The challenges are exacerbated when health data are continuously streamed, as is the case with wearable sensors. Although patients are not always solely responsible for determining which repository should be used, they typically have some input into this decision. Patients can be expected to have idiosyncratic preferences regarding storage decisions depending on their unique contexts. In this paper, we propose a predictive model for the storage of health data that can meet patient needs and make storage decisions rapidly, in real-time, even with data streaming from wearable sensors. The model is built with a machine learning classifier that learns the mapping between characteristics of health data and features of storage repositories from a training set generated synthetically from correlations evident from small samples of experts. Results from the evaluation demonstrate the viability of the machine learning technique used. This study proposes a computational model to evaluate patient room design layout and features that contribute to patient stability and mitigate the risk of fall. While common fall risk assessment tools in nursing have an acceptable level of sensitivity and specificity, they focus on intrinsic factors and medications, making risk assessment limited in terms of how the physical environment contributes to fall risk. We use literature to inform a computational model (algorithm) to define the relationship between these factors and the risk of fall. We use a trajectory optimization approach for patient motion prediction. Based on available data, the algorithm includes static factors of lighting, flooring, supportive objects, and bathroom doors and dynamic factors of patient movement. This preliminary model was tested using four room designs as examples of typical room configurations. Results show the capabilities of the proposed model to identify the risk associated with different room layouts and features.ensive investigation comparing the model with actual patient falls data is needed to further refine model development.Individuals with Alzheimer's disease and other forms of dementia (referred to as AD) deteriorate over time, and there will likely be a corresponding increase in levels of burden and stress for caregivers. Despite the significant contributions made by informal caregivers, there are no widely available mechanisms that meet the information needs of informal caregivers. Using a qualitative approach, the current study focuses on AD caregiver information seeking. The study involved conducting interviews to answer the following research questions (1) What motivating factors lead informal caregivers of people with AD to seek out information?; (2) Is there a relationship between information seeking and resulting perceived stress levels?; and (3) Why do informal caregivers choose to utilize certain resources more than others during their information seeking process? Findings revealed that caregivers' largest motivation for seeking information is to learn how to better care for their loved one. Caregivers tend to rely on mediated resources that they find credible, and interpersonal resources such as people with similar experiences to their own. Many participants were satisfied with information available, but others felt that their interactions with healthcare professionals created more stress and emotional anguish than anticipated. This study offers an initial step in finding ways to meet the needs of those who seek to mitigate their stress through information seeking. By studying the information needs of the caregiving population, healthcare workers and communicators will be more knowledgeable about the relationship between information seeking and stress and coping. The smart phone contains various mobile applications specifically targeting their contents, such as information, messages, e-mail, education and entertainment towards youths. Problematic and excessive smart phone usage can cause many health problems including anxiety, depression and sleep disorders. The aim of this study is to analyse the relationship between smart phone usage, sleep quality and depression. Eight hundred and four students who owned smart phones were given the Information Form, Smart Phone Addiction Scale-Short Version, Pittsburgh Sleep Quality Index (PSQI) and Beck Depression Inventory (BDI). GSK2245840 in vivo The descriptive statistics, independent sample -test, one-way ANOVA, correlation analysis and multivariate regression analysis were used for analysis data. The mean age of the students in the sample was 20.93 ± 2.44. It comprised female (65.0%) and male (35.0%) students. All of the students used smart phones. The daily smart phone usage duration was 7.85 ± 4.55 hour. According to the multivariate linear regression analysis results, significant relationships were statistically determined in the positive way between the smart phone addiction and PSQI point (    .01) and BDI point (    .01). Consequently, a relationship exists between smart phone usage, poor sleep quality and depressive symptoms in university students. The university students, whose depression point is high and sleep quality is poor, should be followed up with regarding the smart phone addiction.Consequently, a relationship exists between smart phone usage, poor sleep quality and depressive symptoms in university students. The university students, whose depression point is high and sleep quality is poor, should be followed up with regarding the smart phone addiction.One of the fastest-moving fields in today's world of applied science, nanotechnology allows the control and design of matter on an extremely small scale, so it has now become an integral part of various industries and scientific areas, such as agriculture, food sector, healthcare and engineering. Understanding the interactions between nanopesticides and edible plants, as well as non-target animals, is crucial in assessing the potential impact of nanotechnology products on the environment, agriculture and human health. The dramatic increase in efforts to use nanopesticides renders the risk assessment of their toxicity and genotoxicity highly crucial due to the potential adverse impact of this relatively uncharted territory. Such widespread use naturally increases our exposure to nanopesticides, raising concerns over their possible adverse effects on humans and non-target organisms, which might include severe impairment of both male and female reproductive capacity. We therefore need better insight into such effects to derive conclusive evidence on the safety or toxicity/genotoxicity of nanopesticides, and Drosophila melanogaster (fruit fly) can prove an ideal model organism for the risk assessment and toxicological classification of nanopesticides, as it bears striking similarities to various systems in human body.