bluuebunny/crossref_metadata_2025_split huggingface dataset:
Quality of reporting of clinical trials in dogs and cats: An update
Authors: Sargeant Jan M. , Plishka Mikayla , Ruple Audrey , Selmic Laura E. , Totton Sarah C. , Vriezen Ellen R. 
Published: June 2021

AbstractBackgroundComprehensive reporting of clinical trials is essential to allow the trial reader to evaluate the methodological rigor of the trial and interpret the results. Since publication of the updated Consolidated Standards of Reporting Trials (CONSORT) guidelines for reporting of parallel clinical trials in humans, extensions for reporting of abstracts and crossover trials have been published.ObjectivesTo describe the types of trials using dogs and cats published from 2015 to 2020 and to evaluate the quality of reporting of a sample of recently published parallel and crossover trials.AnimalsNone.MethodsA comprehensive search was conducted to identify parallel or crossover design clinical trials using dogs and cats published from January 1, 2015 onwards. Quality of reporting was evaluated on a subset of trials published during 2019. The reporting of items recommended in the CONSORT reporting guidelines for abstracts, parallel trials, and crossover trials was evaluated independently by 2 reviewers using standardized forms created for this study. Disagreements among reviewers were resolved by consensus. Results were tabulated descriptively.ResultsThe frequency of reporting of trial features varied from low to high. There remain deficiencies in the quality of reporting of key methodological features and information needed to evaluate and interpret trial results.Conclusions and Clinical ImportanceThere is still a need for authors, peer‐reviewers, and editors to follow reporting guidelines such as CONSORT to maximize the value of clinical trials and to increase confidence in the validity of the trial results.
Paths to computational fluency for natural resource educators, researchers, and managers
Authors: Erickson Richard A. , Burnett Jessica L. , Wiltermuth Mark T. , Bulliner Edward A. , Hsu Leslie 
Published: June 2021

AbstractNatural resource management and supporting research teams need computational fluency in the data and model‐rich 21st century. Computational fluency describes the ability of practitioners and scientists to conduct research and represent natural systems within the computer's environment. Advancement in information synthesis for natural resource management requires more sophisticated computational approaches, as well as reproducible, reusable, extensible, and transferable methods. Despite this importance, many new and current natural resource practitioners lack computational fluency and no common set of recommended resources and practices exist for learning these skills. Broadly, attaining computational fluency entails moving beyond the simple use of computers to applying sound computational principles and methods and including computational experts (such as computer scientists) on research teams. Our path for computational fluency includes using open‐source tools when possible; reproducible data management, statistics, and modeling; understanding and applying the benefits of basic computer programming to carry out more complex procedures; tracking code with version control; working in controlled computer environments; and using advanced computing resources.Considerations for Resource Managers

Natural resource management increasingly uses computer‐generated results to inform and guide decision making. Open science requires that these data and software be reproducible. In turn, open science promotes computational fluency among natural resource managers, researchers, and educators.
Based upon our experiences and perspectives working to support natural resource professionals, many natural resource managers would like to attain computationally fluency, yet lack formal training.
We provide a path to computational fluency that emphasizes: using open‐source tools when possible; reproducible data management, statistics, and modeling; understanding and applying the basic computer programming philosophy to carry out both simple and more complex procedures; tracking code with version control; working in controlled computer environments; and using advanced computing resources.
Natural resource managers, educators, and researchers can use their computationally fluency skills to promote open science in their own work.
Influence of early biliary complications on survival rates after pediatric liver transplantation—A positive outlook
Authors: Berchtold Valeria , Messner Franka , Weissenbacher Annemarie , Oberhuber Rupert , Entenmann Andreas , Aldrian Denise , Vogel Georg , Margreiter Christian , Ulmer Hanno , Krapf Johanna , Cardini Benno , Schlitt Hans J. , Margreiter Raimund , Öfner Dietmar , Müller Thomas , Schneeberger Stefan 
Published: June 2021

