Different Types Of Relationships Research Articles

A heterogeneous traffic spatio-temporal graph convolution model for traffic prediction

Smart cities require advanced traffic management systems. Traffic forecasting is an essential task of the advanced transportation system. Traffic spatio-temporal data are often heterogeneous. Most existing traffic prediction models predominantly use separate components to extract the temporal and spatial features of traffic data. However, this overlooks the intrinsic connections between the spatio-temporal features of traffic data. To directly mine the spatio-temporal heterogeneity, this study constructs a global heterogeneous traffic spatio-temporal graph and proposes the Heterogeneous Traffic Spatio-Temporal Graph Convolution (HTSTGC). To reduce the complexity of the model, Simple Graph Convolution (SGC) is used to extract semi-structured meta-graph information. The receptive fields that capture temporal and spatial features can be flexibly adjusted separately through clever design, which can balance the performance and efficiency of the model. Finally, the feature fusion module applies Gated Graph Neural Network (GGNN) to fuse temporal and spatial features. The results on the PEMS datasets reveal that jointly modeling different types of relationships can improve the traffic prediction performance of the model. The proposed HTSTGC has better performance than the baseline methods in most cases. The research results can support urban traffic control, traffic pollution reduction, and sustainable urban development.

Probing the Representational Structure of Regular Polysemy via Sense Analogy Questions: Insights from Contextual Word Vectors.

Regular polysemes are sets of ambiguous words that all share the same relationship between their meanings, such as CHICKEN and LOBSTER both referring to an animal or its meat. To probe how a distributional semantic model, here exemplified by bidirectional encoder representations from transformers (BERT), represents regular polysemy, we analyzed whether its embeddings support answering sense analogy questions similar to "is the mapping between CHICKEN (as an animal) and CHICKEN (as a meat) similar to that which maps between LOBSTER (as an animal) to LOBSTER (as a meat)?" We did so using the LRcos model, which combines a logistic regression classifier of different categories (e.g., animal vs. meat) with a measure of cosine similarity. We found that (a) the model was sensitive to the shared structure within a given regular relationship; (b) the shared structure varies across different regular relationships (e.g., animal/meat vs. location/organization), potentially reflective of a "regularity continuum;" (c) some high-order latent structure is shared across different regular relationships, suggestive of a similar latent structure across different types of relationships; and (d) there is a lack of evidence for the aforementioned effects being explained by meaning overlap. Lastly, we found that both components of the LRcos model made important contributions to accurate responding and that a variation of this method could yield an accuracy boost of 10% in answering sense analogy questions. These findings enrich previous theoretical work on regular polysemy with a computationally explicit theory and methods, and provide evidence for an important organizational principle for the mental lexicon and the broader conceptual knowledgesystem.

Characterization of cellular responses and cell lysis to elevated hydrodynamic stress from benchtop perfusion bioreactors.

The effective design of perfusion cell culture is currently challenging regarding balancing the operating parameters associated with the hydrodynamic conditions due to increased system complexity. To address this issue, cellular responses of an industrial CHO cell line to different types of hydrodynamic stress in benchtop perfusion bioreactors originating from agitation, sparging, and hollow fibers (HF) in the cell retention devices were systematically investigated here with the analysis of cell lysis. It was found that cell lysis was very common and most associated with the sparging stress, followed by the HF and lastly the agitation, consequently heavily impacting the estimation of process descriptors related to biomass. The results indicated that the agitation stress led to a reduced cell growth with a shift toward a more productive phenotype, suggesting an energy redirection from biomass formation to product synthesis, whereas the sparging stress had a small impact on the intracellular metabolic flux distribution but increased the cell death rate drastically. For HF stress, a similar cell maintenance profile was found as the sparging while the activity of glycolysis and the TCA cycle was significantly impeded, potentially leading to the lack of energy and thus a substantial decrease in cell-specific productivity. Moreover, a novel concept of volume average shear stress was developed to further understand the relations of different types of stress and the observed responses for an improved insight for the perfusion cell culture.

