Miracle-dz/UniFAR-FTUs huggingface dataset:
Dentinal Microcracks after Root Canal Instrumentation Using Instruments Manufactured with Different NiTi Alloys and the SAF System: A Systematic Review

Aim: The aim of this systematic analysis was to assess the prevalence of dentinal microcracks at various levels (3, 6, and 9 mm from the apex) after using instruments made with conventional, R-Phase, and M-Wire NiTi alloys and the SAF system. Materials and Methods: Electronic searches were conducted in the databases Embase, Cochrane Library, Scopus, PubMed, and Web of Science. To arrange search methods, “MeSH” terms and/or keywords typically associated with the subject were paired with the Boolean operators “AND” and “OR. ” Additional searches were conducted on the websites of four separate endodontic journals. After reading the titles and excluding duplicates, 1000 of the 1343 documents originally found were eliminated. Upon reviewing the abstracts, 310 of the remaining 343 experiments were also eliminated. Based on qualifying requirements, only 13 of the remaining 33 articles were included in the qualitative review. Results: All systems triggered dentinal microcracks; however, when chemo-mechanical preparation was performed using Self-Adjusting File (SAF) and systems manufactured with R-phase technology—K3XF and Twisted File Adaptive (TFA)—less of these defects were found when compared to those manufactured with traditional NiTi—ProTaper Universal and Mtwo—and with M-Wire—ProTaper Next, Reciproc, and WaveOne. Conclusions: A lower prevalence of dentinal microcracks was observed after using SAF and endodontic systems manufactured with R-phase.
An X-ray diffraction study of the pressure-induced hydration in cordierite at 4–5 GPa

Abstract The elastic and structural behavior of natural cordierite compressed in aqueous medium up to 6 GPa was studied by means of in situ synchrotron powder diffraction with a diamond-anvil cell. In the range between 1-4 GPa the elastic behavior is regular and slightly anisotropic, with linear compressibilities βa:βb:βc = 4:4:5, the most rigid a-b plane coinciding with the orientation of 6-membered rings. A distinct decrease of compressibility in the range of 4-5 GPa indicates a pressure-induced hydration (PIH), which is confirmed by the structure refinements. The addition of about 60% of the initial water content into the cordierite channels proceeds through positional disordering of the H2O sites inside the channel cavity and a stepwise filling of the H2O position inside the 6-membered rings, leading to the phase transition at about 4.7 GPa. The appearance of H2O molecules inside 6-membered rings prevents their contraction and even causes their slight enlargement along the a direction, apparently related to the orientation of H-bonds. This results in an anisotropic deformation of the unit cell and an increase of the a parameter in the HP phase at 4.9 GPa, as well as a decrease of linear compressibility along a upon the further compression up to 6 GPa (βa:βb:βc = 5:9:10).
A case study on player selection and team formation in football with machine learning

: Machine learning has been widely used in different domains to extract information from raw data. Sports is one of the popular domains for researchers to work on recently. Although score prediction for matches is the most preferred application area for artificial intelligence, player selection, and team formation is also an application area worth working on. There are some studies in the literature about player selection and team formation which are examined in this study. The study has two important contributions: First one is to apply seven different machine learning algorithms on our dataset to find the best player combination for the U13 team of Altınordu Football Academy and comparing the results with that of the coach’s lineup and lineups of 20 matches played in 2019–2020 season. Second is combining the data obtained from the trainings of the players and coach evaluations of the players and feeding the machine to make more accurate predictions. The data from the trainings is gathered with Hit/it Assistant and the coach evaluations of the players are stated by the golden standard according to eighteen criteria stated in the literature. Synthetically generated data is also used in the final dataset to obtain more accurate classification results. Another remarkable aspect of the study is that no match data is used to form the team to be proposed for the next match, instead real match data is only used for evaluation. The results show that machine learning algorithms can be used for player selection and team formation process because random forest algorithm, which is executed on WEKA environment, can make player selections with 93.93% reliability and the lineup suggestions of these algorithms are 97.16% similar to coach’s ideal team and also the best performing algorithm has an average performance of 89.36% for team formation when compared with the match lineups of 2019–2020 football season.
Enzymatic Promiscuity for Organic Synthesis and Cascade Process

Enzymatic promiscuity, namely the possibility that one active site of an enzyme can catalyze several different chemical transformations, has been an important research field and valuable synthesis tool for protein engineering and synthetic chemistry. This review will focus on promiscuous enzyme-mediated reactions that are useful in organic synthesis. Examples include promiscuous hydrolysis reactions, Michael addition, Markovnikov addition, aldol condensation, Henry reaction, Mannich reaction, Epoxidation reaction, Baeyer-Villiger oxidation, radical polymerization and tandem reactions catalyzed by natural enzymes or engineered enzymes with existing or induced catalytic promiscuity. Among these examples, many enzymes from different family have been verified to be able to catalyze the same type of reaction through distinct mechanism. Many complex compounds or important intermediates can be synthesized using promiscuous enzymes or through novel synthesis pathways. These examples support the enormous potential of enzymatic promiscuity in synthetic chemistry.
Ultrasonic Emulsification of Milk Proteins Stabilized Primary and Double Emulsions: A Review

