High-quality Information Research Articles

Enhancing ePaper Reader Intentions through Website Design, Information Quality, and Usefulness

This study examines how website design, information quality, and utility affect ePaper use, with brand trust and attitude as mediators. This study examines the factors that influence ePaper readers’ intentions to use the service. The quantitative study uses a survey questionnaire to collect data. A convenience sample of ePaper readers was selected. Structural Equation Modelling (SEM) was used to analyses data and determine variable relationships. The study found that website design, information quality, and usability affect brand trust, attitude, and ePaper use. A well-designed website with high-quality information and handy features boosts brand trust and attitude, which affects ePaper service use. This study adds to the literature by examining the relationships between website design, information quality, utility, brand trust, attitude, and ePaper use. The results suggest that ePaper suppliers should priorities website design, high-quality content, and useful features to build brand confidence and reader loyalty. It will boost their desire to use the service. Future investigations should aim to examine the potential effects of supplementary variables that could potentially influence individuals’ intents to utilized ePaper, including those related to user experience and social dynamics.

Short communication: Do veterinary diagnoses coming from electronic recording system of veterinary treatments have the potential to be used for breeding in small populations? The case of the dual-purpose Alpine Grey cattle breed

In cattle breeding, emphasis has historically been placed on productive traits, so-called primary traits, while health and fitness traits have been overlooked due to their low heritability and partly negative genetic correlations with productivity. In addition, the recording of phenotypes caused problems due to a lack of high-quality information and / or high costs of recording. This oversight has additionally led to increased incidences of fertility issues and health problems in cattle populations, resulting in economic losses and consumer concerns regarding animal welfare and food safety. This study investigates the potential use of veterinary diagnoses, collected via the Italian electronic recording system of veterinary treatments, for breeding decisions in the local dual-purpose Alpine Grey cattle breed for which the consideration of functional traits in breeding has also become imperative over the last years. The dataset comprised 97 821 records spanning from 2021 to 2023, representing 8 273 individuals and 18 110 lactations of the Alpine Grey breed. Each record documented the administration of treatments for mammary, respiratory, and urogenital pathologies. After data editing and model development, heritability estimates were obtained for each pathology using univariate linear animal models and gibbs sampling algorithms. Results indicate that mammary pathologies affected the highest number of cows (11%), followed by respiratory (4%) and urogenital (5%) pathologies. Heritability estimates ranged from 0.01 to 0.04 across all traits, confirming generally low heritability values. Respiratory pathologies exhibited the highest heritability (0.04; sd 0.04), followed by mammary (0.03; sd 0.01) and urogenital (0.01; sd 0.01) pathologies. This study, although preliminary, provides valuable insights into the health status of local dual-purpose Alpine Grey cattle and highlights the potential of considering veterinary diagnoses for improving animal health and welfare in dairy cattle breeding. However, the low heritability of these traits, which is consistent with literature data, clearly shows the current limitations of using them for breeding. Further, the diagnostic key of the Italian electronic recording system of veterinary treatments should be specified for the use of health data for breeding purposes, as it is formulated in too general manner in its current form. Nevertheless, their significance for animal welfare and economic sustainability underlines the need for their consideration in dairy cattle breeding. Therefore, further research with larger sample sizes and consideration of additional indicators, such as somatic cell counts, is recommended to enhance the accuracy of genetic evaluations and improve breeding strategies for sustainable cattle production.

Maternal Medication Use in Pregnancy: A Narrative Review on Assessing and Communicating the “Risk” of Birth Defects to the Patient

The state of knowledge regarding the teratogenic effects of maternal use of medications during pregnancy is constantly evolving and is often uncertain. Timely access to high-quality information may reduce prolonged harmful exposures, decrease the number of preventable birth defects, empower patients with accurate information about the risks of exposure, and prevent unnecessary patient anxiety and pregnancy termination. In this narrative review, we describe the process by which the teratogenic risk of medications is assessed by experts in medicine, genetics, and epidemiology and how identifiable risks can be effectively communicated to patients. Risk assessment of birth defects in human pregnancy involves collecting and synthesizing available data through a proper and rule-driven evaluation of scientific literature. Expert consensus is a practical approach to determine whether a given exposure produces damage after careful consideration of gestational timing, dose and route of the exposure, maternal and fetal genetic susceptibility, as well as evidence for biological plausibility. The provision of teratogen risk counseling through appropriate interpretation of information and effective knowledge translation to the patient is critical for the prevention of birth defects and maximizing healthy pregnancies.

