Miracle-dz/UniFAR-FTUs huggingface dataset:
MULTIPURPOSE MEASUREMENT MODELS FOR ADJUSTMENT BY THE LEAST-SQUARES METHOD

The development of multipurpose measurement models is the precondition for software development for simultaneous adjustment of the large scope and complicated combinations of the measurement results by the least-squares method. Multipurpose measurement models for software can be a helpful tool for processing the final measurement results provided by different measurement methods applying the mentioned software; processing the measurement results of measurement standards comparisons, interlaboratory comparison, and calibration procedures; estimating the additive and multiplicative systematic components of measurement errors and their uncertainty; processing complicated combinations by binding or linking up of the interlaboratory comparison and calibration results in the time; simultaneous processing of the measurement results obtained by various methods e.g. by the method of direct measurements and comparisons; fast-changing the multipurpose measurement models from linear to non-linear type. Processing of the results by software based on the multipurpose measurement model algorithm can help to established a comprehensive measurement traceability network by pooling the single traceability chains.
Work Pressures Stemming From School Authorities and Burnout Among Physical Education Teachers: The Mediating Role of Psychological Needs Thwarting

Purpose: To examine the role of psychological need thwarting in mediating physical education teachers’ work pressures stemming from school authorities and burnout. Method: A total of 345 physical education teachers (M = 47.46; SD = 8.79) completed some online validated questionnaires. Results: Structural equation modeling first revealed that pressures from school authorities predicted needs thwarting which, in turn, predicted burnout. In a second model, in which burnout was deemed as a multidimensional construct, autonomy and competence thwarting was found to predict both emotional exhaustion and depersonalization. Discussion: These findings suggest that when teachers find themselves pressured by school authorities to act in certain way, they are more likely to feel more exhausted and to adopt more cynical attitudes toward their students due to the thwarting of their basic needs. Practical implications related to school and national policies are discussed. Conclusion: External pressures affect PE teachers’ emotional states and educational policies should address this issue.
Effect of Postpartum Depression in Mothers With 0–1-Year-Old Infants on Father–Infant Attachment

Objective: This analytical, cross-sectional study was designed to determine the effect of postpartum depression in mothers with 0–1 year old infant on father–infant attachment. Methods: The study included 207 mothers and 207 fathers with 0–12-month-old infants. The data were collected using the Introductory Information Form, Edinburgh Postpartum Depression Scale (EPDS), and Father–Infant Attachment Scale (FIAS). The data were analyzed using standard deviation, arithmetic mean, Mann–Whitney U test, and correlation analysis. Results: Total 18.3% of the mothers were at risk of postpartum depression, and the mean total EPDS score was 7.19 ± 5.17. The mean total FIAS score was 74.95 ± 6.21. There was no statistically significant difference between the mean sub-scale and total FIAS scores and the mean EPDS scores (p > 0.05). There was a significant, weak, and negative correlation (r = −0.15; r = −0.181) between the EPDS scores and the sub-scale and total FIAS scores (p < 0.05). Conclusion: We concluded that postpartum depression (PPD) in mothers has a negative effect on father–infant attachment. The primary responsibilities of midwives should include determining the factors affecting parent–infant attachment and providing training and consultancy to establish parent–infant communication. A secure father–infant attachment can be achieved by ensuring the participation of fathers during the pregnancy, delivery, and postpartum periods and careful evaluation of mothers in terms of PPD.
The Social Capital among Elderly Population of Chandigarh: Cross Sectional Study

