bluuebunny/crossref_metadata_2025_split huggingface dataset:
Validation of spine SBRT using a 3D printed Anthropomorphic phantom
Authors: Dekker KH , Alexander KM , de Moraes F Ynoe , Olding T 
Published: January 2022

Abstract
A segment of a spine was 3D-printed based on real patient anatomy, using metal-doped high density plastic to radiographically mimic bone. This spine was submerged in a water tank to create an anthropomorphic phantom. The spine print incorporated a slot for Gafchromic EBT3 film dosimeters and fiducials for alignment of measured and calculated dose distributions. Spine SBRT treatment plans were generated for both 6 MV and 10FFF energies based on oncologist-drawn contours transferred from real anatomy. Plans were delivered under image guidance using our clinical procedures, to evaluate the dosimetric accuracy of our planning system in high density inhomogeneities and the geometric accuracy of delivery. Results show that the Acuros XB algorithm (dose-to-water) agrees well with film measurements throughout the measured region, including within the bone substitute material. Alignment of the steep dose gradients in planned and measured doses was within 0.5 mm in the ANT-POST direction and within 0.9 mm in the SUP-INF direction, both within machine tolerances. Our results give us confidence in our ability to plan and accurately deliver spinal SBRT treatments.
Effects of PVA-GTA-I radiochromic gel dosimeter components on optical dose-response
Authors: Hayashi Shin-ichiro , Ono Kaoru , Fujino Keisuke , Kurihara Ryosuke 
Published: January 2022

Abstract
In recent years, a novel radiochromic gel dosimeter was developed that utilizes the color development of a polyvinyl alcohol-iodide (PVA-I) complex. In this study, we explore the effects of different iodide salts (LiI, NaI, KI, CsI, NH4I, CaI2, and ZnI2) and PVAs with different degrees of polymerization (500, 1000, and 1500) and saponification (80, 88, and 98 mol%) were investigated on a PVA-GTA-I gel dosimeter using PVA that was chemically crosslinked with glutaraldehyde (GTA) as a matrix. The results showed that these substitutions had negligible effect on dose-responses, such as sensitivity and dose-rate independence.
Further investigation of lung tumour peripheral doses using normoxic polymer gel dosimetry techniques
Authors: Venning A , Mundayadan Chandroth M , Morgan C , Roberts M 
Published: January 2022

Abstract
This work builds upon previous investigations of lung tumour peripheral doses for 6 MV, 6 MV FFF, and 10 MV FFF conformal arc therapy beams calculated in the Monaco® TPS and delivered using an Elekta® Agility™ linac. An improved patient lung phantom is developed with measurements using the normoxic PAG gel dosimeter and compared against dose planes from the TPS. The gel dosimeter measurements indicate that the TPS is overestimating the secondary build-up in the lung tumour peripheral region. It has been determined that in lung tumours, 6 MV FFF is the optimal beam energy for peripheral dose coverage and that there is a dosimetric compromise using 10 MV FFF.
RSC: Optically stimulated emission of LiF:Mg, Cu, P - towards 3D optically stimulated luminescence dosimetry
Authors: Nyemann Jacob S , Jensen Mads L , Nielsen Camilla L , Bondgård Karl-Emil T , Turtos Rosana M , Julsgaard Brian , Petersen Jørgen B B , Muren Ludvig P , Balling Peter 
Published: January 2022

Abstract
With the introduction of highly conformal treatment modalities, dose verification in 3D is becoming more important than ever for patient-specific quality assurance of radiotherapy. Reusability of 3D dosimeters may be the path to cope with the cost-benefit issues caused by batch-to-batch fluctuations and intense calibration protocols in existing 3D systems. We present the idea of an envisioned (optically stimulated luminescence) OSL-based 3D readout system, which exploits the inherently reusable dosimetry properties of OSL. We provide the emission spectra of the OSL active material LiF:Mg, Cu, P (MCP) for three stimulation wavelengths (460 nm, 532 nm, and 664 nm), and summarize recently published optical characterization results to highlight the requirements of a readout system for an MCP-based dosimeter.
International Conference on Electronic and Advanced Materials 2021
Authors: Saud Norainiza , Bakar Sharifah Shahnaz Syed , Ramli Mohd Izrul Izwan , Nazeri Muhammad Firdaus Mohd 
Published: January 2022

