Miracle-dz/UniFAR-FTUs huggingface dataset:
Psychological adjustment in individuals with coronary heart disease

Psychological adjustment is one of the most important things needed by individuals with chronic disease to have a positive life regardless of the impact caused by their illness. This study aims to determine the dynamics of psychological adjustment in individuals with coronary heart disease (CHD). The method used in this research is phenomenological qualitative approach. Respondents in this research are four people (three men and one woman) who suffered from coronary heart disease. Data were collected through a semi-structured interview, then analyzed by using interpretive analysis technique. The results showed that all respondents had not succeeded in making adjustments to their chronic illness. Three of the four respondents were able to overcome the emotional, cognitive, and physiological effects of chronic disease, but still failed in behavioral aspect especially in terms of lifestyle changes and routine check-up. In contrast, one other respondent showed a negative adjustment on physiological and emotional aspects, but instead had a much more positive adjustment in behavioral aspect because of the high level of compliance in terms of self-management. Factors found to influence psychological adjustment processes in individuals are belief, coping, and family support.
Prooxidant activity as a criterion for the hazard class of biogenic preparations

The presence of various pathogens in municipal wastewaters and the prevention of large-scale invasions is one of the acute problems of modern ecology. Reducing the load on the ecosystem and capital costs during the disinfection of wastewater, their sediments and soil from various pathogens (for example, helminth eggs) is one of the most important tasks of modern ecology. The most successful solution is the use of recycling technologies to obtain reagents that do not require preparation of the disinfection object and subsequent additional treatment. The production of biogenic preparations from agricultural waste (potato seedlings Solanum tuberosum) is low-cost and does not require complex equipment. However, the effect on various links of metabolism, in particular, the effect on free radical processes and lipid peroxidation in the cell, has not been studied enough, which does not allow us to speak about the toxicity of the drug and its effect on the ecosystem of the disinfected object. The study of the prooxidant activity of raw materials for the production of biogenic preparations) in standard model systems of biochemical studies (human erythrocytes) will allow to establish the hazard class.
Reaction-based conjugated polymer fluorescent probe for mercury(II): good sensing performance with “turn-on” signal output

Based on a Hg2+-promoted deprotection reaction of dithioacetal, the conjugated polymer PDT was designed. PDT exhibited significant fluorescence enhancement along with an apparent red-shift upon the addition of mercury ions. Due to the specific chemical reaction, the response was very fast with good selectivity towards Hg2+. Other metal ions, including K+, Na+, Li+, Ba2+, Mg2+, Pb2+, Ca2+, Mn2+, Fe2+, Co2+, Fe3+, Cu2+, Ni2+, Al3+, Cd2+, Cr3+, and Zn2+, had nearly no influence on the sensing process. Moreover, PDT showed high sensitivity with the detection limit of 1.0 × 10−6 mol L−1 and 1 × 10−5 mol L−1 in solution and as test strips, respectively, as a result of the molecular wire and signal amplification effects.
An Activity Recognition Framework for Monitoring Non-Steady-State Locomotion of Individuals with Parkinson’s Disease

Background: Fundamental knowledge in activity recognition of individuals with motor disorders such as Parkinson’s disease (PD) has been primarily limited to detection of steady-state/static tasks (e.g., sitting, standing, walking), and identification of non-steady-state locomotion on uneven terrains (stairs, ramps) has not received much attention. Furthermore, previous research has mainly relied on data from a large number of locations which could adversely affect user convenience and system performance. Methods: Here, individuals with mild stages of PD and healthy subjects performed non-steady-state circuit trials comprising stairs, ramp, and changes of direction. An offline analysis using a linear discriminant analysis (LDA) classifier and a Long-Short Term Memory (LSTM) neural network was performed for task recognition. The performance of accelerographic and gyroscopic information from varied lower/upper-body segments were tested across a set of user-independent and user-dependent training paradigms. Results: Comparing the F1 score of a given segment across classifiers showed improved performance using LSTM compared to LDA. Using LSTM, even a subset of information (e.g., feet data) in subject-independent paradigms appeared to provide F1 score > 0.8. However, employing LDA was shown to be at the expense of being limited to using a subject-dependent paradigm and/or biomechanical data from multiple body locations. Conclusion: The findings could inform a number of applications in the field of healthcare monitoring and developing advanced lower-limb assistive devices by providing insights into classification schemes capable of handling non-steady-state and unstructured locomotion in individuals with mild Parkinson’s disease.
Efficient Time-to-Digital Converters in 20 nm FPGAs With Wave Union Methods

