bluuebunny/crossref_metadata_2025 huggingface dataset:
Modeling of ferroelectric tunnel junctions based on the Pt/BaTiO3/Nb:SrTiO3 stack
Authors: Dossena M. , Malavena G. , Spinelli A. S. , Monzio Compagnoni C.
Published: August 2022

In this paper, we report a comprehensive modeling investigation of the Pt/BaTiO3/Nb:SrTiO3 stack designed to operate as a Ferroelectric Tunnel Junction (FTJ). The analysis accounts for some specific features of the materials in the stack that are typically overlooked, such as the electric field dependence of the dielectric constant of Nb:SrTiO3 and the dependence of the dielectric constant of BaTiO3 on its thickness. Modeling results are validated through a systematic and consistent comparison with experimental data for the current–voltage characteristics of devices with different stack parameters, at different temperatures. From that, the resistive memory window of an FTJ based on the Pt/BaTiO3/Nb:SrTiO3 stack is then explored over its design space. Results allow to comprehensively assess the ultimate performance of the device, providing hints for the successful development of next-generation FTJ-based memory technologies.
Direct recognition of Raman spectra without baseline correction based on deep learning
Authors: Liu Yuping , Wu Junchi , Wang Yuqing , Dong Sicen
Published: August 2022

Raman spectroscopy, widely used for material analysis, has formed an extensive spectral library. In practical applications, it is usually necessary to preprocess Raman spectroscopy of the target material and then identify the material through spectral-library comparisons. Baseline correction is an important step during pre-processing and it usually requires a special algorithm. However, it demands time and high-level professional skill, confining Raman spectroscopy to laboratories rather than large-scale applications. Therefore, to improve its efficiency and take advantage of the big data in the spectral library, this paper proposes a simple data-augmented deep learning method to achieve Raman spectroscopy recognition without baseline correction. In this method, a simple mathematical baseline (linear or sine function) is added to the spectrum in the database to complete data augmentation. Its training set is used to train the deep learning model. The trained deep-learning model can identify 20 minerals with 100% accuracy without additional baseline correction. Therefore, the method is effective for rapid and direct recognition of Raman spectra.
Control mechanism of the three-dimensional shock wave/boundary layer interaction with the steady and pulsed micro-jets in a supersonic crossflow
Authors: Du Zhao-bo , Shen Chi-bing , Huang Wei , Fan Bo , Han Yi
Published: August 2022

The separation induced by shock wave/boundary layer interactions (SWBLI) is detrimental to the performance of the flow field, and thus, needs to be reduced by using passive or active approaches. In this study, the authors numerically evaluate flow control induced by steady and pulsed micro-jets to capture the mechanism of control of three-dimensional (3D) SWBLIs. The volume of the separation zone is accurately calculated to assess the control effect. The results predicted by the 3D Reynolds-averaged Navier–Stokes equations coupled with the two-equation κ-ω turbulence model of shear stress transport show that the proposed method of using steady or pulsed micro-jets can significantly reduce the volume of the separation zone induced by shock wave/boundary layer interactions. The best comprehensive effect of control over the flow field was obtained by using a high frequency angled jet—namely, case P5—as it reduced the volume of the separation zone by 19.43% with only a small loss in the total pressure. The upwash and downwash motions induced by the streamwise counter-rotating vortex pairs of the jet constituted a key factor influencing the control of the separation zone.
Characteristic analysis and blind area prediction of aeromagnetic scalar gradient detection method
Authors: Chang Ming , Xu Lei , Pang Xin , Zhang Jiawei , Li Houpu , Lin Mingzhen
Published: August 2022

To analyze the characteristics of the aeromagnetic scalar gradient detection method, a uniformly magnetized ellipsoid is used to simulate an unexploded ordnance, and a magnetic field detection model is established in the International Geomagnetic Reference Field based on rotation matrices. Furthermore, the spatial distribution of the target’s magnetic field is simulated. The results indicate that the scalar gradient detection curve is closely related to the unmanned aerial vehicle (UAV) heading, geomagnetic direction, and target attitude. According to the measured data, the aeromagnetic detection system exhibits differences in the detection of different headings, indicating that some “blind areas” exist in the scalar gradient magnetic detection method. The experimental measurement by a quadrotor UAV equipped with two optical pump magnetometers verifies that the scalar gradient detection method can effectively eliminate the geomagnetic field as well as the interferences of the UAV itself. Furthermore, the angular relationship between the target magnetic field contour distribution and the heading is found to be the main reason that the scalar gradient detection system enters the “blind detection area.” Therefore, a flight strategy of “positive direction + orthogonal grid” is proposed. This method effectively reduces the missed detection rate of scalar gradient detection and provides strategic guidance for the detection path of aeromagnetic scalar gradient system.
Low frequency and broadband sound attenuation by meta-liner under grazing flow and high sound intensity
Authors: Wu Fei , Ju Zegang , Geng Ziteng , Zhao Jiang , Hu Man , She Guilin , Pu Huayan , Luo Jun , Xiang Ping
Published: August 2022

