bluuebunny/crossref_metadata_2025_split huggingface dataset:
Customer Awareness of Medical Wearable Health Care Technology and Policy Management in India
Authors: John Byju , Rai Nitin Ranjan 
Published: September 2024

Patient and healthcare providers can take preventive actions based on a review of vital signs obtained remotely with wearable medical devices thanks to Internet of Things-based wearable sensors and an integrated cloud platform. Provide essential home care that is as clean and well-manicured as the intensive care unit (ICU), complete with remote vital sign monitoring. In India, there is no governing agency for healthcare. Healthcare professionals are concerned about the dependability of medical devices in the absence of sufficient laws, which makes them prefer to employ conventional medical devices. Based on related dangers, medical gadgets are to be divided into four types. Converging, analysing, and applying massive data collected by wearable medical devices for use in illness diagnosis, mitigation, and preventive therapy is a challenge to the medical business. By exchanging real-time data, the use of wearable medical devices would not only aid to strengthen patient-physician relationships but also decrease the need for human interventions. To uncover consumer awareness difficulties in the adoption of these devices, this paper explores the possibilities of the Internet of Things in the medical wearable business.
Public Health Monitoring System In COVID-19 Conditions Using Machine Learning-Based Sentimental Analysis
Authors: Haval Dr. Abhijeet Madhukar , Afzal Md 
Published: September 2024

Twitter is a significant forum for individuals to discuss and disseminate health-related data. The system offers substantial data for immediate monitoring of contagious diseases (such as COVID-19), relieving disease-prevention organizations from the laborious tasks associated with Personal Health Measures (PHM). PHM identification is a crucial technique for staying informed about the status of an epidemic. It aims to determine an individual's health by analyzing web text data. This research investigates the process of identifying PHM related to COVID-19 using data from Twitter. The research has constructed a COVID-19 PHM dataset with tweets labeled with four distinct categories of health disorders connected to COVID-19: self-mention (SM), other-mention (OM), awareness, and non-healthcare (NHC). The research achieved favorable outcomes in the PHM identification task. The categorizing results enable prompt health tracking and oversight for digital epidemiology. The study assesses the impact of the attention strategy and training methodology on the predictive capabilities.
Satisfaction Level of Medical Tourism Services Based on Classified Factors - A Discriminatory Approach
Authors: Girimurugan B. , A S Poornima , C Natarajan , Padma Prabha C  , Adhinarayanan Poomagal , N Senthil Prabhu 
Published: September 2024

Medical tourism has the potential to promote high-class learning, well equipped human resources, flattering visa policies, nations’ charisma, and sound flourished infrastructure facilities available in the nation. Countries have recognized this opportunity as a means in support of overall communal and economic upliftment. More governments are valuing these advantages and developing plans and policies for the sector as a result, WTTC states that by means of the appropriate policies, guidance and regulation mechanisms, medical tourism can achieve sustainable growth among the developed and developing nations. With a sample of 600 respondents, the data related to satisfaction in the medical tourism services is directly observed at the hospitals from the International Patients in the six states of Tamil Nadu, Kerala, Karnataka, Telungana, Andhrapradesh, and Pondicherry. Data collected are compiled for the analytical purpose and the hypothesis is framed with regard to the opinion of the patients regarding various facilities and services offered and their satisfaction level towards the medical tourism services. In this present study, the Canonical Discriminate statistics was successfully employed for identifying the satisfaction over various reasons for preferring the medical tourism services in India. The outcome of the study is the classification of different groups under the satisfaction derived from the medical tourism services of four continuous variables are assigned and they are general reasons, treatment reason, cost reason, and sources reason with all 600 respondents. Findings reveal that Treatment Factor is the strong reason contributing towards the construction of low satisfaction group, followed by general reasons. The study highlights that cost is the least reason for low level satisfaction and reveals that cost of medical tourism services are nominal in India.
The Impact of Wearable Devices on Public Health Outcomes in The Treatment of Chronic Diseases Through Continuous Physiological Monitoring
Authors: Kumar Sanjay , Sharma Priti 
Published: September 2024

