bluuebunny/crossref_metadata_2025 huggingface dataset:
An Improved Low-Cost Dual-Antenna GNSS Dynamic Attitude Determination Method in Complex Environments
Authors: Xu Kan , Tao Jun , Lei Xiaoting , Zhang Jie , Liu Chengyi , Chen Liang
Published: October 2024

GNSS attitude determination has been widely adopted due to its high efficiency, absence of cumulative errors, and ease of installation. However, practical navigation and attitude determination systems often rely on low-cost receivers that struggle with substantial multipath effects, frequent cycle slips, and satellite signal loss, significantly impairing attitude determination accuracy in challenging urban environments. To address this issue, this contribution proposes a constrained dynamic prediction model (C-Dynamics), which enables more accurate initial coordinates and thereby increases the effectiveness of the constrained LAMBDA (CLAMBDA) technique. To evaluate the practical performance of C-Dynamics, two sets of real-world data collected from a vehicle platform were analyzed. The results demonstrate that C-Dynamics significantly enhances the accuracy of initial coordinate estimations across various environments. Compared with the lambda method, the CLAMBDA method + C-Dynamics method (CLAMBDA+CD) improves the fixing rate in the urban environment by 5.6%, and the accuracy of the heading angle, pitch angle, and baseline length improved by 66%, 70.9%, and 84.2%, respectively. Moreover, in challenging high obstruction environments, the fixing rate increased by 43.5%, while the accuracy of heading angle, pitch angle, and baseline length improved by 76.4%, 69.2%, and 94%, respectively. The proposed algorithm effectively addresses the low fixing rate and insufficient accuracy of the LAMBDA method in high obstruction environments and holds practical value for widespread adoption in the mass market.
A Base-Map-Guided Global Localization Solution for Heterogeneous Robots Using a Co-View Context Descriptor
Authors: Duan Xuzhe , Wu Meng , Xiong Chao , Hu Qingwu , Zhao Pengcheng
Published: October 2024

With the continuous advancement of autonomous driving technology, an increasing number of high-definition (HD) maps have been generated and stored in geospatial databases. These HD maps can provide strong localization support for mobile robots equipped with light detection and ranging (LiDAR) sensors. However, the global localization of heterogeneous robots under complex environments remains challenging. Most of the existing point cloud global localization methods perform poorly due to the different perspective views of heterogeneous robots. Leveraging existing HD maps, this paper proposes a base-map-guided heterogeneous robots localization solution. A novel co-view context descriptor with rotational invariance is developed to represent the characteristics of heterogeneous point clouds in a unified manner. The pre-set base map is divided into virtual scans, each of which generates a candidate co-view context descriptor. These descriptors are assigned to robots before operations. By matching the query co-view context descriptors of a working robot with the assigned candidate descriptors, the coarse localization is achieved. Finally, the refined localization is done through point cloud registration. The proposed solution can be applied to both single-robot and multi-robot global localization scenarios, especially when communication is impaired. The heterogeneous datasets used for the experiments cover both indoor and outdoor scenarios, utilizing various scanning modes. The average rotation and translation errors are within 1° and 0.30 m, indicating the proposed solution can provide reliable localization support despite communication failures, even across heterogeneous robots.
A Hierarchical Coarse–Fine Adaptive Fusion Network for the Joint Classification of Hyperspectral and LiDAR Data
Authors: Pan Haizhu , Li Xuan , Ge Haimiao , Wang Liguo , Shi Cuiping
Published: October 2024

Hyperspectral image (HSI) and light detection and ranging (LiDAR) data have gained significant attention due to their excellent complementarity, which can be combined to improve the accuracy of land cover classification. However, there are still many challenges, such as unrelated physical characteristics, different data structures, and a lack of labeled samples. Many methods fail to leverage the full potential of multi-source data, particularly hierarchical complementary information. To address these problems, a hierarchical coarse–fine adaptive (HCFA) fusion network with dynamic convolution and a transformer is proposed for multi-source remote sensing land cover classification. Although fusing hierarchical information can improve the classification accuracy of the model, improper hierarchical feature selection and optimization may negatively affect the classification results. Therefore, a coarse–fine mutual learning strategy is proposed to dynamically fuse hierarchical information. Additionally, the disparity between multi-source data continues to prevent the realization of effective fusion. To tackle this challenge, cross-tokenization and cross-token attention are implemented to enhance information interaction. Furthermore, to improve the model representation with limited computational cost, we combine the advantages of dynamic convolution with a transformer. Validation on three standard datasets demonstrates that HCFA achieves high accuracy with just 1% of the training set while maintaining low computational costs.
Infrared Small Target Detection Based on Weighted Improved Double Local Contrast Measure
Authors: Wang Han , Hu Yong , Wang Yang , Cheng Long , Gong Cailan , Huang Shuo , Zheng Fuqiang
Published: October 2024

