Miracle-dz/UniFAR-FTUs huggingface dataset:
Challenges in the reflux ablation in a patient with chronic venous leg ulcer – case report

Introduction : Venous leg ulcer is a severe health problem for patients with a long history of venous hypertension. The treatment includes invasive procedures including both surgical removal of insufficient veins and their intravenous ablation. The lat-ter technique, although often used especially in patients with refractory or recurrent ulceration, sometimes encounters difficulties that reduce the chances of its success. Material and methods : In a patient with a 30-year history of venous ulceration due to insufficient saphenous vein, intravenous laser ablation was performed due to the inability to heal the ulcer for a long time with various forms of conservative treatment. Results : Within a few weeks of the surgery, the venous ulcer was effectively healed. After 8 months, recanalization of the proximal section of the great saphenous vein, connecting with the varicose veins of the thigh, leg and venous plexuses in the area of the ulcer, was found, but without the accompanying opening of the wound. Despite the constant use of compression therapy by the patient, about a year after the procedure, the ulcer reopened. Next, sclerotherapy of the insufficient great saphenous vein trunk as well as varicose veins was performed, resulting in the wound healing again. Conclusions : The procedure of intravenous ablation of insufficient venous trunks is an effective form of venous ulcer treatment. However, in order to maintain a long-term positive effect of treatment, it is advisable to eliminate all sources of venous insufficiency as early as possible and carefully monitor the patient at all stages of therapy.
Anticipatory Allocation of Communication and Computational Resources at the Edge Using Spatio-Temporal Dynamics of Mobile Users

Multi-access Edge Computing represents a key enabling technology for emerging mobile networks. It offers intensive computational resources very close to the end-users, useful for task offloading purposes. Many scientific contributions already proposed approaches for optimally allocating these resources over time. However, most of them fail to take advantage of the prediction of both users’ mobility and service demands over a look-ahead temporal horizon. To bridge this gap, this paper formulates a novel methodology for anticipatorily allocating communication and computational resources at the network edge, based on the prediction of spatio-temporal dynamics of mobile users. The conceived architecture exploits a Software-Defined Networking approach to monitor users’ mobility, a Convolutional Long Short-Term Memory to predict over different look-ahead horizons the number of users within a given number of cells and their related service demands, and Dynamic Programming to optimally allocate users’ requests among available Multi-access Edge Computing servers. Computer simulations investigate the effectiveness of the proposed approach in a realistic autonomous driving use case and compare its behavior against a baseline solution. Obtained results demonstrate its unique ability to dynamically and fairly distribute users’ requests among the resources available at the network edge, while ensuring the targeted quality of service level.
Dynamics in the Contents of Self-Stereotyping and its Implication in Inter-Group Relations

Objectives. Developing a comprehensive model to understand intergroup relationship through integrating two constructs usually used to be examined discretely; self-stereotyping and stereotyping. Background. Today’s understanding of intergroup behavior is firmly grounded in concepts related to stereotypes. In literature, apparently, there are, two dominant approaches in studying stereotype’s effect on intergroup relations. The first approach focuses on the effect of dominant group’s stereotype on intergroup relation, while the second approach focuses on studying the impacts of self stereotyping on victims. Furthermore, minority groups’ self-sterotyping is considered to be derived from the dominant groups’ stereotype. As a result, the prevailing approaches are insensitive to the dynamics in self-stereotype and its implication to the intergroup relationship. In this article, it is claimed that the etiology of intergroup behavior could be better understood by considering a mutually interacting groups’ perspective. Methodology. Systematic approach of reviewing the prevailing literature pertaining to stereotyping and self-stereotyping and integrative analysis method to develop new perspective. Conclusion. Intergroup relation involves the interaction of two or more groups each of them having stereotypes regarding their own group and outgroup. Thus, in this paper, we argued that, the etiology of intergroup behavior cannot be adequately understood without employing the belief system of mutually interacting groups. Hence, we integrated self-stereotyping and other’s stereotypes and the behaviors that emerge during intergroup relations is predicted using the dynamics in the content/valence of minority group members’ self-stereotyping simultaneously with the dominant groups’ stereotype. The integration of these two approaches appears to offer the most adequate explanation for the complex nature of intergroup behavior.
A Chromium Discharge Minimizing Tanning Technology Assisted by O2 Low Temperature Plasma

