Miracle-dz/UniFAR-FTUs huggingface dataset:
FLEXGRID - DEVELOPMENT AND COMPARISON OF DISTRIBUTION NETWORK FLEXIBILITY MARKET ARCHITECTURES

The goal of FLEXGRID [3] is to facilitate energy sector stakeholders (DSOs, TSOs, producers, retailers, flexibility providers) to: i) easily and effectively create advanced Energy Services (ESs), ii) interact in a dynamic and efficient way with their environment (electricity grid) and the rest of the market stakeholders, and, iii) automate and optimize the planning and operation of their ESs. In this way, FLEXGRID envisages secure, sustainable, competitive, and affordable ESs. In order to facilitate bottom-up investments, modern smart grids have to cope up with the challenging distribution network management. Thus, FLEXGRID develops flexibility market architectures, which allow DSOs (which it considers as the core of the future smart grid architecture) to: a) integrate through an open market Distributed Energy Resources (DERs) in a scalable way and, b) efficiently interact with energy sector stakeholders. More specifically, in this paper we present the predominant flexibility market architectures that FLEXGRID develops. In addition, we discuss their requirements, their advantages, their disadvantages and their compatibility with today's smart grids.
How Competition between Action Representations Affects Object Perception during Development

ABSTRACT Recent evidence in adults indicates that object perceptual processing is affected by the competition between action representations. In the absence of a specific motor plan, reachable objects associated with distinct structural (grasping) and functional (using) actions (e.g., calculator) elicit slower judgments than objects associated with similar actions (e.g., ball). This effect is believed to reflect the cost entailed by the conflict between action representations. The present study aims to identify age-related changes in this conflict cost and investigate its underlying mechanisms. Five age groups from 8 to adulthood participated (n = 119). Participants performed perceptual judgments on different 3D objects in a virtual environment in order to assess their conflict cost (Experiment 1). Action priming effects and Simon effects were further assessed in the same participants as independent indices of the ability to activate action representations and to monitor conflict, respectively (Experiments 2 and 3). Experiment 1 demonstrated that the conflict cost is present in children as young as 8 and follows a non-linear, U-shaped developmental trajectory between 8 and adulthood. Experiments 2 and 3 indicated that action priming effects showed a similar U-shaped curve, whereas Simon effects were stable across age groups. Action priming effects further predicted conflict costs at 10. Results suggest that the conflict cost relies on the ability to activate action representations from visual objects, which undergoes important changes during early adolescence. The role of general conflict monitoring abilities in conflict cost development requires further investigation. The findings will fuel models of action selection and embodied views of development.
Seismogenic Anomalies in Atmospheric Gravity Waves as Observed from SABER/TIMED Satellite during Large Earthquakes

Atmospheric disturbances caused by seismic activity are a complex phenomenon. The Lithosphere–Atmosphere–Ionosphere Coupling (LAIC) (LAIC) mechanism gives a detailed idea to understand these processes to study the possible impacts of a forthcoming earthquake. The atmospheric gravity wave (AGW) is one of the most accurate parameters for explaining such LAIC process, where seismogenic disturbances can be explained in terms of atmospheric waves caused by temperature changes. The key goal of this work is to study the perturbation in the potential energy associated with stratospheric AGW prior to many large earthquakes. We select seven large earthquakes having Richter scale magnitudes greater than seven ( M > 7.0 ) in Japan (Tohoku and Kumamoto), Mexico (Chiapas), Nepal, and the Indian Ocean region, to study the intensification of AGW using the atmospheric temperature profile as recorded from the Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) satellite. We observe a significant enhancement in the potential energy of the AGW ranging from 2 to 22 days prior to different earthquakes. We examine the conditions of geomagnetic disturbances, typhoons, and thunderstorms during our study and eliminate the possible contamination due to these events.
Occupational and Physical Therapy Strategies for the Rehabilitation of COVID-19-Related Guillain-Barré Syndrome in the Long-term Acute Care Hospital Setting: Case Report

