bluuebunny/crossref_metadata_2025 huggingface dataset:
Spatiotemporal Thermal Control Effects on Thermal Grill Illusion
Authors: Saga Satoshi , Kimoto Ryotaro , Kaguchi Kaede
Published: January 2023

The thermal grill illusion induces a pain sensation under a spatial display of warmth and coolness of approximately 40 °C; and 20 °C. To realize virtual pain display more universally during the virtual reality experience, we proposed a spatiotemporal control method to realize a variable thermal grill illusion and evaluated the effect of the method. First, we examined whether there was a change in the period until pain occurred due to the spatial temperature distribution of pre-warming and pre-cooling and verified whether the period until pain occurred became shorter as the temperature difference between pre-warming and pre-cooling increased. Next, we examined the effect of the number of grids on the illusion and verified the following facts. In terms of the pain area, the larger the thermal area, the larger the pain area. In terms of the magnitude of the pain, the larger the thermal area, the greater the magnitude of the sensation of pain.
Integrated Damage Location Diagnosis of Frame Structure Based on Convolutional Neural Network with Inception Module
Authors: Ren Jianhua , Cai Chaozhi , Chi Yaolei , Xue Yingfang
Published: January 2023

Accurate damage location diagnosis of frame structures is of great significance to the judgment of damage degree and subsequent maintenance of frame structures. However, the similarity characteristics of vibration data at different damage locations and noise interference bring great challenges. In order to overcome the above problems and realize accurate damage location diagnosis of the frame structure, the existing convolutional neural network with training interference (TICNN) is improved in this paper, and a high-precision neural network model named convolutional neural network based on Inception (BICNN) for fault diagnosis with strong anti-noise ability is proposed by adding the Inception module to TICNN. In order to effectively avoid the overall misjudgment problem caused by using single sensor data for damage location diagnosis, an integrated damage location diagnosis method is proposed. Taking the four-story steel frame model of the University of British Columbia as the research object, the method proposed in this paper is tested and compared with other methods. The experimental results show that the diagnosis accuracy of the proposed method is 97.38%, which is higher than other methods; at the same time, it has greater advantages in noise resistance. Therefore, the method proposed in this paper not only has high accuracy, but also has strong anti-noise ability, which can solve the problem of accurate damage location diagnosis of complex frame structures under a strong noise environment.
The Best of Both Worlds: A Framework for Combining Degradation Prediction with High Performance Super-Resolution Networks
Authors: Aquilina Matthew , Ciantar Keith George , Galea Christian , Camilleri Kenneth P. , Farrugia Reuben A. , Abela John
Published: January 2023

To date, the best-performing blind super-resolution (SR) techniques follow one of two paradigms: (A) train standard SR networks on synthetic low-resolution–high-resolution (LR–HR) pairs or (B) predict the degradations of an LR image and then use these to inform a customised SR network. Despite significant progress, subscribers to the former miss out on useful degradation information and followers of the latter rely on weaker SR networks, which are significantly outperformed by the latest architectural advancements. In this work, we present a framework for combining any blind SR prediction mechanism with any deep SR network. We show that a single lightweight metadata insertion block together with a degradation prediction mechanism can allow non-blind SR architectures to rival or outperform state-of-the-art dedicated blind SR networks. We implement various contrastive and iterative degradation prediction schemes and show they are readily compatible with high-performance SR networks such as RCAN and HAN within our framework. Furthermore, we demonstrate our framework’s robustness by successfully performing blind SR on images degraded with blurring, noise and compression. This represents the first explicit combined blind prediction and SR of images degraded with such a complex pipeline, acting as a baseline for further advancements.
Fractal Texture Enhancement of Simulated Infrared Images Using a CNN-Based Neural Style Transfer Algorithm with a Histogram Matching Technique
Authors: Kim Taeyoung , Bang Hyochoong
Published: January 2023

Here, we propose a CNN-based infrared image enhancement method to transform pseudo-realistic regions of simulation-based infrared images into real infrared texture. The proposed algorithm consists of the following three steps. First, target infrared features based on a real infrared image are extracted through pretrained VGG-19 networks. Next, by implementing a neural style-transfer algorithm to a simulated infrared image, fractal nature features from the real infrared image are progressively applied to the image. Therefore, the fractal characteristics of the simulated image are improved. Finally, based on the results of fractal analysis, peak signal-to-noise (PSNR), structural similarity index measure (SSIM), and natural image quality evaluator (NIQE) texture evaluations are performed to know how the simulated infrared image is properly transformed as it contains the real infrared fractal features. We verified the proposed methodology using a simulation with three different simulation conditions with a real mid-wave infrared (MWIR) image. As a result, the enhanced simulated infrared images based on the proposed algorithm have better NIQE and SSIM score values in both brightness and fractal characteristics, indicating the closest similarity to the given actual infrared image. The proposed image fractal feature analysis technique can be widely used not only for the simulated infrared images but also for general synthetic images.
AQMDRL: Automatic Quality of Service Architecture Based on Multistep Deep Reinforcement Learning in Software-Defined Networking
Authors: Chen Junyan , Liao Cenhuishan , Wang Yong , Jin Lei , Lu Xiaoye , Xie Xiaolan , Yao Rui
Published: January 2023

