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Abstract The proposed smart system for Student Performance Assessment (SPA) is a system that evaluates students' knowledge and skill attainment in a specific course by measuring their achievements of the Course Learning Outcomes (CLOs). The instructor defines the aspects, weights, and rating scale used by SPA to analyze each course. The system calculates the average of students' marks in each learning outcome and compares them with the CLO targets and scores to determine the effectiveness of the teaching and learning methods used. The system uses facts and rules extracted from the course syllabus and Bloom's Taxonomy to build its knowledge base. This paper presents the development of the SPA inference engine, which is used to find CLO targets based on the course level. The inference engine uses efficient procedures and a prediction process to determine the correct target and score, providing a reliable and understandable methodology for reasoning about the information in the knowledge base and formulating conclusions. SPA is a highly responsive and intelligent system that can be a valuable tool for measuring students' achievements. Its characteristics include high performance, reliability, and intelligibility, and its combination of cognitive systems and cognitive theory has led to remarkable progress in measuring student performance. Limitations include dependency on accurate course content and initial setup time, potential bias in CLO weight assignments, challenges in integrating SPA with existing institutional databases, the need for continuous updates to the knowledge base to reflect curriculum changes, and potential resistance from educators to adopt new technologies. Future improvements could involve adaptive learning integrations, enhanced user interfaces, and broader applicability across diverse educational settings.

Abstract The intrusion detection process is important in various applications to identify unauthorized Internet of Things (IoT) network access. IoT devices are accessed by intermediators while transmitting the information, which causes security issues. Several intrusion detection systems are developed to identify intruders and unauthorized access in different software applications. Existing systems consume high computation time, making it difficult to identify intruders accurately. This research issue is mitigated by applying the Interrupt-aware Anonymous User-System Detection Method (IAU-S-DM). The method uses concealed service sessions to identify the anonymous interrupts. During this process, the system is trained with the help of different parameters such as origin, session access demands, and legitimate and illegitimate users of various sessions. These parameters help to recognize the intruder’s activities with minimum computation time. In addition, the collected data is processed using the deep recurrent learning approach that identifies service failures and breaches, improving the overall intruder detection rate. The created system uses the TON-IoT dataset information that helps to identify the intruder activities while accessing the different data resources. This method’s consistency is verified using the metrics of service failures of 10.65%, detection precision of 14.63%, detection time of 15.54%, and classification ratio of 20.51%.

INTRODUCTION: Electronic cigarettes (e-cigarettes) are often advertised as a safer alternative to traditional smoking. However, recent data suggest they may not be as safe as previously believed. This study aims to evaluate the genotoxicity and mutagenicity of e-cigarette liquids. METHODS: We randomly selected eight varieties of e-cigarette liquids from the local market in Jeddah, Saudi Arabia. We evaluated their genotoxicity using the Genotoxicity SOS-Chromo Test™ Kit. In this investigation, a rat liver S9 fraction was utilized to emulate liver metabolic function to measure any chemical substance’s mutagenic potential. The SOS-Chromo Test was performed by recording the β-galactosidase and alkaline phosphatase activity with and without the metabolic activation enzyme (S-9). RESULTS: All samples, except for the first two dilutions of sample 2, were nongenotoxic in the absence of the S9 activation enzyme, according to the genotoxicity analysis. However, when tested in the presence of the S9 enzyme, samples 2, 4, and 7 exhibited mutagenic activity at varying concentrations. CONCLUSION: Contrary to common belief, e-cigarettes are not safe. The present investigation confirms the presence of both toxicants and carcinogens in some e-cigarette liquids. This exposure could increase users’ risk of various health complications.

An integration of Geophysical – Electromagnetic and hydro-chemical methods was used to investigate the potential of groundwater contamination due to Al-Hosayniat Municipal Landfill (HL) southeast of Mafraq city. Twenty-five Time Domain Electromagnetic (TDEM) soundings were carried out inside and outside the HL in addition to one WNW-ESE Very Low Frequency (VLF) profile. The TDEM's results help to define the subsurface geology and structures down to 240m depth. The results also depict the presence of a major fault striking NW-SE that crosses the whole landfill, this fault is documented in the surface geological map, and it was delineated and imaged by TDEM measurements and detected at larger depths. VLF results also detected the presence of a major fault beneath the landfill. The presence of the fault just beneath the HL may enhance the leachate movement which can imply groundwater contamination hazards for a longer period as it is a preferential pathway for contaminants. The results of water quality analyses of adjacent wells show higher concentrations of Na+, SO-24, TDS in some wells and this can be ascribed to agricultural activities and over-pumping in the area rather than landfill leachate. The heavy metal concentrations were found to be within the permissible limits and did't indicate direct contamination due to the HL. However, Fluorite (F-) shows a high concentration of (1.3mg/l) in WS4 and WS5 wells and approaches the permissible limit (1.5mg/l) according to the Jordan Water Standards (JWS). Bromide (Br) shows high concentration of (0.6mg/l) in well Al-2462.

Abstract Background Eating disorders (EDs) are a complex group of disorders that affect mental and physical health and pose a challenge for medical professionals to diagnose. This study aims to screen for eating disorders among Syrian university undergraduates. Methods and materials A cross-sectional study was conducted among Syrian university students using a self-administered questionnaire. The questionnaire consisted of three sections: socio-demographic characteristics, dietary habits and barriers to healthy eating, and the Sick, Control, One, Fat, Food (SCOFF) score. The chi-square test was used to evaluate the factors related to eating disorders. Results Of 728 students, 45.3% were males and 54.7% females. The majority of participants studied medicine 32.3%, followed by pharmacy 25.7% and dentistry 17.6%. Overweight and obese participants represented 21.4% and 6.8% respectively. Merely 36% of students reported adopting a healthy diet, a greater percentage of females 58.8% reported having snacks compared to males 48.5% (P = 0.006), a higher percentage of females 38.8% reported consuming fast food compared to males 26.6% (P

Abstract Background Falls are a common cause of fractures in older adults. This study aimed to investigate the factors associated with spontaneous falls among people aged ≥ 60 years in southern Iran. Methods The baseline data of 2,426 samples from the second stage of the first phase of a prospective cohort, the Bushehr Elderly Health (BEH) program, were included in the analysis. A history of spontaneous falls in the year before recruitment was measured by self-report using a standardized questionnaire. Demographic characteristics, as well as a history of osteoarthritis, rheumatoid arthritis, low back pain, Alzheimer’s disease, epilepsy, depression, and cancer, were measured using standardized questionnaires. A tandem gait (heel-to-toe) exam, as well as laboratory tests, were performed under standard conditions. A multiple logistic regression model was used in the analysis and fitted backwardly using the Hosmer and Lemeshow approach. Results The mean (standard deviation) age of the participants was 69.34 (6.4) years, and 51.9% of the participants were women. A total of 260 (10.7%, 95% CI (9.5–12.0)%) participants reported a spontaneous fall in the year before recruitment. Adjusted for potential confounders, epilepsy (OR = 4.31), cancer (OR = 2.73), depression (OR = 1.81), low back pain (OR = 1.79), and osteoarthritis (OR = 1.49) increased the risk of falls in older adults, while the ability to stand ≥ 10 s in the tandem gait exam (OR = 0.49), being male (OR = 0.60), engaging in physical activity (OR = 0.69), and having high serum triglyceride levels (OR = 0.72) reduced the risk of falls. Conclusion The presence of underlying diseases, combined with other risk factors, is significantly associated with an increased risk of falls among older adults. Given the relatively high prevalence of falls in this population, it is crucial to pay special attention to identifying and addressing these risk factors.

