Miracle-dz/UniFAR-FTUs huggingface dataset:
UAV-enabled communications using NOMA for 5G and beyond: research challenges and opportunities

Future wireless networks are expected to provide ubiquitous connectivity to a wide range of devices with varying traffic patterns, wherever and whenever it is required. Unmanned aerial vehicles (UAVs) are also considered a potential technique for accommodating massive connections and providing seamless coverage. They can be used as flying base stations (BSs) to take advantage of line-of-sight (LoS) connections and effectively support wireless communication coverage and throughput for 5G and beyond. However, the use of highly mobile and energy-constrained UAVs for wireless communications brings plenty of new challenges. 5G wireless networks require non-orthogonal multiple access (NOMA) to be able to meet heterogeneous requirements for low latency, high dependability, massive connection, better fairness, and high throughput. This paper presents an overview of NOMA-based UAV enabled communications by introducing the background of UAV communication and NOMA schemes. Power allocation schemes are also explored as they are critical controlling mechanisms for performance optimization of NOMA-UAV systems. We also categorize UAV-enabled communication applications for usage in both routine professional settings and emergency scenarios. Finally, we address several open research questions that need to be solved for NOMA, as well as new opportunities and future research trends to be exploited.
Implementation English Teacher’s Creativity in Transition Period at Junior High School

Implementation of face-to-face learning during the transition period makes students confused and tends to be passive when carrying out learning in class. That was because previous learning was carried out online and tended not to run effectively so it was necessary to apply teacher creativity in the classroom to increase student understanding and activity. This study aimed to analyze how teachers implement their creativity towards face-to-face learning during the post-pandemic transitional period. Embedded mixed methods were used as a research design where qualitative data became the main data while quantitative data were used as supporting data. The English teacher in junior high school is choosen as the research subject. Data collection are using self-rated questionnaire, classroom observation, and in-depth interview. For the data analyze technique the researcher assess by assigning a point to each statement on the sheet of observations. The results of the study show that 1) Teachers tend not to meet the criteria of being creative teachers in implementing learning during the transition period where teachers fall into the unlikely creativity category in implementing creativity. Teachers who tend to be uncreative can make students more passive and difficult to understand learning in class, especially during the transition period where students who previously learned less effectively through online platforms are now switching to face-to-face learning which requires them to understand the subject matter well.
A new C-band polarimetric radar with simultaneous transmission for hydrometeor classification and rainfall measurement

Enterprise Electronics Cooperation (EEC) has developed a state-of-the-science C-band polarimetric radar based on simultaneous transmission of horizontal (Zh) and vertical (Zv) wavelengths for commercial applications, termed SIDPOL. Deployments of the SIDPOL include the United Kingdom Meteorological Service. Recently, as part of the SIDPOL system, Weather Decision Technologies (WDT) has work with the National Severe Storms Laboratory (NSSL) to integrate the hydrometeor classification algorithms slated to be part of the WSR-88D platform. This paper discusses the SIDPOL system and the NSSL algorithm components.
MODEL SELECTION FOR FUNCTIONAL MIXED MODEL VIA GAUSSIAN PROCESS REGRESSION

In recent years, a functional mixed model (FMM) has attracted considerable attention in longitudinal data analysis, because of its flexibility. The FMM consists of a fixed effect or a population mean function and some subject-specific functional random effects. In this paper, we introduce the FMM constructed by using a basis expansion technique and a Gaussian process regression, and consider the model evaluation and selection problem for the estimated model. When estimating the unknown parameters included in the FMM by the maximum penalized marginal likelihood method, the FMM is extremely sensitive to the choice of tuning parameters. In order to appropriately select them, we derive two model selection criteria for the FMM based on the perspective of information or Bayesian theories by using a marginalization approach. We conduct Monte Carlo simulations to investigate the effectiveness of our proposed modeling procedures. The proposed modeling procedures for the FMM are applied to the analysis of a longitudinal gene expression data.
Performance Estimation of Solar Imagery Using Different Types of Atmospheric Turbulence Models

This paper proposed several approaches for estimating the optical turbulence of the Earth’s atmosphere and their effect on solar images’ resolution using ground-based telescopes based on von Kárman, Kolmogorov, and modified von Kárman PSDs models. The results showed a strong correlation coefficient for the modified von Kármán model of atmospheric representation. As can be seen in the case where solar adaptive optics have been properly designed, they typically decrease aberration considerably and provide greatly improved imagery.
Fake News on Social Media A Threat to Social Conditions: Critical Discourse Study

