bluuebunny/crossref_metadata_2025 huggingface dataset:
A Survey of Visible-Light-Communication-Based Indoor Positioning Systems
Authors: Wang Ruofan , Niu Guanchong , Cao Qi , Chen Chung Shue , Ho Siu-Wai
Published: August 2024

There is a growing demand for indoor positioning systems (IPSs) in a wide range of applications. However, traditional solutions such as GPS face many technical challenges. In recent years, a promising alternative has been emerging, the visible light communication (VLC)-based IPS, which offers a combination of high accuracy, low cost, and energy efficiency. This article presents a comprehensive review of VLC-based IPSs, providing a tutorial-like overview of the system. It begins by comparing various positioning systems and providing background information on their inherent limitations. Experimental results have demonstrated that VLC-based systems can achieve localization accuracy to within 10 cm in controlled environments. The mechanisms of VLC-based IPSs are then discussed, including a comprehensive examination of their performance metrics and underlying assumptions. The complexity, operating range, and efficiency of VLC-based IPSs are examined by analyzing factors such as channel modeling, signal processing, and localization algorithms. To optimize VLC-based IPSs, various strategies are explored, including the design of efficient modulation schemes, the development of advanced encoding and decoding algorithms, the implementation of adaptive power control, and the application of state-of-the-art localization algorithms. In addition, system parameters are carefully examined. These include LED placement, receiver sensitivity, and transmit power. Their impact on energy efficiency and localization accuracy is highlighted. Altogether, this paper serves as a comprehensive guide to VLC IPSs, providing in-depth insights into their vast potential and the challenges that they present.
Early Poplar (Populus) Leaf-Based Disease Detection through Computer Vision, YOLOv8, and Contrast Stretching Technique
Authors: Bolikulov Furkat , Abdusalomov Akmalbek , Nasimov Rashid , Akhmedov Farkhod , Cho Young-Im
Published: August 2024

Poplar (Populus) trees play a vital role in various industries and in environmental sustainability. They are widely used for paper production, timber, and as windbreaks, in addition to their significant contributions to carbon sequestration. Given their economic and ecological importance, effective disease management is essential. Convolutional Neural Networks (CNNs), particularly adept at processing visual information, are crucial for the accurate detection and classification of plant diseases. This study introduces a novel dataset of manually collected images of diseased poplar leaves from Uzbekistan and South Korea, enhancing the geographic diversity and application of the dataset. The disease classes consist of “Parsha (Scab)”, “Brown-spotting”, “White-Gray spotting”, and “Rust”, reflecting common afflictions in these regions. This dataset will be made publicly available to support ongoing research efforts. Employing the advanced YOLOv8 model, a state-of-the-art CNN architecture, we applied a Contrast Stretching technique prior to model training in order to enhance disease detection accuracy. This approach not only improves the model’s diagnostic capabilities but also offers a scalable tool for monitoring and treating poplar diseases, thereby supporting the health and sustainability of these critical resources. This dataset, to our knowledge, will be the first of its kind to be publicly available, offering a valuable resource for researchers and practitioners worldwide.
Enhancing Performance of Continuous-Variable Quantum Key Distribution (CV-QKD) and Gaussian Modulation of Coherent States (GMCS) in Free-Space Channels under Individual Attacks with Phase-Sensitive Amplifier (PSA) and Homodyne Detection (HD)
Authors: Alshaer Nancy , Ismail Tawfik , Mahmoud Haitham
Published: August 2024

In recent research, there has been a significant focus on establishing robust quantum cryptography using the continuous-variable quantum key distribution (CV-QKD) protocol based on Gaussian modulation of coherent states (GMCS). Unlike more stable fiber channels, one challenge faced in free-space quantum channels is the complex transmittance characterized by varying atmospheric turbulence. This complexity poses difficulties in achieving high transmission rates and long-distance communication. In this article, we thoroughly evaluate the performance of the CV-QKD/GMCS system under the effect of individual attacks, considering homodyne detection with both direct and reverse reconciliation techniques. To address the issue of limited detector efficiency, we incorporate the phase-sensitive amplifier (PSA) as a compensating measure. The results show that the CV-QKD/GMCS system with PSA achieves a longer secure distance and a higher key rate compared to the system without PSA, considering both direct and reverse reconciliation algorithms. With an amplifier gain of 10, the reverse reconciliation algorithm achieves a secure distance of 5 km with a secret key rate of 10−1 bits/pulse. On the other hand, direct reconciliation reaches a secure distance of 2.82 km.
An Improved Longitudinal Driving Car-Following System Considering the Safe Time Domain Strategy
Authors: Xu Xing , Wu Zekun , Zhao Yun
Published: August 2024

