bluuebunny/crossref_metadata_2025 huggingface dataset:
EGMT-CD: Edge-Guided Multimodal Transformers Change Detection from Satellite and Aerial Images
Authors: Xiang Yunfan , Tian Xiangyu , Xu Yue , Guan Xiaokun , Chen Zhengchao
Published: December 2023

Change detection from heterogeneous satellite and aerial images plays a progressively important role in many fields, including disaster assessment, urban construction, and land use monitoring. Currently, researchers have mainly devoted their attention to change detection using homologous image pairs and achieved many remarkable results. It is sometimes necessary to use heterogeneous images for change detection in practical scenarios due to missing images, emergency situations, and cloud and fog occlusion. However, heterogeneous change detection still faces great challenges, especially using satellite and aerial images. The main challenges in satellite and aerial image change detection are related to the resolution gap and blurred edge. Previous studies used interpolation or shallow feature alignment before traditional homologous change detection methods, which ignored the high-level feature interaction and edge information. Therefore, we propose a new heterogeneous change detection model based on multimodal transformers combined with edge guidance. In order to alleviate the resolution gap between satellite and aerial images, we design an improved spatially aligned transformer (SP-T) with a sub-pixel module to align the satellite features to the same size of the aerial ones supervised by a token loss. Moreover, we introduce an edge detection branch to guide change features using the object edge with an auxiliary edge-change loss. Finally, we conduct considerable experiments to verify the effectiveness and superiority of our proposed model (EGMT-CD) on a new satellite–aerial heterogeneous change dataset, named SACD. The experiments show that our method (EGMT-CD) outperforms many previously superior change detection methods and fully demonstrates its potential in heterogeneous change detection from satellite–aerial images.
A Novel Approach to Evaluate GNSS-RO Signal Receiver Performance in Terms of Ground-Based Atmospheric Occultation Simulation System
Authors: Li Wei , Sun Yueqiang , Bai Weihua , Du Qifei , Wang Xianyi , Wang Dongwei , Liu Congliang , Li Fu , Kang Shengyu , Song Hongqing
Published: December 2023

The global navigation satellite system radio occultation (GNSS-RO) is an important means of space-based meteorological observation. It is necessary to test the Global Navigation Satellite System Occultation signal receiver on the ground before the deployment of space-based occultation detection systems. The current approach of testing the GNSS signal receiver on the ground is mainly the mountaintop-based testing approach, which has problems such as high cost and large simulation error. In order to overcome the limitations of the mountaintop-based test approach, this paper proposes an accurate, repeatable, and controllable GNSS atmospheric occultation simulation system and builds a load performance evaluation approach based on the ground-based GNSS atmospheric occultation simulation system on the basis of it. The GNSS atmospheric occultation simulation system consists of the visualization and interaction module, the GNSS-RO simulation signal generation module, the GNSS-RO simulator module, the GNSS-RO signal receiver module, and the GNSS-RO inversion and evaluation module, combined with the preset atmospheric model to generate GNSS-RO simulation signals with a high degree of simulation, and comparing the atmospheric parameters of the inversion performance of the GNSS-RO signal receiver with the parameters of the preset atmospheric model to obtain the error data. The overall performance of the GNSS-RO signal receiver can be evaluated based on the error information. The novel approach to evaluate the GNSS-RO signal receiver performance proposed in this paper is validated by using the FY-3E (FengYun-3E) receiver qualification parts that have been verified in orbit, and the results confirm that the approach can meet the requirements of the GNSS-RO receiver performance test. This study shows that the novel approach to evaluate the GNSS-RO signal receiver performance in terms of the ground-based atmospheric occultation simulation system can efficiently and accurately be used to carry out the receiver test and provides an effective solution for the ground-based test of GNSS-RO signal receivers.
DMCH: A Deep Metric and Category-Level Semantic Hashing Network for Retrieval in Remote Sensing
Authors: Huang Haiyan , Cheng Qimin , Shao Zhenfeng , Huang Xiao , Shao Liyuan
Published: December 2023

The effectiveness of hashing methods in big data retrieval has been proved due to their merit in computational and storage efficiency. Recently, encouraged by the strong discriminant capability of deep learning in image representation, various deep hashing methodologies have emerged to enhance retrieval performance. However, maintaining the semantic richness inherent in remote sensing images (RSIs), characterized by their scene intricacy and category diversity, remains a significant challenge. In response to this challenge, we propose a novel two-stage deep metric and category-level semantic hashing network termed DMCH. First, it introduces a novel triple-selection strategy during the semantic metric learning process to optimize the utilization of triple-label information. Moreover, it inserts a hidden layer to enhance the latent correlation between similar hash codes via a designed category-level classification loss. In addition, it employs additional constraints to keep bit-uncorrelation and bit-balance of generated hash codes. Furthermore, a progressive coarse-to-fine hash code sorting scheme is used for superior fine-grained retrieval and more effective hash function learning. Experiment results on three datasets illustrate the effectiveness and superiority of the proposed method.
A Point Cloud Segmentation Method for Dim and Cluttered Underground Tunnel Scenes Based on the Segment Anything Model
Authors: Kang Jitong , Chen Ning , Li Mei , Mao Shanjun , Zhang Haoyuan , Fan Yingbo , Liu Hui
Published: December 2023

