bluuebunny/crossref_metadata_2025 huggingface dataset:
An Investigation of Willingness to Pay for Geopark Management and Conservation: A Case Study of Geotourists in the Greater China Region
Authors: Fang Wei , Gou Gloria Rui , Cheung Lewis T. O. , Fok Lincoln , Chow Alice S. Y. , Zhang Ke
Published: February 2024

Willingness to pay (WTP) for geological protection is essential for market-based geopark conservation tactic formulations. Whether geotourists are willing to pay and how much they prefer to pay might be influenced by different determinants. The present study aims to (1) investigate the probability of paying and the payment amount for geopark conservation and (2) examine how factors influence the intention and amount to pay for the upkeep of geoparks. The results suggest that geotourists would contribute financially to geological conservation and geopark management. The findings also revealed that geotourists’ intentions and payment for conservation were associated with educational background and monthly income level. In addition, geotourist attachment and satisfaction concerning visiting geoparks positively affected intention and payment. This study might provide empirical references for geopark management and conservation in the Greater China Region.
Discrete-Time Survival Models with Neural Networks for Age–Period–Cohort Analysis of Credit Risk
Authors: Wang Hao , Bellotti Anthony , Qu Rong , Bai Ruibin
Published: February 2024

Survival models have become popular for credit risk estimation. Most current credit risk survival models use an underlying linear model. This is beneficial in terms of interpretability but is restrictive for real-life applications since it cannot discover hidden nonlinearities and interactions within the data. This study uses discrete-time survival models with embedded neural networks as estimators of time to default. This provides flexibility to express nonlinearities and interactions between variables and hence allows for models with better overall model fit. Additionally, the neural networks are used to estimate age–period–cohort (APC) models so that default risk can be decomposed into time components for loan age (maturity), origination (vintage), and environment (e.g., economic, operational, and social effects). These can be built as general models or as local APC models for specific customer segments. The local APC models reveal special conditions for different customer groups. The corresponding APC identification problem is solved by a combination of regularization and fitting the decomposed environment time risk component to macroeconomic data since the environmental risk is expected to have a strong relationship with macroeconomic conditions. Our approach is shown to be effective when tested on a large publicly available US mortgage dataset. This novel framework can be adapted by practitioners in the financial industry to improve modeling, estimation, and assessment of credit risk.
Multi-View Data-Based Layover Information Compensation Method for SAR Image Mosaic
Authors: Liu Rui , Wang Feng , Jiao Niangang , You Hongjian , Hu Yuxin , Zhou Guangyao , Chen Yao
Published: February 2024

Currently, massive Synthetic Aperture Radar (SAR) images acquired from numerous SAR satellites have been widely utilized in various fields, and image mosaicking technology provides important support and assistance for these applications. The traditional mosaic method selects specific SAR images that can cover the region of interest (ROI) from redundant data to produce “One Map”. However, an SAR image suffers from severe geometric distortion, especially in mountainous areas, which inevitably reduces the utilization of mosaic image. Therefore, a multi-view data-based layover information compensation (MDLIC) method for SAR image mosaic is proposed, aiming to take full advantage of multi-view data to compensate for the missing information in the layover area of the SAR image. This is performed to improve the information content of the mosaic image and realize efficient thematic information extraction and analysis. First, the calculation of the object-space extent of all images and the division of object-space grid are completed on the basis of geometric and radiometric preprocessing. Then, according to the transformation relationship between the object-space and the image-space, the sampling rate image of each image corresponding to the object-space grid is generated, which determines the layover area and the layover degree in each image. Finally, the information compensation strategy is implemented in accordance with the sampling rate image to realize the compensation of the layover information. The feasibility and effectiveness of the MDLIC method are verified by using multiple SAR images from the Chinese Gaofen-3 01 satellite as datasets for experiments. The experimental results indicate that the MDLIC method can obtain mosaic images with richer information compared with the traditional method, while still providing satisfactory results.
Seismic Landslide Susceptibility Assessment Using Newmark Displacement Based on a Dual-Channel Convolutional Neural Network
Authors: Li Yan , Ming Dongping , Zhang Liang , Niu Yunyun , Chen Yangyang
Published: February 2024