Car-following models are crucial in adaptive cruise control systems, making them essential for developing intelligent transportation systems. This study investigates the characteristics of high-speed traffic flow by analyzing the relationship between headway distance and dynamic desired distance. Building upon the optimal velocity model theory, this paper proposes a novel traffic car-following computing system in the time domain by incorporating an absolutely safe time headway strategy and a relatively safe time headway strategy to adapt to the dynamic changes in high-speed traffic flow. The interpretable physical law of motion is used to compute and analyze the car-following behavior of the vehicle. Three different types of car-following behaviors are modeled, and the calculation relationship is optimized to reduce the number of parameters required in the model’s adjustment. Furthermore, we improved the calculation of dynamic expected distance in the Intelligent Driver Model (IDM) to better suit actual road traffic conditions. The improved model was then calibrated through simulations that replicated changes in traffic flow. The calibration results demonstrate significant advantages of our new model in improving average traffic flow speed and vehicle speed stability. Compared to the classic car-following model IDM, our proposed model increases road capacity by 8.9%. These findings highlight its potential for widespread application within future intelligent transportation systems. This study optimizes the theoretical framework of car-following models and provides robust technical support for enhancing efficiency within high-speed transportation systems.
The Method and Experiment of Detecting the Strength of Structural Components Utilizing the Distributed Strain of Sensing Optical Fibers Demodulated by OFDR
Authors: Chen Bin , Yang Jun , Zhang Dezhi , Liu Wenxiang , Li Jin , Zhang Min , Li Ang , Wang Zhao
Published: August 2024

Defects occurring during the welding process of metal structural components directly affect their overall strength, which is crucial to the load-bearing capacity and durability of the components. This signifies the importance of accurate measurement and assessment of weld strength. However, traditional non-destructive testing methods such as ultrasonic and non-contact camera inspection have certain technical limitations. In response to these issues, this paper analyzes the detection principle of weld strength, revealing that weld defects reduce the effective area of the structural bearing section and cause stress concentration around them. Through repeated experimental data analysis of samples, strain distribution data along the one-dimensional direction caused by defects such as slag inclusion and porosity were obtained. Experimental results show that this method can identify defect types in welds, including slag inclusion, porosity, and unevenness, and accurately measure the location and size of defects with a precision of 0.64 mm, achieving qualitative analysis of weld defects. Additionally, by deploying distributed optical fiber sensors (DOFS) at different vertical distances along the weld direction, the propagation law of stress induced by different types of weld defects on samples was thoroughly analyzed. This further validates the advantages of this method in weld strength detection, including high spatial resolution, high sensitivity, and non-destructive measurement.
A Method for the Spatial Interpolation of EEG Signals Based on the Bidirectional Long Short-Term Memory Network
Authors: Hu Wenlong , Ji Bowen , Gao Kunpeng
Published: August 2024

The precision of electroencephalograms (EEGs) significantly impacts the performance of brain–computer interfaces (BCI). Currently, the majority of research into BCI technology gives priority to lightweight design and a reduced electrode count to make it more suitable for application in wearable environments. This paper introduces a deep learning-based time series bidirectional (BiLSTM) network that is designed to capture the inherent characteristics of EEG channels obtained from neighboring electrodes. It aims to predict the EEG data time series and facilitate the conversion process from low-density EEG signals to high-density EEG signals. BiLSTM pays more attention to the dependencies in time series data rather than mathematical maps, and the root mean square error can be effectively restricted to below 0.4μV, which is less than half the error in traditional methods. After expanding the BCI Competition III 3a dataset from 18 channels to 60 channels, we conducted classification experiments on four types of motor imagery tasks. Compared to the original low-density EEG signals (18 channels), the classification accuracy was around 82%, an increase of about 20%. When juxtaposed with real high-density signals, the increment in the error rate remained below 5%. The expansion of the EEG channels showed a substantial and notable improvement compared with the original low-density signals.
Calibration and Validation of Simulation Parameters for Maize Straw Based on Discrete Element Method and Genetic Algorithm–Backpropagation
Authors: Zeng Fandi , Diao Hongwei , Liu Yinzeng , Ji Dong , Dou Meiling , Cui Ji , Zhao Zhihuan
Published: August 2024

There is a significant difference between the simulation effect and the actual effect in the design process of maize straw-breaking equipment due to the lack of accurate simulation model parameters in the breaking and processing of maize straw. This article used a combination of physical experiments, virtual simulation, and machine learning to calibrate the simulation parameters of maize straw. A bimodal-distribution discrete element model of maize straw was established based on the intrinsic and contact parameters measured via physical experiments. The significance analysis of the simulation parameters was conducted via the Plackett–Burman experiment. The Poisson ratio, shear modulus, and normal stiffness of the maize straw significantly impacted the peak compression force of the maize straw and steel plate. The steepest-climb test was carried out for the significance parameter, and the relative error between the peak compression force in the simulation test and the peak compression force in the physical test was used as the evaluation index. It was found that the optimal range intervals for the Poisson ratio, shear modulus, and normal stiffness of the maize straw were 0.32–0.36, 1.24 × 108–1.72 × 108 Pa, and 5.9 × 106–6.7 × 106 N/m3, respectively. Using the experimental data of the central composite design as the dataset, a GA–BP neural network prediction model for the peak compression force of maize straw was established, analyzed, and evaluated. The GA–BP prediction model’s accuracy was verified via experiments. It was found that the ideal combination of parameters was a Poisson ratio of 0.357, a shear modulus of 1.511 × 108 Pa, and a normal stiffness of 6.285 × 106 N/m3 for the maize straw. The results provide a basis for analyzing the damage mechanism of maize straw during the grinding process.
MSSFNet: A Multiscale Spatial–Spectral Fusion Network for Extracting Offshore Floating Raft Aquaculture Areas in Multispectral Remote Sensing Images
Authors: Yu Haomiao , Hou Yingzi , Wang Fangxiong , Wang Junfu , Zhu Jianfeng , Guo Jianke
Published: August 2024