Chronic illnesses contribute to elevated levels of disability and death. The active involvement of patients in the treatment of chronic diseases is a crucial element of healthcare systems that are focused on chronic diseases. Wearable devices provide real-time health information focused on the patient, enabling them to make informed decisions about self-management. Although wearables are believed to offer advantages in enhancing the self-management of chronic illnesses, their impact on healthcare outcomes still needs to be well comprehended. This study sought to investigate the effect of wearables on healthcare results in adults with chronic conditions by conducting a comprehensive analysis of existing evidence for physiological monitoring. A narrative systematic literature review was performed by searching six databases for randomized and observational research published from January 2018 to July 2023. These studies focused on utilizing a wearable intervention in a group of individuals with chronic diseases to evaluate its effect on a predetermined end measure. The outcomes were defined as any impact on patient or practitioner experience, cost-effectiveness, or healthcare outcomes resulting from the wearable intervention. The findings from the research included in the analysis were gathered according to 6 main themes, which were used as the foundation for a qualitative summary. This research adhered to the requirements outlined in the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) declaration.
Genetic Algorithm-based Optimization Model for Emergency Shelters Location Allocation in Public Health Emergency Situations
Authors: Vij Dr. Priya , Prashant Patil Manisha 
Published: September 2024

Deploying emergency resources after a disaster is crucial for reducing the event’s impact. A multi-objective (MO) resource allocation (RA) method is developed to achieve efficient distribution of relief items and the best choice of transportation routes, taking into account the variable and persistent nature of rescue operations. The MO Cellular Genetical Approach (MOC-GA) addresses the concept by incorporating auxiliary populations and neighborhood architecture into the cellular automaton. The comparative experiment conclusively demonstrates that MOC-GA performs favorably compared to Particle Swarm Optimisation (PSO), Ant Colony Optimisation (ACO), and Dragon Fly Optimisation (DFO) in several metrics such as the Pareto Front (PF), hypervolume, mean objective function value, and PF ratios. The findings demonstrate that MOC-GA effectively addresses the MO dynamical emergency RA approach, offering decision-makers a more comprehensive range of superior and diversified candidate rescue strategies than alternative methods. This research analyzes the existing rescue methods and proposes a theoretical rescue strategy to aid decision-makers in making scientifically informed decisions.
Multi-Step Forecasting Method For Influenza Pandemic Using Long Short-Term Memory Model To Improve Public Health Conditions
Authors: Deshmukh Dr. Rajesh Keshavrao , Shrivastav Mohit 
Published: September 2024

The flu spread is a major global public health problem that gets attention around the world because it can cause serious illness, cost a lot of money, and kill a lot of people every year. To avoid influenza-like disease (ILD) and run healthcare systems well, predicting when an influenza outbreak will happen is essential. Researchers have used Machine Learning (ML) techniques but have yet to find the best ways to see complicated, non-linear trends in sequential data about flu outbreaks. A Long Short-Term Memory (LSTM) and a Genetic Algorithm (GA) are used together in this study to show a new way to predict multi-step influenza breakouts. Every week, the study gathers information on ILD, which covers flu and other illnesses with signs similar to the flu. When measuring performance during busy times, the suggested model does much better than other advanced Machine Learning (ML) methods and Fully Connected Neural Networks (F-CNN).
Spatial Variation of The Impact of Salmonella Bacteria on The Soil of The Plateau (Najaf – Karbala) Using Modern Technologies
Authors: Al-Salami Maryam A. Rahim , Menkhi Saadia Akol 
Published: September 2024

