Information Sharing Research Articles

Social Media Information Sharing: Is It a Catalyst for Green Consumption among Gen X and Gen Y Cohorts?

This study aims to identify the influence of Generation X and Generation Y on green buying behavior in the Indian context. Based on social cognitive theory and the generational cohort theory, the authors test the impact of Perceived Environmental Responsibility and Social Media Information Sharing on the relationship between Generation X and Y cohorts and their green buying behavior. This study uses the Quan-Qual approach to triangulate the results. A structured questionnaire was used to collect data from 427 respondents, and the hypotheses were tested with covariance-based structural equation modeling using AMOS software (AMOS 26 software). To probe further into the findings, in-depth interviews were conducted with 60 respondents from Gen X and Gen Y cohort groups, and the data were analyzed using NVIVO software. The findings reveal that the Gen X cohort seems to engage more in green buying behavior compared to the Gen Y cohort. Also, Social Media Information Sharing mediates the green buying behavior for Gen X and moderates it for Gen Y. Moreover, the qualitative inquiry confirms that the Perceived Environmental Responsibility of both Gen X and Gen Y cohorts does not significantly impact their green buying behavior.

Several Analyses of Chinese Manufacturing Entrepreneurs' Enterprise Performance Improvement through Information Sharing

Several Analyses of Chinese Manufacturing Entrepreneurs' Enterprise Performance Improvement through Information Sharing

A voluntary transfusion recipient registry in Korea as a database for blood group antibodies.

Several types of transfusion-related registries have been developed to improve patient outcomes and blood banks. In Korea, a transfusion program functioning as a blood group antibody database and a reference laboratory has been in operation since July 2013. This study was conducted to determine the current status of blood group antigens and antibodies in Korea and propose a model for registries in the field of transfusion medicine. Cases with unexpected red cell antibodies were registered online in the voluntary transfusion registry. Specific antigen-negative frequencies were calculated based on the recorded data. To determine the frequencies of RhCE antigens, data added via the Blood Information Sharing System were also analyzed. Data added to the registries between July 2013 and June 2022 were included in the analysis. Among 9,048 antibody cases registered from 29 hospitals, anti-E alone was identified most commonly, followed by anti-E and c, anti-C and e, anti-Lea, and anti-M (2,202, 1,792, 757, 618, and 383 cases, respectively). The frequencies of E-, E-c-, C-e-, Le(a-), and M- were 49.1%, 41.6%, 9.1%, 69.4%, and 21.8%, respectively. The distributions of antibodies and antigen frequencies were estimated through the transfusion registry. Antigen frequencies were calculated based on the results of antigen typing of red blood cell components performed at the time of issuing. The online transfusion registry serving as a blood group antibody database is useful for determining the frequencies of blood group antigens and antibodies.

How Do Supply Chain Management Practices Influence Organizational Performance? A Case Study of Bangladesh's Agro-Food Industry

Introduction: Supply chain management practices are essential for enhancing organizational performance, particularly in the Agro-food industry. This study examines the impact of specific SCM factors—Strategic Supplier Partnership, Customer Relationship, Level of Information Sharing, and Quality of Information Sharing—on organizational performance in Bangladesh. Methodology: Data were collected via a structured questionnaire targeting key stakeholders in Bangladesh's Agro-food industry, using a 5-point Likert scale to measure responses. Structural equation modeling (SEM) was utilized to analyze the relationships between SCM practice factors and organizational performance factors such as marketing and financial performance. Findings: The analysis revealed that customer relationships, quality of information sharing, and strategic supplier partnerships have significant positive relationships with organizational performance, particularly in terms of financial and marketing outcomes. However, the level of information sharing did not show a significant impact on both financial and marketing performance. Managerial Implications: The study emphasizes the significance of prioritizing strategic supplier partnerships, customer relationships, and high-quality information sharing for improving organizational performance in Bangladesh's Agro-food industry. Strengthening these SCM practices can lead to better financial and marketing performance, providing valuable insights for managers to develop targeted strategies.