The International Conference on Electronic and Advanced Materials (ICEAM) 2021 was held online on the 2nd November 2021 streaming from Digital Management and Development Centre, Universiti Malaysia Perlis (UniMAP). This conference was organized by the Center of Excellence Geopolymer & Green Technology (CEGeoGTech), UniMAP, with Tin Solder Technology Research Group (TSTRG) MALAYSIA and The Electronics Packaging Research Society (EPRS) as co-organizer. The conference was also sponsored by the Tin Industry (R&D) Board. The conference was held virtually in order to follow the standard operation procedure imposed by the Malaysian government due to the COVID-19 movement control order and travel restriction.
The conference serves as the best platform for sharing and exchanging ideas on Advanced Materials topics such as electronic materials, ceramic materials and geopolymer, bio-materials, polymers and rubber, and composites. The conference aimed to provide a platform for scientists, researchers, and students in the related field to discuss current research progress, challenges, and practical solutions that could be adopted to keep abreast in this challenging world. The program provides networking and cooperation amongst academicians, scientists, researchers, and engineers from the materials sciences and engineering fields.
List of Organizing committee is available in this pdf.
High Entropy Alloy Towards Functional Materials Application: A Review
Authors: Nadzri Nur Izzati Muhammad , Khemar Athirah , Wahab Juyana A. , Mahat Mohd Muzamir 
Published: January 2022

Abstract
The development of high entropy alloy (HEA) is considered a new bloom area as the ideas open up the exploration of a various multi-disciplinary elements in many applications. It is a novel class of complex materials identified in the phase diagram’s core, and they have been proven to outperform typical alloys. This HEA involves mixing unique combinations of mechanical and functional qualities across an infinite space of alloy composition. It is also possessing a great understanding of the thermodynamic behaviour of the materials which influence greatly their physical and mechanical properties. This review paper aims to open up the possibilities of developing HEA on different types of approaches based on the core effects which is very useful in functional materials applications especially in semiconductor, refractory and many more. However, to obtain this material, the most crucial thing is to understand and develop their synthesising routes of HEA production, which has become the main priority. Therefore, this paper focuses on discussing different metal elements that are commonly used in HEA and few fabrication routes on HEA. Some of recent functional materials based HEA is introduced by their enhance properties compare to conventional alloys.
Factors of Controlling the Formation of Titanium Dioxide (TiO2) Synthesized using Sol-gel Method – A Short Review
Authors: Abdul Razak K. , Che Halin D. S. , Abdullah M. M. A. , Mohd Salleh M. A. A. , Mahmed N. , Azani A. , Chobpattana V. 
Published: January 2022

Abstract
There have been experiments on TiO2 thin films synthesized utilizing sol-gel techniques. The sol-gel method is a straightforward technology that gives numerous benefits to the researcher, for instance, material’s reliability, reproducibility, and controllability. Following from there, it can be utilized to make high-quality nano-structured thin films. According to previous studies, the TiO2 films’ characteristics occur to be highly dependent on the production parameters and initial materials utilized. Controlling the formation of TiO2 thin films with the sol-gel method was momentarily discussed here.
Chemical kinetics modelling for the effect of chimney on diffusion flame in carbon nanotubes synthesis
Authors: Wong Hock Yee , Ho Cheng How , Ho Jee Hou 
Published: January 2022