Acoustic liners are the most effective solution to attenuate the noise of ducts with flows but still suffer from narrow sound absorption bandwidth and heavy bulk. In this work, we present a new type of acoustic meta-liner structure, which is composed of perforated plates and coiled-up Fabry–Pérot (FP) channels with carefully designed equivalent length. By tuning the geometric parameters of the perforated plate and the optimal combination of the coiled-up FP channels, perfect impedance matching is achieved in a broadband range. The strong dissipation of sound energy could also be observed under different speeds of grazing flow and high incident sound intensity. It is analytically, numerically, and experimentally demonstrated that for the structure, over 90% sound absorption is achieved in the broadband range from 500 to 3000 Hz in the absence of flow and the condition of grazing flow with a speed of 30–98 m/s, coupled with a 90–130 dB incident sound pressure level. Moreover, the thickness of the proposed simply structured meta-liner is 44 mm, which is only 8/125th of the wavelength corresponding to 500 Hz. As a deep sub-wavelength liner, it exhibits potential application prospects in the field of fluid–solid coupled machinery such as aero-engine systems and ventilation duct systems.
Ultrasonic evaluation of aging kinetics in amorphous polymer
Authors: Yambangyang P. , Wilson R. , Reboud J. , Cooper J. M. , Demčenko A.
Published: August 2022

The physical aging of amorphous polymers induces changes in material properties, which are challenging to detect in situ in industrial settings. Here, we present a nondestructive nonlinear ultrasonic evaluation technique that enables localized measurements of the combined effects of temperature and time in amorphous polymers, without needing to remove materials. The proposed technique is demonstrated using commercial grade poly(vinyl chloride) samples and is supported by analysis of wave–material interactions. The results show that the physical aging of the polymer is described by the Arrhenius equation with an effective activation energy of 103 kJ/mol over the analyzed temperature range.
Freestanding inorganic oxide films for flexible electronics
Authors: Dai Liyufen , An Feng , Zou Juan , Zhong Xiangli , Zhong Gaokuo
Published: August 2022

Recently, flexible electronic devices are of increasing interest due to their wide range of application fields, including information storage, energy conversion, and wearable and implantable electronics. In particular, freestanding inorganic oxide films are proved to be an extraordinary versatile platform for flexible electronics owing to their super elasticity, outstanding functionalities, tunability, and long-term stability. In this Perspective, we review the up-to-date advances of freestanding inorganic oxide films from the perspectives of synthesis methods, physical properties, and device applications. First, preparation strategies based on epitaxial lift-off technologies are classified into physical and chemical aspects that are to be introduced. Second, we discuss the physical properties of freestanding inorganic oxide films, especially in terms of ferroelectricity, magnetism, multiferroics, etc. Third, we highlight several device applications in the fields of data memory, energy storage, and health care. Finally, we conclude with a future perspective into prospects and challenges regarding the syntheses and applications of freestanding inorganic oxide films.
Resonance damping of gravity–capillary waves on water covered with a visco-elastic film of finite thickness: A reappraisal
Authors: Ermakov S. A. , Khazanov G. E.
Published: August 2022

A new approach to the problem of damping of gravity–capillary waves (GCW) on water covered with a layer of viscous liquid (a film) of finite thickness with two elastic boundaries is developed. It is shown that the rotational component of GCW can be described formally as a “forced” longitudinal or Marangoni wave (MW), and the potential component of GCW plays a role of the “external force.” The resonance-like excitation of the forced MW is demonstrated when the GCW and MW frequencies and wave numbers are approximately close to each other. For a film that is thinner than the viscous boundary layers in film, a single forced MW exists that is located within the boundary layer beneath the water surface. For a thick film, the forced MW is characterized by the existence of two spatially separated MW modes: one is localized in the boundary layer below the upper, air–film interface and another within the boundary layers in the vicinity of the water–film interface. Then, at different elasticities of the interfaces, a double peak dependence of the GCW damping coefficient on wave number can occur due to the resonance with the two forced MW modes. The dependence of the damping coefficient on film thickness is characterized by a strong maximum appearing when the film and boundary layer thickness values are comparable to each other. The developed theory is consistent with existing numerical studies and experiment.
Electron irradiation-induced paramagnetic and fluorescent defects in type Ib high pressure–high temperature microcrystalline diamonds and their evolution upon annealing
Authors: Nunn Nicholas , Milikisiyants Sergey , Danilov Evgeny O. , Torelli Marco D. , Dei Cas Laura , Zaitsev Alexander , Shenderova Olga , Smirnov Alex I. , Shames Alexander I.
Published: August 2022

Defects introduced to synthetic type Ib diamond micrometer-size particles by electron-beam irradiation were studied by electron paramagnetic resonance and photoluminescence (PL) spectroscopy as a function of e-beam fluence and post-irradiation thermal annealing. Increasing electron-beam fluence causes a substantial reduction of the substitutional nitrogen (P1) content, accompanied by progressively higher concentrations of paramagnetic negatively charged vacancies (V−) and triplet interstitials (R1/R2). Annealing results in a drastic decrease in the V− and R1/R2 content and an increase in the negatively charged nitrogen-vacancies (NV− or W15). Analysis of PL spectra allows for identification of color centers in the irradiated diamond samples and following their evolution after annealing. These data facilitate understanding of different factors contributing to the formation of color centers in diamond and promote efforts toward controlled engineering of optical centers in fluorescent diamond particles.
The extremal Sombor index of trees and unicyclic graphs with given matching number
Authors: Zhou Ting , Lin Zhen , Miao Lianying
Published: August 2022

