Information Sharing Research Articles

Utilization of Microcurrent Care Device for Health Intervention and Beauty Care

Purpose: Korea's beauty and health industry has continued to change and develop beyond simple appearance care into the area of health intervention. Accordingly, this study was conducted to confirm the mechanism of application of device using Microcurrent Technology to the human body and various management methods. In addition, the purpose of this study was to find out how Microcurrent device is being used for health interventions in modern people. Method: In this study, we investigated previous research related to microcurrent, health intervention, and beauty care published until recently in PubMed, Google Scholar, and Korea Research Information Sharing Service. Among them, a literature review was conducted on papers judged to be deeply related to the content of this study. Results: In Korea, devices that use Microcurrent Technology are still being used interchangeably without an exact definition. The reason for this is that we are in a transitional period from medical device to beauty device. In this study, the device for skin care and health intervention was defined as Microcurrent Care (MCC), and the mechanism of application to the human body was confirmed. In addition, the method of using and safety management of the MCC device was also presented. Conclusion: In Korean society, as the beauty industry continues to change and develop into an area that improves health and quality of life, the range of use of MCC devices is gradually expanding. In this study, the understanding and utilization areas of MCC devices were identified for the development direction of K-beauty, and suggestions were made for long-term success and sustainability.

통합사례관리에 관한 연구동향 분석 : 2014-2024

This study aimed to identify research trends in integrated case management over the past decade by specifically examining papers published from 2014 to 2024. First, academic papers on 'integrated case management' were searched using the Research Information Sharing Service (RISS). Duplicated papers or those not directly related to integrated case management were excluded, and journal articles and theses were selected. A total of 52 papers were selected in this process, and their general characteristics, such as publication year, type of publication, and academic categories, along with the detailed topics, research methodologies, and main results, were presented. The results revealed the following trends: First, papers on integrated case management have been consistently published, with a noticeable increase since the 2020s. Master's theses, doctoral dissertations, and journal articles have been evenly published, primarily in the field of social welfare studies. Second, the detailed topics covered the practice and job factors of integrated case managers, supervision, ordinances or laws, and development plans. Quantitative research was more frequently used. Quantitative studies mainly investigated the roles and competencies of integrated case managers, while qualitative studies often focused on the practical experiences of case managers. Based on these findings, several recommendations for future research in the field of integrated case management were proposed. It is necessary to build nationwide surveys or statistical data in addition to direct surveys. Expanding research to include the experiences of clients, not just case managers, is essential. Additionally, support for the work environment and burnout of integrated case managers is needed.

Dynamic-anonymous privacy-preserving authenticated aggregation for safety-warning system for the Internet of Vehicles

Currently, the connectivity of vehicles using the latest communication and computing technologies has grown as the Internet of Vehicles (IoV). IoV has been utilized to foster an easy traffic flow by sharing safety-warning messages. However, authentication and confidentiality of shared information is a challenging issue. To solve the problem, various privacy-preserving authenticated data-aggregation schemes have been devised. However, existing schemes have shortcomings specifically concerning a heavy computational cost, and the need for a secure channel. Besides, static pseudonyms may increase the risk of a vehicle’s privacy leakage. In response to these challenges, this study introduces a new pairing-less and secure privacy-preserving authenticated data aggregation (PLS-PPADA) scheme. Leveraging the pairing-less and certificate-based setting, the PLS-PPADA scheme emerges as a robust, efficient, and effective solution for safety-warning systems in IoV. Further, to resolve the risk of privacy leakage due to static pseudonyms, the paradigm of fuzzy identity has been utilized. Thus, it achieves efficiency and dynamic anonymity and also does not require a secure channel for key sharing. A comprehensive security analysis underscores its effective data protection capabilities and efficiency comparison presents it as a compelling alternative to existing state-of-the-art schemes.

Inter-disciplinary Approaches to Risk Management and Information Sharing for Resilience to the Changing Climate

Inter-disciplinary Approaches to Risk Management and Information Sharing for Resilience to the Changing Climate

Episodic Visual Hallucinations, Inference and Free Energy.

