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This brief presents a secondary switch-and-compare technique to improve the energy efficiency of a SAR ADC with minimal area and power overheads. The method exploits the self-calibrated comparator in the SAR ADC to generate a supplementary LSB. Instead of a C-DAC, this switching scheme produces voltage change required to evaluate the supplementary LSB at the calibration node of the comparator. The (N+1) $^{\text {th}}$ bit logic proficiently controls the secondary switching and assists the comparator in resolving the augmented LSB. A proof-of-concept (9+1)-bit 20 kS/s SAR ADC is designed in a standard 180 nm CMOS technology to demonstrate the proposed technique. The post-layout simulation results achieve energy efficiency of 51.2 fJ/conv.-step at 562 nW of average power consumption from $\text{V}_{\text {DD}}$ of 1.8 V.

BACKGROUND A podcast is an unconventional method of disseminating information through audio to the mass. Nowadays, the podcast is an emerging new portable technology and is a valuable resource that provides unlimited access for promoting health in participants. Podcast in relevance to healthcare has been used as a source of medical education but there is a dearth of studies for use of podcast as a source of Health Information. This study will provide new perspectives by implementing the SANGYAN podcast that contains information about COVID-19 and other health conditions. OBJECTIVE The study aims to determine the usefulness and effectiveness of the SANGYAN podcast as a digital health intervention to address misinformation related to COVID-19 and other health conditions among individuals in Chennai, Tamil Nadu. METHODS An intervention study will be conducted with 500 participants from PMCHRI and RHTC in Chennai, Tamil Nadu. Individuals, 18 years and older residing in the selected urban and rural settings visiting the OPD of PMCHRI and RHTC will be recruited. The consented participant’s socio-demographic details will be noted and their health literacy will be assessed using REALM-R. Once, the participants will have listened to the podcast the usability and acceptance, of participants' satisfaction will be assessed. RESULTS Descriptive analysis will be gathered to for continuous variables and frequency analysis of the categorical variables. Bivariate analysis was conducted to understand the correlation of socio-demographic features in response to their perception, usefulness, acceptance, and user satisfaction with the study. All analysis will be performed using SPSS v.24., and the results will be reported at 95% CIs and p CONCLUSIONS The finding from this research project will aid in the development and implementation of data-driven, evidence-based, and human-centered behavior change using podcasts to address public health challenges among populations living in diverse settings. This would also help in enhancing the acceptability of podcasts as a source of health-related information. CLINICALTRIAL

BACKGROUND Health inequity is interlinked with good health and well-being of an individual. Digital health interventions have the potential to reduce the existing health inequities. OBJECTIVE The study aims to identify determinants of social, economic, and health inequity in diverse settings to enhance Healthy Eating and Active Living. It further aims to design and develop a digital health intervention HEAL (Healthy Eating Active Living) incorporating framework of human centred design to enhance Healthy Eating and Active Living among rural and urban population groups of Chennai, Tamil Nadu. METHODS A prospective, 3-year cohort study will be conducted. The study aims to recruit 6350 individuals across rural and urban settings of Chennai, Tamil Nadu. A total of eleven sites have been selected for participation in the study. Data will be gathered for sociodemographic, economic inequity, HEAL profile, depression, anxiety and stress, well-being, sources of health information, perceived access to health care, health literacy, navigation of health literacy, satisfaction with health system. RESULTS The study would help to explore the determinants of social, economic and health inequity across multiple sites. Data analysis will be performed using SAS v9.3 and results will be reported as 95% CI and p-values. The findings of the study will inform the design and development of a tailored, interactive human centered digital intervention to enhance the health and well-being of the population groups of Chennai. CONCLUSIONS The proposed study will help in examining the role of the proposed HEAL intervention to enhance the good health and well-being of the population groups of Chennai.

Liquid-liquid phase separation offers unique spatiotemporal control over myriad complex intracellular biochemical processes through compartmentalization of biomolecules into highly dynamic, liquid-like condensates known as membrane-less organelles. The bacterial nucleoid is thought to be one such phase-separated condensate; however, its formation, regulation, and biophysical characteristics are poorly understood. Our super-resolution imaging data suggests that nucleoids are dynamic assemblages of sub-micron-sized liquid-like droplets. We demonstrate that non-sequence-specific Nucleoid-Associated Proteins (NAPs) such as HU-A, HU-B and Dps, accrete nucleic acids and spontaneously condense with them into liquid-like, multicomponent, multiphasic, heterotypic phase-separated droplets. Upon mixing of HU-B, DNA and Dps, HU-B-enriched droplets are seen to contain demixed Dps-enriched droplets. Our findings indicate scope for the possible existence of multiphasic liquid-like compartments within nucleoids, providing insights into bacterial growth phase-dependent variations in the levels of different NAPs.

