Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficacy of Sonic Versus Ultrasonic Irrigation in Debris Removal from the Root Canal System: A Systematic Review

Introduction: The aim of this study was to systematically review the debridement efficacy of ultrasonic irrigation compared with sonic irrigation during the endodontic treatment. Methods: An electronic search was undertaken on Cochrane Library, Medline, ScienceDirect and Scopus for articles published between January 2010 and January 2021 using appropriate Mesh terms and key words. The inclusion criteria were systematic reviews or in vitro controlled trials on permanent mature teeth or models simulating the root canal system involving a sonic or ultrasonic irrigation group and a control group of conventional needle irrigation. Two reviewers independently selected articles to include according to the inclusion criteria, extracted data from the articles and assessed the methodological quality of the included studies. The data items were arranged in tables. Results: From 811 studies, 17 in vitro studies and 3 systematic reviews were included. EndoActivator and EDDY were the most sonic devices used, whereas several ultrasonic devices were tested for passive ultrasonic irrigation. Debris removal was assessed either on root canal walls or isthmuses or both. The risk of bias and quality of the selected studies were qualified as moderate to high according to the JBI (Joanna Briggs Institute) and the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) checklists. Overall, the findings confirmed superiority of the two agitation techniques over conventional irrigation and most of the studies showed no significant differences between sonic and ultrasonic irrigation in debris removal, however a moderate level of evidence showed superiority of ultrasonic irrigation. Conclusion: It may be concluded that sonic and ultrasonic activation of the irrigants are beneficial in hard tissue debris removal when compared to conventional needle irrigation, yet, the current data could not find significant differences between the two techniques.
Use of liquid lithography to form in vitro intestinal crypts with varying microcurvature surrounding the stem cell niche

Objective. The role of the crypt microarchitecture and surrounding tissue curvature on intestinal stem/proliferative cell physiology is unknown. The utility of liquid lithography in creating polydimethylsiloxane (PDMS) micropillar stamps with controlled tip curvature was assessed. Using these stamps, the impact of microcurvature at the crypt base on intestinal cell and cytoskeletal behavior was studied. Approach. An SU-8 master mold as a support, polyols of varying surface energies as sacrificial liquids, and liquid PDMS as the solidifiable material were combined using liquid lithography to form PDMS micropillar arrays. Vapor phase deposition of organosilane onto the master mold was used to modify the surface energy of the master mold to shape the micropillar tips. Collagen was molded using the micropillar arrays forming a scaffold for culture of human primary colonic epithelial cells. Cell proliferation and cytoskeletal properties were assessed using fluorescent stains. Main results. Liquid lithography using low surface energy polyols (<55 dynes cm−1) generated convex-tipped PDMS micropillars, while polyols with higher surface energies (>55 dynes cm−1) yielded concave-tipped PDMS micropillars. Gradients of octyltrichlorosilane deposition across a master mold with an array of microwells yielded a PDMS micropillar array with a range of tip curvatures. Human primary colonic epithelial cells cultured on micropillar-molded collagen scaffolds demonstrated a stem/proliferative cell compartment at the crypt base. Crypts with a convex base demonstrated significantly lower cell proliferation at the crypt base than that of cells in crypts with either flat or concave bases. Crypts with a convex base also displayed higher levels of G-actin activity compared to that of crypts with flat or concave bases. Significance. Liquid lithography enabled creation of arrays of in vitro colonic crypts with programmable curvature. Primary cells at the crypt base sensed and responded to surface curvature by altering their proliferation and cytoskeletal properties.
Architectural Requirements for Deep Learning Workloads in HPC Environments

Scientific machine learning (SciML) promises to have a transformational impact on scientific exploration, by combining state-of-the-art AI methods with the latest generation of supercomputers. However, to efficiently leverage ML techniques on high-performance computing (HPC) systems, it is critical to understand the performance characteristics of the underlying algorithms on modern computational systems. In this work, we present a new methodology for developing a detailed performance understanding of ML benchmarks. To demonstrate our approach we investigate two emerging SciML benchmark applications from cosmology and climate, ComsoFlow and DeepCAM, as well as ResNet-50, a well-known image classification model. We develop and validate performance models that explore the key architectural artifacts, including memory requirements, data reuse, and performance efficiency across both single- and multiple-GPU computations. Our methodology also focuses on the complexity of data-movement across storage and memory hierarchies, and leverages our performance models to capture key components of runtime execution while highlighting design tradeoffs. Although our work focuses on image-processing methods on GPU-based HPC systems, our approach is applicable to a variety of ML algorithmic domains and emerging AI accelerators. Overall, our insights will help computer architects and data scientists understand performance bottlenecks and optimization opportunities to improve SciML design and system efficiency.
Deep Learning based Tamil Parts of Speech (POS) Tagger

