bluuebunny/crossref_metadata_2025 huggingface dataset:
A Refined Terrace Extraction Method Based on a Local Optimization Model Using GF-2 Images
Authors: Kan Guobin , Gong Jie , Wang Bao , Li Xia , Shi Jing , Ma Yutao , Wei Wei , Zhang Jun
Published: December 2024

Terraces are an important form of surface modification, and their spatial distribution data are of utmost importance for ensuring food and water security. However, the extraction of terrace patches faces challenges due to the complexity of the terrain and limitations in remote sensing (RS) data. Therefore, there is an urgent need for advanced technology models that can accurately extract terraces. High-resolution RS data allows for detailed characterization of terraces by capturing more precise surface features. Moreover, leveraging deep learning (DL) models with local adaptive improvements can further enhance the accuracy of interpretation by exploring latent information. In this study, we employed five models: ResU-Net, U-Net++, RVTransUNet, XDeepLabV3+, and ResPSPNet as DL models to extract fine patch terraces from GF-2 images. We then integrated morphological, textural, and spectral features to optimize the extraction process by addressing issues related to low adhesion and edge segmentation performance. The model structure and loss function were adjusted accordingly to achieve high-quality terrace mapping results. Finally, we utilized multi-source RS data along with terrain elements for correction and optimization to generate a 1 m resolution terrace distribution map in the Zuli River Basin (TDZRB). Evaluation results after correction demonstrate that our approach achieved an OA, F1-Score, and MIoU of 96.67%, 93.94%, and 89.37%, respectively. The total area of terraces in the Zuli River Basin was calculated at 2557 ± 117.96 km2 using EM with our model methodology; this accounts for approximately 41.74% ± 1.93% of the cultivated land area within the Zuli River Basin. Therefore, obtaining accurate information on patch terrace distribution serves as essential foundational data for terrace ecosystem research and government decision-making.
Magnetosphere-Ground Responses and Energy Spectra Analysis of Solar Proton Event on 28 October 2021
Authors: Zhang Fang , Zhang Zhenxia , Zhang Dali , Li Xinqiao , Ding Zhiqiang , Wang Lu , Li Shujie , An Zhenghua , Zhang Jilong
Published: December 2024

Among the coronal mass ejections (CMEs) and solar proton events (SPEs) frequently observed by near-Earth spacecraft, the SPE that occurred on 28 October 2021 stands out as a remarkable research event. This is due to the infrequency of reported ground-level enhancements it induced. The CSES (China seismo-electromagnetic satellite) is equipped with high-energy particle detectors, namely, HEPP and HEPD, capable of measuring protons within an energy range of 2 MeV to 143 MeV. These detectors provide valuable opportunities for studying solar activity. By utilizing the Monte Carlo method to simulate the pile-up effect and accounting for the detector’s dead time, with the assistance of real-time incident counting rates, we successfully corrected the spectra in the 10–50 MeV range. The energy spectrum is important for understanding solar proton events. We used the data from the HEPP (high-energy particle package) and HEPD (high-energy particle detector) to obtain the total event-integrated spectrum, which possessed good continuity. Additionally, we compared the observations from the CSES with those from the NOAA satellite and achieved reasonable agreement. We also searched for ground-based responses to this solar activity in China and discovered Forbush decreases detected by the Yang Ba Jing Muon Telescope experiment. In conclusion, the HEPP and HEPD can effectively combine to study solar activity and obtain a smooth and consistent energy spectrum of protons across a very wide energy range.
Leveraging Deep Spatiotemporal Sequence Prediction Network with Self-Attention for Ground-Based Cloud Dynamics Forecasting
Authors: Li Sheng , Wang Min , Shi Minghang , Wang Jiafeng , Cao Ran
Published: December 2024

Ground-based cloud image features high-spatiotemporal resolution, presenting detailed local cloud structures and valuable weather information, which are crucial for meteorological forecasting. However, the inherent fuzziness and dynamism of ground-based clouds have hindered the development of effective prediction algorithms, resulting in low accuracy. This paper presents CloudPredRNN++, a novel method for predicting ground-based cloud dynamics, leveraging a deep spatiotemporal sequence prediction network enhanced with a self-attention mechanism. Initially, a Cascaded Causal LSTM (CCLSTM) with a dual-memory group decoupling structure is designed to enhance the representation of short-term cloud changes. Next, self-attention memory units are incorporated to capture the long-term dependencies and emphasize the non-stationary characteristics of cloud movements. These components are integrated into cloud dynamic feature mining units, which concurrently extract spatiotemporal features to strengthen unified spatiotemporal modeling. Finally, by embedding gradient highway units and adding skip connection, CloudPredRNN++ is constructed into a hierarchical recursive structure, mitigating the gradient vanishing and enhancing the uniform modeling of temporal–spatial features. Experiments on the sequence ground-based cloud dataset demonstrate that CloudPredRNN++ can predict the future cloud state more accurately and quickly. Compared with other spatiotemporal sequence prediction models, CloudPredRNN++ shows significant improvements in evaluation metrics, improving the accuracy of cloud dynamics forecasting and alleviating long-term dependency decay, thus confirming the effectiveness in ground-based cloud prediction tasks.
SAR-PATT: A Physical Adversarial Attack for SAR Image Automatic Target Recognition
Authors: Luo Binyan , Cao Hang , Cui Jiahao , Lv Xun , He Jinqiang , Li Haifeng , Peng Chengli
Published: December 2024

