Search

The significance of using proper blade design is increasing with advancements in aviation technology. Wings are used in a variety of applications, including aircraft, drones, wind turbines, and more. In this study, Ansys version 2021 is used to do a CFD analysis on a NACA 0012 to study the effect of increasing the angle of attack on the coefficient of lift. Analyses have been performed using Ansys Fluent. The k-w turbulence model was used to analyze geometry, designed in SOLIDWORKS. At a speed of 32 m/s, several angles of attack have been evaluated from 0° to 20° to investigate lift and drag coefficient. The results of this study were compared with the literature for validation. It has been observed that increasing the angle of attack will increase the lift coefficient initially but after a specific angle, flow separation takes place and the lift coefficient starts decreasing with a further increase in the angle of attack

Approximately 23 % of total CO2 emissions are attributed to the construction industry each year; in 2009, the industry produced 5.7 billion tonnes of CO2. Green building design is concerned with constructing a building in such a way that its carbon footprint is reduced throughout its life span. Lecture rooms are key components of educational institutions because they are where students spend the majority of their time. This study aims to propose a methodology to convert the existing designs of building to green buildings by optimizing the airflow. The lecture room at UET was chosen for this investigation because the air velocity distribution inside the room is not uniform due to the current design, which influences the convective heat loss from the room by creating pressure and temperature gradient. SOLIDWORKS 2021 was used to create the model of a lecture theatre. Because the majority of the rooms are naturally ventilated, a fluid dynamics analysis was performed on ANSYS Fluent 19.1 to ensure proper air ventilation. Natural airflow was improved by the addition of an extra column of windows. The total electric energy load has been computed, and the quantity of solar panels necessary to meet the demand has been recommended.

Automation technology is developing intelligent health applications to facilitate patients by providing smart health solution. However, plethora of research is required in the autonomous robotics industry to provide smart solutions to hospitals.Hence, the proposed study aims to develop an intelligent automated infrastructure for hospitals, capable of performing several smart tasks in an Intensive Care Unit (ICU). The developed system will make food and medicine approachable by using a robotic arm on an autonomous robot. Furthermore, robotic arm can be locally controlled by the patient paramedic’s staff. The proposed intelligent robot will monitor patient’s condition by automatically monitoring the vitals conditions such as sleeping, stress, discomfort etc. This proposed system is very useful for diseases where close proximity can spread the disease, such as the covid-19 situation. Moreover, the automation will aid ICU patients by controllable bed functionality supported with patient’s EEG signals or remotely by staff. This research proposed LSTM based neural network for EEG classification and compare the results with other machine learning algorithms. The proposed LSTM network achieve 94% accuracy on self generated dataset. The achieved results are also compared with other machine learning models like SVM and Multilayer Perceptron (MLP). The intelligent navigation feature is also introduced which enables the robot to move autonomously in ICU. In addition to this, it can also establish video conference set up between the patient, staff and family members. The robot can automatically alert the staff in an emergency and assist the patient through an intelligent chatbot.

The Internet of Health Things (IoHT) has grown in importance for developing medical applications with the support of wireless communication systems. IoHT is integrated with many sensors to capture the patients' records and transmits them to hospital centres for analysis and reporting. Controlling and managing health records has been addressed in several ways, however, it is noted that two key research problems for vital communication systems are reliability and reducing data loss. To enhance the sustainability of health applications and effectively use the network infrastructure when transferring sensitive data, this research provides a machine learning approach. Moreover, data collected from the IoHTs are protected and can be securely received for physical process in hospitals using authentication trees. Firstly, the undirected graphs are explored based on the multi‐parametric machine learning approach to minimize the computation overheads and traffic congestion. Secondly, it evaluates the nodes' level behaviour over the heterogeneous traffic load with efficient identification of redundant links. Finally, in‐depth analysis and simulation results have shown that the proposed protocol is more effective than existing approaches for data accuracy and security analysis. [ABSTRACT FROM AUTHOR]

The purpose of this study, titled "green strategies in action," is to investigate the intricate relationship between green marketing, green leadership, and sustainability as mediator & greenwashing as a moderator in relationships to organizational performance. Thus, quantitative surveys and qualitative interviews with corporate executives are used in conjunction with a mixed -methods approach in order to investigate the complex relationships that exist within environmental sustainability projects. Ultimately, the purpose of this study is to determine how green leadership and green marketing work together to have a synergistic effect on sustainability, which in turn has an effect on organizational performance. Moreover, research examines potential moderating influence of greenwashing, highlighting importance of real environmental commitment at the same time. The findings provide organizations with useful insights that can be used to navigate difficulties and possibilities allied with execution of effective green strategies. This helps organizations nurture holistic approach toward sustainable practices and organizational success. [ABSTRACT FROM AUTHOR]

