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ABSTRACT The potential benefits of 5G networks, such as faster data speeds and improved user experiences, come with a critical challenge—efficiently preserving energy in base stations (BSs). Addressing this challenge is crucial, necessitating a focus on maximizing the energy efficiency of these stations. BSs play a vital role in providing coverage and capacity by using different frequency bands adapting to diverse communication needs. The choice of a specific frequency band can impact network performance, data rates, coverage range, and signal propagation. This study emphasizes the crucial challenge of preserving energy in 5G BSs and underscores the significance of strategic frequency band selection for optimizing energy efficiency and network performance. For this, stochastic models are introduced for BSs, specifically the Markov and semi‐Markov models, which help choose frequency bands based on system traffic. Closed‐form solutions for steady‐state system size probabilities in these models are derived. A sensitivity analysis is conducted to assess power consumption in various scenarios. Furthermore, a comparison between analytical findings and simulation outcomes regarding power consumption is presented. The convergence of results in both methodologies emphasizes a significant level of agreement; that is, the introduced stochastic models provide insights and are validated by a close alignment between analytical and discrete‐event simulation outcomes. We have shown the behavior of power consumption with respect to three different distributions named deterministic, exponential, and hypo‐exponential. This research highlights the importance of strategic frequency band selection for 5G BSs to optimize energy efficiency and meet the demands of evolving communication networks. [ABSTRACT FROM AUTHOR]

Summary: With the increasing prevalence of wireless sensor networks, the uninterrupted operation of these networks becomes essential. To ensure continuous functionality, wireless networks rely on available base stations (BSs). However, the persistent operation of BSs comes at the cost of substantial energy consumption. Consequently, given the surge in wireless network traffic and the necessity for uninterrupted BS availability, energy efficiency within the BS becomes a concerning issue. To address this, the study employs a semi‐Markov model to depict the availability of the BS, with states corresponding to the failures of its components (baseband unit, remote unit, and software module). The analysis yields a steady‐state solution, with reward rates assigned to each state based on the energy consumption of individual BS components. This approach enables the determination of the expected energy consumption within this model. Additionally, the BS's throughput is assessed using an M/G/1 queueing model with server breakdown. [ABSTRACT FROM AUTHOR]

Aim: The aim was to study the brain connectivity using diffusion tensor imaging (DTI) among patients with treatment-resistant schizophrenia (TRS) and compare the same with a group of patients without TRS. Methods: Twenty-three patients with TRS and 15 patients without TRS underwent DTI using a 3T magnetic resonance imaging machine. DTI data were processed with the calculation of fractional anisotropy (FA) and apparent diffusion coefficient. Patients were also assessed on Brief Psychiatric Rating Scale, Positive and Negative Symptom Scale, Global Assessment of Functioning Scale, and Clinical Global Impression severity scale. Results: Patients with TRS and non-TRS differed significantly in the FA values in the region of right superior longitudinal fasciculus and right uncinate fasciculus, with more integrity of tracts in the non-TRS group. However, these differences disappeared when Bonferroni correction was used for multiple comparisons. Conclusion: The present study suggests lack of significant difference in DTI findings between patients with TRS and non-TRS. [ABSTRACT FROM AUTHOR]

Context: Aging is a natural process which universally affects all the human beings in the society. As the geriatric population is quiet vulnerable, They might suffer from mental and physical disabilities which consequently threatens their independence. Quality of life among the geriatric population is a global concern as it reflects the status of health and of well being among the set population. Aims: To assess the quality of life of elderly living in rural and urban areas and compare the role of socio-demographic factors influencing the quality of life of elderly. Settings and Design: It is a Community based Cross sectional study conducted in urban and rural field practice areas of MMIMSR, Mullana. Methods and Material: Convenience sampling was used. A total of 200 elderly were included in the study. A pretested semi structured questionnaire was used. Statistical Analysis: Data was analysed using SPSS 20.0. Results: According to the sex of the participants, male participants had a higher mean score for QOL as compared to the female participants. Higher mean score was found in each domain for the participants living with their spouses. Conclusions: The quality of life is better among the individuals who do not suffer from any chronic illness'. The health care services should be strengthened to provide for better healthcare to the elderlies for their morbid conditions. [ABSTRACT FROM AUTHOR]