The wave union (WU) method is a well-known method in time-to-digital converters (TDCs) and can improve TDC performances without consuming extra logic resources. However, an earlier study concluded that the WU method is not suitable for UltraScale field-programmable gate array (FPGA) devices, due to more severe bubble errors. This article proves otherwise and presents new strategies to pursue high-resolution TDCs in Xilinx UltraScale 20 nm FPGAs. Combining our new subtapped delay line (sub-TDL) architecture (effective in removing bubbles and zero-width bins) and the WU method, we found that the wave union method is still powerful in UltraScale devices. We also compared the proposed TDC with the TDCs combining the dual sampling structure and the sub-TDL technique. A binning method is introduced to improve the linearity. Moreover, we derived a formula of the total measurement uncertainties for a single-stage TDL-TDC to obtain its root-mean-square resolution. Compared with the previously published FPGA-TDCs, we presented (for the first time) much more detailed precision analysis for single-TDL TDCs.
Antagonistic Effect of Pseudomonas spp on Pythium aphanidermatum and on Plant Growth of Chilli. (Capsicum annuum L.)

The Pseudomonas (subclass-Proteobacteria, order-Pseudmonadales, familyPseudomonaceae) posses a strictly respiratory metabolism using oxygen, they are motile (one or several polar flagella), nonsporulating rods giving Gram negative reaction and 58– International Journal of Current Microbiology and Applied Sciences ISSN: 2319-7706 Volume 10 Number 01 (2021) Journal homepage: http://www.ijcmas. com
Causal dynamical triangulations and the quest for quantum gravity

Quantum Gravity by Causal Dynamical Triangulation has over the last few years emerged as a serious contender for a nonperturbative description of the theory. It is a nonperturbative implementation of the sum-over-histories, which relies on few ingredients and initial assumptions, has few free parameters and - crucially - is amenable to numerical simulations. It is the only approach to have demonstrated that a classical universe can be generated dynamically from Planckian quantum fluctuations. At the same time, it allows for the explicit evaluation of expectation values of invariants characterizing the highly nonclassical, short-distance behaviour of spacetime. As an added bonus, we have learned important lessons on which aspects of spacetime need to be fixed a priori as part of the background structure and which can be expected to emerge dynamically.
The Cell Wall as Shield Against Cadmium Toxicity: Proteome Changes in Alfalfa Stems Exposed to Cadmium

Due to industrial activities such as mining, industrial production and the agricultural use of metalcontaining fertilizers, environmental pollution with toxic metals has become a serious issue. Among others cadmium (Cd) is of great concern due to its widespread occurrence and high toxicity (Satarug et al., 2003). Plants growing on Cd-polluted soil suffer from growth inhibition, chlorosis, and essential physiological and biochemical processes are impacted. Further, metal accumulation in plant tissues introduces Cd into the animal and human food chain. Being a protective barrier, the cell wall acts as the primary defense against environmental threads. Alterations in cell wall structure were reported when plants are exposed to Cd (Krzesłowska, 2011; Parrotta et al., 2015).
The effects of different footprint sizes and cloud algorithms on the top-of-atmosphere radiative flux calculation from CERES instrument on Suomi-NPP

