlighted the positive effects of the extracts of K. pinnata on key factors contributing to the pathophysiology and complications of diabetes mellitus, a systematic overview focusing on the use of these extracts and their bioactive constituents in the management of the disease is lacking. This literature review summarizes the phytochemical composition, traditional uses, and recent scientific data supporting the antidiabetic potential of K. pinnata, with a particular focus on its effects on glycemic control, as well as inflammatory and oxidative homeostasis, toxicity, safety, and potential clinical implications. The phytochemical constituents discussed include quercetin, kaempferol, apigenin, epigallocatechin gallate (EGCG), avicularin, and bufadienolides, along with a presentation of representative structures. The review also covers the potential mechanisms of action in diabetes mellitus. The survey of available literature highlights the effects of K. pinnata on indices of diabetes mellitus, including enhancing insulin sensitivity, mitigating oxidative stress and inflammation, lowering blood glucose levels, and the potential adverse effects. These results point to the promising prospect for K. pinnata use in the management of diabetes mellitus and its associated complications, while underscoring the need for more rigorous investigations, including well-controlled clinical trials. Full article

Educational data mining (EDM) plays a crucial role in developing intelligent early warning systems that enable timely interventions to improve student outcomes. This study presents a novel approach to student performance prediction by integrating metaheuristic hyperparameter optimization with explainable artificial intelligence for enhanced learning analytics. While Light Gradient Boosting Machine (LightGBM) demonstrates efficiency in educational prediction tasks, achieving optimal performance requires sophisticated hyperparameter tuning, particularly for complex educational datasets where accuracy, interpretability, and actionable insights are paramount. This research addressed these challenges by implementing and evaluating five nature-inspired metaheuristic algorithms: Fox Algorithm (FOX), Giant Trevally Optimizer (GTO), Particle Swarm Optimization (PSO), Sand Cat Swarm Optimization (SCSO), and Salp Swarm Algorithm (SSA) for automated hyperparameter optimization. Using rigorous experimental methodology with 5-fold cross-validation and 20 independent runs, we assessed predictive performance through comprehensive metrics including Coefficient of Determination (R²), Root Mean Squared Error (RMSE), Mean Squared Error (MSE), Relative Absolute Error (RAE), and Mean Error (ME). Results demonstrate that metaheuristic optimization significantly enhances educational prediction accuracy, with SCSO-LightGBM achieving superior performance with R² of 0.941. SHapley Additive exPlanations (SHAP) analysis provides crucial interpretability, identifying Attendance, Hours Studied, Previous Scores, and Parental Involvement as dominant predictive factors, offering evidence-based insights for educational stakeholders. The proposed SCSO-LightGBM framework establishes an intelligent, interpretable system that supports data-driven decision-making in educational environments, enabling proactive interventions to enhance student success. Full article

Sertraline, a selective serotonin reuptake inhibitor (SSRI), has emerged as a candidate for therapeutic repurposing due to its reported antifungal activity. We systematically reviewed in vitro, in vivo, and clinical evidence up to July 2025 (PubMed, Scopus, Web of Science). As a result, 322 records were screened and 63 studies were found to meet the inclusion criteria (PRISMA 2020). We close a critical gap by consolidating relevant evidence on Candida auris, including preclinical in vivo models, which have been under-represented in previous summaries. Outcomes included minimum inhibitory and fungicidal concentrations (MIC/MFC), biofilm inhibition, fungal burden, survival, and pharmacokinetic/pharmacodynamic parameters. Preclinical data indicate its activity against clinically relevant fungi—particularly Cryptococcus neoformans and Candida spp., including C. auris—as well as consistent anti-biofilm effects and synergy with amphotericin B, fluconazole, micafungin, or voriconazole. Mechanistic evidence implicates mitochondrial dysfunction, membrane perturbation, impaired protein synthesis, and calcium homeostasis disruption. However, its potential for clinical translation remains uncertain: in cryptococcal meningitis, small phase II studies suggested improved early fungicidal activity, whereas a phase III randomized trial did not demonstrate a benefit regarding survival. Pharmacokinetic constraints at conventional doses, the absence of an intravenous formulation, and safety considerations at higher doses further limit its immediate applicability. Overall, the available evidence supports sertraline as a promising adjuvant candidate, rather than a stand-alone antifungal. Future research should define PK/PD targets, optimize doses and formulations, and evaluate rational combinations through rigorously designed trials, particularly for multidrug-resistant and biofilm-associated infections. Full article