Landslide susceptibility assessment (LSA) is an essential tool for landslide hazard warning. The selection of earthquake-related factors is pivotal for seismic LSA. In this study, Newmark displacement (Dn) is employed as the earthquake-related factor, providing a detailed representation of seismic characteristics. On the algorithmic side, a dual-channel convolutional neural network (CNN) model is built, and the last classification layer is replaced with two machine learning (ML) models to facilitate the extraction of deeper features related to landslide development. This research focuses on Beichuan County in Sichuan Province, China. Fifteen landslide predisposing factors, including hydrological, geomorphic, geological, vegetation cover, anthropogenic, and earthquake-related features, were extensively collected. The results demonstrate some specific issues. Dn outperforms conventional earthquake-related factors such as peak ground acceleration (PGA) and Arias intensity (Ia) in capturing seismic influence on landslide development. Under the same conditions, the OA improved by 5.55% and AUC improved by 0.055 compared to the PGA; the OA improved by 3.2% and AUC improved by 0.0327 compared to the Ia. The improved CNN outperforms ML models. Under the same conditions, the OA improved by 4.69% and AUC improved by 0.0467 compared to RF; the OA improved by 4.47% and AUC improved by 0.0447 compared to SVM. Additionally, historical landslides validate the reasonableness of the landslide susceptibility maps. The proposed method exhibits a high rate of overlap with the historical landslide inventory. The proportion of historical landslides in the very high and high susceptibility zones exceeds 87%. The method not only enhances accuracy but also produces a more fine-grained susceptibility map, providing a reliable basis for early warning of seismic landslides.
Spatiotemporal Dynamics and Driving Factors of Small and Micro Wetlands in the Yellow River Basin from 1990 to 2020
Authors: Zhai Guangqing , Du Jiaqiang , Li Lijuan , Zhu Xiaoqian , Song Zebang , Wu Luyao , Chong Fangfang , Chen Xiya
Published: February 2024

Comprehending the spatiotemporal dynamics and driving factors of small and micro wetlands (SMWs) holds paramount significance in their conservation and sustainable development. This paper investigated the spatiotemporal evolution and driving mechanisms of SMWs in the Yellow River Basin, utilizing buffer zones, overlay analysis, and the Geodetector model based on Landsat satellite images and an open-surface water body dataset from 1990 to 2020. The results revealed that (1) from 1990 to 2020, SMWs in the Yellow River Basin exhibited an overall pattern of fluctuation reduction. The total area decreased by approximately 1.12 × 105 hm2, with the predominant decline occurring in the 0–1 hm2 and 1–3 hm2 size categories. In terms of spatial distribution, SMWs in Qinghai and Gansu decreased significantly, while the SMWs in Inner Mongolia, Henan, and Shandong gradually increased. (2) From 1990 to 2020, SMWs were mostly converted into grassland and cropland, with some transformed into impervious water surface and barren, and only a small percentage converted into other land types in the Yellow River basin. (3) The alterations in SMWs were influenced by factors, with their interplay exhibiting nonlinear or bilinear enhancement. Among these factors, annual precipitation, elevation, and potential evapotranspiration were the primary natural factors influencing the changes in the distribution of SMWs. On the other hand, land use cover type, gross domestic product (GDP), and road distance were the main anthropogenic factors.
Ship Formation Identification with Spatial Features and Deep Learning for HFSWR
Authors: Wang Jiaqi , Liu Aijun , Yu Changjun , Ji Yuanzheng
Published: February 2024

Ship detection has been an area of focus for high-frequency surface wave radar (HFSWR). The detection and identification of ship formation have proven significant in early warning, while studies on the formation identification are limited due to the complex background and low resolution of HFSWR. In this paper, we first establish a spatial distribution model of ship formation in HFSWR. Then, we propose a cascade identification algorithm of ship formation in the clutter edge. The proposed algorithm includes a preprocessing stage and a two-stage formation identification stage. The Faster R-CNN is introduced in the preprocessing stage to locate the clutter regions. In the first stage, we propose an extremum detector based on connected regions to extract suspicious regions. The suspicious regions contain ship formations, single-ship targets, and false targets. In the second stage, we design a network connected by a convolutional neural network (CNN) and an extreme learning machine (ELM) to identify two densely distributed ship formations from inhomogeneous clutter and single-ship targets. The experimental results based on the factual HFSWR background demonstrate that the proposed cascade identification algorithm is superior to the extremum detector combined with the classical CNN algorithm for ship formation identification. Meanwhile, the proposed algorithm performs well in weak formation and deformed formation identification.
Airborne Hyperspectral Images and Machine Learning Algorithms for the Identification of Lupine Invasive Species in Natura 2000 Meadows
Authors: Sabat-Tomala Anita , Raczko Edwin , Zagajewski Bogdan
Published: February 2024

The mapping of invasive plant species is essential for effective ecosystem control and planning, especially in protected areas. One of the widespread invasive plants that threatens the species richness of Natura 2000 habitats in Europe is the large-leaved lupine (Lupinus polyphyllus). In our study, this species was identified at two Natura 2000 sites in southern Poland using airborne HySpex hyperspectral images, and support vector machine (SVM) and random forest (RF) classifiers. Aerial and field campaigns were conducted three times during the 2016 growing season (May, August, and September). An iterative accuracy assessment was performed, and the influence of the number of minimum noise fraction (MNF) bands on the obtained accuracy of lupine identification was analyzed. The highest accuracies were obtained for the August campaign using 30 MNF bands as input data (median F1 score for lupine was 0.82–0.85), with lower accuracies for the May (F1 score: 0.77–0.81) and September (F1 score: 0.78–0.80) campaigns. The use of more than 30 MNF bands did not significantly increase the classification accuracy. The SVM and RF algorithms allowed us to obtain comparable results in both research areas (OA: 89–94%). The method of the multiple classification and thresholding of frequency images allowed the results of many predictions to be included in the final map.
The Use of High-Resolution Satellite Imagery to Determine the Status of a Large-Scale Outbreak of Southern Pine Beetle
Authors: Crosby Michael K. , McConnell T. Eric , Holderieath Jason J. , Meeker James R. , Steiner Chris A. , Strom Brian L. , Johnson Crawford (Wood)
Published: February 2024

