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This research examines the effects of technostress on educators' productivity within Jordan's higher education sector, highlighting gender differences. Technostress, characterized by techno-overload, techno-invasion, techno-complexity, techno-insecurity, and techno-uncertainty, adversely affects productivity. The study gathered data from 500 educators through a structured online survey, achieving a 73% response rate. Analysis revealed that technostress generally decreases productivity, with a more pronounced negative impact on male educators than females. The findings underscore the necessity for targeted interventions to mitigate technostress, particularly among male educators. Strategies recommended include training programs, policy adjustments, and organizational support to enhance the digital teaching environment. This study contributes to the understanding of technostress in Jordanian educational settings. It offers practical recommendations for enhancing e-learning practices and improving the overall educational experience and productivity in Jordan's higher education sector.

Metacognitive awareness of reading strategy has a significant impact on language learning. It addresses how students organize context-based interactions and how they may employ reading comprehension strategies. Saudi students of English still face reading comprehension issues that significantly affect their overall language learning. Therefore, this mixed method study sought to assess Saudi English students' metacognitive awareness of reading strategies, determine whether gender and university level affect their use of these strategies, and explore English instructors' views on this issue. Two instruments were used to achieve these objectives. Two hundred ten students were surveyed for quantitative data. 15 English instructors were interviewed in a semi-structured manner for qualitative data. Descriptive statistical analysis was used to analyze the quantitative data gathered, while pattern matching and thematic content analysis were applied to analyze the qualitative data. The results demonstrated that Saudi EFL students' metacognitive awareness of reading methods was generally moderate. This awareness was moderate regarding the use of global reading strategies and low regarding support and problem-solving strategies. The metacognitive awareness levels for global strategies vary significantly by gender, in favour of females. Senior and junior university students had better global and support reading methods than freshmen. The qualitative data from the interview supported the quantitative data suggesting Saudi EFL students had low to moderate metacognitive awareness. Their low-to-moderate level explains their poor reading comprehension. Based on these findings, limitations and recommendations are given.

Writing apprehension is an overwhelming feeling of stress and anxiety when writing in a foreign language. In the past ten years, it has been a hot topic of debate. Due to its influence on writing quality, this study aimed to investigate this topic among a group of university EFL students, measuring the level of this apprehension and identifying its main factors. To accomplish this objective, 137 Saudi EFL students were given a questionnaire containing 26 statements spanning four essential writing apprehension factors on a five-point Likert scale. The results indicated that Saudi EFL students generally received moderate writing apprehension. Negativity towards writing was the furthermost common factor of writing apprehension. Self-efficacy in writing was regarded as the least apprehensive factor compared to other writing apprehension factors. The potential causes of this apprehension in each factor were addressed. Statistically significant gender and university level differences were found favouring males and beginner university levels for negativity towards writing. Based on these findings, limitations and future study recommendations are suggested.

The advent of 5G and blockchain technologies has signified rapid growth in the digital space. Their unique capabilities have already redefined communication and decentralized networks; however, their possible combined role is the subject of this research article. Objective: The article investigates the implications of 5G technology being used for integration with blockchain and how its disruptive nature would affect Binance's cryptocurrency trading platform. A mixed-methods research strategy was followed that draws upon data analytics, in-depth case study investigations and expert consultations. The key point was finding out how unparalleled low latencies and high-throughput capacities from 5G would be a procedures-changer for blockchain operations. The results indicate that the network capabilities of 5G can compensate for some blockchain major difficulties, including scalability and energy consumption. Quicker data transfer speeds up validation processes which leads to faster blockchain transaction times. This combination of advantages makes it possible to use microtransactions and facilitates scaling in terms of volume per transaction while retaining good levels of security and decentralization. Furthermore, with opportunities such as rapid cross-border payments and extensive smart contract capabilities available too — Binance certainly seems well-positioned to lead the charge in leveraging this convergence. But then again, the integration is accompanied by other challenges as well – mainly regulatory and technological challenges of innovation brought along by two cutting-edge technologies. The integration of 5th-generation mobile networks and blockchain has massive potential for cryptocurrency projects such as Binance. This partnership is poised to transform the existing blockchain landscape, ushering in a whole new era of fintech experience with an increased capability for speed and operational efficiency along safety systems.

Background: Recent artificial intelligence (AI) breakthroughs provide opportunities to extend human-like cognition. In this study, we explore the benefits of incorporating AI and brain-computer interfaces to improve human cognition in real-time. Objective: The article aims to gauge how much better AI-driven neural interfaces can make a vast array of brain function cognitive abilities, memory recall problem-solving, and creativity. Methodology: The study consisted of neural linkages implanted in a group of 200 participants from three different creative fields: art, music, and scientific investigation. As subjects conducted cognitive tasks while engaging with AI systems, researchers captured both quantitative and qualitative data. We used statistical analysis (t-tests, ANOVA) to assess the impact of AI on cognitive performance in addition to a thematic analysis platform assessing user experiences. Results: The results showed striking increases in cognitive performance as a result of using AI. Memory went up by 23%, creativity rose by 26%, whilst problem-solving capacity — at 24%. Statistical analyses verified these findings, quantified by large effect sizes on all tasks. Participants expressed having positive experiences as well, showing an improved collaboration with AI systems. Conclusion: The article highlights important implications for how Human-AI Symbiosis can be used to dramatically extend cognitive capability in creative and professional domains. The results suggest AI may have the power to alter human cognition for the better, making ethical considerations over its use all the more pressing.