The research concluded through field studies and laboratory work that there is spatial and temporal variation of soil contamination with Salmonella bacteria in the soil of the plateau (Najaf – Karbala) within the center of Najaf district and Al-Haydariyah sub-district by taking (36)sites and(144)models and by four seasons ( summer, autumn, winter and spring) and then diagnosed and analyzed microbiologically and know their types and numbers and the effects caused by these pathological species on humans, animals and plants, as two types of Salmonella bacteria were discovered in the soil of the study area and these two types were (Salmonella enteritidis, Salmonella typhi) was also known to have spatial variation for all the previous two species, as it was found that there are sites that record the highest presence of Salmonella bacteria in the soil of the study area, on the contrary, there are sites that did not record any presence of this bacteria , and the temporal variation between the seasons was also known, as autumn and spring recorded the highest presence and then winter ranked third and finally recorded the summer in terms of the total number of Salmonella bacteria in the soil of the study area, and finally the research concluded that the study area poses an environmental threat to the population in particular and the population of Najaf Governorate In general, what these types of bacteria leave on the environment .
An Overview of the Public Health Risk Management Model: A Food Safety-Related Study
Authors: Maurya Dr. Shyam , Sandilya Ranjit 
Published: September 2024

Food Safety (FS) in the Food Market (FM) is an essential aspect of Public Health (PH) since it impacts people of all ages, races, genders, and income levels. Regional and global FM continues to influence the FS and PH. The globalization of Health Risk (HR) is increasing because food supply networks span several nations the research aimed to identify prevalent PH issues associated with FS in the FM. A theoretical analysis employs the population, people's interests, and environment. The study evaluates the FS using the Mixed Methods Appraisal Tool (MMAT). After analyzing 200 full-text papers, seven prevalent PH issues associated with FS in the FM were identified. This study evaluated the literature to identify the FM's predominant PH hazards related to FS. The findings suggest that local and worldwide FM influences PH. Countries must guarantee the security and reliability of the exported food products and meet their national standards.
Analysis of Food Nutrients Impact for Improving Public Health Using Multi Features Deep Recurrent Neural Network for Health Care Management
Authors: Deshmukh Dr. Rajesh Keshavrao , Fatma Eram 
Published: September 2024

The food nutrients in daily life is incredible source to make good food for good life cycle. In recent analysis, the new food styles and ingredients affects the public health especially children to cause various disease and impacts which leads dangerous to healthier life. By analyzing food nutrients and presence healthy ingredients in daily life is important. World health organization pays number of research to healthcare management and suggestions to improve the public health by recommending different protocols. The risk is identifying presence of nutrients scaling is tedious due to prevailing techniques machine learning techniques does not provide good results to make better good food recommendation. The problem is more data labels and features are need to analyze which increase the false rate to reduce accuracy. To address the issue, to propose a deep LSTM gated recurrent neural network (LSTM-GRNN) based on Support vector feature selection (SVFS) to identify the presence of food nutrients to recommend the good food to improve the public health. Initially the food product and nutrients scaling logs are collected and to make preprocessing based on C-score normalization form feature labels. Then nutrients scaling impact rate is analyzed for each food ingredients presence by extracting the margins from features list. The important feature is selected by using SVSR to reduce the feature margins based on support vector. Then the selected features are trained into LSTM unit with recurrent neural network to identify the presence of nutrients rate in each food and to categorize the class by presence. The higher scaling rate of class is considered as good food to recommend for healthy facts. The proposed system attains higher detection accuracy in precision, recall rate and f1 measure, also the lower false negative rate increase the performance of accuracy compared to the existing system.
Healthcare Access and Financing: A Legal and Public Health Perspective
Authors: Kaur Harleen , Das Abhinav 
Published: September 2024