Understandings of how visual hallucinations appear have been highly influenced by generative approaches, in particular Friston's Active Inference conceptualization. Their core proposition is that these phenomena occur when hallucinatory expectations outweigh actual sensory data. This imbalance occurs as the brain seeks to minimize informational free energy, a measure of the distance between predicted and actual sensory data in a stationary open system. We review this approach in the light of old and new information on the role of environmental factors in episodic hallucinations. In particular, we highlight the possible relationship of specific visual triggers to the onset and offset of some episodes. We use an analogy from phase transitions in physics to explore factors which might account for intermittent shifts between veridical and hallucinatory vision. In these triggered forms of hallucinations, we suggest that there is a transient disturbance in the normal one-to-one correspondence between a real object and the counterpart perception such that this correspondence becomes between the real object and a hallucination. Generative models propose that a lack of information transfer from the environment to the brain is one of the key features of hallucinations. In contrast, we submit that specific information transfer is required at onset and offset in these cases. We propose that this transient one-to-one correspondence between environment and hallucination is mediated more by aberrant discriminative than by generative inference. Discriminative inference can be conceptualized as a process for maximizing shared information between the environment and perception within a self-organizing nonstationary system. We suggest that generative inference plays the greater role in established hallucinations and in the persistence of individual hallucinatory episodes. We further explore whether thermodynamic free energy may be an additional factor in why hallucinations are temporary. Future empirical research could productively concentrate on three areas. Firstly, subjective perceptual changes and parallel variations in brain function during specific transitions between veridical and hallucinatory vision to inform models of how episodes occur. Secondly, systematic investigation of the links between environment and hallucination episodes to probe the role of information transfer in triggering transitions between veridical and hallucinatory vision. Finally, changes in hallucinatory episodes over time to elucidate the role of learning on phenomenology. These empirical data will allow the potential roles of different forms of inference in the stages of hallucinatory episodes to be elucidated.

Cybersecurity in supply chains: Addressing emerging threats with strategic measures

This review paper explores the evolving landscape of cybersecurity threats within supply chains, highlighting strategic measures necessary to mitigate these risks. As supply chains integrate more deeply with digital technologies and global networks, they become more vulnerable to sophisticated cyber attacks that can severely disrupt operational integrity and security. This paper synthesizes existing literature to outline the major cybersecurity challenges faced by supply chains, examining case studies and best practices from various industries. Our findings underscore the prevalent threats such as ransomware, phishing, and insider attacks, which exploit the complex interdependencies and digital interfaces in supply chains. The review identifies effective strategic responses, including the integration of comprehensive cybersecurity frameworks, enhanced inter-organizational cooperation, and the implementation of real-time threat detection systems. Conclusively, the paper advocates for a holistic and proactive approach to cybersecurity, emphasizing continuous improvement and adaptive strategies to protect against the dynamic nature of cyber threats in supply chain contexts. Keywords: Cybersecurity, Supply Chain Management, Risk Assessment, Continuous Monitoring, Incident Response, Blockchain Technology, Artificial Intelligence (AI), Collaboration and Information Sharing, Cyber Threats, Emerging Technologies.

A Systematic Review of Personal Information Sharing in Smart Cities: Risks, Impacts, and Controls

AbstractSmart cities aim to deliver smart services that rely on emerging technologies to their users. In order for users to get the provided services, they need to share their personal information with different parties. However, sharing personal information in smart cities may impact the privacy of that information. Thus, there is a need to address privacy risks relevant to sharing personal information in smart cities. This study aims to address this issue by conducting a systematic literature review (SLR) to identify and extract privacy risks, impacts, and existing controls associated with sharing personal information, considering elements involved and interacting during the sharing activity in smart cities. A set of 83 selected studies in both academic and industry fields were reviewed, and the results were categorised into three main groups: privacy risks, impacts, and controls. Moreover, the implications and future research directions were also reported. The proposed privacy risk taxonomy will provide a much-needed foundation for the industry and research community, intending to research and evaluate privacy risk frameworks and design solutions for sharing personal information in smart cities.

