Miracle-dz/UniFAR-FTUs huggingface dataset:
Anticaries Agent Based on Silver Nanoparticles and Fluoride: Characterization and Biological and Remineralizing Effects—An In Vitro Study

Objective The aim of the study was to characterize and evaluate the stability, antimicrobial activity, cytotoxicity, and remineralizing effects of silver nanoparticles and fluoride anticaries agent (AgF) on staining dental enamel. Materials and Methods An experimental AgF solution was prepared and compared to silver diamine fluoride (SDF). First, the AgF was characterized and the stability was evaluated by transmission electron microscopy (TEM). Streptococcus mutans, Enterococcus faecalis, and Escherichia coli strains were used to evaluate the minimum inhibitory and bactericidal concentration and cytotoxicity performed using L929 fibroblastic cells by MTT test. Caries-like lesions induced by pH-cycling in human enamel were obtained, and then, the superficial microhardness, cross-sectional microhardness (CSMH), scanning electron microscopy (SEM), and energy-dispersive X-ray spectroscopy (EDS) were performed. Photographic images were taken to analyze the enamel staining. Results The AgF showed stableness in long term with bacteriostatic and bactericidal actions without cytotoxicity. Enamel remineralization, in surface and in depth (CSMH), was observed when the AgF was used, and it was similar to SDF. SEM showed enamel precipitation, and EDS observed the presence of P, Ca, Au, Ag, and Cl elements. Contrary to SDF, AgF did not stain the enamel. Conclusion The nano silver fluoride anticaries agent tested presented long-term stability, superficial and in-depth remineralizing capacity with antimicrobial potential and biocompatibility and did not stain the enamel.
Reduced switch count pulse width modulated multilevel inverter

The study develops a new topology for a single phase cascaded H-bridge multilevel inverter (CHBMLI) with a focus to reduce the number of power switching devices in the path for the flow of current. The philosophy combines an array of series connected voltage sources on either side of an H-bridge inverter to derive the new configuration for the MLI. It allows a multicarrier pulse width modulation approach to the process of generating the pulses for synthesising the PWM output voltage. The use of a smaller number of switches to reach the output voltage show cases the ability of the modular architecture to expand the scope of the CHBMLI. The architecture of a field programmable gate array fosters to realise its implementation and validate the simulated results over a range of modulation indices. The performance draws a new directive in the choice of a particular topology for the MLI to suit applications in the real world.
Gaussian Distribution-Based Inertial Control of Wind Turbine Generators for Fast Frequency Response in Low Inertia Systems

Accordingto recent grid codes, large-scale wind turbines (WTs) are required to provide fast frequency response (FFR). The existing stepwise inertial control methods suggest immediate incremental power injection by WTs, followed by the abrupt over-production termination to avoid over-deceleration of the rotor speed. These methods have a drawback that they impose severe secondary frequency drops (SFD), or they consider an unrealistic constant wind speed during their inertial control support. This paper proposes a novel Gaussian distribution-based inertial control (GDBIC) scheme that can improve the frequency nadir without rotor speed over-deceleration. Upon detecting a power imbalance, WT increases the output power with an incremental power and declines it following Gaussian distribution trajectory controlled by a standard deviation parameter, ensuring by this convergence of the rotor speed to a stable equilibrium. The proposed scheme is also capable of responding to a second cascade event. The performance of the GDBIC is tested on the wind-integrated IEEE 9-bus system and the IEEE 39-bus system in DIgSILENT PowerFactory. It is also compared with other methods reported in literature. Furthermore, experimental tests are used to verify the performance of the proposed scheme, using two different hardware-in-the-loop testing facilities. The blade fatigue is studied using Fatigue, Aerodynamics, Structures, and Turbulence (FAST) Code. The simulation and experimental results showed that the release of the kinetic energy in rotors using the proposed GDBIC scheme allows significant improvement of the frequency nadir, with no SFD, as well as contribute to reliable operation during abrupt wind changes.
FDNet: Knowledge and Data Fusion-Driven Deep Neural Network for Coal Burst Prediction