Background: Quantum computing has opened new avenues for technological advancement, solving computationally intensive tasks and providing a unique dimension to drone technology. Drone technologies are widely used in agriculture, surveillance, and logistics because of their superior autonomous navigation, data collection, and delivery capabilities. Objective: Quantum computing and drone technology unlock incredible possibilities. Quantum algorithms will be studied to enhance drone functioning, data processing, and communication systems, enabling resilient, secure, and efficient operations in diverse application areas. Methods: Current quantum algorithms and drone applications were examined to construct an integrated functional model. In parallel, quantum-enabled drones were tested for operating efficiency, data transport, and computation. Also, a comparative study showed quantum integration's advantages over standard drone technology. Results: Quantum-integrated drones outperformed conventional drones in securing connections, data processing speeds, and optimizing route algorithms. Quantum encryption enhanced data transmission channel security, while quantum algorithms optimized pathfinding and navigation, reducing energy consumption and improving operational efficiency. Conclusion: Quantum computing and drone technology bring a new era of safe, efficient, and better autonomous systems across sectors. Quantum encryption and quantum computing boost data security and operating speed. This article lays the groundwork for developing quantum-integrated drone systems for use.

Background: Leadership is the behavior of an individual when directing the activities of a group toward a shared goal. In the health-care system, doctors’ responsibilities extend beyond clinical treatment. They should be efficient to lead a team in the patient care setting and within the broader framework of health-care systems. Good medical leadership is vital in delivering high-quality health care. Incorporating leadership skills in medical undergraduates will help them act as leaders and members of the health-care team. Aims and Objectives: The aims and objectives of the study are to enhance leadership skills and to assess the improvement in leadership skills of the participating medical undergraduate students. Materials and Methods: In this prospective study, medical undergraduate students posted in otorhinolaryngology clinics took part in various activities in two subsequent community otorhinolaryngology programs. Students were given different roles and responsibilities in the community programs to improve their leadership skills. Students filled out a pre- and post-activity self-assessment leadership trait questionnaire form for each community program. Results: 88 students participated in the study. The overall percentage leadership score increased significantly from the first pre-activity questionnaire (Q1) to the first post-activity questionnaire (Q2) (P

Existing works relied on subjective interviews in clinical settings, which does not detect mental health problems at early stages. With the advancement in learning strategies, research are trying to find a better way for detecting mental disorders and dysfunctions at early stages. EEG signal measurements is one of the prolific ways for identifying mental disorders and dysfunction in a non-invasive and ubiquitous way. However, the label scarcity and multiclass classification concerning EEG signal measurements has been a challenge for the research community that hinders the realization of automated mental dysfunction identification using EEG signals. Data imbalance is another issue that indirectly relates to the data scarcity issue. To tackle this challenge, we propose a novel Transformer-based Self-Contrastive and Multitask Learning (SCAM-Learning) framework for mental dysfunction classification using EEG signals. The SCAM-Learning framework uses Transformer networks, self-supervised contrastive learning paradigm, and multitask learning strategy to improve the classification performance. The multitask learning is accomplished by utilizing simple and complex data augmentation strategies to train the network for pretext task. The self-supervised contrastive learning helps in dealing with data and label scarcity issues. We also propose a novel cross contrastive loss that helps in improving interdependent correlation matrix for improving the classification performance. Our experimental results on a publicly available dataset reveal that the proposed method can achieve up to 11.89% performance gain in comparison to the existing state-of-the-art methods.

Background and Aim: To address the problems associated with the availability and prices of conventional feeds, researchers have started to include alternative feeds to reduce the cost of diets and increase profitability. This study examined the influences of olive cake (OC), either alone or in combination with Saccharomyces cerevisiae (SC), in the diet of black kids. Materials and Methods: Thirty kids were distributed into three treatments: A free OC diet (control [CON]), OC (20% OC), and OCSC (20% OC with 1 g SC head/d). While penned individually, the kids were fed daily and allowed to acclimate to their diets and pens for 7 days. After acclimation, the kids were fed the prescribed diets for 63 days. Five kids were chosen from each group on day 50 of the feeding period and moved to the metabolic cages to evaluate nutrient digestibility and N balance. At the end of the study, all kids were slaughtered to evaluate carcass characteristics and meat quality. Results: Feeding the OCSC diet increased (p = 0.035) DM intake for kids compared with the OC and CON. The CP intake was higher (p = 0.021) in the OCSC group than in the CON group. Growth performance parameters were higher (p ≤ 0.031) in the OCSC group than in the CON diet. Feed conversion efficiency was improved (p = 0.052) by incorporating OCSC compared with the CON diet. Consequently, economic return was enhanced (p = 0.003) by feeding the OC and OCSC groups. Kids fed the OCSC had a higher cold carcass weight (p = 0.054) than those fed the CON diet. The OCSC group also showed increased (p ≤ 0.027) loin cut and intermuscular fat weights compared with the OC and CON groups. All measured serum metabolites were similar in CON and groups incorporating OCs or OCSCs. Conclusion: The results showed that adding 20% OC combined with SC to the diet of growing kids improved some growth and carcass characteristic parameters without affecting their health status. Notably, using OC along with SC could be an option for feed cost reduction in kids’ diets.

