Topic Information Research Articles

Enhancing Skin Anti-Aging through Healthy Lifestyle Factors

Lifestyle health has been recognized as an evidence-based innovation that defines how everyday behaviors and routines influence the avoidance and therapy of illness and provides an important adjunctive component to overall health. Specifically, an approach with small changes over time can have a dramatic impact on the health and well-being of individuals not only, in general, but also can be applied to skin health. However, lifestyle health factors to improve skin well-being have not been discussed extensively and/or well promulgated. The narrative for this overview focuses on providing a summary for topic background information, but more importantly, presents four lifestyle factors that can improve dermal health [i.e., factor 1: nutrition—diet; factor 2: rest (sleep); factor 3: movement/physical exercise, and factor 4: social and community associations]. This was accomplished by identifying preceding journal reports/reviews covering especially the last five years (January 2018 to July 2023; 164 out of 205 references cited or 80%) using scientific search databases. The main conclusions of this overview encourage the concept that lifestyle health factors such as nutrition/diet, rest/sleep, movement/physical exercise, and community/social interactions support enhanced skin health and well-being with aging. Plus, social media interventions that aim to promote dietary, sleep and physical activity changes might be an application to improve skin health in the future.

Topic model for long document extractive summarization with sentence-level features and dynamic memory unit

The Transformer-based summarization models rely solely on the attention mechanism for document encoding, making it difficult to accurately capture long-range dependencies in long documents due to the presence of attention redundancy. To address this issue, we propose an extractive summarization framework guided by a topic model (TopicSum) that utilizes a heterogeneous graph neural network to leverage the topic information as document-level features during the sentence selection process, thereby capturing the long-range dependencies among sentences. The sentence-level features in this topic model align with the basic unit of the extractive summarization task. Additionally, a memory mechanism is employed to dynamically store and update the memory module, reducing the potential of repetitive information guiding sentence selection. We evaluated the model on three large document datasets, namely Pubmed, arXiv, and GovReport, and achieved significantly higher Rouge scores than previous works, including extractive and abstractive models. Furthermore, our experiments demonstrate that recent highly regarded large language models such as ChatGPT are insufficient to handle the long document summarization task directly. The proposed approach in this paper exhibits sufficient competitiveness in terms of both generation quality and deployment conditions.

Online information acquisition affects food risk prevention behaviours: the roles of topic concern, information credibility and risk perception

BackgroundThe COVID-19 pandemic has not only brought great challenges to the global health system but also bred numerous rumours about food safety. Food safety issues have once again attracted public attention.MethodsThe data were drawn from the fifth wave of the first Taiwan Communication Survey database. The respondents were selected via multistage stratified random sampling. The sampling units were townships/districts, villages/neighbourhoods and households. The sample consisted of 2098 respondents. This study first used propensity value matching to analyse the direct impact of online food safety information acquisition on preventive behaviours and examined the heterogeneous impact caused by the difference in the degree of topic attention through value matching. Hayes’ PROCESS macro model 6 was applied to confirm the mediating effect and the serial mediating effect.ResultsThe research results show that an increase in the frequency of the acquisition of online food safety information significantly increases individuals’ food risk prevention behaviour. However, only users with high concern about the issue are affected. The food risk prevention behaviour of users with low concern about this issue is not affected by the acquisition of online food safety information. Further analysis shows that risk perception and information credibility both play mediating roles in the impact of online food safety information acquisition on food risk prevention behaviour. Moreover, the transmission and united effects of information credibility and risk perception play a distal mediating role.ConclusionsFood risk prevention behaviours are an important topic for personal health as well as government management. Our study’s findings can provide empirical evidence for risk managers and decision-makers to reevaluate the role of the internet in food risk management.

