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Background: Ageing is not an illness, but the elderly are vulnerable to slowly evolving chronic diseases. The difficulties faced by the elderly are uncountable leading to social and cultural differences in the present Indian society. Objective: This study was conducted to assess the socio-demographic profile, lifestyle practices and morbidities of the elderly and their association, particularly with non-communicable diseases. Methodology: A cross-sectional study was carried out in the field practice area of a medical college in a city in north part of India. A total of 225 elderly patients (60 years old and above) residing in urban area were interviewed using a pre-tested questionnaire through house-to-house visit. Results: Out of the total, there were 51.1% females (n = 115), 99.1% Hindus (n = 223), 51.1% married (n = 115), 79.6% lived in joint families (n = 179) and 52.9% belonged to upper middle class (n = 119). Tobacco consumption was seen in 62.1% (smoking; n = 110) and 19.7% (smokeless; n = 35) elderly. About 18% (n = 32) were consuming alcohol. More than half of the study participants were not doing physical activity. Majority of the elderly in the study (n = 197; 87.6%) had one or more diagnosed diseases at the time of study. Morbidity was found associated with type of family, regular exercise, dietary habit, addiction user, duration of smoking, socioeconomic status, alcohol consumption, smoking and tobacco chewing. Conclusion: Many of the lifestyle practices such as tobacco use, alcohol consumption and physical inactivity were prevalent amongst elderly. Majority of them were suffering from more than one morbidity, which was found to be associated with their unhealthy lifestyle practices. There is a need to target interventions for inculcating healthy lifestyle practices amongst elderly.

The aim of the study is to identify and model the role of payment incentives, driver work-rest patterns and other lifestyle habits influencing the drowsy driving behavior among long-haul truck drivers. To achieve this aim, this study targeted two main objectives: (1) to examine the significant differences between the groups of drowsy and non-drowsy drivers based on the opportunities of monetary incentives and (2) to examine the role of different factors: driver demographics, work-rest patterns, lifestyle and occupational characteristics particularly incentives associated with driving in causing driver sleepiness among Indian truck drivers. The study is based on interview responses from 453 long-haul truck drivers approached in three Indian cities-Mumbai, Indore and Nagpur. Initial principal component analysis of the responses related to financial incentives (occupational characteristics) resulted into two correlated factors: (i) willingness to earn extra payments if offered (WEP) and (ii) incentives available in the current driving experience (ICD) that influence driver work-rest patterns and alertness while driving. Kruskal-Wallis test showed a significant difference among the groups of sleepy and non-sleepy drivers due to these factors (WEP and ICD). Finally, a logistic regression model showed that long driving duration, working days per week, rest patterns, insufficient sleeping hours and history of violations were found significantly associated with drowsy driving among the long-haul truck drivers. Increase in consumption of caffeine and tobacco indicated reduction in driver alertness. According to the model results, the odds of drowsy driving were 77% less for drivers between 46-55 years compared to the young drivers (<25 years). Driving under the influence of financial incentives was observed to increase the odds of falling asleep by 1.58 times among the truck drivers. This was apparently the most interesting and intriguing result of the study indicating the need for further research on the influence of financial or socioeconomic motivations to sleepiness.

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Owing to conflicting objectives, assembly planning has become a difficult task for decision makers to devise an effective plan that can satisfy the majority of system goals. This is a tedious job and its quick and effective solution is the subject of much research. In recent years, artificial immune systems (AISs) have captured the attention of various researchers due to their ability to perform tasks such as learning and memory acquisition. The approach is suitable for solving multi-modal and combinatorial optimization problems. This paper extends the AIS approach by proposing a new methodology, termed the ‘psychoclonal algorithm’, to handle the assembly-planning problem. It inherits its traits from Maslow's need hierarchy theory and the theory of clonal selection. The special features of this algorithm are the various levels of needs, immune memory, and affinity maturation. Various levels of needs and immune memory help to preserve the feasibility of solution, whereas affinity maturation guides the solution to general rather than local optima. The algorithm has been initially validated on a known data set that had been previously solved using both the genetic algorithm and the immune algorithm approach. Using this data set the new psychoclonal algorithm was shown to provide a significant improvement over the other two approaches.

This paper investigates the dynamic ship berth allocation problem for a container handling port, focusing on vessel waiting time at the anchorage due to the unavailability of the berth and quay cranes. A mixed integer linear programming model (MINLP) considering the fuel cost associated with waiting time and operational time of the docked vessel is developed. The hiring of the quay cranes to load/unload the containers from the ship and arrangement of the vessels in different berths is taken into account. Fuel consumed by the vessels while performing their respective port operations is incorporated in the model for addressing the sustainability aspects in berth allocation problem. A Chemical Reaction Optimization Algorithm (CRO) is proposed to solve the problem in a large-scale realistic environment and compared with the results with Block-Based Genetic Algorithm (BBGA), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The computational experiment illustrates and validates the proposed model on a real case scenario of the port located in India. The case shows that the developed model achieves better utilization of port resources and available berths.

Acute pancreatitis complicated by acute myocardial infarction has been reported very rarely. The exact mechanism of the cause of myocardial injury is not known. We report a case of 36 year old male presenting with acute pancreatitis complicated by ST elevation acute myocardial infarction (AMI). The administration of thrombolytic therapy in such patients can have deleterious effects. We report successful performance of primary angioplasty in this complicated patient.

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