bluuebunny/crossref_metadata_2025 huggingface dataset:
Bandit Algorithms for Advertising Optimization: A Comparative Study
Authors: Tian Ziyue
Published: February 2025

In recent years, the rapid development of digital advertising has challenged advertisers to make optimal choices among multiple options quickly. This is crucial for increasing user engagement and return on investment. However, traditional A/B testing often suffers from slow response times and difficulties in adapting to dynamic environments, leading to limited effectiveness. This article explores the application of multi-armed bandit algorithms in digital advertising, focusing on the performance of ε- greedy, Upper Confidence Bound (UCB), Linear Upper Confidence Bound (LinUCB), and Softmax algorithms. Experimental results show that incorporating user characteristics can significantly improve the accuracy and relevance of advertising recommendations. Among the algorithms tested, LinUCB, which utilizes contextual information, outperformed the other three non-contextual algorithms in terms of cumulative return and accuracy. It demonstrated significant advantages after full exploration. However, a limitation of this study is that the static experimental dataset cannot fully simulate the dynamic feedback of real-time advertising environments. This limitation affects the generalizability of the findings in highly changing contexts. Future research should focus on developing algorithms that can address feedback delays and are both adaptive and context-sensitive. This would enhance performance in complex advertising situations. Overall, this study deepens the understanding of multi-armed bandit algorithms in advertising and provides strategic guidance for user-targeted advertising.
A Gaussian process approach for contextual bandit-based battery replacement
Authors: Zhou Tianshi
Published: February 2025

The sharing economy has recently become a distinctive business model in China, offering advantages such as low prices and ease of use. Shared electric vehicles have also become an essential part of urban transportation. This research aims to assist electric vehicle providers in optimizing battery replacement schedules, with the objective of minimizing operating losses while meeting evening peak demand. Efficient resource allocation is crucial to remain competitive with the established public transport system. This study proposes a multi-armed bandit (MAB) approach to identify optimal periods for inspection and battery replacement in new city launches, even without prior knowledge of user behavior patterns. Modifications are made to the traditional MAB algorithm, incorporating the lower confidence bound (LCB), contextual features, kernelization, and the Gaussian Process (GP) to enhance the Upper Confidence Bound (UCB) MAB in solving this problem. Unlike deep learning techniques, the MAB model offers a lightweight, efficient, and easy-to-deploy solution that adapts to dynamic scenarios even with limited training data. Results indicate that this method performs stably in cumulative regret and in selecting the optimal choice within a short timeframe. Adaptable to seasonal and weekend fluctuations, this optimized approach shows potential for enhancing operational strategies not only in shared transportation but also across other sectors of the sharing economy.
Sign language recognition method based on deep learning
Authors: He Mu
Published: February 2025

Sign language recognition, as an interdisciplinary field involving computer vision, pattern recognition, and natural language processing, holds profound research significance and extensive application value. This technology not only helps people with hearing impairments and those with normal hearing achieve barrier-free communication, but it also enhances their daily living experience while driving the development of sciences such as computer vision and artificial intelligence technologies. The subsequent text offers a thorough examination of the technologies involved in sign language recognition. It starts by detailing the methods for gathering data in sign language recognition, giving particular attention to hand modeling and the techniques used for visual feature extraction. Then, it discusses in detail the two methods of sign language recognition, namely traditional methods and artificial intelligence methods. These two methods have their advantages and disadvantages, providing different ideas for developing sign language recognition technology. Finally, the article proposes a prospect for the future development of sign language recognition technology, hoping that it can play a significant role in more fields and create a more convenient and barrier-free communication environment for people with hearing impairments.
Prediction and Analysis of USD /JPY Exchange Rate Based on BP Neural Network
Authors: Hu Xiuning , Liu Yehua , Ye Yitong
Published: February 2025

One of the most important variables in the global financial market is the exchange rate, which is the ratio of change between the currencies of two countries. Moderate currency devaluation can stimulate exports, promote economic growth. This paper uses the back propagation (BP) neural network model to predict the short-term exchange rate of USD/JPY. The research result shows that the exchange rate price prediction trend is basically consistent with the real value trend, and the relative error of USD/JPY is extremely small, controlled within 0.8%, which shows that the superficial BP neural network is relatively accurate in the short-term exchange rate forecast of USD /JPY, which has a positive research impact, that is, it provides help for financial analysis. However, it has certain limitations in terms of timeliness, such as the extension of the prediction time, the prediction accuracy may decrease. Future research may need to be combined with other models and methods to improve its reliability.
Deep Learning-Based Object Detection Algorithms
Authors: Yao Linxi
Published: February 2025

