bluuebunny/crossref_metadata_2025 huggingface dataset:
Ecological Sustainability for “Life on Land”: Wellspring of Indigenous Knowledge
Authors: Lushombo Léocadie Wabo
Published: February 2025

This article argues that indigenous knowledge is significantly resourceful for ecological sustainability, without which humanity will not survive. It addresses the intersection between the 15th United Nations Sustainable Development Goal (SGD) “Life on Land” and African indigenous knowledge systems, including the fundamental support system that can sustain the livelihoods of communities. It reconstructs the vision of ecological sustainability from the indigenous knowledge perspective by first analyzing the inadequacy of the United Nations carbon-pricing approach to reforestation and conservation in developing countries. Then, it uses the ethnosphere methodological approach, affirming the ecological ethical warrants found in indigenous epistemology and cosmology in regard to land protection in dialogue with the sustainability vision of Laudado Si’, Querida Amazonia, and Laudate Deum. This article explores indigenous knowledge’s wellspring for ecological sustainability and what it offers for a more sustainable “Life on Land”. It suggests an approach to ecological sustainability that goes beyond a market-based instrument to CO2 reduction to embrace a view of the “sacramental universe” as essential theological input, without which sustainable “Life on Land” cannot be met. It concludes by showing how African mountain region conservancy practices are not essential in sustaining “Life on Land” not solely because they provide the earth’s freshwater but also because they contain valuable ecologically sensitive cultural and religious wisdom.
Environmental and Closure Costs in Strategic Mine Planning, Models, Regulations, and Policies
Authors: Oliveros-Sepúlveda David , Bascompta-Massanés Marc , Franco-Sepúlveda Giovanni
Published: February 2025

This study explores the evolution of mine planning, with particular emphasis on the integration of environmental and social factors in alignment with the sustainable development. Traditionally, mine planning emphasized technical and economic variables, often overlooking environmental and social impacts. However, the increasing need to align with the Sustainable Development Goals (SDGs) has prompted a shift toward broader definitions that incorporate these factors into resource planning. This paradigm change is crucial for managing risks related to project profitability, which now include environmental considerations. The article also examines how government policies and corporate strategies, including Corporate Social Responsibility (CSR) and Environmental, Social, and Governance (ESG) frameworks have evolved to address these impacts. A review of the literature published over the last 25 years identifies four main thematic areas: (1) inclusion of environmental costs in mine planning, (2) quantitative models for calculating environmental and closure costs, (3) legal frameworks in mining, and (4) innovations in public policies. This study underscores the need for a comprehensive approach in mine planning that balances economic, social, and environmental considerations to ensure sustainability and mitigate risks associated with mine closure and environmental remediation.
Multi-Temporal Relative Sea Level Rise Scenarios up to 2150 for the Venice Lagoon (Italy)
Authors: Anzidei Marco , Tolomei Cristiano , Trippanera Daniele , Alberti Tommaso , Bosman Alessandro , Brunori Carlo Alberto , Serpelloni Enrico , Vecchio Antonio , Falciano Antonio , Deli Giuliana
Published: February 2025

The historical City of Venice, with its lagoon, has been severely exposed to repeated marine flooding since historical times due to the combined effects of sea level rise (SLR) and land subsidence (LS) by natural and anthropogenic causes. Although the sea level change in this area has been studied for several years, no detailed flooding scenarios have yet been realized to predict the effects of the expected SLR in the coming decades on the coasts and islands of the lagoon due to global warming. From the analysis of geodetic data and climatic projections for the Shared Socioeconomic Pathways (SSP1-2.6; SSP3-7.0 and SSP5-8.5) released in the Sixth Assessment Report (AR6) of the Intergovernmental Panel on Climate Change (IPCC), we estimated the rates of LS, the projected local relative sea level rise (RSLR), and the expected extent of flooded surfaces for 11 selected areas of the Venice Lagoon for the years 2050, 2100, and 2150 AD. Vertical Land Movements (VLM) were obtained from the integrated analysis of Global Navigation Satellite System (GNSS) and Interferometry Synthetic Aperture Radar (InSAR) data in the time spans of 1996–2023 and 2017–2023, respectively. The spatial distribution of VLM at 1–3 mm/yr, with maximum values up to 7 mm/yr, is driving the observed variable trend in the RSLR across the lagoon, as also shown by the analysis of the tide gauge data. This is leading to different expected flooding scenarios in the emerging sectors of the investigated area. Scenarios were projected on accurate high-resolution Digital Surface Models (DSMs) derived from LiDAR data. By 2150, over 112 km2 is at risk of flooding for the SSP1-2.6 low-emission scenario, with critical values of 139 km2 for the SSP5-8.5 high-emission scenario. In the case of extreme events of high water levels caused by the joint effects of astronomical tides, seiches, and atmospheric forcing, the RSLR in 2150 may temporarily increase up to 3.47 m above the reference level of the Punta della Salute tide gauge station. This results in up to 65% of land flooding. This extreme scenario poses the question of the future durability and effectiveness of the MoSE (Modulo Sperimentale Elettromeccanico), an artificial barrier that protects the lagoon from high tides, SLR, flooding, and storm surges up to 3 m, which could be submerged by the sea around 2100 AD as a consequence of global warming. Finally, the expected scenarios highlight the need for the local communities to improve the flood resiliency plans to mitigate the consequences of the expected RSLR by 2150 in the UNESCO site of Venice and the unique environmental area of its lagoon.
Study on GPR Image Restoration for Urban Complex Road Surfaces Using an Improved CycleGAN
Authors: Huang Xinxin , Liu Jialin , Yang Feng , Qiao Xu , Gao Liang , Fu Tingyang , Zhao Jianshe
Published: February 2025

