Search Results (1,566)

Concrete cracks and sulfate degradation severely compromise structural durability, highlighting the need for sustainable solutions to enhance longevity and minimize environmental impact. This study assesses the efficacy of bacterial self-healing technology utilizing Bacillus megaterium (BM) and Bacillus sphaericus (BS) in enhancing the resistance of concrete to sulfate attacks and improving its mechanical properties. Bacterial suspensions (1% and 2.5% of cement weight) were mixed with concrete containing silica fume or fly ash (10% of cement weight) and cured in freshwater or sulfate solutions (2%, 5%, and 10% concentrations). Specimens were tested for compressive strength, flexural strength, and microstructure using a Scanning Electron Microscope (SEM), Energy-Dispersive X-ray Spectroscopy (EDS), and X-ray diffraction (XRD) at various ages. The results indicate that a 2.5% bacterial content yielded the best performance, with BM surpassing BS, enhancing compressive strength by up to 41.3% and flexural strength by 52.3% in freshwater-cured samples. Although sulfate exposure initially improved early-age strength by 1.97% at 7 days, it led to an 8.5% loss at 120 days. Bacterial inclusion mitigated sulfate damage through microbially induced calcium carbonate precipitation (MICP), sealing cracks, and bolstering durability. Cracked specimens treated with BM recovered up to 93.1% of their original compressive strength, promoting sustainable, sulfate-resistant, self-healing concrete for more resilient infrastructure. Full article

Spodoptera littoralis (Boisd.) (Lepidoptera: Noctuidae) is a major insect pest that severely affects various crops. Our study provides new insights by combining field efficacy trials with enzymatic analysis to evaluate the effects of emamectin benzoate mixtures with other insecticides (lufenuron, cypermethrin, chlorpyrifos, and spinosad) against S. littoralis. The aim of our work was to investigate the effectiveness of five insecticides, i.e., emamectin benzoate, lufenuron, cypermethrin, chlorpyrifos, and spinosad, for controlling this pest under field conditions during two consecutive seasons (2023–2024). Each insecticide was applied individually at the recommended rate, while some were mixed with emamectin benzoate at half its recommended rate. The results indicated that emamectin benzoate was the most effective insecticide, followed by lufenuron. The joint action of emamectin benzoate (LC₂₅) and its mixtures with other insecticides (chlorpyrifos, spinosad, cypermethrin, and lufenuron) at various concentrations (LC₅₀) against second- and fourth-instar S. littoralis larvae was evaluated. Results showed additive effects with chlorpyrifos, lufenuron, and cypermethrin, while potentiation occurred with cypermethrin (LC₅₀) and chlorpyrifos (LC₅₀). Antagonistic effects were observed in the combination of emamectin benzoate with spinosad (LC₂₅ and LC₅₀). This study concluded that applying insecticides individually is more cost-effective for managing cotton leafworm infestations in cotton crops. Additionally, enzyme activity analysis showed significant changes in alpha-esterase, beta-esterase, carboxylesterase, acetylcholinesterase, and glutathione S-transferase levels in larvae treated with different insecticide combinations. Full article

A multi-objective genetic algorithm is tailored to optimize the design of a wavelength interleaver/deinterleaver device. An interleaver combines data streams from two physical channels into one. The deinterleaver does the opposite job. The WDM requirements for this device include channel spacing of 50 GHz, channel bandwidth of 20 GHz, free spectral range of 100 GHz, maximum channel dispersion of 30 ps/nm, and maximum crosstalk of −23 dB. The challenges for the optimization process include the lack of a closed-form expression for the device performance and the trade-off between the conflicting performance parameters. So, for this multi-objective problem, the proposed approach maneuvers to find a compromise between the performance parameters within a few minutes, saving the designer the laborious design process previously proposed in the literature, which relies on visually inspecting the Z-plane for the dynamics of the transmission poles and zeros. Designs of better performance are achieved, with fewer ring resonators, a channel dispersion as low as 1.6 ps/nm, and crosstalk as low as −30 dB. Full article

