bluuebunny/crossref_metadata_2025 huggingface dataset:
Discrete mean square approximation applied to error calculation in biomolecules and brownian motion
Authors: Randjelovic Branislav , Markovic Bojana , Mitic Vojislav V. , Aleksic Sanja , Milosevic Dusan , Vlahovic Branislav , Tsay Shwu-Chen
Published: November 2021

Advanced research frontiers are extended from biophysics relations on the Earth upto the discovering any type of alive matter within the whole space. Microorganisms’ motion within the molecular biology processes integrates variety of microorgnisms functions. In continuation of our Brownian motion phenomena research, we consistently build molecular-microorganisms structures hierarchy. We recognize everywhere biomimetic similarities between the particles in alive and nonalive matter. The research data are based on real experiments, without external energy impulses. So, we develop the analysis, inspired by fractal nature Brownian motion, as recognized joint parameter between particles in alive and nonalive biophysical systems. This is also in line with advance trends in hybrid submicroelectronic integrations. The important innovation in this paper is that we introduced approximation of trajectory and error calculations, using discrete mean square approximation, what cumulatively provide much more precise biophysical systems parameters. By this paper, we continue to generate new knowledge in direction to get complex relations between the particles clusters in biophysical systems condensed matter.
Estimating the State of Health of Lead-Acid Battery Using Feed-Forward Neural Network
Authors: Selvabharathi D. , Muruganantham N.
Published: November 2021

A Battery Management System (BMS) can prolong the life of the battery but it depends on the accuracy of the adopted scheme. Different techniques have been developed to enhance the BMS by monitoring the State of Health (SOH) of the battery. In this paper, the detection of battery voltage is analyzed by using the cycle counting method, which is a conventional technique and compared with Artificial Neural Network (ANN), a heuristic method. The advantage of the proposed ANN method is that SOH can be monitored without disconnecting the battery from the load. Also, the sampling data to the ANN are derived from various techniques including Open Circuit Voltage (OCV) method, Ambient temperature measurement, and valley point detection. A feed-forward backpropagation algorithm is used to achieve the purpose of real-time monitoring of the LAB. The results show that the precise estimation of SOH can be obtained by Feed-Forward Neural Network (FFNN) when trained with more sampling data.
Power Estimation and Validation of Embedded Multiplier Based on ANN and Regression Technique
Authors: Singh Neerja , Verma Gaurav , Khare Vijay
Published: November 2021

Nowadays, high-end Field-Programmable Gate Arrays (FPGAs) are capable of implementing relatively high-performance systems in the field of Digital Signal Processing (DSP). Due to the abundant application of multipliers, their implementation efficiency and performance have become a critical issue in designing the DSP systems. On the other hand, FPGAs consume a large amount of power due to their complex circuitry. So, the power estimation of FPGA implementations at an early design stage has become a critical design metric. Various models are available in the literature based on Look-up Tables (LUTs), but not much literature is available on speed-optimized multiplier design using DSP slices only. In this paper, an embedded multiplier (12.0 IP core) has been analyzed and customized for different Input/Output (I/O) configurations to estimate the power using Vivado Design Suite (2014.4) targeted to the Zynq-family FPGA device (Zynq evolution and development kit). The embedded multiplier IP has been optimized for performance using two different approaches, i.e., Mults (DSP)-based and LUTs-based. Post-synthesis attributes have been used for formulating the power estimation models based on Artificial Neural Network (ANN) and curve fitting and regression technique. The power values estimated from the proposed models have been authenticated with reference to those assessed from the commercial tool. Based on the results obtained, ANN-based model provides average errors of 0.73% and 0.88% for the LUTs and DSP-based designs, respectively. Whereas, the model based on curve fitting and regression technique provides average errors of 3.61% and 1.59% for the LUTs and DSP-based designs, respectively. The timing analysis has been done to get the design performance and time complexity of the proposed models. Area analysis of the design has also been performed in order to report the resource utilization.
Fuzzy Machine Learning Model in Real-World Physical Domains; A State-of-the-Art Approach
Authors: Dhote Sunita , Pais Rupesh , Vichoray Chandan , Baskar S.
Published: November 2021

Day after day, individuals all across the world come up with fresh ideas for improving the future. Several intriguing discoveries and ideas paved the way for a new age of electronics, telecommunications, business, and medicinal innovation. Using less resources, greater changes in these domains can be achieved. As improving efficiency and productivity allow exponential development in some areas of the global economy, artificial intelligence (AI) and machine learning (ML) is being adopted by a growing number of individuals, corporations, and governments. Since real-world scenarios influence imprecise and unpredictable situations, fuzzy systems have become an inescapable machine learning aspect. Thus, this research presents a qualitative analysis of the significance of fuzzy machine learning systems like fuzzy support vector machines (FSVM) in various physical domains. Based on this analysis, this research extends with the proposal of a fuzzy machine learning-based framework for two different physical domains: (1) intelligent transportation and (2) ecological risk handling. Thereby, this state-of-the-art approach presents fault detection using FSVM (FD-FSVM) model in the intelligent transportation domain. In ecological risk handling, this study proposes an improved FSVM for risk level classification (FSVM-RLC) approach, which uses the persistent organic pollutants data for training and validation. These two domains are chosen randomly to evaluate the classification performance of the fuzzy machine learning algorithms based on their mean absolute error, accuracy, precision, recall, and F1 score. Apart from this, the mean square error and mean absolute error are measured. Compared to existing machine learning models, the individual results of these two approaches show the highest performance. Furthermore, this fuzzy integrated machine learning technique kept consistency in both domains by giving 98.2% and 97.89% accuracy levels for FD-FSVM and FSVM-RLC, respectively.
A NEW APPROXIMATION: FROM BARRIER LOWERING TO INTERFACE STATE DENSITY
Authors: KORKUT ABDULKADIR
Published: November 2021