Introduction: Social capital is defined as social cohesion among communities. This refers to processes between people which establish network, social trust and co-operation and mutual benefit. The present study was conducted to know the status of social capital among elderly population of Chandigarh city, India. Methods: The study was conducted from January to April 2017. The study was community based using convenience sampling. A total of 300 elderly subjects were selected for interview from the electoral rolls. The survey instrument was modified Onyx and Bullen scale consisting of 30 items with responses on likert scale from 1-4. Results: Mean age of respondents was 66.47 years. Male: Female ratio was 1.4:1. 45.7% owned their residence whereas 54.3% lived as tenants. Social capital score of majorities of elderly was good (63.7%) followed by average (19.3%). Highest mean scores were received for questions namely: would you help someone if their vehicle breaks down (3.03±0.69), do you agree to helping yourself when you help others (3.15±0.78) and have you visited your neighborhood in the past week (3.01±0.82). Poorest scorewere received for questions: while on shopping, are likely to run into friends (1.11±0.53) and have you done a favor for a sick neighbor in last 6 months (1.33±0.53). Conclusion: Social capital status among elderly of Chandigarh city was good andhigher scores were seenamong males. Highest mean scores were seen in factor 4 (proactivity in social context) and lowest among family and factor 5 (friend’s connections).
Supply Chain Scheduling Using Double Deep Time-Series Differential Neural Network

The purpose of supply chain scheduling is to be able to find an optimized plan and strategy so as to optimize the benefits of the entire supply chain. This paper proposes a method for processing tightly coordinated supply chain task scheduling problems based on an improved Double Deep Timing Differential Neural Network (DDTDN) algorithm. The Semi-Markov Decision Process (SMDP) modeling of the state characteristics and action characteristics of the supply chain scheduling problem is realized, so as to transform the task scheduling problem of the tightly coordinated supply chain into a multi-stage decision problem. The deep neural network model can help fit the state value function, and the unique reinforcement learning online evaluation mechanism can realize the selection of the best action strategy combination, and optimize it under the condition of only the stator processing time. Finally, the optimal action strategy group is obtained.
User-Load-Compatible Masking Schemes for Raptor-Like Protograph-Based LDPC Codes in Gaussian Multiple Access Channels

In this paper, the backward-compatible coding schemes are explored to achieve the near-capacity performances in dynamic LDPC-coded Gaussian multiple access channels (GMAC). Specifically, some user-load-compatible (ULC) masked edges in a raptor-like protograph are masked with the variation of user load <inline-formula><tex-math notation="LaTeX">$u$</tex-math></inline-formula>. To distinguish the edges in a protograph, a multi-user protograph-based extrinsic information transfer (MU-PEXIT) chart is developed. Based on this chart, an iterative edge selection algorithm combined with a theoretical simplification is proposed to facilitate the selection of independent masked edge sets (MESs) for varying <inline-formula><tex-math notation="LaTeX">$u$</tex-math></inline-formula>. To further decrease the complexity of the independent masking scheme, a nested version is presented, where the MESs for varying <inline-formula><tex-math notation="LaTeX">$u$</tex-math></inline-formula> are nested and derived from the same mother MES. Meanwhile, nested selection algorithms are provided to select the nested MESs. Extensive simulation results demonstrate that the masked 5G-NR LDPC codes can maintain near-capacity performances regardless of the variation of <inline-formula><tex-math notation="LaTeX">$u$</tex-math></inline-formula>. Besides, these ULC masked codes achieve comparable performances with the state-of-the-art LDPC codes which are specially re-designed in a static GMAC.
Ensemble Based Temporal Weighting and Pareto Ranking (ETP) Model for Effective Root Cause Analysis

: Root-cause identification plays a vital role in business decision making by providing effective future directions for the organizations. Aspect extraction and sentiment extraction plays a vital role in identifying the root-causes. This paper proposes the Ensemble based temporal weighting and pareto ranking (ETP) model for Root-cause identification. Aspect extraction is performed based on rules and is followed by opinion identification using the proposed boosted ensemble model. The obtained aspects are validated and ranked using the proposed aspect weighing scheme. Pareto-rule based aspect selection is performed as the final selection mechanism and the results are presented for business decision making. Experiments were performed with the standard five product benchmark dataset. Performances on all five product reviews indicate the effective performance of the proposed model. Comparisons are performed using three standard state-of-the-art models and effectiveness is measured in terms of F-Measure and Detection rates. The results indicate improved performances exhibited by the proposed model with an increase in F-Measure levels at 1%–15% and detection rates at 4%–24% compared to the state-of-the-art models.
Effect of individual entrepreneurship orientation on export intention in microand small enterprises: The moderating role of access to finance

