bluuebunny/crossref_metadata_2025 huggingface dataset:
Continual Reinforcement Learning for Quadruped Robot Locomotion
Authors: Gai Sibo , Lyu Shangke , Zhang Hongyin , Wang Donglin
Published: January 2024

The ability to learn continuously is crucial for a robot to achieve a high level of intelligence and autonomy. In this paper, we consider continual reinforcement learning (RL) for quadruped robots, which includes the ability to continuously learn sub-sequential tasks (plasticity) and maintain performance on previous tasks (stability). The policy obtained by the proposed method enables robots to learn multiple tasks sequentially, while overcoming both catastrophic forgetting and loss of plasticity. At the same time, it achieves the above goals with as little modification to the original RL learning process as possible. The proposed method uses the Piggyback algorithm to select protected parameters for each task, and reinitializes the unused parameters to increase plasticity. Meanwhile, we encourage the policy network exploring by encouraging the entropy of the soft network of the policy network. Our experiments show that traditional continual learning algorithms cannot perform well on robot locomotion problems, and our algorithm is more stable and less disruptive to the RL training progress. Several robot locomotion experiments validate the effectiveness of our method.
Analysis of Quality of Life of Autistic Students on the Canary Islands during the COVID-19 Pandemic and the Educational Response Offered
Authors: Rodríguez-Jiménez María del Carmen , Puerta-Araña Irene , González-Herrera Ana Isabel , González-Afonso Miriam Catalina
Published: January 2024

The COVID-19 pandemic caused the closure of educational centers, forcing the teaching–learning process to become virtual at all levels of the educational system. The main objective of this study was to describe and analyze how students with autism spectrum disorder (ASD) and their parents perceived their quality of life and the educational response offered during the COVID-19 pandemic. This study was conducted with 50 students with grade 1 ASD, aged 8–11 years, and their parents. The methodology used was qualitative and descriptive, with semi-structured, individual interviews. The data were analyzed using the MAXQDA 2020 program. The results show that students with ASD state that they need social contact and that the fact of not experiencing it, as occurred during the COVID-19 situation, had a negative impact on their perception of their quality of life, as well as the perception of their parents, significantly affecting their emotional well-being. Likewise, it was detected that both students with ASD and their parents perceived that the educational response was inadequate during that period.
Learning to Co-Teach: A Systematic Review
Authors: Rytivaara Anna , Ahtiainen Raisa , Palmu Iines , Pesonen Henri , Malinen Olli-Pekka
Published: January 2024

Research on how teachers learn to co-teach is scarce. In this systematic review, the PRISMA method was used to examine the relationship between teacher learning and co-teaching in professional development programmes. Inclusion criteria was used to identify 567 articles on K–12 co-teaching, published in 2009–2018. A detailed analysis of nine articles revealed that the linkage between co-teaching and teacher learning remained narrow. Various programmes showed that the existing understanding of co-teaching or teacher learning was not used efficiently. Considerable variation in the programmes regarding the concepts, methods, and practices highlight the importance of conducting future research.
Enculturating a Protective Professional Community—Processes of Teacher Retention in a Swedish Hard-to-Staff School
Authors: Casely-Hayford Jeffrey , Lindqvist Per , Björklund Christina , Bergström Gunnar , Kwak Lydia
Published: January 2024

This study presented a positive deviant case: a Swedish hard-to-staff school which has had a low rate of teacher turnover over time. In line with the purpose of studying positive deviance in organisations, our exploratory inquiry was geared towards understanding how and why ‘at-risk’ teachers, i.e., teachers who teach in subjects which are known to have high levels of staffing difficulties in Sweden, stayed at this particular school. Using a modified grounded theory approach, our results suggested that teachers remained at the school due to being embedded in a protective professional community that was enculturated by different expressions of collegiality. Finally, these findings are discussed in relation to the theoretical concepts of teachers’ job embeddedness and social capital.
Use of QUIC for Mobile-Oriented Future Internet (Q-MOFI)
Authors: Tauqeer Muhammad , Gohar Moneeb , Koh Seok , Alquhayz Hani
Published: January 2024

With the proliferation of mobile devices and various mobile services, ensuring smooth mobility for users has become a major challenge. The future internet is expected to be more mobile-friendly, with advancing technologies that will transform internet management in the coming decades. These technological advancements will help address mobility issues and provide a better internet experience for mobile devices and users. The transport layer plays a crucial role in the internet protocol suite by enabling communication between applications running on different servers. However, the widely used protocols, TCP and UDP, have several limitations, such as unreliability and slow performance due to three-way handshakes. To tackle these issues, Google introduced quick UDP internet connections (QUIC). QUIC aims to enhance latency, delay, and data transmission reliability. Q-MOFI, a future internet architecture focused on mobile devices and based on QUIC, strives to achieve these goals. Moreover, it enhances throughput by implementing multiplexing. Q-MOFI outperforms traditional UDP-based MOFI in terms of throughput gains, minimizing packet loss, and reducing binding operation latency, even when the number of hosts increases. The efficiency of this model has been validated through experimental testing.
Time-Allocation Adaptive Data Rate: An Innovative Time-Managed Algorithm for Enhanced Long-Range Wide-Area Network Performance
Authors: Wang Kunzhu , Wang Kun , Ren Yongfeng
Published: January 2024