The welfare provisions of the current Constitution centre on healthcare. The Indian Constitution's Article 42 declares that the state has an obligation to take all reasonable steps to promote public health. Additionally, Article 47 of the Indian Constitution, another article containing Directive Principles of State Policy, mandates that the state carry out its primary duties to improve public health by raising the standard of living, improving nutrition, and improving public health. In particular, the state must work to outlaw the use of drugs and alcohol, unless prescribed, as these substances are harmful to health. It follows that welfare regulations clearly place an obligation on the state to increase its assistance in promoting the health of those individuals who are unable to pay for the expensive medical care needed to treat their illnesses. In light of the urgency and importance of the issue, the World Health Organisation designated Universal Health Coverage (UHC) as its top goal. Ensuring that everyone has access to necessary health services of adequate quality and is shielded from the financial burden of medical expenses is the goal of universal health coverage. In underdeveloped nations, financial security against medical expenses is just as important as access to healthcare.
Large-Scale Genomics Applications in Public Health and Precision Medicine Using Big Data and Predictive Analytics
Authors: Biswas Debarghya , Tiwari Ankita 
Published: September 2024

After gaining experience from large population sequencing projects, there is a significant gap between the promise of genomics in Precision Medicine (PM) and the existing state of affairs. To fully exploit this potential, Public Healthcare (PH) must adopt procedures, regulations, and technology that are unfamiliar to them. The development needs to be improved by several historical procedures and regulatory hurdles. In the future, PM will include making genetic big data easily accessible at the point of treatment and implementing tools to govern its use effectively. Significant modifications need to be made in billing, payment, and monitoring to accomplish this goal. New systemic and technological structures must also be developed inside the healthcare sector. The responsibilities of clinical geneticists will expand to include overseeing PM systems. Genetic counselors will guide and assist PM by establishing and upholding PM structures. The trajectory of the research is now hindered by several impediments, resulting in avoidable fatalities. Revamping medical facilities to accommodate genomics has the potential to influence the treatment of patients significantly and enable the achievement of long-awaited advancements in PM.
Footprints of past earthquakes revealed in the afterslip of the 2010 Mw 7.8 Mentawai tsunami earthquake
Authors: Feng Lujia , Barbot Sylvain , Hill Emma M. , Hermawan Iwan , Banerjee Paramesh , Natawidjaja Danny H. 
Published: September 2016

AbstractThe 2010 Mw 7.8 Mentawai tsunami earthquake marks one of the first tsunami earthquakes to have postseismic deformation observed by geodetic instruments. The Sumatran GPS Array has recorded the postseismic deformation following this event continuously for >5 years. The spatiotemporal evolution of the postseismic deformation is well explained by velocity‐strengthening frictional sliding on the Sunda megathrust. Our results show that the 2010 afterslip progressed downdip relative to the 2010 coseismic rupture. The southeastern portion of the afterslip region overlaps the area that slipped during the main shock and afterslip of the 2007 Mw 8.4 Bengkulu earthquake, while the northwestern portion slipped an area without recent large earthquakes. By incorporating pre‐earthquake stress conditions into quasi‐dynamic models, we demonstrate that the preceding cumulative slip from the 2007 sequence might have caused a ∼0.1 MPa difference in pre‐earthquake Coulomb stress between the southeastern and northwestern portions of the afterslip region.
Radar imaging of intense nonlinear Ekman divergence
Authors: Liu Guoqiang , Perrie William , Kudryavtsev Vladimir , He Yijun , Shen Hui , Zhang Biao , Hu Haibo 
Published: September 2016

AbstractIn general, given an oceanic thermal front, there is a strong positive correlation between sea surface temperature (SST) gradients and surface winds, and the marine atmospheric boundary layer is unstable over the warm side of the oceanic thermal front. The Gulf Stream is a notable example of an oceanic thermal front, and its warm side is often detected as enhanced backscatter in synthetic aperture radar (SAR) images. However, in some “anomalous” SAR images, low backscatter is sometimes observed on the warm side of the front, which seems inconsistent. Therefore, we propose a mechanism to interpret the generation of the low backscatter, based on interactions between ocean surface wind waves and intense nonlinear Ekman divergence. This mechanism is verified by showing that patterns in an observed anomalous SAR image are in good agreement with those in the simulated radar signature. In addition, this methodology and analysis demonstrate that SAR is potentially important for detecting and diagnosing small scale air‐sea interactions and upper ocean dynamics with strong vertical transports induced by submesoscale processes.
Ionospheric loss from Mars as predicted by hybrid particle simulations
Authors: Brecht Stephen H. , Ledvina Stephen A. , Bougher Stephen W. 
Published: September 2016

