Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of consumption of I, Se, S and nanoaquacitrates on hematological and biochemical parameters of the organism of rabbits

Stepan Gzhytskyi National University of Veterinary Medicine and Biotechnologies Lviv, Pekarska st., 50, Lviv, 79010, Ukraine. Tel. : +38-068-136-20-54. E-mail: bvh@ukr.net Boiko, О. V. , Honchar, О. F. , Lesyk, Y. V. , Kovalchuk, І. І. , Gutyj, B. V. , & Dychok-Niedzielska, A. Z. (2021). Effect of consumption of I, Se, S and nanoaquacitrates on hematological and biochemical parameters of the organism of rabbits. Regulatory Mechanisms in Biosystems, 12(2), 335–340. doi:10.15421/022145
THE REGULARITIES OF IMMUNOLOGICAL DISORDERS IN PATIENTS WITH THE RAPIDLY PROGRESSING GENERALIZED PARODONTITIS WITH THE DRUG-SENSITIVE AND DRUG-RESISTANT PERIODONTOPATHOGENIC BACTERIA

Generalized periodontitis is one of the most common d ental diseases and is one of the main causes of tooth loss in young and mature people. Recently, the etiology and pathogenesis of this disease, which is prone to a persistent and aggressive course with unstable results of complex treatment, have become a priority in the study of the problem of rapidly progressive generalized periodontitis. The results of the study are based on a comprehensive clinical and immunological examination of 59 patients with a diagnosis of rapidly progressive generalized periodontitis of I-II and II-III severity, aged 30 to 52 years (the average age was 35.4 ± 1.4 years on average), among they were observed 31 (53%) women and 28 (47%) men who did not have other general somatic diseases that could affect the immunological status during © Dorohina A.A. , 2020.
Solving Cold-Standby Reliability-Redundancy Allocation Problems with Particle-Based Simplified Swarm Optimization

Particle swarm optimization (PSO) and simplified swarm optimization (SSO) are two of the state-of-the-art swarm intelligence technique that is widely utilized for optimization purposes. This paper describes a particle-based simplified swarm optimization (PSSO) procedure which combines the update mechanisms (UMs) of PSO and SSO to determine optimal system reliability for reliability-redundancy allocation problems (RRAPs) with cold-standby strategy while aimed at maximizing the system reliability. With comprehensive experimental test on the typical and famous four benchmarks of RRAP, PSSO is compared with other recently introduced algorithms in four different widely used systems, i.e., a series system, a series-parallel system, a complex (bridge) system, and an overspeed protection system for a gas turbine. Finally, the results of the experiments demonstrate that the PSSO can effectively solve the system of RRAP with cold-standby strategy and has good performance in the system reliability obtained although the best system reliability is not obtained in all four benchmarks.
HISTOPATHOLOGY LEARNING DATASET AUGMENTATION: A HYBRID APPROACH

In histopathology, staining quantiﬁcation is a mandatory step to unveil and characterize disease progression and assess drug eﬃciency in preclinical and clinical settings. Super-vised Machine Learning (SML) algorithms allow the automation of such tasks but rely on large learning datasets, which are not easily available for pre-clinical settings. Such databases can be extended using traditional Data Augmentation methods, although generated images diversity is heavily dependent on hyperparameter tuning. Generative Ad-versarial Networks (GAN) represent a potential eﬃcient way to synthesize images with a parameter-independent range of staining distribution. Unfortunately, generalization of such approaches is jeopardized by the low quantity of publicly available datasets. To leverage this issue, we propose a hybrid approach, mixing traditional data augmentation and GAN to produce partially or completely synthetic learning datasets for segmentation application. The augmented datasets are validated by a two-fold cross-validation analysis using U-Net as a SML method and F-Score as a quantitative criterion.
Enrichment Of Color Image Using Random Transfer Functions

-Image enrichment might be a way of improvising an image quality in terms of upper insight of the knowledge present within the image. Enrichment of color images is more burdensome compared to the enrichment of gray scale images. Several methods are preferred for enriching the color images. variety of these methods are furnished during this seminar. One method supported enrichment in HSV space is described intimately . The results and comparison of those methods are
Design and Development of Citizen Surveillance and Social-Credit Information System for Bangladesh