Abstract Background In medical education, the learning environment (LE) significantly impacts students' professionalism and academic performance. Positive LE perceptions are linked to better academic outcomes. Our study, which was conducted 15 years after curriculum reform at King Saud University's College of Medicine, aimed to explore students' perspectives on their LE and identify areas for improvement. By understanding their experiences, we strive to enhance LE and promote academic success. Methods This mixed-method study employed an explanatory sequential approach in which a cross-sectional analytical survey phase was collected first using the Johns Hopkins Learning Environment Scale (JHLES), followed by qualitative focus groups. Findings from quantitative and qualitative methods were integrated using joint display. Results A total of 653 medical students completed the JHLES. The total average score was 81 out of 140 (16.8), and the average subscale scores ranged from 2.27 (0.95) for inclusion and safety to 3.37 (0.91) for community of peers. The qualitative approach encompasses both inductive and deductive analyses, identifying overarching themes comprising proudness, high expectations and competition, and views about the curriculum. The integration of results emphasizes the need for continued efforts to create a supportive and inclusive LE that positively influences students' experiences and academic success. Conclusion This research offers valuable insights for educational institutions seeking to enhance medical education quality and support systems. Recommendations include faculty development, the cultivation of supportive environments, curriculum revision, improved mentorship programs, and initiatives to promote inclusivity and gender equity. Future research should explore longitudinal and comparative studies, innovative mixed methods approaches, and interventions to further optimize medical education experiences. Overall, this study contributes to the ongoing dialog on medical education, offering a nuanced understanding of the complex factors influencing students' perceptions and suggesting actionable strategies for improvement.

This study examined the vibration behavior of a beam with linear spring attachments using finite element analysis. It aims to determine the natural frequency with both consistent/coupled mass and lumped mass matrices. The natural frequencies and corresponding mode shapes were correctly determined which formed the basis of any further noise vibration and severity calculations and impact or crash analysis. In order to obtain eigenfrequencies subject to the attached spring, the characteristic equation was obtained by eigenfunctions expansion whose roots were extracted using the root-finding technique. The finite element method by coupled and lumped mass matrices was then used to determine complete mode shapes against various eigenfrequencies. The mode shapes were then analyzed subject to supports with varying stiffness thereby comparing the analytical and numerical results in case of consistent and lumped masses matrices so as to demonstrate how the present analysis could prove more valuable in mathematical and engineering contexts. Utilizing a consistent mass matrix significantly enhanced accuracy compared to a lumped mass matrix, thereby validating the preference for the former, even with a limited number of beam elements. The results indicated that substantial deflection occurred at the beam's endpoints, supporting the dynamic behavior of the spring-beam system.

Abstract Background Among gynaecological malignancies, endometrial cancer (EC) is the most prevalent type of uterine cancer affecting women. This study explored the proteomic profiles of plasma samples obtained from EC patients, those with hyperplasia (Hy), and a control group (CO). A combination of techniques, such as 2D-DIGE, mass spectrometry, and bioinformatics, including pathway analysis, was used to identify proteins with modified expression levels, biomarkers and their associated metabolic pathways in these groups. Methods Thirty-four patients, categorized into three groups—10 with EC, 12 with Hy, and 12 CO—between the ages of 46 and 75 years old were included in the study. Untargeted proteomic analysis was carried out using two-dimensional difference in gel electrophoresis (2D-DIGE) coupled with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS). Results In all three groups, 114 proteins that were significantly (p ≤ 0.05 and fold change ≥ 1.5) altered were successfully identified using peptide mass fingerprints (PMFs). Compared with those in the control group (CO), the EC samples had 85 differentially expressed proteins (39 upregulated and 46 downregulated), and in the Hy group, 81 proteins were dysregulated (40 upregulated and 41 downregulated) compared to those in the CO group, while 33 proteins exhibited differential regulation (12 upregulated and 21 downregulated) in the EC plasma samples compared to those in the Hy group. Vitamin D binding protein and complement C3 distinguished Hy and EC from CO with the greatest changes in expression. Among the differentially expressed proteins identified, enzymes with catalytic activity represented the largest group (42.9%). In terms of biological processes, most of the proteins were involved in cellular processes (28.8%), followed by metabolic processes (16.7%). STRING analysis for protein interactions revealed that the significantly differentially abundant proteins in the three groups are involved in three main biological processes: signalling of complement and coagulation cascades, regulation of insulin-like growth factor (IGF) transport and uptake by insulin-like growth factor binding proteins (IGFBPs), and plasma lipoprotein assembly, remodelling, and clearance. Conclusion The identified plasma protein markers have the potential to serve as biomarkers for differentiating between EC and Hy, as well as for early diagnosis and monitoring of cancer progression.

Hot storage tanks (HST) are known for their high energy consumption, attributed to variations in usage, heat dissipation within the tank, and heat losses to the surroundings. This study proposes a chimney-type electrically heated HST, which is investigated under static mode to enhance its thermal performance. Different natural circulation areas (chimney areas) with large (9.5 cm diameter), medium (2.5 cm diameter), and small (1.5 cm diameter) sizes were utilized to examine the effect of natural circulation on the HST performance. Additionally, the influence of chimney insulation on the HST performance was also studied. The experiments revealed that the chimney significantly affected the thermal stratification within the tank. Different chimney contact diameters (9.5 cm, 2.5 cm, and 1.5 cm) were tested, showing varying degrees of thermal stratification. The results indicated that smaller chimney contact diameters led to higher thermal stratification and more rapid heating of the top layer temperatures. However, the impact of insulation on thermal performance was inconclusive, suggesting the need for more effective insulation and further investigation into the dynamic mode of operation. The findings also highlighted the faster heating of the top outer layer compared to the larger diameter, emphasizing the significance of the chimney type electrical heater in the hot storage tank.

This research aimed to determine the optimal locations for artificial groundwater recharge in the central Azraq Basin using geophysical methods and geographic information sciences. The study identified the eight most important criteria affecting the selection of artificial groundwater recharge sites in the area. Using the Analytic Hierarchy Process method, the study determined the weight of each criterion and established ratings based on available literature. GIS with Multiple Criteria Decision Analysis were used to select the most suitable sites for groundwater recharge, revealing that 5.7% of the study area has low suitability for groundwater recharge, 39.5% showed moderate, 46% showed high, while the remaining 8.8% of the study area showed very high suitability of groundwater recharge. The study employed the Electrical Resistivity Tomography geophysical survey method to verify the results. The ERT results showed that the groundwater level depth in the six examined sites was close to the subsurface level and ranged from 5m at ERT-2 (Basalt aquifer) to larger than 22m in ERT-4 (Rijam aquifer B4), indicating that these sites were suitable for groundwater recharge. ERT results also showed the presence of faulted layers and reveal the presence of favourable recharge zone. Overall, the study recommended that the area can be recognized as having a high potential for groundwater recharge, and projects should be developed accordingly. The research findings have important implications for managing and conserving water resources in arid regions. The study highlights the usefulness of geophysical methods and GIS in identifying and assessing suitable locations for artificial groundwater recharge. It also showed the importance of considering multiple criteria and using a structured decision-making approach in complex decision-making problems.