Timely detection of insect infestation (or other disturbance) in a forest is vital for an adequate response plan to be developed. To determine the status of an active infestation of southern pine beetle (Dendroctonus frontalis) in the Bienville National Forest, WorldView-2 imagery was utilized. Principal components analysis (PCA) was performed and correlated with spectral reflectance bands to assess differences between the classification of spectral reflectance bands and principal components. Unsupervised classification of combinations of principal components (e.g., combining principal components 1 and 2, principal component 1 alone, and principal component 2 alone) was performed and compared with combinations of principal component correlations with spectral reflectance bands (e.g., all bands, bands 1–5, bands 6–8, and bands 2, 4, and 5). Combining principal components 1 and 2 was more accurate than other methods, closely followed by spectral bands 1–5. Employing PCA will aid resource managers in quickly detecting areas of active insect infestation and allow them to deploy adequate response measures to prevent or mitigate continued outbreaks.
Vineyard Zoning and Vine Detection Using Machine Learning in Unmanned Aerial Vehicle Imagery
Authors: Gavrilović Milan , Jovanović Dušan , Božović Predrag , Benka Pavel , Govedarica Miro
Published: February 2024

Precision viticulture systems are essential for enhancing traditional intensive viticulture, achieving high-quality results, and minimizing costs. This study explores the integration of Unmanned Aerial Vehicles (UAVs) and artificial intelligence in precision viticulture, focusing on vine detection and vineyard zoning. Vine detection employs the YOLO (You Only Look Once) deep learning algorithm, achieving a remarkable 90% accuracy by analysing UAV imagery with various spectral ranges from various phenological stages. Vineyard zoning, achieved through the application of the K-means algorithm, incorporates geospatial data such as the Normalized Difference Vegetation Index (NDVI) and the assessment of nitrogen, phosphorus, and potassium content in leaf blades and petioles. This approach enables efficient resource management tailored to each zone’s specific needs. The research aims to develop a decision-support model for precision viticulture. The proposed model demonstrates a high vine detection accuracy and defines management zones with variable weighting factors assigned to each variable while preserving location information, revealing significant differences in variables. The model’s advantages lie in its rapid results and minimal data requirements, offering profound insights into the benefits of UAV application for precise vineyard management. This approach has the potential to expedite decision making, allowing for adaptive strategies based on the unique conditions of each zone.
Understanding the Roles of Aerosols and Clouds in Environment, Meteorology and Climate with Advanced Lidar Remote Sensing Techniques
Authors: Yin Zhenping , Wang Longlong , He Yun , Wang Xuan
Published: February 2024

This Special Issue lists nine publications, covering the topics of advanced atmospheric lidar techniques, lidar retrievals, and lidar applications. The investigations listed here are diverse, but they are all focused on atmospheric lidars. Some urgent issues, for instance low blind zone detection and polarization detection at a near-infrared wavelength band, were discussed and explored. The results are helpful for extending atmospheric lidar applications. In terms of lidar retrievals, a planetary boundary layer height retrieval and an automatic lidar retrieval for aerosol optical properties were investigated in some of the publications, which can strengthen the atmospheric lidar capabilities. For lidar applications, a detailed analysis of the evolution of stratospheric aerosol and dust–cloud interactions was presented. In this Editorial, the articles published within this Special Issue are reviewed to highlight their innovative contributions and main research findings.
Mapping Small-Scale Willow Crops and Their Health Status Using Sentinel-2 Images in Complex Agricultural Areas
Authors: Beygi Heidarlou Hadi , Oprea-Sorescu Octavian , Marcu Marina Viorela , Borz Stelian Alexandru
Published: February 2024

The goal of this study was to estimate the areas under willow cultivation by farmers, as well as their growth and health status. Due to the extremely small patch size of land cover types in the study area, Sentinel-2 data were used to conduct supervised classification based on the random forest machine learning technique, and a large training dataset was produced from PlanetScope satellite imagery. The results of image classification using Google Earth Engine indicated that the Sentinel data were suitable for identifying willow-cultivated areas. It was found that these areas declined from 875.32 ha in 2017 to 288.41 ha in 2022. The analysis of the growth and health conditions of willow-cultivated plots also revealed that the temporal variations in the NDVI in these plots decreased significantly in 2022 as compared to previous years (p < 0.05). An in-depth analysis revealed a significant positive correlation between NDVI, precipitation, and temperature. It was found that the most efficient components explaining the process of browning the vegetation in the planted willow plots were the increasing temperature and decreasing precipitation. This research may be used to document the national and global monitoring efforts for climate change adaptation.
A Deep Learning-Based Platform for Workers’ Stress Detection Using Minimally Intrusive Multisensory Devices
Authors: Rescio Gabriele , Manni Andrea , Ciccarelli Marianna , Papetti Alessandra , Caroppo Andrea , Leone Alessandro
Published: February 2024

