Miracle-dz/UniFAR-FTUs huggingface dataset:
Piezoelectric Tantalum Aluminum Nitride Grown on Stainless Steel for Low-Frequency Vibration-Driven Energy Harvesters

This work presents Ta-doped AlN films grown on stainless steels (SUS 430) by alternative current reactive magnetron sputtering from Al and Ta targets. Also, the bimorph Ta-AlN/SUS/Ta-AlN-based energy harvester (EH) was developed for targeting low-frequency onsite ambient vibrations (below 100 Hz). Ta-AlN of 5 µm thick on SUS had a highly-(002) orientation with a rocking-curve FWHM of 3 deg. The Ta-AlN/SUS/Ta-AlN-based EH provided the output power of 1.92 mW at 1g and 84 Hz. The normalized power density (NPD) was 33.6 mW.g-2.cm-3. It is the highest among those published low-frequency energy harvesters.
Theoretical study of the structure of calcium in phases IV and V via ab initio metadynamics simulation

We searched for the structure of calcium in phases IV and V by a metadynamics simulation based on ab initio calculations, and found two structures. One is a tetragonal lattice which consists of two helical chains along the $c$ axis. The other is an orthorhombic lattice of four zigzag chains. We have calculated the x-ray diffraction patterns and enthalpies of the two structures discovered by our simulation. From comparisons of the patterns with the experimental x-ray patterns of the phases IV and V and from the pressure dependence of the enthalpies, we conclude that the structure with a helical pattern corresponds to phase IV and the structure with a zigzag pattern to phase V.
CO observations of a molecular cloud complex associated with the bright rim near VY Canis Majoris

Extensive CO observations of a large molecular cloud complex (approx. 15 pc) associated with a bright rim near the peculiar star VY CMa are presented. CO emission peaks in a region along and adjacent to the bright rim which forms the western border of the cloud complex. This emission abruptly decreases across the bright rim, and this decrease suggests a physical association of the rim with the cloud. The molecular complex is found to consist of two clouds which have different radial velocities and physical properties. The possibility that these two clouds may be in near-collision is discussed. The physical association of the bright rim with the cloud complex indicates that the clouds are at the same distance, 1.5 kpc, as the stars which excite the rim. Since VY CMa appears to be located at the tip of the bright rim and has a velocity similar to that of the molecular cloud complex and the stars of the cluster NGC 2362, it is concluded that VY CMa is also at the same distance. The resulting luminosity of VY CMa (5 x 10/sup 5/ L sub solar) indicates that the star is very massive, and places constraints on interpretation of itsmore » evolutionary state. 5 figures, 2 tables. « less
The dimensions of knotted polygons

The authors study the dimensions (mean-square radius of gyration and mean span) of self-avoiding polygons on the simple cubic lattice with fixed knot type. The approach used is a Monte Carlo algorithm which is a combination of the BFACF algorithm and the pivot algorithm, so that the polygons are studied in the grand canonical ensemble, but the autocorrelation time is not too large. They show that, although the dimensions of polygons are sensitive to knot type, the critical exponent (v) and the leading amplitude are independent of the knot type of the polygon. The knot type influences the confluent correction to the scaling term and hence the rate of approach to the limiting behaviour.
Significantly enhanced low-frequency dielectric permittivity in the BaTiO3/poly (vinylidene fluoride) nanocomposite

