Objective: Intervention with iron supplementation, deworming, and information, education and communication (IEC) to improve the haematological status among each of the three trimesters of pregnant women in a rural community. Design: A community-based study was carried out using a two-group pre ± post experimental design in a rural community. Setting: Two rural blocks in Vellore district were selected for the study. KV Kuppam block with a population of 120,000 and the adjacent Gudiyatham block with a population of 132,000 served as study and control areas for the study. Subjects: Using a multistage sampling, initially 50% of the panchayats, the local village administrative units, were randomly selected and all pregnant women were the subjects. In the pre-intervention survey 458 and 387 pregnant women had haemoglobin tested and the post-intervention survey covered 403 and 425 pregnant women in the study and control areas, respectively. Similarly serum ferritin was tested in a randomly selected subsample with 254 and 191 pregnant women before intervention and in 216 and 223 pregnant women after intervention in both study and control areas, respectively. Intervention: Iron supplementation and deworming were provided to all pregnant women in the study area from the fourth month of their pregnancy. An intensive information, education and communication was carried out with facts on anaemia and diet modi®cation to each pregnant woman, using a one-to-one approach in the community, and a group method in the mobile clinics. This was carried out for a period of 18 months. Results: A signi®cant decrease in the prevalence of anaemia was found, from 56.1% to 25.07% (P`0.001), 73.4% to 49.2% (P`0.001) and 68.8% to 56.8% (P`0.01) among women in the ®rst, second and third trimesters, respectively, in the intervention area. Signi®cant (P`0.001) increases in the mean haemoglobin of 0.85 gadl (95% CI 10.18 ± 10.84, 11.09 ± 11.63), 0.59 gadl (95% CI 9.98 ± 10.34, 10.55 ± 10.95) and 0.36 gadl (95% CI 9.93 ± 10.33, 10.25 ± 10.73) were also observed in each of the groups. Conclusion: A comprehensive community-based intervention with iron supplementation, helminthic treatment and increase in knowledge using IEC through effective strategies can improve the haematological status of pregnant women in each trimester.

Objectives: (1) To determine the prevalence of chronic pain of musculoskeletal origin at different body sites among elderly (≥60 years). (2) To study the determinants of chronic pain and the healthcare seeking behavior among elderly with chronic pain. (3) To explore the perceptions of chronic pain among elderly. Materials and Methods: This study was done in 55 field practice villages of the Rural Health Training Centre (RHTC) of the Department of Community Medicine, SMVMCH, Puducherry. An Exploratory Mixed-Method study design, where a qualitative phase (in-depth interviews [IDI]) followed the quantitative phase (Survey). A representative sample of 850 respondents was selected by two-stage cluster sampling. A trained investigator did a house-to-house visit and interviewed selected elderly respondents by using predesigned and pretested questionnaire. The intensity of chronic pain was measured using the Functional Rating Scale and Numeric Rating Scale. The manual content analysis was done for qualitative data. Multiple logistic regression was performed on quantitative data. Results: Qualitative data were described under two broad themes: (1) perceptions of elderly people about chronic pain and 2) healthcare seeking and coping mechanisms. Prevalence of chronic pain among the elderly respondents was found to be 47.6%. Most common site for chronic pain was knee joint (64.5%) followed by a low backache (21.7%). Most of the older people with chronic pain had mild-to-moderate and tolerable pain. Two predictors for chronic pain were the age of the respondent (adjusted OR -1.03, 95% CI = 1.01–1.05) and the presence of at least one chronic morbidity (adjusted OR -1.37, 95% CI = 1.03–1.82). Conclusions and Recommendations: Since the chronic pain was found to be associated with aging and the presence of at least one of the chronic morbidities, it is crucial for treating community physicians to consider the comorbid conditions while managing chronic pain in elderly. Considering the complex nature of chronic pain in old age, the primary care physician should be trained in drug and context-specific nondrug interventions to address the biomedical causes and other cognitive-behavior factors, respectively, through active support from their family and neighborhood.

Vehicular ad hoc networks (VANETs) are used to collect and aggregate real-time speed and position information on individual vehicles to optimize signal control at traffic intersections. VANET can be used to group vehicles into approximately equal-sized platoons, which can then be scheduled using OJF. Greedy forwarding algorithm is proposed to use in the traffic management as it offer the better transfer. Vehicle to infrastructure method is used to transfer the message from the platoon to the vehicle thus increases the safety. The road block information is transfer from platoon to V to I and from one V to I it is transfer to another.Greedy Forwarding algorithm is used to forwarded message to the neighboring node which is "closest" to the destination. In this paper Greedy Forwarding is used to increase the delivery rate and throughput. This algorithm is also used to reduce the load traffic. Greedy algorithm has computationally efficient and can find the error in early stage. Under heavy vehicular traffic load, the greedy algorithm performs the same as the platooning algorithm but still produces low delays, and high throughput. Index Terms-VANET (Vehicular ad hoc networks), GreedyAlgorithm, V to I, I to V.

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Purpose The purpose of the research work is to focus on the deployment of wearable sensors in addressing symptom Analysis in the Internet of Things (IoT) environment to reduce human interaction in this epidemic circumstances. Design/methodology/approach COVID-19 pandemic has distracted the world into an unaccustomed situation in the recent past. The pandemic has pulled us toward data harnessing and focused on the digital framework to monitor the COVID-19 cases seriously, as there is an urge to detect the disease, wearable sensors aided in predicting the incidence of COVID-19. This COVID-19 has initiated many technologies like cloud computing, edge computing, IoT devices, artificial intelligence. The deployment of sensor devices has tremendously increased. Similarly, IoT applications have witnessed many innovations in addressing the COVID-19 crisis. State-of-the-art focuses on IoT factors and symptom features deploying wearable sensors for predicting the COVID-19 cases. The working model incorporates wearable devices, clinical therapy, monitoring the symptom, testing suspected cases and elements of IoT. The present research sermonizes on symptom analysis and risk factors that influence the coronavirus by acknowledging the respiration rate and oxygen saturation (SpO2). Experiments were proposed to carry out with chi-Square distribution with independent measures t-Test. Findings IoT devices today play a vital role in analyzing COVID-19 cases effectively. The research work incorporates wearable sensors, human interpretation and Web server, statistical analysis with IoT factors, data management and clinical therapy. The research is initiated with data collection from wearable sensors, data retrieval from the cloud server, pre-processing and categorizing based on age and gender information. IoT devices contribute to tracking and monitoring the patients for prerequisites. The suspected cases are tested based on symptom factors such as temperature, oxygen level (SPO2), respiratory rate variation and continuous investigation, and these demographic factors are taken for analyzed based on the gender and age factors of the collected data with the IoT factors thus presenting a cutting edge construction design in clinical trials. Originality/value The contemporary study comprehends 238 data through wearable sensors and transmitted through an IoT gateway to the cloud server. Few data are considered as outliers and discarded for analysis. Only 208 data are contemplated for statistical examination. These filtered data are proclaimed using chi-square distribution with t-test measure correlating the IoT factors. The research also interprets the demographic features that induce IoT factors using alpha and beta parameters showing the equal variance with the degree of freedom (df = 206).