Structural Relationships Research Articles

Serotonin enhances neurogenesis biomarkers, hippocampal volumes, and cognitive functions in Alzheimer’s disease

Research on serotonin reveals a lack of consensus regarding its role in brain volume, especially concerning biomarkers linked to neurogenesis and neuroplasticity, such as ciliary neurotrophic factor (CNTF), fibroblast growth factor 4 (FGF-4), bone morphogenetic protein 6 (BMP-6), and matrix metalloproteinase-1 (MMP-1) in Alzheimer’s disease (AD). This study aimed to investigate the influence of serotonin on brain structure and hippocampal volumes in relation to cognitive functions in AD, as well as its link with biomarkers like CNTF, FGF-4, BMP-6, and MMP-1. Data from 133 ADNI participants with AD included cognitive assessments (CDR-SB), serotonin measurements (Biocrates AbsoluteIDQ p180 kit, UPLC-MS/MS), and neurotrophic factors quantified via multiplex proteomics. Gray matter volume changes were analyzed using Voxel-Based Morphometry (VBM) with MRI. Statistical analyses employed Pearson correlation, bootstrap methods, and FDR-adjusted p-values (< 0.05 or < 0.01) via the Benjamini–Hochberg procedure, alongside nonparametric methods. The analysis found a positive correlation between serotonin levels and total brain (r = 0.229, p = 0.023) and hippocampal volumes (right: r = 0.186, p = 0.032; left: r = 0.210, p = 0.023), even after FDR adjustment. Higher serotonin levels were linked to better cognitive function (negative correlation with CDR-SB, r = −0.230, p = 0.024). Notably, serotonin levels were positively correlated with BMP-6 (r = 0.173, p = 0.047), CNTF (r = 0.216, p = 0.013), FGF-4 (r = 0.176, p = 0.043), and MMP-1 (r = 0.202, p = 0.019), suggesting a link between serotonin and neurogenesis and neuroplasticity. However, after adjusting for multiple comparisons and controlling for confounding factors such as age, gender, education, and APOE genotypes (APOE3 and APOE4), none of the correlations of biomarkers remained statistically significant. In conclusion, increased serotonin levels are associated with improved cognitive function and increased brain volume. However, associations with CNTF, FGF-4, BMP-6, and MMP-1 were not statistically significant after adjustments, highlighting the complexity of serotonin’s role in AD and the need for further research.Graphical

Functional and structural cerebellar-behavior relationships in aging.

Healthy aging is associated with deficits in cognitive performance and brain changes, including in the cerebellum. Cerebellar communication with the cortex via closed-loop circuits through the thalamus have been established and these circuits are closely related to the functional topography of the cerebellum. In this study, we sought to elucidate relationships between cerebellar structure and function with cognition in healthy aging. We explored this relationship in 138 healthy adults (aged 35-86, 53% female) using resting-state functional connectivity MRI (fcMRI), cerebellar volume, and cognitive and motor assessments. Behavioral tasks assessed attention, processing speed, working memory, episodic memory, and motor abilities. We expected to find negative relationships between lobular volume with age, and positive relationships between specific lobular volumes with motor and cognitive behavior, respectively. We predicted lower cerebello-cortical fcMRI with increased age. Behaviorally, we expected higher cerebello-frontal and cerebello-association area fcMRI cerebellar connectivity to correlate with better behavioral performance. Correlations were conducted between cerebellar lobules I-IV, V, Crus I, Crus II, vermis VI and behavioral measures. We found lower volumes with increased age as well as both higher and lower cerebellar connectivity relationships with increased age, consistent with literature on functional connectivity and network segregation in aging. Further, we revealed unique associations between cerebellar structure and connectivity with comprehensive behavioral measures in a healthy aging population. Our findings further highlight the role of the cerebellum in aging.