Accurately extracting large-scale offshore floating raft aquaculture (FRA) areas is crucial for supporting scientific planning and precise aquaculture management. While remote sensing technology offers advantages such as wide coverage, rapid imaging, and multispectral capabilities for FRA monitoring, the current methods face challenges in terms of establishing spatial–spectral correlations and extracting multiscale features, thereby limiting their accuracy. To address these issues, we propose an innovative multiscale spatial–spectral fusion network (MSSFNet) designed specifically for extracting offshore FRA areas from multispectral remote sensing imagery. MSSFNet effectively integrates spectral and spatial information through a spatial–spectral feature extraction block (SSFEB), significantly enhancing the accuracy of FRA area identification. Additionally, a multiscale spatial attention block (MSAB) captures contextual information across different scales, improving the ability to detect FRA areas of varying sizes and shapes while minimizing edge artifacts. We created the CHN-YE7-FRA dataset using Sentinel-2 multispectral remote sensing imagery and conducted extensive evaluations. The results showed that MSSFNet achieved impressive metrics: an F1 score of 90.76%, an intersection over union (IoU) of 83.08%, and a kappa coefficient of 89.75%, surpassing those of state-of-the-art methods. The ablation results confirmed that the SSFEB and MSAB modules effectively enhanced the FRA extraction accuracy. Furthermore, the successful practical applications of MSSFNet validated its generalizability and robustness across diverse marine environments. These findings highlight the performance of MSSFNet in both experimental and real-world scenarios, providing reliable, precise FRA area monitoring. This capability provides crucial data for scientific planning and environmental protection purposes in coastal aquaculture zones.
Topical Application of Cha-Koji, Green Tea Leaves Fermented with Aspergillus Luchuensis var Kawachii Kitahara, Promotes Acute Cutaneous Wound Healing in Mice
Authors: Katahira Yasuhiro , Sonoda Jukito , Yamagishi Miu , Horio Eri , Yamaguchi Natsuki , Hasegawa Hideaki , Mizoguchi Izuru , Yoshimoto Takayuki
Published: August 2024

“Koji” is one of the most well-known probiotic microorganisms in Japan that contribute to the maintenance of human health. Although the beneficial effects of some probiotics on ulcer healing have been demonstrated, there have been no reports on the wound healing effects of koji to date. In the present study, we investigated the effects of “cha-koji”, green tea leaves fermented with Aspergillus luchuensis, on cutaneous wound healing, using a linear incision wound mouse model. Topical application of autoclave-sterilized cha-koji suspension on the dorsal incision wound area healed the wound significantly faster and, notably, with less scarring than did the green tea or the control distilled water treatment. Further in vitro experiments revealed that the accelerated effects of cha-koji could be attributed to its increased anti-bacterial activity, enhanced epidermal cell proliferation and migration, augmented expression of the anti-inflammatory cytokine transforming growth factor-β1, reduced expression of inflammatory cytokine interleukin-6 in macrophages, and decreased endoplasmic reticulum stress. In addition, we conducted a skin sensitizing potential test, which revealed that cha-koji had no adverse effects that posed a sensitizing risk. Thus, cha-koji may have a potent therapeutic effect on cutaneous wound healing, opening up a new avenue for its clinical application as a medical aid.
Correction: Johnson et al. Phytochemical Profile of Asplenium aethiopicum (Burm. f.) Becherer Using HPTLC. Separations 2020, 7, 8
Authors: Johnson Marimuthu alias Antonysamy , Gowtham Janarthanan , Janakiraman Narayanan , Renisheya Joy Jeba Malar Tharmaraj , Rocha Janaina E. , Coutinho Henrique D. M.
Published: August 2024

In the original publication [...]
Lifetime Experiences of Housing Insecurity among Gay Men Living with HIV at Midlife: An Exploratory Study Utilizing a Social Determinants of Health Perspective
Authors: Liboro Renato M. , Bell Sherry , Flatt Jason , Pharr Jennifer , Ranuschio Brandon , Barnes Lianne , Navarro Andrea , Ribeiro Alexander , Sheik-Yosef Nadia , Waldron John M.
Published: August 2024

Understanding the socioecological contexts of gay men living with HIV is critical to addressing the environmental conditions that influence their lifetime experiences of housing insecurity and homelessness. In line with the strategies of the US Center for Disease Control and Prevention to end the HIV epidemic, it is of utmost importance to recognize how the allocation of available resources impacts health inequities affecting gay men living with HIV who experience housing insecurity and homelessness. Drawing from the World Health Organization’s social determinants of health (SDoH) framework, we used an exploratory, community-based research (CBR) approach to examine the lifetime experiences and important sources of support of gay men living with HIV at midlife who have experienced housing insecurity in Southern Nevada. Thematic analysis of our semi-structured interviews with 12 gay men living with HIV at midlife revealed three overarching themes: (1) challenges to obtaining housing and related support, (2) informal support from friends, and (3) formal support from community-based sources employing diverse support strategies. In this article, we discuss the different sub-themes we identified under these overarching themes and the implications of our findings from an SDoH perspective. We conclude with a discussion on the dire need to (1) highlight the considerable value of both informal and formal sources of support for the housing insecure, and (2) generate recommendations for interventions to address housing insecurity and homelessness with greater consideration for the impacts of SDoH in the work dedicated to helping gay men living with HIV at midlife overcome housing insecurity as they age.
Symmetry and Historicity in the Game of Life: Modifying Rules to Simulate Evolutionary Dynamics
Authors: Tadić Jovan M. , Korenić Andrej , Perović Slobodan
Published: August 2024

