Miracle-dz/UniFAR-FTUs huggingface dataset:
Numerical Thermomechanical Viscous 2D Material Rheology Modelling Enhanced by Marker-in-Cell (MIC) Technique

The rheology of a material especially related to mechanical behaviour is an important characteristic. Rheology has wide applications for example in nanomaterials, engineering design, biology, and in a large scale case in geodynamics study. Rheology relies on several physical equations such as continuity equation, momentum equation, gravity potential, state equation, heat transfer, and advection equation. The equations seem to be simple, however, the application of those equations to a real case is not easy and causes many numerical problems. Marker-in-Cell (MIC) is a technique to solve a numerical rheology modelling problem especially diffusion of sharp gradients during the advection process. The technique successfully simulates the advection process for thermomechanical viscous material for a 2-dimensional case study. The model was initially set up two parts with laterally contrast difference of material characteristics. The lateral density difference of material induced gravitational force and generated circular movement of material. After the simulation was run for the 20-time step, the whole material moved more clockwise circulation. The model started to be stable when all denser material at the lower layer and lower density material at the upper part. The model was fully stable and become at a stationary state at a time more than 100-time steps. The final simulation result showed that the MIC algorithm successfully simulated the conversion process involving the thermomechanical mechanism with diffusion error control and without breaking the contrast density of the material.
Not all paths lead to success: learning strategies and achievement among undergraduate students

ABSTRACT In modern society, academic success is closely associated with achievement. In order to help students realise their potential, it is essential to understand the factors that promote academic success. The research literature indicates several factors that are associated with academic success, including learning strategies, self-direction, and social involvement. Little attention has been paid to the differential effect that these factors may have on students with different personality traits and levels of ability. The present study examined the learning, self-management, and social learning strategies of 199 second-year undergraduate students, and the relationship between these strategies and their grades over a period of two and a half years. In addition, the personality characteristics and aptitude of the students were also assessed. The findings indicated a correlation of self-management strategies and those associated with the learning environment with grades, and highlighted the importance of social strategies in certain situations. The theoretical implications of the findings and their practical implications for guiding and assisting students are discussed.
Association between Endoscopist Specialty and Colonoscopy Quality: Protocol for a Systematic Review and Meta-analysis

Background Colonoscopy is an important modality in the provision of colorectal cancer screening. Though effective and safe, the performance of screening-related colonoscopy is variable in terms of its overall quality, with endoscopist-related factors playing an important role in this variation. The purpose of this study is to systematically review the association between endoscopist specialty and colonoscopy quality and outcomes. Methods A comprehensive electronic search will be carried out to determine the association between endoscopist specialty and colonoscopy quality metrics and/or outcomes in adult patients undergoing colonoscopy. Two abstracters will independently determine study eligibility, assess study quality, and abstract study data. The primary outcome will be the adenoma detection rate (ADR); secondary outcomes will include cecal intubation rate (CIR), withdrawal time (WT), adverse events (AEs) and post-colonoscopy colorectal cancer (PCCRC). Rates of the above metrics and outcomes will also be compared between clinically relevant subgroups determined a priori. DerSimonian and Laird models will be used to perform meta-analyses for each outcome. Sources of heterogeneity will be explored via meta-regression analyses, if possible. Discussion Given the significant established variation in colonoscopy quality, endoscopist-related factors need to be explored. Our meta-analysis will address the important question of whether the specialty of the endoscopist impacts colonoscopy quality and/or outcomes. Systematic Review Registration PROSPERO CRD42021226251.
Slow release of levofloxacin conjugated on silica nanoparticles from poly(ɛ-caprolactone) nanofibers

