Classroom and laboratory infrastructure, food quality, and the environment cause many health issues to the students at colleges and universities, like back pain, eye issues, shoulder pain, and pain in the spinal cord. These problems affect many students' physical and psychological capacity, and students lose enthusiasm and intelligence. This study was conducted at Mehran University of Engineering and Technology (MUET) to identify the ergonomic problems of students from attending lectures in class, performing different experiments in laboratories and workshops, and food quality at the canteens. This study focuses on ergonomic awareness and following rules and regulations for students and university administration. This research is conducted based on different questionnaires and the Rapid Upper Limb Assessment (RULA) method. Through questionnaires, we analyzed students’ physical and mental problems. The survey was conducted at the before mentioned university from a total of 104 random students. Using the RULA method, we take data from different postures of students while performing practical work in computer laboratories, mechanical workshops, and other laboratories. After analysis, it is found that the RULA score is almost 7, which is dangerous for health and further survey shows many health problems through questionnaires. It is suggested that universities should follow the ergonomics rules, make a good environment for classes, laboratories, and workshops also improve the food quality at the canteen to mitigate the health problems in students.

Maintenance in any manufacturing organization is critical, given its significant role in ensuring business continuity. Maintenance plays a crucial role and has a significant impact on the results of industrial companies. Therefore, it is essential to manage maintenance, observe, understand, and improve actions by adopting well-chosen performance indicators according to the company's needs. These indicators are known as Maintenance KPIs or Key Performance Indicators, which allow for gathering knowledge and exploring the best means to achieve the organization's goals. Maintenance KPIs are critical to keeping track of the function, monitoring performance, and ensuring fulfillment of business expectations. In addition, KPIs drive reliability growth while guiding decisions to improve maintenance efficiency and performance. A helpful maintenance KPIs help to identify the problems causing the maintenance effect and help to select the right strategy to support or correct the actions that produced the results. They also allow to identify the causes of equipment failures (measure the influence of life cycle factors), direct what maintenance does with its time and resources (measure the efficiency and effectiveness of the maintenance group) and identify if maintenance removes failure causes ( measure the improved reliability and operational risk reduction results of maintenance effort) and help drive the business benefits provided by maintenance (measure the contribution to the business value of maintenance).

Essential maintenance KPIs are the most commonly used for maintenance management and are adopted by most industries; among these primary KPIs which are essential for maintenance management, we cite Mean Time Between Failure (MTBF), Mean Time To Repair (MTTR), and Overall Equipment (OEE). Nevertheless, it is crucial to continuously redefine and update KPIs to ensure they are appropriate for the organization's current environment, significantly when the constant market or research methodologies change. Hence, researchers and the industry propose several other maintenance KPIs outside the essential ones used in the industry according to the needs and within the performance improvement framework. These proposed KPIs aim to compensate for the lack of maintenance data, the absence of decision support, and the problems related to specific equipment, also in the context of improving the management strategy, the application of predictive maintenance, and the quality control of a maintenance process or the monitoring of systems reviews. Unfortunately, these indicators are not sufficiently known and are, therefore, not used by the industry. However, we believe that some of them should gain maturity and reach the status of widely used traditional indicators, such as the KPI of obsolescence management in maintenance operations and schedule compliance KPIs that aim to link maintenance planning with production. In addition, although not all proposed KPIs in the literature are generalizable, it has been identified that they can sometimes be specific to problematic situations, equipment categories, and even sectors of industry activity. Therefore, this work aims to inventory the most widely used maintenance KPIs and some of the KPIs proposed by researchers and the industry. In addition, we study the trends and challenges of selecting these KPIs and for what purposes they are used to help their understanding and usability. Indeed, Maintenance managers need to select relevant KPIs aligned with the maintenance strategy and the company objectives. 

The objective of the research is to design and evaluate the grid-connected solar photovoltaic roof-top system at Tetulia, Panchagrah, Bangladesh using PVsyst software. The main factors of this research are the move toward renewable energy like PV with environmental consequences. The overall performance of a photovoltaic cell is determined by the amount of solar irradiation, the type of PV module used, and the orientation of PV module. Now, the grid-connected PV system is the best choice for large-scale renewable energy. For the case study, PVsyst software is used to analyze a 3kW solar PV plant installed on a rooftop for residential load consumption of 8.1kWh/day. The available AC energy generated by the PV panels is 4172kWh/year, and 1871kWh/year of surplus energy is supplied to the grid after daytime power demand is met. The yearly global horizontal irradiation of Tetulia, Panchagrah is 1485.4kWh/m2 and during the night, the quantity of electricity imported from the grid is 1050kWh/year. This technology helps in the prediction of power outages and backup energy storage because it makes use of the energy stored in the batteries.

Massive innovation enhancements all through the world have ignited wild rivalry among organizations, with each endeavoring to draw in clients utilizing an alternate methodology. Augmented Reality is a relatively new approach (AR). AR is a relatively latest technology which can provide better possibilities than other inventions to match. Conduct of genuine items under specific circumstances, which is especially pertinent to this work. Unfortunately, developing realistic 3D material is difficult in and of itself and necessitates a lot of human labor. Data analysis requires the use of scientific visualization. Augmented Reality (AR), on the other hand, is a relatively new trend that includes the overlay of computer visuals on the actual environment. In this review, we utilize both of these tools to analyze logical information. As a result, we may use graphical representations derived from numerical statistics to enrich reality. A simulated system can likewise be put in an authentic climate to get further knowledge into a peculiarity. essential.

This paper reviews research work done in Augmented Reality including the history and technologies that enable augmented reality like the devices and various means of merging real world with virtual world like tracking types. The review gives detailed explanation of various applications where AR is currently being used and discusses future significance of AR where it will be necessary for AR-technology to co-exist with mankind. AR technology is constantly improving and upgrading makes it exponentially more valuable for businesses to adopt AR tech for better profitability. 

A major risk associated with internet usage is the access of websites that contain malicious content, since they serve as entry points for cyber attackers or as avenues for the download of files that could harm users.  Recent reports on cyber-attacks have been registered via websites, drawing the attention of security researchers to develop robust methods that will proactively detect malicious websites and make the internet safer. This study proposes a deep learning method using radial basis function neural network (RBFN), to classify abnormal URLs which are the main sources of malicious websites. We train our neural network to learn benign web characteristics and patterns based on application layer and network features and apply binary cross entropy function to classify websites. We used publicly available datasets to evaluate our model. We then trained and assessed the results of our model against conventional machine learning classifiers. The experimental results show a very successful classification method, that achieved an accuracy of 89.72% on our datasets.