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The purpose of this paper is to discuss an online sensor-based support system which the authors believe can be useful in such scenarios. Persons with a cognitive impairment, such as those with Alzheimer’s disease, suffer from deficiencies in cognitive skills which reduce their independence; such patients can benefit from the provision of further assistance such as reminders for carrying out instrumental activities of daily living (IADLs).

The system proposed processes data from a network of sensors that have the capability of sensing user interactions and on-going IADLs in the living environment itself. A probabilistic learning model is built that computes joint probability distributions over different activities representing users’ behavioural patterns in performing activities. This probability model can underpin an intervention framework that prompts the user with the next step in the IADL when inactivity is being observed. This prompt for the next step is inferred from the conditional probability taken into consideration the IADL steps that have already been completed, in addition to contextual information relating to the time of day and the amount of time already spent on the activity. The originality of the work lies in combining partially observed sensor sequences and duration data associated with the IADLs. The prediction of the next step is then adjusted as further steps are completed and more time is spent towards the completion of the activity, thus updating the confidence that the prediction is correct. A reminder is only issued when there has been sufficient inactivity on the part of the patient and the confidence is high that the prediction is correct.

The results of this study verify that by including duration information the prediction accuracy of the model is increased and the confidence level for the next step in the IADL is also increased. As such, there is approximately a 10 per cent rise in the prediction performance in the case of single sensor activation in comparison to an alternative approach which did not consider activity durations.

Duration information to a certain extent has been widely ignored by activity recognition researchers and has received a very limited application within smart environments.

This study concludes that incorporating progressive duration information into partially observed sensor sequences of IADLs has the potential to increase performance of a reminder system for patients with a cognitive impairment, such as Alzheimer’s disease.

This paper aims to discuss an online sensor-based support system which is believed to be useful for persons with a cognitive impairment, such as those with Alzheimer’s disease, suffering from deficiencies in cognitive skills which reduce their independence. Such patients can benefit from the provision of further assistance such as reminders for carrying out instrumental activities of daily living (iADLs).

The system proposed processes data from a network of sensors that have the capability of sensing user interactions and ongoing iADLs in the living environment itself. A probabilistic learning model is built that computes joint probability distributions over different activities representing users’ behavioural patterns in performing activities. This probability model can underpin an intervention framework that prompts the user with the next step in the iADL when inactivity is being observed. This prompt for the next step is inferred from the conditional probability, taking into consideration the iADL steps that have already been completed, in addition to contextual information relating to the time of day and the amount of time already spent on the activity. The originality of the work lies in combining partially observed sensor sequences and duration data associated with the iADLs. The prediction of the next step is then adjusted as further steps are completed and more time is spent towards the completion of the activity; thus, updating the confidence that the prediction is correct. A reminder is only issued when there has been sufficient inactivity on the part of the patient and the confidence is high that the prediction is correct.

The results verify that by including duration information, the prediction accuracy of the model is increased, and the confidence level for the next step in the iADL is also increased. As such, there is approximately a 10 per cent rise in the prediction performance in the case of single-sensor activation in comparison to an alternative approach which did not consider activity durations. Thus, it is concluded that incorporating progressive duration information into partially observed sensor sequences of iADLs has the potential to increase performance of a reminder system for patients with a cognitive impairment, such as Alzheimer’s disease.

Activity duration information can be a potential feature in measuring the performance of a user and distinguishing different activities. The results verify that by including duration information, the prediction accuracy of the model is increased, and the confidence level for the next step in the activity is also increased. The use of duration information in online prediction of activities can also be associated to monitoring the deterioration in cognitive abilities and in making a decision about the level of assistance required. Such improvements have significance in building more accurate reminder systems that precisely predict activities and assist its users, thus, improving the overall support provided for living independently.

The importance of psychological capital (PsyCap) and perceived organizational support (POS) have been identified over the years, however, the underlying relationship of both constructs with different employee outcomes is still a subject of research. The purpose of this study is to investigate whether POS helps in mediating the effect of PsyCap on employee engagement (EE).

In total, 420 samples (middle-level information technology (IT) professionals) were collected from different IT industry located in India by using online survey questionnaires. The collected data were further analyzed using regression analysis, factor analysis, structural equation modeling, reliability and validity analysis, mediation analysis and model fit indices analysis.

The results of the present study confirmed the full mediating effect of POS on the PsyCap-EE relationship and demonstrated that employees with a higher level of PsyCap, contribute more positively to the POS level which further enhances the employee’s level of engagement at the workplace.

The samples collected for the current study included only middle-level IT professionals of the IT industry in India; therefore, the present study results have limited general applicability. The results and findings of the current study are only on the basis of inferential statistical analysis, and descriptive analysis was not performed on the collected data. Further, the study does not investigate the influence of time.

This study would assist practitioners of human resources in organizational development by enhancing the employee’s positive attitude and commitment toward their study. Further, EE can also be improved by enhancing the levels of POS and PsyCap of employees, which is in line with the findings of the current study.

The current study examines the mediating effect of POS on psychological capital and EE the relationship for the first time.

This paper aims to develop a real-time pothole detection system to improve mapping, localization and path planning, reducing vehicle instability and accident risks. Efficient mapping, accurate localization and optimal path planning stand as prerequisites to realizing accident-free navigation. Despite their significance, existing literature often overlooks the real-time detection of potholes, which poses a considerable risk, particularly during nighttime operations. Potholes contribute to vehicle imbalance, trajectory tracking errors, abrupt braking, wheel skidding, jerking and steering overshoot, all of which can lead to accidents.

Unmanned vehicles constitute a critical domain within robotics research, necessitating reliable autonomous navigation for their optimal functioning. This research paper addresses the gap in current methodologies by leveraging a Convolutional Neural Network (CNN)-based approach to detect potholes, facilitating the generation of an efficient environmental map. Furthermore, a hybrid solution is proposed, integrating an improved Ant Colony Optimization (ACO) algorithm with modified Bezier techniques, complementing the CNN approach for accident-free and time-efficient unmanned vehicle navigation. The conventional Bezier technique is enhanced by incorporating new control points near sharp turns, mitigating rapid trajectory convergence and ensuring collision-free paths.

The hybrid solution, combining CNN with path smoothing techniques, is rigorously tested in various real-time scenarios. Experimental results demonstrate that the proposed technique achieves a 100% reduction in collisions in favorable conditions, a 4.5% decrease in path length, a 100% reduction in sharp turns and a significant 23.31% reduction in total time lag compared to state-of-the-art techniques such as conventional ACO, ACO+ Bezier and ACO+ midpoint Bezier, Improved ACO, hybrid ACO+ A*.

The proposed technique provides a proficient solution in the field of unmanned vehicles for accident-free time efficient navigation in an unstructured environment.