Deep neural network-based synthetic aperture radar (SAR) automatic target recognition (ATR) systems are susceptible to attack by adversarial examples, which leads to misclassification by the SAR ATR system, resulting in theoretical model robustness problems and security problems in practice. Inspired by optical images, current SAR ATR adversarial example generation is performed in the image domain. However, the imaging principle of SAR images is based on the imaging of the echo signals interacting between the SAR and objects. Generating adversarial examples only in the image domain cannot change the physical world to achieve adversarial attacks. To solve these problems, this article proposes a framework for generating SAR adversarial examples in a 3D physical scene. First, adversarial attacks are implemented in the 2D image space, and the perturbation in the image space is converted into simulated rays that constitute SAR images through backpropagation optimization methods. The mapping between the simulated rays constituting SAR images and the 3D model is established through coordinate transformation, and point correspondence to triangular faces and intensity values to texture parameters are established. Thus, the simulated rays constituting SAR images are mapped to the 3D model, and the perturbation in the 2D image space is converted back to the 3D physical space to obtain the position and intensity of the perturbation in the 3D physical space, thereby achieving physical adversarial attacks. The experimental results show that our attack method can effectively perform SAR adversarial attacks in the physical world. In the digital world, we achieved an average fooling rate of up to 99.02% for three objects in six classification networks. In the physical world, we achieved an average fooling rate of up to 97.87% for these objects, with a certain degree of transferability across the six different network architectures. To the best of our knowledge, this is the first work to implement physical attacks in a full physical simulation condition. Our research establishes a theoretical foundation for the future concealment of SAR targets in practical settings and offers valuable insights for enhancing the attack and defense capabilities of subsequent DNNs in SAR ATR systems.
Reconstructing the Three-Dimensional Thermohaline Structure of Mesoscale Eddies in the South China Sea Using In Situ Measurements and Multi-Sensor Satellites
Authors: Zhuang Zhiyuan , Zhang Yanwei , Zhang Liuzhenyi , Ruan Weihan , Lyu Danni , Yu Jiancheng
Published: December 2024

The evolution of the three-dimensional thermohaline structure of mesoscale eddies is crucial for assessing energy and mass transfer during their long-distance propagation in the ocean. However, the understanding and quantitative evaluation of the role that mesoscale eddies play in driving variations of thermohaline in the deep sea remains constrained due to the scarcity of in situ observations, particularly in marginal seas such as the South China Sea (SCS). In this study, we propose an artificial intelligence (AI)–physics-based deep learning model that integrates satellite measurements and Argo data from 2003 to 2021 to reconstruct the three-dimensional thermohaline structure of mesoscale eddies in the SCS. Besides utilizing basic sea surface hydrodynamic parameters obtained from satellite data for model training, an additional branch incorporating eddy physical parameters was introduced to optimize the model. The results demonstrate that the model effectively reconstructs thermohaline properties within mesoscale eddies in the SCS. Compared to Argo observations, the average root mean square error (RMSE) for temperature (salinity) within anticyclonic eddies was 0.34 °C (0.036 PSU), while it was 0.36 °C (0.032 PSU) within cyclonic eddies in the upper 1500 m. Further validation using high-resolution glider observations tracking an anticyclonic eddy originating in the SCS confirms the model’s efficiency, achieving an RMSE of 0.2962 °C (0.0138 PSU) for temperature (salinity). The accuracy of our proposed model significantly outperforms that of HYCOM and GLORYS simulations, with the RMSE reduced by 40% to 60%. The distinctive capabilities provide valuable insights into understanding the fine-scale structures of mesoscale eddies, especially in regions with limited in situ data.
Wind Shear Coefficient Estimation Based on LIDAR Measurements to Improve Power Law Extrapolation Performance
Authors: Machidon Dragos , Istrate Marcel , Beniuga Razvan
Published: December 2024

This study aims to improve power law extrapolation accuracy by proposing a new methodology for wind shear coefficient calculation. Real wind speed profiles, measured with a LIDAR remote sensor at two different sites, were extrapolated according to the power law at heights up to 200 m. Two different definitions of the wind shear coefficient are proposed considering wind speed profiles measured at three, four, and five different heights using either the first height as a reference or all of the heights successively. These two definitions were tested, along with the generally accepted constant values of 1/7, 0.25 at the first site, and 0.2 at the second site, to identify the most efficient one. The extrapolated wind profiles were compared with those measured using LIDAR. The results show that the computed wind shear coefficients provide more accurate results compared with constant wind shear coefficients, as the mean absolute errors at 200 m were reduced from 1.396 m/s to 0.437 m/s at the first site and from 1.203 m/s to 0.411 m/s at the second site.
UnDER: Unsupervised Dense Point Cloud Extraction Routine for UAV Imagery Using Deep Learning
Authors: Bergado John Ray , Nex Francesco
Published: December 2024

