bluuebunny/crossref_metadata_2025 huggingface dataset:
A Framework for Parameters Planning in Hybrid Milling of Flexible Blades
Authors: Dai Xing , Cao Zhengtong , Huang Tao , Zhang Xiao-Ming , Ding Han
Published: January 2025

In the milling of flexible workpieces, like thin-wall blades, cutting force-induced deformation and vibration are great adverse issues that are almost inevitable. Semifinish–finish (SF) hybrid milling is a promising strategy to obtain a high accuracy and quality surface, which separates the object blade into several layers in the radial direction of the blisk, and then for each layer, from blade tip to root, using SF hybrid milling as a cycle to finish the current layer, and then move to the next layer. However, two extra contradictions are introduced in process planning: (1) considering the number of layers: To decrease the machining deformation error, we should increase the number of layers, which however increases the time consumption as well as the number of tool marks because of frequent tool path switching between semifinish and finish; (2) as to the allowance of semifinish: To decrease the machining deformation error, we should increase the allowance in semifinish to enhance stiffness, which however increases tool wear since more material needs to be removed by the tool. To balance the contradictions, this article constructs a framework for parameters planning in SF hybrid milling, in which the allowance, number of layers, as well as lengths of each layer are optimized so that we are able to control the deformation error while maintaining high cutting efficiency. The method is verified by simulation and validation experiments. Compared with traditional nonlayering and uniform layering machining, the maximum deformation error by optimized layering machining is reduced by 76.4% and 48.6%, respectively.
Dataset on Complex Power Systems: Design for Resilient Transmission Networks Using a Generative Model
Authors: Chung In-Bum , Wang Pingfeng
Published: January 2025

Critical infrastructure systems are prone to be a target of disruptions from either natural disasters or deliberate attacks that can cause a great magnitude of negative consequences. To withstand and reduce the impacts of disruptive events, it is beneficial to consider them in the early stage of design to create a configuration that can maintain its performance as much as possible. However, due to the complexity of modern systems, it is difficult to optimize the large-scale system design with complicated constraints directly. To overcome this issue, it is advantageous to tackle the problem with a state-of-the-art data-driven approach. However, a subsequent problem arises due to the limited amount of data for power systems available to the public. Hence, synthesized power systems that resemble existing networks are collected and processed, which can be utilized for implementing machine-learning models. The power system networks are represented as graphs with features holding information on the nodes and edges that relate to electrical components. Using the generated dataset, this research implements a generative approach based on Wasserstein generative adversarial networks (GAN) to create intelligent designs. Afterward, performance measures are employed to induce the generation of a population with more appealing characteristics. To demonstrate the practicality of the dataset on data-driven design, it was applied to train the generative model for a 57-bus system and the trained generator was employed to an optimization framework. The results have shown that the model could generate feasible designs of higher resilience and the framework was capable of finding an optimal network design with improved performance.
Predicting Multijoint Maximal Eccentric and Concentric Strength With Force-Velocity Jump Mechanics in Collegiate Athletes
Authors: McClean Zachary J. , McKenzie Mark , Zukowski Matthew , Foley Landon , Pasanen Kati , Herzog Walter , Nabhan Dustin , Jordan Matthew J.
Published: January 2025

Purpose: Maximal muscle strength is often assessed with single-joint or repetition-maximum testing. The purpose of this study was to evaluate the reliability of countermovement-jump (CMJ) velocity–load testing and assess the relationship between CMJ velocity–load kinetics and concentric–isometric–eccentric multijoint leg-extension strength tested on a robotic servomotor leg press in trained athletes. Methods: University athletes (N = 203; 52% female) completed 3 concentric, isometric, and eccentric maximum voluntary leg-extension contractions on the robotic leg press, followed by CMJ velocity–load testing with an additional external load of 0% (CMJBW), 30% (CMJ30), and 60% (CMJ60) of body mass. A linear model was fit for the CMJ takeoff velocity–load relationship to obtain the load intercept. Force–velocity parameters were obtained for the CMJ eccentric deceleration and concentric phases. Linear mixed-effects models were constructed to predict concentric, isometric, and eccentric leg-press force using the CMJ takeoff velocity–load relationship and CMJ kinetics. Results: Isometric leg-press strength was predicted by load intercept and sex (P < .001, R2 = .565, prediction error = 14%). Concentric leg-press strength was predicted by load intercept, CMJ60 concentric impulse, and sex (P < .001, R2 = .657, prediction error = 10%). Eccentric leg-press strength was predicted by minimum downward velocity, CMJ60 eccentric deceleration impulse, and sex (P < .001, R2 = .359, prediction error = 14%). Conclusions: Given the relevance of muscle-strength testing for sport performance and injury prevention, assessing force–velocity mechanics with loaded CMJ testing is a reliable and viable approach to predict maximal concentric, isometric, and eccentric leg-press strength in competitive athletes.
How FocA facilitates fermentation and respiration of formate by
Escherichia coli
Authors: Sawers R. Gary
Published: January 2025

