The University of Nizwa (UoN) was established in 2002 by the Decree of Sultan Qaboos as the first non-profit university in the Sultanate of Oman. It remains the only institution of its kind in Oman.[citation needed]Upon the satisfaction of all requirements set forth by the Oman Ministry of Higher Education and the Higher Education Council, the University of Nizwa was granted legal status by ministerial decision No. 1/2004 on January 3, 2004.[citation needed] On October 16, 2004, the University of Nizwa opened the doors to its inaugural class of 1,200 students, 88% of whom were Omani women.[citation needed] The existing campus is located near the base of the famous Jabal al-Akhdhar in Birkat al-Mouz, 20 km northwest of Nizwa. The construction of a new campus, located near the Farq-Hail highway, began in March 2010.
We present a phenomenological study of rotating, charged black holes (BHs) in Einstein gravity coupled to a traceless (conformal) matter sector formed by ModMax nonlinear electrodynamics and a Kalb–Ramond (KR) two-form that spontaneously breaks local Lorentz symmetry. Starting from a family of obtained static, Schwarzschild-like solutions with a traceless KR sector, we construct the stationary, axisymmetric counterpart via the Newman–Janis algorithm. The resulting Newman-Kerr-like metric depends on four intrinsic parameters: the electric charge Q, the ModMax nonlinearity γ, the Lorentz-violation amplitude ℓ and the spin a. We analyse horizon structure and separatrices in parameter space, derive the null geodesic equations and obtain the photon capture boundary that defines the BH shadow. Using ray-tracing, we compute shadow silhouettes and a suite of shadow observables (areal radius, characteristic radius Rs, distortion δ, oblateness D) and show how γ and ℓ produce qualitatively distinct effects: γ acts as a screening factor for the electromagnetic imprint, while ℓ introduces angular-dependent metric rescalings that deform shadow shape beyond simple size rescaling. We confront model predictions with EHT angular-radius measurements for M87∗ and Sgr A∗ and derive conservative bounds on the combinations of (Q,γ,ℓ,a). Our results identify an effective charge combination Qeff2≃e−γQ2/(1−ℓ)2 and demonstrate that modest Qeff remains compatible with current EHT images while large Qeff is progressively disfavoured.
Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder arising from the interplay between genetic susceptibility and environmental factors. Much focus has been given to the known environmental contaminants, but the effects of emerging contaminants including micro- and nanoplastics (MNPs) on the pathogenesis of ASD have not been adequately explored. Given their widespread environmental distribution, ability to cross biological barriers, persistence in tissues, and potential neurodevelopmental toxicity, MNPs have emerged as a major public health concern. This review aims to comprehensively examine the potential mechanistic links between MNP exposure and ASD. The available evidence suggests that MNPs trigger generation of ROS, mitochondrial dysfunction, chronic neuroinflammation and disruption of several important neurodevelopmental signaling pathways, such as PI3K/Akt/mTOR, Wnt/β-catenin and AGE–RAGE signaling. In addition, disruption of gut-brain axis may contribute to MNP neurodevelopmental abnormalities through gut microbiota dysbiosis, intestinal barrier dysfunction and aberrant immune activation. MNPs may also serve as carriers for persistent organic pollutants (POPs), endocrine disrupting chemicals (EDCs) and heavy metals, which may act synergistically to enhance the neurotoxic effects of these contaminants through a ‘Trojan horse’ mechanism. These molecular and cellular activities may be interdependent and lead to defects in synaptic plasticity, neurotransmission and neuronal development, potentially contributing to ASD-like behaviour. This review provides an integrated mechanistic perspective and identifies research priorities for environmental risk assessment and the development of preventive strategies.
Asphalt concrete has always been the foundation for constructing resilient and durable road infrastructures. The enhanced performance of asphalt concrete in recent years has facilitated the development of high modulus asphalt concrete (HMAC). The primary objective of this review is to provide insight into the current advanced asphalt technology, including material composition, mix design, and mechanical performance parameters; compare the asphalt technologies adopted by different countries; and discuss the innovations, challenges, and prospects shaping the future of asphalt technology. Besides exploring innovative ways of utilizing environmentally friendly materials and pavement technologies, this paper analyzes climate change and the urgent need to reduce carbon footprints. It also discusses the prospects of utilizing HMAC, including the opportunities and challenges in various infrastructure applications. The literature review revealed that HMAC offers a promising approach to improving asphalt pavement performance, reducing the base layer thickness of asphalt, and reducing costs, energy consumption, and environmental impact. Several countries are enthusiastic about utilizing HMAC due to its exceptional durability and performance compared to conventional asphalt concretes. The future directions for HMAC are emerging technologies and sustainable practices. This review summarizes the insights from recent research, industry developments, and technological innovations to provide a comprehensive perspective on high-performance asphalt concrete. This continuously evolving field of road construction is a valuable resource for researchers, practitioners, and policymakers.
We screened an in-house library of 900 natural and naturally derived compounds to identify potential inhibitors targeting the BPLF1 deubiquitinase of Epstein-Barr virus, a significant regulator of the NF-κB signaling pathway and viral DNA replication. The virtual screening results 11 compounds demonstrated substantial binding affinities with docking scores ranging from − 6.56 kcal/mol to -8.95 Kcal/mol. Out of these, five compounds (C4, C5, C6, C7, and C11) showed desirable drug-like characteristics, favorable pharmacokinetic properties with no toxicity, and non-allergenicity, and were thus considered applicable candidates for further investigation. Their toxicity was evaluated using MTT assays on BJ human cell lines, where compounds were found to be safe and showed no effect on cell viability at a concentration of 30 µM (11.24
This study develops and validates a two-stage comparative framework for predicting Photovoltaic (PV) cleaning schedules by integrating high-resolution operational data with regression-based simulated datasets generated from statistical models trained on real measurements. The work directly addresses the growing need to assess whether model-based regression-based simulated data can reliably substitute real measurements in predictive PV maintenance. These models are employed to generate clean-condition power baselines and to estimate daily energy losses attributable to soiling under two distinct paradigms: (i) using real historical PV performance and environmental measurements, and (ii) using regression-derived, regression-based simulated data representing idealized clean operating conditions. Model performance is rigorously quantified using correlation coefficients (R), coefficients of determination (R2), mean absolute deviations, and binary classification metrics including accuracy, precision, recall, and F1-score. The comprehensive results demonstrate that regression-based simulated datasets exhibit high fidelity with real measurements across key electrical variables. This is evident for datasets generated using PLSR, Ridge Regression, and Robust Regression. Strong correlations are observed for DC power (R2 = 0.9545) and DC current (R2 = 0.9520), with mean deviations consistently below 2.2%. When a threshold-based binary decision rule ("clean" versus "do not clean") is applied, cleaning decisions derived from simulated and real datasets show near-perfect concordance, achieving a mean F1-score of 0.9792. These results indicate that for a fixed performance-loss threshold, models using regression-based simulated data reproduce real-data-based cleaning triggers with an accuracy exceeding 97%. Furthermore, the findings confirm that regression-based simulation frameworks constitute a reliable and scalable foundation for data-driven PV maintenance optimization. By enabling efficient cleaning scheduling, these frameworks can significantly reduce operational expenditure and maximize energy yield, particularly in regions where continuous, high-quality PV monitoring data are limited or difficult to obtain.