AbstractThis paper reports the results of hybrid simulations of the Mars‐solar wind interaction. The focus is on the loss rates of O+ and O2+ from the Martian ionosphere while examining the importance of including 3‐D dimensional neutral atmospheres with their associated neutral winds. The simulation loss rates are found to be in excellent agreement with loss rates reported by Lundin et al. (2013). Further, the results follow the functional form of the loss rates versus solar EUV as determined by Lundin et al. (2013). This paper will discuss numerical issues as well as the implementation of 3‐D neutral atmospheres, including neutral winds and corotation flows.
Disaster loss and social media: Can online information increase flood resilience?
Authors: Allaire Maura C. 
Published: September 2016

AbstractWhen confronted with natural disasters, individuals around the world increasingly use online resources to become informed of forecasted conditions and advisable actions. This study tests the effectiveness of online information and social media in enabling households to reduce disaster losses. The 2011 Bangkok flood is utilized as a case study since it was one of the first major disasters to affect a substantial population connected to social media. The role of online information is investigated with a mixed methods approach. Both quantitative (propensity score matching) and qualitative (in‐depth interviews) techniques are employed. The study relies on two data sources—survey responses from 469 Bangkok households and in‐depth interviews with internet users who were a subset of the survey participants. Propensity score matching indicates that social media enabled households to reduce flood losses by an average of 37% (USD 3708 per household), using a nearest neighbor estimator. This reduction is substantial when considering that household flood losses for the matched sample averaged USD 8278. Social media offered information not available from other sources, such as localized and nearly real‐time updates of flood location and depth. With this knowledge, households could move belongings to higher ground before floodwaters arrived. These findings suggest that utilizing social media users as sensors could better inform populations during disasters. Overall, the study reveals that online information can enable effective disaster preparedness and reduce losses.
Assessment of sex differences in bone deficits among adolescents with anorexia nervosa
Authors: Nagata Jason M. , Golden Neville H. , Peebles Rebecka , Long Jin , Leonard Mary B. , Chang Audrey O. , Carlson Jennifer L. 
Published: September 2016

ABSTRACTObjectiveThe objective of this study was to compare sex differences in bone deficits among adolescents with anorexia nervosa (AN) and to identify other correlates of bone health.MethodElectronic medical records of all patients 9–20 years of age with a DSM‐5 diagnosis of AN who were evaluated by the eating disorders program at Stanford with dual‐energy X‐ray absorptiometry (DXA) between March 1997 and February 2011 were retrospectively reviewed. Whole body bone mineral content Z‐scores and bone mineral density (BMD) Z‐scores at multiple sites were recorded using the Bone Mineral Density in Childhood Study (BMDCS) reference data.ResultsA total of 25 males and 253 females with AN were included, with median age 15 years (interquartile range [IQR] 14–17) and median duration of illness 9 months (IQR 5–13). Using linear regression analyses, no significant sex differences in bone deficits were found at the lumbar spine, total hip, femoral neck, or whole body when controlling for age, %mBMI, and duration of illness. Lower %mBMI was significantly associated with bone deficits at all sites in adjusted models.DiscussionThis is the first study to evaluate sex differences in bone health among adolescents with AN, using novel DSM‐5 criteria for AN and robust BMDCS reference data. We find no significant sex differences in bone deficits among adolescents with AN except for a higher proportion of females with femoral neck BMD Z‐scores <‐1. Degree of malnutrition was correlated with bone deficits at all sites. © 2016 Wiley Periodicals, Inc.(Int J Eat Disord 2017; 50:352–358)