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: BACKGROUND: Pediatric obstructive sleep apnea (OSA) poses a significant health concern, affecting the well-being and quality of life of affected children. This study focuses on the translation, adaptation, and validation of the OSA-18 questionnaire for Arabic-speaking pediatric patients, aiming to contribute to the understanding of OSA and its implications on the quality of life in this population. METHODS: A study was conducted at Jordan University Hospital, involving 186 Arabic-speaking pediatric patients with suspected OSA. The OSA-18 questionnaire was translated and culturally adapted following the World Health Organization guidelines. Overnight polysomnography was conducted to assess OSA severity. Internal consistency, construct validity, convergent validity, and predictive/discriminant validity were analyzed. RESULTS: The study sample (68.8 of males, median age: 10.87) showed a median Apnea-Hypopnea Index (AHI) of 7.0, OSA-18 score of 46.5, and Pediatric Sleep Questionnaire (PSQ) score of 7.0. Internal consistency of OSA-18 was acceptable, except for the 'physical symptoms' domain. Exploratory and confirmatory factor analyses revealed five factors corresponding to different OSA-18 domains. Convergent validity was supported by significant correlations between OSA-18 and PSQ scores. However, no correlation was found between OSA-18 scores and AHI. OSA severity was associated with higher PSQ scores but not with AHI. CONCLUSION: This study provides valuable insights into pediatric OSA in Arabic-speaking children. While the translation and validation of the OSA-18 questionnaire demonstrated acceptable reliability and convergent validity, the lack of correlation between OSA-18 scores and AHI raises questions about the survey's ability to capture the full impact of OSA objectively. Future research should focus on developing more reliable diagnostic tools, especially in resource-limited settings. Keywords: Arabic translation, obstructive sleep apnea, Obstructive Sleep Apnea-18 questionnaire, polysomnography, quality of life, Author(s): Montaha Al-Iede (corresponding author) [1]; Mohammad Ali Alshrouf [2]; Abdallah Al-Ani [3]; Alaa Alkurdi [2]; Areej H. Jaber [2]; Omar Husain [2]; Batool Abusabra [2]; Nihad A. Almasri [2,4] [...]

Optic neuritis (ON) is an inflammatory condition that affects the optic nerve and may be associated with various central nervous system demyelinating conditions, infectious diseases, and systemic autoimmune syndromes. This manuscript sheds light on the epidemiologic patterns and diverse clinical features of ON, emphasizing the importance of early detection and prompt treatment. Various studies have revealed geographic and ethnic variations across ON subtypes, which are likely related to the incidence and prevalence of co-associated disorders. Distinguishing ON subtypes may be challenging and requires use of paraclinical tools. Treatment strategies differ depending on the etiology, further highlighting the importance of accurately identifying specific ON subtypes in a timely manner. Keywords: Optic Neuritis (ON), multiple sclerosis (MS), myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD), neuromyelitis optica spectrum disorders (NMOSD), magnetic resonance imaging (MRI), optical coherence tomography (OCT), Author(s): Abdullah Al-Ani [1]; Fiona Costello (corresponding author) [1,2] Introduction Optic neuritis (ON) refers broadly to any inflammatory process involving the optic nerve, the causes of which are protean. ON [...]

Abstract With regard to underground hydrogen storage projects, presuming that the hydrogen storage site has served as a repository for methane, the coexistence of a blend of methane and hydrogen is anticipated during the incipient stage of hydrogen storage. Therefore, the solubility of hydrogen/methane mixtures in brine becomes imperative. On the contrary, laboratory tasks of such measurements are hard because of its extreme corrosion ability and flammability, hence modeling methodologies are highly preferred. Therefore, in this study, we seek to create accurate data-driven intelligent models based upon laboratory data using hybrid models of adaptive neuro-fuzzy inference system (ANFIS) and least squares support vector machine (LSSVM) optimized with either particle swarm optimization (PSO), genetic algorithm (GA) and coupled simulated annealing (CSA) to predict hydrogen/methane mixture solubility in brine as a function of pressure, temperature, hydrogen mole fraction in hydrogen/methane mixture and brine salt concentration. The results indicate that almost all the gathered experimental data are technically suitable for the model development. The sensitivity study shows that pressure and hydrogen mole fraction in the mixture are strongly related with the solubility data with direct and indirect effects, respectively. The analyses of evaluation indexes and graphical methods indicates that the developed LSSVM-GA and LSSVM-CSA models are the most accurate as they exhibit the lowest AARE% and MSE values and the highest R-squared values. These findings show that machine learning methods could be a useful tool for predicting hydrogen solubility in brine encountered in underground hydrogen storage projects, aiding in the advancement of intelligent, affordable, and secure hydrogen storage technologies.

