bluuebunny/crossref_metadata_2025 huggingface dataset:
UAV Hunter: A Net-Capturing UAV System with Improved Detection and Tracking Methods for Anti-UAV Defense
Authors: Zhang Tao , Lu Ruitao , Yang Xiaogang , Xie Xueli , Fan Jiwei , Tang Bin
Published: October 2024

The abuse of UAVs poses a potential risk to social security, necessitating the investigation of anti-UAV methods to safeguard critical areas. However, the existing UAV countermeasures face challenges such as high environmental impact, restricted spatial deployment, and low cost-effectiveness. To address these limitations, we developed a novel anti-UAV system known as UAV Hunter, which adopts an airborne tether-net capture device with visual aids to counter unauthorized UAVs. This system employs an “Anti-UAV with UAV” scheme, comprising a ground control station and a net-capturing UAV. The operator utilizes the ground control station to determine the mission area and flight path and then controls the flight of the net-capturing UAV. During flight, the net-capturing UAV leverages its dual-mode sensor to continuously monitor the target area. Simultaneously, the onboard computer executes a UAV detection and tracking algorithm to search for unauthorized UAVs in real time. The results are relayed to the operator in real time, facilitating precise adjustments for the net-capturing UAV to launch the rope net accurately. The system successfully realizes the functions of dual-mode real-time detection and tracking, precise net capture, and efficient integrated control. Compared with existing methods, the developed system exhibits accurate recognition, rapid action, diverse application scenarios, and an enhanced human–machine interaction experience. Test results in the open environment further validate the feasibility and functional integrity of the system, demonstrating its capability to effectively capture low-altitude unauthorized UAVs.
Kinodynamic Model-Based UAV Trajectory Optimization for Wireless Communication Support of Internet of Vehicles in Smart Cities
Authors: Eskandari Mohsen , Savkin Andrey V. , Deghat Mohammad
Published: October 2024

Unmanned aerial vehicles (UAVs) are utilized for wireless communication support of Internet of Intelligent Vehicles (IoVs). Intelligent vehicles (IVs) need vehicle-to-vehicle (V2V) and vehicle-to-everything (V2X) wireless communication for real-time perception knowledge exchange and dynamic environment modeling for safe autonomous driving and mission accomplishment. UAVs autonomously navigate through dense urban areas to provide aerial line-of-sight (LoS) communication links for IoVs. Real-time UAV trajectory design is required for minimum energy consumption and maximum channel performance. However, this is multidisciplinary research including (1) dynamic-aware kinematic (kinodynamic) planning by considering UAVs’ motion and nonholonomic constraints; (2) channel modeling and channel performance improvement in future wireless networks (i.e., beyond 5G and 6G) that are limited to beamforming to LoS links with the aid of reconfigurable intelligent surfaces (RISs); and (3) real-time obstacle-free crash avoidance 3D trajectory optimization in dense urban areas by modeling obstacles and LoS paths in convex programming. Modeling and solving this multilateral problem in real-time are computationally prohibitive unless extensive computational and overhead processing costs are imposed. To pave the path for computationally efficient yet feasible real-time trajectory optimization, this paper presents UAV kinodynamic modeling. Then, it proposes a convex trajectory optimization problem with the developed linear kinodynamic models. The optimality and smoothness of the trajectory optimization problem are improved by utilizing model predictive control and quadratic state feedback control. Simulation results are provided to validate the methodology.
Intelligent Swarm: Concept, Design and Validation of Self-Organized UAVs Based on Leader–Followers Paradigm for Autonomous Mission Planning
Authors: Adoni Wilfried Yves Hamilton , Fareedh Junaidh Shaik , Lorenz Sandra , Gloaguen Richard , Madriz Yuleika , Singh Aastha , Kühne Thomas D.
Published: October 2024

Unmanned Aerial Vehicles (UAVs), commonly known as drones, are omnipresent and have grown in popularity due to their wide potential use in many civilian sectors. Equipped with sophisticated sensors and communication devices, drones can potentially form a multi-UAV system, also called an autonomous swarm, in which UAVs work together with little or no operator control. According to the complexity of the mission and coverage area, swarm operations require important considerations regarding the intelligence and self-organization of the UAVs. Factors including the types of drones, the communication protocol and architecture, task planning, consensus control, and many other swarm mobility considerations must be investigated. While several papers highlight the use cases for UAV swarms, there is a lack of research that addresses in depth the challenges posed by deploying an intelligent UAV swarm. Against this backdrop, we propose a computation framework of a self-organized swarm for autonomous and collaborative missions. The proposed approach is based on the Leader–Followers paradigm, which involves the distribution of ROS nodes among follower UAVs, while leaders perform supervision. Additionally, we have integrated background services that autonomously manage the complexities relating to task coordination, control policy, and failure management. In comparison with several research efforts, the proposed multi-UAV system is more autonomous and resilient since it can recover swiftly from system failure. It is also reliable and has been deployed on real UAVs for outdoor survey missions. This validates the applicability of the theoretical underpinnings of the proposed swarming concept. Experimental tests carried out as part of an area coverage mission with 6 quadcopters (2 leaders and 4 followers) reveal that the proposed swarming concept is very promising and inspiring for aerial vehicle technology. Compared with the conventional planning approach, the results are highly satisfactory, highlighting a significant gain in terms of flight time, and enabling missions to be achieved rapidly while optimizing energy consumption. This gives the advantage of exploring large areas without having to make frequent downtime to recharge and/or charge the batteries. This manuscript has the potential to be extremely useful for future research into the application of unmanned swarms for autonomous missions.
Causal Hierarchy in the Financial Market Network—Uncovered by the Helmholtz–Hodge–Kodaira Decomposition
Authors: Wand Tobias , Kamps Oliver , Iyetomi Hiroshi
Published: October 2024

