Miracle-dz/UniFAR-FTUs huggingface dataset:
Organizational learning and change: can they coexist?

Purpose The purpose of this paper is to explore the dynamics of IT-enabled change and organizational learning. This research defines organizational learning in general terms as the capacity of acquiring, transferring and creating knowledge within an organization. Design/methodology/approach This research design represents a common form of ex-post facto analysis, in which the exploration of relationships between variables is the main focus. The questionnaire used in the first phase was self-administered in a report format, wherein managers were asked to describe the organizational context and problems; the IT intervention or managers’ response to the problems; and the benefits that resulted from the introduction of IT. In the follow-up phase, the interviews used a similar version of the survey, focusing on the same themes. Findings This paper contends that IT-enabled change such as process redesign and productivity improvements do not take place simultaneously with an organization’s attempts to increase its knowledge base or its capacity to acquire knowledge. Organizations alter processes and re-engineer their operations to improve productivity, not to enhance their knowledge-acquiring and decision-making capacities. It seems that the attention structure of managers may be limited and, therefore, they cannot focus on improving both ends simultaneously. This may suggest that when an organization alters its processes, it may overlook its ability to acquire knowledge and its learning capacity at least for the short term. Originality/value The findings of this study can serve as important insights regarding managers’ work practices. Findings support a pragmatic view where managers are concerned with showing immediate gains through the introduction of IT which implies a lack of long-term planning. The findings also can serve as an important lesson to managers in that when they alter organizational processes, they should not overlook the issues of organizational learning and knowledge creation. The findings highlight the complexity of organizations and the conflicting objectives which organizations may attempt to achieve when engaging in change efforts such as the adoption of new technologies.
Generated Voltage Control In A Switched Reluctance Motor/generator

This paper reports on the switched reluctance machine operating as motor/generator (SRM/G) excited through an external power source. The half-bridge static converter topology is used to drive the machine in either modes of operation. Transition from motor to generator mode is firstly discussed and results from both simulation and experimental tests are included. Operating as generator, the output generated voltage is a function of the phase magnetization level during excitation; therefore, by controlling the phase excitation level, the output voltage is also controlled. Three output voltage control techniques are proposed and analyzed: 1Variable Angle (VA), effected by controlling the phases turning off angles with a fixed DC link excitation voltage; 2 Chopping (CH), attained by chopping the DC link supply during the phases magnetization while keeping a fixed conduction period; and 3 – Variable Link (VL), reached by controlling the DC link excitation voltage by means of a DC-DC buck converter in a fixed conduction period. For the experimental work, full digital control is implemented. Results obtained with the implementation of the output voltage control techniques are presented. Relative performance comparison is included, showing that the CH followed by VL strategy brings the best outcome.
Joint Offloading and Trajectory Design for UAV-Enabled Mobile Edge Computing Systems

Unmanned aerial vehicles (UAVs) have been considered in wireless communication systems to provide high-quality services for their low cost and high maneuverability. This paper addresses a UAV-aided mobile edge computing system, where a number of ground users are served by a moving UAV equipped with computing resources. Each user has computing tasks to complete, which can be separated into two parts: one portion is offloaded to the UAV and the remaining part is implemented locally. The UAV moves around above the ground users and provides computing service in an orthogonal multiple access manner over time. For each time period, we aim to minimize the sum of the maximum delay among all the users in each time slot by jointly optimizing the UAV trajectory, the ratio of offloading tasks, and the user scheduling variables, subject to the discrete binary constraints, the energy consumption constraints, and the UAV trajectory constraints. This problem has highly nonconvex objective function and constraints. Therefore, we equivalently convert it into a better tractable form based on introducing the auxiliary variables, and then propose a novel penalty dual decomposition-based algorithm to handle the resulting problem. Furthermore, we develop a simplified ${l}_{0}$ -norm algorithm with much reduced complexity. Besides, we also extend our algorithm to minimize the average delay. Simulation results illustrate that the proposed algorithms significantly outperform the benchmarks.
Negative Obstacle Detection Using LiDAR Sensors for a Robotic Wheelchair

