Miracle-dz/UniFAR-FTUs huggingface dataset:
Predikce silových a momentových korekčních faktorů pro válcování kovů pomocí AI

Tato práce se zabývá predikcí korekčních faktorů silových a momentových parametrů pro válcování kovů získaných matematickým modelem, což vede ke zvýšení přesnosti celého procesu. Cílem práce je návrh softwarového řešení, jeho implementace a následné otestování na reálných datech dodaných firmou PT SOLUTIONS WORLDWIDE spol. s r.o. Vzhledem ke komplexnosti matematicko-fyzikálního modelu je v této práci prezentována metoda složená z algoritmů předzpracování dat, neuronových sítí a strojového učení. Dodaný modul v jazyce C++ je momentálně součástí softwarových balíků firmy PTSW, které mají být nasazeny v systémech řízení válcovacích linek.
Field body temperatures in the rainforest frog Mantidactylus ( Brygoomantis ) bellyi from northern Madagascar :

Given that global amphibian population declines are in part associated with environmental thermal preferences of infectious pathogens, assessing variation of field body temperatures can contribute to a preliminary disease risk assessment. We evaluated the field body temperatures of the semi-aquatic frog Mantidactylus (Brygoomantis) bellyi from northern Madagascar during the warm-wet season (NovemberJanuary), identified predictors of body temperature (Tb) and differences between Tb and substrate temperature (Tsubs), and investigated diel and elevational variance of Tb. Body temperatures of 353 individuals of M. bellyi in the field ranged from 17.0 to 25.5°C, matching documented optimal temperature growth of the chytrid fungus, Batrachochytrium dendrobatidis (Bd). Our results also confirm M. bellyi to be a thermoconformer, with body temperatures closely matching those of the surrounding substrate. Body temperature of individual frogs slightly increased as a function of size and differences were Field body temperatures in the rainforest frog Mantidactylus (Brygoomantis) bellyi from northern Madagascar: Variance and predictors 58 Rasolonjatovo et al. : Field body temperatures in Mantidactylus bellyi Tsubs ont été déterminés, et les variances journalières et altitudinales de Tb ont été étudiées. La température corporelle de 353 individus de M. bellyi variait entre 17 et 25,5 °C dans leur milieu naturel, correspondant à la température de croissance optimale connue pour le chytride, Batrachochytrium dendrobatidis (Bd). Les résultats confirment également que M. bellyi est un thermoconformeur, avec une température corporelle proche de celle du substrat. La température corporelle est positivement corrélée avec la taille de la grenouille et a différé selon le sexe, les mâles présentant des températures légèrement plus élevées. Les modèles statistiques n’ont pas corroboré l’élévation comme étant un facteur de prédiction pertinent de Tb, même si une diminution de Tb avec l’élévation a été notée. De plus, cette baisse de Tb était moins explicite le jour que la nuit suggérant un gain de chaleur dû à l’exposition au soleil. La variance la plus élevée de Tb a été retrouvée chez la population vivant au niveau sommital du massif (> 1200 m) et la plus faible à mi-altitude (800 à 1000 m), rendant peutêtre cette dernière population plus vulnérable à la maladie fongique chytridiomycose. Cependant, ces données nécessitent des études supplémentaires de la variation saisonnière de la température corporelle, y compris des mesures faites en saison fraîche et sèche, pour bien comprendre le régime thermique de cette espèce des forêts tropicales humides et ses fluctuations thermiques saisonnières. Mots clés : Amphibia, Mantellidae, altitude, température du substrat, cycle journalier
Bovine respiratory complex: evidence of multiple viral circulation in a breeding farm