Substantial quantities of solid and liquid wastes generate in the chrome tanning process, posing major environmental problem if not managed effectively. In order to minimize the emissions of chromium, a high exhaustion chromium tanning method was designed in this study. Low temperature plasma (LTP) technology has the characteristics of economy, pollution-free and high efficiency. The pioneering works were carried out by applying LTP to surface modification of natural leather before tanning process. The effects of different treatment time on micro-structure, chemical compositions, active groups, tanning absorption rate of leather fiber were studied. The SEM results showed that the leather surface was etched rougher. The XPS data showed that the O1s area ratios increased from 18.8 % to 27.6 % after O2 LTP treatment. The optimal O2 plasma treatment time is 10min. The results show O2 LTP treatment effectively improves the chrome tanning absorption rate from 78.68 % of conventional process to 94.47 % of the novel process, which reduces the emission of heavy metal chromium and protects the environment.
Wireless Powered Cognitive NOMA-Based IoT Relay Networks: Performance Analysis and Deep Learning Evaluation

In this article, we study novel wireless powered cognitive nonorthogonal multiple access (NOMA)-based Internet-of-Things (IoT) relay networks to improve the performance of a cell-edge user under perfect and imperfect successive interference cancelation (SIC). In the secondary networks, a source node communicates with a cell-center user via direct link and with a cell-edge user through the assistance of a master IoT node under cognitive radio constraint. Exact closed-form analytical expressions for the outage probability (OP) of NOMA users and the overall system throughput are derived. To provide further insights, a performance floor analysis is also carried out considering two power-setting scenarios: 1) the transmit powers at the power beacon goes to infinity and 2) the maximum allowable power constraint goes to infinity. Moreover, we develop two iterative algorithms for minimizing OP users and maximizing system throughput subject to time-switching and power-allocation factors in two-hop transmission. Direct derivation of the closed-form expression for the ergodic capacity (EC) becomes unfeasible due to the high complexity of the proposed system model. To overcome this issue, we design a deep neural network (DNN) framework for the EC prediction toward real-time configurations. Our results show that the predicted results based on this DNN framework perfectly align with the simulations, validating our design framework. In addition, the DNN approach exhibits the lowest root-mean-square error and low run-time predictions among other regression models.
Detecting Ethereum Ponzi Schemes Based on Improved LightGBM Algorithm

As more investors adopt to enter the field of blockchain investment, the Ponzi scheme, a traditional investment scam, has emerged as a hidden fraud in smart contracts. Although some proposed solutions have paid attention to detecting Ponzi schemes in the blockchain, two problems remain: features for detecting Ponzi schemes are incomplete, and algorithms for detecting Ponzi schemes are not sufficiently efficient. Therefore, we innovatively extract the bytecode feature and combine it with user transaction and opcode frequencies to get more comprehensive features. With these features, we propose a smart contract Ponzi scheme identification method based on the improved LightGBM algorithm. Experiments conducted on the real data set of Ethereum prove that our proposed method has improved accuracy dramatically in terms of the $F$ -score index and the AUC index compared with the state-of-the-art methods. In addition, model training speed is improved significantly. Therefore, our method more accurately identifies Ponzi schemes in smart contracts, thus reducing investment risk.
Rural Electrification in Mozambique: Challenges and Opportunities

