bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Comparative Study of Hybrid Energy Storage System using Battery and Supercapacitor for Stand-Alone Solar PV System
Authors: Uloom N A , Mansur T M N T , Ali R , Baharudin N H , Abdullah A M 
Published: August 2022

Abstract
The standalone solar power system has long been used to meet the electrical needs of basic building structures. To counter the natural supply–demand imbalance caused by solar energy, standalone solar PV system often include energy storage devices, primarily lead–acid batteries. Due to lead-acid battery limitations, solar systems often have higher operational costs compared to traditional power systems. It has been discovered that a supercapacitor-battery hybrid energy storage device can be used to prolong the cycle life of a battery system by reducing the charge–discharge stress caused by variable power exchange. This research examines the influence of a supercapacitor on a photovoltaic system that makes use of a hybrid energy storage system that includes both batteries and supercapacitors in order to lessen the stress placed on the batteries. The methodology involves data collection for load profile and meteorological information, designing solar PV system, and simulation using Matlab SIMULINK to study the effect of supercapacitor on battery current of the evaluated system. Three different energy storage system topologies in building applications were simulated, and their ability in managing battery stress was investigated and evaluated. From the result, it is clear that by applying passive HES system, 53% of battery current can be reduced compared to battery-only system and 92% of reduction can be achieved by using semi-active HES system.
Electronic Ballast using Half Bridge Resonant Converter
Authors: Sukhi Y , Jeyashree Y , Jenifer A , Anita S , Ahamed A Fayaz 
Published: August 2022

Abstract
This paper presents a front-end ac-dc high power factor single stage topology. The proposed topology has good efficiency, better power factor and small size structure. The electronic ballast is designed with buck boost converter. The electronic ballast supplies power to fluorescent lamp. The lamp power regulation for the variations in the input voltage is assured. The steady state operation of the electronic ballast is done. The design of the converter in carried out from the basic principle. The equations for the electronic ballast circuit is derived from the basic principle and the analysis of the converter is done based on the equations. The elements used in the converter circuit is designed using the derived equations. The proposed converter circuit is developed for testing using 40W lamp and to determine the performance of the converter. The input pf measured has become more than 0.99. The electronic ballast efficiency with PFC circuit and buck boost converter is 94.46%
Feasibility study on the conversion of a small engine into a single-piston expander operating under different pressure and valve timing conditions
Authors: Mohamad M N A , Salim W S I W , Muhammad W N A W 
Published: August 2022

Abstract
There are limited studies on improving the piston expander performance for a wider operating range by adopting the variable valve timing method. This study uses a simple conversion technique to develop a single-piston expander (SPE) from a small two-stroke engine. The SPE is being tested at different operating conditions to study the feasibility of the SPE operating under different intake pressure and valve timing conditions. By fixing the exhaust valve timing, the SPE was tested at four intake pressure; 3, 4, 5, and 6 bar, while the intake valve closing varied from 30° to 110°. From the study, the highest power produced by the SPE was only 64 Watt when tested at 6 bar, with the intake valve opening at TDC and closed at 70°. The results show that the converted SPE is feasible in terms of functionality, but it is not performance-wise because much power has been lost through the recompression process. The study also observed that the intake valve timing could significantly affect the SPE power output, besides the intake pressure alone.
Fusion of Thermal and Depth Image to Improve Human Segmentation for a Mobile Robot
Authors: Hadi H S , Rosbi M , Sheikh U U 
Published: August 2022

Abstract
In machine vision, surveillance systems are a kind of security that concentrates on the safety of the human and property. One of the main tasks of a surveillance system is the detection of humans. This paper presents a system of human detection and the development of a technique of human segmentation using a combination of information thermal and depth in a real indoor setting from a mobile robot. A novel fusion of thermal-depth information (FTDI) is introduced to enhance the efficiency of the segmentation process and expedite processing. In experimental studies, evaluation of the performance for the proposed system is carried out using Ground Truth (GT), in which the proposed system yield is compared to GT. The proposed system performs well with an approximate accuracy of over 90% for all data sets as illustrated in the quantitative results and even outperformed state-of-the-art algorithms. This paper presents the novelty of the work, in which the detection method can improve the classification of persons and their occlusion. The advantages, such as being computationally inexpensive and performs well even under severe occlusion and poor illumination, show that this proposed system is robust.
Image reconstruction by shapefree radial basis function neural networks (RBFNs)
Authors: Paewpolsong P , Sriapai N , Tavaen S , Kaennakham S 
Published: August 2022