Exploring the determinants of malnutrition in 2–5 year Iranian children using structural equation modeling: national food and nutrition surveillance

BackgroundChildhood malnutrition remains a critical public health challenge in low- and middle-income countries, contributing significantly to morbidity and mortality among children aged 2–5 years. This study was undertaken to assess the nutritional status of 2–5 y children and to explore the main determinants of child malnutrition in eight food insecure provinces of Iran.MethodsIn each province, participants were invited to attend the health house/center to complete the questionnaire on the pre-appointed day. In this study, an android application comprising electronic questionnaires was employed for data collection. Anthropometric, dietary, food security and socioeconomic status (SES) assessments were performed. Structural equation modeling (SEM) was done to assess the structural relationship of malnutrition indicators (z-score of height to age (HAZ), z-score of weight to age (WAZ) and z-score of weight to height (WHZ)) with other variables.ResultsOverall, 2247 children aged 42.2 ± 0.3 months were enrolled in the study, of whom 1048 (46.6%) were female and 1438 (64%) were urban residents. Based on Z score criteria, 216 (10.1%) of the studied children had less than − 2SD HAZ, 193 (8.4%) had lower than − 2SD WHZ and 188 (8.1%) had lower than − 2SD WAZ. The prevalence rates of stunting, wasting and underweight were not significantly different between boys and girls. Nevertheless, the occurrence of stunting was significantly higher in children residing in rural than in urban areas (p = 0.025). SEM analysis revealed that higher SES had a positive relationship with HAZ (0.089), WAZ (0.163) and WHZ (0.109). The effect of SES was greatest on WAZ, indicated by the highest absolute value of a path coefficient. There were specific indirect effects of father’s education on HAZ (0.032, p = 0.001), WHZ (0.045, < 0.001) and WAZ (0.061, < 0.001) through effect on SES and DDS. SES had a direct effect on DDS (0.202).ConclusionsOur findings using SEM approach provided more concrete evidence for the effect of the household’s SES on child’s nutritional status. Nevertheless, we still need to monitor the studied population in the context of our surveillance program to document more conclusive causal associations.

Continued engagement intention with social media influencers: the role of experience

PurposeThe emergence of social media platforms has revolutionized how brands develop partnerships with social media influencers (SMIs). However, users are seeking more meaningful engagement with SMIs, and little is known about how brands can shift their focus from transient engagements to continued engagement that builds long-term brand–consumer relationships. Extant research has provided inconsistent findings regarding consumer engagement behavior. To address this knowledge deficit, we contribute to the consumer engagement literature by developing and testing a conceptual model that explores and explains the relationships between the factors that influence continued engagement intention (CEI), a form of behavioral intention.Design/methodology/approachA literature review was conducted to identify gaps and develop a theoretically informed conceptual model and hypotheses. Survey data from 604 Instagram SMI followers were analyzed using partial least squares structural equation modeling using SmartPLS 3.3.3 to assess the structural model relationships and conduct post hoc analysis.FindingsThe findings suggest that it is important to positively influence consumer responses to elicit CEI. Furthermore, homophily attitudes toward SMIs moderate the relationship between SMI experience and CEI.Practical implicationsBrands must work with SMIs to create positive SMI experiences and develop CEI. Furthermore, SMIs should focus on brands that fit their lifestyles to enhance homophily attitudes and forge CEI.Originality/valueThis study contributes to the literature by combining social exchange and flow theories to develop and test a holistic framework for examining CEIs regarding SMIs and brands. The findings show that creating positive SMI experiences benefits brands seeking CEI.

Molecular Modeling Studies of β-aminoacyl containing Homopiperazine derivatives as DPP4 Inhibitors

Dipeptidyl peptidase IV (DPP4) is a promising target for developing novel anti-diabetic and anti-obesity drugs. Pharmacophore based three dimensional quantitative structure activity relationship studies (3D-QSAR) and molecular docking were performed on a series of 56 β-aminoacyl-containing homopiperazine derivatives of DPP4 inhibitors to find out the structural relationship with the activity. The best predictive 3D-QSAR model with pharmacophore based alignment resulted in R2 (training set) value of 0.9407, Q2 (internal test set) value of 0.9053, Pearson-R value of 0.9517 and root mean square error (RMSE) 0.2838. The information rendered by pharmacophore based 3D-QSAR models and docking studies may afford valuable clues to optimize the lead and design of potent, selective inhibitors.

