Research Model Research Articles

The effect of social media marketing activities dimensions on value co-creation behavior: An application of the commitment-trust theory

Previous studies have not paid much attention to the effect of social media marketing activities on value co-creation behavior. Especially, up to now, no one has studied the effects of social media marketing activities dimensions on value co-creation behavior. This study applies the commitment-trust theory to develop and estimate this relationship through brand trust and brand commitment on social media in Vietnam. The snowball sampling technique was applied to gather 504 social media users through social media platforms in Vietnam. The proposed research model was tested through PLS-SEM using SmartPLS 4. The results highlighted that most social media marketing activities dimensions (including trendiness, electronic word of mouth, interaction, and customization) affect brand trust (pc are 0.42, 0.152, 0.112, 0.097, respectively, and p-values are all less than 0.05). Simultaneously, brand trust was found to have a positive effect on brand commitment (pc =0.405, p = 0.000). Furthermore, calculation results revealed that brand commitment contributed significantly and strongly to their co-value behavior towards brands (pc = 0.531, p = 0.000). On the contrary, the data do not support a direct impact of entertainment on brand trust (pc = 0.001, p = 0.990) and brand trust on co-value behavior (pc = 0.025, p = 0.466) at a significance level of less than 5%. Ultimately, the findings also suggest a guide to social media marketers to drive customer value co-creation behavior for brands. AcknowledgmentThis work was supported by the National Foundation for Science & Technology Development of Vietnam under Decision No. 131/QD-HDQL-NAFOSTED dated September 21, 2020.

Research on Environmental Performance and Measurement of Smart City Power Supply Based on Non Radial Network DEA

The continuous development of smart cities has put forward higher requirements for the supply of power systems. In response to the constraints in the environmental performance and measurement of smart city power supply, this paper proposes a research model for smart city power supply environmental performance and measurement based on non-radial network DEA based on the characteristics of DEA model and distance function. This model can combine different stages of power supply to conduct more reasonable statistics and analysis of efficiency in different regions. In addition, correlation coefficients were analyzed for the impact of efficiency factors on the phase ratio in the production and sales stages of the power supply system. The research results indicate that there is a positive correlation between the output value and power generation of electricity sales and the efficiency of the electricity sales stage, with correlation coefficients of 0.57 and 0.092, respectively; The length of newly added lines, capacity of new equipment, and line loss rate are all negatively correlated with their efficiency, with correlation coefficients of −0.42, −0.12, and −0.46, respectively. Based on the above analysis, this study provides more theoretical support for the study of environmental performance and measurement of smart city power supply.

Effects of Different Salinity Stress on the Transcriptomic Responses of Freshwater Crayfish (Procambarus clarkii, Girard, 1852)

Salinization of freshwater ecosystems is a pressing global issue. Changes in salinity can exert severe pressure on aquatic animals and jeopardize their survival. Procambarus clarkii is a valuable freshwater aquaculture species that exhibits some degree of salinity tolerance, making it an excellent research model for freshwater aquaculture species facing salinity stress. In the present study, crayfish were exposed to acute low salt (6 ppt) and high salt (18 ppt) conditions. The organisms were continuously monitored at 6, 24, and 72 h using RNA-Seq to investigate the mechanisms of salt stress resistance. Transcriptome analysis revealed that the crayfish responded to salinity stress with numerous differentially expressed genes, and most of different expression genes was observed in high salinity group for 24h. GO and KEGG enrichment analyses indicated that metabolic pathways were the primary response pathways in crayfish under salinity stress. This suggests that crayfish may use metabolic pathways to compensate for energy loss caused by osmotic stress. Furthermore, gene expression analysis revealed the differential expression of immune and antioxidant-related pathway genes under salinity stress, implying that salinity stress induces immune disorders in crayfish. More genes related to cell proliferation, differentiation, and apoptosis, such as the Foxo, Wnt, Hippo, and Notch signaling pathways, responded to high-salinity stress. This suggests that regulating the cellular replication cycle and accelerating apoptosis may be necessary for crayfish to cope with high-salinity stress. Additionally, we identified 36 solute carrier family (SLC) genes related to ion transport, depicting possible ion exchange mechanisms in crayfish under salinity stress. These findings aimed to establish a foundation for understanding crustacean responses to salinity stress and their osmoregulatory mechanisms.