Abstract
With the growth of artificial intelligence technologies, the research on artificial neural networks (ANNs) has been paid much more attention. Radial basis function neural networks (RBFNs) are a type of ANNs that are referred to as models that replicate the role of biological neural networks. While their applications are growing in a wide range of areas, conventional forms of RBFs contain a highly problem-dependent shape parameter, making it not as convenient as one would expect. This work investigates the numerical effectiveness of RBFs containing no shapes, so they are referred to as ‘shapefree’, under the application of image reconstruction. Nine forms of shapefree RBFs have been gathered and implemented in conjunction with the RBFNs. Two popular images (known as Lena and Plane) are damaged in Salt-and-Pepper manner before being repaired by the networks using these shapefree RBFs. The overall performances are monitored based on error norm, CPU-time and storage, and condition number. This aims to provide useful information regarding choices of RBFs for future uses, to overcome the pain one faces from choosing a suitable value of shape parameter.
Carbon nanotubes’ amount and type: crucial parameters in mechanical performance enhancement on cement paste nanocomposites
Authors: Maria Falara , Athanasia Thomoglou , Fani Gkountakou , Anaxagoras Elenas 
Published: August 2022

Abstract
Single-wall (SWCNTs) and multi-wall (MWCNTs) carbon nanotubes’ affection on mechanical properties of cement-based nanocomposites is investigated in the present research. Three different amounts of CNTs, namely 0.05 %, 0.1 %, and 0.5 wt.% by weigth of cement were incorporated in nanomodified cementitious specimens. Flexural and compressive strength were determined for both types. The results reveal that MWCNT nanocomposites exhibit an improved mechanical performance for both flexure and compression as a function of the concentration up to 0.1 wt.% of the cement in contrast to SWCNT modified cementitious composites where the CNT addition leads either to a slight increase or a decrease. Results clearly reveal that the different type of reinforcement at the nanoscale beneficially alters the structure of the cement paste and enables the material to perform multiple structural functions. This investigation is expected to contribute significantly to create a cement based materials with improved mechanical performance.
Slice-to-voxel reconstruction of porous media using style-based generative adversarial networks
Authors: Huang Yubo , Xiang Zhong 
Published: August 2022

Abstract
Prediction of microstructure properties is of great significance for porous media, and the use of limited two-dimensional data to synthesize three-dimensional (3D) structural samples is an important support for this task. Although generative adversarial network technology has been successfully migrated to this scenario, its applicability is limited by the development of deep network technology. In this work, we use a more advanced StyleGAN architecture to accomplish this task and show that this method can achieve or even exceed the reconstruction effect of the previous voxel-to-voxel method.
Crack failure analysis of a temperature transmitter sleeving of a gas transmission station
Authors: Zhang Hongbo , Ren Guoqi , Wang Gaofeng , Yang Fengping 
Published: August 2022

Abstract
Based on the exterior morphology, fracture morphology and metallographic analysis and energy dispersive spectroscopy analysis, the causes of cracking of a temperature transmitter sleeving tube of a gas station are analyzed, the results showing that the main cause of the sleeving crack was the raw material of sleeving has not been treated with solid solution annealing or the heat treatment was not completely processed. therefore, the lack of treatment makes the material gathered a large number of Cr carbides on grain boundary, this leaded to the low anti intergranular corrosion ability; The thread in the sleeving does not meet the requirements of thread processing specifications, which caused the thread near the port to be squeezed in the service process of the sleeving, resulting in large tensile stress at the outer port of the sleeving. In the case of stress concentration caused by mechanical damage at the port position and the corrosion environment, stress corrosion cracking occurred at the port mechanical damage position and eventually lead to leakage.
Pretreatment method for BX24 Decon water of HD, VX and GD before GC analysis
Authors: Xu Mengxue , Wang Lianyuan , Zhu Haiyan , Zhang Hongpeng , Ge Liang 
Published: August 2022