Coal burst prediction is an important research hotspot in coal mine production safety. This paper presents FDNet, which is a knowledge and data fusion-driven deep neural network for coal burst prediction. The main idea of FDNet is to extract explicit features based on the existing mine seismic physical model and utilize deep learning to automatically extract the implicit features of mine microseismic data. The key innovations of FDNet include an expert knowledge indicator selection method based on a subset search strategy, a mine microseismic data extraction method based on a deep convolutional neural network, and a feature deep fusion method of mine microseismic data based on an attention mechanism. We conducted a set of engineering experiments in Gaojiapu Coal Mine to evaluate the performance of FDNet. The results show that compared with the state-of-the-art data-driven machines and knowledge-driven methods, the prediction accuracy of FDNet is improved by 5% and 16%, respectively.
Trajectory Tracking Strategy for Sliding Mode Control with Double Closed-Loop for Lawn Mowing Robot Based on ESO

The mowing robots work with a multivariable strong coupling underactuated system that is mostly troubled by difficulty controlling and unsatisfactory accuracy. Especially, the frequent external disturbances and parameter changes are likely to get missed and heavy cutting. In this paper, a new trajectory tracking control method based on extended state observer (ESO) is introduced with a particular focus on dual closed-loop sliding mode. Firstly, from the perspective of kinematics, a speed assistant controller was designed to generate the speed control quantity, and secondly, a sliding mode control algorithm based on the improved Fractional Power Rate Reaching Law (IFPRRL) was programmed to control the drive motor that tracked the speed control quantity. By means of comparison, our improved algorithm presented faster arrival time and better robustness along with similar jittering. At the same time, the robustness of the system was further enhanced with the help of an optimized ESO to tackle unmodeled disturbances and uncertain disturbances during the operation. Finally, the experimental analysis of the motor drive circuit and the trajectory tracking control system of the lawn mowing robot were both carried out respectively. The analysis shows that the performance of the proposed reaching law sliding mode control algorithm had some new pleasing changes, such as adjustment time and robustness. The circular trajectory and the detour mowing trajectory were respectively tracked in the double closed-loop sliding mode designed in this paper. The experimental goal was to ensure that the error vector Pe = (x Axis position error xe, y Axis position error ye, Angle error θe) all remaining at (0.01m, 0.01m, 0.01rad) were 5.34s and 5.36s, respectively, and both could be finally converged to 0. The results show that the newly developed controller based on ESO presented smaller arrival time and stronger robustness. The dual-closed-loop control of sliding-mode trajectory tracking method was capable to meet the real-time and precision requirements of the lawnmower robot for quick trajectory tracking.
Antibiotics Efficiency in the Infection Complications Prevention after Third Molar Extraction: A Systematic Review

(1) Background: Antibiotics are used in every medical field including dentistry, where they are used for the prevention of postoperative complications in routine clinical practice during the third molar extraction. (2) Methods: This study is reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The present systematic review aimed to evaluate and systematize the use of antibacterial drugs in order to prevent postoperative complications in outpatient oral surgery for wisdom teeth extraction. We conducted a systematic review using electronic databases such as Medline PubMed, Scopus, and the Cochrane Central Register of Controlled Trials. Considering inclusion and exclusion criteria, we included randomized clinical trials published up to 2021 investigating the antibiotic prescription for third molar extraction. (3) Results: We selected 10 studies after the application of inclusion and exclusion criteria. The results showed that the most widely used antibiotic was amoxicillin both with and without clavulanic acid, in different dosages and duration. There were no statistically significant differences between treatment groups for development of postoperative complications. (4) Conclusions: Based on the analysis of the included studies, penicillin is currently the most widely prescribed group of antibiotics. The widespread use of this antibiotic group can lead to antimicrobial resistance (AMR). Due to increasing prevalence of bacteria resistance to penicillins, clinicians should carefully prescribe these antibiotics and be aware that the widespread use of amoxicillin may do more harm than good for the population.
Analysis on Review Data of Restaurants in Google Maps through Text Mining: Focusing on Sentiment Analysis

