Miracle-dz/UniFAR-FTUs huggingface dataset:
Simulation-based Prediction of Structural Design Failure in Fishing Deck Machinery a Hydraulic Type with Finite Element Method

The fishing deck machinery is the tools used to collect fish in fishing activities. Fishing deck machinery is intended to improve the effectiveness of fishing operations. The mission of the Ministry of Marine Affairs and Fishery Year 2015-2019 in the Regulation of the Minister of Marine and Fisheries No. 45/PERMEN-KP/2015 which is a priority is to provide assistance for fishing facilities for fishermen; it is necessary to develop and optimize fishing deck machinery. To assure the safety and dependability of these fishing deck machinery, calculations, simulation and functional tests are needed. This paper discusses the prediction of structural failure in the design of fishing deck machinery a hydraulic type with finite element method simulation approach. The results of the FEM simulation analysis are (i) the maximum value of von-Mises stress is greater than the ultimate tensile strength of the material; (ii) 1st principal stress value minimum is smaller than the ultimate tensile strength of material; (iii). the Poisson ratio value higher than the Poisson ratio value of the material. Base on the simulation result, the structural design of fishing deck machinery is safety.
Surgical Management Analysis of Emergency Colorectal Cancer Surgical Cases - a Retrospective Observational Study

We present an observational retrospective study which takes into consideration and analyzes the surgical management of emergency colorectal cancer cases. This analysis would contribute to a better understanding and for identification of those characteristics that are essential for reducing emergency colorectal cancer incidence and for improving the management of such cases. A number of 159 patients that underwent surgery for obstructive, bleeding or perforated colorectal cancer between January 2015 and September 2017 were selected to be included in the study. The results obtained by analyzing available data collected from the observation charts, surgery protocols, histopathological protocols were then compared with those in the literature.
Exploring Teaching and Course Assistants' Interventions with Groups During Collaborative Problem-Solving

This study explores the task-related interventions of a teaching assistant and three course assistants with six small groups during collaborative problem-solving activities in undergraduate engineering classrooms. Results indicated that the majority of the interventions were not preceded by monitoring the group activity and were initiated by asking the groups a general question about their activity or progress on the task. The majority of the interventions were dominated by feedback moves which suggests that the teachers were using direct instruction strategies more than dialogic strategies when intervening with the groups’ work.
Statistical Analysis and Extraction of Quantitative Data from Elliptical-Signal-Model bSSFP MRI

Statistical Analysis and Extraction of Quantitative Data from Elliptical-Signal-Model bSSFP MRI Meredith Ireene Dupaix Taylor Department of Electrical and Computer Engineering, BYU Doctor of Philosophy Osteoarthritis is the most common type of arthritis, and is characterized by the loss of articular cartilage in a joint. This eventually leads to painful bone on bone interactions. Since the loss of cartilage is permanent, the main treatment for this disease is pain management until a full joint replacement is needed. To test new potential treatments a consistent way to measure cartilage thickness is needed. The current standard used in the knee to represent cartilage uses joint space width measured from x-rays. This measurement is highly variable, and does not directly show cartilage. Unlike x-rays, magnetic resonance imaging (MRI) can provide direct visualization of soft tissues in the body, like cartilage. One specific MRI method called balanced steady-state free precession (bSSFP) provides useful contrast between cartilage and its surrounding tissues. This allows easy delineation of the cartilage for volume and thickness measurements. Using bSSFP has unique challenges, but can provide quantitative MR tissue parameter information in addition to volume and thickness measurements. Although bSSFP provides useful contrast, it is highly sensitive to variations in the main magnetic field. This results in dark bands of signal null across an image referred to as banding artifacts. There are a few new methods for mitigating this artifact. An analysis of banding artifact reduction methods is presented in this dissertation. The new methods are shown to be better than traditional methods at reducing banding artifact. However, they do not provide as of high signal to noise ratio as traditional methods in most cases. This analysis is helpful in obtaining artifact free images for volume and thickness measurements. Image distortion can be created when there is a magnetic susceptibility mismatch between bordering substances being imaged, like in the sinuses where air and body tissues meet. A map of the main magnetic field variation can be used to fix this distortion in post processing. A novel method for obtaining a map of the main magnetic field variation is developed using bSSFP in this dissertation. In cases where bSSFP contrast is desirable this map can be obtained with no additional scan time. A new way to sift out MR tissue parameters: T2, T1, and M0 is presented in this dissertation using bSSFP. This method obtains biomarkers that can potentially show the presence of Osteoarthritis before cartilage degeneration begins at the same time as anatomical images. Adjunct scans do not need to be run to get these extra parameters saving scan time. Unlike many adjunct scans, it is also resolution matched to the anatomical images.
A Deep Learning-Based Screening Method for Improving the Quality and Reliability of Integrated Passive Devices