Software-defined networking (SDN) has become one of the critical technologies for data center networks, as it can improve network performance from a global perspective using artificial intelligence algorithms. Due to the strong decision-making and generalization ability, deep reinforcement learning (DRL) has been used in SDN intelligent routing and scheduling mechanisms. However, traditional deep reinforcement learning algorithms present the problems of slow convergence rate and instability, resulting in poor network quality of service (QoS) for an extended period before convergence. Aiming at the above problems, we propose an automatic QoS architecture based on multistep DRL (AQMDRL) to optimize the QoS performance of SDN. AQMDRL uses a multistep approach to solve the overestimation and underestimation problems of the deep deterministic policy gradient (DDPG) algorithm. The multistep approach uses the maximum value of the n-step action currently estimated by the neural network instead of the one-step Q-value function, as it reduces the possibility of positive error generated by the Q-value function and can effectively improve convergence stability. In addition, we adapt a prioritized experience sampling based on SumTree binary trees to improve the convergence rate of the multistep DDPG algorithm. Our experiments show that the AQMDRL we proposed significantly improves the convergence performance and effectively reduces the network transmission delay of SDN over existing DRL algorithms.
A Method of Merging Maps for MUAVs Based on an Improved Genetic Algorithm
Authors: Sun Quansheng , Liao Tianjun , Du Haibo , Zhao Yinfeng , Chen Chih-Chiang
Published: January 2023

The merging of environmental maps constructed by individual UAVs alone and the sharing of information are key to improving the efficiency of distributed multi-UAVexploration. This paper investigates the raster map-merging problem in the absence of a common reference coordinate system and the relative position information of UAVs, and proposes a raster map-merging method with a directed crossover multidimensional perturbation variational genetic algorithm (DCPGA). The algorithm uses an optimization function reflecting the degree of dissimilarity between the overlapping regions of two raster maps as the fitness function, with each possible rotation translation transformation corresponding to a chromosome, and the binary encoding of the coordinates as the gene string. The experimental results show that the algorithm could converge quickly and had a strong global search capability to search for the optimal overlap area of the two raster maps, thus achieving map merging.
An Efficient CRT Based Algorithm for Frequency Determination from Undersampled Real Waveform
Authors: Zhang Yao-Wen , Han Xian-Feng , Xiao Guo-Qiang
Published: January 2023

The Chinese Remainder Theorem (CRT) based frequency estimation has been widely studied during the past two decades. It enables one to estimate frequencies by sub-Nyquist sampling rates, which reduces the cost of hardware in a sensor network. Several studies have been done on the complex waveform; however, few works studied its applications in the real waveform case. Different from the complex waveform, existing CRT methods cannot be straightforwardly applied to handle a real waveform’s spectrum due to the spurious peaks. To tackle the ambiguity problem, in this paper, we propose the first polynomial-time closed-form Robust CRT (RCRT) for the single-tone real waveform, which can be considered as a special case of RCRT for arbitrary two numbers. The time complexity of the proposed algorithm is O(L), where L is the number of samplers. Furthermore, our algorithm also matches the optimal error-tolerance bound.
The Development of an Energy Efficient Temperature Controller for Residential Use and Its Generalization Based on LSTM
Authors: Alexandru Tudor George , Alexandru Adriana , Popescu Florin Dumitru , Andraș Andrei
Published: January 2023

Thermostats operate alongside intelligent home automation systems for ensuring both the comfort of the occupants as well as the responsible use of energy. The effectiveness of such solutions relies on the ability of the adopted control methodology to respond to changes in the surrounding environment. In this regard, process disturbances such as severe wind or fluctuating ambient temperatures must be taken into account. The present paper proposes a new approach for estimating the heat transfer of residential buildings by employing a lumped parameter thermal analysis model. Various control strategies are adopted and tuned into a virtual environment. The knowledge gained is generalized by means of a long short-term memory (LSTM) neural network. Laboratory scale experiments are provided to prove the given concepts. The results achieved highlight the efficiency of the implemented temperature controller in terms of overshoot and energy consumption.
High-Precision Tribometer for Studies of Adhesive Contacts
Authors: Lyashenko Iakov A. , Popov Valentin L. , Pohrt Roman , Borysiuk Vadym
Published: January 2023

