Over the years, process safety management (PSM) program development and implementation has benefited the operating facilities in terms of successful avoidance of process safety incidents, and related business losses arising due to equipment damage, production interruption and environmental damage. PSM covered facilities have also reported improved personal injury record through successful implementation of an applicable, regional PSM standard. However, there is still a need to address both the efficiency and effectiveness aspects related to non-digital (manual) PSM program development, implementation, audit, and compliance. There has been continuous reporting of significant process safety incidents even after the promulgation of process safety management regulations both in the United States and globally. The uncertain macroeconomic and political conditions have also forced the governments to allocate less than required fiscal budget to their regional regulatory bodies to address any ongoing operational efficiency concerns in successful enforcement of their PSM regulation. This research paper will investigate and discuss the key issues faced by both the government regulatory bodies and process plant facilities, with the current non-digital (manual) method of PSM implementation, audit, and compliance. Moreover, recent modernization initiatives of safety regulations undertaken by federal regulatory bodies such as OSHA and EPA in the United States are described and discussed in the context of achieving the PSM compliance effectively and efficiently. The paper will also discuss detailed comparison between OSHA’s 1992 regulatory impact analysis study and 2023 information collection requirement burden hour and cost estimate and will outline a methodology to estimate the total economic burden of PSM compliance on the existing and future PSM covered facilities in the United States. The methodology to adjust (correct) OSHA’s PSM compliance cost estimates are based on accounting key regulatory, industrial, organizational, and economic factors prevalent in the global process plant industry.

The issue of drowsiness while operating a motor vehicle is an increasingly common occurrence that has been found to contribute significantly to a substantial number of fatal accidents annually. The urgency of the current situation necessitates implementing a solution to mitigate accidents and fatalities. The present study aims to investigate a less intricate and less expensive but remarkably efficient approach for detecting drowsiness in drivers, in contrast to the existing complex systems developed for this purpose. This paper focuses on developing a simple drowsy driver detection system utilizing the Python programming language and integrating the OpenCV and Dlib models. The shape detector provided by Dlib is employed to accurately determine the spatial coordinates of the facial landmarks within the given video input. This enables the detection of drowsiness by monitoring various factors such as the aspect ratios of the eyes, mouth, and the angle of head tilt. The performance evaluation of the system under consideration is conducted through the utilization of standardized public datasets and real-time video footage. When tested with dataset image inputs, the system showed exceptional recognition accuracy. The performance comparison is done to show the efficacy of the proposed approach. Traveling can be made safer and more effective by combining the proposed system with additional safety features and automation technology in cars.

Object tracking is a popular problem for automatic surveillance systems as well as for the research community. The requirement of an object tracking problem is to predict the output including the object position at the current frame based on the input the position of the object at the previous frame. To present the comparison and experiment of some object tracking methods based on deep learning and suggestions for improvement between them in this paper, we had taken some important steps to conduct this research. First, we find out the studies related to deep learning-based object tracking models. Secondly, we examined image and video data sets for verification purposes. Thirdly, to evaluate the results obtained from existing models, we experimented with a little work related to object tracking based on deep learning networks. Fourth, based on the implemented object tracking models, we had proposed a combination of these methods. And finally, we summarize and give the evaluations for each object tracking model from the results obtained. The results show that object tracking based on Siammask model has the highest results TO score of 0.961356383 on VOT dataset and 0.969301864 on UAV123 dataset, but the possibility of errors is also high. Although the result of the combined method has few scores those are lower than the object tracking based on Siammask model, the combined method is more stable than the object tracking based on Siammask model when TME score of 16.29691993 on VOT dataset and 10.16578548 on UAV123 dataset. The Vehicle ReIdentification method results have scores that are not too overwhelming. However, the TME score is the highest with the TME score of 11.55716097 on the VOT dataset and 4.576163526 on the UAV123 dataset.

The overabundance of information on the internet and ecommerce has resulted to the development of recommender system to discover interesting items or contents that are recommendable to the user. The recommended items might be of no interest or unwanted to the users and can make users to lose interest in the recommendations. In this work, a Collaborative Filtering (CF) based method which exploits the initial top-N recommendation lists of an item-based CF algorithm based on unwanted recommendations penalisation is presented.  The method utilises a relevance feedback mechanism to solicit for users preferences on the recommendations while popularise similarity function minimises the chances of recommending unwanted items. The work explains the proposed algorithm in detail and demonstrates the improvements required on existing CF to provide some adjustments required to improve subsequent recommendations to users. 

This study presents the design modification and performance evaluation of an active solar dryer. The Modified Active Solar Dryer (MASD) is equipped with a non-concentrating flat plate thermal collector and drying chamber to dry various agricultural products. The drying chamber contains trays inside it. These are used to completely circulate airflow inside it. The drying Chamber has a cross-section Area of 0.30m × 0.45m and a height of 0.45m. The Flat plate collector has 0.91 m length, 0.45 m width, 0.12 m depth, and 0.049 m^3 volume. This study performed experimental work and evaluated performance by drying the chili in a Simplified Active Solar Dryer and Modified Active Solar dryer from 8 am to5 pm with average solar irradiance of 883.25W⁄m^2 . The temperature has been recorded at six different points. The difference in temperatures between the dryer input and the surrounding air has been observed continuously. Temperatures at the Thermal Collector and Drying Chamber are the main prime mover of solar drying, so our main concern was on these two temperatures. The temperature achieved by SASD in the thermal collector is in the range of 55 ~ 60˚C meanwhile in the MASD temperature range is 65 ~ 70˚C. In SASD chili was dried in five days meanwhile in Modified Active Solar Dryer it took four days. The moisture content of the chili was reduced to 10% and it took 45hour for SASD and 34 hours for MASD. It is concluded that Modified Active Solar Dryer took 34% less time than the Simplified Active Solar Dryer.