bluuebunny/crossref_metadata_2025 huggingface dataset:
Methods to Improve the Accuracy and Robustness of Satellite-Derived Bathymetry through Processing of Optically Deep Waters
Authors: Jia Dongzhen , Li Yu , He Xiufeng , Yang Zhixiang , Wu Yihao , Wu Taixia , Xu Nan
Published: November 2023

Selecting a representative optical deep-water area is crucial for accurate satellite-derived bathymetry (SDB) based on semi-theoretical and semi-empirical models. This study proposed a deep-water area selection method where potential areas were identified by integrating remote sensing imagery with existing global bathymetric data. Specifically, the effects of sun glint correction for deep-water areas on SDB estimation were investigated. The results indicated that the computed SDB had significant instabilities when different optical deep-water areas without sun glint correction were used for model training. In comparison, when sun glint correction was applied, the SDB results from different deep-water areas had greater consistency. We generated bathymetric maps for the Langhua Reef in the South China Sea and Buck Island near the U.S. Virgin Islands using Sentinel-2 multispectral images and 70% of the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) bathymetry data. Additionally, 30% of the ICESat-2 bathymetry data and NOAA NGS Topo-bathy Lidar data served as the validation data to evaluate the qualities of the computed SDB, respectively. The results showed that the average quality of the SDB significantly improved with sun glint correction application by a magnitude of 0.60 m in terms of the root mean square error (RMSE) for two study areas. Moreover, an evaluation of the SDB data computed from different deep-water areas showed more consistent results, with RMSEs of approximately 0.4 and 1.4 m over the Langhua Reef and Buck Island, respectively. These values were consistently below 9% of the maximum depth. In addition, the effects of the optical image selection on SDB inversion were investigated, and the SDB calculated from the images over different time periods demonstrated similar results after applying sun glint correction. The results showed that this approach for optical deep-water area selection and correction could be used for improving the SDB, particularly in challenging scenarios, thereby enhancing the accuracy and robustness of SDB.
Improved Mapping of Regional Forest Heights by Combining Denoise and LightGBM Method
Authors: Sang Mengting , Xiao Hai , Jin Zhili , He Junchen , Wang Nan , Wang Wei
Published: November 2023

Currently, the integration of satellite-based LiDAR (ICESat-2) and continuous remote sensing imagery has been extensively applied to mapping forest canopy height over large areas. A considerable fraction of low-quality photons exists in ICESAT-2/ATL08 products, which restricts the performance of regional canopy height estimation. To solve these problems, a Local Noise Removal-Light Gradient Boosting Machine (LNR-LGB) method was proposed in this study, which efficiently filtered the unreliable canopy photons in ATL08, constructed an extrapolation model by combining multiple remote sensing data, and finally mapped the 30 m forest canopy height of Hunan Province in 2020. To verify the feasibility of this method, the canopy parameters were also filtered based on ATL08 product attributes (traditional method), and the accuracy of the two models was compared using the 10-fold cross-validation. The conclusions were as follows: (1) compared with the traditional model, the overall accuracy of the LNR-LGB model was approximately doubled, in which R2 increased from 0.46 to 0.65 and RMSE decreased from 6.11 m to 3.48 m; (2) the forest height in Hunan Province ranged from 2.53 to 50.79 m with an average value of 18.34 m. The LNR-LGB method will provide a new concept for achieving high-accuracy mapping of regional forest height.
Forest Loss Related to Brazil Nut Production in Non-Timber Forest Product Concessions in a Micro-Watershed in the Peruvian Amazon
Authors: Alarcon-Aguirre Gabriel , Mamani Mamani Maritza , Canahuire-Robles Rembrandt Ramiro , Vasquez Zavaleta Telesforo , Peña Valdeiglesias Joel , Diaz Revoredo Jorge , Rodríguez Achata Liset , Ramos Enciso Dalmiro , Garate-Quispe Jorge
Published: November 2023

Madre de Dios is considered an important center of biodiversity in Peru due to its extensive Amazonian forests. However, the forests are under growing pressure due to land invasion, agricultural expansion, and gold mining. This makes support for forest management very important. This study aimed to evaluate the relationship between forest loss, land cover, land-use changes, and Brazil nut (Bertholletia excelsa Humb. & Bonpl) production in forest concessions in the Peruvian Amazon (2004–2020). Remote sensing techniques were used to classify images using the random forest algorithm, which was applied to the Landsat-5 thematic mapper, Landsat-7 enhanced thematic mapper, and Landsat-8 operational land imagery. Brazil nut production data from 2004–2020 was provided by the Regional Forest and Wildlife Service of Madre de Dios. In forest concessions, the forest area decreased continuously over the whole study period (160.11 ha/year). During the same time period, the change in forest cover in the concessions from Brazil nut to other uses was 4681 ha. At the same time, the authorization and extraction of Brazil nuts varied during the study period but did not show a downward trend. We found a significant and inverse relationship between the conversion of forest to agricultural land and Brazil nut production. However, there were insignificant relationships between forest loss, the persistence of agricultural and forest areas, and Brazil nut production. Therefore, despite the forest loss in the forest concession areas, Brazil nut production has not decreased. Production may not be affected because land pressure is higher near access roads, affecting only the areas near the roads rather than the actual areas where the Brazil nut-producing trees are located. Our results showed that nut production in non-timber forest product concessions would be negatively affected by deforestation and forest degradation, but only slightly.
DNA-Based Gold Nanoparticle Assemblies: From Structure Constructions to Sensing Applications
Authors: Xie Mo , Jiang Jinke , Chao Jie
Published: November 2023