Shared neural codes of recognition memory

Recognition memory research has identified several electrophysiological indicators of successful memory retrieval, known as old-new effects. These effects have been observed in different sensory domains using various stimulus types, but little attention has been given to their similarity or distinctiveness and the underlying processes they may share. Here, a data-driven approach was taken to investigate the temporal evolution of shared information content between different memory conditions using openly available EEG data from healthy human participants of both sexes, taken from six experiments. A test dataset involving personally highly familiar and unfamiliar faces was used. The results show that neural signals of recognition memory for face stimuli were highly generalized starting from around 200 ms following stimulus onset. When training was performed on non-face datasets, an early (around 200–300 ms) to late (post-400 ms) differentiation was observed over most regions of interest. Successful cross-classification for non-face stimuli (music and object/scene associations) was most pronounced in late period. Additionally, a striking dissociation was observed between familiar and remembered objects, with shared signals present only in the late window for correctly remembered objects, while cross-classification for familiar objects was successful in the early period as well. These findings suggest that late neural signals of memory retrieval generalize across sensory modalities and stimulus types, and the dissociation between familiar and remembered objects may provide insight into the underlying processes.

Move a Little Closer? Information Sharing and the Spatial Clustering of Bank Branches

Abstract We present a model of credit market competition to derive key hypotheses about how information sharing between banks influences the spatial clustering of their branches. We then test these hypotheses using data on 56,555 branches owned by 614 banks across 19 countries. We find that information sharing incentivizes banks to establish branches in localities that are new to them but that are already served by other banks. The resultant branch clustering is associated with reduced spatial credit rationing, as information sharing enables firms to access credit from more distant banks. These findings underscore how information sharing makes it more important for banks to move closer to each other rather than closer to their borrowers.

Outcomes of Concomitant Cardiac Surgical Procedures Performed During Pediatric Lung Transplantation in the United States.

Data on concomitant cardiac surgery (CCS) performed during pediatric lung transplantation (LTx) is limited. Therefore, we conducted a multi-institutional analysis to identify the incidence and outcomes of CCS in pediatric (< 18 years) LTx recipients by merging data (2004-2023) from the United Network for Organ Sharing (UNOS) and Pediatric Health Information System (PHIS) databases. Of the total of 596 pediatric LTx recipients, 87 (15%) underwent CCS. The majority of these cardiac surgeries were atrial septal defect (ASD) closure (90%) followed by aortic arch/descending aortic repair (3%), atrial repair (3%), ventricular septal defect closure (2%), patent ductus arteriosus ligation (2%), and tricuspid valve repair (2%). The median age at LTx was 3years (IQR: 0-12). Pulmonary hypertension (PHT) was the predominant indication for LTx (54%). Survival to discharge was 94% and 5-years survival was 64%. Our findings indicate CCS in children undergoing LTx has acceptable outcomes.

Mitigating Supply Disruption: The Interplay between Responsive Pricing and Information Sharing under Dual Sourcing

Facing supply disruptions that often occur in business, firms can increase redundancy through supplier diversification and manage demand-side problems through responsive pricing and demand information sharing. We consider a Stackelberg–Nash game consisting of two competing suppliers with heterogeneity in terms of reliability and production cost and a manufacturer, and study the manufacturer’s strategy choice problem. The manufacturer has two strategies, namely responsive pricing and information sharing. The interplay between responsive pricing and information sharing is analyzed by defining the value of responsive pricing. The results of the study show that responsive pricing always benefits the manufacturer. Responsive pricing increases the manufacturer’s incentive to use dual sourcing, whereas the manufacturer with committed pricing will only choose single sourcing. Under responsive pricing, the demand potential and the unreliable supplier’s disruption probability similarly affect the manufacturer’s sourcing decision. As the demand potential or the unreliable supplier’s disruption probability increases, the manufacturer will tend to prefer dual sourcing. When the reliable supplier’s production cost is moderate, the manufacturer with responsive pricing will choose to share demand information, while the opposite is true for the manufacturer with committed pricing. At this point, responsive pricing and information sharing will complement each other.