Background Diabetes represents an important public health challenge in India and globally. It affects the quality of life and is one of the leading causes of death and disability. The burden on global health is huge, and about 463 million adults are currently living with diabetes. 77 million people in India in the age group of 20–79 years are affected by this pandemic, and the total cost of health expenditure is 8 billion US dollars. This is a huge burden and a great economic cost on public health. The research aims to explore the concept of diabetes self-management and major research questions would comprise asking what affects self-management in people with diabetes and how m-health applications and interventions can impact the self-management behaviors in development and the utility of the m-health apps in self-management of people with diabetes. Therefore, this research protocol is of great significance and would bring an integrative approach to self-care management. This study protocol was developed on the m-health application and testing nutrition informatics intervention for self-care management of type 2 diabetes at home. Objective To design, develop, and assess the impact of an m-health-enabled nutrition informatics intervention for home-based type 2 diabetes self-management in an Indian setting. Methods The study is divided into three phases, in the first phase a systematic literature review of global and Indian literature using Scoping review method using PRISMA- SCR tool to report on various m-health interventions on Self-care management regimes on type 2 diabetes and Nutrition informatics will evaluate the effectiveness and utility of m-health application developed on Selfcare management with nutrition informatics will be reported. In the second phase m-health application on Selfcare management and nutrition informatics regime will be made and in the third phase of the study evaluation of the m-health application will be done using System Usability Scale (SUS) usability and mixed research evaluation with end-users using Diabetes based management system (DBMS)questionnaire. A mixed research study will be conducted between June 2022 and March 2023. A sample of approximately 250 individuals will be recruited and enrolled using a non-probability complete enumeration sampling method from selected urban settings in Delhi. The inclusion and exclusion criteria for males and females aged 20–79 years old with Type 2 diabetes and access to a smartphone. Data will be collected using globally tested tools like the Diabetes Based Management System (DBMS) questionnaires. The collected data will be used to assess the use and utility of the mobile health application developed. The qualitative study using IDIs and FGD will be done to understand knowledge, attitudes, practices, and beliefs regarding diabetes self-care management. Qualitative methods will be focus group discussion (FGD) and in-depth interviews (IDIs) in the study. Lastly, the study questionnaire system usability survey, a global tested tool (SUS), will be used to assess the usability of mobile applications for self-care management of diabetes. Therefore, this study involves three phases, phase 1 will be scoping review using the PRISMA reporting, phase 2 is a human-centered design (HCD) of m-health informatics application for type 2 diabetes and phase 3 will be the evaluation of the designed application using globally tested tool System usability scale (SUS). Results A sample of 250 individuals will go through a pretested DBMS questionnaire. The data collection will be initiated in June 2022, and the initial results are planned for publication by July 2023. Out of n = 250 individuals, at least 120 individuals will be retained in the study. A qualitative study using descriptive analysis of the gathered data will be performed using SPSS V11, and reporting of the results will be done at 95% CIs and P = .0.05. A qualitative study will be exploratory sequential and the first exploratory study using IDIs and FGDs will be done followed by a quantitative method this exploratory sequential study will draw inference from the mixed study. Conclusions The findings of the study using scoping review would inform the elements essential for the development of m-health to improve self-care management of diabetes at home settings in India and its wider application in the global framework. The m-health application will be designed on m-health nutrition informatics. The usefulness and acceptance of the proposed m-health intervention will be evaluated using the globally tested tool System Usability Scale (SUS) for evaluation.

BACKGROUND Metabolic Syndrome (MetS) is a growing concern among the adult population in India, particularly among the urban poor. The challenge of this group is a combination of risk factors including an urbanized lifestyle, poor access to healthcare, and financial limitations, leading to high levels of obesity, diabetes, and hypertension. OBJECTIVE The current study aims to address this challenge by designing, developing, and piloting a tailored, mobile-enabled interactive digital health intervention to enhance self-management of MetS among individuals living in urban slums of New Delhi, India. METHODS The study employs mixed methods, including both quantitative and qualitative data collection to design and evaluate the effectiveness of the intervention that is built on a multifactorial model in improving the self-management of MetS. Data will be collected at baseline and 12 months from the urban slums of Delhi. RESULTS The findings will be disseminated to both national and international audiences through peer-reviewed publications. The results will contribute to our understanding of the interplay of risk factors in MetS and the impact of tailored digital health interventions in addressing this challenge. CONCLUSIONS The study is expected to provide valuable insights into the role of digital health interventions in enhancing self-management of MetS among the urban poor population. CLINICALTRIAL

Objectives: To do scoping review on the global evidence on m-health interventions and determine the effectiveness of mobile based self-management interventions on type 2 diabetes mellitus Methods: A systematic search of PubMed and Scopus research data base were examined. Studies globally published in English from 2010 to 2020 and studies done on Type 2 diabetes and including age groups 18-75 years are included. The studies included intervention such as Randomized controlled trials (RCTs) of mobile based self-management for type 2 diabetes patients and charting and analysis of outcomes at patient level. The reviewer screened the studies, and it was consequently reviewed by a second reviewer independently. The data was extracted, charted and quality of the studies was assessed. The scoping study methodology using operational framework of Arksey, and O’Malley is used to present study outcomes and 27 pointers PRISMA-SCR checklist is used to present this paperResults: A total of 40 articles, consisting of RCT based interventions studies with 3800 plus participants, were included in the assessment. 40 original research articles were selected for inclusion. The selected studies largely focused on the self-management of type 2 diabetes (N = 40), hypertension (N = 2). Various interventions involved the use of mobile SMS (N = 17) or mobile on phone (N = 12), the content on session education with use of mobile (N = 10) or the placement of devices mobile (N = 4). The interventions were generally effective and often led to improved glycemic control and better health indicators.Conclusions: Mobile based self-management interventions seem to have positive effects on self-care and management and significant outcomes for patients with type 2 diabetes. Overall progress in physiologic indicators, selfcare and improvement in quality of patient life with Type 2 Diabetes. Though, the studies emphasized results in small populations, with little indication of future scaling of the intervention. Moreover, the results indicate a need for further research into the self-management which includes nutrition informatics in self-care management regime, and it needs to focus on region wise availability of informatics on nutrition. This will also help in co-management of diabetes and cardiovascular disease. Self-management appears to be an effective means of improving health outcomes in low- and middle-income countries (LMIC) and therefore mobile led selfcare management interventions with nutritional informatics can be advantageous.

