Miracle-dz/UniFAR-FTUs huggingface dataset:
Betting on yourself: a decision model for Human Resource Allocation enriched with self-assessment of soft skills and preferences

Recently, many approaches were proposed to support human resource management in finding the best human resources for available jobs. However, existing solutions do not effectively evaluate employees’ skills, or they do only partially, neither provide mechanisms to describe subjects’ skills and desiderata. To face this issue, this paper proposes a decision model for assisting human resource management in effectively evaluating the degree of mutual satisfaction in job-employee assignments. In particular, the decision model has been devised with the following core characteristics: i) employees’ skills are modeled by combining hard skills (e.g.: academic training and competencies) and soft skills (e.g.: socio-relational experiences); ii) employees’ soft skills are self-evaluated, giving importance not so much to experiences possessed but rather how such skills have been applied over time; iii) employees and managers can self-evaluate their preferences to enable the achievement of the optimal allocation by maximizing the global mutual satisfaction iv) partial matches between characteristics and desires of both employees and jobs are measured through a set of tailored fuzzy metrics. The proposed decision model has been validated in a real case to support the allocation of newly hired employees among open job positions in a Public Administration. Results showed an adequate ability of the proposed model both to support the description of employees, skills, jobs and preferences, and to suggest the best allocation maximizing the global mutual satisfaction. Summarizing, a decision model for human resource management with innovative characteristics is proposed and used to support decisions for a real allocation problem.
Portable Sigatoka Spot Disease Identifier on Banana Leaves Using Support Vector Machine

Banana plant diseases serve as a threat to farmers which can affect the quality and quantity of banana production. Black Sigatoka Spot is considered as the most economically important banana leaf disease as it affects banana plantains worldwide, causing yield loses up to 50%. It is caused by a fungi called Mycosphaerella fijensis. Sigatoka spots are small reddish-brown flecks that are seen on the leaf and overtime develop into pale yellow, brown streaks surrounded by a yellow halo. The goal of this research is to develop a portable system that can identify the sigatoka spot disease on a banana leaf to help famers accurately identify Sigatoka spot on banana leaves. The output of the system displays the classification of the leaf and the leaf area. The researchers used a USB Camera to capture banana leaf images and were processed by stitching, equalization, segmentation, feature extraction and classification by Support Vector Machine. Overall, the proposed system obtained a 90% accuracy in classifying healthy and sigatoka-infected banana leaves.
Human Detection in Moving Fisheye Camera using an Improved YOLOv3 Framework

Pedestrian detection has large relevance to the understanding of static and moving scenes of video sequences. The increasing demand for safety and security of people has resulted in more research on intelligent visual surveillance in a wide range of applications, such as moving human detection. With the great success of deep learning methods, researchers decided to switch from traditional methods based hand-crafted feature extractors to recent deep learning-based techniques in order to detect and track people. In this work, the topic of person detection with a Top-view moving fisheye camera is addressed. Although the fisheye camera is a useful tool for video monitoring, most of object detection techniques, with (or without) deep learning, concern classical perspective cameras. However, due to the distortions of fisheye images, we are expected to have higher requirements and challenges on the pedestrian detection using this device. In this paper, we propose an end-to-end learning people detection method based on YOLOv3 detector that detects people using oriented bounding boxes. The proposed model customizes the traditional YOLOv3 for the detection of oriented bounding boxes, by regressing the angle of each bounding box using a periodic loss function. With rotation bounding box prediction, our approach is efficient, reaching 98,1% of true detection. The proposed method is evaluated on a new available dataset where rotated bounding boxes represent annotations from several fisheye videos: https://partage.imt. fr/index.php/s/nytmFqiq8jaztkX
Semi-Automatic Construction of Surrounding Domain for Aerodynamic Simulation with PolyCube