Deep Semantic Understanding and Sequence Relevance Learning for Question Routing in Community Question Answering

Question routing (QR) aims to route newly submitted questions to the potential experts most likely to provide answers. Many previous works formalize the question routing task as a text matching and ranking problem between questions and user profiles, focusing on text representation and semantic similarity computation. However, these works often fail to extract matching features efficiently and lack deep contextual textual understanding. Moreover, we argue that in addition to the semantic similarity between terms, the interactive relationship between question sequences and user profile sequences also plays an important role in matching. In this paper, we proposed two BERT-based models called QR-BERTrep and QR-tBERTint to address these issues from different perspectives. QR-BERTrep is a representation-based feature ensemble model in which we integrated a weighted sum of BERT layer outputs as an extra feature into a Siamese deep matching network, aiming to address the non-context-aware word embedding and limited semantic understanding. QR-tBERTint is an interaction-based model that explores the interactive relationships between sequences as well as the semantic similarity of terms through a topic-enhanced BERT model. Specifically, it fuses a short-text-friendly topic model to capture corpus-level semantic information. Experimental results on real-world data demonstrate that QR-BERTrep significantly outperforms other traditional representation-based models. Meanwhile, QR-tBERTint exceeds QR-BERTrep and QR-BERTint with a maximum increase of 17.26% and 11.52% in MAP, respectively, showing that combining global topic information and exploring interactive relationships between sequences is quite effective for question routing tasks.

Rise of social bots: The impact of social bots on public opinion dynamics in public health emergencies from an information ecology perspective

Social bots exacerbate emotional chaos and network disorder during sudden public opinion outbreaks. Drawing on information ecology theory, we analyze how social bots as information person characteristics influence information characteristics represented by topic tendency and sentiment spreading and environmental characteristics represented by information diffusion and network influence, and further discuss the conditional role played by social bots in public opinion dynamics. By comparing different machine learning models, the study selects the XGBoost model with the highest accuracy to identify the type of social bots with specific automation features in the Chinese context. Meanwhile, based on the representative public health emergency, the study uses a large-scale social media dataset (N = 328539) to explore how social bots affect multi-dimensional public opinion dynamics in the information ecosystem. It was found that the information published by social bots and human users has an entirely different dissemination mechanism, and social bots have a significant impact on topic tendency, sentiment spreading, information diffusion, and network influence in cyberspace. Moreover, social bots also play an important moderating role in the information diffusion and network influence of affective information. Relevant findings provide a practical reference for the recognition and prevention of social bots, and help emergency management departments to stabilize public sentiments by intervening in the behavior of social bots, weakening the influence of negative public opinion, and promoting the management of public opinions in emergencies and the maintenance of network order.

Development of Quizwhizzer-Assisted Educational Game-Based Learning Evaluation Tools in Informatics Subjects in Junior High Schools

This study discusses how the process of development, validity testing and practicality testing of educational game-based learning evaluation tools on the subject matter of informatics materials Information Technology and Communication at the State High School 39 Padang. This type of research is Research and Development (R&D). (Analysis, Design, Development, Implementation, and Evaluation). The study conducted validity tests with 3 experts and the number of students practicality tests as much as 20 people. The results of the research showed that Quizwhizzer's educational game-based learning evaluation tool is worthy of use. From the data the validity of media expert 1 was 95.2%, media expert 2 was 97.6%, material expert 95.2, and the test results of 86% in the category “Very Practical”. It can be concluded that the evaluation tool based on educational games with Quizwhizzer in computer science subjects is worthy of use in the class VIII of SMP N 39 Padang.

A latent topic‐aware network for dense video captioning

AbstractMultiple events in a long untrimmed video possess the characteristics of similarity and continuity. These characteristics can be considered as a kind of topic semantic information, which probably behaves as same sports, similar scenes, same objects etc. Inspired by this, a novel latent topic‐aware network (LTNet) is proposed in this article. The LTNet explores potential themes within videos and generates more continuous captions. Firstly, a global visual topic finder is employed to detect the similarity among events and obtain latent topic‐level features. Secondly, a latent topic‐oriented relation learner is designed to further enhance the topic‐level representations by capturing the relationship between each event and the video themes. Benefiting from the finder and the learner, the caption generator is capable of predicting more accurate and coherent descriptions. The effectiveness of our proposed method is demonstrated on ActivityNet Captions and YouCook2 datasets, where LTNet shows a relative performance of over 3.03% and 0.50% in CIDEr score respectively.

