bluuebunny/crossref_metadata_2025 huggingface dataset:
Data Classification and Application Requirements Analysis for Urban Traffic Signal Control in the New-Generation V2X Environment
Authors: Li Xiyao , Zhu Jierui , Lin Xiao , Li Xin , Che Xiaolin
Published: December 2024

In this paper, a methodology for analyzing the supply and demand of urban traffic data is proposed, considering the new-generation V2X environment, with an emphasis on its utilization in urban signal control systems. The supply and requirement of urban traffic data are thoroughly analyzed, providing a reference for data extraction and application within urban transportation systems. First, the characteristics of traffic data under the urban signal control system are analyzed from three perspectives: traffic element objects, road element objects, and traffic attribute objects. Second, the various characteristic elements from these three perspectives are coupled, and then data with practical significance are further screened out to obtain a traffic data set under the urban signal control system. Third, a review of three feasible methods for analyzing traffic data requirements is provided. Finally, based on the expert survey method, the traffic data requirements for isolated signal control systems are analyzed, offering technical support for matching data supply and demand within urban signal control systems.
Robust Vision-Based Traffic Anomaly Detection: DINOv2 Driven Gated Recurrent Unit Network
Authors: Luo Zhongwei , Feng Tao , Li Gen
Published: December 2024

In this study, we aim to develop an robust model for vision-based Traffic Anomaly Detection (TAD) tasks, capable of accurately detecting anomalous frames in driving videos captured by various dashboard cameras. We introduce a two-stage method that initially employs a pre-trained Vision Transformer (ViT) model to extract robust feature vectors from images. These vectors are subsequently utilized in supervised training with a temporal neural network, leveraging a limited dataset annotated with binary anomaly labels. In our experiments, we employed the DINOv2 model with a ViT-S/14 backbone for the ViT component and a Gated Recurrent Unit (GRU) Network for the temporal segment. Our model surpassed currently advanced TAD models by over 2% in Area Under the Receiver Operating Characteristic Curve (AUC) metric on the DADA2000 dataset. Furthermore, it demonstrated robust performance across anomaly categories not encountered during training, underscoring the generalization capabilities of pre-trained large models in facilitating TAD tasks and laying a solid foundation for applications in driving safety under complex conditions.
Autonomous Vehicle Crash Prediction via Improved Two-Layer Stacking in High-Dimensional Small-Sample Data Environment
Authors: Zhu Leipeng , Zhang Zhiqing , Zhang Ying , Wang Huizhu , Liu Xuan
Published: December 2024

Overcoming the challenges of low prediction accuracy and poor generalization ability in autonomous vehicle crash analysis based on high-dimensional small sample data is a significant challenge. By integrating interpretable machine learning, data augmentation, and two-layer stacking algorithms, it is possible to accurately identify the contributing factors of crashes and enhance the performance of the model. The research findings indicate several significant insights: a) Although autonomous vehicles are effective in reducing crashes caused by illegal driver operation, they still pose a potential risk under extreme working conditions. These include ambiguous road markings, severe weather conditions, and untrained traffic scenarios. b) Two-layer stacking integrates the heterogeneity of the base algorithms, resulting in improved prediction accuracy for different injury classes compared to existing models. c) The feature crosses algorithm combines the contributing factors that have strong coupling effects, maximizing the prediction accuracy and generalization ability of the two-layer stacking model. The research results are expected to strongly support the development of the emerging technology of autonomous vehicles, and provide an important reference for crash analysis and risk prevention and control policy design.
AMaze: Non-Stop Source to Destination Optimal Vehicle Scheduling
Authors: Yair Hannah , Dolev Shlomi , Gudes Ehud
Published: December 2024

In the era of autonomous vehicles, optimizing and orchestrating the travel routes for all vehicles on roads is crucial to enhance traffic flow and efficiency. Travel Route Scheduling (TRS) aims to determine the shortest route (in terms of individual arrival time) to the destination while avoiding stops at intersections. Given the current global road state, TRS finds the shortest route for the next to-enter vehicle. This study assumes that vehicles are remotely controlled/instructed and are semi-autonomous or autonomous, with the control center constantly aware of their location, speed, and final destination. Under these assumptions, the algorithms presented in this work offer an optimal solution. The primary algorithm in this work is based on Dijkstra’s algorithm, where the weights of the edges dynamically change.
Modeling and Analyzing Ride-Hailing Services with Eclipse MOSAIC
Authors: Schrab Karl , Schweppenhäuser Moritz , Protzmann Robert , Massow Kay , Radusch Ilja
Published: December 2024