One of the main areas of study in computer vision is object detection. It can identify the type and location of target items and determine whether they are present in pictures or movies. With the development of deep learning, Object detection algorithms have seen significant enhancements in both speed and accuracy, leading to extensive adoption across various domains, including autonomous driving, drone surveillance, and security monitoring. This article examines some of the most well-known algorithms from the deep learning period, classifies them into four types of object identification algorithms—two-stage, one-stage, keypoint-based, and transformer-based — and describes their primary advances, benefits, and drawbacks. Furthermore, this work organizes target detection datasets and performance evaluation indicators that are routinely used in studies and provides detailed explanations of their content and properties. The paper adds to the study and advancement of target detection technology-related domains and serves as a useful resource for practitioners and scholars.
Attention is All Large Language Model Need
Authors: Liu Yuxin
Published: February 2025

With the advent of the Transformer, the attention mechanism has been applied to Large Language Model (LLM), evolving from initial single- modal large models to today's multi-modal large models. This has greatly propelled the development of Artificial Intelligence (AI) and ushered humans into the era of large models. Single-modal large models can be broadly categorized into three types based on their application domains: Text LLM for Natural Language Processing (NLP), Image LLM for Computer Vision (CV), and Audio LLM for speech interaction. Multi-modal large models, on the other hand, can leverage multiple data sources simultaneously to optimize the model. This article also introduces the training process of the GPT series. Large models have also had a significant impact on industry and society, bringing with them a number of unresolved problems. The purpose of this article is to assist researchers in comprehending the various forms of LLM, as well as its development, pre- training architecture, difficulties, and future objectives.
Neural Network Techniques for Image Style Transfer
Authors: Zhang Ziqi
Published: February 2025

With the rapid advancement of deep learning technology, neural networks have achieved remarkable results in the field of image processing. Particularly in image style transfer, neural network-based methods have become a research hotspot. Image style transfer aims to apply the stylistic features of one image to another while preserving its content information, thereby creating images with artistic value. This paper reviews the primary methods of image style transfer, including slow transfer based on image iteration and fast transfer based on model iteration and compared the experimental results of various methods. It further explores the implementation approaches and technical developments of single-style, multi-style, and arbitrary style transfer. Analyzing the strengths and weaknesses of existing methods, this paper examines efficiency improvement, image quality enhancement, and diversity augmentation, highlighting the challenges in content preservation, computational resource requirements, and transfer quality in current style transfer techniques. This paper aims to provide researchers with future research directions.
A method for license plate recognition in low-resolution conditions
Authors: Li Zongwei
Published: February 2025

With the widespread application of license plate recognition technology, the development of license plate recognition technology is becoming increasingly advanced. However, in some low-resolution situations, the efficiency and accuracy of license plate recognition are greatly reduced. To understand the development in this area, this paper conducts research from two perspectives: traditional methods and deep learning methods. Among the traditional methods, this paper discusses methods based on ALBP, methods based on image vertical projection feature analysis and geometric features of character segmentation algorithms, and methods based on character region perception of end-to-end license plate recognition algorithms. Among the license plate recognition methods based on deep learning, this paper discusses algorithms such as AOD-Net, YOLOv5, LPRNet, CRNN+CTC, and FasterR-CNN. The purpose of this review is to help readers understand how researchers have overcome low resolution by improving the existing recognition methods in the development of license plate recognition technology, and point out the shortcomings of each improved method so that readers can understand the development and existing problems in this field.
Image classification method based on superpixels and Graph Neural Network
Authors: Tang Yonghong
Published: February 2025

In image classification tasks, commonly used deep learning network architectures include Convolutional Neural Network (CNN) and Vision Transformer (ViT). Still, both are relatively mature, while Graph Neural Network (GNN) network architecture has been attempted less in image classification. In addition, the architecture and design of GNN are still rapidly developing and have enormous potential. This study aims to create an image classification method based on superpixels and graph neural networks. This project attempted three different approaches to preserve superpixel node features with varying numbers of features, designed two training network architectures with different complexities and conducted six sets of tests on image classification tasks on the fashion-mnist dataset. As a result, the use of superpixel and GNN methods achieved good accuracy in image classification tasks, demonstrating the potential of this method in image recognition tasks. It was also found that adding the boundary coordinates of the region to the training in the selection of superpixel node features can improve the accuracy of the final image classification task.
Explore the Current Application Status of Machine Learning in the Medical Field
Authors: Zhang Zhe
Published: February 2025

The application of machine learning in the medical field can improve the quality and efficiency of medical services and promote the innovative development of healthcare. However, there are still many shortcomings in the development of related fields. This paper explores the current development status of machine learning technology and its application in the medical field and explores the direction of its future development and the defects that can be improved, mainly through the literature research method. This paper finds that many current studies lack mature large-scale datasets as training materials. Similarly, the trained models lack applications in real-world scenarios to validate their prediction or detection performance, and can only be verified in other existing datasets, which requires wider collaboration to enable and improve them. The significance of this study mainly lies in sorting out the current development status of machine learning applications in the medical field in China, illustrating the breadth and limitations of the current applications of AI in the medical field, and providing references and guidance for the future development of related fields.
Bridging Neuroscience and AI: A Comprehensive Investigation of Brain-Inspired Computing Models
Authors: Hu Jinming
Published: February 2025

