Development of lightweight roof tiles using spent mushroom material: enhancing thermal insulation and sustainability

This study evaluates the feasibility of utilizing spent mushroom waste (SMW) as a sustainable partial replacement for clay in the manufacturing of lightweight, thermally insulating roof tiles. Clay was partially replaced with 0–25 wt% SMW and fired at 1,000, 1,050, 1,100, and 1,150 °C. Increasing SMW content decreased bulk density from 2,204.67 to 1,390.88 kg m −3 (at SMW-25, 1,150 °C) and reduced thermal conductivity from 0.95 to 0.237 W m −1  K −1 , owing to porosity generated by organic matter burnout. The transverse breaking strength declined with SMW but recovered at higher firing temperatures; at 1,150 °C, the TBS values were 3,067, 2,785, 2,276, 1867, 1,150, and 987 N for 0–25 % SMW, respectively. Tiles containing 5–20 % SMW fired at 1,100–1,150 °C met the ASTM C1167 requirements for Grade 2–3 roofing tiles, while achieving substantial reductions in weight and thermal conductivity. Tiles passed the ASTM C1167 water permeability test and showed no efflorescence, as per ASTM C67. Based on density reduction, tiles containing 20 % SMW fired at 1,150 °C showed a 36 % decrease in density relative to conventional tiles. These results demonstrate that SMW enables lightweight, thermally insulating roof tiles that satisfy relevant standards while valorizing an abundant bio-residue.

Innovative development of self-healing high-strength concrete using a polymeric air-entraining agent and Bacillus sphaericus : durability performance in aggressive saline environments

The advancement of self-healing concrete represents a transformative step toward sustainable infrastructure, yet the integration of microbial agents with engineered admixtures under marine conditions remains underexplored. This study investigated the synergistic effects of Bacillus sphaericus and polymeric air-entraining agents (AEAs) on the self-healing performance and durability of high-strength concrete (HSC) exposed to saline environments. Two strains of B. sphaericus (UPMB-10 and ATCC 14577) were evaluated for viability, sporulation, and mineralization potential under varying salinity levels, with ATCC 14577 selected for its superior resistance and CaCO 3 yield. Freeze-dried spores enriched with calcium lactate and urea were incorporated into HSC mixes containing different AEA dosages (2–6%), enabling microbial survival within air-void niches. Concrete specimens were subjected to cyclic curing in both tap water and artificial seawater to simulate fluctuating marine exposure. Healing efficiencies reached up to 71 % in tap water, while seawater immersion produced complete crack closure by 28 days and up to 96 % recovery in water tightness by 56 days. X-ray diffraction revealed mineralogical adaptation in seawater, with polymorphic phases such as aragonite and diopside forming alongside calcite, driven by the presence of Ca 2+ and Mg 2+ ions. These diverse precipitates contributed to enhanced crack sealing compared to the predominantly calcitic deposits in tap water. The findings demonstrate that moderate polymeric AEA dosages preserve structural-grade strength (>100 MPa) while supporting microbial viability and self-healing activity. By linking bacterial metabolism with saline-induced mineral diversity, this study introduces an integrative microbial-admixture framework for designing next-generation self-healing HSC tailored for marine infrastructures. The synergy between optimized bacterial strains and controlled air-void incorporation (by suitable polymeric AEAs) offers a durable, autonomous repair mechanism capable of withstanding aggressive coastal environments.

Suppression of non-alcoholic fatty liver disease by thymoquinone associated with inhibition of miRNA-155/ROS/p53/TIMP-1-mediated fibrosis and autophagy upregulation

Burnout Across Healthcare, Educational, and Professional Populations: A Comprehensive Scoping Review