Extraction of dense 3D geographic information from ultra-high-resolution unmanned aerial vehicle (UAV) imagery unlocks a great number of mapping and monitoring applications. This is facilitated by a step called dense image matching, which tries to find pixels corresponding to the same object within overlapping images captured by the UAV from different locations. Recent developments in deep learning utilize deep convolutional networks to perform this dense pixel correspondence task. A common theme in these developments is to train the network in a supervised setting using available dense 3D reference datasets. However, in this work we propose a novel unsupervised dense point cloud extraction routine for UAV imagery, called UnDER. We propose a novel disparity-shifting procedure to enable the use of a stereo matching network pretrained on an entirely different typology of image data in the disparity-estimation step of UnDER. Unlike previously proposed disparity-shifting techniques for forming cost volumes, the goal of our procedure was to address the domain shift between the images that the network was pretrained on and the UAV images, by using prior information from the UAV image acquisition. We also developed a procedure for occlusion masking based on disparity consistency checking that uses the disparity image space rather than the object space proposed in a standard 3D reconstruction routine for UAV data. Our benchmarking results demonstrated significant improvements in quantitative performance, reducing the mean cloud-to-cloud distance by approximately 1.8 times the ground sampling distance (GSD) compared to other methods.
Assessment of PPP Using BDS PPP-B2b Products with Short-Time-Span Observations and Backward Smoothing Method
Authors: Zhao Lewen , Zhai Wei
Published: December 2024

The BeiDou Navigation Satellite System (BDS) offers orbit and clock corrections through the B2b signal, enabling Precise Point Positioning (PPP) without relying on ground communication networks. This capability supports applications such as aerial and maritime mapping. However, achieving high precision during the convergence period remains challenging, particularly for missions with short observation durations. To address this, we analyze the performance of PPP over short periods using PPP-B2b products and propose using the backward smoothing method to enhance the accuracy during the convergence period. Evaluation of the accuracy of PPP-B2b products shows that the orbit and clock accuracy of the BDS surpass those of GPS. Specifically, the BDS achieves orbit accuracies of 0.059 m, 0.178 m, and 0.186 m in the radial, along-track, and cross-track components, respectively, with a clock accuracy within 0.13 ns. The hourly static PPP achieves 0.5 m and 0.1 m accuracies with convergence times of 4.5 and 25 min at a 50% proportion, respectively. Nonetheless, 7.07% to 23.79% of sessions fail to converge to 0.1 m due to the limited availability of GPS and BDS corrections at certain stations. Simulated kinematic PPP requires an additional 1–4 min to reach the same accuracy as the static PPP. Using the backward smoothing method significantly enhances accuracy, achieving 0.024 m, 0.046 m, and 0.053 m in the north, east, and up directions, respectively. For vehicle-based positioning, forward PPP can achieve a horizontal accuracy better than 0.5 m within 4 min; however, during the convergence period, positioning errors may exceed 1.5 m and 3.0 m in the east and up direction. By applying the smoothing method, horizontal accuracy can reach better than 0.2 m, while the vertical accuracy can improve to better than 0.3 m.
Laser Phase Noise Compensation Method Based on Dual Reference Channels in Inverse Synthetic Aperture Lidar
Authors: Liu Dengfeng , Xu Chen , Li Yutang , Song Anpeng , Li Jian , Jin Kai , Luo Xi , Wei Kai
Published: December 2024

Laser phase noise is a critical factor that limits the range and performance of coherent lidar systems, especially in high-resolution applications such as inverse synthetic aperture lidar (ISAL), which demands stringent coherence. The effective suppression of laser phase noise is essential to enable high-resolution imaging over long distances. This paper presents a phase noise compensation technique utilizing dual reference channels (DRCs) based on concatenated generated phase (CGP) principles. The proposed method uses two reference channels with different delay lengths: a long-delay channel for coarse phase noise compensation and a short-delay channel for fine adjustments. We performed ISAL imaging experiments on stationary and rotating targets using a seed laser with a 3.41 MHz linewidth, achieving round-trip distances exceeding 110 times the laser coherence length. Imaging quality closely matched a 100 Hz narrow linewidth laser, approaching theoretical resolution limits. Compared to prior methods based on residual error linear estimation, the DRC method enhances compensation speed tenfold while maintaining accuracy. These results highlight the efficacy of the proposed DRC method in mitigating laser phase noise, significantly improving ISAL imaging performance.
Sensors and Advanced Sensing Techniques for Computer Vision Applications
Authors: Anagnostopoulos Christos-Nikolaos , Krinidis Stelios
Published: December 2024