Responsiveness, Social Connection, Hope, and Life Satisfaction in Everyday Social Interaction: An Experience Sampling Study

What are the specific everyday communication experiences—from across people’s social networks—that contribute to well-being? In the present work, we focus on the effects of perceived partner responsiveness in social interactions on various well-being outcomes. We hypothesized that everyday moments of responsiveness indirectly support two key estimates of well-being (hope and life satisfaction) through feelings of social connection. Data were obtained in an experience sampling study collected across ten days (N = 120). Results of dynamic structural equation modeling (DSEM) showed that responsive interaction predicted increases in hope (but not life satisfaction) through social connection. Results also identified reciprocal within-person links between responsive interaction and social connection throughout the day. These findings underscore the importance of responsive everyday communication for fostering social connection across different types of relationships and for supporting people’s capacity for a hopeful life. We discuss the implications of these results for continued research of responsiveness, hope theory, and well-being from a social interaction lens. On a practical level, the mediation pathway involving hope suggests how small changes in our patterns of everyday social interaction can be consequential to the quality of our lives.

Parallel cognitive maps for multiple knowledge structures in the hippocampal formation.

The hippocampal-entorhinal system uses cognitive maps to represent spatial knowledge and other types of relational information. However, objects can often be characterized by different types of relations simultaneously. How does the hippocampal formation handle the embedding of stimuli in multiple relational structures that differ vastly in their mode and timescale of acquisition? Does the hippocampal formation integrate different stimulus dimensions into one conjunctive map or is each dimension represented in a parallel map? Here, we reanalyzed human functional magnetic resonance imaging data from Garvert et al. (2017) that had previously revealed a map in the hippocampal formation coding for a newly learnt transition structure. Using functional magnetic resonance imaging adaptation analysis, we found that the degree of representational similarity in the bilateral hippocampus also decreased as a function of the semantic distance between presented objects. Importantly, while both map-like structures localized to the hippocampal formation, the semantic map was located in more posterior regions of the hippocampal formation than the transition structure and thus anatomically distinct. This finding supports the idea that the hippocampal-entorhinal system forms parallel cognitive maps that reflect the embedding of objects in diverse relational structures.

The Needs of Adolescents to Communicate with Their Parents in Online and Offline Formats: The Creation and Validation of a Questionnaire

Background. Modern society is characterized by the widespread use of social media, which provides users with new communication, leisure, work, and study opportunities. The growth of online communication time leads to the development of new special communicative needs. These circumstances prompted us to develop the “Questionnaire on Adolescents’ Needs to Communicate with Their Parents Online and Offline” (QANCP). Objective. We set out to study the relationship between adolescents’ hierarchy of communicative needs and the characteristics of child-parent relations by developing and testing the psychometric characteristics of the QANCP. Design. One hundred and twenty-eight teenagers (56 boys and 72 girls, age 15-17) took part in the research. The research methods included theoretical analysis, modeling, questionnaires, and statistical analysis. The tools used were: 1) the customized (authorial) “Questionnaire of adolescents’ needs to communicate with their parents online and offline” (QANCP); and 2) the Inventory of Parent and Peer Attachment (IPPA) by Armsden and Greenberg (1987) in the Sadovnikova and Eliseeva adaptation (Eliseeva, 2013). Results. The results of the qualitative research proved the acceptability of the QANCP scales. Expert assessments showed that the test had sufficient face and content validity. Statistically significant differences in the parameters measuring adolescents’ communicative needs in groups with different types of relations with their parents were established. Conclusion. Testing of the QANCP questionnaire showed that the QANCP is a valid and reliable instrument to measure adolescents’ communication with their parents in online and offline formats. It was established that there is a connection between the characteristics of child-parent relations in adolescence and the hierarchy of communication needs of “adolescent-parent” communication online and offline.

Training an AI-based Writing Assistant for Spanish Learners: The Usefulness of Chatbots and the Indispensability of Human-assisted Intelligence

This article deals with the relationship between human and artificial intelligence in the context of an ongoing Spanish Writing Assistant project, where ChatGPT is used to assist in four key tasks related to either training the underlying language model or preparing future user communication. The project is an interdisciplinary collaboration between lexicographers with experience in language teaching and IT experts from a high-tech company. The article first describes the methodology of the overall project and the specific role of the lexicographers. It then discusses the three tasks in which the latter are directly involved: the construction of a set of two parallel Spanish corpora, one correct and the other with induced errors, the generation of validation material, and the writing of extended grammatical explanations for Spanish learners. Based on a large amount of empirical data, including 35,000 carefully reviewed sentences, the article details the different steps of the interaction between human and chatbot, as well as the experiences and reflections drawn from this process. It concludes that the two parts engage in very different types of relationships depending on the concrete task, and that human knowledge, culture, skills and language intuition are crucial for the chatbot to work properly. Keywords: Spanish writing assistants, language learning, chatbots, human-assisted intelligence, training of language model, corpus building