Impact of the COVID-19 pandemic on the stock market and investor online word of mouth

Investor sentiment based on social media Word-of-Mouth (WoM) has been shown to be predictive of stock returns under normal market conditions. However, the COVID-19 pandemic has brought unprecedented impact and uncertainty to the stock market. In this paper, we examine the predictability of social media-based WoM on day-ahead US stock market returns across three phases of the pandemic: pre-COVID, peak-COVID, and recovery. We collect over 24 million stock-related tweets from Twitter across these three periods and employ text-mining methods for feature engineering. We focus on two channels of WoM information, aggregate sentiments, and disaggregated topics, representing investors' overall opinions and discussion patterns about the stock market. We find that, in contrast to normal market conditions, the investor sentiment indices are not significant in predicting market returns during the peak-COVID and recovery periods, while for the pre-COVID period, our findings are consistent with prior studies. To explore the effect of disaggregated latent topics, we propose an ensemble feature selection approach that identifies relevant topics for each of the three periods. We find that adding topic information significantly improves model prediction during the peak-COVID period. Our findings imply that during a global socio-economic disruption and the subsequent recovery, the landscape of financial markets, examined through the lens of social media-based WoM, is different than during normal market conditions. Our findings also suggest that investors who primarily trade on sentiment may wish to leverage machine learning techniques to identify latent topic information, subsequently incorporating such information into their trading decision-making.

Writing a Good Research Paper

A research paper is a piece of academic writing that provides analysis, interpretation, and arguments based on in-depth independent research. A good research paper solidifies a researcher’s skill in scholarly writing. This paper describes the process it takes to write a good research paper; it highlights the processes necessary to write various sections of a research paper - Abstracts, Introduction, Literature Review, Methodology, Results and Discussion, and Conclusion. Digesting this research paper's content would ensure researchers can document information that would otherwise seep through due to a lack of research writing skills. The aim of this is to ensure that researchers can make original contributions to the scholarly base. An author's career, institution, and nation will be impacted by high-quality publication in indexed journals. Significant rejections could result from poor scientific writing. Therefore, writing a successful scientific publication for a peer-reviewed journal takes considerable time and expertise. A few easy actions and advice are provided to help researchers publish the findings of their work in the right scientific publications with greater expertise. Effective scientific writing frequently adheres to a specific format with important elements. The introduction to a specific issue with relevant background literature and theories to be studied, a list and explanation of the techniques, results, and a discussion that connects the results to the topic's broader information should all be included in a scientific paper's main text. For early researchers aiming to raise the caliber of their submissions for publication, this review piece is essential.

Cancer Informatics 2023: Data Sharing and Federating Learning Point Towards New Collaborative Opportunities.

To summarize significant research contributions on cancer informatics published in 2022. An extensive search using PubMed/MEDLINE was conducted to identify the scientific contributions published in 2022 that address topics in cancer informatics. The selection process comprised three steps: (i) ten candidate best papers were first selected by the two section editors, (ii) external reviewers from internationally renowned research teams reviewed each candidate best paper, and (iii) the final selection of three best papers was conducted by the editorial board of the Yearbook. The three selected best papers demonstrate advances in federated learning, drug synergy prediction, and utilization of clinical note data. Cancer informatics continues to mature as a subfield of biomedical informatics. Applications of informatics methods to data sharing and federated approaches are especially notable in 2022.

