bluuebunny/crossref_metadata_2025 huggingface dataset:
Epileptic Patients with More Clinic Visits Are More Likely to Be Diagnosed with Dementia—A Population-Based Retrospective Cohort Study
Authors: Yen Pao-Sheng , Muo Chih-Hsin , Yeh Chung-Hsin , Sung Fung-Chang
Published: December 2024

Objective: This retrospective cohort study assessed dementia risk in epilepsy patients associated with the compliance to epileptic treatment visits. Methods: We used Taiwanese insurance claims data to establish an epilepsy cohort (N = 39,216) diagnosed in 2000–2015 and a matched control cohort without epilepsy (N = 156,864), evaluating the incident dementia by the end of 2016. Results: The dementia incidence was 2.9-fold higher in the epilepsy cohort than in comparisons (4.68 vs. 1.59 per 1000 person-years). Only 9.3% of epilepsy patients were compliant to ≥80% of scheduled treatment visits, but they exhibited a 7.2-fold higher dementia incidence than those without treatment. The contrast was greater in younger patients than in the elderly (20-fold versus 5.5-fold). Dementia incidence increased with the frequency of neurological consultations, peaking in the first year after epilepsy diagnosis. Conclusions: Epileptic patients with more clinical visits for active treatment had a higher chance of dementia diagnosis, highlighting the importance of close neurological monitoring post-epilepsy diagnosis to address potential dementia complications.
From Binary to Multi-Class Classification: A Two-Step Hybrid CNN-ViT Model for Chest Disease Classification Based on X-Ray Images
Authors: Hadhoud Yousra , Mekhaznia Tahar , Bennour Akram , Amroune Mohamed , Kurdi Neesrin Ali , Aborujilah Abdulaziz Hadi , Al-Sarem Mohammed
Published: December 2024

Background/Objectives: Chest disease identification for Tuberculosis and Pneumonia diseases presents diagnostic challenges due to overlapping radiographic features and the limited availability of expert radiologists, especially in developing countries. The present study aims to address these challenges by developing a Computer-Aided Diagnosis (CAD) system to provide consistent and objective analyses of chest X-ray images, thereby reducing potential human error. By leveraging the complementary strengths of convolutional neural networks (CNNs) and vision transformers (ViTs), we propose a hybrid model for the accurate detection of Tuberculosis and for distinguishing between Tuberculosis and Pneumonia. Methods: We designed a two-step hybrid model that integrates the ResNet-50 CNN with the ViT-b16 architecture. It uses the transfer learning on datasets from Guangzhou Women’s and Children’s Medical Center for Pneumonia cases and datasets from Qatar and Dhaka (Bangladesh) universities for Tuberculosis cases. CNNs capture hierarchical structures in images, while ViTs, with their self-attention mechanisms, excel at identifying relationships between features. Combining these approaches enhances the model’s performance on binary and multi-class classification tasks. Results: Our hybrid CNN-ViT model achieved a binary classification accuracy of 98.97% for Tuberculosis detection. For multi-class classification, distinguishing between Tuberculosis, viral Pneumonia, and bacterial Pneumonia, the model achieved an accuracy of 96.18%. These results underscore the model’s potential in improving diagnostic accuracy and reliability for chest disease classification based on X-ray images. Conclusions: The proposed hybrid CNN-ViT model demonstrates substantial potential in advancing the accuracy and robustness of CAD systems for chest disease diagnosis. By integrating CNN and ViT architectures, our approach enhances the diagnostic precision, which may help to alleviate the burden on healthcare systems in resource-limited settings and improve patient outcomes in chest disease diagnosis.
Direct Immunofluorescence in Oral Lichen Planus and Related Lesions: Sensitivity, Specificity, and Diagnostic Accuracy in a Single Diagnostic Center in Poland
Authors: Osipowicz Katarzyna , Szymański Konrad , Pietrzyk Ewelina , Milczarek Emilia , Kowalewski Cezary , Górska Renata , Woźniak Katarzyna
Published: December 2024

Objectives: Our study aimed to establish the basic reliability parameters of direct immunofluorescence test results in patients with oral lichen planus. Methods: We conducted an evaluation of individual antibody classes in the DIF and ELISA (BP180 antigen), comparing these results with the classical histopathological (HP) examination in a group of patients treated within the standard healthcare in our clinic. Results: Among 66 participants with oral changes indicative of LP, only 50% received histopathological confirmation of the LP diagnosis. Among those with a DIF profile entirely typical for LP (C3+, F+), 57.1% had a positive HP result. Fibrinogen deposits were identified in 42.4% and 36.4% of individuals with positive HP results for F1 and F2, respectively; 78.8% of patients with negative HP and 57.6% with positive HP exhibited no fibrinogen deposits. Simultaneous positivity for F1 and F2 occurred in all cases where F1 was positive. HP confirmed positive DIF for C3 in 50% of cases. Fibrinogen deposits demonstrated the highest diagnostic accuracy (61%). Sensitivity and specificity for fibrinogen deposits were 36% and 42% for F1 and 79% and 82% for F2. The positive predictive values were 67% for F1 and 67% for F2, while the negative predictive values were 58% for F1 and 56% for F2. Overall diagnostic accuracy was reported at 61% for F1 and 59% for F2. Conclusions: Our data indicate the complementarity of HP and immunological test results and the necessity of using both methods together in cases of doubt.
Robust Model-Free Identification of the Causal Networks Underlying Complex Nonlinear Systems
Authors: Yang Guanxue , Lei Shimin , Yang Guanxiao
Published: December 2024