Currently, a variety of Low-Power Wide-Area Network (LPWAN) technologies offer diverse solutions for long-distance communication. Among these, Long-Range Wide-Area Network (LoRaWAN) has garnered considerable attention for its widespread applications in the Internet of Things (IoT). Nevertheless, LoRaWAN still faces the challenge of channel collisions when managing dense node communications, a significant bottleneck to its performance. Addressing this issue, this study has developed a novel “time allocation adaptive Data Rate” (TA-ADR) algorithm for network servers. This algorithm dynamically adjusts the spreading factor (SF) and transmission power (TP) of LoRa (Long Range) nodes and intelligently schedules transmission times, effectively reducing the risk of data collisions on the same frequency channel and significantly enhancing data transmission efficiency. Simulations in a dense LoRaWAN network environment, encompassing 1000 nodes within a 480 m × 480 m range, demonstrate that compared to the ADR+ algorithm, our proposed algorithm achieves substantial improvements of approximately 30.35% in data transmission rate, 24.57% in energy consumption, and 31.25% in average network throughput.
Representative Real-Time Dataset Generation Based on Automated Fault Injection and HIL Simulation for ML-Assisted Validation of Automotive Software Systems
Authors: Abboush Mohammad , Knieke Christoph , Rausch Andreas
Published: January 2024

Recently, a data-driven approach has been widely used at various stages of the system development lifecycle thanks to its ability to extract knowledge from historical data. However, despite its superiority over other conventional approaches, e.g., approaches that are model-based and signal-based, the availability of representative datasets poses a major challenge. Therefore, for various engineering applications, new solutions to generate representative faulty data that reflect the real world operating conditions should be explored. In this study, a novel approach based on a hardware-in-the-loop (HIL) simulation and automated real-time fault injection (FI) method is proposed to generate, analyse and collect data samples in the presence of single and concurrent faults. The generated dataset is employed for the development of machine learning (ML)-assisted test strategies during the system verification and validation phases of the V-cycle development model. The developed framework can generate not only time series data but also a textual data including fault logs in an automated manner. As a case study, a high-fidelity simulation model of a gasoline engine system with a dynamic entire vehicle model is utilised to demonstrate the capabilities and benefits of the proposed framework. The results reveal the applicability of the proposed framework in simulating and capturing the system behaviour in the presence of faults occurring within the system’s components. Furthermore, the effectiveness of the proposed framework in analysing system behaviour and acquiring data during the validation phase of real-time systems under realistic operating conditions has been demonstrated.
Application of the STFT for Detection of the Rotor Unbalance of a Servo-Drive System with an Elastic Interconnection
Authors: Ewert Pawel , Wicher Bartłomiej , Pajchrowski Tomasz
Published: January 2024

The article focuses on the use of short-time Fourier transform (STFT) to detect the unbalance of a drive with a flexible connection between the driving machine and the load. The authors present the unbalance model and justify, through subsequent experiments, why the STFT-based approach is appropriate. The effectiveness of the presented method of analyzing signals from acceleration sensors was confirmed experimentally by designing an artificial neural network for detecting the unbalance.
Bidirectional Temporal Pose Matching for Tracking
Authors: Fang Yichuan , Shi Qingxuan , Yang Zhen
Published: January 2024

Multi-person pose tracking is a challenging task. It requires identifying the human poses in each frame and matching them across time. This task still faces two main challenges. Firstly, sudden camera zooming and drastic pose changes between adjacent frames may result in mismatched poses between them. Secondly, the time relationships modeled by most existing methods provide insufficient information in scenarios with long-term occlusion. In this paper, to address the first challenge, we propagate the bounding boxes of the current frame to the previous frame for pose estimation, and match the estimated results with the previous ones, which we call the Backward Temporal Pose-Matching (BTPM) module. To solve the second challenge, we design an Association Across Multiple Frames (AAMF) module that utilizes long-term temporal relationships to supplement tracking information lost in the previous frames as a Re-identification (Re-id) technique. Specifically, we select keyframes with a fixed step size in the videos and label other frames as general frames. In the keyframes, we use the BTPM module and the AAMF module to perform tracking. In the general frames, we propagate poses in the previous frame to the current frame for pose estimation and association, which we call the Forward Temporal Pose-Matching (FTPM) module. If the pose association fails, the current frame will be set as a keyframe, and tracking will be re-performed. In the PoseTrack 2018 benchmark tests, our method shows significant improvements over the baseline methods, with improvements of 2.1 and 1.1 in mean Average Precision (mAP) and Multi-Object Tracking Accuracy (MOTA), respectively.
Multispectral Object Detection Based on Multilevel Feature Fusion and Dual Feature Modulation
Authors: Sun Jin , Yin Mingfeng , Wang Zhiwei , Xie Tao , Bei Shaoyi
Published: January 2024