Intrinsic disordered nature and prediction of secondary structures of pinoresinol lariciresinol reductases 2 in Flax (Linum usitatissimum)

Flax (Linum usitatissimum) contains a wide range of essential nutrients, such as proteins, polyunsaturated fatty acids (PUFAs), phenolic compounds, fibers, flavonoids, and lignans. The production of lignans in flax is mainly caused by pinoresinol-lariciresinol reductases (PLRs). Amongst these, PLR2s play a pivotal function by contributing to the further reduction of lariciresinol to secoisolariciresinol in the biosynthesis of lignans. However, the in-silico analysis of PLR2 gene family interlinking the intrinsic disordered nature and secondary structures that could support this hypothesis is still missing in Flax. Herein, we present the first study of Intrinsic disorder proteins (IDPs) in flax to gain an understanding of their biological functions. This study was conducted from June to August 2024 at the Department of Biochemistry, Lovely Professional University, Phagwara, Punjab, India. The flax genome assembly from Phytozome was used to retrieve 30 PLR2 genes which encode for 30 PLR2 proteins. Further, we used PONDR database to identify the intrinsic disordered nature of the proteins while GOR database was used to predict the secondary structures. For identifying the amino acid composition, Protparam database was used. The results of PONDR database revealed that all the proteins are somewhat disordered whereas amino acid composition marked the presence of disorder-promoting residues in almost all the proteins. Secondary structure predictions by GOR revealed the presence of coils, helices and strands. To relate the structural functional relationship, it is of the utmost importance to have an in-depth knowledge of the balance between intrinsic disorder and secondary structure as this will provide key insights into the dynamic functionality and regulatory mechanisms that are the foundation of complex biological processes.

Morphology of the Human Pineal Gland Studied by Freeze-Fracturing in Scanning Electron Microscopy

The human pineal gland is the largest producer of the hormone melatonin. Pineal acervuli (brain sand), calcified concretions in the pineal gland, have long been studied because of their association with ageing, melatonin production, and neurological disorders. The solid inorganic matter of the hydroxyapatite crystals often renders sample sectioning impossible, to the extent that the sections lose value. Technically, freeze-fracturing has revealed the detailed structure and cell relationships without tissue damage. In our electron microscopic study, samples of the human pineal gland were obtained during autopsy from 20 donors with mean age 69 years. Samples underwent freeze-fracturing and standard histological procedures, and were analysed by scanning electron microscopy (SEM) in high vacuum. Based on our results, freeze-fracturing enabled identification of a mulberry-like acervulus topography. The acervuli were situated in specific “nest-like” structures, which were surrounded by pinealocytes, interstitial cells, and nerve fibres. A fractured surface of the intrapineal acervuli exhibited a regular lamellar structure. Freeze-fracturing the pineal gland and imaging by SEM enabled complex structural analysis. This approach permits viewing the surface acervuli spherical and internal lamellar architecture. Our results confirmed that the parenchyma of this small but important gland contains two types of acervuli, depending on their size: non-aggregated and aggregated. We propose to include these forms of acervuli in the new edition of the Terminologica Histologica. In conclusion, pineal gland freeze-fracturing by SEM is suitable for complex structural analysis. Our description of our methods can be a guide for other scientists who want to study the pineal gland with electron microscopy methods.

