bluuebunny/crossref_metadata_2025 huggingface dataset:
Principles and Design of Bionic Hydrogel Adhesives for Skin Wound Treatment
Authors: Wang Chunxiao , Zhang Xinyu , Fan Yinuo , Yu Shuhan , Liu Man , Feng Linhan , Sun Qisen , Pan Panpan
Published: July 2024

Over millions of years of evolution, nature has developed a myriad of unique features that have inspired the design of adhesives for wound healing. Bionic hydrogel adhesives, capable of adapting to the dynamic movements of tissues, possess superior biocompatibility and effectively promote the healing of both external and internal wounds. This paper provides a systematic review of the design and principles of these adhesives, focusing on the treatment of skin wounds, and explores the feasibility of incorporating nature-inspired properties into their design. The adhesion mechanisms of bionic adhesives are analyzed from both chemical and physical perspectives. Materials from natural and synthetic polymers commonly used as adhesives are detailed regarding their biocompatibility and degradability. The multifunctional design elements of hydrogel adhesives for skin trauma treatment, such as self-healing, drug release, responsive design, and optimization of mechanical and physical properties, are further explored. The aim is to overcome the limitations of conventional treatments and offer a safer, more effective solution for the application of bionic wound dressings.
Chemosensitive Properties of Electrochemically Synthesized Poly-3-Thienylboronic Acid: Conductometric Detection of Glucose and Other Diol-Containing Compounds under Electrical Affinity Control
Authors: Efremenko Yulia , Mirsky Vladimir M.
Published: July 2024

Due to the presence of the boronic acid moieties, poly-3-thienylboronic acid has an affinity for saccharides and other diol-containing compounds. Thin films of this novel chemosensitive polymer were synthesized electrochemically on the gold surface. The adhesion of the polymer was enhanced by the deposition of a monomolecular layer of thiophenol. The technology was used to fabricate conductometric sensors for glucose and other diol-containing compounds. Simultaneous two- and four-electrode conductivity measurements were performed. The chemical sensitivity to sorbitol, fructose, glucose, and ethylene glycol was studied at different pH and electrode potentials, and the corresponding binding constants were obtained. Depending on the electrode potential, the reciprocal values of the binding constants of glucose to poly-3-thienylboronic acid at neutral pH are in the range of 0.2 mM–1.0 mM. The affinity for glucose has been studied in buffer solutions and in solutions containing the major components of human blood. It was shown that the presence of human serum albumin increases the affinity of poly-3-thienylboronic acid for diol-containing compounds.
Natural Rubber/Styrene–Butadiene Rubber Blend Composites Potentially Applied in Damping Bearings
Authors: Tang Saifeng , Li Zhanxu , Sun Weichong , Liu Yangling , Wang Jian , Wang Xiong , Lin Jun
Published: July 2024

Natural rubber (NR) composites have been widely applied in damping products to reduce harmful vibrations, while rubber with only a single composition barely meets performance requirements. In this study, rubber blend composites including various ratios of NR and styrene butadiene rubber (SBR) were prepared via the conventional mechanical blending method. The effects of the rubber components on the compression set, compression fatigue temperature rising and the thermal oxidative aging properties of the NR/SBR blend composites were investigated. Meanwhile, the dynamic mechanical thermal analyzer and rubber processing analyzer were used to characterize the dynamic viscoelasticity of the NR/SBR blend composites. It was shown that, with the increase in the SBR ratio, the vulcanization rate of the composites increased significantly, while the compression fatigue temperature rising of the composites decreased gradually from 47 °C (0% SBR ratio) to 31 °C (50% SBR ratio). The compression set of the composites remained at ~33% when the SBR ratio was no more than 20%, and increased gradually when the SBR ratio was more than 20%.
Experimental Study on the Transport Properties of 12 Novel Deep Eutectic Solvents
Authors: Fan Jing , Pan Yuting , Gao Dazhi , Qu Hongwei
Published: July 2024

