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The purpose of this paper is to explore illness beliefs among adults with type 2 diabetes (T2DM), studied in a clinical setting in the Indian context. Diabetes management lies primarily in the hands of the patient, which signifies the need for understanding the various dimensions of individuals’ illness beliefs. While past research from abroad has stressed the need for understanding the patient’s perspective in effective illness management, the lack of studies in the Indian context calls for further research in this area.

Drawing on the Self-Regulation Model (Leventhal et al., 1980), semi-structured interviews were carried out to understand the beliefs about diabetes among individuals diagnosed to have T2DM. In total, 70 individuals with T2DM were included, taking into account the disease duration, urban-rural, age and gender distinctions. The data were analyzed using content analysis method.

The results of the analysis revealed numerous sub-themes related to the perceived consequences of diabetes, control or cure issues, timeline and emotional issues as experienced by the subjects.

Carrying out a triangulated research with the various stakeholders, namely, diabetologists, general practitioners and other support staff like dieticians could add more value to this exploratory study.

There is a dearth of research work that explores the illness beliefs that patients’ hold about diabetes, as discussed in the Indian context. It is expected that the insight provided by the study can help the government bodies, healthcare organizations and practitioners design and develop interventions from a patient-centric view. Additionally, such a patient-centric approach will enable individuals to achieve their treatment goals.

Road accidents, an inadvertent mishap can be detected automatically and alerts sent instantly with the collaboration of image processing techniques and on-road video surveillance systems. However, to rely exclusively on visual information especially under adverse conditions like night times, dark areas and unfavourable weather conditions such as snowfall, rain, and fog which result in faint visibility lead to incertitude. The main goal of the proposed work is certainty of accident occurrence.

The authors of this work propose a method for detecting road accidents by analyzing audio signals to identify hazardous situations such as tire skidding and car crashes. The motive of this project is to build a simple and complete audio event detection system using signal feature extraction methods to improve its detection accuracy. The experimental analysis is carried out on a publicly available real time data-set consisting of audio samples like car crashes and tire skidding. The Temporal features of the recorded audio signal like Energy Volume Zero Crossing Rate 28ZCR2529 and the Spectral features like Spectral Centroid Spectral Spread Spectral Roll of factor Spectral Flux the Psychoacoustic features Energy Sub Bands ratio and Gammatonegram are computed. The extracted features are pre-processed and trained and tested using Support Vector Machine (SVM) and K-nearest neighborhood (KNN) classification algorithms for exact prediction of the accident occurrence for various SNR ranges. The combination of Gammatonegram with Temporal and Spectral features of the validates to be superior compared to the existing detection techniques.

Temporal, Spectral, Psychoacoustic features, gammetonegram of the recorded audio signal are extracted. A High level vector is generated based on centroid and the extracted features are classified with the help of machine learning algorithms like SVM, KNN and DT. The audio samples collected have varied SNR ranges and the accuracy of the classification algorithms is thoroughly tested.

Denoising of the audio samples for perfect feature extraction was a tedious chore.

The existing literature cites extraction of Temporal and Spectral features and then the application of classification algorithms. For perfect classification, the authors have chosen to construct a high level vector from all the four extracted Temporal, Spectral, Psycho acoustic and Gammetonegram features. The classification algorithms are employed on samples collected at varied SNR ranges.

The purpose of this study is to investigate the relationship between work–life balance (WLB) and job stress, job commitment and job satisfaction. Further, the role of work environment and training and development as moderators in the relationship between WLB and its consequences is investigated.

Using a structured survey instrument, this paper gathered data from 331 respondents working in a transportation company in southern part of India. After checking the psychometric properties of the structured survey instrument, the authors analyzed data using hierarchical regression and structural equation modeling.

The hierarchical regression results indicated that WLB is (1) negatively related to job stress, (2) positively related to job satisfaction and (3) positively related to job commitment. The results also indicated that (1) job stress is negatively related to job satisfaction, and (2) job commitment is positively related to job satisfaction. The results also support that work environment is a moderator in the relationship between (1) WLB and job stress, and (2) WLB and job satisfaction. Results also documented that training and development is a moderator in the relationship between (1) job stress and job satisfaction, and (2) job commitment and job satisfaction.

Since the present research is based on self-report measures, the limitations of common method bias and social desirability are inherent. However, the authors have taken sufficient care to minimize these limitations. The research has implications for managers in work organizations.

This study contributes to both literature on human resource management and practicing managers. The study suggests that employers need to be aware of the importance of WLB and invest moneys into training and development programs. Results also suggest maintaining congenial work environment to help employees maintain balance between work and life.

The study is expected to contribute to the welfare of the society in terms of identifying the consequences of WLB.

This study provides new insights about the consequences of WLB through moderating role of training and development and work environment. To the authors’ knowledge, this is a conceptual model developed and tested and first of its kind in India.