Entrepreneurial Inclination Among Business Students: a Malaysian Study

Entrepreneurship has been the fundamental topics of discussion among the politicians, economists, and academics. Business creation is especially critical in developing countries to stimulate economic growth. The present study attempts to examine entrepreneurial inclination among students who are a potential source of entrepreneurs. The fi ndings of the present research study indicate that majority of our business students are not entrepreneurial-inclined. They do not seem to possess strong entrepreneurial characteristics and entrepreneurial skills, and they are not keen in starting a new business. The roles of higher institutes of education and the government in promoting entrepreneurship are discussed

Value of information analysis for interventional and counterfactual Bayesian networks in forensic medical sciences

Objectives: Inspired by real-world examples from the forensic medical sciences domain, we seek to determine whether a decision about an interventional action could be subject to amendments on the basis of some incomplete information within the model, and whether it would be worthwhile for the decision maker to seek further information prior to suggesting a decision

Compatible and incompatible abstractions in Bayesian networks

The graphical structure of a Bayesian network (BN) makes it a technology well-suited for developing decision support models from a combination of domain knowledge and data. The domain knowledge of experts is used to determine the graphical structure of the BN, corresponding to the relationships and between variables, and data is used for learning the strength of these relationships. However, the available data seldom match the variables in the structure that is elicited from experts, whose models may be quite detailed; consequently, the structure needs to be abstracted to match the data. Up to now, this abstraction has been informal, loosening the link between the final model and the experts' knowledge. In this paper, we propose a method for abstracting the BN structure by using four 'abstraction' operations: node removal, node merging, state-space collapsing and edge removal. Some of these steps introduce approximations, which can be identified from changes in the set of conditional independence (CI) assertions of a network

Analyzing the Simonshaven Case using Bayesian Networks

This paper is one in a series of analyses of the Dutch Simonshaven murder case, each using a different modeling approach. We adopted a Bayesian network (BN)–based approach which requires us to determine the relevant hypotheses and evidence in the case and their relationships (captured as a directed acyclic graph) along with explicit prior conditional probabilities. This means that both the graph structure and probabilities had to be defined using subjective judgments about the causal, and other, connections between variables and the strength and nature of the evidence. Determining if a useful BN could be quickly constructed by a small group using the previously established idioms‐based approach which provides a generic method for translating legal cases into BNs, was a key aim. The model described was built by the authors during a workshop dedicated to the case at the Isaac Newton Institute, Cambridge, in September 2016. The total effort involved was approximately 26 h (i.e., an average of 6 h per author). With the basic assumptions described in the paper, the posterior probability of guilt once all the evidence is entered is 74%. The paper describes a formal evaluation of the model, using sensitivity analysis, to determine how robust the model conclusions are to key subjective prior probabilities over a full range of what may be deemed “reasonable” from both defense and prosecution perspectives. The results show that the model is reasonably robust—pointing not only generally to a reasonably high posterior probability of guilt but also generally below the 95% threshold expected in criminal law. Given the constraints on building a complex model so quickly, there are inevitably weaknesses; hence, the paper describes these and how they might be addressed, including how to take account of supplementary case information not known at the time of the workshop

Metabolic labelling of cholesteryl glucosides in Helicobacter pylori reveals how the uptake of human lipids enhances bacterial virulence.

Helicobacter pylori infects approximately half of the human population and is the main cause of various gastric diseases. This pathogen is auxotrophic for cholesterol, which it converts upon uptake to various cholesteryl α-glucoside derivatives, including cholesteryl 6'-acyl and 6'-phosphatidyl α-glucosides (CAGs and CPGs). Owing to a lack of sensitive analytical methods, it is not known if CAGs and CPGs play distinct physiological roles or how the acyl chain component affects function. Herein we established a metabolite-labelling method for characterising these derivatives qualitatively and quantitatively with a femtomolar detection limit. The development generated an MS/MS database of CGds, allowing for profiling of all the cholesterol-derived metabolites. The subsequent analysis led to the unprecedented information that these bacteria acquire phospholipids from the membrane of epithelial cells for CAG biosynthesis. The resulting increase in longer or/and unsaturated CAG acyl chains helps to promote lipid raft formation and thus delivery of the virulence factor CagA into the host cell, supporting the idea that the host/pathogen interplay enhances bacterial virulence. These findings demonstrate an important connection between the chain length of CAGs and the bacterial pathogenicity

