Miracle-dz/UniFAR-FTUs huggingface dataset:
THE MAINTENANCE AND ROLE OF ARTHRITIS RELATED EXERCISE SELF-EFFICACY IN OLDER ADULTS

OF THE THESIS The Maintenance and Role of Arthritis Related Exercise SelfEfficacy in Older Adults by Jaclyn Eliza Goldbarg Master of Publich Health with a Concentration in Health Promotion and Behavioral Science San Diego State University, 2013 Despite strong evidence that arthritis symptoms and disability can be reduced through physical activity, less than one quarter of U.S. adults with arthritis meet recommended physical activity guidelines. Research supports the use of self-efficacy enhancing strategies in interventions to improve symptoms and increase physical activity in older adults with arthritis. The aim of this study was to re-evaluate an intervention program called Choosing Arthritis Appropriate Physical Activity (CASAPA) from post-intervention to 1-month follow-up, to determine if improvements seen at post-intervention were maintained, and to examine the predictive ability of physical activity self-efficacy subtypes and intention for predicting physical activity. CASAPA was 4-week, psycho-behavioral, educational intervention designed to increase knowledge, skills, and confidence in selecting arthritis appropriate physical activity, delivered to an ethnically diverse, low-SES sample of older adults (n = 143, M age = 66.93), recruited from non-residential community senior centers in San Diego County. Participants completed written questionnaires at baseline, postintervention, and 1-month follow-up to measure self-efficacy for arthritis related exercise, for overcoming barriers to exercise, and for choosing appropriate physical activity. McNemar’s test was used to determine if improvements in self-efficacy measures seen at postintervention in the intervention group were maintained at 1-month follow-up. Linear regression was used to test the predictive ability of the self-efficacy subtypes and intention for physical activity. Results show no significant change in self-efficacy levels for the intervention or control group, suggesting that the intervention group maintained their improved self-efficacy, and remained higher than controls at 1-month follow-up. Results also show that neither the self-efficacy subtypes nor intention at post-intervention were significant predictors of physical activity at follow-up. Findings support evidence that arthritis related self-efficacy subtypes can be increased and maintained for some time following an intervention, but that further research is needed to understand the roles of these self-efficacy subtypes and physical activity intention on physical activity in older adults with arthritis.
Online Social Information Propagation Analysis Based on Time-Delay Mixture Diffusion Model

Information propagation modeling is an important problem in social media analysis. A lot of existing methods assume that information propagation process follows a uniform pattern. Different from the previous studies, we analyze the factors affecting the information transmission probabilities between users from the aspects of users' interest and community-level structure features, and propose a time-delay mixture diffusion model to describe the dynamics of information propagation process in online social networks (OSNs). In this work, we model the information propagation based on mixture of patterns by considering both content and two levels of structural features. We conduct the experiments on the Sina-Weibo dataset, one of the largest microblogging websites in China. The experimental results show that our proposed model outperforms the baseline models in information diffusion prediction.
Detection and Recognition Method of Misfire for Chamber (Deep-Hole) Blasting Based on RFID

Deep-hole blasting and chamber blasting technology are widely used in mining and large-scale earth-rock excavation or landfill projects, due to their advantages of high efficiency and low cost. However, their use is often accompanied by misfire accidents, and generally misfire is relatively hidden, as well as difficult to quickly detect and identify, making misfire the greatest potential safety hazard in blasting construction. This paper presents a method and system of frequency division multiple access (FDMA) detection and recognition for blasting misfire, based on electromagnetic wave propagation theory and radio frequency identification technology. Next, the feasibility of frequency division multiple access (FDMA) misfire detection method is analyzed by means of theoretical deduction. Combining field and laboratory tests, it is concluded that the existence of underground misfire with a linear distance of 225 m can be detected. In addition, the detection frequency range is 975–5025 Hz, and the recognition accuracy of multi-frequency signal can reach 2 Hz. The stability of the electric detonator in an electromagnetic environment is discussed, and the danger criterion of the electric detonator in the electromagnetic environment is obtained. When the power of the signal transmitter is less than 120 W and the induction area of the electric detonation network is less than 0.5 m2, then the electric detonator is safe. The misfire detection and recognition system has been successfully applied in a deep-hole blasting of an open-pit quarry. It is observed that when the distance between millisecond blasting holes is equal to greater than 5 m, the detection signals between holes will not interfere with each other. This paper provides a reliable, systematic and complete research method for remote wireless detection and identification of misfire, and also provides a basis for the safety of blasting construction and misfire detection in related projects.
An Ultra-Low Power Binarized Convolutional Neural Network-Based Speech Recognition Processor With On-Chip Self-Learning