In 2021, the Sombor index was introduced by Gutman, which is a new vertex-degree-based topological index. The Sombor index of a graph G is defined as , where dG(υ) is the degree of the vertex υ in G. Let  and  be the set of trees and unicyclic graphs on n vertices with fixed matching number m, respectively. In this paper, the trees and the unicyclic graphs with the maximum Sombor index among  and  are characterized, respectively.
The spatial distribution of glycogen and glycogen consumption in muscle cells
Authors: Allen David G.
Published: August 2022

David G. Allen looks at new research from the Nielsen lab.
An equipment multiple failure causes intelligent identification method based on integrated strategy for subway sliding plug door system under variable working condition
Authors: Guo Jing , Chen Gaige , Ma Hongbo , Qi Hui
Published: August 2022

The subway sliding plug door system has been opened and closed frequently for a long time under variable working conditions, and multiple failures are prone to occurring and resulting in train shutdowns and even major safety accidents. Due to the complex physical mechanism of the sliding plug door system, it is difficult for a single signal to accurately represent the failure states of the sliding plug door system. Thus, identifying the multiple failure causes of the subway sliding plug door system has become a challenging problem. Aiming at the problem, an equipment multiple failure causes intelligent identification method based on an integrated strategy is proposed for the subway sliding plug door system under variable working condition. In the proposed method, firstly, the sensitive features that can reflect the degradation state of equipment are obtained by using the random forest to measure the importance of fetatures and sort them. Secondly, feature dimensionality is reduced by using t-distributed stochastic neighbor mbedding (TSNE) to map the screened high-dimensional features to low-dimensional space. Finally, the parameters of the extreme learning machine (ELM) are optimized by using the particle swarm optimization (PSO) algorithm to build a multiple failure causes classification model. The proposed method is verified by the 1:1 benchmark test data of the subway sliding plug door system. The results show that the proposed method has higher classification accuracy, faster calculation speed, and stronger generalization ability. The proposed method is an effective integrated strategy to identify multiple failure causes in the subway sliding plug door system and guide the health management and operational maintenance of the subway sliding plug door system.
Green method for synthesis and characterization of copper oxide nanoparticles using Mulberry plant extract and their antibacterial, antioxidant and photocatalytic activity
Authors: Shkir Mohd
Published: August 2022

The green synthesis of metal oxide nanoparticles is treated to be an eco-friendly path and cost-effective. Mulberry plant extract was used to synthesize CuO nanoparticles in this study. UV spectroscopy absorbance at 370 nm was used to analyze the resulting synthesized nanoparticles. The distribution of nanoparticles and particle sizes were discovered to be in the range of 40–60 nm using scanning electron microscopy. The polydisperse entity of CuO nanoparticles was revealed by TEM morphology and their SAED pattern rings. The plant bio-compounds and nanoparticles reaction involved functional groups were determined from FTIR analysis. Copper nanoparticles have characteristic diffraction peaks in the x-ray diffraction spectrum at 38.81°, and 66.35° which correspond to lattice planes (1 1 1) and (2 0 2), respectively. The organic dye pollutants of MB degraded in 90% at 40 min under visible light irradiation. The antioxidant and antibacterial activity of the CuO nanoparticles was tested against gram-positive and gram-negative microorganisms. The results support the hypothesis that green synthesized CuO nanoparticles could be a viable treatment option for diseases caused by microbial pathogens. This fact-finding result is that Mulberry plant extract-based green synthesized CuO nanoparticles destroyed or considerably inhibited pathogen activity, as well as having high antioxidant activity.
Taking neutrino pictures via electrons
Authors: Lin Guey-Lin , Nguyen Thi Thuy Linh , Spinrath Martin , Van Thi Dieu Hien , Wang Tse-Chun
Published: August 2022

In this paper we discuss the prospects to take
a picture of an extended neutrino source, i.e.,
resolving its angular neutrino luminosity distribution.
This is challenging since neutrino directions
cannot be directly measured but only estimated
from the directions of charged particles they interact with in the detector
material. This leads to an intrinsic blurring effect.
We first discuss the problem
in general terms and then apply our insights to solar
neutrinos scattering elastically with electrons. Despite the aforementioned
blurring we show how with high
statistics and precision the original neutrino distributions
could be reconstructed.
Constraining U(1)
L

μ
–
L

τ
charged dark matter model for muon g – 2 anomaly with AMS-02 electron and positron data
Authors: Zu Lei , Pan Xu , Feng Lei , Yuan Qiang , Fan Yi-Zhong
Published: August 2022