Ride-hailing services enjoy a large popularity in the sector of individualized mobility. Due to broad availability, ease of use, and competitive pricing strategies, these services have established themselves throughout the last decades. With the increased popularity, ride-hailing providers aimed to consistently improve the efficiency of their services, leading to the inception of novel research questions. Many of which can be effectively tackled using simulation. In this paper, we present such a simulation-based approach using Eclipse MOSAIC in hand with a large-scale traffic scenario of Berlin. We use real logbook data and discuss how to integrate them with the simulation scenario. Moreover, we present extensions to MOSAIC required for the modeling of the ride-hailing services, utilizing the powerful Application Simulator. Accordingly, as the primary result of this paper, we managed to extend the MOSAIC framework to be able to answer research questions in the domain of ride-hailing and ride-sharing. Additionally, in an initial exemplary study, we analyze the traffic and environmental impacts of different, yet basic, rebalancing strategies, finding non-negligible differences in mileages and pollutant emissions. To our knowledge, the consideration of environmental factors on a city-wide scale is a novel contribution of this paper, not addressed in previous research.
Study on Arrival Behavior Characteristics of Spectators in Large-and-Medium-Sized Theaters—A Case Study of Shanghai
Authors: Liu Xiaoqian , Chen Xiaohong , Han Shu , Qiao Yingyao
Published: December 2024

As the accelerating construction and development of large-and-medium-sized theaters in modern cities, for the purpose of providing a better experience for all the spectators and attracting more people to enter the theaters for enjoying arts, spectators’ arrival behavior characteristics are collected and studied by the case of Shanghai based on statistical analysis as well as ArcGIS tools. A series of arrival behavior characteristics are found by personal attributes, time attributes and theater transport convenience (TTC), which provide theoretical references and directions for the fine designing development of future large-and-medium-sized theaters.
Event Discrete Decision-Making for Autonomous Vehicles at Non-Signalized Intersections
Authors: Leyer Daniel , Weinreuter Hannes , Heizmann Michael
Published: December 2024

Non-signalized urban intersections are one of the remaining challenging situations when it comes to autonomous driving. Situations may arise that are not clearly regulated by the traffic regulations and therefore require cooperation between vehicles to resolve the situations. This paper introduces a general modeling approach for driving at intersections and decision-making there. The decision process is modeled based on discrete event systems (DES). Within the modeling, limited visibility is considered. Unexpected behavior of other vehicles as well as deadlock situations are also accounted for. The decision-making process is based on explicitly modeled features and events and can thus be explained to and understood by its users. The algorithm is evaluated with extensive simulations and the results show that the model is able to deal with multiple cooperation vehicles and reliably avoids collisions even during unexpected behavior of the cooperation vehicles and deadlock situations.
OnNER: An Ontology for Semantic Representation of Named Entities in Scholarly Publications
Authors: Reza Umayer , Zhang Xuelian , Hahmann Torsten
Published: December 2024

A significant portion of scientific knowledge resides within scholarly publications, both in print and digital formats. Recent advancements in natural language processing and information extraction techniques have enhanced the accessibility of this knowledge for further automated querying and processing. Structured and semantically-aware representations, such as ontologies, play a crucial role in simplifying and integrating access to this vast pool of knowledge. While several ontologies have been developed to capture the structure and discourse of scientific publications, there is a notable scarcity of ontologies for succinctly representing named entities that are present in scholarly documents. This paper introduces the Ontology for Named Entity Representation (OnNER) to address this gap. OnNER is designed to represent named entities – the terms identified and labeled using named entity recognition (NER) methods – from scholarly publications. The ontology provides a structured semantic representation of the named entities, how they are labeled, and where they occur. We discuss the overall design of OnNER, its integration with other ontologies, and demonstrate how the ontology facilitates advanced querying of named entities’ presence and collocation within and across publications.
The Common Core Ontologies
Authors: Jensen Mark , De Colle Giacomo , Kindya Sean , More Cameron , Cox Alexander P. , Beverley John
Published: December 2024

The Common Core Ontologies(CCO) are designed as a mid-level ontology suite that extends the Basic Formal Ontology. In 2017,CUBRC, Inc. made CCO openly available. CCO has since been increasingly adopted by a broad group of users and applications and is proposed as the first standard mid-level ontology. Despite these successes, documentation of the contents and design patterns of the CCO has been comparatively minimal. This paper is a step toward providing enhanced documentation for the mid-level ontology suite through a discussion of the contents of the eleven ontologies that collectively comprise the Common Core Ontology suite.
Representing Energy in the Midlevel Energy Ontology (MENO)
Authors: Stappel Mirjam , Neuhaus Fabian
Published: December 2024

Energy is a fundamental phenomenon of physics, but energy also plays an important role in the representation of many domains, since many processes involve energy transformation or transfer. However, energy is represented very differently in existing ontologies. Even in ontologies that share BFO as top-level ontology, energy is sometimes treated as disposition, as quality, and as a material entity. As we discuss in the paper, there are reasons for each choice, which makes the ontological representation of energy a challenging subject. In this paper we present an ontological analysis of energy in a BFO-based mid-level ontology (MENO), including the different kinds of energy, their relations to dispositions as well as their realisation in processes.
Ontological Analysis of Malfunctions: Some Formal Considerations
Authors: Compagno Francesco , Borgo Stefano
Published: December 2024

