bluuebunny/crossref_metadata_2025_split huggingface dataset:
Development of dosimetric procedures for experimental ultra-high dose rate irradiation at a clinical linear accelerator
Authors: Konradsson E , Petersson K , Adrian G , Lempart M , Blad B , Ceberg S , Knöös T , Bäck S Å J , Ceberg C 
Published: January 2022

Abstract
As radiotherapy using ultra-high dose rates has gained new interest, the dosimetric challenges arising at these conditions needs to be addressed. Ionization chambers suffer from a large decrease in ion collection efficiency due to ion recombination, making on-line dosimetry difficult. In this work we present experimental setups and dosimetric procedures for FLASH irradiation of cells, zebrafish embryos and small animals using a 10 MeV electron beam at a modified clinical linear accelerator, and describe the dosimetric steps required to initiate clinical trials. The dosimetric equipment used for our pre-clinical experiments consisted of radiochromic film, thermoluminescent dosimeters, a Farmer-type ionization chamber and phantom material mimicking the experimental setup for irradiation. In preparation for small animal irradiation, dose profiles and depth dose curves were measured for all collimator sizes. The average dose rates were ≥620 Gy/s, ≥640 Gy/s and ≥400 Gy/s for cells, zebrafish embryos and small animals, respectively.
Dose rate effects in radiochromic leuco crystal violet dosimeters
Authors: Jordan Kevin 
Published: January 2022

Abstract
Radiochromic dosimeters based on leuco crystal violet (LCV) in aqueous solutions and gelatin hydrogels were evaluated for dose and dose rate performance. Optical transmission measurements were performed on samples in custom 10 cm long cuvettes in order to have measurable signals below 10 Gy. Standard gel formulations with 0.7 mM concentration of anionic surfactant sodium dodecyl sulfate (SDS) had a dose rate effect of increasing sensitivity with increasing dose rate of nearly 15% from 1 to 10 Gy/min at 20 Gy. Solutions of LCV and trichloracetic acid also showed similar dose rate effects. Adding LCV increased dose rate effect. Adding SDS, lowered sensitivity but maintained dose rate effect. Adding 0.4% gelatin also lowered sensitivity but reversed dose rate effect. For gels, lowering the pH reduced both sensitivity and dose rate effect. These trends are consistent with LCV solubility contributing to dose rate effects. These data indicate there are several factors related to dose rate effects in leuco crystal violet dosimeters and formulations designed to maximize sensitivity may not minimize dose rate effects.
On the feasibility of using an optical fiber Bragg grating array for multi-point dose measurements in radiation therapy
Authors: Lebel-Cormier Marie-Anne , Boilard Tommy , Bernier Martin , Beaulieu Luc 
Published: January 2022

Abstract
Fiber Bragg gratings (FBGs) have proven to be a valuable dosimeter in nuclear environment where radiation doses reach up to a hundred of kiloGray (kGy). Multiple FBGs can be written in a single fiber to allow multi-point detection which would prove very useful for radiotherapy dosimetry. The purpose here is to adapt this already existing technology to provide a novel dosimeter for radiotherapy measurements. The proposed real-time dosimeter consists of twenty 4 mm-long FBGs, equally distributed over 20 cm. FBGs are written through the coating of a standard polyimide-coated silica fiber with the phase-mask technique and femtosecond pulses. The wavelength dependant variation of each FBG is recorded at 1 kHz with a commercially available interrogator. The use of gamma radiation (clinical radiotherapy accelerator) induces a linear shift (0.070 ± 0.006 pm/Gy) of the FBG’s reflected wavelength, which is independent of the dose rates (2.8-11.6 Gy/min) and the energy (6-23 MV). A statistical error of 0.03 pm is obtained on data points therefore limiting the detectable dose to 0.4 Gy. A dose profile of 6 and 23 MV radiotherapy accelerator is also measured. The presented FBGs dosimeter allows for real-time dose measurement in 2D and the small size of its detector makes it a versatile tool. The length and spacing of FBGs can be easily modified to increase both the spatial resolution and the amount of dose point.
Three-dimensional reproducibility assessment of radiochromic ClearView™ gel in custom vessel
Authors: Jordan Kevin , Xu Lu , Barnett Rob 
Published: January 2022