In this study, a set of the interface state density (ISD) formulae was derived from the Schottky effect. In contrast to the conventional approximation, a new approximation for the ISD formulae gives very unusual values in the case of forward bias or reverse bias. The former ISD formula contains the oxide thickness in the metal–semiconductor interface region, whereas the new approximation formulae do not contain the oxide thickness. They depend on the applied voltage and built-in potential in the case of both bias. Besides, lowering barrier height is called the Schottky effect. A couple of the new ISD formulae can be proportioned to each other. The ratio is a new coefficient. Moreover, the coefficient is inversely proportional with the a cceptor concentration and third power of zero-voltage depletion length. Moreover, that fraction is inversely proportional to the acceptor concentration and cubic power to depletion length.
Nonlinear optical responses of molecular systems with vibronic coupling in fluctuating environments
Authors: Paz Jose Luis , Loroño Marcos , González-Paz Lenin A. , Marquez Edgar , Vera-Villalobos Joan , Mora José R. , Alvarado Ysaias J.
Published: November 2021

The topological profiles of the nonlinear optical properties of a two-level molecular system involving simplified versions of intramolecular coupling immersed in a thermal reservoir have been studied in four-wave mixing (FWM) spectroscopy. Solvent effects have been explicitly considered by modeling the nonradiative interaction with the solute as a random variable. To formulate a solution, it is necessary to introduce stochastic considerations. We assume that system–solvent interactions induce random shifts in the Bohr frequency of the adiabatic states, and their manifestation corresponds to the broadening of the upper level. The Born–Oppenheimer (BO) electronic energy curves for this molecular model consist of two intertwined harmonic oscillator potentials shifted in both their position and their minimum equilibrium energy. The critical quantities for this analysis are the transition and permanent dipole moments, which depend strongly on intramolecular coupling. Our results show how absorption and refractive index are affected by vibronic coupling and solvent stochasticity.
Synchronization for KPZ
Authors: Rosati Tommaso C.
Published: November 2021

We study the long-time behavior of Kardar–Parisi–Zhang (KPZ)-like equations: [Formula: see text] [Formula: see text] on the [Formula: see text]-dimensional torus [Formula: see text] driven by an ergodic noise [Formula: see text] (e.g., space-time white in [Formula: see text]). The analysis builds on infinite-dimensional extensions of similar results for positive random matrices. We establish a one force, one solution principle and derive almost sure synchronization with exponential deterministic speed in appropriate Hölder spaces.
Geometry of semi-Riemannian group manifold and its applications in spacetime admitting Ricci solitons
Authors: Arfah Arfah
Published: November 2021

In this work, we show that semi-Riemannian group manifold admits Ricci solitons and satisfies the dynamical cosmology equation of spacetime. In Sec. 2, we introduce and provide some geometric properties of semisymmetric nonmetric connection in semi-Riemannian space. In Sec. 3, we define and show some geometric properties of group manifold endowed with semisymmetric nonmetric connection in semi-Riemannian space. In the section that follows, we give a condition of a group manifold to be Ricci solitons and gradient Ricci soliton. In Sec. 5, we provide the applications of group manifold admitting Ricci solitons in the theory of general relativity.
HIGH-PERFORMANCE WORK SYSTEMS, ENTREPRENEURIAL ORIENTATION, AND INNOVATION STRATEGY IN DEVELOPING COUNTRIES
Authors: SHAHRIARI MOHSEN , MAHMOUDI-MESINEH MARYAM
Published: November 2021

Innovation strategy has become an important recent challenge in today’s competitive conditions of developing countries. In this paper, the impact of high-performance work systems (HPWSs) on innovation strategy of knowledge-based enterprises is examined, considering the mediating role of entrepreneurial orientation. The statistical sample of study includes 180 managers and employees of Iranian knowledge-based companies. Data were collected using standard questionnaire, and the structural equation method was used for data analysis. The results showed that HPWSs have a positive and significant impact on innovation strategy of knowledge-based companies and about 75% of the total effect of the HPWS on innovation strategy is explained indirectly by the mediating variable of entrepreneurial orientation.
FOSTERING TEAM INNOVATION IN TECH START-UPS: THE ROLE OF TEAM AMBIDEXTERITY AS MEDIATOR BETWEEN SERVANT LEADERSHIP BEHAVIOUR AND TEAM INNOVATION
Authors: ANTONIO TONY , MURWANI FULGENTIUS DANARDANA , BERNARTO INNOCENTIUS , SUDIBYO NIKO
Published: November 2021

