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Background: In supraclavicular brachial plexus block, to prolong the duration of analgesia, many adjuvants have been tried in the past in many studies but an ideal adjuvant remains yet to be discovered. Dexmedetomidine, a selective Alfa-2 adrenergic agonist when added to local anesthetic has been reported to prolong the block duration and post-operative analgesia in various regional blocks. Aims and Objectives: The aims and objectives are to study the onset and duration of sensory and motor blockade, postoperative analgesia, and hemodynamic effects of addition of dexmedetomidine with ropivacaine in supraclavicular brachial plexus block. Materials and Methods: Sixty patients aged between 18 and 60 years, American Society of Anesthesiologists class I and II, of both sexes, scheduled for upper limb surgery under supraclavicular brachial plexus block were randomly allocated into 2 groups. Group-A received 20 mL of 0.5% ropivacaine in brachial plexus block with 1 μg/kg dexmedetomidine as adjuvant perineurally and Group-B received 20 mL 0.5% ropivacaine in brachial plexus block with dexmedetomidine intravenous infusion at 1 μg/kg over 10 min. Intraoperatively non-invasive blood pressure, heart rate, SpO2, and sedation were recorded every 5 min for the first 10 min and every 15 min thereafter till the end. Time of first rescue analgesic, intensity of postoperative pain, and total analgesic required were recorded. Results: Onset of sensory and motor block was faster in Group-A than Group-B. Duration of analgesia was prolonged in Group-A than Group-B. Hemodynamic stability was better maintained in Group-A than Group-B. Sedation was better in Group B. Conclusion: Dexmedetomidine as adjuvant to ropivacaine in supraclavicular brachial plexus block is more efficacious in providing faster onset of motor and sensory blocks and prolonging duration of postoperative analgesia with better hemodynamic stability.

Bangladesh has been experiencing rapid urbanization and economic growth, motivating people to move towards the cities. Consequently, commercial areas of the cities are becoming highly occupied. The issue of overpopulation leads to congestion and shortage of open spaces, both reserved and publicly accessible, in such areas of the cities, affecting the physical and mental health of the users of these areas. To understand this situation, Chattogram in particular, the Chaktai commercial area is identified as the case study area for further investigation and research. This study will examine the present scenario of this densely built commercial area through an empirical analysis and field survey, focusing on collecting data related to major land-use types, street characteristics, major activities, traffic analysis, and environmental conditions. Additionally, an observational approach will be employed, along with one-to-one interviews and Focus Group Discussions (FGD), to gather user feedback on the space. By examining urban regeneration strategies for Asadganj, this study offers guidelines to improve conditions for its users including residents, business holders, and workers, to enhance the area's quality of life.

The ever-changing technology landscape has seen a significant breakthrough with the introduction of edge computing. This innovation has revolutionized various domains, and one of its critical applications is in the domain of accident detection. Edge computing can help enhance road safety and emergency response by enabling real-time processing and analysis of sensory information from onboard sensors, cameras, and other connected devices. By integrating edge computing into accident detection systems, we can overcome the limitations of conventional centralized cloud-based methods and create a safer transportation network. In this article, we have proposed an accident detection framework using Deep Learning (DL) in the edge cloud environment. For accident detection, we have used a Convolutional Neural Network (CNN)- based DL model. The DL model detects the accident in the edge node which is near the data source. The proposed framework provides low latency, minimal network usage, and lower execution time as compared to only cloud-based deployment. Additionally, the proposed accident detection model is accurate up to 95.91% with Precision 0.9574, Recall 0.9574 and F1 score 0.9574 in the cloud-edge environment.