Exploring the Economic Foundations Behind Interpersonal Relationships

This paper focuses on economic issues related to the presence of shopping malls, as economic activities are inevitably influenced by social interactions and interpersonal relationships. Different types of relationships, ranging from weak to strong and positive to negative, form a complex system. The combination of Chinese tradition, cultural background, and the requirements of the current social system has led to the prominence of interpersonal connections. This paper provides a detailed analysis of the significant impact of relationships on transaction subjects. Furthermore, it explores how interpersonal relationships affect the formation and development of enterprises, as well as the allocation and utilization of resources. The paper emphasizes the importance of reducing the negative impact of interpersonal relationships by adopting an approach that prioritizes interpersonal connections. This involves considering new cost and profit expectations and minimizing the reliance on relational functions when constraining economic activities.

Lexical synecdoche in Croatian

The three key mechanisms in deriving the secondary meaning of polysemous lexemes are lexical metaphor, lexical metonymy and lexical synecdoche. Considering that lexical synecdoche has not received much attention so far, this paper will explore its role in creating the polysemous structure of Croatian lexemes. In this paper, lexical synecdoche is viewed as a predictable mechanism with several reversible transfers in its core: part for whole and whole for part, general for individual and individual for general, singular for plural and plural for singular, species for genus and genus for species. The formulas of these transfers are at the same time notations for conceptual metonymies which we consider the starting point for the creativity of lexical synecdoches. We do so because the metonymy and the synecdoche operate within a single conceptual domain, where one entity stands for another on the basis of their spatial, temporal, functional or causal relation. This common relation is why the synecdoche is usually considered a subcategory of metonymy. On the other hand, the metonymy and the synecdoche can be seen as separate lexical mechanisms because they are motivated by different types of relations between entities involved in the transfer. While the metonymic transfer is based on the closeness of entities in conceptual proximity as parts of the same domain, the transfer in synecdoche occurs between the domain itself and its integral part and turns from proximity to inclusion.

INTERGENERATIONAL BONDS ACROSS THE EARLY YEARS OF MARRIAGE

Abstract As adults embark upon marriage, forming their own new nuclear families, they often remain connected to their families of origin. Nevertheless, spouses’ relationships with their parents and in-laws are likely to change across the early years of marriage. Given that empirical studies to date are typically limited to only a couple of measurement occasions, however, the stability and impact of such changes are unclear. Using Repeated Measures Latent Class Analyses, the present study addresses this gap by identifying classes of spouses (N=375 dyads) experiencing different types of relationships with their parents and in-laws across the first 9 years of marriage. Further, correlates and consequences of spouses’ longitudinal sentiment profiles were also examined. Three sentiment profiles (i.e., Mostly Helpful, Mostly Ambivalent, Mostly Indifferent, Mostly Difficult) emerged when looking at spouse’s sentiments toward their own parents and for husbands’ sentiments towards their in-laws, whereas 4 profiles (i.e., Mostly Helpful, Mostly Ambivalent, Mostly Indifferent, Mostly Difficult) were found for wives’ sentiments towards their in-laws. Mostly Ambivalent sentiment profiles showed the least stability, and spouses showed significantly higher odds of being in the Mostly Ambivalent class if they were living in a multigenerational household and if the wife was a 2nd generation immigrant. Spouses whose marriages dissolved showed significantly higher odds of being in the Mostly Indifferent, Mostly Ambivalent, and Mostly Difficult classes, compared to the Mostly Helpful class. These results provide an important starting point for future research aiming to understand how intergenerational bonds may promote or hinder successful marriages.