The robust detection of infrared small targets plays an important role in infrared early warning systems. However, the high-brightness interference present in the background makes it challenging. To solve this problem, we propose a weighted improved double local contrast measure (WIDLCM) algorithm in this paper. Firstly, we utilize a fixed-scale three-layer window to compute the double neighborhood gray difference to screen candidate target pixels and estimate the target size. Then, according to the size information of each candidate target pixel, an improved double local contrast measure (IDLCM) based on the gray difference is designed to enhance the target and suppress the background. Next, considering the structural characteristics of the target edge, we propose the variance-based weighting coefficient to eliminate clutter further. Finally, the targets are detected by an adaptive threshold. Extensive experimental results demonstrate that our method outperforms several state-of-the-art methods.
Efficiency of Optimized Approaches for Gravity Operator Modeling
Authors: Fuseau David , Seoane Lucia , Ramillien Guillaume , Darrozes José , Plazolles Bastien , Rouxel Didier , Schmitt Thierry , Salaün Corinne
Published: October 2024

Numerical tesseroid and radial-type approaches are presented and compared in terms of their efficiency for deriving the regional geoid height, vertical gravity, and gradiometric anomalies from sea floor topography grids. The vertical gradient function is particularly suitable for representing shorter wavelengths of gravity, typically less than 10 km. These two modeling methods were applied to the Great Meteor guyot in the Atlantic Ocean using its bathymetry. To optimize the computation of high-resolution gravity anomalies, the Armadillo, GSL, and OpenMP libraries were used to provide an environment for fast vector implementation, numerical integration for tesseroid calculation, and parallelization for loop iterations, resulting in a computation speed increase. The tesseroid and radial methods remain equivalent up to a resolution of about 1 min, with the radial method being faster when dealing with a large number of model points for the geoid. Aside from optimization enabling high-resolution gravity simulations, these fast modeling data can be used as the main operators in gravimetric inversion or to reduce the terrain effects in gravity observations, revealing gravity and sedimentary layers.
Linearity Assessment from the Plankton, Aerosol, Cloud, and Ocean Ecosystem Ocean Color Instrument Pre-Launch Testing and On-Orbit Operations
Authors: McIntire Jeff , Meister Gerhard , Barsi Julia A. , Gliese Ulrik , Jepsen Kim , Kitchen-McKinley Samuel , Lee Shihyan
Published: October 2024

Satellite observations have become increasingly important in scientific studies of the Earth’s climate, especially for oceanographic science. A next generation sensor known as the Ocean Color Instrument (OCI) was launched in February 2024 onboard the Plankton, Aerosol, Cloud, and ocean Ecosystem (PACE) platform, and will extend the data set of on-orbit scientific observations used for ocean and atmospheric science research. Delivering high quality data from a space-borne sensor requires the instrument to be well calibrated; while much of the calibration can be performed on-orbit, some aspects of the calibration must be measured or the mechanisms verified pre-launch. One aspect of the OCI calibration that is novel to its design (for a space-based sensor) is its ability to perform linearity measurements on orbit. When viewing the Sun reflected off a dim diffuser with ∼2–3% reflectance, the sensor is capable of recording successive pixels with increasing integration times. The result is a series of light levels measured from the same source in a given scan line. These measurements are then used to assess the linearity. During the pre-launch test campaign, this mechanism was verified using a known source in the Earth view portion of the scan. Results from these tests form a baseline that was verified once on-orbit (and the calibration may be replaced if the linearity is shown to change). The linearity measured both prior to launch and post-launch will be assessed in this work.
Background-Aware Cross-Attention Multiscale Fusion for Multispectral Object Detection
Authors: Guo Runze , Guo Xiaojun , Sun Xiaoyong , Zhou Peida , Sun Bei , Su Shaojing
Published: October 2024

Limited by the imaging capabilities of sensors, research based on single modality is difficult to cope with faults and dynamic perturbations in detection. Effective multispectral object detection, which can achieve better detection accuracy by fusing visual information from different modalities, has attracted widespread attention. However, most of the existing methods adopt simple fusion mechanisms, which fail to utilize the complementary information between modalities while lacking the guidance of a priori knowledge. To address the above issues, we propose a novel background-aware cross-attention multiscale fusion network (BA-CAMF Net) to achieve adaptive fusion in visible and infrared images. First, a background-aware module is designed to calculate the light and contrast to guide the fusion. Then, a cross-attention multiscale fusion module is put forward to enhance inter-modality complement features and intra-modality intrinsic features. Finally, multiscale feature maps from different modalities are fused according to background-aware weights. Experimental results on LLVIP, FLIR, and VEDAI indicate that the proposed BA-CAMF Net achieves higher detection accuracy than the current State-of-the-Art multispectral detectors.
Speed of Light in Hollow-Core Photonic Bandgap Fiber Approaching That in Vacuum
Authors: Cao Xiaolu , Luo Mingming , Liu Jianfei , Ma Jie , Hao Yundong , Liu Yange
Published: October 2024