The objective of this work is to develop a negative obstacle detection algorithm for a robotic wheelchair. Negative obstacles — depressions in the surrounding terrain including descending stairwells, and curb drop-offs — present highly dangerous navigation scenarios because they exhibit wide characteristic variability, are perceptible only at close distances, and are difficult to detect at normal operating speeds. Negative obstacle detection on robotic wheelchairs could greatly increase the safety of the devices. The approach presented in this paper uses measurements from a single-scan laser range-finder and a microprocessor to detect negative obstacles. A real-time algorithm was developed that monitors time-varying changes in the measured distances and functions through the assumption that sharp increases in this monitored value represented a detected negative obstacle. It was found that LiDAR sensors with slight beam divergence and significant error produced impressive obstacle detection accuracy, detecting controlled examples of negative obstacles with 88% accuracy for 6 cm obstacles and above on a robotic development platform and 90% accuracy for 7.5 cm obstacles and above on a robotic wheelchair. The implementation of this algorithm could prevent life-changing injuries to robotic wheelchair users caused by negative obstacles.
Pollution sources,environmental residues,and ecological toxicity of pharmaceuticals and personal care products(PPCPs):A review

With the large scale development of pharmaceutical and cosmetic industries,the production and use amount of pharmaceuticals and personal care products(PPCPs) are remarkably increased,resulting in the PPCPs remaining in water,soil,and air environment.Regrettably,as a large class of environmental pollutants,PPCPs have not drawn adequate attention till the late 1990s.Due to the continuous input to the environment in high quantity,the residual concentration of PPCPs is increasing,and gradually shows ecological toxicity to the microorganisms,plants and animals,and even,potential ecological risk to human beings.Based on the sum-up of published literatures,this paper analyzed the pollution sources and environmental remaining of PPCPs,as well as the ecological toxicity of residual PPCPs to the microorganisms,plants,animals,and human beings.The main scientific issues related to PPCPs,which should be researched in the future,were suggested.
Diagnosis of Coronary Artery Disease via a Novel Fuzzy Expert System Optimized by Cuckoo Search

In this paper, we propose a novel fuzzy expert system for detection of Coronary Artery Disease, using cuckoo search algorithm. This system includes three phases: firstly, at the stage of fuzzy system design, a decision tree is used to extract if-then rules which provide the crisp rules required for Coronary Artery Disease detection. Secondly, the fuzzy system is formed by setting the intervals for fuzzy variables and extracted rules. Finally, Cuckoo Search algorithm is used to optimize fuzzy membership functions. The accuracy of our proposed system is evaluated using Cleveland Cardiac Patient Database. The detection rate is 93.48% employing optimized membership functions. Also, 85.76% accuracy is obtained for predicting the risk of coronary artery disease. The superiority of proposed system is obvious by comparing it to the previously methods; it is more accurate and is also easier to implement.
Estimation of Beat-to-Beat Interval and Systolic Time Intervals Using Phono- and Seismocardiograms

Systolic time intervals Pre-Ejection Period (PEP) and Left Ventricular Ejection Time (LVET) are widely used indicators of cardiac functions. While accurate assessment of them requires costly equipment such as echocardiography devices, a satisfactory estimation can be done by analyzing signals from simple accelerometer and microphone attached to human chest. This paper reports a study where heart rate and the systolic intervals were derived from phonocardiogram (PCG) and seismocardiogram (SCG) simultaneously. Both sensors, the microphone for PCG and the accelerometer for SCG were attached on the chest wall, close to sternum (PCG) and apex of the heart (SCG). The signals were acquired from 10 participants in a 33-minute laboratory protocol with synchronized ECG measurements. Both signals went through an identical processing path: band pass filtering, envelope extraction with Hilbert transformation and peak detection from the envelope signal. In heart rate estimation, PCG and SCG reached 84% and 93% accuracy, respectively. The systolic interval accuracy estimation was based on deviation analysis as the absolute reference values for PEP and LVET were not available. In PEP estimation, the average standard deviations during the rest periods of the protocol were 4 ms for PCG and 8 ms for SCG. In LVET estimation, the deviations were nearly 10 fold compared to PEP. However, the results show that both methods can be used for accurate heart rate estimation and with careful mechanical attachment also PEP can be accurately derived from both. Due to sharper envelope signal waveform, PEP estimation was more accurate with PCG than with SCG.
Graph Energy Ranking for Scale-Free Networks using Barabasi-Albert Model

