Internet-based Data Research Articles

Psychometric evaluation of the persian version of Existential Concern Questionnaire (ECQ) in Iranian general population: a cross-sectional study

BackgroundExistential anxiety is the anxiety that goes beyond objective threats and is related to a person’s existence. Due to the lack of appropriate instrument to measure all five domains of existential anxiety in the Iranian population, this study aimed to examine the psychometric properties of the Existential Concern Questionnaire (ECQ) in the Iranian general population.MethodsThis online cross-sectional survey was conducted from October 9 to December 11, 2023 in Tehran, Iran. We used an Internet-based data collection method. A total of 549 participants completed the questionnaires. The research instruments included the Existential Concern Questionnaire, Existential Anxiety Questionnaire, Meaning in Life Questionnaire, Short version of the Beck Depression Inventory, Intolerance of Uncertainty Scale-12, and Death Attitude Profile Revised. Confirmatory factor analysis, convergent validity, and concurrent validity were used to measure the validity of ECQ. Cronbach’s alpha and split-half methods were employed to assess the reliability of the ECQ.ResultsConfirmatory factor analysis showed a good fit of the unidimensional model (CMIN/DF: 2.802, RMR: 0.066, GFI: 0.914, NFI: 0.911, CFI: 0.941, PNFI: 0.769, PCFI: 0.794, RMSEA: 0.057). Also, the correlation of ECQ with other variables was as expected except for the search for meaning in life (r: 0.010, P = 0.808), which indicates the validity of this questionnaire. The reliability of the questionnaire was also acceptable (Cronbach’s Alpha: 0.935).ConclusionsThe results supported the validity and reliability of the ECQ. The mentioned questionnaire can be used in Iranian general population. Considering the importance of existential anxiety in etiology of some disorders, it is suggested that the future studies focus on validation of the ECQ in different clinical samples and cultures.

Monitoring event data extracted from online news for outbreak detection

Abstract Introduction Digital disease surveillance (DDS) detects public health events from internet-based data e.g., online news. Event features depicting epidemiological and social characteristics of health events can be extracted from news using the natural language process techniques. However, few studies have leveraged the event features to support anomaly detection in DDS. We aimed to understand the distribution of the event features and explore anomaly detection using the frequency of these features. Methods We collected event data from COVID-19-related news collected from October 1 to December 31, 2021, sourced from BioCaster, an infectious-disease-focused DDS system. The predefined event features in BioCaster include disease, pathogen, location and 14 binary features, such as if an event was caused by an unclassified virus. We described the distribution of the features and detected changes in the frequency of event features using a Bayesian online change point detection. We compared the change points with the number of new cases and of genomic samples collected. Results We included 170,168 news articles reporting COVID-19 in 155 countries. The event feature indicating that an event was caused by an unclassified virus was identified as positive among 3831 (2.25%) news and 12.91% of news had positive value for the feature indicating cases who had travelled across international borders. The change points detected from these two features were temporally correlated to the emergence of the Omicron variant in corresponding countries, which was more significant in countries with at least 300 news articles. Conversely, event features irrelevant to this case study, e.g., if the cases were military workers, were identified as negative in all news and no change points were detected. Conclusions Our study highlights the potential of monitoring the frequency of event features extracted from online news for anomaly detection in DDS, which relies on sufficient news coverage. Key messages • Monitoring the event features extracted from online news provide is useful approach for automatic anomaly detection in digital disease surveillance. • Increasing media coverage is fundamental for improving the early detection in a digital disease surveillance system.

Development of an Internet-based Product-related Child Injury Textual Data Platform (IPCITDP) in China.