Computer vision is a multidisciplinary field that enables machines to interpret and understand visual information from the world, simulating human vision [...]
An Energy Modulation Interrogation Technique for Monitoring the Adhesive Joint Integrity Using the Full Spectral Response of Fiber Bragg Grating Sensors
Authors: Shin Chow-Shing , Lin Tzu-Chieh , Huang Shun-Hsuan
Published: December 2024

Adhesive joining has the severe limitation that damages/defects developed in the bondline are difficult to assess. Conventional non-destructive examination (NDE) techniques are adequate to reveal disbonding defects in fabrication and delamination near the end of service life but are not helpful in detecting and monitoring in-service degradation of the joint. Several techniques suitable for long-term joint integrity monitoring are proposed. Fiber Bragg grating (FBG) sensors embedded in the joint are one of the promising candidates. It has the advantages of being close to the damage and immune to environmental attack and electromagnetic interference. Damage and disbonding inside an adhesive joint will give rise to a non-uniform strain field that may bring about peak splitting and chirping of the FBG spectrum. It is shown that the evolution of the full spectral responses can closely reveal the development of damages inside the adhesive joints during tensile and fatigue failures. However, recording and comparing the successive full spectra in the course of damage is tedious and can be subjective. An energy modulation interrogation technique is proposed using a pair of tunable optical filters. Changes in the full FBG spectral responses are modulated by the filters and converted into a conveniently measurable voltage output by photodiodes. Monitoring damage development can then be easily automated, and the technique is well-suited for practical applications. Filter spectrum width of 5 nm and initial overlap with the FBG spectrum to give 40% of the maximum output voltage is found to be optimal for measurement. The technique is tested on embedded FBGs from different adhesive lap-joint specimens and successfully reflected the severity of changes in the full spectral shapes during the course of tensile failure. Moreover, the trends in these PD outputs corroborate with the V value previously proposed to describe the qualitative change in FBG spectral shape.
A Reinforcement Learning Based Approach for Efficient Routing in Multi-FPGA Platforms
Authors: Farooq Umer , Mehrez Habib , Hasan Najam Ul
Published: December 2024

Prototyping using multi-FPGA platforms is unique because of its use in real-world testing and cycle-accurate information on the design. However, this is a complex and time-consuming process with multiple sub-steps. Among its sub-steps, inter-FPGA routing is the one that can take a significant percentage of total prototyping time. The share of inter-FPGA routing is projected to increase further over time with the ever-increasing complexity of the target designs. In this work, we propose to integrate a Reinforcement Learning (RL)-based framework to speed up the inter-FPGA routing process. For this purpose, we first find a trade-off between the exploration and exploitation approach (also termed as the ϵ-greedy approach) in our RL-based framework while not affecting the final Quality of Results (QoR). To gauge its effectiveness, we then perform an extensive comparison between the proposed framework and established routing approaches. In this regard, a set of fourteen complex benchmarks is used, and the results of the proposed framework are compared against existing routability- and timing-driven routing approaches. Experimental results reveal that, on average, the proposed RL-based framework speeds up the inter-FPGA routing process by 45% and 32%, compared to routability- and timing-driven routing approaches, respectively. The speedup at the routing step further leads to an overall speedup of the backend flow by 22% and 15%, respectively.
DSC-SeNet: Unilateral Network with Feature Enhancement and Aggregation for Real-Time Segmentation of Carbon Trace in the Oil-Immersed Transformer
Authors: Liu Liqing , Ji Hongxin , Feng Junji , Liu Xinghua , Zhang Chi , He Chun
Published: December 2024

Large oil-immersed transformers have metal-enclosed shells, making it difficult to visually inspect the internal insulation condition. Visual inspection of internal defects is carried out using a self-developed micro-robot in this work. Carbon trace is the main visual characteristic of internal insulation defects. The characteristics of carbon traces, such as multiple sizes, diverse morphologies, and irregular edges, pose severe challenges for segmentation accuracy and inference speed. In this paper, a feasible real-time network (deformable-spatial-Canny segmentation network, DSC-SeNet) was designed for carbon trace segmentation. To improve inference speed, a lightweight unilateral feature extraction framework is constructed based on a shallow feature sharing mechanism, which is designed to provide feature input for both semantic path and spatial path. Meanwhile, the segmentation model is improved in two aspects for better segmentation accuracy. For one aspect, to better perceive diverse morphology and edge features of carbon trace, three measures, including deformable convolution (DFC), Canny edge operator, and spatial feature refinement module (SFRM), were adopted for feature perception, enhancement, and aggregation, respectively. For the other aspect, to improve the fusion of semantic features and spatial features, coordinate attention feature aggregation (CAFA) is designed to reduce feature aggregation loss. Experimental results showed that the proposed DSC-SeNet outperformed state-of-the-art models with a good balance between segmentation accuracy and inference speed. For a 512 × 512 input, it achieved 84.7% mIoU, which is 6.4 percentage points higher than that of the baseline short-term dense convolution network (STDC), with a speed of 94.3 FPS on an NVIDIA GTX 2050Ti. This study provides technical support for real-time segmentation of carbon traces and transformer insulation assessment.
The Circle Pure Rolling Method for Point Cloud Boundary Extraction
Authors: Yang Quanou , Li Zhihui , Liu Zhihui , Jiang Xinyu , Gao Xinglong
Published: December 2024

