Miracle-dz/UniFAR-FTUs huggingface dataset:
A semi-supervised deep network embedding approach based on the neighborhood structure

Network embedding is a very important task to represent the high-dimensional network in a lowdimensional vector space, which aims to capture and preserve the network structure. Most existing network embedding methods are based on shallow models. However, actual network structures are complicated which means shallow models cannot obtain the high-dimensional nonlinear features of the network well. The recently proposed unsupervised deep learning models ignore the labels information. To address these challenges, in this paper, we propose an effective network embedding method of Structural Labeled Locally Deep Nonlinear Embedding (SLLDNE). SLLDNE is designed to obtain highly nonlinear features through utilizing deep neural network while preserving the label information of the nodes by using a semi-supervised classifier component to improve the ability of discriminations. Moreover, we exploit linear reconstruction of neighborhood nodes to enable the model to get more structural information. The experimental results of vertex classification on two real-world network datasets demonstrate that SLLDNE outperforms the other state-of-the-art methods.
A Condition and Fault Prevention Monitoring System for Industrial Computer Numerical Control Machinery

Nowadays, the integration of smart systems within the modern industrial scenario is a continuously growing paradigm. Computer Numerical Control (CNC) machinery can heavily benefit from the introduction of Artificial Intelligence (AI) based monitoring applications. In this paper, we present an industrial condition and fault prevention monitoring system for CNC tools. The developed system is the result of an industrial project aimed at realizing a multi-purpose machine which is currently in pre-commercial stage. The results of this work represent the base platform for the further commercial development, which will be carried on from the industrial partners in accordance with clients feedbacks and specifications. This work presents the hardware architecture of the system, the web-based monitoring platform for remote management, and the AI framework used for fault monitoring. The multi-purpose machine is equipped with accelerometer units to monitor the vibration in multiple points of the structure. The control unit of the machine is connected to the sensing nodes and is used to communicate the actual machine state to a remote web platform. The accelerometric data are analyzed through an AI algorithm to perform fault detection. The fault detection algorithm was trained with the measurements performed on the machine under controlled environment faulty operation. The Internet of Things (IoT) based architecture has proven to be effective to facilitate the supervision of the machining processes, and the AI-based classification shows good classification performances for the fault detection tests.
Greenhouse Monitoring System for Chili Plant via IoT Cloud on Ubidots Platform

In Malaysia, most of greenhouses have still been managed manually via manpower to monitor and manage the plants, such as watering, fertilizing, and using their skill in detecting plant diseases. Thus, this study focuses on developing a greenhouse monitoring system by using IoT technology. It aims to assist the local farmers in monitoring their greenhouse, automatically without using the conventional methods that are tedious and require more labor works. For data collection, parameters for chili plants condition are gauge using temperature and humidity sensors, soil moisture sensor, and light dependent resistor (LDR) sensor. The data is sent to the Ubidots Cloud through microcontroller for the monitoring purpose. Overall, a mini greenhouse prototype for chili plant has been successfully constructed with a system interface on Ubidots dashboard to monitor the condition of the greenhouse plant. Results show that the developed system can be an alternative in monitoring the greenhouse condition and alerting farmers of abnormal conditions.
A Smart Multitransmitter Cooperative False Images Generation Method Against Multichannel SAR-GMTI