Abstract
Chlorine-based decontaminant BX24 is effective for the decontamination of chemical warfare agents (CWAs) VX, GD and HD. In this paper, main factors affecting the agent recovery from decontamination water (decon water) were studied, a pretreatment method was subsequently developed for GC analysis of residual chemical warfare agents in BX24 decon water. Results showed that dichloromethane was the most suitable extractant, especially for sample of VX and GD. Neutralization process should be done to obtain a high recovery, especially for samples under low CWAs concentration and samples with high speed of CWAs degradation like HD decon water. When optimized neutralizer of sodium sulfite or sodium thiosulfate and extractant of dichloromethane were used, where neutralization and extraction were done simultaneously, a recovery above 85% could be obtained for BX24 decon water. The appropriate CWAs concentration ranged from 20 mg/l to 10000 mg/l.
Study on Thermo-oxidative Aging Mechanism of Air Spring Rubber Cord Composites
Authors: Guo Xiaohui , Yuan Xiaojing 
Published: August 2022

Abstract
To study the thermo-oxidative aging mechanism of air spring rubber cord composites, observe the changes in surface micro-morphology, study the thermo-oxidative aging mechanism of air spring rubber cord composites, observe the changes in surface micro-morphology, infrared spectrum, quality, hardness, and mechanical properties during the process of thermo-oxidative aging, and analyze the aging mechanism. The results show that the micro-morphological changes of the rubber surface of the rubber cord composites go through two stages: at 0-72 h, the rubber surface The results show that the micro-morphological changes of the rubber surface of the rubber cord composites go through two stages: at 0-72 h, the rubber surface develops from no cracks to a large number of cracks; at 96-336 h, the rubber surface changes from a cracked state. The size of the crack on the rubber surface affects the rate of change of IRHD hardness. The larger the crack, the greater the hardness growth rate, otherwise the opposite. In the aging process, the rubber is in a state of dynamic development. The increase of cross-linking density causes changes in microscopic The increase of cross-linking density causes changes in microscopic morphology and accessible volume, which affects the IRHD hardness. The aging mechanism is more complicated. Material failure is dominated by the failure of the rubber matrix.
Research on the Fluorescence Properties of Protoporphyrin-Modified Amphiphilic Polysilane
Authors: Pu Wenjing , Li Yue , Han Jiajia , Wang Shenhao , Lu Wei 
Published: August 2022

Abstract
Polysilanes are polymers with unique luminescent properties, which will be enhanced by functional side group modifications. In this paper, amphiphilic functional polysilane is synthesized by Si-H addition reaction between Si-H and alkene bonds. Polymethylsilane is selected as matrix which is full of reactive Si-H groups and azobisisobutyronitrile is used as the initiator. The product shows good luminescence (emission band between 400-650 nm), good solubility (soluble in various solvents such as toluene, tetrahydrofuran, n-hexane, dimethyl sulfoxide, alcohol and water) and environmental friendliness, which can be applied to flexible luminescent devices and solvent-based fluorescence detection in the future.
Preparation and Mechanical Properties of Thermal Insulation Silicone Rubber Composites
Authors: Pan Hongwei , Zang Chongguang , Chen Yijun 
Published: August 2022

Abstract
In this paper, three different functional phases of thermal insulation, hollow ceramic microspheres (HCM), hollow silica microspheres (HSM) and hydroxy silicone oil blowing agent (OH-3#)were selected, and a flexible and efficient thermal insulation composite with low thermal conductivity and high thermal structural strength was designed and prepared using the synergistic effect between the three. The results show that the thermal stability and mechanical performance of composites were significantly enforced.
Self-healing of structured light: a review
Authors: Shen Yijie , Pidishety Shankar , Nape Isaac , Dudley Angela 
Published: August 2022

