Miracle-dz/UniFAR-FTUs huggingface dataset:
Deployment Optimization in Cell-Free Massive MIMO with Joint Aerial and Ground APs

Cell-free massive multiple-input multiple-output (CF-mMIMO) arises much attention recently due to its impressive macro diversity gain and uniform service coverage. Compared with the ground access point (AP), using unmanned aerial vehicles (UAVs) as aerial APs can effectively and quickly achieve wireless communication connections. However, the power consumption of the UAV makes it challenging to serve as long-term APs. In this paper, we propose a CF-mMIMO network with joint ground and aerial APs. The downlink achievable sum rate of users is derived. On top of this, we obtain the optimal UAV deployment coordinates through the alternating iterative algorithm. It is found that our optimal UAV deployment can significantly improve the achievable rate. Moreover, after exploring different network distributions, we find that UAV s should be deployed in the area with sparser ground APs, and when users move, UAVs should promptly relocate to the area with a high user density.
Detection of Antibodies for Pathogenic Leptospira in Wild Mammals and Birds from Southern Chile—First Record of Seropositivity in a Guiña (Leopardus guigna)

Simple Summary Wild animals can harbor infectious agents for several diseases. Some of these pathologies are zoonotic because they can be transmitted from animals to humans. Among these, leptospirosis is a zoonosis transmitted by bacteria of the genus Leptospira, which is present worldwide. In this study, animals at a wildlife rehabilitation center in Puerto Montt, southern Chile, were sampled from January to May 2023. Sixty animals, including birds, mammals, and reptiles, were selected to determine the prevalence of pathogenic Leptospira using a diagnostic test that detects antibodies for the bacteria. The prevalence was 25.00% in the total number of animals sampled, with prevalence of 19.51% in birds (choroy parrots, bandurrias, and Magellanic penguins) and a frequency of 38.89% in mammals (chilla foxes, pudus, and guiñas). Seropositivity to the bacteria is reported for the first time in guiñas. These results indicate that different wild animal species are exposed to Leptospira and that increased awareness about the disease is needed. Prevention measures must be implemented to reduce the risk of transmission between animals, as well as from animals to humans. Abstract Leptospirosis is a neglected bacterial zoonotic disease of worldwide distribution that is present in different animal species. This epidemiological study determined the seroprevalence of pathogenic Leptospira spp. in animals at a wildlife rehabilitation center in Puerto Montt, southern Chile, by sampling 60 animals belonging to three classes (birds, mammals, and reptiles). Diagnosis was performed using the microscopic agglutination test with a panel of eight serovars and serogroups. The results showed that 15 animals had anti-Leptospira antibodies, obtaining a seroprevalence of 25.00%, with Leptospira borgpetersenii serogroup Tarassovi presenting reactivity in 13 of the seropositive animals. Among the classes of mammals, chilla foxes (Lycalopex griseus) and pudus (Pudu puda) were seropositive. A guiña (Leopardus guigna) was also seropositive, which was described for the first time in mammals. Among the classes of birds, choroy parrots (Enicognathus leptorhynchus), bandurrias (Theristicus melanopis), and Magellanic penguins (Spheniscus magellanicus) were seropositive. Routine examinations to diagnose leptospirosis, perform epidemiological surveillance, and apply prevention and control measures are necessary, and additional research focusing on the One Health approach to explore the epidemiological role of different wild animal species in the maintenance and transmission of leptospirosis at the local and global levels are recommended.
Time-Aware Dual LSTM Neural Network with Similarity Graph Learning for Remote Sensing Service Recommendation

Technological progress has led to significant advancements in Earth observation and satellite systems. However, some services associated with remote sensing face issues related to timeliness and relevance, which affect the application of remote sensing resources in various fields and disciplines. The challenge now is to help end-users make precise decisions and recommendations for relevant resources that meet the demands of their specific domains from the vast array of remote sensing resources available. In this study, we propose a remote sensing resource service recommendation model that incorporates a time-aware dual LSTM neural network with similarity graph learning. We further use the stream push technology to enhance the model. We first construct interaction history behavior sequences based on users’ resource search history. Then, we establish a category similarity relationship graph structure based on the cosine similarity matrix between remote sensing resource categories. Next, we use LSTM to represent historical sequences and Graph Convolutional Networks (GCN) to represent graph structures. We construct similarity relationship sequences by combining historical sequences to explore exact similarity relationships using LSTM. We embed user IDs to model users’ unique characteristics. By implementing three modeling approaches, we can achieve precise recommendations for remote sensing services. Finally, we conduct experiments to evaluate our methods using three datasets, and the experimental results show that our method outperforms the state-of-the-art algorithms.
A Novel Link Prediction Approach for MOOC Forum Thread Recommendation Using Personalized PageRank and Machine Learning