Introduction Lomitapide is approved for lowering low-density lipoprotein cholesterol (LDL-C) in homozygous familial hypercholesterolemia, which is a rare genetic disorder. The evidence regarding its safety and efficacy from a small clinical trial requires further validation for effectiveness and safety in the real world. This study aimed to use institutional data on the effectiveness and safety of lomitapide to assist in formulating a perspective on adding it to the formulary. Methods This was a retrospective review of patients who were actively prescribed lomitapide at King Abdulaziz Medical City, Riyadh, Saudi Arabia, from 2019 to 2022. Data collection included demographics, confirmed gene mutation results, duration of lomitapide therapy, baseline, on-treatment, last LDL-C levels, percent reduction in LDL-C after 1-3 months of therapy (whichever was first available), other LDL-C lowering therapies used, liver function tests, adverse effects, and compliance. Results Eight adult patients were included in the review, with a mean age of 25.5 years. Approximately 75% were female, and the duration of treatment with lomitapide ranged from 9 months to 3 years. None of the patients were on continuous LDL apheresis. The mean baseline LDL-C at presentation to our facility was 17.2 mmol/L (range, 11.78–21.97 mmol/L), the mean percent drop in LDL-C with lomitapide was 34.1% (range, 0%–87%), gastrointestinal disturbances were documented in 50% of the patients, and no cases of severe liver toxicities or increase in liver enzymes were seen. Conclusions In our cohort of adult patients, lomitapide showed an overall modest reduction in LDL-C, with no cases of increase in liver enzymes and documented intolerance, indicating that most patients were likely noncompliant. This review revealed important considerations when reimbursing expensive medications for rare diseases. Real-world evidence in real-time can support healthcare systems in price negotiations and reaching mutual agreements that can eventually improve patient access to care.

Thermal comfort in openair situations is a difficult industrial task. In literature there were insufficient studies on how to control the external microclimate in a region, and there are many activities outside the house in open areas that require temperature control, such as the stadiums in hot humid countries, and tourism, and recreational areas in humid and hot climates. Openair conditioning requires huge amount of energy, that negatively affects the Global warming of the earth. To reduce energy consumption microclimate control is proposed. Isolation of the controlled area is performed to reduce the amount of air-conditioned load. In this project the air conditioning of an external open area will be studied. Wind tunnel with two air flows at different temperatures, relative humilities, angle of attach and velocities will be constructed. The two flows will be allowed to intersect to gather at different conditions (different wind speeds ranging from 1 to 7 m/s, as well as a jet flow about 1 m/s and angles 60 to 90 degrees) to construct an isolation Dom for the targeted outside open area. An open area with the use of cross flow that stops the local wind speed in the targeted area and allows to keep the conditioned air for a long time in the open space. This method allows to save huge energy used continuously for the air-conditioning purpose.

This dedication aims to enhance teachers' knowledge and skills in designing frameworks and developing ALM (Higher Order Thinking Skills) based questions. The method utilized involves training and mentoring. Training encompasses delivering content and simulating question design. The mentoring process involves question designing, reviewing, revising, validating, testing, assessing validity and reliability, and analyzing item questions. The outcomes of this endeavor indicate the following findings: The pre-test and post-test display a significant change. The average score for the pre-test was 60.33, whereas the average score for the post-test was 80.67, resulting in a difference of 20.34. The mean score for the question validation stands at 69.67 out of a total score of 76, or 91.67%. The average score for language validation is 67 out of a total score of 80, or 83.75%. This illustrates that the developed questions are deemed appropriate both in terms of question construction and language aspects. Validity and reliability tests demonstrate that the developed question items possess varying degrees of validity, reliability, difficulty levels, and discriminative power.

Abstract Adherence to antihypertensives is crucial for control of blood pressure. This study analyzed factors and interventions that could affect adherence to antihypertensives in the US. PubMed, Scopus, Web of Science, and Embase were searched on January 21, 2022 and December 25, 2023 for studies on the adherence to antihypertensives in the US. Nineteen studies and 23 545 747 patients were included in the analysis, which showed that adherence to antihypertensives was the highest among Whites (OR: 1.47, 95% CI 1.34–1.61 compared to African Americans). Employment status and sex were associated with insignificant differences in adherence rates. In contrast, marital status yielded a significant difference where unmarried patients demonstrated low adherence rates compared to married ones (OR: 0.8, 95% CI 0.67–0.95). On analysis of comorbidities, diabetic patients reported lower adherence to antihypertensives (OR: 0.95, 95% CI 0.92–0.97); furthermore, patients who did not have Alzheimer showed higher adherence rates. Different BMIs did not significantly affect the adherence rates. Patients without insurance reported significantly lower adherence rates than insured patients (OR: 3.93, 95% CI 3.43–4.51). Polypill users had higher adherence rates compared with the free‐dose combination (OR: 1.21, 95% CI 1.2–1.21), while telepharmacy did not prove to be as effective. Lower adherence rates were seen among African Americans, uninsured, or younger patients. Accordingly, interventions such as fixed‐dose combinations should be targeted at susceptible groups. Obesity and overweight did not affect the adherence to antihypertensives.

This work will first focus on the various technical aspects of the design of photorealistic 3D renderings of architectural objects. Special attention will be given to the role of new technologies, such as photogrammetry or texture mapping, in the preservation and enhancement of the architectural heritage. The problem of restoring the visual appearance of 3D architectural heritage objects through texture mapping will also be addressed. Hence, an algorithm that achieve a piecewise texture mapping by minimizing length distortion based on artificial neural networks (ANN) and the theory of elasticity was presented. To begin, a surface triangulation S (comprising triangular facets and a boundary) is created. The obtained mesh is then partitioned into disjoint pieces, with each interior vertex belonging to a single piece. Each subsurface, corresponding to one of the obtained pieces, is then mapped independently from the others by imposing local length-preservation constraints and barycentric coordinates of the interior mesh vertices. The proposed method aims to achieve uniform and seamless texturing, allowing for photorealistic representation of 3D models of heritage buildings and cities. This work provides an effective mathematical framework to obtain geometrically precise texture generating process on surfaces of arbitrary architectural topology.

This systematic literature review investigates the relationship between air pollution exposure and inflammatory bowel disease (IBD), encompassing Crohn's disease (CD) and ulcerative colitis (UC). Despite the growing concern over air pollution's impact on various health outcomes, studies specifically addressing effects on the digestive system, particularly IBD, are relatively rare. This review aims to synthesize the current knowledge on this topic, focusing on the mechanisms underlying these associations and the role of different air pollutants. Following PRISMA guidelines, a systematic literature search of PubMed and Web of Science databases was conducted, yielding 13 epidemiological studies and six mechanistic (toxicological) studies meeting the inclusion criteria. The epidemiological studies examined associations between IBD and various air pollutants, including PM2.5 (particles with an aerodynamic diameter smaller than, or equal to, 2.5 µm), PM10 (particles with an aerodynamic diameter smaller than, or equal to, 10 µm), PM2.5–10 (coarse particles with an aerodynamic diameter in the range of 2.5–10 µm), BC (black carbon), NO2 (nitrogen dioxide), NO (nitrogen monoxide), NOx (nitrogen oxides), N2O (nitrous oxide), CO (carbon monoxide), SO2 (sulfur dioxide), VOC (volatile organic compounds), O3 (ozone), Ox (oxidant capacity), and traffic load. Study methodologies varied among these 13 epidemiological studies, including four cohort studies, two ecological studies, three case-control studies, two studies using two-sample Mendelian randomization, and two longitudinal time-series studies. Eight studies investigated associations with Crohn's disease and ulcerative colitis separately, while five studies analyzed IBD as a whole without distinguishing between CD and UC. Eleven studies found statistically significant associations between air pollution exposure and IBD, although inconsistent results were found in several of these studies. A total number of six mechanistic (toxicological) studies were retrieved. Among these six studies, five were using particulate matter as exposure metric, and one was based on NO2 and O3 as exposure metrics. With a combination of animal, human, and in vitro studies, the results in terms of biological mechanisms indicate that air pollution exposure influences the composition of the gut microbiome, altering metabolic functions within the gut, and creates immunological reactions with inflammation contributing to the development of IBD. Consequently, the results suggest a link between air pollution exposure and both the onset and exacerbation of IBD. However, differences in study design, exposure assessment, and pollutant types make it challenging to draw any firm conclusions. Moreover, the lack of multi-pollutant models in most epidemiological studies makes it difficult to estimate the individual effect of specific air pollutants. This review highlights the need for further research utilizing robust study designs and standardized exposure assessment methods to better understand the impact of air pollution on IBD. By elucidating these associations, policymakers and healthcare professionals can develop effective strategies to mitigate the adverse effects of air pollution on digestive health.