In recent years, point cloud segmentation technology has increasingly played a pivotal role in tunnel construction and maintenance. Currently, traditional methods for segmenting point clouds in tunnel scenes often rely on a multitude of attribute information, including spatial distribution, color, normal vectors, intensity, and density. However, the underground tunnel scenes show greater complexity than road tunnel scenes, such as dim light, indistinct boundaries of tunnel walls, and disordered pipelines. Furthermore, issues pertaining to data quality, such as the lack of color information and insufficient annotated data, contribute to the subpar performance of conventional point cloud segmentation algorithms. To address this issue, a 3D point cloud segmentation framework specifically for underground tunnels is proposed based on the Segment Anything Model (SAM). This framework effectively leverages the generalization capability of the visual foundation model to automatically adapt to various scenes and perform efficient segmentation of tunnel point clouds. Specifically, the tunnel is first sliced along its direction on the tunnel line. Then, each sliced point cloud is projected onto a two-dimensional plane. Various projection methods and point cloud coloring techniques are employed to enhance SAM’s segmentation performance in images. Finally, the semantic segmentation of the entire underground tunnel is achieved by a small set of manually annotated semantic labels used as prompts in a progressive and recursive manner. The key feature of this method lies in its independence from model training, as it directly and efficiently addresses tunnel point cloud segmentation challenges by capitalizing on the generalization capability of foundation model. Comparative experiments against classical region growing algorithms and PointNet++ deep learning algorithms demonstrate the superior performance of our proposed algorithm.
Measuring the Canopy Architecture of Young Vegetation Using the Fastrak Polhemus 3D Digitizer
Authors: Šleglová Kristýna , Brichta Jakub , Bílek Lukáš , Surový Peter
Published: December 2023

In the context of climate change conditions, addressing the shifting composition of forest stands and changes in traditional forest management practices are necessary. For this purpose, understanding the biomass allocation directly influenced by crown architecture is crucial. In this paper, we want to demonstrate the possibility of 3D mensuration of canopy architecture with the digitizer sensor Fastrak Polhemus and demonstrate its capability for assessing important structural information for forest purposes. Scots pine trees were chosen for this purpose, as it is the most widespread tree species in Europe, which, paradoxically, is very negatively affected by climate change. In our study, we examined young trees since the architecture of young trees influences their growth potential. In order to get the most accurate measurement of tree architecture, we evaluated the use of the Fastrak Polhemus magnetic digitizer to create a 3D model of individual trees and perform a subsequent statistical analysis of the data obtained. It was found that the stand density affects the number of branches in different orders and the heights of the trees in the process of natural regeneration. Regarding the branches, in our case, the highest number of branch orders was found in the clear-cut areas (density = 0.0), whereas the lowest branching was on-site with mature stands (density = 0.8). The results showed that the intensity of branching (assessed as the number of third-order branches) depends on the total number of branches of the tree of different branch orders but also on stand density where the tree is growing. An important finding in this study was the negative correlation between the tree branching and the tree height. The growth in height is lower when the branching expansion is higher. Similar data could be obtained with Lidar sensors. However, the occlusion due to the complexity of the tree crown would impede the information from being complete when using the magnetic digitizer. These results provide vital information for the creation of structural-functional models, which can be used to predict and estimate future tree growth and carbon fixation.
SOCA-PRNet: Spatially Oriented Attention-Infused Structured-Feature-Enabled PoseResNet for 2D Human Pose Estimation
Authors: Zakir Ali , Salman Sartaj Ahmed , Takahashi Hiroki
Published: December 2023

In the recent era, 2D human pose estimation (HPE) has become an integral part of advanced computer vision (CV) applications, particularly in understanding human behaviors. Despite challenges such as occlusion, unfavorable lighting, and motion blur, advancements in deep learning have significantly enhanced the performance of 2D HPE by enabling automatic feature learning from data and improving model generalization. Given the crucial role of 2D HPE in accurately identifying and classifying human body joints, optimization is imperative. In response, we introduce the Spatially Oriented Attention-Infused Structured-Feature-enabled PoseResNet (SOCA-PRNet) for enhanced 2D HPE. This model incorporates a novel element, Spatially Oriented Attention (SOCA), designed to enhance accuracy without significantly increasing the parameter count. Leveraging the strength of ResNet34 and integrating Global Context Blocks (GCBs), SOCA-PRNet precisely captures detailed human poses. Empirical evaluations demonstrate that our model outperforms existing state-of-the-art approaches, achieving a Percentage of Correct Keypoints at 0.5 (PCKh@0.5) of 90.877 at a 50% threshold and a Mean Precision (Mean@0.1) score of 41.137. These results underscore the potential of SOCA-PRNet in real-world applications such as robotics, gaming, and human–computer interaction, where precise and efficient 2D HPE is paramount.
Reprojection Error Analysis and Algorithm Optimization of Hand–Eye Calibration for Manipulator System
Authors: Peng Gang , Ren Zhenyu , Gao Qiang , Fan Zhun
Published: December 2023