A Fresnel mirror is introduced at a hollow-core photonic bandgap fiber end by fusion splicing a short single-mode fiber segment, to reflect the light backward to an optical frequency domain reflectometry. The backward Fresnel reflection is used as a probe light to achieve light speed measurement with a high resolution and a high signal-to-noise ratio. Thus, its group velocity is obtained with the round-trip time delay as well as the beat frequency at the reflection peak. Multiple Fresnel peaks are observed from 2180.00 Hz to 13,988.75 Hz, corresponding to fusion-spliced hollow-core fiber segments with different lengths from 0.2595 m to 1.6678 m, respectively. The speed of light in the air guidance is calculated at 2.9753 × 108 m/s, approaching that in vacuum, which is also in good agreement with 2.9672 × 108 m/s given by the numerical analysis with an uncertainty of 10−3. Our demonstration promises a key to hollow-core waveguide characterization for future wide-bandwidth and low-latency optical communication.
MS-YOLO: A Lightweight and High-Precision YOLO Model for Drowning Detection
Authors: Song Qi , Yao Bodan , Xue Yunlong , Ji Shude
Published: October 2024

A novel detection model, MS-YOLO, is developed in this paper to improve the efficiency of drowning rescue operations. The model is lightweight, high in precision, and applicable for intelligent hardware platforms. Firstly, the MD-C2F structure is built to capture the subtle movements and posture changes in various aquatic environments, with a light weight achieved by introducing dynamic convolution (DcConv). To make the model perform better in small object detection, the EMA mechanism is incorporated into the MD-C2F. Secondly, the MSI-SPPF module is constructed to improve the performance in identifying the features of different scales and the understanding of complex backgrounds. Finally, the ConCat single-channel fusion is replaced by BiFPN weighted channel fusion to retain more feature information and remove the irrelevant information in drowning features. Relative to the Faster R-CNN, SSD, YOLOv6, YOLOv9, and YOLOv10, the MS-YOLO achieves an average accuracy of 86.4% in detection on a self-built dataset at an ultra-low computational cost of 7.3 GFLOPs.
Development of an Angular Stiffness Sensor to Measure Dental Implant Stability In Vitro
Authors: Xu Weiwei , Chen Yen-Wei , Nagatomo Kanako , Liu Yifeng , Zhou Jihai , Dard Michel , Shen I. Y.
Published: October 2024

This investigation aims to develop an angular stiffness sensor intended for measuring dental implant stability in bone. The sensor hardware included a tiny eccentric motor and an accelerometer to measure a flex constant of an implant with its abutment. The sensor software included a mechanics-based model to convert the flex constant to angular stiffness at the implant/abutment junction to indicate the stability. The sensor’s accuracy and effectiveness are demonstrated through use of Sawbones slab models that mimic a mandibular premolar section. The models include a Branemark Mk III implant inserted into Sawbones slabs of 5 different densities with a locator abutment. An incremental insertion torque was first recorded while the implant was placed in the Sawbones models. Then benchtop experiments were conducted to measure resonance frequencies and angular stiffness. Results indicated that angular stiffness increased with Sawbones density, showing high correlation with the measured resonance frequency (R=0.977) and the incremental insertion torque (R=0.959). Finally, accuracy of the angular stiffness sensor is calibrated in light of the resonance frequency. Angular stiffness scores 99% and 95% accuracy for Sawbones models mimicking medium cancellous bones with and without a cortical layer, respectively.
Analysis of Field of View for a Moon-Based Earth Observation Multispectral Camera
Authors: Yu Zhitong , Ye Hanlin , Zhou Mengxiong , Li Feifei , Jin Yin , Li Chunlai , Liu Guang , Guo Huadong
Published: October 2024

A Moon-based Earth observation multispectral camera provides a unique perspective for observing large-scale Earth phenomena. This study focuses on the analysis of the field of view (FOV) for such a sensor. Unlike space-borne sensors, the analysis of the FOV for a Moon-based sensor takes into account not only Earth’s maximum apparent diameter as seen from the lunar surface but also the Earth’s and the solar trajectory in the lunar sky, as well as the pointing accuracy and pointing adjustment temporal intervals of the turntable. Three critical issues are analyzed: (1) The relationship between the Earth’s apparent diameter and the Earth’s phase angle is revealed. It is found that the Earth’s maximum apparent diameter encompasses the Earth’s full phase, suggesting the FOV should exceed this maximum. (2) Regardless of the location on the lunar surface, a sensor will suffer from solar intrusion every orbital period. Although the Earth’s trajectory forms an envelope during an 18.6-year cycle, the FOV should not be excessively large. (3) To design a reasonable FOV, it is necessary to consider both the pointing accuracy and pointing adjustment temporal interval comprehensively. All these insights will guide future Moon-based Earth observation multispectral camera design.
GPS-Based Hidden Markov Models to Document Pastoral Mobility in the Sahel
Authors: Scriban Arthur , Nabeneza Serge , Cornelis Daniel , Delay Etienne , Vayssières Jonathan , Cesaro Jean-Daniel , Salgado Paulo
Published: October 2024