We introduce a circle rolling method (CRM) for boundary extraction from 2D point clouds. The core idea is to create a circle that performs pure rolling on the perimeter of the point cloud to obtain the boundary. For a 3D point cloud, a plane adsorbs points on both sides to create a 2D point cloud, and the CRM is used to extract the boundary points and map them back into space to obtain 3D boundary points. Continuously moving this plane can obtain a complete boundary, which is called the moving adsorption rolling method (MARM). In this paper, we solve the interference problems in our method caused by unidirectional overlapping points and porous structures and successfully validate the solutions in practical examples. Our point cloud boundary extraction method is faster in 2D and better for surface concavities extracted in 3D compared to existing methods, and it is unaffected by sparse points within the point cloud.
Multi-Band Scattering Characteristics of Miniature Masson Pine Canopy Based on Microwave Anechoic Chamber Measurement
Authors: Du Kai , Li Yuan , Huang Huaguo , Mao Xufeng , Xiao Xiulai , Liu Zhiqu
Published: December 2024

Using microwave remote sensing to invert forest parameters requires clear canopy scattering characteristics, which can be intuitively investigated through scattering measurements. However, there are very few ground-based measurements on forest branches, needles, and canopies. In this study, a quantitative analysis of the canopy branches, needles, and ground contribution of Masson pine scenes in C-, X-, and Ku-bands was conducted based on a microwave anechoic chamber measurement platform. Four canopy scenes with different densities by defoliation in the vertical direction were constructed, and the backscattering data for each scene were collected in the C-, X-, and Ku-bands across eight incidence angles and eight azimuth angles, respectively. The results show that in the vertical observation direction, the backscattering energy of the C- and X-bands was predominantly contributed by the ground, whereas the Ku-band signal exhibited higher sensitivity to the canopy structure. The backscattering energy of the scene was influenced by the incident angle, particularly in the cross-polarization, where backscattering energy increased with larger incident angles. The scene’s backscattering energy was influenced by the scattering and extinction of canopy branches and needles, as well as by ground scattering, resulting in a complex relationship with canopy density. In addition, applying orientation correction to the polarization scattering matrix can mitigate the impact of the incident angle and reduce the decomposition energy errors in the Freeman–Durden model. In order to ensure the reliability of forest parameter inversion based on SAR data, a greater emphasis should be placed on physical models that account for signal scattering and the extinction process, rather than relying on empirical models.
Intelligent Pattern Recognition Using Distributed Fiber Optic Sensors for Smart Environment
Authors: Pamukti Brian , Afifah Shofuro , Liaw Shien-Kuei , Sung Jiun-Yu , Chu Daping
Published: December 2024

Distributed fiber optic sensors (DFOSs) have become increasingly popular for intrusion detection, particularly in outdoor and restricted zones. Enhancing DFOS performance through advanced signal processing and deep learning techniques is crucial. While effective, conventional neural networks often involve high complexity and significant computational demands. Additionally, the backscattering method requires the signal to travel twice the normal distance, which can be inefficient. We propose an innovative interferometric sensing approach utilizing a Mach–Zehnder interferometer (MZI) combined with a time forest neural network (TFNN) for intrusion detection based on signal patterns. This method leverages advanced sensor characterization techniques and deep learning to improve accuracy and efficiency. Compared to the conventional one-dimensional convolutional neural network (1D-CNN), our proposed approach achieves an 8.43% higher accuracy, demonstrating the significant potential for real-time signal processing applications in smart environments.
Epilepsy Prediction and Detection Using Attention-CssCDBN with Dual-Task Learning
Authors: Qiao Weizheng , Bi Xiaojun , Han Lu , Zhang Yulin
Published: December 2024

Epilepsy is a group of neurological disorders characterized by epileptic seizures, and it affects tens of millions of people worldwide. Currently, the most effective diagnostic method employs the monitoring of brain activity through electroencephalogram (EEG). However, it is critical to predict epileptic seizures in patients prior to their onset, allowing for the administration of preventive medications before the seizure occurs. As a pivotal application of artificial intelligence in medical treatment, learning the features of EEGs for epilepsy prediction and detection remains a challenging problem, primarily due to the presence of intra-class and inter-class variations in EEG signals. In this study, we propose the spatio-temporal EEGNet, which integrates contractive slab and spike convolutional deep belief network (CssCDBN) with a self-attention architecture, augmented by dual-task learning to address this issue. Initially, our model was designed to extract high-order and deep representations from EEG spectrum images, enabling the simultaneous capture of spatial and temporal information. Furthermore, EEG-based verification aids in reducing intra-class variation by considering the time correlation of the EEG during the fine-tuning stage, resulting in easier inference and training. The results demonstrate the notable efficacy of our proposed method. Our method achieved a sensitivity of 98.5%, a false-positive rate (FPR) of 0.041, a prediction time of 50.92 min during the epilepsy prediction task, and an accuracy of 94.1% during the epilepsy detection task, demonstrating significant improvements over current state-of-the-art methods.
Neural Mass Modeling in the Cortical Motor Area and the Mechanism of Alpha Rhythm Changes
Authors: Zhang Yuanyuan , Li Zhaoying , Xu Hang , Song Ziang , Xie Ping , Wei Penghu , Zhao Guoguang
Published: December 2024