An engineering device or system is designed to retain its functioning conditions for the time it is needed. Unfortunately, failures happen and mark the state in which a device stops functioning to a satisfactory level and, thus, is considered malfunctioning. The study of malfunction and its prevention is a central topic in engineering and its analysis poses challenges relevant to applied ontology as well. In this work, we focus on the intersection between the conceptual and ontological understanding of causation and malfunction by studying existing works in these areas. Aiming to compare these approaches from a unified viewpoint, we introduce a causation-based analysis method to take on terminological and conceptual challenges. The goal is to develop a formal taxonomy of malfunctions which can also be used to compare the other approaches in the literature. Our work pays attention to real case scenarios paving the way to the ontological understanding and modeling of failures in practice. Furthermore, it helps exploiting the potentialities of failure analysis and, in particular, of root cause analysis.
Unpacking the Semantics of Risk in Climate Change Discourses
Authors: Adamo Greta , Sperotto Anna , Fumagalli Mattia , Mosca Alessandro , Sales Tiago Prince , Guizzardi Giancarlo
Published: December 2024

The climate change assessment community relies on widely accepted definitions of risk and its components, e.g. hazard, exposure, and vulnerability, provided by the well-known international organisation Intergovernmental Panel on Climate Change (IPCC). Those definitions of risk have been changing through the years and are presented in a general and “common sense” form as they need to be understandable by the public society and accommodate notions of risk as embraced by different research streams. However, these definitions have proven ineffective in operational climate risk assessment procedures, which exposes the critical need for disambiguation. This paper addresses the lack of semantic clarity of risk and cognate concepts in the context of climate change assessment by unpacking the ontological commitments underlying the IPCC’s most recent definitions and glossary using the Common Ontology of Value and Risk (COVER) as a primary guideline. This study provides a more precise and refined ontological foundation of risk in climate change research that better aligns with the complexities of scenarios and assessments, and contributes to climate change research on mitigation and adaptation by supporting more effective communication and assessment of climate-related risks and humanity’s responses to them.
Meaning Holism and Indeterminacy of Reference in Ontologies
Authors: Barton Adrien , Fabry Paul , Ethier Jean-François
Published: December 2024

According to meaning holism, the meanings of all of the words in a language are interdependent. If this was true, then the very practice of building largely interconnected set of ontologies would be threatened. We examine here the extent of the severity of meaning holism for ontology engineering, based on several definitions of the meaning of a class term in an ontology, with regard to the classical analytic/synthetic distinction. We show that meaning holism is not as pervasive in ontologies as traditionally assumed in philosophy of language, and that a conception of meaning of a class term as a collection of statements expressing necessary conditions on this term limits meaning holism further. Still, meaning holism presents substantial challenges for ontology engineering and requires mitigation strategies. We also investigate the related phenomenon of indeterminacy of reference and show how anchoring formal ontologies in natural language can mitigate this problem, even if not fully control it.
Precise Floor Plan Recognition Algorithm Based on Keypoints
Authors: Yin Deshuai , Shi Guanli , Chen Yifan , Luan Lin
Published: December 2024

Vectorization of floor plans (VFP) is an object detection task that involves the localization and recognition of different structural primitives in 2D floor plans(FP). The output of VFP can be further processed for the purpose of plan reconstruction, 3D reconstruction and automatic furniture layout. So far how to make the existing 2D floor plans vectorized faces the problem of recognition inaccuracy and inefficiency. This paper proposed a floor plan recognition algorithm based on key points, which is meaningful and useful. First, the algorithm identifies the effective subject of the FP with the help of the object detection algorithm; then, it builds a deep backbone network to identify the key points and semantic information of the marked plane elements; finally, the algorithm utilizes the post-processing algorithm to optimize and retrieve vectorized data information. Compared with existing methods, the algorithm adopted in this paper enhances the support for the recognition of elements such as sloping walls and bay windows, and effectively improves the recognition accuracy.
LLMs Based Multi-Modal Location Recommendation for Smart Building
Authors: Liu Hang , Suzuki Shintaro , Hyodo Akihiko
Published: December 2024

An effective location recommendation function in smart building management systems can optimize space utilization and enhance user service experience. Despite recent advances in Large Language Models (LLMs) for NLP-based recommender systems, smart building systems often lack communication and coordination with other devices, resulting in subpar interactivity and serviceability. To address these challenges, this paper proposes a multi-modal recommendation system for utilizing and sharing open spaces in smart buildings. The system includes a “vision-based recommendation module” that uses visual Language Models and real-time surveillance images to identify locations based on user-requested keywords. The “knowledge-based recommendation module” utilizes knowledge graph technology to match user requirements with historical feedback data, improving semantic matching and optimizing user experience. The system combines the outputs from both modules using decision fusion technology to provide final location recommendations. Simulation results demonstrate that the proposed system can effectively understand user intentions and provide satisfactory location recommendations. The multi-modal approach outperforms individual recommendation methods.
Optimized Non-Maximum Suppression for Multi-Source Local Discharge PRPD Pattern Identification
Authors: Fang Yizhi , Lin Yuling , Li Tianshu , Qiu Zicong , Huang Yuhua , Yi Xiabo
Published: December 2024