A social network is a vast collection of actors and interactions. It forms one of the complex networks. There are various types of social networks such as acquaintance networks, online social networks, covert networks, citation networks, and collaboration networks, etc. Most of these real-world networks are scale-free, and they follow a power-law distribution. Each of these networks has nodes which have various roles to play, and all nodes are not equally important. Hence we need to rank them based on their importance. In this paper, we propose an algorithm named Graph Energy Ranking (GER) to rank the nodes of scale-free networks built using the Barabasi-Albert model. GER analyses the impact of node deletion on the underlying network and therefore gives a better understanding of the network features. Study of ranking done by existing centrality measures versus GER is performed to observe the similarity in the ranking process.
Effect of Different ECG Leads on Estimated R–R Intervals and Heart Rate Variability Parameters

Heart rate and heart rate variability parameters provide important information on sympathetic and parasympathetic branches of autonomous nervous system. These parameters are usually extracted from electrocardiograms often measured between two electrodes and called an ECG lead. Besides systems intended only for heart rate measurement, ECG measurement devices employ several well-known lead systems including the standard 12-lead system, EASI lead system and Mason-Likar systems. Therefore, the first step is to select the appropriate lead for heart rate variability analysis. The appropriate electrode locations for single-lead measurement systems or the preferred measurement lead in multi-lead measurement are choices that the user needs to make when the heart rate variability is of interest. However, it has not been addressed in the literature, if the lead selection has an effect on the obtained HRV parameters. In this work, we characterized the amount of deviation of heart rate and heart rate variability parameters extracted from nine ECG leads, six from EASI leads and three modified limb leads. The results showed a deviation of 2.04, 2.88, 2.06 and 3.45 ms in SDNN, rMSSD, SD1 and SD2, respectively. A relative difference up to 10% was observed in HRV parameters for single signal frames. Additionally, the discrimination of the R-peaks by amplitudes was evaluated. The A–S lead appeared to have the best performance in all the tests.
Modified Repetitive Controller for Disturbance Rejection of Peristaltic Pump

This paper presents the design and implementation of an modified repetitive controller for periodic disturbance rejection of peristaltic pump. An observer based rejection of periodic disturbance is shown here for speed control of peristaltic pump. The input output data samples are taken over finite interval of time with an data acquisition system. Plant model is obtained by using the System Identification Toolbox in MATLAB. Fitness of the model is investigated by applying different dataset in order to achieve the best fit model. Simulation is performed using MATLAB. A comparative study on time domain performance of the system is carried out here to establish the effectiveness of the proposed controller in terms of time domain performances.
Incremental Abbreviation Detection in Clinical Texts

In this paper, incremental abbreviation detection in clinical texts is considered for the practical context where new clinical texts are added, processed, and exploited over time. We propose a parameter-free semi-supervised method, named nonThreshold-ST, based on Self-training and C4.5. It inherits the prediction capability and simplicity of Self-training, while exploiting additional instances that are the most confidently predicted for enhancing the training dataset in the parameter-free configuration manner. The experimental results show that it outperforms its base supervised learning method on different English clinical text sets. Moreover, its Recall, Precision, F-measure, and Accuracy are among the highest values as compared to those of some other semi-supervised learning methods.
An AN-GA Controlled SEPIC Converter for Photovoltaic Grid Integration