Granger causality can uncover the cause-and-effect relationships in financial networks. However, such networks can be convoluted and difficult to interpret, but the Helmholtz–Hodge–Kodaira decomposition can split them into rotational and gradient components which reveal the hierarchy of the Granger causality flow. Using Kenneth French’s business sector return time series, it is revealed that during the COVID crisis, precious metals and pharmaceutical products were causal drivers of the financial network. Moreover, the estimated Granger causality network shows a high connectivity during the crisis, which means that the research presented here can be especially useful for understanding crises in the market better by revealing the dominant drivers of crisis dynamics.
Trends and Gender Disparities in Grades and Grade Anomalies among Bioscience and Health-Related Major Students before, during, and after COVID-19 Remote Instruction
Authors: Malespina Alysa , Seifollahi Fargol , Singh Chandralekha
Published: October 2024

In this study, we investigate student performance using grades and “grade anomalies” across periods before, during, and after COVID-19 remote instruction in courses for bioscience and health-related majors. Additionally, we explore gender equity in these courses using these measures. We define grade anomaly as the difference between a student’s grade in a course of interest and their overall grade point average (GPA) across all other courses taken up to that point. If a student’s grade in a course is lower than their GPA in all other courses, we refer to this as a “grade penalty”. Students received grade penalties in all courses studied, consisting of twelve courses taken by the majority of bioscience and health-related majors. Overall, we found that both grades and grade penalties improved during remote instruction but deteriorated after remote instruction. Additionally, we find more pronounced gender differences in grade anomalies than in grades. We hypothesize that women’s decisions to pursue STEM careers may be more influenced by the grade penalties they receive in required science courses than men’s, as women tend to experience larger penalties across all periods studied. Furthermore, institutions concerned with equity should consider grade penalties as a straightforward measure and make a conscious effort to consider their implications.
Editorial for Special Issue “Practices in Science and Engineering Education”
Authors: Lee Yew-Jin , Ong Yann Shiou
Published: October 2024

As Co-Editors of this Special Issue on science and engineering practices (SEP), we are delighted to curate five excellent submissions that have come our way over the last three years [...]
Transmit Precoding via Block Diagonalization with Approximately Optimized Distance Measures for Limited Feedback in Dense Cellular Networks with Multiantenna Base Stations
Authors: Kwak Sihoon , Kong Jae-Ik , Min Moonsik
Published: October 2024

This study introduces distance metrics for quantized-channel-based precoding in multiuser multiantenna systems, aiming to enhance spectral efficiency in dense cellular networks. Traditional metrics, such as the chordal distance, face limitations when dealing with scenarios involving limited feedback and multiple receive antennas. We address these challenges by developing distance measures that more accurately reflect network conditions, including the impact of intercell interference. Our distance measures are specifically designed to approximate the instantaneous rate of each user by estimating the unknown components during the quantization stage. This approach enables the associated users to efficiently estimate their achievable rates during the quantization process. Our distance measures are specifically designed for block diagonalization precoding, a method known for its computational efficiency and strong performance in multi-user multiple-input and multiple-output systems. The proposed metrics outperform conventional distance measures, particularly in environments where feedback resources are constrained, as is often the case in 5G and emerging 6G networks. The enhancements are especially significant in dense cellular networks, where accurate channel state information is critical for maintaining high spectral efficiency. Our findings suggest that these new distance measures offer a robust solution for improving the performance of limited-feedback-based precoding in cellular networks.
Dual-Branch Dynamic Object Segmentation Network Based on Spatio-Temporal Information Fusion
Authors: Huang Fei , Wang Zhiwen , Zheng Yu , Wang Qi , Hao Bingsen , Xiang Yangkai
Published: October 2024

