Miracle-dz/UniFAR-FTUs huggingface dataset:
Aedes aegypti in Tahiti and Moorea (French Polynesia): isoenzyme differentiation in the mosquito population according to human population density.

Genetic differences at five polymorphic isoenzyme loci were analyzed by starch gel electrophoresis for 28 Aedes aegypti samples. Considerable (i.e., high Fst values) and significant (i.e., P values >10(-4)) geographic differences were found. Differences in Ae. aegypti genetic structure were related to human population densities and to particularities in mosquito ecotopes in both Tahiti and Moorea islands. In highly urbanized areas (i.e., the Papeete agglomeration), mosquitoes were highly structured. Recurrent extinction events consecutive to insecticidal treatments during dengue outbreaks tend to differentiate mosquito populations. In less populated zones (i.e., the east coast of Moorea and Tahiti), differences in ecotope characteristics could explain the lack of differentiation among mosquitoes from rural environments such as the east coast of Tahiti where natural breeding sites predominate. When the lowest populated zones such as Tahiti Iti and the west coast of Moorea are compared, mosquito are less differentiated in Moorea. These results will be discussed in relation to the recent findings of variation in mosquito infection rates for dengue-2 virus.
Visualization of multispectral video with moving background based on background extraction and fusion

Detecting a target in a visible video is a challenge at times, as RGB sensors do capture color and correct relative luminance, but are under exposed and noisy. In the infrared band, visibility of the same target is better but they lack color and relative luminance. Frame by frame fusion of these two videos can be a solution but computationally expensive and thus impractical. For the video captured by fixed camera, the background can be extracted easily and fused with the moving target to save the computation. But if the camera is moving, then the extraction of the moving background is a big challenge. In this paper, we present an efficient multi spectral video visualization method using estimation of the background in motion. The analysis shows that the proposed method is superior compared to the existing methods quantitatively as well as visually. More importantly it is devised to use in real time to make it more practical.
An Educational Intervention to Train Professional Nurses in Promoting Patient Engagement: A Pilot Feasibility Study

Introduction: Growing evidence recognizes that patients who are motivated to take an active role in their care can experience a range of health benefits and reduced healthcare costs. Nurses play a critical role in the effort to make patients fully engaged in their disease management. Trainings devoted to increase nurses' skills and knowledge to assess and promote patient engagement are today a medical education priority. To address this goal, we developed a program of nurse education training in patient engagement strategies (NET-PES). This paper presents pilot feasibility study and preliminary participants outcomes for NET-PES. Methods: This is a pilot feasibility study of a 2-session program on patient engagement designed to improve professional nurses' ability to engage chronic patients in their medical journey; the training mainly focused on passing patient engagement assessment skills to clinicians as a crucial mean to improve care experience. A pre-post pilot evaluation of NET-PES included 46 nurses working with chronic conditions. A course specific competence test has been developed and validated to measure patient engagement skills. The design included self-report questionnaire completed before and after the training for evaluation purposes. Participants met in a large group for didactic presentations and then they were split into small groups in which they used role-play and case discussion to reflect upon the value of patient engagement measurement in relation to difficult cases from own practice. Results: Forty-six nurses participated in the training program. The satisfaction questionnaire showed that the program met the educational objectives and was considered to be useful and relevant by the participants. Results demonstrated changes on clinicians' attitudes and skills in promoting engagement. Moreover, practitioners demonstrated increases on confidence regarding their ability to support their patients' engagement in the care process. Conclusions: Learning programs teaching nurses about patient engagement strategies and assessment measures in clinical practice are key in supporting the realization of patient engagement in healthcare. Training nurses in this area is feasible and accepted and might have an impact on their ability to engage patients in the chronic care journey. Due to the limitation of the research design, further research is needed to assess the effectiveness of such a program and to verify if the benefits envisaged in this pilot are maintained on a long-term perspective and to test results by employing a randomized control study design.
Cardiac arrhythmia detection using dynamic time warping of ECG beats in e-healthcare systems

Automatic real-time detection and classification of ECG patterns is of great importance in early diagnosis and treatment of life-threatening cardiac arrhythmia. In this paper, we have presented dynamic time warping (DTW) distance based approach for classification of arrhythmic ECG beats, with an aim of using it in smart-phone/mobile environment. The performance of the proposed method is tested on ECG beats of various arrhythmia types selected from MIT-BIH arrhythmia database. We have compared the proposed DTW approach using naïve Bayes classifier with relative band spectral power as feature. The DTW approach has shown superior performance compared to the naïve Bayes classifier. Furthermore, we have verified the performance of the DTW approach on down-sampled ECG beats in order to improve speed of the DTW algorithm. It is observed that the performance of the DTW approach did not deteriorate even after subsampling of ECG beats. The DTW with subsampling has been aimed at real-time arrhythmia detection in wearable mobile healthcare systems in telemedicine scenario for continuous monitoring of ECG records from cardiac patients.
Agility versus discipline: Is reconciliation possible?