Influence of Supply Chain Integration, Information Sharing, and Supplier Relationships on Operational Efficiency of Manufacturing Companies

This research investigates the influence of supply chain integration, information sharing, and supplier relationships on operational efficiency in manufacturing companies across diverse industries. A quantitative research approach is employed, utilizing surveys to collect data from 300 organizations. Correlation analysis, regression analysis, mediation analysis, moderation analysis, and structural equation modeling are utilized to analyze the data and test hypotheses. The results demonstrate significant positive correlations between supply chain integration, information sharing, supplier relationships, and operational efficiency. Regression analysis confirms the significant impact of these dimensions on operational efficiency, with supply chain integration exhibiting the strongest effect. Mediation analysis reveals that information sharing and supplier relationships partially mediate the relationship between supply chain integration and operational efficiency. Moreover, the moderation analysis highlights industry sector as a significant moderating factor. The findings underscore the strategic importance of supply chain integration, information sharing, and supplier relationships in driving operational excellence and offer actionable insights for supply chain management practitioners.

Operating in environments affected by uncertainty: Supply chain finance, timely information sharing using advanced technology, and financial performance in Supply Chain Management 4.0

Digitally enabled supply chains, particularly operating in uncertain environments, have been offering emerging domains for research. The effects of Timely Information Sharing (TIS) on Financial Performance (FP) in the context of Supply Chain Finance (SCF) have been hitherto neglected, especially in the context of environments affected by uncertainty. The study contributes to the related literature by developing an integrated framework interlinked with Information Processing and Contingency theories and it facilitates the understanding of the relationships that exist among SCF, TIS using advanced technology, and FP in the context of the environments affected by uncertainty caused by unpredictable events like terrorist attacks and pandemics. To corroborate the relationships and validate the relative framework, we applied Structural Equation Modelling to the data collected from 261 firms. Our findings show that SCF significantly influences FP and that TIS plays a mediating role in enhancing FP interlinked with modern technology. The study also provides the implications of SCF and TIS in strengthening Supply Chain Management 4.0 operations affected by unprecedented circumstances that hinder FP and its viability within the supply chains’ context.

E-EWS-based Governance Framework for Sharing Cyber Threat Intelligence in the Energy Sector

The integration of traditional energy technologies with modern digital technologies increases the risks of cyber-attacks and data breaches. Sharing cyber threat intelligence (CTI) is important for the common defense. The DYNAMO project has chosen the ECHO Early Warning System (E-EWS) as a tool for CTI information sharing. The management of E-EWS becomes the basis for guiding the ethical and efficient operation of the DYNAMO platform and the wider energy sector. The governance framework defines roles, responsibilities, and procedures that are tailored to sharing information, enhancing collaboration, and ensuring the integrity of shared information. Effective governance promotes transparency, compliance, and trust among stakeholders, which ultimately strengthens the security posture of the DYNAMO platform and improves the energy industry's resilience against cyber threats. This paper proposes a governance framework for the DYNAMO platform, including a committee, data security policies, and NIS2 and GDPR compliance. It emphasizes user-friendly collaboration tools, access control, continuous monitoring, stakeholder training, compliance, and phased implementation. The goal is iterative improvements through continuous evaluation.

CTI Sharing Practices and MISP Adoption in Finland’s Critical Infrastructure Protection

Cyber Threat Intelligence (CTI) sharing is crucial for safeguarding organisations and securing national critical infrastructure. This study delves into the CTI-sharing practices of large, safety-critical Finnish organisations, with a specific interest in the deployment and potential of the Malware Information Sharing Platform (MISP). We gathered insights through qualitative interviews with cybersecurity experts from key sectors: energy, healthcare, and transportation. Our findings reveal that a significant proportion of regional CTI data is still shared through manual methods such as email and chat. While these systems are generally viewed positively, they are also understood to be prone to delays and inaccuracies. The interest in utilising MISP is rising in Finland, yet its implementation is still in the nascent stages. Organisations are looking towards the National Cyber Security Center to lead the establishment of a national MISP instance. The benefits of adopting a national MISP framework could be further amplified by organisations joining Europewide industry-specific MISP instances or leveraging MISP to share threat intelligence with their supply chain partners. However, challenges remain, particularly in balancing threat data sharing with European data protection laws, motivating community contributions, and standardising CTI-sharing tools and practices within a country.

