Miracle-dz/UniFAR-FTUs huggingface dataset:
Combination of Open and Closed Reduction Methods in the Treatment of Multiple Mandible Fractures

Introduction: Mandible fracture is defined as a loss of continuity of the mandible bone. Loss of continuity of the mandible bone can have fatal consequences if not treated properly. Classification of mandibular fractures based on anatomic location can occur in the condyle, coronoid, ramus, mandibular angle, mandibular corpus, symphysis, parasymphysis, and dentoalveolar. This case report aims to explain the management of multiple mandibular fractures with closed and open reduction approaches. The desired outcome for these patients is to restore the patient’s maxillary and mandibular (occlusion) relation, who have experienced an occlusion shift due to mandible fracture. Case report: Three cases of multiple mandible fractures. In a panoramic examination obtained a picture of several fracture lines in the mandible with two or more fracture lines. Treatment is carried out using open reduction and closed reduction techniques. As an initial treatment, interdental wiring was installed and followed by ORIF using a mini screw and plate. Then intermaxillary fixation (IMF) is done using elastic bands in the anterior and posterior regions. Conclusion: This case concludes that the reconstruction of mandibular mutilated fractures using open reduction and close reduction techniques can be carried out continuously in multiple mandibular fractures.
TCRM_A_353199 155..169

Objective: Cardiac microvascular obstruction (CMVO) remains a severe complication in non-ST elevation myocardial infarction (NSTEMI) patients with reperfusion therapy. We aimed at developing and validating the nomogram to predict the possibility of CMVO after primary percutaneous coronary intervention (PCI) by integrating clinical and laboratory-based information. Methods: A total of 325 patients undergoing primary PCI for NSTEMI were recruited and divided into the training cohort (n=226) and the validating cohort (n = 99). The development of the nomogram was based on independent predictors of CMVO, and these variables were selected by multivariable logistic regression analysis. Results: Independent predictors contained in nomogram were identified by multivariable logistic regression analysis, and these independent predictors included neutrophils (OR 1.166, 95% CI 1.044–1.303, P<0.01), hemoglobin (OR 1.037, 95% CI 1.013–1.062, P<0.01), triglyceride (OR 1.343, 95% CI 1.059; 1.704, P=0.015), Killip grade (OR 2.190, 95% CI 1.065–4.503, P=0.033), high thrombus load (OR 3.146, 95% CI 1.424–6.952, P<0.01), no-reflow (OR 3.142, 95% CI 1.419–6.955, P<0.01) and ischemic postconditioning (OR 0.445, 95% CI 0.209–0.944, P=0.035). The nomogram accurately predicted the presentation of CMVO in both the training set and validating set (AUC, 0.835 and 0.881, respectively). The results predicted by nomogram were confirmed to be highly consistent with the results of DE-CMR, both the training and validating cohorts, by Calibration plot and Hosmer-Lemeshow test. Decision curve analysis (DCA) also suggested that the nomogram was applicable in the clinic. Conclusion: The nomogram showed good performance in predicting CMVO, and it could help clinicians optimize the clinical treatments to improve the prognosis of NSTEMI patients.
Case study of geophysical surveys for profiling soil / rock interface in Singapore

Profiling soil / rock interface is an important task in geotechnical site investigation. Instead of drilling limited boreholes, geophysical surveys are sometimes needed due to various objectives and challenges case by case. Although the selection of an appropriate type of geophysical surveys would be the foundation of project success, it might be still a common puzzle to geotechnical engineers who may not have the geophysical background. This paper shares an interesting case history where we have conducted 3 different types of geophysical surveys at the same site for profiling soil / rock interface. From the survey results and the nearby boreholes, it is shown that the seismic survey performs better as it has the least differences and the closest profile trend, followed by gravity. The resistivity survey does not show convincing result as the differences at each reference borehole location are rather large and the general trend does not match the possible real one. Hence, the resistivity survey is found as the least suitable one in this case study.
Distribution of Trace Elements, Rare Earth Elements and Ecotoxicity in Sediments of the Kosva Bay, Perm Region (Russia)