Abstract
Samples of the radiochromic hydrogel ClearView™ were characterized for 3D dosimeter performance in custom vessels and read with a modified Vista16 optical cone beam scanner as part of a small field dosimetry study. Uniform dose responses and small field depth doses revealed each sample was unique. As the gels aged over several weeks at 4 °C, the dose sensitivity decreased. Reconstructions of uniform dose distributions revealed unique features near the vessel interface. The central sub-volumes ranged from uniformity to within 2% throughout the sub-volume to a 12% gradient from base to top of gel. Comparisons of small field depth doses revealed that if the central volume had a uniform dose response, no correction was required. However, if a gradient in sensitivity from bottom to top was measured a correction may or may not be required. It is suspected that the filling protocol of the custom vessels is a factor in the observed variations in 3D dose response.
Marker trajectory assessment in optical cone beam computed tomography scanner geometry
Authors: Marks Gareth , Jaryd Christie , Jordan Kevin 
Published: January 2022

Abstract
Measured trajectories of markers on vessel wall and beads on a plumb are compared with calculated trajectories for transmission images obtained with Vista16™ optical cone beam scanner. Effective pixel size was 0.11 mm. An algorithm was developed to measure trajectories for the plumb line phantom and a semi-automatic version was required for the gel filled vessels due to overlap of markers and seam projections at maximum lateral positions. It was found that for 15 cm diameter vessels, the vessel edge was not visible due to divergence in cone beam geometry. Accounting for this effect was necessary in order to remove artefacts from calculated vessel trajectory. Displacement differences were found to be less than 0.1 mm for the plumb line, 0.3 mm for a beads and 1 mm for markers on gel filled vessel. Minimization of the vessel marker trajectory deviation will provide a more accurate approach to refractive index optimization. No optical aberrations were detected from the trajectory analysis and manufacturer scanner alignment and geometrical calibrations were independently verified.
Development of a novel proton CT system using a 3D scintillator detector
Authors: Beddar S , Darne C D , Robertson D G 
Published: January 2022

Abstract
The goal of this work is to develop a novel proton computed tomography (CT) system using a 3-dimensional (3D) scintillator detector. The detector design of the pCT system consists of a 3D scintillator that generates scintillation light in response to the incident proton beams. A scientific charge-coupled device (CCD) camera placed along the beam axis captures a 2D projection or radiograph of the resulting 3D scintillation light signal. A set of CCD cameras placed laterally to the beam axis capture two unique depth dose distributions of the beam. These additional beam images can be used to improve the resolution of the proton radiograph. In this study, we demonstrate the feasibility of this unique imaging system design for proton radiography using a 3D liquid scintillator detector.
Development of photonic detector system for ultra-fast beam diagnostics in proton radiotherapy: the proof of concept
Authors: Iakovenko Viktor , Jaffray David A. 
Published: January 2022

Abstract
A concept of a photonic detector system for proton beam and Bragg peak position measurements in proton radiation therapy is presented. An approach of using scintillator plates with ultra-fast timing characteristics to detect the temporal fine structure of the beam is described. A detector module is made of a 10 × 10 cm2 plastic scintillator plate with 1mm thickness. The light is collected on the corners of a plate by the optical fibers of pre-defined length, which introduce various known time delays. Using the Anger algorithm, the lateral position of the proton pencil beam traversing scintillator plate is reconstructed. We propose two applications of the system: thin single-plate beam position monitor and multi-plate stack quality control device to measure lateral beam position and relative position of the Bragg peak.
Utilizing Pencil Beam Scan Dynamics and a Scintillation Screen to produce 3D Dose Distribution of Proton Beams
Authors: Rahman Mahbubur , Bruza Petr , Gladstone David J. , Zhang Rongxiao , Pogue Brian W. 
Published: January 2022