ABSTRACT Ultrasound-assisted emulsification is an emerging technology as it provides several advantages over conventional emulsification methods. Oil–water emulsions are generally designed to encapsulate, protect, and deliver oil-soluble functional ingredients while double emulsions are used to deliver both water-soluble and oil-soluble functional ingredients simultaneously. Poor stability and less encapsulation efficiency are the main challenges associated with the double emulsification process. Ultrasound can be used as an efficient technology to obtain more stable primary and double emulsions with desired emulsification properties under control processing conditions. This review discusses the factors affecting the properties of milk protein-stabilized primary and double emulsions under sono-emulsification.
Application of Neural Network Algorithm in Optimal Control of Ethylene Distillation Tower

For some nonlinear dynamic systems with uncertainties or disturbances, neural networks can perform intelligent cognition and simulation on them, achieve a good system description, and further realize intelligent control. Aiming at the ethylene rectification process, in order to avoid the time delay of complex rectification process modeling and large-scale process simulation software interface program, and to improve the simulation operation speed, the optimization model combined with the learning function of the neural network is used for the simulation calculation of the rectification process. It can meet the time and accuracy requirements of online optimization. This article outlines several commonly used neural network algorithms and their related applications in ethylene distillation, aiming to provide reference for the development and innovation of industry technology.
A Population-Based Cross-Sectional Study of Anxiety and Depression Associated with the COVID-19 Pandemic in Nigeria

The COVID-19 pandemic has created mental health challenges among the public. The study objectives were to determine the prevalence of anxiety, depression and associated factors among the general public in Nigeria during the COVID-19 pandemic. The cross-sectional study was conducted between May 2020 and June 2020 using an online questionnaire that captured sociodemographic data, generalized anxiety disorder (GAD-7), depression (PHQ-9), lifestyle practices, self-rated health, and exposure to COVID-19 information through social media. Data were analysed using Statistical Package for Social Science with associations tested at 95% confidence interval. Appropriate institutional ethical board approval was obtained, and informed consent obtained from all participants. 543 adults participated, 101 (18.6%) and 131 (24.1%) respondents were classified as having depressive and anxiety disorders respectively. Females (χ2=11.06, p < 0.01), alcohol use (OR 5.50, p < 0.01, 95% CI 2.18-13.87) and poor self-rated health (OR 2.97, p=0.04, 95% CI 1.08-8.22) were significantly associated with GAD. Alcohol consumption (OR 4.27, p < 0.01, 95% CI 1.56-12.04), currently being on medication (OR 2.96, p < 0.01, 95% CI 1.31-6.70) and poor self-rated health (OR 4.21 p < 0.01, 95% CI 1.73-10.23) were significantly associated with depression. Increasing frequency of information on Covid-19 through social media was significantly associated with decreased anxiety and depression (χ2=7.95, p=0.02). A considerable proportion of respondents experienced anxiety and depression, especially persons with unhealthy lifestyles and poor health. Interventions to improve mental health should target these at-risk groups.
Temporal Evolution of Three‐Dimensional Structures of Metal Ion Layer Around Japan Simulated by a Midlatitude Ionospheric Model

A regional numerical ionospheric model with neutral winds, corresponding to the Ground‐to‐topside model of Atmosphere and Ionosphere for Aeronomy model, was used to investigate the temporal evolution of 3‐D structures of metal ion layers (MILs) around Japan. The MILs that appear specifically in the ionospheric E region, called “sporadic E,” display complicated multi‐layer structures and intense density variations. Although the wind shear theory elucidates the basic formation mechanism of MILs, it does not fully explain the physical mechanism of 3‐D MIL structures, especially their sporadic behavior. Herein, we present two simulation cases in which the effect of the monthly mean and day‐to‐day variations of wind on the MILs around Japan was investigated. The temporal evolutions of the complicated MIL structures were classified into the following four phases: (1) Above 110 km, MILs are generally constrained vertically and horizontally at the zonal‐wind shear null. (2) Below 110 km, MILs lag behind the zonal‐wind shear null, and the 3‐D MIL structures are affected not only by the wind shears but also by the magnitude and direction of the horizontal winds. (3) When stagnating metal ions exist below a descending MIL, some metal ions ascend and merge with the descending MIL, thereby increasing its density. (4) MILs stagnate at around 100 km or descend to <100 km depending on the strength of the vertical winds. Our results reveal that Phases (2–4) are crucial for the formation of complicated MIL structures.
Heat and Cold-related Hospitalization Risk in North-East of Iran: A Time-stratified Case Crossover Design

