Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of Flexible Plate Motion Based on Co-Simulation of STAR-CCM + and ABAQUS

By the advantages of the fluid dynamics software STAR-CCM + and the structure simulation software ABAQUS in their respective fields, this paper adopts the FEM algorithm for structural deformation, and based on the RANS equation for fluid simulation, the accuracy of the co-simulation method of Fluid-Structure Interaction(FSI) was proved by comparing the simulation results under typical conditions with the experimental results. Then the deformations of a large flexible plate with fixed bottom end at high flow rate were numerically analyzed. The co-simulation method can obtain more accurate and detailed flow field and structural deformation, and has certain application value for related theoretical research, engineering structure design and safety evaluation.
Separatory Freezing and Cryoconcentration of Milk and Whey

A significant problem in the food industry is the lack of raw materials. At the same time, a significant part of the resources is not used for obtaining food products. This kind of food raw material includes whey, which contains a large amount of valuable components, in particular whey proteins and lactose. The article discusses the results of research devoted to the development of industrial technology for separating freezing (cryoconcentration) of skim milk and curd whey to obtain concentrated skim milk, as well as whey protein concentrate in low-tonnage production conditions. The research was carried out on products manufactured by dairy enterprises in Kemerovo, Russia.
Path planning of mobile robot based on improved DDQN

Aiming at the problem of overestimation and sparse rewards of deep Q network algorithm in mobile robot path planning in reinforcement learning, an improved algorithm HERDDQN is proposed. Through the deep convolutional neural network model, the original RGB image is used as input, and it is trained through an end-to-end method. The improved deep reinforcement learning algorithm and the deep Q network algorithm are simulated in the same two-dimensional environment. The experimental results show that the HERDDQN algorithm solves the problem of overestimation and sparse reward better than the DQN algorithm in terms of success rate and reward convergence speed, Which shows that the improved algorithm finds a better strategy than the DQN algorithm.
A robust registration algorithm for 3D point cloud data with scale stretches and outliers

Generally, the point cloud data obtained by 3D scanner cannot express the overall information of objects at a single time due to the limited field of view, so registration algorithm is needed to obtain the complete information. For the registration of point cloud data sets with large scale stretches and outliers, this paper proposed a new iterative nearest point algorithm named Improved-ICP. In the case of isotropic point extension, the iterative reweighted least squares method is constructed by incorporating a weight function to the minimum error function. In calculation, the weight function is equivalent to increasing the weight the point pairs, and the weights are obtained by an M-estimation criterion. In the paper, the initial registration was optimized to get global convergence for the algorithm, and compared the Improved-ICP algorithm with the ICP algorithm and the Scale-ICP algorithm to verify the effectiveness of the algorithm. To demonstrate the robustness of the Improved-ICP algorithm, we performed several comparative experiments with different scales and different noise between the Scale-ICP and the proposed algorithm in the presence of abnormal points. Experiment results illustrate the Improved-ICP algorithm has high accuracy and strong robustness to scale and abnormal points.
Accurate Online Tensor Factorization for Temporal Tensor Streams with Missing Values

Given a time-evolving tensor stream with missing values, how can we accurately discover latent factors in an online manner to predict missing values? Online tensor factorization is a crucial task with many important applications including the analysis of climate, network traffic, and epidemic disease. However, existing online methods have disregarded temporal locality and thus have limited accuracy. In this paper, we propose STF (Streaming Tensor Factorization), an accurate online tensor factorization method for real-world temporal tensor streams with missing values. We exploit an attention-based temporal regularization to learn inherent temporal patterns of the streams. We also propose an efficient online learning algorithm which allows each row of the temporal factor matrix to be updated from past and future information. Extensive experiments show that the proposed method gives the state-of-the-art accuracy, and quickly processes each tensor slice.
Title: Chondroitinase ABC Administration in Locomotion Recovery After Spinal Cord Injury: A Systematic Review and Meta-Analysis Running Title: Chondroitinase ABC administration in SCI

