bluuebunny/crossref_metadata_2025 huggingface dataset:
The Effectiveness of a 10-Methacryloyloxydecyl Dihydrogen Phosphate (10-MDP)-Containing Hydrophilic Primer on Orthodontic Molar Tubes Bonded under Moisture Contamination: A Randomized Controlled Trial
Authors: Abduljawad Ahmed , Mohammed-Salih Harraa , Jabir Majid , Almahdy Ahmed
Published: September 2023

The debonding of orthodontic attachments adversely affects orthodontic treatment. This randomized controlled trial aims to compare the effectiveness of 10-MDP-containing hydrophilic primers under moisture contamination and hydrophobic primers under non-contaminated conditions. One hundred thirty-six molar tubes of thirty-four recruits were randomly bonded in a split-mouth cross-quadrant design. For the control group, a conventional hydrophobic primer on non-contaminated enamel was used; tubes of the test group were bonded on saliva-contaminated enamel using a 10-MDP-containing hydrophilic primer. The number of debonded molar tubes and their survival rates were recorded after a six-month follow-up. A chi-square test compared the number of failures and adhesive remnant index scores, using the Kaplan–Meier test for survival rates and Multinomial logistic regression to detect the influence of covariates. Thirty-two patients completed the trial; out of 128 tubes, 10 debonded within six months, the conventional primer scored eight failures with an 87.5% survival rate, and the 10-MDP-containing hydrophilic primer scored two failures with a 96.9% survival rate. The difference in survival rates and the adhesive remnant index between the two groups were statistically significant. Age and gender did not show a statistically significant influence on the number of bond failures. 10-MDP-containing hydrophilic primers may reduce bond failures and increase survival rates, especially in poorly isolated conditions.
Effects of Nitrogen Doping on Pulling Rate Range of Defect-Free Crystal in CZ Silicon
Authors: Sun Chenguang , Lou Zhongshi , Ai Xingtian , Xue Zixuan , Zhang Hui , Chen Guifeng
Published: September 2023

We investigated the effect of nitrogen doping on the pulling rate range of defect-free crystal in silicon with a diameter of 200 mm. It was found that the pulling rate range of defect-free crystal in nitrogen-doped Czochralski silicon is wider and the pulling rate (defect free) is lower than it is in non-nitrogen-doped Czochralski silicon. Under the experiment, the pull rate was from 0.67 mm/min~0.58 mm/min to 0.65 mm/min~0.54 mm/min. To further confirm the above experimental analysis, a numerical simulation process of nitrogen-doped Czochralski and non-nitrogen-doped Czochralski in an industrial system was performed. The V/G value along the S/L interface was the same for both models, but the distribution of Cvi (concentration of vacancy–concentration of self-interstitial) for nitrogen-doped Czochralski crystal silicon was more uniform and flat in a nitrogen-doped single crystal. Furthermore, the nitrogen-doped Czochralski crystal silicon had a smaller void size and a higher oxygen precipitation density. The experimental results are in good agreement with the numerical simulation results.
Human Eye Contrast Sensitivity to Vehicle Displays under Strong Ambient Light
Authors: Qian Yizhou , Yang Zhiyong , Hsiang En-Lin , Yang Qian , Nilsen Kevin , Huang Yu-Hsin , Lin Kuan-Heng , Wu Shin-Tson
Published: September 2023

Vehicle displays are becoming more integrated into our daily lives. Achieving a premium driving experience demands the display panel to have high-resolution density and sufficient brightness, particularly when exposed to intense ambient light, as direct sunlight can obscure the displayed images. Combining Barten’s model and diffraction theory, the performance of both infotainment displays and head-up displays (HUDs) is evaluated. For infotainment displays, over 800 nit brightness is essential for the driver to discern 55 pixel-per-degree (PPD) patterns under direct sunlight. For HUDs, a delicate balance between resolution density, brightness, transparency, and image quality must be exercised. By slightly reducing the resolution density to 50 PPD, the transparent micro-LED panel can concurrently achieve a reduced background image blur, low required display brightness (~4000 nits), and high background transmittance (~90%).
Population Structure and Phylogeography of Marine Gastropods Monodonta labio and M. confusa (Trochidae) along the Northwestern Pacific Coast
Authors: Chiu Yuh-Wen , Bor Hor , Wu Jin-Xian , Shieh Bao-Sen , Lin Hung-Du
Published: September 2023

