bluuebunny/crossref_metadata_2025 huggingface dataset:
A New Retrieval Algorithm of Fractional Snow over the Tibetan Plateau Derived from AVH09C1
Authors: Yin Hang , Xu Liyan , Li Yihang
Published: June 2024

Snow cover products are primarily derived from the Moderate-resolution Imaging Spectrometer (MODIS) and Advanced Very-High-Resolution Radiometer (AVHRR) datasets. MODIS achieves both snow/non-snow discrimination and snow cover fractional retrieval, while early AVHRR-based snow cover products only focused on snow/non-snow discrimination. The AVHRR Climate Data Record (AVHRR-CDR) provides a nearly 40-year global dataset that has the potential to fill the gap in long-term snow cover fractional monitoring. Our study selects the Qinghai–Tibet Plateau as the experimental area, utilizing AVHRR-CDR surface reflectance data (AVH09C1) and calibrating with the MODIS snow product MOD10A1. The snow cover percentage retrieval from the AVHRR dataset is performed using Surface Reflectance at 0.64 μm (SR1) and Surface Reflectance at 0.86 μm (SR2), along with a simulated Normalized Difference Snow Index (NDSI) model. Also, in order to detect the effects of land-cover type and topography on snow inversion, we tested the accuracy of the algorithm with and without these influences, respectively (vanilla algorithm and improved algorithm). The accuracy of the AVHRR snow cover percentage data product is evaluated using MOD10A1, ground snow-depth measurements and ERA5. The results indicate that the logic model based on NDSI has the best fitting effect, with R-square and RMSE values of 0.83 and 0.10, respectively. Meanwhile, the accuracy was improved after taking into account the effects of land-cover type and topography. The model is validated using MOD10A1 snow-covered areas, showing snow cover area differences of less than 4% across 6 temporal phases. The improved algorithm results in better consistency with MOD10A1 than with the vanilla algorithm. Moreover, the RMSE reaches greater levels when the elevation is below 2000 m or above 6000 m and is lower when the slope is between 16° and 20°. Using ground snow-depth measurements as ground truth, the multi-year recall rates are mostly above 0.7, with an average recall rate of 0.81. The results also show a high degree of consistency with ERA5. The validation results demonstrate that the AVHRR snow cover percentage remote sensing product proposed in this study exhibits high accuracy in the Tibetan Plateau region, also demonstrating that land-cover type and topographic factors are important to the algorithm. Our study lays the foundation for a global snow cover percentage product based on AVHRR-CDR and furthermore lays a basic work for generating a long-term AVHRR-MODIS fractional snow cover dataset.
A Modified Look-Up Table Based Algorithm with a Self-Posed Scheme for Fine-Mode Aerosol Microphysical Properties Inversion by Multi-Wavelength Lidar
Authors: Zhou Zeyu , Ma Yingying , Yin Zhenping , Hu Qiaoyun , Veselovskii Igor , Müller Detlef , Gong Wei
Published: June 2024

Aerosol microphysical properties, including aerosol particle size distribution, complex refractive index and concentration properties, are key parameters evaluating the impact of aerosols on climate, meteorology, and human health. High Spectral Resolution Lidar (HSRL) is an efficient tool for probing the vertical optical properties of aerosol particles, including the aerosol backscatter coefficient (β) and extinction coefficient (α), at multiple wavelengths. To swiftly process vast data volumes, address the ill-posedness of retrieval problems, and suit simpler lidar systems, this study proposes an algorithm (modified algorithm) for retrieving microphysical property profiles from the HSRL optical data targeting fine-mode aerosols, building upon a previous algorithm (basic algorithm). The modified algorithm is based on a look-up table (LUT) approach, combined with the k-nearest neighbor (k-NN) and random forest (RF) algorithms, and it optimizes the decision tree generation strategy, incorporating a self-posed scheme. In numerical simulation tests for different lidar configurations, the modified algorithm reduced retrieval errors by 41%, 30%, and 32% compared to the basic algorithm for 3β + 2α, 3β + 1α, and 2β + 1α, respectively, with a remarkable improvement of stability. In two observation scenes of a field campaign, the median relative errors of the effective radius for 3β + 2α were 6% and −3%, and the median absolute errors of single-scattering albedo were 0.012 and 0.005. This method represents a further step toward the use of the LUT approach, with the potential to provide effective and efficient aerosol microphysical retrieval for simpler lidar systems, which could advance our understanding of aerosols’ climatic, meteorological, and health impacts.
Semi-Supervised FMCW Radar Hand Gesture Recognition via Pseudo-Label Consistency Learning
Authors: Shi Yuhang , Qiao Lihong , Shu Yucheng , Li Baobin , Xiao Bin , Li Weisheng , Gao Xinbo
Published: June 2024