This paper addresses the problem of part of speech (POS) tagging for the Tamil language, which is low resourced and agglutinative. POS tagging is the process of assigning syntactic categories for the words in a sentence. This is the preliminary step for many of the Natural Language Processing (NLP) tasks. For this work, various sequential deep learning models such as recurrent neural network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) and Bi-directional Long Short-Term Memory (Bi-LSTM) were used at the word level. For evaluating the model, the performance metrics such as precision, recall, F1-score and accuracy were used. Further, a tag set of 32 tags and 225 000 tagged Tamil words was utilized for training. To find the appropriate hidden state, the hidden states were varied as 4, 16, 32 and 64, and the models were trained. The experiments indicated that the increase in hidden state improves the performance of the model. Among all the combinations, Bi-LSTM with 64 hidden states displayed the best accuracy (94 % ). For Tamil POS tagging, this is the initial attempt to be carried out using a deep learning model.
Correlation of normalized difference water index and baseflow index in small island watershed landscapes

The utilization of remote sensing data in the field of environmental hydrology is experiencing rapid progress. The Normalized Difference Water Index (NDWI) approach to transforming the water content of various land cover types and its implications for small island watersheds' hydrological characteristics is essential. NDWI is an algorithm used to detect water bodies, with the capacity to absorb visible and infrared wavelengths strongly. This study aims to analyze the correlation between the NDWI water index and the BFI baseflow index in the small island landscape of Ambon City. The Landsat 7 ETM + and Landsat 8 OLI image processing methods use ENVI 5.3 software to transform the NDWI algorithm and the BFI + 3.0 digital recursive filtering (RDF) method for hydrological characterization. The results showed that there was a strong correlation between the NDWI water index and the baseflow index (BFI) for the small island watershed of Ambon city. This result is relevant to the geographic area of Ambon City, which is dominated by the ocean 95% and land area 5%, so the application of the NDWI water index and the hydrological conditions of small island watersheds are significant.
Cold Test of a Superconducting Quadrupole-Sextupole Nested CCT Prototype using matrix harmonic coil

IMP (Institute of Modern Physics) has been developing a Quadrupole-Sextupole nested CCT (Canted Cosine Theta Type) prototype for the HFRS (High Energy Fragment Separator) beamline of HIAF (High Intensity Heavy-ion Accelerator Facility) project. Hence a matrix harmonic coil method has been developed for magnetic field quality measurement of multi-pole nested magnets. Furthermore, a whole set of measurement system has been constructed and some special mechanical components, like cold bore, have been set up to cooperate with matrix harmonic coil measurement. After cold test at 4.2 K, the measurement results prove that the prototype property and field quality basically meets our requirement, and matrix harmonic coil also functioning well.
Identification of Mouth Cancer laceration Using Machine Learning Approach

This Paper describes about Identification of Mouth Cancer laceration Using Machine Learning Approach . The SVM algorithm is used for this purpose. Image segmentation operations are performed using: Resizing an image, Gray scale conversion, Histogram equalization and Classifying the Segmented image using SVM. SVM is used to reduce the complexity faced in the existing system comprising of Texture Segmentation and ANN (Artificial Neural Networks) Algorithm. SVM is a simple Machine Learning algorithm when compared to ANN. The outcome of the paper is to segment and classify the Malignancy from the Non-Malignant region using the classifier SVM. SVM performs the classification based on the dataset that contains the trained images.
Evaluation of Agribusiness Training Skills Acquisition Programme on Poverty Reduction among Women in Cross River State, Nigeria

The main thrust of this study was to evaluate Agribusiness training skills acquisition programme on poverty reduction among women in Cross River State, Nigeria. Literature review was carried out accordingly. Survey research design was adopted for the study. This design was considered appropriate because it allows the researcher to make inference and generalization of the population by selecting and studying the sample for the study. A sample of seven hundred and thirty-six (736) respondents was used for the study. The sample was selected through stratified and simple random sampling techniques. The questionnaire was the main instrument used for data collection. The reliability estimate of the instruments was established through test-retest reliability method. One Way analysis of Variance (ANOVA) was the statistical technique employed to test the hypothesis under study. The hypothesis was tested at . 05 level of significance. The result of the analysis revealed that, agribusiness training skill acquisition programme significantly influenced poverty reduction among women in Cross River State – Nigeria. Based on the findings of the study it was recommended that training and development policies, goal and objectives should be carefully planned, drafted to capture the core values, culture of the organization and training needs of the individual staff and the industry in general.
A Simple and Consistent Credit Risk Model for Basel II/III, IFRS 9 and Stress Testing when Loan Data History is Short

