bluuebunny/crossref_metadata_2025_split huggingface dataset:
Petrophysical characterizations and shale distribution analysis of Kissenda reservoir sands, Gabon
Authors: Pradhanitasari N , Fatkhan  
Published: June 2022

Abstract
A gas field in western Gabon is penetrated by an exploration site N field with a major fault dividing the well locations. The upthrown block and the downthrown block have different characterization based on the history of Gabon Basin located in Equatorial West Africa, as part of the salt basin complex. In addition to major fault separating the blocks, four wells are selected in this study to identify each block. Petrophysical properties was undertaken by using wireline logs to generate each well composite. These properties include shale volume, total porosities, water saturation and permeability. The presence of shale in sandstone reservoir can affect the producibility rate of the reservoir. Shale volume and density porosity are cross-plotted using the Thomas-Stieber method to determine the shale distribution of both blocks, including the distribution types of laminar shale, dispersed shale and structural shale. Based on this method, the upthrown block has relatively small grain diameters with mostly laminar-dispersed shale distributions, while in downthrown block the grain diameters are four times larger with more dynamic shale distributions.
The effect of TiO2 photocatalyst towards methylene blue degradation using simple dip coating
Authors: Utami Fisca Dian , Surtiyeni Neni , Amalia Nadya , Sustini Euis , Abdullah Mikrajuddin 
Published: June 2022

Abstract
The purpose of a recent study is to evaluate the performances of TiO2 in destroying organic effluent. TiO2 photocatalyst presented a high performance in eliminating methylene blue (MB). Technical anatase of titania was employed. Dip coating was employed to immobilize titania onto the transparent plastic, followed by putting the additional polymer (alteco glue). From the photocatalytic testing, 100 mL of 25 mg L-1 MB was clearly eliminated after 12 hours of irradiation under solar exposure. Variation of TiO2 concentration and the additional polymer was carried out to study the performance of the catalyst.
Size effect at discharging process of coconut oil as latent thermal energy storage
Authors: ‘Ulhaq S D , Yusuf A , Wonorahardjo S , Sutjahja I M 
Published: June 2022

Abstract
Coconut oil is one of the organic PCM materials with high thermal energy storage capability in the form of sensible and latent heat. Ideally, the latent heat storage occurs at a relatively constant temperature associated with phase change transition. Due to relatively low thermal conductivity, the size effect is important to optimize material performance, particularly at the discharging process related to latent heat release. This study will show the simulation results of the size effect in the solidification process of coconut oil by using ANSYS FLUENT. Three plastic balls with radii 2 cm, 3 cm, and 6 cm filled with coconut oil solidified from 308 K to 293 K, and several temperature detectors were placed in the ball to study the heat exchange process. The input parameters of density, heat capacity, thermal conductivity, and viscosity were taken from previous experimental studies. We reported the time evolution of temperature and liquid fraction until the molten coconut oil solidified from different detector positions in each ball.
Effect of Tetrabutylammonium on Methylene Blue Adsorption
Authors: Ledyastuti M , Sofyan O P , Zulfikar M A 
Published: June 2022

Abstract
Artificial dyes such as methylene blue are commonly detected in textile industry wastewater. Clay is commonly used as an economical and effective adsorbent for methylene blue in textile waste treatment. The addition of surfactants into the system is usually done to increase the adsorption capacity of the clay. In this study, we used a molecular dynamics simulation method to study the effect of tetrabutylammonium organic surfactant on the adsorption process of methylene blue on the clay surface. The modelled clays are pyrophyllite and montmorillonite which are hydrophobic and negatively charged, respectively. The simulation results show that the addition of tetrabutylammonium surfactant causes a 12 % and 40% decrease in the binding of methylene blue to the surfaces of pyrophyllite and montmorillonite, respectively. This is due to the occurrence of competitive adsorption in both systems. Therefore, the addition of tetrabutylammonium surfactant was considered ineffective in increasing the adsorption capacity of pyrophyllite and montmorillonite.
Qualitative Modelling the Dancing Volvox with Two-Body System: Comparing Simple Mechanics with Observation Video
Authors: Nurafni Sevi , Taufik Intan 
Published: June 2022