Existing false moving target generation methods face challenges such as uncontrollable position and velocity when against multichannel synthetic aperture radar ground moving target indication (SAR-GMTI) system, as well as significant computational burden in generating dense false targets. Meanwhile, current false blocking image generation methods struggle to create blocking images with controllable position and coverage shape, and the generated images will be partially canceled against multichannel SAR-GMTI. To tackle these issues, we propose a smart multitransmitter cooperative (MTC) false images generation method against multichannel SAR-GMTI and present an accurate and a fast implementation scheme of the method in this article. The accurate implementation scheme is referred to as the MTC frequency-shifting modulation jamming (MTC-FSMJ) method, which can generate false moving targets with controllable radial velocities and initial positions by utilizing multiple transmitters. However, due to the need for iteration calculations in generating a single false target, the computation load of the MTC-FSMJ method becomes substantial when generating dense false moving targets. When we want to generate dense false moving targets, a fast implementation scheme known as the MTC template-modulated jamming (MTC-TMJ) method provides an excellent alternative. This approach can quickly generate dense false moving targets within the template without iteration calculations. Moreover, by reasonably designing the template, the MTC-TMJ method can also generate false blocking images with controllable positions, while avoiding partial cancellation. Theoretical analysis and simulation experiments have verified the effectiveness of both implementation structures.
Reviewing the Clostridioides difficile Mouse Model: Insights into Infection Mechanisms

Clostridioides difficile is an anaerobic, spore-forming bacterium associated with intestinal infection, manifesting a broad spectrum of gastrointestinal symptoms, ranging from mild diarrhea to severe colitis. A primary risk factor for the development of C. difficile infection (CDI) is antibiotic exposure. Elderly and immunocompromised individuals are particularly vulnerable to CDI. A pivotal aspect for comprehending the complexities of this infection relies on the utilization of experimental models that mimic human CDI transmission, pathogenesis, and progression. These models offer invaluable insights into host–pathogen interactions and disease dynamics, and serve as essential tools for testing potential therapeutic approaches. In this review, we examine the animal model for CDI and delineate the stages of infection, with a specific focus on mice. Our objective is to offer an updated description of experimental models employed in the study of CDI, emphasizing both their strengths and limitations.
Midlatitude atmospheric responses to Arctic sensible heat flux anomalies in Community Climate Model, Version 4

Possible linkages between Arctic sea ice loss and midlatitude weather are strongly debated in the literature. We analyze a coupled model simulation to assess the possibility of Arctic ice variability forcing a midlatitude response, ensuring consistency between atmosphere, ocean, and ice components. We work with weekly running mean daily sensible heat fluxes with the self‐organizing map technique to identify Arctic sensible heat flux anomaly patterns and the associated atmospheric response, without the need of metrics to define the Arctic forcing or measure the midlatitude response. We find that low‐level warm anomalies during autumn can build planetary wave patterns that propagate downstream into the midlatitudes, creating robust surface cold anomalies in the eastern United States.
Exploring the Rumen Microbiota and Serum Metabolite Profile of Hainan Black Goats with Different Body Weights before Weaning

Simple Summary Low body weight is not conducive to the growth and development of mammals in the subsequent stages of weaning. The rumen microbiota plays an important role in the growth, development, production, and health of ruminants. A comprehensive analysis was conducted using a combined 16S rRNA and metabolomics analysis method to determine the impact of the rumen microbiota and serum metabolites on goat kids before weaning weight. This study may help to provide new insights for further regulating the rumen microbiota to improve the body weight of animals. Abstract The critical role of the rumen microbiota in the growth performance of livestock is recognized, yet its significance in determining the body weight of goat kids before weaning remains less understood. To bridge this gap, our study delved into the rumen microbiota, serum metabolome, rumen fermentation, and rumen development in goat kids with contrasting body weights before weaning. We selected 10 goat kids from a cohort of 100, categorized into low body weight (LBW, 5.56 ± 0.98 kg) and high body weight (HBW, 9.51 ± 1.01 kg) groups. The study involved sampling rumen contents, tissues, and serum from these animals. Our findings showed that the HBW goat kids showed significant enrichment of VFA-producing bacteria, particularly microbiota taxa within the Prevotellaceae genera (UCG-001, UCG-003, and UCG-004) and the Prevotella genus. This enrichment correlated with elevated acetate and butyrate levels, positively influencing rumen papillae development. Additionally, it was associated with elevated serum levels of glucose, total cholesterol, and triglycerides. The serum metabonomic analysis revealed marked differences in fatty acid metabolism between the LBW and HBW groups, particularly in encompassing oleic acid and both long-chain saturated and polyunsaturated fatty acids. Further correlational analysis underscored a significant positive association between Prevotellaceae_UCG-001 and specific lipids, such as phosphatidylcholine (PC) (22:5/18:3) and PC (20:3/20:1) (r > 0.60, p < 0.05). In summary, this study underscores the pivotal role of the rumen microbiota in goat kids’ weight and its correlation with specific serum metabolites. These insights could pave the way for innovative strategies aimed at improving animal body weight through targeted modulation of the rumen microbiota.
Trichomonas vaginalis Legumain-2, TvLEGU-2, Is an Immunogenic Cysteine Peptidase Expressed during Trichomonal Infection