Artificial Intelligence (AI) has reached new heights, supported by advancements in hardware and algorithm theory. Areas like robotics and autonomous driving have made significant strides, but brain-inspired computing remains a distinctive field. Although there were early hopes of AI closely connecting with brain science, this integration has been minimal. Neuroscience has mostly inspired some early algorithms, while most neural networks only adopted the idea of neuron connections without fully replicating real neural signals. However, brain-inspired algorithms, such as Spiking Neural Networks (SNNs), have shown promising results, often outperforming traditional algorithms in specific tasks and offering lower power consumption. These advancements could inspire new AI models or improve existing ones. This review explores the development of successful brain-inspired algorithms, starting with the structure and function of neurons, including cerebellar structures. It then discussed spiking neural networks, their principles, and recent research, as well as cerebellar-inspired models. Finally, the article summarizes methods for building these models and their applications in fields like robotics.
A Comprehensive Evaluation of Deepfake Detection Methods: Approaches, Challenges and Future Prospects
Authors: Hu Xixi
Published: February 2025

Advances in technology have made deepfake forgeries easier, posing serious ethical and security risks that highlight the urgent need for better detection methods. This paper provides a comprehensive discussion of various Deepfake detection approaches, including methods based on physical attributes and visual inconsistencies, data-driven techniques (such as spatial and frequency domain detection methods), and those using generative models. Based on the classification and introduction of representative methods, the paper further compares their performance across different datasets, revealing that while current methods can detect deepfake to some extent, they generally suffer from poor generalization and accuracy when dealing with different types of forgeries or low-quality data. In conclusion, this study offers insights into the development of future deepfake detection technologies, emphasizing the importance of combining multiple approaches and improving model generalization to address increasingly complex forgery scenarios. It can serve as a valuable reference for researchers looking to understand the advancements in this field.
Bayesian Optimization of Lasso and XGBoost Models for Comparative Analysis in Housing Price Prediction
Authors: Zheng Runze
Published: February 2025

Fluctuations in housing prices have a profound impact on the broader economy and people's livelihoods. Accurate housing price predictions contribute to enhanced market transparency and the formulation of evidence-based policies. This paper focuses on optimizing two machine learning models, Lasso Regression and XGBoost, using Bayesian optimization for predicting housing prices. By leveraging economic features such as Average Earnings, Gross Domestic Product (GDP), Mortgage rates, Population, and Unemployment Rate, the models aim to improve prediction accuracy in the housing market. The Lasso model, known for its feature selection capability through L1 regularization, was fine-tuned using Bayesian optimization to minimize mean squared error (MSE). The XGBoost model, designed for handling large-scale, non-linear datasets, was also optimized using the same method. After optimization, the Lasso model achieved an MSE of 240,498,369.05 and an R² score of 0.977, while the XGBoost model showed superior performance with an MSE of 80,273,332.19 and an R² score of 0.9914. SHAP analysis was used to interpret the models, revealing that Average Earnings and GDP were the most influential features in both models. The results demonstrate that while both models perform well, XGBoost's ability to handle non-linearity and high-dimensional data makes it more effective in housing price predictions.
A Comprehensive Study of Digital Signatures: Algorithms, Challenges and Future Prospects
Authors: Xu Jingkun
Published: February 2025

This article examines digital signature as a critical security measure for authentication and verification in electronic transactions, distinguishing them from simpler e-signatures by their use of advanced cryptographic techniques. Digital signature leverage asymmetric cryptography to provide higher security, with various standardised algorithms, such as the Digital Signature Algorithm (DSA) and Rivest- Shamir-Adleman (RSA), forming the foundation of secure systems today. The paper outlines the evolution of digital and electronic signature, from early telegraph-based approvals to the modern applications that facilitate secure digital contracts and communications. Key digital signature algorithms are analysed in detail, highlighting their strengths and weaknesses, including RSA, DSA, Elliptic Curve Digital Signature Algorithm (ECDSA), and Edwards-Curve Digital Signature Algorithm (EdDSA). Additionally, the article addresses the technical and implementation challenges of digital signature algorithms, such as high computational demands, complexities in key management, and difficulties in secure implementation. It concludes with an exploration of future trends, such as the development of efficient and quantum-resistant algorithms, improvements in cryptographic hardware, and new strategies to simplify secure key management. This comprehensive overview provides insights into both current digital signature practices and emerging solutions poised to enhance security and efficiency in an evolving digital landscape.
Towards a Secure Blockchain Ecosystem: Current Vulnerabilities and Future Directions in Smart Contract Security
Authors: Zhao Boya
Published: February 2025