The multi-source partial discharge PRPD pattern can realize the pattern recognition of multi-source partial discharge types through the target detection algorithm training and identification of shape features. However, when the characteristics of different discharge pattern overlap, the small target is easily blocked by the large target, resulting in false detection and missed detection. Therefore, this paper proposes a multi-source partial discharge PRPD pattern identification algorithm with optimized non-maximum suppression. The Soft-NMS algorithm was introduced to solve the missed detection caused by overlapping targets; GIoU was used to replace the traditional IoU to calculate the similarity between targets and the loss function was optimized; the YOLOv7 network model was further built to identify the PRPD pattern of four typical discharges shape features. After cross-validation between simulation experiments and charged field data, the results prove that the average detection accuracy of the algorithm can reach 98.2% in simulation experiments and 88.4% in field experiments, effectively reducing the false detection rate and successfully identifying the characteristics of multi-source local discharge PRPD pattern when the targets overlap.
Design and Implementation of a Business Middle Platform Microservice Operation Monitoring Platform
Authors: Li Li , Li Shu-Xian , Hong Yang , Ma Jing , Shi Wen-Juan , Li Jia-Qi
Published: December 2024

In the process of digital transformation in power enterprises, a number of new business systems based on microservices architecture have gradually been constructed. However, traditional passive monitoring methods lack the ability to face microservice architecture systems. At the same time, objective problems such as complexity of invocation logic, difficulty in fault localization, and lack of monitoring perspectives have become prominent. This article proposes a multidimensional monitoring method based on microservice architecture for business systems, extending the monitoring perspective to the business layer. So it could achieve comprehensive capture of the operation of the business system, and enabling timely detection of anomalies from the business layer. Meanwhile, it horizontally constructs invocation links at various levels of the system, vertically connects system resources at each level. Consequently breaking through the bottleneck of the disconnection between business and system, and improving the efficiency and accuracy of anomaly tracing greatly. Based on this design concept, it meets the different needs of various entities such as business, components, and management for system operation and maintenance, effectively improving the refinement and intelligence level of operation and maintenance monitoring. On this basis, relying on unified monitoring indicators, alarm systems, and visual large screen capabilities, it assists in locating key nodes where faults occur. Build an enterprise level operation monitoring platform, form a comprehensive and comprehensive monitoring mode, collaborate to ensure stable business operation, and improve the intelligence level of enterprise operation management.
Single-Input Multi-Output High Conversion Ratio Hybrid Structure Buck-Boost DC-DC Converter for Energy Harvesting
Authors: Su Guojun , Xu Weilin
Published: December 2024

This paper presents a hybrid buck-boost DC-DC converter structure to address the limitations of single-output and low conversion ratios (CR) in traditional energy harvesting systems. To reduce the CR of the inductive boost and enhance the total CR of the converter while improving efficiency, the proposed converter cascades a boost circuit with a reconfigurable switched capacitor (SC), and also includes a supercapacitor. The advantage of this architecture is the ability to use low-voltage low-threshold transistors with lower parasitic capacitance and on-resistance, which achieve high-voltage output while reducing switching and conduction losses. The supercapacitor can store energy under a light load and provide additional energy replenishment in buck mode under heavy load. The reconfigurable SC circuit can enable a 2V or 3V output by configuring the switch array. Adaptive switching frequency control was used to achieve a balance between load capability and switching loss. The proposed DC-DC converter is designed with a 0.18 µm CMOS process and has maximum conversion efficiencies of 91.83%, 89.42%, and 88.09% for the inductive boost stage, 2V SC output stage, and 3V SC output stage, respectively. The output voltage ripple is less than 20 mV at 1V output and less than 30 mV at 2V output and 3V output. The input voltage of the converter is as low as 20 mV. The converter features multi-output, high-conversion-ratio, and enhanced efficiency, which are suitable for energy-harvesting applications.
Integrating Ideological Education in Motion Control Systems Amid Digital Transformation
Authors: Guo Yonghui , Huang Wen
Published: December 2024

In the context of digital transformation, the course of motion control systems is facing new opportunities and challenges. This article explores how to integrate ideological and political education into the teaching reform of this course, in order to enhance students’ comprehensive quality and technical abilities. Firstly, the impact of digital transformation on the course of motion control systems was outlined; Secondly, specific strategies for integrating ideological and political education into the curriculum were elaborated; Finally, specific methods for implementing ideological and political education were proposed, and their impact on teaching effectiveness was analyzed. Research has found that through data visualization and other technological means, students’ learning situations can be effectively monitored and evaluated, thereby guiding teaching activities and enhancing the effectiveness of ideological and political education. This study provides new ideas and methods for the teaching reform of motion control system courses.
Restaurant Recommendation System in Malaysia Using Machine Learning Approach
Authors: Idalisa Nur , Hazhar Muhammad Hazwan Mohd , Muslim Norliana , Albashah Nur Lyana Shahfiqa
Published: December 2024