AbstractBackgroundEarly biliary complications (EBC) constitute a burden after pediatric liver transplantation frequently requiring immediate therapy. We aimed to assess the impact of EBC on short‐ and long‐term patient and graft survival as well as post‐transplant morbidity.MethodsWe analyzed 121 pediatric liver transplantations performed between 1984 and 2019 at the Medical University of Innsbruck for the occurrence of early (<90 days) biliary complications and investigated the influence of EBC on patient and graft survival.ResultsEarly biliary complications occurred in 30 (24.8%) out of the 121 pediatric liver transplant recipients. Patient survival at 15 years (89.2% vs. 84.2%, p = .65) and all‐cause (82.5% vs. 74.0%) and death‐censored graft survival (82.5% vs. 75.1%, p = .71) at 10 years were similar between the EBC and the non‐EBC group. The EBC group had a significantly longer ICU (25 vs. 16 days, p < .001) and initial hospital stay (64 vs. 42 days, p = .002). Livers of patients with EBC were characterized by multiple bile ducts (33.3% vs. 13.2%, p = .027), and patients with EBC had a higher risk to develop late biliary complications (OR 2.821 [95% CI 1.049–7.587], p = .044) and bowel obstruction/perforation (OR 4.388 [95% CI 1.503–12.812], p = .007).ConclusionEarly biliary complications after pediatric liver transplantation is frequent. The occurrence of EBC significantly increased post‐transplant morbidity without affecting mortality. Multiple bile ducts were the only risk factor for the development of EBC in our cohort.
Kemahiran Bertutur Bahasa Melayu oleh Murid Cina: Teori Analisis Kontrastif
Authors: Rosli Nor Zulaiqha , Mohd Rusli Nur Farahkhanna , Abdul Jobar Norfaizah , Zakaria Norazimah 
Published: June 2021

The acquisition of Malay as a second language, either verbally or non verbally among the non-Malay students, is still in question. It is observed that the problems of pronunciation is still prevalent among the non-Malay students. Hence, the objectives of this study are twofold; (i) to identify the level of proficiency of the speaking skill of Malay language among Chinese students, and (ii) to analyse the errors made in the speaking skill of Malay language by Chinese students based on contrastive analysis theory. The respondents were 27 Chinese Form 1 students in SMK Ampang Pecah, Kuala Kubu Baharu, Selangor. The initial design of the study was spurred by library research and observation. The instruments used in data collection included notebooks, questionnaire, recorder and texts for speech test. The data were analyzed by contrastive analysis theory by Robert Lado (1957). The findings showed that there were four aspects of pronunciation errors related consonants produced by the respondents. They were (i) sound replacement, (ii) sound addition, (iii) sound abortion, (iv) and grammatical errors. This study also shows that the pronunciation errors were due to the influence of their native language, which is Mandarin language, and the interlingual factors of the respondents that have caused them to be weak in the mastery of Malay language. In terms of implication, this study provides some understanding on the importance of mastering oral speech in Malay language through appropriate grammatical usage and pronunciation , especially among the second language speakers of Malay.
Teaching Literature among Pre-Service Teachers: Challenges and Solutions
Authors: Harun Ain Suraya , Abdul Samat Norhanim 
Published: June 2021

Teacher trainees should be prepared to teach and exposed to the approaches, models, and techniques of literature teaching. Being ready can also boost their confidence to teach literature so that the lessons can be delivered smoothly. These teachers are so new to teaching that they might face difficulties when teaching English, specifically literature. There are teaching techniques, strategies and approaches that those pre-service teachers can investigate to understand better how to apply in their teaching. Additionally, their lack of exposure towards literature teaching can also affect the performance of teaching. This paper seeks to investigate pre-service teachers’ readiness to teach literature in schools and the challenges faced by them while teaching literature. It also attempts to offer suggestions to improve better literature teaching. This mixed method research study used questionnaires distributed to 22 TESL pre-service teachers from a public university in Johor. Additionally, semi-structured interviews were conducted with two lecturers who have vast experiences in supervising pre-service teachers at schools. Results show that a majority of fourth year TESL students are ready to teach literature with the training and courses provided by the university. Also, among the challenges that they faced are time management and their students’ feelings on the subject. This study hopes to provide insights to training teachers on literature teaching.
Forced Turbulent Convection Along Heated and Cooled Walls With Variable Fluid Properties
Authors: Sufrà Lorenzo , Steiner Helfried 
Published: June 2021

