bluuebunny/crossref_metadata_2025 huggingface dataset:
Comparative Evaluation of Mean Cumulative Regret in Multi-Armed Bandit Algorithms: ETC, UCB, Asymptotically Optimal UCB, and TS
Authors: Lei Yicong
Published: February 2025

This research provides insights into how to address short-term and long-term decision-making in different kinds of the Multi-Armed Bandit (MAB) problem, a classic problem in decision-making under uncertainty. In this study, four algorithms - Explore-Then-Commit (ETC), the Upper Confidence Bound (UCB), Asymptotically Optimal UCB, and Thompson Sampling Algorithms (TS) – are selected to solve the MAB problem with numerical and categorical types. Different types represent different value intervals. Each algorithm is applied to each dataset with two different horizons, which represent the number of iterations, to evaluate its short-term and long-term decision-making ability. All algorithms are then utilized in each dataset to compare which one is most suitable for solving a certain type of MAB problem. This research provides an explicit introduction to the MAB problem and the four algorithms. Furthermore, it concludes that both Asymptotically Optimal UCB and TS are suitable for decision-making in the short and long term. At the same time, Asymptotically Optimal UCB is the most appropriate for the numerical MAB problem, while TS is the most appropriate for the categorical MAB problem. Additionally, UCB only suits short-term decision-making, ETC can be efficient only in the numerical MAB problem.
Research on facial emotion recognition model based on alpha-like algorithm and CNN fusion technology
Authors: Wang Hongren
Published: February 2025

This paper studied a facial emotion recognition model based on the fusion technology of Alpha algorithm and Convolutional Neural Network. By combining the spatial feature extraction ability of CNN with the advantages of alpha algorithm in sequence modeling, the performance of emotion recognition model was improved. This paper introduces the theoretical basis of deep learning and reinforcement learning, and proposes a model combining CNN and Alpha-like algorithm. The experimental results show that the accuracy of the fusion model is improved by about 2.1 times in the emotion classification task, especially in the recognition of anger and disgust, which is significantly higher than the traditional algorithm. However, it is also pointed out that the misclassification problem of the model on some complex emotion categories such as surprise and neutral still exists, and the performance of the model needs to be further improved in the future.
From Extractive to Generative: An Analysis of Automatic Text Summarization Techniques
Authors: Liu Zixu
Published: February 2025

With the explosive growth of digital content, the demand for effective information retrieval and summarization has become increasingly important. This paper provides a comprehensive review of automated text summarization techniques, focusing on the challenge of condensing large volumes of text into concise summaries. The article explores the evolution of automatic summarization methods, from early extractive techniques to modern generative approaches based on deep learning. The review highlights significant milestones in the development of summarization algorithms, including the emergence of Transformer-based models like Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-trained Transformer (GPT), which have significantly improved the quality and coherence of generated summaries. Additionally, the paper examines the diverse applications of summarization technologies across fields such as healthcare, discussing the challenges and solutions presented in the literature. By shedding light on the current advancements and ongoing challenges, this review underscores the crucial role of automated text summarization in enhancing information accessibility, with promising implications for future research and practical applications.
Text Classification Method Based on Graph Neural Networks
Authors: Gao Ruofei
Published: February 2025

The goal of text classification is to assign labels to text units accurately, which is a basic task in natural language processing. This technology has shown great value in many practical application scenarios, covering spam detection, emotional tendency analysis, user intent recognition, and many other aspects. In recent years, because of the excellent performance of graph convolutional neural networks (GCNs) in processing non-European spatial data, it has become a research hotspot in text classification and has been widely adopted. This paper first introduces the background knowledge and working principle of graph convolutional neural networks. Then the text classification method based on GCNs is described. The last thing discussed is the limitations of graph convolutional neural networks, as well as the difficulties and future development directions in this field. This paper serves as a guide for researchers and practitioners in relevant fields to fully comprehend the latest advancements in text classification methods using graph convolutional neural networks, and by revealing its limitations and challenges, it also provides guidance for future research and development work, intending to promote technical progress and application expansion in this field.
A Review of Multi-Armed Bandit Algorithms in Player Modeling and Game Design
Authors: Zhan Xizhi
Published: February 2025

This paper explores the application of multi-armed bandit algorithms (MAB) in game design, focusing on player modeling and game optimization. The effectiveness of multi-armed bandit algorithms in modeling player characteristics such as skill level, play style, and social comparison orientation is investigated. The potential of MAB in optimizing game design elements like difficulty, rewards, and user interface is also explored. The paper presents empirical results from simulations and user studies and concludes by discussing the potential of MAB algorithms in game design and highlighting future research directions.
Comparative Study of Euro-Dollar Exchange Rate Forecasting Based on BP Neural Network and ARIMA
Authors: Lu Yitong
Published: February 2025