Most of the areas of Bangladesh face a wide range of threats, ranging from terrorism and civil unrest to kidnapping, rape, and murder. To reduce the impact of these threats, the authorities must capture real-time information on what is happening in and around the city. Therefore, there is a growing requirement for utilizing new and emerging technologies to make the cities safer. The development of the monitoring system in the technological field has become a blazing topic in the milieu of scholars, and AI techniques have become an essential part of the future era. With the help of AI & deep learning, people's social behavior (Action Recognition) can be evaluated. By implementing the social credit score system, authorities can mark the citizen's activities and analyze them. To detect and recognize the actions of a human, YOLOV2 architecture was used and incorporated the SQL database (SQLite) to record the social credit score. A citizen can visualize their credit score from an HTML-based website.
Robust and Distributionally Robust Optimization Models for Linear Support Vector Machine

In this paper we present novel data-driven optimization models for Support Vector Machine (SVM), with the aim of linearly separating two sets of points that have non-disjoint convex closures. Traditional classiﬁcation algorithms assume that the training data points are always known exactly. However, real-life data are often subject to noise. To handle such uncertainty, we formulate robust models with uncertainty sets in the form of hyperrectangles or hyperellipsoids, and propose dis-tributionally robust optimization models enforcing limits on ﬁrst-order deviations along principal directions. All the formulations reduce to convex programs. The eﬃciency of the new classiﬁers is evaluated on real-world databases in medicine, requiring cancer detection and heart disease recognition. Experiments show that box robust classiﬁers might be overly-conservative, whereas higher levels of accuracy can be achieved when moments of the distributions are assumed exploiting the available information via distributionally robust optimization methods.
Estimating the effects of aerosol, cloud, and water vapor on therecent brightening in India during the monsoon season

Abstract. India is experiencing a leveling-off trend of solar radiation, even a transition from dimming to brightening. This process is significantly complicated because of the active atmospheric action during monsoon season. In this study, we use observations from multiple sensors in the A-Train satellite constellation to evaluate the effects of aerosol, cloud, and water vapor variations on recent changes of solar radiation during monsoon season (June–September) in India from 2006 to 2015. The results show that the increase in aerosol optical depth is paradoxical with the variation of surface shortwave radiation in India. Instead, the decreases in water vapor amount and clouds significantly contribute to the brightening, further affecting the surface warming in India. In general, clouds are reduced and thinned by approximately 9.4 % and 182 m when cloud water path (by 53.4 g m−2) and particle number concentration in cloud-sky condition decrease. The corresponding change of clouds weakens the shortwave cloud radiative effect in surface by approximately 45.5 W m−2. Moreover, the precipitable water in clear-sky decreases by approximately 3.0 mm over the brightening period. Consequently, solar brightening increases by roughly 2.8 W m−2 owing to its weakened absorption. Overall, the decreases in water vapor and clouds result in the increased absorption of direct solar radiation in surface and subsequent surface brightening.
High-Throughput Approaches for the Identification of <italic toggle='yes'>Pseudomonas aeruginosa</italic> Antivirulents

Antimicrobial resistance is a serious medical threat, particularly given the decreasing rate of discovery of new treatments. Although attempts to find new treatments continue, it has become clear that merely discovering new antimicrobials, even if they are new classes, will be insufficient. It is essential that new strategies be aggressively pursued. Toward that end, the search for treatments that can mitigate bacterial virulence and tilt the balance of host-pathogen interactions in favor of the host has become increasingly popular. In this review, we will discuss recent progress in this field, with a special focus on synthetic small molecule antivirulents that have been identified from high-throughput screens and on treatments that are effective against the opportunistic human pathogen Pseudomonas aeruginosa.
The effect of moderate intensity running training on reproductive system alteration induced by acetyl salicylic acid (Aspirin) in adult Wistar rats