Very recently, the Fermi-Lab reported the new experimental combined results on the magnetic momentum of muon with a 4.2σ discrepancy compared with the expectation of the Standard Model [1]. A new light gauge boson X in the Lμ
– Lτ
model provides a good explanation for the g – 2 anomaly. A Dirac fermion dark matter with a large Lμ
– Lτ
charge can explain both the g – 2 anomaly and the dark matter relic density [2]. In this work, we focus on the case that the mass of the dark matter is larger than the mass of muon (i.e. m
Ψ > m
μ) for which the channel ΨΨ ⟶ μ
-
μ
+ opens. Although the cross section (σ v)
μ
-
μ
+
is smaller by a factor of 1/q
Ψ
2 (q
Ψ represents the Lμ
– Lτ
charge of the dark matter) compared with the channel ΨΨ ⟶ X X ⟶ ννν̅ν̅, the resulting secondary electrons and positrons could imprint on their spectra above GeV energies due to the reacceleration effect of cosmic ray propagation. We use the AMS-02 measurements of electrons and positrons to constrain the annihilation cross section of the channel ΨΨ ⟶ μ
-
μ
+, which rules out part of the parameter space of the large Lμ
– Lτ
charged dark matter model to account for the muon g – 2 anomaly.
Measuring the propagation speed of gravitational waves with LISA
Authors: Baker Tessa , Calcagni Gianluca , Chen Anson , Fasiello Matteo , Lombriser Lucas , Martinovic Katarina , Pieroni Mauro , Sakellariadou Mairi , Tasinato Gianmassimo , Bertacca Daniele , Saltas Ippocratis D.
Published: August 2022

The propagation speed of gravitational waves, cT
, has been tightly constrained by the binary neutron star merger GW170817 and its electromagnetic counterpart, under the assumption of a frequency-independent cT
. Drawing upon arguments from Effective Field Theory and quantum gravity, we discuss the possibility that modifications of General Relativity allow for transient deviations of cT
from the speed of light at frequencies well below the band of current ground-based detectors. We motivate two representative Ansätze for cT
(f), and study their impact upon the gravitational waveforms of massive black hole binary mergers detectable by the LISA mission. We forecast the constraints on cT
(f) obtainable from individual systems and a population of sources, from both inspiral and a full inspiral-merger-ringdown waveform. We show that LISA will enable us to place stringent independent bounds on departures from General Relativity in unexplored low-frequency regimes, even in the absence of an electromagnetic counterpart.
Estimating speed-accuracy trade-offs to evaluate and understand closed-loop prosthesis interfaces
Authors: Mamidanna Pranav , Dideriksen Jakob L , Dosen Strahinja
Published: August 2022

Objective. Closed-loop prosthesis interfaces, which combine electromyography (EMG)-based control with supplementary feedback, represent a promising direction for developing the next generation of bionic limbs. However, we still lack an understanding of how users utilize these interfaces and how to evaluate competing solutions. In this study, we used the framework of speed–accuracy trade-off functions (SAF) to understand, evaluate, and compare the performance of two closed-loop user-prosthesis interfaces. Approach. Ten able-bodied participants and an amputee performed a force-matching task in a functional box-and-block setup at three different speeds. All participants were subjected to both interfaces in a crossover study design with a 1 week washout period. Importantly, both interfaces used direct proportional control but differed in the feedback provided to the participant (EMG feedback vs. Force feedback). We estimated the SAFs afforded by the two interfaces and sought to understand how the participants planned and executed the task under the various conditions. Main results. We found that execution speed significantly influenced performance, and that EMG feedback afforded better overall performance, especially at medium speeds. Notably, we found that there was a difference in the SAF between the two interfaces, with EMG feedback enabling participants to attain higher accuracies faster than Force feedback. Furthermore, both interfaces enabled participants to develop flexible control policies, while EMG feedback also afforded participants the ability to generate smoother, more repeatable EMG commands. Significance. Overall, the results indicate that the performance of closed-loop prosthesis interfaces depends critically on the feedback approach and execution speed. This study showed that the SAF framework could be used to reveal the differences between feedback approaches, which might not have been detected if the assessment was performed at a single speed. Therefore, we argue that it is important to consider the speed–accuracy trade-offs to rigorously evaluate and compare user-prosthesis interfaces.
High gas throughput SOLPS-ITER simulations extending the ITER database to strong detachment
Authors: Lore J.D. , Bonnin X. , Park J.-S. , Pitts R.A. , Stangeby P.C.
Published: August 2022

SOLPS-ITER simulations performed for Q
DT = 10, P
SOL = 100 MW burning plasmas on ITER extend the existing database to high values of separatrix averaged neon impurity concentration (⟨c
Ne⟩ ≈ 6%) and divertor neutral pressure (⟨p
div⟩ > 25 Pa) in order to determine the heat flux mitigation capability of these scenarios and whether strongly detached states are accessible. In the existing database of ITER simulations, the level of detachment was limited to cases where the integral ion flux to the outer target was greater than 80% of the value at rollover, with the impurity radiation localized near the target. With the possibility of narrow heat flux channels and increased deposited power due to tile shaping, it is important to explore operation at a higher degree of detachment. Two series of simulations were explored to extend the database of SOLPS simulations. By increasing the deuterium and neon puff rates proportionally, the peak divertor energy flux (q
⊥,max) is decreased from 5 to 3 MW m−2 while ⟨p
div⟩ increased from 11 to 27 Pa. By increasing only the neon puff, q
⊥, max can be reduced to <1MW m−2 while ⟨p
div⟩ is maintained at