Public educational buildings, particularly schools, are often overlooked in energy efficiency initiatives, despite their potential for substantial energy and cost savings. This study presents an integrative, measurement-informed, calibrated model-based approach for assessing and enhancing energy performance in elementary schools located in Israel’s hot-arid climate. By combining multiscale environmental monitoring with a rigorously calibrated Energy Plus simulation model, the study evaluates the impact of three demand-side management (DSM) strategies: night ventilation, external envelope insulation, and a combination of the two. Quantitative results show that night ventilation reduced average indoor temperatures by up to 3.3 °C during peak occupancy hours and led to daily energy savings of 10–15%, equating to approximately 1500–2200 kWh annually per classroom. Envelope insulation further reduced diurnal temperature fluctuations from 7.75 °C to 1.0 °C and achieved an additional 9% energy savings. When combined, the two strategies yielded up to 20% energy savings and improved thermal comfort. The findings provide a transferable framework for evaluating retrofitting options in public buildings, offering actionable insights for policymakers and facility managers aiming to implement scalable, cost-effective energy interventions in educational environments. Full article

Accurate estimation of the Remaining Useful Life (RUL) of lithium-ion batteries is essential for enhancing the reliability and efficiency of energy storage systems. This study explores custom deep learning models to predict RUL using a dataset from the Hawaii Natural Energy Institute (HNEI). Three approaches are investigated: an Encoder-only Transformer model, its enhancement with SimSiam transfer learning, and a CNN–Encoder hybrid model. These models leverage advanced mechanisms such as multi-head attention, robust feedforward networks, and self-supervised learning to capture complex degradation patterns in the data. Rigorous preprocessing and optimisation ensure optimal performance, reducing key metrics such as mean squared error (MSE) and mean absolute error (MAE). Experimental results demonstrated that Transformer–CNN with Noise Augmentation outperforms other methods, highlighting its potential for battery health monitoring and predictive maintenance. Full article

Anti-lock braking systems (ABSs) play a vital role in vehicle safety by preventing wheel lockup and maintaining stability during braking. However, their performance is strongly affected by variations in tire–road friction, which often limits the effectiveness of conventional controllers. This research proposes and evaluates a fuzzy logic controller (FLC)-based ABS using a quarter-vehicle model and the Burckhardt tire–road interaction, implemented in MATLAB/Simulink. Two input variables (slip error and slip rate) and one output variable (brake pressure adjustment) were defined, with triangular and trapezoidal membership functions and 15 linguistic rules forming the control strategy. Simulation results under diverse road conditions—including dry asphalt, concrete, wet asphalt, snow, and ice—demonstrate substantial performance gains. On high- and medium-friction surfaces, stopping distance and stopping time were reduced by more than 30–40%, while improvements of up to 25% were observed on wet surfaces. Even on snow and ice, the system maintained consistent, albeit modest, benefits. Importantly, the proposed FLC–ABS was benchmarked against two recent studies: one reporting that an FLC reduced stopping distance to 258 m in 15 s compared with 272 m in 15.6 s using PID, and another where PID outperformed an FLC, achieving 130.21 m in 9.67 s against 280.03 m in 16.76 s. In contrast, our system achieved a stopping distance of only 24.41 m in 7.87 s, representing over a 90% improvement relative to both studies. These results confirm that the proposed FLC–ABS not only demonstrates clear numerical superiority but also underscores the importance of rigorous modeling and systematic controller design, offering a robust and effective solution for improving braking efficiency and vehicle safety across diverse road conditions. Full article