Internet-based media stories provide valuable information for emerging risks of product-related child injury prevention and control, but critical methodological challenges and high costs of data acquisition and processing restrict practical use by stakeholders. We constructed a data platform through literature reviews and multi-round research group discussions. Developed components included standard search strategies, filtering criteria, textual document classification, information extraction standards and a keyword dictionary. We used ten thousand manually labelled media stories to validate the textual document classification model, which was established using the Bidirectional Encoder Representation from Transformers (BERT). Multiple information extraction methods based on natural language processing algorithms were adopted to extract data for 29 structured variables from media stories. They were evaluated through manual validation of 1000 media stories about product-related child injury. We mapped the geographic distribution of media sources and media-reported product-related child injury events. We developed an internet-based product-related child injury textual data platform, IPCITDP, consisting of four layers - automatic data search and acquisition, data processing, data storage, and data application - concerning product-related child injury online media stories in China. Each layer occurred daily. External validation demonstrated high performance for the BERT classification model we established (accuracy = 0.9703) and the combined information extraction strategies (accuracy >0.70 for 25 variables). As of 31 December 2023, IPCITDP collected 35 275 eligible product-related child injury reports from 13 261 news media websites or social media platform accounts which were geographically located across all 31 mainland Chinese provinces and covered over 97% of prefecture-level cities. The injury cases in IPCITDP were typically reported several months or years earlier than official announcements about the product-related child injury risks. Our data platform added data concerning 15 supplementary variables that the national product-related injury surveillance system lacks. Two examples demonstrate the value of IPCITDP in supplementing existing data and providing early epidemiological detection of emerging signals concerning product-related child injury: magnetic beads and electric self-balancing scooters. Our data platform provides injury data that can support early detection of new product-related child injury characteristics and supplement existing data sources to reduce the burden of product-related injury among Chinese children.

Community-engaged artificial intelligence research: A scoping review.

The degree to which artificial intelligence healthcare research is informed by data and stakeholders from community settings has not been previously described. As communities are the principal location of healthcare delivery, engaging them could represent an important opportunity to improve scientific quality. This scoping review systematically maps what is known and unknown about community-engaged artificial intelligence research and identifies opportunities to optimize the generalizability of these applications through involvement of community stakeholders and data throughout model development, validation, and implementation. Embase, PubMed, and MEDLINE databases were searched for articles describing artificial intelligence or machine learning healthcare applications with community involvement in model development, validation, or implementation. Model architecture and performance, the nature of community engagement, and barriers or facilitators to community engagement were reported according to PRISMA extension for Scoping Reviews guidelines. Of approximately 10,880 articles describing artificial intelligence healthcare applications, 21 (0.2%) described community involvement. All articles derived data from community settings, most commonly by leveraging existing datasets and sources that included community subjects, and often bolstered by internet-based data acquisition and subject recruitment. Only one article described inclusion of community stakeholders in designing an application-a natural language processing model that detected cases of likely child abuse with 90% accuracy using harmonized electronic health record notes from both hospital and community practice settings. The primary barrier to including community-derived data was small sample sizes, which may have affected 11 of the 21 studies (53%), introducing substantial risk for overfitting that threatens generalizability. Community engagement in artificial intelligence healthcare application development, validation, or implementation is rare. As healthcare delivery occurs primarily in community settings, investigators should consider engaging community stakeholders in user-centered design, usability, and clinical implementation studies to optimize generalizability.

Use of Machine Learning Models to Differentiate Neurodevelopment Conditions Through Digitally Collected Data: Cross-Sectional Questionnaire Study.

Diagnosis of child and adolescent psychopathologies involves a multifaceted approach, integrating clinical observations, behavioral assessments, medical history, cognitive testing, and familial context information. Digital technologies, especially internet-based platforms for administering caregiver-rated questionnaires, are increasingly used in this field, particularly during the screening phase. The ascent of digital platforms for data collection has propelled advanced psychopathology classification methods such as supervised machine learning (ML) into the forefront of both research and clinical environments. This shift, recently called psycho-informatics, has been facilitated by gradually incorporating computational devices into clinical workflows. However, an actual integration between telemedicine and the ML approach has yet to be fulfilled. Under these premises, exploring the potential of ML applications for analyzing digitally collected data may have significant implications for supporting the clinical practice of diagnosing early psychopathology. The purpose of this study was, therefore, to exploit ML models for the classification of attention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) using internet-based parent-reported socio-anamnestic data, aiming at obtaining accurate predictive models for new help-seeking families. In this retrospective, single-center observational study, socio-anamnestic data were collected from 1688 children and adolescents referred for suspected neurodevelopmental conditions. The data included sociodemographic, clinical, environmental, and developmental factors, collected remotely through the first Italian internet-based screening tool for neurodevelopmental disorders, the Medea Information and Clinical Assessment On-Line (MedicalBIT). Random forest (RF), decision tree, and logistic regression models were developed and evaluated using classification accuracy, sensitivity, specificity, and importance of independent variables. The RF model demonstrated robust accuracy, achieving 84% (95% CI 82-85; P<.001) for ADHD and 86% (95% CI 84-87; P<.001) for ASD classifications. Sensitivities were also high, with 93% for ADHD and 95% for ASD. In contrast, the DT and LR models exhibited lower accuracy (DT 74%, 95% CI 71-77; P<.001 for ADHD; DT 79%, 95% CI 77-82; P<.001 for ASD; LR 61%, 95% CI 57-64; P<.001 for ADHD; LR 63%, 95% CI 60-67; P<.001 for ASD) and sensitivities (DT: 82% for ADHD and 88% for ASD; LR: 62% for ADHD and 68% for ASD). The independent variables considered for classification differed in importance between the 2 models, reflecting the distinct characteristics of the 3 ML approaches. This study highlights the potential of ML models, particularly RF, in enhancing the diagnostic process of child and adolescent psychopathology. Altogether, the current findings underscore the significance of leveraging digital platforms and computational techniques in the diagnostic process. While interpretability remains crucial, the developed approach might provide valuable screening tools for clinicians, highlighting the significance of embedding computational techniques in the diagnostic process.