Herein, we describe the design of a laboratory setup operating as a high-precision tribometer. The whole design procedure is presented, starting with a concept, followed by the creation of an exact 3D model and final assembly of all functional parts. The functional idea of the setup is based on a previously designed device that was used to perform more simple tasks. A series of experiments revealed certain disadvantages of the initial setup, for which pertinent solutions were found and implemented. Processing and correction of the data obtained from the device are demonstrated with an example involving backlash and signal drift errors. Correction of both linear and non-linear signal drift errors is considered. We also show that, depending on the research interests, the developed equipment can be further modified by alternating its peripheral parts without changing the main frame of the device.
Design of U-Shaped Frequency Tunable Microwave Filters in MEMS Technology
Authors: Giacomozzi Flavio , Proietti Emanuela , Capoccia Giovanni , Sardi Giovanni Maria , Bartolucci Giancarlo , Iannacci Jacopo , Tagliapietra Girolamo , Margesin Benno , Marcelli Romolo
Published: January 2023

U-shaped microwave resonators implemented by RF MEMS switches can be considered the result of a novel design approach for obtaining small-footprint tunable resonators, owing to the bent shape of the resonator and the microsystem solution for changing the frequency of resonance. In this paper, we discuss the design approach for potential configurations of U-shaped structures combined with ohmic RF MEMS switches. Owing to their prospective application in RADAR and satellite systems, the devices were assessed for K-Band operation, specifically for 15 GHz, 20 GHz, and 26 GHz. The ON-OFF states determined by an electrostatic actuation of metal beams composing the RF MEMS ohmic switches allow for selecting different path lengths corresponding to different frequencies. In this contribution, initial configurations were designed and manufactured as a proof-of-concept. The advantages and critical aspects of the designs are discussed in detail.
Recent Progress and Applications of Thermal Lens Spectrometry and Photothermal Beam Deflection Techniques in Environmental Sensing
Authors: Franko Mladen , Goljat Leja , Liu Mingqiang , Budasheva Hanna , Žorž Furlan Mojca , Korte Dorota
Published: January 2023

This paper presents recent development and applications of thermal lens microscopy (TLM) and beam deflection spectrometry (BDS) for the analysis of water samples and sea ice. Coupling of TLM detection to a microfluidic system for flow injection analysis (μFIA) enables the detection of microcystin-LR in waters with a four samples/min throughput (in triplicate injections) and provides an LOD of 0.08 µg/L which is 12-times lower than the MCL for microcystin-LR in water. μFIA-TLM was also applied for the determination of total Fe and Fe(II) in 3 µL samples of synthetic cloudwater. The LODs were found to be 100 nM for Fe(II) and 70 nM for total Fe. The application of µFIA-TLM for the determination of ammonium in water resulted in an LOD of 2.3 µM for injection of a 5 µL sample and TLM detection in a 100 µm deep microfluidic channel. For the determination of iron species in sea ice, the BDS was coupled to a diffusive gradient in the thin film technique (DGT). The 2D distribution of Fe(II) and total Fe on DGT gels provided by the BDS (LOD of 50 nM) reflected the distribution of Fe species in sea ice put in contact with DGT gels.
Human Activity Recognition for AI-Enabled Healthcare Using Low-Resolution Infrared Sensor Data
Authors: Karayaneva Yordanka , Sharifzadeh Sara , Jing Yanguo , Tan Bo
Published: January 2023

This paper explores the feasibility of using low-resolution infrared (LRIR) image streams for human activity recognition (HAR) with potential application in e-healthcare. Two datasets based on synchronized multichannel LRIR sensors systems are considered for a comprehensive study about optimal data acquisition. A novel noise reduction technique is proposed for alleviating the effects of horizontal and vertical periodic noise in the 2D spatiotemporal activity profiles created by vectorizing and concatenating the LRIR frames. Two main analysis strategies are explored for HAR, including (1) manual feature extraction using texture-based and orthogonal-transformation-based techniques, followed by classification using support vector machine (SVM), random forest (RF), k-nearest neighbor (k-NN), and logistic regression (LR), and (2) deep neural network (DNN) strategy based on a convolutional long short-term memory (LSTM). The proposed periodic noise reduction technique showcases an increase of up to 14.15% using different models. In addition, for the first time, the optimum number of sensors, sensor layout, and distance to subjects are studied, indicating the optimum results based on a single side sensor at a close distance. Reasonable accuracies are achieved in the case of sensor displacement and robustness in detection of multiple subjects. Furthermore, the models show suitability for data collected in different environments.
Application of Near Infrared Spectroscopy to Monitor the Quality Change of Sour Cherry Stored under Modified Atmosphere Conditions
Authors: Szabo Gergo , Vitalis Flora , Horvath-Mezofi Zsuzsanna , Gob Monika , Aguinaga Bosquez Juan Pablo , Gillay Zoltan , Zsom Tamás , Nguyen Lien Le Phuong , Hitka Geza , Kovacs Zoltan , Friedrich Laszlo
Published: January 2023