SOCIAL RELATIONSHIPS AND DUAL FUNCTIONALITY IN LATER LIFE

Abstract Longer lives are often accompanied by greater risk of cognitive and physical impairments, but we investigate whether social relationships reduce the risk of those impairments. We examine dual functionality – the absence of both dementia and activities of daily living impairments – and draw upon stress process and social integration theories to examine the impact of positive and negative dimensions of relationship quality with friends and family to better understand how social factors may affect how long people maintain both their physical and cognitive functioning in later life. We use nationally representative panel data from the Health and Retirement Study (2006-2016, N=6,985) and Weibull accelerated failure-time models. Higher strain with family members and with friends was associated with a loss of dual functionality at earlier ages (Time Ratio = .97 [95% CI: .95, .99] and TR = .96 [CI: .94, .98], respectively). Greater social support from family members was beneficial, related to a longer time as dual functional (TR = 1.04, CI: 1.02, 1.06); however, more support from friends was related to loss of dual functionality at earlier ages (TR = .98, CI: .96, .99). Findings reveal the implications of social support and strain in different types of relationships for dual functionality.

Quantitative stock portfolio optimization by multi-task learning risk and return

Selecting profitable stocks for investments is a challenging task. Recent research has made significant progress on stock ranking prediction to select top-ranked stocks for portfolio optimization. However, the stocks are only ranked by predicted stock return, ignoring the stock price volatility risk — a critical aspect for stock selection and investments. Moreover, they preliminarily attempted to capture the effects of related stocks from a singular relation, disregarding the rich information regarding multiple spillover effects from related stocks and the distinctions in effects among various relations. Thus, we propose a risk and return multi-task learning model with a heterogeneous graph attention network (HGA-MT) to predict stock ranking for portfolio optimization. First, to aggregate the multiple spillover effects of related stocks, we introduce graph convolutional networks to fuse the effects of related stocks in each relation and design an attention network to allocate varying weights to different types of relationships. Second, we use a multi-task learning paradigm to learn stock return and volatility risks jointly. The stock ranking results are calculated by simultaneously considering the risk and return. Thus, Top-K ranked stocks are recommended in the portfolio for the next trading day to achieve higher and more stable profits. Extensive experiments prove that HGA-MT outperforms previous state-of-the-art methods in stock ranking and backtesting trading evaluation tasks.

METAVERSE: MODEL CRIMINAL CODE

The Scientific and Technical Revolution 5.0 and WEB 3.0 technologies create conditions for the renovation of various forms of social relations with the use of virtual and augmented reality technologies in the metaverse. According to the proposed theory, the legal regulation of social relations in the metaverse requires the development of a comprehensive electronic jurisdiction based on the latest basic legislation. The formation of legal regulation of the metaverse is a prerequisite for the need to form an electronic jurisdiction of the metaverse, which will include sectoral Metaverse Codes. The metaverse, as the electronic society of the future, does not yet have clear legal boundaries, and the task of scholars is to predict and outline with sufficient certainty the future contours of legal authority for virtual environments. Today, discussions in the scientific community about the feasibility and necessity of legal regulation of the metaverse often revolve around several key issues. First, there is the question of what legal framework should be applied in the metaverse and how conflicts between different legal systems should be resolved. Second, there is a debate about whether current regulatory bodies in the physical world have the capacity to effectively regulate the metaverse through existing laws and regulations. Third, there is the question of how to deal with offences committed in the virtual environment, and whether they should be dealt with under existing tort or criminal law, or whether a separate cross-border electronic jurisdiction should be created. The regulation of social relations in the metaverse should focus on one central goal: to clearly define the status of electronic entities, subjects and objects, to establish their rights, duties and responsibilities, and to define the different types of relations between virtual entities, subjects and objects within a given metaverse, as well as between different metaverses within an electronic jurisdiction and in a cross-border context. An essential component of the Metaverse Electronic Jurisdiction is a Metaverse Model Criminal Code that will outline the norms and offences applicable to analogue, hybrid and electronic jurisdictions. This code will define the types of socially harmful acts or crimes and the corresponding criminal penalties that will be applied within the metaverse. The formation of the electronic jurisdiction of the metaverse and the development of a Metaverse Model Criminal Code is a current scientific and legal issue.