MOOCs are online learning platforms that offer free and open educational resources for global learners. Its online forums enable learners to interact with instructors and peers and thus improve their learning outcomes and social presence. However, learners may struggle to find posts that match their goals and preferences due to the large scale and diversity of MOOCs. A well-designed MOOC forum thread recommendation system can help learners overcome information overload and improve their learning experience. In this paper, we consider the recommendation of forum threads as a link prediction problem and propose a novel link prediction approach based on various Heterogeneous graph feature extraction methods with LightGBM. We conduct experiments on real-world data from a MOOC platform to evaluate the effectiveness of our model. Results show that our approach outperforms various baselines in terms of precision and recall. Our findings demonstrate the effectiveness of combining PageRank with machine learning and our model’s potential to increase student engagement in learning discussion and foster socio-collaborative learning.
Ensemble Learning based on CNN and Transformer Models for Leaf Diseases Classification

Symptoms on the leaves are often the first indication of a plant disease. In order not to affect the process of crop production, farmers need to identify plant diseases on their leaves as quickly as possible. This problem has long been addressed by a variety of computational techniques, such as deep learning models. Today, many specialized deep learning models are built using Transformer or Convolution Neural Networks (CNN). However, the accuracy and performance of individual deep learning models depends on many factors, such as the number of parameters, training time, and the dataset used. Often a single model is not well suited to solving problems such as image classification of leaf diseases. This study proposes an ensemble learning based on CNN and Transformer models. The models used in this study are MobileNetV3, DenseNet201, ResNext50, Vision Transformer and Swin Transformer. The purpose of ensemble learning with these five models is to achieve accuracy and good performance through weighted voting such as hard voting and soft voting. The experimental findings indicate that the utilization of ensemble learning, employing a combination of five models, yields enhanced accuracy and performance in the classification of three distinct types of datasets: corn leaf diseases, grape leaf diseases, and potato leaf diseases. Our experiment also showed that the Vision Transformer model has higher accuracy compared to other models. To perform a detailed analysis, we use the Grad-CAM technique to visualize how all models use the gradient to create a classification score. The results of this experiment can be a recommendation for the agricultural sector so that they can be implemented as early as possible to address the problem of leaf diseases classification.
A Survey of Compute First Networking

With the rapid advancements in cloud computing, edge computing, and intelligent devices, there has been a proliferation of computing resources with a trend toward ubiquitous deployment. However, traditional network architectures have struggled to effectively harness these ubiquitous computing resources. In response to this challenge and the growing need for optimizing and efficiently utilizing both network and computing resources, a novel architectural paradigm known as Compute First Networking (CFN) has emerged. CFN establishes a framework where ubiquitous and heterogeneous computing resources are interconnected through networking, enabling unified orchestration and flexible resource allocation. This research paper commences by providing an in-depth examination of the current state of CFN research, delineates various definitions associated with CFN, elucidates the fundamental architecture and pivotal technologies underpinning CFN, and ultimately delves into prospective avenues for future CFN research endeavors.
SREMIC: Spatial Relation Extraction-based Malware Image Classification

Around 800,000 people fall prey to cyberattacks annually, most often by “malware”. Malware has the potential to become a destructive weapon in Cyber-world. It is a difficult task to manually thwart an assault by malware. It is crucial to properly categorize malware binaries in order to identify their origins. Furthermore, malware structure discovery through basic feature extraction approaches are time-consuming and challenging. Malware classification was previously solved using naive machine learning approaches like support vector machine (SVM) and extreme gradient boosting (XGBoost). Recently, deep learning (DL) has shown to be impactful in finding malicious patterns. Without DL, analysis of the vast amounts of available data tends to impossible. Existing methods (e.g., transfer learning, fusion methodology, ensemble learning) may not be effective on actual malware binary files. Moreover, some single image-based malware classification used rudimentary convolutional neural network (CNN) that does not perform well. Faced with these challenges, we propose in this paper a novel model with of a spatial CNN with sufficient regularization and data augmentation that can identify and classify malware in images effectively and efficiently. Our model is evaluated using datasets like MalImg and Microfsoft-Big. The proposed model achieves validation score of 99.93% for MalImg and 99.72% for Microsoft-Big datasets. Our approach outperforms VGG16, VGG19, ResNet50, EfficientNetB1, and Google's Inception v3, including state-of-the-art (SOTA) techniques.
Optimization of training for professional rugby union players: investigating the impact of different small-sided games models on GPS-derived performance metrics