In urban road detection using Ground Penetrating Radar (GPR), challenges arise from complex and variable road structures and diversified detection environments. These unstable factors decrease GPR detection signal strength and cause signal shape distortion, negatively affecting detection accuracy. This reduces the interpretive accuracy of GPR images, impacting precise diagnosis of underground structures and hidden defects in urban roads. Therefore, understanding and overcoming these challenges is practically important for improving GPR performance and interpretive efficiency in urban road detection. To address these issues, this study proposes an innovative strategy using unsupervised learning for GPR image restoration. Specifically, it utilizes the Cycle-Consistent Adversarial Network (CycleGAN) with the Convolutional Block Attention Module (CBAM) generator and integrates the Multi-Scale Structural Similarity Index (MS-SSIM) loss function to enhance restoration quality. The method is trained and validated using field experimentally collected datasets with and without road surface interference, and the performance is evaluated through qualitative and quantitative analysis of restored GPR B-scan images. The experimental results show that the proposed method improves image restoration by 4.9% in SSIM, 39.15% in PSNR, and 76.88% in MAE, confirming its significant effect in GPR image restoration.
Extraction of Periodic Terms in Satellite Clock Bias Based on Fourier Basis Pursuit Bandpass Filter
Authors: Shen Cong , Wang Guocheng , Liu Lintao , Ren Dong , Hu Huiwen , Sun Wenlong
Published: February 2025

Effective noise management and control of periodic fluctuations in spaceborne atomic clocks are essential for the accuracy and reliability of Global Navigation Satellite Systems. Time-varying periodic terms can impact both the performance evaluation and prediction accuracy of satellite clocks, making it crucial to mitigate these influences in the clock bias. We propose methods based on the Fourier dictionary and basis pursuit, namely the Fourier basis pursuit (FBP) spectrum and the Fourier basis pursuit bandpass filter (FBPBPF), to analyze and extract periodic terms in the satellite clock bias. The FBP method minimizes the L1-norm to improve spectral quality, while the FBPBPF reduces boundary effects and noise. Our experimental results show that the FBP spectrum has a more obvious main lobe and reduces spectral leakage compared to traditional windowed Fourier transforms. In simulation experiments, the FBPBPF achieves periodic term extraction with errors reduced by 6.81% to 26.55% compared to traditional signal processing methods, and boundary extraction errors reduced by up to 63.67%. Using the BeiDou Navigation Satellite System’s precise clock bias for verification, the FBP-based prediction method has significantly improved the prediction accuracy compared to the spectral analysis model. For 6, 12, 18, and 24 h predictions, the average root mean square error of the FBP prediction method is reduced by 15.85%, 11.04%, 6.45%, and 4.01%, respectively.
An Efficient Dynamic Engineering Implementation Architecture for MIMO Radar System
Authors: Wen Ruyue , Zhang Hua , Xu Luping
Published: February 2025

Multiple-input multiple-output (MIMO) technology can significantly improve radar system resolution and target detection ability and become the core technology in millimeter-wave radar systems. Each antenna of a MIMO radar system generates a separate data stream, resulting in an exponential increase in the amount of data. The MIMO radar system needs to process these data efficiently and quickly to ensure the real-time performance of the system. Conventional MIMO radar engineering systems have difficulty meeting the efficient processing requirements for large data volume processing, and an efficient dynamic engineering implementation architecture for a MIMO radar system is necessary. In such cases, we analyze the MIMO radar engineering system principle and propose an efficient dynamic MIMO radar engineering implementation architecture, which reduces the data storage step after the Doppler fast Fourier transform (FFT) and improves the storage efficiency and processing speed. In addition, we validate the effectiveness of the method by implementing it on a field-programmable gate array (FPGA) platform. The results show that the method is better than the conventional MIMO radar engineering implementation architecture regarding storage resource consumption and processing speed. Specifically, for the same antenna channel data volume, the MIMO radar system with the proposed architecture achieves a 50% reduction in storage resource consumption and a 15% improvement in processing speed compared to the conventional architecture. The proposed architecture demonstrates better antenna array compatibility across different antenna arrays. This work provides new ideas and methods for the efficient engineering implementation of MIMO radar systems. This work can support the practical application of MIMO radar, especially the engineering applications of large-scale array MIMO radar systems.
Maritime Target Radar Detection and Tracking via DTNet Transfer Learning Using Multi-Frame Images
Authors: He Xiaoyang , Chen Xiaolong , Du Xiaolin , Wang Xinghai , Xu Shuwen , Guan Jian
Published: February 2025