Background: Acetyl salicylic acid (ASA) or Aspirin is one of the most widely used drugs in the world; one of its side effects is the male reproductive system alteration. The present study aimed to investigate the effect of moderate intensity aerobic exercise on ASA-induced reproductive system alteration in rats. Materials and Methods: In two stages, fifteen rats (N=15) were randomly divided into control (C, n=5), non-treatment (NT, n=5) and aerobic-treatment (AT, n=5) groups; the AT and NT groups used aspirin 12.5 mg/kg-daily/five weeks. The AT group ran at moderate intensity on a rodent-treadmill/four weeks, five sessions a week. Groups C and NT did not receive any intervention. Serum testosterone level, spermatogenesis indices and sperm parameters of rats were evaluated and the data were analyzed by one-way variance and Tukey post-hoc-test at alpha level 0.05 by SPSS. Results: Data statistical analysis showed that there was a significant difference between the groups in terms of serum testosterone level (P=0.02), sperm quantity (P=0.0001), viability (P=0.001), maturity (P=0.0001), sperm DNA fracture (P=0.002), TDI, SI and RI indices, tubule diameter (P=0.0001), tubular epithelium thickness (P=0.0001) and number of Leydig cells (P=0.003), but there was no significant difference between the groups in terms of sperm motility (P=0.07). Tukey post-hoc-test showed that these differences were between NT and AT groups, but no significant difference was observed between C and AT. Conclusion: It can be concluded that four-week moderate exercise helps accelerate the reproductive conditions recovery to just before taking acetyl salicylic acid (ASA) or Aspirin.
Ultrafine Particles: 3 Years of Measurements in the NIST Test House | NIST

NIST has supported research characterizing ultrafine particle sources and dynamics for more than a decade. Over 90 % of ultrafine particles (UFP) produced by stovetop cooking on both gas and electric stoves were <10 nm in diameter. Emission rates of up to 10 min were noted. Coagulation was a dominant removal mechanism and was modeled with some success. Kitchen exhaust fan efficiencies varied from <10 % for particles <5 nm to nearly 100 % for particles >10 nm. Using the NIST test house, penetration coefficients and deposition rates were estimated under real-world conditions for particles in closed-window and open-window configurations. Infiltration factors using dedicated automated air change rate measurements varied from 0.02 for the smallest (<5 nm) particles to >0.5 for larger (30 nm to 100 nm) sizes.
Assessing the ammonium nitrate regime in Paris

Assessing the ammonium nitrate formation regime in the Paris megacity and its representation in the CHIMERE model H. Petetin, J. Sciare, M. Bressi, A. Rosso, O. Sanchez, R. Sarda-Estève, J.-E. Petit, and M. Beekmann LISA/IPSL, Laboratoire Inter-universitaire des Systèmes Atmosphériques, UMR CNRS 7583, Université Paris Est Créteil (UPEC) and Université Paris Diderot (UPD), France LSCE, Laboratoire des Sciences du Climat et de l’Environnement, CNRS-CEA-UVSQ, Gif-sur-Yvette, France Energy Environment Water Research Center (EEWRC), The Cyprus Institute, Nicosia, Cyprus AIRPARIF, Agence de surveillance de la qualité de l’air, Paris, France now at: Laboratoire d’Aérologie, Université Paul Sabatier and CNRS, Toulouse, France now at: Air Lorraine, Villers-les-Nancy, France
JOEPP-08-2020-0151_proof 208..227

Purpose – The present study contributes to our understanding of how to curb burnout among hospital staff over time. The authors extend existing research by examining the mediating role of mission valence in the link between transformational leadership and burnout. Design/methodology/approach – Self-administered questionnaire data from employees in a Canadian general hospital (N 5 185) were analyzed using a time-lagged research design to examine whether transformational leaders can increase employees’ attraction to the organization’s mission (i.e. mission valence) and in turn alleviate long-term burnout. Findings – Structural equationmodeling analysis demonstrated that transformational leadership (time 1) was negatively related to the burnout components of emotional exhaustion and depersonalization (time 2). Further, the results showed that mission valence mediated these relationships. Practical implications – The study findings are important for managers and professionals as they identify transformational leadership as a potent strategy to alleviate employee burnout and clarify the process through which this is achieved, namely, by increasing mission valence. Originality/value – To date, surprisingly little research has explored how transformational leadership influences followers’ burnout. To address this issue, the present study examined the role of transformational leadership on staff burnout through the mechanism of increasing mission valence. Understanding how to mitigate burnout is particularly critical in health care organizations given that burnout not only negatively impacts employee wellbeing but also the wellbeing and quality of care provided to patients.
Digitale Verfahren zur Behandlung psychischer Störungen in der COVID-19-Pandemie

