Entrepreneurs’ Networking Styles and Normative Underpinnings during Institutional Transition

Existing network research has mainly adopted functional and/or structural approaches to study the instrumental goals behind entrepreneurs’ networking as well as the influence of personal position on access to resources and eventual performance. The variety of entrepreneurs’ networking styles and their normative underpinnings have not been adequately explored. Contextualized in China, this study asks: How do entrepreneurs’ understandings of social norms shape their networking styles? Through an inductive comparison of two entrepreneur generations in China, we identify three networking styles: guanxi-oriented networking, market-based networking, and mixed networking. We theorize that three types of norms shape these styles: market-inferred norms, dyadically formed norms, and identity-induced norms. This study provides new insights into the understanding of Chinese entrepreneurs’ distinctive networking styles and their normative underpinnings. Further, it suggests implications both for the wider study of entrepreneurs’ networking behaviors in transition economies, and for practitioners wishing to enhance their network building in China.

Drawing Into Being: Charter Graphics and Their Functions

Prior researchers have identified charter documents as texts that serve an outsize role in stabilizing social reality and mediating work, writing, and network building. While charter documents are typically authoritative and text-only tomes, this article expands the category to include charter graphics, visual texts that serve similarly important genre and network functions. Through retrospective analysis of one charter graphic and its role in a decade-long project by a nonprofit organization, this article demonstrates the potential rhetorical, social, and network functions of charter graphics; distinguishes them from charter documents; and offers suggestions for both practitioners and researchers.

Visualizing Phytochemical-Protein Interaction Networks: Momordica charantia and Cancer

The in silico study of medicinal plants is a rapidly growing field. Techniques such as reverse screening and network pharmacology are used to study the complex cellular action of medicinal plants against disease. However, it is difficult to produce a meaningful visualization of phytochemical-protein interactions (PCPIs) in the cell. This study introduces a novel workflow combining various tools to visualize a PCPI network for a medicinal plant against a disease. The five steps are 1) phytochemical compilation, 2) reverse screening, 3) network building, 4) network visualization, and 5) evaluation. The output is a PCPI network that encodes multiple dimensions of information, including subcellular location, phytochemical class, pharmacokinetic data, and prediction probability. As a proof of concept, we built a PCPI network for bitter gourd (Momordica charantia L.) against colorectal cancer. The network and workflow are available at https://yumibriones.github.io/network/. The PCPI network highlights high-confidence interactions for further in vitro or in vivo study. The overall workflow is broadly transferable and can be used to visualize the action of other medicinal plants or small molecules against other diseases.

Graph Neural Network Modelling as a potentially effective Method for predicting and analyzing Procedures based on Patient Diagnoses

BackgroundCurrently, the healthcare sector strives to increase the quality of patient management and improve the economic performance of healthcare providers. The data contained in electronic health records (EHRs) offer the potential to discover relevant patterns that aim to relate diseases and therapies, and thus discover patterns that could help identify empirical medical guidelines that reflect best practices in the healthcare system. Based on this pattern identification, it is then possible to implement recommendation systems based on the idea that a higher volume of procedures is associated with high-quality models.MethodsAlthough there are several applications that use machine learning methods to identify these patterns, this identification is still a challenge, in part because these methods often ignore the basic structure of the population, considering the similarity of diagnoses and patient typology. To this end, we have developed graph methods that aim to cluster similar patients. In such models, patients are linked when the same or similar patterns can be observed for these patients, a concept that enables the construction of a network-like structure. This structure can then be analyzed with Graph Neural Networks (GNN) to identify relevant labels, in this case the appropriate medical procedures.ResultsWe report the construction of a patient Graph structure based on basic patient’s information like age and gender as well as the diagnoses and trained GNNs models to identify the corresponding patient’s therapies using a synthetic patient database. We compared our GNN models against different baseline models (using the SCIKIT-learn library of python) and compared the performance of the different model methods. We have found that GNNs are superior, with an average improvement of the f1 score of 6.48% respect to the baseline models. In addition, the GNNs are useful for performing additional clustering analyses that allow specific identification of specific therapeutic clusters related to a particular combination of diagnoses.ConclusionsWe found that GNNs are a promising way to model the distribution of diagnoses in a patient population and thus better model how similar patients can be identified based on the combination of morbidities and comorbidities. Nevertheless, network building is still challenging and prone to prejudice, as it depends on how ICD distribution affects the patient network embedding space. This network setup requires not only a high quality of the underlying diagnostic ecosystem, but also a good understanding of how to identify related patients by disease. For this reason, additional work is needed to improve and better standardize patient embedding in graph structures for future investigations and applications of services based on this technology, and therefore is not yet an interventional study.

