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Background: Osteoporosis, defined as skeletal disorder characterized by reduced bone density and strength, is prevalent in both developing and developed countries. Dual energy X-ray absorptiometry (DEXA) is an effective tool to diagnose low bone mineral density (BMD). Objective: This study objective was to know the prevalence and predictors of low BMD in a community-based screening program among the adult population using DEXA scan. Methods: In this community-based study conducted between January and June 2016, a population-based screening program was offered to both males and females of age 50 years and above. BMD was measured to derive T-scores and classify as normal, osteopenic, and osteoporotic individuals. Results: A total of 455 subjects were enrolled where 62.4% proportion was women; 42.6% subjects were in the age group of 50 to 59 years. The prevalence of osteoporosis and osteopenia was 20.9% and 48.1%, respectively. Hypertension (23.7%) and diabetes (8.4%) were common comorbidities. Age and gender (female) were independent predictors of low BMD. Conclusion: Osteoporosis/osteopenia is present in both genders with increasing trend with age advancement. Institution of timely screening will prevent the morbidity associated with fractures due to low BMD.

Introduction: Integrated Child Development Services (ICDS) scheme provides a wide range of health, nutrition and education services to children, women and adolescent girls. Maternal satisfaction usually influences service uptake and utilization. Therefore, in addition to coverage, the quality of services provided and end-user satisfaction need to be assessed. Material and Methods: A cross-sectional community-level study was conducted in the urban block of the Patiala district in Punjab. A total of 54 AWCs were selected from a sample frame of 222 by applying a systematic random sampling technique. From each selected AWC, 20 mothers of registered beneficiaries (0–6 years) were randomly selected for interview. The data collected was entered and analysed using SPSS version 22. Results: The present study revealed that most of the Anganwadis (87.19%) were regularly open for the provision of various ICDS services. However, only 48.02% of children received supplementary nutrition from AWC regularly. Only 32.36% of children who were sick were referred by AWW. Satisfaction with quantity and quality of food served was found to be 61% and 45.90% among mothers, respectively. Conclusion: It was concluded that there is a need to improve the quality of services to overcome the problem of under-utilisation of services despite the wide coverage of ICDS services; regular orientation and training courses for AWWs should be conducted to ensure better service delivery to all beneficiaries.

Domain Name System (DNS) is a protocol for converting numeric IP addresses of websites into a human-readable form. With the development of technology, to transfer information, a method like DNS tunneling is used which includes data encryption into DNS queries. The ability of the DNS tunneling method of transferring data attracts attackers to establish bidirectional communication with machines infected with malwares. This can lead to sending instructions in an obfuscated way or can lead to data exfiltration. Since firewalls and intrusion detection systems detect only specific types of tunneling, were as the Machine Learning Algorithms can analyze and predict based on previous data provided to it, it is being adopted by researchers to detect and predict the occurrence of DNS Tunneling. The identification of anomalies in Network packets can be done by using Natural Language Processing (NLP) technique. The experimental test accuracy showed that the feature extraction method in NLP for detecting DNS tunneling in network packets was found to be 98.42% on the generated Dataset. This paper makes a comparative study of 1 Dimensional Convolution Neural Network (1-D CNN), Simple Recurrent Neural Network (Simple RNN), Long Short-Term Memory (LSTM) algorithm, Gated Recurrent Unit (GRU) algorithm for detecting DNS Tunneling over the generated dataset. To detect this threat of DNS tunneling attack, good quality of the dataset is required. This paper also proposes the generation of a good quality dataset that contains network packets, by the recreation of DNS Tunneling attack using tool dnscat2.