∼
11

Pa. As the neon puff level is increased, the position of the impurity radiation peak is shifted towards the X-point. At the highest neon puff levels with steady-state solutions, the electron temperature is reduced below 1 eV across 50 cm of each divertor target. The new cases extend previously observed tight relationships in power and momentum loss factors to low electron temperature improving their utility for highly detached regimes.
Investigation of GMR Eddy Current Permanent Magnet System for Carbon Steel Corrosion Identification using IoT Unmanned Aerial Vehicles Drone Platform
Authors: Ali Kharudin , Joraimee Mohamad A. , Rifai Damhuji , Hazim Aizuddin Othman M. , Mohamed Ayub Mohd Anuar , Hussian Ammar Husaini , Paw Koh Siaw , Phing Chen Chai , Yaw Chong Tak , Ding Tan Jiang
Published: August 2022

Eddy current test (ECT) is one of the techniques for accurate testing as well as widely used in the aircraft and nuclear industry. Changes in thickness due to corrosion will affect the measurement of defects on pipes or plates. This will make an irrelevant decision as to whether the defect that occurs on the work piece is the thickness of the paint or corrosion. This problem can lead to the occurrence of leaking pipes, apart from the deterioration of the company’s productivity and most importantly endangering the safety of employees. Meanwhile, eddy current testing is one of the methods in NDT used to detect cracks or corrosion on the surface of carbon steel. In this work, GMR Eddy Current sensor is used to measure the corrosion on carbon steel pipes, and a permanent magnet is used to generate the magnetic field for corrosion detection. Also, this system is configured with an IoT Unmanned Aerial Vehicles (UAV) drone platform to facilitate access at different heights during the inspection process. The change of voltage amplitude at the GMR sensor on the test material proves the conditioning of the material either normal or corrosive. The result shows that when corrosion occurs on the pipe, the voltage amplitude increases up to 0.5% higher than normal. Finally, the metal loss is higher when the corrosion area is larger and at the same time it is directly proportional to the voltage amplitude
Gas Turbine Model Parameter Classification During Abnormal Operation Using Support Vector Machine For Maintenance
Authors: Yaw Chong Tak , Yap Keem Siah , Koh Siaw Paw , Tiong Sieh Kiong , Ali K. , Low Foo Wah
Published: August 2022

When there is a contingency in a major transmission system, it is crucial to locate and detect abnormal parameters using an accurately modeled gas turbine (GT) generator. In this paper, a new method was proposed to model, a GT generator system. Firstly, MATLAB/Simulink was used to rebuild GT-based model which was validated using a model in PSS/E. Secondly, an artificial intelligence (AI) based approach, namely Support Vector Machine (SVM) was used for problem recognition in GT power plant. The results showed that, under steady-state, the electrical power of the rebuilt GT model was the same as the model in PSS/E. The advantage of having the MATLAB / Simulink GT model was the ability to visually observe the output from each block which was not possible in PSS/E. In addition, the average training accuracy was over 90% for the detection of GT governor parameter during abnormal behavior. As an implication, the developed model should be considered to apply in real-world grids for operation engineers to detect abnormal governor parameters in GT in the next stage. In turn, it assists in the restoration of GT to its operating condition and minimizes troubleshooting time.
Fault Diagnosis in Wind Energy Management System using Extreme Learning Machine: A Systematic Review
Authors: Yaw Chong Tak , Teoh Siew Li , Koh Siaw Paw , Yap Keem Siah , Chong Kok Hen , Low Foo Wah
Published: August 2022

Fault diagnosis is increasingly important given the worldwide demand on wind energy as one of the promising renewable energy sources. This systematic review aimed to summarize the fault diagnosis using Extreme Learning Machine (ELM) on wind energy. Firstly, two databases (i.e. Engineering Village (EV) and IEEE Explore were searched to identify relevant articles, using three important keywords, including Extreme Learning Machine/ELM, fault and wind. Of the 14 included studies, only eight studies mentioned the use of sensor to collect vibration signals as the fault data. Sensors were commonly installed at four places (gearbox, generator, bearing, or rotor) in the included studies. Only nine studies used either single or fusion feature extractions for the fault data. Two types of ELM (i.e. single/multi-layered or hybrid-ELM) were identified to diagnose fault. In general, studies showed the superiority of the application of ELM in producing accuracy results in fault diagnosis of WT, compared to other algorithms. Future studies should incorporate the use of real-world data, and improve on the reporting on the methodological components of the study, to better inform on the usefulness of ELM for fault diagnosis in real-world wind energy settings.
Servo Motor Controller using PID and Graphical User Interface on Raspberry Pi for Robotic Arm
Authors: Abdul Karim Muhammad Zakwan Bin , Thamrin Norashikin M.
Published: August 2022

The usage of the robotic arm in industries has been growing as the robotic arm can provide many advantages to the industries. However, the usage of the robotic arm has many challenges faced by the industries. One of the problems is the error of the angle position caused by the movement of the robotic arm’s joint which affects the precision of the desired position of the end-effector. Therefore, this paper aims to study the solution of the PID controller to increase the accuracy of the position of the end-effector in the X-Y plane using forward kinematic. It describes the mechanical system, design concept, and the prototype of a 6 DOF robotic arm. Initially, the angle error of the joint for 2D plane movement is captured manually using the trigonometric calculation. Then, to reduce this error, the coefficient of the PID controller, k

p
, k

i
, and k

d
are tuned automatically using Simulink and MATLAB. Then, the controller is programmed on a Raspberry Pi with a servo driver, PCA9685. There are six servo motors used as the joints of the robotic arm, namely MG996R. Each servo motor can be controlled by using a Graphical User Interface (GUI) scripted in Python Programming Language. The end position of the end-effector will be displayed on this GUI. It is found that the angle position error of the 6 DOF robotic arm for X-Y plane movement is reduced from 4.7° to 0° by using the PID controller.
IoT based Robotic Arm for Student Online Learning
Authors: Wan Faizura W. T. , Luqman M.Z.M. , Luqman Hakim A. H. , Mohd Hafiz O. , Zulkifli I.
Published: August 2022