Effective economic activity prediction is crucial for financial market stability, as accurate exchange rate forecasting can significantly impact international trade and investment decisions. This study aims to anticipate the EUR/USD exchange rate utilizing the automatically picked BP neural network structure with the Autoregressive Integrated Moving Average(ARIMA) model, assessing their efficacy in capturing market dynamics. Traditional models, like ARIMA, often struggle to account for the complexities and nonlinearities of financial markets, which are influenced by various economic and political factors. Advanced techniques, such as BP neural networks, are designed to overcome these limitations by better capturing complex patterns within the data. With a mean squared error (MSE) of 2.8899e-06 and a lower relative error, the results demonstrate that the BP neural network model performs significantly better than the ARIMA model in terms of prediction accuracy. The ARIMA model's forecasts are presented as a horizontal line, which indicates that it performs less well than the BP neural network model. The study demonstrates that the improved neural network designed by BP has greater predictive capacity in terms of anticipating exchange rates. This provides decision-makers in the market for securities with useful information that can be utilized in their practice.
The Evolution of U-Net Architectures in Medical Image Segmentation
Authors: Gu Yanzhang
Published: February 2025

As an important field of deep learning, image segmentation has developed rapidly in recent years. Medical image segmentation has always been an important application scenario of image segmentation, and many models with excellent performance have emerged. U-Net has become the focus of research and application recently because of its easy to understand structure and excellent performance. This paper focuses on the structure of U-Net itself and explains the reasons for U-Net's excellent performance as well as some defects. At the same time, this paper also introduces the improvements and adjustments made by different researchers to solve the problems encountered in the practical application of U-Net, including structural improvements, such as the adjustment of modules and the replacement of convolution methods, and non-structural improvements, such as the optimization of data sets and the improvement of loss functions. Finally, the prospects and suggestions for the future development and application of U-Net were put forward.
Research Progress of Skeleton-Based Action Recognition Technologies
Authors: Chen Zexiang
Published: February 2025

Skeletal-based action recognition technology, which analyzes the spatiotemporal sequences of human skeletal joints to identify human behaviors, has garnered widespread attention in computer vision in recent years. This review aims to collate and summarize the research advancements in this domain, with a particular focus on the classification and comparison of feature extraction methodologies. The paper commences by elucidating the acquisition and preprocessing of skeletal data, laying the groundwork for subsequent feature extraction. The thematic focus of the research centers on two predominant feature extraction approaches: those based on customary handcrafted features and those predicated on deep learning methodologies. The methodology encompasses a systematic literature review and comparative analysis, complemented by an introduction to the principal benchmark datasets. The paper juxtaposes the strengths and limitations of various feature extraction techniques through these methodologies and explores their potential in practical applications. In conclusion, this review's importance comes from its thorough analysis of the topic of skeletal-based action recognition. In addition to giving a well-organized summary of the status of the field, it also sheds light on how effective various feature extraction methods are.
Recent Advances of Computer Vision-based Plant Disease Recognition Methods
Authors: Zhang Haochen
Published: February 2025

In the global agroecosystem, plant diseases, as important factors affecting crop yield, quality and ecological balance, have long been the focus of agricultural scientific research and practice. With climate change, the increase in international trade and the transformation of agricultural production methods, the frequency and distribution of plant diseases and the extent of their damage have shown a trend of increasing complexity and intensification. This not only poses a serious challenge to food security, but also poses a potential threat to sustainable agricultural development and biodiversity conservation. The aim of this paper is to provide an overview of current research advances and methods that can be applied to computer- based recognition of plant diseases and pests, as well as an outlook on future developments. The paper focuses on the recognition of plant diseases, categorizes the problem scenarios for recognition (single and multi-class) and in this way summarizes some informative methods (detection and tracking) and learning approaches for recognition.
Application of Image Segmentation Technology Based on Machine Learning in Medical Image Analysis
Authors: Zhang Yurun
Published: February 2025

Medical image analysis heavily relies on the crucial step of image segmentation, which possesses the capability to discern and differentiate various structures within medical imagery. Furthermore, this process holds extensive applicability and research significance across numerous medical domains, encompassing clinical diagnosis, pathological evaluations, surgical planning, and more. With the rapid advancement of machine learning methodologies, especially deep learning, the realm of medical image segmentation has undergone substantial enhancements. This paper delves into machine learning-driven image segmentation techniques, emphasizing the implementation of deep learning principles within medical image analysis. It further examines the significance and evolution of these technologies. Additionally, the article discusses the profound impacts of these technologies on disease diagnosis and clinical practice, particularly in enhancing diagnostic precision and treatment efficacy. Specifically, this paper highlights the pivotal role of models like U-Net, V-Net, and 3D U-Net in elevating the accuracy of medical image segmentation. These models have contributed significantly to the progression of medical imaging technology and have been instrumental in diagnosing and treating various diseases. By comparing the utilization and influence of these models, this paper intends to offer insightful references and guidance to researchers and clinicians in the domain of medical image segmentation.
Deep Learning Based on Facial Expression Recognition from Images to Videos
Authors: Deng Rui
Published: February 2025

