Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparative Assessment of Canal Transportation, Dentin Loss, and Remaining Root Filling Material by Different Retreatment Files An In vitro Cross-Sectional Study

Aim: The purpose of this study was to evaluate the centering ability, canal transportation and efficacy of re-treatment rotary and hand files in removing Gutta Percha from root canals using Cone Beam Computed Tomography(CBCT). Materials and Methods: Sixty extracted human maxillary anteriors were obturated and randomly divided into three groups. Root fillings were removed with ProTaper Universal retreatment system, R-Endo system, and Hedstrom files. CBCT scans were taken. Statistical Analysis and Results: The data were analyzed using the Statistical Package for the Social Sciences (SPSS 15.0, IBM). The mean and standard deviation among the groups was calculated by one-way analysis of variance, Kruskal–Wallis, and Mann– Whitney U-tests, and the comparison among the various groups was done by post hoc Tukey's test. A statistically significant amount of remaining root canal filling material and canal transportation was noted (P < 0.05). Conclusion: No system completely removed the root filling material from root canals. Manual instrumentation resulted in more dentin loss and canal transportation than rotary file system.
Endoscopic Anterior Odontoid Screw Fixation for the Odontoid Fracture: A Cadaveric Pilot Study

Introduction: Anterior odontoid screw fixation technique for specific types of odontoid fracture has been proven to be an effective, yet challenging procedure because of threatened damage to the structures in the vicinity. There are few articles suggesting the role of percutaneous and endoscopic technique as an alternative approach to the standard microscopic way. This is the first cadaveric study using existing “EASY GO” endoscopic system-assisted odontoid screw placement. Study Design: This was a cadaveric study. Objective: The objective of the study is to use the endoscope as a safer minimally invasive approach than the standard microscopic anterior approach to odontoid. Methodology: This is a pilot study on 10 fresh-frozen formalin-fixed adult human cadavers. The cadaver was positioned in a way to simulate reduced odontoid fracture. Tubular dilators were used to dock at C2–3 disk space after identifying the landmarks through the microscopic method. The EASY GO endoscopic system was then introduced, and a handheld drill was used to mark the entry point and pass the K-wire through the planned trajectory. Results: No vascular or adjacent vital structures' injury was observed in any of the cadavers. The initial difficulty in attaining the alignment was overcome by the appropriate positioning of the scope. Conclusions: Endoscopic-assisted technique for odontoid screw fixation shall provide a minimally invasive, safe, and easy surgery.
One-pot synthesis of hierarchical SnO2 hollow nanospindles self-assembled from nanorods and their lithium storage properties

Novel hierarchical SnO2 hollow nanospindles self-assembled from nanorods have been successfully synthesized via a templating approach under hydrothermal conditions. The influence of the reactant concentration on SnO2 products is investigated in detail. It is found that the interplay of the acidic etching of Fe2O3 templates and controlled hydrolysis of SnCl2 is significant for the formation of the hierarchical SnO2 hollow nanostructures. The evolution process and formation mechanism of the hollow structures with nanorods are analyzed from the angle of nucleation and morphology. Moreover, the electrochemical properties of the hollow structures are also studied by charge–discharge cycling, the result displays that the hierarchical SnO2 hollow nanostructure exhibits much better lithium storage properties with higher reversible capacities and enhanced cyclic capacity retention than the commercial SnO2 nanoparticles.
Are Thinking Machines Breaking New Frontiers in Neuro-Oncology? A Narrative Review on the Emerging Role of Machine Learning in Neuro-Oncological Practice