BACKGROUND Non-communicable diseases (NCDs) likely require a longer period of care, for which healthcare systems must acquire technologically advanced solutions to enhance patient care. Swasthya Pahal (health for all) is an innovative, interactive, multilingual, standalone, and internet-enabled computer-based program that aims to improve the self-management of NCDs. OBJECTIVE The objective of the study is to determine the usefulness, acceptance, and effectiveness of the Swasthya Pahal program in hospital and community settings in both rural and urban areas of Chennai, Tamil Nadu. This objective can be met by generating risk factor profiles of individuals enrolled and enhancing their self-management of NCDs using a portable health information kiosk that employs the SMAARTTMmodel. METHODS A quantitative study will be conducted on a convenient sample of 2800 individuals from the selected hospital and community settings in rural (n=1400) and urban areas (n=1400) in Chennai, Tamil Nadu. Data will be collected on socio-demographics, health behaviors, clinical status and Knowledge, Attitude, and Practices. Objective assessments such as weight, blood pressure, and random blood sugar levels will be measured. Additionally, the usefulness, acceptance, and effectiveness of the Swasthya Pahal program will be determined. RESULTS Results will be summarized using descriptive analysis. Appropriate bivariate and multivariate regression analysis will be performed to determine the predictors of the outcome variables of usefulness, acceptance, and effectiveness of Swasthya Pahal in wider settings. All analyses will be performed using SAS v9.1 and reporting of the results done at 95% CI and p CONCLUSIONS The proposed study will help enhance the self-management of NCDs in both rural and urban community settings by the implementation of the Swasthya pahal program based on the SMAARTTM informatics framework.

BACKGROUND Background: Well-being is a multidimensional concept and has been extended to many areas. Student well-being has garnered attention over the last decade due to raised concerns. Digital health interventions have the potential to enhance and improve student well–being. OBJECTIVE Objective: The objective of the study is to design, develop and pilot test a digital health platform to enhance student well-being. METHODS Methods: A sample size of five thousand participants will be recruited across Gujarat and Tamil Nadu, India. Students will be enrolled from, Parul University, Vadodara, Gujarat, and Panimalar Medical College Hospital and Research Institute, (PMCHRI), Panimalar Engineering College, Panimalar Institute of Technology and Panimalar College of Nursing, Chennai, Tamil Nadu. Current undergraduate and graduate students consenting to participate will be recruited using convenience sampling from these institutes. The study will collect baseline data to construct the student well-being index. Based on the risk profile, a random subset of the population will be provided access to the digital health intervention that will deliver tailored interactive messages addressing the various dimensions of well-being among undergraduate and graduate students. The eligible study participants will be of 18 years and older, enrolled in these institutes, and willing to give their consent to participate in the study. RESULTS Results: The proposed research is an unfunded study. The enrolment of the individuals in study will begin from October 2022. Data gathered will be analyzed using SAS v9.3 and results will be reported as 95% CI and p-values. CONCLUSIONS Conclusions: The proposed study will help to determine the factors affecting well-being among college students and help in designing digital health intervention to improve the well-being of the undergraduate and graduate students. CLINICALTRIAL

BACKGROUND The mobile sensor data can provide vital information about patients’ behavior and physical activity. In particular, the mobile accelerometer sensor data, if monitored effectively, can be used further for keeping the watch on the physical activity of an individual and planning an intervention to improve it. These sensor data can also be used for improving clinical decision-making in sedentary lifestyle-related diseases. OBJECTIVE The objective of this study is to use the accelerometer sensor metadata of the mobile device in facilitating the delivery of interventions to enhance physical activity. METHODS A sequential explanatory mixed-method research design would be used between June 2022 to December 2022. The physical activity-related accelerometer sensor data will be collected from individuals with a sedentary lifestyle between the age of 30 to 45 visiting the clinics of selected health care providers in Chennai, Tamil Nadu. Based on the initial data recorded, an individualized activity plan will be created. Periodic monitoring of physical activity levels will be conducted over a three months period. Participants will receive the message from clinicians based on their current physical activity levels for encouraging them to adhere to the prescribed regimen suggested by clinicians. RESULTS The proposed research will help in exploring the role of remote monitoring of sensor data in improving adherence to physical activity plan as prescribed by their clinicians. It will also help in studying the variables that influence the monitoring of remote data collection. CONCLUSIONS This study will help to bring improvement in the physical activity levels of patients with a sedentary lifestyle and with confirmed hypertension and type-2 diabetes through remote monitoring, ongoing messaging, and facilitation through clinical decision-making.

This article presents a discrete-time parametric amplifier (DTPA) as the signal demodulator for chopper amplifier. Unlike the conventional chopper, the DTPA demodulator features noise-efficient gain augmentation while demodulating the chopped signal. The demodulator also enables low-frequency noise cancellation during the inherent track-and-hold (T/H) process of the charge parametrization. The positive feedback loop and the dc servo loop are implemented for applications requiring larger input impedance and dc input offset cancellation using a bandpass transfer function. Design considerations for the DTPA-based demodulator circuit and the merits and demerits of the chopper–DTPA amplifier are discussed as well. The proposed design has been fabricated in a standard 180-nm CMOS technology node. The complete design occupies 0.127 mm2 of the die area and consumes 2.4- $\mu \text{W}$ power from a 1.5 V of $\text{V}_{\text {DD}}$ . The measurement results show that the DTPA demodulator provides 8-dB gain enhancement while improving on the prior art of T/H-based demodulator and the amplifier achieves input referred noise voltage of $2~ \mu \text {V}_{\text {rms}}$ in 143-Hz bandwidth.