Gold nanoparticles (Au NPs) have become one of the building blocks for superior assembly and device fabrication due to the intrinsic, tunable physical properties of nanoparticles. With the development of DNA nanotechnology, gold nanoparticles are organized in a highly precise and controllable way under the mediation of DNA, achieving programmability and specificity unmatched by other ligands. The successful construction of abundant gold nanoparticle assembly structures has also given rise to the fabrication of a wide range of sensors, which has greatly contributed to the development of the sensing field. In this review, we focus on the progress in the DNA-mediated assembly of Au NPs and their application in sensing in the past five years. Firstly, we highlight the strategies used for the orderly organization of Au NPs with DNA. Then, we describe the DNA-based assembly of Au NPs for sensing applications and representative research therein. Finally, we summarize the advantages of DNA nanotechnology in assembling complex Au NPs and outline the challenges and limitations in constructing complex gold nanoparticle assembly structures with tailored functionalities.
Use of Domain Labels during Pre-Training for Domain-Independent WiFi-CSI Gesture Recognition
Authors: Berlo Bram van , Verhoeven Richard , Meratnia Nirvana
Published: November 2023

To minimize dependency on the availability of data labels, some WiFi-CSI based-gesture recognition solutions utilize an unsupervised representation learning phase prior to fine-tuning downstream task classifiers. In this case, however, the overall performance of the solution is negatively affected by domain factors present in the WiFi-CSI data used by the pre-training models. To reduce this negative effect, we propose an integration of the adversarial domain classifier in the pre-training phase. We consider this as an effective step towards automatic domain discovery during pre-training. We also experiment with multi-class and label versions of domain classification to improve situations, in which integrating a multi-class and single label-based domain classifier during pre-training fails to reduce the negative impact domain factors have on overall solution performance. For our extensive random and leave-out domain factor cross-validation experiments, we utilise (i) an end-to-end and unsupervised representation learning baseline, (ii) integration of both single- and multi-label domain classification, and (iii) so-called domain-aware versions of the aformentioned unsupervised representation learning baseline in (i) with two different datasets, i.e., Widar3 and SignFi. We also consider an input sample type that generalizes, in terms of overall solution performance, to both aforementioned datasets. Experiment results with the Widar3 dataset indicate that multi-label domain classification reduces domain shift in position (1.2% mean metric improvement and 0.5% variance increase) and orientation (0.4% mean metric improvement and 1.0% variance decrease) in domain factor leave-out cross-validation experiments. The results also indicate that domain shift reduction, when considering single- or multi-label domain classification during pre-training, is negatively impacted when a large proportion of negative view combinations contain views that originate from different domains within a substantial amount of mini-batches considered during pre-training. This is caused by the view contrastive loss repelling the aforementioned negative view combinations, eventually causing more domain shift in the intermediate feature space of the overall solution.
Detection of Water Leakage in Drip Irrigation Systems Using Infrared Technique in Smart Agricultural Robots
Authors: Türkler Levent , Akkan Taner , Akkan Lütfiye Özlem
Published: November 2023

In the future, the world is likely to face water and therefore food shortages due to reasons such as global warming, population growth, the melting of glaciers, the destruction of agricultural lands over time or their use for different purposes, and environmental pollution. Although technological developments are important for people to live a more comfortable and safer life, it is also possible to reduce and even repair the damage to nature and protect nature itself thanks to new technologies. There is a requirement to detect abnormal water usage in agriculture to avert water scarcity, and an electronic system can help achieve this objective. In this research, an experimental study was carried out to detect water leaks in the field in order to prevent water losses that can occur in agriculture, where water consumption is the highest. Therefore, in this study, low-cost embedded electronic hardware was developed to detect over-watering by means of normal and thermal camera sensors and to collect the required data, which can be installed on a mobile agricultural robot. For image processing and the diagnosis of abnormal conditions, the collected data were transferred to a personal computer server. Then, software was developed for both the low-cost embedded system and the personal computer to provide a faster detection and decision-making process. The physical and software system developed in this study was designed to provide a water leak detection process that has a minimum response time. For this purpose, mathematical and image processing algorithms were applied to obtain efficient water detection for the conversion of the thermal sensor data into an image, the image size enhancement using interpolation, the combination of normal and thermal images, and the calculation of the image area where water leakage occurs. The field experiments for this developed system were performed manually to observe the good functioning of the system.
Uncovering Subtle Gait Deterioration in People with Early-Stage Multiple Sclerosis Using Inertial Sensors: A 2-Year Multicenter Longitudinal Study
Authors: Gervasoni Elisa , Anastasi Denise , Di Giovanni Rachele , Solaro Claudio , Rovaris Marco , Brichetto Giampaolo , Confalonieri Paolo , Tacchino Andrea , Carpinella Ilaria , Cattaneo Davide
Published: November 2023

