bluuebunny/crossref_metadata_2025_split huggingface dataset:
OCT in der Neuroophthalmologie
Authors: Naxer Sabine , Schittkowski Michael 
Published: May 2023

ZusammenfassungDie optische Kohärenztomografie (OCT) wird bei der Diagnostik retinaler und glaukomatöser Erkrankungen routinemäßig eingesetzt. Seitdem eine so hohe Auflösung möglich ist, dass die
einzelnen Netzhautschichten darstellbar und auch segmentierbar sind, hielt die OCT auch Einzug in die Neuroophthalmologie. Dieser Beitrag zeigt aktuelle und zukünftige Einsatzmöglichkeiten
in der Neuroophthalmologie und vermittelt Kenntnisse über mögliche Tücken.
Scalable and ultralow power silicon photonic two-dimensional phased array
Authors: Chalupnik Michelle , Singh Anshuman , Leatham James , Lončar Marko , Soltani Moe 
Published: May 2023

Photonic integrated circuit based optical phased arrays (PIC-OPAs) are emerging as promising programmable processors and spatial light modulators, combining the best of planar and free-space optics. Their implementation on silicon photonic platforms has been especially fruitful. Despite much progress in this field, demonstrating steerable two-dimensional (2D) OPAs that are scalable to a large number of array elements and operate with a single wavelength has proven a challenge. In addition, the phase shifters used in the array for programming the far-field beam are either power hungry or have a large footprint, preventing the implementation of large scale 2D arrays. Here, we demonstrate a two-dimensional silicon photonic phased array with high-speed (∼330 kHz) and ultralow power microresonator phase-shifters with a compact radius (∼3 µm) and 2π phase shift ability. Each phase-shifter consumes an average of ∼250 µW of static power for resonance alignment and ∼50 µW of power for far-field beamforming, a more than one order of magnitude improvement compared to prior OPA works based on waveguide-based thermo-optic phase shifters. Such PIC-OPA devices can enable a new generation of compact and scalable low power processors and sensors.
Directed chemical dimerisation enhances the antibacterial activity of the antimicrobial peptide MSI-78(4–20)
Authors: Li Rong , Handley Thomas N.G. , Li Wenyi , O’Brien-Simpson Neil M. , Hossain Mohammed Akhter , Wade John D. 
Published: May 2023

Antimicrobial resistance (AMR) is on the rise, leading to 700 000 deaths worldwide in 2020. Antimicrobial peptides (AMPs) are antibiotic agents that are active against multi-drug resistant pathogens and also have a reduced risk of AMR development. Previous studies have shown that dimerisation of the proline-rich antibacterial peptide (PrAMP) Chex1–Arg20 can enhance its antimicrobial activity while also reducing its toxicity. To determine if dimerisation via a simple disulfide bond can similarly improve other classes of AMPs, the α-helical cationic peptide MSI-78(4–20) was used as a model. The monomer alone, an S-carboxamidomethyl-capped N-terminal Cys–MSI-78(4–20) analogue and the disulfide-linked dimer were successfully synthesised and their antimicrobial activity and toxicity were determined. It was shown that dimerisation enhanced antimicrobial activity against the Gram-positive opportunistic pathogen Staphylococcus aureus ATCC 29213, the Gram-negative bacteria Escherichia coli ATCC 25922 and Pseudomonas aeruginosa ATCC 47615. The peptides showed no significant haemolytic activity with red blood cells and only induced 50% lactate dehydrogenase (LDH) release in mammalian cells at the highest tested concentration, 15 µM. The MSI-78(4–20) dimer was less cytotoxic than the monomer and S-alkyl monomer. Together, the data support the strategy of AMP chemically directed dimerisation as a means of producing potentially more therapeutically useful antimicrobial agents.
Diabetes in the Cook Islands: a clinical audit
Authors: Tepai Machaela , Nosa Vili , Herman Josephine , May Yin Yin , Kiadarbandsari Atefeh , Sluyter John 
Published: May 2023