The International Energy Agency states that access to electricity is an essential condition for sustainable human development, however, it is estimated that approximately 22% of the world population (about 1.6 billion people) does not have access to electricity, a significant part of these people live in rural areas of developing countries in Sub-Saharan Africa, despite the fact that Africa has enormous potential in renewable and non-renewable energy sources. In Mozambique, approximately 50% of the population does not have access to electricity due to the fact that 66.6% of the population lives in rural areas, where the rate of access to electricity is even worse, paradoxically, Mozambique has a significant potential for renewable energy sources equivalent to 23 TW, this potential when combined with factors such as commitment to ensuring access to electricity for all, forecast of population growth and electricity demand, generates huge investment and long term business opportunities in the electricity sector, however, there are economic, social and cultural challenges that constitute uncertainties that should be considered in the decision-making process for investment in rural electrification infrastructure in the specific context of Mozambique and Sub-Saharan Africa in general. This article aims to discuss the possibilities that Mozambique has to guarantee access to electricity for all by 2030 (emanating from United Nations Sustainable Development Goal 7) emphasizing land use plans and education for rural electrification benefits through the use of renewable energy sources.
The inflectional morphology representation of individual words in the mental lexicon

Models of complex word recognition can be separated into two wide groups: symbolic and connectionist. Symbolic models presume the existence of an explicit morphological representation of individual words; connectionist models do not and consider morphological effects to be a by-product of interaction between phonological, orthographic and semantic information. This study aimed to test whether there are explicit mental representations of inflected lexical units in the mental lexicon. Accordingly, the method of inflected suffix morphological and semantic priming of nouns in the Serbian language was used. In the morphological priming condition, the prime and the target shared the same inflectional suffix. In Experiment 1 overt priming was used, while in Experiment 2, masked priming. The results showed no significant effects of inflected suffix morphological priming, while significant semantic priming effects were recorded. The results obtained in this research are in line with predictions of the connectionist models.
Antiproliferative activity of marine brown algae-derived compounds: A review

Marine environment exploration has increased in the search for new compounds that may be attractive to the industrial field, especially for the development of drugs. Brown marine algae are part of this environment and, because of their production of secondary metabolites, they have become a possible source of bioactive compounds that have important biological actions such as anticoagulant, antioxidant and antiproliferative. However, there are still obstacles to complete knowledge about their structures and activities. This review provides key information about the isolation, composition, and structure and antiproliferative activity in vitro and in vitro of compounds derived from different brown algae.
Generating Explanations for Chest Medical Scan Pneumonia Predictions

With the spread of COVID-19, significantly more patients have required medical diagnosis to determine whether they are a carrier of the virus. COVID-19 can lead to the development of pneumonia in the lungs, which can be captured in X-Ray and CT scans of the patient's chest. The abundance of X-Ray and CT image data available can be used to develop a high-performing computer vision model able to identify and classify instances of pneumonia present in medical scans. Predictions made by these deep learning models can increase the confidence of diagnoses made by analyzing minute features present in scans exhibiting COVID-19 pneumonia, often unnoticeable to the human eye. Furthermore, rather than teaching clinicians about the mathematics behind deep learning and heat maps, we introduce novel methods of explainable artificial intelligence (XAI) with the goal to annotate instances of pneumonia in medical scans exactly as radiologists do to inform other radiologists, clinicians, and interns about patterns and findings. This project explores methods to train and optimize state-of-the-art deep learning models on COVID-19 pneumonia medical scans and apply explainability algorithms to generate annotated explanations of model predictions that are useful to clinicians and radiologists in analyzing these images.
LIXIVIAÇÃO DE ÍONS EM ARGISSOLO AMARELO TRATADO COM BIOCARVÃO DE CASCA DE COCO SECO E CULTIVADO COM GIRASOL (Helianthus annus L.)

O biocarvao de casca de coco seco tem grande potencial para ser usado como insumo agricola, porem seu efeito depende do tipo de solo e da dose aplicada. O presente trabalho avaliou o efeito de diferentes doses de biocarvao de casca de coco seco no desenvolvimento e producao de biomassa do girassol (Helianthus annus L.) e na concentracao de ions nutrientes na solucao do solo, em diferentes profundidades de coleta. O experimento de casa de vegetacao foi desenvolvido em delineamento de blocos casualizados, com 6 tratamentos (doses de biocarvao: 0; 2,5; 5; 10, 20 e 30 t ha-1) e 5 repeticoes. Plantas de girasol foram cultivadas por 4 semanas e avaliadas para altura e massa seca. Extratores de solucao do solo foram instalados a 20 cm e a 40 cm de profundidade no solo, e as coletas foram realizadas aos 6, 13 e 20 dias apos a semeadura para avaliacao do pH, CE e concentracoes de P, K, NH4 e NO3. As plantas cresceram mais nas doses mais altas de biocarvao, porem sem reflexo na massa seca. A presenca de biocarvao nao influenciou a concentracao de NO3 da solucao do solo, mas aumentou a de NH4, na maior dose e aos 20 dias de avaliacao. O biocarvao aumentou a concentracao de P e K, a 20 cm de profundidade. O maior efeito foi observado na concentracao de K e na CE, aumentando em profundidade e com o aumento da dose de biocarvao, em todas as epocas de avaliacao.
Multi Direction-of-Arrival Tracking Using Rigid and Flexible Antenna Arrays