Zusammenfassung Die COVID-19-Pandemie und ihre Folgen (z. B. Kontaktbeschränkungen) stellen die psychiatrisch-psychotherapeutische Versorgung national und international vor neue Herausforderungen. Dieser Beitrag gibt einen Überblick über digitale Verfahren zur Behandlung psychischer Störungen unter Pandemiebedingungen und zur Reduktion pandemiebedingter psychischer Belastung (z. B. aufgrund sozialer Isolation). Diskutiert werden außerdem aktuelle Herausforderungen und Grenzen beim Einsatz digitaler Behandlungsformen sowie Perspektiven für deren langfristigen Einsatz in der psychiatrisch-psychotherapeutischen Versorgung.
Reliability and Robustness of Transformers for Automated Short-Answer Grading

Short-Answer Grading (SAG) is an applica-001 tion for NLP in education where student an-002 swers to open questions are graded. This task 003 places high demands both on the reliability (ac-004 curacy and fairness) of label predictions and 005 model robustness against strategic, "adversar-006 ial" input. Neural approaches are powerful 007 tools for many problems in NLP, and transfer 008 learning for Transformer-based models speci-009 ﬁcially promises to support data-poor tasks 010 as this. We analyse the performance of a 011 Transfomer-based SOTA model, zooming in 012 on class-and item type speciﬁc behavior in 013 order to gauge reliability; we use adversarial 014 testing to analyze the the model’s robustness 015 towards strategic answers. We ﬁnd a strong de-016 pendence on the speciﬁcs of training and test 017 data, and recommend that model performance 018 be veriﬁed for each individual use case. 019
A NATURAL LANGUAGE UNDERSTANDING KNOWLEDGE-BASED CHATBOT OVER LINKED WEB DATA

--- With the abrupt development of the web today, the information in web pages is structured and tagged to ensure that it can be read directly by computers, which is available publicly in the type of knowledge bases (KBs). Ensuring this information is machine-readable, easily reachable, and valuable for a customer is one of the goals of the chatbot over linked web data in real-time. Developing a knowledge-based chatbot over linked data leads to many challenges, including user question understanding, multilingual recognition, and supporting multiple knowledge bases. To deal with these challenges, we propose a solution; 1 st we design and develop interactive user interface system architecture, 2 nd , we plan a machine learning(ML) method for natural language understanding(NLU) to be familiar with user queries via text/voice message methods, then take out the necessary keywords. Information for those keywords is obtained by making queries to the linked website's knowledge base to get desired information using the web scraping technique. For the experiment, we use generic medicine data as a domain, and it's added to the frequently asked queries dataset in the system knowledge base, so the existing system knowledge base gets extended automatically by the chatbot intelligent features, such as addressing the user’s queries, feedback messages, and the system failed responses. This system can be widespread with the new domains for an extensive range of topics.
Functional Neurophysiological Biomarkers of Early-Stage Alzheimer’s Disease: A Perspective of Network Hyperexcitability in Disease Progression

Network hyperexcitability (NH) has recently been suggested as a potential neurophysiological indicator of Alzheimer’s disease (AD), as new, more accurate biomarkers of AD are sought. NH has generated interest as a potential indicator of certain stages in the disease trajectory and even as a disease mechanism by which network dysfunction could be modulated. NH has been demonstrated in several animal models of AD pathology and multiple lines of evidence point to the existence of NH in patients with AD, strongly supporting the physiological and clinical relevance of this readout. Several hypotheses have been put forward to explain the prevalence of NH in animal models through neurophysiological, biochemical, and imaging techniques. However, some of these hypotheses have been built on animal models with limitations and caveats that may have derived NH through other mechanisms or mechanisms without translational validity to sporadic AD patients, potentially leading to an erroneous conclusion of the underlying cause of NH occurring in patients with AD. In this review, we discuss the substantiation for NH in animal models of AD pathology and in human patients, as well as some of the hypotheses considering recently developed animal models that challenge existing hypotheses and mechanisms of NH. In addition, we provide a preclinical perspective on how the development of animal models incorporating AD-specific NH could provide physiologically relevant translational experimental data that may potentially aid the discovery and development of novel therapies for AD.
Design and Evaluation of a 32-bit Carry Select Adder using 4-bit Hybrid CLA Adder