People frequently struggle to make decisions when faced with a wider range of possibilities, especially when selecting a dining restaurant. To address this issue, a recommendation system can assist by analyzing user preferences and previous dining experiences to offer personalized suggestions. This research aims to develop a restaurant recommendation system for Malaysian customers using a machine-learning approach. The study focuses on Non-negative Matrix Factorization (NMF), Probability Matrix Factorization (PMF), Principal Component Analysis (PCA), and Singular Value Decomposition (SVD) approaches. Based on an analysis of 2,496 datasets gathered from the TripAdvisor platform, the findings revealed that the SVD method outperformed other approaches, achieving a Root Mean Square Error of 0.1166. This result positions SVD as the most suitable method for developing a restaurant recommendation system. The proposed system features a user-friendly interface built with Streamlit, allowing users to select their location and receive top restaurant suggestions. Additionally, users can view recommendations based on their past dining experiences. The system retrieves all reviews for the selected restaurants and converts them into a Term Frequency-Inverse Document Frequency (TF-IDF) matrix. Cosine similarity is then employed to measure the relevance of review content using the computed TF-IDF. Finally, the system also recommends similar restaurants based on the user’s chosen options, enhancing the overall dining experience.
Challenges of Implementing Sustainable Manufacturing in the Industrial Metaverse Through Big Data and AI
Authors: Velev Dimiter , Steshina Liudmila , Zlateva Plamena , Petukhov Igor
Published: December 2024

Building sustainable manufacturing models in the Industrial Metaverse through the use of Big Data and Artificial Intelligence represents a new approach to optimizing manufacturing processes, reducing waste and emissions, and increasing efficiency. This new concept is based on the integration of physical and virtual systems that collect and analyze huge volumes of information in real time. Through data analysis, AI can predict resource consumption, identify inefficiencies and automate processes to reduce energy costs and carbon emissions. The main goal of these innovations is not only to optimize manufacturing, but also to contribute to the achievement of global goals for sustainable development through better resource management and a reduction of the ecological footprint. The aim of the paper is to explore the concept of the Industrial Metaverse, define the role of Big Data and AI in sustainable manufacturing, and identify opportunities and challenges.
Infrared Image Defect Detection of Transformer Heat Sink Based on Improved YOLOv8
Authors: Wu Zhongfei , Xu Weidong , Sun Haichao , Bai Xueting , Wang Weiqi , Lan Bo , Li Keyu , Wu Mianxiao
Published: December 2024

The transformer is one of the important components of the power system, the transformer in a long time under the load of high temperature, the use of heat sinks for heat dissipation becomes critical, so to ensure the operation of the radiator is also to improve the safety and reliability of transformer operation. Radiator often due to valve failure, causing uneven heat dissipation, to address this problem, using improved YOLOv8 algorithm for the radiator infrared image target recognition. Based on the YOLOv8 object detection algorithm, the CBAM attention mechanism is first introduced. That is, by introducing channel and spatial attention modules in CNN (Convolutional Neural Network), the detection performance of the model can be improved while ensuring the inference speed. Then, WioU loss function is introduced to optimize the network. It is experimentally verified that it achieves an inference accuracy of 89.1% mAP at the edge-end device, which satisfies the identification of anomalies for temperature unbalanced radiators. And realizes high-precision edge-end insulator temperature anomaly program state identification.
FADI-AEC: Fast Score Based Diffusion Model Guided by Far-End Signal for Acoustic Echo Cancellation
Authors: Liu Yang , Wan Li , Huang Yiteng , Sun Ming , Zhao Changsheng , Ni Zhaoheng , Shi Yangyang , Lei Xin
Published: December 2024

Despite the potential of diffusion models in speech enhancement, their deployment in Acoustic Echo Cancellation (AEC) has been restricted. In this paper, we propose DI-AEC, pioneering a diffusion-based stochastic regeneration approach dedicated to AEC. Further, we propose FADI-AEC, fast score-based diffusion AEC framework to save computational demands, making it favorable for edge devices. It stands out by running the score model once per frame. This approach achieves a significant surge in processing efficiency. Apart from that, we introduce a novel noise generation technique where far-end signals are utilized, incorporating both far-end and near-end signals to increase the score model’s accuracy. We test our proposed method on the ICASSP2023 Microsoft deep echo cancellation challenge evaluation dataset, where our method outperforms some of the end-to-end methods and other diffusion based echo cancellation methods.
Artificial Intelligence in Civil Procedure in Europe – Some Perspectives
Authors: Studzińska Joanna
Published: December 2024

The growing prevalence of artificial intelligence (AI) has prompted concerns, yet it also offers promising avenues for its utilization. There is a growing body of work being conducted on the application of artificial intelligence in the judicial sector. AI has the potential to be utilized in a number of ways within the judicial system. The primary objective of this analysis is to highlight the growing role of AI in civil procedure and its potential to significantly impact judicial systems, access to justice, and the efficiency of court processes. The basic method is an analysis of the current state of the law and existing solutions that allow the use of artificial intelligence in the court systems of Europe and the comparison of their usefulness for improving court proceedings.
Broadband and Firm Entries in Lithuania
Authors: Sarachuk Kirill
Published: December 2024