Nanotechnology is revolutionizing medicine by enabling highly precise diagnostics, targeted therapies, and personalized healthcare solutions. This review explores the multifaceted applications of nanotechnology across medical fields such as oncology and infectious disease control. Engineered nanoparticles (NPs), such as liposomes, polymeric carriers, and carbon-based nanomaterials, enhance drug solubility, protect therapeutic agents from degradation, and enable site-specific delivery, thereby reducing toxicity to healthy tissues. In diagnostics, nanosensors and contrast agents provide ultra-sensitive detection of biomarkers, supporting early diagnosis and real-time monitoring. Nanotechnology also contributes to regenerative medicine, antimicrobial therapies, wearable devices, and theranostics, which integrate treatment and diagnosis into unified systems. Advanced innovations such as nanobots and smart nanosystems further extend these capabilities, enabling responsive drug delivery and minimally invasive interventions. Despite its immense potential, nanomedicine faces challenges, including biocompatibility, environmental safety, manufacturing scalability, and regulatory oversight. Addressing these issues is essential for clinical translation and public acceptance. In summary, nanotechnology offers transformative tools that are reshaping medical diagnostics, therapeutics, and disease prevention. Through continued research and interdisciplinary collaboration, it holds the potential to significantly enhance treatment outcomes, reduce healthcare costs, and usher in a new era of precise and personalized medicine. Full article

Background: Mental health issues among medical students have gained increasing attention globally, with studies indicating a high prevalence of psychological disorders within this population. The use of antidepressants and anti-anxiety medications has become a common response to these mental health challenges. However, it is crucial to understand the extent of their usage and associated effects on students’ mental health and academic performance. This cross-sectional study explored the use of antidepressants and anti-anxiety drugs and their impact on the mental health of medical students in Saudi Arabia. Methods: A cross-sectional survey of 561 medical students from 34 universities was conducted between March and July 2024. An anonymous online questionnaire was used to collect sociodemographic, mental health, and medication usage-related information. Results: Most of the participants were female (71.5%) and aged 21–25 years (62.7%). Approximately 23.8% of them used antidepressants, 5.6% reported using anti-anxiety medications, and 14.0% used both types of medication. Among the medication users, 71.7% were using selective serotonin reuptake inhibitors (SSRIs), and 28.3% were using other medications. Adverse drug reactions were reported by 58.8% of the participants, and 39.6% changed drugs with inadequate efficacy. Notably, 49.0% of the respondents who have ever used medications discontinued their medication without consulting a healthcare professional. Despite these challenges, 62.0% of the participants felt that their medications had a positive impact on their academic performance, 73.4% believed that the benefits outweighed the drawbacks, and 76.2% expressed a willingness to continue taking their medication. In particular, 77.6% agreed that treatment with these drugs could prevent mental breakdowns. Sleep duration, physical activity, and family history of psychiatric disorders were significantly associated with medication use, with p values of 0.002, 0.014, and 0.042, respectively. Conclusions: These results shed light on the need to understand the prescribing practices of antidepressant and anti-anxiety drugs among medical students while promoting the appropriate use of these medications among the students. There is a need to incorporate mental health interventions into counseling services and awareness programs to support students. Future longitudinal studies are needed to explore long-term trends. Full article