ABSTRACT Composite levofloxacin (LVF)/nanofibers have been fabricated through electrospinning. Slow release was achieved by covalently binding LVF to mesoporous silica nanoparticles (MSN) through a cleavable thioester bond and then blending the MSN into poly(ϵ-caprolactone) (PCL) nanofibers. Conjugated LVF–MSN was characterized by FTIR, DSC, TGA, and solid-state C13 NMR. The structure of composite nanofibers was studied by scanning electron microscopy (SEM). Drug release profiles showed that burst release was decreased from 59% in the uniform PCL/LVF electrospun mats to 20% in the PCL/conjugated LVF–MSN mats after 1 day in phosphate buffer at 37°C, and gradual release in the latter was observed over the next 13 days. This slow release is due to the cleavable bond between LVF and MSN that can be hydrolyzed over a time and results in slow release of LVF. The results indicate that confining drug-conjugated MSN into nanofibers are effective ways to slow down the burst release of the drug. GRAPHICAL ABSTRACT
Pereira Problem Solving: Business Research Methodology to Explore Open Innovation

Problem solving skills are increasingly important to be able to tackle the complex problems encountered in the business world. Nowadays is increasingly important to achieve sustainable development, focusing not only on economic profit but also on creating social value. It is widely agreed that the principles of scientific management can lead to more effective solutions for complex problems. Problems have to be looked at objectively, with methodology and intellectual integrity and modesty. Several techniques have been developed to help analyze the causes of the problem or formulate solutions. Although these business research techniques are important tools, they are presented as isolated measures. Pereira Problem Solving methodology presented provides guide to address business and management problems. It is an integrative and easy-to-use instrument that helps organizations adopt scientific management practices and will enhance the efficiency of the solutions encountered.
Development of New ORIGEN2 Library Sets for an Aqueous Homogeneous Reactor (AHR) Dedicated for Mo-99/Tc-99m Medical Radioisotope Production

ABSTRACT New ORIGEN2 library sets were developed based on JENDL-4.0 nuclear data for an aqueous homogeneous reactor (AHR) dedicated for Mo-99/Tc-99m medical radioisotope production which uses nitrate-based, low enriched uranium fuel solution. KEYWORDS: ORIGEN2 library, JENDL4.0 nuclear data, aqueous homogeneous reactor, Mo-99/Tc-99m, medical radioisotope 1 INTRODUCTION ORIGEN2 (Croff, 1980, 1983) is a code developed at Oak Ridge National Laboratory for calculating the buildup, decay and processing of radioactive materials in easily comprehensive form. The materials commonly treated and characterized by the code cover in and out of core fuels (or spent fuels), radioactive wastes, recovered elements such as uranium and plutonium, uranium and thorium ores, mill tailings etc. The code provides a comprehensive result in terms of the radionuclide composition (by its mass, fractional isotopic composition, radioactivity) and its characteristics such as toxicity (radioactive and chemical ingestion and inhalation), neutronics (neutron absorption rate, fission rate, spontaneous fission, (α,n) reaction rate) and photon emission (18 energy group of photon spectrum and total heat). Another feature of the code worthily remarked is its fast computation time. In addition to simulating nuclear fuel cycles, the code has been also used for much wider application areas such as fuel reprocessing, spent fuel shipping, waste disposal, decommissioning, and some aspects of the analysis of potential reactor accidents. The coverage of reactor models in the ORIGEN2 data libraries (distributed with the code) evolves from time to time (and released versions). Within the original ORIGEN2.1 (Croff, 1980) code package, reactor models cover mainly typical reactors operated in the United States of America and Canada, i.e.
Development of dual-side view swept source optical coherence tomography for opaque materials thickness measurement

In this study, we developed a novel dual-side view OCT (DSV-OCT) system for thickness measurement on opaque materials. The dual-side view was achieved on conventional swept source OCT platform by creating two symmetrical sampling arms. This allows to image both sides of the material simultaneously and produce the surface contours of the two sides in a single C-scan. Finally, the thickness of the opaque material can be calculated from the two surface contours above. We evaluated the performance of our DSV-OCT using a microscope slide as sample. The results demonstrated that our DSV-OCT has a good capability for thickness measurement on opaque materials with an accuracy of about 3 μm.
Transductive Mutual Information Encoder Network for Few Shot Learning