Background: The rollout of 6G technology will be the next game-changing force that elevates communication standards, because it aims to offer greater reliability, lower latency and increased network capability. Given their increasing observance in the coming wheeze of technology, it is indispensable to understand the security of Internet of Things (IoT) devices like Arduino Due when deployed in 6G contexts. The latter is a concern growing more serious as the tens of billions of network-connected devices begin to enter critical infrastructure, and even consumer gear. Objective: This study aims to explore the vulnerable areas of Arduino Due in a future 6G scenario and provide detail security solutions to mitigate its threats. The article address traditional risks such as eavesdropping, spoofing and man-in-the-middle-attacks, secondly issues derived from 6G networks. Methodology: The study combine modeling and field-testing to assess the susceptibility of the popular Arduino Due in a 6G-like environment. The research then focused on designing and incorporating modern cryptographic methods, dynamic key management systems, and real-time threat detection technology. The scalability, efficiency and robust security these solutions bring to burgeoning IoT networks were key in their development. Results: The respective security mechanisms were able to strengthen the Arduino Due against various attacks. The results showed reduced success of vulnerability, an increase in integrity of data and numerous hours of system availability improvements. Conclusion: With this study, keeping IoT devices safe is good enough for the future of 6G. In reality, our security solutions successfully enhance the resiliency of the Arduino Due which prove the effectiveness of utilizing our security in boosting resilience to support a resilient 6G technology use cases towards academicians and professionals who expect leveraging 6G technologies with minimum but secured IoT devices

In the fourth industrial revolution robots, voice control interfaces and 5G networks play significant role towards advanced real-time autonomous control systems in an industrial domain as well as a social domain. With Arduino-based robots controlled by voice commands for more natural human-machine communications and 5G enabling low latency, high-reliability connectivity as degradation due to typical interference does not matter. This article explores the efficiency and accuracy of voice-controlled interfaces to function robotic attributes via ultra-low latency associated with 5G network links intended for Arduino robots. Methods: An Arduino robot equipped with a voice-controlled interface that utilizes speech recognition and Natural Language Processing (NLP) to understand and carry out commands. This setup was trialed on a 5G network to guarantee connectivity and operational efficiency. Various scenarios and loads were examined in controlled experiments to evaluate response times, precision, and reliability. The 5G has lower latency response time, which led to better command execution. Accurate interpretation of voice commands and real-time adaption to robotic operations for continuous effective behavior across operating conditions and environmental scenarios are demonstrated to highlight the robustness of voice control interface. The combination of voice-activated Arduino robots and 5G networks has paved the way for industrial automation and smart applications, showcasing an innovative approach to human-robot interaction and collaborative robotics. Nevertheless, it is important to analyze the scalability, security, and ethical considerations of these technologies on a wider scale in different domains and applications to guarantee their safe, effective, and equitable implementation.