Facial expressions, as a vital conduit for human emotional expression, are among the most observable features of machines in the field of computer vision. Consequently, facial expression recognition holds broad potential for applications in artificial intelligence and health monitoring, among others. Given the diversity and complexity of expressions, the development of efficient and accurate models for expression recognition is of significant importance. This paper systematically reviews the foundational knowledge and related research in facial expression recognition, analyzing the application of current primary models in expression recognition. Employing a combination of literature review and experimental analysis, this study evaluates existing facial expression recognition algorithms. Special attention is given to advanced models based on Convolutional Neural Networks (CNNs), with a detailed comparison of their architectures and characteristics, analyzing their performance under various conditions. The paper concludes with a summary of the latest advancements in the field of facial expression recognition and proposes potential directions for future research.
Text to Image Generation: A Literature Review Focus on the Diffusion Model
Authors: Zhou Jingxi
Published: February 2025

This paper reviews the progress in text-to-image generation, which enables the creation of images from textual descriptions. This technology holds promise across various fields, including creative arts, gaming, and healthcare. The main approaches in this area are Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Diffusion Models (DM). While GANs initially made significant advancements in realistic image generation, they faced issues with stability and diversity. VAEs introduced a probabilistic approach, allowing for diverse outputs but often at the cost of image quality. The development of DM, like Stable Diffusion, Imagen, and DALL-E 2, has addressed many limitations, producing high-quality, coherent images through iterative denoising. DM stands out for its stability and ability to generate detailed, semantically accurate images. This review explores the strengths and limitations of each approach, with an emphasis on the advantages of DM. It also discusses future directions, including improving efficiency, enhancing multimodal capabilities, and reducing data requirements to make these models more accessible and versatile for various applications.
The Development and Application of Computers and Information Technology in the Military
Authors: Zhang Yinxiong
Published: February 2025

In the context of the new era of information technology, military operations and research have become increasingly reliant on computers and informatization. Military organizations are adopting cutting-edge technologies such as artificial intelligence, big data analytics, and cyber warfare strategies. This paper aims to explore the development and application of computers and information technology in the military field. Firstly, the paper introduces the evolution and innovations of computer and information technology over the past half-century. It then discusses related disciplines, including big data, the Internet of Things (IoT), cloud computing, and sensor technologies, along with relevant concepts and practical applications, providing theoretical support for the research. The paper also presents the current state of military informatization among major countries and regions worldwide. Furthermore, it elaborates on the future trends and directions of computer and information technology in the military. Finally, the paper highlights the importance of military informatization for national defense security and territorial sovereignty and outlines future developmental directions.
Current Status and Future Prospects of Sentiment Analysis in Social Media Texts
Authors: Zhang Yahan
Published: February 2025

With the flourishing of social media, its diversification, high interactivity, and richness of content are becoming increasingly significant, which has a profound impact on people's communication modes and information acquisition channels. Sentiment analysis of massive and diverse social media text data has become an important tool for grasping users' emotional tendencies and attitudes accurately. This paper aims to comprehensively analyze the current situation of social media text sentiment analysis. It focuses on three classic analysis paths, analyzes the strengths and weakness of each approach respectively, and on this basis sorts out the research methods and achievements of each researchers. In addition, this paper also analyzes the limitations of the current social media text sentiment analysis, discusses in detail the challenges and opportunities of the practical application of these analysis methods, and looks forward to the future development trend. This paper aims to provide a certain theoretical foundation and practical guidance for future related research and promote the further development and optimization of sentiment analysis techniques in the field of social media, to play a more active role in understanding public sentiment, guiding marketing strategies, and monitoring social opinions.
Advances in Digital Signature Algorithms: Performance, Security and Future Prospects
Authors: Lyu Shuhan
Published: February 2025

