Miracle-dz/UniFAR-FTUs huggingface dataset:
An Analysis of the Evolving Intellectual Structure of Health Information Systems Research in the Information Systems Discipline

The rapid evolution of health information systems (Health IS) research has led to many significant contributions. However, while the Health IS subset of information systems (IS) scholarship has considerably grown over the past two decades, this growth has led to questions regarding the current intellectual structure of this area of inquiry. In an effort to more fully understand how Health IS research has contributed to the IS discipline, and what this may mean for future Health IS research in the IS domain, we conduct an in-depth evaluation of Health IS research published in mainstream IS journals. We apply citation analysis, latent semantic analysis (LSA), and social network analysis (SNA) to our data set of Health IS articles in order to: (1) identify Health IS research themes and thematic shifts, (2) determine which Health IS research themes are cohesive (versus disparate), (3) identify which Health IS research themes are central (versus peripheral), (4) clarify networks of researchers (i.e., thought leaders) contributing to these research themes, and (5) provide insights into the connection of Health IS research to its reference disciplines. Overall, we contribute a systematic description and explanation of the intellectual structure of Health IS research and highlight how the existing intellectual structure of Health IS research provides opportunities for future research.
Prevent falls in older adults

Falls are common in the elderly and may lead to serious injuries. Recurrent falls are also frequent and are responsible for significant morbidity and mortality in older adults. The most serious injuries due to these falls are head injury and the fractures. The effect of a fall on an older person can be a devastating event, resulting in chronic pain, loss of independence, and reduced quality of life. Hence, the prevention of fall is better than the treatment of the injuries resulting from it.
Minimal invasive enucleation of leiomyoma of the esophagus: A fast-track approach

IntroductIon Leiomyoma is most commonly located in the lower two-third of esophagus and most commonly occurs as intramural–extramucosal tumor. Sometimes, leiomyoma has been associated with achalasia, hiatus hernia, and diverticulum. [1,2] Minimal invasive surgery for benign esophageal tumor reduces the morbidity than in comparison to open approach. We describe a case of leiomyoma of the esophagus and technique for the laparoscopic enucleation of leiomyoma involving lower one-third of the esophagus.
ASSESSMENT OF RELATIONSHIP BETWEEN SELF-EFFICACY AND SELF- CARE IN COPD PATIENTS

Given the importance of the COPD and role of different factors in self-care behaviors of COPD patients, this study have been conducted to determine the relationship between self-efficacy and self-care in these patients. Materials & Methods: In this descriptive-correlational study 62 COPD patients participated who were hospitalized in hospitals affiliated to the Urmia University of Medical Sciences in 2011. Samples were selected using purposive sampling. After determining the validity and reliability of the questionnaires and completing consent form, the demographic, self-efficacy, and self-care questionnaires were given to be completed. The data were analyzed using descriptive and analytic statistics in SPSS software. Result: The mean score of self-care was 69.56 from the range of 34 through 140. This means that the participants performed self-care behaviors moderately. The mean score of self-efficacy scores was 76.93 from the range of 35 through 175. Results of the Kay Square showed a positive correlation between the self-care and self-efficacy in COPD patients so that with increasing self-efficacy, self-care behaviors are also increased ( = 14.75, p=0.02). Conclusion: Given the high potency of self-efficacy factors in predicting self-care behaviors and low self-efficacy scores of COPD patients, in their educational programs to improve self-care behaviors, more attention should be paid to strengthening the helping factors especially self-efficacy
Limestone-gypsum wet flue gas desulfurization based on Cyber-Physical System

The limestone-gypsum wet flue gas desulfurization system has been widely used due to its remarkable desulfurization effect, but there are still problems such as high operation and maintenance costs, difficult control of process indicators, and large energy consumption. In view of the above problems, this paper proposes to apply the Cyber-Physical System (CPS) to the wet flue gas desulfurization (WFGD) process, and construct a architecture suitable for (WFGD) system according to the CPS 5C architecture and NIST CPS framework, and introduce the architecture in detail around the above problems. Applying the advanced technology of CPS to the (WFGD) system can improve the whole system operation efficiency and reduce energy waste, and has good utilization value for promoting the intelligent upgrade of the factory.
Modeling and stability analysis of parallel-connected PFC Boost converter

Compared with a single PFC Boost converter, the parallel-connected converter is more complex, which brings great obstacles to the system analysis and design. In this paper, the PFC Boost converter with n-modules parallel-connecting, are studied when they operate in synchronous switching mode. Thus, it is conclude that the multi-modules PFC Boost converters can be equivalent to a single-module PFC Boost converter. The modeling method of the system is simplified, which make the analysis of the circuit possible. Due to the wide fluctuation range of input voltage, fast-scale instability will definitely arise in the PFC Boost converter with duty ratio greater than 0.5. Based on the simplified modeling method, an appropriate slope compensation can be designed on reference current to avoid fast-scale bifurcation, while with minimum decrease on power factor. The aforementioned modeling and analysis are proved to be efficient by numerical simulations.
An Automatic Robot Skills Learning System from Robot’s Real-World Demonstrations