The increasing incidence of tick-borne diseases and the growing resistance to conventional acaricides underscore the urgent need for innovative and sustainable tick management strategies. This study investigates the potential of assembly and sex pheromones for the control of larval stages of Rhipicephalus sanguineus, the brown dog tick, which poses significant health risks to dogs. The ticks were collected from various canine hosts in Thrissur, Kerala and subjected to Petri-dish bioassays using vapour patches impregnated with synthetic assembly pheromone (AP) and sex pheromone (SP), both individually and in combination. The efficacy of these semiochemicals was assessed based on the attraction and clustering behaviour of unfed larvae and partially fed adult ticks over a 24-hour period. Results demonstrated a significantly higher attraction to AP-impregnated patches, with notable efficacy observed in causing tick aggregation, particularly beyond 30 min of exposure. While SP also showed potential in attracting ticks, the combination of SP and AP did not result in a synergistic effect. The study highlights the potential of using semiochemicals as an environmentally sustainable alternative to traditional acaricides, highlighting their role in integrated pest management strategies Keywords: Rhipicephalus sanguineus, adenine, guanine, xanthine, 2,6-DCP, Petri-dish bioassay

Image enhancement is the process of improving the quality of a digital image. Improved image quality is one of the goals of the ongoing effort to improve the information content and interpretability of satellite images. Two sets of remote sensing data are collected for this region: one from the SPOT-4 (2018) satellite and the other from the Enhanced Thematic Mapper Plus (ETM+) Landsat 7 (2020) satellite. This study used two images taken at various spatial resolutions of the same location. Two images are shown here: one with a 30 m spatial resolution and the other with a 5 m resolution with multispectral processing. The results indicate that integrating spatial and spectral resolution using geomatics techniques significantly benefits various applications. Before merging the images, it was Root Mean Square Error (RMSE) 11.55 Easting, 5.77 Northing and became 1.52 Easting, 1.45 Northing after merging the images. After implementing the approach, the resulting fusing image exhibits enhanced spatial resolution, and the resulting multispectral image has excellent spatial and spectral resolution. Finally, the improved combined image with great spatial and spectral resolution is prepared for analysis and classification.

Abstract Background Lower extremity amputations (LEAs) significantly contribute to mortality and morbidity, often resulting from peripheral artery disease and diabetes mellitus (DM). Traumatic injuries also account for many LEAs. Despite the global burden, the epidemiology of LEAs, particularly in the Middle East and North Africa (MENA) region, remains underexplored. This study utilizes the Global Burden of Disease (GBD) dataset to analyze temporal trends in LEAs in the MENA region from 1990 to 2019. Methods The study utilized the 2019 GBD dataset, which includes estimates for incidence, prevalence, and disability-adjusted life-years (DALYs) across 369 diseases. Age-standardized incidence rates (ASIRs) for LEAs were extracted for 21 MENA countries. Trends were analyzed using percentage change calculations and Joinpoint regression to identify significant shifts in LEA rates over time. Results From 1990 to 2019, male LEA rates generally decreased, while female rates increased. Significant increases in LEA rates were observed in Syria, Yemen, and Afghanistan, correlating with periods of conflict and instability. Conversely, countries like Iraq, Palestine, Sudan, Lebanon, Iran, and Kuwait saw marked decreases. The study highlighted a complex interplay of socio-political factors, natural disasters, and chronic diseases like DM in shaping LEA trends across the region. Conclusion The study reveals variable LEA trends in the MENA region, influenced by conflicts, natural disasters, and chronic diseases. These findings underscore the need for targeted public health interventions, improved healthcare access, and robust data collection systems to reduce the burden of LEAs and improve patient outcomes in the MENA region.

Abstract Trained deep reinforcement learning (DRL) based controllers can effectively control dynamic systems where classical controllers can be ineffective and difficult to tune. However, the lack of closed‐loop stability guarantees of systems controlled by trained DRL agents hinders their adoption in practical applications. This research study investigates the closed‐loop stability of dynamic systems controlled by trained DRL agents using Lyapunov analysis based on a linear‐quadratic polynomial approximation of the trained agent. In addition, this work develops an understanding of the system's stability margin to determine operational boundaries and critical thresholds of the system's physical parameters for effective operation. The proposed analysis is verified on a DRL‐controlled system for several simulated and experimental scenarios. The DRL agent is trained using a detailed dynamic model of a non‐linear system and then tested on the corresponding real‐world hardware platform without any fine‐tuning. Experiments are conducted on a wide range of system states and physical parameters and the results have confirmed the validity of the proposed stability analysis (https://youtu.be/QlpeD5sTlPU).