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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]