Currently, robots can be used for additional purposes, people trying to perform difficult tasks. These robots also have the potential to become more powerful and use sensors not available to humans, giving supernatural abilities to guards that they may not have. Robotic arms are widely used for industry, hospitals, and education. The main function of the robotic arm is to control movement. The robotic arm is operated manually by humans. Since being attacked by this covid 19 epidemic, robotic learning has been difficult to implement where students are forced to go through online learning. Therefore, the research is allowing the students to implement robotic arms through online learning. The purpose of this study is to implement a robotic arm using the Internet of Things (IoT). The robotic arm has been developed with four Degrees of Freedom (DOF). The robotic arm can operate and be controlled by Blynk applications. By using the esp8266 Wi-Fi module, the robot arm can directly connect to the internet. In this study, the most important is to determine to move robotic arms using IoT. Thus, experiments for robotic arms using IoT were carried out.
QRS Detection in Electrocardiogram Signal of Exercise Physical Activity
Authors: Mohd Apandi Ziti Fariha , Ikeura Ryojun , Hayakawa Soichiro , Tsutsumi Shigeyoshi
Published: August 2022

Accurate estimation of heart beats from electrocardiogram (ECG) signals during exercise activity is a very challenging problem. Unlike standard ECG, the signals recorded during exercise activity can affect the accuracy of QRS detection due to the noises and artifacts arise from body movements activity. However, most of the studies on QRS detection often used clean and standard data for the evaluations and assumed to reflect the overall performance of detectors. In addition, there are not many methods that evaluated using real ECG signal in their studies especially during the exercise activity. Therefore, this study was undertaken to access and evaluated the performance of QRS detection algorithms on the real ECG signal data. Three well-known QRS detection algorithms were re-implemented in this study. The ECG signal recorded under realistic movement conditions and can serving as a realistic data to assess the performance of QRS detection is used. The performance of the algorithm in real ECG signal data in sitting, walking, and jogging was then presented. The results show the algorithms capable to detect the QRS in ECG signal of exercise activity.
Breast Cancer Prediction Using Artificial Neural Networks Back Propagation Method
Authors: Nasien Dewi , Enjeslina Veren , Hasmil Adiya M. , Baharum Zirawani
Published: August 2022

Research on breast cancer has been widely conducted and previously studied with various methods or algorithms to categorize it into benign and malignant groups. In ANN algorithm, one method called back propagation network is utilized to solve complex problems related to identification, pattern recognition prediction, and so forth. The objective of the present study is to investigate the level of accuracy and performance by ANN back propagation in predicting breast cancer. Several stages for this study are formulating the problem, collecting and processing the Wisconsin breast cancer dataset from the Kaggle site. Designing and creating an ANN algorithm system to classify cancer into malignant and benign, then examining the system to perceive the prediction accuracy, and conclude it. The results of the numerical simulation indicate that the created system of MATLAB R2016a software obtained an accuracy of 96.929% with an error of 3.071% by a combination of training parameters with epoch 1000, learning rate 0.01, goal 0.001, and hidden layer 5.
Cyber Attack Detection Dataset: A Review
Authors: Mohd Yusof Nur Nadiah , Sulaiman Noor Suhana
Published: August 2022

As cyber attack become more complicated, it becomes more difficult to identify breaches successfully. The inability to identify intrusions might jeopardize security services’ confidence, compromising data confidentiality, integrity, and availability. Cyber attacks like, Ping of Death, Botnets, also IP spoofing, as well as Social Engineering attacks, are becoming more common. A number of Intrusion Detection System (IDS) approaches developed to encounter cyber security intrusion. In order to discover attack patterns, the IDS performance was evaluated by employing dataset of IDS made up of network traffic properties. Intrusion detection is a classification problem in which different Artificial Intelligence techniques have been utilized to classify between legitimate also malicious network traffic. The multiple IDS datasets used to evaluate the IDS model are listed in this publication. These are new attack categories and recent datasets containing network attack features. This paper presents several IDS dataset with many existing evaluation techniques in model of IDS. Hopefully the outcome can be used in designing efficient and effective systems employing the benchmark and new IDS datasets.
Semantic Segmentation of Traffic Landmarks Using Classical Computer Vision and U-Net Model
Authors: Boudissa Mehieddine , Kawanaka Hiroharu , Wakabayashi Tetsushi
Published: August 2022