The School Administration Affecting the Life Skill and Career of Students in the Opportunity Expansion School, Narathiwat Province, Thailand

This study was aimed to study the school administration that influenced the promotion of life skills and career of students of Educational Opportunity Expansion Schools in Narathiwat Province. The samples were 200 teachers who served as heads of four divisions in Educational Opportunity Expansion Schools in Narathiwat Province, chosen through the purposive sampling technique. Research tools were five-scale questionnaires with reliability of 0.952. Statistics used for this research include percentage, mean, standard deviation, and stepwise multiple regression analysis. It was found that administrative performance and the promotion of life skills and career of students were at the high level, both overall and each aspect. School administrators’ administrative performance that affects students’ life skills and career of Educational Opportunity Expansion Schools in Narathiwat Province consisted of three variables; coordination and network building, assessment and evaluation, and development and encouragement. The multiple regression analysis values were 0.803, and the prediction discrepancy value was 0.153, which could predict the promotion of life skills and career of students of Educational Opportunity Expansion Schools in Narathiwat Province as high as possible 64.5 percent.

Modelling in Synthesis and Optimization of Active Vaccinal Components

Cancer is the second leading cause of mortality worldwide, behind heart diseases, accounting for 10 million deaths each year. This study focusses on adenocarcinoma, which is a target of a number of anticancer therapies presently being tested in medical and pharmaceutical studies. The innovative study for a therapeutic vaccine comprises the investigation of gold nanoparticles and their influence on the immune response for the annihilation of cancer cells. The model is intended to be realized using Quantitative-Structure Activity Relationship (QSAR) methods, explicitly artificial neural networks combined with fuzzy rules, to enhance automated properties of neural nets with human perception characteristics. Image processing techniques such as morphological transformations and watershed segmentation are used to extract and calculate certain molecular characteristics from hyperspectral images. The quantification of single-cell properties is one of the key resolutions, representing the treatment efficiency in therapy of colon and rectum cancerous conditions. This was accomplished by using manually counted cells as a reference point for comparing segmentation results. The early findings acquired are conclusive for further study; thus, the extracted features will be used in the feature optimization process first, followed by neural network building of the required model.

PROACTIVE PERSONALITY AND EMPLOYEES’ INNOVATIVE BEHAVIOURS: THE ROLE OF NETWORK BUILDING ABILITY

The study aims to expand our knowledge of the employees’ innovative behaviours in the service sector by adopting the coevolution theory of the psychological–sociological perspective as the lens in which the study will utilise to investigate the antecedents of each stage of the employees’ innovative behaviours: Idea generation, idea promotion, and idea realisation behaviours. The relation between proactive personality and each stage of employees’ innovative behaviours was tested, focusing on the intermediary role of network building ability. A total of406 questionnaires from employees working in the service sector were collected and analysed using structural equation modelling. The study findings illustrate the importance of building networks for proactive employees to be able to innovate and demonstrated that different stages of employees’ innovative behaviours may require different antecedents and, therefore, separating the analysis for each stage may enrich our knowledge and enhance our understanding of employees’ innovative behaviours.

Reliable network inference from unreliable data: A tutorial on latent network modeling using STRAND

Social network analysis provides an important framework for studying the causes, consequences, and structure of social ties. Standard self-report measures—e.g., as collected through the popular ‘name-generator’ method—however, do not provide an impartial representation of transfers, interactions, or social relationships. At best, they represent perceptions filtered through the cognitive biases of respondents. Individuals may, for example, report transfers that did not really occur, or forget to mention transfers that really did. The propensity to make such reporting inaccuracies is both an individual-level and item-level characteristic—variable across members of any given group. Past research has high- lighted that many network-level properties are highly sensitive to such reporting inaccuracies. However, there remains a dearth of easily deployed statistical tools that account for such biases. To address this issue, we introduce a latent network model that allows us to jointly estimate parameters measuring both reporting biases and a latent, underlying social network. Building upon past research, we conduct several simulation experiments in which network data are subject to various reporting biases, and find that these reporting biases strongly impact our ability to accurately infer fundamental network properties. These impacts are not adequately addressed using standard approaches to network reconstruction (i.e., treating either the union or the intersection of double-sampled data as the true network), but are appropriately resolved through the use of our latent network models. To make implementation of our models easier for end-users, we provide a fully-documented R package, STRAND, and include a tutorial illustrating its functionality when applied to empirical food/money sharing data from a rural Colombian population.