Software development methodologies have been considered as essential to the production of high quality software, especially on time and within budget. During the last decade, a new school of practice that labels itself as the agile development school has emerged with a set of concepts that can be seen as diametrically opposed to the tenets of traditional methodologies. Promising effectiveness and efficiency in software development, agile practices are transforming methodologies for developing information systems. This paper discusses whether the two schools are in total contradiction and whether they can be in some way combined within the same project, showing the relation between the agile software development methodologies and the information systems development field.
OBSESSIVE-COMPULSIVE DISORDER: PREVALENT AND DEBILITATING ILLNESS

Obsessive–compulsive disorder (OCD) is a mental disorder characterized by absurd, recurrent and uncontrollable thoughts (obsessions) that produce anxiety, which are followed by repetitive behaviors (compulsions) aimed at reducing anxiety. OCD may be looked upon as a condition in which the affected person frequently experiences irresistible urges to perform repetitive rituals (compulsions). OCD is a prevalent and debilitating illness that often follows a chronic course. Although the neurobiology and etiology of OCD are not completely understood, growing clinical and preclinical evidence appears to support the abnormalities of glutamatergic neurotransmission, including N-methyl-D-aspartate subtype receptor (NMDAR) function, in the pathophysiology and treatment of OCD. The brain regions impaired in OCD includes dorsolateral prefrontal cortex (DLPC), anterior cingulate cortex (ACC), basal ganglia, orbito-frontal cortex (OFC), striatum, amygdala, thalamus and brainstem OCD may be defined as the irruption in the mind of uncontrollable, egodystonic and recurrent thoughts, impulses or images. In OCD, repetitive rituals serve to counteract the anxiety precipitated by obsessions. The OCD patients realize the irrational nature of thoughts and rituals but feel helpless and hopeless about controlling them. Obsessive-Compulsive disorder can impair all areas of brain functioning and produce devastating effects on patients and their families.
High-dynamic range imaging techniques based on both color-separation algorithms used in conventional graphic arts and the human visual perception modeling

The aim of this research is to derive illuminant-independent type of HDR imaging modules which can optimally multispectrally reconstruct of every color concerned in high-dynamic-range of original images for preferable cross-media color reproduction applications. Each module, based on either of broadband and multispectral approach, would be incorporated models of perceptual HDR tone-mapping, device characterization. In this study, an xvYCC format of HDR digital camera was used to capture HDR scene images for test. A tone-mapping module was derived based on a multiscale representation of the human visual system and used equations similar to a photoreceptor adaptation equation, proposed by Michaelis-Menten. Additionally, an adaptive bilateral type of gamut mapping algorithm, using approach of a multiple conversing-points (previously derived), was incorporated with or without adaptive Un-sharp Masking (USM) to carry out the optimization of HDR image rendering. An LCD with standard color space of Adobe RGB (D65) was used as a soft-proofing platform to display/represent HDR original RGB images, and also evaluate both renditionquality and prediction-performance of modules derived. Also, another LCD with standard color space of sRGB was used to test gamut-mapping algorithms, used to be integrated with tone-mapping module derived.
Distributed Network Defence and Reinforcement Learning

The increasing number of security incidents against computer networks has made insufficient network management and intrusion detection approaches to maintain and to protect these complex systems. Even distributed intrusion detection seems to be not enough if it is used isolated from other disciplines. My research will focus in how network and security agents can learn to detect and to categorise abnormal states of the network using distributed reinforcement learning. We use reinforcement learning because of its capabilities to solve interactive problems when is difficult or impractical to obtain examples of the desired behaviour. We observed that network management systems have failed to offer a proper view of the state of complex network or in networks under security attacks. Intrusion Detection Systems have evolved to distributed systems and they have adopted artificial intelligence techniques to detect intrusions. However these approaches are not enough to identify complex attacks or attacks in a global scale. This work can also contribute in providing new scenarios to evaluate solutions to overcome important issues in the use of reinforcement learning in multiagent systems.
Dependencies revisited for improving data quality

