Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of an Aggregation Tree Over the Skewness of the Final Risk Distribution

Following the implementation of Solvency 2 in Europe and other parts of the world, many (re)insurance companies decided to put in place Internal Models. The central part of an internal model is its aggregation tree and the calibration of the aggregation tree has been the subject of many articles. Among the different articles, “Estimating copula for insurance from scarce observations, expert opinion and prior information: a Bayesian approach”, ASTIN Bulletin (2012) by Arbenz et al. sets a general framework for estimating dependencies. In practice, most of the calibration exercises focus on elements which are similar in nature to “correlation” as this is the easiest part of the dependence structure: Practitioners (including underwriters, claims managers, actuaries and finance managers) usually understand and have a slight feeling for correlations if relevant explanations are provided. But the other impacts of these calibration choices are usually forgotten as practitioners do not have a view on these elements. In particular, the number of layers of the aggregation tree as well as the order of the aggregation play a role on both the overall volatility and the overall skewness of the final risk distribution. But, usually, these elements are not discussed in detail. As volatility and skewness are the main determinants of the risk measure (Value at Risk or Expected Shortfall), understanding the impact of the calibration choices on these elements is certainly important and should also be in the focus of the calibration exercise. As a consequence, this article aims at estimating the impact of the different choices of calibration of the aggregation tree on both volatility and skewness of the overall risk distribution. Examples of calibration are provided to demonstrate the impact of the different (forgotten) elements of choice in a calibration exercise.
Digital Current Control of an Asymmetrical Dual Three-Phase Flux-Switching Permanent Magnet Machine

This article proposes an improved digital discrete-time current control scheme for a dual three-phase flux-switching permanent-magnet (FSPM) machine with significant asymmetry between two sets of armature windings. Resonant controllers have been widely used to eliminate current harmonics and unbalance for multiphase machine. However, when the harmonic frequency is relatively high with respect to the sample frequency, the discretization of resonant controller needs additional pole correction and delay compensation, making the design more complicated and performance degraded. In this article, based on the discrete-time domain machine model, the resonant controllers directly designed in z domain using pole-zero cancelation method are developed and optimized for the practical implementation. These resonant controllers can be applied in either stationary frame or rotational frame and the stability is permitted in a wide frequency range. Experimental results on a prototype dual three-phase FSPM machine validate the effectiveness of the proposed scheme.
A domain-specific measure of centrality for water distribution networks

Purpose In recent years, centrality measures have been extensively used to analyze real-world complex networks. Water distribution networks (WDNs), as a good example of complex networks, exhibit properties not shared by other networks. This raises concerns about the effectiveness of applying the classical centrality measures to these networks. The purpose of this paper is to generate a new centrality measure in order to stick more closely to WDNs features. Design/methodology/approach This work refines the traditional betweenness centrality by adding a hydraulic-based weighting factor in order to improve its fit with the WDNs features. Rather than an exclusive focus on the network topology, as does the betweenness centrality, the new centrality measure reflects the importance of each node by taking into account its topological location, its demand value and the demand distribution of other nodes in the network. Findings Comparative analysis proves that the new centrality measure yields information that cannot be captured by closeness, betweenness and eigenvector centrality and is more accurate at ranking the importance of the nodes in WDNs. Practical implications The following practical implications emerge from the centrality analysis proposed in this work. First, the maintenance strategy driven by the new centrality analysis enables practitioners to prioritize the components in the network based on the priority ranking attributed to each node. This allows for least cost decisions to be made for implementing the preventive maintenance strategies. Second, the output of the centrality analysis proposed herein assists water utilities in identifying the effects of components failure on the network performance, which in turn can support the design and deployment of an effective risk management strategy. Originality/value The new centrality measure, proposed herein, is distinct from the conventional centrality measures. In contrast to the classical centrality metrics in which the importance of components is assessed based on a pure topological viewpoint, the proposed centrality measure integrates both topological and hydraulic attributes of WDNs and therefore is more accurate at ranking the importance of the nodes.
The Influence of Prolonged Conditionally Automated Driving on the Take-Over Ability of the Driver