Clustering explanation based on multi-hyperrectangle

Clustering plays a crucial role in data mining and pattern recognition, but the interpretation of clustering results is often challenging. Existing interpretation methods usually lack an intuitive and accurate description of irregular shapes and high dimensional datas. This paper proposes a novel clustering explanation method based on a Multi-HyperRectangle(MHR), for extracting post hoc explanations of clustering results. MHR first generates initial hyperrectangles to cover each cluster, and then these hyper-rectangles are gradually merged until the optimal shape is obtained to fit the cluster. The advantage of this method is that it recognizes the shape of irregular clusters and finds the optimal number of hyper-rectangles based on the hierarchical tree structure, which discovers structural relationships between rectangles. Furthermore, we propose a refinement method to improve the tightness of the hyperrectangles, resulting in more precise and comprehensible explanations. Experimental results demonstrate that MHR significantly outperforms existing methods in both the tightness and accuracy of cluster interpretation, highlighting its effectiveness and innovation in addressing the challenges of clustering interpretation.

Genetic diversity and population structure analysis of 418 tomato cultivars based on single nucleotide polymorphism markers.

Tomato (Solanum lycopersicum) is a highly valuable fruit crop. However, due to the lack of scientific and accurate variety identification methods and unified national standards, production management is scattered and non-standard, resulting in mixed varieties. This poses considerable difficulties for the cataloging and preservation of germplasm resources as well as the identification, promotion, and application of new tomato varieties. To better understand the genetic diversity and population structure of representative tomato varieties, we collected 418 tomato varieties from the past 20 years and analyzed them using genome-wide single nucleotide polymorphism (SNP) markers. We initially assessed the population structure, genetic relationships, and genetic profiles of the 418 tomato germplasm resources utilizing simplified genome sequencing techniques. A total of 3,374,929 filtered SNPs were obtained and distributed across 12 chromosomes. Based on these SNP loci, the 418 tomatoes samples were divided into six subgroups. The population structure and genetic relationships among existing tomato germplasm resources were determined using principal component analysis, population structure analysis, and phylogenetic tree analysis. Rigorous selection criteria identified 15 additional high-quality DNA fingerprints from 50 validated SNP loci, effectively enabling the identification of the 418 tomato varieties, which were successfully converted into KASP (Kompetitive Allele Specific PCR) markers. This study represents the first comprehensive investigation assessing the diversity and population structure of a large collection of tomato varieties. Overall, it marks a considerable advancement in understanding the genetic makeup of tomato populations. The results broadened our understanding of the diversity, phylogeny, and population structure of tomato germplasm resources. Furthermore, this study provides a scientific basis and reference data for future analysis of genetic diversity, species identification, property rights disputes, and molecular breeding in tomatoes.

A hybrid dynamic graph neural network framework for real-time anomaly detection

ABSTRACT The timely and robust detection of anomalies is essential for resilient and secure operations of critical water infrastructures against operational faults or malicious actions. However, real-world systems exhibit diverse and evolving spatiotemporal relationships among their components, posing an intricate challenge in anomaly detection task. This study proposes a hybrid dynamic graph neural network (GNN) that jointly maps long- and short-term spatiotemporal relationships in multivariate data streams. Those relationships are encoded via a hybrid graph, comprising an optimally learned static subgraph for persistent structural relationships and a complementary dynamic subgraph for dynamically shifting relationships. Additionally, an attention mechanism captures time-varying relational importances and shifts the model's focus towards significant relationships, while minimising contributions of less importance to the final outputs. The proposed architecture is showcased through a synthetic case study of a water distribution system with multivariate data streams from both on-site and soft sensors, and shows strong detection and localisation accuracy against all scenarios of operational faults and malicious actions explored. A comparative analysis with an equivalent static graph model indicates that the addition of the hybrid dynamic component enhances detection accuracy and reduces false alarm rates through more robust characterisation of behaviours, thus allowing actionable insights for more resilient and secure operations.