Enhanced knowledge graph recommendation algorithm based on multi-level contrastive learning

Integrating the Knowledge Graphs (KGs) into recommendation systems enhances personalization and accuracy. However, the long-tail distribution of knowledge graphs often leads to data sparsity, which limits the effectiveness in practical applications. To address this challenge, this study proposes a knowledge-aware recommendation algorithm framework that incorporates multi-level contrastive learning. This framework enhances the Collaborative Knowledge Graph (CKG) through a random edge dropout method, which constructs feature representations at three levels: user-user interactions, item-item interactions and user-item interactions. A dynamic attention mechanism is employed in the Graph Attention Networks (GAT) for modeling the KG. Combined with the nonlinear transformation and Momentum Contrast (Moco) strategy for contrastive learning, it can effectively extract high-quality feature information. Additionally, multi-level contrastive learning, as an auxiliary self-supervised task, is jointly trained with the primary supervised task, which further enhances recommendation performance. Experimental results on the MovieLens and Amazon-books datasets demonstrate that this framework effectively improves the performance of knowledge graph-based recommendations, addresses the issue of data sparsity, and outperforms other baseline models across multiple evaluation metrics.

A 1000FPS@360,000pixels mixed-signal sensing with computing macro featuring analog compression and maximum parallelism for objective detection tasks

Ubiquitous vision sensors continuously collect data from the environment, imposing a significant processing burden. Especially, high-frame-rate convolution processing for large-scale pixel arrays presents substantial challenges. This paper proposes a mixed-signal sensing with computing macro featuring analog compression and maximum parallelism for high-frame-rate object detection tasks. A customized neural network (NN) architecture is designed specifically for object detection. Key circuitry modules, including signal readout, dark current offset, normalization, first-layer convolution, and pooling, are implemented in the analog domain, leading to high-quality information extraction and substantial data compression. Maximum parallelism in the first-layer convolution is realized in our proposed processing flow, ensuring high-frame-rate convolution for large-scale pixel arrays. Additionally, the subsequent NN layers are efficiently deployed on SRAM-based in-memory digital computing. The macro was designed using a 55-nm foundry process. Results show that our macro can achieve 1000 fps@360,000 pixels with a 63.8 TOPS throughput under 4/8-bit hybrid precision, outperforming the state-of-the-art in frame rate by 2–10 times.

Transforming Inclusive Digital Pedagogy in Malaysian Tertiary TVET: Adapting to a New Educational Landscape

The COVID-19 pandemic has greatly sped up the use of Artificial Intelligence (AI) and Information and Communication Technology (ICT) in education. The aim is to create a more inclusive and fair system, especially in Technical and Vocational Education and Training (TVET) for individuals with special needs. This approach focuses on personalized learning, high-quality information, and employer involvement, even though it is challenging to replicate traditional classroom experiences. Using the fuzzy Delphi technique, the methodology adapts inclusive digital teaching to changing needs, stressing infrastructure, accessibility, personalized learning, digital skills development, and teacher capabilities. Malaysia is working to become a global leader in education by using ICT to support student-centered learning and develop digital skills, in line with the National Digital Policy. The results show a strong potential for building a thorough digital education system. This integration has important effects on improving educational infrastructure, accessibility, and teacher skills, helping Malaysia establish itself as a leader in the global education scene.