Only one CERES instrument is onboard the Suomi-NPP and it has been placed in cross-track mode since launch, it is thus not possible to construct a set of angular distribution models (ADMs) specific for CERES on NPP. Edition 4 Aqua ADMs were used for flux inversions for CERES-NPP measurements. However, the footprint size of CERES-NPP is larger than that of CERES-Aqua, as the altitude of the NPP orbit is higher than that of the Aqua orbit. Furthermore, cloud retrievals from VIIRS and MODIS, the imagers fly alongside CERES-NPP and CERES-Aqua, are also different. To quantify the flux uncertainties due to the footprint size difference between CERES-Aqua and CERES-NPP, and due to both the footprint size difference and cloud property difference, a simulation is designed using the MODIS pixel level data which are convolved with the CERES-Aqua and CERES-NPP spectral response functions into their respective footprints. The simulation is designed to isolate the effects of footprint size difference and cloud property difference on flux uncertainty from calibration difference between CERES-NPP and CERES-Aqua. The footprint size difference between CERES-Aqua and CERES-NPP introduces instantaneous flux uncertainties in monthly gridded CERES-NPP of less than 4.0 Wm −2 for SW, and less than 1.0 Wm −2 for both daytime and nighttime LW. The global monthly mean instantaneous SW flux from simulated CERES-NPP has a low bias of 0.4 Wm −2 and an uncertainty of 0.8 Wm −2 , the LW has a bias of about 0.1 Wm −2 and an uncertainty of 0.2 Wm −2 . These uncertainties are within the uncertainties of CERES ADMs. When both footprint size and cloud property differences are considered, the uncertainties of monthly gridded CERES-NPP SW flux can be up to 20 Wm −2 in the Arctic regions where cloud optical depth retrievals from VIIRS differ significantly from MODIS. The global monthly mean instantaneous SW flux from simulated CERES-NPP has a high bias of 1.1 Wm −2 and an uncertainty of 2.4 Wm −2 . LW flux shows less sensitivity to cloud property differences than SW flux, with the uncertainties of about 2 Wm −2 in monthly gridded LW flux and about 0.3 Wm −2 for global area-weighted monthly mean LW flux. These results highlight the importance of consistent cloud retrieval algorithms to maintain the accuracy and stability of the CERES climate data record.
Wideband Wide-Scanning Planar Triangular-Lattice Phased Array With Substrate-Integrated Cavity-Backed U-Slot Patches

A compact wideband and wide-scanning planar triangular-lattice phased array is proposed. The proposed element is a U-slot microstrip antenna surrounded by a substrate-integrated cavity (SIC) structure fed by a 50 <inline-formula> <tex-math notation="LaTeX">$\Omega$ </tex-math></inline-formula> coaxial cable. The scan blindness is effectively shifted out of the operation band as the SIC structure obstructs the surface wave and mutual coupling. The proposed array is arranged in a triangular lattice with fewer elements than a rectangular one. A <inline-formula> <tex-math notation="LaTeX">$4\times 8$ </tex-math></inline-formula> planar array prototype is fabricated and measured over <inline-formula> <tex-math notation="LaTeX">$X$ </tex-math></inline-formula>-band. The antenna provides a bandwidth of over 30% frequency range under ±60° scanning in all azimuth planes.
Truck Drivers Commuting to Workplace Under the COVID-19 Circumstances: A Case Study of Alabama State