Bridging the Intention–Behavior Gap in Organic Food Consumption: Empirical Evidence from China

The transition to a low-carbon economy emphasizes the importance of green and low-carbon consumption; yet, there is often a discrepancy between consumers’ intentions and their actual behavior regarding organic foods. This study aims to identify strategies to bridge this gap. The research model of organic food consumption intention and behavior is constructed, and a structural equation model is used to test the research hypotheses based on a valid sample of 480 residents of Guangdong Province through an online questionnaire survey. Further, the intention‒behavior gap is defined and its determinants are investigated through multiclass logistic regression. Finally, we categorize and forecast the alignment between consumption intentions and behaviors using machine learning algorithms. The results reveal that attitudes, social interactions, and cognitive information play crucial roles in aligning intentions with behaviors. By enhancing social information exchange or improving cognitive understanding, consumers can reduce their intention‒behavior discrepancy. This research offers valuable policy recommendations for fostering green consumption among residents from various perspectives.

Unraveling the dynamics of password manager adoption: a deeper dive into critical factors

Purpose The purpose of this study is to comprehensively explore the password manager adoption landscape, delving into crucial factors such as performance, trust, social influence, self-efficacy, risk perception, security concerns, enjoyment and facilitating conditions. It also aims to contribute meaningful insights to security product research and practice. Design/methodology/approach A survey was used to investigate the characteristics of adoption intention for password managers. In total, 156 participants from a public university located in the Midwest region of the USA voluntarily completed the survey. Partial least squares structural equation modeling was used to estimate and validate causal relationships and the proposed research model. Findings Through empirical validation, this study demonstrates that constructs such as social influence, web-specific self-efficacy and perceived risk directly impact trust in password managers. Facilitating conditions and perceived security controls are identified as direct influencers on performance expectancy, deviating from the pathways of the traditional framework. Moreover, the model introduces novel elements crucial for comprehending password manager adoption, including “web-specific self-efficacy” and “perceived security control.” Originality/value The paper systematically reviews existing research on password managers, shedding light on crucial factors significantly influencing adoption behavior. By introducing deviations from conventional frameworks and theories, the study emphasizes the innovative nature of its model. It also formulates strategies to catalyze wider adoption and promote effective design of password managers, increasing user engagement rates.

The Impact Of Technology Mastery On Employee Turnover With Technostress As Moderator

Employee turnover in Indonesia has a fairly high rate and some of this is due to technology. On the other hand, 66% of companies are switching to digital transformation. This causes the employee turnover rate to increase. Most companies will experience an increase in employee turnover if there is technostress in their workforce. This research aims to examine the moderating effect of technostress on the relationship between technology mastery and employee turnover. The research model was built based on the employee perspective. Primary data was collected through questionnaires given to 151 workers or employees in Semarang City. Hypothesis testing is carried out using linear regression and moderated regression analysis (MRA). The research results show that there is a negative relationship between technology mastery and employee turnover, as well as the moderating role of technostress which influences the influence of technology mastery on employee turnover. This research helps company owners and human resource management to reduce employee turnover rates and can be used as a consideration for company evaluation to reduce technostress. To the best of the researchers' knowledge, this study has never been explored before. This research shows how these three variables act synergistically in a model.

Pengaruh Media Audio Visual Terhadap Hasil Belajar IPA Eksperimen Kelas VII MTs Qotrun Nada Depok

The aim of the research is to determine the effect of implementing Audio Visual Learning Media on science learning outcomes by conducting experiments on class VIII students at MTs Qotrun Nada Depok. The research model used is an experimental method with analysis of the mean difference between two samples. The sample used was 40 students from classes VII.1 and VII.2 as the experimental class and 40 students from classes VII.3 and VII.4 as the control class. Research data to test the hypothesis comes from test scores on Measurement Materials and quantities. The instruments used are validity and reliability. The results of the research are the results of learning science using Audio Visual Learning Media (experimental class), with an average score of 72.30 with a median of 70.59; mode 68.42; variance 177.07; and standard deviation 13.31. It can be concluded that students' science learning outcomes provide good results. The science learning outcomes of students who did not use Audio Visual Learning Media (control class) obtained an average score of 50.85 with a median of 43.75; mode 47.17; variant 190.85; and standard deviation 13.81. This shows that students' science learning outcomes provide quite good results. So it can be concluded that there is an influence of Audio Visual Learning Media on science learning outcomes. From the research results, it was found that tcount = 7.13 was higher than ttable = 1.996 at a significance level of 5%, so H0 was rejected and H1 was accepted. This means that there is a significant difference between students' science learning outcomes taught with Audio Visual Learning Media. Keywords: ,

