Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of the polyphenol content and in vitro antioxidant capacity of fruit-based nutritional supplements commonly consumed by athletic and recreationally active populations

ABSTRACT Background Polyphenol-rich fruit supplements are commonly consumed by recreationally active and athletic populations because of their proposed benefits to both exercise performance and recovery from prior exercise. While it has been proposed that 300 mg of polyphenols pre-exercise enhances performance and 1000 mg per day accelerates recovery from muscle damage, it is difficult for consumers to optimize their intake because the polyphenol content of most fruit supplements is not available. Therefore, this study aimed to profile the phenolic and anthocyanin content and in vitro antioxidant capacity of a range of polyphenol-rich fruit supplements on sale in the UK. Methods Ten polyphenol-rich fruit supplements (six cherry, two pomegranate, one blueberry, and one New Zealand blackcurrant) commonly consumed by athletes were analyzed for total phenols (Folin–Ciocalteu method), total anthocyanins (pH differential method), and in vitro antioxidant capacity (ferric reducing antioxidant power (FRAP) and oxygen radical absorbance capacity (ORAC). Results The ten tested supplements varied markedly per serving in total phenolics (range: 13.8–1007.3 mg/gallic acid equivalents), anthocyanin content (range: 0.19–40.52 mg/cyanidin-3-glucoside), ORAC (range: 150–10,072 µmol of trolox equivalents), and FRAP (range: 72−14,320 µmol of Fe2+ equivalents). Different brands of tart cherry concentrate also exhibited a marked variation in their content of total phenolics (208–591 mg/GAE), anthocyanins (1.5–23.7 mg/cyd-3-glu), and antioxidant capacity (FRAP: 1724–4489 µmol of Fe2+ equivalents; ORAC: 6015–10,072 µmol of TE per serving) per serving. Conclusion As expected, supplements based on different fruits contained different quantities of anthocyanins and polyphenols. However, there was also a substantial variation within different brands of tart cherry supplements. Because limited compositional information is available on the labels of most fruit-based supplements, the data in this article will enable consumers to select the required volume of the ten tested supplements to meet suggested recommendations for polyphenol intake to enhance performance (300 mg pre-exercise) and accelerate recovery (1000 mg per day) from prior exercise.
OAM: An Option-Action Reinforcement Learning Framework for Universal Multi-Intersection Control

Efficient traffic signal control is an important means to alleviate urban traffic congestion. Reinforcement learning (RL) has shown great potentials in devising optimal signal plans that can adapt to dynamic traffic congestion. However, several challenges still need to be overcome. Firstly, a paradigm of state, action, and reward design is needed, especially for an optimality-guaranteed reward function. Secondly, the generalization of the RL algorithms is hindered by the varied topologies and physical properties of intersections. Lastly, enhancing the cooperation between intersections is needed for large network applications. To address these issues, the Option-Action RL framework for universal Multi-intersection control (OAM) is proposed. Based on the well-known cell transmission model, we first define a lane-cell-level state to better model the traffic flow propagation. Based on this physical queuing dynamics, we propose a regularized delay as the reward to facilitate temporal credit assignment while maintaining the equivalence with minimizing the average travel time. We then recapitulate the phase actions as the constrained combinations of lane options and design a universal neural network structure to realize model generalization to any intersection with any phase definition. The multiple-intersection cooperation is then rigorously discussed using the potential game theory. We test the OAM algorithm under four networks with different settings, including a city-level scenario with 2,048 intersections using synthetic and real-world datasets. The results show that the OAM can outperform the state-of-the-art controllers in reducing the average travel time.
Public spaces, a challenge for urban renewal: case of Bardo Urban Park Project