The increasing complexity and scale of electronic health records (EHRs) demand advanced tools for efficient data retrieval, summarization, and comparative analysis in clinical practice. MERA (Medical Electronic Records Assistant) is a Retrieval-Augmented Generation (RAG)-based AI system that addresses these needs by integrating domain-specific retrieval with large language models (LLMs) to deliver robust question answering, similarity search, and report summarization functionalities. MERA is designed to overcome key limitations of conventional LLMs in healthcare, such as hallucinations, outdated knowledge, and limited explainability. To ensure both privacy compliance and model robustness, we constructed a large synthetic dataset using state-of-the-art LLMs, including Mistral v0.3, Qwen 2.5, and Llama 3, and further validated MERA on de-identified real-world EHRs from the MIMIC-IV-Note dataset. Comprehensive evaluation demonstrates MERA’s high accuracy in medical question answering (correctness: 0.91; relevance: 0.98; groundedness: 0.89; retrieval relevance: 0.92), strong summarization performance (ROUGE-1 F1-score: 0.70; Jaccard similarity: 0.73), and effective similarity search (METEOR: 0.7–1.0 across diagnoses), with consistent results on real EHRs. The similarity search module empowers clinicians to efficiently identify and compare analogous patient cases, supporting differential diagnosis and personalized treatment planning. By generating concise, contextually relevant, and explainable insights, MERA reduces clinician workload and enhances decision-making. To our knowledge, this is the first system to integrate clinical question answering, summarization, and similarity search within a unified RAG-based framework. Full article

This study investigates the effects of samarium (Sm) promotion on the catalytic activity of 5 weight percent Ni catalysts for partial oxidation of methane (POM)-based hydrogen production supported on a Si-Al mixed oxide (10SiO₂+90Al₂O₃) system. Several 5% Ni-based catalysts supported on silica–alumina was used to test the POM at 600 °C. Sm additions ranged from 0 to 2 wt.%. Impregnation was used to create these catalysts, which were then calcined at 500 °C and examined using BET, H₂-TPR, XRD, FTIR, TEM, Raman spectroscopy, and TGA methods. Methane conversion (57.85%) and hydrogen yield (56.89%) were greatly increased with an ideal Sm loading of 1 wt.%, indicating increased catalytic activity and stability. According to catalytic tests, 1 wt.% Sm produced high CH₄ conversion and H₂ production, as well as enhanced stability and resistance to carbon deposition. Nitrogen physisorption demonstrated a progressive decrease in pore volume and surface area with the addition of Sm, while maintaining mesoporosity. At moderate Sm loadings, H₂-TPR and XRD analyses showed changes in crystallinity and increased NiO reducibility. Sm incorporation into the support and its impact on the ordering of carbon species were indicated by FTIR and Raman spectra. The optimal conditions to maximize H₂ yield were successfully identified through optimization of the best catalyst, and there was good agreement between the theoretical predictions (87.563%) and actual results (88.39%). This displays how successfully the optimization approach achieves the intended outcome. Overall, this study demonstrates that the performance and durability of Ni-based catalysts for generating syngas through POM are greatly enhanced by the addition of a moderate amount of Sm, particularly 1 wt.%. Full article

Large language models (LLMs) are increasingly being applied to clinical tasks, but it remains unclear whether medical-specific models consistently outperform smaller, generalpurpose ones. This study investigates that assumption in the context of cardiovascular disease (CVD) risk assessment. We fine-tuned eight LLMs—both general-purpose and medical-specific—using textualized data from the Behavioral Risk Factor Surveillance System (BRFSS) to classify individuals as “High Risk” or “Low Risk”. To provide actionable insights, we integrated a Retrieval-Augmented Generation (RAG) framework for personalized recommendation generation and deployed the system within an interactive chatbot interface. Notably, Gemma2, a compact 2B-parameter general-purpose model, achieved a high recall (0.907) and F1-score (0.770), performing on par with larger or medical-specialized models such as Med42 and BioBERT. These findings challenge the common assumption that larger or specialized models always yield superior results, and highlight the potential of lightweight, efficiently fine-tuned LLMs for clinical decision support—especially in resource-constrained settings. Overall, our results demonstrate that general-purpose models, when fine-tuned appropriately, can offer interpretable, high-performing, and accessible solutions for CVD risk assessment and personalized healthcare delivery. Full article