Hofmeister ions are thought to play fundamentally important roles in protein solubility, folding, stability, and function. Salt ions profoundly influence the course of protein misfolding, aggregation, and amyloid formation associated with devastating human diseases. However, the molecular origin of the salt-effect in protein aggregation remains elusive. Here, we report an unusual biphasic amyloidogenesis of a pH-responsive, intrinsically disordered, oligopeptide repeat domain of a melanosomal protein, Pmel17, that regulates the amyloid-assisted melanin synthesis in mammals via functional amyloid formation. We demonstrate that a symphony of molecular events involving charge-peptide interactions and hydration, in conjunction with secondary phenomena, critically governs the course of this biphasic amyloid assembly. We show that at mildly acidic pH, typical of melanosomes, highly amyloidogenic oligomeric units assemble into metastable, dendritic, fractal networks following the forward Hofmeister series. However, the subsequent condensation of fractal networks via conformational maturation into amyloid fibrils follows an inverse Hofmeister series due to fragmentation events coupled with secondary nucleation processes. Our results indicate that ions exert a strong influence on the aggregation kinetics as well as on the nanoscale morphology and also modulate the autocatalytic amplification processes during amyloid assembly via an intriguing dual Hofmeister effect. This unique interplay of molecular drivers will be of prime importance in delineating the aggregation pathways of a multitude of intrinsically disordered proteins involved in physiology and disease.

The COVID-19 pandemic has underlined the need to partner with the community in pandemic preparedness and response in order to enable trust-building among stakeholders, which is key in pandemic management. Citizen science, defined here as a practice of public participation and collaboration in all aspects of scientific research to increase knowledge and build trust with governments and researchers, is a crucial approach to promoting community engagement. By harnessing the potential of digitally enabled citizen science, one could translate data into accessible, comprehensible and actionable outputs at the population level. The application of citizen science in health has grown over the years, but most of these approaches remain at the level of participatory data collection. This narrative review examines citizen science approaches in participatory data generation, modelling and visualisation, and calls for truly participatory and co-creation approaches across all domains of pandemic preparedness and response. Further research is needed to identify approaches that optimally generate short-term and long-term value for communities participating in population health. Feasible, sustainable and contextualised citizen science approaches that meaningfully engage affected communities for the long-term will need to be inclusive of all populations and their cultures, comprehensive of all domains, digitally enabled and viewed as a key component to allow trust-building among the stakeholders. The impact of COVID-19 on people’s lives has created an opportune time to advance people’s agency in science, particularly in pandemic preparedness and response.

Biomolecular condensates formed via liquid-liquid phase separation (LLPS) are involved in a myriad of critical cellular functions and debilitating neurodegenerative diseases. Elucidating the role of intrinsic disorder and conformational heterogeneity of intrinsically disordered proteins/regions (IDPs/IDRs) in these phase-separated membrane-less organelles is crucial to understanding the mechanism of formation and regulation of biomolecular condensates. Here we introduce a unique single-droplet surface-enhanced Raman scattering (SERS) methodology that utilizes surface-engineered, plasmonic, metal nanoparticles to unveil the inner workings of mesoscopic liquid droplets of Fused in Sarcoma (FUS) in the absence and presence of RNA. These highly sensitive measurements offer unprecedented sensitivity to capture the crucial interactions, conformational heterogeneity, and structural distributions within the condensed phase in a droplet-by-droplet manner. Such an ultra-sensitive single-droplet vibrational methodology can serve as a potent tool to decipher the key molecular drivers of biological phase transitions of a wide range of biomolecular condensates involved in physiology and disease.

Floating drug delivery systems (FDDS) was most convenient and widely used drug delivery for systemic intervention, oral administration. The main purpose of developing the FDDS is to enhance therapeutic benefits such as ease of dosing administration, patient compliance, and formulation versatility. In pharmaceuticals industries we are approaches to design single-unit and multiple-unit floating systems, and classification and formulation of FDDS aspects are covered in detail. This review summarizes the tablet, Floating Drug Delivery System, Type, Mechanism, and also the Advantages & Disadvantages. This sort of medication delivery method is beneficial because it has enough buoyancy to float above gastric contents and stay buoyant in the stomach for a long time. Keywords:- FDDS, Floating Drug Delivery System, Therapeutic, Design, Buoyant, Gastric

The quality of satellite images is affected due to the scattering and absorption of sunlight by atmospheric molecules and aerosols. Aerosols are small particles suspended in the atmosphere which affects the air quality. The concentration and characteristics of the atmospheric aerosols can vary from place to place. Aerosols are major constituents for the formation of haze and thus responsible for degradation in the satellite images. During the novel corona virus disease pandemic (COVID-19) countrywide lockdown in India, aerosols level significantly lowered in the north Indian cities in the Indo-Gangetic plain. In this paper, an analysis of image quality was done for Sentinel-2A/2B satellite images for different north Indian cities with respect to the impact of aerosols on image sharpness and surface reflectance values during the pre-lockdown and lockdown period. The image quality of the images was evaluated through the power spectrum, gradient, and reflectance values in the image. It was observed that the sharpness of the satellite images increased during the lockdown period due to the reduction in aerosols levels in the atmosphere. There was an increase in the power spectrum values from 5% (minimum) to 12% (maximum) in the blue band, 4% (minimum) to 10% (maximum) in the green band, and 1.7% (minimum) to 6% (maximum) in the red band in the satellite images of major cities in the Indo-Gangetic plain during the lockdown period. Since haze is an additive component in the images, thus it was also observed that surface reflectance values had decreased in visible bands for the features which have low surface reflectance values like water (30%), vegetation (21%) and barren land (17%) and surface reflectance values had increased in visible bands for the features which have high surface reflectance values like building (19%) during the lockdown period.