Determining and applying ‘good’ postharvest and quality control practices for otherwise highly sensitive fruits, such as sour cherry, is critical, as they serve as excellent media for a wide variety of microbial contaminants. The objective of this research was to report two series of experiments on the modified atmosphere storage (MAP) of sour cherries (Prunus cerasus L. var. Kántorjánosi, Újfehértói fürtös). Firstly, the significant effect of different washing pre-treatments on various quality indices was examined (i.e., headspace gas composition, weight loss, decay rate, color, firmness, soluble solid content, total plate count) in MAP-packed fruits. Subsequently, the applicability of near infrared (NIR) spectroscopy combined with chemometrics was investigated to detect the effect of various storage conditions (packed as control or MAP, stored at 3 or 5 °C) on sour cherries of different perceived ripeness. Significant differences were found for oxygen concentration when two perforations were applied on the packages of ‘Kántorjánosi’ (p < 0.01); weight loss when ‘Kánorjánosi’ (p < 0.001) and ‘Újfehértói fürtös’ (p < 0.01) were packed in MAP; SSC when ‘Újfehértói fürtös’ samples were ozone-treated (p < 0.05); and total plate count when ‘Kántorjánosi’ samples were ozone-treated (p < 0.01). The difference spectra reflected the high variability in the samples, and the detectable effects of different packaging. Based on the investigations with the soft independent modelling of class analogies (SIMCA), different packaging and storage resulted in significant differences in most of the cases even on the first storage day, which in many cases increased by the end of storage. The soft independent modelling of class analogies proved to be suitable for classification with apparent error rates between 0 and 0.5 during prediction regardless of ripeness. The research findings suggest the further correlation of NIR spectroscopic and reference parameters to support postharvest handling and fast quality control.
Ratiometric Temperature Sensing Using Highly Coupled Seven-Core Fibers
Authors: May-Arrioja Daniel A. , Fuentes-Fuentes Miguel A. , Hernández-Romano Iván , Martínez-Manuel Rodolfo , Cuando-Espitia Natanael
Published: January 2023

In this paper, a ratiometric approach to sensing temperature variations is shown using specialty fiber optic devices. We analyzed the transmission response of cascaded segments of multicore fibers (MCFs), and dissimilar lengths were found to generate an adequate scheme for ratiometric operation. The perturbation of optical parameters in the MCFs translates to a rich spectral behavior in which some peaks increase their intensity while others decrease their intensity. Thus, by selecting opposite-behavior peaks, highly sensitive ratiometric measurements that provide robustness against spurious fluctuations can be performed. We implemented this approach using seven-core fiber (SCF) segments of 5.8 cm and 9.9 cm. To test the system’s response under controlled perturbations, we heated one of the segments from ambient temperature up to 150 °C. We observed defined peaks with opposite behavior as a function of temperature. Two pairs of peaks within the interrogation window were selected to perform ratiometric calculations. Ratiometric measurements exhibited sensitivities 6–14 times higher than single-wavelength measurements. A similar trend with enhanced sensitivity in both peak pairs was obtained. In contrast to conventional interferometric schemes, the proposed approach does not require expensive facilities or micrometric-resolution equipment. Moreover, our approach has the potential to be realized using commercial splicers, detectors, and filters.
Integrated Low-Voltage Compliance and Wide-Dynamic Stimulator Design for Neural Implantable Devices
Authors: Oh Yeonji , Hong Jonggi , Kim Jungsuk
Published: January 2023

In this study, a pulse frequency modulation (PFM)-based stimulator is proposed for use in biomedical implantable devices. Conventionally, functional electrical stimulation (FES) techniques have been used to reinforce damaged nerves, such as retina tissue and brain tissue, by injecting a certain amount of charge into tissues. Although several design methods are present for implementing FES devices, an FES stimulator for retinal implants is difficult to realize because of the chip area, which needs to be inserted in a fovea, sized 5 mm x 5 mm, and power limitations to prevent the heat generation that causes tissue damage. In this work, we propose a novel stimulation structure to reduce the compliance voltage during stimulation, which can result in high-speed and low-voltage operation. A new stimulator that is composed of a modified high-speed PFM, a 4-bit counter, a serializer, a digital controller, and a current driver is designed and verified using a DB HiTek standard 0.18 μm process. This proposed stimulator can generate a charge up to 130 nC, consumes an average power of 375 µW during a stimulation period, and occupies a total area of 700 µm × 68 µm.
A Survey of Optimization Methods for Independent Vector Analysis in Audio Source Separation
Authors: Guo Ruiming , Luo Zhongqiang , Li Mingchun
Published: January 2023