Due to the deterioration of traffic landmarks such as (crosswalks, lanes, speed limits, …etc) in Mie prefecture-Japan, the sudden stop rate during road crossings reached 3.4% in the year 2020. It has been deemed necessary by the local authorities to implement a reliable maintenance process for these road signs. Such a process would rely on an automatic camera-based monitoring system in order to keep track of the deterioration status of road signs. The first step consists of achieving successful detection and segmentation of these landmarks. In this paper, we try to accomplish this initial goal by using a dataset of around 13000 high resolution images taken from Mie prefecture streets. In order to achieve our goal of landmark detection we relied on some classical computer vision techniques as well as a deep learning approach. We developed a method to separate landmarks from other objects in a given image by relying on distinctive features of these landmarks. The method in question was then used to build a semi-assisted labelling tool, which was used to annotate a set of around 182 images and used them to train a CNN model to perform semantic segmentation of the road signs. In the end we managed to achieve a successful segmentation with a dice score of 78.90% on the validation set, as well as a solid proof of concept for a CNN based approach to solve this particular problem
Tongue Colour Diagnosis System Using Convolutional Neural Network
Authors: Tan Yi Chen , Shahrizal Rusli M. , Kamarudin Nur Diyana , Ab Rahman Ab Al-Hadi , Izzat Ullah Sheikh Usman Ullah Sheikh , Peng Tan Michael Loong , Shapiai Mohd Ibrahim
Published: August 2022

Tongue diagnosis is known as one of the effective and yet noninvasive techniques to evaluate patient’s health condition in traditional oriental medicine such as traditional Chinese medicine and traditional Korean medicine. However, due to ambiguity, practitioners may have different interpretation on the tongue colour, body shape and texture. Thus, research of automatic tongue diagnosis system is needed for aiding practitioners in recognizing the features for tongue diagnosis. In this paper, a tongue diagnosis system based on Convolution Neural Network (CNN) for classifying tongue colours is proposed. The system extracts all relevant information (i.e., features) from three-dimensional digital tongue image and classifies the image into one of the colours (i.e. red or pink). Several pre-processing and data augmentation methods have been carried out and evaluated, which include salt-and-pepper noises, rotations and flips. The proposed system achieves accuracy of up to 88.98% from 5-fold cross validation. Compared to the reported baseline Support Vector Machine (SVM) method, the proposed method using CNN results in roughly 30% improvement in recognition accuracy.
Comparison of RNN-LSTM and Kalman Filter Based Time Series Human Motion Prediction
Authors: Yunus Andi Prademon , Shirai Nobu C , Morita Kento , Wakabayashi Tetsushi
Published: August 2022

Machine-human and machine-machine interaction is inevitable and needs to be considered apart from human-machine interaction. For the safety of machine interaction, the system is required to prevent any unexpected accident. This paper proposed a system that can forecast human motion to know the movement direction for one second. This research was conducted by using an RGB camera as a reliable alternative. The feature extraction process has been done by OpenPose to obtain the coordinate of human body parts in the frame. Then the coordinate data is converted to the movement data containing the distance and direction in the key points to the next frame for the input in the prediction method. This research aims for the human motion prediction using these methods to compare the performance on the human motion data and realize the prediction of human motion. Mainly, Kalman Filter shows more positive results than RNN-LSTM as the prediction method. Movement like hand gestures is more effortless than motions like hand gestures and steps to the left side. The validity of the system based on the RGB camera with unstable data has been confirmed based on the results.
Deforestation, plantation-related land cover dynamics and oil palm age-structure change during 1990–2020 in Riau Province, Indonesia
Authors: Numata Izaya , Elmore Andrew J , Cochrane Mark A , Wang Cangjiao , Zhao Jing , Zhang Xin
Published: August 2022

The expansion of plantations, such as oil palm, in Indonesia has caused large-scale deforestation. Loss of tropical forest, in particular peatland forest, is a major ecological and environmental threat as well as a source of atmospheric carbon emissions. Understanding the spatio-temporal dynamics of plantation expansion may illuminate pathways to reduce deforestation while maintaining high yields in existing plantations. Beyond mapping forest conversion to plantations, it is also important to understand post-conversion plantation success and crop age. In the case of oil palm, the typical productive lifespan is 25–30 years before replanting or conversion to other land use becomes necessary. Knowledge about the extent of oil palm in different productive growth stages is important for yield estimation and improving management strategies. This study characterizes the land-cover and land-use changes inherent to oil palm plantation expansion and age-structured oil palm dynamics across Riau, the province with the greatest production of oil palm in Indonesia, using a 30 year time-series of Landsat satellite imagery. From 1990 to 2020, Riau lost 4.63 M ha of forest, while oil palm extent grew six-fold, reaching an estimated 3.52 M ha in 2020. Rapid expansion of oil palm plantations in Riau resulted in the predominance of younger age classes (<10 yr-old) and rapidly increasing yields during 2010–2020. Conversion dynamics changed over time such that, after 2014, the <10 yr age class declined by 14%, whereas the 10–20 yr-old (peak yield stage) and ⩾20 yr-old (decline stage) age classes increased by 11% and 3%, respectively. In 28 years of observation (1992–2020), 41% of oil palm planted between 1990 and 1992 underwent at least one cycle of replanting in Riau.
Formation and crossover of multiple helical dipole chains
Authors: Siemens Ansgar , Schmelcher Peter
Published: August 2022