Background: Big Data has permeated numerous technological fields, most notably in robotics, allowing astonishing improvements. Large volumes of data from many sources may increase robotic system functionality and flexibility. Objective: This article investigates Big Data's impacts on robotic learning, decision-making, and adaptability to explain how it increases robotic capabilities and gives a complete analysis of integrated data-driven robotic systems. Methods: A comprehensive review and synthesis of the literature was conducted to investigate the integration and deployment of Big Data in robotics. From databases and case studies and theoretical frameworks on Big Data analytics and robotics were explored. Results: Big Data analytics in robots enhances learning in predictive analytics and machine learning algorithms, boosting decision-making. Industrial robots and self-driving cars improve operational efficiency and flexibility by boosting data processing, anomaly detection, and real-time decision-making. Conclusion: Robots have become more flexible in decision-making using Big Data and robotics. Integration improves robotics and allows innovative applications across disciplines, allowing data-driven robotic advancement. The findings suggest using Big Data analytics to improve robots and study future applications, contributing to technological convergence and societal impacts.

Transaction transparency and trust are highly valued in the digital age. When conventional centralized systems are utilized, concerns arise regarding data security and intermediate control, as these systems frequently fail to meet these standards. In this regard, blockchain technology, which offers secure and decentralized solutions, stands out as a game-changer. The article examines the underlying principles of smart contracts and blockchain technology in order to gain an understanding of their potential to revolutionize online interactions. We delve into the decentralized architecture of the blockchain, focusing on its distributed ledger system that ensures the immutability and transparency of data. Determining the functions of consensus mechanisms such as Proof-of-Work and Proof-of-Stake, which form the technological foundation of blockchain, is crucial for preserving the security and integrity of the network. We further investigate the possibility that intelligent contracts can orchestrate agreements and enable trustless transactions in response to predefined conditions. This study showcases the practical applications of blockchain technology and smart contracts in establishing more reliable processes within the healthcare, supply chain management, and finance sectors. The study not only contemplates potential solutions and forthcoming improvements but also recognizes the constraints of the existing technology, specifically with regard to scalability and energy usage. The emergence of smart contracts and blockchain technology heralds a new era of decentralized and impenetrable digital security. Their revolutionary potential to redefine trust and empower both individuals and corporations is evident. In order to optimize their capabilities, we must work together to address existing challenges and enforce solid legal structures that regulate the ethical and transformative application of these state-of-the-art technologies.

Departing from multimedia services among multiple access networks, the Integrated Multimedia Subsystems (IMS) technology is required to enhance communication flexibility and efficiency. However, the growing need for secure, auditable and non-modifiable systems has made clear the shortcomings of conventional IMS in particular when it comes to security and privacy control. The article is trying to solve the security issues in IMS by using blockchain technology and propose a novel architecture which reduces data integrity, authentication and access control in telecom networks without the direct or indirect involvement of a third party. The study designed a hybrid architecture through integrating IMS and the blockchain platform. We tested the new system with a range of simulations, and we tested some primary KPIs like latency, scalability, security resilience. Assessments were based on expert interviews and comparison studies against existing models in circulation. The integrated blockchain-IMS approach leaded to substantial enhancement in security measures, resulting in 40% decrease in identity theft occurrences and 30% fall in unauthorized data access attempts. It means the system maintains acceptable (not so low, around 2ms base latency but smooth) latency and scales good with increasing networking traffic. The possible solution for enhanced communication network security could be an integration of IMS and Blockchain technology. This architecture guarantees data integrity and privacy and gives a feasible, scalable solution for network management. In the future, blockchain integration should be further optimized to reduce Response-time and guarantee user transparency.

Background: The Industrial Internet of Things (IIoT) has revolutionized operations for businesses, and fog computing is a valuable resource management and job scheduling tool that has facilitated the transformation. In this regard, efficient resource usage will be more useful for the performance and energy cost-saving limit of IIoT system. Objective: The article proposed a new energy-aware metaheuristic approach to enhancing the performance and efficiency of IIoT systems in fog computing environments. The study aims to come up with a methodology that strikes a balance between efficiency in compute requirements and energy consumption. Methodology: A meta-heuristic method inspired by natural processes such as genetic algorithms and simulated annealing is applied to optimize the selection of which jobs should be scheduled. This approach takes into account several parameters like when the job is needed, availability of resources, and usage patterns to efficiently schedule jobs across the network. Results: The results show that the proposed approach drastically improves energy efficiency and system performance. In this paper, the fog orchestration master intends to divide the workload between fog and cloud in an excellent manner and resolve specific Issues of fog computing in IIoT environment. This manages to keep energy usage low and the operating efficiency high. Conclusions: Metaheuristic optimization techniques integrate into fog computing environments for IIoT job schedule complexity. This methodology enhances the sustainability of IIoT operations, and their ability to meet robust performance requirements over time. Our findings provide the crucial insight needed to enable industries that need seamless IIoT integration and plan further research in the field of energy-efficient fog computing.

