Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effect of Financial Performance, Public Ownership, and Company Age on Voluntary Disclosure (Case Study on Private Non - Foreign Exchange National Bank Company Listed in Indonesia Stock Exchange)

This study aims to examine the effect of financial performance (profitability, liquidity, leverage), public ownership, and company age on voluntary disclosure of Private Non - Foreign Exchange National Bank companies either partially or simultaneously. The subject in this study were Private Non - Foreign Exchange National Bank companies that listed in the Indonesian Stock Exchange and published annual financial reports for the 2014 - 2018 period. The number of sample were 6 companies. This study used descriptive and verification methods with quantitative approach. The data of this study were secondary data that used annual financial reports of each company. The design of hypothesis testing was classic assumption test (normality test, autocorrelation test, multicollinearity test, heteroscedasticity test) and multiple linear regression analysis (coefficient of determination, t test and F test). The results show company age partially has significant effect on voluntary disclosure. While financial performance (profitability, liquidity, leverage) and public ownership partially has no effect on voluntary disclosure. The results proved that the longer the company becomes a public company, it is expected that the company will have more experience on the information needs by the investors and will meet their information needs through voluntary disclosure of the company's annual report. Financial performance (profitability, liquidity, leverage), public ownership, and company age has a significant effect on voluntary disclosure simultaneously with a coefficient determination of 24.5%.
Spatio-Temporal Evolution Characteristics and Spatial Differences in Urban Tourism Network Attention in China: Based on the Baidu Index

There is a long-term equilibrium relationship between urban tourism network attention (UTNA) and the volume of tourism. Understanding the spatio-temporal patterns of UTNA before and after the COVID-19 pandemic has important implications for destination management. On the basis of the Baidu index, this study collected the tourism network attention of 337 prefecture-level cities in China from 2018 to 2021 through data mining and analyzed the spatio-temporal evolution characteristics and regional differences in UTNA in China by using the seasonal concentration index, the Zipf model and the Dagum Gini coefficient. The results show that, firstly, the UTNA decreased significantly during the study period, with significant seasonal variability and spatial unevenness; April, July, August and October comprise the high season, while January, February, November and December comprise the low season. Secondly, in terms of regional heterogeneity, the seasonal differences in UTNA are generally greater in the northeast regions than in the central, and western regions, and are the smallest in the eastern regions. Thirdly, the UTNA shows a strong rank-scale characteristic, indicating that Beijing, Chongqing, Shanghai, Guangzhou, Xi’an, and others that are rich in tourism resources are the main high-value cities, and “core-edge” characteristics gradually formed around these municipalities and capital cities. Lastly, of the four regions, the northeast regions had the largest intraregional and inter-regional differences. From the perspective of the contribution to regional difference sources Gnb＞Gt＞Gw, inter-regional disparities are the main reasons for the overall differences. Accordingly, policy suggestions are proposed to further promote the sustainable development of tourism destinations.
The Challenge of Long COVID-19 Management: From Disease Molecular Hallmarks to the Proposal of Exercise as Therapy

Long coronavirus disease 19 (COVID-19) is the designation given to a novel syndrome that develops within a few months after infection by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and that is presenting with increasing incidence because of the numerous cases of infection. Long COVID-19 is characterized by a sequela of clinical symptoms that concern different organs and tissues, from nervous, respiratory, gastrointestinal, and renal systems to skeletal muscle and cardiovascular apparatus. The main common molecular cause for all long COVID-19 facets appears to be related to immune dysregulations, the persistence of inflammatory status, epigenetic modifications, and alterations of neurotrophin release. The prevention and management of long COVID-19 are still inappropriate because many aspects need further clarification. Exercise is known to exert a deep action on molecular dysfunctions elicited by long COVID-19 depending on training intensity, duration, and continuity. Evidence suggests that it could improve the quality of life of long COVID-19 patients. This review explores the main clinical features and the known molecular mechanisms underlying long COVID-19 in the perspective of considering exercise as a co-medication in long COVID-19 management.
Multiplanar Eddy Current Analysis of Interior Permanent Magnets in Synchronous Machines

Eddy currents in permanent magnets of synchronous machines may cause their demagnetization due to overheating. Thus eddy current analysis has been discussed widely. 3D transient analyses of eddy current losses yield most accurate results but are also performed at very high computational cost. A previously introduced more efficient 2D approach for accurate eddy current calculation is adapted in this publication for the use with interior permanent magnet machines. Instead of only considering axial eddy currents inside the magnets, radial eddy current displacement is considered by analyzing the magnets in multiple sectional planes. The computed results comply with the theoretical expectations and the results from a 3D simulation. This confirms the method's applicability as a quick and accurate alternative to costly 3D eddy current simulations.
Topology Aware Cluster Configuration for Minimizing Communication Delay in Edge Computing

