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The need to address energy challenges and environmental pollution has led researchers to focus on utilizing solar energy. In this study, a new solar air heater collector system was developed that incorporates arc-shaped wire roughness and external airflow recycling. The system performance was evaluated under various conditions using energy conservation equations and a semi-analytical method for modeling. The results were validated, confirming the method's accuracy. The findings revealed that the hybrid system significantly improved energy and exergy efficiencies at lower mass flow rates. Increasing the airflow recycle ratio up to 3 in conditions of constant roughness and low mass flow rates enhanced collector performance. However, at high flow rates and recycle ratios, exergy efficiency decreased due to increased pressure drop, despite a rise in energy efficiency, making it less effective than a simple collector system. The results show that the temperature increase is not so much from a mass flow rate of more than 0.05 kg/s. The existence of considered artificial roughness has caused an increase in temperature, especially in mass flow rates of less than 0.035 kg/s. [ABSTRACT FROM AUTHOR]

Our aim was to describe the incidence and mortality of genitourinary (GU) cancers in rural and urban Pennsylvania counties. We calculated age-adjusted incidence and mortality rates of GU (prostate, bladder, and kidney) cancers from 1990 to 2019 in the Pennsylvania Cancer Registry. We defined rurality using the Center for Rural Pennsylvania's population density-based definition. We modeled average annual percent changes (AAPC) in age-adjusted incidence and mortality rates using joinpoint regression. Overall GU cancer incidence decreased in rural and urban counties (AAPC −7.5%, p = 0.04 and AAPC −6.6%, p = 0.02, respectively). Prostate cancer incidence decreased in rural and urban counties by −10.5% (p = 0.02) and −9.1% (p = 0.01), respectively. Kidney cancer incidence increased in both rural and urban counties, respectively (AAPC = +11.2, p = 0.002 and +9.3%, p = 0.01). GU cancer mortality decreased in rural and urban counties (AAPC = −11.6, p = 0.047 and AAPC −12.2, p = 0.01, respectively). Prostate cancer mortality decreased at similar rates in rural and urban counties (AAPC −15.5, p = 0.03 and −15.4, p = 0.02, respectively). Kidney cancer mortality decreased in urban (AAPC −6.9% p = 0.03) but remained stable in rural counties. Bladder cancer incidence and mortality were unchanged in both types of counties. Over three decades, GU cancer incidence and mortality decreased across Pennsylvania counties. [ABSTRACT FROM AUTHOR]

Background: There is a global call for upscaling and optimising the role of community pharmacists (CPs) in the control of non-communicable diseases (NCDs). In the United Arab Emirates (UAE), where NCDs are classified as a public health pandemic, upscaling CPs contributions has become more critical. Several contextual, professional, and educational challenges constrain the role of CPs. Objective: To synthesise the perspectives of key stakeholders in the UAE healthcare system and propose a roadmap for advancing the role of CP s in controlling NCDs in the UAE. Methods: This research followed a qualitative design using the International Pharmaceutical Federation (FIP) framework for quality assurance of pharmacy profession development. Data were collected using semi-structured interviews with 28 experts and senior leaders, then analysed using the thematic analysis technique with the assistance of NVivo software. Results: The analysis yielded three main themes that outlined the prospective roadmap: education, work environment, and policy. Some of the generated subthemes were establishing accredited NCD-specialised programmes, building a national framework for interprofessional education and collaboration, and upscaling the engagement of CPs in public health platforms and initiatives. Conclusion: Improving the role of CPs in controlling the NCD pandemic in the UAE requires coherent and well-structured multidisciplinary endeavours from health policymakers, educational institutions, and all groups of healthcare professionals, including the CPs themselves. [ABSTRACT FROM AUTHOR]

Copyright of Arab Journal for Scientific Publishing is the property of Research & Development of Human Recourses Center (REMAH) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: Hepatocyte ballooning (HB) is a significant histological characteristic linked to the advancement of non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatohepatitis (NASH). Although clinicians now consider liver biopsy the most reliable method for identifying HB, its invasive nature and related dangers highlight the need for the development of non-invasive diagnostic options. Objective: This study aims to develop a novel methodology that combines deep learning and machine learning techniques to accurately identify and measure hepatobiliary abnormalities in liver ultrasound images. Methods: The research team expanded the dataset, consisting of ultrasound images, and used it for training deep convolutional neural networks (CNNs) such as InceptionV3, ResNet50, DenseNet121, and EfficientNetB0. A hybrid approach, combining InceptionV3 for feature extraction with a Random Forest classifier, emerged as the most accurate and stable method. An approach of dual dichotomy classification was used to categorize images into two stages: healthy vs. sick, and then mild versus severe ballooning.. Features obtained from CNNs were integrated with conventional machine learning classifiers like Random Forest and Support Vector Machines (SVM). Results: The hybrid approach achieved an accuracy of 97.40%, an area under the curve (AUC) of 0.99, and a sensitivity of 99% for the 'Many' class during the third phase of evaluation. The dual dichotomy classification enhanced the sensitivity in identifying severe instances of HB. The cross-validation process confirmed the strength and reliability of the suggested models. Conclusions: These results indicate that this combination method can decrease the need for invasive liver biopsies by providing a non-invasive and precise alternative for early identification and monitoring of NAFLD and NASH. Subsequent research will prioritize the validation of these models using larger datasets from multiple centers to evaluate their generalizability and incorporation into clinical practice. [ABSTRACT FROM AUTHOR]