To ensure the security and privacy of the Patient’s confidential and critical data transmitted in a wireless body area network (WBAN), all parties must mutually authenticate. Some existing mutual a...

This paper provides the list of all the features that should be required in designing the authentication protocol for wireless body area network and enhances the implementation of revocation functi...

Liquid-liquid phase separation of intrinsically disordered proteins into mesoscopic, dynamic, liquid-like supramolecular condensates is thought to govern critical cellular functions. These condensates can mature from a functional liquid-like state to a pathological gel-like or solid-like state. Here, we present a unique case to demonstrate that an unusual cascade of intermolecular charge-transfer coupled with a multitude of transient noncovalent interactions and conformational fluctuations can promote liquid phase condensation of a pH-responsive, intrinsically disordered, oligopeptide repeat domain of a melanosomal protein. At neutral cytosolic pH, the repeat domain forms highly dynamic, mesoscopic, permeable, liquid-like droplets possessing rapid internal diffusion and torsional fluctuations. These liquid condensates mature via pervasive intermolecular charge-transfer and persistent backbone interactions driving the liquid-to-solid phase transition into heterogeneous solid-like aggregates that are structurally and morphologically distinct from typical amyloids formed at mildly acidic melanosomal pH. Our findings reveal the regulatory role of the repeat domain as a specific pH-sensor that critically controls the phase transition and self-assembly processes akin to prion-like low-complexity domains modulating intracellular phase separation.

BACKGROUND Child malnutrition is a major global health problem that imposes morbidity and mortality burdens. Malnutrition has long-term effects on gut health which can affect the development of children. Gut microbes play a crucial role in the absorption of nutrients in food. Improving mothers’ awareness regarding gut health is one of the many ways to strengthen against diarrheal conditions and malnutrition among children. OBJECTIVE The objectives of the present study are to design, develop and evaluate a mobile-based intervention to improve gut health through mHealth. The study will further explore the association between socio-demographic characteristics, health status, dietary pattern of child and Water and Sanitation Hygiene Practice (WASH) related knowledge, attitude, practices of a mother living in tribal of Sikar, Rajasthan. METHODS A quasi-experimental randomized trial will be conducted between November 2021 and February 2022. 150 mothers of Moderate Acute Malnutrition (MAM) children will be recruited and enrolled using a non-probability complete enumeration sampling method from the Sapera, Loharu, and Banjara community of Sikar. The study participants will be randomized into two groups: Intervention (mobile linked audio-based messages); and Control group (no-mobile linked audio-based messages). Differences in the dietary pattern of children, mothers’ WASH-related knowledge, attitudes, and practices will be measured across the group. The data will be collected for the focus group, baseline, timeline 1 (after one month of the baseline) and timeline 2 (after one month of timeline 1). The study outcomes include the change in KAP of mothers pre- and post-intervention. RESULTS The study's results will help assess the usefulness and effectiveness of the proposed design and development of a user-centered informatics platform that can deliver multimedia-driven nutrition and health educational modules tailored to facilitate healthy habits and improvement in gut health in children of tribal settings. CONCLUSIONS The study will provide insights toward the barriers and challenges leading to malnutrition and poor gut health of the children in tribal populations and the relationship between gut health and malnutrition in the children. We anticipate that the intervention group will show a significant change in improvement in gut health, dietary pattern, WASH practices, knowledge, attitude, and practice of mother. CLINICALTRIAL Ethical approval was obtained from the UREC (University Research Ethics Committee) of DIT University, Dehradun, Uttarakhand, India (DITU/UREC/2021/07/6).

BACKGROUND Pregnant women are considered to be a “high risk” group with limited access to health facilities in urban slums. Barriers to utilization of health services may lead to maternal and child mortality, morbidity, low birth weight, and children with stunted growth. Application of artificial intelligence (AI) can provide substantial improvements in all areas of healthcare from diagnostics to treatment. There have been several technological advances within the field of AI, however, AI not merely driven by what is technically feasible, but by what is humanly desirable is the need of the hour. OBJECTIVE The objective of our study is to develop and evaluate the AI guided citizen centric platform to enhance the uptake of maternal health services (antenatal care) amongst the pregnant women living in urban slum settings. METHODS A cross-sectional mixed method approach employed to collect data among pregnant women, aged 18-44 years, living in urban slums of South Delhi. A convenience sampling used to recruit 225 participants at the Anganwadi centres (AWC) after obtaining consent from the eligible participants. Inclusion criteria includes pregnant individuals residing in urban slums for more than 3 months, having smartphones, visiting AWC for seeking antenatal care. Quantitative and qualitative data will be collected electronically using Open Data Kit (ODK) based opensource tool from eligible participants. Data will be collected on clinical as well as socio-demographic parameters (based on existing literature). We aim to develop an innovative AI guided citizen centric decision support platform to effectively manage pregnancy and its outcomes among urban poor populations. The proposed research will help policymakers to prioritize resource planning, resource allocation and development of programs and policies to enhance maternal health outcomes. RESULTS The AI guided citizen centric decision support platform will be designed, developed, implemented and evaluated using principles of human centred design and findings of the study will be reported to diverse stakeholders. The tested and revised platform will be deployed for use across various stakeholders such as pregnant women, healthcare professionals, frontline workers, and policymakers. CONCLUSIONS With the understanding, use and adoption of emerging and innovative technologies such as AI, maternal health informatics can be at the forefront to help pregnant women in crisis. The proposed platform will potentially be scaled up to different geographic locations for adoption for similar and other health conditions.