Deep eutectic solvents (DESs) are complex substances composed of two or three components, wherein hydrogen bond donors and acceptors engage in intricate interactions within a hydrogen bond network. They have attracted extensive attention from researchers due to their easy synthesis, cost-effectiveness, broad liquid range, good stability, and for being green and non-toxic. However, studies on the physical properties of DESs are still scarce and many theories are not perfect enough, which limits the application of DESs in engineering practice. In this study, twelve DESs were synthesized by using choline chloride and betaine as HBAs, and ethylene glycol, polyethylene glycol 600, o-cresol, glycerol, and lactic acid as HBDs. The variation rules of their thermal conductivity and viscosity with temperature at atmospheric pressure were systematically investigated. The experimental results showed that the thermal conductivity of the 1:4 choline chloride/glycerol solvent was the largest at 294 K, reaching 0.2456 W·m−1·K−1, which could satisfy the demand for high efficiency heat transfer by heat-transferring workpieces. The temperature–viscosity relationship of the DESs was fitted using the Arrhenius model, and the maximum average absolute deviation was 6.77%.
Animal Matter in Indigenous Place-Thought: A Case from the Moon Pyramid, Teotihuacan
Authors: Sugiyama Nawa
Published: July 2024

This article interrogates an archaeological application of the Indigenous concept of place-thought, defined by Vanessa Watts as an “understanding of the world via a physical embodiment” through studying corporeal animal forms. This latter encompasses the osteological traces of animal matter (sacrificed animals and prepared body parts) that, because of their material vitalities, provide an opportune site of engagement to retrieve ancient interpersonal relationships. Over 100 corporeal animal forms from Burials 2 and 6 are interpreted as agentive persons who brought into being the Moon Pyramid as an altepetl (water mountain) of Teotihuacan. The altepetl is a seminal place-thought in Mesoamerica intimately tied with sovereignty. The author argues that potentate apex predators (eagles, wolves, jaguars, pumas, and rattlesnakes) became part of Teotihuaccan’s community through their captive management and were buried alive to sustain the altepetl as master guardians. A zooarchaeological and isotopic investigation of corporeal animal forms provided lurid details of human–predator interactions, including differential access to the animals, esoteric knowledge about their personhood, and even deceit of that information. She concludes that providing a contextually and historically contingent, data-driven, and inter-personally centered reconstruction of ancient place-thought, though admittedly partial and from a specific perspective, should be attainable given the enhanced methods in archaeology.
Beyond Conversational Dialogue: Constructing a Catholic Dialogical Ethical Model for Multi-Religious Nigeria
Authors: Ajibola Ilesanmi G.
Published: July 2024

This article argues that the Catholic Church in Nigeria needs to move beyond interreligious dialogue that dwells more on Councils’ interactions and discourses to develop a dialogical ethical framework that engages religious multiplicity in a more active capacity. Although Nigeria’s religious diversity necessitates interreligious dialogue, that alone is insufficient for constructing concrete ethics of dialogue. The article thus argued for an ethical framework tagged one family, many flavours. The theological sense of the proposal is rooted in Catholic social teachings but open to engagement with other religious traditions for mutual respect and social justice. The religious scope of the article is on Roman Catholicism and the Nigeria Muslim Ummah. The article addressed related ethical challenges confronting Nigeria’s interreligious landscape as a diverse religious community. Primary and secondary sources were used in gathering information for the article; thus, scriptural texts and traditions in Islam, as well as sources in Roman Catholicism, were theologically engaged. The suggested model acknowledges the importance of retaining one’s religious identity while also recognising the importance of interreligious dialogue and the right of the religious other in ethical matters. The article is envisioned to promote conversations about translating dialogical frameworks into practice.
An Assessment of Changes in the Thermal Environment during the COVID-19 Lockdown: Case Studies from the Greenland and Norwegian Seas
Authors: Shi Weifang , Zhang Xue , Zhang Hongye
Published: July 2024