With the rise of online transactions and digital communications, digital signatures have become essential for verifying document integrity and authenticity across fields like finance, government, and healthcare. This article examines several important digital signature algorithms, analyzing their principles and performance. Each algorithm offers unique advantages: the Rivest – Shamir – Adleman (RSA) provides security through integer factorization, the Digital Signature Algorithm (DSA) through discrete logarithms, and the Elliptic Curve Digital Signature Algorithm (ECDSA) with the efficient elliptic curve cryptography. This paper also discusses recent optimizations, including the Batch and Multi- prime RSA for greater efficiency, the Batch DSA for improved verification speed, and the EdDSA for the enhanced ECDSA performance on Edwards curves. The results show that while optimizations improve computational efficiency, challenges persist, especially with potential quantum threats that could undermine these algorithms' security. Future work should explore hybrid cryptographic schemes and post-quantum cryptography to strengthen digital signature resilience in evolving digital environments.
The Integration of Artificial Intelligence and Blockchain: Applications and Challenges in Economic Security and Data Privacy
Authors: Zhong Hongda
Published: February 2025

Artificial Intelligence (AI) and blockchain integrated have a transforming potential across many industries, most especially in enhancing data security, smoothing supply chains, or improving payment systems. While AI drives efficiency in the analysis of data, blockchain provides a secure, decentralized framework that ensures integrity through guaranteed transparency. The paper discusses the working relationship between AI and blockchain, focusing on their applications within economic transactions, supply chain traceability, and security within the Internet of Things (IoT). By integrating the predictive analytics of AI with blockchain's immutable ledger, a company can potentially handle any given risk in connected systems and amplify cybersecurity. Not that there are no challenges, such as scalability and integration with legacy systems, but this convergence indeed holds great promise and provides a robust solution to the demands placed on the digital economy and directions of future innovation. This review provides valuable insights for researchers and industry professionals seeking to utilize the combined strengths of AI and blockchain, offering a comprehensive understanding of how these technologies can address current security and efficiency challenges while laying a foundation for future advancements in digital infrastructure.
The Comprehensive Investigation and Discussion on the Progress of Ethereum Proof-of-Stake
Authors: Feng Xiaojie
Published: February 2025

Proof of Stake (PoS), as an important consensus algorithm in blockchain technology, has received widespread attention and research in recent years. PoS aims to solve the problems of high energy consumption and low efficiency in Proof of Work (PoW) algorithms. By allowing validators to participate in the consensus process based on their economic interests, it achieves low energy consumption and high efficiency in block verification. This article examines the latest developments in five consensus algorithms for proof of stake, with the aim of identifying errors, omissions, and shortcomings in the original proof of stake. This article finds that although proof of stake has been widely used in the market, PoS algorithm still faces challenges such as how to further improve consensus efficiency, enhance system security, and effectively respond to malicious attacks. In order to address these challenges, this article presents some of its own opinions and proposes some summarized solutions and areas for improvement. This article aims to promote the continuous development of PoS algorithm through these perspectives, in order to adapt to more complex and diverse blockchain application scenarios.
The Application of Proof of Stake: Advantages, Challenges and Future Development
Authors: Zhang Zhenhao
Published: February 2025

With the development of consensus mechanism, researchers emphasize the need to strengthen the Proof of Stake consensus process. Problem like how to keep the transaction platform protocol fair; how to decentralize the digital transaction system etc. have still remained to be solved. The Proof of Stake also has potential to be applied in various fields. So, it is worth reviewing on the research in recent years to summarize the research methods and conclusions. Study is based on the classical research on Proof of Stake consensus mechanism, improvement of protocol and algorithm of Proof of Stake and application of Proof of Stake. Related developer documentation website, conference papers and authoritative papers were considered. The selected paper was systematically reviewed to make comments on their research methods and analysis the advantages and disadvantages of the research method. After that, the research conclusions are summarized, and potential valuable project or future challenge are described. Through researching on current researches in Proof of Stake, it ultimately leads to the conclusion that Proof of Stake consensus mechanism has better energy efficiency and scalability comparing to the Proof of Work. However, the problems of security and centralization have remained to be solve. In the future, these problems can be solved by improving the algorithm of it or develop new protocol like mixed one of Proof of Stake and Proof of Work.
The status of MATLAB in the Software Development Market
Authors: Wang Pin
Published: February 2025

With the continuous development of technology, it is necessary to analyze the applicability of MATLAB in the field of software development according to the mainstream trend of the market. This article aims to outline the design purpose of MATLAB and its applicable user groups, and emphasizes the advantages of MATLAB in scientific computing and engineering applications. By comparing it with other programming languages such as Python, this article further highlights the unique advantages of MATLAB in computing performance and functions, thereby clarifying its importance and application value in related fields. This article concludes that as a programming language specializing in computing, MATLAB is not only loved by professionals in engineering, finance, physics, and other fields but also has a high status in education. Through the experiment of developing small games and comparing the rich functions of Python in software development, it points out the immaturity of MATLAB in UI design, code optimization, data transmission, and other aspects. The subsequent research provides some improvements that MATLAB can make in code performance optimization and loop performance. The significance of this study is to let people who are new to software development choose a more suitable programming language.
A Review of Optimization Strategy-Based Image Processing Techniques in Apple Picking
Authors: Ma Guoqin
Published: February 2025