Background Although several reports have described the diagnosis and treatment of patients with COVID-19-associated Guillain-Barré syndrome (GBS), there is a paucity of literature describing the occupational and physical therapy (OT and PT) strategies used in the long-term acute care hospital (LTACH) setting to rehabilitate these patients. Objective To expand this body of literature, we present a case report highlighting the treatment strategies used to rehabilitate and discharge an individual from an independent LTACH facility, following diagnosis and treatment of COVID-19-related GBS at a regional ACH. Methods A 61-year-old male was admitted to an LTACH for the rehabilitation of GBS following COVID-19 infection and intravenous immunoglobulin treatment. Rehabilitation in the LTACH setting uses a variety of skilled treatment interventions to meet patient-driven goals and maximize their function to the highest level possible in preparation of their discharge to a subacute or homecare setting. In this case, this was accomplished through individual OT and PT sessions, OT/PT cotreatment sessions, and targeted group therapy sessions focused on leg, arm, and fine motor coordination exercises. Results With the OT and PT standard of care, the patient’s improvement was demonstrated by several outcome measures, including manual muscle testing, range of motion, grip strength, and the activity measure for postacute care. The patient was successfully rehabilitated and returned to the community after presenting with COVID-19-associated GBS. Conclusions This report highlights the complex rehabilitation needs patients require to regain independence after diagnosis of COVID-19-associated GBS.
Integrated Control and Performance Analysis of High-Speed Medium-Voltage Drive Using Modular Multilevel Converter

The modular multilevel converter (MMC) is highly attractive to develop high-speed medium-voltage variable frequency drives (MV VFDs) due to its cascaded design, high-quality output, and strong reliability. However, high-speed MV VFDs have demanding requirements in capacitor voltage stabilization, current regulation, dynamic response, etc. The instability and distorted voltage/current could easily occur if MMCs are used as high-speed MV VFDs. This article presents a high-speed VFD with silicon carbide (SiC)-based MMC feeding a permanent magnet synchronous machine. A new integrated control method is specifically proposed to address the aforementioned issues covering the low-/middle-/high-speed range. Performance analysis is conducted in terms of efficiency, power density, output current quality, and speed range. Steady-state and dynamic conditions are evaluated by a full-scale simulation model and a high-power SiC-based MMC testbed. Results show that the proposed method effectively removes the large capacitor voltage fluctuations, resonant circulating current, and saturated output voltage from zero to the highest speed. Compared with existing MMC VFDs of limited speed (<4000 r/min), the SiC-based MMC VFD has three times higher speed (15 000 r/min), improved efficiency (99.43%), high power density, and low current harmonic distortions.
IMPACT OF THE SELECTION OF GRID-FORMING CONVERTERS ON THE STABLE OPERATION OF MEDIUM VOLTAGE TEMPORARY ISLANDED MICROGRIDS

Due to the increasing generation by distributed units at the medium and low voltage level, a temporary islanded microgrid operation of distribution grid structures becomes possible. The islanded operation can be advantageous during a blackout of the transmission grid, as the supply of customers can be ensured using local generation units. In order to enable an autonomous operation, at least one grid-forming unit is required. In this paper, a distributed grid-forming control concept based on several droop-controlled grid-forming converters is focused. By conducting dynamic time domain simulations and applying load changes in the CIGRE medium voltage benchmark grid, the impact of the amount and the positioning of the grid-forming converters is investigated. A particular focus is placed on the interactions between the grid-forming and the existing grid-following converters, which are implemented to behave according to the German grid code VDE-AR-N 4110. The results show a beneficial behaviour of the distributed grid-forming control concept, if no dominant grid-forming converter coupled generation unit is present in the grid. This is due to the fact that the load changes are initially shared among the grid-forming units. Furthermore, an electrically closer positioning of the grid-forming converters can balance that initial power sharing.
Ship classification based on convolutional neural networks