The mechanical properties of screw-thread steel bars used for prestressing concrete and their threaded ribs’ bearing mechanism have not been quantitatively studied, in contrast to the extensive qualitative research on ordinary steel mechanical connection splices. A quantitative investigation was conducted under various design parameters and working conditions to examine the mechanical connection splices of screw-thread steel bars used for prestressing concrete. The splices’ connection performance and their threaded ribs’ bearing mechanism were also examined. Analyzing the force on the threads of the splices under monotonic tensile loading allowed for the theoretical computation of the axial force coefficients for threaded ribs. The validated revised three-dimensional numerical model of splices is based on the findings of the theoretical calculations. Afterwards, rigorous numerical simulations of monotonic tensile loading, repeated tensile and compressive loading with high stress, and repeated tensile and compressive loading with large strain were performed on 45 splices with varying nominal rebar diameters, coupler outer diameters and lengths, and thread rib spacings. The results show that rebar pullout and rebar fracture are the two main ways in which splices might fail. After cyclic loading, the splices’ ultimate bearing capacity changed by 0.83% to 2.81%, and their ductility changed by 2.13% to 4.75% compared to after monotonic tensile loading. Although the splice load-carrying capacity and plastic deformation capacity were reduced by 2.11%~7.48% and 3.98%~25.78%, respectively, when the thread rib spacing was increased from the specified value to 0.6~0.8 times the nominal diameter of the rebar, the splice connection performance was still able to meet the requirements for class I splices. Approximately half of the splices’ load-bearing capability is provided by the 1–2 turns of threads close to the coupler ends; after cyclic loading, their stress rises by between 4.52% and 12.63% relative to monotonic tension. Stresses in all threaded ribs of the splices are increased by 5.49% to 27.76% as the distance between the threaded ribs increases to 1.0 and 1.2 times the nominal diameter of the rebar, which reduces the splice’s load-bearing capacity. Full article

Biomass gasification, a key waste-to-energy technology, is a complex thermochemical process with many input variables influencing the yield and quality of syngas. In this study, data-driven machine learning models are developed to capture the nonlinear relationships between feedstock properties, operating conditions, and syngas composition, in order to optimize process performance. Random Forest (RF), CatBoost (Categorical Boosting), and an Artificial Neural Network (ANN) were trained to predict key syngas outputs (syngas composition and syngas yield) from process inputs. The best-performing model (ANN) was then integrated into a multi-objective optimization framework using the open-source Optimization & Machine Learning Toolkit (OMLT) in Pyomo. An optimization problem was formulated with two objectives—maximizing the hydrogen-to-carbon monoxide (H₂/CO) ratio and maximizing the syngas yield simultaneously, subject to operational constraints. The trade-off between these competing objectives was resolved by generating a Pareto frontier, which identifies optimal operating points for different priority weightings of syngas quality vs. quantity. To interpret the ML models and validate domain knowledge, SHapley Additive exPlanations (SHAP) were applied, revealing that parameters such as equivalence ratio, steam-to-biomass ratio, feedstock lower heating value, and fixed carbon content significantly influence syngas outputs. Our results highlight a clear trade-off between maximizing hydrogen content and total gas yield and pinpoint optimal conditions for balancing this trade-off. This integrated approach, combining advanced ML predictions, explainability, and rigorous multi-objective optimization, is novel for biomass gasification and provides actionable insights to improve syngas production efficiency, demonstrating the value of data-driven optimization in sustainable waste-to-energy conversion processes. Full article

Arabidopsis thaliana is a widely used model in plant biology, where histology (HT), histochemistry (HC), immunohistochemistry (IHC), and immunofluorescence (IF) are applied to study cellular structures, macromolecules, and antigens. Despite their extensive use, protocols lack standardization and exhibit substantial variability in critical aspects such as reagent handling, exposure times, and the proper use of controls. This methodological heterogeneity represents a major gap, limiting reproducibility and comparability between studies. Unlike previous methodological reviews, this work focuses exclusively on A. thaliana, systematically identifies reporting omissions, and proposes a roadmap for standardization. A narrative review of literature retrieved from Scopus and Web of Science was conducted with the aim of analyzing methodological approaches, identifying inconsistencies, and offering recommendations for improved laboratory practices. The analysis revealed frequent omissions in the reporting of critical steps such as dehydration, clearing, antigen retrieval, enzyme blocking, and the incorporation of positive and negative controls, which compromise the reliability of results and hinder inter-laboratory validation. Based on this evidence, three key recommendations are emphasized: (i) organ-specific selection and explicit justification of fixatives and stains; (ii) mandatory incorporation of positive and negative controls in IHC and IF; and (iii) adoption of a minimum reporting checklist to enhance reproducibility. Beyond cell morphology, the reviewed studies demonstrate applications in plant physiology, phytogenetics, and pathophysiology. By combining critical analysis with actionable guidelines, this review contributes a practical reference to strengthen methodological rigor in histological and immunological studies of plants. Full article