The advent of Industry 4.0 necessitates substantial interaction between humans and machines, presenting new challenges when it comes to evaluating the stress levels of workers who operate in increasingly intricate work environments. Undoubtedly, work-related stress exerts a significant influence on individuals’ overall stress levels, leading to enduring health issues and adverse impacts on their quality of life. Although psychological questionnaires have traditionally been employed to assess stress, they lack the capability to monitor stress levels in real-time or on an ongoing basis, thus making it arduous to identify the causes and demanding aspects of work. To surmount this limitation, an effective solution lies in the analysis of physiological signals that can be continuously measured through wearable or ambient sensors. Previous studies in this field have mainly focused on stress assessment through intrusive wearable systems susceptible to noise and artifacts that degrade performance. One of our recently published papers presented a wearable and ambient hardware-software platform that is minimally intrusive, able to detect human stress without hindering normal work activities, and slightly susceptible to artifacts due to movements. A limitation of this system is its not very high performance in terms of the accuracy of detecting multiple stress levels; therefore, in this work, the focus was on improving the software performance of the platform, using a deep learning approach. To this purpose, three neural networks were implemented, and the best performance was achieved by the 1D-convolutional neural network with an accuracy of 95.38% for the identification of two levels of stress, which is a significant improvement over those obtained previously.
An Adaptive Deconvolution Method with Improve Enhanced Envelope Spectrum and Its Application for Bearing Fault Feature Extraction
Authors: He Fengxia , Zheng Chuansheng , Pang Chao , Zhao Chengying , Yang Mingyang , Zhu Yunpeng , Luo Zhong , Luo Haitao , Li Lei , Jiang Haotian
Published: February 2024

To address the problem that complex bearing faults are coupled to each other, and the difficulty of diagnosis increases, an improved envelope spectrum–maximum second-order cyclostationary blind deconvolution (IES-CYCBD) method is proposed to realize the separation of vibration signal fault features. The improved envelope spectrum (IES) is obtained by integrating the part of the frequency axis containing resonance bands in the cyclic spectral coherence function. The resonant bands corresponding to different fault types are accurately located, and the IES with more prominent target characteristic frequency components are separated. Then, a simulation is carried out to prove the ability of this method, which can accurately separate and diagnose fault types under high noise and compound fault conditions. Finally, a compound bearing fault experiment with inner and outer ring faults is designed, and the inner and outer ring fault characteristics are successfully separated by the proposed IES-CYCBD method. Therefore, simulation and experiments demonstrate the strong capability of the proposed method for complex fault separation and diagnosis.
Ca2Lib: Simple and Accurate LiDAR-RGB Calibration Using Small Common Markers
Authors: Giacomini Emanuele , Brizi Leonardo , Di Giammarino Luca , Salem Omar , Perugini Patrizio , Grisetti Giorgio
Published: February 2024

Modern visual perception techniques often rely on multiple heterogeneous sensors to achieve accurate and robust estimates. Knowledge of their relative positions is a mandatory prerequisite to accomplish sensor fusion. Typically, this result is obtained through a calibration procedure that correlates the sensors’ measurements. In this context, we focus on LiDAR and RGB sensors that exhibit complementary capabilities. Given the sparsity of LiDAR measurements, current state-of-the-art calibration techniques often rely on complex or large calibration targets to resolve the relative pose estimation. As such, the geometric properties of the targets may hinder the calibration procedure in those cases where an ad hoc environment cannot be guaranteed. This paper addresses the problem of LiDAR-RGB calibration using common calibration patterns (i.e., A3 chessboard) with minimal human intervention. Our approach exploits the flatness of the target to find associations between the sensors’ measurements, leading to robust features and retrieval of the solution through nonlinear optimization. The results of quantitative and comparative experiments with other state-of-the-art approaches show that our simple schema performs on par or better than existing methods that rely on complex calibration targets.
CNN and Attention-Based Joint Source Channel Coding for Semantic Communications in WSNs
Authors: Liu Xinyue , Huang Zhen , Zhang Yulu , Jia Yunjian , Wen Wanli
Published: February 2024