Skin-friction from temperature and velocity data around a wall-mounted cube

This paper reports an algorithm for measuring the time-averaged skin friction vector field τ¯(X)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\overline{\\pmb {\ au }}(\\pmb {X})$$\\end{document} starting from time-resolved temperature maps, acquired by a functional coating of temperature-sensitive paint. The algorithm is applied to a large area around a wall-mounted cube, immersed in the turbulent boundary layer over a flat plate. The method adopts a relaxed version of the Taylor Hypothesis operating on time-resolved maps of temperature fluctuations T′\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$T'$$\\end{document} measured on the slightly warmer bounding surface. The procedure extracts U¯T(X)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\overline{U}}_T(\\pmb {X})$$\\end{document}, the celerity of displacement of T′\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$T'$$\\end{document}, as the best approximation of the forecasting provided by the frozen turbulence assumption near the wall, where its rigorous application is inappropriate. The τ¯(X)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\overline{\\pmb {\ au }}(\\pmb {X})$$\\end{document} estimation is based on the hypothesis of a linear relationship between U¯T(X)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\overline{U}}_T(\\pmb {X})$$\\end{document} and U¯U(X)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\overline{U}}_U(\\pmb {X})$$\\end{document}, chained to the one between U¯U(X)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\overline{U}}_U(\\pmb {X})$$\\end{document} and U¯τ(X)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\overline{U}}_\ au (\\pmb {X})$$\\end{document}. We assess the outcomes of the proposed algorithm against those derived by the 2D and 3D Lagrangian particle tracking (LPT) methodology ’Shake-The-Box’, whose advent has made available high-quality near-wall flow field information. Furthermore, data from high-density 2D time-resolved LPT allows exploring the suitability of the linear relationships chain between U¯T(X)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\overline{U}}_T(\\pmb {X})$$\\end{document} and U¯τ(X)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\overline{U}}_\ au (\\pmb {X})$$\\end{document} in the proposed context.

Optimising the acquisition conditions of high information quality low-field NMR signals based on a cutting-edge approach applying information theory and Taguchi's experimental designs – Virgin olive oil as an application example

BackgroundDeveloping a new spectrometric analytical method based on a fingerprinting approach requires optimization of the experimental stage, particularly with novel instruments like benchtop low-field NMR spectrometers. To ensure high-quality LF-NMR spectra before developing the multivariate model, an experimental design to optimize instrument conditions is essential. However, difficult-to-control factors may be critical for optimization. Taguchi methodology addresses these factors to obtain a system robust to variation. This study uses the Taguchi methodology to optimize instrument settings for acquiring high-quality 1H and 13C LF-NMR signals in a short time from virgin olive oil (VOO). ResultsTwo experimental trials (for 1H and 13C signals, respectively) were carried out and analysed to find an optimal and robust combination of instrument settings against changes in two difficult-to-control factors: ambient temperature and small deviations of the NMR tube volume (700 ± 50 μL). The responses to be optimised, run time and spectral information quality, were analysed separately and jointly, as some factors showed opposite behaviour in the effect on the responses. Multiple response analysis based on suitable desirability functions yielded a combination of factors resulting in desirability values above 0.8 for 1H LF-NMR signals and almost 1.0 for 13C LF-NMR signals.In addition, a novel approach to assess the information quality of an analytical signal was proposed, addressing a major challenge in analytical chemistry. By applying information theory and calculating information entropy, this approach demonstrated its potential for selecting the highest quality (i.e. most informative) analytical signals. SignificanceThe acquisition instrument conditions of LF-NMR were successfully optimised using Taguchi methodology to acquire highly informative 1H and 13C spectra in a minimum run time. The importance lies in the future development of non-targeted analytical applications for VOO quality control. In addition, the innovative use of information entropy to a priori assess the signal quality represents a significant advance and proposes a solution to a long-standing challenge in analytical chemistry.

A Critical Assessment of the Quality and Readability of Information on Gastric Cancer Available on the Internet for Patients.

