Improving Knowledge Retention and Perceived Control through Serious Games: a Study about Assisted Emergency Evacuation

Digital games for education and training, also called serious games (SGs), have shown beneficial effects on learning in several studies. In addition, some studies are suggesting that SGs could improve user's perceived control, which affects the likelihood that the learned content will be applied in the real world. However, most SG studies tend to focus on immediate effects, providing no indication on knowledge and perceived control over time, especially in contrast with nongame approaches. Moreover, SG research on perceived control has focused mainly on self-efficacy, disregarding the complementary construct of locus of control (LOC). This paper advances both lines of research, assessing user's knowledge and LOC over time, with a SG as well as traditional printed materials that teach the same content. Results show that the SG was more effective than printed materials for knowledge retention over time, and a better retention outcome was found also for LOC. An additional contribution of the paper is the proposal of a novel SG that targets the inclusivity goal of safe evacuation for all, extending SG research to a domain not dealt with before, i.e. assisting persons with disabilities in emergencies

Adiabatic passage and ensemble control of quantum systems

This paper considers population transfer between eigenstates of a finite quantum ladder controlled by a classical electric field. Using an appropriate change of variables, we show that this setting can be set in the framework of adiabatic passage, which is known to facilitate ensemble control of quantum systems. Building on this insight, we present a mathematical proof of robustness for a control protocol -- chirped pulse -- practiced by experimentalists to drive an ensemble of quantum systems from the ground state to the most excited state. We then propose new adiabatic control protocols using a single chirped and amplitude shaped pulse, to robustly perform any permutation of eigenstate populations, on an ensemble of systems with badly known coupling strengths. Such adiabatic control protocols are illustrated by simulations achieving all 24 permutations for a 4-level ladder

A meta model framework for risk analysis, diagnosis and simulation

International audienceThe goal of risk analysis is to identify events that may have one or several undesirable consequences on a system, and to assess the likelihood and severity of these consequences. A lot of methods may be used to conduct risk analysis such as Preliminary Hazard Analysis (PHA), and Failure Mode Effects Analysis (FMEA). In most of these methods, the obtained information may be used to build a risk model. Very often, the next step after risk analysis is, to study the behavior of the system if the undesirable events occur, in order to evaluate its performance in degraded conditions and its robustness or resilience. An approach allowing integrated risk analysis and simulation would be desirable. Such an approach has been proposed for business process management [Tjoa et al., 2011]. The goal of this paper is to present a meta model, suited to socio-technical systems, that allows describing the system to analyze, the result of the risk analysis and the required aspects of dynamical system behavior in order automatically perform simulation under degraded conditions. The model is an extension of the FIS model presented in [Negrichi et al., 2012]. The meta model may also be used for fault diagnosis as it can be used for generating redundancy relation and performing root cause search [Flaus et al., 2011]. Our meta model consists of three main modules: the structural view, the dysfunctional view and the view of the evolution.: • The structural view (SysFis): defines the architecture of the analyzed system, breaks it down into subsystems, and describes the characteristics of each subsystem and the material entities used. This is the basic view. that describes the structure of the installation or the analyzed object in a relatively simple manner, by showing the various interactions systems, and specifying, if necessary their functions and the material components (human, technical or informational) tha

Expressiveness of Temporal Query Languages: On the Modelling of Intervals, Interval Relationships and States

Storing and retrieving time-related information are important, or even critical, tasks on many areas of Computer Science (CS) and in particular for Artificial Intelligence (AI). The expressive power of temporal databases/query languages has been studied from different perspectives, but the kind of temporal information they are able to store and retrieve is not always conveniently addressed. Here we assess a number of temporal query languages with respect to the modelling of time intervals, interval relationships and states, which can be thought of as the building blocks to represent and reason about a large and important class of historic information. To survey the facilities and issues which are particular to certain temporal query languages not only gives an idea about how useful they can be in particular contexts, but also gives an interesting insight in how these issues are, in many cases, ultimately inherent to the database paradigm. While in the area of AI declarative languages are usually the preferred choice, other areas of CS heavily rely on the extended relational paradigm. This paper, then, will be concerned with the representation of historic information in two well known temporal query languages: it Templog in the context of temporal deductive databases, and it TSQL2 in the context of temporal relational databases. We hope the results highlighted here will increase cross-fertilisation between different communities. This article can be related to recent publications drawing the attention towards the different approaches followed by the Databases and AI communities when using time-related concepts

Bodily sensation maps: Exploring a new direction for detecting emotions from user self-reported data