Traditional detection and tracking methods struggle with the complex and dynamic maritime environment due to their poor generalization capabilities. To address this, this paper improves the YOLOv5 network by integrating Transformer and a Convolutional Block Attention Module (CBAM) with the multi-frame image information obtained from radar scans. It proposes a detection and tracking method based on the Detection Tracking Network (DTNet), which leverages transfer learning and the DeepSORT tracking algorithm, enhancing the detection capabilities of the model across various maritime environments. First, radar echoes are preprocessed to create a dataset of Plan Position Indicator (PPI) images for different marine conditions. An integrated network for detecting and tracking maritime targets is then designed, utilizing the feature differences between moving targets and sea clutter, along with the coherence of inter-frame information for moving targets, to achieve multi-target detection and tracking. The proposed method was validated on real maritime targets, achieving a precision of 99.06%, which is a 7.36 percentage point improvement over the original YOLOv5, demonstrating superior detection and tracking performance. Additionally, the impact of maritime regions and weather conditions is discussed, showing that, when transferring from Region I to Regions II and III, the precision reached 92.2% and 89%, respectively, and, when facing rainy weather, although there was interference from the sea clutter and rain clutter, the precision was still able to reach 82.4%, indicating strong generalization capabilities compared to the original YOLOv5 network.
Regional Soil Moisture Estimation Leveraging Multi-Source Data Fusion and Automated Machine Learning
Authors: Li Shenglin , Zhu Pengyuan , Song Ni , Li Caixia , Wang Jinglei
Published: February 2025

Soil moisture (SM) monitoring in farmland at a regional scale is crucial for precision irrigation management and ensuring food security. However, existing methods for SM estimation encounter significant challenges related to accuracy, generalizability, and automation. This study proposes an integrated data fusion method to systematically assess the potential of three automated machine learning (AutoML) frameworks—tree-based pipeline optimization tool (TPOT), AutoGluon, and H2O AutoML—in retrieving SM. To evaluate the impact of input variables on estimation accuracy, six input scenarios were designed: multispectral data (MS), thermal infrared data (TIR), MS combined with TIR, MS with auxiliary data, TIR with auxiliary data, and a comprehensive combination of MS, TIR, and auxiliary data. The research was conducted in a winter wheat cultivation area within the People’s Victory Canal Irrigation Area, focusing on the 0–40 cm soil layer. The results revealed that the scenario incorporating all data types (MS + TIR + auxiliary) achieved the highest retrieval accuracy. Under this scenario, all three AutoML frameworks demonstrated optimal performance. AutoGluon demonstrated superior performance in most scenarios, particularly excelling in the MS + TIR + auxiliary data scenario. It achieved the highest retrieval accuracy with a Pearson correlation coefficient (R) value of 0.822, root mean square error (RMSE) of 0.038 cm3/cm3, and relative root mean square error (RRMSE) of 16.46%. This study underscores the critical role of input data types and fusion strategies in enhancing SM estimation accuracy and highlights the significant advantages of AutoML frameworks for regional-scale SM retrieval. The findings offer a robust technical foundation and theoretical guidance for advancing precision irrigation management and efficient SM monitoring.
A Semantically Guided Deep Supervised Hashing Model for Multi-Label Remote Sensing Image Retrieval
Authors: Liu Bowen , Liu Shibin , Liu Wei
Published: February 2025

With the rapid growth of remote sensing data, efficiently managing and retrieving large-scale remote sensing images has become a significant challenge. Specifically, for multi-label image retrieval, single-scale feature extraction methods often fail to capture the rich and complex information inherent in these images. Additionally, the sheer volume of data creates challenges in retrieval efficiency. Furthermore, leveraging semantic information for more accurate retrieval remains an open issue. In this paper, we propose a multi-label remote sensing image retrieval method based on an improved Swin Transformer, called Semantically Guided Deep Supervised Hashing (SGDSH). The method aims to enhance feature extraction capabilities and improve retrieval precision. By utilizing multi-scale information through an end-to-end learning approach with a multi-scale feature fusion module, SGDSH effectively integrates both shallow and deep features. A classification layer is introduced to assist in training the hash codes, incorporating RS image category information to improve retrieval accuracy. The model is optimized for multi-label retrieval through a novel loss function that combines classification loss, pairwise similarity loss, and hash code quantization loss. Experimental results on three publicly available remote sensing datasets, with varying sizes and label distributions, demonstrate that SGDSH outperforms state-of-the-art multi-label hashing methods in terms of average accuracy and weighted average precision. Moreover, SGDSH returns more relevant images with higher label similarity to query images. These findings confirm the effectiveness of SGDSH for large-scale remote sensing image retrieval tasks and provide new insights for future research on multi-label remote sensing image retrieval.
SAR Small Ship Detection Based on Enhanced YOLO Network
Authors: Guan Tianyue , Chang Sheng , Wang Chunle , Jia Xiaoxue
Published: February 2025