Background: Climate change is a pressing global issue with far-reaching consequences. Understanding and predicting its impacts is crucial for effective mitigation and adaptation strategies. With its ability to process vast amounts of data, deep learning offers a promising avenue for improving climate change impact predictions. Objective: This empirical study aims to evaluate the effectiveness of deep learning models in predicting climate change impacts. We explore whether these models can provide more accurate and reliable predictions than traditional methods. Methodology: Authors employ a comprehensive dataset of historical climate data and associated impact variables. Deep learning architectures, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), are trained and tested using this dataset. We also compare their performance against conventional regression models. Results: Article findings demonstrate that deep learning models outperform traditional methods in predicting climate change impacts.Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) have shown remarkable precision and resilience in identifying intricate connections between climate variables and their consequences, including occurrences of severe weather events and the increase in sea levels. Conclusion: Deep learning approaches significantly promise to enhance our understanding of climate change impacts. They offer more accurate predictions and the potential to inform policy decisions and adaptation strategies effectively. As climate change continues accelerating, leveraging advanced machine learning techniques is essential for a sustainable future.

Background: 5G, when combined with immersive virtual reality (VR) technology, can create a trillion dollar innovation ecosystem across industries from education and healthcare to entertainment. The article studies what impact and implications did this integration provide towards the fostering of IT infrastructure growth. Objective: This article investigates whether the 5G/LTE network capabilities can add value to VR applications and focuses on identification of challenges in managing such technology. The proposed research will cover a general perspective on how this integration impacts overall IT systems and thus aims at offering an extensive understanding of the effect of integrates testing over performance, efficiency and end user experience of IT systems. Methodology: This article combined qualitative research techniques of expert interviews and case studies with quantitative data analysis based on network performance indicators—e.g., focusing the effectiveness of VR applications. The main point is that this process provides a comprehensive picture of where technologies are converging. Results: Intending 5G/LTE can significantly enhance performance with a decrease in latency and an increase in data throughput lends recognition to the idea that many of these applications are for VR applications. They provide a real-world example and demonstrate the potential of full integration. But it has identified some problems such as security concerns, network instability and scalability limitations. Conclusion: The integration of 5G/LTE and VR represents a milestone of the scene multi-view system in modern information technology infrastructure, which leads to an increase in user experience and operation efficiency. Despite obstacles needing attention, our results underscore the significant benefits of the integration and its capacity to inspire innovation across a variety of fields.

Intrapulmonary miliary metastasis is a rare presentation of adenocarcinoma of the lung, characterized by the dissemination of cancer cells throughout the lung parenchyma in different patterns. This case report highlights an unusual presentation of adenocarcinoma of the lung with intrapulmonary miliary metastasis, emphasizing the diagnostic challenges and management considerations. Here, we report a case of a 51-year-old female who presented to the emergency department (ED) with a 2-month history of dry cough, which started after a flu illness and was associated with mild shortness of breath, left-sided chest pain, and miliary nodules on chest imaging. During bronchoscopy, a transbronchial biopsy was taken for further pathological assessment. The results showed histopathological evidence of lung adenocarcinoma.

