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Hybrid Wireless Optical Broadband Access Networks (WOBAN) are the cost effective solution to overcome the increasing demand of the bandwidth. However, there is significant mismatch in the performance of the wireless and optical part. In the wireless part of WOBAN, a packet has to travel through several hops due to which the data packet has to incur a significant delay. In this paper we propose a Delay Sensitive Routing Algorithm (DSRA) which significantly reduces the delay in the wireless part. The proposed DSRA treats the data on the basis of delay sensitivity. Simulation results show a significant improvement in the overall performance of WOBAN.

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Objective: Giant cell arthritis (GCA) is a type of vasculitis which is more common in female gender and is closely associated with Polymyalgia rheumatic. One of its important complication include visual impairment. The burden of disease is expected to be very high by 2050 and there is a need to compile the data on most influential studies on GCA to define future strategy to deal with this dangerous disease. Bibliometrics is a statistical analysis of published literature that reflects the value and impact of a particular publication within the specific field. Aim of our study is identify the most significant contributors and their quality of contribution in the field. Method: We conducted this analysis utilizing SCOPUS database using different related MeSH terms. After a detailed screening, the list of top-50 articles were presented in the results in descending order of their ranks on the basis of their total number of citation. Most of our data comprises of publications from 1971-2012. Result: The top-50 most cited articles on GCA were published between 1971 and 2012 with the median number of citations 274 ranging from 598-187. Annals of Internal Medicine was the top ranked journal with 13 publications from the list. The highly ranked author based on the number of publications was Hunder GG (20 publications) with h-index of 40, retaining affiliation with Mayo Clinic, Rochester, United States. Mayo Clinic was the most frequently mentioned institute among the affiliations. The United States was found to be the most productive country rendering most of the articles (64%). Conclusion: Our bibliometric analysis on Giant cell arteritis identifies the information which may direct future research contributions, identify field experts, guide researchers to fill knowledge gaps, and assist in research fund allocation.

The future will be marked by a highly intelligent, automated, and ubiquitous digital world, requiring fast and reliable connectivity across physical, digital, and biological realms. While Multi-access Edge Computing (MEC) has facilitated swift connectivity between mobile devices and resource-rich cloud servers, current state-of-the-art solutions may struggle to meet the demands of compute-and bandwidth-intensive applications in the envisioned digital society. To make up for the capacity, 5G and the upcoming 6G expand the channel bandwidth, exacerbating spectrum scarcity and increasing network costs. To enhance Quality of Service (QoS) and minimize expenses, a recent proposal suggests parallel offloading using multiple radio access technologies (RATs) available on mobile devices, such as Wi-Fi Direct, Wi-Fi, and 5G. However, these technologies differ in performance, including throughput, delay, and response to physical conditions. Inappropriate marshalling of RATs can lead to issues like out-of-sequence packets, resource wastage, and reduced throughput, resulting in longer service delays. To address this problem, we evaluate RAT performance and develop a convex Continuous Non-Linear Program (CNLP) to optimally utilize their capacities, ensuring load distribution aligns with their performance. Additionally, we optimize capacity distribution at relay nodes to ensure smooth MEC data transfer based on incoming load. Numerical results demonstrate significant improvements in terms of throughput, delay, and QoS compared to other techniques involving multiple RATs for computation offloading.