ABSTRACT The main bottleneck limiting the use of traditional ship classification methods is the manual extraction of ship images before classification. To solve this problem, a ship classification method based on a convolutional neural network (CNN) is proposed in this paper. A CNN model can autonomously extract image features, avoiding complex feature selection and extraction processes. In view of the problem of an insufficient number of ship samples, transfer learning was applied to train the model using the ImageNet dataset, effectively alleviating the over-fitting phenomenon in the training process. Experiments showed that the CNN model had an accuracy of 98% in ship classification using the SHIP-3 dataset. The CNN was robust to external environmental challenges – such as illumination – the accuracy of ship classification in foggy and night-time conditions reaching 75%, greatly exceeding the performance of traditional machine learning algorithms.
Overlapping Community Detection in Social Networks

Overlapping Community Detection in Social Networks by Akshar Panchal Social networking sites are important to connect with the world virtually. As the number of users accessing these sites increase, the data and information keeps on increasing. There are communities and groups which are formed virtually based on different factors. We can visualize these communities as networks of users or nodes and the relationships or connections between them as edges. This helps in evaluating and analyzing different factors that influence community formation in such a dense network. Community detection helps in revealing certain characteristics which makes these groups in the network unique and different from one another. We can use such information to find trends in the network which might help in understanding complex systems. In this project, we will study the problem of detecting local overlapping communities in a stream graph, by proposing a new metric that of common communities of the endpoints of a new edge. Moreover, we discover good seed nodes by finding an offline non overlapping community structure of a small sub graph as a preprocessing step. Additionally, we also evaluate these methods with different and extensive datasets. We experiment with a new web graph dataset along with some other more commonly used datasets. F1 score is used for datasets that have the ground truth. The proposed algorithm outperforms the traditional methods by 17%. For the new web graph dataset we use the overlapping modularity metric to evaluate our approach. This approach yields accurate modularity scores up to 0.71 for the web graph dataset increasing the accuracy by 10%. In the end, we discuss the approaches used and their results along with scope to improve for the future.
False Positives in Credit Card Fraud Detection: Measurement and Mitigation

Credit Card Fraud Detection is a classification problem where different types of classification errors cause different costs. Previous works quantified the financial impact of data-driven fraud detection classifiers using a cost-matrix based evaluation approach, however, none of them considered the significant financial impact of false positives. Analysts reported that fraud prediction in e-commerce still has to deal with false positive rates of 30-70%, and many cardholders reduce card usage after being wrongly declined. In our paper, we propose a new method for assessing the cost of false positives and evaluate several state-of-the-art fraud detection classifiers using this method. Further, we investigate the effectiveness of ensemble learning as previous work supposed that a combination of diverse, individual classifiers can improve performance. Our results show that cost-based evaluation yields valuable insights for practitioners and that our ensemble learning strategy indeed cuts fraud cost by almost 30%.
Alt. Health Influencers: how wellness culture and web culture have been weaponised to promote conspiracy theories and far-right extremism during the COVID-19 pandemic

This article examines the proliferation of alt. health influencers during the COVID-19 pandemic. I analyse the self-presentation strategies used by four alt. health influencers to achieve visibility and status on Instagram over a 12-month period from 11 March 2020, when the pandemic was declared by the World Health Organisation. My analysis reveals the ways in which these influencers appeal to the utopian discourses of early web culture and the underlying principles of wellness culture to build and sustain an online following. While early accounts of micro-celebrity treat participatory culture as democratising and progressive, this article demonstrates how the participatory affordances of social media have been exploited to spread misinformation, conspiratorial thinking and far-right extremism. These findings develop previous work on ‘conspirituality’ by demonstrating how wellness culture and web culture can coalesce for authoritarian ends.
Impact of Industry-Specific Risk Factors on Stock Returns of the Malaysian Oil and Gas Industry in a Structural Break Environment