Wireless Sensor Networks (WSNs) have emerged as an efficient solution for numerous real-time applications, attributable to their compactness, cost-effectiveness, and ease of deployment. The rapid advancement of 5G technology and mobile edge computing (MEC) in recent years has catalyzed the transition towards large-scale deployment of WSN devices. However, the resulting data proliferation and the dynamics of communication environments introduce new challenges for WSN communication: (1) ensuring robust communication in adverse environments and (2) effectively alleviating bandwidth pressure from massive data transmission. In response to the aforementioned challenges, this paper proposes a semantic communication solution. Specifically, considering the limited computational and storage resources of WSN devices, we propose a flexible Attention-based Adaptive Coding (AAC) module. This module integrates window and channel attention mechanisms, dynamically adjusts semantic information in response to the current channel state, and facilitates adaptation of a single model across various Signal-to-Noise Ratio (SNR) environments. Furthermore, to validate the effectiveness of this approach, the paper introduces an end-to-end Joint Source Channel Coding (JSCC) scheme for image semantic communication, employing the AAC module. Experimental results demonstrate that the proposed scheme surpasses existing deep JSCC schemes across datasets of varying resolutions; furthermore, they validate the efficacy of the proposed AAC module, which is capable of dynamically adjusting critical information according to the current channel state. This enables the model to be trained over a range of SNRs and obtain better results.
Polarization Conforms Performance Variability in Amorphous Electrodeposited Iridium Oxide pH Sensors: A Thorough Surface Chemistry Investigation
Authors: Marsh Paul , Huang Mao-Hsiang , Xia Xing , Tran Ich , Atanassov Plamen , Cao Hung
Published: February 2024

Electrodeposited amorphous hydrated iridium oxide (IrOx) is a promising material for pH sensing due to its high sensitivity and the ease of fabrication. However, durability and variability continue to restrict the sensor’s effectiveness. Variation in probe films can be seen in both performance and fabrication, but it has been found that performance variation can be controlled with potentiostatic conditioning (PC). To make proper use of this technique, the morphological and chemical changes affecting the conditioning process must be understood. Here, a thorough study of this material, after undergoing PC in a pH-sensing-relevant potential regime, was conducted by voltammetry, scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS). Fitting of XPS data was performed, guided by raw trends in survey scans, core orbitals, and valence spectra, both XPS and UPS. The findings indicate that the PC process can repeatably control and conform performance and surface bonding to desired calibrations and distributions, respectively; PC was able to reduce sensitivity and offset ranges to as low as ±0.7 mV/pH and ±0.008 V, respectively, and repeat bonding distributions over ~2 months of sample preparation. Both Ir/O atomic ratios (shifting from 4:1 to over 4.5:1) and fitted components assigned hydroxide or oxide states based on the literature (low-voltage spectra being almost entirely with suggested hydroxide components, and high-voltage spectra almost entirely with suggested oxide components) trend across the polarization range. Self-consistent valence, core orbital, and survey quantitative trends point to a likely mechanism of ligand conversion from hydroxide to oxide, suggesting that the conditioning process enforces specific state mixtures that include both theoretical Ir(III) and Ir(IV) species, and raising the conditioning potential alters the surface species from an assumed mixture of Ir species to more oxidized Ir species.
Determination of Gait Events and Temporal Gait Parameters for Persons with a Knee–Ankle–Foot Orthosis
Authors: Yang Sumin , Koo Bummo , Lee Seunghee , Jang Dae-Jin , Shin Hyunjun , Choi Hyuk-Jae , Kim Youngho
Published: February 2024

Gait event detection is essential for controlling an orthosis and assessing the patient’s gait. In this study, patients wearing an electromechanical (EM) knee–ankle–foot orthosis (KAFO) with a single IMU embedded in the thigh were subjected to gait event detection. The algorithm detected four essential gait events (initial contact (IC), toe off (TO), opposite initial contact (OIC), and opposite toe off (OTO)) and determined important temporal gait parameters such as stance/swing time, symmetry, and single/double limb support. These gait events were evaluated through gait experiments using four force plates on healthy adults and a hemiplegic patient who wore a one-way clutch KAFO and a pneumatic cylinder KAFO. Results showed that the smallest error in gait event detection was found at IC, and the largest error rate was observed at opposite toe off (OTO) with an error rate of −2.8 ± 1.5% in the patient group. Errors in OTO detection resulted in the largest error in determining the single limb support of the patient with an error of 5.0 ± 1.5%. The present study would be beneficial for the real-time continuous monitoring of gait events and temporal gait parameters for persons with an EM KAFO.
A Long-Term Comparison between the AethLabs MA350 and Aerosol Magee Scientific AE33 Black Carbon Monitors in the Greater Salt Lake City Metropolitan Area
Authors: Mendoza Daniel L. , Hill L. Drew , Blair Jeffrey , Crosman Erik T.
Published: February 2024

Black carbon (BC) or soot contains ultrafine combustion particles that are associated with a wide range of health impacts, leading to respiratory and cardiovascular diseases. Both long-term and short-term health impacts of BC have been documented, with even low-level exposures to BC resulting in negative health outcomes for vulnerable groups. Two aethalometers—AethLabs MA350 and Aerosol Magee Scientific AE33—were co-located at a Utah Division of Air Quality site in Bountiful, Utah for just under a year. The aethalometer comparison showed a close relationship between instruments for IR BC, Blue BC, and fossil fuel source-specific BC estimates. The biomass source-specific BC estimates were markedly different between instruments at the minute and hour scale but became more similar and perhaps less-affected by high-leverage outliers at the daily time scale. The greater inter-device difference for biomass BC may have been confounded by very low biomass-specific BC concentrations during the study period. These findings at a mountainous, high-elevation, Greater Salt Lake City Area site support previous study results and broaden the body of evidence validating the performance of the MA350.
A Holistic Approach for Enhancing Museum Performance and Visitor Experience
Authors: Philippopoulos Panos I. , Drivas Ioannis C. , Tselikas Nikolaos D. , Koutrakis Kostas N. , Melidi Elena , Kouis Dimitrios
Published: February 2024