Hand gesture recognition is pivotal in facilitating human–machine interaction within the Internet of Things. Nevertheless, it encounters challenges, including labeling expenses and robustness. To tackle these issues, we propose a semi-supervised learning framework guided by pseudo-label consistency. This framework utilizes a dual-branch structure with a mean-teacher network. Within this setup, a global and locally guided self-supervised learning encoder acts as a feature extractor in a teacher–student network to efficiently extract features, maximizing data utilization to enhance feature representation. Additionally, we introduce a pseudo-label Consistency-Guided Mean-Teacher model, where simulated noise is incorporated to generate newly unlabeled samples for the teacher model before advancing to the subsequent stage. By enforcing consistency constraints between the outputs of the teacher and student models, we alleviate accuracy degradation resulting from individual differences and interference from other body parts, thereby bolstering the network’s robustness. Ultimately, the teacher model undergoes refinement through exponential moving averages to achieve stable weights. We evaluate our semi-supervised method on two publicly available hand gesture datasets and compare it with several state-of-the-art fully-supervised algorithms. The results demonstrate the robustness of our method, achieving an accuracy rate exceeding 99% across both datasets.
RETRACTED: Zhang et al. Robot Path Planning Method Based on Indoor Spacetime Grid Model. Remote Sens. 2022, 14, 2357
Authors: Zhang Huangchuang , Zhuang Qingjun , Li Ge
Published: June 2024

Remote Sensing retracts the article “Robot Path Planning Method Based on Indoor Spacetime Grid Model” [...]
Identification and Analysis of Long-Term Land Use and Planting Structure Dynamics in the Lower Yellow River Basin
Authors: Hong Shengzhe , Lou Yu , Chen Xinguo , Huang Quanzhong , Yang Qianru , Zhang Xinxin , Li Haozhi , Huang Guanhua
Published: June 2024

Accurate identification of the spatio-temporal planting structure and analysis of its driving factors in an irrigation district are the important bases for scientific and reasonable utilization of irrigation water resources. In pursuit of this goal, the training sample migration method combined with the random forest algorithm were used to classify land use and planting structure over 2001–2022 in the lower Yellow River Basin. Moreover, an econometric regression model was applied to quantify the driving factors of the change in the crop-planted area. The results illustrated that the identification method has relatively high accuracy in identifying historical periods of land use and planting structures, with the average kappa coefficient equating to 0.953. From 2001 to 2022, the area of cultivated land was the largest, with the proportion of the total area increasing from 45.72% to 58.12%. The planted area of winter wheat–summer maize rotation increased from 74.84% to 88.11% of the cultivated land. While the planted area of cotton declined by 96.36%, about 50% of cotton planting was converted to the winter wheat–summer maize rotation planting. The government policies about grain purchase and storage were the dominant factors for the change in the crop-planted area. This resulted in an increase of 63.32 × 103 ha and 63.98 × 103 ha in the planted area of winter wheat and summer maize, respectively. The findings are of great significance to the allocation of water resources in irrigation districts of the lower Yellow River Basin.
Semantic Segmentation of Some Rock-Forming Mineral Thin Sections Using Deep Learning Algorithms: A Case Study from the Nikeiba Area, South Eastern Desert, Egypt
Authors: Hassan Safaa M. , Laban Noureldin , Abo Khashaba Saif M. , El-Shibiny N. H. , Bashir Bashar , Azer Mokhles K. , Drüppel Kirsten , Keshk Hatem M.
Published: June 2024

Image semantic segmentation using deep learning algorithms plays a vital role in identifying different rock-forming minerals. In this paper, we employ the U-net model for its architecture that guarantees precise localization and efficient data utilization. We implement this deep learning model across two distinct datasets: (1) the first dataset from the ALEX Streckeisen website, and (2) the second dataset from the Gabal Nikeiba area, South Eastern Desert of Egypt. Our model exhibits excellent performance in both datasets, with an average accuracy of precision at 0.89 and 0.83, recall at 0.80 and 0.78, and F1 score at 0.82 and 0.79, respectively, helping in identifying and detecting rock-forming minerals in thin-section images. The model’s most exceptional performance is clearly in eleven different basement rock-forming minerals with precision up to 0.89, recall at 0.80, and F1 score at 0.82 on average. This study is significant as it represents the key to identifying and detecting minerals in the thin sections of rock samples in Egypt and the Arabian–Nubian Shield as a whole. By significantly reducing analysis time and improving accuracy compared to manual methods, it revolutionizes geological research and resource exploration in the region.
Integrating NTL Intensity and Building Volume to Improve the Built-Up Areas’ Extraction from SDGSAT-1 GLI Data
Authors: Liu Shaoyang , Wang Congxiao , Wu Bin , Chen Zuoqi , Zhang Jiarui , Huang Yan , Wu Jianping , Yu Bailang
Published: June 2024

Urban built-up areas are the main space carrier of population and urban activities. It is of great significance to accurately identify urban built-up area for monitoring urbanization dynamics and their impact on Sustainable Development Goals. Using only nighttime light (NTL) remote sensing data will lead to omission phenomena in urban built-up area extraction, especially for SDGSAT-1 glimmer imager (GLI) data with high spatial resolution. Therefore, this study proposed a novel nighttime Lights integrate Building Volume (LitBV) index by integrating NTL intensity information from SDGSAT-1 GLI data and building volume information from Digital Surface Model (DSM) data to extract built-up areas more accurately. The results indicated that the LitBV index achieved remarkable results in the extraction of built-up areas, with the overall accuracy of 81.25%. The accuracy of the built-up area extraction based on the LitBV index is better than the results based on only NTL data and only building volume. Moreover, experiments at different spatial resolutions (10 m, 100 m, and 500 m) and different types of NTL data (SDGSAT-1 GLI data, Luojia-1 data, and NASA’s Black Marble data) showed that the LitBV index can significantly improve the extraction accuracy of built-up areas. The LitBV index has a good application ability and prospect for extracting built-up areas with high-resolution SDGSAT-1 GLI data.
Efficiency and Security Evaluation of Lightweight Cryptographic Algorithms for Resource-Constrained IoT Devices
Authors: Radhakrishnan Indu , Jadon Shruti , Honnavalli Prasad B.
Published: June 2024