Fluid and aerodynamic simulation are commonly used in computer graphics for the visual effect in film and for computational fabrication. Contrast to traditional area such as marine structure engineering or aerospace engineering, computer graphics researchers cares more about the efficiency of simulation. To meet the requirements of interactive and even realtime simulation, various techniques have been proposed, such as model reduction or machine learning based regression. Construction of suitable simulation domain is a critical issue, which is also time consuming in previous researchers. In this paper, we present a semi-automatic technique for quickly construction of a PolyCube based simulation domain. Various tools are developed to help the user easily and quickly construct a PolyCube approximating the input 3D shape. Equipped subdivision and interpolation, we can provide PolyCube domain with various resolution satisfying different needs, and also adapt to the input 3D shape to alleviate problems such as nonlinearity. Experimental results demonstrate the effectiveness of our method.
Received Path Power Prediction for Millimeter-wave Using Machine Learning

This paper discusses the received power prediction of millimeter-wave by machine learning when a user moves simply like walking straight. In general, a large amount of data is required for the neural network to predict the received power. In this paper, the transfer function of the channel is divided into narrower bandwidths, and the received power obtained from the narrower channel is used for the learning data. The RMSE is evaluated to show the effectiveness of the proposed prediction scheme.
Enhancing Block-Online Speech Separation using Interblock Context Flow

Despite recent progress in speech separation, online processing is still challenging. One promising approach is the block-online structure, which has been examined in a few previous studies. However, in a blockwise model, the available context information is limited to the same block. To overcome this limitation, we investigate the enhancement of a block-online speech separation model using interblock context flow. Specifically, we propose a blockwise temporal convolution network with layers between adjacent blocks that allow the propagation of interblock context information. We evaluate this model on single-channel speech mixtures with different context widths, latencies, and intervals in noisy and reverberant environments generated by the image method using the Wall Street Journal 0 corpus. The experimental results indicate that the proposed model outperforms the baseline blockwise model under all the conditions.
LSTM Boosted Multi-layer Residual Spiking Neural Network with Application to Motor Cortical Activities

Artificial neural networks (ANN) have achieved good performance on many real-world problems. But a model similar to the mechanism of the brain often works better, such as Spiking Neural Network (SNN). SNNs are regarded as the third generation of Artificial Neural Networks (ANNs). However, training deep SNNs remains a challenge. Spiking neurons' transfer function is usually non-differentiable, which prevents the use of backpropagation. To solve this problem, we use an approximate function. In order to encourage SNN to have a long-term memory and be more trainable, we utilize Long short-term memory (LSTM) to extract abstract features which will be further passed to SNN. To alleviate the problem of the vanishing gradient in the deep network, we further employ the method of skip-connection. We demonstrate the effectiveness and robustness on a motor cortex dataset and the results indicate that our proposed method consistently outperforms other existing ones.
License Plate Recognition Based on CNN

An improved CNN neural network based on GRU sequence model license plate recognition technology is mentioned in this article. The network realizes the recognition of the 7-digit string of Chinese blue license plates and yellow license plates, and has the advantages of higher recognition accuracy and faster recognition speed. The specially labeled license plate data set is input into the built neural network for training and the model is obtained. Interact with the front-end user through the GUI interface to obtain the recognition result. This paper specifically analyzes the license plate location detection method, describes in detail the CNN neural network construction process, and the trained neural network model analyzes the results of license plate recognition, and finally summarizes and looks forward to the improvement of the recognition system.
A Novel miRNA Y-56 Targeting IGF-1R Mediates the Proliferation of Porcine Skeletal Muscle Satellite Cells Through AKT and ERK Pathways