Copyright of Umm Al-Qura University Journal of Engineering & Architecture (Springer Nature) is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: Chat Generative Pre-Trained Transformer (ChatGPT) has previously been shown to accurately predict colon cancer screening intervals when provided with clinical data and context in the form of guidelines. The National Comprehensive Cancer Network® (NCCN®) guideline on non-muscle invasive bladder cancer (NMIBC) includes criteria for risk stratification into low-, intermediate-, and high-risk groups based on patient and disease characteristics. The aim of this study is to evaluate the ability of ChatGPT to apply the NCCN Guidelines to risk stratify theoretical patient scenarios related to NMIBC. Methods: Thirty-six hypothetical patient scenarios related to NMIBC were created and submitted to GPT-3.5 and GPT-4 at two separate time points. First, both models were prompted to risk stratify patients without any additional context provided. Custom instructions were then provided as textual context using the written versions of the NMIBC NCCN® Guidelines, followed by repeat risk stratification. Finally, GPT-4 was provided with an image of the NMIBC risk groups table, and the risk stratification was again performed. Results: GPT-3.5 correctly risk stratified 68% (24.5 of 36) of scenarios without context, slightly increasing to 74% (26.5 of 36) with textual context. Using GPT-4, the model had accuracy of 83% (30 of 36) without context, reaching 100% (36 of 36) with textual context (P =.025). GPT-4 with image context maintained similar accuracy to GPT-4 without context, with accuracy 81% (29 of 36). ChatGPT generally performed poorly when stratifying intermediate risk NMIBC (33%-63%). When risk stratification was incorrect, most responses were overestimations of risk. Conclusions: GPT-4 can accurately risk stratify patients with respect to NMIBC when provided with context containing guidelines. Overestimation of risk is more common than underestimation, and intermediate risk NMIBC is most likely to be incorrectly stratified. With further validation, GPT-4 can become a tool for risk stratification of NMIBC in clinical practice. [ABSTRACT FROM AUTHOR]

The current study investigates removing tetracycline from water using batch, column, and tank experiments with statistical modelling using ANN for continuous tests. An artificial neural network (ANN) using the Levenberg-Marquardt back-propagation (LMA) training algorithm is constructed to compare the effectiveness of Tetracycline removal from aqueous solution using the sorption technique with prepared adsorbent. Several characterization analyses XRD, FT-IR, and SEM are employed for prepared Brownmillerite (Ca2Fe2O5)–Na alginate beads. The operating conditions of batch tests involved, contact time (0.1–3 h), initial of tetracycline (Co) of (100–250 mg/L), pH (3–12), agitation speed (50–250) rpm and dosage of adsorbent (0.2–1.2 g/50 mL). The outcomes of experiments have demonstrated that the optimum conditions for the batch test to achieve the maximum adsorbent capacity (qmax =7.845 mg/g) are achieved at pH 7, contact time 1.5 h, adsorbent dose 1.2 g/50 mL, agitation speed of 200 rpm, and initial concentration of TC 100 mg/L. Minimum mean square error (MSE) values of 7.09E-04 for 30 hidden neurons and 0.0029 for 59 hidden neurons in the 1D and 2D systems are accomplished, respectively. The artificial neural network model has exhibited excellent performance with correlation coefficients exceeding 0.980 for the operating variables, demonstrating its accuracy and effectiveness in predicting the experimental outcomes. According to sensitivity analysis, the influential parameter in the column test (1D) is the flow rate (mL/min), with a relative importance of 32.769%. However, in the tank test (2D), time (day) is signified as an influential parameter with a relative importance of 31.207%. [ABSTRACT FROM AUTHOR]

The current study investigates removing tetracycline from water using batch, column, and tank experiments with statistical modelling using ANN for continuous tests. An artificial neural network (ANN) using the Levenberg-Marquardt back-propagation (LMA) training algorithm is constructed to compare the effectiveness of Tetracycline removal from aqueous solution using the sorption technique with prepared adsorbent. Several characterization analyses XRD, FT-IR, and SEM are employed for prepared Brownmillerite (Ca2Fe2O5)–Na alginate beads. The operating conditions of batch tests involved, contact time (0.1–3 h), initial of tetracycline (Co) of (100–250 mg/L), pH (3–12), agitation speed (50–250) rpm and dosage of adsorbent (0.2–1.2 g/50 mL). The outcomes of experiments have demonstrated that the optimum conditions for the batch test to achieve the maximum adsorbent capacity (qmax =7.845 mg/g) are achieved at pH 7, contact time 1.5 h, adsorbent dose 1.2 g/50 mL, agitation speed of 200 rpm, and initial concentration of TC 100 mg/L. Minimum mean square error (MSE) values of 7.09E-04 for 30 hidden neurons and 0.0029 for 59 hidden neurons in the 1D and 2D systems are accomplished, respectively. The artificial neural network model has exhibited excellent performance with correlation coefficients exceeding 0.980 for the operating variables, demonstrating its accuracy and effectiveness in predicting the experimental outcomes. According to sensitivity analysis, the influential parameter in the column test (1D) is the flow rate (mL/min), with a relative importance of 32.769%. However, in the tank test (2D), time (day) is signified as an influential parameter with a relative importance of 31.207%. [ABSTRACT FROM AUTHOR]

Here, the abilities of Fe-Si42, Fe-Al21N21, Cu-C60, Cu-B30P30, Fe-SiNT(9, 0), Fe-AlNNT(9, 0), Cu-CNT(6, 0) and Cu-BPNT(6, 0) as nano-catalysts of OER and ORR processes are investigated in alkaline environment. The calculated formation energy of Fe- and Cu-doped nanocages and Fe- and Cu-doped nanotubes (Fe-Si42, Fe-Al21N21, Fe and Cu doped nanotubes) are acceptable values and these structures are stable. The Fe-AlNNT(9, 0) and Cu-BPNT(6, 0) have higher capacity for adsorption of OER/ORR species than other studied catalysts. The *OH removal and *OOH formation on Fe-Si42, Fe-Al21N21, Fe and Cu doped nanotubes are potential-determining steps for OER/ORR processes in alkaline environment. The Fe-AlNNT(9, 0) and Cu-BPNT(6, 0) catalysts for OER/ORR processes have lower over-potential than other studied catalysts. The Fe-AlNNT(9, 0) and Cu-BPNT(6, 0) as effective catalysts are suggested to catalyze the OER/ORR processes in alkaline environment. [ABSTRACT FROM AUTHOR]