Formic acid is an important source of reductant and energy for many microorganisms. Formate is also produced as a fermentation product, e.g., by enterobacteria like
Escherichia coli
. As such, formic acid shares many features in common with dihydrogen, explaining perhaps why their metabolism and physiology show considerable overlap. At physiological pH, formic acid is mainly present as the dissociated formate anion and therefore cannot diffuse freely across the cytoplasmic membrane. Specific and bidirectional translocation of formate across the cytoplasmic membrane is, however, achieved in
E. coli
by the homopentameric membrane protein, FocA. Formic acid translocation from the cytoplasm into the periplasm (efflux) serves to maintain a near-neutral cytosolic pH and to deliver formate to the periplasmically-oriented respiratory formate dehydrogenases, Fdh-N and Fdh-O. These enzymes oxidize formate, with the electrons being used to reduce nitrate, oxygen, or other acceptors. In the absence of exogenous electron acceptors, formate is re-imported into the cytoplasm by FocA, where it is sensed by the transcriptional regulator FhlA, resulting in induction of the formate regulon. The genes and operons of the formate regulon encode enzymes necessary to assemble the formate hydrogenlyase complex, which disproportionates formic acid into H
2
and CO
2
. Combined, these mechanisms of dealing with formate help to maintain cellular pH homeostasis and are suggested to maintain the proton gradient during growth and in stationary phase cells. This review highlights our current understanding of how formate metabolism helps balance cellular pH, how it responds to the redox status, and how it helps conserve energy.
The role of N
6
-methyladenosine (m
6
A) mRNA modifications in herpesvirus infections
Authors: Verhamme Ruth , Favoreel Herman W.
Published: January 2025

Herpesviruses, a family of large enveloped DNA viruses, establish persistent infections in a wide range of hosts. This characteristic requires an intricate network of interactions with their hosts and host cells. In recent years, the interplay between herpesviruses and the epitranscriptome—chemical modifications in transcripts that may affect mRNA biology and fate—has emerged as a novel aspect of herpesvirus-host interactions. In particular, herpesviruses display different mechanisms to modulate and usurp the most abundant mRNA modification, N6-methyladenosine or m
6
A. Some herpesviruses interfere with m
6
A methylation of transcripts, while others enhance or take advantage of m
6
A methylation of viral and/or cellular transcripts. In many cases, herpesviruses appear to modulate the m
6
A methylation process to suppress the antiviral host response. This review highlights the strategies used by members of the different herpesvirus subfamilies to manipulate host m
6
A mediators and how these contribute to virus replication and the antiviral host response. Research aimed at deciphering the interaction of herpesviruses with the m
6
A epitranscriptome not only may lead to new avenues in the design of antiviral and immunomodulatory strategies, but also provides new insights in the regulation and the role of m
6
A transcript methylation in general.
Whole genome sequencing and analysis of benzo(a)pyrene-degrading bacteria Bacillus cereus M72-4
Authors: Jin Aofei , Tursun Dilbar , Tan Lirong , Yang Zhuonan , Duo Zhixian , Qin Yanan , Zhang Rui
Published: January 2025

Benzo(a)pyrene produced by food during high-temperature process enters the body through ingestion, which causes food safety issues to the human body. To alleviate the harm of foodborne benzo(a)pyrene to human health, a strain that can degrade benzo(a)pyrene was screened from Kefir, a traditional fermented product in Xinjiang. Bacillus cereus M72-4 is a Gram-positive bacteria sourced from Xinjiang traditional fermented product Kefir; under benzo(a)pyrene stress conditions, there was 69.39% degradation rate of 20 mg/L benzo(a)pyrene by strain M72-4 after incubation for 72 h. The whole genome of M72-4 was sequenced using PacBio sequencing technology in this study. The genome size was 5754 801 bp and a GC content was 35.24%; a total of 5719 coding genes were predicted bioinformatically. Through functional database annotation, it was found that the strain has a total of 219 genes involved in the transportation and metabolism of hydrocarbons, a total of 9 metabolic pathways related to the degradation and metabolism of exogenous substances, and a total of 67 coding genes. According to the Kyoto Encyclopedia of Genes and Genomes database annotation results, a key enzyme related to benzo(a)pyrene degradation, catechol 2,3-dioxygenase, was detected in the genome data of Bacillus cereus M72-4, encoding genes dmpB and xylE, respectively. There are also monooxygenases and dehydrogenases. Therefore, it can be inferred that this strain mainly degrades benzo(a)pyrene through benzoate metabolic.
Application of Multidimensional Data Analysis in Network Intrusion Detection System
Authors: Xin Jisheng
Published: January 2025