Introduction It is now commonplace for patients to consult the internet with health-related questions. Unfortunately, the quality of information provided to them online is highly variable. Ensuring that patients get high-quality, reliable information is essential for all pathologies. Gastric cancer (GC), with its often subtle early symptoms and signs, is one such pathology where early identification is crucial. Ensuring high-quality information availability online for GC is thus essential to increasing rates of early detection. Aims This study aimed to assess the quality and readability of information posted on websites related to GC. Materials and methods We applied the search term "gastric cancer" or "stomach cancer" to the top three search engines, namely Google, Yahoo, and Bing. Using predefined inclusion and exclusion criteria, we identified 20 unique websites posting information related to gastric cancer (GC). We then assessed the quality and readability of the information posted on these websites. We used recognized tools to complete these assessments, including the JAMA benchmark criteria, the DISCERN tool, the Flesch Reading Ease score (FRES), and the Flesch-Kincaid Grade Level (FKGL). We also developed and used a novel GC-specific content assessment tool. Furthermore, we assessed whether or not each website was awarded the Health on the Internet Seal of Approval. Results The average JAMA score was 1.55, with none of the twenty unique websites scoring the maximum 4 points. The average DISCERN score was 54.8 (68.5%), with no website achieving the maximum of 80. The HON seal was present in only six websites (30%). The average GCSCS score was 11, with only five websites achieving a maximum score of 13 (25%). The average FRES and FKGL were 52.7 and 9.7, respectively. Conclusion Our study underscores the critical need for more high-quality, reliable information about GC online. We also emphasize the importance of ensuring this information is comprehensible to most patients, as it directly impacts their health outcomes.

Fast autofocusing in off-axis digital holography based on search region segmentation and dichotomy

Autofocusing is a critical step in digital holography for reconstructing high-quality intensity and phase information, especially for the dynamic detection of moving objects. Some commonly used autofocusing methods in digital holography are time-consuming as they search over a large range to find a unique reconstruction distance. In this study, we propose a new fast autofocusing method based on search region segmentation and dichotomy. First, a new search region is obtained by segmenting the original region and solving a system of inequality equations. Second, improving the focusing evaluation function and combining this function with the dichotomy method transforms the discriminating minimum of the evaluation function into the detection of the zero-crossing position, reducing the number of searches required for the autofocusing method. The effectiveness of the proposed autofocusing method is validated through numerical simulations and experiments. The proposed method can significantly improve computational efficiency while guaranteeing the detection accuracy of the focusing plane compared with conventional methods.

사상의 자유시장 이론과 플랫폼 거버넌스

The concept of ‘marketplace of ideas’ has been employed as a conventional argument to justify freedom of expression. The Constitutional Court of Korea has also been significantly influenced by this concept, presenting it as a rationale to protect freedom of expression and asserting that “the Internet is the medium most closely approximating the ‘marketplace of ideas’”. However, the proliferation of disinformation, hate speech, and the development of artificial intelligence have urged a re-evaluation of the validity of the ‘marketplace of ideas’, which posits that truth can prevail over falsehoods through unrestricted competition. ‘Marketplace of ideas’, which is advocated from a laissez-faire perspective, also encounters challenges related to market failure when examined from an economic perspective. This phenomenon occurs because market failure is not limited to the market for goods but extends to the market for ideas as well. In the context of freedom of expression, market failure can result in impediments to information distribution, insufficient dissemination of high-quality information, and democratic representation. To address market failure in the realm of free expression, it is imperative to focus on platform governance. In the United States, self-regulation by individual companies or industries, and in Europe, co-regulation by governments, have been proposed as potential solutions to market failure. The divergence between the US and European approaches to platform governance stems from the distinct constitutional traditions of freedom of expression. While platform governance mechanisms, such as self-regulation and co-regulation, offer advantages including flexibility, expedience, and professionalism, they may also generate tensions with the rule of law and potentially result in private censorship when limiting fundamental rights. To ensure the democratic legitimacy of self-regulation, limitations on freedom of expression must be implemented in a manner consistent with the rule of law. Furthermore, to prevent co-regulation from devolving into private censorship, considerable caution must be exercised to ensure that government intervention does not transform into censorship by administrative authority.

