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Warfarin is favored over newer direct oral anticoagulants (DOACs) for many older adults. However, its use necessitates rigorous monitoring due to the fine line between toxic and therapeutic doses. Few studies have evaluated the anticoagulation quality of warfarin among elderly patients in Saudi Arabia. This study aimed to assess and identify factors affecting the anticoagulation quality of warfarin using the time in the therapeutic range (TTR) among older adults attending two hospitals in Saudi Arabia. Additionally, we aimed to evaluate differences in the anticoagulation quality of warfarin when managed by pharmacists or physicians. This cross-sectional study was conducted at King Abdullah bin Abdulaziz University Hospital (KAAUH) and King Fahad Medical City (KFMC) in Riyadh, Saudi Arabia. After calculating the TTR of each patient, the anticoagulation control level was determined using these values: a) good control: >70 %; b) intermediate control: 50–70 %; c) poor control:

The morphology of the lesser Zab River is influenced, by the presence of two dams (Dokan dam and Dibbs dam) that regulates the natural seasonal flows, this will affect the study area infrastructure and it has varied impacts upon water management strategies. Therefore, this study aims to quantify the morphological changes of the river course in the downstream reaches of the Dokan dam using Remote Sensing and GIS techniques using the Digital Elevation Model (DEM 30x30m) for the period from 1980 to 2020. This study shows that the differences in discharge for a long time will affect the distribution of erosion and deposition along the downstream river channel. The river course has been changed slightly at the upper reach of the study area, and the overall change is towards the middle and the lower reaches of the river, showing a gradual increase in the channel length, increasing meandering index, and the formation of alluvium islands at the river meander. Implications of this study could help identify river changes and their influence on the design and planning of water management projects and infrastructure in the area beside the other risks that might arise in terms of hazards such as floods.

Efficient energy extraction in photovoltaic (PV) systems relies on the effective implementation of Maximum Power Point Tracking (MPPT) techniques. Conventional MPPT techniques often suffer from slow convergence speeds and suboptimal tracking performance, particularly under dynamic variations of environmental conditions. Smart optimization algorithms (SOA) using metaheuristic optimization algorithms can avoid these limitations inherent in conventional MPPT methods. The problem of slow convergence of the SOA is avoided in this paper using a novel strategy called Swarm Size Reduction (SSR) utilized with a Particle Swarm Optimization (PSO) algorithm, specifically designed to achieve short convergence time (CT) while maintaining exceptional tracking accuracy. The novelty of the proposed MPPT method introduced in this paper is the dynamic reduction of the swarm size of the PSO for improved performance of the MPPT of the PV systems. This adaptive reduction approach allows the algorithm to efficiently explore the solution space of PV systems and rapidly exploit it to identify the global maximum power point (GMPP) even under fast fluctuations of uneven solar irradiance and temperature. This pioneering ultra-fast MPPT method represents a significant advancement in PV system efficiency and promotes the wider adoption of solar energy as a reliable and sustainable power source. The results obtained from this proposed strategy are compared with several optimization algorithms to validate its superiority. This study aimed to use SSR with different swarm sizes and then find the optimum swarm size for the MPPT system to find the lowest CT. The output accentuates the exceptional performance of this innovative method, achieving a time reduction of as much as 75% when compared with the conventional PSO technique, with the optimal swarm size determined to be six.

The present work includes studying the kinetics of the adsorptionof two sugars (maltose and glucose)on activated carbon (GAC) and comparison between them. These sugars are used as draw solution in the forward osmosis process in MOD-SET system(manipulated osmosis desalination system, MOD combined with solute exchange technique, SET). In this work, batch experiment in a stirred tank was carried out to study kinetics adsorption. The experiment was carried out using HPLC to determine the concentrations of sucrose and maltosebefore and after adsorption. The kinetic models used are pseudo-first order, pseudo-second order, intraparticle diffusion and mass transfer model. The findings indicates that sucrose and maltose sorption onto GAC are both well represented by the pseudo second order model.

This paper presents a study on the potential of osmotic energy for power production. The study includes both pilot plant testing and theoretical modelling as well as cost estimation. A projected cost of £30/MWh of clean electricity could be achieved by using a Hydro-Osmotic Power (HOP) plant if a suitable membrane is used and the osmotic potential difference between the two solutions is greater than 25 bar; a condition that can be readily found in many sites around the world. Results have shown that the membrane system accounts for 50%–80% of the HOP plant cost depending on the salinity difference level. Thus, further development in membrane technology and identifying suitable membranes would have a significant impact on the feasibility of the process and the route to market. As the membrane permeability determines the HOP process feasibility, this paper also describes the effect of the interaction between the fluid and the membrane on the system permeability. It has been shown that both the fluid physical properties as well as the membrane micro-structural parameters need to be considered if further development of the HOP process is to be achieved.