Other than during manual and partly automated driving, the driver will not need to constantly observe and correct the path of his/her car with future conditionally automated driving (CAD) systems. The driver will be only responsible to stay alert and be ready to take-over the driving in a preset handover time, if requested. The aim of this simulator study was to examine the reactions of drivers, when requested to take-over after a long, monotonous conditionally automated drive. The conditionally automated drives of 20 drivers (mean driving time of 2:51 ± 0:18 h) were evaluated. To test their reaction and take-over ability, the drivers experienced four easy take-over situations and were additionally challenged with a demanding situation in their worst state regarding fatigue. There was no significant influence found of their fatigue state on the take-over time and quality. The different take-over times in different situations point merely towards an adaption of the drivers’ reactions onto the specific necessities of the different driving situations.
A Longitudinal Examination of Military Veterans’ Invictus Games Stress Experiences

This study explored patterns of change in stress variables (i.e., stressors, appraisals, emotions) encountered by wounded, injured, and sick military veterans in the build up to, during, and following an international sporting competition. The study also examined interactions between psychosocial variables and salivary biomarkers of stress and how these relate to veterans’ health, well-being, illness, and performance. 40 Invictus Games (IG) athletes and a control group of 20 military veteran athletes completed questionnaires at seven time points over a 12-week period. Furthermore, participants provided morning and evening saliva samples at four time points to measure cortisol and secretory immunoglobulin A. Multilevel growth curve analyses revealed significant changes in growth trajectories of stress-related variables. For example, team and culture stressors and anger and dejection emotions significantly increased in the build up to competition, whilst challenge appraisals and excitement and happiness emotions significantly decreased over the same time-frame. A number of the stress related variables also predicted performance, well-being, and mental health. Specifically, organizational stressors and threat appraisals were found to negatively relate to performance, well-being, and mental health. Furthermore, whilst challenge appraisals and problem focused coping positively related to veterans’ well-being, adopting emotion-focused and avoidance coping strategies negatively predicted well-being and mental health. Turning to emotions, experiencing anger, anxiety, and dejection negatively related to mental health, well-being and performance; whereas happiness and excitement displayed a positive relationship with these outcomes. The findings also highlighted that organizational stressor intensity was positively related to cortisol exposure at competition. To conclude, this study not only provides a novel, longitudinal, interdisciplinary insight into psychological and biological markers of the stress response as it relates to the performance, health, and well-being of military veterans, but also further contributes to theoretical understanding on the transactional nature of stress. Moreover, the findings significantly contribute to practice regarding how best to support this unique population in adaptively responding to and engaging with competitive sport.
The effect of vent inlet aspect ratio and its location on ventilation efficiency

The total energy consumption of buildings in China has been increasing year by year, ventilation energy consumption accounts for more than 20% of the total building energy consumption. The optimisation of ventilation system design aiming at energy conservation is of great significance while keeping healthy indoor air quality. However, the current codes and standards are still lacking specification requirements for vents design, e.g. inlet size ratio or position, which can greatly impact on indoor airflow and ventilation efficiency. In this work, we investigated the aspect ratios of vent inlets and their locations on indoor air quality. Both experimental and numerical simulation methods were adopted. Ventilation efficiency is represented with the local average peak concentration 〈C ̅*〉A,max. Results showed that when vent inlet is in symmetric conditions (i.e. located in the middle of the sidewall) and the aspect ratio between the length and width of the vent inlet equals to 4, ventilation removal efficiency showed the best performance. These findings are of great importance to the engineering applicability and able to provide reference for future design standards.
Artificial neural network model for arrival time computation in gate level circuits

ABSTRACT Advances in the VLSI process technology lead to variations in the process parameters. These process variations severely affect the delay computation of a digital circuit. Under such variations, the various delays, i.e. net delay, gate delay, etc., are no longer deterministic. They are random in nature and are assumed to be probabilistic. They keep changing, based on factors such as process, voltage, temperature, and a few others. This calls for efficient tools to perform timing checks on a design. This work presents a technique to compute the arrival time of a digital circuit. The arrival time (AT) is computed using two different timing engines, namely, static timing analysis (STA) and statistical static timing analysis (SSTA). This work also aims to eliminate number of false paths. It uses a fast and efficient filtering method by utilizing ATPG stuck-at faults and path delay faults. ISCAS-89 benchmark circuits are used for implementation. The results obtained using the probabilistic approach are more accurate than the conventional STA. It has been verified with an Artificial Neural Network (ANN) model. The arrival time calculated using SSTA shows 7% improvement over that of STA. The absolute error is reduced twofold in the case of the ANN model for SSTA.
Applying Narrative Theory to Aid Unexpectedness in a Self-Evaluative Story Generation System