Quality of Internet-Based Information on COVID-19 and Dentistry

Aim: Dentists and their patients were at high risk for COVID-19 infection. As websites serve as a primary information source for many, this cross-sectional study aimed to assess the validity and reliability of information provided by websites regarding COVID-19 and dentistry. Materials and Methods: An online search was conducted on Google.com using the keywords ‘SARS CoV-2 and dentistry’, ‘COVID-19 and dentistry’ and ‘Coronavirus and dentistry’. The first 50 websites for each keyword were screened in incognito mode. After excluding duplicates, inaccessible and irrelevant websites, 88 websites remained for analysis. These websites were evaluated using the Health on the Net Foundation Code of Conduct (HON code) and the Journal of the American Medical Association (JAMA) benchmarks, which assess authorship, attribution, disclosure and currency of information. Descriptive statistical analyses, chi-square tests and one-way ANOVA were performed. Results: A total of 88 websites were found suitable for quality assessment. Only three of the websites had HON code accreditation. The mean JAMA benchmark score was 3.75 ± 0.7. The majority of the websites (62.5%) were academic, with a JAMA score of 3.84 ± 0.5. News and non-profit sites had the highest JAMA scores (4.0). Significant differences in JAMA scores were observed across different types of websites ( p < .05). Conclusions: The study found that the quality of websites providing information about COVID-19 and dentistry was generally high, especially among academic sites. This highlights the importance of high-quality online information to ensure that dentists and patients have access to accurate and reliable data during the pandemic.

A surge of interest: Analysing the increased public interest in lipedema using google trends.

This study aimed to monitor public interest in information related to Lipedema and examine potential temporal, seasonal, and income-related trends that may impact public interest. A dataset was created using the Google Trends tool by inputting the keyword "Lipedema" to obtain the relative search volume (RSV) from 2004 to the present in the United States (US). To examine potential income-related disparities in public interest in Lipedema across the US, RSV values were recorded in the five states with the highest and the lowest median income (Maryland, New Hampshire, New Jersey, Utah, and Washington and Mississippi, West Virginia, Arkansas, New Mexico, Kentucky, respectively). Public interest in Lipedema increased significantly over time (R2 = 0.693, p < .001 for the linear trend; R2 = 0.880, p < .001 for the quadratic trend). There were no significant differences between the RSV values between the month of the year and the season of the year (p = 1.000 and p = .859, respectively). RSV values of the five highest-income and low-income states showed significant increases (R2 = 0.333, p < .001, and R2 = 0.195, p < .001, respectively) More rapid growth in interest in Lipedema in states with high income compared to the states with low income (p < .001). Public interest in Lipedema is increasing in both high-income and low-income states. States with higher incomes are more interested. Searches for lipedema are expected to become more frequent, leading to a rising demand for comprehensive education in medical schools and clinician training programs. In addition, there will also be a need for easily accessible, high-quality information resources.

Abstract B105: Quality and Accessibility of Online Information Regarding CAR-T Therapy in Hematologic Malignancies

Abstract Background Chimeric antigen receptor T-cell (CAR-T) therapies have changed the treatment landscape for various hematologic malignancies and are currently under development and being tested in clinical trials for multiple solid tumors. Given that patients commonly use the internet as a source of health-related information, we sought to investigate the quality, understandability, and readability of online information regarding CAR-T therapy. Methods We conducted a search in a commonly used search engine (Google) of five pre-determined search terms to identify 100 unique web pages regarding CAR-T therapy. Scientific publications were excluded. Validated instruments were used to ascertain health information quality (DISCERN), understandability (Patient Education Materials Assessment Tool; PEMAT), and readability (Flesch-Kincaid, Gunning Fog, and SMOG indices). Results were summarized using descriptive statistics. Results Among the 100 web pages reviewed, 68% originated from academic medical centers, and 64% included links to clinical trials. Only 11% of the web pages included references to primary literature. 17% of web pages were available in a language other than English, with Spanish (15%) and Arabic (7%) being the most common. Moderate-to-high quality information (DISCERN ≥ 3) was presented by 47% of web pages, and only 31% of web pages were considered understandable (PEMAT ≥ 70%). Average readability scores were 10.6 (SD = 2.09), 12.6 (SD = 2.3), and 13.1 (SD = 1.7) using the Flesch-Kincaid, Gunning Fog, and SMOG indices, respectively. Conclusion Our study highlights that online information for CAR-T therapies commonly features low-quality health information that is typically not understandable to the general population. Further, contrary to recommendations from federal agencies and medical societies, available online information requires, on average, high school reading comprehension. Healthcare teams and stakeholders should strive to improve existing content and ensure patients have access to trusted, high-quality information given the unique complexities, processes, and toxicities associated with receiving CAR-T therapy. Citation Format: Luis C. Garcia, Ana I. Velázquez. Quality and Accessibility of Online Information Regarding CAR-T Therapy in Hematologic Malignancies [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr B105.