Purpose: Considering the importance of the internationalization of micro and small enterprises (MSEs) for the national economic growth, this study was conducted in order to determine the possible impact of individual entrepreneurial orientation (IEO) on export intention in MSEs through the moderating effect of access to finance. Methodology: This research consists of a survey of 150 agriculture farm owners from Nepal conducted in January 2020. The researchers used SmartPLS as a research tool to analyse the data. Results: The results of this study indicate that out of five dimensions of IEO, only autonomy has a positive significant effect on export intention, whereas access to finance had no moderating role in the effect of IEO on export intention. Conclusion: Overall, this study provides profound insight about IEO in the context of a less developed country like Nepal and also sheds light on Nepalese farm owners’ IEO and their intention to export.
A High Isolation Multiband MIMO Antenna Without Decoupling Structure for WLAN/WiMAX/5G Applications

—In this paper, 1 × 1 and 2 × 2 Multiple-input Multiple-output (MIMO) antenna is designed for a multiband application. The antenna consists of three concentrated decagon-shaped rings which are responsible for obtaining the three resonance frequencies. The proposed antenna has a unique design technique; without using any decoupling structure the antenna attains better isolation performance. First, a two (1 × 1) element MIMO antenna with three diﬀerent orientations (Orientation-I, II, and III) is studied. Second, a four-element MIMO antenna is designed without any decoupling structure for better performance. The above two antenna designs are fabricated and measured. The dimension of the proposed antenna element is 23.5 mm × 26.5 mm, and it has − 10 dB impedance bandwidth over 2.4–2.52 GHz, 3.66–4 GHz, and 4.62–5.54 GHz, which cover various applications such as WLAN (2.4/5.2 GHz), WiMAX (2.5/5.5 GHz), public safety (4.9 GHz), and 5G (3.6–3.8 GHz). The proposed MIMO antenna diversity performances such as isolation, ECC, Directivity Gain, TARC, Channel Capacity Loss (CCL), and Mean Eﬀective Gain (MEG) are studied.
Nonuniform Power Factor Partial Compensation for Compensating Current Reduction Using Particle Swarm Optimization in Traction Power Supply System

Railway power conditioner (RPC) can provide comprehensive reactive power, unbalanced current, and harmonics compensation in the traction power supply system. Under full compensation (FC), RPC can increase the three-phase power factor (PF) to unity but leads to high current rating and operation loss. In previous studies, partial compensation (PC) for the reduction of RPC current rating was proposed. An analytical solution of PC coefficients was used, which limits design freedom since identical PF is required at three-phase. This uniform power factor partial compensation (UPFPC) limits RPC in achieving a further reduction in its current rating. This article proposes a nonuniform power factor partial compensation (NPFPC), which can further reduce RPC current rating within the acceptable PF region and under the voltage unbalance threshold. However, determining an analytical solution under NPFPC is complicated, considering three degrees of freedom. This is achieved in this article by defining the lower boundary of grid side PFs and optimizing by particle swarm optimization. It is found through investigations that NPFPC can effectively reduce the compensating current rating by 56% compared to FC mode and 53% compared to UPFPC. Simulation and experimental results verify the validity of the proposed NPFPC.
Economic potential of Ukraine: assessment and forecasting