Introduction The global burden of diabetes mellitus (diabetes) is significant and of increasing concern with more pregnant women being diagnosed with gestational diabetes mellitus (GDM). The Cook Islands face mounting pressures to address diabetes alongside competing population health needs and priorities. Cook Islands residents frequently travel to New Zealand to access health services. Inadequate information systems also make it difficult for countries to prioritise preventative measures for investment. In the absence of good data to inform effective diabetes preventative and treatment measures, people with diabetes are likely to progress to complications which will burden society and health systems in the Cook Islands and New Zealand. Aim To determine the prevalence of diabetes and prediabetes, and incidence of GDM, in the Cook Islands. Methods We analysed two Te Marae Ora Cook Islands Ministry of Health datasets, the Non‐Communicable Diseases (NCD) register examining demographic data for the period 1967 to December 2018 and same for the GDM register from January 2009 to December 2018. Results Of the 1270 diabetes cases, 53% were female and half were aged 45–64 years. There were 54 pre-diabetes cases and 146 GDM. Of the 20 GDM cases who developed type 2 diabetes, 80% were diagnosed before the age of 40 years. Data quality was poor. Discussion The Cook Islands diabetes registers provide important data to inform priorities for diabetes-related preventative and treatment measures. A data analyst has been employed to ensure quality, regularly audited data and information systems.
Characterization of PillarHall test chip structures using a reflectometry technique
Authors: Danilenko Aleksandr , Rastgou Masoud , Manoocheri Farshid , Kinnunen Jussi , Korpelainen Virpi , Lassila Antti , Ikonen Erkki 
Published: May 2023

Abstract
Thin film samples where one of the thin layers consists of a vacuum or air are called PillarHalls due to their support structure in silicon wafers. Custom PillarHall samples were provided by Chipmetrics Ltd and characterized by reflectometry with a Cary 7000 spectrometer. Data at 8° of angle of incidence were collected with p-polarization of the incident light within the wavelength range of 550–1800 nm. These data were then analyzed with a dedicated MATLAB code, using fitting software accompanying the transfer matrix method for calculation of the reflectance spectrum. Layer thicknesses and unknown refractive indices were chosen as fitted parameters. The oscillating reflectance spectrum of the PillarHall test chip yielded an air gap thickness of 86 nm with an estimated standard uncertainty of 5 nm. This is close to the nominal value of 100 nm. The results demonstrate that reflectometry data are sensitive to the thickness of the thin air layer deep inside the silicon structure.
An unsupervised transfer learning bearing fault diagnosis method based on depthwise separable convolution
Authors: Li Xueyi , Yuan Peng , Wang Xiangkai , Li Daiyou , Xie Zhijie , Kong Xiangwei 
Published: May 2023

Abstract
Bearings are an essential component of rotating mechanical equipment. Traditional signal processing-based fault diagnosis methods usually require a massive labeled data for training, but bearings generally operate in the equipment under normal fault-free conditions. This paper proposes an improved adaptive batch normalization (AdaBN) transfer learning bearing fault diagnosis method for batch normalization (BN) in traditional deep learning architecture. The AdaBN network preprocesses the raw vibration signals, and then the preprocessed features are input to a depthwise sparable convolution neural model for training. Features are extracted by depthwise convolution and point convolution in the network. AdaBN can freeze all the parameters in the network except the BN layer. Finally, a small amount of labeled data is classified using transfer learning methods. A laboratory data set was used for validation, and the experimental validation showed that the accuracy of the bearing fault diagnosis method using AdaBN reached 85%.
Fracture strength and failure mechanism of graphene-containing grain boundaries and pores
Authors: Wang Shuaiwei , Zhang Cun , Chen Shaohua , Peng Zhilong 
Published: May 2023