The IoT has become an integral part of the technological ecosystem that we all depend on. The increase in the number of IoT devices has also brought with it security concerns. Lightweight cryptography (LWC) has evolved to be a promising solution to improve the privacy and confidentiality aspect of IoT devices. The challenge is to choose the right algorithm from a plethora of choices. This work aims to compare three different LWC algorithms: AES-128, SPECK, and ASCON. The comparison is made by measuring various criteria such as execution time, memory utilization, latency, throughput, and security robustness of the algorithms in IoT boards with constrained computational capabilities and power. These metrics are crucial to determine the suitability and help in making informed decisions on choosing the right cryptographic algorithms to strike a balance between security and performance. Through the evaluation it is observed that SPECK exhibits better performance in resource-constrained IoT devices.
Multi-Parameter Characterization of Liquid-to-Ice Phase Transition Using Bulk Acoustic Waves
Authors: Smirnov Andrey , Anisimkin Vladimir , Voronova Natalia , Kashin Vadim , Kuznetsova Iren
Published: June 2024

The detection of the liquid-to-ice transition is an important challenge for many applications. In this paper, a method for multi-parameter characterization of the liquid-to-ice phase transition is proposed and tested. The method is based on the fundamental properties of bulk acoustic waves (BAWs). BAWs with shear vertical (SV) or shear horizontal (SH) polarization cannot propagate in liquids, only in solids such as ice. BAWs with longitudinal (L) polarization, however, can propagate in both liquids and solids, but with different velocities and attenuations. Velocities and attenuations for L-BAWs and SV-BAWs are measured in ice using parameters such as time delay and wave amplitude at a frequency range of 1–37 MHz. Based on these measurements, relevant parameters for Rayleigh surface acoustic waves and Poisson’s modulus for ice are determined. The homogeneity of the ice sample is also detected along its length. A dual sensor has been developed and tested to analyze two-phase transitions in two liquids simultaneously. Distilled water and a 0.9% solution of NaCl in water were used as examples.
A Review of Predictive Analytics Models in the Oil and Gas Industries
Authors: R Azmi Putri Azmira , Yusoff Marina , Mohd Sallehud-din Mohamad Taufik
Published: June 2024

Enhancing the management and monitoring of oil and gas processes demands the development of precise predictive analytic techniques. Over the past two years, oil and its prediction have advanced significantly using conventional and modern machine learning techniques. Several review articles detail the developments in predictive maintenance and the technical and non-technical aspects of influencing the uptake of big data. The absence of references for machine learning techniques impacts the effective optimization of predictive analytics in the oil and gas sectors. This review paper offers readers thorough information on the latest machine learning methods utilized in this industry’s predictive analytical modeling. This review covers different forms of machine learning techniques used in predictive analytical modeling from 2021 to 2023 (91 articles). It provides an overview of the details of the papers that were reviewed, describing the model’s categories, the data’s temporality, field, and name, the dataset’s type, predictive analytics (classification, clustering, or prediction), the models’ input and output parameters, the performance metrics, the optimal model, and the model’s benefits and drawbacks. In addition, suggestions for future research directions to provide insights into the potential applications of the associated knowledge. This review can serve as a guide to enhance the effectiveness of predictive analytics models in the oil and gas industries.
A Microvascular Segmentation Network Based on Pyramidal Attention Mechanism
Authors: Zhang Hong , Fang Wei , Li Jiayun
Published: June 2024

The precise segmentation of retinal vasculature is crucial for the early screening of various eye diseases, such as diabetic retinopathy and hypertensive retinopathy. Given the complex and variable overall structure of retinal vessels and their delicate, minute local features, the accurate extraction of fine vessels and edge pixels remains a technical challenge in the current research. To enhance the ability to extract thin vessels, this paper incorporates a pyramid channel attention module into a U-shaped network. This allows for more effective capture of information at different levels and increased attention to vessel-related channels, thereby improving model performance. Simultaneously, to prevent overfitting, this paper optimizes the standard convolutional block in the U-Net with the pre-activated residual discard convolution block, thus improving the model’s generalization ability. The model is evaluated on three benchmark retinal datasets: DRIVE, CHASE_DB1, and STARE. Experimental results demonstrate that, compared to the baseline model, the proposed model achieves improvements in sensitivity (Sen) scores of 7.12%, 9.65%, and 5.36% on these three datasets, respectively, proving its strong ability to extract fine vessels.
Literature Review of Deep-Learning-Based Detection of Violence in Video
Authors: Negre Pablo , Alonso Ricardo S. , González-Briones Alfonso , Prieto Javier , Rodríguez-González Sara
Published: June 2024