As a distributed shared transaction ledger, blockchain technology has the characteristics of decentralization, immutable, irreversible and traceable, and is changing the inherent model of traditional industries. Smart contracts, as one of the core applications of blockchain technology, provide the basis for a variety of practical applications. However, the frequent security problems of smart contracts have not only caused huge economic losses, but also hindered the development of blockchain systems. According to relevant studies, the economic losses caused by smart contract security breaches have exceeded billions of dollars. Therefore, the security of smart contracts has become a hot topic at home and abroad. This study discusses the technical vulnerabilities and corresponding solutions of smart contracts from the code level. In-depth analysis of security vulnerabilities in smart contracts can help identify and repair potential security hazards, improve the overall security of contracts, and prevent the theft of funds or abnormal execution of contracts. By raising the security awareness of developers, enterprises and users, it is possible to promote the application of smart contracts in high-security fields such as finance and law and promote the healthy development of the blockchain ecosystem.
Enhancing Fault Tolerance in Distributed Storage Systems through an Extended EVENODD Code
Authors: Lu Kejian , Song Liyao , Xie Yuying
Published: February 2025

With the rapid development of technology, we have entered the era of big data. Therefore, the safe and effective handling of abundant amounts of data is essential. This paper analyzes and lists some commonly used data storage systems and encoding techniques and elaborates on the encoding and decoding process of one of them, namely EVENODD Code, which is widely used in distributed storage systems. At the same time, this article briefly discusses the possibility of improving the EVENODD Code. The original traditional EVENODD Code encoding can recover data with up to two block failures, but the computation process is more complex than that of multi-replica technology. As a result, it is less computationally efficient than multi-replica technology. The text briefly mentions the potential for enhancing the efficiency of data operations with the EVENODD Code, which could make it the optimal choice for large-scale, highly reliable applications such as cloud storage and media streaming systems.
Advancing Data Resilience: An In-Depth Study of Pyramid Codes for Fault Tolerance and Repair Cost Optimization
Authors: Chen Yanqiu
Published: February 2025

In the digital era, distributed storage systems are essential for managing vast volumes of data generated by cloud computing, big data, and the Internet of Things. These systems face challenges such as scalability, fault tolerance, and cost control. Pyramid Codes have emerged as an effective solution, enhancing fault tolerance and reducing repair costs. This paper presents a thorough analysis of Pyramid Codes, examining their structure, performance, and advantages over traditional Reed-Solomon Codes. The study delves into fault tolerance mechanisms, demonstrating Pyramid Codes’ ability to handle single, double, and triple disk failures efficiently. Additionally, strategies are proposed for optimizing reliability, such as increasing parity length and creating additional parity layers, which enhance repair capabilities without substantially increasing costs. The potential applications of Pyramid Codes in scenarios requiring high fault tolerance and low repair costs are discussed, emphasizing their role in advancing distributed storage resilience. Future directions include exploring adaptive frameworks for real-time data repair and optimizing parity distribution to balance security and cost-efficiency, paving the way for robust and efficient storage solutions in distributed systems.
Comparative Analysis of SEC DED, EVEN-ODD, and Pyramid Codes for Distributed Storage Systems: A MATLAB-Based Study
Authors: Huang Shouhan
Published: February 2025

This study presents a comparative analysis of three erasure coding schemes—Single Error Correction Double Error Detection (SEC DED), EVEN-ODD, and Pyramid codes—within distributed storage systems, utilizing MATLAB for simulation. The research focuses on key metrics such as storage overhead, recovery time, and fault tolerance to elucidate the performance characteristics and practical applicability of each scheme. Single Error Correction Double Error Detection coding balances recovery speed and fault tolerance but incurs higher storage overhead due to its double-parity structure. EVEN-ODD coding, renowned for its storage efficiency and rapid recovery capabilities, exhibits limitations in fault tolerance. Conversely, Pyramid codes offer robust multidimensional error correction, making them suitable for large-scale storage environments, albeit at the cost of increased storage and extended recovery times. By examining these schemes under various scenarios, the analysis highlights their respective trade-offs and provides insights for selecting the optimal coding scheme based on specific system requirements. The study concludes with recommendations for enhancing erasure coding effectiveness in distributed environments and suggests areas for future research, including hybrid coding schemes and machine learning optimizations.
Advancements in Coded Computation: Integrating Encoding Matrices with Data Shuffling for Enhanced Data Transmission Efficiency
Authors: Yuan Shijie
Published: February 2025