In response to the burgeoning interest in the Metaverse—a virtual reality-driven immersive digital world—this study delves into the pivotal role of AI in shaping its functionalities and elevating user engagement. Focused on recent advancements, prevailing challenges, and potential future developments, our research draws from a comprehensive analysis grounded in meticulous methodology. The study, informed by credible sources including SD, Scopus, IEEE, and WoS, encompasses 846 retrieved studies. Through a rigorous selection process, 54 research papers were identified as relevant, forming the basis for a specific taxonomy of AI in the Metaverse. Our examination spans diverse dimensions of the Metaverse, encompassing augmented reality, virtual reality, mixed reality, Blockchain, Agent Systems, Intelligent NPCs, Societal and Educational Impact, HCI and Systems Design, and Technical Aspects. Emphasizing the necessity of adopting trustworthy AI in the Metaverse, our findings underscore its potential to enhance user experience, safeguard privacy, and promote responsible technology use. This paper not only sheds light on the scholarly interest in the Metaverse but also explores its impact on human behavior, education, societal norms, and community dynamics. Serving as a foundation for future development and responsible implementation of the Metaverse concept, our research identifies and addresses seven open issues, providing indispensable insights for subsequent studies on the integration of AI in the Metaverse. [ABSTRACT FROM AUTHOR]

Multiple pathologic conditions can lead to a diseased and symptomatic glenohumeral joint for which total shoulder arthroplasty (TSA) replacement may be indicated. The long-term survival of implants is limited. With the increasing incidence of joint replacement surgery, it can be anticipated that joint replacement revision surgery will become more common. It can be challenging at times to retrieve the manufacturer of the in situ implant. Therefore, certain systems facilitated by AI techniques such as deep learning (DL) can help correctly identify the implanted prosthesis. Correct identification of implants in revision surgery can help reduce perioperative complications and complications. DL was used in this study to categorise different implants based on X-ray images into four classes (as a first case study of the small dataset): Cofield, Depuy, Tornier, and Zimmer. Imbalanced and small public datasets for shoulder implants can lead to poor performance of DL model training. Most of the methods in the literature have adopted the idea of transfer learning (TL) from ImageNet models. This type of TL has been proven ineffective due to some concerns regarding the contrast between features learnt from natural images (ImageNet: colour images) and shoulder implants in X-ray images (greyscale images). To address that, a new TL approach (self-supervised pertaining (SSP)) is proposed to resolve the issue of small datasets. The SSP approach is based on training the DL models (ImageNet models) on a large number of unlabelled greyscale medical images in the domain to update the features. The models are then trained on a small labelled data set of X-ray images of shoulder implants. The SSP shows excellent results in five ImageNet models, including MobilNetV2, DarkNet19, Xception, InceptionResNetV2, and EfficientNet with precision of 96.69%, 95.45%, 98.76%, 98.35%, and 96.6%, respectively. Furthermore, it has been shown that different domains of TL (such as ImageNet) do not significantly affect the performance of shoulder implants in X-ray images. A lightweight model trained from scratch achieves 96.6% accuracy, which is similar to using standard ImageNet models. The features extracted by the DL models are used to train several ML classifiers that show outstanding performance by obtaining an accuracy of 99.20% with Xception+SVM. Finally, extended experimentation has been carried out to elucidate our approach's real effectiveness in dealing with different medical imaging scenarios. Specifically, five different datasets are trained and tested with and without the proposed SSP, including the shoulder X-ray with an accuracy of 99.47% and CT brain stroke with an accuracy of 98.60%. [ABSTRACT FROM AUTHOR]

This scoping review explores mobile health (mHealth) technologies and their features affecting medication adherence in cancer patients. Among 11 selected studies, predominantly from the USA, mHealth tools, particularly smartphone apps, were examined for their features in managing cancer patient's medication adherence. The studies highlighted the importance of adherence in continuous cancer therapy, with mHealth tools offering reminders and interactive features, that aim to enhance patient engagement. However, the review identified research gaps, emphasizing the need for broader investigations into diverse mHealth tools beyond apps, including electronic capsules and smart pill dispensers. Additionally, it underscored the absence of information on costs, user input, integration with electronic health records, and data management. While acknowledging potential positive impacts on adherence, the review calls for more comprehensive research to substantiate these findings in clinical oncology. [ABSTRACT FROM AUTHOR]

This study delves into the complex prioritization process for Autism Spectrum Disorder (ASD), focusing on triaged patients at three urgency levels. Establishing a dynamic prioritization solution is challenging for resolving conflicts or trade-offs among ASD criteria. This research employs fuzzy multi-criteria decision making (MCDM) theory across four methodological phases. In the first phase, the study identifies a triaged ASD dataset, considering 19 critical medical and sociodemographic criteria for the three ASD levels. The second phase introduces a new Decision Matrix (DM) designed to manage the prioritization process effectively. The third phase focuses on the new extension of Fuzzy-Weighted Zero-Inconsistency (FWZIC) to construct the criteria weights using Single-Valued Neutrosophic 2-tuple Linguistic (SVN2TL). The fourth phase formulates the Multi-Attributive Border Approximation Area Comparison (MABAC) method to rank patients within each urgency level. Results from the SVN2TL-FWZIC weights offer significant insights, including the higher criteria values "C12 = Laughing for no reason" and "C16 = Notice the sound of the bell" with 0.097358 and 0.083832, indicating their significance in identifying potential ASD symptoms. The SVN2TL-FWZIC weights offer the base for prioritizing the three triage levels using MABAC, encompassing medical and behavioral dimensions. The methodology undergoes rigorous evaluation through sensitivity analysis scenarios, confirming the consistency of the prioritization results with critical analysis points. The methodology compares with three benchmark studies, using four distinct points, and achieves a remarkable 100% congruence with these prior investigations. The implications of this study are far-reaching, offering a valuable guide for clinical psychologists in prioritizing complex cases of ASD patients. [ABSTRACT FROM AUTHOR]