In agrarian systems where animal mobility is crucial for feed management, nutrient cycles and household economy, there is a notable lack of precise data on livestock mobility and herding practices. We introduce a methodology leveraging GPS-based behavioural models to analyse and document pastoral mobility in the Sahel. Over 2.5 years, we conducted a continuous collection of GPS data from transhumant and resident cattle herds in the Senegalese agropastoral semiarid rangelands. We developed a Hidden Markov Model robustly fitted to these data to classify recordings into three states of activity: resting (47% overall), foraging (37%) and travelling (16%). We detail our process for selecting the states and testing data subsets to guide future similar endeavours. The model describes state changes and how temperature affects them. By combining the resulting dataset with satellite-based land-use data, we show the distribution of activities across landscapes and seasons and within a day. We accurately reproduced key aspects of cattle mobility and characterised rarely documented features of Sahel agropastoral practices, such as transhumance phases, nocturnal grazing and in-field rainy season paddocking. These results suggest that our methodology, which we make available, could be valuable in addressing issues related to the future of Sahelian pastoralism.
Decoding Brain Signals from Rapid-Event EEG for Visual Analysis Using Deep Learning
Authors: Rehman Madiha , Anwer Humaira , Garay Helena , Alemany-Iturriaga Josep , Díez Isabel De la Torre , Siddiqui Hafeez ur Rehman , Ullah Saleem
Published: October 2024

The perception and recognition of objects around us empower environmental interaction. Harnessing the brain’s signals to achieve this objective has consistently posed difficulties. Researchers are exploring whether the poor accuracy in this field is a result of the design of the temporal stimulation (block versus rapid event) or the inherent complexity of electroencephalogram (EEG) signals. Decoding perceptive signal responses in subjects has become increasingly complex due to high noise levels and the complex nature of brain activities. EEG signals have high temporal resolution and are non-stationary signals, i.e., their mean and variance vary overtime. This study aims to develop a deep learning model for the decoding of subjects’ responses to rapid-event visual stimuli and highlights the major factors that contribute to low accuracy in the EEG visual classification task.The proposed multi-class, multi-channel model integrates feature fusion to handle complex, non-stationary signals. This model is applied to the largest publicly available EEG dataset for visual classification consisting of 40 object classes, with 1000 images in each class. Contemporary state-of-the-art studies in this area investigating a large number of object classes have achieved a maximum accuracy of 17.6%. In contrast, our approach, which integrates Multi-Class, Multi-Channel Feature Fusion (MCCFF), achieves a classification accuracy of 33.17% for 40 classes. These results demonstrate the potential of EEG signals in advancing EEG visual classification and offering potential for future applications in visual machine models.
Application of Electroencephalography Sensors and Artificial Intelligence in Automated Language Teaching
Authors: Chen Yanlin , Wang Wuxiong , Yan Shen , Wang Yiming , Zheng Xinran , Lv Chunli
Published: October 2024

This study developed an automated language learning teaching assessment system based on electroencephalography (EEG) and differential language large models (LLMs), aimed at enhancing language instruction effectiveness by monitoring learners’ cognitive states in real time and personalizing teaching content accordingly. Through detailed experimental design, the paper validated the system’s application in various teaching tasks. The results indicate that the system exhibited high precision, recall, and accuracy in teaching effectiveness tests. Specifically, the method integrating differential LLMs with the EEG fusion module achieved a precision of 0.96, recall of 0.95, accuracy of 0.96, and an F1-score of 0.95, outperforming other automated teaching models. Additionally, ablation experiments further confirmed the critical role of the EEG fusion module in enhancing teaching quality and accuracy, providing valuable data support and theoretical basis for future improvements in teaching methods and system design.
Optimizing DG Handling: Designing an Immersive MRsafe Training Program
Authors: Li Chi Ho , Chow Elle Wing Ho , Tam Manviel , Tong Pak Ho
Published: October 2024

The rapid growth of e-commerce has significantly increased demands on logistics providers, particularly in the areas of product handling and shipment. One of the most challenging and critical aspects of this process is the handling of dangerous goods (DG). This is a complex and time-intensive operation that requires safety measures and proper packaging and labelling, because mishandling DG can lead to severe injuries, property loss, and legal liability. This puts additional pressure on logistics providers to efficiently manage DG while maintaining speed and safety in the context of e-commerce. To meet this challenge, there is an urgent need to advance training programs and enhance the operational efficiency for DG handling. The use of mixed reality (MR) technology offers a promising solution. By seamlessly integrating virtual elements with real-world environments, MR has the potential to greatly improve the effectiveness and efficiency of the training of DG handling. Earlier research has examined MR in various fields, while there is still a research gap in applying MR specifically to the training of DG handling. This paper seeks to address the current research gap by presenting a novel MR model, named “MRSafe,” for a training program on the safe handling of DG. The model offers users virtual experiences and comprehensive guidance to provide operational decision support.
Study on an Integrated Water Treatment System by Simultaneously Coupling Granular Activated Carbon (GAC) and Powdered Carbon with Ultrafiltration
Authors: Wang Yi , Yu Sijia , Cai Weiwei
Published: October 2024