It is important to understand the behavior of an information network and its features. In this research, we explore this idea by applying a multidimensional data analysis system, a system that is significant in enhancing data intrusion detection. To accomplish this, we gather data from various sources, like network traffic, user behavior, and system logs. Using the examples mentioned, the proposed system detects and prevents cyber threats more accurately. Artificial Intelligence techniques including deep learning, clustering, and principal component analysis (PCA), are used. These are essential in analyzing complex patterns within the data and enabling the detection of sophisticated and evolving intrusion techniques. Multidimensional data allows for the capture of intricate, non-linear relationships. This improves the system’s ability to differentiate between normal and abnormal activities. Real-time data processing and AI-driven algorithms enhance detection speed and enable faster responses to potential intrusions. We tested this system on benchmark datasets. Results showed significant improvements in detection rates and a reduction in false positives compared to traditional NIDS. The integration of AI gave us a more adaptive and scalable approach to intrusion detection. Also, it allowed the system to learn from new attack patterns and continuously refine its capabilities.
Construction of an Intelligent Medical Monitoring System Network for Exercise Rehabilitation Based on the Internet of Things
Authors: Wang Bin
Published: January 2025

Exercise rehabilitation refers to a rehabilitation method that restores or improves human motor function through scientific, systematic, and regular exercise training, and it has a very wide range of applications. This paper explored the construction of an intelligent medical monitoring system network for sports rehabilitation based on the Internet of Things (IoT) technology. The system consists of a wireless sensor acquisition module for collecting sports rehabilitation data, a data storage and analysis module for cloud management and deep learning analysis, and a medical monitoring module for remote supervision and patient interaction. The system aimed to achieve personalized exercise rehabilitation plans through real-time monitoring and data collection of patients, and provide remote medical monitoring services to improve the rehabilitation effect and quality of life of patients. The system architecture adopted the IoT technology and cloud storage technology, and combined the deep learning convolutional neural network (CNN) model to achieve remote monitoring and data analysis. The monitoring center of the system can monitor patients’ physiological indicators, exercise status, and rehabilitation effects in real time through IoT wireless sensors, and upload the data to the cloud platform for storage and analysis. Doctors and patients can access data through mobile phones or computers and communicate online. The data showed that when measuring a patient’s heart rate using wireless sensors, the measurement accuracy mostly exceeded 99.8%, and some samples had detection accuracy up to 100%. In addition, traditional sensors can detect heart rates up to 5.1[Formula: see text]s, while wireless sensors can detect heart rates up to 1.28[Formula: see text]s. The CNN had a maximum recall rate of 99.51%, a precision of 99.68%, and an accuracy rate of 99.8% in the classification of psychological data of rehabilitation patients. The experimental results indicated that the system can achieve comprehensive monitoring and effective management of the patient’s rehabilitation process, and its operation is stable and has good practical value.
Layout Design Method of Indoor Space Environment Artistic Elements Based on GA-SA Hybrid Algorithm
Authors: Fu Qing , li Yan
Published: January 2025

In order to efficiently lock the optimal layout strategy and avoid the trap of local optimal solution of a single SA algorithm, a layout design method of artistic elements in indoor space environment based on GA-SA hybrid algorithm is proposed. On the basis of analyzing the basic principles of indoor space environment layout, we should establish the goal of maximizing hierarchy, the relevance of artistic elements, simplicity, comfort, the distance between adjacent artistic elements and the utilization of effective activity space, and this goal should not be interfered with by space boundaries and artistic elements. The layout optimization model for artistic elements in indoor spatial environments, considering constraints such as human activity accessibility, is solved using a GA-SA hybrid algorithm. This approach involves initializing the population, defining a fitness function, and applying selection, crossover and mutation operations. The Metropolis-based jumping mechanism is incorporated to enhance solution exploration, iterating with a gradually decreasing temperature until the optimal layout is achieved. Experimental results indicate that this method effectively arranges artistic elements within indoor spaces, conserving floor area while creating a more spacious, comfortable and functionally distinct activity zone. The user satisfaction score reached 4.8, demonstrating the effectiveness of this layout strategy.
Research on Interactive Teaching of College English Based on Deep Learning and Virtual Reality
Authors: Lu Jingjing
Published: January 2025

As digital technologies advance, the need for interactive teaching methodologies in higher education has become crucial, particularly in the context of English language learning. This study explores innovative approaches utilizing deep learning and virtual reality to enhance student engagement and learning outcomes in college environments. The primary aim is to investigate the effectiveness of an Intelligent Coral Reef Optimization-driven Redefined Long Short-Term Memory (ICR-RLSTM) model, designed to facilitate a more dynamic and personalized learning experience in college English courses. The dataset comprises diverse English language materials, including texts, audio and interactive exercises, sourced from various educational platforms. Data pre-processing involves standardization techniques and the application of Natural Language Processing (NLP) algorithms to ensure the information is clean and structured for optimal analysis. The proposed interactive teaching model integrates gamification elements and virtual environments to provide students with immersive learning experiences. The ICR-RLSTM algorithm enhances predictive capabilities by optimizing long-term memory retention through intelligent coral reef optimization techniques, adapting to individual learner progress. Preliminary results indicate that the proposed model significantly improves student engagement and knowledge retention compared to traditional methods. The findings suggest that integrating deep learning and virtual reality into English language teaching can create adaptive and effective learning environments. This research contributes valuable insights into enhancing interactive teaching strategies and informs future educational practices.
Research on Intelligent Detection of False Flow in Network Marketing Based on Artificial Intelligence
Authors: Teng Ying
Published: January 2025