In this paper, Artificial Neural Network (ANN) optimization with Genetic Algorithm (GA) is implemented. The optimized training to ANN is provide using Bayesian regulation. For this study, a Photovoltaic (PV) system has considered and optimal power tracking been interpreted with proper adjustment of ANN weights using GA approach, which reduces the Root Mean Square Error (RMSE). In this work, the single-ended primary-inductor converter (SEPIC) has been utilized for better power tracking from PV modules. SEPIC Converter accomplish with impedance matching power device and provides utmost PV power tracking. Space vector pulse width modulation-dSPACE interface been utilized as an inverter control. Simulated responses show the potency of the proposed system under sag, swell and varying loading conditions.
Adaptive control of lower limb robot based on human comfort under minimum inertial parameters

In this paper, aiming at the comfort problem of wearing rehabilitation training robot for different patients with lower extremity dyskinesia, the real human gait data as the reference trajectory is proposed, and a control method based on the minimum inertia parameter with the comfort of human wearable robot as the objective function is designed. To realize the real-time tracking of the reference trajectory by the robot trajectory of lower limb rehabilitation training, the NOKOV motion capture system is used to collect human gait data. The Lagrange method is used to establish the dynamics model of the single leg of the lower limb rehabilitation training robot. Based on the minimum inertia parameters, the adaptive controller with comfort as the objective function is designed. Finally, the effectiveness of this method is verified by simulation based on experimental data.
The Effect of the Similarity Between the Two Names of Twitter Users on the Credibility of Their Publications

Social media networks have become one of the most important sources for information and news sharing. As a result of the ease of publication and the lack of censorship of the published content, they have become a source of spreading rumors and deceptive information. Twitter, as a social media site, has its importance in influencing the world public opinion in general and the Arab street in particular. Many studies have been conducted to examine the credibility of the publication through social networking sites, especially through Twitter in different languages, but the research in Arabic is few and needs further development. In this research, we focus on discovering the credibility of Twitter publishing in Arabic by integrating the Social Media Mining techniques with the Natural Language Processing (NLP). We have extracted the common 40 user and content features for training and testing the most used four classifiers in field of credibility detection. In order to enhance the accuracy of the detection process, we developed a new algorithm to discover the relationship (similarity) between the two names of Twitter user (username and display name). Using the random forest classifier, we outperform the baseline works in terms of accuracy.
Feasibility of Using a Wearable Biosensor Device in Patients at Risk for Alzheimer’s Disease Dementia