Medical science in general and oncology in particular are dynamic, rapidly evolving subjects. Brain and spine tumors, whether primary or secondary, constitute a significant number of cases in any oncological practice. With the rapid influx of data in all aspects of neuro-oncological care, it is almost impossible for practicing clinicians to remain abreast with the current trends, or to synthesize the available data for it to be maximally beneficial for their patients. Machine-learning (ML) tools are fast gaining acceptance as an alternative to conventional reliance on online data. ML uses artificial intelligence to provide a computer algorithm-based information to clinicians. Different ML models have been proposed in the literature with a variable degree of precision and database requirements. ML can potentially solve the aforementioned problems for practicing clinicians by not just extracting and analyzing useful data, by minimizing or eliminating certain potential areas of human error, by creating patient-specific treatment plans, and also by predicting outcomes with reasonable accuracy. Current information on ML in neuro-oncology is scattered, and this literature review is an attempt to consolidate it and provide recent updates.
Proximal Junctional Kyphosis after Pediatric Angular Kyphotic Deformity Correction: Are we Missing Something?

Purpose: Corrective maneuvers in an angular kyphotic deformity have its own problems including early complications such as neurological deficit and late complications such as proximal junctional kyphosis (PJK) and proximal junctional failure (PJF). This article discusses the probable mechanisms, leading to PJK in pediatric severe angular kyphotic deformities and preventive strategies for the same. We will also assess natural course of untreated PJK and its devastating consequences. Materials and Methods: Three patients, two 13-year males presented with progressive, painless thoracolumbar kyphoscoliotic deformity, with segmental kyphosis 100° and 140° and scoliosis of 33° and 78°, respectively, and one 14-year-old female presented with angular kyphotic deformity of 60° with apex at D11-12 level. Results: Posterior vertebral column resection with segmental deformity correction with good coronal and sagittal balance was done. In the follow-up, PJF was seen. Second surgery was done with the extension of instrumentation to D4 along with deformity correction in both the male patients. The female patient did not opt for a revision surgery, and we are following the natural history of this case. Conclusion: In severe thoracolumbar angular kyphotic deformities with normal or negative sagittal balance, it might be a safer option to select the sagittal stable vertebra as upper instrumented vertebra based on the C2 plumb line on the preoperative standing lateral radiographs. However, a study with a larger sample size is needed to validate our hypothesis.
Lifesaving Decompressive Craniectomy for High Intracranial Pressure Attributed to Deep-Seated Meningioma: Emergency Management

Objects: As the most common intracranial extra-axial tumor among adults who tend to grow slowly with minimal clinical manifestation, the patients with meningioma could also fall in neurological emergency and even life-threatening status due to high intracranial pressure (ICP). In those circumstances, decompressive craniectomy (DC) without definitive tumor resection might offer an alternative treatment to alleviate acute increasing of ICP. The current report defines criteria for the indications of lifesaving DC for high ICP caused by deep-seated meningioma as an emergency management. Patients and Methods: This study collected the candidates from 2012 to 2018 at Dr. Soetomo General Hospital, Surabaya, Indonesia. The sample included all meningioma patients who came to our ER who fulfilled the clinical (life-threatening decrease in Glasgow Coma Scale [GCS]) and radiography (deep-seated meningioma, midline shift in brain computed tomography [CT] >0.5 cm, and diameter of tumor >4 cm or tumor that involves the temporal lobe) criteria for emergency DC as a lifesaving procedure. GCS, midline shift, tumor diameter, and volume based on CT were evaluated before DC. Immediate postoperative GCS, time to tumor resection, and Glasgow Outcome Scale (GOS) were also assessed postoperation. Results: The study enrolled 14 patients, with an average preoperative GCS being 9.29 ± 1.38, whereas the mean midline shift was 15.84 ± 7.02 mm. The average of number of tumor's diameter and volume was 5.59 ± 1.44 cm and 66.76 ± 49.44 cc, respectively. Postoperation, the average time interval between DC and definitive tumor resection surgery was 5.07 ± 3.12 days. The average immediate of GCS postoperation was 10.07 ± 2.97, and the average GOS was 3.93 ± 1.27. Conclusion: When emergency tumor resection could not be performed due to some limitation, as in developing countries, DC without tumor resection possibly offers lifesaving procedure in order to alleviate acute increasing ICP before the definitive surgical procedure is carried out. DC might also prevent a higher risk of morbidity and postoperative complications caused by peritumoral brain edema.
Plasmon-enhanced light harvesting: applications in enhanced photocatalysis, photodynamic therapy and photovoltaics