Dependency theory is almost as old as relational databases themselves, and has traditionally been used to improve the quality of schema, among other things. Recently there has been renewed interest in dependencies for improving the quality of data. The increasing demand for data quality technology has also motivated revisions of classical dependencies, to capture more inconsistencies in real-life data, and to match, repair and query the inconsistent data. This paper aims to provide an overview of recent advances in revising classical dependencies for improving data quality.
Geometry Modeling for Virtual Reality Based on CAD Data

In order to satisfy the requirement of geometry modeling for virtual reality system, we process a modeling method based on CAD data. Firstly, the media file that is exported from the CAD data is parsed to gain the mesh data. And then the mesh data was simplified through the improved quadric error metrics algorithm, which makes better shape of triangle. At last, the mesh data is wrote to standard virtual reality model with lever of details via OpenFlight API. Ex- periments show that triangular mesh shape by our algorithm is more reasonable than QEM algorithm, but with higher quality of rendering. With large simplification, rendering frame rate and loading efficiency of model are high improved, and visualization of the virtual reality model almost have no change, which will be sufficient to meet the demand of virtu- al reality system.
Diversity Fairness in Tomlinson–Harashima Precoded Multiuser MIMO Through Retransmission

In this letter, we study the diversity unfairness associated with the conventional Tomlinson-Harashima precoding (THP) in multiuser multiple-input multiple-output downlink transmission. A single-retransmission scheme that combines two THP signals at each user with a complementary multi-user suppression order is investigated. For a system with M antennas at the transmitter and a single antenna at each user, the proposed scheme provides a diversity order M+1 for all users and ensures diversity fairness. We study two retransmission policies, where the users either consider only the current received codeword or combine both codewords for decoding. An asymptotic analysis of the outage probability for both THP retransmission schemes is provided. In addition, a power allocation policy that minimizes the outage probability and accommodates the same coding gain at each user is discussed by formulating a geometric optimization problem.
On the collaboration of an automatic path-planner and a human user for path-finding in virtual industrial scenes

This paper describes a global interactive framework enabling an automatic path-planner and a user to collaborate for finding a path in cluttered virtual environments. First, a collaborative architecture including the user and the planner is described. Then, for real time purpose, a motion planner divided into different steps is presented. First, a preliminary workspace discretization is done without time limitations at the beginning of the simulation. Then, using these pre-computed data, a second algorithm finds a collision free path in real time. Once the path is found, an haptic artificial guidance on the path is provided to the user. The user can then influence the planner by not following the path and automatically order a new path research. The performances are measured on tests based on assembly simulation in CAD scenes.
A Cost-Effective and Robust Shot Change Detection and Classification Technique based on Camera Tracking

Shot is considered a good unit for indexing and retrieval in video databases. Existing automatic shot boundary detection techniques require many threshold parameters which are difficult to select. To address this drawback, we present in this paper a content-based approach based on camera tracking. This is accomplished by tracking the background areas of the video. This strategy can detect and identify both abrupt and gradual changes. Our experimental results, based on six categories of 26 videos, indicate that the proposed method is more reliable than existing schemes although it processes 400% less data.
Associations Between Childhood Abuse, Posttraumatic Stress Disorder, and Implicit Emotion Regulation Deficits: Evidence From a Low-Income, Inner-City Population

Objective: Childhood abuse is associated with a wide range of negative outcomes, including increased risk for development of emotion dysregulation and psychopathology, such as posttraumatic stress disorder (PTSD). The goal of the present study was to examine associations between child abuse, PTSD symptoms, and performance on an emotional conflict regulation task that assesses implicit emotion regulation abilities. Method: The sample consisted of 67 (94% African American) females recruited from a public, urban hospital. Childhood abuse was measured using the Childhood Trauma Questionnaire, and PTSD was measured using the modified PTSD Symptom Scale. Task accuracy and implicit emotion regulation were measured through an emotional conflict regulation behavioral task. Results: A multivariate analysis of covariance showed that exposure to moderate to severe childhood abuse was significantly related to worse emotional conflict regulation scores independent of current PTSD symptoms, depressive symptoms, and adult trauma exposure, suggesting a deficit in implicit emotion regulation. We also found an interaction between PTSD symptoms and abuse exposure in predicting accuracy on the behavioral task; high levels of PTSD symptoms were associated with poorer task accuracy among individuals who reported moderate to severe exposure to childhood abuse. However, no relationship between implicit emotion regulation abilities and overall PTSD symptom severity was found. Conclusions: This study provides preliminary evidence of an implicit emotion regulation deficit for individuals exposed to significant childhood abuse and further supports the growing evidence that addressing various aspects of emotion dysregulation, such as awareness of emotions and strategies to manage strong emotions, in the context of treatment would be valuable.
A hybrid genre-based personalized recommendation algorithm