The Euclidean distance error of calibration results cannot be calculated during the hand–eye calibration process of a manipulator because the true values of the hand–eye conversion matrix cannot be obtained. In this study, a new method for error analysis and algorithm optimization is presented. An error analysis of the method is carried out using a priori knowledge that the location of the augmented reality markers is fixed during the calibration process. The coordinates of the AR marker center point are reprojected onto the pixel coordinate system and then compared with the true pixel coordinates of the AR marker center point obtained by corner detection or manual labeling to obtain the Euclidean distance between the two coordinates as the basis for the error analysis. We then fine-tune the results of the hand–eye calibration algorithm to obtain the smallest reprojection error, thereby obtaining higher-precision calibration results. The experimental results show that, compared with the Tsai–Lenz algorithm, the optimized algorithm in this study reduces the average reprojection error by 44.43% and the average visual positioning error by 50.63%. Therefore, the proposed optimization method can significantly improve the accuracy of hand–eye calibration results.
Reverberation-Robust Self-Calibration and Synchronization of Distributed Microphone Arrays by Mitigating Heteroscedasticity and Outlier Occurrence in TDoA Measurements
Authors: Woźniak Szymon , Kowalczyk Konrad
Published: December 2023

The network of distributed microphone arrays is usually established in an ad hoc manner; hence, network parameters such as the mutual positioning and rotation of the arrays, positions of sources, and synchronization of their recording onset times are initially unknown. In this article, we consider the problem of passively jointly self-calibrating and synchronizing distributed arrays in reverberant rooms. We use a typical two-step approach where, initially, the relative geometry of the network is estimated using Direction of Arrival (DoA) measurements. Subsequently, the absolute scale and synchronization parameters are estimated using Time Difference of Arrival (TDoA) measurements. This article presents methods to improve the robustness and accuracy of estimation of the absolute geometric scaling and synchronization parameters in reverberant conditions, in which TDoA measurements do not follow a normal distribution; furthermore, outliers often occur. To remedy these issues, we propose a Weighted Least Squares (WLS) estimator and schema for weighting the TDoA measurements to increase the estimation accuracy from heteroscedastic TDoA measurements. In addition, we propose an iterative reweighing algorithm with a binary weight to detect and reject TDoA outliers, which exploits the residuals of the parametric model in the least absolute value minimization. A numerical evaluation shows significant improvements in the proposed method over the state of the art in terms of the relative scaling error and mean absolute value of the synchronization parameters.
Multifunction Applications of Filtering Dielectric Resonator Antenna Based on Liquid Crystal
Authors: Xia Ke , Zhang Lei , Zhang Haifeng
Published: December 2023

In this paper, a new type of multifunctional device is realized by designing a filtering dielectric resonator antenna (FDRA) with liquid crystal (LC). The LC is encapsulated by glass plates and placed between the feeding network and the ground. Firstly, the resonance frequencies of the hairpin bandpass filter (|S11| is less than −10 dB) move simultaneously when the dielectric constant of LC changes at different temperatures. Then, the hairpin bandpass filter is extended to an FDRA, and the influence of the dielectric constant of LC on the antenna performance parameters is realized to the function of the temperature sensor. The results show that the dielectric constant of LC has an approximately linear relationship with the resonance frequencies of the FDRA. Simultaneously, the axial ratio, gain, antenna efficiency, E-field distribution, and pattern of the FDRA have changed significantly. Furthermore, the FDRA mainly works in the frequency range of 4.65~5.53 GHz, which has good antenna performance and filtering characteristics. Taking resonance frequency fx as an example, its sensitivity, maximum FOM, minimum detection limit, and minimum resolution are determined to be 95 GHz/RIU, 0.5, 0.1, and 9.68, respectively. The multifunctional device provides a novel approach and solution for the transmission of antenna signals and temperature measurements.
An Efficient Implementation Method for Distributed Fusion in Sensor Networks Based on CPHD Filters
Authors: Wang Liu , Chen Guifen
Published: December 2023

A highly efficient implementation method for distributed fusion in sensor networks based on CPHD filters is proposed to address the issues of unknown cross-covariance fusion estimation and long fusion times in multi-sensor distributed fusion. This method can effectively and efficiently fuse multi-node information in multi-target tracking applications. Discrete gamma cardinalized probability hypothesis density (DG-CPHD) can effectively reduce the computational burden while ensuring computational accuracy similar to that of CPHD filters. Parallel inverse covariance intersection (PICI) can effectively avoid solving high-dimensional weight coefficient convex optimization problems, reduce the computational burden, and efficiently implement filtering fusion strategies. The effectiveness of the algorithm is demonstrated through simulation results, which indicate that PICI-GM-DG-CPHD can substantially reduce the computational time compared to other algorithms and is more suitable for distributed sensor fusion.
Enhancement Methods of Hydropower Unit Monitoring Data Quality Based on the Hierarchical Density-Based Spatial Clustering of Applications with a Noise–Wasserstein Slim Generative Adversarial Imputation Network with a Gradient Penalty
Authors: Zhang Fangqing , Guo Jiang , Yuan Fang , Qiu Yuanfeng , Wang Pei , Cheng Fangjuan , Gu Yifeng
Published: December 2023

