Employing Environmental Data and Machine Learning to Improve Mobile Health Receptivity

Behavioral intervention strategies can be enhanced by recognizing human activities using eHealth technologies. As we find after a thorough literature review, activity spotting and added insights may be used to detect daily routines inferring receptivity for mobile notifications similar to just-in-time support. Towards this end, this work develops a model, using machine learning, to analyze the motivation of digital mental health users that answer self-assessment questions in their everyday lives through an intelligent mobile application. A uniform and extensible sequence prediction model combining environmental data with everyday activities has been created and validated for proof of concept through an experiment. We find that the reported receptivity is not sequentially predictable on its own, the mean error and standard deviation are only slightly below by-chance comparison. Nevertheless, predicting the upcoming activity shows to cover about 39% of the day (up to 58% in the best case) and can be linked to user individual intervention preferences to indirectly find an opportune moment of receptivity. Therefore, we introduce an application comprising the influences of sensor data on activities and intervention thresholds, as well as allowing for preferred events on a weekly basis. As a result of combining those multiple approaches, promising avenues for innovative behavioral assessments are possible. Identifying and segmenting the appropriate set of activities is key. Consequently, deliberate and thoughtful design lays the foundation for further development within research projects by extending the activity weighting process or introducing a model reinforcement.BMBF, 13GW0157A, Verbundprojekt: Self-administered Psycho-TherApy-SystemS (SELFPASS) - Teilvorhaben: Data Analytics and Prescription for SELFPASSTU Berlin, Open-Access-Mittel - 201

Predicting collapse of adaptive networked systems without knowing the network

The collapse of ecosystems, the extinction of species, and the breakdown of economic and financial networks usually hinges on topological properties of the underlying networks, such as the existence of self-sustaining (or autocatalytic) feedback cycles. Such collapses can be understood as a massive change of network topology, usually accompanied by the extinction of a macroscopic fraction of nodes and links. It is often related to the breakdown of the last relevant directed catalytic cycle within a dynamical system. Without detailed structural information it seems impossible to state, whether a network is robust or if it is likely to collapse in the near future. Here we show that it is nevertheless possible to predict collapse for a large class of systems that are governed by a linear (or linearized) dynamics. To compute the corresponding early warning signal, we require only non-structural information about the nodes’ states such as species abundances in ecosystems, or company revenues in economic networks. It is shown that the existence of a single directed cycle in the network can be detected by a “quantization effect” of node states, that exists as a direct consequence of a corollary of the Perron–Frobenius theorem. The proposed early warning signal for the collapse of networked systems captures their structural instability without relying on structural information. We illustrate the validity of the approach in a transparent model of co-evolutionary ecosystems and show this quantization in systems of species evolution, epidemiology, and population dynamics

An overview of generalized entropic forms

The aim of this focus letter is to present a comprehensive classification of the main entropic forms introduced in the last fifty years in the framework of statistical physics and information theory. Most of them can be grouped into three families, characterized by two-deformation parameters, introduced respectively by Sharma, Taneja, and Mittal (entropies of degree $(\alpha,\,\beta$)), by Sharma and Mittal (entropies of order $(\alpha,\,\beta)$), and by Hanel and Thurner (entropies of class $(c,\,d)$). Many entropic forms examined will be characterized systematically by means of important concepts such as their axiomatic foundations {\em \`{a} la} Shannon-Khinchin and the consequent composability rule for statistically independent systems. Other critical aspects related to the Lesche stability of information measures and their consistency with the Shore-Johnson axioms will be briefly discussed on a general ground.Comment: 14 pages, 6 tables, no figures, to appear on EPL: Focus Issues "Progresses on Statistical Physics and Complexity

The Use of Augmented Reality in Retail: A Review of Literature

Novel digital technologies are affording ways to superimpose perceptual information (be it auditory, visual, haptic or olfactory) onto our reality, e.g. in retail environments. These technologies that aim to enhance reality are generally called Augmented Reality (AR) technologies. Today, the field of research focused on AR retail has evolved to mature enough state that an overview of the state-of-the-art, results and ways in which AR has been employed in research is needed. Therefore, in this study we conduct a systematic literature review of the academic corpus focused on AR retail. We report on how and where AR is employed in retail, what technological characteristics of AR are commonly analyzed as well as what potential psychological and behavioral outcomes AR is capable of evoking. Overall, AR is a technology with high potential for in-store and remote (online) shopping in terms of evoking both utilitarian and hedonic experiences

Using augmented reality for shopping : a framework for AR induced consumer behavior, literature review and future agenda

Purpose A current technological trend, which has gained even more traction recently due to the COVID-19 pandemic, is the use of augmented reality (AR) in shopping environments. AR is addressing contemporary challenges rooted in online shopping (e.g. in terms of experientiality and try-on) and is fundamentally reshaping consumers' experiences. The purpose of this study is to provide a synthesized and structured overview of the state-of-the-art research focused on AR shopping. Design/methodology/approach The authors conduct a systematic literature review of the empirical academic corpus focused on shopping via AR technology. Findings The review reveals the diverse psychological (cognitive, affective, and social) as well as behavioral outcomes related to the use of AR in the shopping context. The authors integrate the results into a framework for AR induced consumer behavior in shopping, thereby providing an important overview of the dynamics in AR-related shopping and the factors influencing the adoption of the technology by consumers. Specifically, the authors encountered that the technological abilities of AR (e.g. in terms of interactivity, vividness, informativeness, etc.) are a source for enhanced utilitarian and hedonic shopping experiences that can support intentions to purchase a product, reuse an AR app, or recommend it to others. Importantly, our review reveals the demand for several avenues for future research. Originality/value The authors provide an overview and synthesis of how and where AR is employed in shopping contexts, what theories and technological characteristics of AR are commonly analyzed, and what psychological and behavioral outcomes AR has been found to evoke. Based on our findings, the authors derive a framework that illustrates the dynamics in AR shopping and give an in-depth discourse on 13 future research agenda points related to thematic, theoretical, methodological, and technological matters.©2022 Emerald Publishing Limited. This manuscript version is made available under the Creative Commons Attribution–NonCommercial 4.0 International (CC BY–NC 4.0) license, https://creativecommons.org/licenses/by-nc/4.0/fi=vertaisarvioitu|en=peerReviewed