Because of the serious information overload problem on the internet, the recommender system as one of the most important solutions has been widely used to help users find more valuable information. However, the traditional collaborative filtering method is seriously affected by the rating sparseness and cold start to obtain the precise recommendation. In this paper, a hybrid collaborative filtering method (CGCF) based on the traditional user-based collaborative filtering, as well as, a new approach named genre-based collaborative filtering (GCF) is proposed. GCF uses term frequency-inverse document frequency(TF-IDF) to combine users' former ratings with item genres to quantize individual genre preference. Combining GCF and User-bsed collaborative filtering with dynamic weight, we proposed the CGCF. According to the experiment on Movielens dataset, when comparing with Item-based collaborative filtering, CGCF has reduced MAE by 2.2% and improved Coverage by 16.9%. When comparing with User-based collaborative filtering, CGCF has reduced MAE by 2.5% and improve Coverage by 6.2%. The results demonstrate that the proposed method improves the precision and coverage of recommendation obviously comparing with the traditional ones.
Robust Fuzzy Control for Synchronous Machine with Uncertain Parameters

This paper is concerned with the robust control problem of the synchronous machine systems with uncertain parameters. The uncertain non-linear model is first represented by a Takagi-Sugeno with several fuzzy IF-THEN rules. Next, based on these a T-S fuzzy model, a simple fuzzy controller is designed via the parallel distributed compensation (PDC) technique to realize the stabilization of our system. The robust stability of the closed-loop synchronous machine system are proved based on Lyapunov criterion, some sufficient conditions are derived and the common P is solved by linear matrix inequalities (LMI) toolbox of Matlab, so that the whole closed-loop fuzzy system with the synthesized fuzzy control is asymptotically stable. Furthermore, we also discuss the robustness of the closed loop system with perturbations. Finally, a Simulation results demonstrate that the designed fuzzy controller can stabilize the uncertain synchronous machine system. Key-Words: Continuous systems, T-S model, Robust control, Quadratic stability, uncertain.
Improving virtual host efficiency through resource and interference aware scheduling

Modern Infrastructure-as-a-Service Clouds operate in a competitive environment that caters to any user's requirements for computing resources. The sharing of the various types of resources by diverse applications poses a series of challenges in order to optimize resource utilization while avoiding performance degradation caused by application interference. In this paper, we present two scheduling methodologies enforcing consolidation techniques on multicore physical machines. Our resource-aware and interference-aware scheduling schemes aim at improving physical host efficiency while preserving the application performance by taking into account host oversubscription and the resulting workload interference. We validate our fully operational framework through a set of real-life workloads representing a wide class of modern cloud applications. The experimental results prove the efficiency of our system in optimizing resource utilization and thus energy consumption even in the presence of oversubscription. Both methodologies achieve significant reductions of the CPU time consumed, reaching up to 50%, while at the same time maintaining workload performance compared to widely used scheduling schemes under a variety of representative cloud platform scenarios.
‘I couldn’t just entirely be her sister’: the relational and social policy implications of care between young adult siblings with and without disabilities

ABSTRACT Research has commonly explored siblings of people with disabilities’ roles in care for their brothers or sisters with disabilities. Social policy has also commonly framed young adult siblings of people with disabilities as ‘young carers’. However, there has been less consideration of the implications of care for the relationship shared between young adult siblings with and without disabilities and of what this may mean for social policy. What do different types of care mean for sibling relationships? What are the relational and social policy implications of care between siblings? Drawing on a qualitative study of 25 siblings with disabilities and 21 siblings without disabilities aged 15–29, this article explores how young adult siblings perceive, talk and act with regard to the different types of care enacted between them. The article identifies how, during young adulthood, some types of care can endanger siblings’ capacity to feel like siblings and discusses ways that young adult siblings talk and act in order to – as best they can – keep their role within the bounds of a normative sibling relationship. The findings are discussed in light of implications for social policy, particularly with regard to seeing siblings of people with disabilities as ‘young carers’.
The construction of protein-protein interaction network based on machine learning method

Protein-protein interactions (PPIs) are central to most biological processes. Although efforts have been devoted to the development of methodology for predicting PPIs and to construct protein interaction networks, the application of most existing methods is limited because of less and incomplete information. In the present work, we integrate multi-databases which contain protein information and apply for PPI prediction and construction of the interaction network. This process was based on several protein databases and a learning algorithm-support vector machine. The result suggests that the integrating multi-databases could be applied to the exploration of networks for any newly discovered protein with unknown biological relativity. In addition, supplementary experimental information can be added into the multi-databases and enhance the constructing ability of interaction network.