Cancer misinformation on social media.

Social media is widely used globally by patients, families of patients, health professionals, scientists, and other stakeholders who seek and share information related to cancer. Despite many benefits of social media for cancer care and research, there is also a substantial risk of exposure to misinformation, or inaccurate information about cancer. Types of misinformation vary from inaccurate information about cancer risk factors or unproven treatment options to conspiracy theories and public relations articles or advertisements appearing as reliable medical content. Many characteristics of social media networks-such as their extensive use and the relative ease it allows to share information quickly-facilitate the spread of misinformation. Research shows that inaccurate and misleading health-related posts on social media often get more views and engagement (e.g., likes, shares) from users compared with accurate information. Exposure to misinformation can have downstream implications for health-related attitudes and behaviors. However, combatting misinformation is a complex process that requires engagement from media platforms, scientific and health experts, governmental organizations, and the general public. Cancer experts, for example, should actively combat misinformation in real time and should disseminate evidence-based content on social media. Health professionals should give information prescriptions to patients and families and support health literacy. Patients and families should vet the quality of cancer information before acting upon it (e.g., by using publicly available checklists) and seek recommended resources from health care providers and trusted organizations. Future multidisciplinary research is needed to identify optimal ways of building resilience and combating misinformation across social media.

Whole‐process and multi‐stakeholder‐based solid waste management framework construction for industrial parks: Toward circular economy development

AbstractThe intensive generation of industrial solid waste (ISW) in industrial parks poses a severe threat to the water and soil environment. To address the lack of system‐level guidance strategies for ISW management (ISWM), the paper proposes a systematic ISWM framework within the context of the circular economy principle and applies it to a prominent eco‐industrial park in China. This framework is founded on the metabolic characteristics of the entire ISW process, supported by multi‐stakeholder cooperation, and guaranteed by a shareable information platform and infrastructure. A holistic ISW flow analytical process is introduced to facilitate the implementation of this framework. In the case study, a high‐resolution inventory of ISW is established by 157 enterprises in 2021, covering 116 types of general ISW and 61 types of hazardous waste (HW). By integrating material flow analysis, questionnaires, and interviews, this study quantitatively reveals the underperformance of mini‐scale HW recycling, with a recycling rate of 5.6% in 2021, well below the park‐wide rate of 50.8%. Collaborative inboundary and transboundary disposal emerges as a crucial direction for ISWM, with over 80% of ISW being disposed of outside the park. Following the implementation of circular economy strategies guided by the management framework, notable improvements are observed, including a 26.5% increase in the recycling rate of mini‐scale HW and a 13.5% increase in the proportion of disposal outside Beijing. To foster sustainable ISWM, this study recommends enhancing the decentralized infrastructure of collection–storage–transportation integration for mini‐scale ISW in industrial parks. The developed management framework provides valuable insights and guidance for park‐specific ISWM strategies.

Embedding enhancement with foreground feature alignment and primitive knowledge for few-shot learning

Few-Shot Learning (FSL) targets a model to quickly discriminate new categories with limited samples. While most methods struggle to effectively utilize the knowledge learned in the base classes, resulting in models that fail to alleviate the domain gap between training and evaluation. Conversely, humans often enable rapid discrimination by two ways: reinforcing impressions through comparing unknown categories with related objects in memory; Searching for the maximum shared information within the intra-class. Motivated by this, in this paper, we propose a novel Transformer-based Embedding Enhancement Network (TEEN) that adaptively leverages the knowledge learned from the base classes, which we refer to as ‘primitive knowledge’, to complete and distinguish the embeddings for novel classes. Additionally, to reduce interference from background features, we introduce the Transformer-based Foreground Feature Alignment (TFFA) to enhance the representation of image foreground. Ultimately, TEEN enhances the embeddings through foreground alignment and primitive knowledge completion, achieving improved performance in few-shot classification. Extensive experiments on inductive few-shot tasks demonstrate the effectiveness of our approach, achieving state-of-the-art results in cross-domain few-shot tasks.