Physical aggression is a serious and widespread problem in society, affecting people worldwide. It impacts nearly every aspect of life. While some studies explore the root causes of violent behavior, others focus on urban planning in high-crime areas. Real-time violence detection, powered by artificial intelligence, offers a direct and efficient solution, reducing the need for extensive human supervision and saving lives. This paper is a continuation of a systematic mapping study and its objective is to provide a comprehensive and up-to-date review of AI-based video violence detection, specifically in physical assaults. Regarding violence detection, the following have been grouped and categorized from the review of the selected papers: 21 challenges that remain to be solved, 28 datasets that have been created in recent years, 21 keyframe extraction methods, 16 types of algorithm inputs, as well as a wide variety of algorithm combinations and their corresponding accuracy results. Given the lack of recent reviews dealing with the detection of violence in video, this study is considered necessary and relevant.
Detection of Multi-Layered Bond Delamination Defects Based on Full Waveform Inversion
Authors: Wen Jiawei , Jiang Can , Chen Hao
Published: June 2024

This study aimed to address the challenges encountered in traditional bulk wave delamination detection methods characterized by low detection efficiency. Additionally, the limitations of guided wave delamination detection methods were addressed, particularly those utilizing reflected waves, which are susceptible to edge reflections, thus complicating effective defect extraction. Leveraging the full waveform inversion algorithm, an innovative approach was established for detecting delamination defects in multi-layered structures using ultrasonic guided wave arrays. First, finite element modeling was employed to simulate guided wave data acquisition by a circular array within an aluminum–epoxy bilayer structure with embedded delamination defects. Subsequently, the full waveform inversion algorithm was applied to reconstruct both regular and irregular delamination defects. Analysis results indicated the efficacy of the proposed approach in accurately identifying delamination defects of varying shapes. Furthermore, an experimental platform for guided wave delamination defect detection was established, and experiments were conducted on a steel–cement bilayer structure containing an irregular delamination defect. The experimental results validated the exceptional imaging precision of our proposed technique for identifying delamination defects in multi-layered boards. In summary, the proposed method can accurately determine both the positions and sizes of defects with higher detection efficiency than traditional pulse-echo delamination detection methods.
Enhancing Hospital Efficiency and Patient Care: Real-Time Tracking and Data-Driven Dispatch in Patient Transport
Authors: Huang Su-Wen , Chiou Shyue-Yow , Chen Rung-Ching , Sub-r-pa Chayanon
Published: June 2024

Inefficient patient transport in hospitals often leads to delays, overworked staff, and suboptimal resource utilization, ultimately impacting patient care. Existing dispatch management algorithms are often evaluated in simulation environments, raising concerns about their real-world applicability. This study presents a real-world experiment that bridges the gap between theoretical dispatch algorithms and real-world implementation. It applies process capability analysis at Taichung Veterans General Hospital in Taichung, Taiwan, and utilizes IoT for real-time tracking of staff and medical devices to address challenges associated with manual dispatch processes. Experimental data collected from the hospital underwent statistical evaluation between January 2021 and December 2021. The results of our experiment, which compared the use of traditional dispatch methods with the Beacon dispatch method, found that traditional dispatch had an overtime delay of 41.0%; in comparison, the Beacon dispatch method had an overtime delay of 26.5%. These findings demonstrate the transformative potential of this solution for not only hospital operations but also for improving service quality across the healthcare industry in the context of smart hospitals.
A Novel 3D Reconstruction Sensor Using a Diving Lamp and a Camera for Underwater Cave Exploration
Authors: Massone Quentin , Druon Sébastien , Triboulet Jean
Published: June 2024

Aquifer karstic structures, due to their complex nature, present significant challenges in accurately mapping their intricate features. Traditional methods often rely on invasive techniques or sophisticated equipment, limiting accessibility and feasibility. In this paper, a new approach is proposed for a non-invasive, low-cost 3D reconstruction using a camera that observes the light projection of a simple diving lamp. The method capitalizes on the principles of structured light, leveraging the projection of light contours onto the karstic surfaces. By capturing the resultant light patterns with a camera, three-dimensional representations of the structures are reconstructed. The simplicity and portability of the equipment required make this method highly versatile, enabling deployment in diverse underwater environments. This approach is validated through extensive field experiments conducted in various aquifer karstic settings. The results demonstrate the efficacy of this method in accurately delineating intricate karstic features with remarkable detail and resolution. Furthermore, the non-destructive nature of this technique minimizes disturbance to delicate aquatic ecosystems while providing valuable insights into the subterranean landscape. This innovative methodology not only offers a cost-effective and non-invasive means of mapping aquifer karstic structures but also opens avenues for comprehensive environmental monitoring and resource management. Its potential applications span hydrogeological studies, environmental conservation efforts, and sustainable water resource management practices in karstic terrains worldwide.
A Ground-Based Electrostatically Suspended Accelerometer
Authors: Liu Hanxiao , He Xiaoxia , Wu Chenhui , Zhang Rong
Published: June 2024