The ongoing digitalization had a global impact on economic patterns. With better broadband infrastructure, new businesses rely more on digital tools and technologies, are able to grow faster, and reach customers in remote markets. However, despite the growing importance of high-speed internet, there is a lack of research linking broadband provision to firm entries. This paper fills this gap by examining the relationship between the improvements in mobile internet speed and the firm entries in Lithuania over the 5-year time period from 2014 to 2019, the active phase of implementation of the Digital Agenda for Europe. As a result, we observed a negative and significant at 10% level relationship between changes in internet speed and business creation, which contradicts the recent findings in the existing literature.
Single Image Defocus Deblurring with Progressive Feature Attention Network
Authors: Zhan Hongmin , Ling Shenggui , Shi Rui
Published: December 2024

The goal of single-image defocus deblurring is to reconstruct a clear image from a defocused one. Although existing methods perform well in common blurry scenes, they still face the challenge of feature extraction when meeting severely defocused areas. Therefore, the innovative progressive multi-scale feature extraction module (PMFEM) and the feature attention module (FAM) are proposed. The PMFEM gradually extracts and fuses multi-scale features through convolution kernels of different sizes. Three parallel paths process the input respectively, simplifying and optimizing the feature extraction process layer by layer to achieve progressive processing. The FAM dynamically enhances or suppresses features of specific scales by fusing multi-path feature information, thereby improving the model’s feature expression ability. Experimental results show that our method achieves state-of-the-art performance on the Dual-Pixel Defocus Deblurring (DPDD) and Regression Tree Fields (RTF) datasets. Our code can be obtained at https://github.com/HMin-Z/PFANet.
Research and Application of Text-Based Sentiment Analytics
Authors: Sun Jiadong , Wang Mengnan , Ren Defen , Chen Deji
Published: December 2024

Text-based sentiment analytics is an important research direction in the field of natural language processing, and it is widely applied in identifying emotional tendencies. The proliferation of e-commerce platforms has underscored the criticality of emotional tendencies and user experiences delineated in product reviews for gauging product quality and user satisfaction. This study presents a developed text-based sentiment analysis system tailored to product reviews on e-commerce platforms. Three distinct text-based sentiment classification models based on Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and Bidirectional Encoder Representations from Transformers (BERT) were constructed and implemented. Subsequently, sentiment classification and comparative experiments were conducted utilizing real-world product review data. The experimental outcomes underscore that the BERT model outperforms others in terms of the Area Under the Curve (AUC) metric, yielding superior results in the domain of text sentiment analysis. This paper extensively deliberates on the fusion of deep learning methodologies into sentiment analytics, encapsulating the entire spectrum of data collection, preprocessing, and model analysis, with an overarching goal of providing a reference point for research endeavors in analogous realms.
Společná zemědělská politika Evropské unie
Authors: Dirk Ahner
Published: December 2024

The Common Agricultural Policy of the European Union
Německý ,,Superwahljahr'' a jeho důsledky
Authors: Adolf Müller
Published: December 2024

German „Superwahljahr“ and its Consequences
Švédská zahraniční a bezpečnostní politika - tradice a obnova
Authors: Rutger Lindahl
Published: December 2024

Swedish Foreign and Security Policy – Tradition and Renewal
Etnicko-národní diaspory a bezpečnost
Authors: Gabriel Sheffer
Published: December 2024

Ethno-national Diasporas and Security
Postkomunistická mentalita
Authors: Jindřich Fibich
Published: December 2024

The Post-communist Mentality
Politický islám a mezinárodní vztahy
Authors: Volker Perthes
Published: December 2024

Political Islam and International Relations
Armáda České republiky v nejbližších letech
Authors: František Němec
Published: December 2024

Army of the Czech Republic in the Coming Years
The Reunion as a Catalyst for Divorce: A Case Study of the Padang Religious Court, Indonesia
Authors: Halim Syaflin , Asmaret Desi , Mursal Mursal , Daharis Ade , Hebondri
Published: December 2024

The reunion problem is one of the causes of the spike in divorce rates in Padang City due to indications of infidelity during this activity. So, this research fills the gap in research regarding divorce with reunion cases, which can lead to divorce in the jurisdiction of the Class 1A Religious Court in Padang City. The aim of this study is to identify and analyze the effect of Islamic law on divorce cases triggered by reunions at the Religious Court of Padang Class 1A. A descriptive qualitative approach is employed to collect empirical data from field cases. Key findings reveal that reunions often act as a catalyst that accelerates relationship breakdowns, mainly when prior issues exist. Communication between ex-spouses during hearings shows high emotional tension, with judges and advocates playing crucial roles in maintaining a professional atmosphere. Mediation only sometimes succeeds due to the distrust arising from reunions. However, the application of Islamic legal principles, such as the elimination of harm, provides justice for both parties while striving to preserve marriage when possible. This study implies the need to consider reunions as a significant triggering factor in divorce within the context of Islamic law.
Review of Hybrid Classification Technique for Automated Detection and Severity Analysis of Epilepsy Using EEG Signals
Authors: Khushwant Kaur  , Meena Jindal
Published: December 2024