Instagram Social Media Management Strategy @Disparbudpora_Bondowoso As A Communication Medium In Promoting Bondowoso District Tourism

This research is motivated by the concern of the Bondowoso Regency Government about falling behind Banyuwangi Regency in promoting one of their top tourist attractions, the Ijen Crater. The Ijen Crater has been recognized as part of the Global Geopark. Currently, many local and foreign tourists consider the Ijen Crater a Banyuwangi tourist destination because it spans two regions. The objectives of this research are first to understand the social media management by the @pariwisata_bondowoso account, and second to assess the impact and results of social media management in promoting tourism in Bondowoso Regency. This research employs a qualitative method. Data Data were collected using observation techniques, direct interviews, and documentation. The conclusion drawn is that the tourism promotion strategy implemented by Disparbudpora is quite good in providing informative and engaging content, but there are challenges in conducting evaluations, which have not been optimal. The impact of Disparbudpora Bondowoso's social media management in promoting tourism in Bondowoso Regency is evident from the followers' responses in the comment section, likes, and the reach achieved by the @pariwisata_bondowoso Instagram account. The result of the social media management by Disparbudpora is that followers receive relevant and high-quality information.

A listening model of venture growth: entrepreneurs' listening abilities and ventures' listening capabilities

While we understand the importance of entrepreneurs listening to stakeholders, we lack a sufficient theory-driven understanding of why some entrepreneurs and their ventures can listen to their stakeholders more effectively than others. We offer a listening model of venture growth based on listening theories and the literatures on new ventures and capability development. Listening is initially facilitated by entrepreneurs' cognitive and behavioral processes, but continued venture growth creates a paradox for entrepreneurs. Listening to stakeholders may also deplete entrepreneurs' personal resources, diminishing their listening capacity. This paradox can be overcome by generating their ventures' listening capability—behavioral routines and attention structures for listening—enabling them to acquire and interpret quality information from stakeholders more effectively to build or adapt the capabilities necessary for venture growth. The ventures' listening capability acts as a dynamic capability, which itself can be dynamic. This listening model of venture growth contributes to the entrepreneurship literature on stakeholders, entrepreneurs' abilities, and ventures' capabilities and dynamic capabilities. Executive summaryEntrepreneurs need to acquire resources from stakeholders to create and grow their ventures. Therefore, stakeholder enrollment is a critical task for entrepreneurs. The predominant research on stakeholder enrollment has been on entrepreneurs' overt behaviors to secure the support of stakeholders—a unidirectional communication pattern in which entrepreneurs communicate to audiences and stakeholders listen to and decide whether to commit their resources to ventures. However, entrepreneurs need to communicate and listen to their stakeholders. Although scholars recognize the importance of entrepreneurs listening to stakeholders, we lack sufficient understanding of why some entrepreneurs and their ventures can listen to their stakeholders more effectively than others and thus acquire and use stakeholder support critical for venture growth. Therefore, we ask, Why are some entrepreneurial actors more effective at listening to stakeholders than others?To address this question, we integrate theories of listening and the literatures on new ventures and the creation of organizational capabilities to develop a listening model of venture growth. This model explains the importance and the limitations of entrepreneurs' listening ability in acquiring and interpreting stakeholder information for venture growth. Venture members can learn from and formalize entrepreneurs' listening ability to build ventures' listening capability, which overcomes the entrepreneurs' listening limitations. Ventures' listening capability includes acquiring and interpreting stakeholder information to inform and/or change additional capabilities critical for venture growth.Specifically, the model begins with communication from stakeholders to an entrepreneur and their venture. The entrepreneur engages cognitively in listening to the stakeholders' communication and acquiring and interpreting this stakeholder information. The entrepreneur also listens behaviorally, providing back-channeling that encourages the stakeholders to share more high-quality information. The entrepreneur's listening ability can enhance and change the venture's capabilities. However, the entrepreneur's ability to notice and interpret stakeholder information is limited. Other venture members can learn the entrepreneur's cognitive and behavioral listening to represent an organizational-level listening capability. This listening capability is reflected in the venture's routines and attention structures.The venture's listening capability can build and adapt the venture's nonlistening capabilities. The venture's capabilities drive venture growth. Because the listening capability generates and enables the interpretation of more high-quality stakeholder information, the venture can use this information to change its capabilities to obtain or maintain a tight fit with a changing environment. In doing so, the venture's listening capability represents a dynamic capability—listening can change the venture's capabilities.As the venture grows, the strain on the entrepreneur's listening ability further constrains the entrepreneur as a source of information for building and adapting the venture's capabilities. Venture growth also stretches the venture's listening capability, leading to changes in this listening capability and thus building and adapting the venture's nonlistening capabilities for subsequent venture growth. Venture growth can also lead to an increase in stakeholders and, thus, more stakeholder information to be noticed and interpreted by the venture's listening capability and to adapt the venture's nonlistening capabilities if necessary.With this work, we make three primary contributions to the entrepreneurship literature: We provide theoretical insights into (1) the workings of entrepreneurs' listening ability for promoting venture growth; (2) entrepreneurs' learning from their listening ability and formalizing this ability into organizational capabilities, which is critical for information to enact nonlistening capabilities for venture growth; and (3) a venture's listening capability as a dynamic capability.