Smart technologies are advancing the development of cutting-edge systems by exploring the future network. The Internet of Things (IoT) and many multimedia sensors interact with each other for collecting and transmitting visual data. However, managing enormous amounts of data from numerous network devices is one of the main research challenges. In this context, various IoT systems have been investigated and have provided efficient data retrieval and processing solutions. For multimedia systems, however, controlling inefficient bandwidth utilization and ensuring timely transmission of vital information are key research concerns. Moreover, to transfer multimedia traffic while balancing communication costs for the IoT system, a sustainable solution with intelligence in real-life applications is demanded. Furthermore, trust must be formed for technological advancement to occur; such an approach provides the smart communication paradigm with the incorporation of edge computing. This study proposed a model for optimizing multimedia using a combination of edge computing intelligence and authentic strategies. Mobile edges analyze network states to discover the system's status and minimize communication disruptions. Moreover, direct and indirect authentication determines the reliability of data forwarders and network stability. The proposed authentication approach minimizes the possibility of data compromise and increases trust in multimedia surveillance systems. Using simulation testing, the proposed model outperformed other comparable work in terms of byte delivery, packet overhead, packet delay, and data loss metrics. [ABSTRACT FROM AUTHOR]

Summary: In modern technologies, the industrial internet of things (IIoT) has gained rapid growth in the fields of medical, transportation, and engineering. It consists of a self‐governing configuration and cooperated with sensors to collect, process, and analyze the processes of a real‐time system. In the medical system, healthcare IIoT (HIIoT) provides analytics of a huge amount of data and offers low‐cost storage systems with the collaboration of cloud systems for the monitoring of patient information. However, it faces certain connectivity, nodes failure, and rapid data delivery challenges in the development of e‐health systems. Therefore, to address such concerns, this paper presents an efficient data uncertainty management model for HIIoT using machine learning (EDM‐ML) with declining nodes prone and data irregularity. Its aim is to increase the efficacy for the collection and processing of real‐time data along with smart functionality against anonymous nodes. It developed an algorithm for improving the health services against disruption of network status and overheads. Also, the multi‐objective function decreases the uncertainty in the management of medical data. Furthermore, it expects the routing decisions using a machine learning‐based algorithm and increases the uniformity in health operations by balancing the network resources and trust distribution. Finally, it deals with a security algorithm and established control methods to protect the distributed data in the exposed health industry. Extensive simulations are performed, and their results reveal the significant performance of the proposed model in the context of uncertainty and intelligence than benchmark algorithms. [ABSTRACT FROM AUTHOR]

Nowadays, the Internet of Things (IoT) performs robust services for real-time applications in monitoring communication systems and generating meaningful information. The ZigBee devices offer low latency and manageable costs for wireless communication and support the process of physical data collection. Some biosensing systems comprise IoT-based ZigBee devices to monitor patient healthcare attributes and alert healthcare professionals for needed action. However, most of them still face unstable and frequent data interruption issues due to transmission service intrusions. Moreover, the medical data is publicly available using cloud services, and communicated through the smart devices to specialists for evaluation and disease diagnosis. Therefore, the applicable security analysis is another key factor for any medical system. This work proposed an approach for reliable network supervision with the internet of secured medical things using ZigBee networks for a smart healthcare system (RNM-SC). It aims to improve data systems with manageable congestion through load-balanced devices. Moreover, it also increases security performance in the presence of anomalies and offers data routing using the bidirectional heuristics technique. In addition, it deals with more realistic algorithm to associate only authorized devices and avoid the chances of compromising data. In the end, the communication between cloud and network applications is also protected from hostile actions, and only certified end-users can access the data. The proposed approach was tested and analyzed in Network Simulator (NS-3), and, compared to existing solutions, demonstrated significant and reliable performance improvements in terms of network throughput by 12%, energy consumption by 17%, packet drop ratio by 37%, end-to-end delay by 18%, routing complexity by 37%, and tampered packets by 37%. [ABSTRACT FROM AUTHOR]