Limited longitudinal studies have been conducted on gait impairment progression overtime in non-disabled people with multiple sclerosis (PwMS). Therefore, a deeper understanding of gait changes with the progression of the disease is essential. The objective of the present study was to describe changes in gait quality in PwMS with a disease duration ≤ 5 years, and to verify whether a change in gait quality is associated with a change in disability and perception of gait deterioration. We conducted a multicenter prospective cohort study. Fifty-six subjects were assessed at baseline (age: 38.2 ± 10.7 years, Expanded Disability Status Scale (EDSS): 1.5 ± 0.7 points) and after 2 years, participants performed the six-minute walk test (6MWT) wearing inertial sensors. Quality of gait (regularity, symmetry, and instability), disability (EDSS), and walking perception (multiple sclerosis walking scale-12, MSWS-12) were collected. We found no differences on EDSS, 6MWT, and MSWS-12 between baseline and follow-up. A statistically significant correlation between increased EDSS scores and increased gait instability was found in the antero-posterior (AP) direction (r = 0.34, p = 0.01). Seventeen subjects (30%) deteriorated (increase of at least 0.5 point at EDSS) over 2 years. A multivariate analysis on deteriorated PwMS showed that changes in gait instability medio-lateral (ML) and stride regularity, and changes in ML gait symmetry were significantly associated with changes in EDSS (F = 7.80 (3,13), p = 0.003, R2 = 0.56). Moreover, gait changes were associated with a decrease in PwMS perception on stability (p < 0.05). Instrumented assessment can detect subtle changes in gait stability, regularity, and symmetry not revealed during EDSS neurological assessment. Moreover, instrumented changes in gait quality impact on subjects’ perception of gait during activities of daily living.
Magnetic Anomaly Detection Based on a Compound Tri-Stable Stochastic Resonance System
Authors: Huang Jinbo , Zheng Zhen , Zhou Yu , Tan Yuran , Wang Chengjun , Xu Guangbo , Zha Bingting
Published: November 2023

In the case of strong background noise, a tri-stable stochastic resonance model has higher noise utilization than a bi-stable stochastic resonance (BSR) model for weak signal detection. However, the problem of severe system parameter coupling in a conventional tri-stable stochastic resonance model leads to difficulty in potential function regulation. In this paper, a new compound tri-stable stochastic resonance (CTSR) model is proposed to address this problem by combining a Gaussian Potential model and the mixed bi-stable model. The weak magnetic anomaly signal detection system consists of the CTSR system and judgment system based on statistical analysis. The system parameters are adjusted by using a quantum genetic algorithm (QGA) to optimize the output signal-to-noise ratio (SNR). The experimental results show that the CTSR system performs better than the traditional tri-stable stochastic resonance (TTSR) system and BSR system. When the input SNR is -8 dB, the detection probability of the CTSR system approaches 80%. Moreover, this detection system not only detects the magnetic anomaly signal but also retains information on the relative motion (heading) of the ferromagnetic target and the magnetic detection device.
State Transition Graph-Based Spatial–Temporal Attention Network for Cell-Level Mobile Traffic Prediction
Authors: Shi Jianrun , Cui Leiyang , Gu Bo , Lyu Bin , Gong Shimin
Published: November 2023

Mobile traffic prediction enables the efficient utilization of network resources and enhances user experience. In this paper, we propose a state transition graph-based spatial–temporal attention network (STG-STAN) for cell-level mobile traffic prediction, which is designed to exploit the underlying spatial–temporal dynamic information hidden in the historical mobile traffic data. Specifically, we first identify the semantic context information over different segments of the historical data by constructing the state transition graphs, which may reveal different patterns of random fluctuation. Then, based on the state transition graphs, a spatial attention extraction module using graph convolutional networks (GCNs) is designed to aggregate the spatial information of different nodes in the state transition graph. Moreover, a temporal extraction module is employed to capture the dynamic evolution and temporal correlation of the state transition graphs over time. Such a spatial–temporal attention network can be further integrated with a parallel long short-term memory (LSTM) module to improve the accuracy of mobile traffic prediction. Extensive experiments demonstrate that the STG-STAN can better exploit the spatial–temporal information hidden in the state transition graphs, achieving superior performance compared with several baselines.
Tail Artifact Removal via Transmittance Effect Subtraction in Optical Coherence Tail Artifact Images
Authors: Simoncic Urban , Milanic Matija
Published: November 2023