Inferring causal networks from noisy observations is of vital importance in various fields. Due to the complexity of system modeling, the way in which universal and feasible inference algorithms are studied is a key challenge for network reconstruction. In this study, without any assumptions, we develop a novel model-free framework to uncover only the direct relationships in networked systems from observations of their nonlinear dynamics. Our proposed methods are termed multiple-order Polynomial Conditional Granger Causality (PCGC) and sparse PCGC (SPCGC). PCGC mainly adopts polynomial functions to approximate the whole system model, which can be used to judge the interactions among nodes through subsequent nonlinear Granger causality analysis. For SPCGC, Lasso optimization is first used for dimension reduction, and then PCGC is executed to obtain the final network. Specifically, the conditional variables are fused in this general, model-free framework regardless of their formulations in the system model, which could effectively reconcile the inference of direct interactions with an indirect influence. Based on many classical dynamical systems, the performances of PCGC and SPCGC are analyzed and verified. Generally, the proposed framework could be quite promising for the provision of certain guidance for data-driven modeling with an unknown model.
Advances and Challenges in Species Ecological Niche Modeling: A Mixed Review
Authors: Vasconcelos Rodrigo N. , Cantillo-Pérez Taimy , Franca Rocha Washington J. S. , Aguiar William Moura , Mendes Deorgia Tayane , de Jesus Taíse Bomfim , de Santana Carolina Oliveira , de Santana Mariana M. M. , Oliveira Reyjane Patrícia
Published: December 2024

Species distribution modeling (SDM) is a vital tool for ecological and biogeographical research, allowing precise predictions of species distributions based on environmental variables. This study reviews the evolution of SDM techniques from 1985 to 2023, focusing on model development and applications in conservation, climate change adaptation, and invasive species management. We employed a mixed review with bibliometric and systematic element approaches using the Scopus database, analyzing 982 documents from 275 sources. The MaxEnt model emerged as the most frequently used technique, applied in 85% of the studies due to its adaptability and accuracy. Our findings highlight the increasing trend in international collaboration, particularly between China, the United Kingdom, and Brazil. The study reveals a significant annual growth rate of 11.99%, driven by technological advancements and the urgency to address biodiversity loss. Our analysis also shows that while MaxEnt remains dominant, deep learning and other advanced computational techniques are gaining traction, reflecting a shift toward integrating AI in ecological modeling. The results emphasize the importance of global cooperation and the continued evolution of SDM methodologies, projecting further integration of real-time data sources like UAVs and satellite imagery to enhance model precision and applicability in future conservation efforts.
Why Early Career Researchers Escape the Ivory Tower: The Role of Environmental Perception in Career Choices
Authors: Liu Xinqiao , Zhang Xinyuan , Li Yan
Published: December 2024

As early career researchers, postdocs play an irreplaceable and crucial role in scientific research, especially in highly competitive fields. Given the importance of the postdoc community, it is essential to explore their engagement in the academic labor market. Employment in academic departments should be the ideal career choice for postdocs, but this is not always the case. In recent years, an increasing number of postdocs have chosen to leave the ivory tower, which is often the result of a dynamic integration of the work environment and individual cognition. This study is based on the public data from the “Nature 2023 Postdoc Survey” and empirically analyzes the predictive relationship between different dimensions of environmental perception and postdoc academic career choices, as well as whether there are differences in predictive relationships across various fields. Difference analysis suggested that males and those working in their native country tend to have greater satisfaction in their environment perception. Correlation analysis revealed that postdocs’ environmental perception is significantly and positively correlated with academic career choices. Regression results indicated that institutional environment, organizational environment, living environment, and support environment can all predict postdoc academic career choices, with significant disciplinary differences in these predictive effects. It is recommended that mental health, salary and benefits, job security, and professional training be focused on to improve the working environment for early career researchers. Additionally, it is necessary to increase inclusive support for vulnerable postdoc groups and enhance their expectations for academic careers.
Virtual Environment in Engineering Education: The Role of Guidance, Knowledge and Skills Development in Electronic Circuits Teaching
Authors: Tokatlidis Christos , Rapti Sophia , Tselegkaridis Sokratis , Sapounidis Theodosios , Papakostas Dimitrios
Published: December 2024