Generation of musculoskeletal cells from human urine epithelium-derived presomitic mesoderm cells

BackgroundNumerous studies have shown that somite development is a necessary stage of myogenesis chondrogenesis and osteogenesis. Our previous study has established a stable presomitic mesoderm progenitor cell line (UiPSM) in vitro. Naturally, we wanted to explore whether UiPSM cell can develop bone and myogenic differentiation.ResultsSelective culture conditions yielded PAX3 and PAX7 positive skeletal muscle precursors from UiPSM cells. The skeletal muscle precursors undergo in vitro maturation resulting in myotube formation. MYOD effectively promoted the maturity of the skeletal myocytes in a short time. We found that UiPSM and MYOD mediated UiPSM cell-derived skeletal myocytes were viable after transplantation into the tibialis anterior muscle of MITRG mice, as assessed by bioluminescence imaging and scRNA-seq. Lack of teratoma formation and evidence of long-term myocytes engraftment suggests considerable potential for future therapeutic applications. Moreover, UiPSM cells can differentiate into osteoblast and chondroblast cells in vitro.ConclusionsUiPSM differentiation has potential as a developmental model for musculoskeletal development research and treatment of musculoskeletal disorders.

Fabrication, characterization and numerical validation of a novel thin-wall hydrogel vessel model for cardiovascular research based on a patient-specific stenotic carotid artery bifurcation

In vitro vascular models, primarily made of silicone, have been utilized for decades for studying hemodynamics and supporting the development of implants for catheter-based treatments of diseases such as stenoses and aneurysms. Hydrogels have emerged as prominent materials in tissue-engineering applications, offering distinct advantages over silicone models for fabricating vascular models owing to their viscoelasticity, low friction, and tunable mechanical properties. Our study evaluated the feasibility of fabricating thin-wall, anatomical vessel models made of polyvinyl alcohol hydrogel (PVA-H) based on a patient-specific carotid artery bifurcation using a combination of 3D printing and molding technologies. The model’s geometry, elastic modulus, volumetric compliance, and diameter distensibility were characterized experimentally and numerically simulated. Moreover, a comparison with silicone models with the same anatomy was performed. A PVA-H vessel model was integrated into a mock circulatory loop for a preliminary ultrasound-based assessment of fluid dynamics. The vascular model's geometry was successfully replicated, and the elastic moduli amounted to 0.31 ± 0.007 MPa and 0.29 ± 0.007 MPa for PVA-H and silicone, respectively. Both materials exhibited nearly identical volumetric compliance (0.346 and 0.342% mmHg−1), which was higher compared to numerical simulation (0.248 and 0.290% mmHg−1). The diameter distensibility ranged from 0.09 to 0.20% mmHg−1 in the experiments and between 0.10 and 0.18% mmHg−1 in the numerical model at different positions along the vessel model, highlighting the influence of vessel geometry on local deformation. In conclusion, our study presents a method and provides insights into the manufacturing and mechanical characterization of hydrogel-based thin-wall vessel models, potentially allowing for a combination of fluid dynamics and tissue engineering studies in future cardio- and neurovascular research.

Enhancing Urban Flood Forecasting: Integrating Weather Forecasts and Hydrological Models

Precipitation data in urban hydrological models are derived from an ideal stormwater model, which has some uncertainties and limited prediction times. Therefore, to reliably forecast urban flooding, prolong prediction time periods, and better support associated research in urban flood forecasting, a combination of weather forecasts and urban hydrology is necessary. By applying comprehensive cloud microphysical schemes in the Weather Research and Forecasting (WRF) model to the predecessor torrential rainfall associated with Typhoon Khanun (2017), this study evaluated different configurations of atmospheric-hydrological simulations based on the WRF model and InfoWorks ICM. Results showed that the microphysics scheme could significantly affect spatial and temporal distributions of the simulated torrential rainfall. Generally, the combination of WRF and NSSL schemes produced better performance. Applying the NSSL scheme to the WRF model and combining it with the InfoWorks ICM system can reproduce torrential rainfall and urban flood formations.