Multispectral object detection is a crucial technology in remote sensing image processing, particularly in low-light environments. Most current methods extract features at a single scale, resulting in the fusion of invalid features and the failure to detect small objects. To address these issues, we propose a multispectral object detection network based on multilevel feature fusion and dual feature modulation (GMD-YOLO). Firstly, a novel dual-channel CSPDarknet53 network is used to extract deep features from visible-infrared images. This network incorporates a Ghost module, which generates additional feature maps through a series of linear operations, achieving a balance between accuracy and speed. Secondly, the multilevel feature fusion (MLF) module is designed to utilize cross-modal information through the construction of hierarchical residual connections. This approach strengthens the complementarity between different modalities, allowing the network to improve multiscale representation capabilities at a more refined granularity level. Finally, a dual feature modulation (DFM) decoupling head is introduced to enhance small object detection. This decoupled head effectively meets the distinct requirements of classification and localization tasks. GMD-YOLO is validated on three public visible-infrared datasets: DroneVehicle, KAIST, and LLVIP. DroneVehicle and LLVIP achieved mAP@0.5 of 78.0% and 98.0%, outperforming baseline methods by 3.6% and 4.4%, respectively. KAIST exhibited an MR of 7.73% with an FPS of 61.7. Experimental results demonstrated that our method surpasses existing advanced methods and exhibits strong robustness.
Improved Dynamic Performance of Average-Value Modelled Active Front-End Rectifiers
Authors: Ebadpour Mohsen
Published: January 2024

Active front-end (AFE) rectifiers have become widely employed in power systems to achieve unity power factor and harmonic mitigations. The typical modeling approaches applied for AFE rectifiers in the literature mostly relied on two baselines: the detailed model and the time-average model. The former approach deals with the switching element model (SEM), which leads to significant harmonics in currents with distorted waveforms. The latter approach uses the average-value model (AVM) to overcome the currents’ harmonics as well as provide fast responses. However, even the AVM baseline has shown problems during the starting stage (lack of control signals) and over the dead-time periods, which causes serious issues in the implementation process. This paper presents an improved dynamic AVM for AFE rectifiers by precisely considering the issues mentioned above, along with the practical starting procedure and desirable initialization. The studied AFE rectifier is developed using the voltage-oriented control (VOC) technique based on the different modeling methodologies, including SEM, Conventional AVM, and the proposed AVM. The performance of all models is analyzed and compared using simulation results with MATLAB/Simulink R2023a Function blocks for all the algorithm parts and SimScape elements for the electrical circuit model. The simulation results illustrate that the performance of the proposed AVM approach can closely resemble the behavior of the SEM baseline with low harmonic distortion. To evaluate the performance of the proposed model, several case studies are investigated to verify the AFE rectifier operation, regarding mostly the total harmonic distortion (THD) wherein the THD percentages are improved to 4.78 and 2.5 from 5.14 and 2.78 for low- and high-power loads, respectively.
High Frequency Component Enhancement Network for Image Manipulation Detection
Authors: Pan Wenyan , Ma Wentao , Wu Xiaoqian , Liu Wei
Published: January 2024

With the support of deep neural networks, the existing image manipulation detection (IMD) methods can detect manipulated regions within a suspicious image effectively. In general, manipulation operations (e.g., splicing, copy-move, and removal) tend to leave manipulation artifacts in the high-frequency domain of the image, which provides rich clues for locating manipulated regions. Inspired by this phenomenon, in this paper, we propose a High-Frequency Component Enhancement Network, short for HFCE-Net, for image manipulation detection, which aims to fully explore the manipulation artifacts left in the high-frequency domain to improve the localization performance in IMD tasks. Specifically, the HFCE-Net consists of two parallel branches, i.e., the main stream and high-frequency auxiliary branch (HFAB). The HFAB is introduced to fully explore high-frequency artifacts within manipulated images. To achieve this goal, the input image of the HFAB is filtered out of the low-frequency component by the Sobel filter. Furthermore, the HFEB is supervised with the edge information of the manipulated regions. The main stream branch takes the RGB image as input, and aggregates the features learned from the HFAB by the proposed multi-layer fusion (MLF) in a hierarchical manner. We conduct extensive experiments on widely used benchmarks, and the results demonstrate that our HFCE-Net exhibits a strong ability to capture high-frequency information within the manipulated image. Moreover, the proposed HFCE-Net achieves comparable performance (57.3%, 90.9%, and 73.8% F1 on CASIA, NIST, and Coverage datasets) and achieves 1.9%, 9.0%, and 1.5% improvement over the existing method.
A Noval and Efficient Three-Party Identity Authentication and Key Negotiation Protocol Based on Elliptic Curve Cryptography in VANETs
Authors: Yu Wenping , Zhang Rui , Ma Maode , Wang Cong
Published: January 2024