In this study, we have developed an electrostatically suspended accelerometer (ESA) specifically designed for ground use. To ensure sufficient overload capacity and minimize noise resulting from high suspension voltage, we introduced a proof mass design featuring a hollow, thin-walled cylinder with a thin flange fixed at the center, offering the highest surface-area-to-mass ratio compared to various typical proof mass structures. Preload voltage is directly applied to the proof mass via a golden wire, effectively reducing the maximum supply voltage for suspension. The arrangement of suspension electrodes, offering five degrees of freedom and minimizing cross-talk, was designed to prioritize simplicity and maximize the utilization of electrode area for suspension purposes. The displacement detection and electrostatic suspension force were accurately modeled based on the structure. A controller incorporating an inverse winding mechanism was developed and simulated using Simulink. The simulation results unequivocally demonstrate the successful completion of the stable initial levitation process and suspension under ±1g overload.
Extraction of Moso Bamboo Parameters Based on the Combination of ALS and TLS Point Cloud Data
Authors: Fan Suying , Jing Sishuo , Xu Wenbing , Wu Bin , Li Mingzhe , Jing Haochen
Published: June 2024

Extracting moso bamboo parameters from single-source point cloud data has limitations. In this article, a new approach for extracting moso bamboo parameters using airborne laser scanning (ALS) and terrestrial laser scanning (TLS) point cloud data is proposed. Using the field-surveyed coordinates of plot corner points and the Iterative Closest Point (ICP) algorithm, the ALS and TLS point clouds were aligned. Considering the difference in point distribution of ALS, TLS, and the merged point cloud, individual bamboo plants were segmented from the ALS point cloud using the point cloud segmentation (PCS) algorithm, and individual bamboo plants were segmented from the TLS and the merged point cloud using the comparative shortest-path (CSP) method. The cylinder fitting method was used to estimate the diameter at breast height (DBH) of the segmented bamboo plants. The accuracy was calculated by comparing the bamboo parameter values extracted by the above methods with reference data in three sample plots. The comparison results showed that by using the merged data, the detection rate of moso bamboo plants could reach up to 97.30%; the R2 of the estimated bamboo height was increased to above 0.96, and the root mean square error (RMSE) decreased from 1.14 m at most to a range of 0.35–0.48 m, while the R2 of the DBH fit was increased to a range of 0.97–0.99, and the RMSE decreased from 0.004 m at most to a range of 0.001–0.003 m. The accuracy of moso bamboo parameter extraction was significantly improved by using the merged point cloud data.
Fast Three-Dimensional Profilometry with Large Depth of Field
Authors: Zhang Wei , Zhu Jiongguang , Han Yu , Zhang Manru , Li Jiangbo
Published: June 2024

By applying a high projection rate, the binary defocusing technique can dramatically increase 3D imaging speed. However, existing methods are sensitive to the varied defocusing degree, and have limited depth of field (DoF). To this end, a time–domain Gaussian fitting method is proposed in this paper. The concept of a time–domain Gaussian curve is firstly put forward, and the procedure of determining projector coordinates with a time–domain Gaussian curve is illustrated in detail. The neural network technique is applied to rapidly compute peak positions of time-domain Gaussian curves. Relying on the computing power of the neural network, the proposed method can reduce the computing time greatly. The binary defocusing technique can be combined with the neural network, and fast 3D profilometry with a large depth of field is achieved. Moreover, because the time–domain Gaussian curve is extracted from individual image pixel, it will not deform according to a complex surface, so the proposed method is also suitable for measuring a complex surface. It is demonstrated by the experiment results that our proposed method can extends the system DoF by five times, and both the data acquisition time and computing time can be reduced to less than 35 ms.
A Two-Axis Orthogonal Resonator for Variable Sensitivity Mode Localization Sensing
Authors: Nagasaka Yuta , Baronchelli Alessia , Tanaka Shuji , Tsukamoto Takashiro
Published: June 2024

This paper experimentally demonstrates a mode localization sensing approach using a single two-axis orthogonal resonator. The resonator consists of concentric multi-rings connected by elliptic springs that enable two orthogonal oscillation modes. By electrostatically tuning the anisotropic stiffness between the two axes, the effective coupling stiffness between the modes can be precisely controlled down to near-zero values. This allows the sensitivity of mode localization sensing to be tuned over a wide range. An order of magnitude enhancement in sensitivity is experimentally achieved by reducing the coupling stiffness towards zero. The resonator’s simple single-mass structure offers advantages over conventional coupled resonator designs for compact, tunable mode localization sensors. Both positive and negative values of coupling stiffness are demonstrated, enabling maximum sensitivity at the point where coupling crosses through zero. A method for decomposing overlapping resonance peaks is introduced to accurately measure the amplitude ratios of the localized modes even at high sensitivities. The electrostatic tuning approach provides a new option for realizing variable sensitivity mode localization devices using a simplified resonator geometry.
Photovoltaic Power Injection Control Based on a Virtual Synchronous Machine Strategy
Authors: Albornoz Miguel , Rohten Jaime , Espinoza José , Varela Jorge , Sbarbaro Daniel , Gallego Yandi
Published: June 2024