Abstract
Hydrocarbon flame syntheses of carbon nanotubes (CNTs) on various metals substrates have been reported in literature, but existing methods are limited to usage of high melting temperature metals substrate due to excessive temperature in conventional diffusion flame burner. To address this limitation, high thermal conductivity chimney is innovatively incorporated into the diffusion burner to control flame temperature. Hence, the aim of this study is to investigate the effect of chimney application on the interaction behaviour between flame temperature profile and concentration of post combustion species distribution of diffusion flame. In this study, Computational Fluids Dynamics (CFD) – Chemical Kinetics (Chemkin) coupling method is used to simulate the flow and transport behaviour of laminar methane-air mixture in combustion environment. Kinetics mechanism is imported into species transport model of Chemkin simulator to further determine the reaction between combustion species and temperature profile of the mixture. Heat transfer models are then integrated into simulation to investigate the cooling effect of chimney during the combustion of methane-air mixture. This numerical simulation study provides insights on effects of chimney application towards cooling, species concentration, flame temperature and paving path for controlling the growth of CNT on low melting temperature metal substrate. Numerical models show improvement in temperature controls, increase in gas species concentration and in surface area that is favourable for growth of CNTs.
Conjugated Polymer of Biosensor using Langmuir-Blodgett Technique-A Review
Authors: Mohd Najib Nur ’Izqa Ashiqin , Othman Siti Amira 
Published: January 2022

Abstract
Health biosensor applications based on conjugated polymer have gained many attentions among researchers because of their sensitivity, selectivity, and linearity. Polyaniline, a conjugated conducting polymer has been explored since the early 1960s and one of the famous conducting polymer compare to the others like polypyrrole, polyacetylene, and polythiophene. Polyaniline is ease to synthesis, high conductivity, and good performance in biosensor applications. A doping approach using protonic acids (hydrocholoric acid, 4-dodecylbenzenesulfonic acid, sulfuric acid, phosphoric acid) can be utilized to increase solubility and induce fusibility of the stiff chain of the polymer. A structural modification of doping process could make the polymer become high conductivity and it was universally agreed about that. Polyaniline presents ionic conductivity and electronic combinations that make bio-interfaces exist among the other electrochemical applications. The conductivity of the polymer is one of the promising materials that may be used to improve the analytical properties of sensors. Langmuir-Blodgett technique is a method to produce thin film of polyaniline therefore the conductivity of polyaniline can be measured using four-point probe device.
Effects of molding temperature on delamination of small outline transistor (SOT)
Authors: Badarisman A. H. , Abdul Hamid K. , Ideris H. , Abu Bakar M. , Jalar A. 
Published: January 2022

Abstract
Delamination of small outline transistor (SOT) package has been a challenge to ensure good package reliability. Molding process parameter optimization is a practical & cost-effective alternative to reduce delamination of the plastic package. First, selective molding parameters, namely, molding temperature, transfer speed, transfer pressure & pre-heat temperature was varied in a full factorial experiment to determine the significance of each factor. It is observed from the complete factorial analysis that molding temperature was the most significant factor concerning delamination. Next, one factor at a time (OFAT) experimental design was conducted to confirm moulding temperature’s repeatability. Molding temperature was identified as a significant factor on determining the delamination response of SOT packages. Lowering mold temperature resulting to lower percentage of delamination however proven to have an adverse on package curing density.
The effect of ethylene glycol post-treatment on the electrical conductivity of PEDOT: PSS thin films
Authors: Asri Nurul Ain Najihah , Amir Baharin , Ramli Anis Syahirah , Safian Muhd Fauzi , Zakaria Azlan , Jani Nur Aimi , Mahat Mohd Muzamir 
Published: January 2022

AbstractThe application of organic conducting polymers such as poly (3,4-ethylene dioxythiophene): poly (4-styrene sulfonate) (PEDOT: PSS) is vastly expanding for the development of advanced and flexible organic electronic devices, such as solar cells, light-emitting diodes, and organic electrochemical transistors (OECTs). Also, PEDOT: PSS can perfectly replace high-cost Indium tin oxide (ITO) thin films. In this study, PEDOT: PSS was synthesizedviathe chemical oxidative polymerization method. The film formation was carried out through a feasible drop-casting method onto a cleaned glass substrate. To further enhance the conductivity of pristine PEDOT: PSS, the PEDOT: PSS thin films were post-treated with different concentrations (3, 5, and 7% v/v) of ethylene glycol (EG). Based on the electrochemical impedance spectroscopy (EIS) analysis, it was revealed that the post-treated sample had a higher conductivity value compared to the untreated sample (2.48 × 10-4S/cm), with the highest recorded conductivity value of 2.67 ×10-3S/cm at 5% v/v of EG. This result corresponds to the previous study, which highlighted that the optimum concentration of EG is 5% v/v to achieve the optimum conductivity value for thin film application. Furthermore, the structural properties of the thin films were characterized using Fourier transform infrared (FTIR) spectroscopy to confirm the presence of PEDOT: PSS and EG in the samples.
E-book Retrieval and Reading System Based on Brain-Computer Interface
Authors: Su Xiaomeng , Fengshengwang  , Pingtan  
Published: January 2022