Predictability is the polar opposite of originality, and as such it is a notable obstacle that should be overcome in the pursuit of computational creativity. Accurately modelling a human’s understanding of predictability would be a monumental task, requiring a contextually rich network of social interaction, literature, news, and media. However, by artificially instilling a computer with some basic ideas about what is predictable in a given scenario, it can begin to gain an understanding of how to subvert expectation. This project attempts to implement such a process into a specially designed story generation system known as Chronicle, inspired by Vladimir Propp’s Morphology of the Folk Tale. Chronicle aims to fine-tune narrative direction and progression in a system modelled on predictability. Decisions made during the story generation process are based on probabilities defined by the expectations of the typical reader, and are amassed to formulate an overall predictability rating. The decision making process is manipulated by the system in order to pursue a customisable predictability target. Chronicle was demonstrably accurate at evaluating its output in some cases, and less accurate in other cases. Further refinement is required to increase its efficacy, but it presents a promising step towards negotiating predictability in computational creativity.
Hyperspectral and LiDAR Classification With Semisupervised Graph Fusion

To fuse hyperspectral and Light Detection And Ranging (LiDAR), we propose a semisupervised graph fusion (SSGF) approach. We apply morphological filters to LiDAR and the first few components of hyperspectral data to model the height and spatial information, respectively. Then, the proposed SSGF is used to project the spectral, elevation, and spatial features onto a lower subspace to obtain the new features. In particular, the objective of SSGF is to maximize the class separation ability and preserve the local neighborhood structure by using both labeled and unlabeled samples. Experimental results on the hyperspectral and LiDAR data from the 2013 IEEE Geoscience and Remote Sensing Society (GRSS) Data Fusion Contest demonstrated the superiority of the SSGF.
Functional and Proteomic Analysis of Streptococcus pyogenes Virulence Upon Loss of Its Native Cas9 Nuclease

The public health impact of Streptococcus pyogenes (group A Streptococcus, GAS) as a top 10 cause of infection-related mortality in humans contrasts with its benefit to biotechnology as the main natural source of Cas9 nuclease, the key component of the revolutionary CRISPR-Cas9 gene editing platform. Despite widespread knowledge acquired in the last decade on the molecular mechanisms by which GAS Cas9 achieves precise DNA targeting, the functions of Cas9 in the biology and pathogenesis of its native organism remain unknown. In this study, we generated an isogenic serotype M1 GAS mutant deficient in Cas9 protein and compared its behavior and phenotypes to the wild-type parent strain. Absence of Cas9 was linked to reduced GAS epithelial cell adherence, reduced growth in human whole blood ex vivo, and attenuation of virulence in a murine necrotizing skin infection model. Virulence defects of the GAS Δcas9 strain were explored through quantitative proteomic analysis, revealing a significant reduction in the abundance of key GAS virulence determinants. Similarly, deletion of cas9 affected the expression of several known virulence regulatory proteins, indicating that Cas9 impacts the global architecture of GAS gene regulation.
Comparison of Columnar, Surface, and UAS Profiles of Absorbing Aerosol Optical Depth and Single-Scattering Albedo in South-East Poland

The impact of absorbing aerosols on climate is complex, with their potential positive or negative forcing, depending on many factors, including their height distribution and reflective properties of the underlying background. Measurement data is very limited, due to insufficient remote sensing methods dedicated to the retrieval of their vertical distribution. Columnar values of absorbing aerosol optical depth (AAOD) and single scattering albedo (SSA) are retrieved by the Aerosol Robotic Network (AERONET). However, the number of available results is low due to sky condition and aerosol optical depth (AOD) limitation. Presented research describes results of field campaigns in Strzyżów (South-East Poland, Eastern Europe) dedicated to the comparison of the absorption coefficient and SSA measurements performed with on-ground in-situ devices (aethalomter, nephelometer), small unmanned aerial system (UAS) carrying micro-aethalometer, as well as with lidar/ceilometer. An important aspect is the comparison of measurement results with those delivered by AERONET. Correlation of absorption to scattering coefficients measured on ground (0.79) and correlation of extinction on ground to AOD measured by AERONET (0.77) was visibly higher than correlation between AOD and AAOD retrieved by AERONET (0.56). Columnar SSA was weakly correlated with ground SSA (higher values of columnar SSA), which were mainly explained by hygroscopic effects, increasing scattering coefficient in ambient (wet conditions), and partly high uncertainty of SSA retrieval. AAOD derived with the use of profiles from UAS up to PBL height, was estimated to contribute in average to 37% of the total AAOD. A method of AAOD estimation, in the whole troposphere, with use of measured vertical profiles of absorption coefficient and extinction coefficient profiles from lidars was proposed. AAOD measured with this method has poor correlation with AERONET data, however for some measurements, within PBL, AAOD was higher than reported by AERONET, suggesting potential underestimation in photometric measurement under particular conditions. Correlation of absorption coefficient in profile to on ground measurements decrease with altitude. Measurements of SSA from drones agree well with ground measurements and are lower than results from AERONET, which suggests a larger contribution of absorbing aerosols. As an alternative for AAOD estimation in case of lack of AERONET AAOD data simple models are proposed, which base on AOD scaling with SSA measured with different methods. Proposed solution increase potential of absorption coefficient measurements in vertical profiles and columns of the atmosphere. Presented solutions make measurements of absorption coefficients in vertical profiles more affordable and allow rough estimation of columnar values for the whole atmosphere.
Networked Learning and Learning Analytics: A Study on the Employment of Facebook in a Virtual Training Program