Abstract
This study describes a method for producing volumetric dose profiles of proton beams from 2D slices of optical scintillation images. The method relies on a high frame rate camera acquisition (100 frames per second), the spot scanning capabilities of current proton pencil beam scanning systems, and a water equivalent scintillation screen. The acquired slices are corrected for optical blurring and ionization quenching and stacked to produce volumetric dose distribution. The volumetric optical dose profile had a pass rate of 98.3% for 2%/2mm local gamma analysis, suggesting the method can accurately measure dose profiles. The method can potentially image all clinical proton beams for pencil beam scanning systems and can extend to imaging patient plans, with further verification.
Effects of Organic Solvent Doping on the Structural and Conductivity Properties of PEDOT: PSS Fabric
Authors: Ayub Ayu Natasha , Ismail Nur Atikah , Aizamddin Muhammad Faiz , Bonnia Noor Najmi , Sulaiman Noor Safina , Nadzri Nur Izzati Muhammad , Mahat Mohd Muzamir 
Published: January 2022

Abstract
The development of highly conductive fabrics for application in electronic textiles on non-rigid substrates has gained much interest owing to their potential for realizing next-generation wearable conductive fabrics. Besides, it could be used as a portable device in the energy and healthcare industries. However, pristine PEDOT: PSS has been reported to have low conductivity values due to excessive PSS chains that wrapped up around the PEDOT conductive chain structure. In this context, one of the most successful techniques to include dopant is to use ethylene glycol (EG) to improve the conductivity of poly (3,4-ethylenedioxythiophene): poly (styrene sulfonate) (PEDOT: PSS). Immersing or coating of fabrics with PEDOT: PSS is one of the facile methods used for obtaining functional and smart properties. Our studies prepared conductive polymer; PEDOT: PSS with ethylene glycol (EG) dopant) at different concentrations on polyester (PES) fabric substrates by a facile immersion process. The effect of the different concentrations of EG on the conductivity is shown when Electrochemical Impedance Spectroscopy (EIS) is conducted. It shows that 6% v/v of EG gives the optimum conductivity value up to 4.06 × 10-3 Scm-1. Meanwhile, Scanning Electron Microscopy (SEM) imaging is focusing on the morphology of the immersed fabric. The improvement in crystallinity of the doped PEDOT: PSS was revealed and evaluated using X-ray Diffraction (XRD). As a result, it has been demonstrated that EG is an excellent dopant because it efficiently increases the electrical conductivity and crystallinity of PEDOT: PSS fabric.
Effect of pre-twinning on microstructure and texture evolution in room and cryogenically deformed Mg-0.5Ca alloy
Authors: Chaudry Umer Masood , Noh Yeonju , Han Gukin , Hamad Kotiba , Jun Tea-Sung 
Published: January 2022

Abstract
Introducing {10-12} tension twin lamellas has proved to be the most effective strategy to enhance the performance of Mg alloys. Twins can provide the preferential nucleation sites for recrystallization, hence twinning induced grain refinement can result in higher strength material based on Hall-Petch Effect. In addition, twinning can also modify the crystallographic c-axis distribution in wrought Mg alloys resulting in lower plastic anisotropy. Accordingly, in the present work, the twinning evolution in the Mg-0.5Ca (wt.%) alloy at room and cryogenic temperature was critically investigated. The samples were subjected to pre-compression (5%) at room temperature and -150 °C at the strain rate of 10-3 s-1. The microstructural and textural characterization was carried out using OM, SEM and EBSD to emphasize the twinning evolution during compression at room and low temperatures. The local lattice distortion and the localized deformation in terms of Kernel average misorientation (KAM) was also studied. The results revealed the significant grain refinement (13.7 μm 5.61 μm and 6.02 μm of uncompressed and RT, - 150 °C pre-compressed, respectively), where slightly enhanced twinning fraction in the sample compressed at RT was observed. Finally, remarkable increase in the yield strength was noticed in the pre-twinned samples, which was attributed to the twinning-induced grain refinement based on the Hall-Petch relationship.
Influence of Fly Ash Geopolymer Ceramic Powder Addition on Sn-3.0Ag-0.5Cu Solder Joints
Authors: Somidin Flora , Natasha Ilias Nur Fatin , Mohamad Zaimi Nur Syahirah , Izwan Ramli Mohd Izrul , Abdul Razak Nurul Razliana 
Published: January 2022