Based on the results of the open statistical data analysis, the paper justifies indicators for an integral assessment of the economic potential of Ukraine by types of economic activity of business entities. Since the potential is implemented in the future, the paper uses trend analysis to determine the prospective values of previously selected indicators. At the same time, some relative indicators were decomposed into absolute elements (as each of the absolute indicators has its own dynamics of changes). Stepwise, logarithmic, exponential, and other dependencies are used to predict indicators. In addition, the prospective values of indicators were normalized in order to get the correct calculation of the integral indicator in the paper. The integral indicator of the economic potential of Ukraine for each type of economic activity of business entities for the year 2022 is calculated as a result of generalizing the normalized values of prospective indicators. The calculation results can be used to distribute budget funds that are planned to be used for ensuring the long-term development of the Ukrainian economy sectors.
Time Window Selection of Seismic Signals for Waveform Inversion Based on Deep Learning

Seismic full-waveform inversion (FWI) is a powerful tool to image the high-resolution subsurface properties by fitting synthetic seismogram to observed data. To reduce the nonlinearity of FWI, a critical step is to exclude the noises and select seismic signals that are not cycle skipped for the inversion. Conventionally, the windows are created through a short-term-average-to-long-term-average ratio (STA/LTA) method and then selected by comparing the synthetic and observed data with fine-tuned controlling parameters. It works well in regional and global full-waveform adjoint tomographies, but the efficiency and accuracy become a problem when dealing with a tremendous amount of data in the exploration seismology. Here, we design a fully convolutional network (FCN) to select the time window. We manually select 24,000 high-quality training time windows from field data generated by the traditional STA/LTA-based algorithm (i.e., FLEXWIN). The FCN prediction results on test sets achieve good levels of accuracy, suggesting that the neural network provides results comparable to those manually selected ones. Our tests show that the FCN method is approximately 10,000 times faster than the conventional FLEXWIN algorithm. Moreover, the windowing results of the FCN method are more stable than those of FLEXWIN without any parameter tuning, showing the great potential for FWI. We validate the effectiveness of the window selection strategy in FWI through numerical experiments.
Decomposed Task Scheduling for Security-Critical Mobile Cyber–Physical Systems

With the recent advances in mobile sensing, computing, and communication technologies, mobile cyber–physical systems (MCPSs) become a promising networking paradigm that provides mobile users with various applications and services from cyber space to the physical world. One of the main challenges in the MCPSs is to defend security threats launched by adversaries. Exploiting security services to defend threats is energy consuming, whereas the energy of mobile devices is generally limited since most of mobile devices are battery powered. This necessitates the need to design new methodologies to tackle the tradeoff between security and energy of MCPSs. To this end, this article aims to maximize MCPSs’ security under the constraints of energy and deadline. In this article, we first formulate the MCPS security maximization problem as a mixed-integer nonlinear programming (MINLP) problem and then transform it into a mixed-integer linear programming (MILP) problem without performance degradation. To solve the transformed MILP problem efficiently, we propose a decomposed algorithm to derive the optimum task scheduling solution instead of using MILP solvers that may be very time consuming for MCPSs of large granularity. The derived task scheduling solution decides the assignment, operating frequency, execution order, as well as security service selection for all tasks. We implement a series of simulation-based experiments to validate the proposed decomposed task scheduling scheme. Simulation results demonstrate that the proposed scheme increases system security level by 20.38% and 65.11% on average as compared to a state-of-art approach and a baseline method.
Improving Energy Efficiency in UAV Attitude Control using Deep Reinforcement Learning

—Attitude control refers to controlling the rotational motion of an Unmanned Aerial Vehicle (UAV) about its axes. Any movement requires energy consumption and UAV batteries store limited energy. We propose the use of reinforcement learning to optimise energy usage in UAVs. We use Proximal Policy Optimization (PPO) algorithm to train the model, modifying the existing algorithm to incorporate sigmoid activation function. We have introduced Ornstein Uhlenbeck noise to the policy function with the intention of adding the unpredictability found in real world environments. We have designed the reward function of our algorithm such that the quadcopter aims to change its existing angular velocity to achieve the target angular velocity. While this attitude control takes place, energy is spent due to motor/propeller revolutions. Our reward function minimizes the rapid change in motor speeds which causes fast battery depletion, thus saving energy and enhancing UAV ﬂight time.
New access to chiral pyrrolidine and piperidine β-enamino ketones. Application to the enantioselective synthesis of (–)-hygroline