The process of using powdered activated carbon (PAC) in conjunction with ultrafiltration (UF) has been widely adopted for the treatment of various types of water and wastewater. However, during the application of this integrated PAC-UF process, PAC tends to adhere significantly to the surface of the UF membrane, which exacerbates membrane fouling. To tackle this issue, this study proposed an innovative water treatment approach that simultaneously integrated granular activated carbon (GAC) and PAC/biochar with UF. In this setup, PAC/biochar was intended to enhance water quality, while the fluidized GAC particles were aimed at reducing membrane fouling and the deposition of PAC/biochar on the membrane surface. We systematically analyzed the operational performance of the integrated systems concerning fouling formation, PAC/biochar attachment, effluent quality, and foulant components. The results indicate that both PAC and biochar effectively improved effluent quality in terms of chemical oxygen demand (COD) and hardness, although they significantly deposited on the membrane surface during operation. Notably, PAC was more prone to attach to the membrane than biochar, and the fouling in biochar-UF systems was primarily attributed to the attachment of organic foulants rather than biochar itself. By combining with GAC, up to 46.01% of membrane fouling and 96.11% of PAC/biochar attachment were mitigated due to the strong mechanical action of the fluidized GAC particles. Importantly, the inclusion of fluidized GAC did not significantly affect effluent quality. Consequently, the GAC-PAC/biochar systems proposed in this study demonstrated dual benefits of improving effluent quality and ensuring stable operation, thereby providing a viable solution for efficient and sustainable water treatment.
Lowland Integrated Crop–Livestock Systems with Grass Crops Increases Pore Connectivity and Permeability, Without Requiring Soil Tillage
Authors: Ambus Jordano Vaz , Alves Amanda Romeiro , Scheid Douglas Leandro , Antonino Antonio Celso Dantas , Reichert José Miguel
Published: October 2024

Enhancing integrated crop–livestock systems (ICLSs) to improve land-use efficiency is a critical goal. Understanding the ICLS impacts on lowland soils is key to sustainable agricultural practices. Our objective was to test whether adopting ICLSs in lowlands improves soil structure, pore connectivity, and water and air permeability. This study was conducted in a long-term field trial, consisting of the following production systems with flood-irrigation rice: rice–fallow–rice, under conventional tillage and absence of grazing (RFR-ct); rice-grazed ryegrass–rice, under no-tillage and grazing (RGrR-nt); rice-grazed ryegrass–soybean-grazed ryegrass–rice, under no-tillage and grazing (RGrS/RGrR-nt); and a grazed pasture-consortium (winter) and succession field (summer), with no-till rice every 4 years (P4R-nt). Core samples were collected after grazing (October 2018), harvesting (March 2019), and grazing (October 2019). We analyzed soil air permeability, saturated hydraulic conductivity, pore connectivity by computed tomography. Soil tillage in a semi-direct system generated discontinuous porosity. Systems with intense trampling or less surface protection are affected by shearing on topsoil, reducing pore continuity. ICLSs are mainly composed of ryegrass–rice mitigated the harmful effects of trampling, and improved soil structure and functioning. Systems without soil tillage exhibited higher pore connectivity and pores with vertical orientation. Finally, soil tillage is not required to improve structural quality in ICLSs.
Influence of Technical Reasons on Cost Overruns of Infrastructural Projects: A Sustainable Development Perspective
Authors: Lovrinčević Marijo , Vukomanović Mladen , Perić Romano
Published: October 2024

Cost overruns are a significant issue in infrastructure projects, adversely affecting not only economic performance but also sustainability goals by straining resources and extending project timelines. There is consensus among researchers about the persistence of cost overruns and the importance of accurate estimates. However, there are significant differences in the explanation of the reasons for the overruns. While we recognize the importance of understanding the reasons for cost overruns at all stages of project development, we have focused on the reasons for cost overruns after contract award due to the rarely available real and valid data collected at the source of the information. We analyze the deviations between actual and contracted costs, as well as the dependence of cost deviations of individual project types on the size of the contract. For example, the size of the tunnel significantly affects relative cost deviations, while for standard viaducts or road sections this effect is minimal. The results confirmed that cost overruns are less frequent in standard facilities than in those where the experience factor has little influence on the final cost estimate. For example, deviations in the average value of the contracted costs for highway sections range between −12.1% and 27.9% of the contracted value, and for standardized viaduct projects they range between −16.73% and 6.27% of the contracted value. The type of distribution function of deviations between actual and contractually agreed costs was investigated, and a predictive model for estimating future cost deviations of project programs was presented. The goal is to improve economic sustainability and the related optimization of resources in the development of infrastructure, which contributes to the broader agenda of sustainable development. The results encourage the adoption of improved project documentation and forecasting tools, which are essential for sustainable project management.
Experimental and Statistical Analysis of Iron Powder for Green Heat Production
Authors: Sohrabi Mohammadmahdi , Ghobadian Barat , Najafi Gholamhassan , Prasidha Willie , Baigmohammadi Mohammadreza , de Goey Philip
Published: October 2024