Ships are important targets for marine surveillance in both military and civilian domains. Since the rise of deep learning, ship detection in synthetic aperture radar (SAR) images has achieved significant progress. However, the variability in ship size and resolution, especially the widespread presence of numerous small-sized ships, continues to pose challenges for effective ship detection in SAR images. To address the challenges posed by small ship targets, we propose an enhanced YOLO network to improve the detection accuracy of small targets. Firstly, we propose a Shuffle Re-parameterization (SR) module as a replacement for the C2f module in the original YOLOv8 network. The SR module employs re-parameterized convolution along with channel shuffle operations to improve feature extraction capabilities. Secondly, we employ the space-to-depth (SPD) module to perform down-sampling operations within the backbone network, thereby reducing the information loss associated with pooling operations. Thirdly, we incorporate a Hybrid Attention (HA) module into the neck network to enhance the feature representation of small ship targets while mitigating the interference caused by surrounding sea clutter and speckle noise. Finally, we add the shape-NWD loss to the regression loss, which emphasizes the shape and scale of the bounding box and mitigates the sensitivity of Intersection over Union (IoU) to positional deviations in small ship targets. Extensive experiments were carried out on three publicly available datasets—namely, LS-SSDD, HRSID, and iVision-MRSSD—to demonstrate the effectiveness and reliability of the proposed method. In the small ship dataset LS-SSDD, the proposed method exhibits a notable improvement in average precision at an IoU threshold of 0.5 (AP50), surpassing the baseline network by over 4%, and achieving an AP50 of 77.2%. In the HRSID and iVision-MRSSD datasets, AP50 reaches 91% and 95%, respectively. Additionally, the average precision for small targets (AP) exhibits an increase of approximately 2% across both datasets. Furthermore, the proposed method demonstrates outstanding performance in comparison experiments across all three datasets, outperforming existing state-of-the-art target detection methods. The experimental results offer compelling evidence supporting the superior performance and practical applicability of the proposed method in SAR small ship detection.
Unsupervised SAR Image Change Detection Based on Curvelet Fusion and Local Patch Similarity Information Clustering
Authors: Huang Yuhao , Xin Zhihui , Liao Guisheng , Huang Penghui , Hou Guangyu , Zou Rui
Published: February 2025

Change detection for synthetic aperture radar (SAR) images effectively identifies and analyzes changes in the ground surface, demonstrating significant value in applications such as urban planning, natural disaster assessment, and environmental protection. Since speckle noise is an inherent characteristic of SAR images, noise suppression has always been a challenging problem. At the same time, the existing unsupervised deep learning-based methods relying on the pseudo labels may lead to a low-performance network. These methods are high data-dependent. To this end, we propose a novel unsupervised change detection method based on curvelet fusion and local patch similarity information clustering (CF-LPSICM). Firstly, a curvelet fusion module is designed to utilize the complementary information of different difference images. Different fusion rules are designed for the low-frequency subband, mid-frequency directional subband, and high-frequency subband of curvelet coefficients. Then the proposed local patch similarity information clustering algorithm is used to classify the image pixels to output the final change map. The pixels with similar structures and the weight of spatial information are incorporated into the traditional clustering algorithm in a fuzzy way, which greatly suppresses the speckle noise and enhances the structural information of the changing area. Experimental results and analysis on five datasets verify the effectiveness and robustness of the proposed method.
Hyper Spectral Camera ANalyzer (HyperSCAN)
Authors: Chang Wen-Qian , Hou Hsun-Ya , Li Pei-Yuan , Shen Michael W. , Kuo Cheng-Ling , Lin Tang-Huang , Chang Loren C. , Chao Chi-Kuang , Liu Jann-Yenq
Published: February 2025

HyperSCAN (Hyper Spectral Camera ANalyzer) is a hyperspectral imager which monitors the Earth’s environment and also an educational platform to integrate college students’ ideas and skills in optical design and data processing. The advantages of HyperSCAN are that it is designed for modular design, is compact and lightweight, and low-cost using commercial off-the-shelf (COTS) optical components. The modular design allows for flexible and rapid development, as well as validation within college lab environments. To optimize space utilization and reduce the optical path, HyperSCAN’s optical system incorporates a folding mirror, making it ideal for the constrained environment of a CubeSat. The use of COTS components significantly lowers pre-development costs and minimizes associated risks. The compact size and cost-effectiveness of CubeSats, combined with the advanced capabilities of hyperspectral imagers, make them a powerful tool for a broad range of applications, such as environmental monitoring of Earth, disaster management, mineral and resource exploration, atmospheric and climate studies, and coastal and marine research. We conducted a spatial-resolution-boost experiment using HyperSCAN data and various hyperspectral datasets including Urban, Pavia University, Pavia Centre, Botswana, and Indian Pines. After testing various data-fusion deep learning models, the best image quality of these methods is a two-branches convolutional neural network (TBCNN), where TBCNN retrieves spatial and spectral features in parallel and reconstructs the higher-spatial-resolution data. With the aid of higher-spatial-resolution multispectral data, we can boost the spatial resolution of HyperSCAN data.
A Fast Satellite Selection Algorithm Based on Hierarchical Clustering and Iterative Subset Optimization
Authors: Jing Dan , Li Weidie , Han Liang , Li Xinchen , Li Liangchao , Zhang Yan , Guo Liang , Xing Mengdao
Published: February 2025