BACKGROUND Diabetes represents an important public health challenge in India and Globally. It affects quality of life and is one of the leading causes of death and disability. The burden on global health is huge and about 463 million adults are currently living with diabetes. 77 million people in India in the age group of 20-79 years are affected by this pandemic and total cost to health expenditure is 8 billion US dollars, therefore huge burden, and great economic cost on Public health. The self-management of diabetes, the research priorities include exploring the concept of diabetes self-management and major research questions would comprise of asking what affects self-management in persons with diabetes and how do m-health application and interventions can impact on the self-management behaviors in development, utility of the m-health app in self-management of person with diabetes. Therefore, this project research is of great significance and would bring an integrative approach on self-care management OBJECTIVE To design, develop and evaluate the impact of m-health enabled nutrition informatics intervention for home based self-management of type 2 diabetes in an Indian setting. METHODS A mixed research study will be conducted between January 2022 and January 2023. A sample of approximately 250 individuals will be recruited and enrolled using a nonprobability complete enumeration sampling method from selected urban settings of Delhi inclusion and exclusion criteria with age20-79 years male and female with Type 2 diabetes and have access to Smart phone Data will be collected using which questionnaires. The collected data will be used to assess use and utility of mobile health application developed. The knowledge, attitudes, practices, and beliefs regarding Diabetes self-care management. Lastly, the study questionnaire system usability survey(SUS) will be used to assess the usability of mobile applications on selfcare management of Diabetes RESULTS A pilot of 250 individuals has been conducted to pretest the DBMS questionnaire. The data collection will be initiated from January 2022, and the initial results are planned for publication by October 2022.Descriptive analysis of the gathered data will be performed using SPSS V11, and reporting of the results will be done at 95% CIs and P=.0.05. CONCLUSIONS The findings of the study would inform the elements essential for the development of m-health intervention to improve self-care management of diabetes at home settings. The usefulness and acceptance of the proposed intervention will be conducted. CLINICALTRIAL DITU/UREC/2021/07/10

Data-driven modeling, action, and strategies have become popular, and the education community has witnessed increased interest in data-driven decision-making (DDDM). DDDM values and prioritizes decisions supported by high-quality, verifiable data that has been effectively processed and analyzed. The objective of our study is to describe the design, development, and implementation of a data-driven, evidence-based model of academic development in the context of CUNY Graduate School of Public Health and Health Policy (CUNY SPH) utilizing SMAART (Sustainability Multisector Accessible Affordable Reimbursable Tailored) model. The alignment of academic and student affairs within CUNY SPH brought with it several challenges. Defining roles and responsibilities across different student and academic affair units with a goal of collaborative leadership model and lack of meaningfulness were key challenges. It was important to listen to the experiences and recommendations of various individuals performing various functions in different capacities. A unified framework of key data indicators was needed to create a transparent and equitable model. An innovative interactive SMAART SPH dashboard designed, developed, and implemented to guide data-driven, evidence-based decision-making. Institutions can use a large amount of data from various sources to improve students’ learning experience, enhance research initiatives, support effective community outreach, and develop campus infrastructure to bring in sustainability.

A life of human beings is dependent on the energy which they consume for their everyday purpose, it can be for illumination, running a gadget and many other purposes which contribute towards the development of mankind and also for economic growth. If global energy consumption is taken into consideration, every resource has its own quantity. As the years pass by, one can observe the depletion of naturally available conventional resources of energy. Hence, the world has been shifting to renewable energy sources such as solar, wind, tidal power. Even though, there isn’t any greater transformation in the energy consumption from the conventional sources due to their higher efficiency deliveries. If an alternative energy resource has to be discovered, one has to look for the sources available in the surrounding. When the road traffic is considered for an instance, the number of vehicles travelling are enormous and due to the movement, every vehicle induces a certain number of vibrations into the pavement below which collectively turns out to be plenty if the vibrations from all the vehicles are to be taken in account. The researchers have come up with a technology to harness this energy and make it usable to the consumers. To convert one form of energy to another, a transducer can be used which is capable of transforming the entire input energy into an output voltage. This form of output is said to be called ‘piezoelectricity’ where the incident pressure is potentially converted into electricity that can be utilized for numerous purposes.

The real-world data on short-course of ICI use is sparse and merits exploration. A multi-centric observational study on the safety and efficacy of ICI in oncology patients between August 2014 to October 2020 involving 1011 patients across 13 centers in India. The median age was 59 (min 16- max 98) years with male preponderance (77.9%). The predominant cohort received short-course ICI therapy; the median number of cycles were 5 (95% CI 1 - 27) and median duration of therapy was 3 (95% CI 0.5 - 13) months. ICI were used commonly in second and third line setting in our study (66.4%, n=671). Objective response rate (ORR) (complete or partial response) was documented in 254 (25.1%) of the patients, 202 (20.0%) had stable disease and 374 (37.0%) had progressive disease. The clinical benefit rate was present in 456 (45.1%). Among the patients whom ICI was stopped (n=906), the most common reason for cessation of ICI was disease progression (616, 68.0%) followed by logistic reasons like financial constraints (234, 25.82%). With a median follow up of 14.1 (95%CI 12.9-15.3) months, there were 616 events of progression and 443 events of death and the median progression free survival and overall survival were 6.4 (95%CI 5.5-7.3) and 13.6 (95%CI 11.6-15.7) months respectively in the overall cohort. Among the immune related adverse events (irAEs), autoimmune pneumonitis (29, 3.8%) and thyroiditis (24, 2.4%) were common. Real-world multicentric Indian data predominantly with short-course ICI therapy, has comparable efficacy/safety to international literature with standard ICI therapy. This article is protected by copyright. All rights reserved.