Genomic screened homeobox 1 (Gsx1 or Gsh1) is a neurogenic transcription factor required for the generation of excitatory and inhibitory interneurons during spinal cord development. In the adult, lentivirus (LV) mediated Gsx1 expression promotes neural regeneration and functional locomotor recovery in a mouse model of lateral hemisection spinal cord injury (SCI). The LV delivery method is clinically unsafe due to insertional mutations to the host DNA. In addition, the most common clinical case of SCI is contusion/compression. In this study, we identify that adeno-associated virus serotype 6 (AAV6) preferentially infects neural stem/progenitor cells (NSPCs) in the injured spinal cord. Using a rat model of contusion SCI, we demonstrate that AAV6 mediated Gsx1 expression promotes neurogenesis, increases the number of neuroblasts/immature neurons, restores excitatory/inhibitory neuron balance and serotonergic neuronal activity through the lesion core, and promotes locomotor functional recovery. Our findings support that AAV6 preferentially targets NSPCs for gene delivery and confirmed Gsx1 efficacy in clinically relevant rat model of contusion SCI.

IntroductionAn effective referral system is necessary to ensure quality and an optimum continuum of care. In the Kingdom of Saudi Arabia, an e-referral system known as the Saudi Medical Appointments and Referrals Centre (SMARC), has been fully functioning since 2019. This study aims to explore the rate of medical e-referral request acceptance in the KSA, and to study the factors associated with acceptance.MethodsThis period cross-sectional study utilised secondary collected data from the SMARC e-referral system. The data spans both 2020 and 2021 and covers the entirety of the KSA. Bivariate analyses and binary logistic regression analyses were performed to compute adjusted Odds Ratios (aORs) and 95% confidence intervals.ResultsOf the total 632,763 referral requests across the 2 years, 469,073 requests (74.13%) were accepted. Absence of available machinery was a significant predictor for referral acceptance compared to other reasons. Acceptance was highest for children under 14 with 28,956 (75.48%) and 63,979 (75.48%) accepted referrals, respectively. Patients requiring critical care from all age groups also had the highest acceptance including 6,237 referrals for paediatric intensive care unit (83.54%) and 34,126 referrals for intensive care unit (79.65%). All lifesaving referrals, 42,087 referrals, were accepted (100.00%). Psychiatric patients were observed to have the highest proportion for accepted referrals with 8,170 requests (82.50%) followed by organ transplantations with 1,005 requests (80.92%). Sex was seen to be a significant predictor for referrals, where the odds of acceptances for females increased by 2% compared to their male counterparts (95% CI = 1.01–1.04). Also, proportion of acceptance was highest for the Eastern business unit compared to all other units. External referrals were 32% less likely to be accepted than internal referrals (95% CI = 0.67–0.69).ConclusionThe current findings indicate that the e-referral system is mostly able to cater to the health services of the most vulnerable of patients. However, there remains areas for health policy improvement, especially in terms of resource allocation.

The utilization of metallic nanoparticles in bio-nanofabrication holds significant potential in the field of applied research. The current study applied and compared integrated ultrasonic-microwave-assisted extraction (US/MICE), ultrasonic extraction (USE), microwave-assisted extraction (MICE), and maceration (MAE) to extract total phenolic content (TPC). In addition, the study examined the antioxidant activity of Commiphora gileadensis (Cg) leaf. The results demonstrated that the TPC of US/MICE exhibited the maximum value at 59.34 ± 0.007 mg GAE/g DM. Furthermore, at a concentration of 10 μg/mL, TPC displayed a significant scavenging effect on DPPH (56.69 %), with an EC50 (6.48 μg/mL). Comprehensive metabolite profiling of the extract using UPLC-qTOF-MS/MS was performed to identify active agents. A total of 64 chromatographic peaks were found, out of which 60 were annotated. The most prevalent classes of metabolites found were polyphenols (including flavonoids and lignans), organic compounds and their derivatives, amides and amines, terpenes, and fatty acid derivatives. Transmission electron microscopy (TEM) revealed the aggregate size of the synthesized nanoparticles and the spherical shape of C. gileadensis-mediated silver nanoparticles (Cg-AgNPs). The nanoparticles had a particle size ranging from 7.7 to 42.9 nm. The Cg-AgNPs exhibited more inhibition zones against S. aureus and E. coli. The minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) of Cg-extract, AgNPs, and Cg-AgNPs were also tested. This study demonstrated the feasibility of using combined ultrasonic-microwave-assisted extraction to separate and extract chemicals from C. gileadensis on a large scale. These compounds have potential use in the pharmaceutical industry. Combining antibacterial and biocompatible properties in materials is vital for designing new materials for biomedical applications. Additionally, the results showed that the biocompatibility of the Ag-NPs using C. gileadensis extracts demonstrated outstanding antibacterial properties.

Abstract Background The recent earthquake in Syria has caused widespread devastation, leading to extensive damage and loss of life. Considering the diverse range of disasters and conflicts that have affected Syrian society, health workers must possess essential competencies to effectively manage various types of disasters, including earthquakes. Therefore, this study was undertaken to identify the specific competencies required by Syrian health workers to respond efficiently and effectively to earthquakes. Methods An exploratory qualitative study was conducted at the Medical Education Program MEP of the Syrian Virtual University SVU. Nine members of the research team of the MEP, who represent various health specializations in medicine, dentistry, nursing, and pharmacy, accepted to take part in this study. Among these, three members have been actively involved in providing health care in hospitals and the field during the Syrian earthquake on 6 February 2023. The Delphi process was adopted to identify competencies. Health workers involved in earthquake response were categorized into nine groups including medical doctors, dentists, pharmacists, nurses, psychological support professionals, medical students, allied healthcare professionals, on-site disaster teams, and managers. The final list was accepted if it achieved more than 80% agreement among the participants in the first, second, and final rounds. Results The study identified 74 competencies (12 knowledge items, 35 skills, and 27 attitudes) essential for health workers to respond effectively to earthquakes. They are categorized into five domains: "Preparing the team for the rescue process during and, after earthquakes, Implementation of the rescue process, Education and psychological support, Research, and development". Conclusion A list of earthquake competencies was identified for health workers. It is hoped that this list will enhance a country’s resilience and will enable decision–makers to support health workers in acquiring these competencies within a very strained health system in Syria and other countries.

Abstract Background Early oral feeding has been previously postulated to contribute to developing postoperative complications following head and neck reconstructive surgeries using free flaps. This study assessed the association between the timing of oral feeding (early vs. late) and postoperative complications and length of hospital stay among these patients. Method PubMed, Scopus, Cochrane, and Web of Science were searched using terms such as “oral feeding” and “head or neck cancer.” We utilized RevMan software version 5.4 for the analysis. The study defined early oral feeding as feeding within 5-day post-operation, while late oral feeding was defined as feeding after the fifth postoperative day. Five papers that met the inclusion criteria were included in the analysis, with 1097 patients. Results The results showed that early feeding was not significantly associated with postoperative fistulas (RR 0.49, 95% CI 0.23 to 1.05, p-value = 0.07), hematoma/seroma (RR 0.71, 95% CI 0.33 to 1.51, p-value = 0.38), or flap failure (RR 0.84, 95% CI = 0.38 to 1.87, p-value = 0.67). However, early oral feeding was significantly associated with shorter hospital stays than late oral feeding (MD −3.18, 95% CI −4.90 to −1.46, p-value = 0.0003). Conclusion No significant difference exists between early and late oral feeding regarding the risk of postoperative complications in head and neck cancer (HNC) patients who underwent free flap reconstruction surgery. However, early oral feeding is significantly associated with a shorter hospital stay than late oral feeding. Thus, surgeons should consider implementing early oral feeding after free flap reconstruction in HNC patients.