Information and Communication Technologies can create effective learning experiences and might enhance students’ knowledge and skills. In particular, virtual technologies are emerging as a transformative tool in education, in areas such as electronic circuits. Studies have shown that virtual environments in electronic circuit education could provide students with immersive learning experiences, facilitating the practical application of circuit theory in a cost-effective and safe manner. Yet, this field, and especially the operational amplifiers, is limitedly explored regarding the most efficient approach to knowledge and skills development along with the role of teacher’s guidance. Therefore, this paper investigates the impact of a virtual environment on 138 engineering students’ outcomes using a pre-/post-test design. For the data analysis, among other methods, t-tests, ANCOVA, and repeated measures ANOVA were employed. Based on our results, the virtual setting can affect positively the students’ knowledge and improve their skills, revealing a correlation between these two aspects. Regarding the impact of guidance, our analysis showed that although students who received guidance generally performed better than those who did not, the differences were not statistically significant. Thus, this paper aims to serve as a useful guideline for educators and researchers willing to explore the circuit field utilizing virtual tools.
A Maneuver Coordination Analysis Using Artery V2X Simulation Framework
Authors: Oliveira João , Vieira Emanuel , Almeida João , Ferreira Joaquim , Bartolomeu Paulo C.
Published: December 2024

This paper examines the impact of Vehicle-to-Everything (V2X) communications on vehicle cooperation, focusing on increasing the robustness and feasibility of Cooperative, Connected, and Automated Vehicles (CCAVs). V2X communications enable CCAVs to obtain a holistic environmental perception, facilitating informed decision making regarding their trajectory. This technological innovation is essential to mitigate accidents resulting from inadequate or absent communication on the roads. As the importance of vehicle cooperation grows, the European Telecommunications Standards Institute (ETSI) has been standardizing messages and services for V2X communications, in order to improve the synchronization of CCAVs actions. In this context, this preliminary work explores the use of Maneuver Coordination Messages (MCMs), under standardization by ETSI, for cooperative path planning. This work presents a novel approach by implementing these messages as well as the associated Maneuver Coordination Service (MCS) with a Cooperative Driving System to process maneuver coordination. Additionally, a trajectory approach is introduced along with a message generation mechanism and a process to dynamically handle collisions. This was implemented in an Artery V2X simulation framework combining both network communications and SUMO traffic simulations. The obtained results demonstrate the effectiveness of using V2X communications to ensure the safety and efficiency of Cooperative Intelligent Transportation Systems (C-ITS).
Field-Programmable Gate Array-Based True Random Number Generator Using Capacitive Oscillators
Authors: Hajduk Zbigniew
Published: December 2024

In this paper, novel architecture of the true random number generator (TRNG) is presented. The proposed TRNG uses jitter in capacitive oscillators as a source of entropy. These capacitive oscillators exploit the input/output (I/O) buffers of a field-programmable gate array (FPGA) chip. A specific connection between these buffers allows cyclical charging and discharging of a parasitic capacitance associated with an external FPGA pin. If a few pins of an FPGA chip are not connected to any external components, they can be targeted to build the TRNG. The proposed TRNG requires only three external FPGA pins dedicated to capacitive oscillators, as well as 18 look-up tables (LUTs) and 20 flip-flops (FFs). Its throughput amounts to 11–13 Mbit/s. To pass all NIST SP800-22 statistical tests for a wide range of operating temperatures, the TRNG requires a post-processing circuit. The characteristic feature of the proposed TRNG is that it internally generates a signal indicating that a random bit was just produced. Therefore, no external clock signal is needed to sample the output.
A YOLO Network Based on Depthwise Convolution Attention, Feature Fusion, and KL Divergence (DFK-YOLO): A Deep Learning Method for Infrared Small Target Detection Based on YOLOv7
Authors: Ji Peng , Wu Changhao , Zhang Xiangyue , Liu Hean , He Dongsheng
Published: December 2024

Infrared imaging technology has a wide range of applications across various fields, with one of its most critical uses being the detection of small infrared targets. However, model-driven approaches often lack robustness in identifying these small targets, while current deep learning-based methods face challenges in effectively extracting and integrating features. Additionally, appropriate labeling strategies for small infrared targets remain underdeveloped. To address these limitations, this paper proposes a novel detection method based on YOLOv7. Specifically, an attention module leveraging Depthwise Convolution is incorporated into the backbone of YOLOv7. Furthermore, a new Feature Fusion Neck is designed to replace the original neck component of YOLOv7. Lastly, a novel label assignment strategy is introduced. The proposed method achieves a mAP@0.5 of 99.5% and a mAP@0.75 of 71.6% on a public dataset, surpassing the baseline YOLOv7 by 1% and 4.6%, respectively. Compared to state-of-the-art deep learning object detection methods, the proposed approach demonstrates superior performance.
JSN: Design and Analysis of JPEG Steganography Network
Authors: Su Po-Chyi , Cheng Yi-Han , Kuo Tien-Ying
Published: December 2024

Image steganography involves hiding a secret message within an image for covert communication, allowing only the intended recipient to extract the hidden message from the “stego” image. The secret message can also be an image itself to enable the transmission of more information, resulting in applications where one image is concealed within another. While existing techniques can embed a secret image of similar size into a cover image with minimal distortion, they often overlook the effects of lossy compression during transmission, such as when saving images in the commonly used JPEG format. This oversight can hinder the extraction of the hidden image. To address the challenges posed by JPEG compression in image steganography, we propose a JPEG Steganography Network (JSN) that leverages a reversible deep neural network as its backbone, integrated with the JPEG encoding process. We utilize 8×8 Discrete Cosine Transform (DCT) and consider the quantization step size specified by JPEG to create a JPEG-compliant stego image. We also discuss various design considerations and conduct extensive testing on JSN to validate its performance and practicality in real-world applications.
Towards Deterministic-Delay Data Delivery Using Multi-Criteria Routing over Satellite Networks
Authors: Li Xiaogang , Li Hongyan , He Yaoxu , Ma Han
Published: December 2024