Remote sensing image classification has long attracted the attention of the remote‐sensing community because classification results are the basis for many environmental and socioeconomic applications. The classification involves a number of steps, one of the most important is the selection of an effective image classification technique. This paper provides a comparative study of the supervised learning techniques for remote sensing image classification. The study is being focused on classification of land cover and land use. Supervised learning is a branch of machine learning and is used in this study. The comparison is made among the different techniques of pixel-based supervised classification used for remote sensing image classification. The study has been made on a labelled data set. After the implementation, support vector machine has been found to be the most effective algorithm among the five algorithms of pixel-based supervised classification (i.e. maximum likelihood estimation, minimum distance classifier, principal component analysis, isoclustering and support vector machine).

WSNs have enthused resurgence in research on machine learning-based approaches with the intent of overcoming the physical restraints of sensors. Although resource constrained in nature, WSNs domain has tremendous potential for building powerful applications, each with its own individual characteristics and requirements. This fascinating field of WSNs although comprises of various research issues and challenges, viz. energy efficiency, localization, etc., which needs to rectified. One such prominent challenge is detection of outliers whose chore is to preclude any kind of malicious attacks in the network or lessen the noisy error prone data in millions of wireless sensor networks. To do so, the methodologies developed needs to take care of inherent limits of sensor networks so that the energy intake of motes is minimum and lifespan of the motes is maximized. Consequently, the quality of data must be thoroughly patterned as any kind of outlier in the sensed network may degrade the quality of the data and hence affect the final decision. Thus, it becomes imperious to retain the quality of the data. Numerous ML-based methodologies have been used by several researchers over the time to detect any form of outliers or anomaly present in the network. In this paper, some machine learning methodologies have been discussed which have proved their mettle in outlier detection for sensor networks. This paper presents a brief survey on outlier detection in WSNs data using various ML-based techniques.

In this study, Wireless Optical Communication LOS model for underground trains to station based on FSO is designed and simulated to achieve optimum performance and reduced bit error ratio. The FSO medium is highly dependent on Air Quality and Weather conditions bit error ratio vary depending on the conditions. The aim of this design is to achieve a lower Bit error ratio and to increase the range for the communication. The simulation is designed to improve the quality of communication through FSO channel by reducing the bit error ratio through filtering techniques. The results of this simulation will help future research and implementations of the FSO based wireless optical communication for underground trains. This simulation is based on V2I architecture where the communication is based on FSO technology.

With the rapid pace of technological advancements, public health professions require a core set of informatics skills. The objective of the study is to integrate informatics competencies and job analysis to guide development of an evidence-based curriculum framework and apply it towards creation of a population health informatics program. We conducted content analysis of the Population Health Informatics related job postings in the state of New York between June and July 2019 using the Indeed job board. The search terms included "health informatics" and "population health informatics." The initial search yielded 496 job postings. After removal of duplicates, inactive postings and that did not include details of the positions' responsibilities resulted in 306 jobs. Information recorded from the publicly available job postings included job categories, type of hiring organization, educational degree preferred and required, work experience preferred and required, salary information, job type, job location, associated knowledge, skills and expertise and software skills. Most common job title was that of an analyst (21%, n=65) while more than one-third of the hiring organizations were health systems (35%, n=106). 95% (n=291) of the jobs were fulltime and nearly half of these jobs were in New York City (47%, n=143). Data/statistical analysis (68%, n=207), working in multidisciplinary teams (35%, n=108), and biomedical/clinical experience (30%, n=93) were the common skills needed. Structured query language (SQL), Python, and R language were common programming language skills. A broad framework of integrating informatics competencies, combined with analysis of the skills the jobs needed, and knowledge acquisition based on global health informatics projects guided the development of an online population health informatics curriculum in a rapidly changing technological environment.

Background COVID-19 vaccines are considered to be a key to limiting and eliminating the infectious disease. However, the success of the vaccination program will rely on the rates of vaccine acceptance among the population. Objective This study aims to examine the factors that influence vaccine hesitancy and vaccine acceptance, and to explore the unintended consequences of COVID-19 infections. The study will further explore the association between sociodemographic characteristics; health status; COVID-19–related knowledge, attitude, and practices; and its influence on vaccine hesitancy and acceptance among individuals living in urban and rural settings of Chennai, Tamil Nadu in the southern state of India. Methods A cross-sectional study will be conducted between January 2021 and January 2023. A sample of approximately 25,000 individuals will be recruited and enrolled using a nonprobability complete enumeration sampling method from 11 selected urban and rural settings of Chennai. The data will be collected at one time point by administering the questionnaire to the eligible study participants. The collected data will be used to assess the rates of vaccine acceptance; hesitancy; and knowledge, attitudes, practices, and beliefs regarding COVID-19 and COVID-19 vaccines. Lastly, the study questionnaire will be used to assess the unintended consequences of COVID-19 infection. Results A pilot of 2500 individuals has been conducted to pretest the survey questionnaire. The data collection was initiated on March 1, 2021, and the initial results are planned for publication by June 2021. Descriptive analysis of the gathered data will be performed using SAS v9.1, and reporting of the results will be done at 95% CIs and P=.049. The study will help explore the burden of vaccine acceptance and hesitancy among individuals living in urban and rural settings of Chennai. Further, it will help to examine the variables that influence vaccine acceptance and hesitancy. Lastly, the findings will help to design and develop a user-centered informatics platform that can deliver multimedia-driven health education modules tailored to facilitate vaccine uptake in varied settings. Conclusions The proposed study will help in understanding the rate and determinants of COVID-19 vaccine acceptance and hesitancy among the population of Chennai. The findings of the study would further facilitate the development of a multifaceted intervention to enhance vaccine acceptance among the population. International Registered Report Identifier (IRRID) DERR1-10.2196/29733