Bovine respiratory complex (BRC) is a leading cause of production loss among beef, dairy and feedlot cattle. The infectious agents involved are viruses, bacteria and mycoplasma. Viral agents include Bovine respiratory syncytial virus (BRSV), Bovine parainfluenza-3 virus (BPI3V), Bovine herpesvirus 1 (BHV-1) and Bovine viral diarrhea virus (BVDV). The purpose of this study was to evaluate the circulation of viruses associated with BRC in a beef herd, postweaning, doing a longitudinal assessment of serological tests. Moreover, clinical and hematological findings were described and compared. Thirty two animals between 5 to 7 months old, with no vaccination record, were selected for a daily evaluation of clinical signs consistent with BRC. Blood samples were taken on day 0 (weaning), +14 and +25 (when the study ended). No clinical manifestation of BRC was found and hematological parameters were within the reference range. At the beginning of this study, animals did not show BRSV-specific antibodies (Ab), whereas for the other viral agents the percentage of seropositive animals varied: 53% (BVDV1a), 31% (BPI3V), 25% (BVDV2) and 13% (BHV-1). On day +25, percentages of seropositive animals were 47% (BVDV1a), 50% (BPI3V), 19% (BVDV2), 25% (BHV-1) and 81% (BRSV). Seroconversion percentages for BRSV, BPI3V, BVDV1a and BHV-1 were 34%, 25%, 16% and 13% respectively. Furthermore, 66% of the animals seroconverted to one or more viruses. No seroconversion was observed for BVDV2 and BVDV1b. Thus, there is evidence of circulation and −in some cases− co-infection with BRCassociated viral agents during postweaning. At said production stage, conducting viral follow-up studies to identify and characterize circulating strains is key, particularly in relation to BRSV and PI3, which we have little information about in our country.
The algorithm versus the chimps: On the minima of classiﬁer performance metrics

In this paper we seek the minima of performance metrics for binary classiﬁcation to facilitate comparison between metrics and applications, and to assess the quality of inferential statistics made from non-probability samples. We use these minima to min-max normalize the performance metrics so that they can be interpreted as a percentage of the perfect classiﬁer relative to the proverbial chimps at the zoo † guessing at random. We compare our results with the balanced metrics that have been introduced recently, which are corrected for bias due to class imbalance.
12-lead ECG Arrythmia Classification Using Convolutional Neural Network for Mutually Non-Exclusive Classes

The growing demand for diagnosing of cardiovascular diseases leads to the development of new solutions for automatic classification of recorded ECG signals. Creating a robust and fast algorithm for automatic classification of ECG signal is crucial to improve the quality of healthcare, especially in countries where a lack of experienced specialists is an issue or the healthcare system is overloaded. The aim of the PhysioNet/Computing in Cardiology Challenge 2020 is to create an algorithm for classification of 12-lead ECGs based on ECG signals from multiple databases across the world. The shared training set consisted of 43,101 ECG recordings lasting from 5 to 1800 seconds. We (BioS Team) proposed the machine learning algorithm based on convolutional neural networks. The ECG signals were pre-processed using moving median filters to remove high-frequency noise and baseline wandering. We developed simply convolutional neural network consisting of four main convolutional blocks and one fully connected layer. We achieved a challenge validation score of 0.349, and full test score of 0.279, placing us 14 out of 41 in the official ranking.
Incidence of AM Fungi in Bamboo Species from Wadali , Amravati ( M . S . ) , India

