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The purpose of this paper is to explore three main areas in relation to the interface between challenging behaviour and offending.

The first aim is to explore the labelling of behaviours as challenging or offending behaviour in the light of legal definitions, staff knowledge and beliefs and the mental capacity and mental health acts. The second is to explore challenging behaviour as a risk factor for offending in people with Intellectual Disability (ID).The third aim is to discuss the challenging behaviour as a barrier to discharge from secure services.

There is limited research suggesting that challenging behaviour such as physical aggression can lead to longer stay in forensic services, and this warrants further research.

This paper aims to promote discussion about the interface of offending and challenging behaviour in people with IDs and to promote best practice.

This paper aims to implement and extend the You Only Live Once (YOLO) algorithm for detection of objects and activities. The advantage of YOLO is that it only runs a neural network once to detect the objects in an image, which is why it is powerful and fast. Cameras are found at many different crossroads and locations, but video processing of the feed through an object detection algorithm allows determining and tracking what is captured. Video Surveillance has many applications such as Car Tracking and tracking of people related to crime prevention. This paper provides exhaustive comparison between the existing methods and proposed method. Proposed method is found to have highest object detection accuracy.

The goal of this research is to develop a deep learning framework to automate the task of analyzing video footage through object detection in images. This framework processes video feed or image frames from CCTV, webcam or a DroidCam, which allows the camera in a mobile phone to be used as a webcam for a laptop. The object detection algorithm, with its model trained on a large data set of images, is able to load in each image given as an input, process the image and determine the categories of the matching objects that it finds. As a proof of concept, this research demonstrates the algorithm on images of several different objects. This research implements and extends the YOLO algorithm for detection of objects and activities. The advantage of YOLO is that it only runs a neural network once to detect the objects in an image, which is why it is powerful and fast. Cameras are found at many different crossroads and locations, but video processing of the feed through an object detection algorithm allows determining and tracking what is captured. For video surveillance of traffic cameras, this has many applications, such as car tracking and person tracking for crime prevention. In this research, the implemented algorithm with the proposed methodology is compared against several different prior existing methods in literature. The proposed method was found to have the highest object detection accuracy for object detection and activity recognition, better than other existing methods.

The results indicate that the proposed deep learning–based model can be implemented in real-time for object detection and activity recognition. The added features of car crash detection, fall detection and social distancing detection can be used to implement a real-time video surveillance system that can help save lives and protect people. Such a real-time video surveillance system could be installed at street and traffic cameras and in CCTV systems. When this system would detect a car crash or a fatal human or pedestrian fall with injury, it can be programmed to send automatic messages to the nearest local police, emergency and fire stations. When this system would detect a social distancing violation, it can be programmed to inform the local authorities or sound an alarm with a warning message to alert the public to maintain their distance and avoid spreading their aerosol particles that may cause the spread of viruses, including the COVID-19 virus.

This paper proposes an improved and augmented version of the YOLOv3 model that has been extended to perform activity recognition, such as car crash detection, human fall detection and social distancing detection. The proposed model is based on a deep learning convolutional neural network model used to detect objects in images. The model is trained using the widely used and publicly available Common Objects in Context data set. The proposed model, being an extension of YOLO, can be implemented for real-time object and activity recognition. The proposed model had higher accuracies for both large-scale and all-scale object detection. This proposed model also exceeded all the other previous methods that were compared in extending and augmenting the object detection to activity recognition. The proposed model resulted in the highest accuracy for car crash detection, fall detection and social distancing detection.

The objective of this research work is to study the artificial intelligence (AI)-based product benefits and problems of the agritech industry. The research variables were developed from the existing review of literature connecting to AI-based benefits and problems, and 90 samples of primary data from agritech industry managers were gathered using a survey of a well-structured research questionnaire. The statistical package of IBM-SPSS 21 was utilized to analyze the data using the statistical techniques of descriptive and inferential statistical analysis. Results show that better information for faster decision-making has been ranked as the topmost AI benefit. This implies that the executives of agritech units have a concern about the quality of decisions they make and resistance to change from employees and internal culture has been ranked as the topmost AI problem.