Integrated passive devices (IPDs) have been widely used in advanced packaging of semiconductor chips, to improve their power integrity and impedance matching. There is a growing demand in guaranteeing signal and power integrity for the chips used in safety-critical products, such as those used in automotive, aviation, industrial, and defense systems, where IPDs help improve quality and reliability of the chips. Therefore, IPD testing and screening itself is essential. Note that the cost of replacing failed IPDs is much higher than the cost of manufacturing them, so screening bad IPDs before mounting is also crucial. In this work, we propose a machine learning (ML) based screening methodology to identifying the IPDs that have potential reliability issues. Based on the parametric data of 360,000 IPDs collected from the wafer probing test, the proposed Semiconductor Quality Net (SQnet) is trained to predict the IPDs which have low breakdown voltage, i.e., low reliability. Keeping the overkill rate below 10%, our method can screen out 6 to 15X more bad dies than the existing industrial methods, i.e., DPAT and GDBC.
Incidence of Late‐Diagnosed Hip Dysplasia After Different Screening Methods in Newborns

We compared the effectiveness of different approaches to hip screening in newborns. Three groups were studied: children born in our hospital from 1980 through 1985 who were clinically examined by pediatricians (group A), children born in 1986 and 1987 who were examined by ultrasound (US) in addition to clinical examination (group B), and children born in three district hospitals where neonatal clinical screening had not been performed by pediatricians (group C). There were significant differences between the three groups in the incidence of late-diagnosed hip dysplasia (HD): 2.6 in 1,000, 0.7 in 1,000, and 5.3 in 1,000, respectively. We conclude that US screening of hips in newborns was more effective than clinical screening alone and that clinical screening by pediatricians was superior to screening by doctors with less experience in examining children. US appears to have the potential to eradicate late-diagnosed dislocations, but milder degrees of HD still occur. With regard to the US method, measurement of femoral head coverage appears to be more adequate than subjective evaluation only.
Design of Active Vehicle Pitch and Roll Motions as Feedback for the Driver During Automated Driving

Feedback on state transitions and intentions of the automation system are crucial for the driver during partially automated driving to obtain or rather increase the driver’s system awareness. In this thesis, the design of the active pitch and roll motions as well as their suitability as feedback in different motorway scenarios were evaluated in four driving studies. The results show that active rotational motions can complement visual feedback, resulting in multi-modal feedback.
Sparse Coding in Authorship Attribution for Polish Tweets

The study explores application of a simple Convolutional Neural Network for the problem of authorship attribution of tweets written in Polish. In our solution we use two-step compression of tweets using Byte Pair Encoding algorithm and vectorisation as an input to the distributional model generated for the large corpus of Polish tweets by word2vec algorithm. Our method achieves results comparable to the state-of-the-art approaches for the similar task on English tweets and expresses a very good performance in the classification of Polish tweets. We tested the proposed method in relation to the number of authors and tweets per author. We also juxtaposed results for authors with different topic backgrounds against each other.
Simplification of Multi-Criteria Decision-Making Using Inter-Criteria Analysis and Belief Functions