1. Assistant Professor of Physiology Department of Physiology, School of Medicine, Iran University of Medical Sciences, Tehran, Iran. Researcher at Physiology Research Center, Iran University of Medical Sciences, Tehran, Iran. 2. Research Assistant Professor of Physiology at Radiation Biology Research Center, Iran University of Medical Sciences, Tehran, Iran. 3. Assistant Professor of Respiratory Medicine, Collage of Medicine, University of Sulaimani, Sulaimani, Iraq. 4. Medical Student at School of Medicine, Iran University of Medical Sciences, Tehran, Iran. Researcher at Student Research Committee, Faculty of Medicine, Iran University of Medical Sciences, Tehran, Iran. 5. Research Assistant Professor of Anatomical Sciences at Colorectal Research Center, Iran University of Medical Sciences, Tehran, Iran. Researcher at Nursing Care Research Center, Iran University of Medical Sciences, Tehran, Iran. 6. Medical Student at School of Medicine, Iran University of Medical Sciences, Tehran, Iran. Researcher at Physiology Research Centre, Iran University of Medical Sciences, Tehran, Iran. 7. Professor of Biostatistics, Department of Epidemiology and Biostatistics School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
FOVEATED RENDERING TECHNIQUE IN MODERN GRAPHICS by Xiaoxu Meng

Title of thesis: FOVEATED RENDERING TECHNIQUES IN MODERN COMPUTER GRAPHICS Xiaoxu Meng, Master of Science, 2018 Thesis directed by: Professor Joseph F. JaJa Electrical and Computer Engineering Thesis co-directed by: Professor Amitabh Varshney Computer Science Foveated rendering coupled with eye-tracking has the potential to dramatically accelerate interactive 3D graphics with minimal loss of perceptual detail. I have developed a new foveated rendering technique: Kernel Foveated Rendering (KFR), which parameterizes foveated rendering by embedding polynomial kernel functions in log-polar mapping. This GPU-driven technique uses parameterized foveation that mimics the distribution of photoreceptors in the human retina. I present a two-pass kernel foveated rendering pipeline that maps well onto modern GPUs. In the first pass, I compute the kernel log-polar transformation and render to a reduced-resolution buffer. In the second pass, I have carried out the inverse-log-polar transformation with anti-aliasing to map the reduced-resolution rendering to the full-resolution screen. I carry out user studies to empirically identify the KFR parameters and observe a 2.8X − 3.2X speedup in rendering on 4K UHD (2160p) displays. The eye-tracking-guided kernel foveated rendering can resolve the mutually conflicting goals of interactive rendering and perceptual realism. FOVEATED RENDERING TECHNIQUE IN MODERN GRAPHICS
BIG DATA AND PREDICTIVE ANALYTICS IN MANUFACTURING ENTERPRISES FOR ENHANCED DECISION MAKING

: In the fourth industrial revolution, smart manufacturing is reshaping the manufacturing in the industries. The use of IoT technologies facilitates machine-to-machine communication and the flow of information from source to system, which results in a vast amount of machine data getting generated. Using this data enterprises can gain actionable insights & make better data-driven decisions. Big Data Analytics is one of the emerging & advanced analytic industry4.0 technologies, which help to process large & diverse data sets. Today’s competitive environment forces enterprises to process this high-speed data & drive new opportunities. This paper starts with the definition and characteristics of big data, its sources, and its format. Further, it gives a brief explanation of the Hadoop ecosystem which helps us to understand a suite of services available on the cloud to solve big data problems. Big data technologies provide an opportunity to deploy predictive maintenance. Hence, in this paper, we have leveraged big data analytics for performing predictive analytics using a public dataset. Graphs have been plotted for various variables which help to bring visibility in the frequency of failure in advance & hence save the unplanned downtime. These graphs help to understand the range of any variable from which their machine component fails. In the end, an overview of the benefits of big data analytics in manufacturing has also been presented.
Perhaps Psychology’s Replication Crisis is a Theoretical Crisis that is Only Masquerading as a Statistical One