The satellite Internet can cover up to 70% of the surface of our planet Earth to provide network services for nearly 3 billion people. As such, it is promising to become the building block of future 6G networks. The satellite Internet is capable of providing uniform communication capacity to every part of the Earth’s surface, due to its uniform and symmetrical constellation structure, while the uneven distribution of ground populations leads to globally uneven traffic delivery requests, incurring a mismatch between the capacity and traffic transmission demands. As such, traditional single-criteria (e.g., shortest delay) routing algorithms can lead to severe network congestion and cannot provision delay-deterministic data delivery. To overcome this bottleneck, we propose a multi-criteria routing and scheduling scheme to redirect time-tolerant data, thus preventing congestion for time-sensitive data, based on the spatiotemporal distribution of data traffic. First, we construct a traffic spatiotemporal distribution model, to indicate the network load status. Next, we model the satellite network multi-criteria routing problem as an integer linear programming one, which is NP-hard and challenging to solve within polynomial time. A novel link weight design based on both the link delay and load is introduced, transforming the mathematical programming problem into a routing optimization problem. The proposed correlation scheduling algorithm fully utilizes idle network link resources, significantly improving network resource utilization and eliminating resource competition between non-time-sensitive and time-sensitive services. Simulation results show that compared with traditional algorithms, the proposed method can increase the throughput of time-sensitive data by up to 20.8% and reduce the packet loss rate of time-sensitive services by up to 76.8%.
Improving Process Control Through Decision Tree-Based Pattern Recognition
Authors: Rojek Izabela , Kujawińska Agnieszka , Burduk Robert , Mikołajewski Dariusz
Published: December 2024

This paper explores the integration of decision tree classifiers in the assessment of machining process stability using control charts. The inherent variability in manufacturing processes requires a robust system for the early detection and correction of disturbances, which has traditionally relied on operators’ experience. Using decision trees, this study presents an automated approach to pattern recognition on control charts that outperforms the accuracy of human operators and neural networks. Experimental research conducted on two datasets from surface finishing processes demonstrates that decision trees can achieve perfect classification under optimal parameters. The results suggest that decision trees offer a transparent and effective tool for quality control, capable of reducing human error, improving decision making, and fostering greater confidence among company employees. These results open up new possibilities for the automation and continuous improvement of machining process control. The contribution of this research to Industry 4.0 is to enable the real-time, data-driven monitoring of machining process stability through decision tree-based pattern recognition, which improves predictive maintenance and quality control. It supports the transition to intelligent manufacturing, where process anomalies are detected and resolved dynamically, reducing downtime and increasing productivity.
Optimized AI Methods for Rapid Crack Detection in Microscopy Images
Authors: Lou Chenxukun , Tinsley Lawrence , Duarte Martinez Fabian , Gray Simon , Honarvar Shakibaei Asli Barmak
Published: December 2024

Detecting structural cracks is critical for quality control and maintenance of industrial materials, ensuring their safety and extending service life. This study enhances the automation and accuracy of crack detection in microscopic images using advanced image processing and deep learning techniques, particularly the YOLOv8 model. A comprehensive review of relevant literature was carried out to compare traditional image-processing methods with modern machine-learning approaches. The YOLOv8 model was optimized by incorporating the Wise Intersection over Union (WIoU) loss function and the bidirectional feature pyramid network (BiFPN) technique, achieving precise detection results with mean average precision (mAP@0.5) of 0.895 and a precision rate of 0.859, demonstrating its superiority in detecting fine cracks even in complex and noisy backgrounds. Experimental findings confirmed the model’s high accuracy in identifying cracks, even under challenging conditions. Despite these advancements, detecting very small or overlapping cracks in complex backgrounds remains challenging. Our future work will focus on optimizing and extending the model’s generalisation capabilities. The findings of this study provide a solid foundation for automatic and rapid crack detection in industrial applications and indicate potential for broader applications across various fields.
High-Precision Digital-to-Time Converter with High Dynamic Range for 28 nm 7-Series Xilinx FPGA and SoC Devices
Authors: Garzetti Fabio , Lusardi Nicola , Corna Nicola , Fiumicelli Gabriele , Cattaneo Federico , Bonanno Gabriele , Costa Andrea , Ronconi Enrico , Geraci Angelo
Published: December 2024