Early technology start-up faces a lot of problems such as leader mentality, ecosystem, lack of general knowledge, and innovation. One of the root causes is the absence of leadership role. This study is constructed to fill a research gap on the influence of servant leadership behaviour to team innovation in technology start-up. The authors propose team ambidexterity as mediator to enhance the role of servant leadership behaviour in fostering team innovation. We also investigate the team climate as a moderator to support the team ambidexterity. The samples are 207 start-up members in Indonesia which aggregated into 59 clustered samples. The result shows that the team ambidexterity has a significant mediation effect between servant leadership behaviour and team innovation. Second result is team climate, which is a good moderator on team ambidexterity. These outcomes lead to research opportunities and practical implications to enhance the team innovation among start-ups.
Approximation by mixed positive linear operators based on second-kind beta transform
Authors: Deo Naokant , Pratap Ram
Published: November 2021

In this paper, we consider mixed approximation operators based on second-kind beta transform using Szász–Mirakjan operators. For the proposed operators, we establish some direct results, Voronovskaya-type theorem, quantitative Voronovskaya-type theorem, Grüss–Voronovskaya-type theorem, weighted approximation and functions of bounded variation.
Evaluation of Pre-Training with the da Vinci Skills Simulator on Motor Skill Acquisition in a Surgical Robotics Curriculum
Authors: Battaglia Edoardo , Mueller Bradly , Hogg Deborah , Rege Robert , Scott Daniel , Fey Ann Majewicz
Published: November 2021

Training for robotic surgery can be challenging due the complexity of the technology, as well as a high demand for the robotic systems that must be primarily used for clinical care. While robotic surgical skills are traditionally trained using the robotic hardware coupled with physical simulated tissue models and test-beds, there has been an increasing interest in using virtual reality simulators. Use of virtual reality (VR) comes with some advantages, such as the ability to record and track metrics associated with learning. However, evidence of skill transfer from virtual environments to physical robotic tasks has yet to be fully demonstrated. In this work, we evaluate the effect of virtual reality pre-training on performance during a standardized robotic dry-lab training curriculum, where trainees perform a set of tasks and are evaluated with a score based on completion time and errors made during the task. Results show that VR pre-training is weakly significant ([Formula: see text]) in reducing the number of repetitions required to achieve proficiency on the robotic task; however, it is not able to significantly improve performance in any robotic tasks. This suggests that important skills are learned during physical training with the surgical robotic system that cannot yet be replaced with VR training.
User-Defined Gestures with Physical Props in Virtual Reality
Authors: Moran-Ledesma Marco , Schneider Oliver , Hancock Mark
Published: November 2021

When interacting with virtual reality (VR) applications like CAD and open-world games, people may want to use gestures as a means of leveraging their knowledge from the physical world. However, people may prefer physical props over handheld controllers to input gestures in VR. We present an elicitation study where 21 participants chose from 95 props to perform manipulative gestures for 20 CAD-like and open-world game-like referents. When analyzing this data, we found existing methods for elicitation studies were insufficient to describe gestures with props, or to measure agreement with prop selection (i.e., agreement between sets of items). We proceeded by describing gestures as context-free grammars, capturing how different props were used in similar roles in a given gesture. We present gesture and prop agreement scores using a generalized agreement score that we developed to compare multiple selections rather than a single selection. We found that props were selected based on their resemblance to virtual objects and the actions they afforded; that gesture and prop agreement depended on the referent, with some referents leading to similar gesture choices, while others led to similar prop choices; and that a small set of carefully chosen props can support multiple gestures.
HybridPointing for Touch
Authors: Dickson Terence , Wehbe Rina R. , Matulic Fabrice , Vogel Daniel
Published: November 2021

We propose CursorTap, an extension of Forlines et al.'s mixed, absolute and relative "HybridPointing" to large wall-sized multitouch displays. Our technique uses a relative pointing quasimode activated with one hand, while the other hand controls a distant cursor similar to a large touchpad. A controlled experiment compares the technique to standard absolute touch input as a baseline and a whole-display "Drag" technique representing a common alternate approach. Results show CursorTap is fastest for the common usage scenario of reaching distant targets and then returning to nearby targets. Overall, median selection times across distances are similar with CursorTap, but linearly increase with the other techniques. As further validation, a second study explore show people use CursorTap in a two-person game. The results found just over half of the participants choose to use CursorTap for half of the primary interactions where "enemies" are eliminated using a tap, drag, or lasso "tool".
Smartphone-based Handwritten Signature Verification using Acoustic Signals
Authors: Zhao Run , Wang Dong , Zhang Qian , Jin Xueyi , Liu Ke
Published: November 2021