Abstract
In this paper, we propose a real-time Brain-computer interface(BCI) system that ena bles paralyzed patients to read e-books on the website autonomously according to their intentio ns. The system uses EMOTIV EPOC+ to acquire the user’s electroencephalography(EEG) sign als and decodes them to analyze the user’s real intention when reading e-books on the website, i ncluding selecting and reading operations. The system uses the VRPN communication protocol to transmit the EEG data processing results to the PC to realize the corresponding operations o n the web page. To verify its performance, we have carried out experiment on 5 subjects. And t he result shows that after short-term training, the accuracy of the system can reach more than 9 0%.
Single Document Extractive Summarization Model Based on Heterogeneous Graph Transformer
Authors: Gan Ling , He Peng 
Published: January 2022

Abstract
At present, the graph model-based summary model has problems such as insufficient semantic fusion between nodes and lack of location information. Therefore, this paper proposes a single-document extraction text summary model based on a heterogeneous graph attention neural network, using HGT (Heterogeneous Graph Transformer), Heterogeneous Graph Attention Neural Network to solve the defect of insufficient deep semantic fusion of nodes, and use trainable position coding to solve the defect of missing position information. Experiments show that the model in this paper has improved on the three evaluation indicators of R_1, R_2 and R_L, and the abstracts extracted have better generality.
Dense Pedestrian Detection Based on YOLO-V4 Network Reconstruction and CIoU Loss Optimization
Authors: Zhang Guoliang , Du Zexu , Lu Weijiang , Meng Xiaoyan 
Published: January 2022

Abstract
Online object detection is a fundamental problem in time-critical video analysis applications. Due to the performance limitations of one-stage object detection algorithms in dense pedestrian occlusion, we have improved YOLO-V4 in this paper, including network structure optimization, more efficient multi-scale feature fusion strategy formulation, and more specialized network loss function design. First, a single-output YOLO-V4 network structure is proposed, which integrates image information from multiple scales through the designed ladder fusion strategy. This not only ensures that the aspect ratio estimation of anchors is still driven by training data but also solves the invalid anchor distribution problem of the original network for objects with approximate size. Second, we adjust the resolution ratio of the network output feature map to the original input image to reduce the label rewriting cases of training samples. Finally, the concept of repulsive force is introduced to optimize the bounding box regression loss function, which improves the robustness of the model to the detection of densely occluded pedestrians, and enhances the practical value of YOLO-V4 in actual application scenarios.
Few Shot POP Chinese Font Style Transfer using CycleGAN
Authors: Yu Yong 
Published: January 2022

Abstract
The new style design of Chinese fonts is an arduous task, because there are many types of commonly used Chinese characters and the composition of Chinese characters is complicated. Therefore, the style transfer of Chinese characters based on GAN has become a research hotspot in the past two years. This line of re-search is dedicated to using a small number of artificially designed new style fonts and learning the map-ping from the source font style domain to the target style domain. However, such methods have two problems: 1. The performance on pop (point of purchase) fonts with exaggerated and random style is not satisfying. 2. Plentiful manually designed fonts are still required. In order to solve the above problems, we propose a few-shot font style transfer model based on CycleGAN. It uses meta-knowledge to reduce the use of manually designed fonts and enables each character to fully learn the knowledge contained in all new style fonts to achieve satisfying pop font style transfer effect. We also construct a dataset based on commonly used 3500 Chinese characters and verify the effectiveness of our model.
SOMC:A Object-Level Data Augmentation for Sea Surface Object Detection
Authors: Hu Jianqiu , He Jiazhou , Jiang Pan , Yin Yuwei 
Published: January 2022