With the advent of the era of big data information, artificial intelligence (AI) methods have become extremely promising and attractive. It has become extremely important to extract useful signals by decomposing various mixed signals through blind source separation (BSS). BSS has been proven to have prominent applications in multichannel audio processing. For multichannel speech signals, independent component analysis (ICA) requires a certain statistical independence of source signals and other conditions to allow blind separation. independent vector analysis (IVA) is an extension of ICA for the simultaneous separation of multiple parallel mixed signals. IVA solves the problem of arrangement ambiguity caused by independent component analysis by exploiting the dependencies between source signal components and plays a crucial role in dealing with the problem of convolutional blind signal separation. So far, many researchers have made great contributions to the improvement of this algorithm by adopting different methods to optimize the update rules of the algorithm, accelerate the convergence speed of the algorithm, enhance the separation performance of the algorithm, and adapt to different application scenarios. This meaningful and attractive research work prompted us to conduct a comprehensive survey of this field. This paper briefly reviews the basic principles of the BSS problem, ICA, and IVA and focuses on the existing IVA-based optimization update rule techniques. Additionally, the experimental results show that the AuxIVA-IPA method has the best performance in the deterministic environment, followed by AuxIVA-IP2, and the OverIVA-IP2 has the best performance in the overdetermined environment. The performance of the IVA-NG method is not very optimistic in all environments.
A Non Intrusive Human Presence Detection Methodology Based on Channel State Information of Wi-Fi Networks
Authors: Mesa-Cantillo Carlos M. , Sánchez-Rodríguez David , Alonso-González Itziar , Quintana-Suárez Miguel A. , Ley-Bosch Carlos , Alonso-Hernández Jesús B.
Published: January 2023

In recent times, we have been witnessing the development of multiple applications and deployment of services through the indoors location of people as it allows the development of services of interest in areas related mainly to security, guiding people, or offering services depending on their localization. On the other hand, at present, the deployment of Wi-Fi networks is so advanced that a network can be found almost anywhere. In addition, security systems are more demanded and are implemented in many buildings. Thus, in order to provide a non intrusive presence detection system, in this manuscript, the development of a methodology is proposed which is able to detect human presence through the channel state information (CSI) of wireless communication networks based on the 802.11n standard. One of the main contributions of this standard is multiple-input multiple-output (MIMO) with orthogonal frequency division multiplexing (OFDM). This makes it possible to obtain channel state information for each subcarrier. In order to implement this methodology, an analysis and feature extraction in time-domain of CSI is carried out, and it is validated using different classification models trained through a series of samples that were captured in two different environments. The experiments show that the methodology presented in this manuscript obtains an average accuracy above 90%.
Improved Face Detection Method via Learning Small Faces on Hard Images Based on a Deep Learning Approach
Authors: Mamieva Dilnoza , Abdusalomov Akmalbek Bobomirzaevich , Mukhiddinov Mukhriddin , Whangbo Taeg Keun
Published: January 2023

Most facial recognition and face analysis systems start with facial detection. Early techniques, such as Haar cascades and histograms of directed gradients, mainly rely on features that had been manually developed from particular images. However, these techniques are unable to correctly synthesize images taken in untamed situations. However, deep learning’s quick development in computer vision has also sped up the development of a number of deep learning-based face detection frameworks, many of which have significantly improved accuracy in recent years. When detecting faces in face detection software, the difficulty of detecting small, scale, position, occlusion, blurring, and partially occluded faces in uncontrolled conditions is one of the problems of face identification that has been explored for many years but has not yet been entirely resolved. In this paper, we propose Retina net baseline, a single-stage face detector, to handle the challenging face detection problem. We made network improvements that boosted detection speed and accuracy. In Experiments, we used two popular datasets, such as WIDER FACE and FDDB. Specifically, on the WIDER FACE benchmark, our proposed method achieves AP of 41.0 at speed of 11.8 FPS with a single-scale inference strategy and AP of 44.2 with multi-scale inference strategy, which are results among one-stage detectors. Then, we trained our model during the implementation using the PyTorch framework, which provided an accuracy of 95.6% for the faces, which are successfully detected. Visible experimental results show that our proposed model outperforms seamless detection and recognition results achieved using performance evaluation matrices.
Recent Advanced Development of Acid-Resistant Thin-Film Composite Nanofiltration Membrane Preparation and Separation Performance in Acidic Environments
Authors: Li Mowen , Zhang Wenhai , Zhang Xuehong , Guo Hongxia , Liang Yucang
Published: January 2023

Membrane filtration technology has attracted extensive attention in academia and industry due to its advantages of eco-friendliness related to environmental protection and high efficiency. Polyamide thin-film composite nanofiltration (PA TFC NF) membranes have been widely used due to their high separation performance. Non-acid-resistant PA TFC NF membranes face tremendous challenges in an acidic environment. Novel and relatively acid-resistant polysulfonamide-based and triazine-based TFC NF membranes have been developed, but these have a serious trade-off in terms of permeability and selectivity. Hence, how to improve acid resistance of TFC NF membranes and their separation performance in acidic environments is a pivotal issue for the design and preparation of these membranes. This review first highlights current strategies for improving the acid resistance of PA TFC NF membranes by regulating the composition and structure of the separation layer of the membrane performed by manipulating and optimizing the construction method and then summarizes the separation performances of these acid-resistant TFC NF membranes in acidic environments, as studied in recent years.
Optimization of Steam Distillation Process for Volatile Oils from Forsythia suspensa and Lonicera japonica according to the Concept of Quality by Design
Authors: Chen Xinying , Guo Dongyun , Gong Xingchu , Wan Na , Wu Zhenfeng
Published: January 2023

