Data-Driven Learning in High-Resolution Activity Sampling From Patients With Bipolar Depression: Mixed-Methods Study.

BackgroundBehavioral activation is a pen and paper-based therapy form for treating depression. The patient registers their activity hourly, and together with the therapist, they agree on a plan to change behavior. However, with the limited clinical personnel, and a growing patient population, new methods are needed to advance behavioral activation.ObjectiveThe objectives of this paper were to (1) automatically identify behavioral patterns through statistical analysis of the paper-based activity diaries, and (2) determine whether it is feasible to move the behavioral activation therapy format to a digital solution.MethodsWe collected activity diaries from seven patients with bipolar depression, covering in total 2,480 hours of self-reported activities. A pleasure score, on a 1-10 rating scale, was reported for each activity. The activities were digitalized into 6 activity categories, and statistical analyses were conducted.ResultsAcross all patients, movement-related activities were associated with the highest pleasure score followed by social activities. On an individual level, through a nonparametric Wilcoxon Signed-Rank test, one patient had a statistically significant larger amount of spare time activities when feeling bad (z=–2.045, P=.041). Through a within-subject analysis of covariance, the patients were found to have a better day than the previous, if that previous day followed their diurnal rhythm (ρ=.265, P=.029). Furthermore, a second-order trend indicated that two hours of daily social activity was optimal for the patients (β2=–0.08, t (63)=–1.22, P=.23).ConclusionsThe data-driven statistical approach was able to find patterns within the behavioral traits that could assist the therapist in as well as help design future technologies for behavioral activation.</jats:sec

Using a New Odour-Baited Device to Explore Options for Luring and Killing Outdoor-Biting Malaria Vectors: A Report on Design and Field Evaluation of the Mosquito Landing Box.

Mosquitoes that bite people outdoors can sustain malaria transmission even where effective indoor interventions such as bednets or indoor residual spraying are already widely used. Outdoor tools may therefore complement current indoor measures and improve control. We developed and evaluated a prototype mosquito control device, the 'Mosquito Landing Box' (MLB), which is baited with human odours and treated with mosquitocidal agents. The findings are used to explore technical options and challenges relevant to luring and killing outdoor-biting malaria vectors in endemic settings. Field experiments were conducted in Tanzania to assess if wild host-seeking mosquitoes 1) visited the MLBs, 2) stayed long or left shortly after arrival at the device, 3) visited the devices at times when humans were also outdoors, and 4) could be killed by contaminants applied on the devices. Odours suctioned from volunteer-occupied tents were also evaluated as a potential low-cost bait, by comparing baited and unbaited MLBs. There were significantly more Anopheles arabiensis, An. funestus, Culex and Mansonia mosquitoes visiting baited MLB than unbaited controls (P<=0.028). Increasing sampling frequency from every 120 min to 60 and 30 min led to an increase in vector catches of up to 3.6 fold (P<=0.002), indicating that many mosquitoes visited the device but left shortly afterwards. Outdoor host-seeking activity of malaria vectors peaked between 7:30 and 10:30pm, and between 4:30 and 6:00am, matching durations when locals were also outdoors. Maximum mortality of mosquitoes visiting MLBs sprayed or painted with formulations of candidate mosquitocidal agent (pirimiphos-methyl) was 51%. Odours from volunteer occupied tents attracted significantly more mosquitoes to MLBs than controls (P<0.001). While odour-baited devices such as the MLBs clearly have potential against outdoor-biting mosquitoes in communities where LLINs are used, candidate contaminants must be those that are effective at ultra-low doses even after short contact periods, since important vector species such as An. arabiensis make only brief visits to such devices. Natural human odours suctioned from occupied dwellings could constitute affordable sources of attractants to supplement odour baits for the devices. The killing agents used should be environmentally safe, long lasting, and have different modes of action (other than pyrethroids as used on LLINs), to curb the risk of physiological insecticide resistance

MyTraces: Investigating Correlation and Causation between Users' Emotional States and Mobile Phone Interaction