Optical Coherence Tomography Angiography (OCTA) has revolutionized non-invasive, high-resolution imaging of blood vessels. However, the challenge of tail artifacts in OCTA images persists. In response, we present the Tail Artifact Removal via Transmittance Effect Subtraction (TAR-TES) algorithm that effectively mitigates these artifacts. Through a simple physics-based model, the TAR-TES accounts for variations in transmittance within the shallow layers with the vasculature, resulting in the removal of tail artifacts in deeper layers after the vessel. Comparative evaluations with alternative correction methods demonstrate that TAR-TES excels in eliminating these artifacts while preserving the essential integrity of vasculature images. Crucially, the success of the TAR-TES is closely linked to the precise adjustment of a weight constant, underlining the significance of individual dataset parameter optimization. In conclusion, TAR-TES emerges as a powerful tool for enhancing OCTA image quality and reliability in both clinical and research settings, promising to reshape the way we visualize and analyze intricate vascular networks within biological tissues. Further validation across diverse datasets is essential to unlock the full potential of this physics-based solution.
Calculation of Carbon Emissions and Study of the Emission Reduction Path of Conventional Public Transportation in Harbin City
Authors: Zhang Wenhui , Zhou Ge , Song Ziwen , Shi Xintao , Ye Meiru , Chen Xirui , Xiang Yuhao , Zheng Wenzhao , Zhang Pan
Published: November 2023

As the northernmost megacity in China, the long winters, large population size, and unsustainable transport structure in Harbin determine that the city will produce relatively large carbon emissions. The transportation industry is one of the three greenhouse gas emission sources; therefore, the development of low-carbon transportation is imperative. This work compares commonly used carbon emission measurement methods and chooses a mileage method to classify the carbon emissions of conventional buses of different energy types used in Harbin in 2020. A multi-factor grey prediction model was constructed to predict the population size of Harbin and the number of conventional buses. After that, a scenario analysis method was used to analyze the fuel structure of buses in Harbin from three perspectives: a pessimistic scenario, a baseline scenario, and an optimistic scenario. The carbon emissions of conventional buses were calculated for Harbin from 2023 to 2030. Finally, by combining the prediction results and factors influencing carbon emission, a regular bus path to minimize carbon emissions is proposed. The outcome of this study shows that the carbon emission environment in Harbin will be improved by reducing vehicle energy consumption, optimizing energy structure, standardizing driving behavior, building intelligent transportation, giving priority to public transportation, and improving the road network structure.
Effectiveness of Fine Dust Environmental Education on Students’ Awareness and Attitudes in Korea and Australia Using AR Technology
Authors: Bhang Kon Joon , Huh Jung Rim
Published: November 2023

This study evaluated the influence of fine dust environmental education on 90 elementary and middle school students, taking into account factors such as sex, place of residence, experience with environmental education, and experience with augmented reality (AR). The goal was to assess changes in students’ awareness and attitudes towards fine dust. Results showed that fine dust environmental education positively influenced students’ awareness of fine dust. Further analysis by factors revealed that the education was effective regardless of these factors. In addition to increasing awareness of the seriousness of fine dust, students’ attitudes also changed positively. The study conducted a statistical analysis of changes in 10 types of attitudes among the target students before and after our education. After the implementation of the education, students’ attitudes changed positively in all 10 types of attitudes, including checking the weather, restricting activities, personal hygiene, wearing masks, environmental education, separating garbage and cleaning, classroom silence, classroom ventilation, home cleaning, and home environment with statistical significance. These findings underscore the effectiveness and importance of combining fine-dust environmental education with AR technology and can inform the planning of educational programs aimed at improving students’ awareness and attitudes.
Variation of Stem CO2 Efflux and Estimation of Its Contribution to the Ecosystem Respiration in an Even-Aged Pure Rubber Plantation of Hainan Island
Authors: Song Bo , Wu Zhixiang , Dong Lu , Yang Chuan , Yang Siqi
Published: November 2023

The stem CO2 efflux (Es) plays an important role in the carbon balance in forest ecosystems. However, a majority of studies focus on ecosystem flux, and little is known about the contribution of stem respiration to ecosystem respiration (Reco) for rubber (Hevea brasiliensis) plantations. We used a portable CO2 analyzer to monitor the rate of Es in situ at different heights (1.5 m, 3.0 m and 4.5 m) in an even-aged rubber plantation from 2019 to 2020. Our results showed that Es exhibited a significant seasonal difference with a minimum value in April and a maximum in September. The mean annual rate of Es at 3.0 m in height (1.65 ± 0.52 μmol·m−2·s−1) was slightly higher than Es at 4.5 m in height (1.56 ± 0.59 μmol·m−2·s−1) and Es at 1.5 m in height (1.51 ± 0.48 μmol·m−2·s−1). No obvious differences in vertical variations were found. An area-based method (Ea) and a volume-based method (Ev) were used to estimate stem respiration at stand levels. One-way ANOVA showed that Ea had no obvious differences in vertical variation (p = 0.62), and Ev indicated differences in vertical variation (p < 0.05). Therefore, the Ea chamber-based measurements at breast height were reasonable and practical extrapolation proxies of stem respiration in an even-aged rubber plantation. With the use of the area-based method, the stem carbon values released from a mature rubber forest were estimated to be 1.214 t C·hm−2·a−1 in 2019 and 1.414 t C·hm−2·a−1 in 2020. Ea/Reco and Ev/Reco showed seasonal changes, with a minimum value in April and a maximum value in December. The leaf area index (LAI) and soil volumetric moisture content (VWC) were the major impact factors of Ea/Reco in an even-aged pure rubber plantation.
Novel Applications of Silk Proteins Based on Their Interactions with Metal Ions
Authors: Wen Qingmei , Zhang Lei , Chen Yilu , Su Yi , Yu Jingmou , Chen Pu , Zheng Tao
Published: November 2023