To address the issue of low accuracy in the segmentation of dynamic objects using semantic segmentation networks, a dual-branch dynamic object segmentation network has been proposed, which is based on the fusion of spatiotemporal information. First, an appearance–motion feature fusion module is designed, which characterizes the motion information of objects by introducing a residual graph. This module combines a co-attention mechanism and a motion correction method to enhance the extraction of appearance features for dynamic objects. Furthermore, to mitigate boundary blurring and misclassification issues when 2D semantic information is projected back into 3D point clouds, a majority voting strategy based on time-series point cloud information has been proposed. This approach aims to overcome the limitations of post-processing in single-frame point clouds. By doing this, this method can significantly enhance the accuracy of segmenting moving objects in practical scenarios. Test results from the semantic KITTI public dataset demonstrate that our improved method outperforms mainstream dynamic object segmentation networks like LMNet and MotionSeg3D. Specifically, it achieves an Intersection over Union (IoU) of 72.19%, representing an improvement of 9.68% and 4.86% compared to LMNet and MotionSeg3D, respectively. The proposed method, with its precise algorithm, has practical applications in autonomous driving perception.
Hardware-Based WebAssembly Accelerator for Embedded System
Authors: Kim Jinyeol , Kim Raehyeong , Oh Jongwon , Lee Seung Eun
Published: October 2024

WebAssembly (WASM) has emerged as a novel standard aimed at enhancing the performance of web applications, developed to complement traditional JavaScript. By offering a platform-independent binary code format, WASM facilitates rapid and efficient execution within web browsers. This attribute is particularly advantageous for tasks demanding significant computational power. However, in resource-constrained environments such as embedded systems, the processing speed and memory requirements of WASM become prominent drawbacks. To address these challenges, this paper introduces the design and implementation of a hardware accelerator specifically for WASM. The proposed WASM accelerator achieves up to a 142-fold increase in computation speed for the selected algorithms compared to embedded systems. This advancement significantly enhances the execution efficiency and real-time processing capabilities of WASM in embedded systems. The paper analyzes the fundamentals of WebAssembly and provides a comprehensive description of the architecture of the accelerator designed to optimize WASM execution. Also, this paper includes the implementation details and the evaluation process, validating the utility and effectiveness of this methodology. This research makes a critical contribution to extending the applicability of WASM in embedded systems, offering a strategic direction for future technological advancements that ensure efficient execution of WASM in resource-limited environments.
Advanced Comparative Analysis of Machine Learning and Transformer Models for Depression and Suicide Detection in Social Media Texts
Authors: Bokolo Biodoumoye George , Liu Qingzhong
Published: October 2024

Depression detection through social media analysis has emerged as a promising approach for early intervention and mental health support. This study evaluates the performance of various machine learning and transformer models in identifying depressive content from tweets on X. Utilizing the Sentiment140 and the Suicide-Watch dataset, we built several models which include logistic regression, Bernoulli Naive Bayes, Random Forest, and transformer models such as RoBERTa, DeBERTa, DistilBERT, and SqueezeBERT to detect this content. Our findings indicate that transformer models outperform traditional machine learning algorithms, with RoBERTa and DeBERTa, when predicting depression and suicide rates. This performance is attributed to the transformers’ ability to capture contextual nuances in language. On the other hand, logistic regression models outperform transformers in another dataset with more accurate information. This is attributed to the traditional model’s ability to understand simple patterns especially when the classes are straighforward. We employed a comprehensive cross-validation approach to ensure robustness, with transformers demonstrating higher stability and reliability across splits. Despite limitations like dataset scope and computational constraints, the findings contribute significantly to mental health monitoring and suggest promising directions for future research and real-world applications in early depression detection and mental health screening tools. The various models used performed outstandingly.
Tensor Core-Adapted Sparse Matrix Multiplication for Accelerating Sparse Deep Neural Networks
Authors: Han Yoonsang , Kim Inseo , Kim Jinsung , Moon Gordon Euhyun
Published: October 2024

Sparse matrix–matrix multiplication (SpMM) is essential for deep learning models and scientific computing. Recently, Tensor Cores (TCs) on GPUs, originally designed for dense matrix multiplication with mixed precision, have gained prominence. However, utilizing TCs for SpMM is challenging due to irregular memory access patterns and a varying number of non-zero elements in a sparse matrix. To improve data locality, previous studies have proposed reordering sparse matrices before multiplication, but this adds computational overhead. In this paper, we propose Tensor Core-Adapted SpMM (TCA-SpMM), which leverages TCs without requiring matrix reordering and uses the compressed sparse row (CSR) format. To optimize TC usage, the SpMM algorithm’s dot product operation is transformed into a blocked matrix–matrix multiplication. Addressing load imbalance and minimizing data movement are critical to optimizing the SpMM kernel. Our TCA-SpMM dynamically allocates thread blocks to process multiple rows simultaneously and efficiently uses shared memory to reduce data movement. Performance results on sparse matrices from the Deep Learning Matrix Collection public dataset demonstrate that TCA-SpMM achieves up to 29.58× speedup over state-of-the-art SpMM implementations optimized with TCs.
A Bullet Screen Sentiment Analysis Method That Integrates the Sentiment Lexicon with RoBERTa-CNN
Authors: Liu Yupan , Wang Shuo , Yu Shengshi
Published: October 2024