In order to solve low-quality problems such as data anomalies and missing data in the condition monitoring data of hydropower units, this paper proposes a monitoring data quality enhancement method based on HDBSCAN-WSGAIN-GP, which improves the quality and usability of the condition monitoring data of hydropower units by combining the advantages of density clustering and a generative adversarial network. First, the monitoring data are grouped according to the density level by the HDBSCAN clustering method in combination with the working conditions, and the anomalies in this dataset are detected, recognized adaptively and cleaned. Further combining the superiority of the WSGAIN-GP model in data filling, the missing values in the cleaned data are automatically generated by the unsupervised learning of the features and the distribution of real monitoring data. The validation analysis is carried out by the online monitoring dataset of the actual operating units, and the comparison experiments show that the clustering contour coefficient (SCI) of the HDBSCAN-based anomaly detection model reaches 0.4935, which is higher than that of the other comparative models, indicating that the proposed model has superiority in distinguishing between the valid samples and anomalous samples. The probability density distribution of the data filling model based on WSGAIN-GP is similar to that of the measured data, and the KL dispersion, JS dispersion and Hellinger’s distance of the distribution between the filled data and the original data are close to 0. Compared with the filling methods such as SGAIN, GAIN, KNN, etc., the effect of data filling with different missing rates is verified, and the RMSE error of data filling with WSGAIN-GP is lower than that of other comparative models. The WSGAIN-GP method has the lowest RMSE error under different missing rates, which proves that the proposed filling model has good accuracy and generalization, and the research results in this paper provide a high-quality data basis for the subsequent trend prediction and state warning.
Influence of Different Pt Functionalization Modes on the Properties of CuO Gas-Sensing Materials
Authors: Chen Xiangxiang , Liu Tianhao , Ouyang Yunfei , Huang Shiyi , Zhang Zhaoyang , Liu Fangzheng , Qiu Lu , Wang Chicheng , Lin Xincheng , Chen Junyan , Shen Yanbai
Published: December 2023

The functionalization of noble metals is an effective approach to lowering the sensing temperature and improving the sensitivity of metal oxide semiconductor (MOS)-based gas sensors. However, there is a dearth of comparative analyses regarding the differences in sensitization mechanisms between the two functionalization modes of noble metal loading and doping. In this investigation, we synthesized Pt-doped CuO gas-sensing materials using a one-pot hydrothermal method. And for Pt-loaded CuO, Pt was deposited on the synthesized pristine CuO surface by using a dipping method. We found that both functionalization methods can considerably enhance the response and selectivity of CuO toward NO2 at low temperatures. However, we observed that CuO with Pt loading had superior sensing performance at 25 °C, while CuO with Pt doping showed more substantial response changes with an increase in the operating temperature. This is mainly due to the different dominant roles of electron sensitization and chemical sensitization resulting from the different forms of Pt present in different functionalization modes. For Pt doping, electron sensitization is stronger, and for Pt loading, chemical sensitization is stronger. The results of this study present innovative ideas for understanding the optimization of noble metal functionalization for the gas-sensing performance of metal oxide semiconductors.
Research on Circuit Breaker Operating Mechanism Fault Diagnosis Method Combining Global-Local Feature Extraction and KELM
Authors: Liu Qinzhe , Wang Xiaolong , Guo Zhaojing , Li Jian , Xu Wei , Dai Xiaowen , Liu Chenlei , Zhao Tong
Published: December 2023

In response to the lack of generality in feature extraction using modal decomposition methods and the susceptibility of diagnostic performance to parameter selection in traditional mechanical fault diagnosis of high-voltage circuit breaker operating mechanisms, this paper proposes a Global-Local feature extraction method based on Generalized S-Transform (S-Translate) combined with Gray Level Co-Occurrence Matrix (GLCM) and complemented by Maximum Relevance and Minimum Redundancy (mRMR) feature selection. The GL (Global-Local)-mRMR-KELM fault diagnosis model is proposed, which employs the Kernel Extreme Learning Machine (KELM). In this model, the original time-frequency domain features and the time-frequency features of the Generalized S-Transform matrix of vibration signals under different states of the circuit breaker are first extracted as global features. Then, the GLCM is obtained to extract texture features as local features. Finally, the mRMR and KELM are comprehensively applied to perform feature selection and classification on the dataset, thereby accomplishing the fault diagnosis of the circuit breaker’s operating mechanism. In this study, the 72.5 kV SF6 circuit breaker operating mechanism is taken as the research object, and three types of mechanical faults are simulated to obtain a vibration signal. Experimental results verify the effectiveness of the proposed GL-mRMR-KELM model, achieving a diagnostic accuracy of 96%. This research provides a feasible approach for the fault diagnosis of circuit breaker operating mechanisms.
An Overview on Recent Advances in Biomimetic Sensors for the Detection of Perfluoroalkyl Substances
Authors: Ahmadi Tabar Fatemeh , Lowdon Joseph W. , Bakhshi Sichani Soroush , Khorshid Mehran , Cleij Thomas J. , Diliën Hanne , Eersels Kasper , Wagner Patrick , van Grinsven Bart
Published: December 2023