In this paper we propose a new Belief Function-based Inter-Criteria Analysis (BF-ICrA) for the assessment of the degree of redundancy of criteria involved in a multicriteria decision making (MCDM) problem. This BF-ICrA method allows to simplify the original MCDM problem by withdrawing all redundant criteria and thus diminish the complexity of MCDM problem. This is of prime importance for solving large MCDM problems whose solution requires the fusion of many belief functions. We provide simple examples to show how this new BF-ICrA works.
ACQUIRED AND GENETICALLY PREDISPOSED HEART DISEASE IN CHILDREN

Types of acquired heart disease among children include: cardiomyopathy, endocarditis, myocarditis, pericarditis, Kawasaki disease, arrhythmia, hypertension, rheumatic fever, and obesity. These are diseases that occur after birth, as opposed to congenital heart disease, which is present at birth. Acquired heart disease in children constitutes diagnoses which do not occur in adults (eg. Kawasaki disease), or which may be similar to conditions present in adolescents and adult patients (eg. dilated cardiomyopathy). Most frequently diagnosed are rheumatic fever and Kawasaki disease. Children who are diagnosed and treated for congenital heart disease are at higher risk of developing endocarditis and cardiomyopathy. Although heart disease in childhood has a complex aetiology – and is often caused by unknown factors – a wide palette of diagnostic tests is required to determine its cause, including: genetic, echocardiography, X-ray, MRI, CT, heart catheterisation, endomyocardial biopsy, and nuclear studies. Pathological process directly affects cardiac structures, causing myocardial dysfunction. If myocardinal function is suspected, it is necessary to take a detailed history of the disease, along with a physical examination, and apply a diagnostic and therapeutic algorithm in order to stabilise a haemodynamically compromised patient with acquired heart disease.
On-device Efficient Acoustic Modeling with Simple Gated Convolutional Networks

Automatic speech recognition (ASR) is widely adopted for smartphones and many embedded devices in recent years, and neural network based algorithms show the best performance for ASR. While most of ASR systems are based on server-based processing, there is an increasing demand for on-device speech recognition because of privacy concern and low latency processing. Reducing the power consumption is especially important for on-device speech recognition to lengthen the battery life. Among several neural network models, recurrent neural network (RNN) based algorithms are mostly used for speech recognition, and long short-term memory(LSTM) RNN is most popular because of its superior performance over the other ones. However, executing LSTM RNN demands many DRAM accesses because the cache size of embedded devices is usually much smaller than the parameter size of RNN. Multi-time step parallelization technique computes multiple output samples at a time by fetching one set of parameters, and thus it can reduce the number of DRAM accesses in proportional to the number of time steps computed at a time. However, LSTM RNN does not permit the multi-time step parallelization because of complex feedback structure of the model. This thesis presents neural network models that support efficient on-device speech recognition. First, a few models that permit multi-time step parallel processing are evaluated. The models evaluated include Gated ConvNet, Diagonal LSTM, and QRNN (quasi RNN). Since the performance of these models are not as good as the LSTM, one-dimensional depthwise convolution is added to improve the performance. The one-dimensional convolution helps finding the temporal patterns of speech signal. Second, Simple Gated Convolution Network (Simple Gated ConvNet) is proposed for improved performance when the parameter count is very small. The Simple Gated ConvNet employs the simplest form of Gated ConvNet. Instead it relies on one-dimensional
Pharmacological Characteristics of Lacobacillus gasseri OLL2809 and its Application to the Endometriosis Therapy