The Game of Life (GoL) is a paradigmatic computer simulation that exhibits the emergence of complex properties of the whole from relatively simple sets of heuristic rules operating at lower organizational levels. Therefore, it is widely understood as a valuable tool for investigating global properties of evolutionary processes. Cognizant of refined concepts that emerged in recent debates on the central role of historical contingency (historicity) in evolution, we modify the original GoL rules by introducing an updating feedback loop and a probability factor that reflect the degree of historicity in pattern evolution as both stochastic path dependence and sensitivity to initial conditions. We examine this trait in simulations of the emergence and breaking of bilateral and radial symmetries commonly observed in the evolution of life, most prominently as evolving body plans. We show that the implementation of historicity parameters leads to a more realistic sequential and gradual alternating emergence and the breaking of new symmetries than the original set of rules. Apart from its more realistic representations of evolutionary processes, the new approach allows for easier exploration of the parameter space, demonstrates the sequential and gradual emergence and breaking of symmetries, and provides a publicly available and modular simulation tool. We discuss the exploratory potential of the modified GoL platform we designed as an extended thought experiment to study the parameter space defining the historicity of biological processes.
A Multi-Criteria Method Integrating Distances to Ideal and Anti-Ideal Points
Authors: Roszkowska Ewa , Filipowicz-Chomko Marzena
Published: August 2024

Multi-criteria decision-making methods based on reference points and distances from them are essential for evaluating alternatives across multiple criteria. These methods provide structured approaches to comparing and ranking alternatives relative to specified reference points. The main objective of this paper is to present the Multi-Criteria Method Integrating Distances to Ideal and Anti-ideal Points (MIDIA), which, through a weighted system, allows for the consideration of balance and asymmetry in assessing alternatives based on their distances from the ideal and anti-ideal points. As a multi-criteria algorithm, MIDIA is user-friendly and reflects the human mind’s natural tendency to assess objects based on fundamental concepts—comparison with the ideal solution and the anti-ideal solution—that are familiar from everyday experiences and provide valuable insights from a behavioral perspective. Moreover, the proposed method can be seen as an extension of Hellwig’s approach, designed to facilitate the ranking of alternatives based on two reference points: the ideal point and the anti-ideal point, measuring the distance between the alternative and the ideal point and the distance between the ideal and anti-ideal points. The MIDIA method integrates elements from both TOPSIS and VIKOR, by incorporating the structure of TOPSIS and the compromise perspective of VIKOR, offering a balanced approach to multi-criteria decision-making by focusing on the distances from ideal and anti-ideal points. Illustrative examples are given to demonstrate the usability of the proposed tool in situations where the decision-maker has asymmetrical preferences concerning the importance of ideal and anti-ideal points in ranking alternatives. Moreover, the MIDIA method is applied to one of the Sustainable Development Goals, in the area of education (SDG4), to obtain the rankings of EU member countries in 2022. The results obtained using the MIDIA method were compared with those obtained using the TOPSIS and VIKOR approaches. The study concludes that the ranking of alternatives depends on the coefficients of the importance of the distances to reference points and the data setup.
A Robust Filtered-x Least Mean Square Algorithm with Adjustable Parameters for Active Impulsive Noise Control
Authors: Song Pucha , Yan Kang , Luo Li
Published: August 2024

In active noise control (ANC) systems, the traditional filtered-x least mean square (FxLMS) algorithm has poor control effect on impulsive noise. To overcome this drawback, a robust cost function was designed in this paper by embedding the cost function of the FxLMS algorithm into the framework of hyperbolic tangent function; this paper thus proposes a robust filtered-x least hyperbolic tangent (FxLHT) algorithm in ANC systems. Moreover, the value of λ in the FxLHT algorithm greatly affects the robustness and convergence performance of the algorithm. Therefore, a variable λ-parameter was proposed to enhance the performance of the FxLHT algorithm. Simulation results show that in the active control of impulsive noise, compared with the FxLMS algorithm and other robust ANC algorithms, the proposed FxLHT algorithm and variable λ-parameter FxLHT algorithm not only exhibit good robustness and noise reduction performance but also have a better tracking ability.
Impact of Quality Investment and Vertical Shareholding in Hybrid Competing Supply Chains
Authors: Xiong Shouyao , Zhou Tao
Published: August 2024