A multiscale time-series decomposition learning for crude oil price forecasting

Crude oil price forecasting is important for market participants and policymakers. However, accurately tracking oil prices is quite a challenging task due to the complexity of temporal oil data and the nonlinear relationships involved in the forecasting task. In this study, a multiscale time-series decomposition learning framework is proposed to deal with this issue. First, a multiscale temporal processing module is designed to capture different frequency time-series patterns in historical data at various scales. Then, a multiscale decomposition technique is applied to decompose historical crude oil data into various temporal modes, involving global shared information across multiple scales, as well as local specific information that varies at each scale. Finally, a multiscale fusion mechanism is employed to combine these information, which are further used as inputs to construct nonlinear and complex predictive models for crude oil prices. A series of experiments conducted on Shanghai crude oil market demonstrate that the proposed approach outperforms several econometric and machine learning models.

The Relationship Between Social Media Information Sharing Characteristics and Problem Behaviors Among Chinese College Students Under Recommendation Algorithms.

With the development of information technology and various social media, recommendation algorithms have increasingly more influence on users' social media usage. To date, there has been limited research focused on analyzing the impact of recommendation algorithms on social media use and their corresponding role in the development of problematic behaviors. The present study analyzes the impact of recommendation algorithms on college students' information sharing and internalizing, externalizing problem behaviors to address the aforementioned shortcomings. An online questionnaire survey was conducted among 34,752 college students in China. A latent profile analysis was conducted to explore the various behavioral patterns of Chinese college students' information sharing across the three social media platforms identified for this study. The Bolck-Croon-Hagenaars (BCH) method Regression Mixture Modeling was then used to analyze the differences in internalizing and externalizing problem behaviors among the different subgroups of Chinese college students. The level of information sharing by college students across different social media platforms could be divided into "WeChat Moments low-frequency information sharing", "middle-frequency comprehensive information sharing", "TikTok high-frequency information sharing", and "Sina Weibo high-frequency information sharing". Significant differences were observed regarding internalizing and externalizing problem behaviors among college students in different information-sharing subgroups. This study identified four subgroups with different information-sharing characteristics using latent profile analysis. Among them, college students who are in subgroup of social media information sharing influenced by recommendation algorithms exhibit higher frequency of information sharing and higher level of internalizing and externalizing problematic behaviors. These results expand our understanding of college students' social media usage and problem behaviors from a technological perspective. In future, the negative impacts of recommendation algorithms on college students can be reduced by improving their awareness of these algorithms and optimizing the algorithms themselves.

Application of BIM in Civil Engineering

This paper explores how the use of Building Information Modeling (BIM) can be a beneficial platform for Civil engineers. The utilization of BIM (Building Information Modeling) is becoming a hot issue for many public work projects. Also, its market seems to be expanded as BIM is applied in procurement constructions. This concept is the basis of Building Information Modeling (BIM) technology. There is now a growing interest in the adoption of this technology within the Architecture, Engineering and Construction (AEC) industry. The capacity of BIM for civil engineering to study many possibilities enhances results and offers data-driven confidence that projects can be completed on time and under budget. BIM provides shared information throughout the course of a project, producing powerful outcomes. The method of building information modelling (BIM), which is based on 3D models, is the cornerstone of the digital transformation. The information around design assets may be created and managed by civil engineers, resulting in increased teamwork, shared data, and project completion that is both on schedule and within budget. Next, the idea of Building Information Modelling (BIM) and its practical benefits are described. Main part of the paper is devoted to discussion about what kind of difficulties we may encounter during the implementation of the BIM technology and how they are related to the potential benefits.