Abstract
This paper reports the effect of 1.0 wt.% fly ash (FA) geopolymer ceramic powder addition in Sn-3.0Ag-0.5Cu (SAC305, in wt.%) solder joint. Powder metallurgy route was used to fabricate the new composite solder. Solder balls were formed from the new composite solder and reflowed on Cu substrate. The effect of FA as ceramic reinforcement on the bulk microstructure and the interfacial intermetallic compound layer formation of solder joints were investigated under optical microscope. Microstructure observation showed that the β-Sn dendrite size was refined in SAC305-1.0FA/Cu bulk solder joint sample than that in the non-reinforced sample. The addition of FA geopolymer ceramic powder into the solder matrix also produced a thinner intermetallic compound layer.
Preparation and Characterization of Irradiated Bioplastic from Cassava Peel – A Review
Authors: Nee Fong Chui , Othman Siti Amira 
Published: January 2022

Abstract
The conventional plastic bears unavoidable responsibility for the massive scale generation of garbage. The bulk of the market is mostly made up of fossil-based plastics and the worldwide environmental pollution generated by them is growing increasingly problematic. Agro-waste product is probably a good way to make bioplastic. In fact, biodegradable plastic is indeed a good alternative to replace the petroleum-based plastics. Besides, making good use of agriculture waste can assist in diminishing waste accumulation. Cassava peel is a type of waste from cultivating activities that has the potential in making bio-based plastics. This research will focus on the preparation and characterization of bioplastic from cassava peel. Several related characterization methods such as XRD, SEM, FTIR, TGA will be discussed. The main purpose of this review is to fabricate and produce bioplastic samples from cassava peel with the addition of different ratios of sorbitol and chitosan.
Fast Recognition of Pig Faces Based on Improved Yolov3
Authors: Li Guangbo , Jiao Jun , Shi Guolong , Ma Huimin , Gu Lichuan , Tao Liang 
Published: January 2022

Abstract
The traditional ear labels are easy to get off and cause infection in the intelligent managements of live pigs. Therefore, a noninvasive pig face recognition method based on improved YOLOv3 was designed to carry out simultaneous recognition of multiple live pigs.The YOLOv3 model was used to introduce the dense network into the Darknet53 feature extractor, forming a new backbone network in combination with down-sampling, and the improved SPP unit was added to the YOLOv3 model, constructing the YOLOv3_DB_SPP model. The pig face data set used in the test was divided into 10 categories. After data enhancement, the number of samples was 8 512, and the ratio of training set to test set was about 9:1. The test results showed that: 1) Under different classification probability thresholds, the mean average precision of the YOLOv3_DB_SPP model for detecting pig face data set was higher than that of the YOLOv3 model; 2) When the IOU threshold was 0.5 and the classification probability threshold was 0.1, the mean average precision of YOLOv3_DB_SPP model was 9.87% higher than that of the YOLOv3 model; 3) When detecting long-distance covered small target samples, the mean average precision was also higher in the YOLOv3_DB_SPP model. Thus, the YOLOv3_DB_SPP model improved the feature extraction ability of the basic feature extractor and the precision of the detector.
Research on Safety Helmet Detection Algorithm of Power Workers Based on Improved YOLOv5
Authors: Fu Desu , Gao Lin , Hu Tao , Wang Shukun , Liu Wei 
Published: January 2022

Abstract
The traditional helmet detection algorithm in power industry has low precision and poor robustness. In response to this problem, the helmet detection algorithm based on improved YOLOv5 (You only look once) is put forward in this paper. Firstly, the YOLOv5 network structure is improved. By increasing the size of the feature map, one scale is added to the original three scales, and the added 160*160 feature map can be used for the detection of small targets; Secondly, the K-means is used for re-clustering the helmet data set to get more suitable priori anchor boxes. The experimental results illustrate that the average accuracy of the improved YOLOv5 algorithm is increased by 2.9% and reaching 95% compared with the initial model, and the accuracy of helmet recognition is increased by 2.4% and reaching 94.6%. This algorithm reduces the rates of missing detection and misdetection of small target detection in original network, and has strong practicability and advanced nature. It can satisfy the requirements of real-time detection and has a certain role in promoting the safety of power industry.
Research on Facial Expression Recognition Based on Improved Inception Network
Authors: Yang Chao , Gong Weibin 
Published: January 2022