BACKGROUND Pediatric obstructive sleep apnea (OSA) poses a significant health concern, affecting the well-being and quality of life of affected children. This study focuses on the translation, adaptation, and validation of the OSA-18 questionnaire for Arabic-speaking pediatric patients, aiming to contribute to the understanding of OSA and its implications on the quality of life in this population. METHODS A study was conducted at Jordan University Hospital, involving 186 Arabic-speaking pediatric patients with suspected OSA. The OSA-18 questionnaire was translated and culturally adapted following the World Health Organization guidelines. Overnight polysomnography was conducted to assess OSA severity. Internal consistency, construct validity, convergent validity, and predictive/discriminant validity were analyzed. RESULTS The study sample (68.8% of males, median age: 10.87) showed a median Apnea–Hypopnea Index (AHI) of 7.0, OSA-18 score of 46.5, and Pediatric Sleep Questionnaire (PSQ) score of 7.0. Internal consistency of OSA-18 was acceptable, except for the “physical symptoms” domain. Exploratory and confirmatory factor analyses revealed five factors corresponding to different OSA-18 domains. Convergent validity was supported by significant correlations between OSA-18 and PSQ scores. However, no correlation was found between OSA-18 scores and AHI. OSA severity was associated with higher PSQ scores but not with AHI. CONCLUSION This study provides valuable insights into pediatric OSA in Arabic-speaking children. While the translation and validation of the OSA-18 questionnaire demonstrated acceptable reliability and convergent validity, the lack of correlation between OSA-18 scores and AHI raises questions about the survey’s ability to capture the full impact of OSA objectively. Future research should focus on developing more reliable diagnostic tools, especially in resource-limited settings.

Optic neuritis (ON) is an inflammatory condition that affects the optic nerve and may be associated with various central nervous system demyelinating conditions, infectious diseases, and systemic autoimmune syndromes. This manuscript sheds light on the epidemiologic patterns and diverse clinical features of ON, emphasizing the importance of early detection and prompt treatment. Various studies have revealed geographic and ethnic variations across ON subtypes, which are likely related to the incidence and prevalence of co-associated disorders. Distinguishing ON subtypes may be challenging and requires use of paraclinical tools. Treatment strategies differ depending on the etiology, further highlighting the importance of accurately identifying specific ON subtypes in a timely manner.

Most renewable energy sources are intermittent and seasonal, making energy storage and consumption problematic. Hydrogen gas can save and convey chemical energy, making it a promising sustainable energy source. Electrochemical water electrolysis technology’s sustainable and efficient hydrogen gas production attracts global attention. Higher hydrogen production rates enhance hydrogen volumetric energy capacity by storing intermittent hydrogen gas in high-pressure tanks. Pressurized storage tanks are cost-effective and efficient. Hydrogen gas may be stored economically and efficiently in pressurized tanks, making electrochemical water electrolysis a sustainable energy source. This paper introduced hydrogen as an alternative to natural gas, detailed water electrolysis technologies for hydrogen production, and highlighted how they can manufacture hydrogen efficiently and cost-effectively. The theoretical volume of gaseous hydrogen and oxygen that could be produced by electrolyzing water under typical temperature and pressure (STP) circumstances, assuming a 100% efficiency rate of the process. Since there are always two moles of hydrogen produced by electrolysis and one mole of gas occupies the same volume, the volume of hydrogen developed from water is twice that of oxygen. The volume of liberated oxygen is 0.21 (L/min), and the volume of liberated hydrogen is 0.42 (L/min) with a current density of 30 A, for instance, the tracer’s diffusion coefficient for all conceivable flow rates. A maximum value of 90 liters per hour was determined to be the threshold at which the diffusion coefficient increased with increasing flow rate. It would appear that the diffusion coefficient remains unchanged at flow rates greater than 90 liters per hour.