Combined tuned mass damper and vibro-impacting nonlinear energy sink for the seismic protection of frame structures

It is well established that, for tuned mass dampers, a high mass ratio relative to the protected structure is essential for effective performance under seismic excitation in most scenarios. This paper proposes a novel method to enhance the seismic effectiveness of a tuned mass damper without increasing the mass ratio. The proposed approach introduces an innovative device in which a tuned mass damper is serially coupled with a vibro-impacting mass that slides with negligible friction before impacting the boundaries of its runway. This system is then connected to the first storey of the frame structure via a Kelvin-Voigt visco-elastic device. The study employs a dynamically equivalent four-degree-of-freedom model, with its governing equations derived through a direct formulation approach. Initially, the coupled system is subjected to harmonic excitation, and the resulting behaviour is represented using frequency-response curves, which depict the maximum displacements of the structure in relation to the excitation frequency. Following this, a subsequent analysis involves subjecting the system to three distinct earthquake records to evaluate its performance under seismic excitation. To evaluate the effectiveness of the proposed protective device in reducing the displacements of the frame structure during seismic events, the displacements of the first storey and the drifts of the superstructure are compared to those of a frame structure without the external device. The findings indicate that the proposed device performs effectively across a wide range of system parameters, proving to be especially effective for low- and medium-rise frame structures.

Structural relationships between psychological factors and college adjustment among medical students in South Korea: focusing on helicopter parenting and respectful parenting.

This study aims to determine the impact of helicopter parenting and respectful parenting on medical students' mindset, grit, self-directedness, and college adjustment. This study constructed a hypothetical model based on the relationship between helicopter parenting, respectful parenting, mindset, grit, self-directedness, and college adjustment. It set up a structural model to test the fit of the model based on empirical data. The significance of the mediating effects of the paths was tested using multiple mediation analysis. The fit of the initial measurement model did not meet the goodness-of-fit acceptance criteria; therefore, revised models were established. The revised models all showed good fit indices, and the overall path coefficients were significant. Helicopter parenting and respectful parenting are inversely correlated and affect the mindset of medical students, which, in turn, affects their adjustment to college through grit and self-directedness. The results of the multimodal effects of each pathway showed that helicopter parenting negatively affects college adjustment, while respectful parenting has a positive mediating effect on college adjustment. Parenting attitudes have a significant impact on medical students' mindset, grit, and self-direction, which affects their college adjustment. Parents need to respect their children's autonomy and independence and avoid excessive interference.

Microstructural material design of pearlitic steel lamella for desired mechanical properties

Designing materials for a targeted mechanical property is a complex problem due to its structure and property relationship nature, traditionally relying on experimental methods. Computational approaches offer a promising alternative, but they are often computationally intensive. This limits their applicability for solving the goal-based structure design problem. This study focuses on the design of microstructure to achieve specific mechanical properties by combining generative adversarial networks, deep neural networks, and genetic algorithm optimization techniques. In this work to demonstrate the effectiveness of the proposed approach we select a widely used pearlitic steel lamella microstructure where the phase fraction of ferrite and cementite and the lamella distribution significantly influence the strength and ductility. A method for designing the pearlite microstructure features for the targeted mechanical properties, such as yield strength (σy), ultimate strength (σut), and fracture strain (εf) is developed by applying inverse microstructure design techniques to achieve the targeted mechanical properties, specifically in fully pearlitic steel materials. The findings hold great promise, particularly when coupled with additive manufacturing, in offering an efficient and versatile framework for structural materials design for targeted material applications.

MM-DRPNet: A Multimodal Dynamic Radial Partitioning Network for Enhanced Protein–Ligand Binding Affinity Prediction

Accurate prediction of drug-target binding affinity remains a fundamental challenge in contemporary drug discovery. Despite significant advances in computational methods for protein-ligand binding affinity prediction, current approaches still face substantial limitations in prediction accuracy. Moreover, the prevalent methodologies often overlook critical three-dimensional (3D) structural information, thereby constraining their practical utility in computer-aided drug design (CADD). Here we present MM-DRPNet, a multimodal deep learning framework that enhances binding affinity prediction by integrating protein-ligand structural information with interaction features and physicochemical properties. The core innovation lies in our dynamic radial partitioning (DRP) algorithm, which adaptively segments 3D space based on complex-specific interaction patterns, surpassing traditional fixed partitioning methods in capturing spatial interactions. MM-DRPNet further incorporates molecular topological features to comprehensively model both structural and spatial relationships. Extensive evaluations on benchmark datasets demonstrate that MM-DRPNet significantly outperforms state-of-the-art methods across multiple metrics, with ablation studies confirming the substantial contribution of each architectural component. Source code for MM-DRPNet is freely available for download at https://github.com/Bigrock-dd/MMDRPv1.