Deep learning has made breakthroughs in recent decades and has been widely used in many domains. However, most of those methods heavily rely on large labeled datasets, which results in poor performance when provided with limited labeled data. Few-shot learning (FSL), which aims at learning a novel task with limited samples, has attracted a lot of research recently. The previous metric-based methods ignore the internal bias between the training and testing datasets since the categories of the testing dataset are completely different from the training set. Transfer learning methods also suffer from few labeled data and tends to be overfitting in this situation. This paper proposes Transductive Mutual Information Encoder Network (TMIN) for few-shot learning problems. TMIN typically trains a convolutional neural network with a mutual information maximization module in an unsupervised manner. The trained network maps images to a high dimensional embedding space. Then the embeddings are exploited to measure the similarity between samples by a distance metric. Experiments indicate that the proposed model achieves competitive performance compared with the counterparts.
Efficient and Color-tunable Organic Light-emitting Diodes for Rear Light Application on the Motor Vehicle

By designing double emissive layers, orange light-emitting layer (EML) and red EML and a space layer of blending hole-transporting material (HTM) with electron-transporting material (ETM), we have achieved efficient and color-tunable organic light-emitting diodes (OLEDs). It is found that the red shift of EL spectrum was enhanced when the ratio of HTM to ETM was 5:1. And a maximum current efficiency of 41.17 cd/A was reached. It has been demonstrated that two ultra-thin emissive layers could improve the current efficiency of the OLEDs. The current efficiency of the OLEDs with a 1 nm o;6range EML and a 1 nm red EML was enhanced by 27.4 % as compared to the reference devices. This is a novel approach to realizing voltage-controlled electroluminescence (EL) over different colors and intensity in a single OLED. The color-tunable OLEDs show the potential to be utilized as rear light on motor vehicle. We have investigated the effect of the ratio of HTM to ETM on the efficiency and spectral shift of the OLEDs through the control of recommendation zone.
Scalable Label Propagation for Multi-Relational Learning on the Tensor Product of Graphs

Multi-relational learning on knowledge graphs infers high-order relations among the entities across the graphs. This learning task can be solved by label propagation on the tensor product of the knowledge graphs to learn the high-order relations as a tensor. In this paper, we generalize a widely used label propagation model to the normalized tensor product graph, and propose an optimization formulation and the scalable Low-rank Tensor-based Label Propagation algorithm (LowrankTLP) to infer multi-relations for two learning tasks, hyperlink prediction and multiple graph alignment. The optimization formulation minimizes the upper bound of the noisy-tensor estimating error for multiple graph alignment, by learning with a subset of the eigen-pairs in the spectrum of the normalized tensor product graph. We also provide a data-dependent transductive Rademacher bound for binary hyperlink prediction. We accelerate LowrankTLP with parallel tensor computation which enables label propagation on a tensor product of 100 graphs each of size 1000 in less than half hour in the simulation. LowrankTLP was also applied to predicting the author-paper-venue hyperlinks in publication records, alignment of segmented regions across up to 26 CT-scan images and alignment of protein-protein interaction networks across multiple species. The experiments demonstrate that LowrankTLP indeed well approximates the original label propagation with better scalability and accuracy.
Bulk thermal conductivity studies of Sn/SnO coated and filled multiwalled carbon nanotubes for thermal interface material

ABSTRACT Multiwalled carbon nanotubes (MWCNTs) were successfully coated and filled with Sn using a simple method. Bulk thermal conductivity of Sn/SnO filled and coated multiwalled carbon nanotubes (MWCNTs) of ∼1 mm thickness pellet by laser method reveals surge in hybrid carbon nanotubes in comparison to pristine nanotubes. The thermal diffusivity and thermal conductivity of hybrid nanotubes are increased to 4.41 mm2/sec, 5.39 Wm−1K−1 of as compared to 0.36 mm2/sec and 0.28 Wm−1K−1 of the pristine nanotubes. The enhancement in thermal conductivity is attributed to the presence of Sn coating on surface and inside the carbon nanotubes and the formation of compact structures by reducing the air gaps between nanotubes because of their joining during compression and sintering.
Assessment of recycled glass and expanded clay in a dual media configuration for drinking water treatment