Pelatihan pembuatan video profil sekolah di SMA se-kabupaten Lombok Timur

The rapid development of science and technology, along with the advent of the Fourth Industrial Revolution and Society 5.0, has significantly impacted all aspects of life, including education. In response to these developments, the Ministry of Education and Culture has strengthened the teaching of informatics in the latest curriculum, "Independent Learning." However, many schools are not adequately prepared to handle informatics subjects due to a lack of competent teachers. Therefore, the educational informatics program has the opportunity to engage in community engagement by providing training and assistance related to informatics subjects to students in vocational high schools (SMK). This engagement involves the participation of educational informatics lecturers and several students. The program consists of 3 to 4 sessions with students from grade XII at SMK Qurrota A’yun Joben. The training covers fundamental networking, resource sharing, connectivity, and communication aspects, followed by the creation of school profile videos. Participants are instructed in techniques for capturing footage, video editing, and multimedia design using various applications. The posttest results reveal a significant improvement in participants' skills in graphic design (85%), scenario creation (93%), video capturing techniques (86%), and video editing (96%). This engagement offers SMK Qurrota A’yun the opportunity to promote their school through captivating profile videos, thus enhancing the interest of potential students' parents and facing competition in the age of social media. The training and assistance in school profile video creation effectively enhance participants' multimedia management skills and facilitate school promotion in a more compelling manner. Video profiling proves to be an efficient and effective medium for conveying information and promotions, especially in the current era of social media. Leveraging multimedia technology has become essential in fostering school competitiveness and harnessing the full potential of the community in the Society 5.0 era.

SISTEM INFORMASI PERPUSTAKAAN PADA SD NEGERI 101935 BATANG TERAP BERBASIS WEB

Currently the 101935 Batang Terap publick elementary school library, especially for book borrowers, still uses a system that is still very manual, resulting in errors in the circulation of loan books because the data is not well documented. Therefore, we need a database system application that can manage and process library transaction data in an efficient manner precise and efficient. So the authors are interested and willing to research and create this techno project with the topic entitled Web-Based Library Information System at the 101935 Batang Terap Elementary School. make it easier for librarians to process borrowing and returning data and make it easier to input data, process and process data with an application to produce fast and accurate information. The library information system at the 101935 Batang Terap public elementary school was built using the Sublime text and Xampp applications. The library information system at the 101935 Batang Terap public elementary school was created to be able to provide convenience in the process of borrowing and returning books. With the existence of a library information system at the 101935 Batansg Terap public elementary school, it can assist schools in analyzing and obtaining input material in making decisions. easy to make a report to the head of the librarian.

Formation of Industrial and Logistics Hub in the Border Areas of Kazakhstan and Uzbekistan

Central Asia is a strategic region of the China-Europe continental bridge. The conflict between Russia and Ukraine has seriously affected international supply chains, which has led to the intensification of the development of alternative corridors and the construction of new regional logistics hubs along them. The purpose of the research is to determine, on the basis of these sources, the factors of the formation of industrial and logistics hubs, as well as the directions of their development on the example of the border regions of Kazakhstan and Uzbekistan. In preparing this article, a mixed research strategy was used, which implies quantitative and qualitative methods. It consists not only of the systematic study of the literature on the research topic and statistical information but also involves the implementation of theoretical studies, comparison, generalization and analysis of the results, argumentation and formulation of conclusions. The study identified the main constraining factors for the development of such centres delays in customs inspection, inconsistency in the legal framework of the transportation process, underdevelopment of infrastructure, low level of digitalization and the use of information systems, the use of non-tariff barriers, low level of digitalization and the use of information systems. The creation and development of integrated transport services based on a digital platform will speed up this process. In addition, taking into account these factors, the components of an industrial and logistics hub are proposed: an industrial zone, a logistics zone, a trading zone, a certification zone with appropriate services, as well as a digital platform as a link between all components.

The Contribution of Sleep Texture in the Characterization of Sleep Apnea.