General rules for environmental management to prioritise social ecological systems research based on a value of information approach

This is the final version. Available on open access from Wiley via the DOI in this record1. Globally, billions of dollars are invested each year to help understand the dynamics of social ecological systems (SES) in bettering both social and environmental outcomes. However, there is no scientific consensus on which aspect of an SES is most important and urgent to understand; particularly given the realities of limited time and money. 2. Here we use a simulation‐based “value of information” approach to examine where research will deliver the most important information for environmental management in four SESs representing a range of real‐life environmental issues. 3. We find that neither social nor ecological information is consistently the most important: instead, researchers should focus on understanding the primary effects of their management actions. 4. Thus, when managers are undertaking social actions the highest research priority should be understanding the dynamics of social groups. Alternatively, when manipulating ecological systems it will be most important to quantify ecological population dynamics. 5. Synthesis and applications. Our results provide a standard assessment to determine the uncertain social ecological systems (SES) component with the highest expected impact for management outcomes. First, managers should determine the structure of their SES by identifying social and ecological nodes. Second, managers should identify the qualitative nature of the network, by determining which nodes are linked, but not the strength of those interactions. Finally, managers should identify the actions available to them to intervene in the SES. From these steps, managers will be able to identify the SES components that are closest to the management action(s), and it is these nodes and interactions that should receivepriority research attention to achieve effective environmental decision making.Centre of Excellence for Environmental Decisions, Australian Research Counc

Early Identification of Trauma-induced Coagulopathy: Development and Validation of a Multivariable Risk Prediction Model.

OBJECTIVE: The aim of this study was to develop and validate a risk prediction tool for trauma-induced coagulopathy (TIC), to support early therapeutic decision-making. BACKGROUND: TIC exacerbates hemorrhage and is associated with higher morbidity and mortality. Early and aggressive treatment of TIC improves outcome. However, injured patients that develop TIC can be difficult to identify, which may compromise effective treatment. METHODS: A Bayesian Network (BN) prediction model was developed using domain knowledge of the causal mechanisms of TIC, and trained using data from 600 patients recruited into the Activation of Coagulation and Inflammation in Trauma (ACIT) study. Performance (discrimination, calibration, and accuracy) was tested using 10-fold cross-validation and externally validated on data from new patients recruited at 3 trauma centers. RESULTS: Rates of TIC in the derivation and validation cohorts were 11.8% and 11.0%, respectively. Patients who developed TIC were significantly more likely to die (54.0% vs 5.5%, P < 0.0001), require a massive blood transfusion (43.5% vs 1.1%, P < 0.0001), or require damage control surgery (55.8% vs 3.4%, P < 0.0001), than those with normal coagulation. In the development dataset, the 14-predictor BN accurately predicted this high-risk patient group: area under the receiver operating characteristic curve (AUROC) 0.93, calibration slope (CS) 0.96, brier score (BS) 0.06, and brier skill score (BSS) 0.40. The model maintained excellent performance in the validation population: AUROC 0.95, CS 1.22, BS 0.05, and BSS 0.46. CONCLUSIONS: A BN (http://www.traumamodels.com) can accurately predict the risk of TIC in an individual patient from standard admission clinical variables. This information may support early, accurate, and efficient activation of hemostatic resuscitation protocols

The nucleotide and partial amino acid sequences of rat fetuin

Fetuins are among the major plasma proteins, yet their biological role has remained elusive. Here we report the molecular cloning of rat fetuin and the sequence analysis of a full-length clone, RF619 of 1456 bp with an open reading frame of 1056 bp encoding 352 amino acid residues. The coding part of RF619 was identical with the cDNA sequence of the natural inhibitor of the insulin receptor tyrosine kinase from rat (pp63) except for four substitutions and a single base insertion causing divergence of the predicted protein sequences. Partial amino acid sequences of rat plasma fetuin were in agreement with the predictions based on the RF619 cDNA. Purified rat fetuin inhibited the insulin receptor tyrosine kinase in vitro. Therefore, we conclude that RF619 and pp63 cDNA encode the same protein, i.e. authentic rat fetuin which is a functional tyrosine kinase inhibitor