In this study, the process of steam distillation to collect volatile oils from Forsythia suspensa (F. suspensa) and Lonicera japonica (L. japonica) was optimized according to the concept of quality by design. First, the liquid/material ratio, distillation time, and collection temperature were identified as critical process parameters by a review of the literature and single-factor experiments. Then, a Box–Behnken design was used to study the quantitative relationship between the three process parameters, two raw material properties, and the yield of volatile oil. A mathematical model was established with an R2 value exceeding 0.90. Furthermore, the design space of the volatile oil yield was calculated by a probability-based method. The results of a verification experiment showed that the model was accurate and the design space was reliable. A total of 16 chemical constituents were identified in the volatile oil from mixtures of F. suspensa and L. japonica. The content of β-pinene was the highest (54.75%), and the composition was similar to that of the volatile oil of F. suspensa. The results showed that when F. suspensa and L. japonica were distilled together, the main contribution to the volatile oil was from F. suspensa. The volatile oil yield from the combination of F. suspensa and L. japonica was not higher than that from L. japonica.
Determinants of Capital Structure: Empirical Evidence of Manufacturing Companies in the Republic of Serbia
Authors: Stoiljković Aleksandra , Tomić Slavica , Leković Bojan , Matić Milenko
Published: January 2023

The subject of research in the paper is the capital structure of companies in the Republic of Serbia. The research sample consists of companies that operated in the manufacturing industry in the Republic of Serbia in the period 2006–2020. The aim of the research is to identify firm-specific variables that have significant influence on the capital structure of the analyzed companies. Using a panel data methodology, three leverage models were estimated: long-term leverage, short-term leverage, and total leverage. The research results confirm the importance of company size, profitability, tangibility, and risk in determining the capital structure of companies in the Republic of Serbia. However, the research results show that size, profitability, and tangibility of assets have the opposite effect on long-term leverage compared to short-term and total leverage. That is, the behavior of companies in the Republic of Serbia in the case of long-term leverage is in accordance with the predictions of the trade-off theory, while in the case of short-term and total leverage, the behavior of companies can be explained by the pecking order theory.
Embedded System Performance Analysis for Implementing a Portable Drowsiness Detection System for Drivers
Authors: Kim Minjeong , Koo Jimin
Published: January 2023

Drowsiness on the road is a widespread problem with fatal consequences; thus, a multitude of systems and techniques have been proposed. Among existing methods, Ghoddoosian et al. utilized temporal blinking patterns to detect early signs of drowsiness, but their algorithm was tested only on a powerful desktop computer, which is not practical to apply in a moving vehicle setting. In this paper, we propose an efficient platform to run Ghoddoosian’s algorithm, detail the performance tests we ran to determine this platform, and explain our threshold optimization logic. After considering the Jetson Nano and Beelink (Mini PC), we concluded that the Mini PC is most efficient and practical to run our embedded system in a vehicle. To determine this, we ran communication speed tests and evaluated total processing times for inference operations. Based on our experiments, the average total processing time to run the drowsiness detection model was 94.27 ms for the Jetson Nano and 22.73 ms for the Beelink (Mini PC). Considering the portability and power efficiency of each device, along with the processing time results, the Beelink (Mini PC) was determined to be most suitable. Additionally, we propose a threshold optimization algorithm, which determines whether the driver is drowsy, or alert based on the trade-off between the sensitivity and specificity of the drowsiness detection model. Our study will serve as a crucial next step for drowsiness detection research and its application in vehicles. Through our experiments, we have determined a favorable platform that can run drowsiness detection algorithms in real-time and can be used as a foundation to further advance drowsiness detection research. In doing so, we have bridged the gap between an existing embedded system and its actual implementation in vehicles to bring drowsiness technology a step closer to prevalent real-life implementation.
Utilization of Indole Acetic Acid with Leucadendron rubrum and Rhododendron pulchrum for the Phytoremediation of Heavy Metals in the Artificial Soil Made of Municipal Sewage Sludge
Authors: Chen Xiaoling , Feng Jianru , Mou Huaqian , Liang Zheng , Ding Tianzheng , Chen Shiyu , Li Feili
Published: January 2023

