Requirements for a behaviour change application for alcohol intervention and reduction

Abstract. The goal of this study was to find requirements for a behaviour change application for alcohol intervention and reduction. Motivation for the study comes from a preceding project work that studied a potential system that could be used to monitor and reduce alcohol consumption. The result of this project work suggested utilizing behaviour change theory for creating a mobile application to achieve the desired effect on the user. The study was conducted as a theory driven qualitative research study that gathered information from existing scientific research. The study utilized qualitative semi structured interviews and discussions from an online alcohol support forum for data gathering and analysis. The semi structured interviews were part of the previous project work and were also analysed for this study. Analysis for the forum discussions was done as content analysis. The research found that the application should take advantage of suitable behaviour change techniques such as self-monitoring, having social support features or teaching the user coping skills. It should also maintain user’s engagement by having aesthetic design, being easy to use or by having the option to give feedback on using the application. Further the application could use unique smartphone features to stand out from other digital alcohol support methods. Also, the application should provide evidence of successful behaviour change based on scientific data and be built by using established behaviour change application models. The small number of interviewees was a limitation for this study. Also, using content analysis to get information about people who are actively seeking help for alcohol use was a limiting factor on getting more precise information. In the future a more comprehensive qualitative interview process could be conducted that includes a wide variety of different kinds of potential users

Searching for the Cognitive Basis of Anti-Vaccination Attitudes

Peer reviewe

Performance of adiabatic melting as a method to pursue the lowest possible temperature in 3^3He and 3^3He-4^4He mixture at the 4^4He crystallization pressure

We studied a novel cooling method, in which $^3$He and $^4$He are mixed at the $^4$He crystallization pressure at temperatures below $0.5\,\mathrm{mK}$. We describe the experimental setup in detail, and present an analysis of its performance under varying isotope contents, temperatures, and operational modes. Further, we developed a computational model of the system, which was required to determine the lowest temperatures obtained, since our mechanical oscillator thermometers already became insensitive at the low end of the temperature range, extending down to $\left(90\pm20\right)\,\mathrm{\mu K\approx}\frac{T_{c}}{\left(29\pm5\right)}$ ($T_{c}$ of pure $^3$He). We did not observe any indication of superfluidity of the $^3$He component in the isotope mixture. The performance of the setup was limited by the background heat leak of the order of $30\,\mathrm{pW}$ at low melting rates, and by the heat leak caused by the flow of $^4$He in the superleak line at high melting rates up to $500\,\mathrm{\mu mol/s}$. The optimal mixing rate between $^3$He and $^4$He, with the heat leak taken into account, was found to be about $100..150\,\mathrm{\mu mol/s}$. We suggest improvements to the experimental design to reduce the ultimate achievable temperature further.Comment: 39 pages, 24 figure

Activation of the motivation-related ventral striatum during delusional experience

Delusion is the most characteristic symptom of psychosis, occurring in almost all first-episode psychosis patients. The motivational salience hypothesis suggests delusion to originate from the experience of abnormal motivational salience. Whether the motivation-related brain circuitries are activated during the actual delusional experience remains, however, unknown. We used a forced-choice answering tree at random intervals during functional magnetic resonance imaging to capture delusional and non-delusional spontaneous experiences in patients with first-episode psychosis (n = 31) or clinical high-risk state (n = 7). The motivation-related brain regions were identified by an automated meta-analysis of 149 studies. Thirteen first-episode patients reported both delusional and non-delusional spontaneous experiences. In these patients, delusional experiences were related to stronger activation of the ventral striatum in both hemispheres. This activation overlapped with the most strongly motivation-related brain regions. These findings provide an empirical link between the actual delusional experience and the motivational salience hypothesis. Further use and development of the present methods in localizing the neurobiological basis of the most characteristic symptoms may be useful in the search for etiopathogenic pathways that result in psychotic disorders.Peer reviewe

Navigation and Mapping in Forest Environment Using Sparse Point Clouds

Odometry during forest operations is demanding, involving limited field of vision (FOV), back-and-forth work cycle movements, and occasional close obstacles, which create problems for state-of-the-art systems. We propose a two-phase on-board process, where tree stem registration produces a sparse point cloud (PC) which is then used for simultaneous location and mapping (SLAM). A field test was carried out using a harvester with a laser scanner and a global navigation satellite system (GNSS) performing forest thinning over a 520 m strip route. Two SLAM methods are used: The proposed sparse SLAM (sSLAM) and a standard method, LeGO-LOAM (LLOAM). A generic SLAM post-processing method is presented, which improves the odometric accuracy with a small additional processing cost. The sSLAM method uses only tree stem centers, reducing the allocated memory to approximately 1% of the total PC size. Odometry and mapping comparisons between sSLAM and LLOAM are presented. Both methods show 85% agreement in registration within 15 m of the strip road and odometric accuracy of 0.5 m per 100 m. Accuracy is evaluated by comparing the harvester location derived through odometry to locations collected by a GNSS receiver mounted on the harvester.</p