Background: This study aimed to estimate hospitalization risk/number attributed to air extreme temperatures using time-stratified case crossover study and distributed lag non-linear model in a region of Iran during 2015-2019.Methods: A time-stratified case crossover design based on aggregated exposure data was used in this study. In order to have no overlap bias in the estimations, a fixed and disjointed window by using one-month strata was used in the design. A conditional Poisson regression model allowing for over dispersion (Quasi-Poisson) was applied into Distributed Lag Non-linear Model (DLNM). Different approaches were applied to estimate Optimum Temperature (OT). In the model, the interaction effect between temperature and humidity was assessed to see if the impact of heat or cold on Hospital Admissions (HAs) are different between different levels of humidity.Results: The cumulative effect of heat during 21 days was not significant and it was the cold that had significant cumulative adverse effect on all groups. While the number of HAs attributed to any ranges of heat, including medium, high, extreme and even all values were negligible, but a large number was attributable to cold values; about 10000 HAs were attributable to all values of cold temperature, of which about 9000 were attributed to medium range and about 1000 and less than 500 were attributed to high and extreme values of cold, respectively.Conclusion: This study highlights the need for interventions in cold seasons by policymakers. The results inform researchers as well as policy makers to address both men and women and elderly when any plan or preventive program is developed in the area under study.
Efficient Control of DC Microgrid with Hybrid PV—Fuel Cell and Energy Storage Systems

Direct current microgrids are attaining attractiveness due to their simpler configuration and high-energy efficiency. Power transmission losses are also reduced since distributed energy resources (DERs) are located near the load. DERs such as solar panels and fuel cells produce the DC supply; hence, the system is more stable and reliable. DC microgrid has a higher power efficiency than AC microgrid. Energy storage systems that are easier to integrate may provide additional benefits. In this paper, the DC micro-grid consists of solar photovoltaic and fuel cell for power generation, proposes a hybrid energy storage system that includes a supercapacitor and lithium–ion battery for the better improvement of power capability in the energy storage system. The main objective of this research work has been done for the enhanced settling point and voltage stability with the help of different maximum power point tracking (MPPT) methods. Different control techniques such as fuzzy logic controller, neural network, and particle swarm optimization are used to evaluate PV and FC through DC–DC boost converters for this enhanced settling point. When the test results are perceived, it is evidently attained that the fuzzy MPPT method provides an increase in the tracking capability of maximum power point and at the same time reduces steady-state oscillations. In addition, the time to capture the maximum power point is 0.035 s. It is about nearly two times faster than neural network controllers and eighteen times faster than for PSO, and it has also been discovered that the preferred approach is faster compared to other control methods.
Summary of New Insight into Electron Transport in Metals

This paper gives a summary of a new insight into basic electron transport characteristics in crystalline elemental metals. The general expressions based on the Fermi-Dirac distribution of the effective density of the randomly moving electrons, their diffusion coefficient, drift mobility, and other characteristics, including the Einstein relation between diffusion coefficient and drift mobility, are presented. It is shown that the creation of the randomly moving electrons due to lattice atom vibrations produces the same number of electronic defects, which cause scattering of the randomly moving electrons and related transport characteristics.
Colorectal cancer screening and prevention: A brief review of the current guidelines and modalities

Colorectal cancer (CRC) remains a frequently addressed topic in primary care. Recent studies have been published detailing modifiable risk factors for CRC, as well as preventative measures. Providers must be up to date on screening recommendations and modalities. Colonoscopy is the preferred method of screening for CRC, and the screening recommendations in the United States were recently updated in 2020. It is also common for the practitioner to encounter patients who refuse colonoscopy but are willing to undergo alternative methods of testing. The COVID pandemic has also placed a burden on hospital resources, and colonoscopy may not be logistically feasible in some healthcare settings. Therefore, awareness of the guidelines for the various alternative modalities, along with their respective guidelines for frequency of screening is critical. This article provides a brief review of the risk factors associated with colon cancer, the screening modalities (including colonoscopy, sigmoidoscopy, CT colonography, fecal immunohistochemical testing (FIT), guaiac-based fecal occult blood testing (gFOBT), multi target stool DNA testing (MTs-DNA), and others) and the most recent screening recommendations for the general population.
Estimation of Enterprise’s Investment Attractiveness in the Conditions of Development