As a key regulator of gene transcription and post-transcriptional modification, miRNAs play a wide range of roles in skeletal muscle development. Skeletal muscle satellite cells contribute to postnatal growing muscle fibers. Thus, the goal of this study was to explore the effects of novel miRNA Y-56 on porcine skeletal muscle satellite cells (PSCs). We found that Y-56 was highly expressed in porcine muscle tissues, and its expression was higher in Bama Xiang pigs than in Landrace pigs. The EdU assay, cell counting kit-8, and flow cytometry results showed that Y-56 overexpression suppressed cell proliferation and cell cycle, whereas Y-56 inhibition resulted in the opposite consequences. The results of qRT-PCR and Western blot showed that Y-56 remarkably inhibited the expression levels of cyclin-dependent kinase 4 (CDK4), proliferating cell nuclear antigen (PCNA), and cyclin D1. We identified that IGF-1R was a direct target of Y-56 by dual-luciferase reporter assay. Moreover, IGF-1R overexpression promoted the proliferation and cell cycle process of PSCs and upregulated the expression of CDK4, PCNA, and cyclin D1. Conversely, IGF-1R knockdown had the opposite effect. Furthermore, IGF-1R overexpression partially reversed the inhibition of the cell proliferation and cell cycle process of PSCs and the downregulation of the expression of CDK4, PCNA, and Cyclin D1 caused by Y-56 overexpression. Finally, Y-56 inhibited the protein expression levels of p-AKT and p-ERK. Collectively, our findings suggested that Y-56 represses the proliferation and cell cycle process of PSCs by targeting IGF-1R-mediated AKT and ERK pathways.
Sodium Butyrate Attenuates Taurocholate-Induced Acute Pancreatitis by Maintaining Colonic Barrier and Regulating Gut Microorganisms in Mice

Background Acute pancreatitis (AP) damages the intestinal barrier, which aggravates AP. Butyrate exhibits anti-inflammatory effects in AP, but it is unknown if such a protective effect is associated with the regulation of gut microorganisms. We aim to investigate the effects of sodium butyrate (SB) on pancreatic inflammation, colonic barrier, and gut microorganisms. Methods C57BL/6 mice were divided into groups of sham operation (Sham), AP, 200 mg/kg SB intervention (SB-200), and 500 mg/kg SB intervention group (SB-500). Samples were harvested 24 h after the model was established. The gut microbiota was analyzed using 16S rRNA gene sequencing. Results Pancreatic infiltration of neutrophils, macrophages, and M2-type macrophages was significantly reduced in the SB-500 intervention group. Supplementation of SB-500 improved colon mucosal histology and the expression of ZO-1 and occluding. The relative abundance of Alloprevotella and Muribaculaceae was increased and that of Akkermansia was decreased in the SB-500 group compared with the AP group. Ruminococcaceae was the most significantly increased species and Prevotellaceae was the most significantly decreased species in the SB-500 group compared with the AP group. Conclusion High dose of SB inhibits pancreatic inflammation probably by maintaining the intestinal barrier and regulating gut microbiota in mice with AP.
The Symbiotic Continuum Within Ticks: Opportunities for Disease Control

Among blood-sucking arthropods, ticks are recognized as being of prime global importance because of their role as vectors of pathogens affecting human and animal health. Ticks carry a variety of pathogenic, commensal, and symbiotic microorganisms. For the latter, studies are available concerning the detection of endosymbionts, but their role in the physiology and ecology of ticks remains largely unexplored. This review paper focuses on tick endosymbionts of the genera Coxiella, Rickettsia, Francisella, Midichloria, and Wolbachia, and their impact on ticks and tick-pathogen interactions that drive disease risk. Tick endosymbionts can affect tick physiology by influencing nutritional adaptation, fitness, and immunity. Further, symbionts may influence disease ecology, as they interact with tick-borne pathogens and can facilitate or compete with pathogen development within the vector tissues. Rickettsial symbionts are frequently found in ticks of the genera of Ixodes, Amblyomma, and Dermacentor with relatively lower occurrence in Rhipicephalus, Haemaphysalis, and Hyalomma ticks, while Coxiella-like endosymbionts (CLEs) were reported infecting almost all tick species tested. Francisella-like endosymbionts (FLEs) have been identified in tick genera such as Dermacentor, Amblyomma, Ornithodoros, Ixodes, and Hyalomma, whereas Wolbachia sp. has been detected in Ixodes, Amblyomma, Hyalomma, and Rhipicephalus tick genera. Notably, CLEs and FLEs are obligate endosymbionts essential for tick survival and development through the life cycle. American dog ticks showed greater motility when infected with Rickettsia, indirectly influencing infection risk, providing evidence of a relationship between tick endosymbionts and tick-vectored pathogens. The widespread occurrence of endosymbionts across the tick phylogeny and evidence of their functional roles in ticks and interference with tick-borne pathogens suggests a significant contribution to tick evolution and/or vector competence. We currently understand relatively little on how these endosymbionts influence tick parasitism, vector capacity, pathogen transmission and colonization, and ultimately on how they influence tick-borne disease dynamics. Filling this knowledge gap represents a major challenge for future research.
Comparative study of the multiple fault isolation approaches based on the structures of residual sets