Tele-dermatology for Skin Cancer Management

Teledermatology plays a significant role in the UK National Health Service (NHS) for the management of skin cancers by enhancing early detection, diagnosis, and treatment pathways. It uses digital platforms to allow dermatologists to remotely evaluate skin lesions, including potential malignancies, based on high-quality images and patient information submitted by general practitioners (GPs) or other healthcare providers.This approach has proven particularly valuable in reducing wait times for specialist consultations, which is critical for skin cancers where early detection greatly improves outcomes. Through teledermatology, GPs can rapidly refer suspicious lesions to dermatologists, who assess whether further investigation, such as a biopsy or urgent in-person consultation, is required. It helps prioritize high-risk patients for fast-track appointments under the NHS's two-week referral rule for suspected cancers. Teledermatology also facilitates efficient triaging of cases, enabling healthcare systems to manage resources more effectively. Low-risk cases can often be monitored remotely or managed within primary care, reducing unnecessary hospital visits, and easing the burden on specialist services. During the COVID-19 pandemic, the adoption of teledermatology surged, helping maintain continuity of care for skin cancer patients despite restrictions on in-person visits. Overall, teledermatology in the NHS enhances access to specialist care, speeds up the diagnostic process, and improves the management of skin cancers, making it a crucial tool in modern dermatological practice. This article describes a tele-dermatology service and its outcomes.

Japanese Perception of Brain Death and Implications for New Medical Technologies: Quantitative and Qualitative Social Media Analysis.