The network of any IT system is subject to continuous changes, such as the addition of new nodes, software installations, and the emergence of new vulnerabilities. On the other hand, the importance of nodes within the IT system’s network varies due to various factors, impacting the severity of potential node exploitation. Additionally, the interconnected nature of the nodes means that the security of each node is interdependent on the others nodes. In this context, effective risk assessment methodologies that consider the factors which impact the security of the system are crucial. This paper introduces an innovative methodology that takes into account the aforementioned factors. The proposed approach evaluates vulnerabilities, interconnections, and dynamic changes to deliver a comprehensive and up-to-date security risk assessment. By employing this methodology, administrators gain better control over system security with dynamic evaluations that support well-informed decisions. Furthermore, the methodology facilitates risk assessment for specific nodes and enables the quantification of their security levels. Due to a thorough assessment, the proposed methodology empowers IT administrators to improve the overall security of the system.

Abstract This protocol presents a comprehensive proposal for the establishment of the Saudi Cerebral Palsy Register (SCPR), a crucial project for investigating and addressing the prevalence, etiology, and management of cerebral palsy (CP) in Saudi Arabia. The SCPR will not only provide a robust database for ongoing research and analysis but will also serve as a platform for investigating the causes of CP, implementing preventative strategies, and improving the quality of care and outcomes for people with CP and their families in Saudi Arabia. Detailed case definitions, inclusion/exclusion criteria, and data collection protocols are discussed to ensure the integrity and comparability of the data. The plan also outlines strategic funding, institutional and government endorsement, sustainability considerations, potential challenges and proposed solutions, and expected outcomes and impact. These include creating research and educational opportunities, fostering regional and international collaborations, and significantly contributing to CP prevention strategies. Overcoming anticipated obstacles, such as stigma, institutional policies, and collaborations, and securing both necessary funding and endorsements are highlighted as critical for the success of the SCPR. The project is not only aligned with promote prevention of health risks, a target of Vision 2030 in Saudi Arabia, but is also expected to have a substantial impact on the health and quality of life of people with CP and their families in Saudi Arabia, serving as inspiration for similar efforts worldwide.

Abstract Renewable energy has become increasingly popular worldwide, and solar energy is one of the most sustainable and widely used forms of renewable energy. However, the lack of precision in system sizing often leads to oversizing and significant financial waste. To address this issue, this paper presents the development, testing, validation, and evaluation of an online educational/industrial standard-based tool for optimizing photovoltaic water pumping systems (PVWPSs). The proposed tool considers statistics on solar irradiance, average temperature, and necessary solar panel specifications to aid in calculating the peak power required to meet demand. Using MATLAB software, the tool enables the sizing of a PVWPS for either centrifugal or submersible pumps, including complete PV sizing for on-grid or off-grid systems. The tool also demonstrates the impact of ignoring temperature, sun irradiation, and pipeline losses on the sizing technique. Results indicate that significant temperature changes can negatively impact sizing, requiring further adjustments. Finally, a questionnaire was administered to various user segments to evaluate the tool's efficiency, yielding satisfactory results. This paper presents a valuable tool for optimizing PVWPSs, enabling more precise and efficient system sizing, and reducing financial waste.

The linker histone H1 C‐terminal domain (CTD) plays a pivotal role in chromatin condensation. De novo frameshift mutations within the CTD coding region of H1.4 have recently been reported to be associated with Rahman syndrome, a neurological disease that causes intellectual disability and overgrowth. To investigate the mechanisms and pathogenesis of Rahman syndrome, we developed a cellular model using murine embryonic stem cells (mESCs) and CRISPR/Cas9 genome engineering. Our engineered mES cells facilitate detailed investigations, such as H1‐4 dynamics, immunoprecipitation, and nuclear localization; in addition, we tagged the mutant H1‐4 with a photoactivatable GFP (PA‐GFP) and an HA tag to facilitate pulldown assays. We anticipate that these engineered cells could also be used for the development of a mouse model to study the in vivo role of the H1‐4 protein.

Diabetes is a significant global health concern, with an increasing number of diabetic people at risk. It is considered a chronic disease and leads to a significant number of fatalities annually. Early prediction of diabetes is essential for preventing its progression and reducing the risk of severe complications such as kidney and heart diseases. This study proposes an innovative Ensemble Deep Learning (EDL) clinical decision support system for diabetes prediction with high accuracy. The proposed EDL model uses Deep Learning (DL) architectures such as Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), and Convolutional Neural Networks (CNN), integrated with an ensemble learning-based stacking model. The EDL is implemented based on a stack ensemble model that applies meta-level models, including stack-ANN, stack-CNN, and stack-LSTM, to improve the prediction of diabetes. Three diabetes datasets, such as I. Pima Indian Diabetes Dataset (PIMA-IDD-I), II. Diabetes Dataset Frankfurt Hospital Germany (DDFH-G), and III. Iraqi Diabetes Patient Dataset (IDPD-I) are used to train the novel EDL models. The Extra Tree Classifier (ETC) approach is used to extract the relevant features from the data. The performance of the proposed EDL models is evaluated based on major evaluation metrics such as accuracy, precision, sensitivity, specificity, F-score, Matthews Correlation Coefficient (MCC), and ROC/AUC. Among the proposed EDL models, the stack-ANN achieved robust performance using DDFH-G, PIMA-IDD-I, and IDPD-I datasets with accuracy scores of 99.51%, 98.81%, and 98.45%, respectively. The overall results demonstrate that the proposed EDL models outperform previous studies in predicting diabetes.

Crowdsourcing has emerged as a pivotal data source for diverse smart city applications, ranging from health and traffic to security and safety. However, the integration of users’ location data in crowdsourced information poses a significant privacy challenge. Current privacy protection approaches of location-based services have become inadequate to face the evolving attackers’ techniques and tools. Moreover, these protection methods ignored the issue of preserving the accuracy and reliability of data. This paper introduces a novel approach, termed Double Cloak Area (DCL-Ar), designed to effectively safeguard users’ location privacy and ensure the reliability of data based on crowdsourcing. DCL-Ar differentiates by offering dual-layer protection for identity. The first layer involves users creating an initial cloak zone, while the second layer utilizes fog nodes to establish an extended cloak zone. Furthermore, the proposed method introduces three distinct scenarios for managing collaboration among fog nodes to select the optimal anonymizer and address the limitations of existing protection methods which are related to saving the reliability and the accuracy of data. DCL-Ar maintains maximum entropy, achieving complete uncertainty about user locations, thereby ensuring a high level of privacy protection. Through simulation and comparative analysis, the efficacy of the proposed approach is demonstrated where it provides a superior privacy level without significant performance. Experimental results demonstrate that DCL-Ar outperforms traditional methods, improving cache hit ratios and response times while reducing server query loads. Specifically, our approach reduces the number of queries sent to the service provider (SP) by up to 50% compared to existing methods and maintains a high cache hit ratio of nearly 100% over time. It further impacts on the traditional cloak-area and other protection approaches.