Always-on speech interfaces are prevailing in human-machine interaction, especially on wearable devices, Internet of Things, etc., which benefits from the recent breakthroughs in deep learning. For battery-powered devices, ultra-low power and real-time processing are critical. However, the massive memory access and computation of deep neural networks (DNNs) lead to long latency and huge energy consumption, which hinder their further integration in battery-powered devices. Extremely low-bit quantization shows the potential to enhance energy efficiency and speed by orders of magnitudes, but it suffers from the degradation of recognition accuracy. In this paper, we propose a binarized convolutional neural network (BCNN) based speech recognition processor, integrated with on-chip self-learning mechanism to compensate the accuracy loss caused by low-precision. We optimize the BCNN architecture by eliminating the computation redundancy, compressing the weights and tailoring approximate circuits. Fabricated in 28 nm CMOS, this processor supports real time speech recognition with power consumption of <inline-formula> <tex-math notation="LaTeX">$141~\mu \text{W}$ </tex-math></inline-formula> and energy efficiency of 2.46 pJ/Neuron. Compared with state-of-the-art speech recognition implementations, this processor achieves <inline-formula> <tex-math notation="LaTeX">$2.5\times $ </tex-math></inline-formula> reduction on energy consumption per neuron, and <inline-formula> <tex-math notation="LaTeX">$8.0\times $ </tex-math></inline-formula> energy reduction per speech frame.
Fuzzy PI Multi-loop Control of Wound Rotor Synchronous Machine for Aircraft Variable Frequency AC Generation System

In order to enhance the dynamic performance of the output voltage regulation for wound rotor synchronous machine (WRSM) in the aircraft variable frequency (VF) power system, the fuzzy PI multi-loop control strategy is proposed. Traditional voltage control method is not easy to achieve the dynamic performance and stability requirements of the VFAC generation system under wide speed, so the fuzzy PI algorithm, load current feedback and speed feedback are added to the voltage and excitation current double loop. This new voltage regulation strategy can compensate the load fluctuation by load current feedback and change the initial parameters of the fuzzy PI algorithm by speed feedback to keep the stability of the VF power system. The mathematical model of the WRSM is analyzed and the aircraft VFAC generation system model is established in Simulink. The simulation results indicate that the fuzzy PI multi-loop control strategy has better dynamic performance than traditional voltage control method under different speed and load conditions.
Analysis of Machine Learning Techniques in Fault Diagnosis of Vehicle Fleet Tracking Modules

In this paper, four techniques of machine learning (ML) were applied and analyzed during the diagnosis of failures in vehicle fleet tracking modules. A comparison of the sampling methods was carried out considering the training and testing process using real data provided by DDMX, that acts in vehicle fleet tracking. A methodology was defined for pre-processing the collected data before the application of the ML techniques. Totally 16 models were created using the Random Forest (RF), Naive Bayes (NB), Support Vector Machine (SVM) and Multi Layer Perceptron (MLP) techniques. We have obtained promising results, where the techniques achieved a precision of 99.76% and 99.68% for detection and isolation of faults, respectively, on the provided dataset. These models can serve as prototypes to diagnose faults remotely and states that traditional ML techniques with manual feature extraction are still able to achieve high metrics.
Secure Staging System for Highly Confidential Data Built on Reconfigurable Computing Platform

Cloud use for High Performance Computing (HPC) and High Performance Data Analytics (HPDA) is increasing. The data are transferred to the cloud and usually left there even after the data being processed. There is security concern for such data being left online. We propose secure staging system to prepare not only data but also computing platform for processing the data dynamically just while the data is processed. The data plane of the secure staging system has dynamic reconfigurability with several lower-than-IP-layer partitioning mechanisms. The control plane consists of a scheduler and a resource provisioner working together to reconfigure the partitioning in the data plane dynamically. A field trial system is deployed for treating secure data in dental school to be processed in the computer center with the location distance of 1km. The system shows high score in the Common Vulnerability Scoring System (CVSS) evaluation.
Spacecraft Telemetry Time Series Forecasting With Ensembles of Neural Networks

The paper proposes a two-level model for multidimensional time series forecasting for telemetry data processing. The model under consideration is an ensemble of neural networks such as multilayer perceptron or convolutional neural network. Our results show that use of ensembles of neural networks with dynamic weighing allows reducing the forecasting error in application to space telemetry processing.
$W$ -Band Injection-Locked Frequency Octupler Using a Push–Push Output Structure