Nutrition informatics (NI) is the effective retrieval, organization, storage, and optimum use of information, data and knowledge for food-and-nutrition-related problem-solving and decision-making. There is a growing opportunity to facilitate technology-enabled behavioral change interventions to support NI research and practice. This paper highlights the changing landscape of food and nutrition practices in India to prepare a NI workforce that could provide some valuable tools to address the double burden of nutrition. Management and interpretation of data could help clarify the relationships and interrelationships of diet and disease in India on both national and regional levels. Individuals with expertise in food and nutrition may receive training in informatics to develop national informatics systems. NI professionals develop tools and techniques, manage various projects and conduct informatics research. These professionals should be well prepared to work in technological settings and communicate data and information effectively. Opportunities for training in NI are very limited in developing countries. Given the current progress in developing platforms and informatics infrastructure, India could serve as an example to other countries to promote NI to support achieving SDGs and other public health initiatives.

Background: Acute upper gastrointestinal bleeding (UGIB) is a common medical emergency characterised by haematemesis and/or melaena. All patients presenting with UGIB require prompt assessment using a validated assessment tool. Early assessment identifies patients at high risk of death, of further bleeding and those requiring intervention, including surgery. Aim of the study: To study clinical and endoscopic profile in acute upper gastrointestinal bleeding in tertiary health care centre. Materials and methods: The present study reviewed medical records of 100 patients who presented to the outpatient department with upper GI bleeding and underwent GI endoscopy. The age of the participants selected was more than 18 years. Clinical and endoscopic data of these patients was compiled and analysed. Results: Number of male patients in study group was 79 and number of female patients was 21. The mean age was 50.16 years. It was seen that esophageal varices is the most common findings (33%) followed by peptic ulcer disease (31%). The least common finding was malignancy which was present in only 2 % patients. Conclusion: Within the limitations of the present study, it can be concluded that esophageal varices was the most common finding followed by peptic ulcer disease in our study population. Malignancy was least common finding.

Introduction: Anemia is a common, underestimated problem in cancer patients receiving myelosuppressive chemotherapy and has significant adverse effect on the quality of life and outcome. Darbepoetin has been shown to be effective in this setting, but controversy surrounds it actual use. Methods: We analyzed prospectively collected clinical practice data of patients receiving darbepoetin in a real-world setting for this retrospective audit. Patients with baseline hemoglobin (Hb) of

Background & Objective: Cryptographic protocols had been evident method for ensuring con dentiality, Integrity and authentication in various digital communication systems. However the validation and analysis of such cryptographic protocols was limited to usage of formal mathematical models until few years back. Methods: In this paper, various popular cryptographic protocols have been studied. Some of these protocols (PAP, CHAP, and EAP) achieve security goals in peer to peer communication while others (RADIUS, DIAMETER and Kerberos) can work in multiparty environment. These protocols were validated and analysed over two popular security validation and analysis tools AVISPA and Scyther. The protocols were written according to their documentation using the HLPSL and SPDL for analysis over AVISPA and Scyther respectively. The results of these tools were analysed to nd the possible attack an each protocol. Afterwards The execution time analysis of the protocols were done by repeating the experiment for multiple iterations over the command line versions of these tools.As the literature review suggested, this research also validates that using password based protocols (PAP) is faster in terms of execution time as compared to other methods, Usage of nonces tackles the replay attack and DIAMETER is secure than RADIUS. Results and Conclusion: The results also showed us that DIAMETER is faster than RADIUS. Though Kerberos protocol was found to safe, the results tell us that it is compromisable under particular circumstances.

Cash Conversion cycle is considered to be a useful measure to gauge the efficiency with which working capital of an enterprise is managed. In this research paper an attempt has been made to examine the relationship cash conversion cycle has with return on assets representing the measure of profitability and the extent to which it exerts its influence on the profitability. Size of the firm represented as log of sales and Financial Debt represented as total debt to total assets ratio are taken as control variables. We have considered only Indian textile companies listed on Indian stock exchanges. The study covers the period of nine years. The data was analysed using various statistical techniques including multiple regression analysis. F-test, auto correlation test using Durbin Watson Statistics and multi co linearity test using matrix of coefficients of correlation and variance inflation statistics were carried out to enhance the reliability of the results so obtained. The study revealed that both cash conversion cycle and size of the firm have strong and very significant negative association with profitability. The firms have to pay attention to ensure that length of cash conversion cycle and size of the firm remain optimum. The debt ratio was found to have insignificant negative association with profitability of the firm. The results of this research will provide insight to academicians and industrial experts for better decisions.

Experiential learning is a core competency of curricula across accredited U.S. schools of public health, helping students to hone diverse skill sets that will enable them to readily join the workforce and contribute to solving complex public health problems. At the City University of New York (CUNY) Graduate School of Public Health and Health Policy (SPH), all master’s and doctoral students are required to complete a practicum during their degree programs; yet practicum development and selection processes are often overlooked. To enhance the experiential learning program and Human Research Protection Program processes surrounding student practica at the CUNY SPH, researchers mined an administrative database and analyzed characteristics of practicum projects undertaken by graduate students. Findings from this novel study will facilitate policy and procedural changes within the SPH Office of Experiential Learning, which oversees the practicum program, and across SPH offices, including the Human Research Protection Program, to promote an optimal, student-centered practicum experience. Additionally, these analyses will serve as a model for other schools of public health seeking to develop, refine, and strengthen their practicum programs.