Burnout, defined by emotional exhaustion, depersonalization, and reduced personal accomplishment, is increasingly recognized as a significant threat to staff wellbeing, organizational performance, and patient safety in healthcare and related sectors. Although research on burnout has grown rapidly, the evidence base remains fragmented, limiting understanding of cross-population patterns, measurement approaches, and the effectiveness of interventions. This scoping review systematically maps and synthesizes the existing literature on burnout among healthcare workers, students, teachers, night shift workers, and other professional populations, with particular emphasis on its implications for staff well-being and quality of care. Following Arksey and O’Malley’s framework and PRISMA-ScR guidelines, systematic searches were conducted in MEDLINE, Embase, PsycINFO, CINAHL, Scopus, Web of Science, and Cochrane from inception to December 2024. Eligible studies used validated instruments to assess burnout. Data synthesis employed narrative thematic analysis and systematic literature mapping. Sixty-five studies were included (healthcare workers n=29; students n=18; teachers n=9; night shift workers n=6; other populations n=3). Six key themes emerged: prevalence variations (25–72%), with healthcare workers demonstrating the highest rates (35–68%) and strongest associations with compromised patient safety; diversity of measurement tools; intervention effectiveness patterns, wherein combined individual-organizational approaches demonstrated superiority over single-component strategies (effect size d=0.67, 95% CI: 0.42–0.91 at 12-month follow-up); organizational versus individual risk factors; temporal trends including COVID-19 impacts; and implementation challenges. Methodological heterogeneity limited cross-population comparability and the standardization of interventions. Burnout represents a critical occupational health and patient safety concern. This scoping review highlights significant gaps in cross-population research, the need for standardized measurement approaches, and the importance of multilevel, evidence-based interventions. The findings provide essential insights for researchers, healthcare administrators, and policymakers aiming to design sustainable strategies to protect staff wellbeing and ensure safe, high-quality care.

The Knowledge-Attitude-Behavior Paradox in E-Cigarette Adoption Among University Students at Northern Border University, Saudi Arabia

The proliferation of e-cigarette use among university students presents a critical public health challenge, yet the mechanisms driving adoption in non-Western sociocultural contexts remain inadequately theorized. Guided by the Social Cognitive Theory and the Theory of Planned Behavior, this study investigated the prevalence and predictors of e-cigarette use among university students at Northern Border University in Saudi Arabia. A cross-sectional survey of 670 students assessed knowledge, attitudes, and usage behaviors, with predictors identified via multivariable logistic regression. The prevalence of current use was 20.6% (138/670), with a significant gender disparity where female students had lower odds of use (adjusted OR = 0.334**; 95% CI: 0.203–0.550**). A striking knowledge-attitude-behavior paradox emerged: while 77.6% (520/670) acknowledged addiction potential, substantial gaps in knowledge about respiratory risks (only 45%, 301/670) and nicotine content (35%, 234/670) persisted. Critically, medical students, despite having superior knowledge (53.2%, 141/265 vs 28.4%, 115/405 among non-medical students), exhibited only moderately more protective attitudes (61.5% vs 78.5% disapproving). More favorable attitudes significantly predicted current use (adjusted OR = 1.040 per one-point increase**; 95% CI: 1.008–1.073**), confirming the mediating role of attitudes. These findings indicate that e-cigarette adoption is a socially embedded behavior shaped by gender norms and educational contexts, challenging information-deficit models. This underscores the necessity for theory-driven, multilevel interventions that address cognitive, affective, and normative determinants of behavior to inform culturally sensitive prevention strategies and campus policies.

Advanced Meta-Heuristic Optimization for Accurate Photovoltaic Model Parameterization: A High-Accuracy Estimation Using Spider Wasp Optimization