: The Algerian urban renewal policy, supported by the law on urban orientation, aims to rebuild the city on itself to exploit urban wastelands. From this perspective, open spaces, wastelands, and urban interstices become major planning issues; their requalification makes it possible to ensure the economic management of space and to improve the attractiveness of the city. The results of this article are based on a case study represented by the "Modernization Plan of the Metropolis of Constantine". This new development strategy is primarily based on a program of activities centered on the rehabilitation and upgrading of central urban spaces, as well as the residential proximity of central districts, and the realization of international significant projects that would change the city's image and bring it into the modern era. Ideally, urban public places should be designed to promote unity rather than division. conviviality and community involvement among cities are significant for urban development projects.
A Hybrid RNN based Deep Learning Approach for Text Classification

Despite the fact that text classification has grown in relevance over the last decade, there are a plethora of approaches that have been created to meet the difficulties related with text classification. To handle the complexities involved in the text classification process, the focus has shifted away from traditional machine learning methods and toward neural networks. In this work the traditional RNN model is embedded with different layers to test the accuracy of the text classification. The work involves the implementation of RNN+LSTM+GRU model. This model is compared with RCNN+LSTM and RNN+GRU. The model is trained by using the GloVe dataset. The accuracy and recall are obtained from the models is assessed. The F1 score is used to compare the performance of both models. The hybrid RNN model has three LSTM layers and two GRU layers, whereas the RCNN model contains four convolution layers and four LSTM levels, and the RNN model contains four GRU layers. The weighted average for the hybrid RNN model is found to be 0.74, RCNN+LSTM is 0.69 and RNN+GRU is 0.77. RNN+LSTM+GRU model shows moderate accuracy in the initial epochs but slowly the accuracy increases as and when the epochs are increased. Keywords—F1 score; gated recurrent unit; GloVe; long short term memory; precision; recall; recurrent neural network; regionbased convolutional neural network; text classification
Deep Sentiment Extraction using Fuzzy-Rule Based Deep Sentiment Analysis

In the world of social media, the amount of textual data is increasing exponentially on the internet, and a large portion of it expresses subjective opinions. Sentiment Analysis (SA) also named as Opinion mining, which is used to automatically identify and extract the subjective sentiments from text. In recent years, the research on sentiment analysis started taking off because of a huge of amount of data is available on the social media like twitter, machine learning algorithms popularity is increased in IR (Information Retrieval) and NLP (Natural Language Processing). In this work, we proposed three phase systems for sentiment classification in twitter tweets task of SemEval competition. The task is predicting the sentiment like negative, positive or neutral of a twitter tweets by analyzing the whole tweet. The first system used Artificial Bee Colony (ABC) optimization technique is used with Bag-of-words (BoW) technique in association with Naive Bayes (NB) and k-Nearest Neighbor (kNN) classification techniques with combination of various categories of features in identifying the sentiment for a given twitter tweet. The second system used to preserve the context a Rider Feedback Artificial Tree Optimization-enabled Deep Recurrent neural networks (RFATO-enabled Deep RNN) is developed for the efficient classification of sentiments into various grades. Further to improve the accuracy of classification on n-valued scale Adaptive Rider Feedback Artificial Tree (Adaptive RiFArT)-based Deep Neuro fuzzy network is devised for efficient sentiment grade classification. Finally, this research work proposed a Fuzzy-Rule Based Deep Sentiment Extraction (FBDSE) Algorithm with Deep Sentiment Score computation. Accuracy measure is considered to test the proposed systems performance. It was observed that the fuzzy-rule based system achieved good accuracy compared with machine learning and deep learning based approaches. Keywords—Sentiment analysis; SemEval; recurrent neural networks; LSTM; word embeddings; accuracy; f1-score; fuzzy – rule; deep sentiment extraction
Exploring the interplay between natural and intersexual selection on the evolution of a cognitive trait