The utilization of medicinal plant extracts in therapeutics has been hindered by various challenges, including poor bioavailability and stability issues. Nanovesicular delivery systems have emerged as promising tools to overcome these limitations by enhancing the solubility, bioavailability, and targeted delivery of bioactive compounds from medicinal plants. This review explores the applications of nanovesicular delivery systems in antibacterial and anticancer therapeutics using medicinal plant extracts. We provide an overview of the bioactive compounds present in medicinal plants and their therapeutic properties, emphasizing the challenges associated with their utilization. Various types of nanovesicular delivery systems, including liposomes, niosomes, ethosomes, and solid lipid nanoparticles, among others, are discussed in detail, along with their potential applications in combating bacterial infections and cancer. The review highlights specific examples of antibacterial and anticancer activities demonstrated by these delivery systems against a range of pathogens and cancer types. Furthermore, we address the challenges and limitations associated with the scale-up, stability, toxicity, and regulatory considerations of nanovesicular delivery systems. Finally, future perspectives are outlined, focusing on emerging technologies, integration with personalized medicine, and potential collaborations to drive forward research in this field. Overall, this review underscores the potential of nanovesicular delivery systems for enhancing the therapeutic efficacy of medicinal plant extracts in antibacterial and anticancer applications, while identifying avenues for further research and development. The role of nanovesicular delivery systems in enhancing the therapeutic potential of medicinal plant extracts against bacterial infections and cancer. [ABSTRACT FROM AUTHOR]

Background: Triamcinolone acetonide injections (TAIs) have been suggested to decrease complications following rhinoplasty. This systematic review aimed to assess the efficacy and safety of TAIs following rhinoplasty. Methods: We performed a systematic literature search on Medline, Embase, Google Scholar, and Cochrane Central Register of Controlled Trials from inception to May 2023, without any timeframe limitations. The following terms were used: (Triamcinolone OR steroid injections OR triamcinolone acetonide) AND (Skin thickness OR supratip edema OR supratip deformity OR Pollybeak deformity) AND (rhinoplasty OR external rhinoplasty). We included randomized controlled trials and observational studies (prospective, retrospective, and case series). Results: In total, six of the 1604 articles met our inclusion criteria. A total of 1524 patients were included in this study. Our results included patient demographics, type of rhinoplasty, post-injection follow-up period, site of injection, type of syringe used, timing of the first dose, volume and concentration used, time interval between doses, response to the injection, and complications of injection. Conclusion: To our knowledge, this is the first systematic review to address this issue. Our results demonstrate the ease and safety of TAIs as a first-line treatment, with positive outcomes and limited complications. TAIs can be used early postoperatively to minimize the need for revision surgery. Despite the limited number of studies on TAIs, this study provides the best available evidence that can help surgeons decide when to use the injection, the intervals between doses, and the duration of use. Further randomized controlled trials are required to confirm our findings. Level of Evidence II: This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266. [ABSTRACT FROM AUTHOR]

This study examines the lifecycle of the Dubai Water Canal construction, focusing on risk mitigation strategies implemented across its four distinct phases. By incrementally addressing potential risks, the project minimized exposure to uncertainties and ensured the quality and safety of the end product. Key findings highlight the minimal impact on urban growth and emphasize the importance of comprehensive feasibility studies, geological assessments, and environmental impact assessments in identifying and managing risks. The study uniquely contributes to the discourse on urban development by highlighting the effectiveness of a phased approach in large-scale projects. The research methodology involved detailed environmental and technical assessments, stakeholder engagement, and continuous monitoring throughout the project. Our conclusions underscore the need for proactive risk management, thorough planning, and sustainability to ensure the resilience and success of similar infrastructure initiatives globally. [ABSTRACT FROM AUTHOR]

Supercritical fluids (SCFs) can be used to prepare drugs nanoparticles with improved solubility. SCFs have shown superior advantages in pharmaceutical industry as an environmentally friendly alternative to toxic/harmful organic solvents. They possess gas-like transport characteristics and liquid-like solvation power for solutes. Evaluation of chemotherapeutic drugs’ solubility in supercritical carbon dioxide (SCCO2) has been recently an attractive subject for developing this method in pharmaceutical sector. To reach this purpose, the utilization of accurate models is of great necessity to estimate experimental-based solubility data. In this paper, the authors tried to employ machine learning (ML) approaches to estimate the solubility of Letrozole (LET) drug as chemotherapeutic agent and correlate its values in wide ranges of temperature and pressure. To do this, PAR (Passive Aggressive Regression), RF (Random Forest), and RBF-SVM are the models used (Support Vector Machine with RBF kernel). These models optimized in terms of their hyper-parameters using GA algorithm. The optimized PAR, RF, RBF-SVM models obtained coefficients of determination (R-squared) of 0.8277, 0.9534, and 0.9947. Also, the MSE error rate of the models are 0.1342, 0.0305, and 0.0045, in the same order. The final result of the evaluations shows the optimized RBF-SVM model as the most appropriate model in this research. The model exhibits a maximum prediction error of 0.1289. [ABSTRACT FROM AUTHOR]

The impact of wettability on the co-moving velocity of two-fluid flow in porous media is analyzed herein. The co-moving velocity, developed by Roy et al. (Front Phys 8:4, 2022), is a novel representation of the flow behavior of two fluids through porous media. Our study aims to better understand the behavior of the co-moving velocity by analyzing simulation data under various wetting conditions. We analyzed 46 relative permeability curves based on the Lattice–Boltzmann color fluid model and two experimentally determined relative permeability curves. The analysis of the relative permeability data followed the methodology proposed by Roy et al. (Front Phys 8:4, 2022) to reconstruct a constitutive equation for the co-moving velocity. Surprisingly, the coefficients of the constitutive equation were found to be nearly the same for all wetting conditions. On the basis of these results, a simple approach was proposed to reconstruct the relative permeability of the oil phase using only the co-moving velocity relationship and the relative permeability of the water phase. This proposed method provides new information on the interdependence of the relative permeability curves, which has implications for the history matching of production data and the solution of the associated inverse problem. The research findings contribute to a better understanding of the impact of wettability on fluid flow in porous media and provide a practical approach for estimating relative permeability based on the co-moving velocity relationship, which has never been shown before. [ABSTRACT FROM AUTHOR]