In the process of vehicles transitioning from conventional means of transportation to mobile computing platforms, ensuring secure communication and data exchange is of paramount importance. Consequently, identity authentication has emerged as a crucial security measure. Specifically, effective authentication is required prior to the communication between the On-Board Unit (OBU) and Roadside Unit (RSU). To address vehicle identity authentication challenges in the Internet of Vehicles (VANETs), this paper proposes a three-party identity authentication and key agreement protocol based on elliptic curve public key cryptography. Considering issues such as vehicle impersonation attacks, RSU impersonation attacks, and vehicle privacy breaches in existing schemes within wireless mobile environments, this protocol introduces a trusted registry center that successfully enables mutual authentication between OBU and RSU. The proposed protocol not only enhances the VANETs system’s ability to withstand security threats but also improves the credibility and efficiency of the authentication process.
ZX Fusion: A ZX Spectrum Implementation on an FPGA with Modern Peripherals
Authors: Jacinto Gustavo , Policarpo Duarte Rui
Published: January 2024

The ZX Spectrum was a popular 8-bit home computer by Sinclair Research in the 1980s. Even though some of these computers may still work, the audio tapes, the TV with an analog tuner, and the micro-switch joystick that were used with the original ZX Spectrum are outdated and hard to find in good working order or to replicate. As many other old closed systems are also very difficult to update to support modern peripherals there is a necessity to provide a methodology to adapt such systems to support new peripherals while being compatible with existing software. This implementation is a means by which to validate the methodology before applying it to a physical system. The work proposed in this paper focused on recreating a ZX Spectrum+/48K computer and interfacing it with modern peripherals on an FPGA. This was accomplished by adding a co-processor to assist with the control of the more complex peripherals. Otherwise, the original system would require complex architectural changes and would perform poorly due to the low performance of the Z80 CPU. This work distanced itself from previous works on emulating a ZX Spectrum, as it focused on the use of different upgraded peripherals and the use of a NIOS II soft processor as a co-processor to manage the SD card accesses and save-state functionality. A demonstration of the proposed modernized architecture was made by successfully running a diagnostics ROM and playing original ZX Spectrum games from an SD card for games with a PS/2 keyboard and a pair of joysticks.
An Optimized Device Structure with Improved Erase Operation within the Indium Gallium Zinc Oxide Channel in Three-Dimensional NAND Flash Applications
Authors: Choi Seonjun , Park Jin-Seong , Kang Myounggon , Jung Hong-sik , Song Yun-heub
Published: January 2024

In this paper, we propose an optimized device structure to address issues in 3D NAND flash memory devices, which encounter difficulties when using the hole erase method due to the unfavorable hole characteristics of indium gallium zinc oxide (IGZO). The proposed structure mitigated the erase operation problem caused by the low hole mobility of IGZO by introducing a filler inside the IGZO channel. It facilitated the injection of holes into the IGZO channel through the filler, while the existing P-type doped polysilicon filler material was replaced by a P-type oxide semiconductor. In contrast to polysilicon (band gap: 1.1 eV), this P-type oxide semiconductor has a band gap similar to that of the IGZO channel (2.5 to 3.0 eV). Consequently, it was confirmed through device simulation that there was no barrier due to the difference in band gaps, enabling the seamless supply of holes to the IGZO channel. Based on these results, we conducted a simulation to determine the optimal parameters for the P-type oxide semiconductor to be used as a filler, demonstrating improved erase operation when the P-type carrier density was 1019 cm−3 or higher and the band gap was 3.0 eV or higher.
Machine Fault Diagnosis through Vibration Analysis: Continuous Wavelet Transform with Complex Morlet Wavelet and Time–Frequency RGB Image Recognition via Convolutional Neural Network
Authors: Łuczak Dominik
Published: January 2024

In pursuit of advancing fault diagnosis in electromechanical systems, this research focusses on vibration analysis through innovative techniques. The study unfolds in a structured manner, beginning with an introduction that situates the research question in a broader context, emphasising the critical role of fault diagnosis. Subsequently, the methods section offers a concise summary of the primary techniques employed, highlighting the utilisation of short-time Fourier transform (STFT) and continuous wavelet transform (CWT) for extracting time–frequency components from the signal. The results section succinctly summarises the main findings of the article, showcasing the results of features extraction by CWT and subsequently utilising a convolutional neural network (CNN) for fault diagnosis. The proposed method, named CWTx6-CNN, was compared with the STFTx6-CNN method of the previous stage of the investigation. Visual insights into the time–frequency characteristics of the inertial measurement unit (IMU) data are presented for various operational classes, offering a clear representation of fault-related features. Finally, the conclusion section underscores the advantages of the suggested method, particularly the concentration of single-frequency components for enhanced fault representation. The research demonstrates commendable classification performance, highlighting the efficiency of the suggested approach in real-time scenarios of fault analysis in less than 50 ms. Calculation by CWT with a complex Morlet wavelet of six time–frequency images and combining them into a single colour image took less than 35 ms. In this study, interpretability techniques have been employed to address the imperative need for transparency in intricate neural network models, particularly in the context of the case presented. Notably, techniques such as Grad-CAM (gradient-weighted class activation mapping), occlusion, and LIME (locally interpretable model-agnostic explanation) have proven instrumental in elucidating the inner workings of the model. Through a comparative analysis of the proposed CWTx6-CNN method and the reference STFTx6-CNN method, the application of interpretability techniques, including Grad-CAM, occlusion, and LIME, has played a pivotal role in revealing the distinctive spectral representations of these methodologies.
Integrated Model Text Classification Based on Multineural Networks
Authors: Hu Wenjin , Xiong Jiawei , Wang Ning , Liu Feng , Kong Yao , Yang Chaozhong
Published: January 2024