The proliferation of false flow in online platforms poses significant threats to user security and corporate governance due to its short lifespan and diverse fraudulent methods. To address the need for high reliability and real-time detection, this paper proposes an artificial intelligence (AI)-based active detection system. The system intelligently collects webpage URLs, calculates similarity using edit distance, and flags URLs exceeding a preset threshold as suspected targets for further analysis. Leveraging the ball vector machine (BVM) algorithm, the proposed method effectively classifies pre-processed feature vectors, achieving a detection accuracy of 95.6% and reducing the false alarm rate by 15% compared to support vector machine (SVM)-based methods. Experimental results on 500 legitimate and 500 fake webpages demonstrate its efficiency and suitability for large-scale applications, providing a robust and proactive solution to false flow detection.
Research on Integration of Innovation and Entrepreneurship Education Resources Based on Embedded Neural Network Algorithm
Authors: Wang Zhuhua
Published: January 2025

With the comprehensive promotion of the national innovation-driven development strategy, innovation and entrepreneurship education has become the core battlefield for cultivating talents with innovative spirit, entrepreneurial ability, and social responsibility. This field is not only related to individual growth and career development, but also the core engine for promoting the transformation and upgrading of the national economy and achieving innovation-driven development. This paper introduces advanced embedded network technology to explore new paths for optimizing resource allocation. This paper constructs a multi-level innovation and entrepreneurship education resource integration model based on embedded network technology. Based on the clustering results of embedded networks, this paper further designs a targeted resource integration scheme. Several typical cases were selected for empirical analysis. By comparing and analyzing key indicators such as educational effectiveness, student satisfaction, and entrepreneurial success rate before and after implementing resource integration, the application effectiveness of embedded network technology in innovation and entrepreneurship education resource integration was evaluated. The practical results show that this model can significantly improve the efficiency and quality of educational resource utilization, providing strong support for the sustainable and healthy development of innovation and entrepreneurship education.
Numerical and computational fluid dynamics experimental analysis of novel extended tetra-hybrid Tiwari and Das Sisko nanofluid passed a stenosed artery
Authors: Islam Nazrul , Sajid Tanveer , Ahmad Shabbir , Santos Elizaldo Domingues dos , Junjua Moin-ud-Din , Irshad Kashif , Hendy Ahmed S.
Published: January 2025

The medical industry extensively uses nanoparticles for applications such as wound dressing, artificial organ components, drug delivery, tissue engineering, and cardiovascular disease treatment. The incorporation of nanoparticles into the base fluid enhances the rate of heat transmission and additionally decreases blood pressure. This study aims to examine the effects of a new tetra-hybrid nanofluid model, which includes nanoparticles, on the flow of blood through a stenosed artery with a circular shape. Model partial differential equations (PDEs) incorporate phenomena such as thermal radiation and viscous dissipation. Furthermore, we transform these modeled PDEs into dimensionless ordinary differential equations (ODEs) using self-similarity variables and numerically solve the proposed ODEs using the well-established Lobatto IIIa numerical technique. The impact of several dimensionless parameters on the heat transfer rate, skin friction, velocity, and temperature fields has been computed and analyzed using figures and tables. Moreover, we conducted a computational fluid dynamics (CFD) investigation on a tetra-hybrid nanofluid, using blood as the base fluid. The results indicate that heat transmission is higher in tetra-hybrid nanoparticles compared to tri-hybrid and di-hybrid nanofluids. The volume fraction of nanoparticles in the base fluid increases, resulting in a decrease in the surface drag coefficient and a decrease in the heat transport phenomenon. Amplification in the thermal radiation parameter improves heat transfer, helping to remove toxins and plaque from blood flowing through arteries. An increase in thermal radiation generates excess heat that dilates inflexible arteries, facilitating blood flow. From CFD analysis, it is observed that thermal conduction k and heat transfer coefficient h amplify by improving Reynolds number. Pressure at the outlet interface of the tube decreases from 3058 Pa to 2996 Pa for the case of a 10% volume fraction of nanoparticles. Velocity boundary layer thickness decreases from 1.46 to 1.37 with an increase in the volume fraction of nanoparticles from 1% to 10%. Heat deliverance rate amplifies in the case of tetrahybrid nanofluid 2.5394–2.6147 in contrast to trihybrid nanofluid 2.4008 to 2.4711 by amplifying Rd from 0.7 to 1.1. The surface drag coefficient amplifies by magnifying Re but the Nusselt number diminishes by improving the volume fraction of nanoparticles.
An Intelligent Optimized Compression Framework for Columnar Database
Authors: Jadhawar B. A. , Sharma Narendra
Published: January 2025