The predator–prey model is a fundamental mathematical tool in ecology used to understand the dynamic relationship between predator and prey populations. This study develops a fractional-order delayed dynamical model for a four-species food web, which includes an intermediate predator feeding on two prey species and a top predator preying on all three species. The boundedness of the system’s solutions is first rigorously established using the Laplace transform method. Next, a nonlinear dynamical analysis is performed to determine the existence conditions and local stability of both the trivial and positive equilibrium points. In particular, by treating the time delay as a bifurcation control parameter, explicit criteria for the onset of Hopf bifurcation are derived. Theoretically, when the delay magnitude exceeds a critical threshold, the system loses stability and exhibits sustained oscillatory behavior. Finally, systematic numerical simulations are performed under specific parameter settings. The effects of varying fractional orders and delay magnitudes on the system’s dynamics are quantitatively explored, and the results show strong agreement with the theoretical predictions. Full article

Background: Following childbirth, up to 20% of women may have postpartum depression (PPD), which can adversely affect the mother’s health, the infant’s development, and familial connections. Numerous causes exist, although recent research indicates that micronutrient shortages are modifiable biological factors. This systematic review aims to consolidate existing knowledge regarding the relationship between micronutrient levels and the risk of PPD. Methods: This review was conducted in accordance with PRISMA 2020 guidelines and registered with PROSPERO. We reviewed every study published up to April 1, 2025, on PubMed, Scopus, and Web of Science. Nineteen studies met the inclusion criteria. We employed the Newcastle–Ottawa Scale to assess bias. Results: Nineteen studies were included in the analysis. Vitamin D was the most extensively researched vitamin. The majority of the studies (9 out of 13) identified a significant correlation between low serum 25(OH)D levels and PPD symptoms. Individuals with diminished levels of vitamin B12 and zinc had an elevated risk of PPD. There was insufficient evidence for folate, magnesium, iron, and selenium. This was frequently due to methodological discrepancies, insufficient control of confounding variables, and variations in biomarker timing. The majority of the studies exhibit a low to moderate likelihood of bias. Conclusions: Increasing evidence suggests that deficiencies in specific micronutrients, particularly vitamin D, vitamin B12, and zinc, may contribute to the onset of postpartum depression. The results indicate that targeted nutritional screening and management may be beneficial in perinatal mental health care, notwithstanding the inability to ascertain the exact causative factors. There is a necessity for more rigorous longitudinal investigations and randomised trials to enhance our understanding of processes and assist physicians in making informed judgements. Full article

Background/Objectives: This narrative review addresses ongoing controversies and advancements concerning ultrasound-guided peripheral intravenous (IV) catheter insertion, and the impact of ultrasound guidance on success rate, procedural time, patient and staff experience, complications and costs, as well as requirements for its use. Methods: A literature review was conducted. Results: Growing evidence suggests that ultrasound-guided insertion of peripheral IV catheter represents a superior technique across various patient populations, particularly those presenting with difficult IV access (DIVA). Key findings highlight significant improvements in first-attempt success rates, reduction of procedural complications, and enhanced patient comfort. Ultrasound-guided insertion is also associated with an increase in catheter dwell time, a reduction in repeat procedures and in central line placements, leading to improved resource utilization and the potential for substantial long-term cost-effectiveness, despite the cost of initial investment and training. However, obtaining these improvements involves a critical importance for standardized training, adherence to rigorous aseptic techniques, and generalization of the transformative impact of ongoing technological advancements in ultrasound devices. Conclusions: The collective body of evidence supports the widespread adoption of ultrasound-guided peripheral IV cannulation as an evidence-based best practice in modern healthcare. Full article