The importance of online reviews is prevalent as more people access goods or places online and make decisions to visit or purchase. However, such reviews are generally provided by short sentences or mere star ratings; failing to provide a general overview of customer preferences and decision factors. This study explored and broke down restaurant reviews found on Google Maps. After collecting and analyzing 5,427 reviews, we vectorized the importance of words using the TF-IDF. We used a random forest machine learning algorithm to calculate the coefficient of positivity and negativity of words used in reviews. As the result, we were able to build a dictionary of words for positive and negative sentiment using each word’s coefficient. We classified words into four major evaluation categories and derived insights into sentiment in each criterion. We believe the dictionary of review words and analyzing the major evaluation categories can help prospective restaurant visitors to read between the lines on restaurant reviews found on the Web.
Information Extraction from Websites using Extraction Ontologies

S rozvojem Internetu a růstem množstvi textových dokumentů vzrostla během posledniho desetileti poptavka po nastrojich pro automatickou extrakci informaci (IE - information extraction). Ukolem IE je nalezt v analyzovaných dokumentech udaje předem specifikovaneho semantickeho typu a tyto extrahovat pro potřeby dalsich aplikaci. Analyzovanými dokumenty mohou přitom být webove stranky, e-maily, dokumenty uvnitř firemnich informacnich systemů, ale i běžne textove zdroje jakými jsou noviny a casopisy. Aplikace, ktere nejcastěji využivaji výsledky IE, zahrnuji tradicni textove vyhledavace, ktere lze pomoci IE rozsiřit o tzv. strukturovane vyhledavani, dale systemy pro automaticke zodpovidani otazek nebo např. aplikace pro podporu posuzovani kvality webových stranek. Tato prace se zabýva vývojem metod a nastrojů pro IE, ktere jsou obzvlastě vhodne pro extrakci ze semi-strukturovaných dokumentů, jakými jsou webove stranky, a pro situace kdy existuje malo trenovacich dat. Hlavnim přinosem teto prace je navržený přistup rozsiřených extrakcnich ontologii, který v sobě kombinuje využiti extrakcnich znalosti tři typů: (1) expertem zadane extrakcni znalosti, (2) znalosti naucene z trenovacich dat a (3) znalosti indukovane na zakladě opakujici se formatovaci struktury, ktera se casto ve webových strankach nachazi. Nase hypoteza je, že soucasne využiti vsech tři typů znalosti extrakcnim algoritmem může přispět k celkove přesnosti a robustnosti extrakce. Motivaci pro tuto praci byl malý pocet dosud popsaných metod pro IE, ktere by využivaly vsechny zminěne typy extrakcni znalosti. Jako prvni je v tomto textu popsan statistický trenovaný přistup k IE založený na skrytých markovských modelech, který je dale integrovan s několika trenovanými klasifikatory obrazků tak, aby bylo možno extrahovat jak textove položky, tak obrazky. Přistup je demonstrovan a hodnocen na uloze extrakce popisů bicyklů nabizených různými internetovými obchody. Popsano je i několik algoritmů pro klasifikaci obrazků s použitim různých množin rysů pro klasifikaci. Tyto trenovane přistupy jsou posleze integrovany v ramci navržene metody rozsiřených extrakcnich ontologii, navazujici na praci D.W. Embleyho, kterou rozsiřuje o soucasne využiti vsch tři výse zminěných typů extrakcni znalosti. Zamýslenými přinosy extrakcnich ontologii jsou rychlý vývoj funkcniho prototypu, jeho plynulý přechod do finalni IE aplikace a možnost využit různa množstvi tři typů extracni znalosti podle jejich dostupnosti. Protože extrakcni ontologie je typicky odvozena z vhodne domenove ontologie a zůstava ve středu extrakcniho procesu, minimalizuje tento přistup usili nutne pro zpětnou konverzi extrahovaných výsledků pro populaci zdrojove ontologie ci datoveho schematu. Výsledky navrženeho přistupu jsou prezentovany pro několik realných domen.
Community-Based Research with Immigrant Families: Sustaining an Intellectual Commons of Care, Resistance, and Solidarity in an Urban Intensive Context