The COVID-19 lockdown had a significant impact on human activities, reducing anthropogenic heat and CO2 emissions. To effectively assess the impact of the lockdown on the thermal environment, we used the sliding paired t-test, which we improved from the traditional sliding t-test by introducing the paired t-test for sliding statistical tests, to test the abrupt change in the thermal environment. Furthermore, an additive decomposition model and wavelet analysis method were used to analyze the characteristics of trend and irregular change, coherence, and phase difference of the time series data with respect to the thermal environment. We chose the Greenland Sea and the Norwegian Sea, regions highly sensitive to changes in climate and ocean circulation, as case studies and used remote sensing data of the sea surface temperature (SST) and the atmospheric CO2 concentration data obtained from the Goddard Earth Sciences Data and Information Services Center from January 2015 to December 2021 for the analysis. The results show that although the annual spatial mean SST in 2020 is lower than the mean of all 7 years in most areas of the two seas, there is no evidence of a significant mutation in the decrease in the SST during the lockdown in 2020 compared with the temperatures before, according to the sliding paired t-test. The analysis of the irregular components of the monthly mean SST decomposed by an additive decomposition model also does not show the anomalously low SST during the lockdown in 2020. In addition, the lockdown had almost no impact on the increasing trend of CO2 concentration. The wavelet analysis also shows that there is no obvious anomaly in coherence or phase difference between the periodic variation of the SST and the CO2 concentrations in 2020 compared with other years. These results suggest that the direct effect of the COVID-19 lockdown on the thermal environment of the study area could be negligible.
Operational Forest-Fire Spread Forecasting Using the WRF-SFIRE Model
Authors: Kale Manish P. , Meher Sri Sai , Chavan Manoj , Kumar Vikas , Sultan Md. Asif , Dongre Priyanka , Narkhede Karan , Mhatre Jitendra , Sharma Narpati , Luitel Bayvesh , Limboo Ningwa , Baingne Mahendra , Pardeshi Satish , Labade Mohan , Mukherjee Aritra , Joshi Utkarsh , Kharkar Neelesh , Islam Sahidul , Pokale Sagar , Thakare Gokul , Talekar Shravani , Behera Mukunda-Dev , Sreshtha D. , Khare Manoj , Kaginalkar Akshara , Kumar Naveen , Roy Parth Sarathi
Published: July 2024

In the present research, the open-source WRF-SFIRE model has been used to carry out surface forest fire spread forecasting in the North Sikkim region of the Indian Himalayas. Global forecast system (GFS)-based hourly forecasted weather model data obtained through the National Centers for Environmental Prediction (NCEP) at 0.25 degree resolution were used to provide the initial conditions for running WRF-SFIRE. A landuse–landcover map at 1:10,000 scale was used to define fuel parameters for different vegetation types. The fuel parameters, i.e., fuel depth and fuel load, were collected from 23 sample plots (0.1 ha each) laid down in the study area. Samples of different categories of forest fuels were measured for their wet and dry weights to obtain the fuel load. The vegetation specific surface area-to-volume ratio was referenced from the literature. The atmospheric data were downscaled using nested domains in the WRF model to capture fire–atmosphere interactions at a finer resolution (40 m). VIIRS satellite sensor-based fire alert (375 m spatial resolution) was used as ignition initiation point for the fire spread forecasting, whereas the forecasted hourly weather data (time synchronized with the fire alert) were used for dynamic forest-fire spread forecasting. The forecasted burnt area (1.72 km2) was validated against the satellite-based burnt area (1.07 km2) obtained through Sentinel 2 satellite data. The shapes of the original and forecasted burnt areas matched well. Based on the various simulation studies conducted, an operational fire spread forecasting system, i.e., Sikkim Wildfire Forecasting and Monitoring System (SWFMS), has been developed to facilitate firefighting agencies to issue early warnings and carry out strategic firefighting.
Pseudopolar Format Matrix Description of Near-Range Radar Imaging and Fractional Fourier Transform
Authors: Zou Lilong , Li Ying , Alani Amir M.
Published: July 2024