Many probiotics have been developed for the treatment of diseases. However, there is no probiotics for the treatment of endometriosis. In the pathology of endometriosis involvement of impaired peritoneal immunosurveillance systems has been well established, suggesting the impaired activity of natural killer (NK) cell in the peritoneal cavity. Therefore, Lacobacillus gasseri OLL2809 (L. gasseri OLL2809) was selected from human as a probiotic Lactobacillus to exhibit a strong stimulatory activity for IL-12 (p70) production followed by the activation of NK cell. In a mice endometriosis model, OLL2809 significantly inhibited the growth of the endometrial tissue. In a rat implanted endometriosis model, the healing of the endometriosis was significantly enhanced by the treatment with L. gasseri OLL2809. In clinical study enrolled by patients with endometriosis, L. gasseri OLL2809 showed significant efficacy on the pain intensity at the menstrual period and significant improvement on the verbal rating scale of dysmenorrhea, although serum level of CA-125 was not affected by the ingestion of L. gasseri OLL2809. No clinical adverse effect was observed by the treatment with L. gasseri OLL2809. These findings show that OLL2809 is useful and safety probiotic to improve quality of life (QOL) in the endometrial patients.
A CASE-CONTROL STUDY OF HODGKIN'S DISEASE IN BRAZIL I. EPIDEMIOLOGIC ASPECTS

: A number of epidemiologic variables were investigated in a case-control interview study, conducted in Sao Paulo, Brazil, of 70 Hodgkin's disease (HD) patients, 70 tumor control subjects matched for age and sex, and 128 siblings of the patients. The major epidemiologic findings were a high percentage of total cases among children, with a high sex ratio, a relative predominance of the mixed cellularity histologic subtype, and a bimodal age-specific incidence curve with the highest rates among young adults and the elderly. When the matched controls were used as the comparison group, high socioeconomic status (SES) was found to be associated with an increased risk for HD (p = 0.001). On the basis of the case-sibling comparison, an association between prior tonsillectomy and risk for HD was found (p = 0.04), and the relative risk for HD among tonsillectomized persons as compared to individuals who had not had the operation was 2.5. Other variables, including sibship size, birth order, marital status, occupational exposure, prior use of amphetamines or diphenylhydantoin, intensity of exposure to children and history of viral illnesses were not found to be determinants of risk for HD in this study.
Understanding of Life-Sustaining Treatment in Patients With Chronic Illness and Their Willingness to Complete Advance Directives

We investigated the level of understanding of life-sustaining treatment in Korean patients with chronic illness and examined the influence of understanding of life-sustaining treatment on their willingness to complete advance directives. Data were collected by a survey of 187 outpatients recruited by the proportionate quota sampling method at 2 general hospitals in South Korea, using structured questionnaires. Data were analyzed with hierarchical multivariate logistic regression analysis. According to the results, only 9% of the respondents have an adequate understanding of the percentage of survival to actual hospital discharge after in-hospital cardiopulmonary resuscitation, and 59% understood the possibility of permanent use of a mechanical ventilator when breathing is not restored. Although most respondents showed a preference for advance directives, only 57% wanted to complete advance directives. Understanding of life-sustaining treatment significantly increased the odds of being willing to complete advance directives, controlling for covariates. In conclusion, proactive publicity and proper education regarding life-sustaining treatments by health care providers are critical to encouraging completion of advance directives.
A Sharp Sufficient Condition for Sparsity Pattern Recovery

Sufficient number of linear and noisy measurements for exact and approximate sparsity pattern/support set recovery in the high dimensional setting is derived. Although this problem as been addressed in the recent literature, there is still considerable gaps between those results and the exact limits of the perfect support set recovery. To reduce this gap, in this paper, the sufficient condition is enhanced. A specific form of a Joint Typicality decoder is used for the support recovery task. Two performance metrics are considered for the recovery validation; one, which considers exact support recovery, and the other which seeks partial support recovery. First, an upper bound is obtained on the error probability of the sparsity pattern recovery. Next, using the mentioned upper bound, sufficient number of measurements for reliable support recovery is derived. It is shown that the sufficient condition for reliable support recovery depends on three key parameters of the problem; the noise variance, the minimum nonzero entry of the unknown sparse vector and the sparsity level. Simulations are performed for different sparsity rate, different noise variances, and different distortion levels. The results show that for all the mentioned cases the proposed methodology increases convergence rate of upper bound of the error probability of support recovery significantly which leads to a lower error probability bound compared with previously proposed bounds.
Who May Have Durable Benefit from Robotic Gait Training? A 2-Year Follow-Up Randomized Controlled Trial in Patients with Subacute Stroke