This paper reviews the application of optimization strategy- based image processing techniques in apple harvesting, focusing on the important role of various image processing methods in automated agriculture. These image processing methods include convolutional neural networks (CNN), support vector machines (SVM), artificial neural networks (ANN), and deep learning (DL) techniques. In apple target detection, localization, segmentation, ripeness, and quality detection. These techniques can effectively improve the accuracy and efficiency of automated picking systems, reduce the dependence on labor, and at the same time reduce the risk of fruit damage. However, there are still many challenges in practical applications, such as environmental factors like fruit occlusion, light variations, and complex backgrounds, which pose requirements on the adaptability and robustness of image processing techniques. To address these challenges, this paper explores various optimization strategies to improve the performance in complex environments. This paper looks forward to future research directions and proposes that the lightweight model should be further optimized, the real-time and adaptability of the algorithms should be improved, and low-cost and high-efficiency sensors and data processing techniques should be developed in order to promote the application and popularization of image processing technology in large- scale automated apple picking.
Enhancing Distributed Machine Learning through Data Shuffling: Techniques, Challenges, and Implications
Authors: Zhang Zikai
Published: February 2025

In distributed machine learning, data shuffling is a crucial data preprocessing technique that significantly impacts the efficiency and performance of model training. As distributed machine learning scales across multiple computing nodes, the ability to shuffle data effectively and efficiently has become essential for achieving high-quality model performance and minimizing communication costs. This paper systematically explores various data shuffling methods, including random shuffling, stratified shuffling, K-fold shuffling, and coded shuffling, each with distinct advantages, limitations, and application scenarios. Random shuffling is simple and fast but may lead to imbalanced class distributions, while stratified shuffling maintains class proportions at the cost of increased complexity. K-fold shuffling provides robust model evaluation through multiple training-validation splits, though it is computationally demanding. Coded shuffling, on the other hand, optimizes communication costs in distributed settings but requires sophisticated encoding-decoding techniques. The study also highlights the challenges associated with current shuffling techniques, such as handling class imbalance, high computational complexity, and adapting to dynamic, real-time data. This paper proposes potential solutions to enhance the efficacy of data shuffling, including hybrid methodologies, automated stratification processes, and optimized coding strategies. This work aims to guide future research on data shuffling in distributed machine learning environments, ultimately advancing model robustness and generalization across complex real-world applications.
An In-depth Analysis and Further Extensions of Pyramid Codes
Authors: Zhu Jinyu
Published: February 2025

Pyramid codes, an extension of Maximum Distance Separable (MDS) codes, offer enhanced flexibility and efficiency in data storage, retrieval, and error correction. By introducing redundancy, pyramid codes improve reliability and accessibility in data-intensive systems such as Redundant Array of Independent Disks (RAID) configurations, wireless communications, and cloud storage. This study provides an in-depth exploration of pyramid codes, including definitions, key features, and methodologies for fault correction, particularly through the Gaussian and Laplacian pyramids. Further, the study examines the transition from basic pyramid codes to generalized pyramid codes, which employ advanced strategies such as interference alignment and discrete cosine transforms (DCT) for robust error correction across hierarchical data structures. Generalized pyramid codes enhance flexibility, reduce input-output overhead, and offer adaptable layers for optimized recovery processes. This study underscores the application benefits and practical implications of these codes in distributed storage environments and suggests potential future improvements in efficiency, redundancy management, and error correction capabilities. Through a systematic examination, the research aims to contribute to the progressive evolution of coding systems, ensuring stronger fault tolerance and adaptability in complex data storage and transmission applications.
Optimizing Energy Efficiency and Green Storage in RAID-6 Systems: Comparative Analyses of Error Correction Codes
Authors: Guo Pinyi
Published: February 2025

Redundant Array of Independent Disks Level 6 (RAID-6) systems are critical for ensuring fault tolerance and data reliability in data centers, leveraging dual-parity calculations for data protection. Despite their benefits, these systems are also associated with high energy consumption, significantly impacting the carbon footprint of data facilities. This study evaluates the energy efficiency of three prevalent error correction codes—Reed-Solomon, Row-Diagonal Parity (RDP), and Liberation Codes—applied within RAID-6 systems. Through detailed comparative analysis, the research assesses how each code influences power consumption and computational demands. Initial findings indicate that integrating more efficient coding algorithms could substantially lower energy usage, thus enhancing green computing efforts. Future research aims to implement these optimizations in actual storage setups within cloud and high-performance computing settings, potentially reducing both operational costs and environmental impacts. This proactive approach seeks to align with the increasing demand for sustainable data management practices, offering significant benefits in energy savings and reinforcing the commitment to eco-friendly computing solutions.
Enhanced Distributed Computation for Machine Learning: Coded Strategies and Multidisciplinary Impact
Authors: Tian Xiaolin
Published: February 2025