Near-range radar imaging (NRRI) has evolved into a vital technology with diverse applications spanning fields such as remote sensing, surveillance, medical imaging and non-destructive testing. The Pseudopolar Format Matrix (PFM) has emerged as a promising technique for representing radar data in a compact and efficient manner. In this paper, we present a comprehensive PFM description of near-range radar imaging. Furthermore, this paper also explores the integration of the Fractional Fourier Transform (FrFT) with PFM for enhanced radar signal analysis. The FrFT—a powerful mathematical tool for signal processing—offers unique capabilities in analysing signals with time-frequency localization properties. By combining FrFT with PFM, we have achieved significant advancements in radar imaging, particularly in dealing with complex clutter environments and improving target detection accuracy. Meanwhile, this paper highlights the imaging matrix form of FrFT under the PFM, emphasizing the potential for addressing challenges encountered in near-range radar imaging. Finally, numerical simulation and real-world scenario measurement imaging results verify optimized accuracy and computational efficiency with the fusion of PFM and FrFT techniques, paving the way for further innovations in near-range radar imaging applications.
Underwater Side-Scan Sonar Target Detection: YOLOv7 Model Combined with Attention Mechanism and Scaling Factor
Authors: Wen Xin , Wang Jian , Cheng Chensheng , Zhang Feihu , Pan Guang
Published: July 2024

Side-scan sonar plays a crucial role in underwater exploration, and the autonomous detection of side-scan sonar images is vital for detecting unknown underwater environments. However, due to the complexity of the underwater environment, the presence of a few highlighted areas on the targets, blurred feature details, and difficulty in collecting data from side-scan sonar, achieving high-precision autonomous target recognition in side-scan sonar images is challenging. This article addresses this problem by improving the You Only Look Once v7 (YOLOv7) model to achieve high-precision object detection in side-scan sonar images. Firstly, given that side-scan sonar images contain large areas of irrelevant information, this paper introduces the Swin-Transformer for dynamic attention and global modeling, which enhances the model’s focus on the target regions. Secondly, the Convolutional Block Attention Module (CBAM) is utilized to further improve feature representation and enhance the neural network model’s accuracy. Lastly, to address the uncertainty of geometric features in side-scan sonar target features, this paper innovatively incorporates a feature scaling factor into the YOLOv7 model. The experiment initially verified the necessity of attention mechanisms in the public dataset. Subsequently, experiments on our side-scan sonar (SSS) image dataset show that the improved YOLOv7 model has 87.9% and 49.23% in its average accuracy (mAP0.5) and (mAP0.5:0.95), respectively. These results are 9.28% and 8.41% higher than the YOLOv7 model. The improved YOLOv7 algorithm proposed in this paper has great potential for object detection and the recognition of side-scan sonar images.
Adaptive Nighttime-Light-Based Building Stock Assessment Framework for Future Environmentally Sustainable Management
Authors: Liu Zhiwei , Guo Jing , Zhang Ruirui , Ota Yuya , Nagata Sota , Shirakawa Hiroaki , Tanikawa Hiroki
Published: July 2024

The accumulation of artificially built environment stock during urbanization processes has been actively involved in altering the material and energy use pattern of human societies. Therefore, an accurate assessment of built environment stock can provide insights for decision makers to implement appropriate environmentally sustainable retrofitting strategies. This study presents a building stock estimation enhancement framework (BSEEF) that leverages nighttime light (NTL) to accurately assess and spatially map building stocks. By innovatively integrating a region classification module with a hybrid region-specified self-optimization module, BSEEF adaptively enhances the estimation accuracy across diverse urban landscapes. A comparative case study of Japan demonstrated that BSEEF significantly outperformed a traditional linear regression model, with improvements ranging from 1.81% to 16.75% across different metrics used for assessment, providing more accurate building stock estimates. BSEEF enhances environment/sustainability studies by enabling precise spatial analysis of built environment stocks, offering a versatile and robust framework that adapts to technological changes and achieves superior accuracy without extensive reliance on complex datasets. These advances will make BSEEF an indispensable tool in strategic planning for urban development, promoting sustainable and resilient communities globally.
Active Bidirectional Self-Training Network for Cross-Domain Segmentation in Remote-Sensing Images
Authors: Yang Zhujun , Yan Zhiyuan , Diao Wenhui , Ma Yihang , Li Xinming , Sun Xian
Published: July 2024