Silk secreted by Bombyx mori L. silkworm has become one of the most important biomaterials, due to its excellent biocompatibility, controllable biodegradability, superior processability, and unique mechanical properties. Silk fibroin and sericin, as the two components of silk, contain abundant polar functional groups, and thus can bind metal ions through electrostatic interaction and chelation. Based on this binding, silk proteins not only can be used to fabricate ecofriendly and efficient adsorbents to remove heavy metals from waterbodies, but also can synthesize metal nanostructures (nanoparticles or nanoclusters) to form silk/metal composites with amazing optical or electrochemical characteristics. This binding also can be manipulated to optimize silk’s performance. This review focuses on discussing and summarizing advances in the use of silk fibroin and sericin for heavy metal ion-contaminated water remediation, biosensing materials, and electrochemical materials from the perspective of the interaction between silk proteins and metal ions. The performance enhancement of silk using metal ions is also analyzed. Moreover, the silk proteins’ interactions with metal ions and related structural features that contribute to the above sustainable applications are illustrated to lay a theoretical foundation. This review will favor the expansion of the applications of silk in both the traditional textile field and new biomaterials.
Digital Process Twins as Intelligent Design Technology for Engineering Metaverse/XR Applications
Authors: Stary Christian
Published: November 2023

The last years have seen propagating Metaverse and Mixed Reality (Extended Reality, XR) technologies into everyday applications. Due to their immersion and digitalization capability, sustainability issues can be addressed to optimize resource consumption before processes are put to practice or products are materialized. In particular, Digital Process Twin technologies can execute behavior models of socio-technical Cyber-Physical Systems. They allow both designing variants of system behavior and validating implementation architectures for system operation. In this contribution, I leverage these capabilities to capture the behavior-centered intelligence of Metaverse and XR applications. The more accurately user roles and cyber-physical components can be captured by behavior models representing Metaverse/XR applications, the more accurately the environmental, social, and economic impact of design alternatives can be experienced.
How Circular Economy Disclosure Responds to Institutional Determinants Empirical Evidences in Non-Financial European Firms
Authors: Pozzoli Matteo , Nastari Raffaela , Pisano Sabrina , Venuti Marco
Published: November 2023

Despite the increasing attention that the circular economy (CE) has received at the international level in recent years, the literature has paid limited attention to the importance of institutional factors that may influence firms’ disclosure of CE. Thus, there is a gap in the study of CE disclosure, especially when compared with other studies dedicated to environmental disclosure. This paper aims to fill this gap by investigating the institutional pressures, in terms of coercive, normative, and mimetic factors, that influence firms’ behavior with respect to CE disclosure. This research focuses on a sample of 366 nonfinancial firms, operating in 14 EU countries between 2015 and 2020. The results show that coercive and mimetic institutional pressures positively influence the level of CE disclosure issued by the firms. More specifically, the stringency of the environmental policy (coercive pressure) and the belonging to an environmentally sensitive sector (mimetic pressure) have a positive impact on the CE disclosure provided. With respect to normative pressure, the results are mixed. In fact, only the adoption of Global Reporting Initiatives’ (GRIs) standard requirements is positively and significantly related to CE disclosure. The presence of an external assurance, as well as the commitment to the SDGs, is not significantly related to the CE disclosure. The absence of an analytical standard that organically addresses the issue of CE, by guiding companies in their disclosure, may explain the irrelevance of these factors in the process of convergence of the information produced. This research contributes to this area by filling a gap in the CE literature, providing some insights into the determinants of disclosure and the role of institutional pressures in influencing the level of CE information. In addition, the research adds to previous studies on disclosure by measuring the CE information provided by companies with an indicator developed based on specific environmental items collected from the Refinitiv Eikon database, which could be used in future research. The findings of this paper have some important practical implications. In particular, the results confirm to policymakers that stricter regulations have a positive impact on disclosures related to the CE. Thus, a new specific European regulation should promote more homogeneous and analytical CE disclosure, increasing the sensitivity among firms and practitioners on this topic. A similar approach may be followed for the same purpose by other regional or local policymakers. The paper also emphasizes the necessity of introducing more stringent regulations on assurance and SDGs by the regulatory or professional bodies to achieve greater uniformity of behavior by firms.
The Influence of Urbanization and Environmental Factors on the Financial Performance of Retail and Automotive Industries in China
Authors: Mahmood Faisal , Al-Shattarat Wasim , Hamed Ruba , Al-Shattarat Basiem , Benameur Kameleddine , Hassanein Ahmed , Tahat Yasean
Published: November 2023