The phenomenon of using language in cyberspace or social media is trending with various up-to-date applications with the aim of being more effective and efficient communicating. This research really needs to be done because there is rampant communication that is not wise through social media, in addition, no one has studied this phenomenon from a language perspective. This research focuses on fake news texts in the media social context with a focus on discourse, namely the impact of fake news texts on social conditions. The purpose of this study is to describe in depth the problem of the threat of spreading fake news texts on social media to social conditions. The aim is to find out and analyze threats in the form of the impact of spreading fake news on social media on social conditions. Methods and techniques of data collection by way of observation, documentation of the instrument with a questionnaire. Based on the results of an analysis of the data found related to the spread of fake news texts on social media, it has an impact on social conditions in terms of aspects; (a) Aspects of media literacy, less wise in using social media; (b) The economic aspect, the motivation to live instantly is reflected in unhealthy economic competition; (c) Health aspect, reflected by people with unhealthy lifestyles and obstacles to health stability nationally; (d) Political aspects, the occurrence of dishonest and unfair political implementation in the process of political contestation.
Analysis of entrepreneurial ecosystem factors on productive entrepreneurship of digital start-ups in Indonesia

Technological developments and innovation create challenges for businesses to develop sustainable innovations in the light of global shifts in social, environmental and economic conditions in order to advance or maintain their position. Digital startups are an alternative for businesses to create value, and in the long term improve a country's economy. Entrepreneurial ecosystem theory is widely used to research digital startup ecosystems, but the factors in the model have not been tested quantitatively in terms of their effect on productive entrepreneurship. This study is a quantitative study by embarrassingly distributing the survey to 120 respondents who are founders and C-level at digital start-ups in Indonesia. Regression analysis using SPSS was carried out to see the correlation between variables. The results of this study explain that networking, leadership, finance, human resources and knowledge have an influence on productive entrepreneurship in the startup and digital ecosystem in Indonesia.
Selection of efficient machine learning algorithm on Bot-IoT dataset for intrusion detection in internet of things networks

With the growth of internet of things (IoT) systems, they have become the target of malicious third parties. In order to counter this issue, realistic investigation and protection countermeasures must be evolved. These countermeasures comprise network forensics and network intrusion detection systems. To this end, a well-organized and representative data set is a crucial element in training and validating the system's credibility. In spite of the existence of multiple networks, there is usually little information provided about the botnet scenarios used. This article provides the Bot-IoT dataset that embeds traces of both legitimate and simulated IoT networks as well as several types of the attacks. It provides also a realistic test environment to address the drawbacks of existing datasets, namely capturing complete network information, precise labeling, and a variety of recent and complex attacks. Finally, this work evaluates the confidence of the Bot-IoT dataset by utilizing a variety of machine learning and statistical methods. This work will provide a foundation to enable botnet identification on IoT-specific networks.
Transformational leadership and employees' performance: the mediating role of employees' commitment in private banking sectors in Pakistan

In this competitive era, organisations face issues related to leadership styles in maintaining employee performance and commitment. Leadership, like transformational leadership, motivates employees to achieve the target for an organisation. However, employee performance and commitment are behavioural factors that help them achieve organisational goals. These factors develop emotional attachments like loyalty and faithfulness among the employees towards the organisations. This study examines the relationship between transformational leadership, employee commitment, and employee performance in a developing country like Pakistan. Although, in the twenty-first century, issues related to leadership styles have given new ways for researchers to further insight into employee performance and commitment study. However, this paper aims to determine the impact of transformational leadership on employee performance by mediating the role of employee commitment in the private banking sector. The cross-sectional and descriptive survey was used in the data collection; 466 employees of the banks participated in the study. The research indicates that transformational leadership positively impacts employee performance and commitment. The findings show that transformational leadership has a positive effect on employees' performance and employee commitment.
Bayesian Assessment of a Binary Measurement System with Baseline Data

SYNOPTIC ABSTRACT Binary measurement systems that classify parts as either pass or fail are widely used. In industrial settings, many previously passed and failed parts are often available. We develop a Bayesian model to incorporate baseline information to determine whether a part originated from the stream of previously passed or failed parts as well as the overall pass rate of the inspection system. Simulation studies demonstrate the viability of our proposed method, and we compare our model to simpler models that do not incorporate all baseline information. We show that in some cases incorporation of baseline data can result in the reduction of posterior standard deviations by a factor of two. Additionally, our Bayesian approach has the advantages of allowing the incorporation of expert opinion and not relying on the assumption of normality.
High-precision and high-accuracy polarimetry of exoplanets