To disclose an effect of ceramic particle size on dielectric properties of BaTiO3/ poly(vinylidene fluoride) (BT/PVDF) composites at low frequency, the dielectric properties of the BT/PVDF composites loaded with different diameter BT particles at the same volume concentration were studied at wide frequency ranges from 10−1to107Hz. A significant low-frequency dielectric permittivity increase in the BT/PVDF nanocomposite was noticed, which was not reported previously. The authors considered that the marked aggrandizement of interface and dipole polarization at low frequency is charged with the significant increase in dielectric permittivity.
Applications of Neurocomputing (KC-LSTM) in Prediction Fire - A Comprehensive Study and Understanding

this paper present predictor model combination between the advantage of Knowledge Constriction (KC) and Long Short-Term Memory (LSTM to find the burned area. The dataset of this case syudy collected from five types of sensors, namely Temperature Sensor (Tmp36), Wind Speed Sensor (010C), Relative Humidity Sensor (RH5X), Rain Sensor (WR2-RFC A553100), and Direct Current (Acudc240). The predictor model consists of four stages, the first stage called pre-process, include (1) check the missing values (2) split dataset into multi-intervals and remove the duplicate; the second stage build knowledge constraints, then split the data set into two parts: training and testing. We'll create the predictor with the training data set while putting the based on five cross validations. The third stage, building predictor dependent on the neurocomputing (LSTM) and knowledge constriction called (4F-DLT), the four-stage using the evaluations measure. As a results the 4F-DLT consider a pragmatic predictor for fire forest area reduce the computation and time at the same time increase the accuracy.
Aw … The Museum is so “Dark”: The Effect of Thermal Stimuli for Virtual Reality Experience and Emotion

Virtual Reality (VR) is an emerging technology that can enhance tourists' experiences through digital multisensory, such as human senses and thermal stimuli. Prior studies are missing to understand the impact of thermal stimuli on the VR experience. Therefore, this study tries to fill the research gap in a dark tourism context with three conditions: cold, warm, and no thermal stimuli. Using a randomized lab experiment with 15 participants, this study found that cold thermal stimuli trigger less arousal and valence than warm and neutral conditions. Moreover, we also reported users' emotions by capturing psychophysiological data (electrodermal activity and heart rate) to increase the robustness of the study. We extend this study by interviewing participants to understand cognitive and affective experiences that lead to eudemonic well-being as the outcome of this VR experience.
End-to-End Learning-Based Full-Duplex Amplify-and-Forward Relay Networks

Full duplex (FD) relaying can provide double spectral efficiency. Despite advanced self-interference cancellation techniques, residual self-interference (RSI) limits the performance significantly. We present an autoencoder (AE)-based block coded modulation (BCM) and differential BCM (d-BCM) for an FD amplify-and-forward (FD-AF) relay network that can tackle the deteriorating impacts of RSI. Existing works treat AE frameworks as black-box with minimal/no focus on training convergence, limiting AE’s practical deployment. Focussing on training convergence, firstly, we show that training of AE converges above minimum signal-to-noise-ratio (SNR) and below maximum RSI level, and CSI helps in faster convergence. Secondly, we establish a relationship between training hyper-parameters and AE-based BCM/d-BCM design, by showing that, for any given hyper-parameters, training of the AE has converged to its maximum potential of decoding if AE’s encoder has designed $2^{k}$ codewords, with an emphasis on the minimum required training samples. To open the black-box AE, we reveal five observations in the AE-based designed codewords concerning Euclidean distance, packing density, hamming distance, and kurtosis, that resemble the desired observations of theoretical random coded modulations. By extensive simulations, we show that the proposed AE outperforms the conventional methods considerably for varying SNR, RSI, transmission rate, channel estimation errors, and small/practical block lengths.
Positronium formation in positron-hydrogen scattering

The positronium-formation cross section in positron-hydrogen scattering has been calculated by using the distorted-wave approximation in which the total incident wave function is obtained by the method of polarized orbitals. The two variants of the polarized-orbital method, one due to Temkin and Lamkin and the other due to Callaway et al., have been employed to estimate their relative importance. The present results have been compared with those obtained by elaborate variational methods. The results obtained by using the potential of Temkin and Lamkin have been found to be encouraging.
Are molecular clouds in virial equilibrium