In order to avoid complicated programming difficulties in robot control, we propose an automatic robot learning system which can learn skills from real-world demonstrations by robot. The system utilizes RGB-D camera to record one robot’s demonstrations and then the demonstration data are processed and transferred into robot simulation environment. The policy model is trained entirely in simulation with the advantage of avoiding safety problem which is the key difficulty of real-world training. Then the learned policy is automatically transferred to another robot to reproduce the demonstrated skills. The experiments show that the system could automatically finish entire learning process from recording the robot demonstrations to applying the learned policy to another robot. And with the selected policy learning method, the robot could not only acquire skills but outperform the demonstrator.
Position Control of Hydraulic Series Elastic Actuator with Parameter Self-Optimization

Series elastic actuator (SEA) can reduce the output impedance of robots when interacting with the environment, thereby improving the force control performance. However, owing to complex dynamics and sensitivity to external disturbance, the application of SEA suffers from poor position control. Therefore, this paper proposes an effective method for high-precision position control. A hydraulic SEA is presented with its dynamic model firstly. Then, we apply a disturbance observer (DOB) to load position control based on the dynamic character. The cylinder extension control is considered as velocity-source, therefore simplifying the system model. In addition, we design a feedback controller based on the measured load position for error compensation. The control parameters are further optimized by using a crowding-based dynNP-DE algorithm, in which a crowding strategy is introduced to improve the population diversity. Experiments are conducted to validate the effectiveness of the proposal.
Inertia Phase Control During Gear Shift Process of I-AMT with Overrunning Clutch

The two-speed inverse automated manual transmission AMT (I-AMT) with overrunning clutch can avoid the torque interruption during the gear shift of electric vehicles (EVs) and also simplify the control complexity. The clutch slip control in the inertia phase is of significance. In this paper, a triple-step controller is deduced and designed for the inertia phase control problem in the gear shift process, which realizes the clutch slip speed tracking the optimal trajectory. A plant model of I-AMT with overrunning clutch is built and the controller of the gear shift process is described in detail. Eventually, simulation results under two working scenarios of gear upshift and downshift demonstrate the clutch slip speed can track the reference trajectory well in inertia phase, which validates the effectiveness of the controller.
Intention and Engagement Recognition for Personalized Human-Robot Interaction, an integrated and Deep Learning approach

The quality of the interaction between two individuals depends upon not only exchange (i.e. understanding partner’s intention and reacting to it), but also on how personalized is the interaction. In this work, we have set out to accomplish these objectives for Human Robot Interaction. For this, we have developed a distributed and multimodal data acquisition and interaction manager architecture aiming to enable personalized Human-Robot Interactions. In the proposed approach, high-level perceptual capabilities (i.e. recognizing human activity and engagement) are performed by an Autoencoder, which is a Deep Learning and Unsupervised Learning method. This Autoencoder module is integrated with a facial recognition and a dialog manager (speech recognition and speech generation) to enable personalized interaction. We discuss the advantages of Autoencoders over Supervised Learning methods, and how our proposed architecture can be used to increase the duration of interaction with a robot during unscripted scenarios. Experimental validations are also performed in real Human-Robot interactions using a humanoid robot.
The Research on Phase Spectrum Decomposition Mechanism based on One-Side EMD

Aiming at the problem of Hilbert transformation on mode decomposition for non-Bedrosian signal, this paper proposes a phase spectrum decomposition method based on one-side empirical mode decomposition (EMD) which realizes the straight analysis for non-Bedrosian signal through Hilbert transformation. This paper first proposes the main features of the signal which meets the requirement of EMD, introducing the principle of phase spectrum superposition and one-side EMD to expand EMD application into phase spectrum scope. Then the frequencies relationship between time-amplitude coordinate and phase spectrum are deduced. Finally the process procedures as Hilbert transformation, one-side EMD phase spectrum decomposition and frequency analysis are constructed. From simulation testing, it is proved that one-side EMD phase spectrum decomposition mechanism could conquer the problem of Hilbert transformation for non-Bedrosian signal processing and be higher on stability and accuracy.
Neuro-adaptive Sliding Mode Fault-tolerant Control of Hypersonic Vehicle under Actuator Fault