Obstructive sleep apnea (OSA) is multi-faceted world-wide-distributed disorder exerting deep effects on the sleeping brain. In the latest years, strong efforts have been dedicated to finding novel measures assessing the real impact and severity of the pathology, traditionally trivialized by the simplistic apnea/hypopnea index. Due to the unavoidable connection between OSA and sleep, we reviewed the key aspects linking the breathing disorder with sleep pathophysiology, focusing on the role of cyclic alternating pattern (CAP). Sleep structure, reflecting the degree of apnea-induced sleep instability, may provide topical information to stratify OSA severity and foresee some of its dangerous consequences such as excessive daytime sleepiness and cognitive deterioration. Machine learning approaches may reinforce our understanding of this complex multi-level pathology, supporting patients' phenotypization and easing in a more tailored approach for sleep apnea.

An epidemiological study of unintentional pediatric firearm fatalities in the USA, 2009–2018

BackgroundFirearm injuries are the leading cause of mortality among children and adolescents 1–19 years old in the USA. Many prior studies on this topic lack detailed information about the circumstances of the firearm fatalities and include decedents and shooters of all ages. This study characterizes firearm fatalities in the USA in which children < 15 years old unintentionally killed themselves or another child.MethodsTen years of data from the National Violent Death Reporting System were analyzed. Unintentional firearm fatalities among children were reviewed to identify characteristics of decedents and the children who inflicted the deaths, their relationship, and circumstances of the deaths. There were 279 firearm fatalities during the study period involving children < 15 years old who unintentionally killed themselves or another child < 15 years old.ResultsMost victims were male (81.4%), and 40.9% were 2–4 years old. Most incidents (64.0%) occurred at the victim’s residence, and in 80.9% of cases the firearm owner was a relative of the shooter. In the < 5-year age group, 80.3% of injuries were self-inflicted, and in the 10–14-year age group, 32.3% of shooters were a friend of the victim.ConclusionThis study highlights that children in the USA are shooting themselves and each other in their own homes, and often accessing firearms owned by family members. These findings can be used to guide prevention efforts, such as child access prevention laws, to reduce the number of pediatric firearm fatalities in the future.

Assembly makespan estimation using features extracted by a topic model

Accurate makespan estimation is imperative during production scheduling to increase the flexibility and efficiency of work plans. However, given the complexities of production systems and product customizations, it is challenging to estimate makespans with high accuracy. In this paper, we propose a topic model-based neural network (TM-NN) method to increase the accuracy of makespan estimation for assembly processes. First, unlike traditional methods that use influential factors as inputs, we extract assembly features using a latent Dirichlet allocation model that mines latent topic information from an assembly instruction corpus. Then, the assembly process is represented as a sequence model with both assembly topics and features of the product physical characteristics, the assembly process, the equipment, the personnel, and uncertainty. Finally, we use a structured numerical vector as the input to machine learning-based predictive models, including a neural network, a random forest, and a support vector machine, and estimate makespans. The results show that the proposed TM-NN method effectively extracts latent topics in assembly documents and significantly increases the accuracy of makespan estimation.

TPoet: Topic-Enhanced Chinese Poetry Generation

Chinese poetry generation has been a challenging part of natural language processing due to the unique literariness and aesthetics of poetry. In most cases, the content of poetry is topic related. In other words, specific thoughts or emotions are usually expressed regarding given topics. However, topic information is rarely taken into consideration in current studies about poetry generation models. In this article, we propose a topic-enhanced Chinese poetry generation model called TPoet in which the topic model is integrated into the Transformer-based auto-regressive text generation model. By feeding topic information to the input layer and heterogeneous attention mechanism, TPoet can implicitly learn the latent information of topic distribution. In addition, by setting multiple identifiers such as segment, rhyme, and tone, the model can explicitly learn the constraints of generated poems. Extensive experimental results show that the quality of TPoet-generated poems outperforms the current advanced models or systems, and the topic consistency and diversity in generated poems have been significantly improved as well.