Two Sides of the Same Coin? A Comparison between Internet-based and Paper-based Data Collection for Autism Quotient and Depression, Anxiety and Stress Scale

IntroductionThe utilization of internet-based data collection in mental health research has gained popularity for its convenience and affordability. However, concerns often arise regarding the validity and reliability of data collected via the internet. The Autism Spectrum Quotient (AQ) is a self-report questionnaire to measure the traits associated with autism spectrum disorder (Baron-Cohen et al. J Autism Dev Disord, 2001; 31 5-17) and the online usage of AQ is common and conducted with large numbers of participants across many studies. However, the effect of using internet-based data collection for AQ rather than conventional paper-based procedures is unknown.Objectives To address this issue, we conducted a study comparing the effectiveness of internet-based and paper-based data collection procedures for both the AQ and Depression Anxiety Stress Scale-21 (DASS-21, Lovibond &amp; Lovibond, Behav Res Ther 1995; 33 335–343), which is also a prevalent mental health measurement in the literature and often used for online data collection (Zlomke, Comput Hum Behav 2009; 25 841-843). In addition, to compare internet-based and paper-based methods more fully, we included another variable (type of supervision) where a researcher was either present or absent during the completion of the questionnaires.MethodsA power analysis was conducted, and a minimum of 90 participants were needed to reach a medium effect size of .30 with an adequate power of .80 at a= .05. Accordingly, 96 participants were used and randomly assigned across 4 data collection groups: internet-based (supervision, no supervision) and paper-based (supervision, no supervision). In addition to a Demographic Form, AQ, and DASS-21 were used to obtain the data. Three independent variables were used in the current study: type of presentation (internet-based and paper-based) and type of supervision as between factors, and type of assessment as a within factor.ResultsUsing a 2 x 2 x 2 mixed design ANOVA, no significant main effects were found for any independent variables (all p &gt; .33) or interaction (all ps &gt; .17).ConclusionsThe results of using AQ and DASS-21 were not altered by using internet-based or paper-based data collection procedures, suggesting that both methodologies are equally valid for this purpose. Moreover, these effects were also unaffected by the presence or absence of a researcher during data collection, suggesting that supervision by an authoritative figure does not alter the responses made.Disclosure of InterestNone Declared

Evaluation of the Gonadotoxicity of Cancer Therapies to Improve Counseling of Patients About Fertility and Fertility Preservation Measures: Protocol for a Retrospective Systematic Data Analysis and a Prospective Cohort Study.