Abstract
Volvox cells normally construct a spherical colony of hundred of cells and moving together to perform their basic needs. More than moving a single colony, two attached volvox colonies can ‘dance’ and move together. This system can be modeled as a two-body system, where its center of mass (COM) is performing simple translational (TRAN) motion, while the two colonies center is rotating around the COM with roll (ROLL) or spin (SPIN) motions. From the available observation video, about 10 s or 325 frames can be isolatedfor Sobel edge detection to get a clear picture of the dancing motion of two volvox colonies. It has been observed that the dancing volvox has a superposition motion of the other motion (TRAN, ROLL, SPIN), which has been compared qualitatively with the simple mechanics model of the two-body system. From the model, the translational velocityis about 1.25 m/s, and its roll and spin angular velocity are about 0.785 and 3.063 rad/s, respectively. The results of the study concluded that the mode of motion of the two Volvox colonies was a combination of spin and translational motion.
Electric field-induced modification of Dzyaloshinskii-Moriya interaction in Fe/Mg0(001) with heavy metal insertions
Authors: Pradipto Abdul-Muizz , Nakamura Kohji 
Published: June 2022

Abstract
The effect of external electric field on the Dzyaloshinskii-Moriya interaction (DMI) in Fe/M/MgO(001) (M=Os, Ir, Pt, Au) is investigated by means of first principles full-potential linearized augmented plane wave (FLAPW)-based Density Functional Theory method. We find that, apart from the Au layer insertion, the sign of the DMI is altered by the introduction of the heavy metal, such that the magnetic chirality in Fe/M/MgO(001) is inverted from that of Fe/MgO(001). The magnitudes of DMI in the Fe/Os/MgO and Fe/Ir/MgO are enhanced by 5 - 7 times compared to that without the heavy metal insertion. The increase of the DMI can be attributed to the strength of spin-orbit coupling of the heavy metal layer, implied by the asymmetry of the orbital moments of the heavy metals within the spin spiral structures. When an external electric field is applied, no significant modification to the DMI can be observed. Our work suggests that additional heavy metal layer might be necessary for a fine control of DMI, and hence of the overall magnetic domain-related properties, by external gate voltages.
Some Possible Schemes in Higher Dimensional Stationary Skyrme Blackhole Formulation
Authors: Fadhilla E S , Gunara B E , Atmaja A N 
Published: June 2022

Abstract
In this article we present some possible ways on how to formulate a stationary blackhole in higher dimensional generalized Skyrme model with minimal gravitational coupling. We consider a generalized Skyrme action which includes all possibble higher order kinetic terms as the representations of all strain tensor invariants. Some possible spacetime ansatzs are mentioned, such as Myers-Perry metric and general higher dimensional axisymmetric metric with one rotation plane. Here, we find that there exist some Skyrme field ansatzs, obeying the same symmetries of the spacetime manifold, which lead to a diagonal strain tensor.
The Relative Entropy of One Graviton Creation in Early Universe
Authors: Trenggana A , Zen F P 
Published: June 2022

Abstract
The detection of nonclassicality of primordial gravitational waves (PGWs) is one way to prove the quantum theory of gravity. The nonclassical feature can be seen as a non-gaussianity, that could happen if there is a graviton creation in a squeezed state of Bunch-Davies vacuum. This condition would also produce an entanglement state. In this paper, we study that non-gaussianity by using relative entropy as a measurement. We find that, when the value of α (the entanglement constant) is near to one, the non-gaussianity would get its highest value. For the presence of matter field, the non-gaussianity still has the same characteristic for different values of α. But, in this case, the relative entropy value is constant for every value of the squeezed parameter.
Preparation of Platina Nanoparticles with the Presence of Cellulose Nano Crystals
Authors: Wibowo Arie , Santoso Rachmad , Judawisastra Hermawan , Budhi Yogi Wibisono 
Published: June 2022