The genetic structure and demographic history of marine organisms are influenced by biological and ecological features, oceanic currents, and the paleo-geological effects of sea-level fluctuations. In this study, we used mitochondrial COI + 16S gene analysis to investigate the phylogeographic pattern and demography of Monodonta labio and M. confusa in Taiwan, the Ryukyu Islands, Japan, mainland China, and Korea. Our genetic analysis identified two major lineages that were not evident from the distribution patterns of different populations. The Taiwan Strait, which acted as a land bridge during Pleistocene glaciations, and the lack of strong dispersal barriers to gene flow between ocean basins after glaciations shaped the phylogeographic pattern. The genetic differentiation in the Ryukyu Islands was influenced by the specialist-generalist variation hypothesis and the Kuroshio Current. Bayesian skyline plot analyses suggested that the effective population size of M. labio and M. confusa rapidly increased approximately 0.1 and 0.075 million years ago, respectively. Our approximate Bayesian computation analysis suggested that all M. labio and M. confusa populations experienced a decline in population size following a recent population expansion and constant size, respectively. Our study provides a baseline for future investigations of the biogeographical patterns of marine gastropods in the Northwest Pacific and offers valuable insights for the management, sustainable resource utilization, and conservation of this species.
Quantitative vs. Qualitative SPECT-CT Diagnostic Accuracy in Bone Lesion Evaluation—A Review of the Literature
Authors: Mutuleanu Mario-Demian , Paun Diana Loreta , Lazar Alexandra Maria , Petroiu Cristina , Trifanescu Oana Gabriela , Anghel Rodica Maricela , Gherghe Mirela
Published: September 2023

(1) Background: Considering the importance that quantitative molecular imaging has gained and the need for objective and reproducible image interpretation, the aim of the present review is to emphasize the benefits of performing a quantitative interpretation of single photon emission computed tomography-computed tomography (SPECT-CT) studies compared to qualitative interpretation methods in bone lesion evaluations while suggesting new directions for research on this topic. (2) Methods: By conducting comprehensive literature research, we performed an analysis of published data regarding the use of quantitative and qualitative SPECT-CT in the evaluation of bone metastases. (3) Results: Several studies have evaluated the diagnostic accuracy of quantitative and qualitative SPECT-CT in differentiating between benign and metastatic bone lesions. We collected the sensitivity and specificity for both quantitative and qualitative SPECT-CT; their values ranged between 74–92% and 81–93% for quantitative bone SPECT-CT and between 60–100% and 41–100% for qualitative bone SPECT-CT. (4) Conclusions: Both qualitative and quantitative SPECT-CT present an increased potential for better differentiating between benign and metastatic bone lesions, with the latter offering additional objective information, thus increasing diagnostic accuracy and enabling the possibility of performing treatment response evaluation through accurate measurements.
Diagnostic Excellence in Pediatric Spine Imaging: Using Contextualized Imaging Protocols
Authors: Kadom Nadja , Reddy Kartik , Cooper Maxwell , Knight-Scott Jack , Jones Richard , Palasis Susan
Published: September 2023

Contextual design and selection of MRI protocols is critical for making an accurate diagnosis given the wide variety of clinical indications for spine imaging in children. Here, we describe our pediatric spine imaging protocols in detail, tailored to specific clinical questions.
Temporal Stability of Ciliary Beating Post Nasal Brushing, Modulated by Storage Temperature
Authors: Bricmont Noemie , Bonhiver Romane , Benchimol Lionel , Louis Bruno , Papon Jean-François , Monseur Justine , Donneau Anne-Françoise , Moermans Catherine , Schleich Florence , Calmès Doriane , Poirrier Anne-Lise , Louis Renaud , Seghaye Marie-Christine , Kempeneers Céline
Published: September 2023

Primary ciliary dyskinesia is a heterogeneous, inherited motile ciliopathy in which respiratory cilia beat abnormally, and some ultrastructural ciliary defects and specific genetic mutations have been associated with particular ciliary beating alterations. Ciliary beating can be evaluated using digital high-speed videomicroscopy (DHSV). However, normal reference values, essential to assess ciliary beating in patients referred for a PCD diagnostic, vary between centres, as minor variations in protocols might influence ciliary beating. Consequently, establishment of normal values is essential for each PCD diagnostic centre. We aimed to evaluate whether delay after sampling, and temperature for conservation of respiratory ciliated samples, might modify assessments of ciliary beating. In total, 37 healthy nasal brushing samples of respiratory ciliated epithelia were collected. Video sequences were recorded at 37 °C immediately using DHSV. Then, the samples were divided and conserved at 4 °C or at room temperature (RT). Ciliated beating edges were then recorded at 37 °C, at 3 h and at 9 h post sampling. In six samples, recordings were continued up to 72 h after sampling. Ciliary beating was assessed manually by ciliary beat frequency (CBFM) and ciliary beat pattern (CBP). A semi-automatic software was used for quantitative analysis. Both CBF and CBP evaluated manually and by a semi-automated method were stable 9 h after sampling. CBFM was higher when evaluated using samples stored at RT than at 4 °C. CBP and the semi-automated evaluation of ciliary beating were not affected by storage temperature. When establishing normal references values, ciliary beating can be evaluated at 37 °C up to 9 h after nasal brushing, but the storage temperature modifies ciliary beating and needs to be controlled.
Nerve Root Compression Analysis to Find Lumbar Spine Stenosis on MRI Using CNN
Authors: Shahzadi Turrnum , Ali Muhammad Usman , Majeed Fiaz , Sana Muhammad Usman , Diaz Raquel Martínez , Samad Md Abdus , Ashraf Imran
Published: September 2023