Over a long period of time, a huge amount of technogenic bottom sediments has been accumulating in the Kosva Bay with significant concentrations of amorphous iron and aluminium hydroxides, which, in turn, are active sorbents of pollutants. This study examines the distribution of trace elements and rare earth elements and their toxicity in the Kosva Bay of the Kama Reservoir (Perm Region, Russia). In the middle reach, the Kosva River crosses the Kizel coal basin, where acid mine water is discharged from closed mines. The average content of trace elements in the samples of bottom sediments of the bay varies from 0.10 mg/kg (Se) to 176.36 mg/kg (Ba). The amount of rare earth elements varies from 66.8 to 83.6 mg/kg. The ecological significance of trace elements and rare earth elements was studied using an element-by-element assessment (EF and Igeo), Potential Ecological Risk Index (RI), Mean Probable Effect Concentration Quotient (PECQ), and two bioassays (Daphnia magna Straus and Scenedesmus quadricauda (Turp.) Breb. The highest Hg enrichment was found at two sampling points. Taking into account the average value of Igeo, the pollution by Co, V, Nb, Hg, Sn, Zn, Sm, Ni, Cr, and Gd is the highest and corresponds to extremely contaminated category. The RI values indicate that pollution categories vary from moderate risk to considerable risk. According to mean PECQ values, bottom sediments of the bay have moderate potential toxicity towards biological communities. Results of chronic and acute toxicity on test objects D. magna and Scenedesmus quadricauda Breb show the water extract from bottom sediments having no effects on the test objects. The results of the study show that in order to assess the quality of bottom sediments, an integrated approach, combining chemical and ecotoxicological analyses, is needed.
The effect of dental water jet application on softened human enamel surfaces: A preliminary in vitro study using swept-source optical coherence tomography

Objectives: To evaluate the surface loss of enamel, following water jet application, on acid-induced softened human enamel surfaces. Methods: Ninety enamel windows of 2×4 mm2 were prepared on 45 extracted human premolar teeth. The specimens were divided into 2 groups: Group 1 (n=45) exposed to distilled water; Group 2 (n=45) exposed to citric acid (pH 3.2) for 30 minutes. All specimens were then subjected to a water jet for 20 seconds. Surface microhardness (SMH) and enamel thickness measurements were taken at baseline (t1), postdistilled water/post-erosion (t2) and post-water jet (t3). Enamel thickness measurements were derived from the depth-resolved intensity profile (A-scans) of swept-source optical coherence tomography (SS-OCT) and SMH was used as a measurement of the state of mineralization. Repeated measures ANOVA was used to analyze the changes in SMH and enamel thickness (D) (α=0.05). Results: For Group 2, at t2, there was a 54.28% and 14.14% reduction in SMH and D, respectively. Repeated measures ANOVA showed that the aforementioned mean reduction in SMH and D were statistically significant (P<0.05) at t2 compared to t1 for Group 2. However, at t3, no significant difference (P>0.05) was observed in both measurements, compared to t2 for both groups. Conclusion: Within the limitations of the study, the application of a water jet may not affect the thickness of enamel softened by acidic conditions.
Evaluation of the Shear Stress Reduction Factor for the Liquefaction Potential in the Catania Area (Italy)

In this paper a study concerning the soil liquefaction of some test sites in the city of Catania has been presented. In situ investigations of sandy saturated soils were carried out in order to determine the soil profiles and the geotechnical parameters for potential liquefaction under cyclic loading. The stress-based liquefaction analysis framework for cohesionless soil includes a function that describes fundamental aspects of dynamic site response, i.e. the shear stress reduction coefficient, rd, which is depending of several factors (depth; earthquake and ground motion characteristics; dynamic soil properties). Among the various relationships proposed, including probabilistic, a new variation of rd with depth has been obtained using a deterministic earthquake scenario as input motion. The relationship is based on large numbers of site response analyses for different site conditions and includes the effects of a site's average shear wave velocity over a specified depth. Therefore, the continuous profiles i.e. for the city of Catania allow a more detailed interpretation of soil layers and soil types. Semi-empirical procedures for liquefaction evaluations originally have been also developed using the Standard Penetration Test (SPT) to differentiate between liquefiable and non-liquefiable sites. Seismic Dilatometer Marchetti Tests (SDMT) have been also carried out, with the aim to evaluate the soil profile of shear wave velocity (Vs) and the horizontal stress index (Kd). The available data obtained from the Seismic Dilatometer Marchetti Tests results enabled also to evaluate the potential liquefaction.
Extending Deep Rhythm for Tempo and Genre Estimation Using Complex Convolutions, Multitask Learning and Multi-input Network

Tempo and genre are two inter-leaved aspects of music, genres are often associated to rhythm patterns which are played in specific tempo ranges.In this paper, we focus on the Deep Rhythm system based on a harmonic representation of rhythm used as an input to a convolutional neural network.To consider the relationships between frequency bands, we process complex-valued inputs through complex-convolutions.We also study the joint estimation of tempo/genre using a multitask learning approach. Finally, we study the addition of a second input convolutional branch to the system applied to a mel-spectrogram input dedicated to the timbre.This multi-input approach allows to improve the performances for tempo and genre estimation.
Detection of Brain Disease Using Morphological Operation and Artificial Intelligence Technique

