Factors influencing students’ intention to use internet for academic purposes

This study aimed to investigate the factors influencing students’ intention to use the Internet for academic purposes amongst 204 final year business students in public universities in Malaysia.This study integrated theory of planned behavior (TPB) and theory of acceptance model (TAM) as the base model toward that purpose.The research model employs the variables from both theories namely attitudes, subjective norms, perceived behavioral control, perceived usefulness, perceived ease of use, intention, and behavior.A multiple regression analysis provides empirical support for the applicability of integration of TPB and TAM in predicting students’ intention to use the Internet for academic purposes.Results of the study show that attitudes, perceived behavioral control, and perceived usefulness are statistically significant in influencing intention to use the Internet for academic purposes.Based on the results, it can be concluded that students’ intention to use the Internet for academic purposes could be predicted from their attitudes, perceived behavioral control, and perceived usefulness at 49% level.In view of the results, several implications and recommendations are discussed

Mycotoxin Co-Occurrence in Michigan Harvested Maize Grain

Mycotoxins are secondary metabolites produced by fungi that, depending on the type and exposure levels, can be a threat to human and animal health. When multiple mycotoxins occur together, their risk effects on human and animal health can be additive or synergistic. Little information is known about the specific types of mycotoxins or their co-occurrence in the state of Michigan and the Great Lakes region of the United States. To understand the types, incidences, severities, and frequency of co-occurrence of mycotoxins in maize grain (Zea mays L.), samples were collected from across Michigan over two years and analyzed for 20 different mycotoxins. Every sample was contaminated with at least four and six mycotoxins in 2017 and 2018, respectively. Incidence and severity of each mycotoxin varied by year and across locations. Correlations were found between mycotoxins, particularly mycotoxins produced by Fusarium spp. Environmental differences at each location played a role in which mycotoxins were present and at what levels. Overall, data from this study demonstrated that mycotoxin co-occurrence occurs at high levels in Michigan, especially with mycotoxins produced by Fusarium spp., such as deoxynivalenol

A preliminary analysis of interactive effects between common classroom contingencies and methylphenidate.

To assess the drug-behavior interaction effects with an 8-year-old boy wih attention deficit hyperactivity disorder, common classroom antecedent (e.g., seating arrangement) and consequent (e.g., peer prompts) stimuli were alternated within a school day while drug conditions (methylphenidate vs. placebo) were alternated across days. The results suggested that peer attention maintained disruptive behavior when methylphenidate was absent but not when it was present

Minimal-uncertainty prediction of general drug-likeness based on Bayesian neural networks

Triaging unpromising lead molecules early in the drug discovery process is essential for accelerating its pace while avoiding the costs of unwarranted biological and clinical testing. Accordingly, medicinal chemists have been trying for decades to develop metrics-ranging from heuristic measures to machine-learning models-that could rapidly distinguish potential drugs from small molecules that lack drug-like features. However, none of these metrics has gained universal acceptance and the very idea of 'drug-likeness' has recently been put into question. Here, we evaluate drug-likeness using different sets of descriptors and different state-of-the-art classifiers, reaching an out-of-sample accuracy of 87-88%. Remarkably, because these individual classifiers yield different Bayesian error distributions, their combination and selection of minimal-variance predictions can increase the accuracy of distinguishing drug-like from non-drug-like molecules to 93%. Because total variance is comparable with its aleatoric contribution reflecting irreducible error inherent to the dataset (as opposed to the epistemic contribution due to the model itself), this level of accuracy is probably the upper limit achievable with the currently known collection of drugs. When designing new drugs, there are countless ways to create molecules, yet only a few interact with biological targets. Beker and colleagues provide here a graph neural network based metric for drug-likeness that can guide the search