This paper presents a supervised machine learning approach using eight popular classifiers for fault classification in power transmission lines. The classification of faults, indicated by the behavior of the electrical signals associated with them, plays a pivotal role in maintaining the reliability, stability, and security of electrical grids. However, most of the previous studies on fault analysis in power systems relied on proprietary data and lack of standardized benchmarks, hindering the comparison of algorithms and making performance more erratic. Moreover, the nonavailability of labeled data for all types of faults is the most problematic. This paper proposes to perform fault classification on a data set created from a real-time Simulink model to standardize performance and advance research in this area. A new strategy for non-intensive feature extraction is applied using relatively simpler techniques, eliminating computationally expensive techniques such as wavelets. Feature selection through dimensionality reduction techniques is used to improve model performance and more efficient use of computational resources. The performance of the learning algorithms (e.g. Decision Tree, Random Forest, etc.) has been analyzed with various preprocessing techniques (e.g. data scaling, transformation, etc.) and tuning of parameters, focusing on the accuracy and computational time ( $T_{c}$ ), for performance generalization and efficiency. Performing specific operations on data in sequence of steps provided flexibility and adaptability in processing the data, making it easy to train, evaluate, and validate the learning algorithms. The results demonstrated that the proposed scheme can be effectively used for fault classification with high accuracy and significant reductions in $T_{c}$ under various operating conditions. The study also determined the best estimator for each classifier when building and training the classifier models, offering a variety of options. Logistic Regression, Random Forest and Support Vector Machine were the outperforming classifiers and proved their potential for classifying faults in electric power transmission lines.

This paper aims at identifying the most common constructions that realise metaphor in a group of commonly used Jordanian Arabic proverbs. The metaphorical instances in the data were identified using the Metaphor Identification Procedure (cf. Pragglejaz Group 2007). The analysis of the data focused on the form of the linguistic metaphors in Jordanian proverbs as this aspect of analysis has been paid less attention for favour of idea-content aspect of metaphors since Aristotle. It was found that metaphor is mainly realised by the constructions: genitive, grammatical metaphor, post-modification, pre-modification, and sentence metaphor. The qualitative and quantitative analysis of the data showed that the most common types of metaphor realization were sentence metaphor and grammatical metaphor. A future paper with a larger size of data might show further constructions that realise metaphor in Jordanian Arabic.

This paper suggests a novel Model-Free Predictive Control (MFPC) approach for three-phase quasi Z-source inverters (qZSI). While Model Predictive Control (MPC) is popular for qZSI due to its ease of use and performance, it suffers from sensitivity to parameter mismatch. The proposed MFPC overcomes this limitation by relying on an ultra-local model (ULM), eliminating the need for precise parameter matching. This approach establishes a comprehensive mathematical model for key qZSI control variables, including load current, inductor current, and capacitor voltage. Additionally, a unique method seamlessly integrates shoot-through status into the MFPC framework without compromising qZSI operation. Simulation results across various operating conditions demonstrate how much better the suggested MFPC is than the traditional MPC, especially under parameter mismatch scenarios. Notably, the MFPC achieves a significant improvement, exceeding a 37% reduction in total harmonic distortion (THD). Furthermore, the practicality of the proposed MFPC strategy is validated through hardware-in-the-loop (HIL) testing using a C2000TM microcontroller.

Background: Psychological distress such as anxiety and depression can negatively affect medical students' academic performance and future careers. Aim: This study examined the prevalence and impact of psychological distress on medical students' academic achievement, their associated learner characteristics, and how students handle stress in western Saudi Arabia. Setting and Design: This cross-sectional study incorporated a mixed-methods design, including quantitative and qualitative components for Phase 2 medical students. Methods: A self-administered questionnaire was used for quantitative components to examine the prevalence of psychological distress among medical students. In addition, a qualitative study component was conducted with students with severe psychological distress using structured interviews with a predetermined list of responses to which to select and assess factors that lead to severe psychological distress and how students handle stress. This study began on January 15, 2018, at the medical college Taif University and lasted 3 months. Statistical Analysis Used: The quantitative and qualitative data were coded, verified, and analyzed by a statistician using the Statistical Package for the Social Sciences software (SPSS version 22.0). Chi-squared tests were used to examine differences between proportions. Results: This study (n = 289) revealed that more than 69% of medical students experienced some form of psychological distress, 52% suffered from mild and moderate psychological distress, and 17% with severe psychological distress. There were no statistically significant differences between genders and years of study regarding psychological distress. Overall, there was no statistically significant association between grade point average and psychological distress scores. Conclusions: This study revealed a high prevalence of psychological distress among medical students. This study recommends establishing a psychological counseling unit at medical schools in western Saudi Arabia to detect psychological distress in medical students and timely referrals of those who need medical advice.

Due to a continuous change in people’s lifestyle and dietary habits, gastrointestinal diseases are on the increase, with dietary changes being a major contributor to a variety of bowel problems. Around two million people around the world die due to gastrointestinal (GI) diseases. Endoscopy is a medical imaging technology helpful in diagnosing gastrointestinal diseases like polyps and esophagitis. Its manual diagnosis is time-consuming; hence, computer-aided techniques are now widely used for accurate and fast GI disease diagnosis. In this paper, the Kvasir dataset of 4000 endoscopic images, comprising 500 images of each of the eight gastrointestinal tract disease classes have been classified using seven grid search fine-tuned transfer learning models. The fine-tuned transfer learning models employed in this paper are ResNet101, InceptionV3, InceptionResNetV2, Xception, DenseNet121, MobileNetV2, and ResNet50. The grid search algorithm has been used to determine the architectural and fine-tuning hyperparameters. The fine-tuned ResNet101 model performed the best, with a learning rate 0.001 and a batch size of 32 for the SGD optimizer at 40 epochs. These hyperparameters were optimized through grid search along with new set of layers added to the model. The newly added layers include one flatten layer, two dropout layers and five dense layers optimized using grid search. The grid search fine-tuned ResNet101 model obtained an accuracy of 0.90, a precision of 0.92, a recall of 0.92, and an f1-score of 0.91. Further, the grid search fine-tuned ResNet101 model was integrated with an attention mechanism to enhance performance by focusing on essential image features, notably in medical imaging where some regions may contain vital diagnostic information. The proposed grid search fine-tuned and attention mechanism integrated ResNet101 model achieved an accuracy of 0.935, precision of 0.93, recall of 0.94 and an f1-score of 0.93.

Background: Blunt cerebrovascular injury (BCVI) is an injury to the carotid or vertebral artery that is nonpenetrating and results from stretching or impingement of the vessel walls due to sudden head movement. Recent studies showed that the incidence of BCVI is around 1%–9%, with stroke being the most dreaded complication. Methodology: This retrospective study was conducted after approval by the medical research ethics committee at our tertiary care center, including all consecutive neck computed tomography angiography scans for blunt trauma patients from January 2017 to December 2020. The patient demographics, symptoms, and signs at presentation and imaging were reviewed through the electronic patient records for the presence of BCVI using the modified Denver scoring and Biffl criteria. Patients younger than 18 years, those with penetrating trauma, and patients with no head-and-neck scans were excluded from the study. The treatment and outcome of the positive cases were studied. Results: There were 871 patients with blunt trauma during the study. Of this number, 16 patients were identified with BCVI (1.83%). Most of the patients were male (75%), and the mean age of the cohort was 31 years (18–45 years). Among those with BCVI, five had carotid artery injuries (31%), and 11 had vertebral artery injuries (68.8%). BCVI Grades 2 and 4 were the most prevalent injuries. Ischemic infarction was seen in three patients (18%). Five patients (31.3%) with BCVI received antiplatelet therapy. None of the patients underwent endovascular or surgical treatment. Moreover, there was no BCVI-related mortality. There was a statistically significant between the Biffl grading, type of artery injury, and the presence of neurological deficit at the time of initial presentation, which both turned to be significant with a P = 0.017 and P = 0.049, respectively. Conclusion: BCVI is a serious injury with the potential for neurological complications. The artery involved and Biffl grading influence the presentation. Initiation of therapy with antithrombotic medications for stroke prevention is essential in all patients. The patients with positive findings need follow-up imaging, and an internationally recognized guideline is essential to manage this cohort.