We report here a new access to chiral pyrrolidine and piperidine β-enamino ketones by condensation of (S)-phenylglycinol with ω-oxo alkynones. As an illustration of the synthetic potential of the target compounds, the total enantioselective synthesis of alkaloid (-)-hygroline was achieved.
Strength training promotes anthropometric and functional benefits in sedentary subjects: does a personal trainer matter?

Purpose. the purpose of this study was to compare the effects of strength training performed with and without the super­ vision of a personal trainer on the anthropometric, functional, and biochemical responses of sedentary adults. Methods. Overall, 38 sedentary men were divided into 3 groups: control group ( n = 12), no personal trainer group ( n = 14), and personal trainer group ( n = 12). Participants of all groups were submitted to pre­training data collection, including anthropometric evaluation, functional and cardiometabolic tests, and blood collection for biochemical parameters. then, the subjects were involved in strength training of 50 minutes 3 times per week for 8 weeks. After the strength training program, the participants were submitted to the same data collection. Results. the personal trainer group presented a decrease in waist circumference, systolic blood pressure, sum of skinfolds, abdominal resistance, pectoral resistance, one­repetition maximum on bench and leg press compared with the no personal trainer group ( p < 0.05). the no personal trainer group had an improvement only in the sum of skinfolds ( p < 0.05). Conclusions. the results of the present study indicate that 8 weeks of strength training with a personal trainer can produce important changes in body composition and blood pressure in sedentary subjects.
Internet of Things (IoT) Based Modeling for Dynamic Security in Nuclear Systems with Data Mining Strategy

The data mining design incorporated with big data based cloud computing system is investigated for the nuclear terrorism prevention where the conventional physical protection system (PPS) is modified. The networking of terror related bodies is modeled by simulation study for nuclear forensic incidents. It is needed for the government to detect the terrorism and any attempts to attack to innocent people without illegal tapping. Although the mathematical algorithm of the study can’t give the exact result of the terror incident, the potential possibility could be obtained by the simulations. The result shows the shape oscillation by time. In addition, the integration of the frequency of each value can show the degree of the transitions of the results. The value increases to -2.61741 in 63.125th hour. So, the terror possibility is highest in later time
Review of Autonomous Intelligent Vehicles for Urban Driving and Parking

With the concept of Internet-of-Things, autonomous vehicles can provide higher driving efficiency, traffic safety, and freedom for the driver to perform other tasks. This paper first covers enabling technology involving a vehicle moving out of parking, traveling on the road, and parking at the destination. The development of autonomous vehicles relies on the data collected for deployment in actual road conditions. Research gaps and recommendations for autonomous intelligent vehicles are included. For example, a sudden obstacle while the autonomous vehicle executes the parking trajectory on the road is discussed. Several aspects of social problems, such as the liability of an accident affecting the autonomous vehicle, are described. A smart device to detect abnormal driving behaviors to prevent possible accidents is briefly discussed.
CYCLODEXTRINS - FIELDS OF APPLICATION. PART II ¶

This paper represents an analysis of potential and current applications of cyclodextrins as biologically active substances in medicine. The main applications described here include use of cyclodextrins as agents that form inclusion complexes with endogenous substances (membrane lipids, cellular cholesterol), agents that form inclusion complexes with exogenous substances with their man role as guest molecules (sugammadex, FBCx), agents that block endogenous and exogenous macromolecules (ion channels, anthrax toxin, α-hemolysin), and agents which activity is based on the chemical nature of them and of their derivatives (cyclodextrin polysulphate derivatives). The fi rst classifi cation for medically important biological activity of cyclodextrins has been proposed.