Bullet screen, a form of online video commentary in emerging social media, is widely used on video websites frequented by young people. It has become a novel means of expressing emotions towards videos. The characteristics, such as varying text lengths and the presence of numerous new words, lead to ambiguous emotional information. To address these characteristics, this paper proposes a Robustly Optimized BERT Pretraining Approach (RoBERTa) + Convolutional Neural Network (CNN) sentiment classification algorithm integrated with a sentiment lexicon. RoBERTa encodes the input text to enhance semantic feature representation, and CNN extracts local features using multiple convolutional kernels of different sizes. Sentiment classification is then performed by a softmax classifier. Meanwhile, we use the sentiment lexicon to calculate the emotion score of the input text and normalize the emotion score. Finally, the classification results of the sentiment lexicon and RoBERTa+CNN are weighted and calculated. The bullet screens are grouped according to their length, and different weights are assigned to the sentiment lexicon based on their length to enhance the features of the model’s sentiment classification. The method combines the sentiment lexicon can be customized for the domain vocabulary and the pre-trained model can deal with the polysemy. Experimental results demonstrate that the proposed method achieves improvements in precision, recall, and F1 score. The experiments in this paper take the Russia–Ukraine war as the research topic, and the experimental methods can be extended to other events. The experiment demonstrates the effectiveness of the model in the sentiment analysis of bullet screen texts and has a positive effect on grasping the current public opinion status of hot events and guiding the direction of public opinion in a timely manner.
A Novel Two-Channel Classification Approach Using Graph Attention Network with K-Nearest Neighbor
Authors: Wang Yang , Yin Lifeng , Wang Xiaolong , Zheng Guanghai , Deng Wu
Published: October 2024

Graph neural networks (GNNs) typically exhibit superior performance in shallow architectures. However, as the network depth increases, issues such as overfitting and oversmoothing of hidden vector representations arise, significantly diminishing model performance. To address these challenges, this paper proposes a Two-Channel Classification Algorithm Based on Graph Attention Network (TCC_GAT). Initially, nodes exhibiting similar interaction behaviors are identified through cosine similarity, thereby enhancing the foundational graph structure. Subsequently, an attention mechanism is employed to adaptively integrate neighborhood information within the enhanced graph structure, with a multi-head attention mechanism applied to mitigate overfitting. Furthermore, the K-nearest neighbors algorithm is adopted to reconstruct the basic graph structure, facilitating the learning of structural information and neighborhood features that are challenging to capture on interaction graphs. This approach addresses the difficulties associated with learning high-order neighborhood information. Finally, the embedding representations of identical nodes across different graph structures are fused to optimize model classification performance, significantly enhancing node embedding representations and effectively alleviating the over-smoothing issue. Semi-supervised experiments and ablation studies conducted on the Cora, Citeseer, and Pubmed datasets reveal an accuracy improvement ranging from 1.4% to 4.5% compared to existing node classification algorithms. The experimental outcomes demonstrate that the proposed TCC_GAT achieves superior classification results in node classification tasks.
A Management Framework and Optimization Scheduling for Electric Vehicles Participating in a Regional Power Grid Demand Response under Battery Swapping Mode
Authors: Yang Xiaolong , Du Ruoyun , Ji Zhengsen , Wang Qian , Qu Meiyu , Gao Weiyao
Published: October 2024

With the rapid development of new energy vehicle industry and battery technology, in addition to charging mode to supplement energy mode for electric vehicles, battery swapping mode is also about to become an important way for electric vehicles to recharge power. Therefore, in this context, this paper plans the demand response management framework of electric vehicles participating in the regional power grid under the battery swapping mode from the first time. On this basis, the time distribution of battery-swapping demand was proposed by the time series analysis model of different vehicle types of electric vehicles. Then, in order to reduce the peak-valley load difference in the regional power grid as the optimization management goal, the charging schedule optimization scheduling model of electric vehicles participating in the demand response of the regional power grid under the battery swapping mode was constructed. The case analysis shows that under the battery swapping mode, by participating in the demand response through the optimal management and scheduling of the charging load of the power battery, can help the grid balance the contradiction between supply and demand in the peak and valley and promote the full consumption of new energy.
Intelligent Technologies for Vehicular Networks
Authors: Blanco Fernández Yolanda
Published: October 2024

Intelligent transport systems (ITS) [...]
Bias and Deviation Map-Based Weighted Graph Search for NLOS Indoor RTLS Calibration
Authors: Kim Jeong-Ho , An Hyun-Gi , Komuro Nobuyoshi , Kim Won-Suk
Published: October 2024