Investment attractiveness is an essential element of intensifying investment processes. The high level of investment attractiveness provides a stable inflow of investments into the country, additional revenues to the state budget, a better social environment in the country.As a proposed innovative transformation technology, tools for digitalization of public administration are described, aimed at improving the quality of services provided, harmonizing relationships between public administration entities, and optimizing and redistributing powers from state to municipal authorities.The article's primary goal is to develop a toolkit for assessing the investment attractiveness of an enterprise in the context of development.The authors studied the theoretical basis, analyzed the current state of investment activity in Ukraine. Based on the analysis, a methodology for the integral assessment of the investment attractiveness of an enterprise was proposed. The proposed method has the following advantages: an integrated approach, coverage of a large set of indicators and coefficients, accurate mathematical calculation of the indicator, which serves as a criterion for assessing investment attractiveness, allows you to consider the dynamics of indicators over several years.
Coproducing digital archives with 2SLGBTQ+ Atlantic Canadian youth amidst the COVID-19 pandemic

PurposeThe authors explore the coproduction of a digital archive with 50 2SLGBTQ+ youth across Atlantic Canada during the COVID-19 pandemic in order to catalyze broader public participation in understanding 2SLGBTQ+ youth-led activism in this place and time through art production.Design/methodology/approachThrough a mail-based participatory visual research project and an examination of collage, zines and DIY facemasks, the authors highlight how the production, sharing and archiving of youth-produced art adds to methodological discussions of exhibiting and digital archiving with 2SLGBTQ+ youth as a form of activist intervention.FindingsIn reflexively examining the cocuration of art through social media and project website, the authors argue that coproducing digital archives is an important part of knowledge mobilization. Also, the authors consider how the work has been interacted with by a broader public, so far in an exclusively celebratory manner and note the benefits and challenges of this type of engagement to the youth and to the understandings of 2SLGBTQ+ youth archives.Originality/valueThe authors suggest that these modes of engaging in participatory visual research at a distance offer original contributions in relation to how participation can be understood in a digital and mail-based project. The authors see participant control of how to share works within digital archives as a contribution to the understanding of people's capacity to negotiate and take ownership of these spaces. These strategies are participant-centered and suggest ways that archiving can be made more accessible, especially when working with communities who are socially marginalized or otherwise excluded from the archival process.
Probabilistic Flight Delay Predictions Using Machine Learning and Applications to the Flight-to-Gate Assignment Problem

The problem of flight delay prediction is approached most often by predicting a delay class or value. However, the aviation industry can benefit greatly from probabilistic delay predictions on an individual flight basis, as these give insight into the uncertainty of the delay predictions. Therefore, in this study, two probabilistic forecasting algorithms, Mixture Density Networks and Random Forest regression, are applied to predict flight delays at a European airport. The algorithms estimate well the distribution of arrival and departure flight delays with a Mean Absolute Error of less than 15 min. To illustrate the utility of the estimated delay distributions, we integrate these probabilistic predictions into a probabilistic flight-to-gate assignment problem. The objective of this problem is to increase the robustness of flight-to-gate assignments. Considering probabilistic delay predictions, our proposed flight-to-gate assignment model reduces the number of conflicted aircraft by up to 74% when compared to a deterministic flight-to-gate assignment model. In general, the results illustrate the utility of considering probabilistic forecasting for robust airport operations’ optimization.
SoHAM: A Sound-Based Human Activity Monitoring Framework for Home Service Robots

Monitoring daily activities is essential for home service robots to take care of the older adults who live alone in their homes. In this article, we proposed a sound-based human activity monitoring (SoHAM) framework by recognizing sound events in a home environment. First, the method of context-aware sound event recognition (CoSER) is developed, which uses contextual information to disambiguate sound events. The locational context of sound events is estimated by fusing the data from the distributed passive infrared (PIR) sensors deployed in the home. A two-level dynamic Bayesian network (DBN) is used to model the intratemporal and intertemporal constraints between the context and the sound events. Second, dynamic sliding time window-based human action recognition (DTW-HaR) is developed to estimate active sound event segments with their labels and durations, then infer actions and their durations. Finally, a conditional random field (CRF) model is proposed to predict human activities based on the recognized action, location, and time. We conducted experiments in our robot-integrated smart home (RiSH) testbed to evaluate the proposed framework. The obtained results show the effectiveness and accuracy of CoSER, action recognition, and human activity monitoring. Note to Practitioners—This article is motivated by the goal to develop companion robots that can assist older adults living alone. Among many capabilities, monitoring human daily activities is an essential one for such robots. Though computer vision or wearable sensors-based methods have been developed by other researchers, they are not practical due to the privacy concern and intrusiveness. Sound-based daily activity recognition can address these concerns and offer a viable solution. In this regard, our proposed method adopts microphones on the robot and a small set of motion sensors distributed in the home. The proposed theoretical framework was tested in a small-scale mock-up apartment with promising results. Before such companion robots can be deployed to real homes for elderly care, there is a need to improve the robustness of the algorithms. More thorough tests in various realistic home environments should be conducted to fully evaluate the performance of the robots. In addition, privacy concern related to audio capture should be further mitigated.