In the current investigation, a novel methodology was employed to assess iron powder as a recyclable and sustainable energy carrier. Concurrently, an examination of the modeling of iron powder ignition and the ensuing heat output from the burner was undertaken. The flame temperature was determined by examining the light intensity emitted by the particles as they melted, which is directly related to the particle’s cross-sectional area. An account of the characterization of the experimental procedure, validation, and calibration is presented. Through measurements, distinct one-to-one correlations have been established between the scales of flame combustion and the temperatures of particles of varying sizes of iron. Additionally, a theoretical model for the combustion of expanding particles, particularly iron, within the diffusion-limited regime has been rigorously developed. This model delves into the spectra acquired from particle flames within the burner, utilizing Partial Least Squares Regression (PLSR) and Principal Component Analysis (PCA). This study investigates the use of optical fiber spectroscopy to predict flame temperature and assess iron powder size. The aim was to investigate how different sizes of iron powder affect flame temperature and to create calibration models for non-destructive prediction. The study shows that smaller particles had an average temperature of 1381 °C while larger particles reach up to 1842 °C, demonstrating the significant impact of particle size on combustion efficiency. The results were confirmed using advanced statistical methods, including PLSR and PCA, with PCA effectively differentiating between particle sizes and PLSR achieving an R2 value of 0.90 for the 30 µm particles.
Involving Rural Youth in Agroecological Nature-Positive Farming and Culinary Agri-Ecotourism for Sustainable Development: The Indian Scenario
Authors: Rana Jai Chand , Bisht Ishwari Singh , Mathur Prem , Fadda Carlo , Mittra Sarika , Ahlawat Sudhir Pal , Vishwakarma Harinder , Yadav Rashmi
Published: October 2024

Industrial food systems produce most of the food that we consume globally today. But many observers acknowledge that industrial agriculture cannot be sustained in the long run. As such, agroecological practices that encourage sustainable farming practices with a focus on local markets and the local economy are being promoted more and more to change the way food systems work. The current research examines how rural youth can best be involved in food system transformation as a potential agent of change. The research considers the potential of payments for environmental services (PESs) in the study area. The economic potential of gastronomic agri-ecotourism in various traditional agricultural landscapes of different agroecosystems of India has also been investigated. Gastronomic agri-ecotourism has the potential to provide economic benefits to traditional agricultural landscapes and agroecosystems while also promoting sustainable tourism practices. The results of an exploratory study on rural youth participation in nature-friendly agroecological regenerative farming and culinary agri-ecotourism initiatives are presented in this paper in an effort to increase the sustainability of conventional farming and food systems. This study, which was carried out in four different agroecological settings in India, sheds light on the opportunities and problems faced by rural youth, as well as their motivations.
Investigating Factors Influencing the Selection of Micro-Mobility in a Tourist City: Focus on Jeju City
Authors: Kang Hyunmin , Yim Heejung , Kim Seoyoung , Lee Okkeun , Kim Hyochang
Published: October 2024

This study provides an analysis of micro-mobility use in Jeju City, focusing on how individual choices between e-bikes, e-scooters, and e-motorcycles are influenced by a combination of personal, environmental, and contextual factors. Drawing on data from a local micro-mobility provider from April to December 2022, the research employs multinomial logistic regression to examine the impact of various determinants on the selection of micro-mobility options. The results reveal unique findings, with significant variations in mode choice correlating with gender, age, and region. Also, usage time of micro-mobility emerged as a crucial determinant, suggesting a relationship between trip length and selected mobility. Additionally, environmental variables, particularly temperature, were found to substantially impact users’ choices, reflecting the sensitivity of micro-mobility demand to weather fluctuations. Insights from this study suggest the importance of integrating responsive service design for micro-mobility that addresses the multifaceted needs of users in tourist cities, emphasizing environmental considerations.
Characteristics of Climate Change in Poyang Lake Basin and Its Impact on Net Primary Productivity
Authors: Yang Yang , Wu Chenchen , An Tongrui , Yue Tianxiang
Published: October 2024

Climate change exerts substantial impacts on human society and the carbon cycle of terrestrial ecosystems. Studying the spatiotemporal characteristics of regional climate change and its impact on carbon sequestration is an important topic in ecology and environmental science. This study utilized meteorological and land use/cover data to explore these dynamics. Statistical methods such as the Mann–Kendall (M-K) test and wavelet analysis were used to simulate the changes in annual average temperature and precipitation in the Poyang Lake Basin from 1980 to 2020. The Carnegie–Ames–Stanford Approach (CASA) model was used to estimate the interannual variation in net primary productivity (NPP) in the region over the past 40 years. Additionally, the present study examined the influence of various factors on NPP changes. The main results are as follows: (1) Over the past four decades, the average temperature in the Poyang Lake Basin was 17.85 °C, while the average precipitation was 1621.35 mm. The average annual temperature rises at a rate of 0.27 °C per decade. (2) A significant shift in the average annual temperature occurred in the early 21st century, and annual precipitation exhibited multiple abrupt changes during the mid-to-late 1990s. Both temperature and precipitation changes follow a 25-year cycle, with temperature hotspots located in the south and precipitation hotspots in the northeast. (3) The impact of climate change on the change in NPP in the Poyang Lake Basin is about 70%, with the annual average temperature having a significant effect on the increase in NPP. This study can provide a scientific foundation for formulating policies aimed at mitigating climate-related disasters and enhancing carbon cycling in terrestrial ecosystems.
Eco-Friendly Tannic Acid-Based Concrete Coating with Anti-Chloride Performance via One-Step Assembly
Authors: Xiao Zhong , Chang Zhe , Liu Ying , Ma Yichao , Wei Fei , Xiao Di
Published: October 2024