Managing modern museum content and visitor data analytics to achieve higher levels of visitor experience and overall museum performance is a complex and multidimensional issue involving several scientific aspects, such as exhibits’ metadata management, visitor movement tracking and modelling, location/context-aware content provision, etc. In related prior research, most of the efforts have focused individually on some of these aspects and do not provide holistic approaches enhancing both museum performance and visitor experience. This paper proposes an integrated conceptualisation for improving these two aspects, involving four technological components. First, the adoption and parameterisation of four ontologies for the digital documentation and presentation of exhibits and their conservation methods, spatial management, and evaluation. Second, a tool for capturing visitor movement in near real-time, both anonymously (default) and eponymously (upon visitor consent). Third, a mobile application delivers personalised content to eponymous visitors based on static (e.g., demographic) and dynamic (e.g., visitor movement) data. Lastly, a platform assists museum administrators in managing visitor statistics and evaluating exhibits, collections, and routes based on visitors’ behaviour and interactions. Preliminary results from a pilot implementation of this holistic approach in a multi-space high-traffic museum (MELTOPENLAB project) indicate that a cost-efficient, fully functional solution is feasible, and achieving an optimal trade-off between technical performance and cost efficiency is possible for museum administrators seeking unfragmented approaches that add value to their cultural heritage organisations.
Lightweight Detection Method for X-ray Security Inspection with Occlusion
Authors: Wang Zanshi , Wang Xiaohua , Shi Yueting , Qi Hang , Jia Minli , Wang Weijiang
Published: February 2024

Identifying the classes and locations of prohibited items is the target of security inspection. However, X-ray security inspection images with insufficient feature extraction, imbalance between easy and hard samples, and occlusion lead to poor detection accuracy. To address the above problems, an object-detection method based on YOLOv8 is proposed. Firstly, an ASFF (adaptive spatial feature fusion) and a weighted feature concatenation algorithm are introduced to fully extract the scale features from input images. In this way, the model can learn further details in training. Secondly, CoordAtt (coordinate attention module), which belongs to the hybrid attention mechanism, is embedded to enhance the learning of features of interest. Then, the slide loss function is introduced to balance the simple samples and the difficult samples. Finally, Soft-NMS (non-maximum suppression) is introduced to resist the conditions containing occlusion. The experimental result shows that mAP (mean average precision) achieves 90.2%, 90.5%, 79.1%, and 91.4% on the Easy, Hard, and Hidden sets of the PIDray and SIXray public test set, respectively. Contrasted with original model, the mAP of our proposed YOLOv8n model increased by 2.7%, 3.1%, 9.3%, and 2.4%, respectively. Furthermore, the parameter count of the modified YOLOv8n model is roughly only 3 million.
Research on Inter-Frame Feature Mismatch Removal Method of VSLAM in Dynamic Scenes
Authors: Yang Zhiyong , He Yang , Zhao Kun , Lang Qing , Duan Hua , Xiong Yuhong , Zhang Daode
Published: February 2024

Visual Simultaneous Localization and Mapping (VSLAM) estimates the robot’s pose in three-dimensional space by analyzing the depth variations of inter-frame feature points. Inter-frame feature point mismatches can lead to tracking failure, impacting the accuracy of the mobile robot’s self-localization and mapping. This paper proposes a method for removing mismatches of image features in dynamic scenes in visual SLAM. First, the Grid-based Motion Statistics (GMS) method was introduced for fast coarse screening of mismatched image features. Second, an Adaptive Error Threshold RANSAC (ATRANSAC) method, determined by the internal matching rate, was proposed to improve the accuracy of removing mismatched image features in dynamic and static scenes. Third, the GMS-ATRANSAC method was tested for removing mismatched image features, and experimental results showed that GMS-ATRANSAC can remove mismatches of image features on moving objects. It achieved an average error reduction of 29.4% and 32.9% compared to RANSAC and GMS-RANSAC, with a corresponding reduction in error variance of 63.9% and 58.0%, respectively. The processing time was reduced by 78.3% and 38%, respectively. Finally, the effectiveness of inter-frame feature mismatch removal in the initialization thread of ORB-SLAM2 and the tracking thread of ORB-SLAM3 was verified for the proposed algorithm.
Multi-Dimensional Wi-Fi Received Signal Strength Indicator Data Augmentation Based on Multi-Output Gaussian Process for Large-Scale Indoor Localization
Authors: Tang Zhe , Li Sihao , Kim Kyeong Soo , Smith Jeremy S.
Published: February 2024