Instead of storing data in rows, a columnar database is a type of Database Management System (DBMS). To speed up the processing and reply to a question, a columnar database’s job is to efficiently write and read data to and from hard disc storage. One of the most crucial methods in the creation of column-oriented database systems is compression. For columns with Zero-length string types, all heavier and light-in-weight compression techniques have limitations. Processing of transactions online, these databases are substantially more effective for online analytical processing than for online transactional processing. This indicates that although they are made to examine transactions, they are not very effective at updating them. To overcome these issues a Zero Length Recurrent based Fruit Fly Optimization (ZLRFF) model is used. Additionally, a reduction technique is known as ZLRFF was designed to achieve a high compression ratio and allow straight lookups on compressed material without decompression first. ZLRFF’s main goal is to divide a Zero-length string written column vertically into smaller columns that can each be compressed using a separate lightweight compression technique. To search directly on compressed data, we also provide a search technique we call FF-search. Extensive testing demonstrates that ZLRFF supports direct searching on compressed data in addition to achieving a decent compression ratio, which enhances query performance.
Optimized Feature Selection Approach with Elicit Conditional Generative Adversarial Network Based Class Balancing Approach for Multimodal Sentiment Analysis in Car Reviews
Authors: Kothuri Sri Raman , Rajalakshmi N. R.
Published: January 2025

Multimodal Sentiment Analysis (MSA) is a growing area of emotional computing that involves analyzing data from three different modalities. Gathering data from Multimodal Sentiment analysis in Car Reviews (MuSe-CaR) is challenging due to data imbalance across modalities. To address this, an effective data augmentation approach is proposed by combining dynamic synthetic minority oversampling with a multimodal elicitation conditional generative adversarial network for emotion recognition using audio, text, and visual data. The balanced data is then fed into a granular elastic-net regression with a hybrid feature selection method based on dandelion fick’s law optimization to analyze sentiments. The selected features are input into a multilabel wavelet convolutional neural network to classify emotion states accurately. The proposed approach, implemented in python, outperforms existing methods in terms of trustworthiness (0.695), arousal (0.723), and valence (0.6245) on the car review dataset. Additionally, the feature selection method achieves high accuracy (99.65%), recall (99.45%), and precision (99.66%). This demonstrates the effectiveness of the proposed MSA approach, even with three modalities of data.
ICE: Incremental Subspace Clustering of High-Dimensional Categorical Data
Authors: Pang Ning , Zhang Chaowei , Zhang Jifu , Qin Xiao
Published: January 2025

Subspace clustering is an effective way to analyze high-dimensional data. The main problems of the conventional subspace clustering techniques are as follows: first, conventional clustering methods can not describe categorical attribute space in more detail; second, most subspace-clustering techniques failure to process dynamic data effectively; finally, lack of effective noise recognition leads to the decline of the efficiency of incremental subspace-clustering analysis. We address the above problems by an incremental subspace-clustering algorithm — called ICE. With attribute subspace constructed by a rough set-based weight computing method, ICE obtains clustering results through initial and incremental clustering stage. Utilizing the original cluster results generated from initial clustering stage, we adopt merging and splitting operation to dynamic adjust cluster-structure in incremental clustering stage. Before achieving the final results, a polymerization-based noise recognition technique is employed to automatically identify noise from sparse clusters without human threshold intervention. We implement ICE on synthetic and real-world datasets. The experimental results reveal that incremental subspace-clustering method can achieves satisfactory performance on extensibility, accuracy and robustness.
Risk Index Based Uncertain Portfolio Selection with Monotone Increasing Multiplicative Background Risk
Authors: Ma Di , Huang Xiaoxia , Choe Kwang-Il
Published: January 2025

Investors usually invest not only in risky assets but also in risk-free assets and face not only portfolio risk but also background risk. This paper discusses an uncertain portfolio selection problem in risky assets and risk-free assets with monotone increasing multiplicative background risk (MBR), which is prevalent but less research has been done. To do so, we first propose an uncertain mean-risk index model based on uncertainty theory where the security return and MBR are regarded as uncertain variables and give the deterministic form of the model. Then for further analysis, we discuss the critical constraint and optimality condition of the model. Based on the discussion, we study the influence of uncertain MBR on the investors’ decisions. Finally, we provide the case analysis to illustrate the application of our method and the analysis results.
Hardware-Aware Color-Distorted Image Classifier for Intelligent White Balance
Authors: Huang Chengqiang , Zhu Yongxin , Wang Hui , Feng Songlin
Published: January 2025

Automatic white balance (AWB) is an important module for cameras and classification of the color-distorted image is critical to realize intelligent AWB. Though accurate classifiers usually can be achieved via deep neural network models, they cannot fit into embedded hardware due to their complexity. To increase the classification accuracy and decrease latency, a lightweight convolutional neural network (CNN) with a histogram layer for AWB (AWBHNet) is constructed, which consists of one histogram layer, one regular convolutional layer, three depth separable convolutional layers, four pooling layers, two fully connected layers and two dropout layers. One-tenth of ImageNet is utilized as the normal image dataset. To generate various distorted colors, histogram shifting and matching are proposed to randomly adjust the histogram position or shape. Furthermore, the extent of shifting or matching is randomly generated to ensure the diversity of color distortion. Subsequently, the proposed AWBHNet and other CNNs are successively trained. Experiments show that the accuracy of the classifier trained by AWBHNet is 0.9150, which is at least 1.33% higher than each regular or lightweight network. Finally, intelligent AWB is realized on smartphones, the inference latency of AWBHNet is 47.6% lower than the best existing network.
Sedimentology, lithostratigraphy, paleogeography and water level changes in a lacustrine basin: the depositional model of Lake Pannon
Authors: Sztanó Orsolya , Balázs Attila , Bartha István Róbert , Ben Mahrez Hana , Horányi Anna , Kovács Ádám , Šujan Michal , Tőkés Lilla , Törő Balázs , Vallner Fanni , Varga Gábor , Magyar Imre
Published: January 2025