Adder circuits play a remarkable role in modern microprocessor. Adders are widely used in critical paths of arithmetic operation such as multiplication and subtraction. A Carry Select Adder (CSA) design methodology using a modified 4-bit Carry Look-Ahead (CLA) Adder has been proposed in this research. The proposed 4-bit CLA used hybrid logic style based logic circuits for Carry Generate (Gi) and Carry Propagate (Pi) functions in order to improve performance and reduce the number of transistors required. The modified 4-bit CLA is introduced as the basic unit for implementation of 32-bit CSA. The proposed design of hybrid CLA based 32-bit CSA has been compared with conventional static CMOS based 32-bit CSA and 32-bit Ripple Cary Adder (RCA) by conducting simulation using Cadence Virtuoso tools. Power consumption and delay in the proposed 32-bit CSA are found as 322.6 μW and 0.556 ns whereas those parameters in the conventional 32-bit CSA were 455.4 μW and 0.667 ns respectively. We have done all the simulation using Cadence Virtuoso 90 nm technology library.
Student Profile Modeling Using Boosting Algorithms

The student profile has become an important component of education systems. Many education systems objectives, such as e-recommendation, e-orientation, e-recruitment, and dropout prediction are essentially based on the profile for decision support. Machine learning plays an important role in this context, where several studies have been carried out either for classification, prediction, or clustering purpose. In this paper, the authors present a comparative study of different boosting algorithms, which have been used successfully in many fields and for many purposes. In addition, the authors applied feature selection methods Fisher score, information gain combined with recursive feature elimination to enhance the preprocessing task and models’ performances. Using multi-label dataset to predict the class of the student performance in mathematics. This article shows that the light gradient boosting machine (LightGBM) algorithm achieved the best performance when using information gain with recursive feature elimination method compared to the other boosting algorithms.
Deep Learning Model based Face Recognition with Mask

- Face detection has evolved as a popular problem in image processing and Computer Vision. Many new algorithms are developed using convolutional constructions to make the algorithm as accurate as possible. These convolutional structures have made it easier to extract pixel details. We aim to design a binary face divider that can find any face present in the frame no matter how aligned. We present how to make an accurate face mask from any image of incorrect size. Starting from an RGB image of any size, the method uses VGG Pre-defined Training Weights - 16 Architecture rendering feature. The training was done through Fully Convolutional Networks to differentiate the face and face present in the image. Gradient Decline is used for training while Binomial Cross Entropy is used as a loss function. Progressively the output image from FCN is processed to remove unwanted noise and to avoid false guesses if any and to create a binding box around the face. In addition, the proposed model also shows good results in visual facial recognition. In line with this it is also able to get many face masks in one frame. Tests were performed on the Multi Parsing Human Dataset to detect an estimated pixel density of 93.884% on split face masks.
The role of convolutional neural networks in scanning probe microscopy: a review

Progress in computing capabilities has enhanced science in many ways. In recent years, various branches of machine learning have been the key facilitators in forging new paths, ranging from categorizing big data to instrumental control, from materials design through image analysis. Deep learning has the ability to identify abstract characteristics embedded within a data set, subsequently using that association to categorize, identify, and isolate subsets of the data. Scanning probe microscopy measures multimodal surface properties, combining morphology with electronic, mechanical, and other characteristics. In this review, we focus on a subset of deep learning algorithms, that is, convolutional neural networks, and how it is transforming the acquisition and analysis of scanning probe data.
Sim-to-Real Adaptation for Mobile Robots Based on LiDAR Odometry

: In this paper, we propose a method for the simulation-to-reality prob-1 lem in the domain of wheeled mobile robotics. While most applications rely on 2 a low-level velocity controller, both in simulation and on the real robot, it may 3 be desirable to directly control other action spaces, e