Research Model of the Degree of Technological Humanism in Manufacturing Companies in the Transformation towards Industry 5.0

Adopting the concept of Industry 4.0 enables manufacturing companies to gain advantages from digital production management, though issues have been identified related to human resources, management and organisation. Problem areas concern workplace ergonomics to the general wellbeing and satisfaction of workers, and social responsibility. Industry 5.0 promotes progress based on human-machine collaboration and sustainable, human-centred value creation. The research model of the degree of technological humanism, which aims to ensure that technological progress improves human wellbeing in manufacturing companies in the transformation towards Industry 5.0, is a demanding area that has not been sufficiently researched or unambiguously defined so far. The presented model can facilitate the digital transition of manufacturing companies towards Industry 5.0, as it contains the components necessary for the transformation of organisational processes, thereby enhancing and accelerating the implementation of smart manufacturing.

Breaking down silos: The Gastroenterology Immunology Neuroscience (GIN) Discovery Program - a new model for research.

The Gastroenterology Immunology Neuroscience (GIN) Discovery Program represents a new model for research that overcomes the limitations imposed by traditional "research silos" in science. By uniting these three fields, the GIN Program aims to enhance the understanding and treatment of chronic conditions through a system-wide perspective focusing on the gut-immune-brain axis. Key initiatives include monthly interdisciplinary seminars, an annual symposium, and GINnovate, a commercialization and entrepreneurship event. Additionally, the program offers a seed grant competition for early and mid-career researchers, promoting advancements in gut-immune-brain axis research through the power of collaboration. The GIN Program in a short period of time has facilitated the formation of a vibrant community, captivating attention from both national and international institutions. This effort to break down barriers in research aims to inspire similar models that prioritize open communication, mutual respect and a commitment to impactful science.

Projected near-surface wind speed and wind energy over Central Asia using dynamical downscaling with bias-corrected global climate models

Wind energy development in Central Asia can help alleviate drought and fragile ecosystems. Nevertheless, current studies mainly used the global climate models (GCMs) to project wind speed and energy. The simulated biases in GCMs remain prominent, which induce a large uncertainty in the projected results. To reduce the uncertainties of projected near-surface wind speed (NSW) and better serve the wind energy development in Central Asia, the Weather Research and Forecasting (WRF) model with bias-corrected GCMs was employed. Compared with the outputs of GCMs, dynamical downscaling acquired using the WRF model can better capture the high- and low-value centres of NSWS, especially those of Central Asia's mountains. Meanwhile, the simulated NSWS bias was also reduced. For future changes in wind speed and wind energy, under the Representative Concentration Pathway 4.5 (RCP4.5) scenario, NSWS during 2031–2050 is projected to decrease compared with that in 1986–2005. The magnitude of NSWS reduction during 2031–2050 will reach 0.1 m s−1, and the maximum reduction is projected to occur over the central and western regions (>0.2 m s−1). Furthermore, future wind power density (WPD) can reveal nonstationarity and strong volatility, although a downward trend is expected during 2031–2050. In addition, the higher frequency of wind speeds at the turbine hub height exceeding 3.0 m s−1 can render the plain regions more suitable for wind energy development than the mountains from 2031 to 2050. This study can serve as a guide in gaining insights into future changes in wind energy across Central Asia and provide a scientific basis for decision makers in the formulation of policies for addressing climate change.