: Artificial Intelligence is being used in medical field for various applications like disease diagnosis. One specific application of this using ANN and ANFIS in medical disease diagnosis is the analysis of brain MRI with the aid of image segmentation technique. Image segmentation is a field of digital image processing in which an image is splitted in various parts using one of available techniques such as edge detection or cluster dependent area. In this paper brain MRI Image is segmented using image segmentation method and it is converted in frequency domain using DWT operation then after applying morphological operation and watershed operation an image is compared with a set of images showing various diseases using artificial intelligence and hence disease is diagnosed . The simulation results are enhanced to somewhat 94% using AI technique .
Prediction of Customer Churn in Telecom Industry: A Machine Learning Perspective

The business world is becoming increasingly saturated in today's competitive environment. There is a great deal of competition in the telecommunications industry, especially due to various vibrant service providers. As a result, they have had difficulty retaining their existing customers. As attracting new customers is much more costly than retaining current ones, now is the time to ensure the telecom industry maintains value by retaining customers over acquiring new ones. Numerous machine learning and data mining methods have been proposed in the literature to predict customer churners using heterogeneous customer records over the past decade. This research gives a brief idea on the Customer Churn problem, and explores how various machine learning techniques can be used to predict customer churn via models such as XGBoost, GradientBoost, AdaBoost, ANN, Logistic Regression and Random Forest, and also compare the effectiveness of the models in term of accuracy. Keyword : Machine Learning, Customer Churn, Prediction Model, Random Forest, XGBoost, AdaBoost, GBoost
Green Space, Violence, and Crime: A Systematic Review

Purpose: To determine the state of evidence on relationships among urban green space, violence, and crime in the United States. Methods and Results: Major bibliographic databases were searched for studies meeting inclusion criteria. Additional studies were culled from study references and authors’ personal collections. Comparison among studies was limited by variations in study design and measurement and results were mixed. However, more evidence supports the positive impact of green space on violence and crime, indicating great potential for green space to shape health-promoting environments. Conclusion: Numerous factors influence the relationships among green space, crime, and violence. Additional research and standardization among research studies are needed to better understand these relationships.
Remixing AIs: mind swaps, hybrainity, and splicing musical models

Musical models created using machine learning techniques can be hacked, repurposed and spliced in many new ways. The current generation of models is not at any level of simulated consciousness sufficient to trigger immediate ethical blocks, and stands open to systems surgery. Beyond morphing of inputs and outputs, hidden layers of deep learning models may themselves be actively stimulated and substituted, from unit weights and biases to activation functions. Layers from multiple networks may be swapped and interpolated, from single units to complete layers. The hybridity of musical formation takes place at the level of model internals, in artistic transformations beyond standard transfer learning. This activity of AI code bending is dubbed here ‘hybrainity’, and alongside theoretical discussion of its origins, potential and ethics, examples of hacking particular machine learning models for new creative projects are provided, including applications in live performance and audiovisual generation.
Effect of Locally Loosened Soil on Wave Propagation

In-situ down-hole or cross-hole wave surveys are useful as a means of measuring spatial distribution of elastic wave velocities. However, detecting an existing subsurface cavity using this method is not easy, partially because the characteristics of wave propagation around a cavity are not fully understood. This contribution aims to develop a better understanding of how elastic wave signals are affected due to locally loosened soil around a subsurface cavity. Laboratory model tests are conducted, and it was found that compression waves propagating near to the cavity decelerate and attenuate, and frequency-domain responses are also affected by the local loosening. To understand particle-scale responses of loosened soil during elastic wave propagation, discrete element method simulations are adopted. Two types of soil elements composed of dense-loose mixed layers are examined; the simulation results reveal that the wave velocities are an averaged response of the entire soil element, whereas the frequency response is dominated by the loosest portion of the soil element. Besides, a reflection of wave energy is observed at a boundary where the soil density changes sharply.
A Novel Hybrid Network for Arabic Sentiment Analysis using fine-tuned AraBERT model

The pre-trained word embedding models become widely used in Natural Language Processing (NLP), but they disregard the context and sense of the text. We study in this paper, the capacity of pre-trained BERT model (Bidirectional Encoder Representations from Transformers) for the Arabic language to classify Arabic tweets using a hybrid network of two famous models;Bidirectional Long Short Term Memory (BiLSTM) and Gated Recurrent Unit (GRU) inspired by the great achievement of deep learning algorithms. In this context, we finetuned the Arabic BERT (AraBERT) parameters and we used it on three merged datasets to impart its knowledge for the Arabic sentiment analysis. For that, we lead the experiments by comparing the AraBERT model in one hand in the word embedding phase, with a statics pretrained word embeddings method namely AraVec and FastText, and on another hand in the classification phase, we compared the hybrid model with convolutional neural network (CNN), long short-term memory (LSTM), BiLSTM, and GRU, which are prevalently preferred in sentiment analysis. The results demonstrate that the fine-tuned AraBERT model, combined with the hybrid network, achieved peak performance with up to 94% accuracy.
Towards an Evaluation of Symbolic Music Encodings for RNN Music Generation