Abstract There has been an increased focus on the role of natural and sexual selection in shaping cognitive abilities, but the importance of the interaction between both forces remains largely unknown. Intersexual selection through female mate choice might be an important driver of the evolution of cognitive traits, especially in monogamous species, where females may obtain direct fitness benefits by choosing mates with better cognitive abilities. However, the importance given by female to male cognitive traits might vary among species and/or populations according to their life‐history traits and ecology. To disentangle the effects of natural and sexual selection, here we use an agent‐based simulation model and compare the model's predictions when females mate with the first randomly encountered male (i.e., under natural selection) versus when they choose among males based on their cognitive trait values (i.e., under natural and intersexual selection). Males and females are characterized, respectively, by their problem‐solving ability and assessment strategy. At each generation, agents go through (1) a choosing phase during which females assess the cognitive abilities of potential mates until eventually finding an acceptable one and (2) a reproductive phase during which all males compete for limited resources that are exploited at a rate, which depends on their cognitive abilities. Because males provide paternal care, the foraging success of mated males determines the breeding success of the pair through its effect on nestling provisioning efficiency. The model predicts that intersexual selection plays a major role in most ecological conditions, by either reinforcing or acting against the effect of natural selection. The latter case occurs under harsh environmental conditions, where intersexual selection contributes to maintaining cognitive diversity. Our findings thus demonstrate the importance of considering the interaction between both selective forces and highlight the need to build a conceptual framework to target relevant cognitive traits.
Intelligent Covid-19 Vaccine Supplychain Management System

In light of the trend of countries to give vaccines to their people, it was concluded that the best solution to confront the COVID-19 pandemic is to give the vaccine, as it prevents its transmission from person to person, and if the infection is infected, the symptoms are much less than the degree of danger of the person who did not meet the vaccine. In this paper, we presented tables on the types of vaccines, a brief explanation of the Coronavirus, and the idea of our software project. Vaccine handling skills, where gives users the opinions of the people who received the vaccine and the doctors also according to the age group and the disease.
Gene-Edited Foods and the Public: The First Representative Survey Study of the United States

ABSTRACT Recent gene editing tools and techniques continue to develop at a swift pace and gene-edited foods boast significant promise to create identifiable benefits for end-use consumers, although there are currently few publicly identifiable products in the commercial marketplace. While competing stakeholders are active in this space, few public-facing media stories have surfaced and there have been few studies of public opinion of gene-edited foods. This article reports findings of the first representative survey study of public opinion toward gene-edited foods in the United States. This work finds that Americans are divided on their perceptions of this new technology and provides robust and granular assessment and identification of socioeconomic and belief-based classifications to better describe the current state of public opinion in this area.
A Modified Deep Learning Framework for Arrhythmia Disease Analysis in Medical Imaging Using Electrocardiogram Signal

Arrhythmias are anomalies in the heartbeat rhythm that occur occasionally in people's lives. These arrhythmias can lead to potentially deadly consequences, putting your life in jeopardy. As a result, arrhythmia identification and classification are an important aspect of cardiac diagnostics. An electrocardiogram (ECG), a recording collecting the heart's pumping activity, is regarded the guideline for catching these abnormal episodes. Nevertheless, because the ECG contains so much data, extracting the crucial data from imagery evaluation becomes extremely difficult. As a result, it is vital to create an effective system for analyzing ECG's massive amount of data. The ECG image from ECG signal is processed by some image processing techniques. To optimize the identification and categorization process, this research presents a hybrid deep learning-based technique. This paper contributes in two ways. Automating noise reduction and extraction of features, 1D ECG data are first converted into 2D pictures. Then, based on experimental evidence, a hybrid model called CNNLSTM is presented, which combines CNN and LSTM models. We conducted a comprehensive research using the broadly used MIT_BIH arrhythmia dataset to assess the efficacy of the proposed CNN-LSTM technique. The results reveal that the proposed method has a 99.10 percent accuracy rate. Furthermore, the proposed model has an average sensitivity of 98.35 percent and a specificity of 98.38 percent. These outcomes are superior to those produced using other procedures, and they will significantly reduce the amount of involvement necessary by physicians.
Clinical Presentation and MRI Characteristics of Appendicular Soft Tissue Lymphoma: A Systematic Review