The development of phytoremediation by garden plants is an effective way to deal with the dilemma of municipal sewage sludge disposal. In this study, two ornamental plants were used as phytoremediation plants to rehabilitate heavy-metal-contaminated municipal sewage sludge in field experiments, and the role of exogenous phytohormone IAA was also tested. Ornamental plants Loropetalum chinense var. rubrum (L. rubrum) and Rhododendron pulchrum (R. pulchrum) adapted well to the artificial soil made of municipal sewage sludge, and the concentrations of Cu, Zn, Pb, and Ni were decreased by 7.29, 261, 20.2, and 11.9 mg kg−1, respectively, in the soil planted with L. rubrum, and 7.60, 308, 50.1, and 17.7 mg kg−1, respectively, in the soil planted with R. pulchrum, accounted for 11–37% of the total amounts and reached significant levels (p < 0.05), except Cd. The concentration of Pb in all parts of the two ornamental plants was increased, as well as most heavy metals in L. rubrum root. As a result, three months after transplant, the phyto-extraction amounts in L. rubrum were 397, 10.9, and 1330 μg for Ni, Cd, and Pb, respectively, increased by 233% to 279%. The phyto-extraction amount in R. pulchrum were 1510, 250, and 237 μg for Zn, Pb, and Cu, respectively, increased by 143% to 193%. These results indicated a potential to remediate heavy metals of the two ornamental plants, especially L. rubrum. The results of correlation analysis implied that the interaction of heavy metals in the plant itself played an important role in the uptake of heavy metals. This seemed to explain why applying IAA in the experiment had little effect on plant growth and phytoremediation of heavy metals. This study provided a green and feasible idea for the proper disposal of municipal sewage sludge.
Bearing Fault Diagnosis Based on Multi-Scale CNN and Bidirectional GRU
Authors: Saghi Taher , Bustan Danyal , Aphale Sumeet S.
Published: January 2023

Finding a reliable approach to detect bearing faults is crucial, as the most common rotating machine defects occur in its bearings. A convolutional neural network can automatically extract the local features of the mechanical vibration signal and classify the patterns. Nevertheless, these types of networks suffer from the extraction of the global feature of the input signal as they utilize only one scale on their input. This paper presents a method to overcome the above weakness by employing a combination of three parallel convolutional neural networks with different filter lengths. In addition, a bidirectional gated recurrent unit is utilized to extract global features. The CWRU-bearing dataset is used to prove the performance of the proposed method. The results show the high accuracy of the proposed method even in the presence of noise.
MEDIA PEMBELAJARAN HURUF HIJAIYAH UNTUK ANAK USIA DINI BERBASIS GAME ANDROID
Authors: Zy Ahmad Turmudi , Edora Edora
Published: December 2022

Sebagai umat beragama kita harus membentengi diri kita sedini mungkin dengan pembekalan ilmu agama. Salah satunya yaitu dalam membaca Al-quran. Bagi anak usia dini tahapan awal membaca Al-quran yaitu mampu menguasai huruf hijaiyah. Permasalahan dalam penelitian ini yaitu kurangnya minat belajar anak serta minimnya media yang diaplikasikan dalam proses pembelajaran huruf hijaiyah. Media yang masih konvensional sering berakibat pencapaian kurang optimal . Dari itu perlu, dibuatlah sebuah media pembelajaran untuk anak usia dini berupa game edukasi berbasis android supaya menambah alternatif media pembelajaran bagi anak usia dini. Penelitian ini menerapakan metode MDLC (Multimedia Development Life Cyrcle). Pengujian kebutuhan fungsional dalam aplikasi ini melalui tahapan pengujian black box. Hasil pengujian black box yaitu sebesar 100% artinya aplikasi ini selaras dengan kebutuhan pengguna dan semua fungsi berjalan sesuai kebutuhan fungsional. Maka dari itu, bisa disimpulkan bahwa game edukasi ini atraktif dan efektif dalam meningkatan kecakapan belajar huruf hijaiyah
Jazz in the context of «global village» principle
Authors: Paliy Iryna
Published: December 2022

This article addresses one of the manifestations of interaction between different music domains, using the sphere of jazz as an example. In the course of evolution, the art of music has developed five relatively independent domains based on fundamentally different principles of musical thought. Globalization, which has intensified in the 20th century and continues to grow in the 21st, gave birth to a new domain called by us “unionique music”. This domain is the object of our research. The relevance of this study stems from the fact that unionique music compositions do not fit the standard principles of genre classifications used by academic music scholars. At the same time, an important feature that unites these compositions is the combination of such spheres of music as jazz, rock, “traditional” music of Western European academic tradition, ethnic music and folklore. Nevertheless, the degree of impact of a particular music domain in different compositions has different indicators. This article focuses on the analysis of unionique music compositions that contain expressive means of various domains, but at the same time are founded on the jazz-based principle of music playing.
Henri Duparc in the history of the mélodie genre
Authors: Zharkikh Tetiana
Published: December 2022