Advances in sedimentology and basin analysis provide the foundation for a comprehensive basin-fill model and lithostratigraphic framework of Lake Pannon, Central Europe, highlighting the dynamical changes of the depositional environments. The lake's ca. 8 Myr long evolution began with transgression and deepening, followed by normal regression, forming up to 7 km thick sediment fill. Initial coarse-grained coastal deposits are overlain by offshore to deep-water marls, with varying carbonate- and organic-matter content and include both anoxic laminites and sediment gravity-flow deposits. Later confined and unconfined turbidite systems developed due to interactions between basin-floor relief and shelf-slope progradation. Stacked deltaic cycles followed by clayey alluvial plain deposits with anastomosing and meandering sandy channel fills comprise the upper part of the succession. Repeated aggradational to progradational clinothem architectures, thickness and distribution of muddy to sandy delta lobes on the shelf, and distribution of turbidite lobes in the deep basins reflect lake-level fluctuations. While climate primarily controlled lake level, coastal sedimentation is identified as a new factor driving long-term lake-level rise. Therefore, Lake Pannon's stratigraphy demonstrates that, in contrast to marine systems, in supply-dominated endorheic lakes accommodation space is created by sedimentation.
MSA-Net: A Multi-Scale Information Diffusion Model Awaring User Activity Level
Authors: Tang Yinzhou , Piao Jinghua , Wang Huandong , Wang Yue , Li Yong
Published: January 2025

Modeling information diffusion on social networks can be used to guide the prediction and control of information propagation and improve the structure and functionality of social networks. Existing information diffusion prediction methods can predict information diffusion paths and its volume by modeling social network structure and user behavior. However, none of the existing methods take user activity level, which is proved to be critical in modeling the information diffusion process, into account, thus weaken the prediction accuracy. To solve this problem, this paper proposes a Multi-Scale Activity Network (MSA-Net) to capture topological and historical affect features for different scales and to predict the users who will be affected at a specific future timestamp with the help of user activity level. Specifically, we first learn the network representation of three scales or levels: micro-scale, meso-scale, and macro-scale, which refers to the user level, intra-community level, and inter-community level, respectively. Then, we introduce the user activity level for each user by using user degree and average number of tweets per time unit to model the individual differences of users to achieve a more accurate prediction. Extensive experiments based on real-world datasets show that MSA-Net achieves a 6.14% improvement in terms of precision, a 6.74% improvement in terms of recall metrics, a 4.26% improvement in terms of F1-score, a 3.15% improvement in terms of MAP, and a 25.78% improvement in terms of NRMSE over the best existing baseline. The code and data are available at https://github.com/tsinghua-fib-lab/MSA-Net.
Causal Inference for Recommendation: Foundations, Methods and Applications
Authors: Xu Shuyuan , Ji Jianchao , Li Yunqi , Ge Yingqiang , Tan Juntao , Zhang Yongfeng
Published: January 2025

Recommender systems are important and powerful tools for various personalized services. Traditionally, these systems use data mining and machine learning techniques to make recommendations based on correlations found in the data. However, relying solely on correlation without considering the underlying causal mechanism may lead to various practical issues such as fairness, explainability, robustness, bias, echo chamber and controllability problems. Therefore, researchers in related area have begun incorporating causality into recommendation systems to address these issues. In this survey, we review the existing literature on causal inference in recommender systems. We discuss the fundamental concepts of both recommender systems and causal inference as well as their relationship, and review the existing work on causal methods for different problems in recommender systems. Finally, we discuss open problems and future directions in the field of causal inference for recommendations.
Evaluating Self-Supervised Learning for WiFi CSI-Based Human Activity Recognition
Authors: Xu Ke , Wang Jiangtao , Zhu Hongyuan , Zheng Dingchang
Published: January 2025

With the advancement of the Internet of Things (IoT), WiFi Channel State Information (CSI)-based Human Activity Recognition (HAR) has garnered increasing attention from both academic and industrial communities. However, the scarcity of labeled data remains a prominent challenge in CSI-based HAR, primarily due to privacy concerns and the incomprehensibility of CSI data. Concurrently, Self-Supervised Learning (SSL) has emerged as a promising approach for addressing the dilemma of insufficient labeled data. In this paper, we undertake a comprehensive inventory and analysis of different categories of SSL algorithms, encompassing both previously studied and unexplored approaches within the field. We provide an in-depth investigation and evaluation of SSL algorithms in the context of WiFi CSI-based HAR, utilizing publicly available datasets that encompass various tasks and environmental settings. To ensure relevance to real-world applications, we design experiment settings aligned with specific requirements. Furthermore, our experimental findings uncover several limitations and blind spots in existing work, shedding light on the barriers that need to be addressed before SSL can be effectively deployed in real-world WiFi-based HAR applications. Our results also serve as practical guidelines and provide valuable insights for future research endeavors in this field.
EDA-Copilot: A RAG-Powered Intelligent Assistant for EDA Tools
Authors: Xiao Zhe , He Xu , Wu Haoying , Yu Bei , Guo Yang
Published: January 2025