The increasing participation of photovoltaic sources in power grids presents the challenge of enhancing power quality, which is affected by the intrinsic characteristics of these sources, such as variability and lack of inertia. This power quality degradation mainly generates variations in both voltage magnitude and frequency, which are more pronounced in microgrids. In fact, the magnitude problem is particularly present in the distribution systems, where photovoltaic sources are spread along the grid. Due to the power converter’s lack of inertia, frequency problems can be seen throughout the network. Grid-forming control strategies in photovoltaic systems have been proposed to address these problems, although most proposed solutions involve either a direct voltage source or energy storage systems, thereby increasing costs. In this paper, a photovoltaic injection system is designed with a virtual synchronous machine control strategy to provide voltage and frequency support to the grid. The maximum power point tracking algorithm is adapted to provide the direct voltage reference and inject active power according to the droop frequency control. The control strategy is validated through simulations and key experimental setup tests. The results demonstrate that it is possible to inject photovoltaic power and provide voltage and frequency support.
HeMoDU: High-Efficiency Multi-Object Detection Algorithm for Unmanned Aerial Vehicles on Urban Roads
Authors: Shi Hanyi , Wang Ningzhi , Xu Xinyao , Qian Yue , Zeng Lingbin , Zhu Yi
Published: June 2024

Unmanned aerial vehicle (UAV)-based object detection methods are widely used in traffic detection due to their high flexibility and extensive coverage. In recent years, with the increasing complexity of the urban road environment, UAV object detection algorithms based on deep learning have gradually become a research hotspot. However, how to further improve algorithmic efficiency in response to the numerous and rapidly changing road elements, and thus achieve high-speed and accurate road object detection, remains a challenging issue. Given this context, this paper proposes the high-efficiency multi-object detection algorithm for UAVs (HeMoDU). HeMoDU reconstructs a state-of-the-art, deep-learning-based object detection model and optimizes several aspects to improve computational efficiency and detection accuracy. To validate the performance of HeMoDU in urban road environments, this paper uses the public urban road datasets VisDrone2019 and UA-DETRAC for evaluation. The experimental results show that the HeMoDU model effectively improves the speed and accuracy of UAV object detection.
Intervention-Induced Changes in Balance and Task-Dependent Neural Activity in Adults with Acquired Brain Injury: A Pilot Randomized Control Trial
Authors: Hernandez-Sarabia Jesus A. , Schmid Arlene A. , Sharp Julia L. , Stephens Jaclyn A.
Published: June 2024

Advances in neuroimaging technology, like functional near-infrared spectroscopy (fNIRS), support the evaluation of task-dependent brain activity during functional tasks, like balance, in healthy and clinical populations. To date, there have been no studies examining how interventions, like yoga, impact task-dependent brain activity in adults with chronic acquired brain injury (ABI). This pilot study compared eight weeks of group yoga (active) to group exercise (control) on balance and task-dependent neural activity outcomes. Twenty-three participants were randomized to yoga (n = 13) or exercise groups (n = 10). Neuroimaging and balance performance data were collected simultaneously using a force plate and mobile fNIRS device before and after interventions. Linear mixed-effects models were used to evaluate the effect of time, time x group interactions, and simple (i.e., within-group) effects. Regardless of group, all participants had significant balance improvements after the interventions. Additionally, regardless of group, there were significant changes in task-dependent neural activity, as well as distinct changes in neural activity within each group. In summary, using advances in sensor technology, we were able to demonstrate preliminary evidence of intervention-induced changes in balance and neural activity in adults with ABI. These preliminary results may provide an important foundation for future neurorehabilitation studies that leverage neuroimaging methods, like fNIRS.
Alignment Prediction of Air Spring Vibration Isolation System for Marine Shafting Based on Genetic Algorithm–Back Propagation Neural Network
Authors: Liu Song , Shi Liang , Liu Zhi-Wei
Published: June 2024

Shafting alignment plays an important role in the marine propulsion system, which affects the safety and stability of ship operation. Air spring vibration isolation systems (ASVISs) for marine shafting can not only reduce mechanical noise but also help control alignment state by actively adjusting air spring pressures. Alignment prediction is the first and a key step in the alignment control of ASVISs. However, in large-scale ASVISs, due to factors such as strong interference and raft deformation, alignment prediction faces problems such as alignment measurement sensors failure and difficulty in establishing a mathematical model. To address this problem, a data model for predicting alignment state is developed based on a back propagation (BP) neural network, fully taking advantage of its self-learning and self-adaption abilities. The proposed model exploits the collected data in the ASVIS instead of the alignment measurement data to calculate the alignment state, providing another alignment prediction approach. Then, in order to solve the local optimum issue of BP neural network, we introduce the genetic algorithm (GA) to optimize the weights and thresholds of the BP neural network, and an improved GA-BP model is designed. The GA-BP model can leverage the advantages of the global search capability of GA as well as the BP neural network’s fast convergence in local search. Finally, we conduct experiments on a real ASVIS and evaluate the prediction models using different criteria. The experimental results show that the proposed prediction model with the GA-BP neural network can accurately predict the alignment state, with a mean-square error (MSE) of 0.0114. And compared to the BP neural network, the GA-BP neural network reduces the MSE by approximately 74%.
Developing a Multi-Spectral NIR LED-Based Instrument for the Detection of Pesticide Residues Containing Chlorpyrifos-Methyl in Rough, Brown, and Milled Rice
Authors: Rodriguez-Macadaeg Fatima , Armstrong Paul R. , Maghirang Elizabeth B. , Scully Erin D. , Brabec Daniel L. , Arthur Frank H. , Adviento-Borbe Arlene D. , Yaptenco Kevin F. , Suministrado Delfin C.
Published: June 2024