Abstract
The effect of temperature depending material properties on heat and momentum transfer along heated/cooled walls in turbulent pipe flow was investigated using direct numerical simulations (DNS). For the considered thermal wall conditions, always associated with a molecular Prandtl number well over unity Prw = 10, the significantly dampened/enhanced turbulent motion caused by the increase/decrease of the viscosity with distance to the heated/cooled wall turned out to clearly dominate over the opposite trend of the enthalpy fluctuations. The Nusselt number and, quantitatively less pronounced, the wall friction coefficient are accordingly decreased/increased for the heated/cooled case. A comparison against a well-established Nu-correlation unveils the limits of the generally applied approach, which is essentially based on uniform bulk flow conditions and subsequently modified accounting for material property variation, when applied to heated and cooled conditions. An enhanced disparity of the turbulent normal stresses is observed inside the inertial subrange for the heated case, indicating a stronger deviation from isotropic turbulence, which possibly challenges mostly isotropic standard turbulence models.
Experimental Investigation on Cavity Pressure Inside Sheet Cavitation
Authors: Kobayashi Keisuke , Katayama Yusuke , Watanabe Satoshi , Tsuda Shin-ichi 
Published: June 2021

Abstract
The gas content is one of the important factors in cavitation, which may increase the pressure inside the cavity through the diffusive mass transfer of the dissolved gas into the cavity. In the present study, we try to directly measure the cavity pressure inside the sheet cavity at the throat of a converging-diverging nozzle. Then the influences of the flow velocity and the gas content (amount of dissolved oxygen) on the gas partial pressure are investigated. It is found that, even in low gas content level, the cavity pressure is slightly but apparently higher than the saturated vapor pressure, indicating the presence of gas partial pressure. It is observed that the gas partial pressure in significantly developed cavitation is almost constant regardless of the flow velocity but slightly increases against the increase of the saturation level of dissolved gas. It is also found that the gas partial pressure inside cavity depends on the degree of cavitation development; the gas partial pressure decreases with the development of cavitation.
Model of Human Actions Recognition Based on 2D Kernel
Authors: Radulescu Bogdan Alexandru , Radulescu Victorita 
Published: June 2021

Abstract
Action Recognition is a domain that gains interest along with the development of specific motion capture equipment, hardware and power of processing. Its many applications in domains such as national security and behavior analysis make it even more popular among the scientific community, especially considering the ascending trend of machine learning methods. Nowadays approaches necessary to solve real life problems through human actions recognition became more interesting. To solve this problem are mainly two approaches when attempting to build a classifier, either using RGB images or sensor data, or where possible a combination of these two. Both methods have advantages and disadvantages and domains of utilization in real life problems, solvable through actions recognition. Using RGB input makes it possible to adopt a classifier on almost any infrastructure without specialized equipment, whereas combining video with sensor data provides a higher accuracy, albeit at a higher cost. Neural networks and especially convolutional neural networks are the starting point for human action recognition. By their nature, they can recognize very well spatial and temporal features, making them ideal for RGB images or sequences of RGB images. In the present paper is proposed the convolutional neural network architecture based on 2D kernels. Its structure, along with metrics measuring the performance, advantages and disadvantages are here illustrated. This solution based on 2D convolutions is fast, but has lower performance compared to other known solutions. The main problem when dealing with videos is the context extraction from a sequence of frames. Video classification using 2D Convolutional Layers is realized either by the most significant frame or by frame to frame, applying a probability distribution over the partial classes to obtain the final prediction. To classify actions, especially when differences between them are subtle, and consists of only a small part of the overall image is difficult. When classifying via the key frames, the total accuracy obtained is around 10%. The other approach, classifying each frame individually, proved to be too computationally expensive with negligible gains.
Vision-Based Motion Planning of Cable-Driven Parallel Robots in the Presence of Moving Obstacle Using Sampling-Based Method
Authors: Xu Jiajun , Park Kyoung-Su 
Published: June 2021