Introduction: One of the most common reasons for visiting dental clinics is dental pain. Seeking timely and professional dental care is a crucial step to maintain good oral health. Aims: The aim of the present study was to explore the prevalence of acute dental pain, the different management methods, and the use and practicality of teledentistry and online dental consultations among the population of Taif, Saudi Arabia. Materials and Methods: A total of 556 adults were enrolled in the study by answering an online survey that was distributed through different social media websites. Data were collected on the type of dental pain, pain-related characteristics using the Modified Dental Pain Screening Questionnaire, self-and formal care methods for pain relief, and the utilization of teledentistry services. Data analysis was performed using descriptive statistics, the Chi-square test, and the logistic regression model. Results and Discussion: Of the 556 participants, approximately 68% reported having dental pain, and almost 39% reported that the pain originated from a tooth. In addition, 73.2% reported that the pain was exacerbated after eating/drinking something cold. Regarding self-care methods, prescribed drugs were found to be mostly taken by those aged 41–50 years (adjusted odds ratio [AOR] = 3.38, P = 0.01), whereas nonprescribed drugs and home remedies were mostly taken by those aged 51 years or older (AOR = 2.25, P = 0.02; AOR = 2.65, P = 0.007; respectively). For formal-care methods, those who obtained professional help to control their pain were more likely to be dentists/dental students (AOR = 6.17, P = 0.02). Furthermore, a borderline effect was observed regarding teledentistry usage, with most users who were connected to a dentist being 31–40 years old (P = 0.09) and less likely to be men (P = 0.08). Conclusion: The prevalence of acute dental pain and self-medication practices was notably high among the study population. Therefore, it is imperative to educate the general public on appropriate management strategies for this type of pain. In addition, the utilization of teledentistry services was minimal within this population. [ABSTRACT FROM AUTHOR]

Background: Patient satisfaction levels serve as a crucial performance indicator of the quality of health care in any health institute. Aim: This study aimed to investigate the satisfaction level of patients with the dental care services provided by Taif University Dental Hospital (TUDH) in an effort to highlight areas that need further improvements. Settings and Design: This study was conducted at TUDH in Taif, Saudi Arabia, and employed a cross-sectional analytical observational research approach. Materials and Methods: A simple random sampling technique was employed over a period of 3 months from November 2023 to January 2024. A self-administrated questionnaire was used to assess patient satisfaction with the dental services provided by final-year dental students. Statistical Analysis: The Statistical Package for the Social Sciences (SPSS) software (Version 25.0, Chicago, IL, USA) was used for all statistical analyses. Results: This study involved 94 patients; satisfaction levels were assessed across various domains within a health-care setting. Notably, doctors' interactions received consistently high mean scores (ranging from 1.10 to 1.20), as did faculty supervisors' interactions (ranging from 1.36 to 1.52). X-ray procedures and personnel demonstrated similar satisfaction levels (mean scores between 1.17 and 1.23), while dental assistants received positive evaluations (mean scores around 1.24). Reception staff satisfaction was moderate (mean scores from 1.26 to 1.54), and patient relation office satisfaction varied (mean scores from 1.38 to 1.72). Younger patients (under 40 years) reported higher satisfaction across all domains compared to older participants. In addition, males and non-Saudi nationals expressed greater satisfaction than their female and Saudi counterparts, although these findings were not statistically significant. Education level also played a role, with more educated individuals (holding a College/University degree or higher) showing greater satisfaction, particularly in reception staff and patient relation office domains (with borderline significant effects; P = 0.03 and P = 0.093, respectively). Conclusions: The majority of the patients were satisfied with the provided dental care services at TUDH. [ABSTRACT FROM AUTHOR]

This study focuses on developing a machine learning (ML) model capable of predicting relative density and equivalent strain in samples produced through hot isostatic pressing (HIP) of Ti-6Al-4 V powders. The model is trained using data from numerical simulations, incorporating processing parameters and powder size and distribution as input features. Results demonstrate strong predictive performance, with R2 values of 0.951 and 0.911 for relative density and equivalent strain, respectively. The findings also reveal that the effectiveness of ML predictions is greatly influenced by the weight functions assigned to processing parameters as input features, while the impact of powder size and distribution weighting on optimal prediction is comparatively minimal. This suggests that particle behavior may demonstrate a higher level of consistency in response to the HIP process compared to the variability created by processing parameters. The outcomes of the ML predictions are further utilized to provide a detailed discussion on how variations in temperature, pressure, and powder size and distribution impact changes in relative density and equivalent strain in a specimen. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the association between pretreatment blood flow velocity in the choroid and optic nerve head (ONH) and retinal oxygen metabolism in the acute uveitic phase and the development of 'sunset glow fundus' in Vogt‐Koyanagi‐Harada (VKH) disease. Methods: Retrospective analysis of 41 patients (82 eyes). Laser speckle flowgraphy and retinal oximetry measurements were performed at the presentation. The main outcome measure was the development of 'sunset glow fundus'. Results: Twenty patients (40 eyes) presented in the phase preceding anterior segment inflammation (early presentation), and 21 patients (42 eyes) presented with anterior segment inflammation (late presentation). In ONH, mean blur rate (MBR)‐vessel, representing blood flow velocity in retinal vessels, was significantly lower in the late presentation group, while choroidal MBR was not significantly different. The late presentation group had significantly lower oxygen saturation in retinal venules, a higher arteriovenous oxygen saturation difference and a smaller calibre of retinal arterioles compared with the early presentation group. Eyes that subsequently developed 'sunset glow fundus' had significantly lower ONH MBR‐vessels, lower oxygen saturation in retinal venules, a higher arteriovenous oxygen saturation difference and a smaller calibre of retinal arterioles compared with eyes without 'sunset glow fundus'. ONH MBR‐vessel had a significant negative correlation with arteriovenous oxygen saturation difference and a significant positive correlation with calibre of retinal arterioles. Conclusions: In the acute uveitic phase of VKH disease, the development of 'sunset glow fundus' is associated with pretreatment reduced retinal blood flow velocity, calibre of retinal arterioles and oxygen saturation in retinal venules, as well as an increased arteriovenous oxygen saturation difference. [ABSTRACT FROM AUTHOR]