In the interconnected age of big data, cloud computing, and the Internet of Things, the demand for robust data processing and transmission systems is critical. This study delves into the fundamental principles, technological advantages, and applications of coded computation, emphasizing the integration of encoding matrices and data shuffling techniques. Encoding matrices enhance data reliability, fault tolerance, and security, reducing transmission and storage costs. Data shuffling techniques, by reordering data, decrease communication overhead and computational burden, thereby optimizing the coding computation process. This paper analyzes various data shuffling methods, their integration with encoding matrices, and their impact on computational efficiency and data transmission. The application of these technologies promises substantial improvements in the efficiency of data systems, offering vital advancements for modern computing environments. By refining the design of encoding matrices and data shuffling strategies, the potential to elevate the performance of coded computations is explored, with implications for the progressive development of information technology.
Research on promotional pricing decisions of retailers considering customers’ add-on items return behavior
Authors: Song Sujuan , Peng Wei , Zeng Yuyang
Published: February 2025

Abstract: This study aims to investigate a retailer’s optimal decisions under “Value Increasing” promotion, where speculative consumers will deliberately purchase add-on items to qualify for discounts if the purchase amount is less than the “Value Increasing” promotional threshold and then return the add-on items after successful payment. The models without and with the “Value Increasing” promotion are established to investigate the effects of speculative consumers’ add-on items refund behavior on the optimal pricing strategies and the optimal profits. The results show that participating in the “Value Increasing” promotional campaigns does not always benefit retailers. When the promotional discounts degree meets the incentive compatibility conditions, a low probability of the product being added by speculative consumers or a small proportion of speculative consumers makes retailers benefit more from participating in the “Value Increasing” promotional campaigns. However, when these conditions are not met, not participating in the “Value Increasing” promotion is better for retailers. Moreover, compared to without “Value Increasing” promotional campaigns, retailers will set a higher regular price to offset the losses associated with speculative returns under the “Value Increasing” promotional campaigns, which may result in consumers’ final price after the discount not necessarily be lower than the price they would pay under non-promotional campaigns.
Ätherische Öle und Aromatherapie – Grundlagen und Ergebnisse
Authors: Jobst Detmar
Published: February 2025

Die Erfahrung zeigt, dass ätherische Öle das Wohlbefinden fördern und Selbstheilungskräfte stärken können. Die Verwendung ätherischer Öle erfordert Kenntnisse über ihre Biochemie und sichere Anwendung mit Trägersubstanzen. Forschungsergebnisse belegen die antimikrobielle Wirkung vieler ätherischer Öle gegen verschiedene Bakterien und Pilze. Trotz vielversprechender Ergebnisse stehen der Entwicklung von aromatischen Antibiotika Herausforderungen gegenüber wie die mangelnde Haltbarkeit und die therapeutische Breite zwischen Wirksamkeit und Unverträglichkeit.
Tourism and Quebec’s Cree Community of Waskaganish: Navigating the Past, Embracing the Present, and Shaping the Future
Authors: Blueboy Jonathan , Ibrahim Zainub
Published: February 2025

The Cree community of Waskaganish is an Indigenous community on the James Bay in Northern Quebec. Waskaganish is in an early stage of tourism development and currently encounters a relatively limited number of visitors who participate in a variety of informal activities, including traditional cultural Cree activities such as fishing, hunting, and trapping. Current legislation restricts cultural practices such as fishing and hunting to members of the Cree Nation. However, locals informally involve visitors in these activities, which while illegal, is unenforced. A recent Cree Nation Governance Agreement was passed in 2017 enabling the Cree Nation Government to pass laws and regulations that supersede federal or provincial laws in designated category 1A lands. The Cree Nation now has the opportunity to consider modifying these regulations to allow for formal tourism opportunities which could benefit the local economy and protect and showcase the local culture and environment. This study explores the preparedness and potential for legal changes that support formalized tourism in Waskaganish and finds that tourism may be well-positioned to support the preservation of the local environment, culture, and identity while offering economic opportunities in Waskaganish and other neighboring Indigenous communities.

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© The Authors 2025
Photoacoustic detection of genetically encoded fluorophores for neuronal subtype identification
Authors: Lusk Joel , Marschall Ethan , Miranda Christopher , Aridi Christina , Smith Barbara
Published: February 2025

Objective. Elucidating neurological processes in the mammalian brain requires improved methods for imaging and detecting neuronal subtypes. Transgenic mouse models utilizing Cre/lox recombination have been developed to selectively label neuronal subtypes with fluorophores, however, light-scattering attenuation of both excitation light and emission light limits their effective range of detection. Approach. To overcome these limitations, this study investigates the use of a near-infrared fluorophore, iRFP713, for subtype labeling of neurons found within brain regions that are typically inaccessible by optical methods. Towards this goal, a custom photoacoustic system is developed for detection of iRFP in neurons in brain slices, expressed via Cre/lox, and within in vitro cell culture. Results. In this study, a custom system is developed to detect iRFP in neuronal cells both in brain slices and in vitro. Furthermore, this work validates iRFP expression in the brains of transgenic mice and neuronal cell culture.
Dynamics of soliton excitations in inhomogeneous magnetic chains
Authors: Kamburova R S , Primatarowa M T , Chamati H
Published: February 2025