Abstract
In the past decades, cable-driven parallel robots (CDPRs) have been proven the extraordinary performance for various applications. However, the multiple cables lead the robot easy to interfere with environments. Especially the large workspace of CDPR may introduce unknown moving obstacles. In this study, a sampling-based path planning method is presented for a CDPR to find the collision-free path in the presence of the moving obstacle. The suggested method is based on rapidly exploring random tree (RRT) algorithm which gives CDPRs advantages to handle complex constraints such as cable collision and dynamic feasible workspace (DFW). Moreover, we conduct the forward simulation to check the feasibility in a closed-loop system. The moving parts of both CDPRs and the moving obstacle are assumed as convex bodies, so that Gilbert-Johnson-Keerthi (GJK) algorithm is adopted to detect collision in real-time. Finally, the related simulation is carried out to illustrate the algorithm. The experiment is also presented using the drone as a moving obstacle and YOLO vision algorithm to detect the drone. The experiment results demonstrate the reliability of the suggested method.
A New Approach to Enhance Artificial Intelligence for Robot Picking System Using Auto Picking Point Annotation
Authors: Tsai Cheng-Han (Lance) , Chang Jen-Yuan (James) 
Published: June 2021

Abstract
Artificial Intelligence (AI) has been widely used in different domains such as self-driving, automated optical inspection, and detection of object locations for the robotic pick and place operations. Although the current results of using AI in the mentioned fields are good, the biggest bottleneck for AI is the need for a vast amount of data and labeling of the corresponding answers for a sufficient training. Evidentially, these efforts still require significant manpower. If the quality of the labelling is unstable, the trained AI model becomes unstable and as consequence, so do the results. To resolve this issue, the auto annotation system is proposed in this paper with methods including (1) highly realistic model generation with real texture, (2) domain randomization algorithm in the simulator to automatically generate abundant and diverse images, and (3) visibility tracking algorithm to calculate the occlusion effect objects cause on each other for different picking strategy labels. From our experiments, we will show 10,000 images can be generated per hour, each having multiple objects and each object being labelled in different classes based on their visibility. Instance segmentation AI models can also be trained with these methods to verify the gaps between performance synthetic data for training and real data for testing, indicating that even at mAP 70 the mean average precision can reach 70%!
Convolutional Neural Network Design for Improvement of Machining Quality Monitoring
Authors: Chang Ting-Yu , Chang Jen-Yuan (James) 
Published: June 2021

Abstract
Automatically monitoring finishing quality by computers can achieve efficient product quality management and can improve overall production efficiency. To be able to offer quantitative measures and to achieve this goal, this paper discusses and suggests the utilization of artificial intelligence (AI) technology to predict product finishing quality by use of signals such as vibrations captured by accelerometers generated during manufacturing. To reduce the cost of inspecting products one by one, a deep one-dimensional convolutional neural network (CNN) is proposed to predict machined surface quality. In this method, dense residual skip-connections are used to improve the complexity of the model to improve the accuracy of predicted values. With the adaptation of the pooling layer in the proposed model, it is observed that the number of parameters used in the model is greatly reduced. Not only the predicted accuracy is optimized with the proposed model, the parameters that need to be stored and the computation resource that is consumed in the inference stage are significantly reduced as well. Compared with methods reported in the literature, through calibrated experimental verifications, the proposed model used in this work can improve the prediction accuracy by 10 percent, without any additional signal preprocessing efforts. The work presented in this paper is thought to have engineering implications in quantifying machining quality in the machine tools industry.
Calculation of Electromagnetic Forces for EDS Type Maglev Using 3-D Finite Element Model
Authors: Lim Seonbin , Yoo Junsun , Park No-Cheol 
Published: June 2021