Product quality is a key factor affecting consumers’ willingness to buy, providing greater advantages to an enterprise than product price. This paper investigates the impact of two factors, price and quality, on the operational decisions of hybrid competing supply chains. Supply chain I, which consists of a manufacturer and a retailer, is a decentralized structure. Supply chain II, where the manufacturer and retailer are integrated, is a centralized structure. Quality investment and vertical shareholding are introduced into the decentralized supply chain. Models are constructed for three different scenarios, examining whether the manufacturer makes a quality investment and whether the retailer holds shares in the quality investment. By comparing the equilibrium results, solved by the Stackelberg game method, the following conclusions are drawn: (1) Quality investment and shareholding can enhance product quality and price. (2) The retail price in a centralized supply chain is consistently lower than that in a decentralized one, leading to generally higher total profits for centralized supply chain. (3) The total profit of the decentralized supply chain only exceeds that of the centralized ones when the degree of substitution between products is lower than 0.6285 and the quality effort cost factor is within a specific range. While centralized supply chain is generally more advantageous, decentralized supply chain can outperform him under specific conditions.
How Can Scientific Crowdsourcing Realize Value Co-Creation? A Knowledge Flow-Based Perspective
Authors: Qiu Ran , Wang Guohao , Yu Liying , Xing Yuanzhi , Yang Hui
Published: August 2024

Presently, the practice of scientific crowdsourcing still suffers from user loss, platform operational inefficiency, and many other dilemmas, mainly because the process mechanism of realizing value co-creation through interaction between users and platforms has not yet been elaborated. To fill this gap, this study takes Kaggle as the research object and explores the realization process and internal mechanism of scientific crowdsourcing value co-creation from the perspective of knowledge flow. The results show that the operation process of Kaggle-based scientific crowdsourcing can be decomposed into five progressive evolutionary stages, including knowledge sharing, knowledge innovation, knowledge dissemination, knowledge application, and knowledge advantage formation. The knowledge flow activates a series of value co-creation activities of scientific crowdsourcing, forming a dynamic evolution and continuous optimization of the value co-creation process that includes the value proposition, value communication, value consensus, and all-win value. Institutional logic plays a key role as a catalyst in the value co-creation of scientific crowdsourcing, effectively facilitating the realization of value co-creation by controlling and guiding the flow of knowledge. The study unlocks the “gray box” from knowledge flow to value co-creation, providing new theoretical support and guidance for further enhancing the value co-creation capacity and accelerating the practice of scientific crowdsourcing.
Optimized Decisions for Smart Tourism Destinations: A Cross-Generational Perspective Using an Improved Importance–Performance Analysis
Authors: Botezat Elena-Aurelia , Ban Olimpia-Iuliana , Popa Adela Laura , Coita Dorin-Cristian , Tarcza Teodora Mihaela
Published: August 2024

Our study introduces an enhanced version of the Importance–Performance Analysis (IPA) method, a powerful tool that can be applied across various domains. This method plays a crucial role in our research, aiding in making well-informed decisions about smart tourism destination attributes. We achieved this by evaluating how 911 consumers from four different generations (Baby Boomers, Generation X, Millennials, and Generation Z) rated these attributes based on their most recent tourist destination visit. Unlike traditional methods that often rely on subjective opinions or complex statistical models, the Improved IPA (IIPA) method offers a clear approach to decision-making. It enables decision-makers to focus on the most crucial attributes that drive consumer interest, thereby optimizing resource allocation and marketing efforts. Specifically, to remain competitive, decision-makers for smart tourist destinations should focus on queuing-time forecast and applications, websites, and content accessible for travelers with disabilities for Baby Boomers; e-complaint handling for Generation X; smart emergency response system for Millennials; and tourist-flow forecast, real-time traffic broadcast, electronic-entrance guard systems, and accessible data about physical design features of accommodation, restaurants, and tourist attractions for Generation Z. Theoretically, this study advances the research on managerial decision-making by demonstrating the effectiveness of the IIPA as a clear and straightforward method for making optimal decisions about product or service attributes. In practice, the study provides decision-makers with valuable insights into the importance of different categories of smart attributes in shaping the overall holiday experience at a tourist destination for Baby Boomers, Generation X, Millennials, and Generation Z tourism consumers.
A Testing and Evaluation Method for the Car-Following Models of Automated Vehicles Based on Driving Simulator
Authors: Zhang Yuhan , Shao Yichang , Shi Xiaomeng , Ye Zhirui
Published: August 2024

The continuous advancement of connected and automated driving technologies has garnered considerable public attention regarding the safety and reliability of automated vehicles (AVs). Comprehensive and efficient testing is essential before AVs can be deployed on public roads. Current mainstream testing methods involve high costs in real-world settings and limited immersion in numerical simulations. To address these challenges and facilitate testing in mixed traffic scenarios involving both human-driven vehicles (HDVs) and AVs, we propose a testing and evaluation approach using a driving simulator. Our methodology comprises three fundamental steps. First, we systematically classify scenario elements by drawing insights from the scenario generation logic of the driving simulator. Second, we establish an interactive traffic scenario that allows human drivers to manipulate vehicles within the simulator while AVs execute their decision and planning algorithms. Third, we introduce an evaluation method based on this testing approach, validated through a case study focused on car-following models. The experimental results confirm the efficiency of the simulation-based testing method and demonstrate how car-following efficiency and comfort decline with increased speeds. The proposed approach offers a cost-effective and comprehensive solution for testing, considering human driver behavior, making it a promising method for evaluating AVs in mixed traffic scenarios.
Using Principal Component Analysis for Temperature Readings from YF3:Pr3+ Luminescence
Authors: Rajčić Anđela , Ristić Zoran , Periša Jovana , Milićević Bojana , Aldawood Saad , Alodhayb Abdullah N. , Antić Željka , Dramićanin Miroslav D.
Published: August 2024