A recent study showed the potential of the DA Perten 7200 NIR Spectrometer in detecting chlorpyrifos-methyl pesticide residue in rough, brown, and milled rice. However, this instrument is still lab-based and generally suited for point-of-sale testing. To provide a field-deployable version of this technique, an existing light emitting diode (LED)-based instrument that provides discrete NIR wavelength illumination and reflectance spectra over the range of 850–1550 nm was tested. Spectra were collected from rough, brown, and milled rice at different pesticide concentrations and analyzed for quantitative and qualitative measurement using partial least squares regression (PLS) and discriminant analysis (DA). Simulations for two LED-based instruments were also evaluated using corresponding segments of spectra from the DA7200 to represent LED illumination. For the simulation of the existing LED-based instrument (LEDPrototype1) fitted with 850, 910, 940, 970, 1070, 1200, 1300, 1450, and 1550 nm LED wavelengths, resulting R2 ranged from 0.52 to 0.71, and the correct classification was 70.4% to 100%. The simulation of a second LED instrument (LEDPrototype2) fitted with 980, 1050, 1200, 1300, 1450, 1550, 1600, and 1650 nm LED wavelengths showed R2 of 0.59 to 0.82 and correct classifications of 66% to 100%. These LED wavelengths were selected based on the significant wavelength regions from the PLS regression coefficients of DA7200 and the commercial availability of LED wavelengths. Results showed that it is possible to use a multi-spectral LED-based instrument to detect varying levels of chlorpyrifos-methyl pesticide residue in rough, brown, and milled rice.
MDC-RHT: Multi-Modal Medical Image Fusion via Multi-Dimensional Dynamic Convolution and Residual Hybrid Transformer
Authors: Wang Wenqing , He Ji , Liu Han , Yuan Wei
Published: June 2024

The fusion of multi-modal medical images has great significance for comprehensive diagnosis and treatment. However, the large differences between the various modalities of medical images make multi-modal medical image fusion a great challenge. This paper proposes a novel multi-scale fusion network based on multi-dimensional dynamic convolution and residual hybrid transformer, which has better capability for feature extraction and context modeling and improves the fusion performance. Specifically, the proposed network exploits multi-dimensional dynamic convolution that introduces four attention mechanisms corresponding to four different dimensions of the convolutional kernel to extract more detailed information. Meanwhile, a residual hybrid transformer is designed, which activates more pixels to participate in the fusion process by channel attention, window attention, and overlapping cross attention, thereby strengthening the long-range dependence between different modes and enhancing the connection of global context information. A loss function, including perceptual loss and structural similarity loss, is designed, where the former enhances the visual reality and perceptual details of the fused image, and the latter enables the model to learn structural textures. The whole network adopts a multi-scale architecture and uses an unsupervised end-to-end method to realize multi-modal image fusion. Finally, our method is tested qualitatively and quantitatively on mainstream datasets. The fusion results indicate that our method achieves high scores in most quantitative indicators and satisfactory performance in visual qualitative analysis.
Dr. Octavio Castillo—Pioneer of Urolaparoscopy
Authors: Vitagliano Gonzalo Juan
Published: June 2024

It was November 2003 in the city of Rosario, Argentina [...]
The Digitisation of Italian Schools and the Pandemic Trigger: Actors and Policies in an Evolving Organisational Field
Authors: Carbone Domenico , Calvi Cristina
Published: June 2024

This article analyses the ongoing processes in the organisational field of Italian schools in light of the innovations induced by digital education policies. Specifically, it focuses on the relationship between actors and digital policies concerning the experience of distance learning (DL) that characterised the period of the COVID-19 pandemic. The paper reflects on DL outcomes regarding the three expectations that have often characterised the rhetoric associated with the promotion of digital educational policies, namely: the raising of learning levels, the development of digital competences and the increase in school inclusion. Through an analysis of a series of empirical studies exploring the point of view of the paper, this paper highlights what progress has been made in the digital schooling in Italy and what are still its main limitations. The results of the study show both the limits of the effectiveness of educational policies constructed with a top-down approach and highlight the potential for policy recalibration offered by a reorganisation of the decision-making process through the active involvement of all the actors in the educational system.
Potential for Frugal Innovation in a Brazilian Regional System: A Study Based on a Multicriteria Approach
Authors: Moreira Gabriel Gomes , dos Santos Robério José Rogério , de Carvalho Victor Diogho Heuer , Rosário Francisco José Peixoto , dos Santos Agnaldo José
Published: June 2024