The interaction of nonlinear excitations with extended inhomogeneities in an on-site anisotropic Heisenberg spin chain is studied numerically. The inhomogeneous segment is modeled by the change in the anisotropy constant of a set of consecutive spins. The modified anisotropy leads to linear and nonlinear additional terms in the nonlinear Schrödinger (NLS) equation for the spin amplitude derived for classical spin vectors and wide excitations. For a homogeneous chain with easy axis anisotropy the NLS equation possesses a bright type soliton solution. When the modification of the anisotropy constant is negative the spin set acts as a complex linear and nonlinear potential with a dominant linear well character and for a given range of initial velocities of the incoming soliton and the mismatch anisotropy the scattering pattern exhibits periodically repeating regions of trapping and transmission as a function of the length of the inhomogeneity. It is shown that the runaway of the soliton is due to a resonance between the period of the shape oscillations of the soliton inside the inhomogeneity and the length of the latter.
Adsorption of ammonia and hydrazine on a metal oxide layer
Authors: Maslenska Rada , Ivanova Nikoleta
Published: February 2025

Metal oxides, and specifically magnetite, are complex systems when embedded in molecular dynamic model systems. This allows to consider a number of phenomena such as adsorption of molecules on the surface. The initial coordinates of Fe3O4 in the {111} plane were generated with CrystalMaker. Ten molecules each of ammonia and hydrazine are placed near the surface of the metal oxide. Water molecules are added to the constructed systems so that overlapping of atoms was avoided. Atomistic molecular dynamics simulations are performed with Clay force field with an imposed charge modification for the iron atoms. The NPT ensemble is used at a temperature of 298 K and a pressure of 1 bar and TIP3P water model, with a trajectory duration of 100 ns. The interaction of ammonia and hydrazine is considered and their initial molecular motion is shown. Root mean square deviation (RMSD) and radial distribution functions (RDF) of both molecules were calculated. Adsorption of some of the ammonia and hydrazine molecules is reported.
Investigation of Ti/TiN/AlTiCrN hard coating deposited by Close Field Unbalanced Magnetron Sputtering
Authors: Chitanov Vasiliy , Zlatareva Ekaterina , Kolchev Stefan , Kolaklieva Lilyana , Kakanakov Roumen , Cholakova Tetiana , Pashinski Chavdar
Published: February 2025

A Ti/TiN/AlTiCrN hard coating was developed and deposited with Close Field Unbalanced Magnetron Sputtering. Technological experiments were performed to optimize the ratio between the composed metals Al, Ti, and Cr, and the ratio metals/nitrogen. Nanoindentation measurements obtained nanohardnes of 32 GPa. A scratch test with a normal load up to 30 N indicated good adhesion to the substrate. A tribological test with a rotational drive (ball on plate) was made with a load of 3 N and a path length of 50 m. The measured coefficient of friction was 0.34. It should be noted that the reported results are obtained at temperatures as low as 270 °C, which determines the coating as universal. The obtained results are compatible with ones obtained on the same coating deposited by high-temperature and power techniques as Cathodic Arc Deposition and HIPIMS. The investigated coating is aimed to improve the performance of tools made of materials with different thermal stability.
A new through traction power supply system considering power quality improvement and multi-energy access
Authors: Liu Xinyue , Wang Guo , Min Yongzhi , Su Pengfei
Published: February 2025

The key to solving the power quality problem of the traditional traction power supply system was the through-connected traction power supply system. This study analyses the impact of the traditional through-connected traction power supply system on power quality and constructs a new through-connected traction power supply system that considers the improvement of power quality and multi-energy access. This is achieved by combining the multi-energy access scheme. A comparative analysis is conducted between the new system and the traditional through-connected traction power supply system in terms of four aspects: power quality, multi-energy access, economy and low-carbon, and stability and flexibility of the system. The results show that the advanced traction power supply device considering power quality improvement and multi-energy access has positive effects in facilitating new energy consumption, effective harmonic suppression, and cost optimization.
Networks as a Source of Competitive Career Advantage
Authors: Porter Caitlin M , Pearson Ashley H
Published: February 2025

Although it is well established that professional networking is a precursor to career success, little guidance exists for how to support employees in developing their professional networks. As such, the purpose of this chapter is to provide insights into how to develop networks for a competitive career advantage. To that end, the chapter identifies three aspects of professional networks that are consistently associated with career success outcomes—that is, large, nonredundant networks; an inner circle of developers; and higher status contacts—and reviews research evidence describing how and why each aspect promotes career success. Then, the chapter draws from career management scholarship to provide a framework for training and development specialists to guide employees in developing their networks for a competitive career advantage. Finally, it discusses three lingering questions about networks as a source of a competitive career advantage.
Knowledge Networks in Organizations
Authors: Parker Andrew , Brennecke Julia
Published: February 2025