Cytotoxic treatments such as chemo- and radiotherapy and immune therapies are required in cancer diseases. These therapies have the potential to cure patients but may also have an impact on gonadal function and, therefore, on fertility. Consequently, fertility preservation treatments such as freezing of gametes and gonadal tissue might be required. However, as detailed data about the necessity to perform fertility preservation treatment are very limited, this study was designed to fill this data gap. Primary objective of this study is to analyze the impact of cancer therapies and chemotherapies on the ovarian reserve and sperm quality. Secondary objectives are to analyze the (1) impact of cancer therapies and chemotherapies on other fertility parameters and (2) probability of undergoing fertility preservation treatments in relation to specific cancer diseases and treatment protocols and the probability to use the frozen gametes and gonadal tissue to achieve pregnancies. First, previously published studies on the gonadotoxicity of chemo- and radiotherapies among patients with cancer will be systematically analyzed. Second, a prospective cohort study set up by approximately 70 centers in Germany, Switzerland, and Austria will collect the following data: ovarian function by analyzing anti-Müllerian hormone (AMH) concentrations and testicular function by analyzing sperm parameters and total testosterone immediately before and around 1 year after gonadotoxic therapies (short-term fertility). A follow-up of these fertility parameters, including history of conceptions, will be performed 5 and 10 years after gonadotoxic therapies (long-term fertility). Additionally, the proportion of patients undergoing fertility-preserving procedures, their satisfaction with these procedures, and the amount of gametes and gonadal tissue and the children achieved by using the frozen material will be analyzed. Third, the data will be merged to create the internet-based data platform FertiTOX. The platform will be structured in accordance with the ICD (International Classification of Diseases) classification of cancer diseases and will be easily be accessible using a specific App. Several funding bodies have funded this study. Ten systematic reviews are in progress and the first one has been accepted for publication. All Swiss and many German and Austrian ethics committees have provided their approval for the prospective cohort study. The study registry has been set up, and a study website has been created. In total, 50 infertility centers have already been prepared for data collection, which started on December 1, 2023. The study can be expected to bridge the data gap regarding the gonadotoxicity of cancer therapies to better counsel patients about their infertility risk and their need to undergo fertility preservation procedures. Initial data are expected to be uploaded on the FertiTOX platform in 2026. ClinicalTrials.gov NCT05885048; https://clinicaltrials.gov/study/NCT05885048. DERR1-10.2196/51145.

Outcomes and Costs of the Transition From a Paper-Based Immunization System to a Digital Immunization System in Vietnam: Mixed Methods Study.

The electronic National Immunization Information System (NIIS) was introduced nationwide in Vietnam in 2017. Health workers were expected to use the NIIS alongside the legacy paper-based system. Starting in 2018, Hanoi and Son La provinces transitioned to paperless reporting. Interventions to support this transition included data guidelines and training, internet-based data review meetings, and additional supportive supervision visits. This study aims to assess (1) changes in NIIS data quality and use, (2) changes in immunization program outcomes, and (3) the economic costs of using the NIIS versus the traditional paper system. This mixed methods study took place in Hanoi and Son La provinces. It aimed to analyses pre- and postintervention data from various sources including the NIIS; household and health facility surveys; and interviews to measure NIIS data quality, data use, and immunization program outcomes. Financial data were collected at the national, provincial, district, and health facility levels through record review and interviews. An activity-based costing approach was conducted from a health system perspective. NIIS data timeliness significantly improved from pre- to postintervention in both provinces. For example, the mean number of days from birth date to NIIS registration before and after intervention dropped from 18.6 (SD 65.5) to 5.7 (SD 31.4) days in Hanoi (P<.001) and from 36.1 (SD 94.2) to 11.7 (40.1) days in Son La (P<.001). Data from Son La showed that the completeness and accuracy improved, while Hanoi exhibited mixed results, possibly influenced by the COVID-19 pandemic. Data use improved; at postintervention, 100% (667/667) of facilities in both provinces used NIIS data for activities beyond monthly reporting compared with 34.8% (202/580) in Hanoi and 29.4% (55/187) in Son La at preintervention. Across nearly all antigens, the percentage of children who received the vaccine on time was higher in the postintervention cohort compared with the preintervention cohort. Up-front costs associated with developing and deploying the NIIS were estimated at US $0.48 per child in the study provinces. The commune health center level showed cost savings from changing from the paper system to the NIIS, mainly driven by human resource time savings. At the administrative level, incremental costs resulted from changing from the paper system to the NIIS, as some costs increased, such as labor costs for supportive supervision and additional capital costs for equipment associated with the NIIS. The Hanoi and Son La provinces successfully transitioned to paperless reporting while maintaining or improving NIIS data quality and data use. However, improvements in data quality were not associated with improvements in the immunization program outcomes in both provinces. The COVID-19 pandemic likely had a negative influence on immunization program outcomes, particularly in Hanoi. These improvements entail up-front financial costs.