Background: In late December 2019, COVID-19, commonly referred to as the Coronavirus was identified in China because of the main explanation for recent human respiratory health cases. The virus was first detected in Wuhan City, and during a space of months, it had covered the whole globe. There are yet no therapeutics and vaccines available. The virus has engendered huge drastic changes to world healthcare, economic, transportation, and education systems around the world. Purpose: the general purpose of this study was to explore truck drivers’ views regarding commuting to the workplace under the COVID-19 circumstances of selected counties in Alabama. Methods: The research paper goal necessitated the truck drivers’ views regarding commuting to the workplace. Following a summary of the literature review research phase, the researcher conducted a variety of semi-structured interviews with truck drivers in Alabama through Survey Monkey by a postgraduate student in June-July 2020. Overall, 50 truck drivers have completed the survey. The info was stored on Survey Monkey servers within the Center for Urban and Rural Research (CURR), Department of Community and Regional Planning, Alabama A&M University. Results: The data analysis reveals their main workplace before the COVID-19 pandemic 94% of the truck drivers residing in Alabama especially from Jefferson county provided information about commuting to workplaces, while 4% to the places of educations (lecture room0 and a couple of production site. Furthermore, 92% of the truck drivers reported NO change within the means of transport in commuting trips during the COVID-19 pandemic, while 8% indicated changes within the means of transport. Conclusion: This research paper contributes important new empirical analysis of the truck drivers’ views regarding commuting to the workplace under the COVID-19 pandemic to some extent where there's an abundance of conceptual papers and opinion pieces but still scant evidence on the particular road safety of the pandemic for researchers to think about on potential person and situation factors related to COVID-19 that would affect road safety during and after the pandemic. Collaborative efforts by researchers and public and personal sectors are going to be needed to collect data and develop truck drivers' road safety strategies in reference to the new reality of COVID-19.
The Study of Events Approach Applied to the Impact of Mergers and Acquisitions on the Performance of Consulting Engineering Companies

Recent research suggests that one of the main motivations for mergers and acquisitions is the attempt to acquire companies to incorporate intangible assets. Such assets provide important sources of sustainable competitive advantages and opportunities for growth. This article analyzes the strategies of engineering companies, as well as value creation in acquisition events of multinational companies, by using the study of the events method, providing an innovative way to be applied to this phenomenon. This method is used in our research to study the influence of the announcement of acquisitions on the abnormal accumulated returns of the acquiring companies, and is allowed to confirm that influence. In general, the average accumulated returns were positive and statistically significant in the three windows of the method, according to the significance tests used. The results validate the hypothesis that the events generate synergy gains for market players, emphasizing the importance of growth via acquisitions for the sector under analysis.
Estimation of productive losses caused by withdrawal of antibiotic growth promoter from pig diets – Meta-analysis

This study was designed to simulate productive and economic losses due to the withdrawal of antibiotic growth promoters (AGP) from pig diets. Articles that compared diets with AGP (AGP+) or without AGP (AGP–) for pigs were collected from electronic databases and the performance results were entered in a database. A meta-analysis was performed following the sequence: graphical analysis, correlation, and variance-covariance. The performance results observed in the meta-analysis, feed cost, and AGP costs were used to build equations to estimate the economic effect of withdrawing AGP. The database comprised 81 scientific articles containing 103 experiments totalizing 42,923 pigs. Avilamycin (24.7 %) was the most frequent AGP in the database, followed by Colistin (15.4 %), Tiamulin (11.7 %), Tylosin (8.0 %), Lincomycin (9.4 %), and Bacitracin (5.4 %). Weight gain (p < 0.05) increased in AGP+ diets during postweaning (6.5 %). However, there was no effect of AGP on weight gain of growing-finishing pigs. There was better (p < 0.05) feed conversion in pigs fed AGP+ diets in all rearing phases. Weight gain and feed conversion improved (p < 0.05) with the addition of Avilamycin, Bacitracin, and Tylosin. AGP withdrawal in the post-weaning phase increased feed costs by US$ 0.86 per animal and in growing-finishing phase the increase was US$ 3.11. Thus, pigs fed AGP+ diets have a better performance than pigs fed AGPdiets and the withdrawal of AGP increases feed costs.
Differential Mode Noise Prediction and Analysis in Single-Phase Boost PFC for the New Frequency Range of 9–150 kHz

High penetration of power electronics due to the concentration of switching frequency in the range of 9–150 kHz may create new challenging issues. Currently, regarding the recent version standard (IEC 61000-6-3), there is a lack of enough insight and fundamental studies despite reported electromagnetic interference (EMI) noise problems in this frequency range. Hence, this article proposes a time-frequency analytical modeling method for characterizing differential mode (DM) noise in a single-phase power factor correction (PFC) converter in this new frequency range. Provided comparative simulation analysis shows the proposed method's ability to estimate DM noise with a 9–150 kHz frequency range at high accuracy utilizing the double Fourier analysis method. Moreover, the obtained experimental results on a 1-kW single-phase boost PFC converter validate the proposed EMI modeling approach's effectiveness, demonstrating an error of less than 1.8 dB for the considered experimental case studies.
The Effect of R&D Expenditures on Earnings Management: A Research on Bist-All Shares