This chapter gives an overview of the literature on knowledge networks in organizations. It highlights that there are different types of knowledge (i.e., tacit versus explicit knowledge) and details the challenges to the transfer of knowledge within organizations (i.e., the formal structure and the boundaries between knowledge domains). It also indicates different ways in which these challenges can be overcome. The chapter reviews the most relevant network theories and presents research findings regarding the antecedents and consequences of knowledge networks to help practitioners understand how and why they should attend to these networks in their organizations. The chapter concludes by specifying three areas of interest that scholars, as well as practitioners, should pay attention to in the future: (a) how virtual and hybrid work influence knowledge networks, (b) how new technology- and artificial intelligence–based search influence knowledge networks, and (c) how knowledge networks dynamically change over time.
How to Reveal, Develop, and Leverage Networks for Greater Impact
Authors: Pryor Greg , Cullen-Lester Kristin L
Published: February 2025

This book serves as a guide, offering a “beacon and blueprint” for practitioners who are revealing, developing, and leveraging organizational networks. As the beacon, it identifies desired outcomes, guiding one’s work and, as the blueprint, it offers a simple yet powerful path connecting network analysis insights to actions to develop and leverage networks. Together, creating the clarity social capital solutions need to maximize impact for an organization. This chapter offers guiding principles for revealing, developing, and leveraging networks. These principles surfaced as collective perspectives from the book’s contributors, clarifying what “must go right and can’t go wrong” for a project to succeed. The chapter concludes with a discussion of how to further address the social capital imperative by pioneering the next frontier of network science application.
Losing sleep? The effects of exposure to shootings near one’s residence on self-reported sleep quantity in Chicago, 2020-2021
Authors: McLone Suzanne , Coleman Chad , Matzke Hannah , Fox Matthew , Jay Jonathan
Published: February 2025

Firearm violence is a public health crisis in the US and negatively impacts individuals and communities. We used data from The Healthy Chicago Survey, a citywide survey of 8,754 respondents from 2020-2021, and police-reported shootings to estimate the effect of residential exposure to shootings on sleep duration. Exposure was measured as fatal or non-fatal shootings occurring within 400 meters of the respondent’s residence, within 7 days before taking the survey. Sleep was categorized into <6, 6-<7, 7-<9, and >=9 hours/day. We used log-binomial regression models to estimate risk ratios (RR). We used probabilistic quantitative bias analysis to estimate the magnitude and direction of the impact of differential outcome misclassification of self-reported sleep on results. Of 8584 survey respondents, 589 (6.9%) were exposed to at least one shooting. The estimated associations between shootings and very short and moderately short sleep duration were null, while the RR for long sleep duration was 1.10 (95% confidence interval: 1.00, 1.22); results of the quantitative bias analysis for long sleep duration were null (RR=1.04 [95% simulation interval: 0.92, 1.15]). Our findings suggest that simply living close to a recent shooting may not affect sleep duration among adults living in Chicago, but further investigation is warranted.
Postoperative Outcomes in Body Contouring Procedures Following GLP-1 Receptor Agonist Use: A 10-Year Analysis
Authors: Liang Joey , Pillai Tara , Chopra Sakshi , Cathey Jackson M , Patel Ash
Published: February 2025

Background

Utilization of GLP-1 receptor agonists (GLP-1ras) has increased dramatically in recent years. While GLP-1ras appear safe to use overall, it remains unclear if pre-operative GLP-1ra use impacts body contouring surgery outcomes.

Objectives

This study evaluated the post-operative course of patients receiving body contouring procedures by GLP-1ra use.

Methods

This retrospective cohort study examined patients who received panniculectomies, brachioplasties, thighplasties, and abdominoplasties at a large academic institution between November 2014 and November 2024. Propensity score matched analysis paired cohorts by age, smoking status, and body mass index and hemoglobin A1c at the time of surgery. Univariate analyses and multivariable logistic regression were performed.

Results

In total, 590 patients were included in this study. Of these, 100 patients utilized GLP-1ras before surgery. Propensity score matched analysis paired 72 GLP-1ra and 143 non-GLP-1ra patients. 30-day readmissions and emergency department visits in matched and unmatched cohorts were similar by GLP-1ra use (p > 0.05). Post-operative cellulitis occurred less frequently among GLP-1ra users in matched cohorts (p = 0.008); however, GLP-1ra use did not predict cellulitis on multivariable logistic regression. Duration of GLP-1ra use and timing of discontinuation prior to surgery did not predict the incidence of wound dehiscence, seromas, or hematomas. Patients using GLP-1ras for obesity had lower rates of wound dehiscence than patients using GLP-1ras for diabetes (p = 0.028).