KERJA SAMA UN WOMEN DENGAN GOJEK DALAM MENDUKUNG AGENDA SUSTAINABLE DEVELOPMENT GOALS-5: GENDER EQUALITY DI INDONESIA

This article aims to describe the collaboration between UN Women and Gojek which was agreed in July 2020 in pushing for the achievement encouraging the SDGs-5: Gender Equality in Indonesia. The theory used is gender equality in the perspective of feminism, feminist institutionalism theory, and sustainable development in the type of qualitative research. Data collection was taken through internet-based research and data analysis techniques using interactive data analysis models. The results of the research show that the cooperation by UN Women and Gojek is divided into three framework areas of work namely increasing women's capacity, strengthening the capacity of MSMEs, and promoting women's safety in public spaces. The collaboration between UN Women and Gojek is shown as one of the parties involved in helping achieve the SDGs 5 agenda. This is also evidenced by data from the WEF report on the 2022 Global Gender Gap, where the gender equality index in Indonesia has increased from last year

Prevalence of and risk factors for myopia among urban and rural children in Northeast China: protocol for a school-based cross-sectional study

IntroductionThe worldwide prevalence of myopia is high and continues to increase. In this study, a school screening programme for myopia will be implemented using the whole-process information method. The purpose...

Privacy-preserving recommendation system based on social relationships

In the era of internet-based data, recommendation systems are crucial for helping users access personalized content and facilitating business promotion and sales. Recommendation systems based on social relationships have gained popularity due to their ability to use social influence and enhance the impact and credibility of recommendations. However, building a successful recommendation system faces the challenge of protecting user data privacy, as it requires a significant amount of sensitive user data. For this challenge, we propose a privacy-preserving recommendation system based on social relationships in the dual-cloud model. To achieve the secure computation of this system, we also design and improve some underlying secure protocols such as secure equality protocol and secure comparison protocol. Our underlying protocol has the advantages of high efficiency and provable security. As a result, our recommendation system can provide excellent recommendations while ensuring privacy protection. Experiments show that our system can perform secure recommendation calculations on 128-bit data with semi-honest security in approximately 5.5 s.

The Influence of Merdeka Belajar – Kampus Merdeka to Improve the Quality of Graduates in Higher Education: A Reflective Analysis

Objective: Higher education is vital in producing qualified graduates ready to face work challenges. This research used a case study approach at Asahan University. This study aimed to analyze the effect of the Merdeka Belajar-Kampus Merdeka program at Asahan University. Method: The approach was used as an experimental research approach. The respondents constituted students and lecturers at the Faculty of Teacher Training and Education at Asahan University, which accounts for 26 participants. In sampling from the population, researchers used purposive sampling with sample characters, namely students and lecturers of the Faculty of Teacher Training and Education, students and lecturers involved in the MBKM program batch 1 to batch 6. Data collection was carried out using 20 questionnaires, which were analyzed using a Likert Scale. The data was collected using the Google form integrated with Internet-Based Data Analysis (IBDA) and analyzed using the Microsoft Office Excel formula. Results: Therefore, based on the questionnaire distributed using Google Forms, it was found that the program MBKM that Asahan University has carried out affects the competence of graduates involved in the program, with a significance of 64.4% with significant criteria. Novelty: Based on the analysis and discussion results, the Learning Management System integrated the MBKM program to require a clear, structured concept and scientific transparency in all aspects to increase the number of graduates who contribute to the development of Indonesia Maju 2045.

Collaborative Governance Review on G20’s Global Health Infrastucture Priority Issue – The Indonesia Presidency 2022

In 2022, Indonesia will serve as the forum's president. This brings Indonesia to the attention of the global community. Due to restrictions, the COVID-19 pandemic has been plaguing the world for about two years, making it difficult to implement the G20 in Indonesia. Thus, the three main topics of discussion at the 2022 G20 will be: 1) digital transformation; 2) sustainable energy transition; and 3) global health architecture. This paper employs the Collaborative Governance model developed by Ansell &amp; Gash (2008) to examine how the Indonesian G20 Presidency in 2022 collaborates on important global health architecture issues. A review of the literature and internet-based data analysis using the Vos Viewer application comprise the qualitative research methodology. The results of this research are: 1) the relevance of collaborative governance theory to the G20 forum. Seeing how G20 members and participants interact in discussing priority issues of global health architecture. 2) describe the G20 collaboration process using the Ansell &amp; Gash (2008) collaborative governance model. 3) pentahelix as a tool to further emphasize the role of G20 members and participants in the ongoing collaboration process. This research has theoretical implications which prove that collaborative governance can be used to analyze the G20 forum and answer the challenges of the Covid-19 Pandemic.