The Sharing of Pandemic-Related Information From U.S. Government Twitter Accounts

The rapid onset of COVID-19 created a desire for information about the virus and government-directed response efforts. This study explores the sharing of Twitter posts from U.S. government officials and organizations during the onset of the COVID-19 pandemic. This study followed two paths of investigation. First, significant differences in the retweet frequency of government sources were determined based on the source (public officials and agencies, Republican- or Democrat-led states) and differentiated by equal time periods (40 days pre- or post-pandemic declaration). The study findings show that the frequency at which government accounts were retweeted in the U.S. was significantly impacted by the pandemic period, with the post-pandemic period significantly more active as governments wrestled with unprecedented economic, policy, and societal concerns related to the crisis. A greater reliance on public officials as opposed to public agencies or departments was seen, as was a greater tendency to retweet the posts of government officials from Democrat-led states. The second path examined the content of government tweets to determine the linguistic factors which predict retweet frequency. Study results found that both positive and negative emotional language in tweets predicted the frequency of retweeting government sources, though the results varied based on the source. The study results provide insight into the government-affiliated information sources users chose to share with their networks as well as the types of language that appeal to citizens during times of crisis.

Predictors of massive transfusion protocols activation in patients with trauma in Korea: a systematic review.

Massive transfusion protocols (MTPs) implementation improves clinical outcomes of the patient's resuscitation with hemorrhagic trauma. Various predictive scoring system have been used and studied worldwide to improve clinical decision. However, such research has not yet been studied in Korea. This systematic review aimed to assess the predictors of MTPs activation in patients with trauma in Korea. The PubMed, Embase, Cochrane Library, Research Information Sharing Service databases, KoreaMed, and KMbase were searched from November 2022. All studies conducted in Korea that utilized predictors of MTPs activation in adult patients with trauma were included. Ten articles were eligible for analysis, and the predictors were assessed. Clinical assessments such as systolic and diastolic blood pressure, shock index (SI), prehospital modified SI, modified early warning system (MEWS) and reverse SI multiplied by the Glasgow Coma Scale (rSIG) were used. Laboratory values such as lactate level, fibrinogen degradation product/fibrinogen ratio, and rotational thromboelastometry (ROTEM) were used. Imaging examinations such as pelvic bleeding score were used as predictors of MTPs activation. Our systematic review identified predictors of MTPs activation in patients with trauma in Korea; predictions were performed using tools that requires clinical assessments, laboratory values or imaging examinations only. Among them, ROTEM, rSIG, MEWS, SI, and lactate level showed good effects for predictions of MTPs activation. The application of predictors for MTP's activation should be individualized based on hospital resource and skill set, also should be performed as a clinical decision supporting tools.

OPTIMIZING THE GROWTH AND DEVELOPMENT OF TODDLERS THROUGH EMPOWERING CADRES AND MOTHERS OF TODDLERS WITH ASIKIA ACTIVITIES

The toddler period is a vulnerable time, especially with regard to growth, as it can affect the development of children in the future. Therefore, it is necessary to intervene to increase the interaction of health professionals with parents to have a positive impact on child development. The Maternal and Child Health Information Sharing Agenda (ASIKIA) is an activity carried out in an effort to empower the community, especially cadres and mothers of toddlers in optimizing the growth and development of infants and toddlers. Community empowerment activities were carried out through cadre mentoring activities on March 14, 2023 and toddler mother class activities on March 16, 2023. The empowerment model used is Unlimited Totality Health Empowerement (UTHE) which uses several kinds of models in activities in the form of lectures, group discussions, demonstrations and discussions. The activities carried out proved to be able to increase the knowledge of cadres about stimulation of growth and development of toddlers as shown by the results of the pre-test and post-test where before being given counseling, cadre knowledge in the good category was 50% and after being given counseling cadre knowledge increased to the good category as much as 88%. Likewise, the knowledge of mothers of toddlers about stunting showed results before being given counseling, the knowledge of mothers of toddlers in the good category was 17% and after being given counseling, the knowledge of mothers of toddlers increased to the good category by 100%. In addition, the success of the activity is also indicated by the ability of cadres to correctly assess child development using the "SIP (Developmental Stimulation) Bag" media. ASIKIA can be used as a means of sharing knowledge and active discussion between the service team and activity participants on various maternal and child health issues.