Abstract
Grain boundaries and pores commonly manifest in graphene sheets during experimental preparation. Additionally, pores have been intentionally incorporated into graphene to fulfill specific functions for various applications. However, how does the simultaneous presence of pores and grain boundaries impact the mechanical properties of graphene? This paper establishes uniaxial tension models of single-layer graphene-containing pores and three types of experimentally observed. The effect of interaction between pores and grain boundaries on the fracture strength of graphene was studied respectively for three types of grain boundaries by employing molecular dynamics simulations and considering factors such as pore size, the distance between pores and grain boundaries, and loading angle. A competitive mechanism between the intrinsic strength of pristine graphene with grain boundaries (referred to as pristine GGBs), which varies with the loading angle and the fracture strength of graphene sheets with pores that changes with the size of the pores, governs the fracture strength and failure modes of GGBs with pores. When the former exceeds the latter, the fracture strength of GGBs with pores primarily depends on the size of the pores, and fractures occur at the edges of the pores. Conversely, when the former is lower, the fracture strength of GGBs with pores relies on the loading angle and the distance between pores and grain boundaries, leading to grain boundary rupture. If the two strengths are comparable, the failure modes are influenced by the distance between pores and grain boundaries as well as the loading angle. The findings further elucidate the impact of coexisting grain boundaries and pores on the fracture behavior of graphene, providing valuable guidance for the precise design of graphene-based devices in the future.
Combined experimental and theoretical investigation on SONOS pFLASH switch
Authors: Wei Yidan , Liu Guozhu , Wei Jinghe , Liu Xudong , Liu Yong , Sun Jinping , Zhao Wei , Wei Yingqiang , Sui Zhiyuan , Liu Meijie 
Published: May 2023

Abstract
In this paper, a new push–pull pFLASH switch is designed and fabricated based on a 110 nm eFLASH process, which includes two 2 T-pFLASH transistors and one signal transmission transistor. The pFLASH transistors are programmed and erased by band-to-band tunneling-induced hot electron and Fowler–Nordheim tunneling to realize its ‘on/off’ function, and the current of the signal transmission transistor can be effectively tuned by the drain voltage of the 2 T-pFLASH. In order to clarify the degradation mechanism of the electronic properties, first principles calculations are performed from the atomic scale. Nitrogen vacancies have been proven to play a crucial role in nitrides. In addition, the effects of vacancies on the charge retention are investigated in terms of electronic structure. The nitrogen vacancies have proven to be detrimental to the electron storage by the simulated localization energies. Therefore, this study will provide a newly designed field-programmable gate array configuration unit, whose electrical mechanisms are revealed by theoretical simulations, which can also become the design foundation for future FLASH switches.
Depth-resolved attenuation coefficient estimation from optical coherence tomography data in case of incomplete signal attenuation in the imaging depth range
Authors: Moiseev Alexander , Potapov Arseniy , Sherstnev Evgeny , Gelikonov Grigory , Gelikonov Valentin , Sirotkina Marina , Shilyagin Pavel , Ksenofontov Sergey , Gladkova Natalia 
Published: May 2023

Abstract
Attenuation coefficient estimation from optical coherent tomography (OCT) data is one of the emerging methods for additional information extraction from the OCT data. With the reasonable assumptions of uniform proportion of the light, scattered backwards, relative to the light, scattered in all directions and the assumption of complete light attenuation within the imaging depth range, the attenuation coefficient can be estimated in every pixel of the OCT data volume, i.e. with the depth resolution. In the present paper the numerically effective method for lifting the second assumption was proposed. With numerical simulations and experiments it was shown, that the proposed method allows attenuation coefficient estimation even if OCT signal was not completely attenuated within the imaging depth range. Since the proposed method lifts one of the requirements for depth-resolved attenuation coefficient estimation, it allows the extension of the depth-resolved attenuation estimation method to the new applications.
Wheeled Robot Visual Odometer Based on Two-dimensional Iterative Closest Point Algorithm
Authors: Wang Chenggong , Li Gen , Wang Ruiqi , Li Lin 
Published: May 2023

Abstract
According to the two-dimensional motion characteristics of planar motion wheeled robot, the visual odometer was dimensionally reduced in this study. In the feature point matching part of visual odometer, the contour constraint was used to filter out the mismatched feature point pairs (abbreviated as FPP). This method could also filter out the matched FPP, and the feature of FPP was correct color image matches, however, their depth image error was large. This offered higher quality matched FPP for the subsequent interframe motion estimation. Dimension reduction was performed in the interframe motion estimation part, and the two-dimensional Iterative Closest Point (ICP) algorithm was used for camera motion estimation. The experiments indicated that the proposed algorithm effectively improved the computational speed and precision of planar motion wheeled robot visual odometer. This research indicates that the dimension reduction processing of ICP algorithm can effectively improve the operation speed and calculation accuracy of planar motion wheeled robot visual odometry, which provides a good reference and data support for the subsequent research of wheeled robot visual odometry in the future.
AD-STGCRN: A Model for Traffic Flow Prediction
Authors: Tang Jie , Wen Pei , Sun Cheng , Xu Dahong , Xiong Bing , Zhao Jiajia , Zhang Jin 
Published: May 2023