This article draws from a decade-long community-based research partnership oriented toward learning from and supporting immigrant youth and families as they advocate for themselves in the face of educational inequity. In particular, we focus on examining the trajectory and insights of the partnership in light of ongoing educational, health, and sociopolitical crises during the pandemic and the racial uprisings against police violence. We sought to understand how the work shifted in response to these global crises and also what sustained our collaboration during these times. As we showcase through representative examples of our inquiries, members of different immigrant communities in our partnership drew on their individual and collective experiences to engage in research as an act of care, to address pragmatic and immediate needs in their schooling, and to contend with traumatic legacies of oppression. Expanding networks of care and the intellectual legacy of the collaboration itself–what we refer to as an intellectual commons–created the foundation to sustain and amplify our work together during a time of social transformation.
Smartphones and Tablets: Regulations, Routines, and Habits of Spanish Families

Abstract Smartphones and tablets generate familiar challenges to be effectively managed in order to live together properly. The influence of both devices on routines and habits in families is described in this article, analyzing whether there are regulations in place to manage their use and determining the presence of significant differences according to the independent variables analyzed. A cross-sectional quantitative design is developed with a sample of 1,082 subjects from all over Spain. Two measurement scales have been applied relating to routines and habits and to family regulations regarding the use of smartphones and tablets. The results indicate the low impact of devices on intra-family routines and habits, as well as the existence of rules regulating their use in the home, mainly when there are minors and the adults are over 35 years old. It seems that families are aware of the need to regulate the impact of these devices.
The sheltopusik (Pseudopus apodus) in southwestern Ukraine? Insights from the museum collection

We provide data on the historical record of Pseudopus apodus (Anguidae) from Odesa, Ukraine based on the specimen re-discovered in the museum collection of the National Science and Natural History Museum of the National Academy of Sciences of Ukraine in Kyiv (NMNH) in 2021. The data show that the specimen belongs most likely to the nominotypical subspecies, P. a. apodus (Pallas, 1775). We discussed the possible origin of the record and we concluded that the specimen was introduced to Odesa most probably from its native range (Crimea or Caucasus). The uniqueness of the record and the past human-mediated interactions thus suggest an allochthonous origin rather than the historical sign of the relict population. Thus, our data clarify the situation on species distribution in the north-western Black Sea region from where we have still limited knowledge.
Farsi Version of Visual Aesthetics of Website Inventory (FV-VisAWI): Translation and Psychometric Evaluation

Abstract The Visual Aesthetic of Website Inventory (VisAWI) is the most commonly used self-report instrument for evaluating website aesthetics. However, at this time, there is no valid and reliable instrument in Farsi for Iranian users to evaluate website aesthetics. This study aimed to translate, validate, and evaluate the psychometric properties of a Farsi version of VisAWI. To do this, the cross-cultural adoption method was used in this study. First, the VisAWI was translated using forward-backward translation. Then, in order to evaluate scale validity, content and face validity were determined. The final version of a scale inventory with 12 items was given to 200 students to evaluate one of the most visited websites in Iran which is the first e-commerce website. The reliability of scale was assessed using Cronbach’s alpha and test-retest. Confirmatory factor analysis was used to evaluate the validity of scale. Cronbach’s alpha was 0.79 for this version. Confirmatory factor analysis confirmed the four-factor structure model was best fitted for the data. Our results showed the scaled psychometric Farsi version of VisAWI items tended to be valid and reliable for Iranian users.
Adaptive Client Clustering for Efficient Federated Learning over Non-IID and Imbalanced Data