Cognitive empathy mediates the relationship between gray matter volume size of dorsomedial prefrontal cortex and social network size: A voxel-based morphometry study

Social networks are an important factor in developing and maintaining social relationships. The social brain network comprises brain regions that differ in terms of their location, structure, and functioning, and these differences tend to vary among individuals with different social network sizes. However, it remains unknown how social cognitive abilities such as empathy can affect social network size. The goal of our study was to examine the relationship between brain regions in the social brain network, empathy, and individual social network size by using the Social Network Index, which measures social network diversity, size, and complexity by assessing 12 different types of relationships. We performed voxel-based morphometry and mediation analyses using data from questionnaires and structural magnetic resonance imaging data in a sample of 204 young adults. Our findings showed that the gray matter volume of the dorsomedial prefrontal cortex (dmPFC) was inversely associated with social network size and cognitive empathy mediated this association, suggesting that decreased gray matter volume in the dmPFC is associated with greater utilization of cognitive empathy, which, in turn, seems to increase social network size. A potential mechanism explaining this inverse relationship could be cognitive pruning, a phenomenon that occurs in the brain between early adolescence and adulthood, but future longitudinal studies are needed. In conclusion, our findings provide information about the neurocognitive mechanisms involved in the formation and maintenance of social networks.

The Joys and Stresses of Social Relationships and the Effect on Resiliency During the Covid-19 Pandemic: A National Survey of US Older Adults.

The COVID-19 pandemic placed unprecedented stress on older adults. Resiliency could mitigate deleterious effects of pandemic stressors. We described trends in resilience among US adults aged 50-80 years approximately one and half years after the onset of the pandemic, compared with before the pandemic, and evaluated associations between relationships and resilience. Data were from the National Poll on Healthy Aging (N=2,100) in August 2021. Respondents rated their resiliency as compared with before the pandemic (more, about the same, or less) and different types of relationships (spouse/partner, friends, neighbors) as sources of joy and/or stress during the pandemic (a lot, some, very little, and not at all). Multinomial logistic regressions (complete-case analysis) estimated the relationships between each joyful and stressful relationships and resiliency. Most participants reported feeling as resilient as before the pandemic (70.6%) with 14.5% feeling less resilient. More women than men felt less resilient than before the pandemic (16.7% vs. 12.8%, p=0.006). Feeling a lot of stress from one's spouse or neighbors was associated with increased likelihood of feeling less resilient than before the pandemic (OR=3.7; 95% CI: 1.8, 7.7 and OR=4.5; 95% CI: 1.5, 13.9, respectively) which was stronger among women (OR=15.1; 95% CI: 4.8, 45.6) than men (OR=1.03; 95% CI: 0.4, 2.7). Social relationships may have been more important for women than for men in supporting resiliency during the pandemic. Understanding patterns of resiliency can help to inform policymaking and support the well-being of older adults.

Construction of a knowledge graph for breast cancer diagnosis based on Chinese electronic medical records: development and usability study

BackgroundElectronic medical records (EMRs) contain a wealth of information related to breast cancer diagnosis and treatment. Extracting relevant features from these medical records and constructing a knowledge graph can significantly contribute to an efficient data analysis and decision support system for breast cancer diagnosis.MethodsAn approach was proposed to develop a workflow for effectively extracting breast cancer-related features from Chinese breast cancer mammography reports and constructing a knowledge graph for breast cancer diagnosis. Firstly, the concept layer of the knowledge graph for breast cancer diagnosis was constructed based on breast cancer diagnosis and treatment guidelines, along with insights from clinical experts. .Next, a BiLSTM-Highway-CRF model was designed to extract the mammography features, which formed the data layer of the knowledge graph. Finally, the knowledge graph was constructed by combining the concept layer and the data layer in a Neo4j graph data platform, and then applied in visualization analysis, semantic query and computer assisted diagnosis.ResultsMammographic features were extracted from a total of 1171 mammography examination reports. The overall extraction performance of the model achieved an accuracy rate of 97.16%, a recall rate of 98.06%, and a F1 score of 97.61%. Additionally, 47,660 relationships between entities were identified based on the four different types of relationships defined in the concept layer. The knowledge graph for breast cancer diagnosis was constructed after inputting mammographic features and relationships into the Neo4j graph data platform. The model was assessed from the concept layer, data layer, and application layer perspectives, and showed promising results.ConclusionsThe proposed workflow is applicable for constructing knowledge graphs for breast cancer diagnosis based on Chinese EMRs. This study serves as a reference for the rapid design, construction, and application of knowledge graphs for diagnosis and treatment of other diseases. Furthermore, it offers a potential solution to address the issues of limited data sharing and format inconsistencies present in Chinese EMR data.