A correlation between CO luminosities and virial theorem masses of Galactic molecular clouds has been claimed to prove conclusively that molecular clouds are in gravitational virial equilibrium. This correlation is an artifact of the size-line width relation exhibited by molecular clouds, which does not require clouds to be virialized. Virial theorem masses do not provide an estimate of N(H2)/I(co). Comparison of the virial theorem-derived values of N(H2)/I(co) with the gamma-ray value shows that the masses of lower luminosity clouds are considerably overestimated by virial theorem analyses; these clouds are probably strongly influenced by nongravitational forces. 24 refs.
Online User Reviews Investigation Towards Madura Island Tourism using Latent Semantic Analysis

Modern tourism relies on customer experience feedback that is often shared via social media in images, videos, or texts. The massive growth of user-generated content, especially text, opens the opportunity for research to help us analyze and extract the information from the documents. Natural Language Processing methods enable us to extract data from users’ opinions in unstructured text, which is helpful for business strategy making. In this study, we summarize the terms often discussed in the Google Maps review regarding Madura Island using the topic modeling technique. One well-known topic modeling method is Latent Semantic Analysis, an unsupervised learning method that extracts the documents based on the appearance frequency of words. This method then calculates the distance between words and clusters of similar terms. By extracting 11240 reviews from Google Maps, we obtain ten words representing the themes that often emerge in user reviews. The emerging themes then are beneficial for further analysis to decide the business development strategy for the tourism sector.
Phonon softening and the metal-insulator transition in VO2

The phase transition in pure VO2 is ascribed to softening, in the high temperature semimetallic phase, of an optical phonon at the R point of the Brillouin zone. This softening arises from an electron-phonon interaction the strength of which is a manifestation of the tightly bound character of the d electrons. The insulating nature of the low temperature phase is considered to be due to band splitting and uncrossing produced by the crystalline distortion. This change in the bands is the static, large amplitude, limit of the electron-phonon interaction responsible for the phonon entropy of the semimetal. Detailed numerical calculations are made for one dimensional chains along the c axis. Good quantitative agreement is obtained with known experimental data.
Evaluation of color-discrimination ellipsoids in two-color spaces

We obtained the color-differential thresholds for 20 stimuli and 3 observers within 2 color-representation spaces. First, in the CIE-1931 color space we compare the results of our experiments, which followed chromatic-discrimination criteria, with those obtained by other authors that followed color-matching criteria. Second, the representation of our data in the cone-excitation space shows that all the discrimination ellipses are nearly oriented along the S-cone-variation axis. In our experiments the variations in the threshold of each color channel according to its level of excitation are similar to those found by other authors using different experimental conditions and methods, but certain discrepancies appear when the influence that one color channel may exert on the other is analyzed. The characteristics of luminance discrimination are shown to be independent of the chromaticity of the stimuli.
LIDS: Lightweight Dynamic Scheduling Technique for 6G-enabled Massive LoRa based IoT Systems

The sixth-generation (6G) wireless communication networks are envisioned to deliver improved Quality of Services (QoS) such as data-rate, latency, localization, etc., compared to 5G services. This demand will mainly arise due to the massive Internet of Things (IoT) connections among billions of embedded devices. It is worth noting that these IoT devices are constrained in nature in terms of memory, computation and most importantly power. Low-power wireless computing necessitates a link-layer technology that considers the aforementioned IoT device requirements such as low-power, low data-rate, long-range and energy efficient reliable communications. Long Range Radio (LoRa) technology has recently become widely popular to facilitate low-power, long-range communication, while other similar technologies struggle to achieve long-range transmissions. The LoRA standard defines several PHY and MAC sub-layer specifications for Low-Power Wide Area Networks (LPWAN) that can be used to build several IoT applications. With 6G communications, an IoT application is expected to consist of hundreds of devices connected at a time, and this will need a proper scheduling mechanism among these devices for optimal performance. The LoRa standard does not define any scheduling mechanism and leaves it to the research community to explore. In this paper, we propose a distributed scheduling mechanism under the supervision of a common LoRa gateway. The proposed mechanism focuses on a Lightweight DynamicScheduling (LIDS) scheme to minimize loss in data transmissions through collisions. The proposed mechanism optimizes the power consumption to achieve the desired level of QoS requirements such as throughput and latency.
Brand Engagement In S-commerce: A Study of Malaysia Customers