This paper proposed a neuro-adaptive sliding mode fault-tolerant control (NASMFTC) method for the problem of actuator control gain loss fault in hypersonic vehicles. Described as the elevator yaw angle and the engine throttle setting valve fault. The controller combines the robust adaptive algorithm with the radial basis function (RBF) based neural network (NN) method for the hypersonic vehicle longitudinal motion model. The proposed fault-tolerant control method has the following advantages: 1. No need to estimate the value of the actuator fault; 2. The designed control algorithm is simple in structure and does not require a large amount of online calculation, can quickly deal with the occurrence of faults; 3. Can ensure that the system still has the ability to stably track in the case of constant or time-varying actuator faults. The final simulation experiment verifies the effectiveness of the proposed method.
Enhanced Player Interaction Using Motion Controllers for First-Person Shooting Games in Virtual Reality

The main purpose of virtual reality (VR) is to enhance realism and the player experience. To do this, we focus on VR interaction design methods, analyze the existing interaction solutions including both accurate and rough interaction methods, and propose a new method for creating stable and realistic player interactions in a first-person shooter (FPS) game prototype. In this research, we design and modify the existing mapping methods between physical and virtual worlds, and create interfaces such that physical devices correspond to shooting tools in virtual reality. Moreover, we propose and design prototypes of universal interactions that can be implemented in a simple and straightforward way. Proposed interactions allow the player to perform actions similar to those of real shooting, using both hands such as firing, reloading, attaching and grabbing objects. In addition, we develop a gun template with haptic feedback, and a visual collision guide that can optionally be enabled. Then, we evaluate and compare our methods with the existing solutions. We then use these in a VR FPS game prototype and conduct a user study with participants, and the resulting user study proves that the proposed method is more stable, player-friendly and realistic.
Research on the game scheduling optimization of heterogeneous integrated energy system with wind power uncertainties

In many types of heterogeneous integrated energy system, energy hub of optimal to ensure energy security, stable and economic operation system is important. However, numerous energy hub device, and there is serious uncertainty of wind power access, the hub connected coupling between serious at the same time , makes it hard for the use of traditional optimization scheduling method to solve many kinds of heterogeneous integrated energy hub of mutual influence and mutual restriction between problem. To this end, the Nash equilibrium theory to solve many kinds of heterogeneous integrated energy system optimization scheduling problem. According to the coupling relationship of different energy carrier based on power flow, set up many kinds of heterogeneous energy line set in the system. The game model between the players can effectively and accurately realize the optimal dispatch through a robust optimization technology and overcome the uncertainty caused by wind power integration of the energy hub. The results show the applicability and robustness of the game model in dealing with the optimization scheduling problem.
Detection and Tracking Method for Maneuvering Targets Using Improved Gradient-based Optical Flow Field

In order to address the problem of larger error on detecting and tracking the maneuvering targets by using the common approach on the optical flow field, this paper presented a novel method based on the improved gradient-based optical flow field. According to the Horn-Schunck algorithm, the weight of optical flow was explored on the gradient threshold of sub-image to boost the detecting performance. With respect to the targets tracking, the unscented Kalman filtering gain was optimized. Further, the adjustment coefficient of unscented transformation was adaptively derived based on the trace of state variance matrix of the Sigma sampling points. The experiments indicated that the proposed method can effectively detect and track the maneuvering targets with the accurate estimation.
Alterations in Chromatin Folding Patterns in Cancer Variant-Enriched Loci

In this study, we focus on the following question: do genomic regions enriched in cancer variant mutations have significantly different chromatin folding patterns? We utilize publicly available Hi-C data to characterize chromatin folding patterns in healthy (GM12878) and cancer (K562) cells based on status of A/B compartmentalization and random vs non-random chromatin physical interactions. We then perform statistical testing to assess if chromatin folding patterns in cancer variant-enriched loci are significantly different from non-enriched loci. Our results indicate that loci with cancer variant status have significantly altered ($\mathbf{FDR} < 0.05$) chromatin folding patterns. Source code is available via git repository https://bitbucket.org/aperezrathke/chr-loc-cancer-bhi.
Consensus building for interval MAGDM problems based on integer programming models

This paper is primarily interested in optimization consensus models in the context of multiple attribute group decision making (MAGDM) where the uncertain information can be represented by interval numbers. The preservation of the original opinion scales in the modified preferences, and the scarce number of consensus approaches for the interval MAGDM problem are the major motivations to develop new optimal consensus models. Two general consensus rules from a cost perspective are used as the objectives: one is to minimize the amount of preference changes and the other is to minimize the number of modifications. Based on the two rules, two consensus models based on the Manhattan distance are presented. The presented models can be solved by integer linear programming techniques. The main advantage of the proposed consensus models comparing to the existing approaches is that the generated preferences are belonged to the original scales. These models can be extended to other uncertain situations and can find wild applications in decision science.
Harmonics Reduction for Four-Leg Distribution Network-Connected Single Phase Transformerless PV Inverter System Using Diagonal Recurrent Neural Network