—Federated learning (FL) is an emerging distributed and privacy-preserving machine learning framework. However, the performance of traditional FL methods is seriously impaired by the real-world data, which appear to be non-independent and identically distributed (non-IID). The recent clustered federated learning (CFL) methods eliminate the impact of non-IID data by grouping clients with similar data distribution into the same cluster. Unfortunately, existing CFL methods heavily rely on the pre-setting of the cluster number, failing to achieve adaptive client clustering. Even worse, we experimentally observe that imbalanced data across clients largely degrade their correctness of client clustering. In this paper, we present a novel CFL method without manual intervention, named AutoCFL, which can eliminate both effects of non-IID and imbalanced data simultaneously. To deal with imbalanced data, the local training adjustment strategy adaptively adjusts the number of local training epochs for each client. To further improve the clustering correctness and adaptability, the weighted voting-based client clustering strategy automatically groups each client into an appropriate cluster. Extensive experiments are conducted to evaluate the design of AutoCFL with three popular datasets under various data settings. Experimental results demonstrate that AutoCFL outperforms the state-of-the-art methods under non-IID and imbalanced data settings, e.g. , on average improving the model accuracy by 9 . 24% when compared to the standard FL method, i.e. , FedAvg , while significantly reducing communication costs by 4 . 67 × in an adaptive client clustering manner.
Human Reliability and Ship Stability

Abstract : This report briefly reviews ship stability and capsize issues, risk assessment, and Human Factors issues related to risk of capsize during design and operation of warships. A generic approach to Human Reliability Analysis (based on Kirwan 1994) is described in some detail. Based on this approach, a four part two year plan is proposed to establish and apply a Human Reliability Analysis approach to estimate Human Factors risks related to warship capsize and management of stability.
Transparent Interconnection of Lots of Links (TRILL) over IP

The Transparent Interconnection of Lots of Links (TRILL) protocol is implemented by devices called TRILL Switches or RBridges (Routing Bridges). TRILL supports both point-to-point and multi-access links and is designed so that a variety of link protocols can be used between TRILL switch ports. This document standardizes methods for encapsulating TRILL in IP(v4 or v6) to provide a unified TRILL campus.
Analysis of Self-Regulation of Eating Behaviors within Polish Adolescents’ COVID-19 Experience (PLACE-19) Study

The self-regulation of eating behaviors (suppression of behavioral short-term impulse to consume food products in the interest of pursuing long-term weight goals), has been an important determinant for adopting a healthy lifestyle in the period of the COVID-19 pandemic in adults, but there have been no such studies conducted in the population of adolescents in this period. The aim of the presented study is to analyze self-regulation of eating behaviors in the population-based sample of Polish adolescents within the Polish Adolescents’ COVID-19 Experience (PLACE-19) Study. The Self-Regulation of Eating Behavior Questionnaire (SREBQ) was used to assess self-regulation of eating behaviors in the studied population of 1126 Polish adolescents (818 females and 308 males), aged 15–20, recruited based on a random quota sampling within a national sample. Based on the screening questions of the SREBQ, 145 individuals were excluded from the analysis. The participants of the study were categorized based on their gender, body mass index, body mass index change during the COVID-19 pandemic, and food products declared as tempting. The major factors associated with SREBQ score were body mass index change and tempting food products. The respondents losing weight during the COVID-19 pandemic were characterized by a higher SREBQ score than those maintaining stable body mass or gaining weight (3.4 vs. 3.2 vs. 3.2; p = 0.0001). The respondents declaring both sweet and salty food products as tempting were characterized by a lower SREBQ score than those declaring only sweet, only salty, or declaring no tempting products (3.2 vs. 3.4 vs. 3.4 vs. 3.4; p < 0.0001). The major factors associated with SREBQ categories were gender, body mass index change, and tempting food products. A higher share of respondents characterized by a high self-regulation of eating behaviors was observed for males than for females (27.4% vs. 18.8%; p = 0.0142); for respondents losing weight during the COVID-19 pandemic than for respondents gaining weight (25.9% vs. 15.5%; p = 0.0423); as well as for respondents declaring no tempting products than those declaring both sweet and salty food products (38.3% vs. 18.2%; p < 0.0001). It was concluded that the self-regulation of eating behaviors in adolescents is closely associated with food products perceived as tempting by them, as well as with gender. During the COVID-19 pandemic, the low self-regulation of eating behaviors was a significant determinant of the body mass gain. Taking this into account, female adolescents characterized by a low self-regulation of eating behaviors especially should be subjected to a dedicated intervention program to prevent overweight and obesity.