Nowadays, many sellers have started to participate in s-commerce. This has opened a new opportunity for sellers to sell the company's products and services. Due to the unique environment in social media, online sellers need to thoroughly focus on improving consumer engagement in s-commerce. The main objective of this study is to determine the influence of customer participation, customer involvement, eWOM, and self-expressive brand on s-commerce brand engagement. The study collected 150 usable data using a self-administered online questionnaire. The Structural Equation Model (SEM) was used to analyse the data of the study. The result of the study showed that customer involvement, eWOM, and self expressive brand will influence brand engagement. Based on the results of the study, recommendations were made for the company.
Comparative Semantic Resume Analysis for Improving Candidate-Career Matching

A resume, in general, is a commonly and widely used way for a person to present their competence and qualifications. It is usually written in different personalized methods in a variety of inconsistent styles in various file formats (pdf, txt, doc, etc.). The process of selecting an appropriate candidate based on whether their resume matches a list of job requirements is usually a tedious, difficult, time-consuming, and effort-consuming task. This task is deemed significant for extracting relevant information and useful attributes that are indicative of good candidates. This study aims to assist human resource departments to improve the candidate career matching process in an automated and more efficient manner based on inferring and analyzing comparative semantic resume attributes using machine learning (ML) and natural language processing (NLP) tools. The ranking support vector machine (SVM) algorithm is then used to rank these resumes by attribute using semantic data comparisons. This produces a more accurate ranking able to detect the tiny differences between candidates and give more unique scores to get an enhanced list of candidates ranked from the best to worst match for the vacancy. The experimental results and performance comparison show that the proposed comparative ranking based on semantic descriptions surpasses the standard ranking based on mere regular scores in terms of a distinction between candidates and distribution of resumes across the ranks with accuracy up to 92%.
Linear trend of occurrence and intensity of heavy rainfall events on Northeast Brazil

A comprehensive dataset of daily rainfall covering the period from 1972 to 2002 was used to compute the climatology and trend of occurrence and intensity of heavy, weak and normal events of rainfall on Northeast Brazil (NEB). For selection of rainfall events, we used the technique of quantiles and the trend was identified using the nonparametric Mann–Kendall test. The heavy events are modulated by La Niña and El Niño occurrences and in general they presented negative trend concerning to number of episodes and positive trend to daily rainfall.
Analysis of Critical Success Factors in Information Technology Projects: A National Shipping Company Case Study

The positive of the Covid-19 pandemic is being a catalyst for the company's digital transformation plan. However, the targets set have not been achieved with the increasing demand for digitizing business processes and not being matched by increased resources. This research is focused on the IT projects that became quick wins for the Information Technology Division, which experienced delays in the middle of the year, but the target can still be achieved at the end of the year. This research aims to analyze the Critical Success Factors (CSFs) of Information Technology projects in a National Shipping Company. The Analytical Hierarchy Process (AHP) was used in this research to determine the ranking of the Critical Success Factors (CSFs) found so that company can set priorities in making decisions and project success can be achieved. Based on the analysis, the top three success factors that become the highest priority are User Involvement, IT Readiness, and Top Management Commitment & Support. In contrast, the bottom three success factors with the lowest priority are Trends, Hardware/Software Selection, and Change Management.
A Comparison of the Application of Artificial Intelligence in the Educational System