Recently, UWB-based technology providing centimeter-level accuracy has been developed and widely utilized in indoor real-time location tracking systems. However, location accuracy varies due to factors such as frequency interference, collisions, reflected signals, and whether line-of-sight (LOS) conditions are met, and it can be challenging to ensure high accuracy in specific environments. Fortunately, when anchor positions are fixed, the locations of large obstacles such as columns or furniture remain relatively stable, leading to similar patterns of positioning bias at specific points. This study proposes an algorithm that corrects inaccurate positioning to more closely reflect the actual location based on bias and deviation maps generated using natural neighbor interpolation. Initially, positioning bias and deviations at specific points are sampled, and bias and deviation maps are created using natural neighbor interpolation. During location tracking, the algorithm detects candidate clusters and determines the centroid to estimate the actual location by applying the bias and deviation maps to the measured positions derived through trilateration. To validate the proposed algorithm, experiments were conducted in a non-LOS (NLOS) indoor environment. The results demonstrate that the proposed algorithm can reduce the positioning bias of a UWB-based RTLS by approximately 71.34% compared to an uncalibrated system.
A Versatile Platform for PV System Integration into Microgrids
Authors: Gómez-Ruiz Gabriel , Sánchez-Herrera Reyes , Clavijo-Camacho Jesús , Cano Juan M. , Ruiz-Rodríguez Francisco J. , Andújar José M.
Published: October 2024

Advancing decarbonization critically depends on the integration of PV systems into microgrids. However, this integration faces challenges, including the variability of photovoltaic solar energy production, the demands of energy management, and the complexities of grid synchronization and communication. To address these challenges, a PV emulator platform is an essential tool. This paper presents a novel four-layer PV emulator platform that seamlessly integrates power systems, control systems, measurement instrumentation, and communication processes. The proposed platform enables the emulation of I-V curves and the dynamic adjustment of operating points—including both the maximum power point (MPP) and power reserve point (PRP)—as well as temperature and irradiance while providing sufficient power capacity for microgrid integration. To validate its effectiveness, the platform was assessed for its capability to adjust operating points, such as MPPs or PRPs, under varying irradiance and temperature conditions. The results show that the platform effectively adjusts operating points with a deviation of less than 5% from theoretical values and successfully tracks a sequence of operating points. This performance underscores the platform’s potential in integrating and managing PV systems within microgrid environments, thereby advancing the path to decarbonization.
Forecasting Heart Disease Risk with a Stacking-Based Ensemble Machine Learning Method
Authors: Wu Yuanyuan , Xia Zhuomin , Feng Zikai , Huang Mengxing , Liu Huizhou , Zhang Yu
Published: October 2024

As one of the main causes of sickness and mortality, heart disease, also known as cardiovascular disease, must be detected early in order to be prevented and treated. The rapid development of computer technology presents an opportunity for the cross-combination of medicine and informatics. A novel stacking model called SDKABL is presented in this work. It uses three classifiers, namely K-Nearest Neighbor (KNN), Decision Tree (DT), and Support Vector Machine (SVM) at the base layer and the Bidirectional Long Short-Term Memory based on Attention Mechanisms (ABiLSTM) model at the meta layer for the ultimate prediction. For lowering the temporal complexity and enhancing the model’s accuracy, the dimensionality reduction approach is seen to be crucial. Principal Component Analysis (PCA) was utilized in SDKABL to minimize dimensionality and facilitate feature fusion. Using several performance measures, including precision, F1-score, accuracy, recall, and Receiver Operating Characteristic (ROC) score, the performance of SDKABL was compared to that of other independent classifiers. The experimental findings demonstrate that our proposed model combining individual classifiers with the stacking method helps improve the prediction model’s accuracy.
Challenges and Advances in Analyzing TLS 1.3-Encrypted Traffic: A Comprehensive Survey
Authors: Zhou Jiuxing , Fu Wei , Hu Wei , Sun Zhihong , He Tao , Zhang Zhihong
Published: October 2024

The widespread adoption of encrypted communication protocols has significantly enhanced network security and user privacy, simultaneously elevating the importance of encrypted traffic analysis across various domains, including network anomaly detection. The Transport Layer Security (TLS) 1.3 protocol, introduced in 2018, has gained rapid popularity due to its enhanced security features and improved performance. However, TLS 1.3’s security enhancements, such as encrypting more of the handshake process, present unprecedented challenges for encrypted traffic analysis, rendering traditional methods designed for TLS 1.2 and earlier versions ineffective and necessitating the development of novel analytical techniques. This comprehensive survey provides a thorough review of the latest advancements in TLS 1.3 traffic analysis. First, we examine the impact of TLS 1.3’s new features, including Encrypted ClientHello (ECH), 0-RTT session resumption, and Perfect Forward Secrecy (PFS), on existing traffic analysis techniques. We then present a systematic overview of state-of-the-art methods for analyzing TLS 1.3 traffic, encompassing middlebox-based interception, searchable encryption, and machine learning-based approaches. For each method, we provide a critical analysis of its advantages, limitations, and applicable scenarios. Furthermore, we compile and review key datasets utilized in machine learning-based TLS 1.3 traffic analysis research. Finally, we discuss the main challenges and potential future research directions for TLS 1.3 traffic analysis. Given that TLS 1.3 is still in the early stages of widespread deployment, research in this field remains nascent. This survey aims to provide researchers and practitioners with a comprehensive reference, facilitating the development of more effective TLS 1.3 traffic analysis techniques that balance network security requirements with user privacy protection.
An Analytical Approach for IGBT Life Prediction Using Successive Variational Mode Decomposition and Bidirectional Long Short-Term Memory Networks
Authors: Deng Kaitian , Xu Xianglian , Yuan Fang , Zhang Tianyu , Xu Yuli , Xie Tunzhen , Song Yuanqing , Zhao Ruiqing
Published: October 2024