Abstract
Self-healing of light refers to the ability of a light field to recover its structure after being damaged by a partial obstruction placed in its propagation path. Here, we will give a comprehensive review of the history and development of self-healing effects, especially highlighting its importance in vector vortex beams carrying spin and orbital angular momenta. Moreover, an unified zoology of self-healing, structured light is proposed to unveil a deeper understanding of its physical mechanism and provide a bird’s eye view on diverse forms of self-healing effects of different kinds of complex structured light. Finally, we outline the open challenges we are facing, potential opportunities and future trends for both fundamental physics and applications.
Quantitative evaluation of biaxial compressive strain and its impact on proton conduction and diffusion in yttrium-doped barium zirconate epitaxial thin films
Authors: Hyodo Junji , Yamazaki Yoshihiro 
Published: August 2022

Abstract
Proton-conducting oxides, including 20 mol% yttrium-doped BaZrO3 (BZY20), have attracted considerable attention as electrolytes for environmentally friendly electrochemical cells, such as proton ceramic fuel cells (PCFCs) and proton-conducting solid oxide cells. These oxides exhibit fast proton conduction due to the complex physicochemical phenomena of hydration, chemical lattice expansion, proton migration, proton trapping, and local distortion. Using a proton-conducting oxide as an electrolyte film in electrochemical devices introduces an interface, which thermally and chemically generates mechanical strain. Here, we briefly review the current state of research into proton-conducting oxides in bulk samples and films used in electrochemical devices. We fabricated 18 and 500 nm thick 20 mol% BZY20 epitaxial films on (001) Nb-doped SrTiO3 single-crystal substrates to form a model interface between proton-conductive and non-proton-conductive materials, using pulsed laser deposition, and quantified the mechanical strain, proton concentration, proton conductivity, and diffusivity using thin-film x-ray diffractometry, thermogravimetry, secondary ion mass spectrometry, and AC impedance spectroscopy. Compressive strains of −2.1% and −0.85% were measured for the 18 and 500 nm thick films, respectively, and these strains reduced both the proton conduction and diffusion by five and one orders of magnitude, respectively, at 375 °C. Analysis based on a simple trapping model revealed that the decrease in proton conduction results from the slower diffusion of mobile protons with a negligible change in the proton trapping contribution. The model shows that the high ohmic resistance reported for a high-performance PCFC with a power density of 740 mW cm−2 at 600 °C can be solely explained by the estimated compressive strain in the cells. This study shows that minimizing biaxial compressive strain by appropriate choices of the electrolyte–electrode combination and fabrication process is important for maximizing the performance of electrochemical cells.
Superconducting routing platform for large-scale integration of quantum technologies
Authors: Thomas C , Michel J-P , Deschaseaux E , Charbonnier J , Souil R , Vermande E , Campo A , Farjot T , Rodriguez G , Romano G , Gustavo F , Jadot B , Thiney V , Thonnart Y , Billiot G , Meunier T , Vinet M 
Published: August 2022

Abstract
To reach large-scale quantum computing, three-dimensional integration of scalable qubit arrays and their control electronics in multi-chip assemblies is promising. Within these assemblies, the use of superconducting interconnections, as routing layers, offers interesting perspectives in terms of (1) thermal management to protect the qubits from control electronics self-heating, (2) passive device performance with significant increase of quality factors and (3) density rise of low and high frequency signals thanks to minimal dispersion. We report on the fabrication, using 200 mm silicon wafer technologies, of a multi-layer routing platform designed for the hybridization of spin qubit and control electronics chips. A routing level couples the qubits and the control circuits through one layer of Al0.995Cu0.005 and superconducting layers of TiN, Nb or NbN, connected between them by W-based vias. Wafer-level parametric tests at 300 K validate the yield of these technologies while low temperature electrical measurements in cryostat are used to extract the superconducting properties of the routing layers. Preliminary low temperature radio-frequency characterizations of superconducting passive elements, embedded in these routing levels, are presented.
General spiking neural network framework for the learning trajectory from a noisy mmWave radar
Authors: Liu Xin , Yan Mingyu , Deng Lei , Wu Yujie , Han De , Li Guoqi , Ye Xiaochun , Fan Dongrui 
Published: August 2022

