Princess Nourah bint Abdulrahman University

Interpretable neural network forecasting of CO₂ emissions from renewable and non-renewable energy use in Saudi Arabia

Energy-intensive economies need to precisely forecast CO₂ emissions associated with energy use to be well-prepared for sustainability. This study aims to develop a precise predictive framework for modeling Saudi Arabia’s CO 2 emissions using artificial neural networks (ANNs). A c...

Beyond the Burgers paradigm: the stabilizing role of higher-order nonlinearities in magnetized heavy ion-acoustic shocks

Abstract This paper investigates the role of higher-order nonlinearities in the propagation of heavy ion-acoustic shock waves (HIASWs) within a magnetized, collisionless plasma comprising inertial heavy ions and kappa-distributed electrons and positrons. Employing a reductive per...

Novel analysis of physics-informed neural networks for solving Cahn-Allen, FitzHugh-Nagumo and Fisher–KPP Models

This paper presents a hybrid semi-analytical/deep-learning framework for nonlinear PDEs, combining the New Iterative Method (NIM) with Physics-Informed Neural Networks (PINNs). A truncated NIM series provides a closed-form baseline satisfying the initial condition exactly, while...

Assessment of Nutritional Risk Representations in Publicly Available Data in Saudi Arabia: A Triangulated Scoping Study

Background: Saudi Arabia maintains official statistics on its food security, in addition to conducting surveys on health parameters and performing national health assessments. Also, several academic papers on dietary patterns are published every year. Many published documents ana...

Proportional Autoregressive Quasi-Lindley Half-Logistic Unit Process with Application in Modeling Crime Dynamics

The manuscript proposes a novel bounded proportional autoregressive (PAR) process based on the quasi-Lindley half-logistic unit (QHU) distribution, termed the QHU-PAR(1) process. Fundamental probabilistic properties of the process are established, including its Markov structure,...

Integrating Azotobacter inoculation with nano-NPK foliar application: a sustainable strategy for enhancing growth, physiology, and sugar yield in sugar beet (Beta vulgaris L.)

Sugar beet is a key crop for the sugar industry, increasingly reliant on eco-friendly inputs to meet rising global demand. Plant growth-promoting rhizobacteria (PGPR) enhance plant growth via root associations, while nanotechnology offers solutions to limitations of traditional f...

AI-Based Optimization for Biofuel Production: Strategies for Utilizing Degraded Land for Climate Change Mitigation, Green Finance Mobilization, and Achieving United Nations Sustainable Development Goals

Global land degradation affects approximately 2 billion hectares, threatening food security, biodiversity, and climate stability while undermining the United Nations Sustainable Development Goals (SDGs). The concurrent urgency to decarbonize the energy system and mobilize green f...

In vitro study and in silico reveal Teucrium Polium L. methanolic extract as an apoptosis inducer and a potential Bcl-2 inhibitor for breast cancer

Therapy of breast cancer (BC) overexpressing the pro-survival protein Bcl-2 remains a major clinical challenge. The discovery of natural anticancer drugs with potential as Bcl-2 inhibitors has generated increasing interest. The medicinal plant Teucrium polium L. (TPL) exhibits ph...

A hybrid semi-analytical and neural network approach for solving nonlinear Kolmogorov and Rosenau-Hyman equations

This paper presents a hybrid semi-analytical and physics-informed neural network framework for solving linear and nonlinear partial differential equations. The proposed approach combines the New Iterative Method (NIM) with Physics-Informed Neural Networks (PINNs): a low-order ana...

Adaptive Output Feedback Control with Prescribed-Time Performance for Nonlinear Cyber–Physical Systems Under Output Constraints, Actuator Backlash, and Malicious Attacks

This paper proposes an adaptive fuzzy prescribed-time output feedback control approach for strict-feedback nonlinear cyber–physical systems (CPSs) subjected to malicious attacks, output constraints, and backlash-like hysteresis. To approximate unknown system nonlinearities, fuzzy...

Maternal Psychological Distress and Parent-Reported Child Behavioural and Emotional Outcomes Among Mothers of Children with Autism Spectrum Disorder: A Cross-Sectional Study

Background: Mothers of children with autism spectrum disorder (ASD) face numerous challenges in meeting their children’s needs. This study aimed to assess parent-reported child behavioural and emotional outcomes and the levels of anxiety, depression, and stress among mothers of c...

Determinants of self-esteem among children in Jordan

Self-esteem is an important factor affecting personality development. School-aged children can have lower self-esteem levels compared to people of other age groups and it can be influenced by several factors which vary across cultures. This study aimed to assess self-esteem and e...

Tailored novel nanocomposites for advanced disposal of crystal violet dye from water

Crystal violet is a toxic cationic dye that poses substantial threats to aquatic biota and public health because of its persistence, bioaccumulation potential, and documented cytotoxic and carcinogenic effects. Efficient removal of this pollutant from contaminated water therefore...

AI ‐Enabled 6G Space‐Air‐Ground–Integrated Networks for Ultra‐Reliable Low Latency Internet of Medical Things Healthcare

ABSTRACT This paper explores an AI‐assisted resource scheduling and cooperative learning model in a space–air–ground combined network (SAGIN) to possess ultra‐reliable low‐latency Internet of Medical Things (IoMT) applications. The generated healthcare data by the IoMT devices ar...

Optimal integration of electric vehicle charging stations and distributed generator in microgrids using bio-inspired optimization techniques

The prompt adoption of Electric Vehicles (EVs) offers substantial challenges to modern power distribution systems, incorporating enlarged power demand, voltage variability, and elevated energy losses. To solve such problems, this paper proposes an integrated optimization scheme f...

Triple band Yagi antenna design and performance prediction for next generation (6G) wireless applications using supervised regression models

This study presents a high-performance Yagi antenna designed for sub-terahertz (sub-THz) and terahertz (THz) wireless communication applications, addressing the growing demands of emerging technologies such as high-resolution imaging, short-range secure communication, sensing, sp...

Synthesis of WO3/MnO2 Heterojunction Thin Film by AACVD for Photoelectrochemical Water Splitting

Aerosol-assisted chemical vapor deposition (AACVD) was used to prepare a thin film of the heterojunction MnO2/WO3 onto an FTO glass substrate. The WO3/MnO2 heterojunction thin film exhibited a monoclinic WO3 structure characterized by a nanorod-like shape and substantial interfac...

Decoding multidimensional water quality in arid industrial lakes: a comprehensive CCME-WQI, HPI, and GIS-based evaluation of the Arab al-Ulayqat system

Effective water quality management is crucial in arid regions, where inland water systems are highly vulnerable to anthropogenic pressure and climatic stress. This study aimed to assess water quality in Egypt’s Arab al-Ulayqat Lakes using physicochemical, microbiological, heavy-m...

Compact wideband circularly polarized antenna and its self-decoupled MIMO configuration for 28-GHz band targeting 5G/6G millimeter-wave networks

Abstract Millimeter-wave communication has become a cornerstone of emerging 5G and future 6G systems due to its ability to support multi-gigabit data rates, ultra-low latency, and dense device connectivity. These performance requirements enforced constraints that are not limited...

Leakage-Aware Multi-Representation EEG Fusion with Confidence-Gated Learnable MLP for Robust Epileptic Seizure Detection

Automated epileptic seizure detection from Electroencephalogram (EEG) signals remains challenging due to the non-stationary nature of EEG activity, strong inter-patient variability, and the tendency of window-based evaluation protocols to produce overly optimistic results when te...