For real-time edge computing applications working under stringent deadlines, communication delay between IoT devices and edge devices needs to be minimized. Since the generalized assignment problem being NP-Hard, an optimal assignment of IoT devices to the edge cluster is hard. We propose the application RL based heuristics to obtain a near-optimal assignment of IoT devices to the edge cluster while ensuring that none of the edge devices are overloaded. We demonstrate that our algorithm outperforms the state-of-the-art.
Real-Time Face Mask Detector Using Deep Learning

Coronavirus Disease 2019, also known as Covid-19, is an infectious respiratory disease that was first identified in December 2019 in Wuhan, China. Covid-19 then caused a global pandemic in 2020, which is still ongoing (as of Feb 2022). The Covid-19 virus is known to be transmitted through small droplets exhaled by an infected person. Wearing a face mask is one of the most effective ways to reduce the spread of the Covid-19 virus. Face masks can prevent direct contact of the nose and mouth with the external environment, preventing inhalation of small droplets and thus lowering the risk of infection. Because face masks are so important in everyday life, there has been a lot of research done on them. However, most research has focused on detecting the presence of a face mask, rather than how the face mask is worn or the type of face mask. The primary goal of this project would be to create a face mask detector using deep learning with additional features and to investigate the possibilities of additional features and system enhancements.
The Value of High-Frequency Repetitive Transcranial Magnetic Stimulation of the Motor Cortex to Treat Central Pain Sensitization Associated With Knee Osteoarthritis

Aim Chronic pain associated with knee osteoarthritis may develop in connection with a maladaptive process of pain sensitization in the central nervous system. Repetitive transcranial magnetic stimulation (rTMS) has been proposed to treat various pain syndromes related to central sensitization phenomenon, but was never applied in the context of knee osteoarthritis. Methods A 71-year-old woman presenting clinical evidence of central sensitization of pain associated with left knee osteoarthritis underwent monthly sessions of rTMS delivered at 10 Hz over the right motor cortex. Results From the week following the third session, she began to improve on various clinical aspects, including pain. After 10 sessions (i.e., almost one year of follow-up), pain was reduced by 67%, especially regarding neuropathic components, while sleep disorders and fatigue also improved by 57–67%. The central sensitization inventory (CSI) score was reduced by 70%. Conclusion This observation suggests that high-frequency motor cortex rTMS could be a therapeutic option to treat neuropathic pain and psychological symptoms associated with central sensitization developing in the context of chronic osteoarthritis of the knee joint.
Robust Sensor Fusion for Autonomous UAV Navigation in GPS denied Forest Environment

Forestry and precision biodiversity data collection using UAVs can be cost-effective and time efficient solution. However, navigating in the forest canopy autonomously can be quite challenging because of its GPS denied environment, cluttered, dynamic, and large scale. Most of the commercial UAVs used in forest applications apply GPS-based navigation which is not suitable for navigating under the canopy. In this paper, an autonomous UAV flight mission in a cluttered forest-like canopy environment is presented. A robust multi-sensor fusion-based robust navigation method which has failure detection features is proposed to enable safe and reliable autonomous navigation. The autonomy architecture utilizes the navigation, planning, and control capabilities of the UAV in a simultaneous manner. To achieve autonomous missions in forest environment, the proposed system has been tested rigorously in a simulated environment and the result shows the capability of autonomous flights in such a challenging environment. The performance of the autonomous flight was evaluated based on mean velocity and path length with respect to the increasing number of trees in the forest.
Functionally Graded Electrical Windings Enabled by Additive Manufacturing

The ever increasing demand for high power-density and energy efficient electrical machines and wound passive components is driving exploration of alternative fabrication methods such as additive manufacturing of windings which can offer greater design options than conventional approaches. Alongside unparalleled geometric freedom, additive manufacturing could allow architected material properties to be realised through process control. In this paper, the potential performance benefits of functionally grading electrical conductivity to optimally balance DC and AC winding loss components are investigated. The practical feasibility of manufacturing such windings is demonstrated, and the achievable variance in electrical conductivity for CuCrZr is shown.
A novel approach to detect malware in portable executables of major operating systems

Innovation is the very base on which contempo- rary technology thrives, with every new innovation there is a possibility for a novel exploit. Malevolent software representssolemn cyber hazard in the convolutional cyber spatial space, the internet. Conventional malware detection techniques have failed to keep up with the pace of mounting intricacy and variants used in latest malware outbreaks. In order to camouflage their identity, tools are used to perform alterations on the binary. Moreover, this technique allows the malicious binary to pass the signature-based tests on most of the engines used for analysis. Such binaries when executed performs metamorphic attacks on the host system. The last two decades have provided us with lots of research and ways to analyse a binary but very little is donein the domain with these adverse variants. To patch the breach, this paper aims to deliver a compressive advanced static analysis malware analyser for detecting malicious binaries for all major operating systems which specializes in the art of sleuthing packedportable executables.
Safety Positioning for UAV Swarms in Harsh Environments