This paper presents a comparative study of the applicability features of the three chosen multiple fault isolation approaches based on the binary evaluated structures of residual sets. Firstly, all considered approaches are briefly described. Then, their computational effectiveness is discussed. Next, the low-level implementation of the compared approaches is characterised and the set of experimental results is presented. Finally, the results of experiments are discussed and the appropriate recommendations for diagnostic engineering practice are formulated.
Natural Products as a Novel Therapeutic Strategy for NLRP3 Inflammasome-Mediated Gout

Gout is the most common form of inflammatory arthritis. It occurs when monosodium urate crystals (MSU) are deposited within joints due to hyperuricemia and persistent elevations of serum uric acid levels. Traditional gout treatment such as urate-lowering therapy is difficult to continue for a long period of time due to the risk of side effects. Recent studies have shown that the modulation of MSU-induced inflammatory responses is dependent on the inflammatory cytokine IL-1β, which has a central role in a chain of processes involving multiple cytokines and mediators. In this regard, the NLRP3 inflammasome is known to play a crucial part and thus has been proposed as a novel target in the treatment for gout. However, the biochemical mechanism for NLRP3 inflammasome activation has not yet been clearly elucidated. Therefore, this report can provide an overview of natural extractions targeted to prevent or treat NLRP3 inflammasome-mediated gout in the MSU-induced gout model. In addition, the research and development of such natural products are suggested as a potential strategy in the treatment of gout.
Do Gender Conformity Pressure and Occupational Knowledge Influence Stereotypical Occupation Preferences in Middle Childhood?

This study investigates how perceived occupational knowledge, gender stereotypes, and pressure to conform to gender norms influence children’s career interests in a sample of fourth and fifth grade children (n = 178, Mage = 9.78 years, 46.6% girls). Children were interested in and perceived that they knew more about own gender dominated occupations, compared to other gender dominated occupations. Gender moderated the effect of gender conformity pressure and gender stereotypes on interest in female-dominated but not male-dominated occupations. Boys were less interested in female-dominated occupations when they felt pressure to conform to gender norms and held more stereotypical beliefs about those occupations. These results suggest that perceived occupational knowledge is an important, yet overlooked, factor in understanding gender differences in children’s occupational interests.
Joint Energy Disaggregation of Behind-the-Meter PV and Battery Storage: A Contextually Supervised Source Separation Approach

An increasing number of residential customers have installed hybrid rooftop solar battery systems (HRSBSs). Currently, most HRSBSs are installed behind-the-meter (BTM), where only customers’ net load is measured by smart meters. This invisibility poses significant challenges to the system operation. Disaggregating BTM PV generation and battery charging/discharging profile of customers can enhance the grid-edge observability. This article proposes an energy disaggregation method to jointly separate the PV generation and battery charging/discharging power from the net load. First, a Home Smart Battery Management model is built to simulate the battery charging/discharging profile. Second, an optimal disaggregation model is established based on a contextually supervised source separation method. Furthermore, the feature vectors of PV, load, and battery are extracted as the input of the disaggregation model. Case studies on two datasets from Australia and China show that the proposed method has a promising disaggregation performance.
Shuttle Transfer of mRNA Transcripts via Extracellular Vesicles From Male Reproductive Tract Cells to the Cumulus–Oocyte Complex in Rabbits (Oryctolagus cuniculus)