Detecting polarization of the light reflected from an exoplanet requires extremely high-precision polarimeters and highaccuracy calibration techniques. The polarimetric precision of a few parts per million (ppm), approaching the photon noise, was demonstrated for the Sun and bright distant stars by several groups and instruments. However, the accuracy of absolute polarimetric calibration strongly depends on the polarimeter design and observing conditions, which results in largely unknown systematic errors hindering the exoplanet polarization detection. Here we discuss some of the crucial aspects of exoplanet polarimetric data acquisition, e.g., effects of seeing, sky polarization, telescope polarization, etc. We simulate examples of polarimetric measurements with various levels of random and systematic errors. They demonstrate that sparse measurements (ten or less) and unknown systematic errors can hinder exoplanet signal detection even when the signal is significantly larger than the polarimetric precision. We discuss various approaches which help improve random errors (precision) and mitigate systematic errors (accuracy) caused by various effects. We also discuss the performance of polarimeters with different designs and indicate their strengths and weaknesses in terms of precision and accuracy.
Dynamic Ultrasonic Wave Generators as an Alternative Technology to Field Rat Repellents

The case of crop failure caused by pest attacks is one of the problems in agriculture that is always interesting to study. Apart from weather factors, pest attacks on rice and corn agricultural commodities often occur just before harvest time. Currently, farmers often use toxic materials and even electricity as a means to repel and eradicate rat pests. However, this method can be dangerous for the farmers themselves. There have even been several cases of death due to electrical equipment installed in agricultural areas. Based on these problems, a pest repellent will be made by utilizing ultrasonic waves from a solar-powered power source so that it can be used efficiently, practically and safely. This tool is built using IC NE556C, Solar Panel, LDR Sensor (Light Dependent Resistor), and Ultrasonic Piezo PTC 4000 Speaker. IC NE556C timer and ultrasonic wave multivibrator are used to generate ultrasonic waves randomly according to the program. The purpose of making this tool is as an alternative to field mouse pest repellent using ultrasonic waves by utilizing solar energy in handling pests in agricultural areas.
Regional Collaborative Prediction of Air Pollutants Based on CNN-BiLSTM Model

The development of industry has brought serious air pollution problems. It is very important 7 to establish a high-precision and high-performance air quality prediction model and take corresponding 8 control measures. In this paper, based on four years of air quality and meteorological data in Tianjin, 9 China, the relationship between various meteorological factors and air pollutant concentrations are 10 analyzed, the abnormal data are detected and preprocessed. A hybrid deep learning model consisting of 11 convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) is proposed 12 to predict pollutant concentrations, and the effects of three different database input modes are compared. 13 In addition, the Bayesian optimization algorithm is applied to obtain the optimal combination of hyper-14 parameters for the proposed deep learning model, which makes the model have higher generalization 15 ability. Furthermore, based on the air quality data of multi stations in the region, a regional collaborative 16 prediction method is designed, the concept of strongly-correlated station (SCS) is defined, and the results 17 of collaborative prediction are modified using the idea of SCS, effectively improving the accuracy of 18 prediction. 19
Computer simulation of confined liquid crystal dynamics.

Results are presented from a series of simulations undertaken to determine whether dynamic processes observed in device-scale liquid crystal cells confined between aligning substrates can be simulated in a molecular system using parallel molecular dynamics of the Gay-Berne model.In a nematic cell, on removal of an aligning field, initial near-surface director relaxation can induce flow, termed 'backflow', in the liquid. This, in turn, can cause director rotation, termed 'orientational kickback', in the centre of the cell. Simulations are performed of the relaxation in nematic systems confined between substrates with a common alignment on removal of an aligning field. Results show that relaxation timescales of medium sized systems are accessible. Following this, simulations are performed of relaxation in hybrid aligned nematic systems, where each surface induces a different alignment. Flow patterns associated with director reorientation are observed. The damped oscillatory nature of the relaxation process suggests that the behaviour of these systems is dominated by orientational elastic forces and that the observed director motion and flow do not correspond to the macroscopic processes of backflow and kickback. Chevron structures can occur in confined smectic cells which develop two domains of equal and opposite layer tilt on cooling. Layer tilting is thought to be caused by a need to reconcile a mismatch between bulk and surface smectic layer spacing. Here, simulations are performed of the formation of structures in confined smectic systems where layer tilt is induced by an imposed surface pretilt. Results show that bookshelf, chevron and tilted layer structures are observable in a confined Gay-Berne system. The formation and stability of the chevron structure are shown to be influenced by surface slip.
“We Are, Therefore We Should”: Evidence That In-Group Identification Mediates the Acquisition of In-Group Norms