Chloride ion erosion in seawater is a major cause of durability damage to reinforced concrete structures. Most of the currently used anti-corrosion coatings are organic polymer coatings, which are prone to aging and peeling off and polluting the environment. Inspired by the underwater adhesion behavior of mussels, a green substance-tannic acid (TA) is found and used as the main material of anti-chloride coatings. Three assembly methods of green concrete chloride-resistant coatings fabricated by the oxidative self-polymerization of tannic acid, coordination-driven one-step assembly and multistep assembly of tannic acid (TA), and trivalent iron cation (Fe(III)) on a concrete surface are proposed. Compared to the other two assembly methods and existing coatings, the one-step assembly of the TA and Fe(III) coating was recommended to be the first choice because of its good continuity; shortest time-consumption (just 10 min); lowest price (only one-third of epoxy coating); and the best chloride-resistant effectiveness per unit thickness reaching 52.17%, far better the multistep assembly method and the oxidative self-polymerization method by 12.67% and 2.42%, which is 79-times higher than that of epoxy resin A. This study offers a TA-based concrete coating fabricated by the one-step assembly method with an excellent anti-chloride performance and cheap price, which is promising for a wide range of applications for the chloride-resistant corrosion protection of steel-reinforced concrete in seawater environments.
Application of Building Information Modelling in Construction and Demolition Waste Management: Systematic Review and Future Trends Supported by a Conceptual Framework
Authors: Lins Eduardo José Melo , Palha Rachel Perez , Sobral Maria do Carmo Martins , Araújo Adolpho Guido de , Marques Érika Alves Tavares
Published: October 2024

The architecture, engineering, construction, and operations industry faces an urgent need to enhance construction and demolition waste management in urban areas, driven by increasing demolition and construction activities and a desire to align with sustainable practices and the circular economy principles. To address this need, a systematic literature review on the building information modelling methodology was conducted, employing a structured protocol and specific tools for the analysis of academic studies, based on PRISMA guidelines and StArt software (version 3.4 BETA). Ninety relevant studies published between 1998 and 2024, were analysed and selected from the Web of Science, Scopus, and Engineering Village databases. Findings indicate that China leads in publications with 34%, followed by Brazil (8%) and the United Kingdom (7%). The analysis emphasises the use of drones and LiDAR scanners for precise spatial data, processed by 3D reconstruction tools like Pix4D and FARO As-Built. Revit excels in 3D modelling, providing a robust platform for visualisation and analysis. Visual programming tools such as Dynamo automate processes and optimise material reuse. The study presents a conceptual framework that integrates these technologies with the principles of the circular economy, clarifying the interactions and practical applications that promote the sustainable management of demolition waste from urban buildings and process efficiency. Although the approach promotes material reuse and sustainability, it still faces barriers such as the need for waste segregation at the source, the adaptation of innovative technologies, like the iPhone 15 Pro LiDAR and thermal cameras, as well as associated costs. These factors may limit its adoption in larger-scale projects, particularly due to the increased complexity of buildings.
The Matching Relationship Between the Distribution Characteristics of High-Grade Tourist Attractions and Spatial Vitality in Xinjiang
Authors: Zikirya Bahram , Xing Yueqing , Zhou Chunshan
Published: October 2024

The development of the tourism industry serves as a crucial pathway for guiding urban spatial vitality, making the study of the matching relationship between the spatial distribution characteristics of tourist attractions and regional spatial vitality particularly important for the advancement of the tourism sector. This study combines Amap POI data and Weibo sign-in data, employing various quantitative methods, including Kernel Density Estimation (KDE), Hotspot Analysis (Getis-Ord Gi*), and the Geographically Weighted Regression (GWR) model, to thoroughly explore the distribution characteristics of different grades of tourist attractions in Xinjiang and their matching relationship with spatial vitality. The findings indicate that AAAAA attractions are primarily concentrated in Urumqi and its surrounding areas, where spatial vitality highly matches the distribution of attractions. The distribution of AAAA attractions shows regional differences, exhibiting higher matching degrees in certain areas of southern and western Xinjiang, while some regions in northern Xinjiang demonstrate lower matching degrees. Conversely, AAA attractions are more widely distributed in remote areas, where the matching between vitality and attraction distribution is low, particularly in southern and eastern Xinjiang, revealing a notable mismatch between tourism resources and spatial vitality. By analyzing the matching relationship between tourism resources and spatial vitality, this study provides a scientific basis for optimizing the allocation of tourism resources in Xinjiang and enhancing regional tourism spatial vitality. Additionally, this study also offers valuable insights for tourism managers and planners to formulate more precise tourism development policies.
Prediction of Energy-Related Carbon Emissions in East China Using a Spatial Reverse-Accumulation Discrete Grey Model
Authors: Wang Shubei , Yuan Xiaoling , Jin Zhongguo
Published: October 2024