Based on the original deep network architecture, this paper replaces the deep integrated network by integrating shallow FastText, a bidirectional gated recurrent unit (GRU) network and the convolutional neural networks (CNNs). In FastText, word embedding, 2-grams and 3-grams are combined to extract text features. In recurrent neural networks (RNNs), a bidirectional GRU network is used to lessen information loss during the process of transmission. In CNNs, text features are extracted using various convolutional kernel sizes. Additionally, three optimization algorithms are utilized to improve the classification capabilities of each network architecture. The experimental findings using the social network news dataset demonstrate that the integrated model is effective in improving the accuracy of text classification.
Zero-Shot Learning-Based Recognition of Highlight Images of Echoes of Active Sonar
Authors: Liu Xiaochun , Yang Yunchuan , Yang Xiangfeng , Liu Liwen , Shi Lei , Li Yongsheng , Liu Jianguo
Published: January 2024

Reducing the impact of underwater disturbance targets and improving the ability to recognize real moving targets underwater are important directions of active sonar research. In this paper, the highlight model of underwater targets was improved and a method was proposed to acquire highlight images of the echoes of these targets. A classification convolutional neural network called HasNet-5 was designed to extract the global features and local highlight features of the echo highlight images of underwater targets, which achieved the true/false recognition of targets via multi-classification. Five types of target highlight models were used to generate simulation data to complete the training, validation and testing of the network. Tests were performed using experimental data. The results indicate that the proposed method achieves 92% accuracy in real target recognition and 94% accuracy in two-dimensional disturbance target recognition. This study provides a new approach for underwater target recognition using active sonar.
Dual-Branch Cross-Attention Network for Micro-Expression Recognition with Transformer Variants
Authors: Xie Zhihua , Zhao Chuwei
Published: January 2024

A micro-expression (ME), as a spontaneous facial expression, usually occurs instantaneously and is difficult to disguise after an emotion-evoking event. Numerous convolutional neural network (CNN)-based models have been widely explored to recognize MEs for their strong local feature representation ability on images. However, the main drawback of the current methods is their inability to fully extracting holistic contextual information from ME images. To achieve efficient ME learning representation from diverse perspectives, this paper uses Transformer variants as the main backbone and the dual-branch architecture as the main framework to extract meaningful multi-modal contextual features for ME recognition (MER). The first branch leverages an optical flow operator to facilitate the motion information extraction between ME sequences, and the corresponding optical flow maps are fed into the Swin Transformer to acquire motion–spatial representation. The second branch directly sends the apex frame in one ME clip to Mobile ViT (Vision Transformer), which can capture the local–global features of MEs. More importantly, to achieve the optimal feature stream fusion, a CAB (cross attention block) is designed to interact the feature extracted by each branch for adaptive learning fusion. The extensive experimental comparisons on three publicly available ME benchmarks show that the proposed method outperforms the existing MER methods and achieves an accuracy of 81.6% on the combined database.
An Efficient Reduction of Timer Interrupts for Model Checking of Embedded Assembly Programs
Authors: Yamane Satoshi , Kriyama Taro , Wu Yajun
Published: January 2024

In verifying programs for embedded systems, it is essential to reduce the verification time because state explosion may occur during model checking. One solution is to reduce the number of interrupt handler executions. In particular, when periodic interrupts such as timer interrupts are incorporated, it is necessary to know the physical time. In this paper, we define a control flow automaton (CFA) that can handle time and propose an algorithm based on interrupt handler execution reduction (IHER). The proposed method reduces the number of interrupt executions, including timer interrupts. A case study verified the effectiveness of this algorithm.
Detection of Small Lesions on Grape Leaves Based on Improved YOLOv7
Authors: Yang Mingji , Tong Xinbo , Chen Haisong
Published: January 2024