In the current regulatory environment, banks are required to quantify credit risk by means of default probabilities, loss rates conditional on default and expected exposures for a number of purposes: Regulatory capital calculation, loan loss provisioning and stress testing. The nature of each credit risk parameter might be different for each application, e.g., forward looking default probabilities are needed for loan loss provisioning while regulatory capital is based on long-term averages. Those different requirements for each purpose create a substantial burden especially for small and medium-sized banks. This article describes a simple framework that allows the consistent calculation of credit risk parameters for all risk applications. It assumes that a bank is using a scorecard based on loan-level data where the data history might only span a couple of years. This data is combined with a macroeconomic model in a suitable way to derive risk parameters compliant with all regulatory requirements.
Reassurance in Information Acquisition

Conventionally, information acquisition is motivated by its instrumental value—the value derived from adapting action to new knowledge. In this paper, we analyse a model of individual preferences that anticipate elation and disappointment—the emotional responses to good and bad news. We assume loss aversion (bad news looms larger than good news) and diminishing sensitivity. While emotions are generally presumed to cause information avoidance, our key finding is that a decision maker faced with a large potential loss of low probability may seek non-instrumental information for the purpose of reassurance. When information is weakly instrumental, the pursuit of reassurance can cause a decision maker who is less likely to face a loss to value information more than a decision maker who is more likely to face the same loss. This paradox disappears at higher levels of information instrumentality. We provide empirical support for this effect, first in the context of an incentivized controlled experiment involving a compound lottery choice (N = 403), and then in the context of a survey (N = 1349) about the desire to undertake COVID-19 testing, carried out at the early stages of the pandemic.
Human-Algorithm Interaction: Algorithmic Pricing in Hybrid Laboratory Markets

This paper investigates pricing in laboratory markets when human players interact with an algorithm. We compare the degree of competition when exclusively humans interact to the case of one firm delegating its decisions to an algorithm. We further vary whether participants know about the presence of the algorithm. When one of three firms in a market is an algorithm, we observe significantly higher prices compared to humanonly markets. Firms employing an algorithm earn significantly less profit than their rivals. For four-firm markets, we find no significant differences. (Un)certainty about the actual presence of an algorithm does not significantly affect collusion, although humans seem to perceive algorithms as more disruptive.
Preparation and Properties of Sodium Carboxymethyl Cellulose/Sodium Alginate/Chitosan Composite Film

A sodium alginate/chitosan solution was prepared by dissolving sodium alginate, chitosan, and glycerol in an acetic acid solution. This solution was then combined with a sodium carboxymethyl cellulose solution and the mixture was cast onto a glass plate and dried at a constant temperature of 60 °C. Then, a carboxymethyl cellulose/sodium alginate/chitosan composite film was obtained by immersing the film in a solution of a cross-linking agent, CaCl2, and air-drying the resulting material. First, the most advantageous contents of the three precursors in the casting solution were determined by a completely random design test method. Thereafter, a comprehensive orthogonal experimental design was applied to select the optimal mass ratio of the three precursors. The composite film obtained with sodium alginate, sodium carboxymethyl cellulose, and chitosan contents of 1.5%, 0.5%, and 1.5%, respectively, in the casting solution displayed excellent tensile strength, water vapor transmission rate, and elongation after fracture. Moreover, the presence of chitosan successfully inhibited the growth and reproduction of microorganisms. The composite film exhibited antibacterial rates of 95.7% ± 5.4% and 93.4% ± 4.7% against Escherichia coli and Staphylococcus aureus, respectively. Therefore, the composite film is promising for antibacterial food packaging applications.
Anaerobic Ammonium Oxidation Linked to Microbial Reduction of Natural Organic Matter in Marine Sediments

Identification of microbial processes driving the loss of nitrogen from the oceans is of paramount relevance as these processes affect primary productivity in these ecosystems, which ultimately affects global biogeochemical cycles. Denitrification and anammox (anaerobic ammonium oxidation coupled to nitrite reduction) are the only identified processes so far that lead to nitrogen loss in marine environments. Here we provide stoichiometric and spectroscopic evidence, as well as tracer analysis with [15N]ammonium, revealing that anaerobic ammonium oxidation linked to the microbial reduction of natural organic matter (NOM) fuels nitrogen loss in marine sediments from the eastern tropical North Pacific coast. Tracer analysis revealed that the NOM-dependent anammox process was responsible for producing ∼1.5 μg of 15N2 (g of sediment)−1 day–1 after incubation for 27 days in sediment incubations amended with Pahokee peat, while intrinsic NOM present in the sediment promoted the production of ∼0.4 μg of 15N2 (g o.
Burnout and associated factors among nurses working in public hospitals of Harari region and Dire Dawa administration, eastern Ethiopia. A cross sectional study