In order to better analyze and predict energy-related carbon emissions in East China to address climate change, this paper enhances the predictive capabilities of grey models in spatial joint prediction by creating the reverse-accumulation spatial discrete grey model RSDGM (1,1,m) and accumulation spatial discrete grey breakpoint model RSDGBM (1,1,m,t), which took the impact of system shocks into consideration. The efficiency of the models is confirmed by calculating the energy-related carbon emissions in East China from 2010 to 2022. Future emissions are predicted, and the spatial spillover effect of emissions in East China is discussed. The conclusions are as follows: (1) The RSDGM (1,1,m) theoretically avoids errors in background values and parameter calculations, reducing computational complexity. Empirically, the model exhibits high performance and reflects the priority of new information in spatial joint analysis. (2) The RSDGBM (1,1,m,t) captures the impact of shocks on system development, improving the reliability of carbon emissions prediction. (3) Jiangsu and Shandong are positively affected by spatial factors in terms of carbon emissions, while Shanghai and Zhejiang are negatively affected. (4) It is estimated that carbon emissions in East China will increase by approximately 23.8% in 2030 compared to the level in 2022, with the levels in Zhejiang and Fujian expected to increase by 45.2% and 39.7%, respectively; additionally, the level in Shanghai is projected to decrease. Overall, East China still faces significant pressure to reduce emissions.
Correlation of Sarcopenia with Coronary Artery Disease Severity and Pericoronary Adipose Tissue Attenuation: A Coronary CT Study
Authors: Albano Domenico , Monti Caterina Beatrice , Risoleo Giovanni Antonio , Vignati Giacomo , Rossi Silvia , Conte Edoardo , Andreini Daniele , Secchi Francesco , Fusco Stefano , Galia Massimo , Vitali Paolo , Gitto Salvatore , Messina Carmelo , Sconfienza Luca Maria
Published: October 2024

Objective: To investigate the association between sarcopenia, as appraised with CT-derived muscle metrics, and cardiovascular status, as assessed via coronary CT angiography (CCTA) using the Coronary Artery Disease-Reporting and Data System (CAD-RADS) and with pericoronary adipose tissue (pCAT) metrics. Methods: A retrospective observational study conducted on patients who underwent CCTA. The cross-sectional area (CSA) and attenuation values of the paravertebral muscles at the T8 level and the pectoralis major muscles at the T6 level were measured. The patient height was employed for the normalization of the skeletal muscle CSA. The pCAT attenuation around the coronary arteries was assessed, and the CAD severity was graded using the CAD-RADS reporting system. Regression analyses were performed to assess the impact of demographics, clinical factors, and CT variables on the CAD-RADS and pCAT. Results: A total of 220 patients were included (132 males, median age 65 years). Regression analyses showed the associations of CAD with age and sex (p < 0.001). Familiarity with CAD was related to the left anterior descending artery pCAT (p = 0.002) and circumflex artery pCAT (p = 0.018), whereas age was related to the left anterior descending artery pCAT (p = 0.032). Weak positive correlations were found between the lower muscle density and lower pCAT attenuation (ρ = 0.144–0.240, p < 0.039). Conclusions: This study demonstrated weak associations between the sarcopenia indicators and the cardiovascular risk, as assessed by the CAD severity and pCAT inflammation. However, these correlations were not strong predictors of CAD severity, as age and traditional cardiovascular risk factors overshadowed the impact of sarcopenia in the cardiovascular risk assessment.
Exploring Strategies to Market SMEs’ Activities: The Role of Personality Traits and Travel Motivations in Tourist Activities
Authors: Moisa Delia Gabriela , Parapanos Demos , Heap Tim
Published: October 2024

Traditionally, professionals in the tourism sector have been encouraged to segment their market based on socio-demographic variables. However, the multifaceted nature of tourist activity calls for consideration of psychological factors to effectively shape marketing strategies. This study aims to provide insights for tourism industry practitioners by investigating the roles of tourists’ personalities and travel motivations in shaping their activities and their commitment to sustainability in Cumbria, home to UNESCO World Heritage Sites like the Lake District National Park and Hadrian’s Wall. Data were collected using quantitative methods to identify correlations between personality traits, travel motivations, and tourist activities. Additionally, a qualitative focus group with SME representatives from Cumbria provided insights into their understanding of the model. The findings provide significant insights into how the relationships between personality traits, travel motivations, and tourist activities can benefit tourism SMEs. By comprehending tourists’ behaviour, SMEs can tailor their offerings to appeal to the right audience, mitigating the adverse effects of overtourism, and therefore do more than allude to the concept of sustainability. Using these insights, SMEs could make informed decisions and create more effective marketing strategies, fostering sustainable practices that benefit both their customers and the long-term viability of their businesses and the destination.