Available online on http://www.ijlsci. in ISSN: 2320-964X (Online) ISSN: 2320-7817 (Print) The bamboos are evergreen perennial flowering plants in the subfamily Bambusoideae of the grass family Poaceae. They are of notable economic and cultural significance in South Asia, Southeast Asia and East Asia, being used for building materials, as a food source, and as a versatile raw product. It is an extremely versatile material with countless uses including construction, clothes, food and fuel. Due to its multiple utility and accessibility to common man, it is also known as green gold. There is a need to take efforts for making bamboo a supplementary business for agriculture along with its conservation and nurturing. Bamboo business also will prove financially beneficial for the farmers. The diversified collection of Bamboo plants from different parts of the country is well maintained at Wadali Forest Garden at Amravati. The main motive behind the plantation is to empower the farmer. The farmer who grows oranges and soybeans requires good species of bamboo. They are encouraged to grow bamboo species without having them to purchase it from outside, the bamboo plants are sold to them on the basis of species. Among the microbial communities Mycorrhizae forms symbiotic associations with the roots of most plant species. This symbiosis, that exists in between plant root and Arbuscular Mycorrhizal Fungi (AMF) is of great use and interest to ecologist due to potential influence of it on natural ecosystem processes. As we have the great source of Bamboos in Amravati, it was utilized for the present research work particularly in the field of Mycorrhizal Biotechnology. The main aim of the present study was to investigate the AMF association with three bamboo species i.e. B. balcooa, B. vulgaris, B. tulda. All the Bambusa species studied exhibited AM fungal association. The root association characterized by the presence of hyphal, arbuscule and vesicles typical of AM fungi were seen. The results indicated that the Bambusa balcooa Roxb. exhibited maximum % root colonization. The total number of spores per 100 g of all samples of rhizospere soils found in the range of 60 to 150 (Bambusa balcooa), 46 to 56 (B. vulgaris) and 40 to 80 (Bambusa tulda). Glomus tortuosum Schenk and Smith was found to be highly associated with all the three species of Bomboos. The trap culture is maintained to develop monoculture of viable spores. For sustainable development these native most dominant and some more species of AMF can be taken into account in near future as biofertilizers for Bamboos after their mass multiplication.
A Learning-based Robotic Bin-picking with Flexibly Customizable Grasping Conditions

A practical robotic bin-picking system requires a high grasp success rate for various objects. Also, the system must be capable of coping with various constraints and their changes flexibly. To resolve these issues, this study proposes a novel deep learning-based method that exploits a simulator to generate desired grasping actions. The features of this method are as follows: (1) Grasping conditions for any object can be flexibly customizable in the simulated environment to improve the real-world grasping actions. (2) Sensor input (RGB image) is directly regressed to grasping actions by using convolutional processing. Owing to these features, the system using the proposed method can grasp objects with geometric variations, semi-transparent objects, and objects with a biased center of gravity. Experimental results on a real robot system show that the proposed method exhibits a high grasp success rate for four types of objects with different physical and geometric properties as well as additional constraints of grasping condition.
Protective effects of Lycium barbarum polysaccharide (LBP) on rats with renal ischemia-reperfusion injury (IRI)

Renal ischemia-reperfusion injury (RIRI) is a common issue in clinics. Previous studies showed multiple pharmaceutical roles of Lycium barbarum polysaccharides (LBP); however very few studies focused on its preventive function on RIRI. This study thus investigated the effect of LBP on renal functions in rat RIRI models and the protective mechanisms. SD rats were randomly assigned into three groups: sham group, ischemia-reperfusion (IR) group and IR + LBP group. Serum urea and blood creatine levels were analyzed, along with superoxide dismutase (SOD) and malondialdehyde (MDA) quantification. Real-time PCR and Western blot were employed to determine mRNA and protein expression of rat Bcl-2 and Bax. Enzyme linked immunosorbent assay (ELISA) was used to measure the expression of tumor necrosis factor-alpha (TNF-α) and interleukin-1 beta (IL-1β) in rat serum. IR rats had significantly elevated serum urea and creatine levels, along with decreased SOD or MDA contents. IR rats also showed decreased mRNA and protein expression of Bcl-2 yet increased Bax expression, plus enhanced secretion of TNF-α and IL-1β (P < 0.05 compared to sham group). LBP treatment decreased serum urea or creatine levels in IR rat, enhanced SOD activity, decreased MDA content, increased mRNA or protein level of Bcl-2 but decreased Bax expression, and suppressed the secretion of TNF-α and IL-1β (P < 0.05 compared to IR group). In summary, LBP can exert protective effects on RIRI via improving oxidative stress (OS), suppressing apoptosis, and decreasing secretion and inflammatory cytokines.
GC‒MS analysis of fatty acids of Malvaviscus arboreus leaves