The precise detection of small lesions on grape leaves is beneficial for early detection of diseases. In response to the high missed detection rate of small target diseases on grape leaves, this paper adds a new prediction branch and combines an improved channel attention mechanism and an improved E-ELAN (Extended-Efficient Long-range Attention Network) to propose an improved algorithm for the YOLOv7 (You Only Look Once version 7) model. Firstly, to address the issue of low resolution for small targets, a new detection head is added to detect smaller targets. Secondly, in order to increase the feature extraction ability of E-ELAN components in YOLOv7 for small targets, the asymmetric convolution is introduced into E-ELAN to replace the original 3 × 3 convolution in E-ELAN network to achieve multi-scale feature extraction. Then, to address the issue of insufficient extraction of information from small targets in YOLOv7, a channel attention mechanism was introduced and improved to enhance the network’s sensitivity to small-scale targets. Finally, the CIoU (Complete Intersection over Union) in the original YOLOv7 network model was replaced with SIoU (Structured Intersection over Union) to optimize the loss function and enhance the network’s localization ability. In order to verify the effectiveness of the improved YOLOv7 algorithm, three common grape leaf diseases were selected as detection objects to create a dataset for experiments. The results show that the average accuracy of the algorithm proposed in this paper is 2.7% higher than the original YOLOv7 algorithm, reaching 93.5%.
Hazardous and Toxic Material Decontamination Facility Training in Virtual Reality
Authors: Lee Jeannie S. A. , Tan Teck Jun , Teng David Kuan Peng , Ng Yih Yng , Chen Kan
Published: January 2024

Patient decontamination is the act of removing or neutralising hazardous substances from an affected individual. To ensure adequate emergency preparedness, regulations require hospitals to train personnel in decontamination procedures regularly. To supplement in-person training, a virtual reality (VR) system is being developed for the training of hospital staff members in the mass decontamination of hazardous and toxic materials (HAZMAT) and/or radioactively contaminated casualties. As a demonstration of the concept, a primary VR prototype is designed to help users familiarize themselves with a chemical scanner tool, intended for examining victims for residual chemical hazards. This initial prototype showcases the benefits of using VR to create training simulations, complementing existing decontamination training methods in a secure and cost-effective manner. The proposed approach features a modularized user-centric design, a novel scanning simulation, and a high-realism virtual environment and workflow to enhance training effectiveness. A pilot user study and assessment suggest that new users were able to achieve a significant level of competency with VR, compared to users who underwent traditional training.
Economic Dispatch Optimization Strategies and Problem Formulation: A Comprehensive Review
Authors: Marzbani Fatemeh , Abdelfatah Akmal
Published: January 2024

Economic Dispatch Problems (EDP) refer to the process of determining the power output of generation units such that the electricity demand of the system is satisfied at a minimum cost while technical and operational constraints of the system are satisfied. This procedure is vital in the efficient energy management of electricity networks since it can ensure the reliable and efficient operation of power systems. As power systems transition from conventional to modern ones, new components and constraints are introduced to power systems, making the EDP increasingly complex. This highlights the importance of developing advanced optimization techniques that can efficiently handle these new complexities to ensure optimal operation and cost-effectiveness of power systems. This review paper provides a comprehensive exploration of the EDP, encompassing its mathematical formulation and the examination of commonly used problem formulation techniques, including single and multi-objective optimization methods. It also explores the progression of paradigms in economic dispatch, tracing the journey from traditional methods to contemporary strategies in power system management. The paper categorizes the commonly utilized techniques for solving EDP into four groups: conventional mathematical approaches, uncertainty modelling methods, artificial intelligence-driven techniques, and hybrid algorithms. It identifies critical research gaps, a predominant focus on single-case studies that limit the generalizability of findings, and the challenge of comparing research due to arbitrary system choices and formulation variations. The present paper calls for the implementation of standardized evaluation criteria and the inclusion of a diverse range of case studies to enhance the practicality of optimization techniques in the field.
Energy Demand in AR Applications—A Reverse Ablation Study of the HoloLens 2 Device
Authors: Skurowski Przemysław , Myszor Dariusz , Paszkuta Marcin , Moroń Tomasz , Cyran Krzysztof A.
Published: January 2024

This study presents a comprehensive analysis of power consumption in the context of AR applications on the HoloLens 2. As an ablation study, the research systematically dissects various aspects, shedding light on both hardware and software components. The power consumption was examined in diverse scenarios, ranging from the device at idle to running AR applications of increasing complexity. Overall, the study provides insights into the power management aspects associated with Hololens 2 AR devices and applications, offering cues for optimizing energy efficiency and enhancing the user experience. The findings confirm that display-related factors, such as the screen brightness and content complexity, significantly impact power consumption. However, the results reveal that power usage does not scale linearly with the number of objects in an AR scene due to overhead factors and object occlusion. Furthermore, usage scenarios such as lighting conditions and large/minor user motions are analyzed. Moreover, the influence of different development platforms, Unity and Unreal, on power consumption is discussed, highlighting their respective contributions to energy usage. These findings might be useful for researchers, developers, and users of AR technology.
Forest Biomass in Bioenergy Production in the Changing Geopolitical Environment of the EU
Authors: Kożuch Anna , Cywicka Dominika , Górna Aleksandra
Published: January 2024