Per- and polyfluoroalkyl substances (PFAS) are a class of materials that have been widely used in the industrial production of a wide range of products. After decades of bioaccumulation in the environment, research has demonstrated that these compounds are toxic and potentially carcinogenic. Therefore, it is essential to map the extent of the problem to be able to remediate it properly in the next few decades. Current state-of-the-art detection platforms, however, are lab based and therefore too expensive and time-consuming for routine screening. Traditional biosensor tests based on, e.g., lateral flow assays may struggle with the low regulatory levels of PFAS (ng/mL), the complexity of environmental matrices and the presence of coexisting chemicals. Therefore, a lot of research effort has been directed towards the development of biomimetic receptors and their implementation into handheld, low-cost sensors. Numerous research groups have developed PFAS sensors based on molecularly imprinted polymers (MIPs), metal–organic frameworks (MOFs) or aptamers. In order to transform these research efforts into tangible devices and implement them into environmental applications, it is necessary to provide an overview of these research efforts. This review aims to provide this overview and critically compare several technologies to each other to provide a recommendation for the direction of future research efforts focused on the development of the next generation of biomimetic PFAS sensors.
Connecting Us Back to Ourselves: Aesthetic Experience as a Means to Growth after Trauma
Authors: Bennett Jill , Kenning Gail , Wobcke Marianne , Gitau Lydia
Published: December 2023

This article examines the experience and effects of a trauma-responsive program that uses creative methods to address the ongoing psychosocial impacts of transgenerational trauma and youth suicide, which disproportionately affect First Nations people in Australia. Our aim is to understand how the aesthetic (sensory-affective) dimensions of such a program serve to promote experiences of growth after trauma, manifesting in a sense of connection to both self and community. The paper focuses on the second of two immersive, experiential workshops delivered seven months apart in the regional town of Warwick in Queensland, Australia. In the light of self-reports of growth and personal transformation following the initial workshop, the paper examines the key drivers of such growth, focusing in particular on how trauma-related experience is metabolised through cultural containment. It builds on Bion’s concept of container/contained, combining analysis of the affordances of immersion. Framed in cultural rather than medical terms, the larger goal of the paper is to establish how cultural programs fill a gap in trauma informed support, facilitating the processing of trauma.
Carbon Footprint of Greenhouse Production in EU—How Close Are We to Green Deal Goals?
Authors: Ravani Maria , Georgiou Konstantinos , Tselempi Stefania , Monokrousos Nikolaos , Ntinas Georgios
Published: December 2023

Sustainable greenhouse production has been brought to the forefront as one of the pillars in achieving the objectives set by the Green Deal strategy in 2020, for drastically decreasing net emissions from agriculture. The scope of this review was to capture the current situation regarding the sustainability of greenhouse production in the European Union and to present ways to decrease the carbon footprint. For this reason, a systematic search of studies was conducted, focusing on the investigation of the environmental assessment of conventional greenhouses in EU along with a bibliometric analysis to identify the relationships between the studies. In total, 52 papers were selected for an in-depth analysis that led to addressing the posed research questions. The study reveals that Spain and Italy were the most active countries in the literature for the calculation of the carbon footprint in greenhouses, the value of which showed a large variation per crop and per country and was significantly affected by the use of non-renewable energy sources. It was observed that practical solutions to reduce the carbon footprint of greenhouses have already been implemented and proposed, which indicates a positive inclination towards achieving the Green Deal objectives.
From Thinking Green to Riding Green: A Study on Influencing Factors in Electric Vehicle Adoption
Authors: Rafiq Farrukh , Parthiban Eden Samuel , Rajkumari Yaisna , Adil Mohd , Nasir Mohd , Dogra Nikhil
Published: December 2023

Recent changes in national and international environmental policy place a lot of emphasis on electric vehicles (EVs) as they reduce ecological damage by eliminating emissions. However, given the products’ novelty, consumers have expressed mixed emotions about EV purchases. Skepticism surrounding EV reliability is a significant concern for potential adopters, primarily due to a lack of essential support. As a result, to resolve the problem of consumers’ mixed emotions toward EVs, it is necessary to understand how consumer environmental concerns (EC), attitudes, subjective norms and perceived behavioral control shape consumers’ intentions. Leveraging the modified and expanded version of the Theory of Planned Behavior (TPB), the current study developed and validated a conceptual model and hypotheses through cross-sectional research. Employing the snowball sampling method, we collected data from 274 respondents via an online survey. The study establishes that social pressure can impact individual ATT, fostering enduring benefits for respondentsʹ EC and behavioral intentions. Consequently, active involvement by EV manufacturers in advocating sustainable consumption is paramount to catalyzing future market growth. Our research is a ground-breaking attempt to determine EV interest among consumers. The nuanced findings hold implications for marketers/practitioners involved on the supply side of the EV business.
Sustainable Healthcare Resilience: Disaster Preparedness in Saudi Arabia’s Eastern Province Hospitals
Authors: AlDulijand Noora A. , Al-Wathinani Ahmed M. , Abahussain Mohammed A. , Alhallaf Mohammad A. , Farhat Hassan , Goniewicz Krzysztof
Published: December 2023