The precise estimation of the operational lifespan of insulated gate bipolar transistors (IGBT) holds paramount significance for ensuring the efficient and uncompromised safety of industrial equipment. However, numerous methodologies and models currently employed for this purpose often fall short of delivering highly accurate predictions. The analytical approach that combines the Pattern Optimization Algorithm (POA) with Successive Variational Mode Decomposition (SVMD) and Bidirectional Long Short-term Memory (BiLSTM) network is introduced. Firstly, SVMD is employed as an unsupervised feature learning method to partition the data into intrinsic modal functions (IMFs), which are used to eliminate noise and preserve the essential signal. Secondly, the BiLSTM network is integrated for supervised learning purposes, enabling the prediction of the decomposed sequence. Additionally, the hyperparameters of BiLSTM and the penalty coefficients of SVMD are optimized utilizing the POA technique. Subsequently, the various modal functions are predicted utilizing the trained prediction model, and the individual mode predictions are subsequently aggregated to yield the model’s definitive final life prediction. Through case studies involving IGBT aging datasets, the optimal prediction model was formulated and its lifespan prediction capability was validated. The superiority of the proposed method is demonstrated by comparing it with benchmark models and other state-of-the-art methods.
Multi-Objective Optimization Operation of Multi-Agent Active Distribution Network Based on Analytical Target Cascading Method
Authors: Zhao Yiran , Xue Yong , Zhang Ruixin , Yin Jiahao , Yang Yang , Chen Yanbo
Published: October 2024

In the context of the green energy transition, the rapid expansion of flexible resources such as distributed renewable energy, electric vehicles (EVs), and energy storage has significantly impacted the operation of distribution networks. This paper proposes a multi-objective optimization approach for active distribution networks (ADNs) based on analytical target cascading (ATC). Firstly, a dynamic optimal power flow (DOPF) calculation method is developed using second-order conic relaxation (SOCR) to address power flow and voltage issues in the distribution network, incorporating active management (AM) elements. Secondly, this study focuses on aggregating the power of flexible resources within station areas connected to distribution network nodes and incorporating these resources into demand response (DR) programs. Finally, a two-layer model for collaborative multi-objective scheduling between station areas and the active distribution network is implemented using the ATC method. Case studies demonstrate the model’s effectiveness and validity, showing its potential for enhancing the operation of distribution networks amidst the increasing integration of flexible resources.
Pharmaceutical Wastewater and Sludge Valorization: A Review on Innovative Strategies for Energy Recovery and Waste Treatment
Authors: Pech-Rodríguez W. J. , Meléndez-González P. C. , Hernández-López J. M. , Suarez-Velázquez G. G. , Sarabia-Castillo César R. , Calles-Arriaga C. A.
Published: October 2024

Currently, a large amount of pharmaceutical waste (PW) and its derivatives are being produced and, in some cases, inadequate management or treatment practices are applied. In this regard, this research explores the adoption of several alternatives to deal with these problems, including biocarbon within the framework of the circular economy. Photocatalytic nanomaterials have been also extensively discussed as a feasible way to remove pharmaceutical compounds in wastewater. Although there are existing reports in this area, this document provides a detailed study of the synthesis process, experimental conditions, the integration of photocatalysts, and their impact on enhancing photocatalytic efficiency. Additionally, the low cost and ease of fabrication of lab-scale microbial fuel cells (MFCs) are thoroughly examined. This innovative technology not only facilitates the degradation of hazardous compounds in wastewater but also harnesses their energy to generate electricity simultaneously. The aforementioned approaches are covered and discussed in detail by documenting interesting recently published research and case studies worldwide. Furthermore, this research is of significant importance because it addresses the valorization of PW by generating valuable by-products, such as H2 and O2, which can occur simultaneously during the photodegradation process, contributing to more sustainable industrial practices and clean energy technologies.
Numerical Simulation of Secondary Hydrate Formation Characteristics and Effectiveness of Prevention Methods
Authors: Li Xian , Lu Hongfeng , Zhang Panpan , Yu Lu , Xiao Changwen , Li Yan
Published: October 2024