Handwritten signature verification techniques, which can facilitate user authentication and enable secure information exchange, are still important in property safety. However, on-line automatic handwritten signature verification usually requires dynamic handwritten patterns captured by a special device, such as a sensor-instrumented pen, a tablet or a smartwatch on the dominant hand. This paper presents SonarSign, an on-line handwritten signature verification system based on inaudible acoustic signals. The key insight is to use acoustic signals to capture the dynamic handwritten signature patterns for verification. Particularly, SonarSign exploits the built-in speakers and microphones of smartphones to transmit a specially designed training sequence and record the corresponding echo for channel impulse response (CIR) estimation, respectively. Based on the sensitivity of CIR to the tiny surrounding environment changes including handwritten signature actions, SonarSign designs an attentional multi-modal Siamese network for end-to-end signatures verification. First, multi-modal CIR streams are fused to extract representative signature pattern features from spatio-temporal dimensions. Then an attentional Siamese network is elaborated to verify whether the given two signatures are from the same signatory. Extensive experiments in real-world scenarios show that SonarSign can achieve accurate and robust signatures verification with an AUC (Area Under ROC (Receiver Operating Characteristic) Curve) of 98.02% and an EER (Equal Error Rate) of 5.79% for unseen users.
In Vivo Dynamics of the Latent Reservoir for HIV-1: New Insights and Implications for Cure
Authors: Siliciano Janet D. , Siliciano Robert F.
Published: November 2021

Although antiretroviral therapy (ART) can reduce viremia to below the limit of detection and allow persons living with HIV-1 (PLWH) to lead relatively normal lives, viremia rebounds when treatment is interrupted. Rebound reflects viral persistence in a stable latent reservoir in resting CD4+ T cells. This reservoir is now recognized as the major barrier to cure and is the focus of intense international research efforts. Strategies to cure HIV-1 infection include interventions to eliminate this reservoir, to prevent viral rebound from the reservoir, or to enhance immune responses such that viral replication is effectively controlled. Here we consider recent developments in understanding the composition of the reservoir and how it can be measured in clinical studies. We also discuss exciting new insights into the in vivo dynamics of the reservoir and the reasons for its remarkable stability. Finally we discuss recent discoveries on the complex processes that govern viral rebound.
Dynamic Response Analysis of Medium-Speed Maglev Train with Track Random Irregularities
Authors: Yu Peichang , Song Mengxiao , Chen Qiang , Wang Lianchun , He Guang , Leng Peng
Published: November 2021

In order to analyze the dynamic response of medium-speed maglev train in the speed range of 0–200 km/h, the suspension performance, suspended energy consumption, and riding comfort of the train stimulated by random track irregularities are discussed in this paper. Firstly, the model of medium-speed maglev train including car body, air spring vibration isolation system, and the suspension system is established. Then, a controller based on flux inner feedback loop and PID outer feedback loop is designed for the suspension system. The established model is stimulated by the actual track power spectrum in full speed range. The simulation results show that the fluctuation of suspension gap is less than ±4 mm. Furthermore, thanks to the adding of permanent magnet, the power consumption is significantly reduced, which is of benefit to the electromagnet heating problem and on-board levitation power supply system. The riding comfort of the train moving on the irregular track using Sperling index is assessed. The experimental results validate the effectiveness of the proposed analytical calculation model of medium-speed maglev train. It is shown that medium-speed maglev train achieved good performance, significant power reduction, and satisfactory riding comfort.
Remote Facial Expression and Heart Rate Measurements to Assess Human Reactions in Glass Structures
Authors: Bedon Chiara , Mattei Silvana
Published: November 2021

In engineering applications, human comfort fulfillment is challenging because it depends on several aspects that can be mathematically controlled and optimized, like in case of structural, energy, or thermal issues, and others. Major troubles can indeed derive from combined human reactions, which are related to a multitude of aspects. The so‐called “emotional architecture” and its nervous feelings are part of the issue. The interaction of objective and subjective parameters can thus make the “optimal” building design complex. This paper presents a pilot experimental investigation developed remotely to quantify the reactions and nervous states of 10 volunteers exposed to structural glass environments. As known, intrinsic material features (transparency, brittleness, etc.) require specific engineering knowledge for safe mechanical design but can in any case evoke severe subjective feelings for customers, thus affecting their psychological comfort and hence behaviour and movements. This study takes advantage of static/dynamic Virtual Reality (VR) environments and facial expression analyses, with Artificial Intelligence tools that are used to measure both Action Units (AUs) of facial microexpressions and optical heart rate (HR) acquisitions of volunteers exposed to VR scenarios. As shown, within the limits of collected records, the postprocessing analysis of measured signals proves that a rather good correlation can be found for measured AUs, HR data trends, and emotions under various glazing stimuli. Such a remote experimental approach could be thus exploited to support the early design stage of structural glass members and assemblies in buildings.
Mature Ovarian Teratoma: Neurological Implications in a Young Woman
Authors: Schiavi Michele Carlo , Manganelli Fabio , Morgani Claudia , Cignini Pietro , Yacoub Veronica , Carletti Valerio , Grilli Debora , Di Pinto Anna , Bisogni Francesco , Ligato Azzurra , Galanti Francesco , Valensise Herbert Carmelo Carlo , Palazzetti Pier Luigi
Published: November 2021