Short- and Long-Term Neurobehavioral Effects of Developmental Exposure to Valproic Acid in Zebrafish

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by impairments in social interaction and communication, anxiety, hyperactivity, and interest restricted to specific subjects. In addition to the genetic factors, multiple environmental factors have been related to the development of ASD. Animal models can serve as crucial tools for understanding the complexity of ASD. In this study, a chemical model of ASD has been developed in zebrafish by exposing embryos to valproic acid (VPA) from 4 to 48 h post-fertilization, rearing them to the adult stage in fish water. For the first time, an integrative approach combining behavioral analysis and neurotransmitters profile has been used for determining the effects of early-life exposure to VPA both in the larval and adult stages. Larvae from VPA-treated embryos showed hyperactivity and decreased visual and vibrational escape responses, as well as an altered neurotransmitters profile, with increased glutamate and decreased acetylcholine and norepinephrine levels. Adults from VPA-treated embryos exhibited impaired social behavior characterized by larger shoal sizes and a decreased interest for their conspecifics. A neurotransmitter analysis revealed a significant decrease in dopamine and GABA levels in the brain. These results support the potential predictive validity of this model for ASD research.

Listening to Your Own Willingness to Continue Using Contact Tracing Apps: The Roles of Personal Norms

COVID-19 became a global epidemic after its breakout in December 2019. With the help of information technology, many countries developed contact tracing systems to reduce the spread of the epidemic. Although the WHO declared in May 2023 that COVID-19 is no longer a global public health emergency, the virus is still constantly changing and endangering individuals worldwide. In Taiwan, Social Distancing APP is a voluntary contact tracing app that has been downloaded and utilized approximately 9.5 million times. However, adoption remains low. To prevent the spread of the epidemic and encourage more people to use it, understanding the factors influencing continuance intentions to use social distancing apps is an important topic. The research model is composed of two parts: the modified expectation confirmation model (ECM) and personal norms. Our research model was assessed using data from a sample of 501 users of social distancing apps and was analyzed using the partial least squares structural equation model (PLS-SEM). The results of this study suggest that (1) personal norms are the most important factor influencing continuance intention, followed by satisfaction, perceived usefulness, and descriptive norms; (2) personal norms fully mediate the relationship between injunctive norms and continuance intention; (3) personal norms partially mediate the relationship between descriptive norms and continuance intention; (4) personal norms negatively moderate the relationship between satisfaction and continuance intention; and (5) personal norms positively moderate the relationship between disappointment and continuance intention. This study also discusses the implications of a modified ECM and personal norms for continuance intention to use contact tracing apps.

Assessing the Nexus between Supplier and Customer Integration and Environmental Cost Performance: Insights into the Role of Digital Transformation

Since the beginning of the 21st century, environmental protection and digital supply chains have increasingly garnered attention. As firms transition to green practices, how to achieve competitive advantage by reducing environmental costs has become one of the key concerns for many business managers. This study aims to examine how supplier and customer integration affect a firm’s environmental cost performance while considering the moderating effect of digital transformation. Following a survey conducted by a professional research agency on 800 Chinese manufacturing firms specializing in green products, the research model was tested using structural equation modeling and hierarchical regression analysis. The results indicate a significant positive relationship between both supplier integration and customer integration with a firm’s environmental cost performance. Additionally, positive digital transformation reinforces the relationship between supplier integration and customer integration. However, there are limitations due to the study’s background, scope, and sample size. This study underscores the impact of supplier integration and customer integration on a firm’s environmental cost performance, as well as the crucial moderating role of digital transformation. It contributes to the growing literature on sustainable supply chain management and provides valuable insights for relevant practitioners.

MedPodGPT: A multilingual audio-augmented large language model for medical research and education.

The proliferation of medical podcasts has generated an extensive repository of audio content, rich in specialized terminology, diverse medical topics, and expert dialogues. Here we introduce a computational framework designed to enhance large language models (LLMs) by leveraging the informational content of publicly accessible medical podcast data. This dataset, comprising over 4, 300 hours of audio content, was transcribed to generate over 39 million text tokens. Our model, MedPodGPT, integrates the varied di-alogue found in medical podcasts to improve understanding of natural language nuances, cultural contexts, and medical knowledge. Evaluated across multiple benchmarks, MedPodGPT demonstrated an average improvement of 2.31% over standard open-source benchmarks and showcased an improvement of 2.58% in its zero-shot multilingual transfer ability, effectively generalizing to different linguistic contexts. By harnessing the untapped potential of podcast content, MedPodGPT advances natural language processing, offering enhanced capabilities for various applications in medical research and education.

