A Definition and Categorization System for Advanced Materials: The Foundation for Risk‐Informed Environmental Health and Safety Testing

Novel materials with unique or enhanced properties relative to conventional materials are being developed at an increasing rate. These materials are often referred to as advanced materials (AdMs) and they enable technological innovations that can benefit society. Despite their benefits, however, the unique characteristics of many AdMs, including many nanomaterials, are poorly understood and may pose environmental safety and occupational health (ESOH) risks that are not readily determined by traditional risk assessment methods. To assess these risks while keeping up with the pace of development, technology developers and risk assessors frequently employ risk‐screening methods that depend on a clear definition for the materials that are to be assessed (e.g., engineered nanomaterial) as well as a method for binning materials into categories for ESOH risk prioritization. The term advanced material lacks a consensus definition and associated categorization or grouping system for risk screening. In this study, we aim to establish a practitioner‐driven definition for AdMs and a practitioner‐validated framework for categorizing AdMs into conceptual groupings based on material characteristics. Results from multiple workshops and interviews with practitioners provide consistent differentiation between AdMs and conventional materials, offer functional nomenclature for application science, and provide utility for future ESOH risk assessment prioritization. The definition and categorization framework established here serve as a first step in determining if and when there is a need for specific ESOH and regulatory screening for an AdM as well as the type and extent of risk‐related information that should be collected or generated for AdMs and AdM‐enabled technologies.     

Managing big data in the retail industry of Singapore: Examining the impact on customer satisfaction and organizational performance

Much of the research on big data analytics has been centered on technical or system development. Research has been carried out on the usage of big data analytics to understand customer relationships and experience, amongst others. Still, there is a lack of research in the retail industry considering big data management, examining the impact on customer satisfaction and organizational performance in the retail sector. Retailers explore analytics to gain a unified picture of their customers and operations across the store or online channels and make strategic decisions contributing to the growth of the retail industry. Thereof, this study has been conducted by majorly focusing on the Singapore retail industry to clarify the feasibility of big data management analytics. Quantitative research method was employed involving 500 participants from the retail industry of Singapore. The results of the study stated that amongst the different big data analytics utilized within the retail industry of Singapore, social media analytics had been majorly answered by the participants. Future researchers can study about the upcoming retail trends in Singapore and how the effects of big data analysis changed in the past few years and deal with the unexpected future recessions in the retail industry within Singapore.     

Third-person effect in the context of public relations and corporate communication

Inaccurate perceptions of audience can cause public relations practitioners to overreact or underreact to a situation, which brings about unintended consequences. We draw on the third-person effect research to identify examples and outline the conditions when the overestimation or underestimation of media effects can motivate key players to engage in restrictive, corrective, promotional, and resistance behaviors. Then, we recommend approaches that can help public relations practitioners better reflect and manage corporate communication. In short, the approaches involve: (a) determining the nature of the presumed effect from the audience perspective and going beyond the categorical positive/negative message, (b) assessing the level of the presumed effect and considering also the possibility of the first- as well as the second-person effect, and (c) assessing the influence of the perceived effect on behaviors that have the potential to directly as well as indirectly affect the company.     

Validating protein structure using kernel density estimates

Measuring the quality of determined protein structures is a very important problem in bioinformatics. Kernel density estimation is a well-known nonparametric method which is often used for exploratory data analysis. Recent advances, which have extended previous linear methods to multi-dimensional circular data, give a sound basis for the analysis of conformational angles of protein backbones, which lie on the torus. By using an energy test, which is based on interpoint distances, we initially investigate the dependence of the angles on the amino acid type. Then, by computing tail probabilities which are based on amino-acid conditional density estimates, a method is proposed which permits inference on a test set of data. This can be used, for example, to validate protein structures, choose between possible protein predictions and highlight unusual residue angles.     

The effects of food stamp receipt on weight gained by expectant mothers

With over 66% of Americans overweight, expectant mothers are unusual because they are encouraged to gain weight while pregnant. Food stamp receipt (FSR) may facilitate recommended weight gain by providing resources for food and nutrition. I examine the effects of FSR on the amount of weight gained by low-income expectant mothers using NLSY79 data. Results indicate FSR decreases the probability gaining insufficient weight but does not exacerbate the probability of gaining too much weight. Examining the effects of FSR on pregnancy weight gain is important because low birth weight is more likely when expectant mothers gain insufficient weight.     

Reward‐Modulated Response Inhibition,Cognitive Shifting,and the Orbital Frontal Cortex in Early Adolescence

Immaturities in cognitive shifting are associated with adolescent risk behaviors. The orbital frontal cortex (OFC) regulates reward processing and response inhibition. This study tested the relationship between cognitive shifting, OFC activity, and reward‐modulated response inhibition in young adolescents. An fMRI antisaccade (AS) paradigm examined the effects of reward conditions on inhibitory response and OFC processing. A validated self‐report inventory assessed cognitive shifting. Compared with neutral, reward trials showed better AS performance and increased OFC activation. Cognitive shifting positively associated with AS performance in reward and neutral trials. Poorer cognitive shifting predicted greater OFC activation. Results indicate lower OFC efficiency, as greater activation to achieve correct performance, underlies cognitive shifting problems. These neurocognitive impairments are relevant for understanding adolescent risk behaviors.     

Modeling the mental health service utilization decisions of university undergraduates: A discrete choice conjoint experiment

Objective: We modeled design factors influencing the intent to use a university mental health service. Participants: Between November 2012 and October 2014, 909 undergraduates participated. Method: Using a discrete choice experiment, participants chose between hypothetical campus mental health services. Results: Latent class analysis identified three segments. A Psychological/Psychiatric Service segment (45.5%) was most likely to contact campus health services delivered by psychologists or psychiatrists. An Alternative Service segment (39.3%) preferred to talk to peer-counselors who had experienced mental health problems. A Hesitant segment (15.2%) reported greater distress but seemed less intent on seeking help. They preferred services delivered by psychologists or psychiatrists. Simulations predicted that, rather than waiting for standard counseling, the Alternative Service segment would prefer immediate access to E-Mental health. The Usual Care and Hesitant segments would wait 6 months for standard counseling. Conclusions: E-Mental Health options could engage students who may not wait for standard services.     

Pool Screening: An Example of Independent Nonidentical Bernoulli Trial

Pool screening is a widely used study design to make inference about the probability of a subject being positive for an infection when the probability is extremely low. When the pool sizes are unequal, outcomes of pool screening follow independent nonidentical Bernoulli distributions. This paper presents different calculation methods for the probability distribution of T, the number of positive pools. Marcus and Lopes recursive method is recommended.