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This research endeavors to advance energy efficiency (EE) within heterogeneous networks (HetNets) through a comprehensive approach. Initially, we establish a foundational framework by implementing a two-tier network architecture based on Poisson process distribution from stochastic geometry. Through this deployment, we develop a tailored EE model, meticulously analyzing the implications of random base station and user distributions on energy efficiency. We formulate joint base station and user densities that are optimized for EE while adhering to stringent quality-of-service (QoS) requirements. Subsequently, we introduce a novel dynamically distributed opportunistic sleep strategy (D-DOSS) to optimize EE. This strategy strategically clusters base stations throughout the network and dynamically adjusts their sleep patterns based on real-time traffic load thresholds. Employing Monte Carlo simulations with MATLAB, we rigorously evaluate the efficacy of the D-DOSS approach, quantifying improvements in critical QoS parameters, such as coverage probability, energy utilization efficiency (EUE), success probability, and data throughput. In conclusion, our research represents a significant step toward optimizing EE in HetNets, simultaneously addressing network architecture optimization and proposing an innovative sleep management strategy, offering practical solutions to maximize energy efficiency in future wireless networks. [ABSTRACT FROM AUTHOR]

This study investigates the influence of government effectiveness, health expenditure, and sustainable development goals (SDGs) on life expectancy in Pakistan. To accomplish this, a systematic analysis was conducted on time series data spanning from 2000 to 2020. Cointegration analysis was utilized to evaluate the long-term integration of all variables, while a comprehensive causality test was performed to investigate the short-term links among government effectiveness, health expenditure, SDGs, and life expectancy. The findings of the Johansen Cointegration test definitively confirmed the presence of long-term cointegration among all variables. In addition, the results of the Granger causality test show that there is a one-way causal relationship between government performance, health spending, and SDGs to life expectancy in the short term. The validation of both enduring and immediate connections among these factors emphasizes the crucial significance of healthcare services in Pakistan. Therefore, it is important to push for more healthcare investments and increased national budget allocations by the Pakistani government. Prioritizing the allocation of resources towards healthcare, bolstering the efficiency of the administration, and attaining SDG targets are all crucial for enhancing life expectancy in Pakistan. The study's results also carry significant policy implications, underscoring the necessity of strategically implementing health expenditure and SDG targets to enhance human capital and population well-being, as demonstrated by the increased life expectancy. [ABSTRACT FROM AUTHOR]

Renewable energy sources play a critical role in all governments' and organizations' energy management and sustainability plans. The solar cell represents one such renewable energy resource, generating power in a population-free circumference. Integrating these renewable sources with the smart grids leads to the generation of green smart grids. Smart grids are critical for modernizing electricity distribution by using new communication technologies that improve power system efficiency, reliability, and sustainability. Smart grids assist in balancing supply and demand by allowing for real-time monitoring and administration, as well as accommodating renewable energy sources and reducing outages. However, their execution presents considerable problems. High upfront expenditures and the need for substantial and reliable infrastructure changes present challenges. Despite these challenges, shifting to green smart grids is critical for a resilient and adaptable energy future that can fulfill changing consumer demands and environmental aims. To this end, this work considers developing a reliable Internet of Things (IoT)-based green smart grid. The proposed green grid integrates traditional grids with solar energy and provides a control unit between the generation and consumption parts of the grid. The work deploys intelligent IoT units to control energy demands and manage energy consumption effectively. The proposed framework deploys the paradigm of distributed edge computing in four levels to provide efficient data offloading and power management. The developed green grid outperformed traditional grids in terms of its reliability and energy efficiency. The proposed green grid reduces energy consumption over the distribution area by an average of 24.3% compared to traditional grids. [ABSTRACT FROM AUTHOR]