When calculating positions, finding the optimal satellite subset is often a compromise between real-time performance and accuracy. This paper proposes a satellite selection method based on hierarchical clustering and iterative optimization. First, hierarchical clustering groups satellites on a two-dimensional projection plane are used to obtain a basic satellite subset. The relationship between GDOP and the number of satellites involved in positioning calculations is analyzed, showing that once the number of satellites reaches a certain threshold, further increases do not significantly enhance positioning accuracy. Then, the impact of a given satellite on the GDOP of the current satellite subsets is analyzed. Based on this, the most important satellite is iteratively added to the current satellite subset, gradually optimizing the spatial geometric configuration of the satellite subset. Simulations show that this method can quickly select an optimal satellite subset that meets positioning accuracy requirements under different GDOP demands, significantly improving computational efficiency compared to the traditional methods.
Enconv1d Model Based on Pseudolite System for Long-Tunnel Positioning
Authors: Li Changgeng , Zhang Yuting , Liu Changshui
Published: February 2025

Pseudolite positioning systems offer precise localization when GPS signals are unavailable, advancing the development of intelligent transportation systems. However, in confined indoor environments such as kilometer-long tunnels, where vehicles move at high speeds, traditional pseudolite algorithms struggle to establish accurate physical models linking signals to spatial domains. This study introduces a deep learning-based pseudolite positioning algorithm leveraging a spatio-temporal fusion framework to address challenges such as signal attenuation, multipath effects, and non-line-of-sight (NLOS) effects. The Enconv1d model we developed is based on the spatio-temporal characteristics of the pseudolite observation signals. The model employs the encoder module from the Transformer to capture multi-step time constraints while introducing a multi-scale one-dimensional convolutional neural network module (1D CNN) to assist the encoder module in learning spatial features and finally outputs the localization results of the Enconv1d model after the dense layer integration. Four experimental tests in a 4.6 km long real-world tunnel demonstrate that the proposed framework delivers continuous decimeter-level positioning accuracy.
Kinetic Energy Cascade in the Frequency Domain from Satellite Products
Authors: Geng Qianqian , Su Xin , Chen Ru , Huang Gang , Shi Wanli
Published: February 2025

Multiscale oceanic motions continuously transfer kinetic energy across various spatiotemporal scales through kinetic energy cascade. Satellite altimetry offers long-term daily ocean data at 0.25-degree resolution, enabling the analysis of energy cascades in both wavenumber and frequency domains. While energy cascade studies in the wavenumber domain are well-developed, frequency domain analyses remain limited. In this study, using 24 years of velocity data from satellite altimetry, we analyze the surface frequency-domain kinetic energy cascade primarily using the coarse-graining method. Compared to other approaches in literature, the coarse-graining approach shows superiority in diagnosing energy cascade in the frequency domain. Using this approach in the Kuroshio Extension region, we compare the spatiotemporal variability of energy cascades between the frequency and wavenumber domains. A pronounced low-frequency forward cascade, distinct from the wavenumber domain results, is identified. We propose a theory linking this low-frequency forward cascade with eddy generation through eddy–mean flow interactions. Significant variability is also observed in frequency domain energy cascades. Further analysis shows that wind forcing only plays a minor role in modulating the temporal variability of the energy cascade. Our findings are crucial for evaluating the model’s fidelity and advancing investigation of climate variability from the perspective of energy transfer.
MD-Former: Multiscale Dual Branch Transformer for Multivariate Time Series Classification
Authors: Du Yanling , Chu Shuhao , Wang Jintao , Shi Manli , Huang Dongmei , Song Wei
Published: February 2025

Multivariate Time Series Classification (MTSC) is a challenging task in real-world applications. Current approaches emphasize modeling multiscale relationships over time. However, the Multivariate Time Series (MTS) also exhibits multiscale cross-channel relationships. Furthermore, the long-term temporal relationships in time series are difficult to capture. In this paper, we introduce MD-Former, a Multiscale Dual-Branch Attention network leveraging the Transformer architecture to capture multiscale relationships across time and channels for MTSC. In MD-Former, MTS is embedded into 2D vectors using Channel-Patching (CP) to retain channel information. Following this, we develop two branches: the Interlaced Attention Branch (IAB) and the Channel-Independent Attention Branch (CIAB). The IAB facilitates the fusion of information across channels and time, while the CIAB prevents the loss of information resulting from excessive fusion. Both the IAB and CIAB consist of multiple layers, each representing a distinct time scale. Finally, we utilize features from each layer of both IAB and CIAB as inputs to the Multiscale Classification Head (MCH) for feature fusion and classification. Experimental results show that MD-Former achieves performance levels that are comparable to SOTA methods in MTSC.
An Efficient and Low-Complexity Transformer-Based Deep Learning Framework for High-Dynamic-Range Image Reconstruction
Authors: Lopez-Cabrejos Josue , Paixão Thuanne , Alvarez Ana Beatriz , Luque Diodomiro Baldomero
Published: February 2025