Abstract
Aiming at the problem that the existing prediction models fail to utilize the serial correlation, temporal correlation and spatial correlation of data from multiple perspectives to achieve accurate prediction, a dual-mode spatiotemporal graph convolutional neural network model combined with attention mechanism is proposed. This paper has been tested and validated in very stochastic traffic forecasting. experiments on two publicly available traffic datasets, PeMSD04 and PeMSD08, show that AD-STGCRN has higher prediction accuracy, with the model’s prediction errors reduced by an average of 18%, 16% and 25% for MAE, RMSE and MAPE respectively on PeMSD04 compared to other prediction models. On PeMSD08 they were reduced by an average of 18%, 16% and 20% respectively.
Deep Learning Based Cross-View Human Detection System
Authors: Zhang Jining , Yang Yihan , Gao Di , Wang Ruijie , Wang Jun 
Published: May 2023

Abstract
Human detection is an important research branch in the field of computer vision, and it is widely used in vehicle-assisted driving, intelligent monitoring, intelligent transportation and other aspects. In order to solve the problems of poor real-time performance, low detection accuracy and inability to detect under occlusion in traditional human detection methods. This paper proposes a deep learning based trainable cross-view human detection system, where the encoder-decoder network focuses on solving the feature association problem after perspective transformation. Then, the system was tested on the WildTrack dataset. The results show that our cross-view human detection system outperforms conventional systems in terms of speed and accuracy across the board, achieving a satisfactory performance rate of 0.71 MODA in the presence of extensive occlusion.
Raspberry Pi-Embedded Intelligent Control System for Irrigation and Fertilization Based on deep learning
Authors: Jia Weibing , Wei Zhengying 
Published: May 2023

Abstract
More than two-thirds of freshwater consumed worldwide are used for irrigation, and the existing control system for irrigation and fertilization is not intelligent and efficient enough. It is necessary to improve the utilization efficiency of irrigation water and fertilizer, and the intelligent level of irrigation and fertilization system. We have built tomato fertilizer deficiency and fertilization decision model based on leaf images and deep learning, this paper develops an Raspberry Pi-embedded intelligent control system for irrigation and fertilization based on previous deep learning models. The system included sensors, image acquisition, control part, control panel and remote control, the type of main hardware and sensors were selected, and the circuit of the system were designed. The C language is used to compile the source code of the deep learning framework, and the Python language is used to compress the deep learning model. The result shows that the compression rate of the deep learning model can reach 36.7% and the decision-making time can be shortened by 36% under the condition of ensuring the model accuracy. The Raspberry Pi-embedded intelligent control system can help farmers improve the utilization efficiency of irrigation and fertilizer.
A single-stage automatic license plate recognition network with Balanced-IoU loss
Authors: Liu Sen , Xie Yufei , Wu Longbin , Song Kunpeng , Gong Kaiwen , Duan Xiaohui 
Published: May 2023

Abstract
In this paper, we propose a license plate recognition model, which can detect and recognize the license plate in a single forward. The features of the input image are extracted by our 15-layer convolutional neural network. In detection branch, we use a loss function with better nonlinear to fit the detection process of license plate. To catch the location of license plate with less information loss, we add Intersection over Ground-truth (IoG) into the Intersection over Union (IoU) and get Balanced-IoU loss (BIoU loss). The combination of these two loss functions can make the model get a better predictive result. In recognition branch, we introduce an attention mechanism to make better recognition results. The experimental results show that our network can get 99.91% recognition precision, which is over 3% higher than state-of-the-art single-stage ALPR network RPnet.
Single Camera Person Re-identification with Self-paced Joint Learning
Authors: Zhang Rumeng , Li Mengyao , Lv Xueshuai , Gao Ling 
Published: May 2023