Semantic segmentation with cross-domain adaptation in remote-sensing images (RSIs) is crucial and mitigates the expense of manually labeling target data. However, the performance of existing unsupervised domain adaptation (UDA) methods is still significantly impacted by domain bias, leading to a considerable gap compared to supervised trained models. To address this, our work focuses on semi-supervised domain adaptation, selecting a small subset of target annotations through active learning (AL) that maximize information to improve domain adaptation. Overall, we propose a novel active bidirectional self-training network (ABSNet) for cross-domain semantic segmentation in RSIs. ABSNet consists of two sub-stages: a multi-prototype active region selection (MARS) stage and a source-weighted class-balanced self-training (SCBS) stage. The MARS approach captures the diversity in labeled source data by introducing multi-prototype density estimation based on Gaussian mixture models. We then measure inter-domain similarity to select complementary and representative target samples. Through fine-tuning with the selected active samples, we propose an enhanced self-training strategy SCBS, designed for weighted training on source data, aiming to avoid the negative effects of interfering samples. We conduct extensive experiments on the LoveDA and ISPRS datasets to validate the superiority of our method over existing state-of-the-art domain-adaptive semantic segmentation methods.
Development of an Imaging Spectrometer with a High Signal-to-Noise Ratio Based on High Energy Transmission Efficiency for Soil Organic Matter Detection
Authors: Fan Jize , Wang Yuwei , Gu Guochao , Li Zhe , Wang Xiaoxu , Li Hanshuang , Li Bo , Hu Denghui
Published: July 2024

Hyperspectral detection of the change rate of organic matter content in agricultural remote sensing requires a high signal-to-noise ratio (SNR). However, due to the large number and efficiency limitation of the components, it is difficult to improve the SNR. This study uses high-efficiency convex grating with a diffraction efficiency exceeding 50% across the 360–850 nm range, a back-illuminated Complementary Metal Oxide Semiconductor (CMOS) detector with a 95% efficiency in peak wavelength, and silver-coated mirrors to develop an imaging spectrometer for detecting soil organic matter (SOM). The designed system meets the spectral resolution of 10 nm in the 360–850 nm range and achieves a swath of 100 km and a spatial resolution of 100 m at an orbital height of 648.2 km. This study also uses the basic structure of Offner with fewer components in the design and sets the mirrors of the Offner structure to have the same sphere, which can achieve the rapid adjustment of the co-standard. This study performs a theoretical analysis of the developed Offner imaging spectrometer based on the classical Rowland circular structure, with a 21.8 mm slit length; simulates its capacity for suppressing the +2nd-order diffraction stray light with the filter; and analyzes the imaging quality after meeting the tolerance requirements, which is combined with the surface shape characteristics of the high-efficiency grating. After this test, the grating has a diffraction efficiency above 50%, and the silver-coated mirrors have a reflection value above 95% on average. Finally, the laboratory tests show that the SNR over the waveband exceeds 300 and reaches 800 at 550 nm, which is higher than some current instruments in orbit for soil observation. The proposed imaging spectrometer has a spectral resolution of 10 nm, and its modulation transfer function (MTF) is greater than 0.23 at the Nyquist frequency, making it suitable for remote sensing observation of SOM change rate. The manufacture of such a high-efficiency broadband grating and the development of the proposed instrument with high energy transmission efficiency can provide a feasible technical solution for observing faint targets with a high SNR.
Segmentation of 3D Point Clouds of Heritage Buildings Using Edge Detection and Supervoxel-Based Topology
Authors: Salamanca Santiago , Merchán Pilar , Espacio Alejandro , Pérez Emiliano , Merchán María José
Published: July 2024