We investigate the classical equilibrium properties and metamorphosis of the ground state of interacting dipoles with fixed locations on a helix. The dipoles are shown to align themselves along separate intertwined dipole chains forming single, double, and higher-order helical chains. The number of dipole chains, and their properties such as chirality and length scale on which the chains wind around each other, can be tuned by the geometrical parameters. We demonstrate that all possible configurations form a self-similar bifurcation diagram which can be linked to the Stern–Brocot tree and the underlying Farey sequence. We describe the mechanism responsible for this behavior and subsequently discuss corresponding implications and possible applications.
Lie bi-algebras on the non-commutative torus
Authors: Landi Giovanni , Rajeev S G
Published: August 2022

We use the canonical trace on the non-commutative torus

T

θ

2

to construct a Lie bi-algebra splitting of the algebra of its smooth functions. In the special case of rational parameter

θ
=

M

N

(the algebra is then generated by clock and shift matrices) this Lie bi-algebra is

G
L

̲

(

N

)

=

U

̲

(

N

)

⊕

B

̲

(

N

)

, corresponding to unitary and upper triangular matrices. The Lie bi-algebra has a remnant in the classical limit N → ∞: the elements of

U

̲

(

N

)

tend to real functions while

B

̲

(

N

)

tends to a space of complex analytic functions. The limit results into an infinite dimensional Lie bi-algebra for the smooth functions on the commutative torus.
Entropy growth during free expansion of an ideal gas
Authors: Chakraborti Subhadip , Dhar Abhishek , Goldstein Sheldon , Kundu Anupam , Lebowitz Joel L
Published: August 2022

To illustrate Boltzmann’s construction of an entropy function that is defined for a microstate of a macroscopic system, we present here the simple example of the free expansion of a one dimensional gas of non-interacting point particles. The construction requires one to define macrostates, corresponding to macroscopic variables. We define a macrostate M by specifying the fraction of particles in rectangular boxes ΔxΔv of the single particle position-velocity space {x, v}. We verify that when the number of particles is large the Boltzmann entropy, S
B(t), of a typical microstate of a nonequilibrium ensemble coincides with the Gibbs entropy of the coarse-grained time-evolved one-particle distribution associated with this ensemble. S
B(t) approaches its maximum possible value for the dynamical evolution of the given initial state. The rate of approach depends on the size of Δv in the definition of the macrostate, going to zero at any fixed time t when Δv → 0. Surprisingly the different curves S
B(t) collapse when time is scaled with Δv as: t ∼ τ/Δv. We find an explicit expression for S
B(τ) in the limit Δv → 0. We also consider a different, more hydrodynamical, definition of macrostates for which S
B(t) is monotone increasing, unlike the previous one which has small decaying oscillations near its maximum value. Our system is non-ergodic, non-chaotic and non-interacting; our results thus illustrate that these concepts are not as relevant as sometimes claimed, for observing macroscopic irreversibility and entropy increase. Rather, the notions of initial conditions, typicality, large numbers and coarse-graining are the important factors. We demonstrate these ideas through extensive simulations as well as analytic results.
Factors affecting safety leadership of construction project owners in Indonesia
Authors: Indrayana D V , Pribadi K S , Marzuki P F , Iridiastadi H
Published: August 2022

Construction is an industry with a high possibility of risk. The construction industry accounts for the highest number of work accidents globally, with more than 60,000 fatal accidents per year. A construction safety culture through mature safety leadership can be the fundamental factor to improve safety performance. Safety leadership is the process of interaction between leaders and followers to achieve organizational safety goals. In many cases of work accidents in construction projects in Indonesia, the leadership of the project owner is rarely discussed. It is more often determined that the responsible stakeholder is the contractor. Leadership by the project owner is the main factor for generating new and effective safety management measures. This study aims to determine the factors that influence the Safety Leadership of construction project owners in Indonesia. This study uses the Delphi round technique to analyze experts’ judgments to avoid biases. This study results in obtaining several factors and indicators that are considered to have an influence on the safety leadership of construction project owners in Indonesia.
The effect of land subsidence on hydrodynamic conditions in Pekalongan coast
Authors: Martin R J , Kurniawan A , Nugroho E O , Wijaya F S , Muhammad I F , Yola  , Fauzi N
Published: August 2022

Due to land subsidence at Pekalongan, some areas at Pekalongan coast submerged at a rate up to 10 cm/year. This study was conducted to examine the effect of land subsidence on the hydrodynamic conditions in Pekalongan through a numerical model simulation using Delft3D. There are 2 scenarios for the numerical model, scenario 1 in 2016 and scenario 2 in 2021 after the occurrence of land subsidence at a rate of 10 cm/year. Model calibration was carried out on water level elevation and current velocity by comparing the simulation data and field data based on the variation of the Chezy number. The calibrated Chezy number parameter was 60 m0.5/s. The sensitivity test is carried out by changing the input parameters such as water level elevation, river discharge, and wave height. The sensitivity test results shows that the most sensitive parameter is wave height. The comparison of the simulation results of the two scenarios shows that the current velocity decreased by about 0.05 m/s to 0.1 m/s and the wave height increased by about 0.05 meters to 0.15 meters after land subsidence occurred. Land subsidence causes the waters in the study area to become deeper, and the results of the study show that current velocity tends to decrease and wave height tends to increase because of the land subsidence.