Distributed restoration for integrated electricity-gas-heating energy systems with an iterative loop scheme

Electricity, gas and heating networks are increasingly coupled to form an integrated energy system (IES), which is beneficial in improving the operational performance of the system during power outages. Generally, IES involves multiple energy operators, making the centralized framework impracticable. To this end, a novel distributed restoration method for IES is proposed in this paper. The IES restoration problem is formulated as a mixed-integer second-order cone programming (MISOCP) model to minimize the total load losses, which considers the coupling effects among the electricity, gas and heating networks. Then, considering the independent management and information privacy of different energy networks, the distributed restoration model is formulated by decomposing shared boundary information using the alternating direction method of multipliers (ADMM). For the sake of the non-convexity caused by binary load state variables, an iterative loop scheme is introduced in ADMM procedure. This allows us to solve a relaxed distributed model in the first stage, and perform iterative loops in the second stage, which can improve the convergence of ADMM and make the distributed solution feasible. Finally, numerical studies are conducted on 33-7-6-node IES and 123-24-32-node IES to validate the effectiveness and feasibility of the proposed method.

Epistatic Features and Machine Learning Improve Alzheimer's Disease Risk Prediction Over Polygenic Risk Scores.

Polygenic risk scores (PRS) are linear combinations of genetic markers weighted by effect size that are commonly used to predict disease risk. For complex heritable diseases such as late-onset Alzheimer's disease (LOAD), PRS models fail to capture much of the heritability. Additionally, PRS models are highly dependent on the population structure of the data on which effect sizes are assessed and have poor generalizability to new data. The goal of this study is to construct a paragenic risk score that, in addition to single genetic marker data used in PRS, incorporates epistatic interaction features and machine learning methods to predict risk for LOAD. We construct a new state-of-the-art genetic model for risk of Alzheimer's disease. Our approach innovates over PRS models in two ways: First, by directly incorporating epistatic interactions between SNP loci using an evolutionary algorithm guided by shared pathway information; and second, by estimating risk via an ensemble of non-linear machine learning models rather than a single linear model. We compare the paragenic model to several PRS models from the literature trained on the same dataset. The paragenic model is significantly more accurate than the PRS models under 10-fold cross-validation, obtaining an AUC of 83% and near-clinically significant matched sensitivity/specificity of 75%. It remains significantly more accurate when evaluated on an independent holdout dataset and maintains accuracy within APOE genotype strata. Paragenic models show potential for improving disease risk prediction for complex heritable diseases such as LOAD over PRS models.

Beyond the rule. Development of a multi-criteria tool for designing and evaluating an inclusive context

There are rules that provide shared information with the aim of guiding the behaviour of individuals or the community regarding spaces, processes and products. Therefore, the goal is to start a normalisation and standardisation procedure, which allows to solve a specific problem. With regard to the culture and practice of accessibility of spaces and environments, the reference legislation (Law 13/89, Presidential Decree 24 July 1996, n. 503, ISO 21541:2021, Ministerial Decree 236/89, UNI 17210:2021) is not only rather obsolete, but also excludes a large part of potential users. This paper aims to open a debate on the current operational tools in order to evaluate and design an inclusive context, proposing a new, more performing and universal one. The culture of accessibility is not only the scrupulous and scientific observance of the rules. It also means combining both quantitative and qualitative needs; therefore, providing environmental well-being. Thanks to the critical description of reference or experimental evaluation or design tools (HCD participatory methodologies for the definition of needs analyses, Quality Function Deployment for the tracking of technical specifications, ICF with a focus on UNI activities, laws, decrees and regulations to observe the Rule), this paper describes some projects that attempted to go beyond the rule, providing an inclusive context and space to meet people’s actual needs. Therefore, putting some operational tools into functional synergy (Rules, Inclusive Methodologies, ICF, QFD) to define a new multi-criteria tool can be an excellent starting point to develop, for each specific environmental context, a list of expectations that are important for planning and evaluation.