The exploitation of natural gas hydrates by the pressure reduction method is affected by the decomposition heat absorption effect, and the range of the formation temperature reduction area is expanding. At the same time, the temperature reduction phenomenon is more significant around the production wells under the influence of gas throttling and expansion effects, and hydrate formation will occur under certain temperature and pressure conditions, leading to blockage of effective seepage channels in the reservoir in the region and elevation of seepage resistance, which may affect the output of hydrate decomposition gas. A numerical simulation model is constructed for the purpose of studying the secondary hydrate generation pattern around the well, analyzing the impact of secondary hydrates around wells on the production capacity, and assessing the effectiveness of prevention methods to inform the actual production of hydrates. The results demonstrate that secondary hydrate is typically formed in the near-well area of the upper part of the production well, and the secondary hydrate around the upper part of the production well is the first to be formed, exhibiting the highest saturation peak and the latest decomposition. The formation of the secondary hydrate can be predicted based on the observed change in temperature and pressure, and the rate of secondary hydrate formation is markedly rapid, whereas the decomposition rate, approximately 0.285 mole/d, is relatively slow. Additionally, the impact of secondary hydrates on cumulative gas production is insignificant, and the effect of secondary hydrates on capacity can be ignored. Hot water injection, wellbore heating, and reservoir reconstruction can effectively eliminate secondary hydrates around the well. Reservoir reconstruction represents a superior approach to the elimination of secondary hydrates, which can effectively enhance production capacity while preventing the generation of secondary hydrates.
Control of Liquid Hydrocarbon Combustion Parameters in Burners with Superheated Steam Supply
Authors: Kopyev Evgeny , Kuznetsov Viktor , Minakov Andrey , Alekseenko Sergey , Sharypov Oleg
Published: October 2024

A numerical simulation of reacting mixture flow in a full-scale combustion chamber of a prototype burner with a fuel-sprayed jet of superheated steam and a controlled excess air ratio was performed based on a verified model. The influence of steam jets on the combustion parameters of the created prototype device was analyzed based on the results, and a comparison with data from various atmospheric burners, including evaporative and spray types, direct-flow and vortex types, and those with natural and forced (regulated) air supply, was made. Various schemes for supplying steam to burner devices were discussed. It was shown that the relative steam consumption is a parameter for controlling the emission of toxic combustion products, such as NOx and CO, for all designs. A high burner performance is achieved when superheated steam is supplied at more than 250 °C with a relative steam flow rate of >0.6. The design features of the burner systems and operational parameters that ensure high thermal and environmental efficiency when burning various types of fuel and waste are identified.
Correction: Gupta et al. The Role of Transactive Energy in the Future Energy Industry: A Critical Review. Energies 2022, 15, 8047
Authors: Gupta Neeraj , Prusty B Rajanarayan , Alrumayh Omar , Almutairi Abdulaziz , Alharbi Talal
Published: October 2024

In the original publication [...]
Analysis of the Interplay between Indoor Air Quality and Thermal Comfort in University Classrooms for Enhanced HVAC Control
Authors: Lamberti Giulia , Leccese Francesco , Salvadori Giacomo
Published: October 2024

While aspects of indoor environmental quality, such as thermal comfort, indoor air quality (IAQ), acoustic, and visual comfort, are usually studied separately, their interactions are crucial yet often overlooked. Understanding how these factors influence each other is essential for a comprehensive perception of the indoor environment. This paper investigates the relationship between indoor air quality (IAQ) and thermal comfort using an extensive field investigation conducted in university classrooms during the heating season, collecting 712 samples of subjective responses correlated with environmental measurements. Key findings reveal significant correlations between subjective responses related to the thermal environment and those related to air quality. Perceived control over the thermal environment shows stronger correlations with IAQ responses than with thermal responses, particularly with perceived ventilation (r = 0.41), COVID-19 risk (r = 0.28), and air quality (r = 0.28). Additionally, environmental parameters demonstrate stronger correlations with thermal responses than with IAQ responses. Higher CO2 concentration is associated with increased thermal sensation and decreased thermal preference and perceived control. Conversely, IAQ responses remain relatively stable with changes in indoor operative temperature. The difference between the operative temperature to which the occupants are exposed and their expressed neutral temperature widens as CO2 concentration rises, indicating a reduced adaptive capacity of occupants which is associated with increasing CO2 levels. These insights are crucial for providing HVAC system management strategies that consider the interaction between different aspects of IEQ, aiming to improve occupants’ well-being and reduce energy consumption.
Machine Learning and Cointegration for Wind Turbine Monitoring and Fault Detection: From a Comparative Study to a Combined Approach
Authors: Knes Paweł , Dao Phong B.
Published: October 2024

Data-driven models have become powerful tools for structural and condition monitoring of engineering systems, particularly wind turbines. This paper presents a comparative analysis of common machine learning (ML) algorithms (artificial neural networks, linear regression, random forests, and gradient boosting) and a cointegration-based approach for fault detection using Supervisory Control and Data Acquisition (SCADA) data. While ML models offer early fault prediction, the cointegration method is simpler, requires less training data, and has lower computational costs. However, it is less effective for early detection. To balance these trade-offs, we propose a cascading monitoring framework, where the ML model provides long-term predictions (outer monitoring process) and the cointegration model offers short-term verification (inner monitoring process). The cointegration model serves to confirm anomalies flagged by the ML model. By combining both models in a cascade structure, the system reduces the risk of false alarms triggered by uncertainties in the ML model alone. Furthermore, the short-term cointegration-based prediction model helps pinpoint immediate risks and mitigate the issue of prolonged downtime. This combination enhances both accuracy and reliability, as demonstrated through testing on a five-year SCADA dataset from a commercial wind turbine with a known gearbox fault.
Multidimensional Exploration of Wood Extractives: A Review of Compositional Analysis, Decay Resistance, Light Stability, and Staining Applications
Authors: Gao Chenggong , Cui Xinjie , Matsumura Junji
Published: October 2024