Application an internet facilitation in a community-based cervical cancer screening project

ObjectiveTo evaluate the feasibility of an internet-facilitated community model for cervical cancer screening using self-collected HPV testing as primary screening.MethodA population-based cervical cancer screening program was conducted in the suburb of Shenzhen, China, from September 2014 to July 2017. Women with 25–60 years of age and no pregnancy were eligible for participation. Participants could register for screening by logging in a website by themselves or with the aids of local community workers. A unique barcode was issued to each applicant upon successful registration. After registration, women could get sampling kits from community screening site/study clinic, collect vaginal samples privately or in group, and provide their sample for Hr-HPV tests on Cobas4800 and SeqHPV assays. Testing reports were checkable through personal account for all participant and phone calls were given to all women positive of Hr-HPV. Participants positive of both or either the 2 assays were identified as the positives. The positives could return the study clinic for triage or search medical care in other clinics. Colposcopy directed or ramdom biopsies were performed on all positives who returned to the study clinics.ResultsA total of 10,792 community women registered for screening, among whom, 10,010 provided their vaginal samples for tests. 99.5% of the participants were confirmed to have correct personal identifiable information and samples, and 98.9% of them got HPV testing results from both or either assays. No adverse event was reported.ConclusionWhen self-collected HPV testing is used as the primary testing, the internet-based data platform facilitates the screening in registration, data collection, and data tracking, and increases the screening coverage. Internet-facilitated community model is promising to cervical cancer control and applicable in regions with variety of resources.

SoilSpatvis: WEB Application for Geographical Data Visualization with R Language for Assessing Soil Pollution

ABSTRACT Recently, user-friendly internet-based applications are becoming popular in parallel with rapidly developing technology. With the growing information technology, computing power, and wealth of data available, analyzing spatial data has become an important subject for scientists who are interested in earth sciences. Thus, in terms of the software used for spatial data analysis, WEB-based applications have become the essential and increasingly popular for many applications. Here, a WEB-based software developed with R language is introduced. The software named as SoilSpatVis provides spatial data analysis and visualization. SoilSpatVis includes a desktop application that performs internet-based geographic data analysis using the K-means method, one of the machine learning algorithms. In the study, different soil pollution parameters collected from some sample sites in the border town of Kütahya, Turkey were used. These parameters were transferred into RStudio software which supports R language. The data were firstly clustered using the K-means algorithm and a visualization section was created for cluster analysis. The “Visualizations” page has been created which allows composing different graphs according to the desired parameter, and the “About” page provides information about the team that developed the application. Various geographic analysis tools are being added to the application and software codes are shared via GitHub.

Text mining of veterinary forums for epidemiological surveillance supplementation

Web scraping and text mining are popular computer science methods deployed by public health researchers to augment traditional epidemiological surveillance. However, within veterinary disease surveillance, such techniques are still in the early stages of development and have not yet been fully utilised. This study presents an exploration into the utility of incorporating internet-based data to better understand smallholder farming communities within the UK, by using online text extraction and the subsequent mining of this data. Web scraping of the livestock fora was conducted, with text mining and topic modelling of data in search of common themes, words, and topics found within the text, in addition to temporal analysis through anomaly detection. Results revealed that some of the key areas in pig forum discussions included identification, age management, containment, and breeding and weaning practices. In discussions about poultry farming, a preference for free-range practices was expressed, along with a focus on feeding practices and addressing red mite infestations. Temporal topic modelling revealed an increase in conversations around pig containment and care, as well as poultry equipment maintenance. Moreover, anomaly detection was discovered to be particularly effective for tracking unusual spikes in forum activity, which may suggest new concerns or trends. Internet data can be a very effective tool in aiding traditional veterinary surveillance methods, but the requirement for human validation of said data is crucial. This opens avenues of research via the incorporation of other dynamic social media data, namely Twitter, in addition to location analysis to highlight spatial patterns.