The “replication crisis” may well be the single most important challenge facing empirical psychological research today. It appears that highly trained scientists, often without understanding the potentially dire long-term implications, have been mishandling standard statistical procedures in the service of attaining statistical “significance. ” Exacerbating the problem, most academic journals do not publish research that has not produced a “significant” result. This toxic combination has resulted in journals apparently publishing many Type I errors and declining to publish many true failures to reject H0. In response, there has been an urgent call from some psychologists that studies be registered in advance so that their rationales, hypotheses, variables, sample sizes, and statistical analyses are recorded in advance, leaving less room for post hoc manipulation. In this chapter, I argue that this “open science” approach, though laudable, will prove insufficient because the null hypothesis significance test (NHST) is a poor criterion for scientific truth, even when it is handled correctly. The root of the problem is that, whatever statistical problems psychology may have, the discipline never developed the theoretical maturity required. For decades it has been satisfied testing weak theories that predict, at best, only the direction of the effect, rather than the size of effect. Indeed, uncritical acceptance of NHST by the discipline may have served to stunt psychology’s theoretical growth by giving researchers a way of building a successful career without having to develop models that make precise predictions. Improving our statistical “hygiene” would be a good thing, to be sure, but it is unlikely to resolve psychology’s growing credibility problem until our theoretical practices mature considerably.
MOLECULAR DISTINGUISHING OF STAPHYLOCOCCUS SPECIES BASED ON 16S RRNA, ANDCOA GENE

Staphylococcus species is important pathogenic bacteria responsible for a range of diseases in humans.Hospital-acquired infections and antibiotic-resistant strains attributed to this bacteria have become endemic in hospitals in many countries and are associated with serious public health issues. The new strains cause severe community-acquiredinfectioninimmuno-compromised patients, as well as in healthy people. 83 urine samples were collected from patients suffering from urinary tract infection in Hilla city, samples were cultured inplated ofnutrient agar,manitol salt agar and blood agar,then phenotypic characteristics of Staphylococcus was described after gram staining, incubation at 37 C Biochemical investigations were done according to Macfaddin,2000. DNA of Staphylococcus isolates was extracted and purified using Extraction and purification Kit from Geneaid company,the results of present study the percentage of S-saprophyticu was (8.4%) while percentage of S-aureus was(19.27). For 16 s-rRNA Primer genotypingThe results revealed that the presence a single band (1500 bp). Coa Gene, showed that the amplifying of the nitrogen base sequence showed that all isolates were made of120 base pair size product. Conclusion:using 16 sRNA primers for molecular assays is a good techniqueto detect pathogenic bacteria, including select agents
A Neural Approach for Load Torque Identification in Electrical Machinery

: - The induction motors are largely used in several industry sectors. The dimensioning of an induction motor has still been inaccurate because in most of the cases the load behavior in its shaft is completely unknown. The proposal of this paper is to use artificial neural networks as tool for dimensioning of induction motors rather than conventional methods, which use classical identification techniques and mechanical load modeling. Simulation results are also presented to validate the proposed approach.
An Empirical Analysis on Why People Run Away from In-Feed Ads under Manufacturing Industry Innovation

Thanks to the innovation in manufacturing industry,the rapid development ofdigital technologies such as mobile Internet and social media enables the appearance of up-to-date intelligent marketing methods. As one of them, In-Feed advertising becoming a fashionable approach due to its precise positioning and growing market share, problems such as loss of privacy and lack of trust arise which promote the avoidance behavior of consumer. Based on these facts, our study designs three experiments to explore the mechanism between the In-Feed advertising and the avoidance behavior of consumer. Furthermore, in order to give instructions on enhancing the communication effect of In-Feed ads, we tries to clarify the boundary conditions of consumers processing ads information. Experiment 1 examines the moderating effects of ad reviews between perceived relevance, threat appraisal and coping appraisal; Experiments 2 and 3 respectively examine the moderating effects of the connection strength and interaction frequency between consumers and ad-likers between the above variables. These three experimental results provide theoretical support and practical guidance for intelligent digital marketing and advertising.
Analysing Mental Health Evaluation Techniques from a Statistical Perspective