The method of measuring temperature using luminescence by analyzing the emission spectra of Pr3+-doped YF3 using principal component analysis is presented. The Pr3+-doped YF3 is synthesized using a solid-state technique, and its single-phase orthorhombic crystal structure is confirmed using X-ray diffraction. The emission spectra measured within the 93–473 K temperature range displays characteristic Pr3+ f-f electronic transitions. The red emission from the 3P0,1→3H6,3F2 electronic transition mostly dominates the spectra. However, at low temperatures, the intensity of the green emissions from the 3P0,1→3H5, deep-red 3P0,1→3F4, and the deep-red emissions from the 3P0,1→3F4 transitions are considerably lower compared to the intensity of the red emissions. Temperature variations directly impact the photoluminescent spectra, causing a notable increase in the green and deep-red emissions from the 3P1 excited state. We utilized the entire spectrum as an input for principal component analysis, considering each temperature as an independent group of data. The first principal component explained 99.3% of the variance in emission spectra caused by temperature and we further used it as a reliable temperature indicator for luminescence thermometry. The approach has a maximum absolute sensitivity of around 0.012 K−1. The average accuracy and precision values are 0.7 K and 0.5 K, respectively.
A Formal Verification Approach for Linux Kernel Designing
Authors: Wang Zi , Lan Yuqing , He Xinlei , Lv Jianghua
Published: August 2024

Although the Linux kernel is widely used, its complexity makes errors common and potentially serious. Traditional formal verification methods often have high overhead and rely heavily on manual coding. They typically verify only specific functionalities of the kernel or target microkernels and do not support continuous verification of the entire kernel. To address these limitations, we introduce LMVM (Linux Kernel Modeling and Verification Method), a formal method based on type theory that ensures the correct design of the Linux architecture. In the model, the kernel is treated as a top-level type, subdivided into the following sublevels: subsystem, dentry, file, struct, function, and base. These types are defined in the structure and relationships. The verification process includes checking the design specifications for both type relationships and the presence of each type. Our contribution lies primarily in the following two points: 1. This is a lightweight verification. As long as the modeling is complete, architectural errors in the design phase can be identified promptly. 2. The designed “model refactor” module supports kernel updating, and the kernel can be continuously verified by extending the kernel model. To test its usefulness, we develop a set of security communication mechanisms in the kernel, which are verified using our method.
Advanced Oxidation Processes of Organic Contaminants
Authors: Liu Mingzhu , Zhang Shengnan , Wang Zunyao
Published: August 2024

Concern is growing about the occurrence of emerging organic contaminants in various eco-environments [...]
Impact of COVID-19 Lockdown on NO2 Pollution and the Associated Health Burden in China: A Comparison of Different Approaches
Authors: Li Zhiyuan
Published: August 2024

So far, a large number of studies have quantified the effect of COVID-19 lockdown measures on air quality in different countries worldwide. However, few studies have compared the influence of different approaches on the estimation results. The present study aimed to utilize a random forest machine learning approach as well as a difference-to-difference approach to explore the effect of lockdown policy on nitrogen dioxide (NO2) concentration during COVID-19 outbreak period in mainland China. Datasets from 2017 to 2019 were adopted to establish the random forest models, which were then applied to predict the NO2 concentrations in 2020, representing a scenario without the lockdown effect. The results showed that random forest models achieved remarkable predictive accuracy for predicting NO2 concentrations, with index of agreement values ranging between 0.34 and 0.76. Compared with the modelled NO2 concentrations, on average, the observed NO2 concentrations decreased by approximately 16 µg/m3 in the lockdown period in 2020. The difference-to-difference approach tended to underestimate the influence of COVID-19 lockdown measures. Due to the improvement of NO2 pollution, around 3722 non-accidental premature deaths were avoided in the studied population. The presented machine learning modelling framework has a great potential to be transferred to other short-term events with abrupt pollutant emission changes.
The Effect of α-Fe2O3(0001) Surface Containing Hydroxyl Radicals and Ozone on the Formation Mechanism of Environmentally Persistent Free Radicals
Authors: Liang Danli , Liu Jiarong , Wang Chunlin , Tu Kaipeng , Wang Li , Qiu Lili , Zhang Xiuhui , Liu Ling
Published: August 2024