With the rise of Large Language Models (LLMs), researchers have become increasingly interested in their applications in EDA flows, particularly in specific subdomains like serving as knowledge assistants and generating RTL code. In this study, we present a Retrieval-Augmented Generation (RAG) framework tailored to EDA task processing, named EDA-Adaptive RAG. This framework addresses the implicit semantics of EDA data and facilitates efficient knowledge acquisition through classification and enhanced retrieval, significantly enhancing LLMs ability to acquire EDA knowledge. Furthermore, we aim to integrate RAG into the design process as an EDA assistant application. Using RTL code generation as a case study, we demonstrate that the performance of RTL code generation can be enhanced through highly relevant retrievals provided by our RAG. The experimental analysis involves EDA Q&A tasks and RTL code generation evaluation. It is shown that our method outperforms the latest works in terms of both answer stability and code quality.
Point Cloud-Based Deep Learning in Industrial Production: A Survey
Authors: Liu Yi , Zhang Changsheng , Dong Xingjun , Ning Jiaxu
Published: January 2025

With the rapid development of 3D acquisition technology, point clouds have received increasing attention. In recent years, point cloud-based deep learning has been applied to various industrial scenarios, promoting industrial intelligence. However, there is still a lack of review on the application of point cloud-based deep learning in industrial production. To bridge this gap and inspire future research, this article provides a review of current point cloud-based deep learning methods applied to industrial production from the perspective of different application scenarios, including pose estimation, defect inspection, measurement and estimation, and so on. Considering the real-time requirement of industrial production, this article also summarizes real-time point cloud-based deep learning methods in each application scenario. Then, this article introduces commonly used evaluation metrics and public industrial point cloud datasets. Finally, from the aspects of the dataset, speed and industrial product specificity, the challenges faced by current point cloud-based deep learning methods in industrial production are discussed, and future research directions are prospected.
Performance Comparison of Al2O3 Gate Dielectric Grown on 4H-SiC Substrates via Thermal and Plasma-Enhanced Atomic Layer Deposition Methods
Authors: Guo Yun-Duo , Wang An-Feng , Huang Qi-Min , Wang Zhen-Yu , Ma Hong-Ping , Zhang Qing-Chun
Published: January 2025

This study systematically compared the material and electrical properties of Al2O3 films deposited on n-type 4H-SiC substrates using thermal and plasma-enhanced atomic layer deposition (ALD), referred to as PE-Al2O3 and T-Al2O3, respectively. Atomic force microscopy data indicates that the roughness of Al2O3 deposited on SiC substrates by both ALD procedures is low. X-ray photoelectron spectroscopy analysis suggests that the proportion of hydroxides on T-Al2O3 surfaces is greater than that on PE-Al2O3. Based on O 1s energy loss spectra and fitting of absorption spectra, the bandgap of Al2O3 films ranges from 6.1 to 6.2 eV, with PE-Al2O3 exhibiting a slightly higher bandgap. As for C–V data analysis of MOS capacitors, PE-Al2O3/SiC possesses a lower interface defect density and border traps in oxide layer than T-Al2O3/SiC. Further I–V testing demonstrates that the breakdown field of PE-Al2O3 is 8.7 MV cm−1, with leakage current maintained at the order of 10–8 A cm−2. In contrast, T-Al2O3 displays a breakdown field of 7.2 MV cm−1 and a significant “soft” breakdown. The effective barrier height of PE-Al₂O₃/SiC is determined to be 1.10 eV based on Fowler-Nordheim tunneling mechanism fitting, which is greater than 0.952 eV for T-Al2O3. These results confirm the advantages of using the PEALD method.
Supplementary Table S1 from Deep Learning Predicts Subtype Heterogeneity and Outcomes in Luminal A Breast Cancer Using Routinely Stained Whole-Slide Images
Authors: Kurian Nikhil Cherian , Gann Peter H. , Kumar Neeraj , McGregor Stephanie M. , Verma Ruchika , Sethi Amit
Published: January 2025

4-part table showing hospital sources for cases in the training, validation, initial testing, and final testing subsets.
Hospital Variability in the Use of Vasoactive Agents in Patients Hospitalized for Acute Decompensated Heart Failure for Clinical Phenotypes
Authors: Shiraishi Yasuyuki , Niimi Nozomi , Kohsaka Shun , Harada Kazumasa , Kohno Takashi , Takei Makoto , Jimba Takahiro , Nakano Hiroki , Matsuda Junya , Shindo Akito , Kitano Daisuke , Tsukamoto Shigeto , Koba Shinji , Yamamoto Takeshi , Takayama Morimasa
Published: January 2025

BACKGROUND:

The absence of practice standards in vasoactive agent usage for acute decompensated heart failure has resulted in significant treatment variability across hospitals, potentially affecting patient outcomes. This study aimed to assess temporal trends and institutional differences in vasodilator and inotrope/vasopressor utilization among patients with acute decompensated heart failure, considering their clinical phenotypes.