Background Burnout is a syndrome of emotional exhaustion, low personal accomplishment and depersonalization experienced by a health professional and it is more common in nurses due to high workload and job stress that is mostly caused by working proximity to patients and taking care of them. Burnout compromises the provision of quality health care. Despite this, there is no information in Ethiopia on burnout among nurses in study area. Objectives To determine the magnitude of burnout and associated factors among nurses working in public hospitals of Harari regional state and Dire Dawa administration, eastern Ethiopia, February 1–29, 2020. Methods Institutional based quantitative cross-sectional study was employed from February 1–29 among 412 randomly selected nurses who have been working in hospitals for the last 6 months. Simple random sampling method was employed and data was collected by self-administered, standardized, reliable and valid, questionnaire (Maslachs Burnout Inventory- Human Services Survey). Data was entered into EpiData Version 3.1 and exported to statistical package for social science version 20 for analysis. All covariate with P-value less than 0.25 in bivariable analysis were candidate for multivariable analysis. Level of statistical significance was declared at p-value < 0.05. Results Among 412 nurses taking part in this study, 183(44.4%) of nurses with 95% CI, had experienced burnout. Married marital status [AOR:2.3,95%CI:(1.2–4.3)], poor current health status [AOR:4.8, 95% CI:(1.1–21.4)] and fair current health status [AOR:12, 95% CI:(4.5–32)], working greater than eight hour per-day[AOR:0.52, 95%CI:(0.29–0.92)], intention to leave a job [AOR:0.48,95%CI:(0.2–0.88), being working in emergency room [AOR:0.3,95%CI:(0.1–0.98)] and using a different medication related to work related health problems were factors associated with nurses’ burnout. Conclusion The nurses’ burnout in this study is high and it is attributed by marriage, perceiving health status as poor and fair, whereas, having the intention to leave job, being working in emergency room and using a medication in relation to work related health problems reduced risk of developing burnout. So, the concerned bodies should provide trainings which focus on stress copying mechanisms and assertiveness program.
Early administration of fibrinogen concentrate in patients with polytrauma with thromboelastometry suggestive of hypofibrinogenemia: A randomized feasibility trial

OBJECTIVE: To evaluate the clinical effects of early administration of fibrinogen concentrate in patients with severe trauma and hypofibrinogenemia. METHODS: We conducted an open randomized feasibility trial between December 2015 and January 2017 in patients with severe trauma admitted to the emergency department of a large trauma center. Patients presented with hypotension, tachycardia, and FIBTEM findings suggestive of hypofibrinogenemia. The intervention group received fibrinogen concentrate (50 mg/kg), and the control group did not receive early fibrinogen replacement. The primary outcome was feasibility assessed as the proportion of patients receiving the allocated treatment within 60 min after randomization. The secondary outcomes were transfusion requirements and other exploratory outcomes. Randomization was performed using sequentially numbered and sealed opaque envelopes. ClinicalTrials.gov: NCT02864875. RESULTS: Thirty-two patients were randomized (16 in each group). All patients received the allocated treatment within 60 min after randomization (100%, 95% confidence interval, 86.7%-100%). The median length of intensive care unit stay was shorter in the intervention group (8 days, interquartile range [IQR] 5.75-10.0 vs. 11 days, IQR 8.5-16.0; p=0.02). There was no difference between the groups in other clinical outcomes. No adverse effects related to treatment were recorded in either group. CONCLUSION: Early fibrinogen replacement with fibrinogen concentrate was feasible. Larger trials are required to properly evaluate clinical outcomes.
Knowledge-Embedded Message-Passing Neural Networks: Improving Molecular Property Prediction with Human Knowledge

The graph neural network (GNN) has become a promising method to predict molecular properties with end-to-end supervision, as it can learn molecular features directly from chemical graphs in a black-box manner. However, to achieve high prediction accuracy, it is essential to supervise a huge amount of property data, which is often accompanied by a high property experiment cost. Prior to the deep learning method, descriptor-based quantitative structure–property relationships (QSPR) studies have investigated physical and chemical knowledge to manually design descriptors for effectively predicting properties. In this study, we extend a message-passing neural network (MPNN) to include a novel MPNN architecture called the knowledge-embedded MPNN (KEMPNN) that can be supervised together with nonquantitative knowledge annotations by human experts on a chemical graph that contains information on the important substructure of a molecule and its effect on the target property (e.g., positive or negative effect). We evaluated the performance of the KEMPNN in a small training data setting using a physical chemistry dataset in MoleculeNet (ESOL, FreeSolv, Lipophilicity) and a polymer property (glass-transition temperature) dataset with virtual knowledge annotations. The results demonstrate that the KEMPNN with knowledge supervision can improve the prediction accuracy obtained from the MPNN. The results also demonstrate that the accuracy of the KEMPNN is better than or comparable to those of descriptor-based methods even in the case of small training data.