Abstract
Platina play a crucial role in catalytic converter device as one of catalyst that convert toxic gases in the exhaust gas emission to less toxic gas pollutant. The performance of this device can be significantly elevated by implementing platina nanoparticles (Pt NPs) because catalytic performance can be increased by decreasing its particles size. However, nanoparticles are not stable and tend to agglomerate, thus prohibit formation of nano particles. In this context, utilization of cellulose nano crystals (CNCs) in fabrication of Pt NPs are attractive due to their ability to encourage nucleation and prevent agglomeration of nanoparticles. In this study, Pt NPs was prepared by reduction of H2PtCl6 solution at various temperature (25, 33 and 40 °C) with the presence of CNCs. Based on visual observation, darker precipitate was observed by increasing temperature. Further characterization using UV-Vis spectroscopy showed that about 90% of Pt4+ in solution were successfully convert to dark particles at 40 °C. Transmission electron microscopy (TEM) and selected area electron diffraction (SAED) revealed that the dark particles in sample is Pt NPs with particles size 16.5 ± 3.2 nm.
Modification of screen-printed carbon electrode (SPCE) by the Nafion functionalized silicon nanoparticles (SiNP/Naf) materials in non-enzymatic electrochemical sensor for uric acid (UA) detection
Authors: Huda N , Sambegoro P L , Septiani N L W , Iqbal M , Sholehah A , Yuliarto B 
Published: June 2022

Abstract
A non-enzymatic electrochemical sensor has been successfully developed to detect uric acid (UA) based on a modified screen-printed carbon electrode (SPCE) using Nafion functionalized silicon nanoparticles. Silicon nanoparticles (SiNPs) material is used because of their advantages, including its abundant availability, good biocompatibility, and adjustable porosity and surface area according to the synthesis method used. Nafion is a sulfonated tetrafluoroethylene-based fluoropolymer-copolymer that has been widely used for electrochemical sensors and is functioned as a stabilizer of the sensor. In this study, SiNPs material was successfully synthesized through the non-thermal chemical vapor deposition method. XRD diffractogram and SEM image confirmed its structure and morphology. The crystallinity of the produced SiNPs is 69.85%. SEM-EDS characterization was also carried out to confirm the success of the SPCE modification by SiNPs and SiNPs/Naf materials. The changes in the morphology of SPCE and the EDS spectrum that were measured indicated the success of the SPCE modification process for each stage. Cyclic voltammetry, electrochemical impedance spectroscopy, and differential pulse voltammetry study were used to analyze the electrochemical characteristics and performance of non-enzymatic electrochemical sensors based on SPCE/SiNPs/Naf structures against uric acid analytes. Based on the results of electrochemical analysis, the sensitivity, detection limit, and quantification limit of the sensor are 0.01 μA.mM-1. cm-2, 0.21 μM, and 0.69 μM respectively in the linear measurement range of uric acid concentration of 10 – 1000 μM. Then, a sensor stability study was also carried out which resulted in an RSD value of 4.83%.
Review of Learning Result of The Physics Base on SSCS (Search, Solve, Create, and Share) Model in Terms of Critical Thinking Skills
Authors: Putri Q S , Suharno  , Budiawanti S 
Published: June 2022

Abstract
Since 2020, the whole world has been affected by Covid 19. The impact of the Covid 19 pandemic is not only on the social sector but also on the education sector. So that education must continue to be carried out using distance learning or online. However, changes in the learning process also affect the teaching and learning conditions of students and teachers. Therefore, it is necessary to apply an appropriate learning model. One of them uses the SSCS learning model. SSCS is a learning model that can improve critical thinking skills. The SSCS model has four stages: search, problem-solving (solve), create, and share. At the search stage, students are taught to understand the problem. Next, students make a plan to solve the problem. In the next step, students build a solution from the problem-solving strategy that has been arranged in the previous stage. Finally, the solutions that have been constructed are disseminated and evaluated for continuous improvement. The purpose of this paper is to provide a comprehensive description of the development of the SSCS model, especially in physics learning that has been carried out to improve critical thinking skills. It was concluded that distance and direct knowledge using the SSCS learning model experienced an increase in abilities and scores of around 40% - 50%.
Photovoltaic Performance of TiO2 Mesoporous Films with Different Working Areas for Dye-sensitized Solar Cells
Authors: Nasikhudin  , Astarini Nadiya Ayu , Rahman Muhamad Fadhil , Diantoro Markus , Aripriharta  , Osman Zurina 
Published: June 2022