Plasmonic nanostructures have played a significant role in the development of modern materials science and technology. Plasmon-enhanced solar light harvesting to enhance the efficiency of solar to fuel energy conversion has been one of the most important research areas of the last decade to help meet the worlds growing energy demand. Over the years, both organic and inorganic semiconductor materials, with high stability, environmental compatibility and photocatalytic activity, have been widely used as photocatalysts for direct conversion of solar energy into fuels. However, the efficiency of semiconductors is limited by their inability to absorb visible light due to high band gap. During last few years, great amount of research has been carried out to improve the efficiency of photocatalysts and photovoltaic devices by integration of plasmonic nanoparticles (NPs) with semiconductor materials. The presence of plasmonic NPs leads to increase in the absorption cross-section of semiconductors via strong field enhancement, extension of light absorption to longer wavelengths and enhances electron–hole charge separation in semiconductor medium, thus maximize the efficiency of photocatalytic and photovoltaic devices. In this review, we summarize recent advances made toward the integration of plasmonic nanostructures with semiconductor photocatalytic systems for enhanced light harvesting applications, including dye degradation, water splitting for H2 generation, photodynamic therapy, chemical transformation and photovoltaics.
Characterization of Extracellular Dextranase from a Novel Halophilic Bacillus subtilis NRC-B233b a Mutagenic Honey Isolate under Solid State Fermentation

Bacillus subtilis NRC-B233b was isolated from Libyan honey sample proved to be a potent dextranase producer by applying solid state fermentation and utilizing corn flour as the sole carbon source. The optimized culture conditions for dextranase productions were 37°C, pH 10, 32 h, and 20% (v/w) moisture content. A unique character of this isolate is its ability to produce steady dextranase irrespective to the presence of NaCl in the medium. The addition of 0.175 Mm CrCl3 increased the enzyme production by about 4.5 fold. Further improvement in enzyme production was achieved by simple UV mutation which increased the enzyme production up to about 2842 U/g. The crude extract has been partially purified about 112-fold from crude extract by only two purification steps involving ultra-filtration. The partially purified dextranase showed its maximum activity at pH 9.2 and 70°C. It retained full activity (100%) at 75°C for one hour. Dextranase activity increased about 4 fold in the presence of 10% NaCl. This enzyme showed variable degradation effect on different types of dextran and its derivatives. The treatment of viscous sugar cane juice with the enzyme preparation resulted in clear visual dextran hydrolysis. These results suggest that the dextranase produced by Bacillus subtilis NRC-B233b is industrially applicable.
Use of boundary element methods in field emission computations

The boundary element method is well suited to deal with some potential field problems encountered in the context of field emission. A boundary element method is presented in the specific case of three‐dimensional problems with azimuthal symmetry. As a check, computed results are displayed for well‐known theoretical examples. The code is then employed to calculate current from a field emission tip and from the same tip with a protrusion. Finally an extension of the boundary element code is employed to calculate space‐charge effects on emitted current.
Simplifying the free-radical polymerization of styrene : microwave-assisted high-temperature auto polymerizations

We have investigated the combination of the thermally auto-initiated free radical polymerization of styrene and precipitation polymerization in order to develop a fast and environmentally friendly approach to produce polystyrene. To achieve high reaction temperatures in a short period of time, microwave irradiation was utilized as the heating source. Styrene was used without any purification, e.g., without distillation or column filtration. Due to the auto-initiation of styrene at high temperatures no radical initiator was required. Different water- or ethanol-to-styrene ratios were heated far beyond their boiling points and at relatively high pressures for the auto-initiated polymerization of styrene. The obtained molecular weights could be controlled by the ethanol-to-styrene ratio in the case of ethanol as the solvent although the monomer conversions were rather low under the applied conditions. Moreover, the effect of a commercially available stable free nitroxide was investigated on the control over the polymerization. It has been observed that it is possible to control the molecular weight of the polymer by changing the ratio of styrene to free nitroxide (varied from 10:1 to 400:1) and moderate polydispersity indices (PDI = 1.3 to 1.9) could be obtained. Finally, the developed polymerization processes only require a simple purification step due to the precipitation of the polystyrene in the reaction solvent.
Time‐dependent Density Functional‐based Tight‐bind Method Efficiently Implemented with OpenMP Parallel and GPU Acceleration