Over the last ten years, the need for high-resolution time-domain digital signal production has grown exponentially. More than ever, applications call for a digital-to-time converter (DTC) that is extremely accurate and precise. Skew compensation and camera shutter operation represent just a few examples of such applications. The advantages of adopting a flexible and rapid time-to-market strategy focused on fast prototyping using programmable logic devices—such as field-programmable gate arrays (FPGAs) and system-on-chip (SoC)—have become increasingly evident. These benefits outweigh those of performance-focused yet expensive application-specific integrated circuits (ASICs). Despite the availability of various architectures, the high non-recurring engineering (NRE) costs make them unsuitable for low-volume production, especially in research or prototyping environments. To address this trend, we introduce an innovative DTC IP-Core with a resolution, also known as least significant bit (LSB), of 52 ps, compatible with all Xilinx 7-Series FPGAs and SoCs. Measurements have been performed on a low-end Artix-7 XC7A100TFTG256-2, guaranteeing a jitter lower than 50 ps r.m.s. and offering a high dynamic range up to 56 ms. With resource utilization below 1% and a dynamic power dissipation of 285 mW for our target FPGA, the design maintains excellent differential and integral nonlinearity errors (DNL/INL) of 1.19 LSB and 1.56 LSB, respectively.
Finding All Solutions with Grover’s Algorithm by Integrating Estimation and Discovery
Authors: Lee Sihyung , Nam Seung Yeob
Published: December 2024

Grover’s algorithm leverages quantum computing to efficiently locate solutions in unstructured search spaces, outperforming classical approaches. Since Grover’s algorithm requires prior knowledge of the number of solutions (M) within a search space of size N, previous studies assume M is estimated beforehand and focus on identifying all solutions. Here, we propose a two-step process that integrates both the estimation of M and the discovery of the solutions, optimizing the interactions between the two steps. To enhance efficiency, the estimation step captures as many solutions as possible, leaving the discovery step to focus on the remaining ones. To ensure accuracy, the discovery step continues searching until the probability of finding additional solutions becomes sufficiently low. We implemented and evaluated our methods, showing that over 80% of solutions were found during the estimation phase, allowing the discovery phase to conclude earlier, while identifying over 99% of solutions on average. In theory, the process requires NM × log(M) Grover’s iterations in the worst case, but in practice, it typically terminates after iterations proportional to N. We expect that our methods will be applicable to various search problems and inspire further research on efficiently finding all solutions.
Research on Safe-Economic Dispatch Strategy for Renewable Energy Power Stations Based on Game-Fairness Empowerment
Authors: Zhang Zhen , Xian Wenjun , Tan Weijun , Li Jinghua , Liu Xiaofeng
Published: December 2024

The optimal dispatching of renewable energy power stations is particularly crucial in scenarios where the stations face energy rationing due to the large proportion of renewable energy integrated into the power system. In order to achieve safe, economical, and fair scheduling of renewable energy power stations, this paper proposes a two-stage scheduling framework. Specifically, in the initial stage, the maximum consumption space of renewable energy for the system can be optimized by optimizing the formulated safe-economic dispatch model. In the second stage, the fair allocation mechanism of renewable energy power stations is proposed based on the game-fairness empowerment approach. In order to obtain a comprehensive evaluation of renewable energy power stations, an evaluation index system is constructed considering equipment performance, output characteristics, reliability, flexibility, and economy. Subsequently, the cooperative game weighting method is proposed to rank the performance of renewable energy power stations as the basis for fair dispatching. Simulation results show that the proposed scheduling strategy can effectively ensure the priority of renewable energy power stations based on their comprehensive ranking, and improve the safety, economy, and fairness of power station participation in scheduling.
Production of Bio-Oil via Pyrolysis of Banana Peel and Tire Waste for Energy Utilization
Authors: Rodrigues Joaquim Augusto Pinto , Pinto Natália Aguiar Brittes Tinoco , Leite Leonardo Amaral dos Santos Barroso , Pereira Amaro Olímpio
Published: December 2024

The energy crisis and environmental degradation are pressing challenges, intensified by population growth and the excessive generation of solid waste. Converting waste into energy, especially through pyrolysis, is a viable and sustainable alternative. This thermal process transforms waste such as banana peels and used tires into high-value products, such as gas, char, and bio-oil. This study aims to evaluate the production of bio-oil from the pyrolysis and co-pyrolysis of these materials, considering different proportions and temperatures, as well as using an Artificial Neural Network (ANN) to predict the composition of the bio-oils. The pyrolysis tests with 100% banana peel and 75% banana peel mixed with 25% tire showed a decrease in bio-oil yield with increasing temperature, with a drop of around 30% when comparing 500 °C to 400 °C. In contrast, co-pyrolysis with 50% of each material and 100% of the tire resulted in increases in bio-oil yield as the temperature rose. A Fourier Transform Infrared Spectroscopy (FTIR) analysis of the bio-oils showed the presence of relevant functional groups, while an elemental analysis and ANN provided accurate predictions of carbon, hydrogen, and nitrogen content. The results suggest that the co-pyrolysis of waste tires and banana peels is a viable alternative for the production of bio-oil.
Investigation of the Effect of Diverse Dictionaries and Sparse Decomposition Techniques for Power Quality Disturbances
Authors: Anjali Vivek , Panikker Preetha Parakkatu Kesava
Published: December 2024