Subgrid modeling for compound flooding in coastal systems

ABSTRACT Compound flooding, the concurrence of multiple flooding mechanisms such as storm surge, heavy rainfall, and riverine flooding, poses a significant threat to coastal communities. To mitigate the impacts of compound flooding, forecasts must represent the variability of flooding drivers over a wide range of spatial scales while remaining timely. One approach to develop these forecasts is through subgrid corrections, which utilize information at smaller scales to “correct” water levels and current velocities averaged over the model scale. Recent studies have shown that subgrid models can improve both accuracy and efficiency; however, existing models are not able to account for the dynamic interactions of hydrologic and hydrodynamic drivers and their contributions to flooding along the smallest flow pathways when using a coarse resolution. Here, we have developed a solver called CoaSToRM (Coastal Subgrid Topography Research Model) with subgrid corrections to compute compound flooding in coastal systems resulting from fluvial, pluvial, tidal, and wind-driven processes. A key contribution is the model’s ability to enforce all flood drivers and use the subgrid corrections to improve the accuracy of the coarse-resolution simulation. The model is validated for Hurricane Eta 2020 in Tampa Bay, showing improved prediction accuracy with subgrid corrections at 42 locations. Subgrid models with coarse resolutions (R2 = 0.70, 0.73, 0.77 for 3-, 1.5-, 0.75-km grids) outperform standard counterparts (R2 = 0.03, 0.14, 0.26). A 3-km subgrid simulation runs roughly 50 times faster than a 0.75-km subgrid simulation, with similar accuracy.

Loss of cped1 does not affect bone and lean mass in zebrafish.

Human genetic studies have nominated Cadherin-like and PC-esterase Domain-containing 1 (CPED1) as a candidate target gene mediating bone mineral density (BMD) and fracture risk heritability. Recent efforts to define the role of CPED1 in bone in mouse and human models have revealed complex alternative splicing and inconsistent results arising from gene targeting, making its function difficult to interpret. To better understand the role of CPED1 in adult bone mass and morphology, we turned to zebrafish, an emerging model for orthopaedic research. We analyzed two different cped1 mutant lines and performed deep phenotyping to characterize more than 200 measures of adult vertebral, craniofacial, and lean tissue morphology. We also examined alternative splicing of zebrafish cped1 and gene expression in various cell/tissue types. Our studies fail to support an essential role of cped1 in adult zebrafish bone. Specifically, homozygous mutants for both cped1 mutant alleles, which are expected to result in loss-of-function and impact all cped1 isoforms, exhibited no significant differences in the measures examined when compared to their respective wildtype controls, suggesting that cped1 does not significantly contribute to these traits. We identified sequence differences in critical residues of the catalytic triad between the zebrafish and mouse orthologs of CPED1, and discuss how these differences, as well as distinct alternative splicing, could underlie different functions of CPED1 orthologs in the two species. Our studies demonstrate that cped1 is not required for normal adult zebrafish bone mass, lean mass, or bone and lean mass morphology, adding to evidence that variants at 7q31.31 can act independently of CPED1 to influence BMD and fracture risk.

The transcriptome of MHV-infected RAW264.7 cells offers an alternative model for macrophage innate immunity research.

Macrophages are the primary innate immune cells encountered by the invading coronaviruses, and their abilities to initiate inflammatory reactions, to maintain the immunity homeostasis by differential polarization, to train the innate immune system by epigenic modification have been reported in laboratory animal research. In the current in vitro research, murine macrophage RAW 264.7 cell were infected by mouse hepatitis virus, a coronavirus existed in mouse. At 3-, 6-, 12-, 24-, and 48-h post infection (hpi.), the attached cells were washed with PBS and harvested in Trizol reagent. Then The harvest is subjected to transcriptome sequencing. The transcriptome analysis showed the immediate (3 hpi.) up regulation of DEGs related to inflammation, like Il1b and Il6. DEGs related to M2 differential polarization, like Irf4 showed up regulation at 24 hpi., the late term after viral infection. In addition, DEGs related to metabolism and histone modification, like Ezh2 were detected, which might correlate with the trained immunity of macrophages. The current in vitro viral infection study showed the key innated immunity character of macrophages, which suggested the replacement value of viral infection cells model, to reduce the animal usage in preclinical research.