Brain death has been used to decide whether to keep sustained care and treatment. It can facilitate tissue, organ, and body donation for several purposes, such as transplantation and medical education and research. In Japan, brain death has strict diagnostic criteria and family consent is crucial, but it has been a challenging concept for the public since its introduction, including knowledge and communication issues. We analyzed data across YouTube and Twitter in Japan to uncover actors and assess the quality of brain death communication, providing recommendations to communicate new medical technologies. Using the keyword "" (brain death), we collected recent data from YouTube and Twitter, classifying the data into 5 dimensions: time, individuality (type of users), place, activity, and relations (hyperlinks). We employed a scale to evaluate brain death information quality. We divided YouTube videos into 3 groups and assessed their differences through statistical analysis. We also provided a text-based analysis of brain death-related narratives. Most videos (20/61, 33%) were uploaded in 2019, while 10,892 tweets peaked between July 3 and 9, 2023, and June 12 and 18, 2023. Videos about brain death were mostly uploaded by citizens (18/61, 27%), followed by media (13/61, 20%) and unknown actors (10/61, 15%). On the other hand, most identified users in a random sample of 100 tweets were citizens (73/100, 73%), and the top 10 retweeted and liked tweets were also mostly authored by citizens (75/100, 75%). No specific information on location was uncovered. Information videos contained guides for accreditation of the National Nursing Exam and religious points of view, while misinformation videos mostly contained promotions by spirituality actors and webtoon artists. Some tweets involved heart transplantation and patient narratives. Most hyperlinks pointed to YouTube and Twitter. Brain death has become a common topic in everyday life, with some actors disseminating high-quality information, others disseminating no medical information, and others disseminating misinformation. Recommendations include partnering with interested actors, discussing medical information in detail, and teaching people to recognize pseudoscience.

Three-Dimensional Reconstruction of Indoor Scenes Based on Implicit Neural Representation.

Reconstructing 3D indoor scenes from 2D images has always been an important task in computer vision and graphics applications. For indoor scenes, traditional 3D reconstruction methods have problems such as missing surface details, poor reconstruction of large plane textures and uneven illumination areas, and many wrongly reconstructed floating debris noises in the reconstructed models. This paper proposes a 3D reconstruction method for indoor scenes that combines neural radiation field (NeRFs) and signed distance function (SDF) implicit expressions. The volume density of the NeRF is used to provide geometric information for the SDF field, and the learning of geometric shapes and surfaces is strengthened by adding an adaptive normal prior optimization learning process. It not only preserves the high-quality geometric information of the NeRF, but also uses the SDF to generate an explicit mesh with a smooth surface, significantly improving the reconstruction quality of large plane textures and uneven illumination areas in indoor scenes. At the same time, a new regularization term is designed to constrain the weight distribution, making it an ideal unimodal compact distribution, thereby alleviating the problem of uneven density distribution and achieving the effect of floating debris removal in the final model. Experiments show that the 3D reconstruction effect of this paper on ScanNet, Hypersim, and Replica datasets outperforms the state-of-the-art methods.

MIDF-DMAP: Multimodal information dynamic fusion for drug molecule activity prediction

BackgroundComputer-aided drug development can alleviate limitations in drug research and development processes, such as long cycles, high costs, and limited targeting. Deep learning models can be used to predict the activity of drug compound molecules. However, existing models still need to adequately address semantic redundancy, conflicts, and noise between different data modalities. It results in low accuracy in predicting molecular activity and many false positive results that need further resolution. ObjectiveThis study aims to design a parallel deep learning model for processing multimodal molecular information fusion, dynamically adjusting the weights of different modal features during the fusion process. This approach ensures that high-quality modal information is more significant in molecular activity prediction, improving the model’s predictive accuracy. MethodsFirst, three modal features of drug molecules are extracted: one-dimensional fingerprints, two-dimensional topological structures, and three-dimensional geometric structures. Then, a dynamic weighting mechanism is proposed to fuse these three modal features organically. Finally, the fused features are input into the model classifier to obtain molecular activity prediction results. ResultsExperimental results show that the proposed model improved the Receiver Operating Characteristic-Area Under the Curve (ROC-AUC) value by 29.96% compared to the Weave model and 26.38% compared to the GC model on the BBBP dataset. Compared to the GraphMVP model and the multi-channel substructure graph model MSGG, the ROC-AUC value increased by 20.44% and 15.80%, respectively. This method also achieved good results on imbalanced samples and small molecule datasets. ConclusionThis study provides an effective multimodal dynamic fusion method for virtual drug screening, enriching computer-aided drug development theory and improving drug research and development efficiency.