In a world increasingly vulnerable to environmental and health crises, sustainable healthcare systems are crucial. This study focuses on the resilience and sustainability of healthcare services in Saudi Arabia’s Eastern Province, assessing their readiness to endure and adapt amidst disaster scenarios. Conducted through a cross-sectional survey involving 522 hospital employees, the research provides an in-depth analysis of various sustainability-related aspects of healthcare preparedness. This includes examining both structural and non-structural safety elements, standard operating procedures, staff training in disaster response, data security, and sustainable management of resources during emergencies. The results reveal a noteworthy commitment to sustainable disaster preparedness: 53.7% of hospitals, predominantly governed by the Ministry of Health, have implemented comprehensive disaster recovery plans. Furthermore, the study found a strong representation of female participants (54.2%) in these hospitals. A significant 64.1% of respondents confirmed the existence of disaster recovery plans in their workplaces, and an even higher percentage, 70.6%, reported receiving specific training in disaster response. These findings underscore the proactive efforts of Eastern Province hospitals in fostering a sustainable and resilient healthcare framework, demonstrating an approach that prioritizes preparedness beyond immediate crisis response. This proactive stance is pivotal in ensuring the continuity of healthcare services amidst the ever-evolving nature of global disasters.
Blockchain-Empowered Decentralized Philanthropic Charity for Social Good
Authors: Ahmed Istiaque , Fumimoto Kai , Nakano Tadashi , Tran Thi Hong
Published: December 2023

The charity sector impacts society significantly in many areas, including providing education, healthcare, hunger relief, drinking water, disaster relief, environmental preservation, and assistance to underserved people. The existing charity organizations have numerous limitations, such as poor management, high operation costs, and a lack of transparency in the donation execution flow. The authentication of users and institutions is a big problem in the existing system. This research resolves the issues of transparency and reliability with an immutable and traceable distributed ledger. We empower the existing centralized charity works with the electronic know-your-customer (eKYC) authentication approach and cryptographic HASH. Information privacy is implemented using the filters within smart contracts. The implementation of eKYC to ensure authenticity and to secure data flow through the channel are two significant contributions of this work. A coin-toss function for data selection and a random time delay between pieces of data are used to avoid attacks based on guesswork. We aim for this framework to send 100% of donations to the beneficiaries and become a hyper-liquid medium to fill the United Nations Sustainable Development Goals (SDG) funding gap. We also introduce the concept of service charity to broaden the ability for people to offer their services and skills as charity.
Spatio-Temporal Dynamics of Terminal Lakes in the Hexi Interior, China
Authors: Ma Qin , Yao Xiaojun , Zhang Cong , Yang Chen , Yang Kang , Tian Zhijuan , Li Jiawei
Published: December 2023

The evolution of a terminal lake at the end of a river not only reflects the climate change characteristics within the basin but also the impact of regional human activities, especially in arid areas. In the Hexi Interior of China, three terminal lakes (e.g., Halaqi Lake, East Juyanhai Lake, and Qingtu Lake) situated in the Shule River, Heihe River and Shiyang River, respectively, have been increasingly studied to support regional ecological protection and sustainable oasis development. In this study, Landsat TM/ETM+/OLI and Sentinel-2 MSI imagery were used to examine Halaqi Lake spanning from 2017 to 2022, East Juyanhai Lake from 1990 to 2022, and Qingtu Lake from 2009 to 2022. The focus of this investigation was to characterize changes in lake area and the impact of climate change and human activities. The results revealed a dramatic change in Halaqi Lake, which suddenly emerged in 2017, initially covering an area of 13.49 km2, gradually vanishing nearly in 2021, and reappearing in 2022 with a reduced area of 9.53 km2. The area of East Juyanhai Lake was 54.39 km2 in 1990 but reduced to 40.84 km2 by 2022. Throughout this period, it encountered episodes of drying up in 1992, 1995, 2001, and 2002. Qingtu Lake emerged in 2009, with an area of 0.09 km2, and subsequently expanded to 2.60 km2 by 2022. Climate change and human activities collectively influence the area fluctuations of these three terminal lakes. Among these factors, temperature changes have a greater impact on the lake area in East Juyanhai. Global warming has worsened glacier melting in the Qilian Mountains, resulting in increased inflow in certain years and substantial lake area expansion. Human activities are the primary drivers of changes in Halaqi Lake and Qingtu Lake. Industrial water consumption is the key factor influencing area changes in Halaqi Lake, whereas water usage in forestry, animal husbandry, and fisheries plays a dominant role in the area changes of Qingtu Lake. Furthermore, the introduction of ecological water conveyance projects has had an indispensable effect on rejuvenating and preserving the watershed areas of these three terminal lakes. It is important to emphasize that human-driven water resource management is the primary cause of sudden changes in the lake areas.
Temporal and Spatial Changes of Agriculture Green Development in Beijing’s Ecological Conservation Developing Areas from 2006 to 2016
Authors: Li Hong , Zhang Weiwei , Xiao Xiao , Lun Fei , Sun Yifu , Sun Na
Published: December 2023