ABSTRACT The growing popularity of Social Networking Sites (SNS) that are embedded with networked infrastructures serves as an ideal platform for developing a networked learning environment connecting geographically dispersed learners. Unlike the traditional learning systems which provide only limited sources of data, the learners engaged in virtual networked social environments tend to produce huge volumes of digital footprints that cannot be analyzed using conventional analytical techniques. Two new branches of analytical sciences – Educational Data Mining (EDM) and Learning Analytics (LA) – are being employed for processing digital data derived from online educational platforms in order to obtain meaningful inferences and data-driven insights. Hence, the present experimental study involving a small group of geographically dispersed learners intend to examine the engagement level and interaction patterns that occur within a feminist networked learning environment created in Facebook using popular EDM and learning analytical techniques such as K-means Clustering, Social network analysis and correlation mining. Upon analysis, it was found that the peer network influence played a vital role in activating passive learners, eventually leading to the development of a closely bound networked learning community over time.
Pothole Detection Using Computer Vision and Learning

Techniques for identifying potholes on road surfaces aim at developing strategies for real-time or offline identification of potholes, to support real-time control of a vehicle (for driver assistance or autonomous driving) or offline data collection for road maintenance. For these reasons, research around the world has comprehensively explored strategies for the identification of potholes on roads. This paper starts with a brief review of the field; it classifies developed strategies into several categories. We, then, present our contributions to this field by implementing strategies for automatic identification of potholes. We developed and studied two techniques based on stereo-vision analysis of road environments ahead of the vehicle; we also designed two models for deep-learning-based pothole detection. An experimental evaluation of those four designed methods is provided, and conclusions are drawn about particular benefits of these methods.
Soft Rehabilitation and Nursing-Care Robots: A Review and Future Outlook

Rehabilitation and nursing-care robots have become one of the prevalent methods for assistant treatment of motor disorder patients in the field of medical rehabilitation. Traditional rehabilitation robots are mostly made of rigid materials, which significantly limits their application for medical rehabilitation and nursing-care. Soft robots show great potential in the field of rehabilitation robots because of their inherent compliance and safety when they interact with humans. In this paper, we conduct a systematic summary and discussion on the soft rehabilitation and nursing-care robots. This study reviews typical mechanical structures, modeling methods, and control strategies of soft rehabilitation and nursing-care robots in recent years. We classify soft rehabilitation and nursing-care robots into two categories according to their actuation technology, one is based on tendon-driven actuation and the other is based on soft intelligent material actuation. Finally, we analyze and discuss the future directions and work about soft rehabilitation and nursing-care robots, which can provide useful guidance and help on the development of advanced soft rehabilitation and nursing-care robots.
The Effect of Neuro-feedback on Indicators Obsessive-Compulsive Disorder and Anxiety

The aim of this study was to compare the effectiveness of neuro-feedback training on the reduction of indactors obsessive-compulsive, and anxiety. The sample of this study was patients with obsessive-compulsive disorder and anxiety, which were selected through purposeful sampling and in the situations of entry and exit. They were randomly appointed in two groups of neuro-feedback and drug therapy. The Madesley and Beck Anxiety Inventory was used to collect the data from the subjects. In this study, only control group used drug. Finally, using SPSS software, data were analyzed by Covariance method. The findings of this study indicate that neuro-feedback training lead to the significant reduction of checkpoints, rinsing, Kennedy and anxiety (P <0.05). The results of this study show the effectiveness of this method as a complementary method to other treatments such as pharmacotherapy and psychotherapy in the treatment of obsessive-compulsive disorder and anxiety.