Most of the existing work concerning the analysis of emotional states and mobile phone interaction has been based on correlation analysis. In this paper, for the first time, we carry out a causality study to investigate the causal links between users’ emotional states and their interaction with mobile phones, which could provide valuable information to practitioners and researchers. The analysis is based on a dataset collected in-the-wild. We recorded 5,118 mood reports from 28 users over a period of 20 days. Our results show that users’ emotions have a causal impact on different aspects of mobile phone interaction. On the other hand, we can observe a causal impact of the use of specific applications, reflecting the external users’ context, such as socializing and traveling, on happiness and stress level. This study has profound implications for the design of interactive mobile systems since it identifies the dimensions that have causal effects on users’ interaction with mobile phones and vice versa. These findings might lead to the design of more effective computing systems and services that rely on the analysis of the emotional state of users, for example for marketing and digital health applications

Do Labour Market Institutions Matter?: Micro-Level Wage Effects of International Outsourcing in Three European Countries

This paper studies the impact of outsourcing on individual wages in three European countries with markedly different labour market institutions: Germany, the UK and Denmark. To do so we use individual level data sets for the three countries and construct comparable measures of outsourcing at the industry level, distinguishing outsourcing by broad region. Estimating the same specification on different data show that there are some interesting differences in the effect of outsourcing across countries. We discuss some possible reasons for these differences based on labour market institutions.International outsourcing, individual wages, labour market institutions

Abiotic and Biotic Factors Influence Refuge Use at the Community and Organismal Level

Animal behavior is influence by a wide range of factors. One factor that can heavily influence behavior is the presence or absence of refuge (i.e., refuge provides a direct benefit to animals’ fitness). Many animals seek refuge to avoid predation despite clear costs to other life processes. The decision to use refuge is complex and anthropogenic activities may alter the abundance of refuge. Artificial refuge structures can be successful in conservation efforts and are an effective means to measure biodiversity. Thus, I used cover boards to examine how habitat structure and season influence vertebrate abundance and diversity in the southeastern U.S. Vertebrate abundance was driven by proximity to roads where abundance was higher at sites that were further from roads. Season influenced the diversity of vertebrate classes where diversity was greater in the summer than in the fall and winter. My results provide evidence that anthropogenic, biotic, and temporal factors can influence vertebrate abundance and biodiversity. In addition to temporal and spatial factors, biotic factors can influence refuge use. These factors can create tradeoffs that are well-studied in some contexts of life history evolution. One such tradeoff that affects refuge use is the thermoregulation-predator avoidance tradeoff. This tradeoff may be plastic in response to environmental conditions such as pathogen exposure. Thus, I examined the dynamics of a thermoregulation-predator avoidance tradeoff using the cornsnake (Pantherophis guttatus) in a controlled lab setting. Immune activation did not elicit behavioral fever or change shelter use when shelter was available across the entire thermal gradient. Although snakes strongly prioritized shelter use, their prioritization shifted during immune challenge. Snakes injected with LPS that were forced to choose between preferred temperature and shelter use maintained thermoregulation, but they spent up to 9-fold more time exposed relative to when they were injected with saline. These results demonstrate the plasticity of the widespread tradeoff between thermoregulation and shelter use

Generalized Global Solar Radiation Forecasting Model via Cyber-Secure Deep Federated Learning

Recently, the increasing prevalence of solar energy in power and energy systems around the world has dramatically increased the importance of accurately predicting solar irradiance. However, the lack of access to data in many regions and the privacy concerns that can arise when collecting and transmitting data from distributed points to a central server pose challenges to current predictive techniques. This study proposes a global solar radiation forecasting approach based on federated learning (FL) and convolutional neural network (CNN). In addition to maintaining input data privacy, the proposed procedure can also be used as a global supermodel. In this paper, data related to eight regions of Iran with different climatic features are considered as CNN input for network training in each client. To test the effectiveness of the global supermodel, data related to three new regions of Iran named Abadeh, Jarqavieh, and Arak are used. It can be seen that the global forecasting supermodel was able to forecast solar radiation for Abadeh, Jarqavieh, and Arak regions with 95%, 92%, and 90% accuracy coefficients, respectively. Finally, in a comparative scenario, various conventional machine learning and deep learning models are employed to forecast solar radiation in each of the study regions. The results of the above approaches are compared and evaluated with the results of the proposed FL-based method. The results show that, since no training data were available from regions of Abadeh, Jarqavieh, and Arak, the conventional methods were not able to forecast solar radiation in these regions. This evaluation confirms the high ability of the presented FL approach to make acceptable predictions while preserving privacy and eliminating model reliance on training data