This article explores methods to accelerate distributed computation, focusing on its application in machine learning. It discusses two primary concepts: coded multiplication and data shuffling, along with a non-linear core to Random Access Memory (RAM) approach, presenting new avenues for future research. The challenges and future developments of coded computation in machine learning are examined, along with a general discussion on its applications across various scientific fields. The need for systems capable of handling massive data volumes has led to the adoption of large-scale distributed systems. These systems, however, introduce complexities such as straggler nodes and system failures, which can disrupt computational efficiency. Coded computation offers a solution by introducing redundancy that guards against system failures and enhances data noise mitigation. This paper highlights the strategic importance of coded matrix computation and data shuffling in improving fault tolerance and reducing communication costs within machine learning operations, thereby enhancing the overall efficiency and reliability of large-scale data analytics.
Enhanced EVENODD Encoding Techniques: Innovations for Improved Data Resilience
Authors: Bi Sheng
Published: February 2025

The scale and complexity of modern storage systems necessitate robust data protection mechanisms, where EVENODD coding algorithms play a critical role. Despite its widespread application, traditional EVENODD algorithms exhibit limitations that demand advanced solutions for today’s data storage challenges. This paper introduces an innovative approach to the encoding and decoding processes of EVENODD code, highlighting significant enhancements that offer better protection and operational efficiency. The study explores new methods that reduce computational complexity and accelerate data recovery processes, ensuring superior data integrity and system reliability. The enhancements are vital for large-scale storage systems where high data availability and rapid recovery are paramount. The analysis also delves into the practical implementations and potential challenges of these novel methods, proposing strategic solutions to integrate them effectively in existing systems. This research aims to set a foundational framework for future studies and practical applications in data storage and protection technology, pushing the boundaries of current data resilience capabilities.
Extraction and Analysis of Spatial Heterodyne Potassium Signals Based on Principal Component Analysis and Non-Local Means Method
Authors: Yang SiQian , Wang XinQiang , Xiong Wei , Wang FangYuan , Ye Song
Published: February 2025

The use of flame suppressants in jet-propelled aircraft significantly reduces the infrared radiation of their exhaust plumes, thereby increasing the difficulty of target detection based on infrared radiation. Potassium salts, as a component of flame suppressants, produce characteristic signals when burned. To probe into new methods for detecting flying targets, a spatial heterodyne spectrometer is utilized to detect the weak signals from potassium salt combustion against a sky background, combined with data processing techniques. In the experiment, a potassium lamp is employed to simulate the potassium combustion signals and placed in a sky background for data acquisition. Preliminary processing results revealed that the signals were submerged within the atmospheric background. Principal Component Analysis (PCA) is then applied to separate the atmospheric background from the weak potassium lamp signals in the mixed signals, followed by the introduction of the Non-Local Means (NLM) denoising algorithm to suppress noise. Finally, Principal Component Regression (PCR) is used to restore the potassium lamp signals. Quantitative analysis demonstrated that the potassium lamp signals could be effectively extracted at a signal-to-noise ratio (SNR) of 0.1310, and the signal intensity exhibited a linear relationship with the current, with a correlation coefficient of 0.9823. Thus, the combination of spatial heterodyne detection technology with PCA and NLM methods is feasible for detecting potassium combustion signals against an atmospheric background to identify jet-propelled flying targets.
Stylopization by Xenos spp. (Xenidae, Strepsiptera) in invasive alien hornet, Vespa velutina, in South Korea
Authors: Kim Il-Kwon , Kim Chang-Jun , Choi Jeong-Hwan , Kang Hyun Jun , Choi Moon Bo
Published: February 2025