Background Alzheimer’s disease (AD) is the most common and most costly chronic neurodegenerative disease globally. AD develops over an extended period prior to cognitive symptoms, leaving a “window of opportunity” for targeted risk-reduction interventions. Further, this pre-dementia phase includes early physiological changes in sleep and autonomic regulation, for which wearable biosensor devices may offer a convenient and cost-effective method to assess AD-risk. Methods Patients with a family history of AD and no or minimal cognitive complaints were recruited from the Alzheimer’s Prevention Clinic at Weill Cornell Medicine & New York-Presbyterian. Of the 40 consecutive patients screened, 34 (85%) agreed to wear a wearable biosensor device (WHOOP). One subject (2.5%) lost the device prior to data collection. Of the remaining subjects, 24 were classified as normal cognition and were asymptomatic, 6 were classified as subjective cognitive decline, and 3 were amyloid-positive (one with pre-clinical AD, one with pre-clinical Lewy-Body Dementia, and one with mild cognitive impairment due to AD). Sleep-cycle, autonomic (heart rate variability [HRV]) and activity measures were collected via WHOOP. Blood biomarkers and neuropsychological testing sensitive to cognitive changes in pre-clinical AD were obtained. Participants completed surveys assessing their sleep-patterns, exercise habits, and attitudes towards WHOOP. The goal of this prospective observational study was to determine the feasibility of using a wrist-worn biosensor device in patients at-risk for AD dementia. Unsupervised machine learning was performed to first separate participants into distinct phenotypic groups using the multivariate biometric data. Additional statistical analyses were conducted to examine correlations between individual biometric measures and cognitive performance. Results 27 (81.8%) participants completed the follow-up surveys. Twenty-four participants (88.9%) were satisfied with WHOOP after six months, and twenty-three (85.2%) wanted to continue wearing WHOOP. K-means clustering separated participants into two groups. Group 1 was older, had lower HRV, and spent more time in slow-wave sleep (SWS) than Group 2. Group 1 performed better on two cognitive tests assessing executive function: Flanker Inhibitory Attention/Control (FIAC) (p=.031), and Dimensional Change Card Sort (DCCS) (p=.061). In Group 1, DCCS was correlated with SWS (ϱ=.68, p=0.024) and HRV (ϱ=.6, p=0.019). In Group 2, DCCS was correlated with HRV (ϱ=.55, p=0.018). There were no significant differences in blood biomarkers between the two groups. Conclusions Wearable biosensor devices may be a feasible tool to assess AD-related physiological changes. Longitudinal collection of sleep and HRV data may potentially be a noninvasive method for monitoring cognitive changes related to pre-clinical AD. Further study is warranted in larger populations.
Investigation of The Neural Drive During Vibration Exercise by High-density Surface-electromyography

Mechanical vibration applied directly to the muscle belly or tendon has been reported to elicit a specific reflex loop named tonic vibration reflex (TVR), which involves motor unit (MU) activation synchronized and un-synchronized within the vibration cycle. Indirect application of vibration to the muscle by vibration exercise (VE) has also been suggested to evoke TVR, as evidenced by the spectral peaks observed at the vibration frequency in the surface electromyography (sEMG). However, other studies interpreted these spectral peaks as the result of motion artifacts (MAs). The aim of the present study is, therefore, to investigate MU activation patterns during VE in order to clarify the nature of those spectral peaks. To this end, low-intensity isometric contractions were executed with and without VE, and high-density sEMG measurements were performed during the contraction tasks. MU action potential (MUAP) trains were extracted by decomposing the recorded high-density sEMG signals. The spectra of the MUAP trains were then calculated and compared between vibration and no-vibration conditions. Clear MU synchronization was observed during VE, confirming the spectral peaks at the vibration frequency to be mainly due to the reflex loop rather than MAs.
Varactor Loaded Pattern Reconfigurable Patch Antenna With Shorting Pins

A varactor loaded pattern reconfigurable patch antenna with shorting pins is presented. Continuous beam reconfigurability and null scanning are realized by introducing two sets of shorting pins and only four varactors into a square patch antenna. For this antenna, the edge-shorting pins are placed along with the parallel edges of the square patch for different <inline-formula> <tex-math notation="LaTeX">$E$ </tex-math></inline-formula>-fields. Another set of shorting pins are fixed symmetrically along the diagonal of the square patch for directivity and null depth enhancement. To achieve continuous pattern reconfiguration, varactors are connected with the edge-shorting pins to provide continuously tunable phase difference. The mechanism of the pattern reconfiguration and effects of the shorting pins are discussed based on a transmission-line model. The proposed antenna has a compact size of <inline-formula> <tex-math notation="LaTeX">$0.4\,\,\lambda _{0} \times 0.4\,\,\lambda _{0} \times 0.06\,\,\lambda _{0}$ </tex-math></inline-formula>. Both the simulated and measured results confirm that the proposed antenna has the capabilities of beam steering and null scanning. The measured main beam scans from −55°to −15.5°and 19° to 54°, with a gain reduction of 3.22 dB relative to maximum (7.55 dBi). Meanwhile, the coverage of radiation pattern null is from −74° to 62°.