Trichomonas vaginalis is the causative agent of trichomoniasis, the most prevalent nonviral, neglected sexually transmitted disease worldwide. T. vaginalis has one of the largest degradomes among unicellular parasites. Cysteine peptidases (CPs) are the most abundant peptidases, constituting 50% of the degradome. Some CPs are virulence factors recognized by antibodies in trichomoniasis patient sera, and a few are found in vaginal secretions that show fluctuations in glucose concentrations during infection. The CPs of clan CD in T. vaginalis include 10 genes encoding legumain-like peptidases of the C13 family. TvLEGU-2 is one of them and has been identified in multiple proteomes, including the immunoproteome obtained with Tv (+) patient sera. Thus, our goals were to assess the effect of glucose on TvLEGU-2 expression, localization, and in vitro secretion and determine whether TvLEGU-2 is expressed during trichomonal infection. We performed qRT-PCR assays using parasites grown under different glucose conditions. We also generated a specific anti-TvLEGU-2 antibody against a synthetic peptide of the most divergent region of this CP and used it in Western blot (WB) and immunolocalization assays. Additionally, we cloned and expressed the tvlegu-2 gene (TVAG_385340), purified the recombinant TvLEGU-2 protein, and used it as an antigen for immunogenicity assays to test human sera from patients with vaginitis. Our results show that glucose does not affect tvlegu-2 expression but does affect localization in different parasite organelles, such as the plasma membrane, Golgi complex, hydrogenosomes, lysosomes, and secretion vesicles. TvLEGU-2 is secreted in vitro, is present in vaginal secretions, and is immunogenic in sera from Tv (+) patients, suggesting its relevance during trichomonal infection.
The Spatiotemporal Distribution and Molecular Characterization of Circulating Dengue Virus Serotypes/Genotypes in Senegal from 2019 to 2023

Dengue virus is becoming a major public health threat worldwide, principally in Africa. From 2016 to 2020, 23 outbreaks were reported in Africa, principally in West Africa. In Senegal, dengue outbreaks have been reported yearly since 2017. Data about the circulating serotypes and their spatial and temporal distribution were limited to outbreaks that occurred between 2017 and 2018. Herein, we describe up-to-date molecular surveillance of circulating DENV serotypes in Senegal between 2019 to 2023 and their temporal and spatial distribution around the country. For this purpose, suspected DENV-positive samples were collected and subjected to dengue detection and serotyping using RT-qPCR methods. Positive samples were used for temporal and spatial mapping. A subset of DENV+ samples were then sequenced and subjected to phylogenetic analysis. Results show a co-circulation of three DENV serotypes with an overall predominance of DENV-3. In terms of abundance, DENV-3 is followed by DENV-1, with scarce cases of DENV-2 from February 2019 to February 2022. Interestingly, data show the extinction of both serotype 1 and serotype 2 and the only circulation of DENV-3 from March 2022 to February 2023. At the genotype level, the analysis shows that sequenced strains belong to same genotype as previously described: Senegalese DENV-1 strains belong to genotype V, DENV-2 strains to the cosmopolitan genotype, and DENV-3 strains to Genotype III. Interestingly, newly obtained DENV 1–3 sequences clustered in different clades within genotypes. This co-circulation of strains belonging to different clades could have an effect on virus epidemiology and transmission dynamics. Overall, our results highlight DENV serotype replacement by DENV-3, accompanied by a wider geographic distribution, in Senegal. These results highlight the importance of virus genomic surveillance and call for further viral fitness studies using both in vitro and in vivo models, as well as in-depth phylogeographic studies to uncover the virus dispersal patterns across the country.
Neural Implicit 3D Reconstruction with Double Supervision