The invasive hornet Vespa velutina Lepeletier, which first invaded South Korea in 2003, has spread throughout the country, significantly affecting apiaries, ecosystems, and human health. Xenos spp. (Xenidae, Strepsiptera) are primarily parasitic to social wasps, with V. analis being the only known host in Korea. Until recently, no parasites or parasitoids on V. velutina had been discovered. In 2020, strepsipteran parasites were discovered on 11 hornet workers in Andong City, South Korea. These parasites, comprising four larvae and seven pupae, were all male, except for one individual of an undetermined sex. Molecular analysis and morphological examination identified the parasites as Xenos moutoni (du Buysson, 1903) and X. oxyodontes Nakase & Kato, 2013. This marks the first recorded instance of strepsipteran parasites on V. velutina in regions invaded by this hornet. Although the exact infection rate of these parasites could not be determined, it appears that native strepsipteran parasites have adapted to a non-native Vespa species. Stylopization, the condition caused by these parasites, is known to negatively affect hornet colonies: infected workers do not contribute to nest activities, hindering nest development, and infected reproductive individuals (males and new queens) do not mate, which impedes the establishment of new colonies. However, due to the hornet’s high reproductive rate and compensatory mechanisms, the overall control effect of the parasites is likely to be minor.
Molecular characterization and subtype analysis of Blastocystis sp. in captive wildlife in Henan, China
Authors: Cui Zhaohui , Huang Xiyao , Zhang Sulan , Li Kaifang , Zhang Aili , Li Qichen , Zhang Yutong , Li Junqiang , Qi Meng
Published: February 2025

Blastocystis sp. is a zoonotic intestinal protozoan that is ubiquitous globally, residing in the gastrointestinal tracts of both humans and various animals. In the present study, a PCR-sequencing tool based on the SSU rRNA gene was employed to investigate the prevalence and subtypes of Blastocystis spp. in 204 fresh fecal samples collected from 20 captive wildlife species from a bird park in Henan Province, Central China. Overall, Blastocystis was present in 13.73% (28 out of 204) of the samples and 25% (5 out of 20) of the species. A total of four zoonotic subtypes of Blastocystis sp. were found: ST1, ST3, ST5, and ST27, with the latter being the most prevalent, accounting for 35.71% (10 out of 28) of the 5 species positive for Blastocystis sp. To the best of our knowledge, this is the first report of Blastocystis ST27 in birds in China, namely bar-headed goose (Anser indicus) and peafowl (Pavo muticus). The data suggest that captive wildlife, particularly those in bird parks, may frequently be infected with this zoonotic pathogen. Consequently, these animals may serve as potential reservoirs for zoonotic infections in humans.
Adolescents’ informal exposure to English as a second language in the context of Galicia
Authors: Alonso Rosa Alonso , Fernández-Dobao Ana
Published: February 2025

This study examines the effects of informal English exposure on vocabulary, grammar, and writing. A total of 178
Galician adolescent learners of English completed a placement test, a language background and an out-of-school English activities
questionnaire, and three language tasks: a grammaticality judgement test, a vocabulary test, and a writing task. Findings revealed
that the most common out-of-school activities were listening to music, watching films without subtitles, and gaming. Positive
correlations were found between learners’ performance on the language tasks and the activities of watching films without
subtitles, watching films with captions, reading, writing, and, to a smaller extent, listening to music, gaming, and speaking
online. Gender-related differences were noticed in the frequency of some out-of-school activities and the correlations with
learners’ performance on the three language tasks.
The politics of positionality and naming practices in socio-cultural relations
Authors: Weuffen Sara , Fricker Aleryk
Published: February 2025

This paper explores power/knowledge relations of nomenclature practices associated with non-Indigenous
positionality statements. Focusing on the ways in which knowledge shapes meaning and understanding, and calls for decolonising
scholarly practices, we interrogate the socio-cultural-political nexus of positionality statements to highlight some inherent
challenges that ought to be addressed in contemporary research spaces. Taking a post-structuralist discursive approach, we examine
four common self-referential non-Indigenous positioning words — settler, colonist, white, and
non-Indigenous — to discuss the linguistic implications for relationality to, and with, First Nations Peoples
and standpoints. To demonstrate the importance of understanding nuances of language, examples of positionality statements are
provided throughout, and explanations provided that highlight the potential implications for cross-cultural mean making. Our
purpose in writing on this topic is to provocate that positionality statements, when written and/or spoken with considered and
informed use, are critical to decolonising the harmful practices and assumed superiority of the western ways of thinking and
doing.
It’s your fault
Authors: Kamalova Alina
Published: February 2025

While the problem still remains decriminalised in Russia, this study explores domestic violence victims’ storytelling in four episodes of the popular Russian talk show Pust’ Govoryat, which aired from 2017 to 2020. By drawing upon De Fina’s (2021) narratives-as-practice approach and van Leeuwen’s (2008) CDA theory, I critically examine storytelling as a discourse practice itself and situate constructed identities, norms, and values in wider dominant discourses of country’s social and political life. Corpus-assisted discourse analysis (Baker 2012) helps to analyse the representation of abusers and victims. Qualitative and quantitative analyses suggest that the show, broadcast on the main state-funded federal channel, propagates a family ideology that supports victim-blaming, normalises and depolitisises domestic violence. The research indicates that such an anti-feminist, misogynist, and patriarchal discourse becomes an integral part of the state’s anti-Western and anti-liberal ideology and resonates with the general increase in epistemological violence in today’s Russia.
Social Capital for Improved Food Security in Southern Malawi: Gender Dynamics and Changes Over Time
Authors: Craig Ailish , Hutton Craig , Musa Frank B. , Sheffield Justin
Published: February 2025