The time‐dependent density functional‐based tight‐bind (TD‐DFTB) method is implemented on the multi‐core and the graphical processing unit (GPU) system for excited state calculations of large system with hundreds or thousands of atoms. Sparse matrix and OpenMP multithreaded are used for building the Hamiltonian matrix. The diagonal of the eigenvalue problem in the ground state is implemented on the GPUs with double precision. The GPU‐based acceleration fully preserves all the properties, and a considerable total speedup of 8.73 can be achieved. A Krylov‐space‐based algorithm with the OpenMP parallel and GPU acceleration is used for finding the lowest eigenvalue and eigenvector of the large TDDFT matrix, which greatly reduces the iterations taken and the time spent on the excited states eigenvalue problem. The Krylov solver with the GPU acceleration of matrix‐vector product can converge quickly to obtain the final result and a notable speed‐up of 206 times can be observed for system size of 812 atoms. The .
Selective hydrogenation of cyclohexenone on iron–ruthenium nano-particles suspended in ionic liquids and CO2-expanded ionic liquids

Iron and ruthenium mono- and bimetallic nanoparticles (NPs) with different metal ratios (Fe–Ru 9 : 1, Fe–Ru 3 : 1, and Fe–Ru 1 : 1) have been prepared in imidazolium ionic liquids (ILs) via decarbonylation of the corresponding metal carbonyl precursors. The as-prepared ionic liquid suspended nanoparticles were tested as catalysts for the selective hydrogenation of cyclohexenone to cyclohexanone at 50 °C in a high pressure batch reactor equipped for in situ Attenuated Total Reflection (ATR) infrared spectroscopy. After reaction, supercritical carbon dioxide was applied to extract the products from the reaction mixture, thus facilitating a green approach to solventless transition metal catalysis. The course of the reaction and extraction was followed by ATR-IR spectroscopy. From the IL suspended monometallic NPs, Ru showed much higher activity than Fe. Addition of 50 at% iron to Ru (Fe–Ru 1 : 1) resulted in a remarkable decrease of the average particle size of the NPs from 2.9 nm to 1.6 nm. Among the different NPs prepared, the bimetallic Fe–Ru 1 : 1 was most active followed by the pure Ru. Addition of CO2 to the reaction mixture greatly increased the reaction rate (4 times faster with 60 bar of CO2 than without), as demonstrated for the highly active Fe–Ru 1 : 1 NPs suspended in 1-butyl-3-methylimidazolium tetrafluoroborate ([BMIm][BF4]). At 60 bar of CO2 high selectivity >95% to cyclohexanone, at a TOF of ca. 300 h−1, was achieved and the catalyst could be reused 5 times without noticeable deactivation.
Time Series Data-Driven Online Prognosis of Wind Turbine Faults in Presence of SCADA Data Loss

While recent years have witnessed the high potential of various machine learning based schemes in wind turbine fault prognosis (WTFP), none of them effectively tackle the challenging yet practical issue of severe operational data loss during WTFP. To address this issue, this paper develops an intelligent time series (TS) data analytics approach for missing data-tolerant WTFP. Instead of struggling to impute missing data, a powerful TS subsequence nearest neighbor (SNN) profiling technique is introduced to directly characterize TS data with missing values and transform them into discerning SNN profiles. Taking such profiles as inputs, the long short-term memory based recurrent network is further leveraged to perform in-depth faulty feature learning from temporal evolution trends. This eventually results in a discriminative multi-fault classification model, which can implement reliable online WTFP in severe missing data contexts. Experimental test results with field data collected from an actual wind turbine demonstrate the efficacy of the proposed approach.
Improving Wildfire Severity Classification of Deep Learning U-Nets from Satellite Images