ABSTRACT The aim of this study was to investigate the overall performance of different types of recycled glass media with the optimized coagulation conditions for drinking water treatment. Tests were performed using a laboratory-based filtration unit, with dual filter media configurations and synthetic raw water. The glass media showed comparable performance for both filtration and backwashing and avoided the breakthrough of particles in the effluent. Moreover, the promising performance of the glass media was a slow head loss development which should grant longer filtration runs, less backwashing requirement and thus possible clean water and energy savings. Finally, the optimized combination of filtration media and coagulation operations can lead to the best filtration performance.
Polymer-based spherical matrix array for high resolution real-time volumetric optoacoustic micro-angiography

Optoacoustic (OA) tomography has recently demonstrated ultrafast imaging rates, owing to its unique ability to capture volumetric image data with a single nanosecond-duration flash of light. The achievable spatial resolution of volumetric tomography is however limited by the narrow bandwidth and lack of scalability of the piezo-composite arrays currently employed for OA signal detection. Here we report on the first implementation of high-density spherical matrix array technology based on flexible polyvinylidene difluoride films featuring ultrawideband (38 MHz) sub-millimeter sized elements thus enabling volumetric imaging with ~35 μm resolution, superior image fidelity and excellent signal to noise performance permitting real-time imaging of deep tissues without employing signal averaging. We demonstrate real-time volumetric in vivo imaging capabilities of the new approach by performing cerebral angiography in mice. The new technology leverages the true potential of OA for high spatio-temporal resolution visualization of rapid biodynamics.
ME‐Net: Multi‐encoder net framework for brain tumor segmentation

MRI plays a vital role to evaluate brain tumor diagnosis and treatment planning. However, the manual segmentation of the MRI image is strenuous. With the development of deep learning, a large number of automatic segmentation methods have been developed, but most of them stay in 2D images, which leads to subpar performance. Aiming at segmenting 3D MRI, we propose a model for brain tumor segmentation with multiple encoders. Our model reduces the difficulty of feature extraction and greatly improves model performance. We also introduced a new loss function named “Categorical Dice,” and set different weights for different segmented regions at the same time, which solved the problem of voxel imbalance. We evaluated our approach using the online BraTS 2020 Challenge verification. Our proposed method can achieve promising results compared to the state‐of‐the‐art approaches with Dice scores of 0.70249, 0.88267, and 0.73864 for the intact tumor, tumor core, and enhancing tumor.
Effect of anticholinergic medications on the risk of dementia: a Protocol of systematic review and meta-analysis

Elham Maraghi Ahvaz Jondishapour University of Medical Sciences Faculty of Public Health Ali Reza Safarpour Shiraz Medical School: Shiraz University of Medical Sciences Maryam Hajmohammadi Ahvaz Jondishapour University of Medical Sciences Faculty of Public Health Ali Akbar Oroojan Medical University of Dezful Amal Saki Malehi Ahvaz Jondishapour University of Medical Sciences Faculty of Public Health Hadis Ashra zadeh (  Ashra zadeh.h1993@gmail.com ) Ahvaz Jondishapour University of Medical Sciences Faculty of Nursing and Midwifery https://orcid.org/0000-0002-9369-5854
Observer‐based trajectory control for directional drilling process