Location fingerprinting using Received Signal Strength Indicators (RSSIs) has become a popular technique for indoor localization due to its use of existing Wi-Fi infrastructure and Wi-Fi-enabled devices. Artificial intelligence/machine learning techniques such as Deep Neural Networks (DNNs) have been adopted to make location fingerprinting more accurate and reliable for large-scale indoor localization applications. However, the success of DNNs for indoor localization depends on the availability of a large amount of pre-processed and labeled data for training, the collection of which could be time-consuming in large-scale indoor environments and even challenging during a pandemic situation like COVID-19. To address these issues in data collection, we investigate multi-dimensional RSSI data augmentation based on the Multi-Output Gaussian Process (MOGP), which, unlike the Single-Output Gaussian Process (SOGP), can exploit the correlation among the RSSIs from multiple access points in a single floor, neighboring floors, or a single building by collectively processing them. The feasibility of MOGP-based multi-dimensional RSSI data augmentation is demonstrated through experiments using the hierarchical indoor localization model based on a Recurrent Neural Network (RNN)—i.e., one of the state-of-the-art multi-building and multi-floor localization models—and the publicly available UJIIndoorLoc multi-building and multi-floor indoor localization database. The RNN model trained with the UJIIndoorLoc database augmented with the augmentation mode of “by a single building”, where an MOGP model is fitted based on the entire RSSI data of a building, outperforms the other two augmentation modes and results in the three-dimensional localization error of 8.42 m.
Improving Vehicle Heading Angle Accuracy Based on Dual-Antenna GNSS/INS/Barometer Integration Using Adaptive Kalman Filter
Authors: Jiao Hongyuan , Xu Xiangbo , Chen Shao , Guo Ningyan , Yu Zhibin
Published: February 2024

High-accuracy heading angle is significant for estimating autonomous vehicle attitude. By integrating GNSS (Global Navigation Satellite System) dual antennas, INS (Inertial Navigation System), and a barometer, a GNSS/INS/Barometer fusion method is proposed to improve vehicle heading angle accuracy. An adaptive Kalman filter (AKF) is designed to fuse the INS error and the GNSS measurement. A random sample consensus (RANSAC) method is proposed to improve the initial heading angle accuracy applied to the INS update. The GNSS heading angle obtained by a dual-antenna orientation algorithm is additionally augmented to the measurement variable. Furthermore, the kinematic constraint of zero velocity in the lateral and vertical directions of vehicle movement is used to enhance the accuracy of the measurement model. The heading errors in the open and occluded environment are 0.5418° (RMS) and 0.636° (RMS), which represent reductions of 37.62% and 47.37% compared to the extended Kalman filter (EKF) method, respectively. The experimental results demonstrate that the proposed method effectively improves the vehicle heading angle accuracy.
Enhancing Security and Flexibility in the Industrial Internet of Things: Blockchain-Based Data Sharing and Privacy Protection
Authors: Tong Weiming , Yang Luyao , Li Zhongwei , Jin Xianji , Tan Liguo
Published: February 2024

To address the complexities, inflexibility, and security concerns in traditional data sharing models of the Industrial Internet of Things (IIoT), we propose a blockchain-based data sharing and privacy protection (BBDSPP) scheme for IIoT. Initially, we characterize and assign values to attributes, and employ a weighted threshold secret sharing scheme to refine the data sharing approach. This enables flexible combinations of permissions, ensuring the adaptability of data sharing. Subsequently, based on non-interactive zero-knowledge proof technology, we design a lightweight identity proof protocol using attribute values. This protocol pre-verifies the identity of data accessors, ensuring that only legitimate terminal members can access data within the system, while also protecting the privacy of the members. Finally, we utilize the InterPlanetary File System (IPFS) to store encrypted shared resources, effectively addressing the issue of low storage efficiency in traditional blockchain systems. Theoretical analysis and testing of the computational overhead of our scheme demonstrate that, while ensuring performance, our scheme has the smallest total computational load compared to the other five schemes. Experimental results indicate that our scheme effectively addresses the shortcomings of existing solutions in areas such as identity authentication, privacy protection, and flexible combination of permissions, demonstrating a good performance and strong feasibility.
A Survey on Open Radio Access Networks: Challenges, Research Directions, and Open Source Approaches
Authors: Azariah Wilfrid , Bimo Fransiscus Asisi , Lin Chih-Wei , Cheng Ray-Guang , Nikaein Navid , Jana Rittwik
Published: February 2024

The open radio access network (RAN) aims to bring openness and intelligence to the traditional closed and proprietary RAN technology and offer flexibility, performance improvement, and cost-efficiency in the RAN’s deployment and operation. This paper provides a comprehensive survey of the Open RAN development. We briefly summarize the RAN evolution history and the state-of-the-art technologies applied to Open RAN. The Open RAN-related projects, activities, and standardization is then discussed. We then summarize the challenges and future research directions required to support the Open RAN. Finally, we discuss some solutions to tackle these issues from the open source perspective.
Taxation Preferences and the Uptake of Hybrid and Electric Vehicles in Poland’s Ten Largest Cities: A Case Study
Authors: Witkowski Jacek , Kusio Tomasz , Fiore Mariantonietta , Olesiński Zbigniew
Published: February 2024