R&D expenditures are important in increasing the level of information and technological development. Efficiency in production, cost reduction and competitive advantage are achieved with the added value created by successful R&D activities. However, in the process of accounting and reporting the R&D expenditures, some manipulative applications can be implemented by the business management for achieving personal or corporate targets. The purpose of this research is to reveal the effect of R&D expenditures on earnings management. In this context, from 2007 to 2018, 65 companies that made R&D expenditures included in BIST-All Shares Index were examined. The earnings management effect calculated on the Modified Jones Model, taking into account the current period, one-year and two-year time lag of R&D expenditures was tested with panel data analysis. As a result of the research, it was determined that R&D expenditures negatively affect earnings management in the current period and positively in lagged periods. In addition, while size and leverage have negative effects on earnings management in the current period, one-year and two-year time lag, no statistically significant relationship was found in terms of return on assets.
Improving student satisfaction through social media marketing activities: The mediating role of perceived quality

The connection between education and technology offers an exciting opportunity for universities to utilize social media marketing activities not only to recruit new students or to create brand image and reputation, but also to enhance student satisfaction. Despite having a large number of studies indicating the importance of social media as a tool for student recruitment, the studies on the importance of social media marketing activities (SMMA) for students who are already enrolled are rare. This study aims at examining the relationship between SMMA and student satisfaction, considering perceived quality as a mediator in the higher education sector in North Cyprus. Utilizing a cross-sectional online survey, the data for this study was collected during October- November 2020 from 424 international students enrolled in North Cyprus universities. The model was assessed and the data was analyzed using Structural Equation Model (SEM) in AMOS statistical software. The findings suggest that SMMA has a positive relationship with the perceived quality of education, and perceived quality mediates the relationship between SMMA and student satisfaction positively.
A Stochastic Lomax Diffusion Process: Statistical Inference and Application

In this paper, we discuss a new stochastic diffusion process in which the trend function is proportional to the Lomax density function. This distribution arises naturally in the studies of the frequency of extremely rare events. We first consider the probabilistic characteristics of the proposed model, including its analytic expression as the unique solution to a stochastic differential equation, the transition probability density function together with the conditional and unconditional trend functions. Then, we present a method to address the problem of parameter estimation using maximum likelihood with discrete sampling. This estimation requires the solution of a non-linear equation, which is achieved via the simulated annealing method. Finally, we apply the proposed model to a real-world example concerning adolescent fertility rate in Morocco.
Interior Permanent Magnet Synchronous Motor Drive System with Machine Learning-Based Maximum Torque per Ampere and Flux-Weakening Control

An interior permanent magnet synchronous motor (IPMSM) drive system with machine learning-based maximum torque per ampere (MTPA) as well as flux-weakening (FW) control was developed and is presented in this study. Since the control performance of IPMSM varies significantly due to the temperature variation and magnetic saturation, a machine learning-based MTPA control using a Petri probabilistic fuzzy neural network with an asymmetric membership function (PPFNN-AMF) was developed. First, the d-axis current command, which can achieve the MTPA control of the IPMSM, is derived. Then, the difference value of the dq-axis inductance of the IPMSM is obtained by the PPFNN-AMF and substituted into the d-axis current command of the MTPA to alleviate the saturation effect in the constant torque region. Moreover, a voltage control loop, which can limit the inverter output voltage to the maximum output voltage of the inverter at high-speed, is designed for the FW control in the constant power region. In addition, an adaptive complementary sliding mode (ACSM) speed controller is developed to improve the transient response of the speed control. Finally, some experimental results are given to demonstrate the validity of the proposed high-performance control strategies.