Drones, also known as unmanned aerial vehicles (UAVs), are becoming more and more widespread for a variety of uses, including delivery, inspection, and monitoring. On the other hand, safe communication between drones is a crucial problem that must be solved. The current lightweight authentication mechanisms designed to protect UAV communications have failed to mitigate jamming attacks effectively. In this work, we design a novel approach to secure UAV communication by the combination of frequency hopping and chaotic mapping. The designed approach mitigates jamming attacks, as Chaotic maps’ unpredictability and randomness create a secure frequency hopping sequence. We describe in detail about planning, analysis, and application of the designed approach, and analyze the performance using a network simulator. The results show that our proposed work successfully mitigates jamming attacks and establishes secure UAV communication. Our work significantly enhances the design of a secure drone communication system. By incorporating our designed approach, flying robots can operate in a wide range of applications with high security and reliability.

Objectives: Double J (DJ) stent is widely used in cases of ureteric strictures as a temporary solution; however, it has certain limitations, such as the need for frequent exchange. Alternatively, a long-lasting thermoexpandable stent (Memokath™ 051) has been used to relieve ureteral obstructions in comorbid patients with multiple reported advantages, such as longer durability which avoids the need of frequent exchanges. In addition, it can be used in certain cases of urethral strictures. Our study is the first in the region to report the experience and outcome of Memokath stent with ureteral and urethral strictures. Materials and Methods: After local IRB approval, we retrospectively reviewed records of 21 patients who underwent insertion of Memokath 051 stent in the ureter and Memokath 045 in the urethra between 2013 and 2021. Indications of insertion, indwelling duration, and causes of removal were collected and analyzed by SPSS. Results: Twenty-one patients received 21 Memokath stents for 11 ureteral strictures and 10 urethral strictures. Fifty-five percent of ureteral strictures were malignant, and 27.3% of the benign ureteric strictures were transplant ureters. Ureteric memokath stents remained functioning for a mean of 16.1 months. Mean indwelling time for transplant ureteric memokath stents was 24.3 months. Total 4 ureteric stents had migrated, 4 stents blocked, and 2 stents were removed as planned and 1 stent removed due to febrile urinary tract infection (UTI). Ten urethral stents remained in place for mean of 14 months. Three stents were removed as planned with resolution of stricture, two were removed due to blockage, three stents had UTI, one was removed due to pain, and one stent remained functioning until the patient expired. Conclusion: Our outcome is comparable to other published studies, suggesting that ureteric Memokath stent is a better option with adequate indwelling time, especially in transplant ureters, in comparison with DJ stents. In addition, Memokath stents can be used in selected recurrent urethral strictures where surgical reconstruction is not feasible.

Abstract Neurodegenerative disorders present complex pathologies characterized by various interconnected factors, including the aggregation of misfolded proteins, oxidative stress, neuroinflammation and compromised blood–brain barrier (BBB) integrity. Addressing such multifaceted pathways necessitates the development of multi-target therapeutic strategies. Emerging research indicates that probucol, a historic lipid-lowering medication, offers substantial potential in the realm of neurodegenerative disease prevention and treatment. Preclinical investigations have unveiled multifaceted cellular effects of probucol, showcasing its remarkable antioxidative and anti-inflammatory properties, its ability to fortify the BBB and its direct influence on neural preservation and adaptability. These diverse effects collectively translate into enhancements in both motor and cognitive functions. This review provides a comprehensive overview of recent findings highlighting the efficacy of probucol and probucol-related compounds in the context of various neurodegenerative conditions, including Alzheimer’s disease, Parkinson’s disease, Huntington’s disease, and cognitive impairment associated with diabetes.

Abstract CKD is a byproduct of the cement industry, and its accumulation in the surrounding represents one of many issues associated with this industry. In this study, CKD was utilized in the fabrication of one-mix geopolymer cement (GP) composite as an economical and environmental solution for disposal of this byproduct. The mechanical properties and durability behavior during various deterioration actions were inspected. The obtained findings demonstrated that, replacing slag by CKD in the fabricated GP could cause an elongation in the setting times and reduction in the compression strength of approximately 50%. However, GPs containing CKD offered an accepted resistance to irradiation by γ-rays and to firing action. Reinforcing the GPs with nano Fe3O4 (NF) or nano TiO2 (NT) accelerated the geopolymerization reaction and offered mechanical properties surprising the control mix, this was related to the micro-filling and catalytic actions of the NPs which supported the formation of symmetrical and organized clusters of CSHs and CASH gel as shown in SEM micrographs. The reinforcing mixes surpassing the control mix in the protection against intrusion of sulfate ions which they could retain about 92% of their strength after 4 months of exposure while the control mix retained 80%. Furthermore, they showed a superior resistance to the destructive effect of irradiation by high dose gamma rays up to 1500 kGy and they retained ~ 75% of their strength after irradiation while the control mix was kept at only 35%. The fabricated composites are recommended for usage in many applied construction fields.

The human body is a typical non-rigid object, and its 3D reconstruction is a classic problem in the field of computer vision. Due to the inherent complexity and dynamism of the human body, it is not suitable for existing non-rigid 3D motion reconstruction algorithms that assume that the number of shape bases of non-rigid bodies is known. The number of shape bases is very important for 3D reconstruction methods. If the number of shape bases is estimated incorrectly in contour deformation estimation, it can lead to unreliable or even complete failure of the reconstruction algorithm. Therefore, this paper designs a 3D non-rigid motion human object reconstruction algorithm based on reliable estimation of contour deformation degree. This algorithm leverage Scale Invariant Feature Transform (SIFT) to obtain non-rigid moving human target features. Firstly, the contour appearance model of the moving human sequence is used to extract the contour feature sequence, which is preprocessed based on the contour appearance depth feature; Furthermore, the deformation degree of the contour is reconstructed and the calculation process of the number of shape bases was optimized, which is no longer simply defined as known. This method optimizes and solves the problem of missing data, improves the reliability of estimating the degree of contour deformation, and completes target reconstruction. The experimental results show that the three-dimensional reconstruction algorithm can accurately reconstruct the changes in the movements of athletes’ shots; The accuracy of 3D reconstruction can reach 95.98%; Moreover, PSNR, SSIM, and MSE indexes performed well with smaller fluctuation range, and the distribution of three-dimensional reconstructed scattering points is very close to the three-dimensional position distribution of real scattering points, and the three-dimensional reconstruction effect is good with strong reliability.

Abstract A brain tumor is an unnatural expansion of brain cells that can’t be stopped, making it one of the deadliest diseases of the nervous system. The brain tumor segmentation for its earlier diagnosis is a difficult task in the field of medical image analysis. Earlier, segmenting brain tumors was done manually by radiologists but that requires a lot of time and effort. Inspite of this, in the manual segmentation there was possibility of making mistakes due to human intervention. It has been proved that deep learning models can outperform human experts for the diagnosis of brain tumor in MRI images. These algorithms employ a huge number of MRI scans to learn the difficult patterns of brain tumors to segment them automatically and accurately. Here, an encoder-decoder based architecture with deep convolutional neural network is proposed for semantic segmentation of brain tumor in MRI images. The proposed method focuses on the image downsampling in the encoder part. For this, an intelligent LinkNet-34 model with EfficientNetB7 encoder based semantic segmentation model is proposed. The performance of LinkNet-34 model is compared with other three models namely FPN, U-Net, and PSPNet. Further, the performance of EfficientNetB7 used as encoder in LinkNet-34 model has been compared with three encoders namely ResNet34, MobileNet_V2, and ResNet50. After that, the proposed model is optimized using three different optimizers such as RMSProp, Adamax and Adam. The LinkNet-34 model has outperformed with EfficientNetB7 encoder using Adamax optimizer with the value of jaccard index as 0.89 and dice coefficient as 0.915.