Introduction: The primary objective of this study was to assess the effectiveness of the clinical decision tool (CDT) in trauma patients, providing a comparable ability to rule out thoracolumbar (TL) fractures as traditional imaging methods. The goal is to facilitate early clearance of the TL spine without an immediate requirement for radiological tests, thereby minimizing unnecessary utilization of TL-spine imaging. Methods: A prospective, observational study was conducted on trauma patients with suspected TL injury. To achieve early TL clearance, the CDT assessed criteria such as absence of pain, tenderness, and pain-free axial movement and flexion. The study enrolled alert trauma patients with thoracic and/or lumbar spine injuries, defined by the Glasgow Coma Scale of 15. The study excluded patients not aligning with CDT criteria, such as those who received intravenous opioid analgesia within 4 h and those unable to stand due to suspected pelvic or lower limb injuries. Results: Following the completion of the CDT steps, there were 31 true negative cases, signifying the absence of TL fractures according to both CDT and imaging studies. The sensitivity of the CDT was 99.38% (95% confidence interval [CI]: 96.59%–99.98%), specificity 9.1% (95% CI: 6.30%–12.73%), negative predictive value (NPV) 96.87% (95% CI: 81.02%–99.56%), positive predictive value (PPV) 34.19% (95% CI: 33.38%–35.00%), negative likelihood ratio (LHR) 0.07 (95% CI: 0.01–0.49), and positive LHR 1.09 (95% CI: 1.06–1.13). The sensitivity, specificity, NPV, PPV, negative LHR, and positive LHR varied with each step in the CDT. Notably, the overall sensitivity was high; however, the stepwise sensitivity decreased, albeit with an improvement in specificity with each further step in the tool. The overall sensitivity in the study cohort (n = 500) was high; however, the stepwise sensitivity decreased, albeit with an improvement in the specificity. Conclusions: The CDT to rule out TL fracture is a feasible bedside stepwise tool in fully awake trauma patients after a thorough clinical neurological examination on arrival. The tool could help Level II or III trauma centers avoid secondary triage to the higher center.

Abstract This is an interim analysis of the Beta-blocker (Propranolol) use in traumatic brain injury (TBI) based on the high-sensitive troponin status (BBTBBT) study. The BBTBBT is an ongoing double-blind placebo-controlled randomized clinical trial with a target sample size of 771 patients with TBI. We sought, after attaining 50% of the sample size, to explore the impact of early administration of beta-blockers (BBs) on the adrenergic surge, pro-inflammatory cytokines, and the TBI biomarkers linked to the status of high-sensitivity troponin T (HsTnT). Patients were stratified based on the severity of TBI using the Glasgow coma scale (GCS) and HsTnT status (positive vs negative) before randomization. Patients with positive HsTnT (non-randomized) received propranolol (Group-1; n = 110), and those with negative test were randomized to receive propranolol (Group-2; n = 129) or placebo (Group-3; n = 111). Propranolol was administered within 24 h of injury for 6 days, guided by the heart rate (> 60 bpm), systolic blood pressure (≥ 100 mmHg), or mean arterial pressure (> 70 mmHg). Luminex and ELISA-based immunoassays were used to quantify the serum levels of pro-inflammatory cytokines (Interleukin (IL)-1β, IL-6, IL-8, and IL-18), TBI biomarkers [S100B, Neuron-Specific Enolase (NSE), and epinephrine]. Three hundred and fifty patients with comparable age (mean 34.8 ± 9.9 years) and gender were enrolled in the interim analysis. Group 1 had significantly higher baseline levels of IL-6, IL-1B, S100B, lactate, and base deficit than the randomized groups (p = 0.001). Group 1 showed a significant temporal reduction in serum IL-6, IL-1β, epinephrine, and NSE levels from baseline to 48 h post-injury (p = 0.001). Patients with severe head injuries had higher baseline levels of IL-6, IL-1B, S100B, and HsTnT than mild and moderate TBI (p = 0.01). HsTnT levels significantly correlated with the Injury Severity Score (ISS) (r = 0.275, p = 0.001), GCS (r = − 0.125, p = 0.02), and serum S100B (r = 0.205, p = 0.001). Early Propranolol administration showed a significant reduction in cytokine levels and TBI biomarkers from baseline to 48 h post-injury, particularly among patients with positive HsTnT, indicating the potential role in modulating inflammation post-TBI. Trial registration: ClinicalTrials.gov NCT04508244. It was registered first on 11/08/2020. Recruitment started on 29 December 2020 and is ongoing. The study was partly presented at the 23rd European Congress of Trauma and Emergency Surgery (ECTES), April 28–30, 2024, in Estoril, Lisbon, Portugal.