Epilepsy affects an estimated 70 million people worldwide and is defined by the realization of two or more unprovoked seizures. Introduction Electroencephalography (EEG) has long been a cornerstone in the diagnosis and management of epilepsy, but it remains challenging for interpretation due to its vast complexities. Hybrid classification methods provide the encouraging results considerably through automatic detection. These methods are combining Machine learning, Signal processing and Statistical techniques which will lead to better accuracy in detecting epilepsy and measure its severity. There are other hybrid approaches, with these having the capacity to adapt and normalize EEG signal variability amidst noise which make results more reliable and less prone to fluctuation; some examples of this being Support Vector Machines (SVM) used in combination with Convolutional Neural Networks for example. They also provide possibilities for continuous and real-time monitoring as required in clinical settings, to enable timely interventions. Nonetheless, problems like data diversity, real-time processing of it to generate predictions for each patient and model interpretability still remain. These rely on a mixture of named algorithms and human-chosen algorithmic choices, reflect the types biases that flow through every system in science but optimization for real world applications will require extensive evaluation of each other these issues. A significant upshot demands these algorithms to be more generalizable, real-time and clinically integrated in the future research work so that automation improves epilepsy management efficiently.
Comparative Analysis of Container Orchestration Platforms: Kubernetes vs. Docker Swarm
Authors: Venkat Marella
Published: December 2024

Novel software architecture patterns, including microservices, have surfaced in the last ten years to increase the modularity of applications and to simplify their development, testing, scaling, and component replacement. In response to these emerging trends, new approaches such as DevOps methods and technologies have arisen to facilitate automation and monitoring across the whole software construction lifecycle, fostering improved collaboration between software development and operations teams. The resource management (RM) strategies of Kubernetes and Docker Swarm, two well-known container orchestration technologies, are compared in this article. The main distinctions between RM, scheduling, and scalability are examined, with an emphasis on Kubernetes' flexibility and granularity in contrast to Docker Swarm's simplicity and use. In this article, a case study comparing the performance of two popular container orchestrators—Kubernetes and Docker Swarm—over a Web application built using the microservices architecture is presented. By raising the number of users, we compare how well Docker Swarm and Kubernetes perform under stress. This study aims to provide academics and practitioners with an understanding of how well Docker Swarm and Kubernetes function in systems built using the suggested microservice architecture. The authors' Web application is a kind of loyalty program, meaning that it offers a free item upon reaching a certain quantity of purchases. According to the study's findings, Docker Swarm outperforms Kubernetes in terms of efficiency as user counts rise.
Screening of agroresidues for economical and sustainable Phytase Production by Bacteria
Authors: Suchita Bharambe  , Swati Peshwe  , Jayashree Bhuktar  , Anjali Naik
Published: December 2024

Phytase, a phytate hydrolysing enzyme has substantial application in food, feed and agriculture sector for improvising the availability of phosphorus and reduces the environmental pollution. The present study emphasizes on cost effective production of bacterial phytase. Total 25 agroresidues were screened for economic production of phytase using bacteria. A total of 30 bacterial isolates were obtained from poultry and goat shed soil and litter sample showing highest phytase activity. The efficient isolates SP-46 and SP-52 were screened for selection of efficient combination bacterial isolate and agroresidues for cost-effective production of phytase. The results showed that utilization of agroresidues not only serve as efficient substrate but also contribute to sustainable agro-waste valorisation and reduces need of expensive synthetic media for phytase production.
The Impacts of Extensive Agricultural Investment on Local Livelihood Income in Western Ethiopia
Authors: Ararso Negatu , Wakweya Rusha Begna
Published: December 2024

In western Ethiopia, large-scale agricultural investment has been growing, but its effects on local livelihood incomes have received less attention. This study investigates the impact of extensive agricultural investment on local livelihood incomes in Sibu Sire District, western Ethiopia. A total of 180 households were selected using simple random sampling. Descriptive statistics such as t-test, ANOVA, and Chi-square test were employed for data analysis. The results showed that there was a significant difference in household livelihood income prior-investment expansion among the three groups (F2, 178 = 17.41; P = 0.001), whereas there was no significant difference among the 3 groups post-investment expansion (F2, 177 = 0.9; P = 0.9). There was also no significant difference in social service provision among the groups. Regarding technology transmission, the respondents revealed that there was no significant difference among the groups. The study found that insiders (84.4%), outsiders (91.7%), and the control group (100.0%) had not introduced new technology. Moreover, crops and animal production by insiders and outsiders decreased compared to the control group. Therefore, the study suggests that the government needs to improve investment policy and make interventions to improve local socio-economic conditions in the area.
Analysis of the Effect of Palm Oil Productivity on Indonesian Palm Oil (CPO) Supply Chain Management 2015-2021 Through Government Policy as a Mediation Variable
Authors: Lumbanraja Pretty Luci , Lumbanraja Penny Chariti
Published: December 2024