Unmanned aerial vehicle (UAV) technology has been widely utilized in military and civilian fields such as rescue, disaster relief, urban planning, and material transportation. Obtaining the current position information of a UAV is of great significance in the performance of its tasks. However, positioning technology may fail in harsh environments. This article proposes the following solutions to address this problem: we eliminate interference and recover useful information by a dark channel algorithm and an attention generation adv ersarial network; visual and geomagnetic signals are fused to improve the safety of positioning technology; and a two-path convolutional network is proposed to optimize the feature extraction method and extract the continuity information of data. Moreover, open research work and challenges are also discussed.
Study of (cid:2) polarization in relativistic nuclear collisions at √ s NN = 7 . 7–200 GeV

We present a calculation of the global polarization of (cid:2) hyperons in relativistic Au–Au collisions at RHIC Beam Energy Scan range √ s NN = 7 . 7 − 200 GeV with a 3 + 1-dimensional cascade + viscous hydro model, UrQMD + vHLLE. Within this model, the mean polarization of (cid:2) in the out-of-plane direction is predicted to decrease rapidly with collision energy from a top value of about 2% at the lowest energy examined. We explore the connection between the polarization signal and thermal vorticity and estimate the feed-down contribution to (cid:2) polarization due to the decay of higher mass hyperons.
Pinpoint Achilles’ Heel in RFID Localization: Phase Calibration of RFID Antenna based on Linear Localization Model

In the context of Industrial Internet of Things (IIoT), RFID technologies have been widely applied to locate or track tagged objects for achieving item-level intelligence. However, prior localization work encounters two main issues. First, the phase measurement usually contains physical deviation. Existing localization work generally takes the physical center of an RFID antenna as its phase center, which is a key factor in improving localization accuracy but actually different from the physical center in practice. Second, the non-linear localization model is likely to be too complex to run on edge nodes with limited computing resources. In this paper, we present a LInear localizatiON solution, called LION, to perform the phase calibration for antennas with no need for the complex computation nor strong limitations. Specifically, we provide a novel lightweight model to pinpoint the actual antenna position quickly and accurately. Compared to previous localization methods, we reduce the intersection of circles or hyperbolas into radical lines, which greatly reduces the computation cost while guaranteeing the high accuracy. Further, to adapt to the complex environment with various ambient noise and multi-path effect, we leverage the weighted least square method to determine the optimal position. Moreover, we propose an adaptive parameter selection scheme to automatically choose optimal parameters for localization. In this way, LION is able to perform the accurate localization robustly. We implement LION using commercial RFID devices, and evaluate its performance extensively. Experimental results show the necessity of phase calibration as well as the high time efficiency of LION, e.g., the average accuracy improves by 6× and 2.1× for 2D and 3D localization, and the average time consuming is 0.02s and 1.8s for 2D and 3D cases.
A Hybrid Approach to Protect Server from IP Spoofing Attack

The rapid expansion of the internet has resulted in distributed denial of service cyberattacks being one of the most critical problems in a data centre environment where numerous servers are placed. A Distributed Denial of Service attack especially IP spoofing, carried out by countless specialists, creates a significant number of packets, which can quickly exhaust the processing and communication capabilities of a target in a short amount of time. Defending against such attack require a number of actions, including detection, classification, and traceback in order to mitigate the problem. In this research, we present a hybrid strategy for the identification and prevention of distributed denial of service (DDoS) attacks based on IP spoofing. Our suggested detection system makes use of both distance calculation and machine learning-based detection methods individually, but in an integrated manner, and it integrates the results of the two indicators to upgrade the system identification precision by combining their results. Our proposed hybrid system is tested against the CAIDA 2007 dataset in order to determine its detection performance. As a result, DDoS attack based on IP spoofing on the internal network is detected and spoofed IPs are found out and analyzed for the protection of server in order to forestall unapproved access and to maintain security of information handled by server. Also, SNORT based prevention technique is used so as to protect the server from attack. We conclude that the suggested hybrid system outperforms the non-hybrid detection systems in terms of detection performance and helps in preventing the attack by finding out the spoofed IP packet causing the attack thus protecting the system from attack.
New Method of Estimating Binary's Mass Ratios by Using Superhumps