Artificial intelligence (AI) has made considerable strides in educational technologies in recent decades. The goal of this study was to analyze and explain Artificial Intelligence in Education (AIED). AI, a concept coined in the distant past to refer to the method of imputing human intelligence to machines, has made great strides in the quickly advancing modern society. Thus, the idea of fusing education and artificial intelligence emerged, ushering in the twenty-first century. The recent study, however, shows that instructional techniques are benefiting from artificial intelligence studies. This article gives a broad overview of various implementation methods for AI-based technologies in the education industry. These tools have made education easier and more interesting for both professors and students. We now have an educational environment that is already set up thanks to AI, complete with a range of specialist system applications, collaborative robots, intelligent AI assistants, and a smart learning environment. Additionally, this paper mentions a number of models that have been proposed for this subject and has good possibilities going forward. As a result, we shall draw the conclusion from this review study that artificial intelligence appears to have a bright future in the field of education.
Spin pumping by nonreciprocal spin waves under local excitation

We measured spatial distribution of spin pumping by nonreciprocal magnetostatic surface waves (MSSWs) using inverse spin Hall effect (ISHE) in a YIG/Pt system under local excitation. The local intensity of spin pumping by MSSWs shows center displacement of ± 0.1 mm from the center of a waveguide according to the reversal of a bias field, which is due to the nonreciprocal propagation of MSSWs over a macroscopic distance. The combination of spin pumping and ISHE enables electrical investigation of local spin dynamics in a magnetic insulator.
Behavior Privacy Preserving in RF Sensing

Recent years have witnessed the booming development of RF sensing, which supports both identity authentication and behavior recognition by analysing the signal distortion caused by human body. In particular, RF-based identity authentication is more attractive to researchers, because it can capture the unique biological characteristics of users. However, the openness of wireless transmission raises privacy concerns since human behaviors could expose massive private information of users, which impedes the real-world implementation of RF-based user authentication applications. It is difficult to filter out the behavior information from the collected RF signals. In this article, we propose a privacy-preserving deep neural network named BPCloak to erase the behavior information in RF signals while retaining the ability of user authentication. We conduct extensive experiments over mainstream RF signals collected from three real wireless systems, including the WiFi, radio frequency identification (RFID), and millimeter-wave (mmWave) systems. The experimental results show that BPCloak significantly reduces the behavior recognition accuracy, i.e., 85%+, 75%+, and 65%+ reduction for WiFi, RFID, and mmWave systems respectively, merely with a slight penalty of accuracy decrease when using these three systems for user authentication, i.e., 1%-, 3%-, and 5%-, respectively.
Network Traffic Classifications using Gated Recurrent Units with Weighted Cross-entropy

Intrusion detection systems (IDS) have been used to identify several types of attacks. Several issues can affect the classification of attacks, such as classification results which can be biased due to unbalanced data that have been used in the training of the classifier. Moreover, the detection rate of these IDS has to be improved to detect as many as possible of several attacks. In this paper, we propose to use a complex sequential model such as Gated Recurrent Units to classify different kinds of attacks. We use the NSL-KDD dataset to train our model. This dataset has unbalanced data which might affect the results of our classifier. To fix this issue, we use Dropout and weighted cross entropy loss function to overcome the issue of unbalanced data. Our results show that there is an enhancement in the detection rate of the classifier. we have achieved a higher detection rate compared with previous studies.
Pruning Boolean Expressions to Shorten Dynamic Slices

This paper presents a novel extension to dynamic slicing that we call pruned slicing. The proposed slicing approach produces smaller slices than traditional dynamic slicing. This is achieved by reasoning over Boolean expressions. We have implemented a prototype in Python and empirically evaluated its performance on three different benchmarks: TCAS, QuixBugs and the Refactory dataset. We show that pruned slicing reduces the size of dynamic slices on average by 10.96 percent for TCAS. For QuixBugs and the Refactory dataset, the slice size remains the same, but the number of Boolean expressions within the slice is reduced. Further, the empirical evaluation shows that pruned dynamic slicing comes with a low computational overhead compared to dynamic slicing. Pruned slicing can also be used in combination with relevant slicing.