Anti-NMDAR encephalitis is an autoimmune syndrome associated with antibodies against NMDA receptors. In some cases, it is associated with various tumors; one of them is ovarian teratoma, which mostly affects women below the age of 30 years. Here, we report a case of ovarian teratoma associated with anti-NMDAR encephalitis treated with both laparoscopic surgery and immunotherapy. Multidisciplinary approach is the cornerstone for the management of this syndrome.
Research on Image Denoising and Super-Resolution Reconstruction Technology of Multiscale-Fusion Images
Authors: Li Size , Qian Pengjiang , Zhang Xin , Chen Aiguo
Published: November 2021

Image denoising and image super-resolution reconstruction are two important techniques for image processing. Deep learning is used to solve the problem of image denoising and super-resolution reconstruction in recent years, and it usually has better results than traditional methods. However, image denoising and super-resolution reconstruction are studied separately by state-of-the-art work. To optimally improve the image resolution, it is necessary to investigate how to integrate these two techniques. In this paper, based on Generative Adversarial Network (GAN), we propose a novel image denoising and super-resolution reconstruction method, i.e., multiscale-fusion GAN (MFGAN), to restore the images interfered by noises. Our contributions reflect in the following three aspects: (1) the combination of image denoising and image super-resolution reconstruction simplifies the process of upsampling and downsampling images during the model learning, avoiding repeated input and output images operations, and improves the efficiency of image processing. (2) Motivated by the Inception structure and introducing a multiscale-fusion strategy, our method is capable of using the multiple convolution kernels with different sizes to expand the receptive field in parallel. (3) The ablation experiments verify the effectiveness of each employed loss measurement in our devised loss function. And our experimental studies demonstrate that the proposed model can effectively expand the receptive field and thus reconstruct images with high resolution and accuracy and that the proposed MFGAN method performs better than a few state-of-the-art methods.
Global Dynamics, Bifurcation Analysis, and Chaos in a Discrete Kolmogorov Model with Piecewise-Constant Argument
Authors: Khan Abdul Qadeer
Published: November 2021

The local behavior with topological classifications, bifurcation analysis, chaos control, boundedness, and global attractivity of the discrete-time Kolmogorov model with piecewise-constant argument are investigated. It is explored that Kolmogorov model has trivial and two semitrival fixed points for all involved parameters, but it has an interior fixed point under definite parametric condition. Then, by linear stability theory, local dynamics with different topological classifications are investigated around trivial, semitrival, and interior fixed points. Further for the discrete Kolmogorov model, existence of periodic points is also investigated. It is also investigated the occurrence of bifurcations at interior fixed point and proved that at interior fixed point, there exists no bifurcation, except flip bifurcation by bifurcation theory. Next, feedback control method is utilized to stabilize chaos existing in discrete Kolmogorov model. Boundedness and global attractivity of the discrete Kolmogorov model are also investigated. Finally, obtained results are numerically verified.
Development of the Fog Seal Layer Characterized by Durability in Terms of Skid Resistance
Authors: Jiang Yingjun , Fan Jiangtao , Lv Pengyu , Yuan Kejia , Yi Yong , Tian Tian , Bai Chenfan
Published: November 2021

In this paper, based on the thermosetting properties, bonding performance, high‐temperature water damage resistance, and aging resistance properties of epoxy‐emulsified asphalt, the best formulation of epoxy emulsified asphalt as a skid resistance fog sealing asphalt is studied. Through accelerated wheel wear and other tests, the effects of the ratio of the amount of skid resistance particles to the amount of epoxy emulsified asphalt, the total amount of skid resistance fog seal asphalt, and the construction process on the skid resistance durability of the skid resistance fog seal were studied. The results show that the epoxy emulsified asphalt formula code A80 (E2) (emulsified asphalt : water‐based epoxy = 80 : 20) is the best formula; when the proportion of emery and epoxy emulsified asphalt is 1 : 1, the British Pendulum Number (BPN) reduction rate of the skid resistance fog seal‐test piece worn 100,000 times was 27.4%. Compared with the same period, the reduction rate was reduced by 4.6% on average, with the smallest mass loss rate and the best skid resistance durability. When the total amount of skid resistance fog seal asphalt is 1.2 kg/m2, the BPN reduction rate of the skid resistance fog seal specimen worn 100,000 times was 27.4%, which is an average reduction of 5.8% over the same period, and the mass loss rate is the smallest, and the skid resistance durability optimal. When using the premixed process, the BPN of the specimen was reduced by 13.2% after abrasion of 100,000 times. Compared with the “one asphalt and one sand” and “double asphalt and one sand” molded specimens, the mass loss rate was reduced by 15.0%, 4eU&7.2%, showing the best skid resistance performance. The results can provide guidance for the design and construction of AC‐13 and other pavement skid resistance fog seals.
An Inertial Accelerated Algorithm for Solving Split Feasibility Problem with Multiple Output Sets
Authors: Jia Huijuan , Liu Shufen , Dang Yazheng
Published: November 2021