Accurate parameter extraction of photovoltaic (PV) models plays a critical role in enabling precise performance prediction, optimal system sizing, and effective operational control under diverse environmental conditions. While a wide range of metaheuristic optimisation techniques have been applied to this problem, many existing methods are hindered by slow convergence rates, susceptibility to premature stagnation, and reduced accuracy when applied to complex multi-diode PV configurations. These limitations can lead to suboptimal modelling, reducing the efficiency of PV system design and operation. In this work, we propose an enhanced hybrid optimisation approach, the modified Spider Wasp Optimization (mSWO) with Opposition-Based Learning algorithm, which integrates the exploration and exploitation capabilities of the Spider Wasp Optimization (SWO) metaheuristic with the diversity-enhancing mechanism of Opposition-Based Learning (OBL). The hybridisation is designed to dynamically expand the search space coverage, avoid premature convergence, and improve both convergence speed and precision in high-dimensional optimisation tasks. The mSWO algorithm is applied to three well-established PV configurations: the single diode model (SDM), the double diode model (DDM), and the triple diode model (TDM). Real experimental current–voltage (I–V) datasets from a commercial PV module under standard test conditions (STC) are used for evaluation. Comparative analysis is conducted against eighteen advanced metaheuristic algorithms, including BSDE, RLGBO, GWOCS, MFO, EO, TSA, and SCA. Performance metrics include minimum, mean, and maximum root mean square error (RMSE), standard deviation (SD), and convergence behaviour over 30 independent runs. The results reveal that mSWO consistently delivers superior accuracy and robustness across all PV models, achieving the lowest RMSE values of 0.000986022 (SDM), 0.000982884 (DDM), and 0.000982529 (TDM), with minimal SD values, indicating remarkable repeatability. Convergence analyses further show that mSWO reaches optimal solutions more rapidly and with fewer oscillations than all competing methods, with the performance gap widening as model complexity increases. These findings demonstrate that mSWO provides a scalable, computationally efficient, and highly reliable framework for PV parameter extraction. Its adaptability to models of growing complexity suggests strong potential for broader applications in renewable energy systems, including performance monitoring, fault detection, and intelligent control, thereby contributing to the optimisation of next-generation solar energy solutions.

An Intelligent Multi-Stage GA–SVM Hybrid Optimization Framework for Feature Engineering and Intrusion Detection in Internet of Things Networks

The rapid growth of IoT networks necessitates efficient Intrusion Detection Systems (IDS) capable of addressing dynamic security threats under constrained resource environments. This paper proposes a hybrid IDS for IoT networks, integrating Support Vector Machine (SVM) and Genetic Algorithm (GA) for feature selection and parameter optimization. The GA reduces the feature set from 41 to 7, achieving a 30% reduction in overhead while maintaining an attack detection rate of 98.79%. Evaluated on the NSL-KDD dataset, the system demonstrates an accuracy of 97.36%, a recall of 98.42%, and an F1-score of 96.67%, with a low false positive rate of 1.5%. Additionally, it effectively detects critical User-to-Root (U2R) attacks at a rate of 96.2% and Remote-to-Local (R2L) attacks at 95.8%. Performance tests validate the system’s scalability for networks with up to 2000 nodes, with detection latencies of 120 ms at 65% CPU utilization in small-scale deployments and 250 ms at 85% CPU utilization in large-scale scenarios. Parameter sensitivity analysis enhances model robustness, while false positive examination aids in reducing administrative overhead for practical deployment. This IDS offers an effective, scalable, and resource-efficient solution for real-world IoT system security, outperforming traditional approaches.

Distributed Connected Dominating Set Algorithm to Enhance Connectivity of Wireless Nodes in Internet of Things Networks

The sustainability of the Internet of Things (IoT) involves various issues, such as poor connectivity, scalability problems, interoperability issues, and energy inefficiency. Although the Sixth Generation of mobile networks (6G) allows for Ultra-Reliable Low-Latency Communication (URLLC), enhanced Mobile Broadband (eMBB), and massive Machine-Type Communications (mMTC) services, it faces deployment challenges such as the short range of sub-THz and THz frequency bands, low capability to penetrate obstacles, and very high path loss. This paper presents a network architecture to enhance the connectivity of wireless IoT mesh networks that employ both 6G and Wi-Fi technologies. In this architecture, local communications are carried through the mesh network, which uses a virtual backbone to relay packets to local nodes, while remote communications are carried through the 6G network. The virtual backbone is created using a heuristic distributed Connected Dominating Set (CDS) algorithm. In this algorithm, each node uses information collected from its one- and two-hop neighbors to determine its role and find the set of expansion nodes that are used to select the next CDS nodes. The proposed algorithm has O(n) message and O(K ) time complexities, where n is the number of nodes in the network, and K is the depth of the cluster. The study proved that the approximation ratio of the algorithm has an upper bound of 2.06748 (3.4306 MCDS + 4.8185) . Performance evaluations compared the size of the CDS against the theoretical limit and recent CDS clustering algorithms. Results indicate that the proposed algorithm has the smallest average slope for the size of the CDS as the number of nodes increases.