Multiple ancestral components and complex origins of the Yugur people in Gansu province revealing by 35 Y-STR.

The Yugur people represent one of the ethnic groups residing within the Hexi Corridor, distinguishable by their small population size, linguistic diversity, intricate ancestral components, serving as a quintessential exemplar of the populations inhabiting this corridor. There are still many controversial issues in the academic community regarding the origin, migration, and formation process of the Yugur. In this study, we explored the formation process of the Yugur from the perspective of molecular anthropology, based on the paternal genetic characteristics of the Yugur people. And the study will synthesize multiple disciplines, encompassing ethnology, history and linguistics, in order to offer a thorough analysis. Within this research endeavor, a high-resolution kit comprising 35 Y-STRs was employed to examine 237 male specimens from the Yugur people in Gansu province. Y-SNP haplogroups were deduced through the utilization of Y-STR data. The paternal genetic data from diverse populations documented in published literature were merged to construct a 16 Y-STR dataset, a 25 Y-STR dataset, and a dataset detailing haplogroup frequencies. In this study, we employed haplotype network analysis, principal component analysis, multidimensional scaling analysis, phylogenetic tree construction, and genetic distance calculations to delve into the genetic structure, haplotype distribution, and genetic relationship with neighboring populations of the Yugur people. The findings of this study reveal that the Yugur people are a blend of ancestral lineages from both Eastern and Western Eurasian origins, with approximately 13% of their genetic component traced back to Western Eurasian populations. The Yugur people in Gansu province exhibits a more intimate genetic relationship with the Han, Tibetan, and Mongolian populations inhabiting nearby regions, while showing distinct genetic differences with Turkic-speaking groups like the Uyghur. Based on the merged data, we identified Q1b1a3-L330 and R1a1a-M17 shared with Turkic-speaking people, C2a1a1-F1756, C2a1a3-M504, C2a1a3a-F3796, C2a1a2-M48 and C2b1a1a1a-M407 shared with Mongolic-speaking people, D1a1a-M15 and D1a1b1-P47 shared with Tibetans, and multiple paternal lineages shared with Han people, which are the main paternal lineages of Yugur people, indicating multiple ancestral components and complex origins of Yugur. In this study, we provided a clearer genetic landscape which further supports the formation process and population characteristics of the Yugur people recorded in history, ethnology, and linguistics, and lays the foundation for more detailed studies on population genetics and forensic genetics in the future.

Improving the Process of Managing Psychosocial Risks in Organizations.

Psychosocial risks (PSRs) are identified as one of the main modern occupational safety issues, primarily related to occupational stress, and need to be reduced to safe levels in accordance with international requirements. The research purpose is to improve the process of managing the PSRs in the occupational safety and health management systems of employees, taking into account the impact of psychosocial dangers in accordance with the requirements of ISO 45001:2018 and ISO 45003:2021 standards. To develop the process of managing the PSRs, a system analysis method is applied, which allows determining the structural relationships between the variable elements of dangerous psychosocial factors described in the ISO 45003:2021 standard. The bow-tie model has been improved to determine the relationship between psychosocial dangers and the dangerous event occurrence - experiencing stress by an employee, taking into account the influence of various dangerous psychosocial factors, which provides for an additional risk level assessment for restoring the health of an employee who has experienced stress. The process of psychosocial risk management consisting of ten steps is proposed, which involves identifying the relationship between psychosocial dangers and consequences for human health, taking into account the impact of various dangerous psychosocial factors based on questionnaires, followed by analysis and processing of the duration and intensity of experiencing stress. To reduce the impact of subjective assessments of the duration and intensity of experiencing stress on the psychosocial risk level, it is proposed to take into account the value of individual perception of experiencing stress (stress resistance) and the employees' health condition. The scientific novelty is to determine the psychosocial risk level as the sum of the risk values from the impact of each dangerous psychosocial factor, which are characteristic of aspects of work organization, social conditions at work, working environment, equipment, dangerous tasks and the level of employee health. The practical value is the development of scales for assessing the impact of dangerous psychosocial factors and a form for documenting the process of managing psychosocial risks with the indication of precautionary measures to control them.