Support given by health professionals before and after a patient's death to relatives involved in general palliative care at home in Sweden: Findings from the Swedish Register of Palliative Care.

General palliative care (PC) is provided more at home, leading to increased involvement of relatives. Although support for relatives is a fundamental component of PC, there are deficiencies in the support provided to relatives when general PC is provided at home. This study aimed to describe the support provided by health professionals before and after a patient's death to relatives involved in general PC at home. A cross-sectional register study was implemented, with data from the Swedish Register of Palliative care. The sample consisted of 160 completed surveys from relatives who had been involved in general PC at home, with 160 related surveys answered by health professionals. Only the questions about support to relatives were used from the surveys. The findings showed that although many relatives appear to receive support in general PC at home, not all relatives receive optimal support before or after a patient's death. The findings also indicated differences in whether relatives received some support before and after a patient's death depending on the type of relative. There were also differences in responses between health professionals and relatives regarding if relatives received counseling from a doctor about whether the patient was dying. There is potential for improvements regarding support for relatives, especially after a patient's death, which has been confirmed in previous studies. The differences in whether relatives received support before and after a patient's death depending on the type of relative highlight the need for future research on how to support different types of relatives before and after a patient's death when general PC is provided at home.

An analytic strategy for data processing of multimode networks

AbstractComplex network data structures are considered to capture the richness of social phenomena and real-life data settings. Multipartite networks are an example in which various scenarios are represented by different types of relations, actors, or modes. Within this context, the present contribution aims at discussing an analytic strategy for simplifying multipartite networks in which different sets of nodes are linked. By considering the connection of multimode networks and hypergraphs as theoretical concepts, a three-step procedure is introduced to simplify, normalize, and filter network data structures. Thus, a model-based approach is introduced for derived bipartite weighted networks in order to extract statistically significant links. The usefulness of the strategy is demonstrated in handling two application fields, that is, intranational student mobility in higher education and research collaboration in European framework programs. Finally, both examples are explored using community detection algorithms to determine the presence of groups by mixing up different modes.

Types of fiber and gut microbiota composition and diversity among arab females

ObjectivesDietary fiber is recognizedas an important nutrient for gut health. However, research on the relations of different types of fibers (soluble and insoluble) to the human microbiota health is limited. This study aimed to identify whether higher habitual intake of soluble and/or insoluble fiber have a different influence on the composition, diversity, and abundance of microbiota. MethodsWe examined the fecal microbial composition of 92 healthy females aged 18 and above using the novel shotgun metagenomics sequencing technique. The habitual fiber intake was determined using the Saudi food frequency questionnaire. Pearson’s correlation was used for the correlations between total, soluble, and insoluble fiber and gut microbiota. α- and β-diversities were applied to acquire the distinctions in the relative abundances of bacterial taxa. ResultsOur findings show that higher dietary fiber, particularly insoluble fiber, was significantly correlated with the abundances of Bacteroides_u_s, Bacteroides uniformis, and Lactobacillus acidophilus (r = 0.26, 0.29, 0.26, p-value < 0.05, respectively). Non-significant difference was noted in the microbial α-diversity and β-diversity in low and high soluble/insoluble dietary fiber. ConclusionsCurrent findings suggest that insoluble dietary-fiber intake is favorably correlated with the health of the human gut microbiota. However, further investigations are necessary to identify the effect of types of fiber on the specific species identified in this study.

Productive practices in Andean rural areas and their relationship to extractive markets (Atacama Desert, northern Chile, 1915–2019)

AbstractExtractivism has marked the history of Latin America whose operations are in rural territories inhabited mainly by indigenous populations. Mining has had a remarkable expansion in rural territories of the Andes. Critical studies of these processes have focused on the disruptive aspects and conflict between companies, local populations, and States. However, mining has also been intertwined with the territories based on contradictory relationships at different timescales. To examine this issue, we carried out a historical reconstruction of the productive practices of the Caspana community indigenous (northern Chile) and their different forms of connection with mining development. We combine diverse data sources and methodological approaches: oral histories obtained from ethnography, censuses, explorers’ records, and academic literature. We identify different types of relationships over time, according to the different forms of indigenous participation in the extractive markets and the deployment and rearrangement of diverse economic strategies by the indigenous population.