Abstract
The deep learning model is a data-driven model and more high-quality data will bring it better results. In the task of Unmanned Surface Vessel’s object detection based on optical images or videos, the object is sparser than the target in the natural scene. The current datasets of sea scenes often have some disadvantages such as high image acquisition costs, wide range of changes in object size, imbalance in the number of different objects and so on, which limit the generalization of the model for the detection of sea surface objects. In order to solve problems of insufficient scene and poor effect in current sea surface object detection, an object-level data augmentation for sea surface objects called SOMC is proposed. According to the different scenarios faced by the USV when performing autonomous obstacle avoidance, patrol and other tasks, SOMC generates suitable scenarios by mixing and copying targets conveniently, providing the possibility of unlimited expansion of the sea surface object. The experiment selected images in the video taken by the camera on top of the USV. A sufficient amount of comparative experiment prove that the SOMC integrates with existing excellent data augmentations and achieved an improvement in the detection effect, which proves the effectiveness and practicability of the SOMC in the perception task of the USV.
A Q-matrix Model Based on Binarized Neural Network
Authors: Li Jingjiang , Jiang Chong , Ye Shiwei 
Published: January 2022

Abstract
Q-matrix theory plays an important role in the field of cognitive diagnosis assessment. It is time-consuming and laborious for experts to define Q-matrix from a large number of data. In order to deal with this problem, this article comes up with a Q- matrix generation model based on binarized neural network. We combine the neural network with the Boolean operation relations of Q-matrix, A matrix and R matrix in item response theory, so that the model can mine Q-matrix more effectively.
Towards Electricity Price and Electric Load Forecasting Using Multi-task Deep Learning
Authors: Liu Yali , Chai Tingting , Zhang Zhaoxin , Long Gang 
Published: January 2022

Abstract
The continuous development of the power Internet of Things (IOT) has enabled power market participants to obtain a large amount of data. Simultaneously, the power IOT has an increasing demand for power load and electricity price forecasting; Since the forecasting of electricity load and electricity price is a single task, and the model calculation accuracy is not high, this brings great challenges to the accurate forecasting of electricity load and electricity price. In this paper, two power load and electricity price forecasting models via multi-task deep learning are established perform high-precision joint forecasting of power load and electricity price Experimental results demonstrate that the prediction results of the proposed deep learning models are superior to the other compared approaches in terms of the main task and the auxiliary task, and show superior prediction performance, verifying the practicability and superiority of the power load and electricity price multi-task forecasting model.
A Survey of Entity Alignment of Knowledge Graph Based on Embedded Representation
Authors: Huang Jing , Wang Jiaqi , Li Yahui , Zhao Wenbin 
Published: January 2022

AbstractThis paper summarizes the main methods of knowledge representation learning. Representation learning represents the entity information of the knowledge graph as a low dimensional vector. Its vector dimension is low, which helps to improve the computational efficiency and make full use of the semantic information between entities. In order to embed two KGs into a unified semantic space, joint embedding is used to achieve this goal. With the development of research, there are many improved embedding methods, such as iteration, multi view embedding, knowledge graph entity alignment based on graph neural network and so on.
Short-term Traffic Flow Forecast on Basis of PCA- Interval Type-2 Fuzzy System
Authors: Qu Zhengyang , Li Jun 
Published: January 2022

Abstract
According to the time series of urban traffic flow, a prediction method on basis of type-2 fuzzy logic is proposed under the theoretical framework of fuzzy logic. The prediction model of the interval type-2 fuzzy system is established, and the BP algorithm is used to adjust the coefficients of the antecedent and consequent of fuzzy rules. In this paper, the algorithm is verified by the measured data of road network, and compared with other fuzzy methods, BP algorithm and support vector machine (SVM) According to the experimental data that type-2 fuzzy logic system has higher prediction accuracy.