A new O-ring flat sheet membrane module design was used to investigate the performance of Vacuum Membrane Distillation (VMD) for water desalination using two commercial polytetrafluoroethylene (PTFE) and polyvinylidene fluoride (PVDF) flat sheet hydrophobic membranes. The design of the membrane module proved its applicability for achieving a high heat transfer coefficient of the order of 103 (W/m2 K) and a high Reynolds number (Re). VMD experiments were conducted to measure the heat and mass transfer coefficients within the membrane module. The effects of the process parameters, such as the feed temperature, feed flow rate, vacuum degree, and feed concentration, on the permeate flux have been investigated. The feed temperature, feed flow rate, and vacuum degree play an important role in enhancing the performance of the VMD process; therefore, optimizing all of these parameters is the best way to achieve a high permeate flux. The PTFE membrane showed better performance than the PVDF membrane in VMD desalination. The obtained water flux is relatively high compared to that reported in the literature, reaching 43.8 and 52.6 (kg/m2 h) for PVDF and PTFE, respectively. The salt rejection of NaCl was higher than 99% for both membranes.

Background/Aim: Eosinophilic esophagitis (EE) is a clinicopathologic entity characterized by esophageal symptoms in association with a dense eosinophilic infiltrate currently defined as >15 eosinophils per high power field in the appropriate clinical context. This is the first pediatric study in Saudi Arabia to give the experience with EE and examine its symptom, histology and endoscopy results. Materials and Methods: Retrospective chart review of all patients diagnosed with EE at National Guard Hospital, Jeddah Between 2007 and 2009. The authors identified EE on histologic criteria (≥15 eosinophils per high-power field) together with their clinical context. The authors reviewed medical records for details of clinical presentation, laboratory data, radiologic, endoscopic, and histologic findings, and the results of treatment. Results: We identified 15 patients in our database in the last three years. 100% of the patients were males. The median age at presentation was 10 years (range, 3-17 years). The commonly reported symptoms were failure to thrive (86%), epigastric abdominal pain (53%), poor eating (40%), dysphagia with solid food (26%), food impaction (13%), and vomiting (20%). Asthma was reported in 46% and allergic rhinitis in 40%. Peripheral eosinophilia (>0.7 Χ 10/l) was found in 66%. High serum IgE Level (>60 IU/ml) was found in 60%. Upper endoscopic analysis revealed esophageal trachealization in 46%, esophageal erythema in 46%, white specks on the esophageal mucosa in 33%, esophageal narrowing in 13%, and normal endoscopy in 13%. The mean eosinophils per high-power field was 30.4 (range, 20-71). Histologic characteristics included degranulated eosinophils (86%), basal cell hyperplasia (93%) and eosinophils clusters (micro-abscess) in 73%. The treatment of EE revealed that they used swallowed corticosteroid in 50%, proton pump inhibitors in 66%, elemental diet/ food elimination in 13% and systemic corticosteroid in 13%. Conclusions: Failure to thrive and abdominal pain in a male, atopic school-aged child was the most common feature of EE. Peripheral eosinophilia, high serum IgE and endoscopic esophageal erythema and trachealization should significantly raise the clinical index of suspicion for the diagnosis of EE.

The production of power using the process of pressure–retarded osmosis (PRO) has been studied both experimentally and theoretically for simulated sea water vs. river water and deionized water under two cases: the first is for simulated real conditions of sea water and river water and second under low brine solution concentration to examine the full profile of the power- pressure. The influence of concentration polarization (CP) on water flux has been examined as well.

Abstract This study concentrates on the implementation of Marine Predator Algorithm (MPA) scheme for tuning of a power system stabilizer’s (PSS’s) parameters to damp the low-frequency oscillations in a power system. To this, the single machine infinite bus system (SMIB), the Western System Coordinating Council (WSCC) and the New England 10 machine 39-bus power system are utilized for testing and comparing different metaheuristic algorithms using different fitness functions. Optimal PSS parameters of SMIB test system are validated using CU-SLRT Std, a real-time digital simulator. The comparative studies demonstrate that the MPA optimized PSS yields improvements of up to 98.62% in the Particle Swarm Optimization (PSO) at 69.42%, Whale Optimization Algorithm (WOA) at 71.79%, Flower Pollination Algorithm (FPA) at 72.39%, African vulture optimization algorithm (AVOA) at 78.04%, Wild Horse Optimization (WHO) algorithm at 68.57% under various operating scenarios. The superiority of the MPA optimized PSS has been validated using Hardware-in-the-loop implementation for the SMIB test system.

The objective of this study is to explore synchronizationa spects of discrete fractional neural networks, encompassing both constant and variable orders. By employing nonlinear feedback control techniques, we establish a sufficient criterion to ensure the synchronization of discrete fractional neural networks with constant orders. Moreover, under certain specified circumstances, we employ the Lyapunov functional to analyze the synchronization of discrete fractional neural networks with variable orders. This synchronization condition is entirely reliant on system parameters, facilitating straightforward verification and implementation. To assess the efficacy and practicality of the proposed methodologies, we present two illustrative numerical examples. These examples vividly demonstrate the successful synchronization of discrete fractional neural networks.