This study probes the intersection of urbanization, environmental degradation, and corporate performance in China’s retail and automotive sectors. Utilizing data from 23 regions spanning 2000–2022, the research elucidates the impacts of urban growth, infrastructure development, and particulate matter 2.5 pollution (PM2.5) on these industries. The study uses panel data from 2000 to 2022 and the corresponding analytical random effect model, along with pre- and post-estimation tests for the main results. The findings reveal that while urban population growth bolsters retail sales, it adversely affects the automotive industry. Urban infrastructure does not influence conventional sectors, yet it negatively impacts online retail sales. PM2.5 pollution depresses retail and automotive sales but boosts online sales, underscoring the rising environmental consciousness among consumers. The urban population significantly moderates the relationship between PM2.5 pollution and sales across sectors, hinting at unique consumption patterns in populated urban areas. This study provides critical insights for policymakers aiming for sustainable economic growth, and encourages further research with more region-specific factors and extended time frames.
Evolution of Green Vehicle Routing Problem: A Bibliometric and Visualized Review
Authors: Li Hui , Zhou Jian , Xu Kexin
Published: November 2023

The Green Vehicle Routing Problem (GVRP) has garnered considerable attention as a prominent subject within the field of green logistics. Scholars, organizations, and legislators have dedicated substantial efforts to comprehensively investigate and implement solutions for this problem. To thoroughly understand the research status in this field, this paper provides a bibliometric review of all-round GVRPs between 2000 and 2021 based on 1230 publications filtered from the Web of Science Core Collection with the help of CiteSpace V5.8.R3 and VOSviewer 1.6.13. The results of bibliometric features analyses indicate that GVRP has entered into a stage of prosperity in the past decade, and over 2500 researchers from 72 countries and regions have contributed to the development of this field. Furthermore, combining the keywords and co-citation analyses, we obtain nine subfields of GVRP, elaborate their study content evolution history, and accordingly apply the research potential evaluation model to envisage its future directions. The findings show that fuel consumption and electric vehicles are major research hotspots with the most optimistic prospects, and cold chain logistics, which has both high maturity and high recent attention, is the current mainstream of GVRP. The findings may provide guidance for future research in this field.
Bridging National Policies with Practical Rural Construction and Development: Research on a Decision Support System Based on Multi-Source Big Data and Integrated Algorithms
Authors: Jiao Yan , Cai Weiguang , Chen Mingman , Jia Ziyu , Du Tiantian
Published: November 2023

While national policies play a crucial role in shaping local development, effective governance is essential for rural revitalization. However, the successful implementation and impact of these policies in rural areas can vary due to unique local circumstances, limited information, and a lack of sophisticated decision making tools. Closing the divide between overarching national policies and practical rural development is an immediate necessity. This study begins by creating a comprehensive five-dimensional evaluation system encompassing industrial economy, public utilities, transportation and logistics, policy and institutions, and resources and the environment. It then summarizes four typical development modes—the suburban fusion mode, the characteristic industry-oriented mode, the humanistic and ecological resource-based mode, and the balanced development mode with less distinct characteristics—through an analysis of the Chinese government’s policy framework for rural construction. Subsequently, it introduces a decision support system for rural construction and development founded on multi-source heterogeneous big data and integrated algorithms. This system was tested using 782 townships as samples for classification, evaluation, and decision support. The results leverages insights into current rural development trends to efficiently align with national policies and provide customized implementation recommendations tailored to local resource characteristics. This contributes to the practical execution of rural revitalization strategies and the advancement of scientific rural decision making.
Ground-Level Particulate Matter (PM2.5) Concentration Mapping in the Central and South Zones of Peninsular Malaysia Using a Geostatistical Approach
Authors: Rusmili Siti Hasliza Ahmad , Mohamad Hamzah Firdaus , Choy Lam Kuok , Azizah R. , Sulistyorini Lilis , Yudhastuti Ririh , Chandraning Diyanah Khuliyah , Adriyani Retno , Latif Mohd Talib
Published: November 2023

Fine particulate matter is one of the atmospheric contaminants that exist in the atmosphere. The purpose of this study is to evaluate spatial–temporal changes in PM2.5 concentrations in the central and south zones of Peninsular Malaysia from 2019 to 2020. The study area involves twenty-one monitoring stations in the central and south zones of Peninsular Malaysia, using monthly and annual means of PM2.5 concentrations. The spatial autocorrelation of PM2.5 is calculated using Moran’s I, while three semi-variogram models are used to measure the spatial variability of PM2.5. Three kriging methods, ordinary kriging (OK), simple kriging (SK), and universal kriging (UK), were used for interpolation and comparison. The results showed that the Gaussian model was more appropriate for the central zone (MSE = 14.76) in 2019, while the stable model was more suitable in 2020 (MSE = 19.83). In addition, the stable model is more appropriate for both 2019 (MSE = 12.68) and 2020 (8.87) for the south zone. Based on the performance indicator, universal kriging was chosen as the best interpolation method in 2019 and 2020 for both the central and south zone. In conclusion, the findings provide a complete map of the variations in PM2.5 for two different zones, and show that interpolation methods such as universal kriging are beneficial and could be extended to the investigation of air pollution distributions in other areas of Peninsular Malaysia.
Social and Economic Impact Assessment of Coal Power Phase-Down at the Provincial Level: An Entropy-Based TOPSIS Approach
Authors: Zhao Changhong , Chen Jiaxuan , Yang Xiaowen , Yuan Jiahai
Published: November 2023