This paper is concerned with the problem of simultaneously tracking the direction-of-arrival (DOA) of far-field multiple moving sources/users in wireless communications using the vector-signal received by an antenna array of $N$ elements. The antenna array can be rigid (fixed array locations) or flexible (time-varying array locations), and it is used in conjunction with a “manifold extender”, a spatiotemporal state-space model and a Kalman-type tracking approach for non-stationary wireless channels. In particular, two tracking approaches are proposed. The first is based on an arrayed Extended Kalman Filter (arrayed-EKF) algorithm and the second on an arrayed Unscented Kalman Filter (arrayed-UKF) algorithm. Furthermore, if the array is rigid the spatiotemporal state-space model incorporates the DOAs and the angular velocities of the sources, while if it is flexible it also includes the array locations in the set of state-variables. The performance of the two approaches using both rigid and flexible arrays is evaluated using computer simulation studies and compared with a subspace tracking algorithm and a particle filter method under the same conditions. The results show that the arrayed-UKF and the arrayed-EKF show superior tracking performance, especially for low SNRs.
Artificial Intelligence Enabled Cyberspace Security Defense

: Cyberspace is regarded as the fifth largest activity space following land, sea, air, and space. Protecting cyberspace security is a major issue related to national security, national sovereignty, and the legitimate rights and interests of the people. With the rapid development of artificial intelligence (AI) technology and its application in various fields, cyberspace security has been facing new challenges. This study analyzes the new risks of cyberspace security in the era of AI, such as more intelligent network attacks, more frequent large-scale network attacks, higher concealment of network attacks, stronger confrontation game of network attacks, and easier exposure to stealing of important data. AI technology has significant advantages in dealing with massive data, multi-source heterogeneous data, and real-time dynamic data, which can significantly improve the defense capability of cyberspace. This study introduces some key problems and technologies of AI-enabled cyberspace security defense, particularly the construction of a cyberspace security knowledge brain and the detection of network attacks. Furthermore, we propose the corresponding countermeasures and suggestions from three aspects: the construction of a dynamic and scalable network security knowledge brain, the promotion of intelligent detection against network attacks, and the evaluation of AI technologies’ security.
Responses of soil bacterial community and enzyme activity to experimental warming of an alpine meadow

Soil microbes and enzymes play vital roles in the decomposition and transformation of soil material, nutrient cycling, energy flow and physicochemical properties. However, the responses of soil microbes and enzymes to warming remains poorly understood, which limits our ability to predict the consequences of future climate warming. To understand better how climate warming will alter the activity of soil microbes and enzymes, we established a field experiment to manipulate temperature in an alpine meadow in northwestern Sichuan, China. After 5 years of experimental warming, soil bacterial communities were assessed by MiSeq sequencing of 16S rRNA gene amplicons. The results showed that the experimental warming treatments increased the soil acid phosphatase, invertase and urease activity, and available nutrients, but decreased the catalase activity and soil organic matter (SOM) at the 0–10‐cm soil depth. However, the warming treatment increased catalase activity, SOM, available nitrogen (AN) and available potassium (AK), but decreased invertase activity at the 10–20‐cm soil depth. Warming significantly increased the relative abundance of Chloroflexi at the 0–10‐cm depth, whereas it significantly increased the relative abundance of Cyanobacteria and decreased the relative abundance of Firmicutes at the 10–20‐cm depth. Similarly, warming altered the relative abundance of bacterial genera, for example Candidatus Solibacter increased, whereas Pseudomonas and Janthinobacterium decreased. Warming did not significantly affect diversity or structure of the soil bacterial community. The soil acid phosphatase and urease activity, and SOM, total potassium (TK), AK and total phosphorus (TP) contents were strongly correlated with the soil bacterial community. Overall, despite the pronounced changes in soil enzyme activity and plant nutrient contents under warming, composition and diversity of the soil bacterial community were not changed. This study helps to elucidate soil responses in grasslands to climate warming.
1,25-Dihydroxycholecalciferol Improved the Growth Performance and Upregulated the Calcium Transporter Gene Expression Levels in the Small Intestine of Broiler Chickens