Conclusions

Compared to non-GLP-1ra controls, GLP-1ra users have similar post-operative outcomes following body contouring surgery. Wound dehiscence is more prevalent among patients taking GLP-1ras for diabetes compared to weight loss, potentially due to worse glycemic control or associated comorbidities.
SATINN v2: automated image analysis for mouse testis histology with multi-laboratory data integration
Authors: Yang Ran , Celino-Brady Fritzie T , Dunleavy Jessica E M , Vigh-Conrad Katinka A , Atkins Georgia Rae , Hvasta Rachel L , Pombar Christopher R X , Yatsenko Alexander N , Orwig Kyle E , O’Bryan Moira K , Lima Ana C , Conrad Donald F
Published: February 2025

Analysis of testis histology is fundamental to the study of male fertility, but it is a slow task with a high skill threshold. Here, we describe new neural network models for the automated classification of cell types and tubule stages from whole-slide brightfield images of mouse testis. The cell type classifier recognizes 14 cell types, including multiple steps of meiosis I prophase, with an external validation accuracy of 96%. The tubule stage classifier distinguishes all 12 canonical tubule stages with external validation accuracy of 63%, which increases to 96% when allowing for ±1 stage tolerance. We addressed generalizability of SATINN, through extensive training diversification and testing on external (non-training population) wildtype and mutant datasets. This allowed us to use SATINN to successfully process data generated in multiple laboratories. We used SATINN to analyze testis images from 8 different mutant lines, generated from 3 different labs with a range of tissue processing protocols. Finally, we show that it is possible to use SATINN output to cluster histology images in latent space, which, when applied to the 8 mutant lines, reveals known relationships in their pathology. This work represents significant progress towards a tool for robust, automated testis histopathology that can be used by multiple labs.
A Systematic Review and Meta-Analysis Comparing the 2019 and 2005 Bosniak Classification Systems for Assessing Renal Cysts and Cystic Renal Masses: Diagnostic Accuracy and Inter-rater Agreement Evaluation
Authors: Niknejad Mohammad Taghi , Mohajeri Shiva , Javadrashid Reza , Shahir Eftekhar Mohammad , shojaeshafiei Farzaneh , Baradaran Mansoureh , Hatami Bahareh , Klontzas Michail E , Shahidi Ramin
Published: February 2025

Objectives

The Bosniak Classification System (BCS) is integral in clinical decision-making for Renal cysts and cystic renal masses, with updates in 2005 and 2019 aiming to enhance diagnostic accuracy and clinical utility. Despite these revisions, challenges in inter-rater agreement and practical applicability persist, underscoring the need for comprehensive evaluation of both versions' effectiveness in guiding patient care.

Methods

This meta-analysis adhered to PRISMA guidelines and systematically reviewed studies comparing the 2005 and 2019 BCS versions using CT or MRI. We searched PubMed, Embase, Scopus, and Web of Science, and used QUADAS-2 for quality assessment. In this study, we focused on diagnostic performance and inter-observer agreement. Statistical analysis involved bivariate random-effects modeling and assessing pooled sensitivity, specificity, and inter-rater reliability.

Results

We included eleven articles. The 2019 Bosniak classification showed higher diagnostic performance for CT (sensitivity: 85.7%, specificity: 81.9%) and MRI (sensitivity: 96.2%, specificity: 70.9%) compared to the 2005 version. Inter-rater reliability was better with the 2019 classification (CT kappa: 0.813, MRI kappa: 0.601) than with the 2005 version. The quality assessment indicated low risk of bias overall, though some studies had high risk in specific areas.

Conclusion

The Bosniak 2019 classification provides improved diagnostic specificity and inter-rater reliability compared to the 2005 version. Its adoption may enhance clinical decision-making and reduce overtreatment in managing cystic renal masses.

Advances in knowledge

This paper is novel in being the first meta-analysis to demonstrate that the Bosniak 2019 classification system significantly enhances inter-rater agreement, and overall diagnostic performance compared to the 2005 version, particularly in CT imaging, thus offering a more accurate and reliable tool for evaluating cystic renal masses.
Putting Countries on the Map? Pastoral Visits of John Paul II and International Trade
Authors: Popov Alexander
Published: February 2025

During his Pontificate from 1979 until 2005, Pope John Paul II visited 130 countries, more than the 263 Popes before him combined. I document a significant increase in exports to trading partners with a relatively high share of Catholics following a Pastoral visit, leading to a non-negligible increase in aggregate exports. The biggest beneficiaries in terms of increased trade are visited countries that are at lower stages of economic development and have relatively fewer Catholics and relatively weaker trade links. The effect is absent for other prominent episodes, such as visits by celebrated secular leaders or global sports events.