Investigating the physiological mechanisms in the motor cortex during rehabilitation exercises is crucial for assessing stroke patients’ progress. This study developed a single-channel Jansen neural mass model to explore the relationship between model parameters and motor cortex mechanisms. Firstly, EEG signals were recorded from 11 healthy participants under 20%, 40%, and 60% maximum voluntary contraction, and alpha rhythm power spectral density characteristics were extracted using the Welch power spectrum method. Furthermore, a single-channel neural mass model was constructed to analyze the impact of parameter variations on the average power of simulated signals. Finally, model parameters were adjusted to achieve feature fitting between the simulated signals and the average power of the alpha rhythm. Results showed that alpha rhythm average power in the contralateral cortical regions increased with higher grip force levels. Similarly, the power of the simulated signals also increased with specific parameter (J, Ge, and Gi) increases, closely approximating the measured EEG signal changes. The findings suggest that increasing grip force activates more motor neurons in the motor cortex and raises their firing rate. Neural mass modeling provides a computational neuroscience approach to understanding the dynamic changes in alpha rhythms in the motor cortex under different grip force levels.
Using Infrared Raman Spectroscopy with Machine Learning and Deep Learning as an Automatic Textile-Sorting Technology for Waste Textiles
Authors: Tsai Pei-Fen , Yuan Shyan-Ming
Published: December 2024

With the fast-fashion trend, an increasing number of discarded clothing items are being eliminated at the stages of both pre-consumer and post-consumer each year. The linear economy produces large volumes of waste, which harm environmental sustainability. This study addresses the pressing need for efficient textile recycling in the circular economy (CE). We developed a highly accurate Raman-spectroscopy-based textile-sorting technology, which overcomes the challenge of diverse fiber combinations in waste textiles. By categorizing textiles into six groups based on their fiber compositions, the sorter improves the quality of recycled fibers. Our study demonstrates the potential of Raman spectroscopy in providing detailed molecular compositional information, which is crucial for effective textile sorting. Furthermore, AI technologies, including PCA, KNN, SVM, RF, ANN, and CNN, are integrated into the sorting process, further enhancing the efficiency to 1 piece per second with a precision of over 95% in grouping textiles based on the fiber compositional analysis. This interdisciplinary approach offers a promising solution for sustainable textile recycling, contributing to the objectives of the CE.
Event-Based Visual/Inertial Odometry for UAV Indoor Navigation
Authors: Elamin Ahmed , El-Rabbany Ahmed , Jacob Sunil
Published: December 2024

Indoor navigation is becoming increasingly essential for multiple applications. It is complex and challenging due to dynamic scenes, limited space, and, more importantly, the unavailability of global navigation satellite system (GNSS) signals. Recently, new sensors have emerged, namely event cameras, which show great potential for indoor navigation due to their high dynamic range and low latency. In this study, an event-based visual–inertial odometry approach is proposed, emphasizing adaptive event accumulation and selective keyframe updates to reduce computational overhead. The proposed approach fuses events, standard frames, and inertial measurements for precise indoor navigation. Features are detected and tracked on the standard images. The events are accumulated into frames and used to track the features between the standard frames. Subsequently, the IMU measurements and the feature tracks are fused to continuously estimate the sensor states. The proposed approach is evaluated using both simulated and real-world datasets. Compared with the state-of-the-art U-SLAM algorithm, our approach achieves a substantial reduction in the mean positional error and RMSE in simulated environments, showing up to 50% and 47% reductions along the x- and y-axes, respectively. The approach achieves 5–10 ms latency per event batch and 10–20 ms for frame updates, demonstrating real-time performance on resource-constrained platforms. These results underscore the potential of our approach as a robust solution for real-world UAV indoor navigation scenarios.
Research on Intelligent Monitoring and Protection Equipment of Vital Signs of Underground Personnel in Coal Mines: Review
Authors: Liang Yuntao , Liu Yingjie , Lu Changjia , Cui Dawei , Yang Jinghu , Zhou Rui
Published: December 2024