Lumbar spine stenosis (LSS) is caused by low back pain that exerts pressure on the nerves in the spine. Detecting LSS is a significantly important yet difficult task. It is detected by analyzing the area of the anteroposterior diameter of the patient’s lumbar spine. Currently, the versatility and accuracy of LSS segmentation algorithms are limited. The objective of this research is to use magnetic resonance imaging (MRI) to automatically categorize LSS. This study presents a convolutional neural network (CNN)-based method to detect LSS using MRI images. Radiological grading is performed on a publicly available dataset. Four regions of interest (ROIs) are determined to diagnose LSS with normal, mild, moderate, and severe gradings. The experiments are performed on 1545 axial-view MRI images. Furthermore, two datasets—multi-ROI and single-ROI—are created. For training and testing, an 80:20 ratio of randomly selected labeled datasets is used, with fivefold cross-validation. The results of the proposed model reveal a 97.01% accuracy for multi-ROI and 97.71% accuracy for single-ROI. The proposed computer-aided diagnosis approach can significantly improve diagnostic accuracy in everyday clinical workflows to assist medical experts in decision making. The proposed CNN-based MRI image segmentation approach shows its efficacy on a variety of datasets. Results are compared to existing state-of-the-art studies, indicating the superior performance of the proposed approach.
Predicting Future Land Use Utilizing Economic and Land Surface Parameters with ANN and Markov Chain Models
Authors: Rani Ankush , Gupta Saurabh Kumar , Singh Suraj Kumar , Meraj Gowhar , Kumar Pankaj , Kanga Shruti , Đurin Bojan , Dogančić Dragana
Published: September 2023

The main aim of this study is to comprehensively analyze the dynamics of land use and land cover (LULC) changes in the Bathinda region of Punjab, India, encompassing historical, current, and future trends. To forecast future LULC, the Cellular Automaton–Markov Chain (CA) based on artificial neural network (ANN) concepts was used using cartographic variables such as environmental, economic, and cultural. For segmenting LULC, the study used a combination of ML models, such as support vector machine (SVM) and Maximum Likelihood Classifier (MLC). The study is empirical in nature, and it employs quantitative analyses to shed light on LULC variations through time. The result indicates that the barren land is expected to shrink from 55.2 km2 in 1990 to 5.6 km2 in 2050, signifying better land management or increasing human activity. Vegetative expanses, on the other hand, are expected to rise from 81.3 km2 in 1990 to 205.6 km2 in 2050, reflecting a balance between urbanization and ecological conservation. Agricultural fields are expected to increase from 2597.4 km2 in 1990 to 2859.6 km2 in 2020 before stabilizing at 2898.4 km2 in 2050. Water landscapes are expected to shrink from 13.4 km2 in 1990 to 5.6 km2 in 2050, providing possible issues for water resources. Wetland regions are expected to decrease, thus complicating irrigation and groundwater reservoir sustainability. These findings are confirmed by strong statistical indices, with this study’s high kappa coefficients of Kno (0.97), Kstandard (0.95), and Klocation (0.97) indicating a reasonable level of accuracy in CA prediction. From the result of the F1 score, a significant issue was found in MLC for segmenting vegetation, and the issue was resolved in SVM classification. The findings of this study can be used to inform land use policy and plans for sustainable development in the region and beyond.
Entrepreneurship Education Pedagogical Approaches in Higher Education
Authors: Rodrigues Ana Luísa
Published: September 2023

Entrepreneurship education, as an emerging scientific field, has undergone significant evolution at the conceptual and praxis levels. The concept of entrepreneurship was considered from a broad perspective as the capacity to act upon opportunities and ideas, thereby creating social, cultural, or financial value in diverse contexts. The study and cultivation of this competence are highly relevant to enhancing employability and equipping young people with the skills, knowledge, and attitudes necessary within an entrepreneurial culture for the exercise of active citizenship. This study aims to consolidate the concept and analyse the pedagogical approaches in entrepreneurship education, particularly focusing on experiential learning. It also explores the most frequently used instructional methods in higher education programmes. The research is based on a comprehensive literature review, complemented by a case study of the entrepreneurship education programme implemented at the University of Lisbon, in Portugal, enriched by data collected from one of its curricular units. Experiential learning emerges as a valid approach in entrepreneurship teaching, especially when teachers effectively combine practical experience with theory while learners assume the shared responsibility of learning from experience. As the most recommended instructional methods, one can point to collaborative pedagogical models, namely problem-based learning, project-based learning, peer assessment, design thinking, formative feedback, service learning, and active methodologies that integrate digital technologies.
The Creation of Situated Boundary Objects in Socio-Educational Contexts for Boundary Crossing in Higher Education
Authors: Fuertes-Alpiste Marc , Molas-Castells Núria , Rubio Hurtado Maria Jose , Martínez-Olmo Francesc
Published: September 2023