In 2 field studies (Ns = 71 and 113), we tested the prediction that in-group identification would mediate the acquisition of group norms by new group members. Study 1 demonstrated that participants surveyed after a team-development program reported greater awareness of in-group norms of teamwork and cooperation, compared to those surveyed at the start. Moreover, there was evidence that this effect was mediated by increased in-group identification. Study 2 replicated this finding, and showed that the effects were specific to the norm of teamwork. Acquisition of alternative norms of individualism and competitiveness did not increase after participation in the program, and did not correlate with identification. Practical implications and future work are discussed. jasp_794 1857.1876
Nursing Research Priorities based on the Nurse-sensitive Indicators: Scoping Review

Patient-centeredness, safety, and quality of care are major healthcare concerns worldwide. Clinical indicators are essential for monitoring and evaluating safety and quality of care within the health care system. Therefore, research on clinical indicators can improve the quality of care, promote good practices, clarify key concepts, and identify gaps vital to nursing care. The study aims to explore nurse-sensitive indicators to find significant research areas and prioritize them. The study design is exploratory research conducted through scoping review, followed by Delphi consultation to identify nurse-sensitive indicators applicable in the Saudi Arabia health care system and prioritize these indicators as research areas. The most frequently cited indicators are patient fall, medication error, patient satisfaction, catheter-associated infection, pain, pressure ulcer, the incidence of deep vein thrombosis or pulmonary embolism, appropriate levels of nursing skill mix, and nursing initial and need assessment. The result shows that (33) NSIs are essential to the quality of nursing care and patient safety in Saudi Arabia. Patient-centeredness, patient safety, a healthy work environment, and a nursing informatics system are significant research dimensions. We found that nurse satisfaction, patient satisfaction, pressure ulcers, patient falls, nurse-patient communication, patient engagement, and pain management are research priorities. Further studies must be conducted on NSIs that address patient-centeredness, safety, a healthy work environment, and a nursing informatics system. It can be observed that most of the studies focused on outcome indicators, and there is a need to study patient-reported indicators as a significant patient-centered concept.
Another Look at the Acceptability of Bare Singular NPs in Episodic Sentences in Brazilian Portuguese

Bare (determiner-less) singular NPs in Brazilian Portuguese have been variously analyzed as indefinite terms, as kind terms, or as ambiguous between the two. It has furthermore been noted (Schmitt and Munn 1999, and subsequent literature) that bare singulars, unlike bare plurals, are degraded in the preverbal subject position of episodic sentences, but that their acceptability is improved when they are embedded inside a list. We conducted an experimental study examining the effect of NP type, syntactic position and list context on the acceptability of bare NPs in Brazilian Portuguese. Our results indicate that the low acceptability of bare singulars in subject position of episodic sentences results from the additive effects of three separate factors, rather than from a constraint against bare singulars in subject position. Implications of these findings for theories of bare NP interpretation in Brazilian Portuguese are discussed.
A novel triggering condition of event‐triggered control based on heuristic dynamic programming for discrete‐time systems

In this paper, an event‐triggered heuristic dynamic programming algorithm for discrete‐time nonlinear systems with a novel triggering condition is studied. Different from traditional heuristic dynamic programming algorithms, the control law in this algorithm will only be updated when the triggering condition is satisfied to reduce the computational burden. Three neural networks are employed, which are model network, action network, and critic network. Model functions, control laws, and value functions are estimated using neural networks, respectively. The main contribution of this algorithm is the novel triggering condition with simpler form and fewer assumptions. Additionally, a proof of the stability for discrete‐time systems using Lyapunov technique is given. Finally, two simulations are shown to verify the effectiveness of the developed algorithm.
H a m s Oncogene Derived From Rat Mutations in Cell Lines Tracheal Implants Exposed In Vivo to 7,12=Dimethyl benz[a]anthracene

The frequency of Ha-ras mutations was determined as a function of neoplastic progression in cell lines derived from rat tracheal implants exposed in vivo to 7,12-dimethylbenz[alanthracene. Restriction fragment-length polymorphism (RFLP) analysis revealed an A --t T transversion in the second base of codon 61 in 2 of 11 cell lines. One of the positive cell lines was tumorigenic, but the other was neither tumorigenic nor anchorage independent, thus indicating a lack of correlation between neoplastic stage and ras mutation. Densitometry analysis of the RFLP bands indicated that approximately 50% of the cells within these two heterogeneous populations contained the mutation. Direct sequence analysis of polymerase chain reaction-amplified DNA confirmed these results and did not reveal any other mutations in this region of the Ha-rasgene.