This study examines the impact of industry-specific risk factors such as oil price, gas price, and exchange rate on stock returns of Malaysian oil and gas firms in a structural break environment by employing the break least square approach of Bai and Perron (1998, 2003). Existing studies fall short of providing such empirical evidence. The results document evidence of structural breaks in the relationship between industry risk factors and the stock returns of the oil and gas industry. Industry-specific risk factors are shown to significantly affect the stock returns of oil and gas industry sub-sectors alongside market-based risk factors. The results reveal that the beta values of oil price, gas price, and exchange rate vary across sub-periods hence confirming that exposure of oil and gas stocks to industry risk factors varies over time and across sub-periods. The effects of oil, gas, and exchange rate risk factors also differ across the sub-industry, with impacts and directions largely dependent on the core business activities of the oil and gas sub-industries. The empirical results offer implications for asset managers and investors.
A Pilot Study of Gene Expression Analysis in Peripheral Blood Mononuclear Cells in Response to a Hypocaloric Mediterranean Diet

Background Few studies have examined gene expression in peripheral blood mononuclear cells (PBMCs) after a dietary intervention. Objective Our study is aimed at evaluating in a pilot study the peripheral blood gene expression in obese patients after weight loss secondary to a hypocaloric Mediterranean diet. Design A sample of 11 obese subjects without metabolic syndrome was enrolled. Biochemical, anthropometric parameters and microarray analysis were performed at baseline and after 6 months of dietary intervention. Results The mean age was 43.1 ± 6.3 years, and the mean body mass index (BMI) was 38.6 ± 8.1 kg/m2. All the next improvements were statistically significant: body weight −7.4 ± 1.9 kg, BMI -2.5 ± 0.2 kg, fat mass −5.7 ± 1.2 kg, waist circumference −5.8 ± 1.2 cm, triglycerides −17.4 ± 6.5 mg/dl, C-reactive protein −3.1 ± 1.5 mg/dL, insulin −2.1 ± 1.0 mUI/L, and HOMA-IR −0.7 ± 0.2 units. We identified 634 differentially expressed genes: 262 genes with relative higher expression levels and 372 with lower expression levels. Cluster analysis showed 35 genes in nutritional disease and 17 genes in endocrine system. The most relevant gene was thyroid peroxidase (TPO), and this gene was overexpressed, and the next genes carbonic anhydrase VI (CA6), caveolin protein 1 (CAV1) and solute carrier family type 12 (SLLC12A3), soluble carrier family type 12 (SLLC12A3), beta 3 receptor (ADRB3), and glutamate receptor ionotropic N methyl D aspartate 2 A (GRIN2A) were all underexpressed. Conclusion In PBMC from obese patients after a diet with a Mediterranean pattern, the expression of 634 genes, of the endocrine system and of nutritional disease, is modified.
Structural and functional analysis of the human cone‐rod homeobox transcription factor

The cone-rod homeobox (CRX) protein is a critical K50 homeodomain transcription factor responsible for the differentiation and maintenance of photoreceptor neurons in the vertebrate retina. Mutant alleles in the human gene encoding CRX result in a variety of distinct blinding retinopathies, including retinitis pigmentosa, cone-rod dystrophy, and Leber congenital amaurosis. Despite the success of using in vitro biochemistry, animal models, and genomics approaches to study this clinically relevant transcription factor over the past 24 years since its initial characterization, there are no high-resolution structures in the published literature for the CRX protein. In this study, we use bioinformatic approaches and small-angle x-ray scattering (SAXS) structural analysis to further understand the biochemical complexity of the human CRX homeodomain (CRX-HD). We find that the CRX-HD is a compact, globular monomer in solution that can specifically bind functional cis-regulatory elements encoded upstream of retina -specific genes. This study presents the first structural analysis of CRX, paving the way for a new approach to studying the biochemistry of this protein and its disease-causing mutant protein variants.
Sapienic Acid Metabolism Influences Membrane Plasticity and Protein Signaling in Breast Cancer Cell Lines