1,25-Dihydroxycholecalciferol (1,25-(OH)2-D3) is the final active product of vitamin D. This study aimed to investigate the effects of 1,25-(OH)2-D3 on growth performance, bone development, and calcium (Ca) transporter gene expression levels in the small intestine of broiler chickens. On the day of hatching, 140 female Ross 308 broilers were randomly allotted into two treatments with five replicates (14 birds per replicate). Two levels of 1,25-(OH)2-D3 (0 and 1.25 µg/kg) were added to the basal diet without vitamin D. Results showed that the addition of 1.25 µg/kg 1,25-(OH)2-D3 increased the average daily feed intake and the average daily gain and decreased the feed conversion ratio and mortality in 1- to 19-day-old broiler chickens compared with the basal diet without vitamin D (P<0.05). 1,25-(OH)2-D3 also enhanced the length, weight, ash weight, and the percentage contents of ash, Ca, and P in the tibia and femur of broilers (P<0.05). The mRNA expression levels of the Ca-binding protein (CaBP-D28k) in the duodenum, jejunum, and ileum of 19-day-old broilers increased to 88.1-, 109.1-, and 2.7-fold, respectively, after adding 1,25-(OH)2-D3 (P<0.05). The mRNA expression levels of the plasma membrane Ca ATPase 1b (PMCAlb) in the duodenum and the sodium (Na)/ Ca exchanger 1 (NCX1) in the duodenum and the jejunum were also enhanced to 1.57-2.86 times with the addition of 1,25-(OH)2-D3 (P<0.05). In contrast, the mRNA expression levels of PMCA1b and NCX1 in the ileum and that of vitamin D receptor (VDR) in the small intestine were not affected by 1,25-(OH)2-D3 (P>0.05). These data indicate that 1,25-(OH)2-D3 upregulated Ca transporter gene transcription and promoted Ca2+ absorption in the small intestine, especially in the proximal intestine (duodenum and jejunum), thereby improving growth performance and bone mineralization in broiler chickens.
Antimicrobial and Antioxidant Properties of Phenolic Extracts from Haloxylon articulatum Bioss Growth in Oued Souf – Algeria

This study intends to investigate plants that grow in southeast Algeria namely Haloxylon articulatum Bioss. which are commonly used by medical science for a treatment. More especially this study shed light on the antioxidant effect and biological activity of the plant whereby extraction of the active ingredients phenol is taken into account. The active ingredients were identified through the scanning device using High-performance liquid chromatographic (HPLC). Inorder to get deep insight into the body of knowledge towards extraction process; current study utilized both qualitative and quantitative approach. The data was analyzed separately on the anti-bacterial activity and oxidation of the phenolic extracts. The obtained results revealed a significant effect on the proliferation of some bacterial strains and free radical. In addition, of the phenolic extracts have shown less effective than the antibiotic chosen "polymyxin B" on bacteria; Staphylococcus aureus ATCC 6816 and Staphylococcus aureus methicillin resistant. As phenolic extracts have shown greater efficacy than the antibiotic chosen "polymyxin B" on bacteria Bacillus cereus ATCC 14579. Hence, based on the empirical evidences it can be stated that from various concentrations approach; the sensitivity of bacteria Bacillus cereus ATCC 14579 against phenolic extracts.