There is a growing awareness of the need to develop professional skills among university students, which is related to connecting learning to real life. In order to foster this connection, teachers may carry out activities that involve crossing boundaries, using theory in the practice of the professional context. This study presents a teaching experience consisting of a collaborative inquiry-based learning activity mediated by a WebQuest. Students analysed real digital literacy or digital inclusion projects implemented by local organisations to propose improvements by means of creating a digital educational product (a boundary object). This involved a change in context from the university environment to the socio-educational and professional setting. The aim of this study is to examine the students’ perception of this experience. For this purpose, a case study was conducted with a group of 39 first-year students of the bachelor’s degree in Social Education of the University of Barcelona. A questionnaire was administered and the responses were analysed from the perspective of Hermans’ Dialogical Self Theory and Star’s boundary objects. The results show that the students perceive the activity as a bridge between the two contexts, that they view this transition positively—albeit with certain limitations—and that they consider digital technology to have facilitated boundary crossing. In conclusion, we consider that the examined experience is useful in respect to closing the gap between academic and professional skills and contributes to the theoretical foundations for learning between contexts.
“Too Young to Learn English?”—Nurturing Preschool Children’s English Language Learning across an Early Years Curriculum: A Case Study
Authors: Lucas Carmen
Published: September 2023

School readiness can be used as a key target to support children’s social, emotional, language, and a wide range of communication skills, particularly to promote equity for children in a democratic society. This is especially important for Portuguese children attending early years foundation settings, as considering “school readiness” creates a much broader educational base for school subjects to subsequently build upon and, in particular, nurturing early years foundation stage Portuguese children’s literacy development in English, the privileged language of international communication. However, little is known about how the English language can be best taught at such young ages, as well as the unique outcomes from such an early exposure, at least in the Portuguese context. Language play has been argued to be a “universal” feature common to all human beings, regardless of their age. This study set out to prepare young preschool children with an English language background for the primary school years. As such, this study examined the impact of exposing children to classroom-based storybook reading sessions and original cartoon-based created materials, hence creating a print-referencing-style environment carried out during an academic year. The impacts on preschoolers’ early literacy development were examined, focusing specifically on the domain of print knowledge. The study reported here set out to determine to what extent a play-based, cross-curricular pedagogic programme for learning English, fastened in CLIL principles (English across the curriculum), could be effective in promoting early literacy development within an early years setting in Portugal, thus preparing children for the formal years of primary school to come. Fifteen native Portuguese preschool children (3–5 years old) were the focus of this study. Their engagement in English lessons was investigated through audio recordings of peer and group conversations. Cross-reference content analyses were carried out separately, with audio recordings of lessons and researcher’s field notes as part of triangulation of the data.
Framework to Assess the Impact of International Research Experiences in Civil Engineering on Graduate Students
Authors: Mehrabi Daniel , Grau David , Ariaratnam Samuel T. , Schneider Verena
Published: September 2023

With globalization, the demand for competent engineering graduates with international acumen increases. In order to respond to this rising demand, the National Science Foundation (NSF) funded the International Research Experiences in Civil, Construction, and Environmental Engineering (IRECCEE) program to understand the impact of international research experiences on students through a multidisciplinary lens. The program provides Ph.D. students in civil engineering disciplines the opportunity to gain valuable international research experience and establish long-lasting collaborations with international researchers. The program funds students to travel and perform research work at an international research institution. For every student, such research experience becomes the intervention. This paper discusses the program and the corresponding research framework with the aim that the research community can leverage such framework. The mixed-method research framework was designed to comprehensively capture the immediate and sustained impact of the program on the students’ competencies. With a multidisciplinary approach, the framework captures impacts on students’ intercultural competence, professional development, intellectual growth, personal development, and academic development. The data collection includes survey designs and interviews. In order to capture immediate and sustained impacts, data is collected before and multiple times after the intervention.
Video Tutorials in Teacher Education: Benefits, Difficulties, and Key Knowledge and Skills
Authors: Tomczyk Łukasz , Mascia Maria Lidia , Guillen-Gamez Francisco David
Published: September 2023

Shaping the competences of future teachers in a way that is appropriate to the current stage of development of the information society is, in the post-COVID era, a topical and important subject. The accumulation of experience in recent years with the use of digital edtech has forced the search for optimal solutions to support the learning and teaching process. Among such (still undervalued) solutions are video tutorials. This article answers the three research questions: (1) What are the benefits of using video tutorials in education? (2) What difficulties do future teachers face when creating educational tutorials? (3) What key knowledge and skills are required of teachers who wish to create video tutorials? The study (qualitative, based on structured interviews) involved 16 pre-service teachers (Poland) who attended a course on the introduction to the use of information technology in 2022/2023 at one of the leading academic universities in Poland. The collected data, showing the complexity of the use of this type of edtech, provide not only a simple diagnosis regarding the processes of creating and using video tutorials by future generations of teachers, but also allow for the modernisation of academic programmes that prepare pedagogical graduates to work in an increasingly digitalised world of education.
Body-Related Attentional Bias in Adolescents Affected by Idiopathic Scoliosis
Authors: Bertuccelli Margherita , Rubega Maria , Cantele Francesca , Favero Claudia , Ermolao Andrea , Formaggio Emanuela , Masiero Stefano
Published: September 2023