Abstract
Electrodynamic (EDS) type suspension is appropriate for a high-speed maglev train as control is not essential to levitate a train. However, the trains might oscillate large in the vertical direction since there is no control for the EDS type maglev train. Linear Synchronous Motor (LSM) is used to propel trains as it can generate large propulsion force with high efficiency. LSM generate periodic, not uniform, Electromagnetic (EM) force and it cause vertical and lateral vibration. Therefore, EM forces acting on a maglev vehicle should be investigated to determine the vibration characteristics of a high-speed maglev train.
An excitation force on bogies is nonlinearly changed by air gap between ground coils and magnets on bogies. Therefore, relative position of guide and a bogie could be defined to contracting time variant excitation force. The study focuses on verifying vertical and lateral vibration due to LSM that makes repeating propulsion force to a maglev vehicle. We made a transient 3-D EM analysis model that could simulate levitation and guidance force of a maglev vehicle. The model was constructed based on straight rail of the conventional maglev system, L0 series in Japan. We compared the simulation results, levitation, and guidance forces on single ground coil, with another study results. Also, total EM forces on a bogie in simulation results was compared with real track experimental results of another study. We could evaluate ride comport and running stability using our transient EM force by coupling with rigid body simulation.
FPGA-Based Implementation of Torque Controller for 6-DOF Articulated Robots
Authors: Tan-Phat Phan , Chao Paul C.-P. , Zih-Wei Huang 
Published: June 2021

Abstract
This paper aims to design an impedance position-based proportional-integral-derivative (PID) controller based on forward and inverse kinematics of HIWIN RA605 articulated robot, and Field-Programmable Gate Array (FPGA) implementation for a PID torque controller. In order to control the robot, the FPGA needs to output commands to communicate with the AC servo motor drivers. An FPGA-based controller for a 6-degree-of-freedom (DOF) articulated robot that implements several tasks such as hardware implementation, encoder counters, noise cancellation algorithm, analog generators, PID controller, and communication are included, the processing time of the FPGA is 16 μs. Meanwhile, the whole process of the system only takes 82 μs to complete.
Design of a 2 DOF Shape Memory Alloy Actuator Using SMA Springs
Authors: Ali Hussein F. M. , Kim Youngshik 
Published: June 2021

Abstract
In this paper, we developed two degree of freedom shape memory alloy (SMA) actuator using SMA springs. This module can be applied easily to various applications: device holder, artificial finger, grippes, fish robot, and many other biologically inspired applications, where small size and small wight of the actuator are very critical. This actuator is composed of two sets of SMA springs: one set is for the rotation around the X axis (roll angle) and the other set is for the rotation around the Y axis (pitch angle). Each set contains two elements: one SMA spring and one antagonistic SMA spring. We used an inertia sensor (IMU) and two potentiometers for angles feedback.
The SMA actuator system is modeled mathematically and then tested experimentally in open-loop and closed-loop control. We designed and experimentally tuned a proportional integrator derivative (PID) controller to follow the set points and to track the desired trajectories. The main goal of the presented controller is to control roll and pitch angles simultaneously in order to satisfy set points and trajectories within the work space. The experimental results show that the two degree of freedom SMA actuator system follows the desired setpoints with acceptable rise time and overshoot.
Identification of Factors Affecting Disk Drive’s Performance in Data Server by Use of Decision Tree Learning Method
Authors: Liao Yi-Ju , Chang Jen-Yuan (James) 
Published: June 2021

Abstract
To identify factors affecting magnetic disk drive’s data recording performance in data server, decision tree learning method is proposed and validated in this paper. Aiming at improving classification efficiency of various causes of HDD performance degradation, the ID3 algorithm of decision tree was first used showing the training set model would be able to achieve 100% accuracy. The maximum information entropy and information gain theory of ID3 algorithm were then adopted, from which accuracy range of 0.5–0.6 can be further achieved. The proposed method was validated to be effective for leveraging the data sever into Industry 4.0 ready smart machine.