Influence of ICT Resources Utilization on Social Information Sharing among Undergraduates in Federal University Oye Ekiti, Nigeria

The study examined the influence of ICT resource utilization on social information sharing among undergraduates at Federal University Oye Ekiti, Nigeria. It employed the descriptive survey design. The population comprises all undergraduates in the faculty of education at Federal University Oye Ekiti. A total of 200 students volunteered to participate in this study using a simple random sampling technique. The main instrument for data collection was a semi-structured question which was validated with a Cronbach’s alpha score of 0.7 and above. Data collected from respondents was analyzed using descriptive statistics, frequency, table, mean, and standard deviation. The formulated hypothesis was analyzed with Spearman rank regression analysis. Results indicated a generally high extent of social information-sharing practices among undergraduates in the selected university with an average mean score of 3.4 on a scale of 4 points. Findings further indicated that computers (mean=3.0, RII=0.9) and Google Meet (mean=3.0, RII=0.9) were the most commonly used among the ICT resources used by undergraduates. Others include smartphones (mean = 2.9, RII = 0.9), Google (mean = 2.6, RII = 0.8), software programs (mean = 2.6, RII= 0.8), emails (mean = 2.6, RII= 0.8), projectors (mean = 2.2, RII= 0.7), interactive online teaching platforms (mean = 2.2, RII= 0.7) and Slack (mean = 2.1, RII= 0.7). the study revealed a statistically significant influence of ICT resource utilization and social information-sharing practices among undergraduates in the selected university (F=48.817, p&lt;0.05). The study concluded that the use of ICT resources should be encouraged by the university authority to move the institution to the next level through massive investment in digital educational transformation which has the potential to boost the ranking of the university among its contemporaries across the globe.

Study of Cybersecurity Laws and Regulations

In the modern digital era, cybersecurity has become a crucial field due to the increase in cyberthreats and assaults, which pose serious hazards to people, businesses, and countries. To meet these changing difficulties, a wide range of national and international cybersecurity rules and regulations have been developed. This research paper explores a thorough analysis of these laws and regulations. The first section of the report gives an overview of the state of cybersecurity throughout the world, emphasising how frequently and how sophisticatedly cyber-attacks are becoming. After that, it takes a close look at the laws that control cybersecurity in both the public and private domains. The paper addresses the jurisdictional issues that emerge in the globally interconnected realm of cyberspace and investigates the fundamental ideas that support cybersecurity legislation, such as data protection, breach notification, and responsibility. Additionally, the study paper examines important cybersecurity laws from across the globe, such as the Cybersecurity Information Sharing Act of the United States, the California Consumer Privacy Act, and the General Data Protection Regulation (GDPR) of the European Union (CISA). The efficacy of these measures in protecting confidential data and reducing cyber risks is assessed.

Sublethal effects of early-life exposure to common and emerging contaminants in birds

The plight of wild birds is becoming critical due to exposure to environmental contaminants. Although laboratory studies have provided insights into the developmental effects of chemical exposures, less is known about the adverse effects of environmental chemicals in developing wild birds. Early life stages are critical windows during which long-term organization of physiological, behavioral, and neurological systems can occur. Thus, contaminant exposure at early life stages can directly influence survival and reproductive success, with consequences for population stability and resilience in wild species. This review synthesizes existing knowledge regarding both short- and long-term effects of early-life exposure to widespread contaminants in birds. We focus especially on wild birds and on contaminants of concern within the Gulf of Mexico as an example of a habitat under anthropogenic stress from exposure to a complex mixture of chemicals and changing land uses that exacerbate existing vulnerabilities of wildlife in this region. Chemical contaminants for discussion in this review are based on avian mortality records from the Wildlife Health Information Sharing Partnership (WHISPers) database and on additional review of the literature regarding avian contaminants of concern for the northern Gulf of Mexico, and include oil and associated polycyclic aromatic hydrocarbons, dioxin and dioxin-like compounds, flame retardants, pesticides, heavy metals, and plastics. We provide an overview of effects in bird species at both the pre-hatching and post-hatching early life stages, discuss differences in sensitivities by route of exposure, life stage, and life history, and provide recommendations for future research. We find that additional research is needed on altricial species, post-hatching early-life exposure, long-term effects, and on ecologically relevant contaminant concentrations and routes of exposure. Given the increasing frequency and intensity of anthropogenic stressors encountered by wild animals, understanding both lethal and sublethal impacts of contaminants on the health of individuals and populations will be critical to inform restoration, management, and mitigation efforts.