Abstract
The photovoltaic performance of Dye-sensitized Solar Cells (DSSC) could be optimized by playing the working area of the mesoporous TiO2 photoanode. A small working area will enhance the photovoltage and power conversion efficiency. In comparison, a large working area will improve the recombination rate and reduce the photon particle transport rate at the photoanode. The TiO2 based photoanode layer consists of a blocking layer and mesoporous paste deposited using spin coating and screen printing technique, respectively with various working areas of 0.25, 0.30, and 0.56 cm2. The cells were characterized using XRD, SEM-EDX, UV-Vis spectroscopy, and solar simulator. XRD confirmed that the TiO2 was in the anatase phase. SEM image represented a high surface area of mesoporous TiO2 indicated by porosity level up to 70%. The elemental analysis of TiO2 using EDX observed the presence of Ti and O peaks. The TiO2 mesoporous-based photoanode immersed in 0.07 mM of N719 solution possessed an absorbance range in the ultraviolet to the visible light. The working area of 0.25 cm2 exhibited a promising power conversion efficiency of 2.02% with a circuit current density of 10.8 mA/cm2 under an illumination light of 100 mW/cm2.
SiamAVOT: A High-performance Siamese Network Tracker for Aerial Visual Object Tracking
Authors: Deng Pingyu , Qiu Xuyi , Yao Ziyu , Li Lei 
Published: June 2022

Abstract
Aerial vision object tracking technology plays an increasingly critical part in intelligent perception and task cognition when aircraft perform various reconnaissance missions. However, the particularity in object types and visual angles in aerial vision imagery render the tracking process more prone to deformation, scale variation, motion blur and occlusion, making this technique very challenging. A high-performance Siamese network tracker for aerial visual object tracking, SiamAVOT, is proposed to deal with this problem in this paper. First, we utilize the SiamCAR infrastructure to introduce the Bottleneck Attention Module (BAM) attention mechanism after its backbone network for fusion of low and high level features to improve recognition for deformation object features. Then, we change to Distance-IoU (DIoU) loss for the bounding box regression during the training process to improve the network’s ability to predict scale variation. Finally, a Kalman filter online learning module that integrates time and space trajectory information is designed to solve the object motion blur and occlusion disappearance problem in the inference process. The proposed SiamAVOT achieves leading performance on the UAV123 and AVOT40 aerial datasets and can run in real-time at 72 FPS.
Crack Detection based on Convnext and Normalization
Authors: Zhang Hongfei , Liu Chun , Ho James , Zhang Zhiwei 
Published: June 2022

AbstractCracks, mostly caused by irregular expansion and contraction, indicating potential damage to a building, are of great significance to building quality assessment and predicting potential disasters like earthquakes. In this paper, after a comparison of the conventional methods using image processing techniques to detect the cracks and the newly-proposed CNN method, ConvNeXt is involved, which shows more sufficiency and stability. At the stage of the experiment, due to the specialization of the datasets and the methodology, a serial of image processing is engaged as the preprocessing before the data are used to train the CNN model. By using two crack-specialized datasets, namely, Concrete Crack Images for Classification and the SDNET2018, over 60,000 images are selected as the training samples. After adequate training and the AdamW involved as the optimizer, an accuracy of 99.0% on the dataset is reached and the expected results of accuracy of 99.0% are obtained.
Single Image Super-Resolution Algorithm based on Fixed-Point Multi-Model Gaussian Process Regression
Authors: Lu Ziwei , Yu Hongfei 
Published: June 2022