The purpose of this study is to explore the dimensions of technology on which information technology (IT) professionals assess and evaluate a given technology. The authors believe that these dimensions influence their career outcomes and hold the potential to explain the intent to leave among IT professionals.

This study hypothesizes that the likelihood of a given technology being assessed favorably depends on various technology features perceived by IT professionals. These features influence their career prospects, thus influencing work outcomes like turnover intention. This study uses a survey-based quantitative technique to test the proposed research model. Data has been recorded from 312 IT professionals working in different service-based IT firms.

The findings of this study indicate that the technology IT professionals work with impacts their work and career-related outcomes. IT professionals evaluate and understand technology to assess the favorability of these technologies. Individual preferences to work with a specific technology are driven by that technology's possible influence on career outcomes such as the availability of job opportunities.

This study proposes that assigned technology influences career outcomes among IT professionals and has a potential to explain their turnover intentions. The authors have found that technologies fulfilling their career expectations, such as better work opportunities and job security, might be favorable for IT professionals. Unmet expectations with the assigned technology affect the turnover intention among IT professionals. Though this study examines turnover intention as an outcome, future studies can explore the attributes of technology relevant to IT professionals and their impact on other significant consequences such as work exhaustion and job satisfaction.

With a growing attrition rate and significant demand for skilled IT professionals, the importance of studying their behavior has become essential for both academia and the industry. Despite ample research, there is still a gap between theory and practice. This study on IT professionals proposes that understanding technology and how it is perceived, understood and evaluated by IT professionals might provide significant insights into their work behavior.

This study contributes to the literature by describing the unexplored phenomenon of the impact of technology assignment on IT professionals' work outcomes. This study is valuable in exploring a new dimension to explain turnover intention.

The purpose of this paper is to explain and come to an understanding of origin and growth relating to the concept of environment and sustainable development in domestic and international laws; also to show the rationality of enlisting the right to the environment as a basic right in the constitution, that would help to protect this value from the detrimental activities of private entities and states, and also to show the rationality of institutional development.

This paper attempts to focus on environment and sustainable development in Bangladesh. The research is based upon theoretical sources and empirical data.

An environment is a set of natural, artificial or man‐made, physical, chemical and biological elements that make the existence, transformation and development of living organisms possible. The appearance of the environmental law arose from the need to conserve the environment in order to avoid its destruction and, as a result, the danger that an adequate quality of life might disappear. By conservation, we understand all those measures that are necessary to preserve the environment and natural resources. In modern times, human development corresponds to the concept of sustainable development and environment. It is a transverse theme derived from joint consideration of the issue of the environment and environmental protection, and of all that concerns production in the development of a country or nation.

The paper is original in calling for making the constitution a weapon, in as much as the environment should be fit for human development so that productive activities may meet present needs without compromising those of future generations. The environment is an independent value and needs as strict protection as other commonly agreed values, such as the right to property, or the right to life and health.

This paper aims to study inhibitory effect of 4-aminothiophenol on the corrosion of mild steel (MS) in 0.5 M HCl.

In this study, electrochemical experiments, quantum chemical calculations, potentiodynamic measurements, linear polarization resistance and scanning electron microscopy were used.

The experimental results suggest that this compound is efficient corrosion inhibitor and the inhibition efficiencies increase with increasing their (from 0.5 to 10.0 mM.) concentrations. This reveals that inhibitive actions of inhibitors were mainly due to adsorption on mild steel surface. The adsorption of these inhibitors was found to obey Langmuir adsorption model. The computed quantum chemical features show good correlation with empirical inhibition efficiencies.

The 4-aminothiophenol is suitable inhibitor for application in closed-circuit systems against corrosion. The study is original and has great impact in industrial area. The obtained theoretical results have been adapted with the experimental data.