This letter presents an injection-locked frequency octupler (ILFO) fabricated using a 100-nm GaAs pHEMT. A frequency quadrupler, followed by an injection-locked push–push oscillator, is employed to achieve a high multiplication factor of eight with a simple structure. Compared to conventional amplifier-multiplier chains, the proposed structure is advantageous for dc power and chip area consumption, phase noise, and circuit stability. The high multiplication factor allows the use of a low-frequency, high-performance, phase-locked-loop (PLL) as input. The ILFO achieves a locking range from 90.5 to 95.2 with 1-dBm input power. The output power is measured to be −0.4 dBm at 91.2 GHz. The phase-noise degradation from the input source to the output is 19.7 dB at 100-kHz offset.
Can creativity be taught? The case study of chemical engineers

This article aims to reveal what effect 15 hours of training in the field of creative thinking has on the activation of the creative potential of its participants. The experimental and the control group were first-year students of the Faculty of Chemical Engineering and Technology at Cracow University of Technology. These students represent an important sector of knowledge that contributes to the development and competitiveness of the economy. So, it is important that chemical engineers have the capacity to respond to changing conditions of production. The success of chemical engineers is not only dependent on a formal or explicit knowledge, but also on not explicable or implicit knowledge, so important in science disciplines, such as chemical engineering. Classified information is essential for the creation of tacit knowledge, which is crucial for the ingenuity, and the development of creative attitude of humans. This study used the Jellen and Urban preand post-test of creative drawing, which was administered along with the KAHN Inventory. Results of the tests of chemical engineering students are analysed and presented in this article.
Analysis of Low-Cost Virtual and Augmented Reality Technology in Case of Motor Rehabilitation

We evaluated the effectiveness of Low-cost Immersive Virtual Reality (IVR) and Desktop Augmented Reality (DAR) in Motor Rehabilitation of Spasmodic Torticollis (ST) with a case study of 12 subjects. Goal: To verify by functional (kinesiological) criteria and computational criteria (interaction and involvement) which was the best approach technique for the treatment of ST. Method: The comparative cross-sectional study method was applied, analyzing a cut of the moment of the subjects compared with control groups at the same moment. Result: The two technologies, IVR and DAR are presented as an instrument that adds a playful object to the therapy and favors the active participation of the patient during the treatment. However, IVR tends to have higher fidelity for range of motion data generation.
Component Model Calibration Using Transient Thermal Test Methods and Multiple Measurement Setups

Fine-tuning of finite-element thermal models to measurement data is presented, with the goal to obtain one thermal model that can accurately predict thermal response of a discrete power MOSFET in very different cooling scenarios. In particular heat paths through the plastic mold and through pins are considered in addition to the more widely studied main path through the thermal slug (mounting base). Different heat paths are activated during transient thermal measurements in dedicated test setups. Accurate 3D model geometry is obtained by means of simple reverse engineering of component dimensions. Manual tuning of material property values is the final step to obtain a detailed finite-element model which reproduces accurately the experimental results. This involves an iterative process, where material data is successively refined to reduce the mismatch of measured and simulated thermal impedance curves and structure functions.
An adaptive Virtual Impedance Control Method to Enhance PCC Voltage Quality of Islanded Microgrid

for traditional island microgrids, each DG unit is usually equipped with an LCL filter to fully absorb the switching harmonics of the inverter. However, the resonance problem of the filter between the DG units greatly affects the power quality of the DG unit output. In this paper, a simple method is developed for simultaneous local load harmonic current compensation and active damping of resonance in an islanded microgrid. The proposed method realizes the power sharing of the DG unit by the droop control, and based on the flexible tuning of the virtual impedance of the DG unit output, the virtual impedance is separated from the current regulation and the local load harmonic current compensation. Simulation results are provided to verify the correctness of the proposed method.
Handling Socio-Technical Barriers Involved in Crowd-Based Requirements Elicitation

Currently a large number of software systems target a huge customer base termed as market-driven or market-based systems. Requirements elicitation of such a kind of software systems is not possible following a traditional approach as there are many geo-graphically dispersed customers in these systems. These days crowd-based requirements elicitation (CBRE) has been proposed and utilized by researchers and practitioners as a new method of requirements identification which offers benefits of handling many diverse system's stakeholders. CBRE is done by involving crowd members through crowdsourcing. This method in turn involves many social and technical barriers, as the participants (crowd) utilized during this process belong to varying geographic regions with varying cultures and diversity, and due to the in-volvement of crowdsourcing platform during elicitation process. The objective of this PhD research proposal is to identify these socio-technical barriers involved in crowd-based requirements and devising a requirements elicitation strategy to better handle the issues involved in CBRE. An exploratory case study methodology will be utilized to explore the socio-technical barriers, the root causes as well as pros and cons of these barriers. Based on the exploratory case study results, a strategy will be devised for requirements elicitation, involving crowd participants. While, a confirmatory case study will be conducted to check the effectiveness of this strategy. The results in the form of a framework of socio-technical barriers in CBRE will be achieved.
Dichotomy Solution Based Model Predictive Control for Permanent Magnet Linear Synchronous Motors