Attentional biases toward body-related information increase body dissatisfaction. This can lead at-risk populations to develop psychopathologies. This phenomenon has not been extensively studied in girls affected by idiopathic scoliosis. This work aimed to study the cognitive processes that could contribute to the worsening and maintaining of body image disorders in adolescent idiopathic scoliosis. Twenty-eight girls were recruited and tested for body image dissatisfaction through the Scoliosis-Research-Society-22-revised (SRS-22r) questionnaire. Attentional biases towards disease-related body parts were assessed using a computerized visual match-to-sample task: girls were asked to answer as fast and accurately as possible to find the picture matching a target by pressing a button on a computer keyboard. Reaction times (RTs) and accuracy were collected as outcome variables and compared within and between groups and conditions. Lower scores in SRS-22r self-image, function, and total score were observed in scoliosis compared to the control group (p-value < 0.01). Faster response times (p-value = 0.02) and higher accuracy (p-value = 0.02) were detected in the scoliosis group when processing shoulders and backs (i.e., disease-relevant body parts). A self-body advantage effect emerged in the scoliosis group, showing higher accuracy when answering self-body stimuli compared to others’ bodies stimuli (p-value = 0.04). These results provide evidence of body image dissatisfaction and attentional bias towards disease-relevant body parts in girls with scoliosis, requiring clinical attention as highly predisposing to psychopathologies.
Enhancing Ransomware Attack Detection Using Transfer Learning and Deep Learning Ensemble Models on Cloud-Encrypted Data
Authors: Singh Amardeep , Mushtaq Zohaib , Abosaq Hamad Ali , Mursal Salim Nasar Faraj , Irfan Muhammad , Nowakowski Grzegorz
Published: September 2023

Ransomware attacks on cloud-encrypted data pose a significant risk to the security and privacy of cloud-based businesses and their consumers. We present RANSOMNET+, a state-of-the-art hybrid model that combines Convolutional Neural Networks (CNNs) with pre-trained transformers, to efficiently take on the challenging issue of ransomware attack classification. RANSOMNET+ excels over other models because it combines the greatest features of both architectures, allowing it to capture hierarchical features and local patterns. Our findings demonstrate the exceptional capabilities of RANSOMNET+. The model had a fantastic precision of 99.5%, recall of 98.5%, and F1 score of 97.64%, and attained a training accuracy of 99.6% and a testing accuracy of 99.1%. The loss values for RANSOMNET+ were impressively low, ranging from 0.0003 to 0.0035 throughout training and testing. We tested our model against the industry standard, ResNet 50, as well as the state-of-the-art, VGG 16. RANSOMNET+ excelled over the other two models in terms of F1 score, accuracy, precision, and recall. The algorithm’s decision-making process was also illuminated by RANSOMNET+’s interpretability analysis and graphical representations. The model’s openness and usefulness were improved by the incorporation of feature distributions, outlier detection, and feature importance analysis. Finally, RANSOMNET+ is a huge improvement in cloud safety and ransomware research. As a result of its unrivaled accuracy and resilience, it provides a formidable line of defense against ransomware attacks on cloud-encrypted data, keeping sensitive information secure and ensuring the reliability of cloud-stored data. Cybersecurity professionals and cloud service providers now have a reliable tool to combat ransomware threats thanks to this research.
Enhancement of Electromagnetic Scattering Computation Acceleration Using LSTM Neural Networks
Authors: Yang Yuanpeng , Xinyang Shi , Wang Qingyao , Fang Chonghua
Published: September 2023

This paper presents the electromagnetic long short-term memory neural network (EM-LSTMNN) approach to accelerate radar cross-section (RCS) calculations in optimizing low RCS for electrically large targets. The proposed method converts the conventional electromagnetic numerical calculation into an efficient numerical calculation using the LSTM neural network, resulting in a significant improvement in RCS computation speed. To assess the effectiveness of this approach, a downscaled model of a large-sized ship is employed as the target for low RCS optimization. Each modification made to the target’s mesh data during the optimization process is stored in the dataset as a new element. As the ship scaling model undergoes modifications during the optimization process, the new mesh data are recorded, thus adding a new element to the dataset at each time step. This forms a time series representation of the mesh model. By utilizing the dataset collected throughout the optimization process, the proposed EM-LSTMNN model is trained using data-driven approaches, with a training time of approximately 106 s. It is worth noting that this training time is significantly smaller compared to existing methods that employ fully connected neural networks. The performance of the proposed approach is demonstrated by comparing the RCS calculation results obtained through this method with those obtained through traditional electromagnetic simulations.
Analyzing Miners’ Dynamic Equilibrium in Blockchain Networks under DDoS Attacks
Authors: Liu Xiao , Huang Zhao , Wang Quan , Jiang Xiaohong , Chen Yin , Wan Bo
Published: September 2023