The quality of power signals is strongly influenced by nonlinear loads in Electrical Power systems. Representation of electrical signals using different Sparse techniques is an interesting area of research as it moderates the volume of data to be stored. The storage of signals in Sparse form will make data storage easier and more efficient. Earlier studies concentrated on blindly choosing Overcomplete Hybrid Dictionaries (OHDs) for Sparse representation. The effect of different dictionaries in representing electrical signals has also not been reviewed in them. This paper presents an investigation of the effect of various dictionaries and the sparsity constant on the representation of electrical signals. The validation for statements presented in this paper is carried out by representing power signals with diverse power line disturbances like Swell, DC offset, and random oscillation, with the help of various dictionaries in the simulation platform. The Sparse representation of the power signals was generated using the Orthogonal Matching Pursuit algorithm. The resultant Sparse representation was then compared with the original signal. The difference between them was found to be negligible with the help of different metrics. The ratio of the obtained signal from Sparse representation, the original signal (A/R ratio), and the Mean Squared Error were taken as the metrics. The MATLAB platform was used for performing the simulation study.
Life Cycle Assessment of Torrefied Residual Biomass Co-Firing in Coal-Fired Power Plants: Aspects of Carbon Dioxide Emission
Authors: Cho Kyungil , Lee Yongwoon
Published: December 2024

This study investigates the carbon dioxide (CO2) emission characteristics of using torrefied biomass (residual wood and wood chip) as co-firing materials in coal-fired power plants, based on life cycle assessment techniques. We quantify the greenhouse gas (GHG) mitigation potential of substituting coal with biomass under different torrefaction temperatures, biomass types, and co-firing ratios. Results indicate that higher co-firing ratios significantly reduce CO2 emissions. Torrefaction at 270 °C was identified as optimal, balancing high energy yield and minimized emissions, while 310 °C torrefaction showed limited mitigation benefits due to lower mass yields and higher carbon content. Pelletization and torrefaction enhanced biomass properties, but the energy intensity of these processes affected the overall emission balance. This study underscores the potential of biomass to replace imported coal and contribute to carbon neutrality, while highlighting the importance of optimizing biomass processing conditions. Future work should focus on refining torrefaction parameters and assessing other biomass characteristics to enhance operational efficiency in coal-fired power plants.
Simulated Warming Increases Litter Decomposition and Release Rates of Some Metallic Elements and Recalcitrant Components in Different-Aged Chinese Fir Plantations
Authors: Zhang Lixian , Guo Wenjuan , Chen Yulong , Li Zhihao , Liu Qi , Heal Kate V. , Li Shubin , Zhou Lili
Published: December 2024

With global warming, understanding the effect of elevated temperature on the decomposition of Chinese fir needle litter has significant implications for nutrient cycling, yield, and management of economically important Chinese fir plantations. We conducted simulated warming decomposition experiments in incubators at 25 °C, 30 °C, and 35 °C on Chinese fir needle litter from middle-aged, mature, and overmature stands. Changes in litter mass and concentrations of some metallic elements and recalcitrant components were measured in litter sampled at different decomposition time-steps up to 264 days (d). Warming to 35 °C significantly increased the mass loss rate of needle litter from overmature stands throughout the experiment (except at 72 d). The effect of warming on litter mass loss rate for middle-aged and mature stands was lower and is attributed to higher litter quality in these stands. Compared to 25 °C, warming to 30 °C and 35 °C increased the needle litter decomposition rate across all developmental stages by 17.3% and 48.3%, respectively. Potassium (K), calcium (Ca), and magnesium (Mg) were mostly released during needle litter decomposition in all Chinese fir developmental stages. Lignin, condensed tannins, total phenols, and cellulose were enriched in needle litter, while the release of hemicellulose from near the start of the decomposition experiment was attributed to its lower molecular weight compared with other carbohydrates in litter. Compared with 25 °C, warming to 35 °C increased the release rates from litter of K, Ca, and Mg by 14.7%, 24.6%, and 21.5%, and the release rates of lignin, total phenols, cellulose, and hemicellulose by 7.5%, 8.8%, 10.4%, and 13.7%. Needle litter iron (Fe), aluminum (Al), and sodium (Na) in different development stages and manganese (Mn) in the overmature stands were mostly enriched during the experiment. Warming significantly promoted the enrichment of Fe, Al (except for mature stands), and Na, and reduced the enrichment of Mn. In summary, the sensitivity of needle litter to temperature in overmature stands is higher than that in middle-aged and mature stands, suggesting that forest managers can extend the rotation length of Chinese fir plantations to increase the yield of large-diameter timber, litter decomposition, and ecosystem nutrient return.
Development and Characterization of a Novel Environmentally Friendly Larch-Tannin-Modified Phenol–Formaldehyde Resin for Plywood Manufacturing
Authors: Li Taotao , Hu Zhiyuan , Liu Zhanjun , Li Cheng
Published: December 2024