As an irreplaceable ecological barrier, an ecological conservation developing area (ECDA) is vital for the integrated construction of urban and rural areas and the optimization and adjustment of industrial structures. However, few empirical studies have been conducted on the spatiotemporal variations of agricultural green development (AGD) in the ECDAs of large cities. Based on the green agricultural traits of Beijing and the accessible data, we evaluated the AGD and analyzed its spatial and temporal heterogeneity in Beijing’s ECDAs by constructing a framework with 13 indicators. The results stated that energy consumption is a vital factor in green agriculture production and that the agricultural output value per unit of arable land area is the key to green agricultural revenue. From 2006 to 2016, the AGD index of the ECDA had an increasing trend, until 2012 when it followed a decreasing tendency. The AGD index of the northern region was higher than in the southern ECDA. The obstacle degree model was used to verify the AGD limiting factors, where poor infrastructure, slow agritourism, low labor productivity, and low resource use efficiency varied by districts in the ECDA. Given these findings, our study is conducive to AGD evaluation at the district (county) level for the ECDAs of large cities and provides important policy implications.
Identifying the Level of Symmetrization of Reaction Time According to Manual Lateralization between Team Sports Athletes, Individual Sports Athletes, and Non-Athletes
Authors: Badau Dana , Badau Adela , Joksimović Marko , Manescu Catalin Octavian , Manescu Dan Cristian , Dinciu Corina Claudia , Margarit Iulius Radulian , Tudor Virgil , Mujea Ana Maria , Neofit Adriana , Teodor Dragos Florin
Published: December 2023

The present study aimed to investigate the impact of practicing sports activities on manual skills, focused on reaction time depending on manual laterality. The objectives of the study were to identify the differences in improving simple, optional, and cognitive reaction times in the manual executions of student athletes who practice team sports involving the manual handling of the ball (volleyball, basketball, handball) in comparison with student athletes who practice individual sports and with non-athletic students; to identify the differences regarding the reaction time of the right- and left-handed executions depending on the manual lateralization of the subjects (right- and left-handedness) between the three experimental samples: team sports group (TSG), individual sports group (ISG), and the group of non-athletes (NAG) through the use of computer tests. The study included 335 subjects who were divided into three groups: TSG with 102 subjects, ISG with 112 subjects, and NAG with 121 subjects. The subjects of the study were given five computer tests to evaluate three types of reaction time: simple reaction time (Start/Stop Test), choice reaction time (Check Boxes Test, Hit-the-dot Test), and time of cognitive reaction (Trail making Test part A and B). The results were analyzed regarding right- and left-handedness, as well as the execution hand (right hand or left hand) in solving the tests. The results of the study highlighted significant statistical differences between the three groups: TSG, ISG, and NAG. The best results were recorded by TSG in all tests, and the lowest by NAG. Statistically significant differences were also recorded between the executions with the dominant hand compared to the executions with the non-dominant hand in relation to right- and left-handedness. The study highlighted that the smallest differences in all the study groups were recorded in the simple reaction time test, where the differences between the right-handed and left-handed executions were the lowest, reflecting the best level of symmetrization of the motor executions.
The Distribution Characteristics and Potential Risk Assessment of Lead in the Soil of Tieguanyin Tea Plantations in Anxi County, China
Authors: Zhan Yuanyuan , Zhu Qin , Li Xiaolin , Tao Changwu , Su Huogui , Wu Yuede , Lin Jinshi , Zhang Yue , Huang Yanhe , Jiang Fangshi
Published: December 2023

Assessing the distribution and risks associated with the soil lead content in the Tieguanyin tea plantations of Anxi County is critical, given the county’s significance as the primary Tieguanyin tea production area in Fujian Province. This study examined the distribution characteristics of soil lead in Anxi County’s tea plantations according to the Kriging spatial interpolation of the parameters of the semivariance function of the exponential model. Moreover, the sources of lead content were analyzed, considering geological backgrounds and anthropogenic influences. Ecological risks and the issuance of early warnings were also assessed. The soil lead content in the rocks of the Tieguanyin tea plantations in Anxi County followed the order: andesite > dacite > rhyolite > granite. The soil lead content gradually decreased from the center toward the east and west, forming four distinct north–south parallel zones. High-lead-content areas were identified at the border of Jiandou, Bailai, and Hushang; in the central part of Lutian; and in the southern part of Huqiu. The high levels of soil lead in the tea plantations possibly originated from industrial and mining activities, automobile exhaust, and agricultural activities. The distribution of single-factor pollution indices and potential risk evaluation based on the Soil Environmental Quality Standard, Environmental Technical Conditions for Tea Production Area, and Environmental Technical Conditions for Organic Tea Production Area indicated that the soil in Tieguanyin tea plantations in Anxi County was clean and safe for tea cultivation.
Diversity in Proprotein Convertase Reactivity among Human Papillomavirus Types
Authors: Izaguirre Gonzalo , Phan Lam Minh Uyen , Asif Shaan , Alam Samina , Meyers Craig , Rong Lijun
Published: December 2023