This paper presents an analysis of the effects of solar irradiance on a single phase PV inverter system connected to a four-leg distribution system (FLDN). The proposed method aims to reduce current harmonic distortion (THDi) in the distribution system due to different and fluctuating irradiation. For this purpose, PV system with a capacity of 3.5 kW is connected to FLDN low voltage through an inverter voltage source (VSI) using intelligent control. This intelligent controller consist of: MPPT controller, constructive diagonal recurrent neural network controller (CDRNN), and synchronization and DQ transformation. The MPPT controller system automatically varies the voltage reference signal to obtain DC voltage, which extracts the maximum power from the PV string, while the DQ transformation block is used to convert the AC inverter output voltage and current into the synchronous reference frame. All of these signals are processed by the CDRNN controller to produce a PWM. Based on the simulation results using Matlab/Simulink, the THDi of FLDN was reduced from 5.09% into 4.63% in descent solar irradiance and 5.43% become 4.98% in ascend irradiance condition.
Influencing Factors of Flow Field of Ionic Wind Induced by Corona Discharge in a Multi-Needle-to-Net Electrode Structure Under Direct-Current Voltage

The morphologies and velocity of ionic wind induced by direct-current (DC) corona discharge were experimentally measured. DC voltage was applied to short air gaps of high-voltage electrode–needle electrode and grounding electrode–net electrode to generate coronas, so as to allow the generation of ionic wind induced by corona discharge. The purpose of the study is to optimise the multi-needle-to-net electrode structure to improve the velocity of the ionic wind and keep it in a relatively stable state. For this purpose, the influences of various parameters on the velocity of the ionic wind were explored. The research results showed that the side length of meshes and the distribution mode of needles significantly affected the velocity of the ionic wind. The maximum velocity of the ionic wind, when the side length of meshes was 0.85 mm was 3 and 1.28 times those when the side lengths of the meshes were 2 and 0.425 mm, respectively. This indicated that there was an optimal side length that can maximise the velocity of the ionic wind. Increasing the number of needles and optimising the distribution mode of needles can effectively improve the velocity, that is, the improvement of the number and distribution uniformity of needles was conducive to increase the velocity of the ionic wind. Increasing the voltage amplitude can increase the velocity of the ionic wind, however, it was possible that noise was significant and in this case, the corresponding voltage cannot be considered as the working voltage of the ionic wind generator.
Early Forest Fire Region Segmentation Based on Deep Learning

As the forest fire can bring about great property loss and ecological disaster, artificial intelligence-based forest fire monitoring system has gained popularity in recent years to enable the fire alarm quickly and accurately. In this paper, considering that the fire area is very small and hard to be detected using traditional method for detection early forest fire, we propose a novel forest fire monitoring framework based on convolutional neutral networks. In order to validate that the proposed framework can improve effectiveness and accuracy of detecting the early forest fires, many groups of fire detection experiments using a self-generated forest fire dataset and two real forest fire monitor videos are conducted. The experiment results demonstrate its capability to work in various challenging fire and illumination conditions presented in the study, and show that the framework can effectively detect the early forest fire.
H∞ Filtering for Nonlinear Networked Control Systems with Mixed Random Delays and Packet Dropouts

In this paper, the problem of H∞ filter design is investigated for a class of nonlinear networked control systems on the basis of the interval type-2 fuzzy set theory. First, mixed random delay and packet dropouts are taken into consideration in the filter design process. And the membership functions and number of fuzzy rules of the filter are different from the plant. Second, the fuzzy filtering error system is built based on interval type-2 fuzzy model. Then, by using Lyapunov function, stability judgment theory, Wirtinger inequality and linear matrix inequality, the filter stability condition is given. Finally, simulation example is provided to show the effectiveness of the method proposed in this paper.
Sensor Calibration and Trajectory Planning in 3d Vision-guided Robots

In order to improve the welding success rate and precision of irregular workpieces and realize the automation of welding process, an automatic welding system based on 3D vision was designed. In order to solve the problem of high-precision calibration of the relative positional relationship between the sensor and the robot end in the Eye-in-hand system, the multi-Point calibration algorithm is used to calibrated the position of the visual sensor, and the final maximum error does not exceed 0.2 mm. In the eye in hand system, the sensor is fixed relative to the end of the robot, but when scanning the weld, the position of the end of the robot is constantly changing, so in order to convert the weld to a robot-based coordinate system, it must be able to obtain real-time Robot end position. A linear fitting method is proposed to calculate the position of the robot end-effector when the sensor scans to the specified weld section. Since the welding trajectory is identified as a discrete sequence, the trajectory of the robot needs high-order guidance, and the existing trajectory planning algorithm cannot satisfy the high accuracy requirement, the robot trajectory interpolation based on NURBS curve is proposed. In order to verify the NURBS trajectory planning. After calculating we find that each joint acceleration of the robot is continuous and the actuator passes passing points. Finally, the above algorithms are used to complete a software system, which is applied to the welding of the bicycle tripod and obtains a good welding effect.