High-dynamic-range (HDR) image reconstruction involves creating an HDR image from multiple low-dynamic-range images as input, providing a computational solution to enhance image quality. This task presents several challenges, such as frame misalignment, overexposure, and motion, which are addressed using deep learning algorithms. In this context, various architectures with different approaches exist, such as convolutional neural networks, diffusion networks, generative adversarial networks, and Transformer-based architectures, with the latter offering the best quality but at a high computational cost. This paper proposes an HDR reconstruction architecture using a Transformer-based approach to achieve results competitive with the state of the art while reducing computational cost. The number of self-attention blocks was reduced for feature refinement. To prevent quality degradation, a Convolutional Block Attention Module was added, enhancing image features by using the central frame as a reference. The proposed architecture was evaluated on two datasets, achieving the best results on Tel’s dataset in terms of quality metrics. The computational cost indicated that the architecture was significantly more efficient than other Transformer-based approaches for reconstruction. The results of this research suggest that low-complexity Transformer-based architectures have great potential, with applications extending beyond HDR reconstruction to other domains.
Study on Vibration Characteristics of Functionally Graded Material Composite Spherical Piezoelectric Transducer
Authors: Wang Sha , Shan Junjie
Published: February 2025

Non-uniform composite structures for transducers exhibit considerable potential in enhancing impedance matching and efficiency. Here, a functionally graded material composite spherical piezoelectric transducer (FGM-cSPT) is proposed, and a three-port electromechanical equivalent circuit model is established. The correctness of the theoretical model is verified using the finite element method and experiment. Based on the theoretical model, the effects of the non-uniform coefficient and the geometric dimension of FGM-cSPT on the electromechanical vibration characteristics (resonance frequency, anti-resonance frequency, and effective electromechanical coupling coefficient) of the transducer are analyzed. The results show that the non-uniform coefficient and geometric dimension can effectively regulate the vibration characteristics of the FGM-cSPT, which can be used to guide engineering design. Our methodology will offer possibilities for designing FGM-cSPTs and may promote applications in various fields, such as marine exploitation, structural health detection, and energy collection.
Volunteering, Social Trust and Life Satisfaction of the Young-Old in China: Based on Urban-Rural Differences
Authors: Hu Xuanyu , Xie Lili
Published: February 2025

With the increasingly severe situation of population aging in China, how to improve the well-being of older adults is an important topic of social discussion. However, the situation of older adults in urban and rural China greatly differs. Current research has focused on older people in urban areas, with insufficient attention paid to the urban-rural differences in older groups. At the same time, volunteering is considered to be a significant strategy to improve the lives of older adults, and the young-old are the main participants in volunteer activities, but the impact and influence mechanism of volunteering on life satisfaction among urban and rural young-old have yet to be clarified. Therefore, the goal of this study is to analyze the influence of volunteering on the lives of the young-old in a more comprehensive way, mainly exploring three questions: (1) The impact of volunteering on life satisfaction of the young-old. (2) The influence mechanism of volunteering on their life satisfaction. (3) The urban-rural differences in the impact and influence mechanism. To answer the above questions, this study was conducted from the perspective of urban-rural differences, based on Activity Theory and Social Capital Theory, and adopted quantitative research methods, including multiple linear regression and structural equation models. Secondary data, which comes from the Chinese Social Survey in 2021, was used in this study, and we chose the Chinese older adults aged 55–70 years old as the object of study. The results show that volunteering positively correlates with life satisfaction, while social trust is a significant positive mediating mechanism. Furthermore, the rural young-old benefit more from volunteering. The findings imply that it is of great significance to appropriately encourage the young-old to participate in volunteer activities, formulate targeted policies for different older people based on urban-rural differences, and gradually break the obstacles of the urban-rural dual system comprehensively.
Quantum-Inspired Latent Variable Modeling in Multivariate Analysis
Authors: Kyriazos Theodoros , Poga Mary
Published: February 2025

Latent variables play a crucial role in psychometric research, yet traditional models often struggle to address context-dependent effects, ambivalent states, and non-commutative measurement processes. This study proposes a quantum-inspired framework for latent variable modeling that employs Hilbert space representations, allowing questionnaire items to be treated as pure or mixed quantum states. By integrating concepts such as superposition, interference, and non-commutative probabilities, the framework captures cognitive and behavioral phenomena that extend beyond the capabilities of classical methods. To illustrate its potential, we introduce quantum-specific metrics—fidelity, overlap, and von Neumann entropy—as complements to correlation-based measures. We also outline a machine-learning pipeline using complex and real-valued neural networks to handle amplitude and phase information. Results highlight the capacity of quantum-inspired models to reveal order effects, ambivalent responses, and multimodal distributions that remain elusive in standard psychometric approaches. This framework broadens the multivariate analysis theoretical and methodological toolkit, offering a dynamic and context-sensitive perspective on latent constructs while inviting further empirical validation in diverse research settings.
Green Innovation and the Urban–Rural Income Gap: Empirical Evidence from China
Authors: Wen Jinda , Chen Haonan
Published: February 2025