Design Es handelt sich um eine einfach verblindete, randomisierte und kontrollierte Studie (RCT). Zu den Studienteilnehmern gehörten nicht gehfähige Patienten im Mittel in den ersten 20 Tagen nach Schlaganfall. Strata LowMotricity (LM)mit <29 Punkten auf dem Motricity Index (0–100) entspricht Patienten mit lediglich maximal palpabler Muskelkontraktion des betroffenen Beins. Strata High Motricity (LM) mit > =29 Punkten auf dem Motricity Index entspricht Patienten mit lediglich mindestens palpabler Muskelkontraktion des betroffenen Beins.
Describing Images by Semantic Modeling using Attributes and Tags

Unlike tags whose descriptive power scales linearly, attributes model an exponential number of semantic classes. We hypothesize that integrating human semantic parsing improves person-related visual attribute prediction. In this regard, we propose Semantic Segmentation-based Gating (SSG) and Pooling (SSP). In SSG, we create multiple copies where each preserves the activations within a single semantic region and suppresses otherwise. This mechanism prevents semantically inconsistent regions to be mixed. SSP behaves similarly but at the classifier layer. To tackle the heavy memory utilization of SSP and SSG, Symbiotic Augmentation is proposed, where we learn to generate only one mask per activation channel.
On the use of participatory methodologies for video game research: Exploring disaster risk reduction in video games

Video game scholars examining the shortcomings of previous video game research reference the need for new and innovative methodologies. Existing video game research seemingly inhibits organic learning experiences by setting specific research targets or providing players with gameplay instructions, hence utilising methodological approaches that study the learning process from the outside. With the increasing popularity of both serious and mainstream disaster video games, a necessity exists for innovative research to explore how video games can be used as learning tools. Based upon the researchers’ own enquiry, this article demonstrates the potential use, benefits and challenges of participatory methodologies for the conduct of video game research. This article pushes back upon traditional video game research methods, reviewing the methodological approaches of existing video game literature and demonstrates how participatory methodologies are currently being used for disaster video game research. An examination of participatory methodologies, being used in disaster video game research, reviews the strengths and challenges of each research approach. Rationalising the potential of participatory methodologies, in the context of constructivist learning theory and active participation, to foster the learning process and explore learning from the inside. As such this article provides an innovative methodological framework, which can be used as a template when considering future video game research.
The investigation on the effect of rice bran addition on the rheological and sensory properties of muffin cake

Rice bran, one of the main by-products of rice milling industry, has been recognized as an excellent source of protein, dietary fiber and allied micronutrients. Current research was conducted to utilize indigenous rice bran (RB) as well as preparation of value-added products. Rice bran sample, stabilized by oven. In order to determine the effect of full fat stabilized rice bran (in four levels 0, 10, 20 and 30%) on dough rheological and muffin cake sensory properties of one type of wheat flour (with 75% extraction rate) a completely randomized experiment and 3 replications was conducted. The rheological behavior of dough containing rice bran was studied using farinograph. Water absorption, dough development time (30% rice bran) and valorimeteric value were increased and dough stability and dough softening were decreased by addition of rice bran in flour than the control. Sensory evaluation with Friedman test revealed that there is no significant difference between treatments at 5% level (expect the odor). Cakes containing 20% rice bran flour got the highest scores for sensory evaluation (except for the characteristic color). it is concluded that the quantity and quality rheological and sensory properties of paste and muffin cakes was improved with the addition of rice bran flour. Thus rice bran could be used for wheat substituting and a good functional ingredient for value adding of food products. Moreover the present study suggests that T3 (30% RB + 70% wheat flour) can produce superior quality cakes to prove effectiveness of RB as bakery powder. Key word: Rice bran, Rheological properties, sensory evalution, paste, muffin cake.