Co-creating Transmedia Learning

The way the world moves forward forces us to prepare ourselves through 21st-century educational processes: those that are active, realistic, systemic, and cross-disciplinary. However, facilitating such processes is not trivial as they require skills and resources that educators can seldom possess. Narratives, primarily in the form of interactive digital narratives (IDNs), are potent tools for 21st-century education, but they too require significant resources from educators. Transmedia learning, which is the use of educational media revolving around a story universe, can potentially support educators in applying IDNs, primarily if it lets them and their students add new narratives to a shared story universe, thus freeing them from having to create narratives from scratch. However, how to conduct this co-creation process is underexplored. Through a theoretical investigation of the literature on 21st-century education, IDNs, transmedia learning, narrative co-creation, and shared universes, we propose a framework for co-designing a transmedia learning ecosystem. The framework divides a transmedia learning ecosystem into learning content, narrative, and interactivity aspects, to be handled collaboratively by educators, narrative experts, and interactivity experts. Furthermore, each aspect is systemic and hierarchical, which allows for designing educational IDNs that expand upon each other, thus letting each educator work on one that suits their expertise while ensuring that the end products provide a comprehensive learning experience. We applied the framework in a case study of co-designing a transmedia learning ecosystem for 21st-century informatics education. The case study’s preliminary result is a general design of the ecosystem, which shows how co-design tasks can be delegated efficiently to produce IDN media that deliver informatics subjects hierarchically and engagingly. We also discuss our future research directions, including developing the ecosystem further to produce actual IDN media and expanding the framework to support other IDN creation activities, such as design implementation and IDN media validation.

Pengembangan Media Pembelajaran Berbasis Tiktok Pada Mata Pelajaran Informatika Kelas X TKJ Di SMK Negeri 2 Padang

This learning media development research aims to produce learning media products that are valid and practical so that they are suitable for use in informatics learning class X at SMK Negeri 2 Padang. The method used in this research and development (Research and Development). With the ADDIE development model (Analyze, Design, Development, Implementation, Evaluation). The source of the data in this study was to carry out a feasibility test in the form of validation using a material validation questionnaire carried out by 3 experts namely, 2 class x informatics teachers at SMK Negeri 2 Padang and 1 lecturer from the informatics education study program, 3 media experts namely a lecturer in the informatics education study program. Data analysis was performed using a Likert scale measurement. With 4 answer options (very valid, valid, less valid, invalid). After these measurements, the material validation results were 89%, media expert validation was 91%. From the results of the analysis it can be concluded that the development of TikTok-based learning media is in very valid and practical criteria and can be used in learning activities in class X informatics subjects. TKJ at SMK Negeri 2 Padang.

Efektivitas Media Pembelajaran Berbasis Tiktok Pada Mata Pelajaran Informatika Kelas X TKJ

The rapid development of technology at this time, educators are expected to be able to create interesting and innovative learning media for students. So that it has an impact on student learning outcomes later. One of the learning media that can be utilized is the TikTok application. The TikTok application is an application in the form of short videos that are packaged properly. And the majority of users are millennials and school children. The purpose of this study was to determine the effectiveness of TikTok-based learning media used in informatics class X TKJ at SMK Negeri 2 Padang. The method used is an experiment. By using One Group Pretest-Posttest Design. The sample is determined by simple random sampling technique. For data analysis techniques using the analysis prerequisite test, namely the normality test, hypothesis testing using the paired sample test with a result of 0.000, and the N-Gain test. Based on the results of the N-Gain test in the experimental class, it was obtained at 63,765 or 63.7%. In the interpretation of the N-Gain percentage category if 56-75% is said to be quite effective. So the N-Gain score of 63.7% is said to be quite effective. So that the effectiveness of TikTok-based learning media in class X TKJ informatics subjects at SMK Negeri 2 Padang is quite effective.