Malvaceae plants have been reported to produce a wide range of structurally varied fatty acids. Therefore, in this work, the fatty acid content of Malvaviscus arboreus Cav. leaves was investigated using gas chromatography-mass spectrometry (GC‒MS) technique. A total of 17 fatty acids were characterized, with noticeable abundance of saturated acids (50.98%) that were principally dominated by palmitic acid (32.15%). Moreover, a number of mono-, di-, and tri-unsaturated fatty acids were detected constituting 24.92% of the total identified acids, of which 5,12-octadecadienoic acid (7.66%) was the major unsaturated acid identified. As a common feature of several genera of the Malvaceae family, some unusual fatty acids were identified from the saponifiable fraction of M. arboreus leaves, namely 9-methyl tetradecanoic, 3-methoxy pentadecanoic, and 9-heptadecynoic acids, that along with tetracosanoic, 9-octadecenoic, 13-octadecenoic, 5,12-octadecadienoic, and 9,11,15-octadecatrienoic acids, were also interestingly reported herein for the first time in the genus Malvaviscus.
Meconium Testing for Prenatal Acetaminophen Exposure and Attention Deficit Hyperactivity Disorder- Considering Confounding by Indication

On September 28, 2020, Baker and colleagues reported on a prospective study showing an association between meconium acetaminophen measures and the diagnosis of Attention Deficit Hyperactivity Disorder (ADHD) in the offspring. They concluded that” caution should be used in administering acetaminophen during pregnancy” based on the fact that the use of meconium is a “biological marker” strengthens the association to the verge of causation. We believe that this “biological assumption” may be wrong. Meconium measurements of acetaminophen are merely a proof of exposure. We argue that it is very likely that the association between acetaminophen and ADHD are the result of bias by indication.
Research Progress and Trend of Person Re-identification

: Person re-identification refers to the matching technology of the same pedestrian image under different non-overlapping cameras, which has important application value in strengthening social management, preventing the occurrence of criminal acts and realizing event reconstruction. Person re-identification mainly relies on human visual representation and artificial design features and is greatly influenced by illumination, image resolution, pedestrian posture and shooting angle. Therefore, person re-identification faces great challenges. In this paper, the existing pedestrian representation feature learning technology and measurement technology are reviewed and analyzed, and the existing problems and possible solutions are pointed out. Person re-identification has great significance for researchers in this field to grasp the status quo and put forward new research ideas.
Running head: FROM SURFACE REALISM TO TRAINING CONSIDERATIONS 1

Traditionally, it was believed that realistic, high-fidelity simulation could result in effective training-systems. By showing the potentials of low-fidelity simulations, numerous research projects cast doubt on the traditional belief in high-fidelity simulation. Because the construct of fidelity has guided the training-system design for decades, a growing disbelief in fidelity has created a void in design, leaving designers without resources in their decision-making. This article first presents a historical review that shows how fidelity has been used in research and design, and how it was later challenged by researchers. Then, in filling the void of fidelity, attempts at providing design guidelines, such as traineeand task-analysis, are reviewed and their strengths and limitations are discussed. Toward the end, the lessons from the review are used to call for the creation of design methods. An example of such a method is discussed that uses the videorecording of expert performance as a resource in design.
Rheological characteristics of proanthocyanidin polymers from Pinus radiata. I. Rheological behavior of water-soluble extract fractions and phlobaphenes

The rheological characteristics of the solutions of the whole extracts and their fractions from the Pinus radiata bark were investigated. The viscosity changes upon addition of aqueous NaOH solution into the solutions of the hot water extracts are due mainly to the dissolution of the phlobaphene fraction. The polyelectrolyte character was established from the solutions of the hot-water extracts. The kinetics of the size growth of naturally forming phlobaphenes could be observed by photon correlation spectroscopy (PCS) from the solutions of hot-water extracts. Mild sonication could degrade phlobaphenes partly and this indicates that the formation of phlobaphenes is partly associated with colloidal interaction. © 1995 John Wiley & Sons, Inc.
DECT-2020 New Radio: The Next Step toward 5G Massive Machine-Type Communications