This paper presents a novel segmentation algorithm specially developed for applications in 3D point clouds with high variability and noise, particularly suitable for heritage building 3D data. The method can be categorized within the segmentation procedures based on edge detection. In addition, it uses a graph-based topological structure generated from the supervoxelization of the 3D point clouds, which is used to make the closure of the edge points and to define the different segments. The algorithm provides a valuable tool for generating results that can be used in subsequent classification tasks and broader computer applications dealing with 3D point clouds. One of the characteristics of this segmentation method is that it is unsupervised, which makes it particularly advantageous for heritage applications where labelled data is scarce. It is also easily adaptable to different edge point detection and supervoxelization algorithms. Finally, the results show that the 3D data can be segmented into different architectural elements, which is important for further classification or recognition. Extensive testing on real data from historic buildings demonstrated the effectiveness of the method. The results show superior performance compared to three other segmentation methods, both globally and in the segmentation of planar and curved zones of historic buildings.
Compact 3D-Printed Unit for Separation of Simple Gas Mixtures Combined with Chemiresistive Sensors
Authors: Zvonkova Magdalena , Adamek Martin , Skowronkova Nela , Dlabaja Stepan , Matyas Jiri , Jasso Miroslav , Adamkova Anna , Mlcek Jiri , Salek Richardos Nikolaos , Buran Martin
Published: July 2024

Inexpensive chemiresistive sensors are often insufficiently selective as they are sensitive to multiple components of the gas mixture at the same time. One solution would be to insert a device in front of the sensor that separates the measured gas mixture and possibly isolates the unwanted components. This study focused on the fabrication and characterization of a compact unit, which was fabricated by 3D printing, for the separation and detection of simple gas mixtures. The capillary, the basic part of the compact unit, was 4.689 m long and had a diameter of 0.7 mm. The compact unit also contained a mixing chamber on the inlet side and a measuring chamber with a MiCS-6814 sensor on the outlet side. Mixtures of ethanol and water at different concentrations were chosen for characterization. The measured calibration curve was found to have a reliability of R2 = 0.9941. The study further addressed the elements of environmental friendliness of the materials used and their sustainability.
An Identification Method for Road Hypnosis Based on Human EEG Data
Authors: Wang Bin , Wang Jingheng , Wang Xiaoyuan , Chen Longfei , Zhang Han , Jiao Chenyang , Wang Gang , Feng Kai
Published: July 2024

The driver in road hypnosis has not only some external characteristics, but also some internal characteristics. External features have obvious manifestations and can be directly observed. Internal features do not have obvious manifestations and cannot be directly observed. They need to be measured with specific instruments. Electroencephalography (EEG), as an internal feature of drivers, is the golden parameter for drivers’ life identification. EEG is of great significance for the identification of road hypnosis. An identification method for road hypnosis based on human EEG data is proposed in this paper. EEG data on drivers in road hypnosis can be collected through vehicle driving experiments and virtual driving experiments. The collected data are preprocessed with the PSD (power spectral density) method, and EEG characteristics are extracted. The neural networks EEGNet, RNN, and LSTM are used to train the road hypnosis identification model. It is shown from the results that the model based on EEGNet has the best performance in terms of identification for road hypnosis, with an accuracy of 93.01%. The effectiveness and accuracy of the identification for road hypnosis are improved in this study. The essential characteristics for road hypnosis are also revealed. This is of great significance for improving the safety level of intelligent vehicles and reducing the number of traffic accidents caused by road hypnosis.
Integrating LiDAR Sensor Data into Microsimulation Model Calibration for Proactive Safety Analysis
Authors: Igene Morris , Luo Qiyang , Jimee Keshav , Soltanirad Mohammad , Bataineh Tamer , Liu Hongchao
Published: July 2024