Subgraph-Aware Graph Kernel Neural Network for Link Prediction in Biological Networks.

Identifying links within biological networks is important in various biomedical applications. Recent studies have revealed that each node in a network may play a unique role in different links, but most link prediction methods overlook distinctive node roles, hindering the acquisition of effective link representations. Subgraph-based methods have been introduced as solutions but often ignore shared information among subgraphs. To address these limitations, we propose a Subgraph-aware Graph Kernel Neural Network (SubKNet) for link prediction in biological networks. Specifically, SubKNet extracts a subgraph for each node pair and feeds it into a graph kernel neural network, which decomposes each subgraph into a combination of trainable graph filters with diversity regularization for subgraph-aware representation learning. Additionally, node embeddings of the network are extracted as auxiliary information, aiding in distinguishing node pairs that share the same subgraph. Extensive experiments on five biological networks demonstrate that SubKNet outperforms baselines, including methods especially designed for biological networks and methods adapted to various networks. Further investigations confirm that employing graph filters to subgraphs helps to distinguish node roles in different subgraphs, and the inclusion of diversity regularization further enhances its capacity from diverse perspectives, generating effective link representations that contribute to more accurate link prediction.

Shared Information for a Markov Chain on a Tree

Shared Information for a Markov Chain on a Tree

The Effect of Supply Chain Management Practices on Competitive Advantage in Manufacturing Industry in Pakistan: The Moderating Role of Big Data Analytics

The purpose of the current study is to examine that what big data analytics (BDA) may have a moderating effect on the relationship between supply chain management practices (SCMPs) and competitive advantage (CA) in Pakistani manufacturing companies. Data was gathered quantitatively from several manufacturing businesses located industrial in Karachi. The study hypothesis was tested using SPSS method and linear regression analysis, sample size is 154 persons working in small, medium and large sized manufacturing companies with multiple categorizations based on enterprise size, job &amp; experience. The findings demonstrate that SCMPs have a major favorable impact on competitive advantage (CA). In particular, information quality (IQ), customer relationship management (CRM), and strategic supplier partnerships (SSP) all significantly improve competitive advantage (CA), information sharing (ISh) not significantly on competitive advantage (CA). Furthermore, investigates the association between SCMPs and CA in Pakistani manufacturing enterprises, as well as the moderating effect of big data analytics (BDA). The findings validate H1, H2, and H3 and demonstrate the beneficial effects of Supplier Strategic Partnership (SSP), customer relationship management (CRM), and information quality (IQ) on competitive advantage (CA). This study is to look at how big data analytics (BDA) may be able to moderate the relationship between supply chain management practices (SCMPs) and competitive advantage (CA) in Pakistani manufacturing companies. The findings demonstrate that SCMPs significantly improve CA. Information quality (IQ), customer relationship management (CRM), and strategic supplier partnerships (SSP) in particular significantly improve competitive advantage (CA). Information sharing (ISh), on the other hand, had no effect on competitive advantage (CA). Some flaw in the way the firms under investigation handle big data analytics (BDA). It emphasizes that for big data analytics (BDA) systems to produce the intended outcomes, managers must integrate them with other business systems and capabilities.