Larch tannin, a valuable forest product resource, offers the benefits of being natural, renewable and environmentally friendly. With growing environmental concerns, the widespread use of phenolic resins in the wood industry has been limited due to the depletion of fossil resources and formaldehyde emission issues. In this study, larch tannin was modified using a NaOH/urea solution to enhance phenolic resin properties. The curing properties and thermal stability of the adhesives were analyzed using a differential scanning calorimeter (DSC) and thermogravimetric analysis (TGA), and the formaldehyde emissions were also measured. The results showed that this treatment effectively reduced the resin’s gel time. LTPF-U-4 resin demonstrated the highest bonding strength of 1.09 MPa and exhibited low formaldehyde emissions, thereby meeting the requirements for Class I plywood (≥0.7 MPa) and complying with the E0 grade of plywood standards as outlined in the China National Standard GB GB/T 17657-2013. The sodium hydroxide/urea-treated larch tannin substitution improved the bonding performance of the LTPF-U resins; its impact on bonding strength is limited. Nevertheless, the treatment significantly reduces the formaldehyde emissions of plywood bonded with LTPF-U resins. The NaOH/urea treatment not only reduced the thermal stability of the resin but also improved its curing properties and lowered its curing temperature. This research offers valuable insights for developing modified phenolic resins, which have significant practical implications.
Resistance to Pulling Seedlings Out of the Nursery Container
Authors: Kormanek Mariusz , Małek Stanisław , Tabor Sylwester
Published: December 2024

An important issue in container nurseries is the production of seedlings of appropriate quality. These seedlings must meet specific biometric parameters and possess traits that ensure their suitability for later use in forest cultivation. One such traitis the ease of pulling the seedling out of the container cell, characterized by the pulling resistance. This resistance depends on many factors, including theseedling parameters, substrate, and container. In this work, a prototype measuring station was used to record seedling pull-out resistance as a function of their vertical displacement. Tests were conducted on 30 seedlings of each species: pine, beech, and oak. These were grown in polystyrene containers with cell volumes of 150 cm3 for pine (Pinus sylvestris L.) and 300 cm3 for beech (Fagus sylvatica L.) and oak (Quercus robur L.). Significant differences were observed in seedling extraction resistance between species, as well as between resistances associated with containers of different cell volumes. The maximum pull-out resistance of pine seedlingswas the lowest at 12.4 ± 8.58 N, followed by 22.5 ± 1.87 N for beech, and 33.5 ± 18.08 N for oak (extraction speed of 2.5 cm·s−1).Similarly, the total energy required to extract pine seedlings was 0.358 ± 0.196 J, compared to 0.502 ± 0.316 J for beech and 1.479 ± 0.774 J for oak. The pattern of pull-out resistance regardingvertical displacement was consistent across all species. A correlation was found between the maximum pull-out resistance and the total energy required to extract the seedlings, along with biometric parameters such as root collar diameter, shoot height, dry mass of seedling parts, root volume, the extent of root ball overgrowth, and the volumetric density of the substrate within the cell.These relationships should be considered when designing devices for the automatic extraction of seedlings from polystyrene containers.
Improving Fire Suppression Efficiency in Electric Vehicles: A Study on Optimized Upward Spray Devices
Authors: Oh Jin-Dong , Kim Chan-Hoo , Park Sung-Young
Published: December 2024

Fire accidents in electric vehicles are mainly caused by battery fires, and one of the most effective fire suppression methods is to spray water from the bottom of a vehicle in an upward direction. In this study, analyses and experiments were conducted to improve the spray angle of a fluidic oscillator used for attaching an upward spray device. Through these analyses, the factors resulting in the maximum spray angle were derived from the four design variables of the fluidic oscillator, which were reconstructed for further analysis. The model that combined the radius of the mixing chamber curvature, inlet wedge width, and outlet wedge width exhibited the largest spray angle (84°) among the combination models that included the outlet wedge width variable. To evaluate the fire suppression performance of the fluidic oscillator nozzle, a cooling-rate comparison experiment was conducted with a recently used orifice nozzle. The results showed that the fluidic oscillator nozzle leads to a faster overall cooling rate than the orifice nozzle, rendering it more suitable for suppressing battery fires. After the production of the upward spray device, practical tests showed that it could spray a large area under a vehicle, thereby suggesting its applicability in actual fire scenes.
Study on Public Perceptions and Disaster Prevention Framework of Tunnel Fires Based on Social Media and Artificial Intelligence
Authors: Lai Chuyao , Zhang Yuxin , Tang Xiaofan , Guo Chao
Published: December 2024