In many applications, including underwater robotics, the coverage problem requires an autonomous vehicle to systematically explore a defined area while minimizing redundancy and avoiding obstacles. This paper investigates coverage path-planning strategies to enhance the efficiency of underwater gliders particularly in maximizing the probability of detecting a radioactive source while ensuring safe navigation. We evaluate three path-planning approaches: the Traveling Salesman Problem (TSP), Minimum Spanning Tree (MST), and the Optimal Control Problem (OCP). Simulations were conducted in MATLAB R2020a, comparing processing time, uncovered areas, path length, and traversal time. Results indicate that the OCP is preferable when traversal time is constrained, although it incurs significantly higher computational costs. Conversely, MST-based approaches provide faster but fewer optimal solutions. These findings offer insights into selecting appropriate algorithms based on mission priorities, balancing efficiency and computational feasbility. Full article

Vehicular Communication Networks (VCNs) are essential to intelligent transportation systems, where real-time data exchange between vehicles and infrastructure supports safety, efficiency, and automation. However, achieving high Quality of Service (QoS)—especially under delay-sensitive conditions—remains a major challenge due to the high mobility and dynamic topology of vehicular environments. While some efforts have explored routing protocol optimization, few have systematically compared multiple optimization approaches tailored to distinct traffic and delay conditions. This study addresses this gap by evaluating and enhancing two widely used routing protocols, QOS-AODV and GPSR, through their improved versions, CM-QOS-AODV and CM-GPSR. Two distinct optimization models are proposed: the Traffic-Oriented Model (TOM), designed to handle variable and high-traffic conditions, and the Delay-Efficient Model (DEM), focused on reducing latency for time-critical scenarios. Performance was evaluated using key QoS metrics: throughput (rate of successful data delivery), packet delivery ratio (PDR) (percentage of successfully delivered packets), and end-to-end delay (latency between sender and receiver). Simulation results reveal that TOM-optimized protocols achieve up to 10% higher PDR, maintain throughput above 0.40 Mbps, and reduce delay to as low as 0.01 s, making them suitable for applications such as collision avoidance and emergency alerts. DEM-based variants offer balanced, moderate improvements, making them better suited for general-purpose VCN applications. These findings underscore the importance of traffic- and delay-aware protocol design in developing robust, QoS-compliant vehicular communication systems. Full article

The increasing integration of renewable energy sources and the rising demand for electricity has intensified concerns over voltage stability in radial distribution systems. These networks are particularly susceptible to voltage collapse under heavy loading conditions, posing serious system reliability and efficiency risks. Integrating distributed generation (DG) has emerged as a strategic solution to strengthen voltage profiles and reduce power losses. To address this challenge, this study proposes a novel distribution voltage stability index (NDVSI) for accurately assessing voltage stability and guiding optimal DG placement and sizing. The NDVSI provides a reliable tool to identify weak buses and their neighboring nodes that critically impact stability. By targeting these locations, the method ensures DG units are installed where they offer maximum improvement in voltage support and minimum power losses. The approach is implemented using MATLAB R2019a (MathWorks Inc., Natick, MA, USA) and validated on three benchmark radial distribution systems, including IEEE 12-bus, 33-bus, and 69-bus systems, demonstrating its scalability and effectiveness across different grid complexities. Comparative analysis with existing voltage stability indices confirms the superiority of NDVSI in both diagnostic precision and practical application. The proposed approach offers a technically sound and economically viable tool for enhancing the reliability, stability, and performance of modern distribution networks. Full article

Background: Severe maternal hypertension is linked to adverse perinatal outcomes. Both nifedipine and hydralazine are commonly used antihypertensive agents in this setting. Methods: A comprehensive literature search was conducted in PubMed, Cochrane Library, and EMBASE from inception to April 2024 to identify randomized controlled trials comparing oral or sublingual nifedipine with intravenous hydralazine for the management of severe hypertension, with or without preeclampsia/eclampsia. A random-effects meta-analysis was performed using RevMan. Results: Seven randomized controlled trials were included. The pooled analysis demonstrated no significant difference between the two agents regarding time to achieve optimal blood pressure control (MD = −1.08 min, 95% CI = −6.66 to 4.49), caesarean delivery (OR = 0.62, 95% CI = 0.38 to 1.03), neonatal birth weight (MD = 57.65 g, 95% CI = −209.09 to −324.40), NICU admissions (OR = 0.90, 95% CI = 0.41 to 1.98), and 5-min APGAR scores (MD = 0.1, 95% CI = −0.20 to 0.39). However, patients receiving nifedipine had significantly lower odds of experiencing medication-related adverse events (OR = 0.62, 95% CI = 0.40 to 0.97). Conclusions: Nifedipine and intravenous hydralazine showed comparable efficacy in achieving optimal blood pressure control and similar maternal and neonatal outcomes. However, nifedipine was associated with significantly fewer maternal adverse effects, indicating superior tolerability. Full article