Massive machine type communications (mMTC) is one of the cornerstone services that must be supported by 5G systems. 3GPP has already introduced LTE-M and NB-IoT, referred to as cellular IoT (CIoT), in 3GPP Releases 13, 14, and 15, and submitted these technologies as part of 3GPP's IMT-2020 technology submission to ITU-R. Even though NB-IoT and LTE-M have been shown to satisfy 5G mMTC requirements, it is expected that these CIoT solutions will not address all aspects of IoT and ongoing digitalization, including the support for direct communication between “things” with flexible deployments, different business models, as well as support for even higher node densities and enhanced coverage. In this article, we first describe the Digital Enhanced Cordless Tele-communications (DECT) DECT-2020 standard recently published by ETSI for mMTC communications. We then evaluate its performance and compare it to the existing low-power wide area network solutions, showing that it outperforms those in terms of supported density of nodes while keeping delay and loss guarantees at the required level.
Blockchain Assisted Decentralized Federated Learning (BLADE-FL): Performance Analysis and Resource Allocation

Federated learning (FL), as a distributed machine learning paradigm, promotes personal privacy by local data processing at each client. However, relying on a centralized server for model aggregation, standard FL is vulnerable to server malfunctions, untrustworthy server, and external attacks. To address this issue, we propose a decentralized FL framework by integrating blockchain into FL, namely, blockchain assisted decentralized federated learning (BLADE-FL). In a round of the proposed BLADE-FL, each client broadcasts the trained model to other clients, aggregates its own model with received ones, and then competes to generate a block before its local training of the next round. We evaluate the learning performance of BLADE-FL, and develop an upper bound on the global loss function. Then we verify that this bound is convex with respect to the number of overall aggregation rounds K, and optimize the computing resource allocation for minimizing the upper bound. We also note that there is a critical problem of training deficiency, caused by lazy clients who plagiarize others trained models and add artificial noises to disguise their cheating behaviors. Focusing on this problem, we explore the impact of lazy clients on the learning performance of BLADE-FL, and characterize the relationship among the optimal K, the learning parameters, and the proportion of lazy clients. Based on MNIST and Fashion-MNIST datasets, we show that the experimental results are consistent with the analytical ones. To be specific, the gap between the developed upper bound and experimental results is lower than 5%, and the optimized K based on the upper bound can effectively minimize the loss function.
Reinforcement Learning based Recommender Systems: A Survey

Recommender systems (RSs) have become an inseparable part of our everyday lives. They help us find our favorite items to purchase, our friends on social networks, and our favorite movies to watch. Traditionally, the recommendation problem was considered to be a classification or prediction problem, but it is now widely agreed that formulating it as a sequential decision problem can better reflect the user-system interaction. Therefore, it can be formulated as a Markov decision process (MDP) and be solved by reinforcement learning (RL) algorithms. Unlike traditional recommendation methods, including collaborative filtering and content-based filtering, RL is able to handle the sequential, dynamic user-system interaction and to take into account the long-term user engagement. Although the idea of using RL for recommendation is not new and has been around for about two decades, it was not very practical, mainly because of scalability problems of traditional RL algorithms. However, a new trend has emerged in the field since the introduction of deep reinforcement learning (DRL), which made it possible to apply RL to the recommendation problem with large state and action spaces. In this paper, a survey on reinforcement learning based recommender systems (RLRSs) is presented. Our aim is to present an outlook on the field and to provide the reader with a fairly complete knowledge of key concepts of the field. We first recognize and illustrate that RLRSs can be generally classified into RL- and DRL-based methods. Then, we propose an RLRS framework with four components, i.e., state representation, policy optimization, reward formulation, and environment building, and survey RLRS algorithms accordingly. We highlight emerging topics and depict important trends using various graphs and tables. Finally, we discuss important aspects and challenges that can be addressed in the future.