This article conducts a comprehensive stability analysis for h-Fractional Order Differences (h-FoDs) in both linear and non-linear states, considering commensurate and incommensurate orders. The stability findings are encapsulated in the form of relevant theorems. Numerical examples are provided to validate the theoretical results, showcasing their applicability across various domains.

Abstract This research introduces a new technique to control constrained nonlinear systems, named Lyapunov-based neural network model predictive control using a metaheuristic optimization approach. This controller utilizes a feedforward neural network model as a prediction model and employs the driving training based optimization algorithm to resolve the related constrained optimization problem. The proposed controller relies on the simplicity and accuracy of the feedforward neural network model and the convergence speed of the driving training based optimization algorithm. The closed-loop stability of the developed controller is ensured by including the Lyapunov function as a constraint in the cost function. The efficiency of the suggested controller is illustrated by controlling the angular speed of three-phase squirrel cage induction motor. The reached results are contrasted to those of other methods, specifically the fuzzy logic controller optimized by teaching learning-based optimization algorithm, the optimized PID with particle swarm optimization algorithm, the neural network model predictive controller based on particle swarm optimization algorithm, and the neural network model predictive controller using driving training based optimization algorithm. This comparative study showcase that the suggested controller provides good accuracy, quickness and robustness due to the obtained values of the mean absolute error, mean square error root mean square error, enhancement percentage, and computing time in the different simulation cases, and it can be efficiently utilized to control constrained nonlinear systems with fast dynamics.

Abstract This paper presents a novel architecture to enhance the performance of grid‐connected photovoltaic (PV) systems through the introduction of several key novelties. Firstly, a packed U‐cell seven‐level (PUC7)‐based single‐phase solar active filter is implemented, offering a comprehensive solution for harmonics mitigation, reactive power compensation, and efficient power extraction from the PV source, while facilitating the injection of real power into the grid. Secondly, the p‐q power injection algorithm is modified to accommodate the extraction of solar power from the PV generator to the grid, simultaneously addressing the need for harmonic current injection to improve power quality. This modification ensures dynamic performance by extracting reference current with harmonic content and solar power information, thereby enhancing the system's overall efficiency. Lastly, the proposed architecture undergoes real outdoor testing, validating its performance in various key aspects including maximum power tracking, reduction of total harmonic distortion in comparison with previous work, operation at unity power factor, and testing the effective operation of the multifunction feature. These contributions collectively demonstrate the effectiveness of the proposed system in enhancing power injection quality and reactive power compensation under real outdoor conditions of PV systems connected to the grid.

This data article presents a detailed dataset collected as part of the University of Sharjah's (UoS) strategic initiative towards transforming into a smart campus by 2030. Collected from January 1st, 2024, to June 20nd, 2024, from key facilities including offices, labs, and communal spaces, the dataset encompasses precise energy consumption metrics and environmental conditions monitored via Internet of Things (IoT) sensors. It features appliance-specific power data (watts, voltage, kWh) alongside environmental parameters such as temperature, humidity, and occupancy rates. Distinctively, this dataset includes Markov Transition Field (MTF) visualizations, converting time series data into analytical 2D images, which facilitates advanced data interpretations suitable for Deep and transfer learning applications. Aimed at supporting research in energy management and intelligent system development, this dataset offers comprehensive insights into the operational dynamics of a transitioning smart campus, providing both raw and processed forms of data to accommodate diverse research needs.

Our paper is focused on optimal and control of an isolated photovoltaic system with batteries. The control is made by the application of a power management control (PMC). Batteries are kept safe from deep discharges and overloads by the PMC, maintaining a continuous supply to the load. The ease, with which this method can be implemented, as well as its effectiveness without imposing a large computing strain on the user, is noteworthy. The batteries and PV panels in the system under study are connected to a bidirectional converter enabling the batteries to be charged and drained in accordance with weather conditions. The simulation results, clearly highlight good performance of the proposed control across two different profiles.

This research paper focuses on the analysis of a discrete FitzHugh–Nagumo reaction–diffusion system. We begin by discretizing the FitzHugh–Nagumo reaction–diffusion model using the second-order and L1-difference approximations. Our study examines the local stability of the equilibrium points of the system. To identify conditions that ensure the global asymptotic stability of the steady-state solution, we employ various techniques, with a primary focus on the direct Lyapunov method. Theoretical results are supported by numerical simulations that demonstrate the practical validity of the asymptotic stability conclusions. Our findings provide new insights into the stability characteristics of discrete FitzHugh–Nagumo reaction–diffusion systems and contribute to the broader understanding of such systems in mathematical biology.