성인평생학습자의 자기성찰과 시민역량 간의 구조적 관계: 대인관계능력과 비판적 사고의 간접효과

The purpose of this study is to analyze the indirect effects of interpersonal skills and critical thinking based on the structural relationship between self-reflection and civic competency of adult lifelong learners. To this end, data were processed using SPSS 27.0 and Amos 27.0 programs based on the data surveyed on 799 adult lifelong learners living across the country. Frequency analysis was performed to examine demographic characteristics, descriptive statistics to find out the normality distribution of each variable, Cronbach's α to verify the reliability of the measurement tool, and confirmatory factor analysis was performed to verify the validity. In addition, correlation analysis was performed to examine the correlation between research variables and structural equation model analysis to verify the hypothesis. As a result of the study, it was found that self-reflection had a significant positive (+) effect on civic competency, interpersonal ability, and critical thinking. Second, it was found that interpersonal skills had a significant effect on critical thinking and civic competency, and critical thinking had a significant effect on civic competency. Third, it was tested that interpersonal skills and critical thinking had a significant indirect effect between self-reflection and civic competency. Therefore, in this study, interpersonal skills and critical thinking are important to improve the self-reflection and civic competence of adult lifelong learning, and educational support is needed to improve lifelong education policy directions and programs.

Structural Relationship between Information Attributes, System Quality, Satisfaction, and Intention to Use of Airbnb: Information System Success Model Approach

Structural Relationship between Information Attributes, System Quality, Satisfaction, and Intention to Use of Airbnb: Information System Success Model Approach

The Structural Relationship among Social Support, Relative Deprivation and Hopelessness: Focused on the Men in their 50s ", Asia-pacific Journal of Convergent Research Interchange, KCTRS, ISSN : 2508-9080 (Print); 2671

The Structural Relationship among Social Support, Relative Deprivation and Hopelessness: Focused on the Men in their 50s ", Asia-pacific Journal of Convergent Research Interchange, KCTRS, ISSN : 2508-9080 (Print); 2671

중국 평생학습자의 자기주도적 학습과 주관적 행복감 관계에서 참여동기와 자기효능감의 매개효과 분석

The purpose of this study is to analyze the structural relationships between self-directed learning, participation motivation, self-efficacy, and subjective happiness among Chinese lifelong learners. The study aims to identify the factors influencing the subjective happiness of adult learners and highlight the need for policy support to enhance it. The research was conducted through an online survey targeting lifelong learners in Shanxi Province, China, collecting 754 valid responses. Statistical analysis was performed using SPSS 28.0 and Amos 28.0. The study found, first, that self-directed learning positively impacts subjective happiness. The more learners engage in self-directed learning, the higher their level of happiness, and their participation motivation and self-efficacy are also strengthened. This suggests that as learners become more immersed in their learning, their motivation and confidence increase, leading to a positive impact on subjective happiness. Second, participation motivation had a positive effect on self-efficacy, and self-efficacy was identified as an important factor in enhancing subjective happiness. The higher the learner’s motivation to participate in learning, the more their self-efficacy improves, which, in turn, contributes to the increase in happiness. Third, the analysis revealed an indirect effect where participation motivation and self-efficacy mediate the relationship between self-directed learning and subjective happiness. Self-directed learning not only directly increases happiness but also indirectly boosts subjective happiness through participation motivation and self-efficacy. This study emphasizes the importance of self-directed learning in improving the subjective happiness of adult learners in China. It also suggests that educational programs and policy support are needed to enhance participation motivation and self-efficacy, which play critical roles in promoting happiness.