The formation of environmentally persistent free radicals (EPFRs) is mediated by the particulate matter's surface, especially transition metal oxide surfaces. In the context of current atmospheric complex pollution, various atmospheric components, such as key atmospheric oxidants ·OH and O3, are often absorbed on particulate matter surfaces, forming particulate matter surfaces containing ·OH and O3. This, in turn, influences EPFRs formation. Here, density functional theory (DFT) calculations were used to explore the formation mechanism of EPFRs by C6H5OH on α-Fe2O3(0001) surface containing the ·OH and O3, and compare it with that on clean surface. The results show that, compared to EPFRs formation with an energy barrier on a clean surface, EPFRs can be rapidly formed through a barrierless process on these surfaces. Moreover, during the hydrogen abstraction mechanism leading to EPFRs formation, the hydrogen acceptor shifts from a surface O atom on a clean surface to an O atom of ·OH or O₃ on these surfaces. However, the detailed hydrogen abstraction process differs on surfaces containing oxidants: on surfaces containing ·OH, it occurs directly through a one-step mechanism, while, on surfaces containing O3, it occurs through a two-step mechanism. But, in both types of surfaces, the essence of this promotional effect mainly lies in increasing the electron transfer amounts during the reaction process. This research provides new insights into EPFRs formation on particle surfaces within the context of atmospheric composite pollution.
Comparative Study of the Effects of Drugs Targeting Adrenergic Receptors on the Early Life Stages of Zebrafish
Authors: Lv Junsheng , Sun Fengzhu , Li Zaitian , Qin Yueyun , Sheng Ruozhu , Sun Liwei
Published: August 2024

Owing to the presence of drugs targeting adrenergic receptors in aquatic ecosystems, considerable attention has been directed towards their environmental distribution and fate in recent decades. However, their potential impacts on non-target aquatic organisms, particularly fish, have received relatively limited investigation. In this study, moxisylyte (MOX) and propranolol (PRO) were selected as representatives of α- or β-adrenergic receptor antagonist, respectively, and we assessed their effects on the early life stages of zebrafish, especially on the nervous and cardiovascular systems. Although both compounds exhibited marginal effects on zebrafish survival, hatching and gross abnormality following exposure to concentrations ranging from 1 to 625 μg/L, they adversely affected the development of cardiovascular and nervous systems, but through different mechanisms of action, as evidenced by variations in gene transcriptional responses and enzyme activities. Notably, cardiovascular responses appear promising for use as potential biomarkers for exposure to drugs targeting adrenergic receptors. This study enhances our understanding of the ecotoxicological risks posed by α- and β-blockers in fish. Nonetheless, further investigation is needed to elucidate the precise mechanisms underlying the impacts of drugs targeting adrenergic receptors due to our limited knowledge of the physiological functions of the adrenergic system in fish.
N-Acetylcysteine Ineffective in Alleviating Hangover from Binge Drinking: A Clinical Study
Authors: Podobnik Boris , Demšar Lenart , Šarc Lucija , Jerin Aleš , Osredkar Joško , Trontelj Jurij , Roškar Robert , Brvar Miran
Published: August 2024

Alcohol hangover (veisalgia) is a fairly common phenomenon. The pathogenesis of veisalgia is not understood and treatment has not yet been established. Occasionally, students take N-acetylcysteine (NAC) before binge drinking to alleviate hangover. The aim of this study was to evaluate the effect of NAC on serum levels of electrolytes, enzymes, oxidative stress biomarkers and symptoms of veisalgia in binge drinking. In this randomized, double-blind, placebo-controlled study, healthy students were randomly assigned into two groups: one receiving NAC and the other receiving a placebo. Blood samples were taken before drinking, 30 min after a 1.5 h long drinking session, and the subsequent morning. Serum levels of electrolytes, urea, enzymes, ethanol, 8-Hydroxydeoxyguanosine (8-OHdG) and N-epsilon-hexanoyl-lysine were measured. The participants completed the Acute Hangover Severity Scale (AHSS) assessment based on symptoms, and 40 students (20 male), aged 23 ± 2 years, were included in the study. Their mean blood ethanol concentration was 1.4 g/kg. Serum sodium levels were increased after drinking, and urea decreased the following morning compared to their levels before drinking in both groups. Serum 8-OHdG levels were increased after drinking and remained elevated until the following morning, compared to the levels before drinking, in both groups. NAC had no effect on sodium, urea and 8-OHdG levels or the symptoms of veisalgia. In conclusion, binge drinking causes a transient increase in serum sodium and as a prolonged increase in oxidative marker 8-OHdG levels. NAC had no effect on the sodium and 8-OHdG levels.
Forecasting Disease Burden with a Dynamic Transmission Model of Human Papillomavirus and Recurrent Respiratory Papillomatosis in the United States
Authors: Palmer Cody , Morais Edith , Tota Joseph
Published: August 2024

Juvenile- and adult-onset recurrent respiratory papillomatosis (JORRP and AORRP) are rare but serious conditions that are caused by oral human papillomavirus (HPV) infections. The proliferation of wart-like growths throughout the respiratory tract can result in medical problems, including death. The current treatment scheme is surgery, though prevention of HPV infection through vaccination is available. A previously developed model for JORRP and AORRP was adapted to the United States using data on disease burden and HPV infection. The model was validated against post-vaccination reductions in disease and used to forecast the future burden of JORRP and AORRP, estimating the impact that HPV vaccination will have on these diseases. Between 2007 (the beginning of HPV vaccination in the US) and 2021, this model estimates that approximately 1393 lives, 22,867 Quality-Adjusted-Life-Years, and over USD 672 million in treatment costs have been saved by HPV vaccination. There is also a substantial reduction in JORRP and AORRP burden, with a 95% reduction in incidence by 2040. Moreover, between 2040 and 2121, the model predicts 3–11 total cases of HPV6/11-related JORRP in the US, and 36–267 total cases of HPV6/11-related AORRP. HPV vaccination in the United States has driven, and will continue to drive, substantial reductions in the public health and economic burden of HPV6/11-related JORRP and AORRP.
Outcomes of COVID-19 and Influenza in Cerebral Palsy Patients Hospitalized in the United States: Comparative Study of a Nationwide Database
Authors: Quazi Mohammed A. , Shakir Muhammad Hassan , Faiz Zohaa , Quraishi Ibrahim , Nasrullah Adeel , Ikram Hafiz Abdullah , Sohail Amir H , Sultan Sulaiman , Sheikh Abu Baker
Published: August 2024