Studies have shown that vehicle trajectory data are effective for calibrating microsimulation models. Light Detection and Ranging (LiDAR) technology offers high-resolution 3D data, allowing for detailed mapping of the surrounding environment, including road geometry, roadside infrastructures, and moving objects such as vehicles, cyclists, and pedestrians. Unlike other traditional methods of trajectory data collection, LiDAR’s high-speed data processing, fine angular resolution, high measurement accuracy, and high performance in adverse weather and low-light conditions make it well suited for applications requiring real-time response, such as autonomous vehicles. This research presents a comprehensive framework for integrating LiDAR sensor data into simulation models and their accurate calibration strategies for proactive safety analysis. Vehicle trajectory data were extracted from LiDAR point clouds collected at six urban signalized intersections in Lubbock, Texas, in the USA. Each study intersection was modeled with PTV VISSIM and calibrated to replicate the observed field scenarios. The Directed Brute Force method was used to calibrate two car-following and two lane-change parameters of the Wiedemann 1999 model in VISSIM, resulting in an average accuracy of 92.7%. Rear-end conflicts extracted from the calibrated models combined with a ten-year historical crash dataset were fitted into a Negative Binomial (NB) model to estimate the model’s parameters. In all the six intersections, rear-end conflict count is a statistically significant predictor (p-value < 0.05) of observed rear-end crash frequency. The outcome of this study provides a framework for the combined use of LiDAR-based vehicle trajectory data, microsimulation, and surrogate safety assessment tools to transportation professionals. This integration allows for more accurate and proactive safety evaluations, which are essential for designing safer transportation systems, effective traffic control strategies, and predicting future congestion problems.
A Fusion Positioning System Based on Camera and LiDAR for Unmanned Rollers in Tunnel Construction
Authors: Huang Hao , Hu Yongbiao , Wang Xuebin
Published: July 2024

As an important vehicle in road construction, the unmanned roller is rapidly advancing in its autonomous compaction capabilities. To overcome the challenges of GNSS positioning failure during tunnel construction and diminished visual positioning accuracy under different illumination levels, we propose a feature-layer fusion positioning system based on a camera and LiDAR. This system integrates loop closure detection and LiDAR odometry into the visual odometry framework. Furthermore, recognizing the prevalence of similar scenes in tunnels, we innovatively combine loop closure detection with the compaction process of rollers in fixed areas, proposing a selection method for loop closure candidate frames based on the compaction process. Through on-site experiments, it is shown that this method not only enhances the accuracy of loop closure detection in similar environments but also reduces the runtime. Compared with visual systems, in static positioning tests, the longitudinal and lateral accuracy of the fusion system are improved by 12 mm and 11 mm, respectively. In straight-line compaction tests under different illumination levels, the average lateral error increases by 34.1% and 32.8%, respectively. In lane-changing compaction tests, this system enhances the positioning accuracy by 33% in dim environments, demonstrating the superior positioning accuracy of the fusion positioning system amid illumination changes in tunnels.
Deep Learning-Based Simultaneous Temperature- and Curvature-Sensitive Scatterplot Recognition
Authors: Liu Jianli , Ke Yuxin , Yang Dong , Deng Qiao , Hei Chuang , Han Hu , Peng Daicheng , Wen Fangqing , Feng Ankang , Zhao Xueran
Published: July 2024

Since light propagation in a multimode fiber (MMF) exhibits visually random and complex scattering patterns due to external interference, this study numerically models temperature and curvature through the finite element method in order to understand the complex interactions between the inputs and outputs of an optical fiber under conditions of temperature and curvature interference. The systematic analysis of the fiber’s refractive index and bending loss characteristics determined its critical bending radius to be 15 mm. The temperature speckle atlas is plotted to reflect varying bending radii. An optimal end-to-end residual neural network model capable of automatically extracting highly similar scattering features is proposed and validated for the purpose of identifying temperature and curvature scattering maps of MMFs. The viability of the proposed scheme is tested through numerical simulations and experiments, the results of which demonstrate the effectiveness and robustness of the optimized network model.
A Method for Real-Time Recognition of Safflower Filaments in Unstructured Environments Using the YOLO-SaFi Model
Authors: Chen Bangbang , Ding Feng , Ma Baojian , Wang Liqiang , Ning Shanping
Published: July 2024