Appendicular soft tissue lymphoma (ASTL) is rare and is frequently misinterpreted as soft tissue sarcoma (STS). Studies investigating magnet resonance imaging (MRI) characteristics of ASTL are scarce and showed heterogenous investigation criteria and results. The purpose of this study was to systematically review clinical presentations and MRI characteristics of ASTL as described in the current literature. For that purpose, we performed a systematic literature review in compliance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Patient demographics, clinical presentation and MRI imaging characteristics of ASTL were investigated, resulting in a total of nine included studies reporting a total of 77 patients. Signal intensity of lymphoma compared to muscle tissue was mostly described as isointense (53%) or slightly hyperintense (39%) in T1-weighted images and always as hyperintense in proton-and T2-weighted images. Multicompartmental involvement was reported in 59% of cases and subcutaneous stranding in 74%. Long segmental involvement was present in 80% of investigated cases. Involvement of neurovascular structures was reported in 41% of cases and the presence of traversing vessels in 83% of patients. The presence of these findings should lead to the inclusion of ASTL in the differential diagnosis of soft tissue masses.
Dispersion of SARS‐CoV‐2 RNA across a wastewater treatment plant and its workers

Within urban and suburban sewersheds, SARS‐CoV‐2 released through faeces is transported through sewage systems into municipal wastewater treatment plants (WWTPs). Studies have shown that viral RNA is detectable in untreated wastewater but not in WWTP effluent. In this study, we investigated treatment steps between the influent and final treated effluent to identify the point at which viral RNA is below detection. Additionally, we examined air surrounding high turbulence treatment steps to test for the presence of SARS‐CoV‐2 RNA in WWTP‐generated bioaerosols. To examine potential worker exposure to SARS‐CoV‐2, WWTP workers were tested for the presence of viral RNA. The data show that despite high viral RNA concentration in the influent, SARS‐CoV‐2 RNA concentration decreased significantly (p < 0.02) in the main treatment steps and was below detection in the effluent. Additionally, SARS‐CoV‐2 RNA was below detection in air samples (n = 42), and the worker rate of infection was not significantly different (p = 0.99) from the rate of infection in the surrounding community. These results suggest that WWTP workers may have minimal exposure to SARS‐CoV‐2 during routine outdoor work procedures and that the WWTP successfully reduces the amount of viral RNA entering effluent receiving waters, providing a vital public health service to communities.
Efficacy and Safety of Electroacupuncture for Pain Control in Herpes Zoster: A Systematic Review and Meta-Analysis

Introduction Herpes zoster is caused by the reactivation of the latent varicella-zoster virus, which leads to acute pain that may disturb routine activities and affect patients' quality of life. Electroacupuncture (EA) has been commonly used for treating herpetic pain in clinical treatment. However, no relevant studies have been performed to evaluate the efficacy and safety of EA for acute control in herpetic neuralgia patients. The purpose of the current study was to conduct a systematic review and meta-analysis to address the deficiencies of the current research. Methods Three English (PubMed, Cochrane Library, and Web of Science) and four Chinese (China National Knowledge Infrastructure (CNKI), Chinese Biomedical Literature database (CBM), Wan-fang database, and the Chinese Scientific Journals Full-text Database (VIP)) were comprehensively searched from inception to 31 December 2021. Two independent reviewers evaluated the retrieved data based on the eligibility criteria in advance. In addition, the Cochrane Risk of Bias Tool was used to assess the methodological quality of the included studies. Outcome indexes in this study included the visual analog scale, the time to cessation of pustules, the time to scabs, the time to rash healing, adverse reactions, and the incidence of postherpetic neuralgia. Sensitivity and subgroup analyses were also performed to evaluate the intervention effect specifically. In addition, publication bias was analyzed. Results Six randomized controlled trials (167 participants in the experimental groups and 174 participants in the control groups) were identified as reporting the application of EA for acute herpes zoster pain and were included in this study. The results from our meta-analysis revealed that EA was superior to control treatment according to visual analog scale, the time of rash healing, and the incidence of postherpetic neuralgia. However, in terms of the time to cessation of pustules, scabs, and adverse reactions, the results showed that EA compared with the control group showed no significant difference. In addition, subgroup analyses indicated that 2/100 Hz-EA has more significant effects on herpetic pain. Sensitivity analyses revealed that the results of EA for acute pain control and the rash healing time in herpetic neuralgia patients were stable. However, a publication bias was observed. Conclusion Our meta-analysis results showed that EA could offer certain advantages in treating acute pain in herpetic neuralgia patients. However, small sample sizes, heterogeneity in study design, and variable methodological quality weaken these inferences. In addition, weak evidence was found for the safety of EA.
On Probabilistic Generalization of Backdoors in Boolean Satisfiability