The coal industry is a high risk, high difficulty industry, and the annual global mine accident rate is high, so the safety of coal mine underground operations has been a concern. With the development of technology, the application of intelligent security technology in coal mine safety has broad prospects. In this paper, the research progress of vital signs monitoring and support equipment for underground personnel in coal mines is reviewed. The two main methods to ensure the safety of miners are discussed. They consist of directly monitoring human vital signs through portable devices such as smart helmets and smartwatches and indirectly monitoring underground environmental parameters. In addition, the application of information technology, sensor technology and artificial intelligence in vital signs monitoring is briefly discussed, and some future research directions are proposed. For example, through big data and artificial intelligence technology, vital signs data can be compared with historical data, individual health trends and potential risks can be analyzed, and we can provide personalized health management programs for miners. These technologies not only improve the safety of underground coal mine operation, but also provide an important guarantee for the realization of intelligent and safe coal mine production.
Multi-Armed Bandit Approaches for Location Planning with Dynamic Relief Supplies Allocation Under Disaster Uncertainty
Authors: Liang Jun , Zhang Zongjia , Zhi Yanpeng
Published: December 2024

Natural disasters (e.g., floods, earthquakes) significantly impact citizens, economies, and the environment worldwide. Due to their sudden onset, devastating effects, and high uncertainty, it is crucial for emergency departments to take swift action to minimize losses. Among these actions, planning the locations of relief supply distribution centers and dynamically allocating supplies is paramount, as governments must prioritize citizens’ safety and basic living needs following disasters. To address this challenge, this paper develops a three-layer emergency logistics network to manage the flow of emergency materials, from warehouses to transfer stations to disaster sites. A bi-objective, multi-period stochastic integer programming model is proposed to solve the emergency location, distribution, and allocation problem under uncertainty, focusing on three key decisions: transfer station selection, upstream emergency material distribution, and downstream emergency material allocation. We introduce a multi-armed bandit algorithm, named the Geometric Greedy algorithm, to optimize transfer station planning while accounting for subsequent dynamic relief supply distribution and allocation in a stochastic environment. The new algorithm is compared with two widely used multi-armed bandit algorithms: the ϵ-Greedy algorithm and the Upper Confidence Bound (UCB) algorithm. A case study in the Futian District of Shenzhen, China, demonstrates the practicality of our model and algorithms. The results show that the Geometric Greedy algorithm excels in both computational efficiency and convergence stability. This research offers valuable guidelines for emergency departments in optimizing the layout and flow of emergency logistics networks.
A Review of the Healthcare and Social Care Needs of the Older Prisoner Population in England and Wales
Authors: Gavin Paul , Porter Cody Normitta , MacDonald Finley
Published: December 2024

In recent years, research on older prisoners in England and Wales has expanded significantly, and the term “older prisoner” is now well entrenched in the prison-based literature. Those prisoners who are over the age of 50 are now the fastest growing demographic in the prison system and they present with a wide range of specific health and social care needs. This paper provides an overview of the main challenges facing older prisoners in the context of their health and social care needs, including the development of mental health problems such as Alzheimer’s and dementia, as well as challenges related to mobility and engaging in structured activities in prison. This paper also considers the issue of dying in prison for older prisoners as well as the challenges of resettlement and reintegration post-release.
Assessing the Circular Economy in Regions of Chile by Using Multiple-Criteria Decision-Making (MCDM)
Authors: Komasi Hossein , Karbassi Yazdi Amir , Eskandari Sani Mohammad , Tan Yong
Published: December 2024

The circular economy has gained significant global attention, particularly in developed countries, as a novel economic model to reduce waste and optimize resource utilization. This study assesses the capacity of Chilean regions to adopt the circular economy model. Six Chilean regions were selected for this purpose. The research process is carried out and employs a descriptive–analytical approach, conducting a comparative analysis of the six regions based on nine waste-related indicators. The Rank Alternative by Fractional Separation of Ideal Solutions (RAFSI) method, a multi-criteria decision-making (MCDM) technique, was used with trapezoidal fuzzy numbers for data analysis. A sensitivity analysis was employed to ensure the robustness of the results and determine the range of variation and flexibility of the model. The findings indicate that the Antofagasta region ranked first based on the research indicators, followed by Coquimbo, Arica y Parinacota, Valparaíso, Atacama, and Tarapacá. These results highlight significant disparities in the performance of Chile’s northern regions, emphasizing the key factors influencing circular economy adoption. This underscores the crucial need for region-specific strategies to enhance sustainability and waste management, as one size does not fit all in this context.
Urban Forestry in Sub-Saharan Africa: Challenges, Contributions, and Future Directions for Combating Climate Change and Restoring Forest Landscapes
Authors: Ale Sawaba , Adjonou Kossi , Segla Kossi Novigno , Komi Kossi , Zoungrana Jean-Bosco Benewinde , Aholou Coffi , Kokou Kouami
Published: December 2024