The ability of detecting emotions is essential in different fields such as user experience (UX), affective computing, and psychology. This paper explores the possibility of detecting emotions through user-generated bodily sensation maps (BSMs). The theoretical basis that inspires this work is the proposal by Nummenmaa et al. (2014) of BSMs for 14 emotions. To make it easy for users to create a BSM of how they feel, and convenient for researchers to acquire and classify users’ BSMs, we created a mobile app, called EmoPaint. The app includes an interface for BSM creation, and an automatic classifier that matches the created BSM with the BSMs for the 14 emotions. We conducted a user study aimed at evaluating both components of EmoPaint. First, it shows that the app is easy to use, and is able to classify BSMs consistently with the considered theoretical approach. Second, it shows that using EmoPaint increases accuracy of users’ emotion classification when compared with an adaptation of the well-known method of using the Affect Grid with the Circumplex Model, focused on the same set of 14 emotions of Nummenmaa et al. Overall, these results indicate that the novel approach of using BSMs in the context of automatic emotion detection is promising, and encourage further developments and studies of BSM-based methods

Smartphone-Based Therapeutic Exercises for Men Affected by Premature Ejaculation: A Pilot Study

Introduction: Smartphone-delivered healthcare interventions allow patients to access services on demand when needed, improving motivation and compliance. However, the use of mobile health apps has been scarcely explored in sexual medicine. Aim: To evaluate the effects of integrating psychological treatment for premature ejaculation (PE) with a mobile coaching app that offers therapeutic exercises on the patient's smartphone. Methods: This study comprised 35 heterosexual men with primary psychogenic PE (mean age 34 years, standard deviation = 9.15). All patients entered a cycle of 15 sessions of psychodynamic psychotherapy integrating behavioral therapy, each lasting about 45 minutes. The patients were randomly assigned to 2 groups, each of which performed daily homework exercises (physiotherapy exercises for reinforcing the pelvic floor muscles and cognitive exercises for distancing from sexual failure.) The first group (15 patients) received verbal and printed instructions only (treatment as usual\u2014TAU), whereas the second group (17 patients) experienced the exercises with guidance from the mobile app (app). In both groups, the exercises started after the seventh session. Patients were advised to perform the exercises 3 times a day for 3 months. Main Outcome Measures: The primary outcome measures were the Premature Ejaculation Diagnostic Tool and the Premature Ejaculation Profile. Results: Analysis of the data revealed significant pre-post improvements in Premature Ejaculation Diagnostic Tool and Premature Ejaculation Profile scores for the app group compared with those of the TAU group (P <.01). The frequency of patients with no-PE condition for the app group after treatment was significantly higher than the frequency of patients with no-PE condition for the TAU group (P <.001). Conclusion: Results suggest that a mobile coaching app performs better than TAU in improving both the behavioral skills of ejaculatory delay and sexual self-confidence within a psychological treatment for PE. Future studies should collect follow-up data and explore the potential of mobile coaching apps in combined pharmacotherapy and psychotherapy interventions. Optale G, Burigat S, Chittaro L. et al. Smartphone-Based Therapeutic Exercises for Men Affected by Premature Ejaculation: A Pilot Study. J Sex Med 2020;XX:XXX\u2013XXX

Efficacy of Immersive Virtual Reality Combined With Multisensor Biofeedback on Chronic Pain in Fibromyalgia: A Pilot Randomized Controlled Trial

Objective: Fibromyalgia (FM) is a syndrome marked by chronic pain, fatigue, and mood disorders. Nonpharmacologic strategies are recommended to avoid overuse of opioids or nonsteroidal anti-inflammatory drugs, but current approaches often provide limited relief. This study aimed to preliminarily assess the efficacy and feasibility of a new combined intervention of immersive virtual reality with multisensor biofeedback (IVR-BF) in FM management. Methods: In this single-center, pilot, open-label, randomized controlled trial, adult patients with FM were randomly assigned 1:1 to either the treatment (TR) group, receiving IVR-BF immediately, or a waitlist control (WL) group, receiving IVR-BF after the TR group completed treatment. The primary outcome was reduction in visual analog scale (VAS) pain scores in the TR group, after five IVR-BF sessions, compared to the WL group, after the waiting period. Secondary outcomes included improvements in FM impact (FM Impact Questionnaire [FIQ] score) and qualitative aspect of pain (Short-form McGill Pain Questionnaire [SF-MPQ] score). A longitudinal analysis was conducted across all patients to examine the trends in VAS pain, SF-MPQ, and FIQ score during the trial. Results: Fifty patients were screened, and 20 female patients (10 TR and 10 WL) completed the trial and were analyzed. Those in the TR group showed significantly lower VAS pain scores compared to those in the WL group (P = 0.011), along with significant improvement in the FIQ score (P = 0.018). The longitudinal analysis revealed progressive improvements in VAS pain, SF-MPQ, and FIQ score, supported by physiologic improvements (heart rate variability, respiratory rate, skin conductance). No significant safety concerns were reported. Patients expressed a high level of satisfaction with the IVR experience. Conclusion: IVR-BF is a feasible treatment that shows potential in reducing pain and improving quality of life in patients with FM, supporting the need for larger trials to further evaluate its efficacy. (Figure presented.)