Introduction: Professional rugby union players can improve their performance by engaging in small-sided games (SSGs), which simulate the movement patterns of the game. This study collected metrics related to running performance and mechanical workload and their relative values from both forward and back positions, aiming to explore the impact of different SSGs factors on athlete workload, as well as the workload difference between official games (OGs) and SSGs. Methods: The monitored GPS data were collected from SSGs with different player numbers and pitch sizes (five sessions), SSG rules (5 weeks, four sessions per week), and OGs conducted throughout the year. Additionally, the study compared changes in players’ sprinting performance before and after two SSG sessions. Results: Backs had greater workload than forwards. Less space and number of players SSG (4 vs. 4, 660 m2) was conducive to facilitating training for players in acceleration and deceleration. Conversely, larger spaces were associated with improved running performance. However, the introduction of a floater had no significant impact on performance improvement. Additionally, the 7 vs. 4 model (seven players engaged with four opponents) resulted in the greatest workload during medium-hard accelerations (F = 52.76–88.23, p < 0.001, ηp 2 = 0.19–0.28). Japan touch model allowed for more high-speed running training (F = 47.93–243.55, p < 0.001, ηp 2 = 1.52). The workload performed by SSGs can almost cover that of OGs (F = 23.36–454.21, p < 0.05, ηp 2 = 0.03–0.57). In the context of ηp 2, values around 0.01, 0.06 and 0.14 indicate small, medium and large effects respectively. Discussion: However, given the significantly higher workload of SSGs and the slight decrease in sprinting performance, further research is required to examine the training patterns of SSGs. This study provided insight into the impact of player numbers, pitch size, and rules on rugby-specific SSGs. Coaches should optimize SSG setups for enhanced training outcomes, ensuring the long-term development of physical capacity, technical and tactical skills.
When is Digital Technology Beneficial Toward Corporate Innovation? A Meta-Analysis

Recent years have witnessed a proliferation of digital innovation research on the relationship between digital technology and corporate innovation. However, the evidence across different disciplines and contexts regarding the innovation outcomes of digital technology has been contradictory. Using the meta-analysis approach, we examined 176 253 observations from 113 studies and found a positive relationship between digital technology and corporate innovation. From a knowledge-based perspective, we argue that the positive impact of digital technology on corporate innovation stems from knowledge generation, knowledge flow, and knowledge absorption. Furthermore, we posit that the benefits that digital technology brings to corporate innovation depend upon the external conditions in which the corporations are situated. In particular, our study presents empirical evidence showing that the relationship is stronger in countries with weak institutional support of innovation and a weak rule of law. In this study, we find no evidence to suggest that the effect size varies across various industrial contexts and innovation paradigms. Moreover, we find that the positive impacts of digital technology on corporate innovation appear to grow with time. Our article contributes both to technology management and corporate innovation management, delivering possibilities for and insights into managerial practices.
Detection and Visualization of Neglected Tropical Skin Diseases Using EfficientNet and Grad-CAM

Early skin disease detection is crucial for both effective treatment and the prevention of spreading to others. Neglected Tropical Skin Diseases (skin-NTDs) primarily affect low-income and developing countries, receiving inadequate attention and resources in comparison to other health issues. In this study, an automated system has been developed for detecting five skin-NTDs, capable of recognizing the diseases from raw lesion images without requiring any pre-processing. A dataset was created in the initial phase of the study since no publicly available benchmarks are available. The EfficientNet family of pre-trained models was utilized to train the classifier, and the EfficientNet-B3 was selected based on the experimental results. Additionally, the proposed work has developed a Grad-CAM based visualization technique to identify the most influential regions within images for the classification of specific diseases. The proposed model exhibited an overall classification accuracy of 91.53% on the test data. The proposed work will offer benefits to frontline medical staff and local residents in low-income countries.
Identification of Sarcocystis and Trichinella Species in Muscles of Gray Wolf (Canis lupus) from Lithuania

Simple Summary The gray wolf (Canis lupus) is the largest representative of the family Canidae widespread in Eurasia and North America. Sarcocystis and Trichinella parasites were previously reported in the muscles of gray wolves. Apicomplexan Sarcocystis forms sarcocysts in the muscles of intermediate hosts and develops sporocysts in the intestines of definite hosts. Members of the genus Trichinella are cosmopolitan hazardous nematodes. The species composition of these parasites in gray wolves from Lithuania has not been studied so far. We examined muscle samples from 15 gray wolves, and species of parasites were confirmed using DNA analysis methods. Microscopically, Trichinella larvae were observed in 12 animals, and sarcocysts formed by Sarcocystis spp. were noticed in four. Trichinella britovi was also identified in the examined wolves. Current data show that zoonotic T. britovi is the dominant Trichinella species in gray wolves from nearby countries. In the case of Sarcocystis, two animals harbored S. svanai, and another two individuals were infected by S. svanai and S. arctica. Future studies are needed to assess the pathogenesis of the identified Sarcocystis spp. Abstract Apicomplexan Sarcocystis and Trichinella nematodes are food-borne parasites whose life cycle is carried-out in various wildlife and domestic animals. The gray wolf (Canis lupus) is an apex predator acting as an ecosystem engineer. This study aimed to identify the species of Sarcocystis and Trichinella found in the muscles of gray wolves in Lithuania. During the 2017–2022 period, diaphragm, heart, and hind leg samples of 15 animals were examined. Microscopical analysis showed the presence of two types of Sarcocystis parasites in 26.7% of the analyzed muscle samples. Based on the sequencing of five loci, nuclear 18S rDNA, 28S rDNA, ITS1, mitochondrial cox1, and apicoplast rpoB, S. arctica, and S. svanai were identified. The current work presents the first report of S. svanai in gray wolf. Phylogenetically, S. svanai clustered together with S. lutrae, infecting various carnivorans, and S. arctica was most closely related to S. felis from domestic cats. Trichinella spp. were found in 12 gray wolves (80%). For the first time, Trichinella species were molecularly identified in gray wolves from Lithuania. Trichinella britovi was confirmed in all of the isolated Trichinella larvae using a multiplex PCR. Gray wolves in Lithuania may serve as a major source of zoonotic pathogens due to the presence of these parasites.
Associations between Mediterranean Diet Adherence, Quality of Life, and Mental Health in Patients with Multiple Sclerosis: A Cross-Sectional Study