Regional innovation systems (RISs) can stimulate knowledge sharing and collaboration, attracting investments and promoting economic and social progress. This is often linked to what is known as frugal innovation, involving small businesses developing and selling sustainable, low-cost products that meet local needs. This study aims to present and apply a model to measure regional innovation potential, using a multicriteria approach based on the principles of frugal innovation (FI). The analytical hierarchical process (AHP) was used to generate factor weights, enabling score calculation to provide insights into FI potential on a literature-based five-point scale. Data were collected in two stages: (i) from twelve main participants—working in a rural RIS—who responded to a questionnaire, and (ii) from cities through official government channels to collect information about their innovation development initiatives. The results reveal that the RIS analyzed still lacks assistance in the development of public policies to support the development of an innovative culture, indicating the need for appropriate mechanisms to boost innovation actions. The outputs of this study can help cities and regions to analyze their innovation potential, assist public managers in decision-making, support the creation of innovation-stimulating mechanisms, help RISs to address deficiencies, and promote local development.
The Perception of Eros and the Cinema Audience Experience in Cádiz (Spain)
Authors: Rodríguez-Cunill Inmaculada , Domínguez-López Juan José , Carlos-García Sonia
Published: June 2024

The perception of the concept of Eros has evolved through shared cinematic experiences, to the point of shaping collective imagery in Cadiz, Spain. This city is known for its creativity and an extraordinary amount of performances during the period of carnival, and is represented annually by anonymous citizens. The research method employed consisted of an exhaustive analysis of bibliographic, press, and archival references on audience behavior from the introduction of the cinematograph to the present day. The authors have designed a table that organizes the emergence of movie theaters in the city and completed the background information, delving into the historical, geographical, and idiosyncratic factors that have contributed to collective creativity in the city. From there, we analyzed the evolution of the concept of Eros through the perspectives of Byung Chul Han and Georges Bataille. As a result, we recovered the value of projection interruptions in the analog environment as an opportunity for collective interaction, confronting them with the demands of technological perfection. We demonstrated the resilience of the analog through new experiences that show the evolution of the need for collective contact. Future studies will focus on other contexts, such as supermarkets and terraces, to contribute to a broader understanding of urban spaces, social cohesion, and perceptions of Eros.
Utilization of Natural Soils as a Remediation Method for Electric Arc Furnace and Ladle Slags
Authors: Korkmaz Bilal , Dayioglu Asli Yalcin
Published: June 2024

Steel slags are solid residual materials formed as by-products throughout the process of steel production within the steelmaking industry. These wastes have good physical properties such as high stiffness and friction angle for use as road fill materials or in geotechnical applications. However, the presence of heavy metals and high alkalinity levels constitute significant environmental hazards and set limitations on using slags in engineering applications. While there have been investigations into the mechanical characteristics of steel slags, research on assessing potential harm when utilizing the materials in engineering applications is rare. This study examines the mitigation methods to address the environmental problems associated with steel slags. To do this, two different steel slags with different production techniques were treated with soils of different properties such as fine and coarse sand, bentonite, kaolin, and natural clay. The pH and electrical conductivity (EC) values of pure steel slags were determined using the water leach test (WLT). Variations in pH and EC values of steel slags subjected to treatment were evaluated through both WLT and sequential water leach (SWLT) tests. As a result, the high strength, stiffness, and drainage capability of EAF and LS steel slags make these materials suitable for road filling. This is further backed by their soaked and unsoaked CBR values. During the water leach tests, notable decreases in pH were observed with a 60% natural clay (NC) solution, resulting in a decrease of 1.2 and 0.7 in EAF and LS, respectively. The addition of sand had a negligible impact on pH due to its inert characteristics. Moreover, in sequential water leach tests, the most significant decrease in pH was observed with NC (with a reduction of 2.0 points for EAF and 0.9 points for LS) through enhanced ion exchange and extended periods of dilution and buffering. Also, the use of NC resulted in substantial decreases in EC for EAF and LS, with reductions of 77% and 81%, respectively. Moreover, heavy metal concentrations in leachate waters from pure steel slags have been detected, and the effect of treatment on aluminum and iron concentrations has been determined. The results indicate that the use of natural soil significantly drops the pH and lowers the trace metal concentrations within the leachate.
Modeling and Analysis of a Novel Levitation Magnet with Damping Coils for High-Speed Maglev Train
Authors: Fu Shanqiang , Deng Zigang , Han Weitao , Gao Xinmai , Zhou Ying
Published: June 2024

In this work, a novel levitation magnet with damping coil is proposed to address the existing issues and improve reliability. The fault mechanism of the existing magnet is analyzed and validated using a coupling model combined with inductive voltage experiments. The magnetic yoke with damping coils is designed and the equivalent magnetic circuit (EMC) model of the magnet is established. The nonlinearity of magnetic materials and the magnetic flux fluctuation due to the tooth-slot effect are considered in the EMC model. Simultaneously, the transient finite element (FEM) model is built. The magnetic flux of yoke, the inductive current of the damping coil, and the magnetic force are analyzed. A good agreement is found between EMC and FEM. Additionally, the static magnetic force is tested on the magnet test platform to validate EMC and FEM models. Results reveal that compared with the existing magnet, the magnetic flux fluctuation with damping coils is significantly reduced, and the inductive voltages with the damping coil are significantly decreased. The novel magnet with damping coils featuring excellent magnetic characteristics is more advantageous for the system’s security and durability.