A mobile application to report and detect 3D body emotional poses

Most research into automatic emotion recognition is focused on facial expressions or physiological signals, while the exploitation of body postures has scarcely been explored, although they can be useful for emotion detection. This paper first explores a mechanism for self-reporting body postures with a novel easy-to-use mobile application called EmoPose. The app detects emotional states from self-reported poses, classifying them into the six basic emotions proposed by Ekman and a neutral state. The poses identified by Schindler et al. have been used as a reference and the nearest neighbor algorithm used for the classification of poses. Finally, the accuracy in detecting emotions has been assessed by means of poses reported by a sample of users

Visual Data Mining

Occlusion is one of the major problems for interactive visual knowledge discovery and data mining in the process of finding patterns in multidimensional data.This project proposes a hybrid method that combines visual and analytical means to deal with occlusion in visual knowledge discovery called as GLC-S which uses visualization of n-D data in 2D in a set of Shifted Paired Coordinates (SPC). A set of Shifted Paired Coordinates for n-D data consists of n/2 pairs of common Cartesian coordinates that are shifted relative to each other to avoid their overlap. Each n-D point A is represented as a directed graph A* in SPC, where each node is the 2D projection of A in a respective pair of the Cartesian coordinates. The proposed GLC-S method significantly decrease cognitive load for analysis of n-D data and simplify pattern discovery in n-D data. The GLC-S method iteratively splits n-D data into non-overlapping clusters (hyper-rectangles) around local centers and visualizes only data within these clusters at each iteration. The requirements for these clusters are to contain cases of only one class and be the largest cluster with this property in SPC visualization. Such sequential splitting allows: (1) avoiding occlusion, (2) finding visually local classification patterns, rules, and (3) combine local sub-rules to a global rule that classifies all given data of two or more classes. The computational experiment with Wisconsin Breast Cancer data(9-D), User Knowledge Modeling data(6-D), and Letter Recognition data(17-D) from UCI Machine Learning Repository confirm this capability. At each iteration, these data have been split into training (70%) and validation (30%) data. It required 3 iterations in Wisconsin Breast Cancer data, 4 iterations in User Knowledge Modeling and 5 iterations in Letter Recognition data and respectively 3, 4, 5 local sub-rules that covered over 95% of all n-D data points with 100% accuracy at both training and validation experiments. After each iteration, the data that were used in this iteration are removed and remaining data are used in the next iteration. This removal process helps to decrease occlusion too. The GLC-S algorithm refuses to classify remaining cases that are not covered by these rules, i.e.,., do not belong to found hyper-rectangles. The interactive visualization process in SPC allows adjusting the sides of the hyper-rectangles to maximize the size of the hyper-rectangle without its overlap with the hyper-rectangles of the opposing classes. The GLC-S method splits data using the fixed split of n coordinates to pairs. This hybrid visual and analytical approach avoids throwing all data of several classes into a visualization plot that typically ends up in a messy highly occluded picture that hides useful patterns. This approach allows revealing these hidden patterns. The visualization process in SPC is reversible (lossless). i.e.,., all n-D information is visualized in 2D and can be restored from 2D visualization for each n-D case. This hybrid visual analytics method allowed classifying n-D data in a way that can be communicated to the user’s in the understandable and visual form

The great melting pot. Common sole population connectivity assessed by otolith and water fingerprints

Quantifying the scale and importance of individual dispersion between populations and life stages is a key challenge in marine ecology. The common sole (Solea solea), an important commercial flatfish in the North Sea, Atlantic Ocean and the Mediterranean Sea, has a marine pelagic larval stage, a benthic juvenile stage in coastal nurseries (lagoons, estuaries or shallow marine areas) and a benthic adult stage in deeper marine waters on the continental shelf. To date, the ecological connectivity among these life stages has been little assessed in the Mediterranean. Here, such an assessment is provided for the first time for the Gulf of Lions, NW Mediterranean, based on a dataset on otolith microchemistry and stable isotopic composition as indicators of the water masses inhabited by individual fish. Specifically, otolith Ba/Ca and Sr/Ca profiles, and delta C-13 and delta O-18 values of adults collected in four areas of the Gulf of Lions were compared with those of young-of-the-year collected in different coastal nurseries. Results showed that a high proportion of adults (>46%) were influenced by river inputs during their larval stage. Furthermore Sr/Ca ratios and the otolith length at one year of age revealed that most adults (similar to 70%) spent their juvenile stage in nurseries with high salinity, whereas the remainder used brackish environments. In total, data were consistent with the use of six nursery types, three with high salinity (marine areas and two types of highly saline lagoons) and three brackish (coastal areas near river mouths, and two types of brackish environments), all of which contributed to the replenishment of adult populations. These finding implicated panmixia in sole population in the Gulf of Lions and claimed for a habitat integrated management of fisherie