Novel Analysis of S i O 2 + Z n O + M W C N T -Ternary Hybrid Nanofluid Flow in Electromagnetic Squeezing Systems

The present investigation inspects the unsteady, incompressible MHD-induced flow of a ternary hybrid nanofluid made of S i O 2(silicon dioxide), Z n O (zinc oxide), and M W C N T (multi-walled carbon nanotubes) suspended in a water-ethylene glycol base fluid between two perforated squeezing Riga plates. This problem is important because it helps us understand the complicated connections between magnetic fields, nanofluid dynamics, and heat transport, all of which are critical for designing thermal management systems. These findings are especially useful for improving the design of innovative cooling technologies in electronics, energy systems, and healthcare applications. No prior study has been done on the theoretical study of the flow of ternary nanofluid ( S i O 2 + Z n O + M W C N T / W a t e r − E t h y l G l y c o l , ( 60 : 40 ) ) past a pierced squeezed Riga plates using the boundary value problem solver 4th-order collocation (BVP4C) numerical approach to date. So, the current work has been carried out to fill this gap, and the core purpose of this study is to explore the aspects that enhance the heat transfer of base fluids ( H 2 O / E G ) suspended with three nanomaterials S i O 2 , Z n O , and M W C N T . The Riga plates introduce electromagnetic forcing through an embedded array of magnets and electrodes, generating Lorentz forces to regulate the flow. The squeezing effect introduces dynamic boundary movement, which enhances mixing; however, permeability, due to porosity, replicates the true material limits. Similarity transformations of the Navier-Stokes and energy equations result in a highly nonlinear set of ordinary differential equations that govern momentum and thermal energy transport. The subsequent boundary value problem is solved utilizing the BVP4C numerical approach. The study observes the impact of magnetic parameters, squeezing velocity, solid volume percentages of the three nanoparticles, and porous medium factors on velocity and temperature fields. Results show that magnetic fields reduce the velocity profile by 6.75% due to increased squeezing and medium effects. Tri-hybrid nanofluids notice a 9% rise in temperature with higher thermal radiation.

FedPA: Federated Learning with Performance-Based Averaging for Efficient Medical Image Classification

Federated learning is a decentralized model training paradigm with significant potential. However, the quality of Federated Network’s client updates can vary due to non-IID data distributions, leading to suboptimal global models. To address this issue, we propose a novel client selection strategy called FedPA (Performance-Based Federated Averaging). This proposed model selectively aggregates client updates based on a predefined performance threshold. Only clients whose local models achieve an F1 score of 70% or higher after training are included in the aggregation process. Clients below this threshold receive the updated global model but do not contribute their parameters. In this way, the low-performance clients are still in the process of learning and, after some rounds, will be able to contribute. If no client meets the performance threshold in a given round, the system falls back to standard FedAvg aggregation. This ensures the global model continues to improve even when most clients perform poorly. We evaluate FedPA on a subset of the MURA dataset for abnormality detection in radiographs of four bone types. Compared to baseline federated learning algorithms such as Federated Averaging (FedAvg), Federated Proximal (FedProx), Federated Stochastic Gradient Descent (FedSGD), and Federated Batch Normalization (FedBN), FedPA consistently ranks first or second across key performance metrics, particularly in accuracy, F1 score, and recall. Moreover, FedPA demonstrates notable efficiency, achieving the lowest average round time ( ≈ 2270 s) and minimal memory usage ( ≈ 645.58 MB), all without relying on GPU resources. These results highlight FedPA’s effectiveness in improving global model quality while reducing computational overhead, positioning it as a promising approach for real-world federated learning applications in resource-constrained environments.