The importance of sapienic acid (6c-16:1), a monounsaturated fatty acid of the n-10 family formed from palmitic acid by delta-6 desaturase, and of its metabolism to 8c-18:1 and sebaleic acid (5c,8c-18:2) has been recently assessed in cancer. Data are lacking on the association between signaling cascades and exposure to sapienic acid comparing cell lines of the same cancer type. We used 50 μM sapienic acid supplementation, a non-toxic concentration, to cultivate MCF-7 and 2 triple-negative breast cancer cells (TNBC), MDA-MB-231 and BT-20. We followed up for three hours regarding membrane fatty acid remodeling by fatty acid-based membrane lipidome analysis and expression/phosphorylation of EGFR (epithelial growth factor receptor), mTOR (mammalian target of rapamycin) and AKT (protein kinase B) by Western blotting as an oncogenic signaling cascade. Results evidenced consistent differences among the three cell lines in the metabolism of n-10 fatty acids and signaling. Here, a new scenario is proposed for the role of sapienic acid: one based on changes in membrane composition and properties, and the other based on changes in expression/activation of growth factors and signaling cascades. This knowledge can indicate additional players and synergies in breast cancer cell metabolism, inspiring translational applications of tailored membrane lipid strategies to assist pharmacological interventions.
IOTA Based Anomaly Detection Machine learning in Mobile Sensing

In this proposed method, iMCS can detect and prevent fake sensing activities of mobile users using machine learning techniques. Our iMCS solution uses behavioral analysis based on participants' reliability scores to detect variation in behavior of users and introduces a new role in a distributed system of MCS architecture to validate the collected data. To evaluate the incentive based on the participant's sensory data and data quality, to properly distribute profit among the participants, we employ the Shapley Value approach. The evaluation results demonstrate that our method is effective in both quality estimations and incentive sharing.
The RADARSAT Constellation Mission Core Applications: First Results

The Canadian RADARSAT Constellation Mission (RCM) has passed its early operation phase with the performance evaluation being currently active. This evaluation aims to confirm that the innovative design of the mission’s synthetic aperture radar (SAR) meets the expectations of intended users. In this study, we provide an overview of initial results obtained for three high-priority applications; flood mapping, sea ice analysis, and wetland classification. In our study, the focus is on results obtained using not only linear polarization, but also the adopted Compact Polarimetric (CP) architecture in RCM. Our study shows a promising level of agreement between RCM and RADARSAT-2 performance in flood mapping using dual-polarized HH-HV SAR data over Red River, Manitoba, suggesting smooth continuity between the two satellite missions for operational flood mapping. Visual analysis of coincident RCM CP and RADARSAT-2 dual-polarized HH-HV SAR imagery over the Resolute Passage, Canadian Central Arctic, highlighted an improved contrast between sea ice classes in dry ice winter conditions. A statistical analysis using selected sea ice samples confirmed the increased contrast between thin and both rough and deformed ice in CP SAR. This finding is expected to enhance Canadian Ice Service’s (CIS) operational visual analysis of sea ice in RCM SAR imagery for ice chart production. Object-oriented classification of a wetland area in Newfoundland and Labrador by fusion of RCM dual-polarized VV-VH data and Sentinel-2 optical imagery revealed promising classification results, with an overall accuracy of 91.1% and a kappa coefficient of 0.87. Marsh presented the highest user’s and producer’s accuracies (87.77% and 82.08%, respectively) compared to fog, fen, and swamp.
Daily wind-based interruption prediction in Enedis' Medium Voltage Overhead Lines using time series data

Overhead lines represent about 50% of the French Medium Voltage/Low Voltage electrical network. Therefore, most of the French electrical network is vulnerable to weather conditions. In order to optimize financial investments, and plan maintenance campaigns, a reliability model would be helpful, especially if based on three main features of the electrical network: the intrinsic reliability linked to natural ageing, the sensibility to frequent weather conditions, and its vulnerability to extreme and rare climatic hazards which it is more difficult to hedge. The history of electrical failures is used to tune and build this model in order to propose a failure rate made of those three features. Then we will present a machine learning model which predict a few days ahead of time, the number of failures caused by the prevailing winds of a French area.