In response to the burgeoning interest in the Metaverse—a virtual reality-driven immersive digital world—this study delves into the pivotal role of AI in shaping its functionalities and elevating user engagement. Focused on recent advancements, prevailing challenges, and potential future developments, our research draws from a comprehensive analysis grounded in meticulous methodology. The study, informed by credible sources including SD, Scopus, IEEE, and WoS, encompasses 846 retrieved studies. Through a rigorous selection process, 54 research papers were identified as relevant, forming the basis for a specific taxonomy of AI in the Metaverse. Our examination spans diverse dimensions of the Metaverse, encompassing augmented reality, virtual reality, mixed reality, Blockchain, Agent Systems, Intelligent NPCs, Societal and Educational Impact, HCI and Systems Design, and Technical Aspects. Emphasizing the necessity of adopting trustworthy AI in the Metaverse, our findings underscore its potential to enhance user experience, safeguard privacy, and promote responsible technology use. This paper not only sheds light on the scholarly interest in the Metaverse but also explores its impact on human behavior, education, societal norms, and community dynamics. Serving as a foundation for future development and responsible implementation of the Metaverse concept, our research identifies and addresses seven open issues, providing indispensable insights for subsequent studies on the integration of AI in the Metaverse. [ABSTRACT FROM AUTHOR]

Multiple pathologic conditions can lead to a diseased and symptomatic glenohumeral joint for which total shoulder arthroplasty (TSA) replacement may be indicated. The long-term survival of implants is limited. With the increasing incidence of joint replacement surgery, it can be anticipated that joint replacement revision surgery will become more common. It can be challenging at times to retrieve the manufacturer of the in situ implant. Therefore, certain systems facilitated by AI techniques such as deep learning (DL) can help correctly identify the implanted prosthesis. Correct identification of implants in revision surgery can help reduce perioperative complications and complications. DL was used in this study to categorise different implants based on X-ray images into four classes (as a first case study of the small dataset): Cofield, Depuy, Tornier, and Zimmer. Imbalanced and small public datasets for shoulder implants can lead to poor performance of DL model training. Most of the methods in the literature have adopted the idea of transfer learning (TL) from ImageNet models. This type of TL has been proven ineffective due to some concerns regarding the contrast between features learnt from natural images (ImageNet: colour images) and shoulder implants in X-ray images (greyscale images). To address that, a new TL approach (self-supervised pertaining (SSP)) is proposed to resolve the issue of small datasets. The SSP approach is based on training the DL models (ImageNet models) on a large number of unlabelled greyscale medical images in the domain to update the features. The models are then trained on a small labelled data set of X-ray images of shoulder implants. The SSP shows excellent results in five ImageNet models, including MobilNetV2, DarkNet19, Xception, InceptionResNetV2, and EfficientNet with precision of 96.69%, 95.45%, 98.76%, 98.35%, and 96.6%, respectively. Furthermore, it has been shown that different domains of TL (such as ImageNet) do not significantly affect the performance of shoulder implants in X-ray images. A lightweight model trained from scratch achieves 96.6% accuracy, which is similar to using standard ImageNet models. The features extracted by the DL models are used to train several ML classifiers that show outstanding performance by obtaining an accuracy of 99.20% with Xception+SVM. Finally, extended experimentation has been carried out to elucidate our approach’s real effectiveness in dealing with different medical imaging scenarios. Specifically, five different datasets are trained and tested with and without the proposed SSP, including the shoulder X-ray with an accuracy of 99.47% and CT brain stroke with an accuracy of 98.60%. [ABSTRACT FROM AUTHOR]

Purpose: Percutaneous thermal ablation is an effective treatment for primary and metastatic liver tumors and is a recommended local therapy for early-stage hepatocellular carcinoma (HCC). Reported evidence shows an increase in the ablation zone volume over the first 24-h post-liver ablation. This report compares ablation zone volumes immediately at the completion (T = 0) of 26 microwave ablations of liver tumors to 24-h post-procedure (T = 24) volumes. Materials and methods: 20 patients, 13 (65%) males, underwent a total of 26 hepatic microwave ablations (MWA) under ultrasound guidance. Contrast-enhanced CT (CECT) or MRI was performed immediately and another CECT 24 h post operatively. Evaluation of the ablation zone and comparison of the two post-operative scans were done using BioTrace software. The expansion of ablation zones on post-op CECTs was matched point by point per direction. The distance between each 2 points was measured and grouped by distance. The incidence of each specific distance was then converted into a percentage, first for each case separately, then for all cases altogether. Data were tested by a matched paired one-sided t test. Results: The median lesion diameter was 1.5 cm (range 0.5–3.3) with 16 (62%) HCC cases and 9 hepatic metastases (4 neuroendocrine carcinoma, 4 colorectal carcinomas, 1 breast carcinoma, 1 pancreatic cancer). The data show a consistent volume expansion greater than 30% (p = 7.7e−5) 24-h post-ablation, where the median expansion is 57%. Distances between T = 0 and T = 24 equal to 3–7 mm occur in over 35% of the cases. Conclusion: The ablation zone expansion at 24-h post-op was not uniform. The final ablation zone is difficult to predict at the time of the procedure. The awareness of the ablation zone expansion is important when treating near-critical structures, managing the heat sink effect, and preserving liver parenchyma. [ABSTRACT FROM AUTHOR]