METHODS:

Data were extracted from a government-funded multicenter registry covering the Tokyo metropolitan area, comprising consecutive patients hospitalized in intensive/cardiovascular care units with a primary diagnosis of acute decompensated heart failure between January 2013 and December 2021. Clinical phenotypes, that is, pulmonary congestion or tissue hypoperfusion, were defined through a comprehensive assessment of clinical signs and symptoms, vital signs, and laboratory findings. We assessed the frequency and temporal trends in phenotype-based drug utilization of vasoactive agents and investigated institutional characteristics associated with adopting the phenotype-based approach using generalized linear mixed-effects models, with random intercepts to account for hospital-level variability.

RESULTS:

Among 37 293 patients (median age, 80 years; 43.7% female), 88.6% and 21.2% had pulmonary congestion and tissue hypoperfusion status, respectively. Throughout the study period, both overall and phenotype-based vasodilator utilizations showed significant declines, with overall usage dropping from 61.4% in 2013 to 48.6% in 2021 (
P
trend
<0.001). Conversely, no temporal changes were observed in overall inotrope/vasopressor utilization from 24.6% in 2013 to 25.8% in 2021 or the proportion of phenotype-based utilization. Notably, there was considerable variability in phenotype-based drug utilization among hospitals, with a median ranging from 48.3% to 77.8%. In multivariable-adjusted models, a higher number of board-certified cardiologists were significantly associated with lower rates of phenotype-based vasodilator utilization and reduced inappropriate inotrope/vasopressor utilization, while tertiary care hospitals were linked to more appropriate inotrope/vasopressor utilization.

CONCLUSIONS:

Substantial variability existed among hospitals in phenotype-based drug utilization of vasoactive agents for patients with acute decompensated heart failure, highlighting the need for standardized treatment protocols.

REGISTRATION:

URL:
https://www.umin.ac.jp/ctr/index.htm
; Unique identifier: UMIN000013128.
Pharcidopsyche novemplicata, a curious new genus and species of the Psychidae, representing new subfamily Pharcidopsychinae (Lepidoptera, Tineoidea)
Authors: SAIGUSA TOYOHEI , SUGIMOTO MIKA
Published: January 2025

The psychid genus Pharcidopsyche Saigusa et Sugimoto, gen. nov. and its type species, Pharcidopsyche novemplicata Saigusa et Sugimoto, sp. nov. (Lepidoptera, Psychidae) are described based on the adults, larvae and larval case from Sabah, Borneo. Pharcidopsychinae, subfam. nov. is established for the new genus. The adult and larval morphology, larval case and biology of the type species are described.
Contrasting Trends in Colorado Fire Weather Index from Reanalysis and Observations
Authors: Silver Max M. , Lundquist Julie K.
Published: January 2025

Recent wildfires in Colorado raise the question of whether rising global temperatures have increased fire weather occurrences in Colorado. The U.S. National Weather Service defines fire weather as when “forecast weather conditions will result in a significant threat for the ignition and/or spread of wildfires.” We use two datasets to address the question: “How has the occurrence of fire weather changed in Colorado?” Using 22 years of observed weather conditions from a meteorological tower at the National Renewable Energy Laboratory and 67 years of ERA5 reanalysis data, we assess changing trends in Colorado fire weather as defined by hot, dry, and windy conditions. Additionally, we explore if the difference in recorded wind speeds between observational data and reanalysis data can be explained by differences in spatial and temporal resolution and what are the implications in the context of quantifying fire weather occurrences. The observational data are limited in temporal extent and spatial representativeness, but they capture exact real-world conditions at a location in complex terrain. The reanalysis data are available for an extended period of time and for the entire state, but the data are of relatively coarse spatial and temporal resolution and may fail to capture extremes. To quantify fire risk, we calculate the hot–dry–windy index (HDWI), which relies on wind speed and vapor pressure deficit. No statistically significant trend in the HDWI appears in the observational dataset. However, according to the reanalysis data, strong increasing trends in HDWI values emerge across all of Colorado. This apparent conflict between observational and reanalysis data suggests that reanalysis data may not be representative. Further, more long-term observational datasets are required to assess fire risk.

Significance Statement

The purpose of this study is to understand if the occurrences of fire weather are changing in Colorado. Using a 22-yr observational dataset representative of one location, we fail to find a significant trend in fire weather occurrences over this period. However, when we examine a reanalysis dataset for the same time period and for a longer 67-yr time period, fire weather occurrences seem to be increasing. We find that reanalysis often underestimates wind speed when compared to observations. The coarser spatial and temporal resolution of reanalysis data do not fully explain the bias. This conflict between observations and reanalysis data suggests that more long-term observational datasets are required to accurately assess fire risk.