The paper proposes an inertial accelerated algorithm for solving split feasibility problem with multiple output sets. To improve the feasibility, the algorithm involves computing of projections onto relaxed sets (half spaces) instead of computing onto the closed convex sets, and it does not require calculating matrix inverse. To accelerate the convergence, the algorithm adopts self-adaptive rules and incorporates inertial technique. The strong convergence is shown under some suitable conditions. In addition, some newly derived results are presented for solving the split feasibility problem and split feasibility problem with multiple output sets. Finally, numerical experiments illustrate that the algorithm converges more quickly than some existing algorithms. Our results extend and improve some methods in the literature.
The Model of Makerspace Development Element and Performance Analysis Based on NVivo Classification
Authors: Wang Yingyan , Zeng Rui
Published: November 2021

Makerspace is an innovation and entrepreneurship service platform. Its ultimate goal is to realize value cocreation among multiple subjects. The development of Makerspace and the realization of value are closely related to the Makerspace Development Policy. With the help of NVivo/ software, we analyzed 17 Makerspace Development Policies. Through the selection of samples, statistical analysis of word frequency, and coding analysis, the 7 core elements of entrepreneurial incentives, fee reductions and exemptions, employment support, entrepreneurial environment, talent support, financial support, and service optimization are summarized to form an empirical research survey of the development policy elements of the makerspace project. The author uses the game evolution method to analyze the mechanism of the internal relationship of innovation factors, builds a model of the development factors of the makerspace, selects 52 makerspace companies to conduct a questionnaire survey, using the model to conduct empirical research on the performance of existing policies, and finally proposes a model for makerspace development.
An Empirical Analysis of the Price Volatility Characteristics of China’s Soybean Futures Market Based on ARIMA-GJR-GARCH Model
Authors: Xu Yang , Xia Zhihao , Wang Chuanhui , Gong Weifeng , Liu Xia , Su Xiaodi
Published: November 2021

As the main force in the futures market, agricultural product futures occupy an important position in the China’s market. Taking the representative soybean futures in Dalian Commodity Futures Market of China as the research object, the relationship between price fluctuation characteristics and trading volume and open position was studied. The empirical results show that the price volatility of China’s soybean futures market has a “leverage effect.” The trading volume and open interest are divided into expected parts and unexpected parts, which are added to the conditional variance equation. The expected trading volume coefficient is estimated. Also, the estimated value of the expected open interest coefficient is, respectively, smaller than the estimated value of the unexpected trading volume coefficient and the estimated value of the unexpected open interest coefficient. Therefore, the impact of expected trading volume on the price fluctuation of China’s soybean futures market is less than that of unexpected trading volume on the price of soybean futures market. This paper adds transaction volume as an information flow to the variance of the conditional equation innovatively and also observes transaction volume as the relationship between conditional variance and price fluctuations.
The Effect of Roxadustat on Transfusion-Dependent Myelodysplastic Syndrome Complicated by Chronic Kidney Disease
Authors: Hara Ryujiro , Goto Naoki , Furuya Daisuke , Kitahara Toshihiko , Numata Hiroki , Watanabe Shigeki , Kawada Hiroshi , Ando Kiyoshi
Published: November 2021

Haematopoietic insufficiency is the treatment target of lower-risk myelodysplastic syndrome (MDS). Although erythropoiesis-stimulating agents (ESAs) are generally effective for treating anaemia, resistance can develop. Hypoxia-inducible factor-prolyl hydroxylase (HIF-PH) improves renal anaemia by promoting endogenous erythropoietin production and normalizing iron metabolism. HIF-PH inhibitors could be used to treat MDS, but their efficacy and safety have not been studied. A 78-year-old female patient with essential thrombocythemia gradually developed anaemia and was diagnosed with therapy-related MDS 4 years later. The anaemia temporarily improved with ESAs, but the patient became transfusion dependent. At the same time, anaemia and chronic renal failure due to nephrosclerosis progressed, and the patient was diagnosed with MDS with renal anaemia. After switching from ESAs to roxadustat, an HIF-PH inhibitor, anaemia improved, and the patient was no longer transfusion dependent. No progression of the underlying disease or any adverse events was observed 4 months after initiating roxadustat.
Impact of the COVID-19 Pandemic and Related Lockdown Measures on Lifestyle Behaviors and Well-Being in Children and Adolescents with Severe Obesity
Authors: Welling Mila S. , Abawi Ozair , van den Eynde Emma , van Rossum Elisabeth F.C. , Halberstadt Jutka , Brandsma Annelies E. , Kleinendorst Lotte , van den Akker Erica L.T. , van der Voorn Bibian
Published: November 2021