We propose a new dynamical method of estimating binary’s mass ratios by using the period of superhumps in SUUMa-type dwarf novae during the growing stage (the stage A superhumps). This method is based on the working hypothesis that the period of superhumps in the growing stage is determined by the dynamical precession rate at the 3W1 resonance radius, and is suggested in our new interpretation of the superhump period evolution during a superoutburst (2013, PASJ, 65, 95). By comparing objects having known mass ratios, we show that our method can provide sufficiently accurate mass ratios comparable to those obtained by eclipse observations in quiescence. One of the advantages of this method is that it requires neither an eclipse nor any experimental calibration. It is particularly suitable for exploring the low mass-ratio end of the evolution of cataclysmic variables, where the secondary is not detectable by conventional methods. Our analysis suggests that previous determinations of the mass ratio by using superhump periods during a superoutburst were systematically underestimated for lowmass-ratio systems, and we provided a new calibration. It reveals that most WZSge-type dwarf novae have either secondaries close to the border of the lower main-sequence or brown dwarfs, and most of the objects have not yet reached the evolutionary stage of period bouncers. Our results are not in contradiction with an assumption that an observed minimum period ( 77min) of ordinary hydrogen-rich cataclysmic variables is indeed the minimum period. We highlight how important the early observation of stage A superhumps is, and propose an effective future strategy of observation.
A low-cost high throughput microfluidics system for multi-thousand droplets per second sorting based on time resolved fluorescence measurement

Time-resolved fluorescence measurement screening and sorting has been proven to be interesting for biological applications. Fluorescence lifetime (FLT) measurement provides additional information to the measurement of the intensity or polarization of the fluorescence emission. Furthermore, droplet microfluidics enables higher throughput than well plate readers. Few researches have been recently done about the microfluidic droplet sorting based on the fluorescence lifetime measurement. Some of the proposed solutions support high throughput but they do not extract the FLT directly from the fluorescence decay. In this paper, we present an alternative low-cost system for the microfluidic droplets sorting. We implemented a FLT measurement system based on the time-correlated single photon counting (TCSPC) technique on a cyclone V SoC-FPGA. For the excitation light source, we use a simple pulsed laser diode and a single photon avalanche diode (SPAD) as a photodetector. The optical system is an ad hoc microscope. The droplet generation is done with flow focusing technique in a PDMS-based microfluidics chip. This system was successfully tested in real-time at a droplet rate of more than 3000 droplets per second.
Multivariate Classification of Dry Beans using Pipelined Dimensionality Reduction Technique

Machine learning methods have been employed frequently in the agricultural sector for crops analysis. These artificial intelligence-based models facilitate the understanding about crop sub species, their diseases and other significant issues concerning their yield. It helps in classification of several species of similar crops and also to identify specific sub-species among several others. The machine learning models can also be utilized to classify different types of beans, grains and pulses. It greatly assists in the evaluation of high-quality grains and their uniformity in size. Many features define each variety of the beans but not all the features equally contribute in the process of classification. In this paper, a Pipelined Linear Discriminant Analysis methodology has been used to remove insignificant features and estimators were applied for classification. Seven machine learning models were investigated and the overall prediction accuracy was 92%. Support Vector Machine, Random Forest and Multilayer Perceptron had higher accuracy of 93%. This methodology also efficiently overcomes the problem of overfitting and at the same time increases the overall performance of the models. We further investigate the impact of dimensionality reduction on the dataset.
Dual Polarity Reluctance-Permanent Magnet Synchronous Motor

The change of the number of poles in three-phase electrical machines has always been applied exclusively to induction motors. The main advantage of varying the number of poles in induction motors is the possibility to operate with two different synchronous speeds. This paper investigates the possibility to vary the number of poles in synchronous motors as well. In synchronous machines when the stator number of poles changes the rotor number of poles has to be modified accordingly. A hybrid reluctance–permanent magnets synchronous motor is presented. Its operating principle is described through finite element analysis. Such a motor operation is then analysed by experimental tests on the prototype.
Eddy Currents in the End-Windings of High Power Density Traction Machines

The main requirement for electrical machines used for traction motor applications are torque density and efficiency. Conductors with large cross section area, such as form wound-or hairpin-winding, are a popular option to increase both. However, they suffer from increased eddy current loss when being exposed to time varying magnetic fields. In this paper, such additional eddy currents in the end-windings of hairpin winding traction machines are investigated and assessed. It is shown, that commonly used analytical approaches underestimate the losses significantly at high speed and hence are not suitable for an accurate evaluation. As alternative, an FEA based workflow is applied to evaluate the influence of additional eddy currents within the full drive map of an example 150kW study case traction machine. The results show an significant increase of eddy currents, especially for high speed operation points at high load and underline the importance of accurate evaluation.