During times of low food security, many coping mechanisms may be adopted to try and alleviate hunger. One such coping mechanism is the use of social capital where food or cash is accessed through social relations and networks. Using qualitative rural rapid appraisal methods in rural southern Malawi, this study aims to understand the social capital-food security nexus while also considering gender and temporal changes. We found that during periods of food insecurity, bonding and bridging social capital are utilised; however, there have been changes over time in how they are used. Most noticeable is the conditional sharing of food within bonding social capital, decreased activity of the village savings and loans associations and a reduction of linking social capital over the past 5 years. The way men and women use social capital to access food is different, with men having bonding social capital over a larger spatial area, meanwhile, women use the village saving and loans associations. Social capital is being used for improved food security, but it varies by gender and is not always available to the most vulnerable in society. Finally, limits to using social capital for food security are evident, a significant finding given the high levels of food insecurity in Malawi.

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Effects of Silicone Oil Coating on Interfacial Space Charge Behavior in XLPE–EPDM Composite Insulation Structures
Authors: Zhao Juntao , Ren Yuanyang , Wang Yang , Lv Zepeng , Gao Jinghui , Zhou Yumei , Yu Liqin , Wu Kai
Published: February 2025

As one of the weakest points in high-voltage direct current (HVDC) cables and accessories, the accumulation of space charges at the crosslinked polyethylene (XLPE)/ethylene propylene diene monomer (EPDM) interface coated with silicone oil is crucial to insulating properties. The physical mechanisms underlying this charge accumulation and dissipation phenomenon remain unclear, particularly at the molecular level. Thus, the interfacial space charge accumulation and dissipation behavior at EPDM/XLPE, EPDM/non-polar dimethyl silicone oil (PDMS)/XLPE, and EPDM/polar fluorinated silicone oil (PMTFS)/XLPE interfaces was measured using pulsed electroacoustic (PEA) method, and molecular simulation techniques were employed to calculate the electronic properties across those interfaces. It was found that the transformation law of the interfacial charge polarity does not completely align with the Maxwell–Wagner (MW) model, which is related to the contact type of the interface (with or without silicone oil and the type of silicone oil) and the voltage polarity. The presence of a high interfacial potential barrier is an important factor behind the fact that the transformation law of the interfacial charge polarity does not align with the MW model. The high hole potential barrier (greater than 1 eV) of EPDM/XLPE and EPDM/PMTFS is the reason why the interfacial charges of EPDM/XLPE and EPDM/PMTFS/XLPE remain always positive as the applied negative voltage and temperature increases. Due to the low potential barrier of the EPDM/PDMS/XLPE interface, the polarity of the interfacial charge is always consistent with the polarity of the voltage applied to the medium with a greater conductivity. At 40°C and 60°C, EPDM/XLPE and EPDM/PMTFS/XLPE positive interface charge accumulation is significantly reduced compared to that observed at room temperature under a negative voltage, which is attributed to the enhanced charge injection and migration of XLPE with rising temperature. This study provides theoretical insights for finding an effective coating material to reduce charge accumulation at the cable accessory interface.
Optimizing sea level muon flux modelling: A 2D fit approach and integration into Geant4 generators
Authors: Frosin Catalin , Bonechi Lorenzo , Borselli Diletta , Ciulli Vitaliano , D'Alessandro Raffaello , Gonzi Sandro , Viliani Lorenzo
Published: February 2025

Starting from the measurements conducted in 2004 with the ADAMO silicon spectrometer, we have performed a 2D fit procedure to diverse combinations of muon flux datasets documented in the literature. The fit employed three different formulas describing the energy and angular distribution of atmospheric muons at the Earth’s surface. The analysis revealed that not all measurements exhibitcomplete compatibility. However, we have identified a formula capable of furnishing comprehensive spectrum coverage utilizing a singular set of parameters. Despite inherent limitations, we have achieved a satisfactory agreement with the Guan parameterization. Subsequently, we have integrated this optimized parameterization into the EcoMug library and a Geant4-based muon generator. The model’s predictive capabilities have been validated by comparing it with experimental flux measurements and against other measurements in the literature. Through this process, we have shown and tested the reliability and accuracy of different muon flux modelling, facilitating advancements in domains reliant on the precise characterization of atmospheric muonphenomena.