To investigate public perceptions regarding tunnel fire disasters and optimize the tunnel fire disaster prevention framework, this study takes the emerging social media platform Douyin as a case study, conducting an in-depth analysis of 2133 short videos related to tunnel fires on the platform. A computational communication method was used for analysis, Latent Dirichlet Allocation was used to cluster the discussion topics of these tunnel fire short videos, and a spatiotemporal evolution analysis of the number of videos posted, user comments, and emotional inclinations across different topics was performed. The findings reveal that there is a noticeable divergence in public opinion regarding emergency decision making in tunnel fires, related to the complexity of tunnel fire incidents, ethical dilemmas in tunnel fire escape scenarios, and insufficient knowledge popularization of fire safety practices. The study elucidates the public’s actual needs during tunnel fire incidents, and a dynamic disaster prevention framework for tunnel fires based on social media and artificial intelligence is proposed on this basis to enhance emergency response capabilities. Utilizing short videos on social media, the study constructs a critical target dataset under real tunnel fire scenarios. It proposes a computer vision-based model for identifying critical targets in tunnel fires. This model can accurately and in real-time identify key targets such as fires, smoke, vehicles, emergency exits, and people in real tunnel fire environments, achieving an average detection precision of 77.3%. This research bridges the cognitive differences between the general public and professionally knowledgeable tunnel engineers regarding tunnel fire evacuation, guiding tunnel fire emergency responses and personnel evacuation.
Rowing Against the Tide: The Golden Mussel (Limnoperna fortunei) Leaves DNA Footprints Along Its Invasion Route in South American Rivers
Authors: Ferreira Augusto Luiz , Neto Renato Luiz Bot , Marín-Ruiz Vanessa , Rosa Leonardo , Almeida Mara Cristina , de Andrade Patrícia Dammski Borges , Christo Susete Wambier , Artoni Roberto Ferreira
Published: December 2024

The invasion of the golden mussel has resulted in considerable environmental and socioeconomic alterations, which present a considerable threat to the native biodiversity and sustainability of the region. Understanding the distribution and biological characteristics of this Asian mussel, as well as its interaction with human activities, is of the utmost importance to developing effective strategies for mitigating and preventing its further spread. This study examines the dispersal route and incidence of golden mussels, tracing their movement from initial populations in Argentina to their arrival in the São Francisco River Basin (SFR). The presence of the mussel was confirmed through an integrative assessment that included shell taxonomic analyses and mitochondrial DNA signatures. This assessment identified populations located 7.5 km from the river’s mouth, close to the Atlantic Ocean, in areas such as shrimp farms, artisanal ports, and marinas. The analysis of mitochondrial DNA revealed the presence of South-American-specific and shared ancestral haplotypes in the SFR, Grande River, and Argentina. These findings indicate that intracontinental colonization toward the northeast region of South America originated from Asian populations that entered South America via Argentina. The absence of Asian-specific signatures in the SFR, combined with a geomorphological structure unsuitable for large ports or transoceanic vessels, supports the hypothesis of the intracontinental dispersal of Limnoperna fortunei.
Edible Coating for Fresh-Cut Fruit and Vegetable Preservation: Biomaterials, Functional Ingredients, and Joint Non-Thermal Technology
Authors: Ma Mengjie , Liu Yueyue , Zhang Shuaizhong , Yuan Yongkai
Published: December 2024

This paper reviews recent advances in fresh-cut fruit and vegetable preservation from the perspective of biomacromolecule-based edible coating. Biomaterials include proteins, polysaccharides, and their complexes. Compared to a single material, the better preservation effect was presented by complexes. The functional ingredients applied in the edible coating are essential oils/other plant extracts, metals/metal oxides, and organic acids, the purposes of the addition of which are the improvement of antioxidant and antimicrobial activities and/or the mechanical properties of the coating. The application of edible coating with other preservation technologies is an emerging method, mainly including pulsed light, short-wave ultraviolet, modified atmosphere packaging, ozonation, and γ-irradiation. In the future, it is crucial to design coating formulations based on preservation goals and sensory characteristics. The combination of non-thermal preservation technology and edible coating needs to be strengthened in research on food preservation. The application of AI tools for edible coating-based preservation should also be focused on. In conclusion, edible coating-based preservation is promising for the development of fresh-cut fruits and vegetables.
Spatial Metabolomics Reveals the Effects of Dietary Capsaicin Intervention on Interscapular Adipose Tissue Metabolome in Mice
Authors: Yang Haoqing , Zheng Peiying , Hu Jiamiao , Lin Zhongjing , Sriboonvorakul Natthida , Lin Shaoling
Published: December 2024

Capsaicin is a polyphenol with a well-known anti-obesity potential, which could activate brown adipose tissue and promote the browning of white adipose tissue. Indeed, conventional proteomics have been used to investigate the browning effects of capsaicin on adipose tissue. However, the existence of a layer of white adipose tissue above the interscapular brown adipose tissue poses a great challenge to obtain intact interscapular brown adipose tissue without including adjacent white adipose tissue. Therefore, the traditional method normally focuses on changes occurring in the bottom layer of interscapular brown adipose tissue. Spatial metabolomics is an omics method that enables the analysis of metabolite distributions in tissue sections. Therefore, in the current study, spatial metabolomics was utilized to investigate the effects of dietary capsaicin intervention on interscapular brown adipose tissue and adjacent white adipose tissue. The results indicated several noteworthy findings that capsaicin treatment may induce similar metabolite alterations across various regions of brown adipose tissue irrespective of their proximity to WAT, while it also markedly influences the metabolites in the adjacent white adipose tissue. A KEGG pathway analysis further revealed these changes were associated with key characteristics of beige energy metabolism pathways, such as thermogenesis, glycerol phospholipid metabolism, and pentose phosphate pathway. Taken together, this study may supplement useful details to understand the mechanisms of capsaicin enhancing BAT activity and promoting WAT browning.