The choice of encoding for symbolic music data used in music generation models has typically been done in a case-by-case basis up to now. In this paper we attempt to evaluate and study the behaviour of a baseline shallow LSTM network trained on Irish folk music, us-ing three diﬀerent encodings: a MIDI-like event-based encoding, a ﬁxed timestep-based encoding, and the popular ABC notation. We use objec-tive statistical measures on the network output and also visualise the LSTM parameters to gain insight on the way it processes each encoding.
PV-Battery Hybrid Energy System For Electric Vehicle – Supervision and Control Strategy

In today era, India has high solar potential, offers great opportunity in economic development. In this paper, PV with battery bank is studied and modelled to confirm feasibility for proposed hybrid electric vehicle (EV). The maximum power point tracking (MPPT) approach is applied to extract maximum power for PV system. In this work, perturb and observe (PandO) and fuzzy logic controller (FLC) techniques are compared for tracking MPPT. In the presented work smart power management of a PV system with battery storage is applied. Supervision unit is provided to allow maximum power transfer from PV generator, protect batteries against overcharge and deep discharge and satisfies the needs of energy. The application is designed to handle variable load condition of EV and PV source, subjected to varying environmental conditions. However, FLC provides better performance allows this method for MPPT tracking. The simulation results (SimPowerSystems) environment confirmed proposed supervisor makes autonomous PV system capable of working normally charging and discharging batteries to supply EV load without interruption.
Bimetallic AgeCu nanosheets assembled flower-like structure for oxygen reduction reaction

Fabricating low-cost and Pt-free fuel cell catalysts towards highly active and durable oxygen-reduction reaction catalysis remains a great challenge. Herein, we synthesize a bimetallic AgeCu nanosheets assembled flower-like structure by our developed cyclic scanning electrodeposition (CSE) method. The formation mechanism of this structure is investigated systematically, where Cu2þ can affect oxidative etching during the formation process. The prepared bimetallic nanostructure is studied for kinetics towards oxygen reduction reaction (ORR) in alkaline media. The electrochemical test results indicate that the ORR catalytic ability of bimetallic AgeCu nanostructure is competitive with Pt/C-20%, better than Pt disk, and far superior to the pure Ag nanostructure. The electrons transfer number (n) is close to four, which is further proved by scanning electrochemical microscope (SECM) experiments. Density functional theory (DFT) calculations reveal that the high ORR activity is attributable to the widely distributed AgeCu steps, which is formed by decorating Cu atoms on the Ag nanosheets. © 2020 Elsevier B.V. All rights reserved.
The Serological Sciences Network (SeroNet) for COVID-19: Depth and Breadth of Serology Assays and Plans for Assay Harmonization

SeroNet institutions have developed or implemented 61 diverse COVID-19 serological assays and are collaboratively working to harmonize these assays using reference materials to establish standardized reporting units. This will facilitate clinical interpretation of serology results and cross-comparison of research data. ABSTRACT In October 2020, the National Cancer Institute (NCI) Serological Sciences Network (SeroNet) was established to study the immune response to COVID-19, and “to develop, validate, improve, and implement serological testing and associated technologies” (https://www.cancer.gov/research/key-initiatives/covid-19/coronavirus-research-initiatives/serological-sciences-network). SeroNet is comprised of 25 participating research institutions partnering with the Frederick National Laboratory for Cancer Research (FNLCR) and the SeroNet Coordinating Center. Since its inception, SeroNet has supported collaborative development and sharing of COVID-19 serological assay procedures and has set forth plans for assay harmonization. To facilitate collaboration and procedure sharing, a detailed survey was sent to collate comprehensive assay details and performance metrics on COVID-19 serological assays within SeroNet. In addition, FNLCR established a protocol to calibrate SeroNet serological assays to reference standards, such as the U.S. severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) serology standard reference material and first WHO international standard (IS) for anti-SARS-CoV-2 immunoglobulin (20/136), to facilitate harmonization of assay reporting units and cross-comparison of study data. SeroNet institutions reported development of a total of 27 enzyme-linked immunosorbent assay (ELISA) methods, 13 multiplex assays, and 9 neutralization assays and use of 12 different commercial serological methods. FNLCR developed a standardized protocol for SeroNet institutions to calibrate these diverse serological assays to reference standards. In conclusion, SeroNet institutions have established a diverse array of COVID-19 serological assays to study the immune response to SARS-CoV-2 and vaccines. Calibration of SeroNet serological assays to harmonize results reporting will facilitate future pooled data analyses and study cross-comparisons. IMPORTANCE SeroNet institutions have developed or implemented 61 diverse COVID-19 serological assays and are collaboratively working to harmonize these assays using reference materials to establish standardized reporting units. This will facilitate clinical interpretation of serology results and cross-comparison of research data.