With the rollout of the carbon peak and neutrality targets, conventional coal-fired power plants will gradually be phased down in China’s power system in an orderly manner. The economic and social impact of the energy transition is a vital topic that requires scientific measurements and evaluation. In this paper, we establish a comprehensive approach to assess the impact of provincial coal power phase-down with 11 indicators covering dimensions of economy, society, and industry. An entropy-based Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) approach is adopted to calculate entropy weight, relative closeness, and other evaluation benchmark data. Then, the influence degree in 30 provinces is ranked based on the assessment. The results show that there is a significant regional imbalance in the process of coal power phase-down, among which Shanxi, Inner Mongolia, and other coal base provinces are the most vulnerable regions bound by their huge raw coal production and coal industry employment. Although the coal power industry is less affected than the coal industry, it will face pressure from the optimization of coal power units, followed by the dual impact of taxation and employment issues. Finally, the potential impacts of coal power phase-down and policy implications are proposed.
Co-Design Methods for Non-Standard Multi-Storey Timber Buildings
Authors: Orozco Luis , Krtschil Anna , Wagner Hans Jakob , Bechert Simon , Amtsberg Felix , Knippers Jan , Menges Achim
Published: November 2023

To meet climate change goals and respond to increased global urbanisation, the building industry needs to improve both its building technology and its design methods. Constrained urban environments and building stock extensions are challenges for standard timber construction. Co-design promises to better integrate disciplines and processes, promising smaller feedback loops for design iteration and building verification. This article describes the integrated design, fabrication, and construction processes of a timber building prototype as a case study for the application of co-design methods. Emphasis is placed on the development of design and engineering methods, fabrication and construction processes, and materials and building systems. The development of the building prototype builds on previous research in robotic fabrication (including prefabrication, task distribution, and augmented reality integration), agent-based modelling (ABM) for the design and optimisation of structural components, and the systematisation of timber buildings and their components. The results presented in this article include a functional example of co-design from which best practises may be extrapolated as part of an inductive approach to design research. The prototype, with its co-designed process and resultant flat ceilings, integrated services, wide spans, and design adaptability for irregular column locations, has the potential to expand the design potential of multi-storey timber buildings.
Integral Operators Applied to Classes of Convex and Close-to-Convex Meromorphic p-Valent Functions
Authors: Totoi Elisabeta-Alina , Cotirla Luminita-Ioana
Published: November 2023

We consider a newly introduced integral operator that depends on an analytic normalized function and generalizes many other previously studied operators. We find the necessary conditions that this operator has to meet in order to preserve convex meromorphic functions. We know that convexity has great impact in the industry, linear and non-linear programming problems, and optimization. Some lemmas and remarks helping us to obtain complex functions with positive real parts are also given.
The Prevalence, Seroprevalence, and Risk Factors of Tick-Borne Encephalitis Virus in Dogs in Lithuania, a Highly Endemic State
Authors: Simkute Evelina , Pautienius Arnoldas , Grigas Juozas , Urbute Paulina , Stankevicius Arunas
Published: November 2023

The rising awareness and increasing number of case reports of tick-borne encephalitis (TBE) in dogs indicate that the virus might be an important tick-borne pathogen in dogs, especially in endemic areas. Therefore, the aim of the present study was to investigate the prevalence rate of TBEV RNA and TBEV-specific antibodies in clinical samples of dogs living in a highly endemic region of Lithuania and to evaluate the main risk factors for severe disease course and death. The blood samples (n = 473) of dogs were collected in two veterinary clinics in central Lithuania. Tick-borne encephalitis virus (TBEV) RNA was detected in 18.6% (88/473; CI 95% 15.2–22.4) and TBEV-specific antibodies were found in 21.6% (102/473; CI 95% 17.9–25.6) of dog blood serum samples after confirmation with a virus neutralization test. The death/euthanasia rate was 18.2% (16/88; CI 95% 10.8–27.8) in PCR-positive dogs. Male dogs were more likely to develop neurological symptoms (p = 0.008). Older dogs (p = 0.003), dogs with the presence of neurological symptoms (p = 0.003), and dogs with the presence of TBEV-specific antibodies (p = 0.024) were more likely to experience worse outcomes of the disease. The results of the present study demonstrate that TBEV is a common and clinically important pathogen in dogs in such endemic countries as Lithuania.
Presence and Persistence of Andes Virus RNA in Human Semen
Authors: Züst Roland , Ackermann-Gäumann Rahel , Liechti Nicole , Siegrist Denise , Ryter Sarah , Portmann Jasmine , Lenz Nicole , Beuret Christian , Koller Roger , Staehelin Cornelia , Kuenzli Andrea B. , Marschall Jonas , Rothenberger Sylvia , Engler Olivier
Published: November 2023