Introduction: COVID-19 lockdown measures have large impact on lifestyle behaviors and well-being of children. The aim of this mixed-methods study was to investigate the impact of COVID-19 lockdown measures on eating styles and behaviors, physical activity (PA), screen time, and health-related quality of life (HRQoL) in children (0–18 years) with severe obesity. Methods: During the first COVID-19 wave (April 2020), validated questionnaires were completed and semi-structured telephone interviews were conducted with parents of children with severe obesity (adult body mass index [BMI]-equivalent ≥35 kg/m2) and/or with the children themselves. Changes in pre-pandemic versus lockdown scores of the Dutch Eating Behavior Questionnaire Children, Pediatric Quality of Life Inventory, and Dutch PA Questionnaire were assessed. Qualitative analyses were performed according to the Grounded Theory. Results: Ninety families were approached of which 83 families were included. Characteristics of the included children were: mean age 11.2 ± 4.6 years, 52% female, mean BMI SD-score +3.8 ± 1.0. Emotional, restrained, and external eating styles, HRQoL, and (noneducational) screen time did not change on group level (all p > 0.05). However, weekly PA decreased (mean difference −1.9 h/week, p = 0.02) mostly in adolescents. In the majority of children, mean weekly PA decreased to ≤2 h/week. Children with high emotional or external eating scores during lockdown or pre-existent psychosocial problems had the lowest HRQoL (p < 0.01). Qualitative analyses revealed an increased demand for food in a significant proportion of children (n = 21), mostly in children <10 years (19/21). This was often attributed to loss of daily structure and perceived stress. Families who reported no changes (n = 15) or improved eating behaviors (n = 11) attributed this to already existing strict eating schemes that they kept adhering to during lockdown. Conclusion: This study shows differing responses to COVID-19 lockdown measures in children with severe obesity. On group level, PA significantly decreased and in substantial minorities eating styles and HRQoL deteriorated. Children with pre-existent psychosocial problems or pre-pandemic high external or emotional eating scores were most at risk. These children and their families should be targeted by health care professionals to minimize negative physical and mental health consequences.
Efficient organization of nodes in wireless sensor networks (clustering location-based LEACH)
Authors: El Ouadi Mohammed Réda , Hasbi Abderrahim
Published: November 2021

The rapid development of connected devices and wireless communication has enabled several researchers to study wireless sensor networks and propose methods and algorithms to improve their performance. Wireless sensor networks (WSN) are composed of several sensor nodes deployed to collect and transfer data to base station (BS). Sensor node is considered as the main element in this field, characterized by minimal capacities of storage, energy, and computing. In consequence of the important impact of the energy on network lifetime, several researches are interested to propose different mechanisms to minimize energy consumption. In this work, we propose a new enhancement of low-energy adaptive clustering hierarchy (LEACH) protocol, named clustering location-based LEACH (CLOC-LEACH), which represents a continuity of our previous published work location-based LEACH (LOC-LEACH). The proposed protocol organizes sensor nodes into four regions, using clustering mechanism. In addition, an efficient concept is adopted to choose cluster head. CLOC-LEACH considers the energy as the principal metric to choose cluster heads and uses a gateway node to ensure the inter-cluster communication. The simulation with MATLAB shows that our contribution offers better performance than LEACH and LOC-LEACH, in terms of stability, energy consumption and network lifetime.
Automated hierarchical classification of scanned documents using convolutional neural network and regular expression
Authors: Arief Rifiana , Benny Mutiara Achmad , Maulana Kusuma Tubagus , Hustinawaty Hustinawaty
Published: November 2021

This research proposed automated hierarchical classification of scanned documents with characteristics content that have unstructured text and special patterns (specific and short strings) using convolutional neural network (CNN) and regular expression method (REM). The research data using digital correspondence documents with format PDF images from pusat data teknologi dan informasi (technology and information data center). The document hierarchy covers type of letter, type of manuscript letter, origin of letter and subject of letter. The research method consists of preprocessing, classification, and storage to database. Preprocessing covers extraction using Tesseract optical character recognition (OCR) and formation of word document vector with Word2Vec. Hierarchical classification uses CNN to classify 5 types of letters and regular expression to classify 4 types of manuscript letter, 15 origins of letter and 25 subjects of letter. The classified documents are stored in the Hive database in Hadoop big data architecture. The amount of data used is 5200 documents, consisting of 4000 for training, 1000 for testing and 200 for classification prediction documents. The trial result of 200 new documents is 188 documents correctly classified and 12 documents incorrectly classified. The accuracy of automated hierarchical classification is 94%. Next, the search of classified scanned documents based on content can be developed.
Asymmetric image encryption scheme based on Massey Omura scheme
Authors: Al Saffar Najlae Falah Hameed , Al-Saiq Inaam R. , Abo Alsabeh Rewayda Razaq Mohsin
Published: November 2021

Asymmetric image encryption schemes have shown high resistance against modern cryptanalysis. Massey Omura scheme is one of the popular asymmetric key cryptosystems based on the hard mathematical problem which is discrete logarithm problem. This system is more secure and efficient since there is no exchange of keys during the protocols of encryption and decryption. Thus, this work tried to use this fact to propose a secure asymmetric image encryption scheme. In this scheme the sender and receiver agree on public parameters, then the scheme begin deal with image using Massey Omura scheme to encrypt it by the sender and then decrypted it by the receiver. The proposed scheme tested using peak signal to noise ratio, and unified average changing intensity to prove that it is fast and has high security.
Study of power management of standalone DC microgrids with battery supercapacitor hybrid energy storage system
Authors: Seger Al-Salloomee Ali Gaeed , Khosroabadi Saeed , Abdullah Albukariat Ali Abdulabbas
Published: November 2021