ChatGPT's Capabilities for Use in Anatomy Education and Anatomy Research

ChatGPT's Capabilities for Use in Anatomy Education and Anatomy Research

음식축제 관광의 활성화를 위한 K-푸드의 홍보방향 : 소셜 빅데이터를 활용한 떡볶이 연관어 분석을 중심으로

In order to present a vision for food tourism necessary for the sustainable growth of K-food and take a global leap, and to revitalize food tourism, we must prepare for the future of food tourism by actively promoting K-food. Therefore, promotion through social media is active in food tourism. Customers share information about the company through social media and communicate with other customers. Because of these characteristics, a significant number of restaurant companies use social media as a marketing channel. The big data analysis methods, which finds information by analyzing vast amounts of Internet-based data, is useful for understanding the meaning and relationship of information. The analysis method was analyzed using the data collection site SomeTrend for ‘text mining’ and ‘semantic network analysis’, and the main keywords were collected as related words of ‘Tteokbokki’, ‘Food Festival’, and ‘K-Food’. Social big data can be used as a way to interpret trends. Based on these research methods, it is a theoretical exploration of ways to connect food festivals and tourism strategies for Tteokbokki, a representative food based on traditional knowledge of K-Food, to meet the needs of food tourism and globalize K-Food. And it is a study to present effective implications from a practical point of view.

A health surveillance data-based assessment of the impact of routine paediatric rotavirus vaccination on all-cause acute childhood diarrhoea

Background: To improve the fight against faecal-orally transmitted rotavirus infection, which is the leading cause of severe diarrhoea among children aged &lt; 5 years, Ghana has incorporated rotavirus vaccination into its expanded programme of immunization. Rotavirus diarrhoea constitutes a significant portion of all-cause acute childhood diarrhoea (ACD) in children. Objective: This study was designed to investigate the impact of routine rotavirus vaccination on ACD cases. Methods: The study was completed through a cross-sectional review of health institutional childhood diarrhoea surveillance data from 2012 to 2021, which includes the year when rotavirus vaccination was introduced. The study dataset was abstracted from the DHIMS-2 internet-based health data repository and was descriptively analyzed by administrative regions using Epi InfoTM version 3.5.1(CDC, USA). Results: The burden of ACD in terms of both absolute and mean values remained the lowest among infants aged under 28 days across all regions, as they had not yet received rotavirus vaccination. In contrast, children aged 1 to 4 years, who are typically exposed to rotavirus serotypes 1 and 2, consistently experienced the highest burden of ACD. With increasing rotavirus vaccination coverage, children aged 1 month to 4 years recorded a marginal, but sustained decline in mean ACD cases from 2016 to 2021. The ACD also similarly declined slowly among the rotavirus vaccine naïve infants aged &lt; 28 days. Despite a spike in 2017, the burden of ACD was low in the Ahafo administrative region. However, increasing rotavirus vaccination coverage did not appear to have a significant impact on reducing ACD in the Ashanti, Bono, Bono East, Eastern, and Northeast administrative regions. From 2012 to 2021, the Central, Greater Accra, Oti, Upper East, Volta, and Western administrative regions recorded a marginal decline in the burden of ACD among children aged 1 to 4 years, and this was accompanied by an increase in rotavirus vaccination coverage. The ACD cases rose as rotavirus vaccination coverage decreased in the Western North Region between 2015 and 2019. Conclusion: Routine rotavirus vaccination may have contributed to a reduction in severe childhood ACD cases even though this study cannot exclude the impact of other anti-diarrhoeal interventions. To evaluate the population effectiveness of rotavirus vaccines, it is recommended to widely promote routine rotavirus disease surveys, which can be nested within ACD surveillance.

3G, HSPA AND 4G LTE NETWORK RELIABILITY AND ACHIEVABLE INTERNET SPEEDS ON AIRBORNE AIRCRAFT UP TO 10.000 FT ABOVE GROUND LEVEL

Nowadays, we are witnesses of 5G mobile technology advancements and many countries plan to or already have introduced this technology to public usage. However, the older technologies such as 3G, HSPA and 4G LTE, because of their physical properties, ways of datagram transfers and frequency bands used, are still very suitable for usage in cases that assume longer distances between terminal device and cell towers. Having that in mind, a research was conducted during 2021. and 2022. in order to test i.e. verify or refute the hypothesis that 3G, HSPA and 4G LTE technologies will be reliable enough to provide a satisfactory connection on aircraft flying up to 3 km (10.000 ft) above ground level, quality enough to establish Internet connection speeds of at least 1Mbps, which would be enough to serve as a medium for innovated, Internet-based data links similar to CPDLC/ACARS. Through a series of 14 airplane flights, both above plains and mountain regions, first-hand data was gathered and analyzed accordingly, so reliable conclusions about presented hypothesis could be made. Empirical research results were accompanied with research results made on well-known computer radio propagation simulator TAP7.