When infecting humans, Andes orthohantavirus (ANDV) may cause a severe disease called hantavirus cardiopulmonary syndrome (HCPS). Following non-specific symptoms, the infection may progress to a syndrome of hemorrhagic fever combined with hyper-acute cardiopulmonary failure. The case fatality rate ranges between 25–40%, depending on the outbreak. In this study, we present the follow-up of a male patient who recovered from HCPS six years ago. We demonstrate that the ANDV genome persists within the reproductive tract for at least 71 months. Genome sequence analysis early and late after infection reveals a low number of mutations (two single nucleotide variants and one deletion), suggesting limited replication activity. We can exclude the integration of the viral genome into the host genome, since the treatment of the specimen with RNAse led to a loss of signal. We demonstrate a long-lasting, strong neutralizing antibody response using pseudovirions expressing the ANDV glycoprotein. Taken together, our results show that ANDV has the potential for sexual transmission.
Cardiomyopathy Does Not Exacerbate the Severity of Pneumonia Caused by a SARS-CoV-2 Delta Variant in the J2N-k Hamster Model
Authors: Iwatsuki-Horimoto Kiyoko , Ito Mutsumi , Okuda-Hamabata Moe , Takagi Hisayoshi , Imai Masaki , Kawaoka Yoshihiro
Published: November 2023

Cardiovascular disease is one of many risk factors that have been linked to increased severity or mortality in coronavirus disease 2019 (COVID-19) patients; however, the exact role of SARS-CoV-2 in the pathogenesis of cardiac inflammatory injury has not been established. A previous study reported that SARS-CoV-2 causes more severe disease with cardiomyopathy in a J2N-k animal model. Here, we investigated the sensitivity of J2N-k hamsters, as a cardiomyopathy animal model, to a delta strain of SARS-CoV-2 compared to J2N-n control animals. We found that J2N-k hamsters were less susceptible to this delta strain than J2N-n animals, and we found no evidence that cardiomyopathy is a risk factor in this animal model. Since the previous study reported that SARS-CoV-2 causes more severe disease with cardiomyopathy in the same animal model, further analysis of the relationship between cardiomyopathy and SARS-CoV-2 infection is needed.
Molecular Characterization of a Novel Enamovirus Infecting Raspberry
Authors: Koloniuk Igor , Fránová Jana , Přibylová Jaroslava , Sarkisova Tatiana , Špak Josef , Tan Jiunn Luh , Zemek Rostislav , Čmejla Radek , Rejlová Martina , Valentová Lucie , Sedlák Jiří , Holub Jan , Skalík Jan , Blystad Dag-Ragnar , Sapkota Bijaya , Hamborg Zhibo
Published: November 2023

Raspberry plants, valued for their fruits, are vulnerable to a range of viruses that adversely affect their yield and quality. Utilizing high-throughput sequencing (HTS), we identified a novel virus, tentatively named raspberry enamovirus 1 (RaEV1), in three distinct raspberry plants. This study provides a comprehensive characterization of RaEV1, focusing on its genomic structure, phylogeny, and possible transmission routes. Analysis of nearly complete genomes from 14 RaEV1 isolates highlighted regions of variance, particularly marked by indel events. The evidence from phylogenetic and sequence analyses supports the classification of RaEV1 as a distinct species within the Enamovirus genus. Among the 289 plant and 168 invertebrate samples analyzed, RaEV1 was detected in 10.4% and 0.4%, respectively. Most detections occurred in plants that were also infected with other common raspberry viruses. The virus was present in both commercial and wild raspberries, indicating the potential of wild plants to act as viral reservoirs. Experiments involving aphids as potential vectors demonstrated their ability to acquire RaEV1 but not to successfully transmit it to plants.
Correction: Rivas et al. Tendon-Derived Mesenchymal Stem Cells (TDSCs) as an In Vitro Model for Virological Studies in Wild Birds. Viruses 2023, 15, 1455
Authors: Rivas José , Dubois Axel , Blanquer Aude , Gérardy Mazarine , Ziegler Ute , Groschup Martin H. , Grobet Luc , Garigliany Mutien-Marie
Published: November 2023

In the original publication [...]
Electric Vehicle NiMH Battery State of Charge Estimation Using Artificial Neural Networks of Backpropagation and Radial Basis
Authors: Hernández Jordy Alexander , Fernández Efrén , Torres Hugo
Published: November 2023

The state of charge of a battery depends on many magnitudes, but only voltage and intensity are included in mathematical equations because other variables are complex to integrate into. The contribution of this work was to obtain a model to determine the state of charge with these complex variables. This method was developed considering four models, the multilayer feed-forward backpropagation models of two and three input variables used supervised training, with the variable-learning-rate backpropagation training function, five and seven neurons in the hidden layer, respectively, achieving an optimal training. Meanwhile, the radial basis neural network models of two and three input variables were trained with the hybrid method, the propagation constant with a value of 1 and 80 neurons in the hidden layer. As a result, the radial basis neural network with the variable-learning-rate training function, considering the discharge temperature, was the one with the best performance, with a correlation coefficient of 0.99182 and a confidence interval of 95% (0.98849; 0.99516). It is then concluded that artificial neural networks have high performance when modeling nonlinear systems, whose parameters are difficult to measure with time variation, so estimating them in formulas where they are omitted is no longer necessary, which means an accurate SOC.