Congestion Control (CC) is a fundamental mechanism to achieve effective and equitable sharing of network facilities. As future networks evolve towards more complex paradigms, traditional CC methods are required to become more powerful and reliable. On the other hand, Machine Learning (ML) has become increasingly popular for solving challenging and sophisticated problems, and scientists have started to turn their interest from rule-based approaches to ML-based methods. This paper employs machine learning models to con- struct a performance evaluation scheme to predict network metrics for the Self-Clocked Rate Adaptation for Multimedia (SCREAM) algorithm. It uses a rigorous data preprocessing pipeline and a systematic application of ML methods to enhance the performance of the regression model for SCReAM's performance metrics. Also, we constructed a dataset that pro- vides SCREAM's input parameters and output metrics, such as network queue delay, smoothed Round Trip Time (sRTT), and network throughput. Each prediction process has several phases: choosing the best initial regressor model, hyperparameter tuning, ensemble learning, stacking regressors, and utilizing the holdout data. Each model's performance was evaluated through various regression metrics; this study will mainly focus on the coefficient of determination (R2) score. The improvement between the initial best-selected model and the final improved model determined that we were able to increase R2 up to 96.64% for network throughput, 99.4% for network queue delay, and 100% for sRTT. [ABSTRACT FROM AUTHOR]

The PBE-GGA (Perdew Burke-Ernzerhof Generalized Gradient Approximation) for the exchange-correlation potentials, based on first-principles density functional theory (DFT) study is used to investigate the structural, optical, and electrical aspects of XSnI3 (X = Rb, K, Tl, and Cs) materials. According to the DFT calculation, the energy band gaps (Eg) of XSnI3 (X = Rb, K, Tl, and Cs) materials are 2.76, 2.01, 1.90, and 0.34 eV respectively. The direct energy bandgap (Eg) indicates that halide perovskite materials are appropriate semiconductors for solar cell application. A thorough analysis of optical conductivity indicates that, the optical conductance peaks of XSnI3 (X = Rb, K, Tl, and Cs) halide perovskite materials reach maximum values of 2.3, 2.2, 4.5, and 5.2 eV, respectively, in the ultraviolet spectrum and shift slightly at higher energy bands. The maximal optical conductivity of XSnI3 (X = Rb, K, Tl, and Cs) materials were (1.6 × 105 Ω−1 cm−1, 1.8 × 105 Ω−1) cm−1, 2.2 × 105 Ω−1 cm−1 and 2.4 × 105 Ω−1 cm−1 respectively. The XSnI3 (X = Rb, K, Tl, and Cs) is a group of materials with enhanced surface area for light photon absorption and enhanced optical conductivity, energy absorption, and refractive index properties make them suitable for perovskite solar cell application. Highlights: The PBE-GGA (Perdew Burke-Ernzerhof Generalized Gradient Approximation) for the exchange-correlation potentials, based on first-principles density functional theory (DFT) study is used to investigate the structural, optical, and electrical aspects of XSnI3 (X = Rb, K, Tl, and Cs) materials. According to the DFT calculation, the energy band gaps (Eg) of XSnI3 (X = Rb, K, Tl, and Cs) materials are 2.76, 2.01, 1.90, and 0.34 eV respectively. The direct energy bandgap (Eg) indicates that halide perovskite materials are appropriate semiconductors for solar cell application. A thorough analysis of optical conductivity indicates that the optical conductance peaks of XSnI3 (X = Rb, K, Tl, and Cs) halide perovskite materials reach maximum values of 2.3, 2.2, 4.5, and 5.2 eV, respectively, in the ultraviolet spectrum and shift slightly at higher energy bands. The XSnI3 (X = Rb, K, Tl, and Cs) is a group of materials with enhanced surface area for light photon absorption and enhanced optical conductivity, energy absorption, and refractive index properties make them suitable for perovskite solar cell application. [ABSTRACT FROM AUTHOR]

Meeting the rising global demand for healthcare diagnostic tools is crucial, especially with a shortage of medical professionals. This issue has increased interest in utilizing deep learning (DL) and telemedicine technologies. DL, a branch of artificial intelligence, has progressed due to advancements in digital technology and data availability and has proven to be effective in solving previously challenging learning problems. Convolutional neural networks (CNNs) show potential in image detection and recognition, particularly in healthcare applications. However, due to their resource-intensiveness, they surpass the capabilities of general-purpose CPUs. Therefore, hardware accelerators such as application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), and graphics processing units (GPUs) have been developed. With their parallelism efficiency and energy-saving capabilities, FPGAs have gained popularity for DL networks. This research aims to automate the classification of normal and abnormal (specifically Diabetic Foot Ulcer—DFU) classes using various parallel hardware accelerators. The study introduces two CNN models, namely DFU_FNet and DFU_TFNet. DFU_FNet is a simple model that extracts features used to train classifiers like SVM and KNN. On the other hand, DFU_TFNet is a deeper model that employs transfer learning to test hardware efficiency on both shallow and deep models. DFU_TFNet has outperformed AlexNet, VGG16, and GoogleNet benchmarks with an accuracy 99.81%, precision 99.38% and F1-Score 99.25%. In addition, the study evaluated two high-performance computing platforms, GPUs and FPGAs, for real-time system requirements. The comparison of processing time and power consumption revealed that while GPUs outpace FPGAs in processing speed, FPGAs exhibit significantly lower power consumption than GPUs. [ABSTRACT FROM AUTHOR]

Background: A pandemic classification for COVID-19 was later issued. The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) was the cause of it. It spreads quickly and can result in severe respiratory failure at an early stage. Clinical research has demonstrated that the risk of infection is increased by both aging and chronic conditions. As such, blood group effects on the COVID-19 infection and its progression remain unknown. Aim: This study investigates possible relationships between patient blood group types, risk of SARS-CoV-2 infection, and clinical outcomes in COVID-19 in diabetic patients. Methodology: Age (41-60) was shown to be the age group most susceptible to infection with the SARS-COVID-19 virus, according to a statistical analysis of data from 90 individuals infected with COVID-19. Based on gender, the findings indicated that women had a greater incidence of diabetes than men did, with women having a 46% incidence of diabetes and men having a 44% infection rate. The blood types A and O were the most prevalent. According to the results of the current investigation, blood type O+ individuals had a higher prevalence of diabetes than blood type O-individuals. Based on gender, the findings indicated that women had a greater incidence of diabetes than men did, with women having a 46% incidence of diabetes and men having a 44% infection rate. Results: The results of the present study suggest that while blood group A might have a role in increased susceptibility to COVID-19 infection, blood group O might be somewhat protective. However, blood group type does not seem to influence clinical outcomes once infected. [ABSTRACT FROM AUTHOR]