Abstract
Emerging usages for millimeter wave (mmWave) radar have drawn extensive attention and inspired the exploration of learning mmWave radar data. To be effective, instead of using conventional approaches, recent works have employed modern neural network models to process mmWave radar data. However, due to some inevitable obstacles, e.g., noise and sparsity issues in data, the existing approaches are generally customized for specific scenarios. In this paper, we propose a general neuromorphic framework, termed mm-SNN, to process mmWave radar data with spiking neural networks (SNNs), leveraging the intrinsic advantages of SNNs in processing noisy and sparse data. Specifically, we first present the overall design of mm-SNN, which is adaptive and easily expanded for multi-sensor systems. Second, we introduce general and straightforward attention-based improvements into the mm-SNN to enhance the data representation, helping promote performance. Moreover, we conduct explorative experiments to certify the robustness and effectiveness of the mm-SNN. To the best of our knowledge, mm-SNN is the first SNN-based framework that processes mmWave radar data without using extra modules to alleviate the noise and sparsity issues, and at the same time, achieve considerable performance in the task of trajectory estimation.
A review of non-cognitive applications for neuromorphic computing
Authors: Aimone James B , Date Prasanna , Fonseca-Guerra Gabriel A , Hamilton Kathleen E , Henke Kyle , Kay Bill , Kenyon Garrett T , Kulkarni Shruti R , Mniszewski Susan M , Parsa Maryam , Risbud Sumedh R , Schuman Catherine D , Severa William , Smith J Darby 
Published: August 2022

Abstract
Though neuromorphic computers have typically targeted applications in machine learning and neuroscience (‘cognitive’ applications), they have many computational characteristics that are attractive for a wide variety of computational problems. In this work, we review the current state-of-the-art for non-cognitive applications on neuromorphic computers, including simple computational kernels for composition, graph algorithms, constrained optimization, and signal processing. We discuss the advantages of using neuromorphic computers for these different applications, as well as the challenges that still remain. The ultimate goal of this work is to bring awareness to this class of problems for neuromorphic systems to the broader community, particularly to encourage further work in this area and to make sure that these applications are considered in the design of future neuromorphic systems.
Comparative studies of AlphaFold, RoseTTAFold and Modeller: a case study involving the use of G-protein-coupled receptors
Authors: Lee Chien , Su Bo-Han , Tseng Yufeng Jane 
Published: August 2022

Abstract
Neural network (NN)-based protein modeling methods have improved significantly in recent years. Although the overall accuracy of the two non-homology-based modeling methods, AlphaFold and RoseTTAFold, is outstanding, their performance for specific protein families has remained unexamined. G-protein-coupled receptor (GPCR) proteins are particularly interesting since they are involved in numerous pathways. This work directly compares the performance of these novel deep learning-based protein modeling methods for GPCRs with the most widely used template-based software—Modeller. We collected the experimentally determined structures of 73 GPCRs from the Protein Data Bank. The official AlphaFold repository and RoseTTAFold web service were used with default settings to predict five structures of each protein sequence. The predicted models were then aligned with the experimentally solved structures and evaluated by the root-mean-square deviation (RMSD) metric. If only looking at each program’s top-scored structure, Modeller had the smallest average modeling RMSD of 2.17 Å, which is better than AlphaFold’s 5.53 Å and RoseTTAFold’s 6.28 Å, probably since Modeller already included many known structures as templates. However, the NN-based methods (AlphaFold and RoseTTAFold) outperformed Modeller in 21 and 15 out of the 73 cases with the top-scored model, respectively, where no good templates were available for Modeller. The larger RMSD values generated by the NN-based methods were primarily due to the differences in loop prediction compared to the crystal structures.