As human life continues improving, applications like virtual reality (VR) and augmented reality (AR) necessitate increasingly higher-quality 3D reconstructions. With the advancements in neural implicit 3D surface and volume rendering, multi-view 3D reconstruction has garnered significant attention. A prevailing issue in this domain is that the direct combination of neural implicit surface and volume rendering typically considers only photometric consistency loss, leading to an under-constrained surface problem. To address this problem, we develop a double-supervised approach and an integrated network for implicit surface and volume rendering, enabling the generation of a 3D surface model of an object from a set of multi-view images. In our approach, the object surface is represented by a signed distance function, and a 3D surface reconstruction model is jointly trained with photometric consistency constraints and geometry constraints. Experiments demonstrate that by enhancing prior geometry supervision and integrating the double-supervised network architecture into neural implicit 3D reconstruction, we can achieve accurate and high-quality 3D reconstruction.
Organizational Resilience in Times of Crisis: Its Relation with Work-Life Balance and Work Engagement

Various studies have recognized the importance of the resilience aspect for individuals to be able to rise and overcome all challenges, especially the Covid-19 pandemic. One of the important aspects for individuals to be able to survive is the aspect of balance between personal and work-life balance (WLB). On the other hand, this aspect of WLB is also a determining factor for individual engagement with the organization (work engagement) so that employees can be loyal to the organization. For this reason, this study aims to examine the work-life balance and organizational resilience to work engagement for employees who work from home during the Covid-19 pandemic. This study uses an explanatory quantitative approach with data collection methods using questionnaires that will be distributed online and processed using Partial Least Square (PLS) using 130 respondents. The results showed a positive and significant effect between the work-life balance on work engagement, work-life balance on organizational resilience, and organizational resilience on work engagement. The results also show the mediating role of organizational resilience on the effect of work-life balance on work engagement.
Evaluation of dual polarization radar for rainfall-runoff modelling: a case study in Sydney, Australia

Dual polarization radars have advantages that may translate into more accurate measurements of rainfall than are provided by conventional, single polarization radars. An investigation was performed into how much additional benefit will be delivered to practical rainfall-runoff modelling by dual polarization radar over the single polarization radar approach. The benefit of dual polarization is compared against the impact of using calibration raingauge networks for single polarization radar rainfall. The investigation is framed in the context of a case study: a widespread rainfall event of approximately 2 days duration producing a small flood in a partially urbanized basin of 354 km 2 area.
SiC trench MOSFET with heterojunction diode for low switching loss and high short‐circuit capability

A SiC trench MOSFET with a merged heterojunction diode is proposed and numerically analysed here. The merged heterojunction diode can effectively suppress the turn-on of the parasitic body diode in the proposed SiC trench MOSFET. In addition, a P + shield layer surrounding the gate oxide layer can dramatically alleviate the gate oxide corner from the concentration of the electric field and improve the static and dynamic performances of the proposed device. As a result, not only the breakdown voltage is increased by 24% but also the miller charge and the switching losses of the proposed structure are reduced by 43 and 48.6%, respectively, when compared with those of the conventional SiC trench MOSFET with a grounded P + shield layer. Moreover, the short-circuit capability and its failure mechanism are numerically studied for the proposed structure. Finally, a feasible fabrication procedure is provided to realise the fabrication of this new device.
ConvIDS: A Convolutional LSTM Based Intrusion Detection Model for In-Vehicle CAN Bus