Wireless power transfer systems (WPTSs) are critical for efficient and reliable electric vehicle (EV) charging, but challenges such as misalignment and coupling variations limit their performance. This paper addresses a proposed design approach for WPTSs by optimizing the following two widely used coil types: ring and spiral circular coils. An analytical estimation of inductive characteristics is conducted to establish a foundation for system optimization. The study framework focuses on coil geometrical parameters and relative placements, accounting for horizontal, vertical, and angular misalignments to ensure a consistent performance under varying coupling conditions. COMSOL simulations accurately determine inductive parameters, validating the theoretical analysis for a 200 W charging coil prototype. Experimental investigations of coupling coefficients for coreless and cored charging pads highlight the superior performance of the Square I-Core-based spiral winding configuration in enhancing the coupling coefficient while ensuring that it remains below the critical value required for stable system operation. The agreement between the analytical results, simulation data, and experimental findings underscores the reliability of the proposed design approach. Full article

The increasing global discovery of plant species presents both opportunities and challenges, particularly in distinguishing between beneficial and poisonous varieties. While computer vision techniques show promise for classifying plant species and predicting toxicity, the lack of comprehensive datasets including images, scientific names, descriptions, local names, and poisonous status complicates these predictions. In this paper, we propose an Explainable Deep Inherent Learning approach that leverages advanced computer vision techniques for effective plant species classification and poisonous status prediction. The proposed Deep Inherent Learning method was validated using different explanation techniques, and Explainable AI (XAI) was employed to clarify decision-making processes at both the local and global levels. Additionally, we provide visual information to enhance trust in the proposed method. To validate the efficacy of our approach, we present a case study involving 2500 images of 50 different plant species from the Arabian Peninsula, enriched with essential metadata. This research aims to reduce the incidence of poisoning from harmful plants, thereby benefiting individuals and society. Our experimental results demonstrate strong performance, with the XAI model achieving accuracy, Precision, Recall, and F1-Score of 0.94, 0.96, 0.96 and 0.97, respectively. By enhancing interpretability, our study fosters greater trust in AI-driven plant classification systems. Full article

For power electronic interfaces, Direct Power Control (DPC) has emerged as a leading control technique, especially in applications such as synchronous motors, induction motors, and other electric drives; renewable energy sources (such as photovoltaic inverters and wind turbines); and converters that are grid-connected, such as Virtual Synchronous Generator (VSG) and Static Compensator (STATCOM) configurations. DPC accomplishes several significant goals by avoiding the inner current control loops and doing away with coordinating transformations. The application of STATCOM based on three- and five-level diode-clamped inverters is covered in this work. The study checks the abilities of DPC during power control adjustments during diverse grid operation scenarios while detailing how multilevel inverters affect system stability and power reliability. Proportional Integral (PI) controllers are used to control active and reactive power levels as part of the control approach. This study shows that combining DPC with Sinusoidal Pulse Width Modulation (SPWM) increases the system’s overall electromagnetic performance and control accuracy. The performance of STATCOM systems in power distribution and transient response under realistic operating conditions is assessed using simulation tools applied to three-level and five-level inverter topologies. In addition to providing improved voltage quality and accurate reactive power control, the five-level inverter structure surpasses other topologies by maintaining a total harmonic distortion (THD) below 5%, according to the main findings. The three-level inverter operates efficiently under typical grid conditions because of its straightforward design, which uses less processing power and computational complexity. Full article