Supply chain management is important for palm oil companies so it is necessary to analyze the capacity and quality of trading actors, analysis related to control and distribution. So that production output supply chain mapping can be carried out to ensure production, distribution and marketing systems are effective and efficient. This research aims to determine the influence of palm oil productivity measurement variables on palm oil supply chain management through government policy as an intervening variable. This research is exploratory in nature using inferential analysis techniques. Information used in the time setting framework for the 2015-2021 period. This research uses simple linear regression statistical data analysis with the Structural Equation Model (SEM) with the alternative Partial Least Square (PLS) method. The research results show that the influence of palm oil productivity is significantly positive on government policy (0.000) < (0.05) with an estimated magnitude of 0.552. And the influence of government policy is significantly positive on supply chain management (0.000) < (0.005) with an estimated magnitude of 0.834. In this case, there is a positive and significant influence of palm oil productivity on supply chain management through government policy.
МОТИВАЦІЯ ПЕРСОНАЛУ ПІДПРИЄМСТВ: СУЧАСНІ ТРЕНДИ В УМОВАХ ВОЄННОГО СТАНУ
Authors: Черевань Ірина , Коваленко Юлія , Голець Сергій
Published: December 2024

Стаття присвячена дослідженню питання мотивації персоналу в умовах воєнного стану. Відмічено, що зміни у середовищі функціонування українських підприємств трансформують поняття мотивації персоналу, зміщуючи акценти на мотиви і стимули, які будуть дієвими у нових екстерналіях. Здійснено аналіз трансформації підходів до розуміння мотиваційних механізмів в довоєнний період та в умовах сучасного розвитку підприємств. Відзначено зміни у фокусі спрямування мотиваційних заходів у бік забезпечення безпечної та психологічно-комфортної роботи персоналу українських компаній. Проведено аналіз мотиваційних заходів, які використовуються підприємствами різних сфер економічної активності з метою сприяння підтримці вмотивованості персоналу та забезпечення ефективної мотиваційної політики. Обгрунтовано основні тренди мотивації персоналу в умовах воєнного стану.
ЕЛЕКТРОМАГНІТНА ЕМІСІЯ СЕРЕДОВИЩА ЯК РЕАКЦІЯ НА СЕЙСМОТЕКТОНІЧНІ ПРОЦЕСИ В СЕЙСМОГЕНЕРУВАЛЬНИХ РЕГІОНАХ
Authors: Ігнатишин Василь Васильович , Iжак Тібор Йосипович , Молнар Д Стефан Стефанович
Published: December 2024

Актуальність дослідження: Закарпаття – сейсмонебезпечний регіон України. На вияв місцевої сейсмічності впливає геодинамічний стан регіону, який відбивається на зміні геофізичних полів. Важливо вивчити зміни електромагнітної емісії в періоди інтенсифікації сейсмотектонічних процесів, диференціюючи дослідження згідно з режимами опитування приладів, їх частоти. Актуально оптимізувати пошук ефективної частоти вимірювання електромагнітної емісії серед доступних для вимірювання. Предметом дослідження є варіації електромагнітної емісії в центральній частині Закарпаття, сучасних горизонтальних рухів кори в зоні Оашського глибинного розлому, їх динамічних характеристик, вияву місцевої сейсмічності вивчення їх взаємозв’язків та перспектив подальших наукових досліджень; розрахунок ступеня кореляції низки параметрів досліджуваних геофізичних полів. Мета дослідження: вивчення взаємозв’язків геофізичних полів, зокрема електромагнітної емісії, поля деформацій у центральній частині Закарпатського внутрішнього прогину, місцевої сейсмічної активності. Методологія дослідження: розрахунки кінематичних характеристик сучасних рухів кори, порівняння, кореляційний аналіз низки спостережень. Результати дослідження: показано, що інтенсивні горизонтальні зміщення земної кори в зоні Оашського глибинного розлому супроводжуються аномальними варіаціями електромагнітної емісії, сейсмічною активізацією. Практичне значення: отримані результати важливі для пошуку ефективних методів вивчення процесів підготовки та виявів екологічно небезпечних процесів, їх екологічних аспектів, поповнення банку геофізичних даних. Висновки: досліджено зв’язок електромагнітної емісії центральної частини Закарпаття із сучасними горизонтальними рухами кори, сейсмічною активністю регіону у 2022 році. Показано, що ефективним методом вивчення сейсмотектонічних процесів у регіоні є використання прискорення сучасних горизонтальних рухів кори. Перспектива подальших досліджень. Результати досліджень можна використати при вивченні фізичних характеристик геологічних структур сейсмонебезпечних регіонів, важливо дослідити зв’язки електромагнітної емісії із сейсмотектонікою регіону в інших діапазонах частот. Це важливо при пошуку оптимальних методик дослідження напружено-деформованого стану порід, їх майбутнього прогнозування.