The paper proposes a probabilistic generalization of the well-known Strong Backdoor Set (SBS) concept applied to the Boolean Satisfiability Problem (SAT). We call a set of Boolean variables B a ρ-backdoor, if for a fraction of at least ρ of possible assignments of variables from B, assigning their values to variables in a Boolean formula in Conjunctive Normal Form (CNF) results in polynomially solvable formulas. Clearly, a ρ-backdoor with ρ=1 is an SBS. For a given set B it is possible to efficiently construct an (ε, δ)-approximation of parameter ρ using the Monte Carlo method. Thus, we define an (ε, δ)-SBS as such a set B for which the conclusion "parameter ρ deviates from 1 by no more than ε" is true with probability no smaller than 1 - δ. We consider the problems of finding the minimum SBS and the minimum (ε, δ)-SBS. To solve the former problem, one can use the algorithm described by R. Williams, C. Gomes and B. Selman in 2003. In the paper we propose a new probabilistic algorithm to solve the latter problem, and show that the asymptotic estimation of the worst-case complexity of the proposed algorithm is significantly smaller than that of the algorithm by Williams et al. For practical applications, we suggest a metaheuristic optimization algorithm based on the penalty function method to seek the minimal (ε, δ)-SBS. Results of computational experiments show that the use of (ε, δ)-SBSes found by the proposed algorithm allows speeding up solving of test problems related to equivalence checking and hard crafted and combinatorial benchmarks compared to state-of-the-art SAT solvers.
Semantic Visual Segmentation of a Mobile Robot Environment Using Deep Learning Model

This paper addresses the use of deep learning techniques in 3D point cloud labeling of environment representations for the task of a semantic visual localization of mobile robots. In contrast to standard problems resolved with Convolutional Neural Networks (CNNs), the paper deals with applying CNNs to segment point clouds that are, unlike images, unordered and unstructured. The used point clouds contain laser measurements of 3D positions (x,y,z) as well as captured RGB camera images from the scanned scene to colorize the point cloud (RGB values). The main focus of the paper is on implementation and evaluation of a hand-crafted convolution layer and the ConvPoint CNN architecture that introduces continuous convolutions for point cloud processing. The solution was implemented in the Python programming language using the PyTorch deep learning framework.
Short-Term Reliability Evaluation of Composite Power Systems in the Presence of DR Programs

Demand response (DR) programs are getting more attention due to their many advantages for the operation and reliability of power systems. Evaluating the short-term impacts of supplementary resources, like DR, on system reliability is crucial in power system operation planning, as it can assist system operators in managing remedial resources and effectively establishing operational plans. However, short-term reliability assessment requires particular considerations, not usually included in conventional approaches. In the short term, the failure probability of system components depends on time and the initial states. Considering DR programs adds another layer of complexity to this problem due to their operational and management requirements. This paper discusses the distinctions between conventional and short-term reliability evaluations and proposes an algorithm for operating reliability assessments. The results indicate that taking a short-term view of system reliability might result in more realistic evaluations of system reliability and hence more robust planning of remedial resources.