Background: The Mediterranean diet (MD) is well-known as a diet which may exert a protective effect against neurodegenerative diseases, including multiple sclerosis (MS). To date, only a few clinical surveys have assessed the potential effects of the MD in patients with MS. The purpose of the present study is to evaluate the potential effects of MD compliance on disease disability, quality of life, physical activity, depressive symptomatology, and blood biochemical parameters related to nutritional status in MS patients, considering several socio-demographic, anthropometric, and lifestyle characteristics. Methods: This is a cross-sectional study conducted on 558 adults with MS aged 18–64 years. Relevant questionnaires were utilized to evaluate socio-demographic and anthropometric parameters, disease disability (Expanded Disability Status Scale, EDSS), multidimensional health-related quality (MS Quality of Life-54, MSQOL-54), physical activity levels (International Physical Activity Questionnaire, IPAQ), depression (Beck Depression Inventory II, BDI-II), and MD adherence (MedDietScore), while several blood biochemical parameters were retrieved from the patients’ medical records. Results: Enhanced MD compliance was independently associated with a decreased frequency of overweight/obesity, as well as abdominal obesity, in patients suffering from MS. Elevated MD compliance was also independently associated with a decreased incidence of advanced disease disability, a higher prevalence of elevated physical activity, an improved quality of life, and lower depressive symptoms, as well as higher levels of certain blood biochemical parameters, which are effective indicators of iron deficiency and malnutrition. Conclusions: The present study found that higher MD adherence may slow down disease disability, promoting a better quality of life and mental health in adults with MS. Future prospective surveys are required to obtain conclusive results.
Isoflavones Effects on Vascular and Endothelial Outcomes: How Is the Gut Microbiota Involved?

Isoflavones are a group of (poly)phenols, also defined as phytoestrogens, with chemical structures comparable with estrogen, that exert weak estrogenic effects. These phytochemical compounds have been targeted for their proven antioxidant and protective effects. Recognizing the increasing prevalence of cardiovascular diseases (CVD), there is a growing interest in understanding the potential cardiovascular benefits associated with these phytochemical compounds. Gut microbiota may play a key role in mediating the effects of isoflavones on vascular and endothelial functions, as it is directly implicated in isoflavones metabolism. The findings from randomized clinical trials indicate that isoflavone supplementation may exert putative effects on vascular biomarkers among healthy individuals, but not among patients affected by cardiometabolic disorders. These results might be explained by the enzymatic transformation to which isoflavones are subjected by the gut microbiota, suggesting that a diverse composition of the microbiota may determine the diverse bioavailability of these compounds. Specifically, the conversion of isoflavones in equol—a microbiota-derived metabolite—seems to differ between individuals. Further studies are needed to clarify the intricate molecular mechanisms behind these contrasting results.
Multimodal image seamless fusion

Abstract. An iterative joint bilateral filter is used to obtain a natural weight map. Images from different modalities are merged by a weighted-sum rule in the spatial domain. Saliency maps are determined by the gradient of the pairwise raw images. Comparing the pairwise values of saliency maps, a coarse weight map is attained to determine which pixel is preferred. Since such a coarse weight map obtained by pairwise comparison is not a natural weight map subjectively, i.e., it is inconsistent with human visual system, the weight map is modified by using an iterative joint bilateral filter. With the iterative joint bilateral filter, the weight map becomes natural. We use the refined weight map to obtain the fused image and we seamlessly merge images from different modalities effectively. Experiments were conducted on several pairs of multimodal images to verify the effectiveness and superiority of the proposed image fusion algorithm compared to the state-of-the-art methods.