In this study, a weighted federated learning approach is proposed for electrocardiogram (ECG) arrhythmia classification. The proposed approach considers the heterogeneity of data distribution among multiple clients in federated learning settings. The weight of each client is dynamically adjusted according to its contribution to the global model improvement. Experiments on public ECG datasets show that the proposed approach outperforms traditional federated learning and centralized learning methods in terms of accuracy and robustness. On the client side, the suggested federated learning (FL) approach had an accuracy of 0.93, sensitivity of 0.98, specificity of 0.82, miss classification rate of 0.07, precision of 0.06, FPR of 0.01, and FNR of 0.01. FL has 0.98 accuracy, 0.99 sensitivity, 0.91 specificity, 0.02 miss classification rate, 0.10 precision, 0.01, FPR, and 0.01 FNR on the server. The server-side federated learning approach outperforms the client-side in accuracy, sensitivity, specificity, miss classification rates, and precision. The results indicate that the proposed weighted federated learning approach is a promising solution for ECG arrhythmia classification in a distributed environment. In short, the proposed federated learning approach applied to ECG arrhythmia detection aims to address privacy concerns and improve accuracy, while still maintaining the centralized framework and advanced algorithmic approach.

AbstractThis research study addresses the complex interaction in between advertising and marketing capabilities, financial performance, and the moderating impact of consumer relationship management (CRM) in Jordanian small and medium enterprises (SMEs) in the service field. Partial Least Squares Structural Equation Modeling (PLS-SEM) analysis was used for this study. The study clearly verifies a significant and favorable relationship in between advertising alignment and financial performance, highlighting the tactical value of customer-centric approaches. It likewise highlights the prominent function of value development in driving monetary success and the positive effect of operational capabilities on financial efficiency. Furthermore, the research study discovers the reliable moderation of CRM in the connections amongst marketing alignment, value development, operational capacities, and financial performance. These searchings for highlight the central role of CRM in improving the effect of marketing abilities on financial end results and offer useful understandings for Jordanian SMEs in the solution sector looking for to optimize financial performance and boost client connections.

AbstractThis study focused on current research on creative accounting and external auditors as well as prospective topics for future research. In the Web of Science Core Collection, a bibliometric and visualization tool for the Web of Science and viewer-based literature, 465 research articles dealing with creative accounting and external auditors authored between 1981 and 2022 were analyzed. Cluster analysis, all-keyword co-occurrence analysis, and bibliographic coupling mapping are all investigated in the study. This research enables us to propose future research paths that may be useful in reflecting on the significant impact that technology will have on the growth of creative accounting and external auditors. The survey discovered four main research trends: the first trend shown is ‘The Future of External Auditing and Financial Reporting Quality’ (red cluster). The second trend focuses on the future of auditors in detecting creative accounting (the green cluster). The third trend is Financial Statement Audit Quality Management for Earnings Management and Fraud Detection (blue and purple clusters). The fourth trend is related to Preventing the Next Financial fraud: A Global Creative Accounting (yellow cluster).

IntroductionReferrals are an integral part of any healthcare system. In the Kingdom of Saudi Arabia (KSA) an electronic referral (e-referral) system known as the Saudi Medical Appointments and Referrals Centre (SMARC) began formally functioning in 2019. This study aims to showcase the Saudi experience of the e-referral system and explore the epidemiology of referrals nationally.MethodsThis retrospective descriptive study utilised secondary collected data between 2020 and 2021 from the SMARC system. Cross tabulations with significance testing and colour-coded maps were used to highlight the patterns across all regions.ResultsThe study analysed over 600,000 referral requests. The mean age of patients was 40.70 ± 24.66 years. Males had a higher number of referrals (55.43%). Referrals in 2021 were higher than those in 2020 (56.21%). Both the Autumn and Winter seasons had the highest number of referrals (27.09% and 27.43%, respectively). The Surgical specialty followed by Medicine had the highest referrals (26.07% and 22.27%, respectively). Life-saving referrals in the Central region were more than double those in other regions (14.56%). Emergency referrals were also highest in the Southern regions (44.06%). The Central and Eastern regions had higher referrals due to unavailable sub-speciality (68.86% and 67.93%, respectively). The Southern regions had higher referrals due to both unavailable machine and unavailable beds (18.44% and 6.24%, respectively).ConclusionThis study shows a unique system in which referrals are between secondary, tertiary, and specialised care. It also highlights areas of improvement for equitable resource allocation and specialised care in slightly problematic areas as well as the use of population density in future planning.

This study examines the crucial role of electronic word-of-mouth (EWOM) in mediating the impact of marketing mix strategies on international students’ study destination decisions. The theoretical framework proposed in this research integrates consumer behavior theories to enhance our understanding of the relationship between marketing mix strategies and international students’ enrollment choices. Building upon previous research that underscores the importance of marketing mix strategies in shaping consumer choices, this study focuses specifically on the context of international students deciding to study in Jordan. To achieve its objectives, a questionnaire was administered to a sample of 2000 international students who recently enrolled in both public and private universities in Jordan, with a response rate of 61%. Through rigorous empirical analysis, this study provides compelling evidence that the quality of the product/program, price, place, and promotion significantly influence the decision-making process of international students. Moreover, the study reveals that EWOM plays a critical mediating role in this relationship, underscoring the significance of online reviews, ratings, and recommendations in shaping consumer perceptions and choices. These findings underscore the need for marketers to proactively monitor and manage online reviews and harness the power of EWOM as a potent marketing tool to positively impact consumer decision-making.

This research explores the impact of blockchain technology on the quality of Accounting Information Systems (AIS) and subsequent business performance in Jordan's commercial banking sector. By surveying 388 participants from various banks, the study applies a quantitative methodology to assess how blockchain usage influences AIS quality and bank performance. Findings indicate a robust positive effect of Blockchain on AIS quality, significantly enhancing business performance. This study confirms the transformative potential of Blockchain in emerging markets and illuminates the pivotal role of AIS quality as a mediator in this relationship. These insights are critical for policymakers, financial institutions, and technology strategists, emphasizing the strategic importance of blockchain technology in enhancing operational efficiency and competitiveness in the banking industry of developing economies.

The main objective of this study is to investigate the efficiency of coagulation-flocculation process for surface water treatment, using the conventional coagulants (ferric chloride (FeCl3) and polyferric sulphate (PFS)), magnetic nanoparticles (MNPs), Fe-based metal-organic framework MIL-53(Fe), magnetic metal organic framework (MMOF), and evaluate the effect of combination of these coagulants on the coagulation process. MNPs were synthesized using the co-precipitation method and MIL-53(Fe) was synthesized using solvothermal process. The synthesized coagulants were characterized using point of zero charge (pHpzc), X-ray diffraction (XRD), Fourier Transform Infrared (FTIR) and Brunauer–Emmett–Teller (BET) techniques. The point zero of charge pH (pHPZC) values for the prepared MNPs, MIL-53(Fe) and MMOF were for found to be 7.8, 5.0 and 3.0, respectively, while the respective surface area for MIL-53(Fe) and MMOF were 23.8 and 11.8 m2/g. In addition, pore sized MMOF was found to be 59.455 Å which is larger than that for MIL-53(Fe) (34.705 Å). The results obtained from this study revealed that turbidity removal exceeded 98 % when using 30 mg/L of FeCl3 at pH 7. The same removal efficiency required less dose of PFS (10 mg/L) at a pH 8.5. The combination of MNPs with FeCl3 enhanced the removal efficiency to more than 99 % at the optimized condition of FeCl3. However, using MIL-53(Fe) slightly enhanced the FeCl3 coagulation at low dosage (10 mg/L) at pH 7, whereas, MMOF recorded better enhancement for the FeCl3 coagulation at the same conditions, reaching up to 98 %. Moreover, the combination of PFS and MNPs provided the best coagulation performance with turbidity removal of more than 99 % at low dose and at pH 7.