This paper is concerned with observer‐based trajectory control of directional drilling system with rotary steering devices. Since the trajectory orientation cannot be measured directly, most of the existing results implicitly assume that the trajectory orientation is equal to the measurement of orientation of the bottom hole assembly (BHA) or the drill bit, which would bring in certain control errors in practice. To avoid such control errors, in this paper, a trajectory control approach is established, which includes a state observer to estimate the trajectory orientation based on the measurement of the BHA orientation. Firstly, the relationship between the trajectory orientation and the BHA orientation is analyzed, and a trajectory evolution model is introduced according to the analysis. Secondly, based on this trajectory evolution model, a variable transformation technique is applied such that a trajectory tracking control problem is formulated. Thirdly, a feedback control strategy is devised based on the estimation of the trajectory orientation for trajectory tracking. Fourthly, stability analysis of the closed‐loop system is discussed, and the design of the controller and state observer is done. Finally, a typical case is used to illustrate the correctness and effectiveness of our approach.
Assessment of aquatic genotoxicity of the Lake Sevan basin, Armenia using natural bioindicators

Water pollution can cause genomic instability, thereby threatening aquatic wildlife and human health. For a correct assessment of the impact of the mixture of pollutants, chemical analysis of environmental contaminants should be simultaneously applied with genotoxicity assays. The сomet assay is a sensitive, versatile and extensively used method for genotoxicity assessment in a water environment and was successfully applied in aquatic wild species, including fish and crayfish. Earlier we investigated the genotoxicity of waters of the Lake Sevan basin (Simonyan et al. 2016, Simonyan et al. 2019). Lake Sevan is the largest lake of the Caucasus Region, situated in the Republic of Armenia, in the Gegharkunik Province. Water pollution in the lake is a result of both anthropogenic and natural pressures. A combination of comet assay (Tice et al. 2000) and chemical analysis was applied for evaluation of water genotoxicity using gibel carp Carassius auratus gibelio and crayfish Astacus leptodactylus as sentinel organisms. Fish and water samples were collected from the south–southwest of the village Shorzha (40 28′33′′N and 45 14′22′′E), Peninsula of Lake Sevan (40 33′46′′N and 45 01′37′′E), the estuaries of the Rivers Gavaraget (40 25′12′′N and 45 09′53′′E) and Dzknaget (40 36′55′′N and 44 58′13′′E). Сrayfish and water samples were collected from the estuary of the River Masrik (40 13′25′′N and 45 38′21′′E) and near the villages of Artanish (40 27′19′′N and 45 25′12′′E) and Tsapatakh (40 24′34′′N and 45 28′22′′E). According to the chemical analysis of waters, ‡ § ‡ | ‡ 0 0 0 0 0 0 0 0 0 0 0 0 0 0 © Sargsyan A et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. the south–southwest of the village Shorzha and the estuary of the River Masrik were chosen as reference sites. The content of contaminants in water was analyzed according to the standard methods in the Environmental Impact Monitoring Center, Ministry of Environment of the Republic of Armenia. The obtained results showed that the levels of DNA damage were significantly higher in both species in polluted areas than in reference sites. A significant positive correlation between DNA damage in C. auratus gibelio and contents of Al, Fe, Cu and Mn in water was shown. DNA damage in A. leptodactylus correlated with Al, Fe, Cu and Mo. C. auratus gibelio and A. leptodactylus were found to be sensitive mostly to the same pollutants, which indicate that these metals may be of primary concern as contaminants of the studied aquatic environment. Thus, the current study indicates that C. auratus gibelio and A. leptodactylus are sensitive bioindicators for monitoring of water pollution in the Lake Sevan basin.
Multilayer Backpropagation Neural Networks for Implementation of Logic Gates

ANN is a computational model that is composed of several processing elements (neurons) that tries to solve a specific problem. Like the human brain, it provides the ability to learn from experiences without being explicitly programmed. This article is based on the implementation of artificial neural networks for logic gates. At first, the 3 layers Artificial Neural Network is designed with 2 input neurons, 2 hidden neurons & 1 output neuron. after that model is trained by using a backpropagation algorithm until the model satisfies the predefined error criteria (e) which set 0.01 in this experiment. The learning rate (α) used for this experiment was 0.01. The NN model produces correct output at iteration (p)= 20000 for AND, NAND & NOR gate. For OR & XOR the correct output is predicted at iteration (p)=15000 & 80000 respectively.