The article examines the potential utilization of forest biomass in bioenergy production in Europe, taking into account limiting and developmental factors. The methodology includes a strategic analysis and the use of PEST analysis to evaluate the market for wood biomass. In the context of the current geopolitical situation and the decarbonization goals of the EU, the authors recommend accelerating energy transformation and highlighting forest biomass as an alternative within renewable energy sources. A literature review indicates the need to revise EU assumptions to enable the use of wood for bioenergy production, taking into account the needs of the wood industry. The analysis of economic factors shows competitiveness of forest biomass against coal, yet challenges arise regarding resource availability and competition with other energy sources. Emphasis is placed on the necessity of sustainable forest resource management and technological innovation. In the context of an energy crisis, the article underscores the role of innovation and recycling in alleviating shortages in energy markets. Conclusions highlight the imperative to develop a sustainable energy strategy for forest resource management and engage EU countries in the development of new biofuel and renewable energy sources for energy security and environmental protection.
Energy Yield of Spectral Splitting Concentrated Solar Power Photovoltaic Systems
Authors: Arnaoutakis Georgios E. , Katsaprakakis Dimitris A.
Published: January 2024

Combined concentrated solar power with photovoltaics can provide electricity and heat at the same system while maximizing the power output with reduced losses. Spectral splitting is required in such systems to separate the infrared part of the solar spectrum towards the thermal system, while the visible and near-infrared radiation can be converted by the photovoltaic solar cell. The performance of concentrated solar power plants comprising reflective beam splitters for combined generation of electricity and heat is presented in this work. A 50 MW power plant is considered in this work as a case of study in Southern Crete, Greece. The solar power plant consists of parabolic trough collectors and utilizes beam splitters with varying reflectivity. The dynamic performance of the power plant is modeled, and the annual energy yield can be calculated. Up to 350 MWt of thermal power can be delivered to the photovoltaic system utilizing a 50% reflecting splitter. The penalty to the high-reflectivity system is limited to 16.9% and the annual energy yield is calculated as 53.32 GWh. During summer months, a higher energy yield by up to 84.8 MWh/month is produced at 80% reflectivity compared to 90% as a result of the number of parabolic troughs. The reported energy yields with reflectivity by dynamic modeling can highlight discrete points for improvement of the performance in concentrated solar power photovoltaics.
Evolution of Freshwater Runoff in the Western Adriatic Sea over the Last Century
Authors: Sani Teresa , Marini Mauro , Campanelli Alessandra , Machado Toffolo Mariana , Goffredo Stefano , Grilli Federica
Published: January 2024

The evaluation of the hydrography and biogeochemistry of the Adriatic Sea over the last century was summarized in this review to point out any changes in river runoff and provide an overview of the cause and effect of these trends on marine ecosystems. Although several rivers flow into the Adriatic, the most affected area is the northern Adriatic, where the Po River loads into the basin half of the total freshwater input, carrying river runoff and causing algal blooms and hypoxia phenomena. These fresh waters of the northern Adriatic flow predominantly along the entire western side, reaching the southernmost part of the basin up to the Mediterranean Sea. Here, and in the whole basin, variations in river runoff and nutrient concentration have been observed through the years. Starting from 1960 until the end of the century, an increase in nutrient discharge and phytoplankton activity was reported, with negative repercussions on local fisheries, species richness, and recreational activities within the basin. However, a recent decrease in river inflow has been observed along the coastal belt, which can trigger negative consequences for the food web of the marine ecosystem. These trends, more broadly, corroborate the vulnerability of the Adriatic Sea and stress the importance of implementing strategies for the defense of the relevant ecosystems within its confines.
Unmanned Aerial Vehicle–Light Detection and Ranging-Based Individual Tree Segmentation in Eucalyptus spp. Forests: Performance and Sensitivity
Authors: Yan Yan , Lei Jingjing , Jin Jia , Shi Shana , Huang Yuqing
Published: January 2024

As an emerging powerful tool for forest resource surveys, the unmanned aerial vehicle (UAV)-based light detection and ranging (LiDAR) sensors provide an efficient way to detect individual trees. Therefore, it is necessary to explore the most suitable individual tree segmentation algorithm and analyze the sensitivity of the parameter setting to determine the optimal parameters, especially for the Eucalyptus spp. forest, which is one of the most important hardwood plantations in the world. In the study, four methods were employed to segment individual Eucalyptus spp. plantations from normalized point cloud data and canopy height model generated from the original UAV-LiDAR data. And the parameter sensitivity of each segmentation method was analyzed to obtain the optimal parameter setting according to the extraction accuracy. The performance of the segmentation result was assessed by three indices including detection rate, precision, and overall correctness. The results indicated that the watershed algorithm performed better than other methods as the highest overall correctness (F = 0.761) was generated from this method. And the segmentation methods based on the canopy height model performed better than those based on normalized point cloud data. The detection rate and overall correctness of low-density plots were better than high-density plots, while the precision was reversed. Forest structures and individual wood characteristics are important factors influencing the parameter sensitivity. The performance of segmentation was improved by optimizing the key parameters of the different algorithms. With optimal parameters, different segmentation methods can be used for different types of Eucalyptus plots to achieve a satisfying performance. This study can be applied to accurate measurement and monitoring of Eucalyptus plantation.
An Efficient and Lightweight Detection Model for Forest Smoke Recognition
Authors: Guo Xiao , Cao Yichao , Hu Tongxin
Published: January 2024