Uncontrolled wildfires are dangerous events capable of harming people safety. To contrast their increasing impact in recent years, a key task is an accurate detection of the affected areas and their damage assessment from satellite images. Current state-of-the-art solutions address such problem through a double convolutional neural network able to automatically detect wildfires in satellite acquisitions and associate a damage index from a defined scale. However, such deep-learning model performance is strongly dependent on many factors. In this work, we specifically focus on a key parameter, i.e., the loss function, exploited in the underlying neural networks. Besides the state-of-the-art solutions based on the Dice-MSE, among the many loss functions proposed in literature, we focus on the Binary Cross-Entropy (BCE) and the Intersection over Union (IoU), as two representatives of the distribution-based and region-based categories, respectively. Experiments show that the BCE loss function coupled with a double-step U-Net architecture provides better results than current state-of-the-art solutions on a public labeled dataset of European wildfires.
Towards Training Robust Private Aggregation of Teacher Ensembles Under Noisy Labels

Deep learning models trained on large-scale data have achieved encouraging performance in many real-world tasks. Meanwhile, publishing those models trained on sensitive datasets, such as medical records, could pose serious privacy concerns. To counter these issues, one of the current state-of-the-art approaches is Private Aggregation of Teacher Ensembles, or PATE, which achieved promising results in preserving the utility of the model while providing a strong privacy guarantee. PATE combines an ensemble of "teacher models" trained on sensitive data and transfers the knowledge to a "student" model through the noisy aggregation of teachers’ votes for labeling unlabeled public data which the student model will be trained on. However, the knowledge or voted labels learned by the student are noisy due to private aggregation. Learning directly from noisy labels can significantly impact the accuracy of the student model. In this paper, we propose the PATE++ mechanism, which combines the current advanced noisy label training mechanisms co-teaching(+) with the original PATE framework to enhance its accuracy. A novel structure of Generative Adversarial Nets with one generator and two discriminators is developed in order to integrate them effectively. Furthermore, we discuss the intrinsic limitations of the "update-by-disagreement" method in the co-teaching+ mechanism and develop a novel noisy label detection mechanism for semi-supervised model training to further improve student model performance when training with noisy labels. We evaluate our method on Fashion-MNIST and SVHN to show the improvements on the original PATE on all measures.
Novel MIMO-UWB-Based AG-UG-AG Routing With Microbit Sensors for WUSN

In this letter, empirical with analytical methodology to decode-and-forward packets over aboveground-to-underground-to-aboveground systems in the wireless underground sensor network environment is corroborated. Multiple-input–multiple-output ultrawideband (MIMO-UWB) radio frequency links were empirically characterized and exploited using microbit moisture-measuring white light-emitting diodes in different volumetric water content and orientation depths. Relay-routing and selection using the novel maximum available channel capacity technique was accomplished. The simulation performance demonstrates relay-routing and selection impacts on channel conditions, overall outage probability, transmission power, and energy consumption, without depreciated aggregate system throughput.
Machine Learning Methods for Anomaly Detection in Industrial Control Systems

This paper examines multiple machine learning models to find the model that best indicates anomalous activity in an industrial control system that is under a software-based attack. The researched machine learning models are Random Forest, Gradient Boosting Machine, Artificial Neural Network, and Recurrent Neural Network classifiers built-in Python and tested against the HIL-based Augmented ICS dataset. Although the results showed that Random Forest, Gradient Boosting Machine, Artificial Neural Network, and Long Short-Term Memory classification models have great potential for anomaly detection in industrial control systems, we found that Random Forest with tuned hyperparameters slightly outperformed the other models.