Abstract
Facial expression recognition is one of the hot research fields in computer vision. Deep learning method is widely used due to its excellent performance in feature characterization. However, convolutional neural network, which is commonly used in the field of image classification, is prone to challenges such as too many parameters, difficulty in training and too many network layers. We proposed an improved inception network to solve the above. In this model, small-scale filter was used to replace large-scale one and multi-channel features were extracted. In addition, a smaller stochastic gradient value introduced in the ReLU makes the weights of some neurons be able to update. AM-softmax is adopted as the loss function with respect of the issue that the large intra-class distance about expression features. The recognition rates of the proposed model on CK+ and FER2013 are 98.32% and 71.12%, respectively. The experimental results verify the effectiveness of the proposed algorithm.
Similarity-Based Echo State Network for Remaining Useful Life Prediction
Authors: Wang Hao , Chen Yuzhi , Zhao Hongming , Cui Shitong , Zhao Xudong 
Published: January 2022

Abstract
The health state of aero engines will affect economic benefits, availability, and safety of aircrafts. Fault prognosis is considered to be one of useful methods to evaluate the health state of aero engines and avoid unscheduled down time. Prognosis is the ability to forecast the evolution of engine deterioration. The two major objectives of prognosis are: forecasting the impending failures and predicting the engines’ remaining useful life (RUL). This paper proposes a method that combines echo state network (ESN) with similarity-based methods for RUL prediction. First, principal component analysis (PCA) is used to pre-process data to obtain degradation trajectories, which handles the fusion problem of multiple data. Second, in order to use historical information accurately, the Euclidean distance is applied to calculate the similarity among different degradation trajectories. Third, the trajectory with the highest similarity is used in the ESN training process. Then, a numerical simulation on the NASA C-MAPSS dataset verifies the accuracy of the method. Finally, the comparison with other methods shows the superiority of the method.
Few-Shot Image Classification Based on Ensemble Metric Learning
Authors: Wang Hang , Chen Duanbing 
Published: January 2022

Abstract
In the case of few labelled image data samples, image classification is a difficult challenge, which is called few-shot image classification. Recently, many methods based on metric learning have been proposed. Most of these methods mainly focus on the representations of global image-level features or local feature-level descriptors. However, these methods calculate similarity from a single metric learning perspective. Motivated by ensemble learning, a novel Ensemble Metric Learning (EML) method for few-shot image classification is proposed, which not only utilizes label propagation, but also considers image-level and local feature-level descriptor metrics. The experimental results show that the proposed method can effectively improve the classification accuracy by ensemble learning.
A Light Model for Super-Resolution of Remote Sensing Images
Authors: Tingting Song 
Published: January 2022

Abstract
Remote sensing image super-resolution is an important research topic, which helps to improve the quality of remote sensing images. However, because remote sensing images are usually quite large while the satellite equipment often have low computing capacity and small memory, it is appealing to develop lightweight and fast models to perform the super-resolution task for the remote sensing images. In this paper, we empirically study the effectiveness of conventional super-resolution approaches for remote sensing images, and propose an effective way to reduce the model parameters and computational cost. Specifically, motivated by Res2Net, we design a new multi-scale hierarchy residual block to replace the ResBlock in EDSR to provide a more diverse receptive field for each residual block. The proposed modified method has fewer parameters and faster speed compared with the original EDSR. Moreover, we also build two benchmark super-resolution datasets (i.e., DOTA-SR and LEVIR-SR) from DOTA and LEVIR-CD, respectively, for experimental evaluation. We perform experiments on the two datasets, and results show that our method is light and have comparable performance over the existing super-resolution baselines.
Teacher-Student Based Domain Adaptation for Person Re-identification
Authors: Ding Jianpeng 
Published: January 2022

Abstract
In traditional deep Convolutional Neural Network (CNN) based person re-identification (Re-ID) methods, there must be thousands of training samples with annotated information under the same data distribution, in other words, the data used to train the CNN model is collected from the same scene. However, if we need to deploy a trained Re-ID model at the airport, but the data for training the model is collected on campus, the traditional methods will suffer considerable performance degradation because the data come from different distributions. It is a non-trivial task to straightforwardly re-collect sufficient annotations from the new scene, due to the high cost of time and human labor. Therefore, the domain adaptation paradigm is used to resolve the data distribution problem. Specifically, an efficacious method for domain adaptive person Re-ID based on the teacher-student framework is proposed in our research. Our method has been evaluated by numerous experiments and proved its effectiveness.