An intelligent Health Monitoring System for Patients with Chronic Obstructive Pulmonary Disease based on Internet of Things

Abstract: Chronic obstructive pulmonary disease is an important cause of global mortality. These patients are in dire need of treatments to maintain lung function and reduce consecutive visits and hospital stays. IoT acts as a specialized assistant in healthcare, providing a wide range of potential applications such as remote monitoring. Therefore, this study has been compiled with the aim of designing a care system based on the Internet of Things for patients with chronic obstructive pulmonary disease. In this project, we designed a system for monitoring chronic obstructive pulmonary disease patients using an Arduino board and embedding three air quality sensors, heart rate and pulse oximetry, temperature sensor and Bluetooth module, and then implemented it on a patient with obstructive pulmonary disease. . The information obtained from the sensors is sent via Bluetooth to the mobile application that has the ability to share and store information and informs the doctor and the person about the changes. By using the pulse oximetry sensor, the patient increases the monitoring and part of his pulmonary rehabilitation, and by using the temperature and air quality sensor, he identifies the undesirable and harmful air and by informing the doctor and the person, he prevents the exacerbation of the symptoms of chronic obstructive pulmonary disease. This system increases the rehabilitation and self-care of patients, and by providing suitable environmental conditions, prevents unnecessary medical interventions and consecutive visits, and increases the quality of life of patients and reduces costs.

Herbal Medicines for the Improvement of Immune Function in Patients with Cancer: A Protocol for Systematic Review and Meta-Analysis

Objectives: Patients with cancer eventually fail to respond to therapy when malignant cells develop effective ways to evade immunosurveillance. Conventional cancer treatments, such as radiation therapy and chemotherapy, aim to cure the disease or prolong the patient’s life. However, the toxicity and side effects of conventional treatments limit their efficacy. Herbal medicine is a typical complementary and integrative form of medicine for cancer treatment in Asia. This protocol evaluates the effectiveness of herbal medicines in improving the immune function of patients with cancer.Methods: The following electronic databases will be searched: MEDLINE via PubMed, EMBASE via Elsevier, Cochrane Central Register of Controlled Trials, China National Knowledge Infrastructure (CNKI), and Korean databases including Regional Information Sharing Systems (RISS), National Digital Science Library (NDSL), and Oriental Medicine Advanced Searching Integrated System (OASIS). Additionally, prospective randomized controlled trials that evaluate the effectiveness of herbal medicines on immune function in patients with cancer will be included in this review. All outcomes related to the immune function of patients with cancer (e.g., CD3, CD4, CD8, CD4/CD8 ratio, CD19 (B cells), dendritic cells (CD11), CD56 (NK cells), and macrophages) will be included in this review.Results: This review is expected to provide data on the effectiveness of herbal medicines on improving immune functions in patients with cancers.Conclusion: This systematic review will help patients and clinicians establish new management options for cancer treatment.

Emergence and Transformation of K-Smart Beauty Care

Purpose: K-smart beauty is securing an important position in the global beauty industry through innovation based on a scientific approach and the latest technology. This satisfies consumers' diverse appearance management needs and conveys the positive values of the new K-culture. In this study, we aim to examine changes in Korean beauty care behavior for the pursuit of health interventions and aesthetics that meet consumer requirements and preferences. Method: This study investigated academic research published by the Korea Research Information Sharing Service(www.riss.kr), data from Korea's government-funded research institutes, and media articles to investigate the recent change process of K-beauty. Purpose of the study a literature review was conducted by organizing the contents that corresponded to the above. Results: The beauty industry in Korean society is going through a process of specialization, segmentation, and cutting-edge technology, and is transforming into smart beauty through continuous innovation. Changing consumer awareness of beauty care was at work on this basis, and various factors such as technological development and distribution of beauty equipment, diversification of service provision to consumers, and globalization of K-culture were confirmed to have a complex synergistic effect. Conclusion: In Korean society, the perception of beauty care is changing beyond simple appearance care to the concept of smart beauty, which includes health interventions. It has been confirmed that these changes enable the provision of efficient and economical services and are gradually moving towards data-based customized services for everyone while protecting the environment.