Dichotomy solution-based model predictive control (DS-MPC) for permanent magnet linear synchronous motors (PMLSM) is proposed in this paper. The conventional finite control set model predictive control (FCS-MPC) can be combined with a pulse width modulation (PWM) modulator due to an effective cost function optimization algorithm which is from the idea of dichotomy. In the algorithm, all the voltage vectors in the constrained vector plane are dynamically selected and calculated through iteration. The proposed DS-MPC for PMLSM not only keeps the same dynamic transient performance as FCS-MPC but also greatly decreases the thrust force ripple in steady state. Meanwhile, in order to enhance the robustness of PMLSM drive systems, DS-MPC is combined with a sensorless control algorithm based on model reference adaptive system (MRAS). The proper designed MRAS can easily obtain the positon and speed information based on DS-MPC. Simulation results illustrate that DS-MPC together with MRAS has an attractive performance for PMLSM drives.
Repetitive process stability theory applied to the design of distributed controllers for spatially interconnected systems

This paper considers differential linear repetitive processes that are a distinct class of two-dimensional continuous-discrete linear systems of both physical and systems theoretic interest. A well developed stability theory for these processes exists and this paper gives substantial new results on the use of this theory for controller design. The application area is distributed feedback controllers for spatially interconnected systems. In particular, the differential linear repetitive process setting is used to design controllers for spatially interconnected systems that are composed of several linear continuous-time subsystems, where each directly interacts with neighboring subsystems. This design is based on converting the problem to one of $\mathcal{H}_{\infty}$ control for a differential linear repetitive process, leading to design based on linear matrix inequality computations. Specifically, sufficient conditions for the existence of a dynamic feedback controllers are developed together with the design algorithms for the associated controller matrices. A simulation based case study on the model of spring-mass system is given to demonstrate the feasibility and effectiveness of the new design.
Dynamic pricing of demand response based on elasticity transfer and reinforcement learning

In this paper, we study the dynamic pricing of electricity service provider in the day-ahead spot market. Since the wholesale electricity price that the electricity service provider obtains from the utility grid is constantly changing, and the user's response behavior is unknown, setting a suitable retail price is a big challenge for the electricity service provider. In response to this problem, we propose a method based on elasticity transfer and reinforcement learning, which transfers the elasticity of the implemented demand response region to the region where the user elasticity is unknown, as the initial reference for dynamic pricing, and then uses the SARAS learning algorithm for practical exploration and learning. The simulation results show that the elasticity transfer to the new region as the initial reference can significantly improve the learning rate compared to the system without the initial reference. Therefore, the proposed method can maximize the price of the electricity service provider and the user by setting the optimal price at a faster rate without prior knowledge of the user.
Vehicle Rear-Lamp Detection at Nighttime via Probabilistic Bitwise Genetic Algorithm

Rear-lamp detection of a vehicle at nighttime is an important technique for advanced driver-assistance systems. We present a detection method by employing a variant of genetic algorithm, which utilizes bitwise genetic operation instead of classic crossover and mutation. That is, the detection task is cast to a localization problem under an evolutionary optimization framework. Specifically, geometric parameters of a rectangle pair form a model to represent the detected rear-lamp pair. The fitness function for evaluating each candidate solution is combinatorial, which consists of multiple fitness functions designed under handcrafted rules from the observation. In addition, the solution space is narrowed down by extracting the red-light sources, which yields in more efficient solution exploration. Experiment with a publicly available dataset which involves images captured in various traffic situations shows the effectiveness of our method qualitatively and quantitatively.
An Ordered Search for Subset Selection in Support Vector Orthogonal Regression

Subset selection is an important task in many problems, especially when dealing with high dimensional problems, such as classification, regression, and others. In this sense, this work proposes an ordered search to select variables in orthogonal regression problems based on support vectors. The admissible search is based on a monotone property of the radius parameter. Thus, we use the radius of the SV-regression as an evaluation measure for the search, making it able to find the subsets with the smallest radius in each dimension of the problem without exhaustively exploring all possibilities. The main reason for choosing the orthogonal regression is due to the fact that this model also considers the existence of error in dependent variables. The obtained results, represented by the test error, when compared to the LASSO and a recursive feature elimination technique, demonstrate the efficiency of the method.