Mental wellbeing is an epitome of success for human beings. Recently, a lot of mental diseases that include anxiety, suicidal tendency, depression, bipolar disorder, obsessive compulsive disorder (OCD), etc. have deep-seated among all age groups. This is due to the fact that the social complexity has increased at work, in relationships, at home and in majorly all aspects of human life. Early detection of these problems can help in treating and if possible, eradicating this illness from a person’s life. Researchers have proposed different techniques that can be put into action to effectively identify these diseases. These techniques utilize different activities of normal day-to-day human behaviour, like speech analysis, social media behavioural patterns, visual-activity pattern analysis, etc. Due to a large variety of available techniques, researchers find it difficult to standardize techniques for detection and evaluation of such diseases. Thus, in this paper we have performed an in-depth study about the state-of-the art methods to identify mental health issues. Readers will be able to get a bird’s eye view of these methods and will be able to identify the best practices involved in mental health analysis. Moreover, this survey also recommends some possible improvements in the existing methods, in order to further improve the system’s performance.
An Underwater Behavior Recognition System for Marine Life

Bycatch, unintended capture of marine species, is a prominent issue that affects sea animals like sea turtles and damages the habitat. Several cyber physical systems have been implemented to reduce bycatch, with varied success rates. Smart Nets is an object recognition system that detects sea turtles and uses LED illumination levels as stimuli to warn turtles and big sea animals of potential danger. We aim to build on Smart Nets by developing a behavior recognition system that identifies sea turtle response behavior to stimuli, specifically Uturns and reversals. This would enable automated optimized stimuli to achieve effective results in reducing the bycatch
Cycloastragenol and Astragaloside IV activate telomerase and protect nucleus pulposus cells against high glucose-induced senescence and apoptosis

In diabetes-induced intervertebral disc degeneration (Db-IVDD), senescence and apoptosis of nucleus pulposus cells (NPCs) are major contributing factors. Telomere attrition and telomerase downregulation are some of the main reasons for senescence and eventual apoptosis. The derivatives of the Chinese herb Astragalus membranaceus, Cycloastragenol (CAG) and Astragaloside IV (AG-IV), are reportedly effective telomerase activators against telomere shortening; however, their effect in Db-IVDD have not been explored. The present study simultaneously investigated the regulation of these derivatives on senescence, apoptosis, telomeres and telomerase a model of high-glucose (HG)-induced stress using rat primary NPCs. The NPCs were stimulated with HG (50 mM) to evoke HG-induced stress, and the effects of CAG and AG-IV were observed on: i) The expression level of senescence marker p16; ii) β-Gal staining; iii) the expression levels of apoptosis markers cleaved-caspase 3 (c-C3), BAX and Bcl-2; iv) telomerase activation with telomerase reverse transcriptase (TERT) mRNA and protein expression, while telomere length was measured with reverse transcription-quantitative PCR. Cell proliferation was determined using the Cell Counting Kit-8 assay. Results demonstrated an upregulation in the expression levels of p16, c-C3 and BAX, and increased β-Gal staining; while the expression level of Bcl-2 was downregulated in a concentration-dependent manner. Pre-treatment of the NPCs with CAG and AG-IV downregulated the protein expression levels of p16, c-C3 and BAX, and decreased the percentage of β-Gal and FITC staining; while upregulating the Bcl-2 expression. These effects protected the cells from HG stress-induced senescence and apoptosis. HG also downregulated the expression profile of TERT and shortened the telomere length in a glucose concentration-dependent manner. While pretreatment with CAG and AG-IV upregulated TERT expression and ameliorated the telomere attrition. CAG and AG-IV also increased cell proliferation and improved cell morphology in HG conditions. Overall, these findings indicated that CAG and AG-IV suppressed HG stress-induced senescence and apoptosis, in addition to enhancing telomerase activation and lengthening of the Telomere. Therefore, CAG and AG-IV prolonged the replicative capability and longevity of the NPCs and they have the potential to be therapeutic agents in Db-IVDD.
A spatial model of forest and land fire vulnerability level in the Dairi District, North Sumatra, Indonesia