With the popularity of intelligent vehicles, there are numerous concerns regarding the security of the in-vehicle networks (IVNs). Controller Area Network (CAN) is one of the primary networks utilized in vehicles due to its high reliability and low cost. However, the absence of security mechanisms makes it vulnerable to cyber attacks. Once the attacker successfully gains access to the target CAN bus through the physical or wireless interface, various types of injection attacks can be conducted. To detect malicious attack on communication of the CAN bus, an efficient intrusion detection system (IDS) is required. Since the data of the CAN bus is typically in the form of time series, recurrent neural network (RNN) based methods have been proven effective for in-vehicle intrusion detection. In this paper, we proposed a novel approach named ConvIDS. The method is based on CAN image transforming and Convolutional Long Short-Term Memory Network (ConvLSTM). The experiments on real vehicle datasets demonstrate that the proposed model outperforms traditional neural network based methods in aspects of all metrics.
The Effect of Whole-Body Electromyostimulation Program on Physical Performance and Selected Cardiometabolic Markers in Obese Young Females

Background and Objectives: Whole-body electromyostimulation is under investigation as a potential aid for obesity-related health problems, supplementing a comprehensive, evidence-based obesity management strategy that includes lifestyle, diet, and exercise. The study investigated the impact of a whole-body electromyostimulation training program on physical performance and cardiometabolic markers in young obese females. Materials and Methods: Twenty-eight obese females, aged over 18 years with BMI over 30 and body fat over 28% and no underlying health conditions or medication, were divided into a whole-body electromyostimulation group (15 participants) and a control group (13 participants). The whole-body electromyostimulation program lasted 12 weeks, with two 20 min sessions weekly, using bipolar, rectangular current. Assessments pre and post intervention included body composition, blood pressure, lipid profile, C-reactive protein levels, maximal oxygen consumption, and jumping and sprint performance. Two-way ANOVA and t-tests were used for analysis. Results: Statistical analysis revealed significant (group × time) interactions for body composition, systolic blood pressure, maximal oxygen consumption, jumping and sprint performance, and plasma levels of lipids and C-reactive protein. Post hoc analyses for the whole-body electromyostimulation group indicated improvements in body composition indices (p < 0.01), systolic blood pressure (p = 0.003), maximal oxygen consumption (p = 0.010), and both jumping and 30 m sprint performance (p < 0.001 and p = 0.001, respectively) after the intervention. Furthermore, plasma levels of lipids (p < 0.01) and C-reactive protein (p = 0.002) showed significant improvements following the training program. In contrast, no significant changes were observed for these variables in the control group. Conclusions: A 12-week whole-body electromyostimulation program significantly improved body composition (skeletal muscle mass, body mass index, body fat, and waist circumference), physical performance (maximal oxygen consumption, jumping and sprint performance), and certain cardiometabolic (plasma level of lipids) and inflammatory markers (C-reactive protein) in obese young women. Further research is needed to explore the broader effects of whole-body electromyostimulation on physical and cardiometabolic health.
Assembling Extra Features with Grouped Pointwise Convolutions for MobileNets

Taking into account depthwise separable convolutions (DSCs) in MobileNets leads to a deep architecture having a low memory footprint and effective complexity. Such learned models still lack feature diversification for image representation. Several approaches have been introduced to deal with this issue via exploiting various dilation features to enrich more discriminative information. However, these dilation features are just embedded partly into some layers of MobileNets. Otherwise, the number of the learnable parameters would be sharply increased if they had been completely deployed for the whole backbone of the concerned networks. To this end, we propose to assemble grouped dilation features for MobileNets by addressing a depthwise separable dilated convolution located in parallel with the corresponding DSC. The resultant feature maps will be concatenated and permuted together, and then fed into a grouped convolution for learning diverse information. This grouped operation insignificantly increases the number of the learning parameters. Also, we propose to make an adaptation of a residual mechanism for enhancing the performance of MobileNetV1. Experimental results on benchmark datasets for image classification have validated the competence of our proposals.