Semen is known to contain an ovulation-inducing factor (identified as a nerve growth factor, NGF) that shows a significant increase in ovulation after semen deposition in induced ovulatory species. However, the interplay between the male reproductive tract cells and oocyte maturation through messenger RNA (mRNA) cargo is yet to be investigated. Extracellular vesicles (EVs) from the primary culture of rabbit prostate (pEVs), epididymis (eEVs), and testis (tEVs) were isolated to examine their contents for several mRNA transcripts through relative quantitative PCR (RT-qPCR). The expressions of NGF, neurotrophin (NTF3), vascular endothelial growth factor A (VEGFA), A disintegrin and metalloprotease 17 (ADAM17), midkine (MDK), kisspeptin (KISS1), and gonadotrophin-releasing hormone (GNRH1) were examined in isolated EVs. EVs were characterized through transmission electron microscopy. EV uptake by cumulus cell culture was confirmed through microscopic detection of PKH26-stained EVs. Furthermore, the effects of pEVs, eEVs, and tEVs were compared with NGF (10, 20, and 30 ng/ml) supplementation on oocyte in vitro maturation (IVM) and transcript expression. KISS1, NTF3, MDK, ADAM17, GAPDH, and ACTB were detected in all EV types. GNRH1 was detected in tEVs. NGF was detected in pEVs, whereas VEGFA was detected in eEVs. pEVs, eEVs, and 20 ng/ml NGF showed the highest grade of cumulus expansion, followed by tEVs and 10 ng/ml NGF. Control groups and 30 ng/ml NGF showed the least grade of cumulus expansion. Similarly, first polar body (PB) extrusion was significantly increased in oocytes matured with eEVs, pEVs, tEVs, NGF20 (20 ng/ml NGF), NGF10 (10 ng/ml NGF), control, and NGF30 (30 ng/ml NGF). Additionally, the expression of NGFR showed a 1.5-fold increase in cumulus cells supplemented with eEVs compared with the control group, while the expression of PTGS2 (COX2) and NTRK showed 3-fold and 5-fold increase in NGF20-supplemented cumulus-oocyte complexes (COCs), respectively. Oocyte PMP15 expression showed a 1.8-fold increase in IVM medium supplemented with eEVs. Additionally, oocyte NGFR and NTRK expressions were drastically increased in IVM medium supplemented with pEVS (3.2- and 1.6-fold, respectively) and tEVs (4- and 1.7-fold, respectively). This is the first report to examine the presence of mRNA cargo in the EVs of male rabbit reproductive tract cells that provides a model for the stimulation of female rabbits after semen deposition.
Deep Learning in Plant Phenological Research: A Systematic Literature Review

Climate change represents one of the most critical threats to biodiversity with far-reaching consequences for species interactions, the functioning of ecosystems, or the assembly of biotic communities. Plant phenology research has gained increasing attention as the timing of periodic events in plants is strongly affected by seasonal and interannual climate variation. Recent technological development allowed us to gather invaluable data at a variety of spatial and ecological scales. The feasibility of phenological monitoring today and in the future depends heavily on developing tools capable of efficiently analyzing these enormous amounts of data. Deep Neural Networks learn representations from data with impressive accuracy and lead to significant breakthroughs in, e.g., image processing. This article is the first systematic literature review aiming to thoroughly analyze all primary studies on deep learning approaches in plant phenology research. In a multi-stage process, we selected 24 peer-reviewed studies published in the last five years (2016–2021). After carefully analyzing these studies, we describe the applied methods categorized according to the studied phenological stages, vegetation type, spatial scale, data acquisition- and deep learning methods. Furthermore, we identify and discuss research trends and highlight promising future directions. We present a systematic overview of previously applied methods on different tasks that can guide this emerging complex research field.