An in-depth understanding of the impact of green innovation on the urban–rural income gap is essential for developing countries seeking to address urban–rural imbalances and promote sustainable economic development. This study focuses on China, utilizing provincial panel data from 2007 to 2022, and employs the two-way fixed effects model, the mediating effects model, and the moderating effects model. The study’s key findings are: (1) Green innovation positively reduces the urban–rural income gap. Specifically, the regression results indicate that a one-unit increase in green innovation corresponds to a 0.017-unit reduction in the urban–rural income gap. (2) The heterogeneity analysis reveals that the effect of green innovation on the urban–rural income gap is more pronounced in regions with higher levels of economic development, in non-food-producing areas, and when green utility patents are adopted. Additionally, green innovation narrows the urban–rural income gap predominantly in high-skill regions. (3) Examining the influencing mechanism confirms that green innovation reduces the urban–rural income gap by promoting population urbanization, eco-urbanization, labor force restructuring, and mitigating wage income inequality. (4) The moderating effects analysis indicates that environmental pollution exacerbates the impact of green innovation on the urban–rural income gap; specifically, higher levels of environmental pollution amplify the effect of green innovation in reducing the gap. These findings offer valuable insights for addressing urban–rural income inequality and fostering sustainable socio-economic development in developing countries.
Application of Controlled-Source Audio-Frequency Magnetotellurics (CSAMT) for Subsurface Structural Characterization of Wadi Rum, Southwest Jordan
Authors: Basaloom Abdullah , Alzahrani Hassan
Published: February 2025

The UNESCO World Heritage Centre announced in 2011 that the Wadi Rum Protected Area (WRPA) is a global landmark for natural and cultural attraction, which represents an emerging industrial suburban and a critical socio-economic significance to the country of Jordan. The study area in Wadi Rum is located northeast of the Gulf of Aqaba between the African and Arabian plates. The region is historically characterized by significant tectonic activity and seismic events. This study focuses on characterizing the subsurface structural features of Wadi Rum through the application of the geophysical method of controlled-source audio-frequency magnetotellurics (CSAMT). CSAMT data were collected from 16 sounding stations, processed, and qualitatively interpreted. The qualitative interpretation involved two main approaches: constructing sounding curves for each station and generating apparent resistivity maps at fixed depths (frequencies). The results revealed the presence of at least four distinct subsurface layers. The surface layer exhibited relatively low resistivity values (<200 Ω·m), corresponding to alluvial and wadi sediments, as well as mud flats. Two intermediate layers were identified: the first showed very low resistivity values (80–100 Ω·m), likely due to medium-grained bedded sandstone, while the second displayed intermediate resistivity values (100–800 Ω·m), representing coarse basal conglomerates and coarse sandstone formations. The deepest layer demonstrated very high resistivity values (>1000 Ω·m), which were likely attributed to basement rocks. Analysis of resistivity maps, combined with prior geological information, indicates that the subsurface in the study area features a graben-like structure, characterized by two detected faults trending in the northeast (NE) and southwest (SW) directions. The findings of this study, by providing critical insights into the subsurface structure, make a considerable contribution to the urban sustainability of the region, which is necessary for the careful assessment of potential hazards and the strategic planning of future infrastructure development within the protected area.
Analysis of Key Factors of Cost Overrun in Construction Projects Based on Structural Equation Modeling
Authors: Dong Sizi , Ahmed Mukhtar , Chatpattananan Vuttichai
Published: February 2025

Cost overruns are prevalent in global construction industries, propelled by the swift expansion of the construction sector. To tackle this difficulty, it is essential to comprehend the primary elements leading to these overruns. This research examines the correlation between systematic elements affecting construction costs via a literature review, expert interviews, exploratory factor analysis (EFA), and structural equation modeling (SEM). A theoretical framework analyzes contract management, resource distribution, and external environmental factors affecting cost overruns. Twenty-four criteria were selected via literature review and expert consultation, and data from 212 questionnaires were analyzed. The results identify 22 critical elements classified into project management, resources, supply chain, and external and environmental influences. Project management and resources, together with supply chain, exerted the most significant influence. SEM study indicates that management inadequacies intensify resource problems, whereas proficient internal management alleviates external difficulties. This research offers practical recommendations for mitigating budget excesses and enhancing project cost-effectiveness.
Experimental Investigation of Rock Damage Induced by Ultrasonic High-Frequency Vibration Under High Confining Pressure
Authors: Feng Jinyu , Yan Tie , Hou Zhaokai
Published: February 2025