Purpose: To assess the safety and effectiveness of percutaneous transsplenic access (PTSA) for portal vein (PV) interventions among patients with PV disease. Materials and methods: Adult patients with PV disease were enrolled if they required percutaneous catheterization for PV angioplasty, embolization, thrombectomy, variceal embolization, or transjugular intrahepatic portosystemic shunt (TIPS) placement for a difficult TIPS or recanalization of a chronically occluded PV. The procedures were performed between January 2018 and January 2023. Patients were excluded if they had an active infection, had a chronically occluded splenic vein malignant infiltration of the needle tract, had undergone splenectomy, or were under age 18 years. Results: Thirty patients (15 women, 15 men) were enrolled. Catheterization of the PV through PTSA succeeded for 29 of 30 patients (96.7%). The main adverse effect recorded was flank pain in 5 of 30 cases (16.7%). No bleeding events from the spleen, splenic vein, or percutaneous access point were recorded. Two cases (6.7%) each of hepatic bleeding and rethrombosis of the PV were reported, and a change in hemoglobin levels (mean [SD], − 0.5 [1.4] g/dL) was documented in 14 cases (46.7%). Conclusion: PTSA as an approach to accessing the PV is secure and achievable, with minimal risk of complications. Minimal to no bleeding is possible by using tract closure methods. [ABSTRACT FROM AUTHOR]

3D printing (3DP) is increasingly utilized to achieve quick turnaround on various geometric designs and prototypes, being the growing part of additive manufacturing technology (AMT). The 3DP technique effectively improves the production of complex models in terms of low-cost, time-consuming production, and with less material volume. The key to results optimisation with 3DP is the preparation of the geometry. The following techniques can effectively reduce the required time of the 3D printing process for complex and non-linear CAD files. The fused deposition modelling/fabrication (FDM/FFF) techniques become the first choice in many applications, including biomedical ones. Still, some obstacles exist in the geometry roughness and quality zones. This paper proposes an optimisation method for 3D printed shapes used in biomedical devices and instrumentation by minimising the support structure attached to the model using the FDM technique. In this research, we proposed a method for dynamic compensation against gravity-affected parts extended from the main object's geometry using a forward planar learning (FPL) algorithm to minimise cusp height in 3D printed objects. After the slicing stage, the outcomes proved to be of good quality, optimised the object's surfaces, and minimised the printing time by 32%-38%. The proposed method is promising in defining a better setting for slicing and toolpath for FDM 3D printing. However, this method was not tested on other 3DP methods (Stereolithography (SLA), Selective laser sintering (SLS), and Digital Light Processing (DLP)), as more verification efforts need to be done on these 3D printing processes. [ABSTRACT FROM AUTHOR]

Background: MRI fusion prostate biopsy has improved the detection of clinically significant prostate cancer (CSC). Continued refinements in predicting the pre-biopsy probability of CSC are essential for optimal patient counseling. We investigated potential factors related to improved cancer detection rates (CDR) of CSC in patients with PI-RADS ≥ 3 lesions. Methods: The pathology of 980 index lesions in 980 patients sampled by transrectal mpMRI-targeted prostate biopsy across four medical centers between 2017–2020 was reviewed. PI-RADS lesion distribution included 291 PI-RADS-5, 374 PI-RADS-4, and 315 PI-RADS-3. We compared CDR of index PI-RADS ≥ 3 lesions based on location (TZ) vs. (PZ), PSA density (PSAD), and history of prior negative conventional transrectal ultrasound-guided biopsy (TRUS). Results: Mean age, PSA, prostate volume, and level of prior negative TRUS biopsy were 66 years (43–90), 7.82 ng/dL (5.6–11.2), 54 cm3 (12–173), and 456/980 (46.5%), respectively. Higher PSAD, no prior history of negative TRUS biopsy, and PZ lesions were associated with higher CDR. Stratified CDR highlighted significant variance across subgroups. CDR for a PI-RADS-5 score, PZ lesion with PSAD ≥ 0.15, and prior negative biopsy was 77%. Conversely, the CDR rate for a PI-RADS-4 score, TZ lesion with PSAD < 0.15, and prior negative biopsy was significantly lower at 14%. Conclusions: For index PI-RADS ≥ 3 lesions, CDR varied significantly based on location, prior history of negative TRUS biopsy, and PSAD. Such considerations are critical when counseling on the merits and potential yield of prostate needle biopsy. [ABSTRACT FROM AUTHOR]

Although the insurance industry in Iraq has been relatively less developed compared to other Arab countries, it has experienced significant expansion, particularly due to the implementation of reforms in the sector. Nevertheless, the political and economic transformations in the country have profoundly influenced the insurance sector. The study discovered a strong association between the financial indicators of the insurance sector and the economic, political, and security conditions in the country during different research periods. As economic and political conditions improved, this positively affected the output of the insurance sector, which saw a gradual increase in its production volume and its contribution to GDP. This was evident during the first and second periods. When crises and negative political changes occurred, the activity of the insurance sector declined. The study recommended the need to increase attention to the insurance sector by stimulating the work of insurance companies, expanding types of insurance, and increasing investment levels to increase profits. It also recommended activating insurance participation for individuals and business sectors by expanding insurance awareness and clarifying the importance of referring to insurance companies for various economic activities to reduce the risks that a person may be exposed to personally. [ABSTRACT FROM AUTHOR]