In the last years, renewable energy (RE) is increasing widely in the energy sector, and microgrid technology is overgrowing. In this paper, stand-alone microgrid using solar photovoltaic (PV) energy as a source of renewable energy is simulated to provide power for direct current (DC) loads with hybrid energy storage system (HESS) which consists of battery and supercapacitor bank. The proposed microgrid system is tested under various cases of load and variable irradiance to confirm and validate the proposed management strategy to remain the DC bus voltage within a stable limit. The performance of DC microgrid is comparing with and without supercapacitor (SC) bank and notes a desirable decrease in the magnitude of transient voltage when using HESS. The sun power SPR-E19-320 standard was simulated to analyze system performance taking into account the constant load demand. Note that HESS helps reduce transient of DC voltage very effectively in all situations. Very large transients arise due to sudden changes in load demand is also compensated by HESS. The results obtained indicate that the stand-alone DC microgrid with HESS is very beneficial for reducing transient of DC-link voltage that occurs due to sudden change in load or fault. The proposed system is performed by MATLAB/Simulink environment.
Embedded fuzzy controller for water level control
Authors: Martinez Baquero Javier Eduardo , Ortega Jairo Cuero , Moreno Robinson Jimenez
Published: November 2021

This article presents the design of a fuzzy controller embedded in a microcontroller aimed at implementing a low-cost, modular process control system. The fuzzy system's construction is based on a classical proportional and derivative controller, where inputs of error and its derivate depend on the difference between the desired setpoint and the actual level; the goal is to control the water level of coupled tanks. The process is oriented to control based on the knowledge that facilitates the adjustment of the output variable without complex mathematical modeling. In different response tests of the fuzzy controller, a maximum over-impulse greater than 8% or a steady-state error greater than 2.1% was not evidenced when varying the setpoint.
Optimal integral sliding mode controller controller design for 2-RLFJ manipulator based on hybrid optimization algorithm
Authors: Yahya Randa Jalaa , Abbas Nizar Hadi
Published: November 2021

A newly hybrid nature-inspired algorithm called HSSGWOA is presented with the combination of the salp swarm algorithm (SSA) and grey wolf optimizer (GWO). The major idea is to combine the salp swarm algorithm's exploitation ability with the grey wolf optimizer's exploration ability to generate both variants' strength. The proposed algorithm uses to tune the parameters of the integral sliding mode controller (ISMC) that design to improve the dynamic performance of the two-link flexible joint manipulator. The efficiency and the capability of the proposed hybrid algorithm are evaluated based on the selected test functions. It is clear that when compared to other algorithms like SSA, GWO, differential evolution (DE), gravitational search algorithm (GSA), particles swarm optimization (PSO), and whale optimization algorithm (WOA). The ISMC parameters were tuned using the SSA, which was then compared to the HSSGWOA algorithm. The simulation results show the capabilities of the proposed algorithm, which gives an enhancement percentage of 57.46% compared to the standard algorithm for one of the links, and 55.86% for the other.
Real-time traffic sign detection and recognition using Raspberry Pi
Authors: Md Isa Ida Syafiza Binti , Ja Yeong Choy , Azyze bin Mohd Shaari Azyze Nur Latif
Published: November 2021

Nowadays, the number of road accident in Malaysia is increasing expeditiously. One of the ways to reduce the number of road accident is through the development of the advanced driving assistance system (ADAS) by professional engineers. Several ADAS system has been proposed by taking into consideration the delay tolerance and the accuracy of the system itself. In this work, a traffic sign recognition system has been developed to increase the safety of the road users by installing the system inside the car for driver’s awareness. TensorFlow algorithm has been considered in this work for object recognition through machine learning due to its high accuracy. The algorithm is embedded in the Raspberry Pi 3 for processing and analysis to detect the traffic sign from the real-time video recording from Raspberry Pi camera NoIR. This work aims to study the accuracy, delay and reliability of the developed system using a Raspberry Pi 3 processor considering several scenarios related to the state of the environment and the condition of the traffic signs. A real-time testbed implementation has been conducted considering twenty different traffic signs and the results show that the system has more than 90% accuracy and is reliable with an acceptable delay.
Comparative study to realize an automatic speaker recognition system
Authors: Abakarim Fadwa , Abenaou Abdenbi
Published: November 2021

In this research, we present an automatic speaker recognition system based on adaptive orthogonal transformations. To obtain the informative features with a minimum dimension from the input signals, we created an adaptive operator, which helped to identify the speaker’s voice in a fast and efficient manner. We test the efficiency and the performance of our method by comparing it with another approach, mel-frequency cepstral coefficients (MFCCs), which is widely used by researchers as their feature extraction method. The experimental results show the importance of creating the adaptive operator, which gives added value to the proposed approach. The performance of the system achieved 96.8% accuracy using Fourier transform as a compression method and 98.1% using Correlation as a compression method.