Ultrasonic vibration can effectively improve the efficiency of rock breaking. In the actual process of drilling and breaking rocks, rocks will be affected by symmetrical ground stress in the formation. Therefore, in this paper, symmetrical confining pressure is applied to four surfaces of rocks by self-developed experimental equipment to simulate the symmetrical ground stress of the formation, and 64 groups of mixed tests are carried out with confining pressure, static load and vibration time as variables. The damage degree is evaluated by comparing the compressive strength of rock before and after vibration. The experimental results show that ultrasonic vibration can reduce the compressive strength of rock, increase the static load and vibration time during vibration, and increase the damage degree of rock. When the symmetric confining pressure increases, the formation and expansion of rock cracks can be inhibited, and the damage effect of ultrasonic high-frequency vibration on rock can be reduced. At the same time, a theoretical model is established to explain these phenomena.
Partnerships and Community Building as Collaborative Assistance: Insights on Goal Presence, Hierarchy, and Integration from Urban Park Plans
Authors: Perry Elizabeth E. , Schiappa Ellie A. , McCurdy Allison
Published: February 2025

Urban parks provide areas for human wellbeing and green space benefits in densely populated landscapes but cannot accomplish all their goals in isolation. They require assistance from collaborations to address challenges. The need for these collaborations is often codified in planning documents. We assisted Rock Creek Park (National Park Service, Washington, D.C.) in their considerations of where to place “partnerships” in their strategic plan by sourcing and summarizing goal topics, hierarchies, and relationships from peer park plans. Using textual coding and network analysis approaches, we examined strategic planning documents from park system entities across the 20 largest urban areas in the United States. We found that, topically, Rock Creek Park’s five initial strategic planning goal topics—safety, access, stewardship, community engagement, and employee engagement—were common and both inward and outward-facing goals. Hierarchically, “partnerships” was routinely considered as a primary goal (a stand-alone topic) and as an integrated secondary goal (supportive within other topics). Additionally, we identified “community building” as an important, outward facing “assistance” goal, differentiated from “partnerships” in audience and encompassing how a park shows up for the urban community and demonstrates its value to the region. We discuss these findings toward urban park planning processes.
From Trade Fairs to Urban Development: Exploring Destination Loyalty and City Branding Through the Thessaloniki International Fair
Authors: Kourkouridis Dimitris , Salepaki Asimenia
Published: February 2025

This study examines how participation in the Thessaloniki International Fair (T.I.F.) influences exhibitors’ satisfaction and loyalty toward the host city of Thessaloniki. It investigates factors such as event organization, logistical support, business networking opportunities, and exhibitors’ interactions with local infrastructure and services. Using social exchange theory (S.E.T.) as a framework, this research investigates how positive exchanges, including cultural experiences, local hospitality, and professional interactions at the fair, drive destination loyalty. A mixed-methods approach was used, combining quantitative surveys and qualitative interviews with German exhibitors, to capture comprehensive insights into the factors shaping their perceptions. Results indicate high satisfaction with Thessaloniki’s cultural and culinary offerings, with local hospitality emerging as a significant factor in fostering emotional connections and loyalty intentions. However, transportation and accessibility were identified as areas for improvement, as these logistical issues detract from the overall experience and impose perceived ‘costs’ within the exchange. The findings highlight these factors’ roles in shaping destination loyalty and offer actionable recommendations for improving exhibitor experiences. The findings emphasize the broader implications for urban development, highlighting how trade fairs can serve as catalysts for city branding and infrastructural improvements, thereby strengthening Thessaloniki’s position in the competitive M.I.C.E. tourism sector.
Utilizing Viral Metagenomics to Characterize Pathogenic and Commensal Viruses in Pediatric Patients with Febrile Neutropenia
Authors: Sarana da Silva Anielly , de Campos Gabriel Montenegro , Altizani Gabriela Marengone , de Carvalho Enéas , Barros Alice Chagas , Cella Eleonora , Kashima Simone , Sampaio Sandra Coccuzzo , Elias Maria Carolina , Giovanetti Marta , Scrideli Carlos Alberto , Slavov Svetoslav Nanev
Published: February 2025

Febrile neutropenia (FN) is one of the most common complications in pediatric oncology patients. It has a complex etiologic nature, which in the majority of cases remains unclear. Intervention often follows empirical treatment protocols, mainly using broad-spectrum antibiotics. To evaluate potential viral etiologic agents, this study applied viral metagenomics to paired plasma and oropharyngeal samples obtained from pediatric patients with oncological diseases diagnosed with FN. Metagenomic sequencing was performed on 15 pediatric patients with oncological diseases and FN at the outpatient clinic of Pediatric Oncology at the University Hospital of the Faculty of Medicine of Ribeirão Preto, University of São Paulo. As a control group, we included 15 pediatric patients with oncological diseases in remission or undergoing treatment. Clinically relevant viruses identified by metagenomics in FN patients predominantly included herpesviruses and viruses found in the respiratory tract, like adenoviruses. Direct molecular confirmation was performed on all of them. Anelloviruses, represented by various genera and species in all groups, were also highly prevalent. The data obtained in this study show that viruses might also have possible implications for the etiology of FN. However, due to the complex nature of this disease, more studies are necessary to evaluate their causal relationship. The results obtained in our study may serve to improve patient treatment and ensure adequate management.