Extractives, which naturally evolve as fundamental defense mechanisms in wood against environmental stresses, hold an essential place in the field of wood conservation science. Despite their low content in woody substrates, extractives are chemically complex and can be extracted accurately by solvents with different polarities, covering key components such as aliphatic, terpenoid, and phenolic compounds. The application of solvent extraction allows for the effective recovery of these extracts from forestry waste, thereby creating new opportunities for their reuse in wood modification and enhancing the economic value and potential applications of forestry waste. In the wood industry, extractives not only act as efficient preservatives and photo-stabilizers, significantly improving the decay resistance and photodegradation resistance of wood, but also serve as ideal dyes for fast-growing wood due to their abundant natural colors, which lend the product a distinct aesthetic appeal. The aim of this paper is to provide a comprehensive review of the origin and distribution characteristics of wood extractives and to examine the impact of solvent selection on extraction efficiency. At the same time, the mechanism of extractives in enhancing wood decay resistance and slowing down photodegradation is deeply analyzed. In addition, specific examples are presented to illustrate their wide utilization in the wood industry. This is intended to provide references for research and practice in related fields.
Mechanism of Spontaneous Acceleration of Slow Flame in Channel
Authors: Yarkov Andrey , Yakovenko Ivan , Kiverin Alexey
Published: October 2024

This paper is devoted to the numerical analysis of the spontaneous acceleration of a slow flame in a semi-closed channel. In particular, the flow development in the channel ahead of the propagating flame is analyzed. The applied detailed numerical model allows the clear observation of all features intrinsic to the reacting flow evolution in the channel, including the formation of perturbations on the scale of the boundary layer and their further development. In all considered cases, perturbations of the boundary layer emerge in the early stages of flame acceleration and decay afterward. The flow stabilizes more rapidly in a narrow channel, where the velocity profile is close to the Poiseuille profile. At the same time, the compression waves generated in the reaction zone travel along the channel. The interaction between compression waves in the area of combustion products can lead to the formation of shock waves. The effect of shock waves on the flow in the fresh mixture causes an increase in the flame area and a corresponding flame acceleration. In addition, shock waves trigger boundary-layer instability in wide channels. The perturbations of the boundary layer grow and evolve into vortexes, while further vortex–flame interaction leads to significant flame acceleration.
Lactococcus garvieae as a Novel Pathogen in Cultured Pufferfish (Takifugu obscurus) in China
Authors: Xu Ruilong , He Zhongning , Deng Yiyang , Cen Yihao , Mo Zequan , Dan Xueming , Li Yanwei
Published: October 2024

In October 2023, a disease outbreak in pufferfish (Takifugu obscurus) farms in Zhongshan City, Guangdong, China, caused high mortality. Diseased fish (mean length: 15 ± 1 cm) exhibited swimming disorders, fin rot, hemorrhage, and an enlarged spleen. Histopathological observations generally revealed inflammation, necrosis, and congestion in the spleen, kidneys, and brain tissues. The most severe pathological changes included interstitial edema and tubular atrophy in the kidneys, hemosiderin deposition in the spleen, massive red blood cell infiltration, and a decrease in lymphocytes. A single strain of bacteria (Tol-1) was isolated from the diseased pufferfish and identified as a Gram-positive streptococcus strain, exhibiting α-hemolysis on sheep blood agar plates. Through biochemical characterization, 16S rDNA sequencing, morphological analysis, and specific primer-based identification, the Tol-1 strain was identified as Lactococcus garvieae, serotype I. Antimicrobial susceptibility testing indicated that Tol-1 was sensitive to Chloramphenicol, Ampicillin, Cephalexin, and Doxycycline, but resistant to Kanamycin, Gentamicin and Ciprofloxacin. In addition, 15 common virulence factors were detected in the Tol-1 strain, including adhPav, adhPsaA, adhC I–II, adh, and hly 1–3. Pufferfish (mean length: 17 ± 1 cm) subjected to artificial infection via intraperitoneal injection (IP) with the Tol-1 strain exhibited clinical symptoms and histopathological damage similar to those observed in naturally infected fish. An infection dose of 1 × 105 CFU/fish resulted in 80% mortality. The study fulfilled Koch’s postulates, indicating that the disease outbreak in pufferfish was caused by L. garvieae, which exhibited a high mortality rate in pufferfish despite the subtle clinical symptoms. These results serve as a warning for pufferfish farming areas and provide a scientific basis for future prevention and control efforts.