This study addresses a significant gap in the existing literature by introducing novel perspectives. First, it provides a comprehensive assessment of the impact of ESG sustainability and information and communication technology (ICT) on energy transition using updated quarterly data from 2002 Q3 to 2024 Q4. Second, it uniquely integrates climate policy uncertainty (CPU) and financial development (FD) as core explanatory variables, which have been largely neglected in prior research. Third, this study applies advanced quantile-based methodologies, including the Quantile Autoregressive Distributed Lag (QARDL) model and Quantile Cointegration (QC) techniques, to enhance empirical rigor and ensure policy relevance across the entire conditional distribution. The results showed that at lower quantiles (τ = 0.05–0.30), FD positively influences ET, supporting early-stage clean energy adoption. ICT shows a short-term negative effect (τ = 0.05–0.40). Based on these findings, policymakers should strengthen financial development to accelerate clean energy adoption at early stages, while addressing the short-term negative impacts of ICT by promoting supportive digital and energy policies that align technology use with sustainability goals. Full article

Background: Electrochemical impedance spectroscopy (EIS) is indispensable for disentangling charge-transfer, capacitive, and diffusive phenomena, yet reproducible parameter estimation and objective model selection remain unsettled. Methods: We derive closed-form impedances and analytical Jacobians for seven equivalent-circuit models (Randles, constant-phase element (CPE), and Warburg impedance (ZW) variants), enforce physical bounds, and fit synthetic spectra with 2.5% and 5.0% Gaussian noise using hybrid optimization (Differential Evolution (DE) → Levenberg–Marquardt (LM)). Uncertainty is quantified via non-parametric bootstrap; parsimony is assessed with root-mean-square error (RMSE), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC); physical consistency is checked by Kramers–Kronig (KK) diagnostics. Results: Solution resistance ($R_s$) and charge-transfer resistance ($R_{ct}$) are consistently identifiable across noise levels. CPE parameters $(Q,n)$ and diffusion amplitude $\left(\sigma \right)$ exhibit expected collinearity unless the frequency window excites both processes. Randles suffices for ideal interfaces; Randles+CPE lowers AIC when non-ideality and/or higher noise dominate; adding Warburg reproduces the ${45}^{\circ }$ tail and improves likelihood when diffusion is present. The $(R_{ct}+Z_W)\Vert \mathrm{CPE}$ architecture offers the best trade-off when heterogeneity and diffusion coexist. Conclusions: The framework unifies analytical derivations, hybrid optimization, and rigorous statistics to deliver traceable, reproducible EIS analysis and clear applicability domains, reducing subjective model choice. All code, data, and settings are released to enable exact reproduction. Full article

(1) Background: With the increasing incidence of cancer, the need for handling cytotoxic drugs has also grown. However, manipulating these drugs exposes healthcare professionals to significant risks, including occupational exposure to hazardous chemicals. Therefore, it is important to adopt protective measures, including personal protective equipment (PPE) and correct ergonomic practices, to ensure safe drug preparation and minimize health risks for the operators. However, while chemical exposure and PPE have been extensively addressed in the literature, the combined impact of ergonomic practices and protective measures remains insufficiently emphasized, representing a critical gap this review aims to address. Accordingly, the objective of this literature review was to analyze the ergonomic and individual protection practices during the handling of cytostatic drugs and all the implications that bad ergonomic practices and/or poor individual protection have on the operator’s health; (2) Methods: In order to perform this integrative review, a structured literature search was conducted using online databases (Web of Science^®, Google Scholar^®, and PubMed^®) from January 2005 to June 2025. (3) Results: A total of 19 articles were analyzed, with 17 focusing on PPE and 17 on ergonomics. The findings emphasize that PPE, such as gloves, masks, gowns, sleeves and safety glasses, plays a critical role in the safe handling of cytotoxic drugs, particularly when combined with other safety measures. Additionally, maintaining correct ergonomic posture is important in preventing musculoskeletal disorders; (4) Conclusions: This review emphasizes the significance of integrating appropriate PPE use with sound ergonomic procedures. Although PPE is still the secondary line of defense against occupational exposure, ergonomic issues must also be addressed to avoid chronic musculoskeletal problems. Continuous training, rigorous attention to safety procedures, and ergonomic enhancements should be prioritized by healthcare facilities as a key element of occupational safety programs to reduce the short-term and long-term health hazards for personnel handling dangerous drugs. Full article