Proof of work (PoW) is one of the most widely used consensus algorithms in blockchain networks. It mainly uses the competition between mining nodes to obtain block rewards. However, this competition for computational power will allow malicious nodes to obtain illegal profits, bringing potential security threats to blockchain systems. A distributed denial of service (DDoS) attack is a major threat to the PoW algorithm. It utilizes multiple nodes in the blockchain network to attack honest miners to obtain illegal rewards. To solve this problem, academia has proposed a DDoS attack detection mechanism based on reinforcement learning methods and static game modeling methods based on mining pools. However, these methods cannot effectively make miners choose the strategy with the best profit over time when facing DDoS attacks. Therefore, this paper proposes a dynamic evolutionary game model for miners facing DDoS attacks under blockchain networks to solve the above problems for the first time. We address the model by replicating the dynamic equation to obtain a stable solution. According to the theorem of the Lyapunov method, we also obtain the only stable strategy for miners facing DDoS attacks. The experimental results show that compared with the static method, the dynamic method can affect game playing and game evolution over time. Moreover, miners’ strategy to face DDoS attacks gradually shifts from honest mining to launching DDoS attacks against each other as the blockchain network improves.
A Lightweight Modified YOLOv5 Network Using a Swin Transformer for Transmission-Line Foreign Object Detection
Authors: Zhang Dongsheng , Zhang Zhigang , Zhao Na , Wang Zhihai
Published: September 2023

Transmission lines are often located in complex environments and are susceptible to the presence of foreign objects. Failure to promptly address these objects can result in accidents, including short circuits and fires. Existing foreign object detection networks face several challenges, such as high levels of memory consumption, slow detection speeds, and susceptibility to background interference. To address these issues, this paper proposes a lightweight detection network based on deep learning, namely YOLOv5 with an improved version of CSPDarknet and a Swin Transformer (YOLOv5-IC-ST). YOLOv5-IC-ST was developed by incorporating the Swin Transformer into YOLOv5, thereby reducing the impact of background information on the model. Furthermore, the improved CSPDarknet (IC) enhances the model’s feature-extraction capability while reducing the number of parameters. To evaluate the model’s performance, a dataset specific to foreign objects on transmission lines was constructed. The experimental results demonstrate that compared to other single-stage networks such as YOLOv4, YOLOv5, and YOLOv7, YOLOv5-IC-ST achieves superior detection results, with a mean average precision (mAP) of 98.4%, a detection speed of 92.8 frames per second (FPS), and a compact model size of 10.3 MB. These findings highlight that the proposed network is well suited for deployment on embedded devices such as UAVs.
A Waveform Design for MIMO Sensing on Two-Dimensional Arrays with Sparse Estimation
Authors: Liu Yiwen , Pan Chong , Liu Xiaolong , Zhu Ying , Liu Yifu
Published: September 2023

With the increasing demand for target sensing in future mobile communication networks, integrated sensing and communications (ISAC) not only brings new system architecture and multi-functional integration, but also introduces new challenges. In order to further reduce the data volume of the sensing system signal processing, this paper proposes a waveform optimization design method and target parameter estimation technique for a compressed sensing multiple-input multiple-output (MIMO) radar based on a 2D antenna array. This scheme enables accurate estimation of target angles in the sparse time-domain sampling scenario and L-shaped antenna array. In this paper, extensive simulation results demonstrate the good performance of the proposed scheme for various compression ratios and signal-to-noise ratios (SNRs).
Addressing Long-Distance Dependencies in AMR Parsing with Hierarchical Clause Annotation
Authors: Fan Yunlong , Li Bin , Sataer Yikemaiti , Gao Miao , Shi Chuanqi , Gao Zhiqiang
Published: September 2023

Most natural language processing (NLP) tasks operationalize an input sentence as a sequence with token-level embeddings and features, despite its clausal structure. Taking abstract meaning representation (AMR) parsing as an example, recent parsers are empowered by transformers and pre-trained language models, but long-distance dependencies (LDDs) introduced by long sequences are still open problems. We argue that LDDs are not actually to blame for the sequence length but are essentially related to the internal clause hierarchy. Typically, non-verb words in a clause cannot depend on words outside of it, and verbs from different but related clauses have much longer dependencies than those in the same clause. With this intuition, we introduce a type of clausal feature, hierarchical clause annotation (HCA), into AMR parsing and propose two HCA-based approaches, HCA-based self-attention (HCA-SA) and HCA-based curriculum learning (HCA-CL), to integrate HCA trees of complex sentences for addressing LDDs. We conduct extensive experiments on two in-distribution (ID) AMR datasets (AMR 2.0 and AMR 3.0) and three out-of-distribution (OOD) ones (TLP, New3, and Bio). Experimental results show that our HCA-based approaches achieve significant and explainable improvements (0.7 Smatch score in both ID datasets; 2.3, 0.7, and 2.6 in three OOD datasets, respectively) against the baseline model and outperform the state-of-the-art (SOTA) model (0.7 Smatch score in the OOD dataset, Bio) when encountering sentences with complex clausal structures that introduce most LDD cases.
A New Framework of the EAP System in Semiconductor Manufacturing Internet of Things
Authors: Song Tairan , Qiao Yan , He Yunfang , Li Jie , Wu Naiqi , Liu Bin
Published: September 2023