Massive wildfires have become more frequent, seriously threatening the Earth’s ecosystems and human societies. Recognizing smoke from forest fires is critical to extinguishing them at an early stage. However, edge devices have low computational accuracy and suboptimal real-time performance. This limits model inference and deployment. In this paper, we establish a forest smoke database and propose a model for efficient and lightweight forest smoke detection based on YOLOv8. Firstly, to improve the feature fusion capability in forest smoke detection, we fuse a simple yet efficient weighted feature fusion network into the neck of YOLOv8. This also greatly optimizes the number of parameters and computational load of the model. Then, the simple and parametric-free attention mechanism (SimAM) is introduced to address the problem of forest smoke dataset images that may contain complex background and environmental disturbances. The detection accuracy of the model is improved, and no additional parameters are introduced. Finally, we introduce focal modulation to increase the attention to the hard-to-detect smoke and improve the running speed of the model. The experimental results show that the mean average precision of the improved model is 90.1%, which is 3% higher than the original model. The number of parameters and the computational complexity of the model are 7.79 MB and 25.6 GFLOPs (giga floating-point operations per second), respectively, which are 30.07% and 10.49% less than those of the unimproved YOLOv8s. This model is significantly better than other mainstream models in the self-built forest smoke detection dataset, and it also has great potential in practical application scenarios.
Mining Social Media Data to Capture Urban Park Visitors’ Perception of Cultural Ecosystem Services and Landscape Factors
Authors: Chen Yaxin , Hong Chuanchun , Yang Yifan , Li Jiaxin , Wang Yu , Zheng Tianyu , Zhang Yinke , Shao Feng
Published: January 2024

Urban parks not only enhance urban ecology but also play a crucial role in providing cultural ecosystem services (CESs) for the well-being of urban residents. Both artificial and natural landscape factors within parks contribute significantly to the supply of cultural ecosystem services. To explore public perceptions of landscape factors and CESs, this study focused on 25 urban parks in Hangzhou. Social media data uploaded by park visitors from 2018 to 2023 were collected to establish a corresponding CES indicator framework. Combining computer vision with text mining, we assessed the preferences and correlations between visitor-perceived CESs and park landscape factors. The results indicated that the majority of park visitors perceive CESs (80.00%) with overall satisfaction higher than importance. Among them, aesthetic experiences and recreation showed both high satisfaction and importance. In shared social media photos, arbors (19.01%), herbaceous flowers (8.99%), and groves (8.22%) were frequently presented as landscape factors. The study revealed close correlations between user gender, landscape factors, and perceived CES categories, with females contributing more to the perception of both. There were internal correlations within CES categories, with spiritual services, aesthetic experiences, and recreation showing the most significant associations. Different landscape factors impacted CES categories to varying degrees, and biological landscapes formed by plant and animal factors were considered to provide more CESs. These findings are significant for enhancing the quality of ecological services and biodiversity in parks.
Tree Diameter at Breast Height (DBH) Estimation Using an iPad Pro LiDAR Scanner: A Case Study in Boreal Forests, Ontario, Canada
Authors: Guenther Matthew , Heenkenda Muditha K. , Morris Dave , Leblon Brigitte
Published: January 2024

The aim of this study was to determine whether the iPad Pro 12th generation LiDAR sensor is useful to measure tree diameter at breast height (DBH) in natural boreal forests. This is a follow-up to a previous study that was conducted in a research forest and identified the optimal method for (DBH) estimation as a circular scanning and fitting ellipses to 4 cm stem cross-sections at breast height. The iPad Pro LiDAR scanner was used to acquire point clouds for 15 sites representing a range of natural boreal forest conditions in Ontario, Canada, and estimate DBH. The secondary objective was to determine if tested stand (species composition, age, density, understory) or tree (species, DBH) factors affected the accuracy of estimated DBH. Overall, estimated DBH values were within 1 cm of actual DBH values for 78 of 133 measured trees (59%). An RMSE of 1.5 cm (8.6%) was achieved. Stand age had a large effect (>0.15) on the accuracy of estimated DBH values, while density, understory, and DBH had moderate effects (0.05–0.14). No trend was identified between accuracy and stand age. Accuracy improved as understory density decreased and as tree DBH increased. Inertial measurement unit (IMU) and positional accuracy errors with the iPad Pro scanner limit the feasibility of using this device for forest inventories.