Background: Mental illness is a significant challenge and becoming more relevant in today’s fast paced world. According to WHO, mental health is “a state of well-being in which the individual realizes his or her own abilities, can cope with the normal stresses of life, can work productively and fruitfully, and is able to make a contribution to his or her community”. The aim of the present study was to assess the knowledge about mental illness and attitude and practice of the public toward people with mental illness.  Methods: An observational, descriptive study with cross-sectional design was done among 200 adults of Bagh bazar slum, urban field practice area of department of Community Medicine, R. G. Kar Medical College and Hospital, Kolkata, West Bengal, India in May 2019 with a predesigned, pretested schedule.Results: Only 2.5% says that they are willing to live with a people with mental illness and only 1% has actually done so. Health-care seeking behavior shows that 54.5% will go to a general practitioner in case of any mental illness though only 2.5% believed that people with severe mental illness can fully recover.  Attitude toward mental illness showed mixed picture as also in knowledge.Conclusions: Health education and public awareness regarding mental illness can decrease the stigma, prejudice; discrimination attached with it and improves help-seeking behaviour of the community. This study provides insights into the cognitive and affective aspect of mental illness among adult population of the study area. It will also help in implementing better policies for increasing public awareness regarding mental illness.

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This paper introduces a new approach to the well-studied nurse scheduling problem. Nurse scheduling problem involves multiple inter-related parameters concerning nurse and patients, which makes the problem too complex. As a result, many of the traditional NSPs are forced to consider only the nurse-respective parameters for generating the schedules. In this paper, we have considered patient recovery as the ultimate objective of nurse scheduling. To achieve this, we have considered patient's needs and priorities besides the nurse skills, and environment (e.g. logistic) parameters as basic constraints. This paper aims to minimize soft constraints as well as to improve patient's satisfaction and quality of service of nurse assignment. We have defined a number of hard and soft constraints based on patient's requirements, ailments, preferences for a particular nurse, nurse's skill parameters, penalty, demands on duties, matching quotient with patient's requirements, location, etc. The assignment of nurses to patients for a particular shift depend on the relation between patient's need and the skill factor of the nurse, besides, of course, the availability factor of the nurse. This helps in achieving efficiency of the overall solution, besides properly supporting qualitative issues. In this regards, two objective functions are devised here to maximize the nurse's rewards and minimize the scheduling computational cost. The resulting algorithm has been tested on real-case scenarios of a nursing centre, providing evidence of the actual advantages of the proposed solution.

Part 5: Industrial Management and Other ApplicationsInternational audienceRemote care of patient is now becoming a subject of major concern in healthcare services. Proposed work describes a pervasive system to assist continuously the patients who are at remote place from the connected hospital using a priority based classification and assignment of nurses to the high risk patients. The challenge lies in storage and management of the vast amount of real-time data originating from heterogeneous sources under dynamic situations. This paper attempts to design a new system consisting of several modules for managing real-time heterogeneous data. The overwhelming data could cause difficulty to decide over numerous patients to whom the care should be given first and then onwards. To solve this, the proposed system attempts to derive fuzzy rules to make decision based on priority among selected groups in a dynamic environment. This proposed model formulates an indexed hash key for high risk patients and proper nurse relatively