In modern semiconductor manufacturing, the computer-integrated manufacturing system plays an essential role in automation with plenty of software systems. Among them, the equipment automation program (EAP) is one of the fundamental systems to support the interconnection of various types of equipment. For the traditional EAP, the communication and logic models are tightly coupled. The occurrence of any exception in EAP may make the EAP power down such that no equipment is reachable. Additionally, it can handle a couple of manufacturing tools only. The extension of manufacturing tools in a semiconductor fab makes the investment in EAP unbearable. Thus, fabs are highly desired to solve such problems of the traditional EAP. To do so, this work designs a new framework for a distributed EAP system with new technologies being adopted to enhance the usage and stability of EAP. Additionally, this design philosophy makes the distributed EAP system more compatible and expansible. Further, this EAP system can be upgraded as communication and big data technologies advance. Experiments are carried out to verify the stability of the designed distributed EAP system.
41.6 Gb/s High-Depth Pre-Interleaver for DFE Error Propagation in 65 nm CMOS Technology
Authors: Zhan Yongzheng , Li Tuo , Zou Xiaofeng , Hu Qingsheng , Li Lianming , Zhang Lu
Published: September 2023

A high-speed, high-depth pre-interleaver in the proposed symbol pre-interleaving Bit MUX (PBM) was implemented to mitigate decision feedback equalizer (DFE) error propagation in a 400 G Ethernet Serializer–Deserializer (SerDes) interface. Based on the SerDes interface link architecture with 5-tap DFE, the performance of the PBM under DFE error propagation was simulated theoretically, which could obtain an interleaving gain of 0.35 dB. In the pre-interleaver, in order to significantly increase the transmission rate while keeping the larger interleaving depth, characteristic polynomial parallelization with the logic expansion method and register-based memory with interleaving technology were adopted. Finally, the pre-interleaver was fabricated with 65 nm CMOS technology, with a total area of 0.615 mm2, including the I/O pad. The measurement results show that the horizontal opening degree of the output signal can reach 0.925 UI at the data rate of 41.6 Gb/s. The total power consumption is 38.52 mW at the supply voltage of 1.2 V and frequency of 1.3 GHz.
Efficient CU Decision Algorithm for VVC 3D Video Depth Map Using GLCM and Extra Trees
Authors: Wang Fengqin , Wang Zhiying , Zhang Qiuwen
Published: September 2023

The new generation of 3D video is an international frontier research hotspot. However, the large amount of data and high complexity are core problems to be solved urgently in 3D video coding. The latest generation of video coding standard versatile video coding (VVC) adopts the quad-tree with nested multi-type tree (QTMT) partition structure, and the coding efficiency is much higher than other coding standards. However, the current research work undertaken for VVC is less for 3D video. In light of this context, we propose a fast coding unit (CU) decision algorithm based on the gray level co-occurrence matrix (GLCM) and Extra trees for the characteristics of the depth map in 3D video. In the first stage, we introduce an edge detection algorithm using GLCM to classify the CU in the depth map into smooth and complex edge blocks based on the extracted features. Subsequently, the extracted features from the CUs, classified as complex edge blocks in the first stage, are fed into the constructed Extra trees model to make a fast decision on the partition type of that CU and avoid calculating unnecessary rate-distortion cost. Experimental results show that the overall algorithm can effectively reduce the coding time by 36.27–51.98%, while the Bjøntegaard delta bit rate (BDBR) is only increased by 0.24% on average which is negligible, all reflecting the superior performance of our method. Moreover, our algorithm can effectively ensure video quality while saving much encoding time compared with other algorithms.
Defect Detection Method of Phosphor in Glass Based on Improved YOLO5 Algorithm
Authors: Qin Yong , Pan Zhenye , Shao Chenhao
Published: September 2023

Phosphor in Glass (PiG) is easy to be stirred unevenly during production and processing, and improper use of instruments and other factors lead to defective products. In this paper, we propose an improved YOLOv5 target detection algorithm. Firstly, the Coordinate Attention (CA) is introduced into the backbone network to enable the network to notice detect targets in a larger range. Secondly, the Bidirectional Feature Pyramid Network (BiFPN) is used to fuse different scale information in the neck part to obtain the output feature map with rich semantic information. At the same time, the weighted bidirectional feature fusion pyramid structure adjusts the contribution of different scale input feature maps to the output by introducing weights. This optimization enhances the feature fusion effect, reduces the loss of feature information in the convolution process, and improves detection accuracy. Then, the GIOU_Loss function is replaced with the EIOU_Loss function to speed up the convergence. Finally, the comparative experiment is carried out with the self-made PiG dataset. The experimental results show that the average accuracy mAP of this method is 12.35% higher than that of the original method (YOLOv5s), with a detection speed is 53.92 FPS, aligning with the actual needs of industrial detection.
A Hierarchical Control Strategy Based on Dual-Vector Model Predictive Current Control for Railway Energy Router
Authors: Lian Jingru , Dai Chaohua , Zhou Fulin , Chen Weirong
Published: September 2023