Abstract. Thoha AS, Triani H. 2021. A spatial model of forest and land fire vulnerability level in the Dairi District, North Sumatra, Indonesia. Biodiversitas 22: 3319-3326. Fires often occur every dry season and have a significant impact on ecosystems and human activities. One of the important roles in reducing the risk of forest and land fires is the availability of updated vulnerability level maps in all vulnerable areas. The objective of this study was to determine the relationship between the driving factors for forest and land fires and hotspots and to obtain a spatial model of the distribution of vulnerability to forest and land fires in the Dairi District of North Sumatra Province. This study uses a composite mapping analysis method to obtain a spatial model and the distribution of vulnerable areas to forest and land fires. Six variables in the form of maps were used in building the model, including land cover, population density, distance from the road, distance from the river, and distance from the settlement. This study showed that the most important variable for vulnerability level model of forest and land fires was the distance from the settlement. This study also found that open land, the farthest distance to the road, the farthest distance to the river, the farthest distance to the settlement, and the densest population were the driving factors for increased fire activity. The spatial model of the vulnerability level to forest and land fires in Dairi District was Y = 0.022X1 + 0.214X2 + 0.113X3 + 0.482X4 + 0.169X5. Land cover having high-very high vulnerability level belonged to open land dominated by grass. The largest areas with a high-very high forest fire vulnerability level in Dairi District were spread over Tanah Pinem Sub-district.
LooPy: interactive program synthesis with control structures

One vision for program synthesis, and specifically for programming by example (PBE), is an interactive programmer's assistant, integrated into the development environment. To make program synthesis practical for interactive use, prior work on Small-Step Live PBE has proposed to limit the scope of synthesis to small code snippets, and enable the users to provide local specifications for those snippets. This paradigm, however, does not work well in the presence of loops. We present LooPy, a synthesizer integrated into a live programming environment, which extends Small-Step Live PBE to work inside loops and scales it up to synthesize larger code snippets, while remaining fast enough for interactive use. To allow users to effectively provide examples at various loop iterations, even when the loop body is incomplete, LooPy makes use of live execution, a technique that leverages the programmer as an oracle to step over incomplete parts of the loop. To enable synthesis of loop bodies at interactive speeds, LooPy introduces Intermediate State Graph, a new data structure, which compactly represents a large space of code snippets composed of multiple assignment statements and conditionals. We evaluate LooPy empirically using benchmarks from competitive programming and previous synthesizers, and show that it can solve a wide variety of synthesis tasks at interactive speeds. We also perform a small qualitative user study which shows that LooPy's block-level specifications are easy for programmers to provide.
Planned Behavior and Intention to Study Abroad Concerning Family Rally Relationship: Case in Vietnam

Vietnamese students studying abroad in recent years have received worldwide attention. This study explores the relationship between planned behaviour, i.e., attitude towards behaviour, subjective norm and perceived behavioural control and coherence towards intention to enrich the literature – Vietnamese students studying abroad at university. In addition, the study also explores the factors related to family cohesion to the intention of Vietnamese students to study abroad. This study was conducted through a cross-sectional survey using a targeted sampling technique (n=350). The SEM model was used to test the hypotheses. The SEM model proved that: The decisive factor affects the intention to study abroad with the coefficient of Regression Weight = 0.222. The subjective norm factor (norm) affects the intent variable with the coefficient of Regression Weight = 0.138; The behavioural intention control factor affects the intent factor with Regression Weights = 0.276; The factor through family cohesion (cohesion) affects the intent factor with Regression Weights = 0.159. The conclusions of this study provide valuable data for study abroad managers, universities, study abroad consulting service providers, and families who want to invest in their children to study abroad.