Since the 1960s, the countries of sub-Saharan Africa have been affected by an urban crisis due to the demographic explosion in cities and the resulting land pressure. These issues have resulted in the proliferation of spontaneous housing areas, which call the future of urban vegetation and its degree of inclusion in city planning into question. To analyze the current scientific knowledge on the development of urban landscapes, particularly in sub-Saharan Africa, a literature review on urban forestry covering the period of 1998–2022 was carried out in order to better guide future research works towards a greater consideration of urban forestry and its contribution to combating the adverse effects of climate change. Out of the 110 scientific papers selected, 50 were considered relevant because of their direct link with urban forestry. Based on the analysis of these papers, a classification of the articles according to five themes was carried out. These themes are the management of green spaces in urban areas, the functions of trees in an urban environment, the development of urban forestry, the characterization of urban tree flora, and the financing of urban forestry. Thus, the “function of trees in an urban environment and the characterization of urban tree flora” are the most addressed issues, being raised in 42% and 28% of articles, respectively. “Financing urban forestry” is practically not addressed (2%). The analysis of the 50 selected articles reveals a significant geographic concentration of urban forestry research. Countries such as South Africa, Kenya, and Ghana are more represented in the literature, while vast regions in Central and Western Africa (e.g., the Democratic Republic of Congo, Chad, and Niger) are the object of minimal or no documented research with regard to urban forestry. Future research should address these underrepresented themes, as they are crucial for the long-term sustainability of urban forestry projects. Particularly, research on financing mechanisms is essential to unlocking sustainable funding sources for urban forestry, a critical barrier to its expansion.
Determining Vulnerability Indicators of Buildings for Sea-Level Rise and Floods in Urban Coastal Areas
Authors: Çal İsa , Ciravoğlu Ayşen
Published: December 2024

Projected sea-level rise and floods due to climate change impacts are the hazards threatening urban coastal areas. In the literature on mitigation and adaptation, it is determined that studies in the field of architectural design for the assessment of risks and vulnerabilities to these hazards are not yet at a sufficient level. This study aims to determine the vulnerability indicators of buildings due to architectural design decisions in the urban coastal areas facing the risk of sea-level rise and flood hazards. In this direction, it is argued that the decisions that are taken regarding the building and its environment during the architectural design process can be interpreted as vulnerability indicators in vulnerability assessments of buildings to be made in the context of these hazards. In this context, an indicator-based assessment framework is proposed as a method of examining the vulnerability and climate resilience capacity of design practices in urban coastal areas. The first stage of the research methodology includes the results of a literature review to identify indicators of building vulnerability. In the second stage, these indicators were presented for expert opinions and analyzed with the Delphi method and an assessment framework was created. This assessment framework is designed to serve as a decision-making tool or checklist for decision makers, facilitating the integration of vulnerability indicators into the design, implementation, and retrofitting of buildings in urban coastal areas. Due to its hierarchical, yet flexible, and adaptable structure, it can be used by architects, urban planners, and policy makers in terms of assessing buildings and its environments so that actions for adaptation can be implemented.
Assessing Temperature Change Impact in the Wake of Ongoing Land Use Change: A Case Study at Lake Dianshan
Authors: Liu Hua , Zhou Xuefei
Published: December 2024

Climate change exerts both direct and indirect influences on the eutrophication of surface water ecosystems in various ways. This study aimed to evaluate the impact of temperature fluctuations on trophic levels through various interdisciplinary coupling analysis methods after land use change, which including water and sediment sample analysis, hydraulic model, remote sensing, and historic data analysis. For the historical analysis, six satellite images of Lake Dianshan were examined to assess algal bloom occurrences and the coverage of Eichhornia crassipes from 2013 to 2023. The correlation between trophic indicators and temperature was analyzed using statistical methods. For the monthly analysis, a total of 27 sediment samples and 54 water samples collected from Lake Dianshan were assessed to determine how seasonal temperature variations influence eutrophication status. The trophic indicators have higher concentration at inlet sampling sites compared to outlet sites, which validated the potential external pollution source. The trophic level of Lake Dianshan is influenced not only by climate change but also significantly by urban human activities. The management of eutrophication has substantially improved the water quality of Lake Dianshan over the past few decades. Furthermore, increasing temperatures demonstrate a positive correlation with the proliferation of cyanobacteria, particularly in urban areas.
Assessing Street Environments for Older Adults in Urban Villages Using POIs and Street View Images—A Case Study of Guangzhou, China
Authors: Li Dongyuan , Ni Yang
Published: December 2024

In China’s urban villages, typically low-income communities, the living environments of older adults are often overlooked. Furthermore, quantitative research in these areas has been limited by the complexity of their street environments. This research bridges these gaps through an innovative interdisciplinary quantitative approach, integrating points of interest (POIs) and street view images (SVIs) to assess the street environments for older adults using ArcGIS and deep learning-based Fully Convolutional Networks. Specifically, Huangcun in Guangzhou, China, was selected for its typicality, with features like high density, complex street environments, and a significant older adult population. The findings reveal that while most age-friendly facilities meet basic needs, there are significant deficiencies in accessibility, comfort, and safety which call for targeted environmental improvements. This study contributes to sustainable urban development by providing street-by-street insights that empower urban planners to rectify spatial inequalities and to foster environments conducive to aging in place. By developing and applying a novel methodology that enhances urban age-friendliness assessments, this research extends the application of such models across diverse urban settings globally, promoting the integration of sustainability in urban planning.