The multiport and multidirectional energy flow of railway energy routers (RERs) poses a significant challenge when integrating photovoltaic (PV) systems and energy storage systems (ESSs). To address this issue, this paper proposes an improved hierarchical control strategy for RERs with a reference signal generation layer and an inverter control layer. In the reference signal generation layer, a time-segmentation energy allocation strategy based on a state machine is proposed to manage the multidirectional energy flow in RERs resulting from PV systems and ESSs while minimizing peak power demand. In the inverter control layer, a dual-vector model predictive current control (MPCC) strategy is designed for back-to-back inverters. The dual-vector MPCC strategy eliminates the need for individual PWM blocks, thereby enhancing RER current-tracking accuracy and efficiency. The prominent advantage of the dual-vector MPCC strategy is its ability to achieve high current-tracking accuracy while minimizing active power losses. Simulations and hardware-in-the-loop experiments are conducted to validate the feasibility and effectiveness of the proposed method.
Recommendation Method of Power Knowledge Retrieval Based on Graph Neural Network
Authors: Hou Rongxu , Zhang Yiying , Ou Qinghai , Li Siwei , He Yeshen , Wang Hongjiang , Zhou Zhenliu
Published: September 2023

With the development of the digital and intelligent transformation of the power grid, the structure and operation and maintenance technology of the power grid are constantly updated, which leads to problems such as difficulties in information acquisition and screening. Therefore, we propose a recommendation method for power knowledge retrieval based on a graph neural network (RPKR-GNN). The method first uses a graph neural network to learn the network structure information of the power fault knowledge graph and realize the deep semantic embedding of power entities and relations. After this, it fuses the power knowledge graph paths to mine the potential power entity relationships and completes the power fault knowledge graph through knowledge inference. At the same time, we combine the user retrieval behavior features for knowledge aggregation to form a personal subgraph, and we analyze the user retrieval subgraph by matching the similarity of retrieval keyword features. Finally, we form a fusion subgraph based on the subgraph topology and reorder the entities of the subgraph to generate a recommendation list for the target users for the prediction of user retrieval intention. Through experimental comparison with various classical models, the results show that the models have a certain generalization ability in knowledge inference. The method performs well in terms of the MR and Hit@10 indexes on each dataset, and the F1 value can reach 87.3 in the retrieval recommendation effect, which effectively enhances the automated operation and maintenance capability of the power system.
Machine-Learning-Based Vulnerability Detection and Classification in Internet of Things Device Security
Authors: Hulayyil Sarah Bin , Li Shancang , Xu Lida
Published: September 2023

Detecting cyber security vulnerabilities in the Internet of Things (IoT) devices before they are exploited is increasingly challenging and is one of the key technologies to protect IoT devices from cyber attacks. This work conducts a comprehensive survey to investigate the methods and tools used in vulnerability detection in IoT environments utilizing machine learning techniques on various datasets, i.e., IoT23. During this study, the common potential vulnerabilities of IoT architectures are analyzed on each layer and the machine learning workflow is described for detecting IoT vulnerabilities. A vulnerability detection and mitigation framework was proposed for machine learning-based vulnerability detection in IoT environments, and a review of recent research trends is presented.
A Review of Communication Technologies in Mud Pulse Telemetry Systems
Authors: Li Chenxi , Xu Zhengguang
Published: September 2023

Mastering real-time and accurate downhole information is crucial for ensuring drilling safety, improving drilling efficiency, and maximizing economic benefits. In recent years, logging while drilling (LWD) technologies, represented by mud pulse telemetry (MPT), achieved significant success. However, they still face challenges such as a large amount of downhole information conflicting with low information transmission rates, severe channel distortion, strong noise interference, and weak surface receiving signals. Therefore, this review collects and updates the papers, patents, and conference articles in the relevant field over the past ten years. Starting from the basic structure of communication systems, the downhole signal coding and modulation technologies of the MPT system are discussed. The attenuation reflection brought by the mud channel and various noise interferences in the system are described. The surface noise cancellation, channel equalization, synchronization, and decoding are studied in detail. By analyzing the theoretical principles, development breakthroughs, and relevant challenges that can improve the development of LWD technologies, this review aims to assist researchers in the field in obtaining the latest references and strategies.
A Dynamic Ensemble Selection of Deepfake Detectors Specialized for Individual Face Parts
Authors: Kawabe Akihisa , Haga Ryuto , Tomioka Yoichi , Shin Jungpil , Okuyama Yuichi
Published: September 2023

The development of deepfake technology, based on deep learning, has made it easier to create images of fake human faces that are indistinguishable from the real thing. Many deepfake methods and programs are publicly available and can be used maliciously, for example, by creating fake social media accounts with images of non-existent human faces. To prevent the misuse of such fake images, several deepfake detection methods have been proposed as a countermeasure and have proven capable of detecting deepfakes with high accuracy when the target deepfake model has been identified. However, the existing approaches are not robust to partial editing and/or occlusion caused by masks, glasses, or manual editing, all of which can lead to an unacceptable drop in accuracy. In this paper, we propose a novel deepfake detection approach based on a dynamic configuration of an ensemble model that consists of deepfake detectors. These deepfake detectors are based on convolutional neural networks (CNNs) and are specialized to detect deepfakes by focusing on individual parts of the face. We demonstrate that a dynamic selection of face parts and an ensemble of selected CNN models is effective at realizing highly accurate deepfake detection even from partly edited and occluded images.