Abstract
Existing re-identification (re-ID) methods rely on a large number of cross-camera identity tags for training, and the data annotation process is tedious and time-consuming, resulting in a difficult deployment of real-world re-ID applications. To overcome this problem, we focus on the single camera training (SCT) re-ID setting, where each identity is annotated in a single camera. Since there is no annotation across cameras, it takes much less time in data acquisition, and enables fast deployment in new environments. To address SCT re-ID, we proposed a joint comparison learning framework and split the training data into three parts, single-camera labeled data, pseudo labeled data, and unlabeled instances. In this framework, we iteratively (1) train the network and dynamically update the memory to store the three types of data, (2) assign pseudo-labels to the unlabeled images using a clustering algorithm. In the model training phase, we jointly train the three types of data to update the CNN model, and this joint training method can continuously takes advantages of both labeled, pseudo labeled or unlabeled images. Extensive experiments are conducted on three widely adopted datasets, including Market1501-SCT and MSMT17-SCT, and show the superiority of our method in SCT. Specifically, the mAP of our method significantly outperforms state-of-the-art SCT methods by 42.6% and 30.1%, respectively.
A Failure Diagnosis Method of the Disc Cutter Based on ARIMA
Authors: Yang Yun , Zhang Yiyun , Wu Dun , Li Yumei , Lan Hao 
Published: May 2023

Abstract
As one of the main rock-breaking tools on the cutterhead of the shield machine, the disc cutter often fails due to the abnormal wear, which in turn affects the tunneling efficiency of the shield machine, in order to find and replace the failed disc cutter in time, a means to effectively detect the wear state and a method to diagnose the failure of the disc cutter are required.In this paper, a failure diagnosis method combining the disc cutter wear measurement and ARIMA prediction is proposed, the theoretical model of wear change law under three common wear forms such as the normal wear, the eccentric wear and the polygon wear were established, and realize the prediction of the disc cutter wear by data cleaning, time reconstruction, model ordering, residual test and other treatments of wear time series, and the error threshold of the disc cutter failure was set by experiment, and a failure diagnosis method of the disc cutter is constructed. The method was applied to an actual case, and the diagnostic accuracy is 83.3%.
RP-Bert: An approach to detect and classify network intrusions based on a combination of Transfer learning and rules
Authors: Lei Shuxin , Zhang Xiaosong , Yi Junkai 
Published: May 2023

Abstract
We investigate the model generalization problem that exists for network traffic intrusion detection. Existing works have begun to apply some advanced algorithms and models from NLP domain to network security domain, but have some challenges. One of the biggest problem is that the amount of data for some existing network intrusions is not sufficient to train the current oversized models. For this purpose, we improve on ET-Bert and propose a method to detect and classify network intrusion based on the combination of migration learning and rules (RP-Bert). And we test and validate it on a large open source dataset BoT-loT, which Recall is achieved, Precision is achieved, F1 Score is achieved, Accuracy is achieved.
Micro-expression Recognition Based on DCBAM-EfficientNet Model
Authors: Qi Fengyu , Yang Chuanying , Shi Bao , Ma Shaoying 
Published: May 2023

Abstract
To address the problems of low accuracy of existing deep learning-based micro-expression recognition models, numerous network parameters, and the difficulty of mobile deployment of micro-expression recognition models, this paper proposes DCBAM-EfficientNet, a micro-expression recognition model that uses the lightweight network EfficientNet as the backbone network and incorporates the attention module. The network can guarantee the accuracy of micro-expression recognition with relatively few network parameters. The attention mechanism allows the more expressive micro-expression features to be highlighted, and the CBAM attention is improved into a DCBAM model, where the large convolution kernel in the spatial attention module of CBAM is replaced by a dilated convolution with the same receptive field, reducing the network parameters while better preserving the spatial features of the image. The integration of the DCBAM model into the main structure of EfficientNet enables better integration of contextual information. Data enhancement is used to process the micro-expression dataset to decrease the occurrence of overfitting and improve the generalization ability of the model. The results demonstrate that the optimized model DCBAM-EfficientNet can effectively promote the recognition accuracy of micro-expressions, significantly reduce the quantity and volume of model parameters, and provide a reference for the deployment of mobile micro-expression recognition models.