Abstract
To solve the problem of large amount of computation in matrix inversion of Gaussian process regression model, a super-resolution algorithm based on local Gaussian process regression of fixed point multi-model is proposed. Firstly, the training samples are classified by Gaussian mixture model, and image patches are randomly selected as fixed points in each type of training samples, and its K nearest neighbor patch are searched. Secondly, local Gaussian process regression model by using its low-mid-frequency components and the corresponding high frequency components. Again, low resolution test image patch is classified, only the K nearest neighbor test image patch is searched during reconstruction. And then, find the nearest fixed point in each kind of image patch, and use the local Gaussian process regression model based on the fixed point to predict its corresponding high-frequency component. Finally, corresponding high frequency information is predicted by using this trained model, which improves the reconstruction efficiency. Experimental results have demonstrated that the proposed algorithm is superior in both quantitative and qualitative aspects against other algorithms.
Scene Classification of Remote Sensing Images Using EfficientNetV2 with Coordinate Attention
Authors: Wang Zengkun , Cao Yang , Yu Hongfei , Sun Caihua , Chen Xuejian , Jin Zhanggen , Kong Weili 
Published: June 2022

Abstract
The high intra class diversity of remote sensing image scene often leads to the problem of difficult classification of remote sensing image scenes. Therefore, this paper proposes the CA-EfficientNetV2 model, embedding the coordinate attention into the head of the EfficientNetV2 network to enhance the classification effect. The coordinate attention is used to generate the position relationship between image spaces and channels so as to learn features efficiently. We trained three improved models CA-EfficientNetV2-S, CA-EfficientNetV2-M and CA-EfficientNetV2-L on UC Merced remote sensing dataset respectively. The classification accuracy reached 99.55%, 97.49% and 97.09% respectively. Among them, CA-EfficientNetV2-S had the best effect, which was improved by 0.8% compared with the original network.
Research on Skin Disease Health Detection of College Students based on Deep Learning
Authors: Zheng Guiyue , Li Meng , Zhang Fuchun , Wang Baiyang , Ji Yuelong 
Published: June 2022

Abstract
Skin disease is a common diseases. Although it is very common, the manifestations of skin diseases are various, and the diagnosis process is relatively difficult, and the requirements for dermatologists are also very high. And the recognition efficiency of detection also needs to be improved. To this end, this paper mainly investigates the potential of classifying skin diseases in deep learning convolutional neural networks to enhance the recognition accuracy. The VGG16 neural network was trained using the training data of the ISIC 2019 challenge, and the training data was augmented to increase the accuracy of the model, and accurately classify 3 skin diseases with 97% accuracy. This method can help doctors to make auxiliary diagnosis and provide technical support for skin disease diagnosis.
Analysis and Design of Two-Stage Cascaded LED Load Wireless Power Transfer System
Authors: Zhang Bin , Hei Guoyu , Li Huirong 
Published: June 2022

Abstract
This paper proposes a two-stage cascaded wireless power transfer (WPT) system aimed at increasing the transfer radius and supplying power to multiple low-power loads. The proposed WPT scheme is regarded as a cascading of two WPT systems. The system realizes constant current power supply for two-stage LED load and put emphasis on solving the problems of limited power supply radius and power supply of multiple constant current/voltage loads in WPT system. Four basic power supply types of transmission network are discussed in this paper, and the compensation network structures and parameters suiting for these types are determined by analyzing every stage’s input and output characteristic using two-port network T matrix. The result of research validates that constant current is achieved in general. Moreover, the two stage’s maximum efficiency is 68.04% when the LED total number changes from 6 to 13, which proves the feasibility of the proposed scheme.
Single-phase disconnection fault location of active distribution network based on negative-sequence differential impedance
Authors: Ma Tianxiang , Duan Xin , Zhang Zhiyuan , Shen Hongliang , Xu Yan , Zou Nan 
Published: June 2022

Abstract
Accidents such as overvoltage, personal injury and motor damage caused by singlephase disconnection faults occur frequently in active distribution networks. In response to this problem, firstly, the negative-sequence differential impedance is defined and a protection method based on negative-sequence differential impedance is proposed, the method uses the amplitude of negative-sequence differential impedance as an action quantity and uses its phase to construct an adaptive braking threshold. Secondly, in case of protection failure under overload, the zero-sequence voltage amplitude difference is proposed as an auxiliary criterion, and the influence of different types of distributed generators on the zero-sequence voltage difference on both sides of the fault fracture is analyzed. Finally, it is verified by simulation that the method can be positioned reliably and is not affected by the neutral point grounding method, transition resistance, load and distributed generators.