This paper disseminates our research findings that we conducted on university students in the years 2021, 2022, and 2023, with the year 2021 taken as the base year. Our research mined and excavated a concealed prevalence of social anxiety as an important and crucial facet of study anxiety in the university students of Pakistan. Using the Liebowitz Social Anxiety Scale (LSAS), we found a significant increase in the social anxiety level among university students in the past three years after the COVID-19 lockdown. Our data showed that the 'very severe anxiety' level soared up to 52.94% in the year 2023 from just 5.98% in the year 2021, showing a net increase of 47.06%. Statistical analyses demonstrate noteworthy differences in the overall social anxiety levels among the students, reaching significance at the 5% level and a discernable upward trend in the social anxiety levels as study anxiety. We also employed predictive analytics, including binary classifiers and generalized linear models with a 95% confidence interval, to identify individuals at risk. This study highlights a dynamic shift with escalating social anxiety levels among the university students and thus emphasizing its awareness, which is significantly important for the timely intervention, potentially preventing symptom escalation and improving outcomes. [ABSTRACT FROM AUTHOR]

The fifth-generation (5G) cellular infrastructure is expected to bring about the widespread use of connected vehicles. This technological progress marks the beginning of a new era in vehicular networks, which includes a range of different types and services of self-driving cars and the smooth sharing of information between vehicles. Connected vehicles have also been announced as a main use case of the sixth-generation (6G) cellular, with ultimate requirements beyond the 5G (B5G) and 6G eras. These networks require full coverage, extremely high reliability and availability, very low latency, and significant system adaptability. The significant specifications set for vehicular networks pose considerable design and development challenges. The goals of establishing a latency of 1 millisecond, effectively handling large amounts of data traffic, and facilitating high-speed mobility are of utmost importance. To address these difficulties and meet the demands of upcoming networks, e.g., 6G, it is necessary to improve the performance of vehicle networks by incorporating innovative technology into existing network structures. This work presents significant enhancements to vehicular networks to fulfill the demanding specifications by utilizing state-of-the-art technologies, including distributed edge computing, e.g., mobile edge computing (MEC) and fog computing, software-defined networking (SDN), and microservice. The work provides a novel vehicular network structure based on micro-services architecture that meets the requirements of 6G networks. The required offloading scheme is introduced, and a handover algorithm is presented to provide seamless communication over the network. Moreover, a migration scheme for migrating data between edge servers was developed. The work was evaluated in terms of latency, availability, and reliability. The results outperformed existing traditional approaches, demonstrating the potential of our approach to meet the demanding requirements of next-generation vehicular networks. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is an emerging technology that has recently gained significant interest, especially with the dramatic increase in connected devices. However, IoT networks are not yet standardized, and the design of such networks faces many challenges, including scalability, flexibility, reliability, and availability of such networks. Routing is among the significant problems facing IoT network design because of the dramatic increase in connected devices and the network requirements regarding availability, reliability, latency, and flexibility. To this end, this work investigates deploying a multipath routing scheme for dense IoT networks. The proposed method selects a group of routes from all available routes to forward data at a maximum rate. The choice of data transmission routes is a complex problem for which numerical optimization methods can be used. A novel method for selecting the optimum group of routes and coefficients of traffic distribution along them is proposed. The proposed method is implemented using dynamic programming. The proposed method outperforms the traditional route selection methods, e.g., random route selection, especially for dense IoT networks. The model significantly reduced the number of intermediate nodes involved in routing paths over dense IoT networks by 34%. Moreover, it effectively demonstrated a significant decrease of 52% in communication overhead and 40% in data delivery time in dense IoT networks compared to traditional models. [ABSTRACT FROM AUTHOR]

Next-generation cellular networks are expected to provide users with innovative gigabits and terabits per second speeds and achieve ultra-high reliability, availability, and ultra-low latency. The requirements of such networks are the main challenges that can be handled using a range of recent technologies, including multi-access edge computing (MEC), artificial intelligence (AI), millimeterwave communications (mmWave), and software-defined networking. Many aspects and design challenges associated with the MEC-based 5G/6G networks should be solved to ensure the required quality of service (QoS). This article considers developing a complex MEC structure for fifth and sixth-generation (5G/6G) cellular networks. Furthermore, we propose a seamless migration technique for complex edge computing structures. The developed migration scheme enables services to adapt to the required load on the radio channels. The proposed algorithm is analyzed for various use cases, and a test bench has been developed to emulate the operator's infrastructure. The obtained results are introduced and discussed. [ABSTRACT FROM AUTHOR]