The purpose of this article is to explore whether and based on what criteria local governments employ reduced tax rates on means of transportation to encourage hybrid and electric vehicles. The study also aims to determine if there has been a more rapid increase in the number of means of transportation powered entirely or partially by electricity in local government units that have implemented more substantial incentives. The study encompasses the ten largest cities in Poland and the years 2018–2020. To achieve the above research objectives, an analysis of the texts of the resolutions of the city councils was carried out in terms of the tax rates in effect during the selected period. On the basis of the relevant amounts provided in the resolutions, the amount of concessions resulting from the application of lower tax rates was calculated, and the criteria for the use of these concessions related to the level of emissivity were presented. To assess the potential correlation between the scale of tax preferences and the dynamics of growth in the number of hybrid and electric vehicles, a statistical tool in the form of Spearman’s rank correlation coefficient was used for a thorough data analysis. The results suggest that municipalities use their authority to introduce differentiated tax rates on means of transportation. However, those utilizing hybrid and electric vehicles are more likely to employ these powers on an equal basis, with owners of vehicles adhering to lower emission standards covered by EURO standards. In addition, there was no positive correlation between the amount of tax credits and the rate of growth in the number of reduced-emission vehicles. This work can foster a green mindset and societal nature-based new thinking.
A Comprehensive Review of Sustainability in Natural-Fiber-Reinforced Polymers
Authors: Prasad Vishnu , Alliyankal Vijayakumar Amal , Jose Thomasukutty , George Soney C.
Published: February 2024

Fiber-reinforced polymer composites (FRCs) from renewable and biodegradable fiber and sustainable polymer resins have gained substantial attention for their potential to mitigate environmental impacts. The limitations of these composites become evident when considered in the context of high-performance engineering applications, where synthetic fiber composites like glass or carbon FRCs typically dominate. A balance between the performance of the composite and biodegradability is imperative in the pursuit of what may be termed an environmentally conscious composite. This comprehensive review article provides some insight into the sustainability of FRCs, alongside detailing the sustainability considerations at various stages—materials, performance, applications, and end of life. The discussion also covers the different types of sustainable natural fibers and the types of polymer resins with some of the current achievements in the mechanical and functional properties of such composites, followed by a broad survey of their potential applications across diverse engineering applications.
Automatic and Efficient Detection of Loess Landslides Based on Deep Learning
Authors: Ji Qingyun , Liang Yuan , Xie Fanglin , Yu Zhengbo , Wang Yanli
Published: February 2024

Frequent landslide disasters on the Loess Plateau in northwestern China have had a serious impact on the lives and production of the people in the region due to the fragile ecological environment and severe soil erosion. The effective monitoring and management of landslide hazards is hindered by the wide range of landslide features and scales in remotely sensed imagery, coupled with the shortage of local information and technology. To address this issue, we constructed a loess landslide dataset of 11,010 images and established a landslide detection network model. Coordinate Attention (CA) is integrated into the backbone with the aid of the YOLO model to capture precise location information and remote spatial interaction data from landslide images. Furthermore, the neck includes the Convolutional Block Attention Module (CBAM), which prompts the model to prioritize focusing on legitimate landslide objectives while also filtering out background noise to extract valid feature information. To efficiently extract classification and location details from landslide images, we introduce the lightweight Decoupled Head. This enhances detection accuracy for landslide objectives without excessively increasing model parameters. Furthermore, the utilization of the SIoU loss function improves angle perception for landslide detection algorithms and reduces the deviation between the predicted box and the ground truth box. The improved model achieves landslide object detection at multiple scales with a mAP of 92.28%, an improvement of 4.01% compared to the unimproved model.
Nurturing Sustainability Changemakers through Transformative Learning Using Design Thinking: Evidence from an Exploratory Qualitative Study
Authors: Macagno Thomas , Nguyen-Quoc Anh , Jarvis Suzi P.
Published: February 2024

Globally, society faces significant problems, from climate crisis to persistent poverty. Education for Sustainable Development offers an approach to achieving a sustainable society. The challenge is creating changemakers with the knowledge, mindset, and competencies to innovate. Teaching sustainability tends to be discipline-specific, taught content-heavy via “spoon-feeding” and neglecting student agency changes. To overcome these challenges, we developed a programme to “teach” innovation for sustainability that combines transformative learning and design thinking as the pedagogy. Classes were anchored by solving a real-world sustainability challenge, facilitating experience transformation into new knowledge. Design Thinking is a transdisciplinary methodology that helps different disciplines collaborate on addressing complex problems. However, there is limited empirical evidence to support the effectiveness of this transformative pedagogy in sustainability education. What is the value of combining Design Thinking and Transformative Learning as a pedagogy in sustainability education? Based on the University College Dublin Innovation Academy’s Professional Diploma of Innovation for Sustainability, we address this research question by exploring the impacts of the programme design. Data were collected during and after the programme through students’ reflections and observation field notes of teaching sections. Findings show that the programme design supported developing a sustainability mindset, sustainability literacy, and creative confidence. All characteristics support becoming a changemaker.