Prediction of effective genome size in metagenomic samples

We introduce a novel computational approach to predict effective genome size (EGS; a measure that includes multiple plasmid copies, inserted sequences, and associated phages and viruses) from short sequencing reads of environmental genomics (or metagenomics) projects. We observe considerable EGS differences between environments and link this with ecologic complexity as well as species composition (for instance, the presence of eukaryotes). For example, we estimate EGS in a complex, organism-dense farm soil sample at about 6.3 megabases (Mb) whereas that of the bacteria therein is only 4.7 Mb; for bacteria in a nutrient-poor, organism-sparse ocean surface water sample, EGS is as low as 1.6 Mb. The method also permits evaluation of completion status and assembly bias in single-genome sequencing projects

Screen for ISG15-crossreactive Deubiquitinases

Background: The family of ubiquitin-like molecules (UbLs) comprises several members, each of which has sequence, structural, or functional similarity to ubiquitin. ISG15 is a homolog of ubiquitin in vertebrates and is strongly upregulated following induction by type I interferon. ISG15 can be covalently attached to proteins, analogous to ubiquitination and with actual support of ubiquitin conjugating factors. Specific proteases are able to reverse modification with ubiquitin or UbLs by hydrolyzing the covalent bond between their C-termini and substrate proteins. The tail regions of ubiquitin and ISG15 are identical and we therefore hypothesized that promiscuous deubiquitinating proteases (DUBs) might exist, capable of recognizing both ubiquitin and ISG15. Results: We have cloned and expressed 22 human DUBs, representing the major clades of the USP protease family. Utilizing suicide inhibitors based on ubiquitin and ISG15, we have identified USP2, USP5 (IsoT1), USP13 (IsoT3), and USP14 as ISG15-reactive proteases, in addition to the bona fide ISG15-specific protease USP18 (UBP43). USP14 is a proteasome-associated DUB, and its ISG15 isopeptidase activity increases when complexed with the proteasome. Conclusions: By evolutionary standards, ISG15 is a newcomer among the UbLs and it apparently not only utilizes the conjugating but also the deconjugating machinery of its more established relative ubiquitin. Functional overlap between these two posttranslational modifiers might therefore be more extensive than previously appreciated and explain the rather innocuous phenotype of ISG15 null mice

Development and Application of the Owner-Bird Relationship Scale (OBRS) to Assess the Relation of Humans to Their Pet Birds

Only a few birds besides domestic pigeons and poultry can be described as domesticated. Therefore, keeping a pet bird can be challenging, and the human-avian relationship will have a major influence on the quality of this cohabitation. Studies that focus on characterizing the owner-bird relationship generally use adapted cat/dog scales which may not identify its specific features. Following a sociological approach, a concept of human-animal relationship was developed leading to three types of human-animal relationship (impersonal, personal, and close personal). This concept was used to develop a 21-item owner-bird-relationship scale (OBRS). This scale was applied to measure the relationship between pet bird owners (or keepers) (n = 1,444) and their birds in an online survey performed in Germany. Factor analysis revealed that the relationship between owner and bird consisted of four dimensions: the tendency of the owner to anthropomorphize the bird;the social support the bird provides for the owner;the empathy, attentiveness, and respect of the owner toward the bird;and the relationship of the bird toward the owner. More than one quarter of the German bird owners of this sample showed an impersonal, half a personal, and less than a quarter a close personal relationship to their bird. The relationship varied with the socio-demographic characteristics of the owners, such as gender, marital status, and education. This scale supports more comprehensive quantitative research into the human-bird relationship in the broad field of human-animal studies including the psychology and sociology of animals as well as animal welfare and veterinary medicine

A dynamical approach to the spatiotemporal aspects of the Portevin-Le Chatelier effect: Chaos,turbulence and band propagation

Experimental time series obtained from single and poly-crystals subjected to a constant strain rate tests report an intriguing dynamical crossover from a low dimensional chaotic state at medium strain rates to an infinite dimensional power law state of stress drops at high strain rates. We present results of an extensive study of all aspects of the PLC effect within the context a model that reproduces this crossover. A study of the distribution of the Lyapunov exponents as a function of strain rate shows that it changes from a small set of positive exponents in the chaotic regime to a dense set of null exponents in the scaling regime. As the latter feature is similar to the GOY shell model for turbulence, we compare our results with the GOY model. Interestingly, the null exponents in our model themselves obey a power law. The configuration of dislocations is visualized through the slow manifold analysis. This shows that while a large proportion of dislocations are in the pinned state in the chaotic regime, most of them are at the threshold of unpinning in the scaling regime. The model qualitatively reproduces the different types of deformation bands seen in experiments. At high strain rates where propagating bands are seen, the model equations are reduced to the Fisher-Kolmogorov equation for propagative fronts. This shows that the velocity of the bands varies linearly with the strain rate and inversely with the dislocation density, consistent with the known experimental results. Thus, this simple dynamical model captures the complex spatio-temporal features of the PLC effect.Comment: 17 pages, 18 figure