Patients with cerebral palsy (CP) are particularly vulnerable to respiratory infections, yet comparative outcomes between COVID-19 and influenza in this population remain underexplored. Using the National Inpatient Sample from 2020–2021, we performed a retrospective analysis of hospital data for adults with CP diagnosed with either COVID-19 or influenza. The study aimed to compare the outcomes of these infections to provide insights into their impact on this vulnerable population. We assessed in-hospital mortality, complications, length of stay (LOS), hospitalization costs, and discharge dispositions. Multivariable logistic regression and propensity score matching were used to adjust for confounders, enhancing the analytical rigor of our study. The study cohort comprised 12,025 patients—10,560 with COVID-19 and 1465 with influenza. COVID-19 patients with CP had a higher in-hospital mortality rate (10.8% vs. 3.1%, p = 0.001), with an adjusted odds ratio of 3.2 (95% CI: 1.6–6.4). They also experienced an extended LOS by an average of 2.7 days. COVID-19 substantially increases the health burden for hospitalized CP patients compared to influenza, as evidenced by higher mortality rates, longer hospital stays, and increased costs. These findings highlight the urgent need for tailored strategies to effectively manage and reduce the impact of COVID-19 on this high-risk group.
The Dual Role of TRIM7 in Viral Infections
Authors: Gonzalez-Orozco Maria , Rodriguez-Salazar Carlos A. , Giraldo Maria I.
Published: August 2024

The E3 ubiquitin ligase TRIM7 is known to have dual roles during viral infections. Like other TRIM proteins, TRIM7 can regulate the IFN pathway via the regulation of the cytosolic receptors RIG-I or MDA-5, which promote the production of type I interferons (IFN-I) and antiviral immune responses. Alternatively, under certain infectious conditions, TRIM7 can negatively regulate IFN-I signaling, resulting in increased virus replication. A growing body of evidence has also shown that TRIM7 can, in some cases, ubiquitinate viral proteins to promote viral replication and pathogenesis, while in other cases it can promote degradation of viral proteins through the proteasome, reducing virus infection. TRIM7 can also regulate the host inflammatory response and modulate the production of inflammatory cytokines, which can lead to detrimental inflammation. TRIM7 can also protect the host during infection by reducing cellular apoptosis. Here, we discuss the multiple functions of TRIM7 during viral infections and its potential as a therapeutic target.
Comparative Transcriptome Analysis Unveils Regulatory Factors Influencing Fatty Liver Development in Lion-Head Geese under High-Intake Feeding Compared to Normal Feeding
Authors: Kong Jie , Yao Ziqi , Chen Junpeng , Zhao Qiqi , Li Tong , Dong Mengyue , Bai Yuhang , Liu Yuanjia , Lin Zhenping , Xie Qingmei , Zhang Xinheng
Published: August 2024

The lion-head goose is the only large goose species in China, and it is one of the largest goose species in the world. Lion-head geese have a strong tolerance for massive energy intake and show a priority of fat accumulation in liver tissue through special feeding. Therefore, the aim of this study was to investigate the impact of high feed intake compared to normal feeding conditions on the transcriptome changes associated with fatty liver development in lion-head geese. In this study, 20 healthy adult lion-head geese were randomly assigned to a control group (CONTROL, n = 10) and high-intake-fed group (CASE, n = 10). After 38 d of treatment, all geese were sacrificed, and liver samples were collected. Three geese were randomly selected from the CONTROL and CASE groups, respectively, to perform whole-transcriptome analysis to analyze the key regulatory genes. We identified 716 differentially expressed mRNAs, 145 differentially expressed circRNAs, and 39 differentially expressed lncRNAs, including upregulated and downregulated genes. GO enrichment analysis showed that these genes were significantly enriched in molecular function. The node degree analysis and centrality metrics of the mRNA–lncRNA–circRNA triple regulatory network indicate the presence of crucial functional nodes in the network. We identified differentially expressed genes, including HSPB9, Pgk1, Hsp70, ME2, malic enzyme, HSP90, FADS1, transferrin, FABP, PKM2, Serpin2, and PKS, and we additionally confirmed the accuracy of sequencing at the RNA level. In this study, we studied for the first time the important differential genes that regulate fatty liver in high-intake feeding of the lion-head goose. In summary, these differentially expressed genes may play important roles in fatty liver development in the lion-head goose, and the functions and mechanisms should be investigated in future studies.