Statement of the problem. Henri Duparc (1848–1933) has firmly entered the history of the French melodie genre. However, his work has not been properly studied by modern domestic musicologists. Although two composers, Gabriel Fauré and Henri Duparc, are generally recognized as the most prominent representatives of this genre, the vocal work of G. Fauré attracted the attention of many scientists and performers, in contrast to the music of H. Duparc. The restoration of historical justice in relation to the heritage of the French composer justifies the topic of this article. The purpose of the article is an attempt to comprehend H.Duparc’s interpretation of the melodie genre by analyzing one of his vocal miniatures – “Phidylé” a setting of a poem by Leconte de Lisle and highlight the composer’s role in the history of the genre. The historical and biographical contextual approaches, genre, structuralfunctional, phono-semantic methods of analysis and the study of the performance dramaturgy of H. Duparc’s miniature led to a number of conclusions. Conclusions and results of the study. In Duparc’s interpretation of the poem, two ideas interact: the exact reflection in music the poem meaning and the transformation of the poem due to its musical detailization. The study revealed the features of the composer’s style and the role of the composer as the founder of the melodie genre: – it was H. Duparc who founded such features of the genre as a deep emotional and lyrical intonation and sensuality compared to a poetic text; – original reproduction of the signs of “endless melody” (influence of R. Wagner’s work) and opera monologue; – equality of parts in the “duet” of the vocalist and pianist-accompanist; – “orchestral” sound of the piano texture. Thus, H. Duparc stands at the origins of the “golden age” in the history of the melodie genre as a representative and defender of the late romanticism aesthetics. The vocal works of H. Duparc gave a powerful impetus to the genre of French melodie. The reproduction of the dramatic tension of feelings, the search for perfection in music testify to the significance of the composer’s contribution to the melodie genre, while the small number of his works is certainly compensated by their musicality. Since the vocal works of H. Duparc create a certain difficulty for foreign performers in the verbal and phonetic aspects, as well as in the artistic and interpretive aspect, it seems necessary to continue their in-depth analysis.
ЕErnest Chausson: periodization of artistic path in the context of the style formation
Authors: Milanina Aliona
Published: December 2022

Statement of the problem. In the history of French music the work of Ernest Chausson (1855–1899) played a connecting role between the late romanticism of S. Franck and the early impressionism of C. Debussy. The personality and work of the French composer are sufficiently deeply studied in European and American musicology; it is natural for Ukrainian researchers to join the process of this study. Despite the outstanding importance of the composer’s work in both French and world music, there are no problematic studies devoted to the study of his legacy in domestic science. The music of the French master, in particular, vocal music, remains little-known in Ukraine. One of the debatable issues of studying the work of E. Chausson is the periodization of his legacy, since the discrepancies existing in the works of researchers hinder the understanding of his creative evolution. After all, the chronological distribution of the artist’s work takes into account complex historical, socio-psychological and artistic processes, as well as the specificity of his creative method and related stylistic changes. The purpose of the article is to summarize the known periodizations of E. Chausson’s work and to develop synthesized one in order to remove existing disagreements. Solving this innovative task required the study of a number of works of foreign scientists using historical, historiographical, comparative approaches, as well as genre and style analysis when referring to the creative work of E. Chausson.Information on the periodization of E. Chausson’s creative heritage is provided both by encyclopedic publications (Warszawski, 2005; Latham, 2009, etc.), and by some scholars who refer to the music of E. Chausson (Chiang, 2006; Moroncini, [2022]; Hanon, 2022). At the same time, certain chronological differences can be traced in the information provided by scientists. Results and conclusions. The application of the basic position of domestic musicology regarding the change of the “intonational image of the world” as a criterion for distinguishing historical periods (Chekan, 1997) and the experience of foreign scientists made it possible, as a result, to prove the feasibility of dividing E. Chausson’s work into three periods: early (1877–1886), where tangible lateromantic influences of J. Massenet, S. Frank and R. Wagner, middle, mixed, (1886–1889), marked by the interaction of late-romantic and impressionistic traits, and the final (1889–1899) stage of formation of the composer’s individual style. Such a periodization, without excluding existing ones, will contribute to a clearer understanding of E. Chausson’s creative evolution, which should facilitate the work of a musician-performer.
Regional dimension of ukraine's gender policy (on the example of Donetsk region)
Authors:   Mariupol State University, Hunza Yuliia , Lysak Viktoriiа ,   Mariupol State University
Published: December 2022

The regional dimension of the creation and implementation of gender policy in Ukraine on the example of Donetsk region is analyzed. The scientific achievements of Ukrainian researchers on gender policy and normative and legal documents of gender implementation in Ukraine and Donetsk region are described. The peculiarities of Donetsk region and the policy of gender equality in the region are clarified. Factors influencing gender policy at the national and regional levels are revealed. The mechanisms of formation and implementation of gender policy in Donetsk region and Ukraine, through the administrative-territorial structure of the region, its components and competencies in the field of gender research are analyzed. The main criteria for the formation of Ukraine's gender policy are highlighted. The implementation of Ukraine's gender policy at the regional level on the example of Donetsk region in the socio-economic and socio-political spheres is considered and an assessment of their effectiveness is given. The issue of the state of gender relations in the economic, social and political spheres of Ukrainian society is generalized. The obtained results of scientific research testify to the state of gender policy in all spheres of life. Since gender equality is the basis for the development of society, the results can be used to further implement the gender approach in the socio-political and legislative spheres.