The cleavage of viral surface proteins by furin is associated with some viruses’ high virulence and infectivity. The human papillomavirus (HPV) requires the proteolytic processing of its capsid proteins for activation before entry. Variability in reactivity with furin and other proprotein convertases (PCs) among HPV types was investigated. HPV16, the most prevalent and carcinogenic HPV type, reacted with PCs with the broadest selectivity compared to other types in reactions of pseudoviral particles with the recombinant PCs, furin, PC4, PC5, PACE4, and PC7. Proteolytic preactivation was assessed using a well-established entry assay into PC-inhibited cells based on the green fluorescent protein as a reporter. The inhibition of the target cell PC activity with serpin-based PC-selective inhibitors also showed a diversity of PC selectivity among HPV types. HPV16 reacted with furin at the highest rate compared to the other types in time-dependent preactivation reactions and produced the highest entry values standardized to pseudoviral particle concentration. The predominant expression of furin in keratinocytes and the high reactivity of HPV16 with this enzyme highlight the importance of selectively targeting furin as a potential antiviral therapeutic approach.
Cytokines, Serological, and Histopathological Assessment of Recombinant Vaccination Strategies for Combatting Infectious Bursal Disease in Broiler Chickens
Authors: Gewaily Mahmoud S. , El-Khyat Fares , Tahoon Abd Elnaby , Al-Rasheed Mohammed , Abdo Safaa E. , Gado Ahmed , Elmasry Mohamed , Ismail Mahmoud M.
Published: December 2023

Infectious bursal disease (IBD) represents a greatly transmissible viral disease found worldwide, causing significant health and production challenges in young chickens. The aim of this research was to assess the immune reaction induced by different vaccines targeting IBD. These vaccines included recombinant (Vac1; HVT-IBD vector), immune complex (Vac2; Bursa-Plex®), and intermediate plus (Vac3; Bursine plus) IBD vaccines. Our assessment relied on serological and histopathological analyses, as well as the pattern of immune-related cytokine expression in the bursal tissue. The vaccinated groups, along with a control positive (CP) group, were subjected to a vvIBDV challenge on their 28th day of life, while the control negative (CN) group received a mock vaccination with PBS. Our study revealed that Vac1 resulted in the most favorable growth performance, as well as maintained normal liver and kidney function, mitigating the impact of IBDV infection. Serological analysis using VP2 ELISA kits indicated that Vac1 induced the strongest immunological response among all vaccines. Histopathological examination demonstrated that Vac1 caused minimal lymphoid depletion observed in the lymphoid organs, followed by Vac2. Analysis of cytokine expression profiles showed significant upregulation in all vaccinated groups, particularly Vac1, during the pre-challenge period. Following IBDV infection, Vac1 resulted in a noteworthy increase in the expression of IL2 and IFN-γ, Vac2 showed a significant upregulation in TNF-α and granzyme, and both Vac1 and Vac3 exhibited increased levels of IL1β and IL10. In conclusion, our study suggests that the various vaccines triggered immune responses against IBD through both humoral and cell-mediated immunity. However, recombinant followed by immune complex vaccines appeared to induce more robust immunity while also being safer for broiler chickens in contrast to the intermediate plus vaccine.
Detection of Floating Objects on Water Surface Using YOLOv5s in an Edge Computing Environment
Authors: Li He , Yang Shuaipeng , Zhang Rui , Yu Peng , Fu Zhumu , Wang Xiangyang , Kadoch Michel , Yang Yang
Published: December 2023

Aiming to solve the problems with easy false detection of small targets in river floating object detection and deploying an overly large model, a new method is proposed based on improved YOLOv5s. A new data augmentation method for small objects is designed to enrich the dataset and improve the model’s robustness. Distinct feature extraction network levels incorporate different coordinate attention mechanism pooling methods to enhance the effective feature information extraction of small targets and improve small target detection accuracy. Then, a shallow feature map with 4-fold down-sampling is added, and feature fusion is performed using the Feature Pyramid Network. At the same time, bilinear interpolation replaces the up-sampling method to retain feature information and enhance the network’s ability to sense small targets. Network complex algorithms are optimized to better adapt to embedded platforms. Finally, the model is channel pruned to solve the problem of difficult deployment. The experimental results show that this method has a better feature extraction capability as well as a higher detection accuracy. Compared with the original YOLOv5 algorithm, the accuracy is improved by 15.7%, the error detection rate is reduced by 83% in small target task detection, the detection accuracy can reach 92.01% in edge testing, and the inference speed can reach 33 frames per second, which can meet the real-time requirements.