The identification of safflower filament targets and the precise localization of picking points are fundamental prerequisites for achieving automated filament retrieval. In light of challenges such as severe occlusion of targets, low recognition accuracy, and the considerable size of models in unstructured environments, this paper introduces a novel lightweight YOLO-SaFi model. The architectural design of this model features a Backbone layer incorporating the StarNet network; a Neck layer introducing a novel ELC convolution module to refine the C2f module; and a Head layer implementing a new lightweight shared convolution detection head, Detect_EL. Furthermore, the loss function is enhanced by upgrading CIoU to PIoUv2. These enhancements significantly augment the model’s capability to perceive spatial information and facilitate multi-feature fusion, consequently enhancing detection performance and rendering the model more lightweight. Performance evaluations conducted via comparative experiments with the baseline model reveal that YOLO-SaFi achieved a reduction of parameters, computational load, and weight files by 50.0%, 40.7%, and 48.2%, respectively, compared to the YOLOv8 baseline model. Moreover, YOLO-SaFi demonstrated improvements in recall, mean average precision, and detection speed by 1.9%, 0.3%, and 88.4 frames per second, respectively. Finally, the deployment of the YOLO-SaFi model on the Jetson Orin Nano device corroborates the superior performance of the enhanced model, thereby establishing a robust visual detection framework for the advancement of intelligent safflower filament retrieval robots in unstructured environments.
Efficient Vibration Measurement and Modal Shape Visualization Based on Dynamic Deviations of Structural Edge Profiles
Authors: Zhu Andong , Gong Xinlong , Zhou Jie , Zhang Xiaolong , Zhang Dashan
Published: July 2024

As a non-contact method, vision-based measurement for vibration extraction and modal parameter identification has attracted much attention. In most cases, artificial textures are crucial elements for visual tracking, and this feature limits the application of vision-based vibration measurement on textureless targets. As a computation technique for visualizing subtle variations in videos, the video magnification technique can analyze modal responses and visualize modal shapes, but the efficiency is low, and the processing results contain clipping artifacts. This paper proposes a novel method for the application of a modal test. In contrast to the deviation magnification that exaggerates subtle geometric deviations from only a single image, the proposed method extracts vibration signals with sub-pixel accuracy on edge positions by changing the perspective of deviations from space to timeline. Then, modal shapes are visualized by decoupling all spatial vibrations following the vibration theory of continuous linear systems. Without relying on artificial textures and motion magnification, the proposed method achieves high operating efficiency and avoids clipping artifacts. Finally, the effectiveness and practical value of the proposed method are validated by two laboratory experiments on a cantilever beam and an arch dam model.
Trajectory Analysis of 6-DOF Industrial Robot Manipulators by Using Artificial Neural Networks
Authors: Çetinkaya Mehmet Bahadır , Yildirim Kürşat , Yildirim Şahin
Published: July 2024

Robot manipulators are robotic systems that are frequently used in automation systems and able to provide increased speed, precision, and efficiency in the industrial applications. Due to their nonlinear and complex nature, it is crucial to optimize the robot manipulator systems in terms of trajectory control. In this study, positioning analyses based on artificial neural networks (ANNs) were performed for robot manipulator systems used in the textile industry, and the optimal ANN model for the high-accuracy positioning was improved. The inverse kinematic analyses of a 6-degree-of-freedom (DOF) industrial denim robot manipulator were carried out via four different learning algorithms, delta-bar-delta (DBD), online back propagation (OBP), quick back propagation (QBP), and random back propagation (RBP), for the proposed neural network predictor. From the results obtained, it was observed that the QBP-based 3-10-6 type ANN structure produced the optimal results in terms of estimation and modeling of trajectory control. In addition, the 3-5-6 type ANN structure was also improved, and its root mean square error (RMSE) and statistical R2 performances were compared with that of the 3-10-6 ANN structure. Consequently, it can be concluded that the proposed neural predictors can successfully be employed in real-time industrial applications for robot manipulator trajectory analysis.
Leveraging Wearable Sensors in Virtual Reality Driving Simulators: A Review of Techniques and Applications
Authors: Boboc Răzvan Gabriel , Butilă Eugen Valentin , Butnariu Silviu
Published: July 2024

Virtual reality (VR) driving simulators are very promising tools for driver assessment since they provide a controlled and adaptable setting for behavior analysis. At the same time, wearable sensor technology provides a well-suited and valuable approach to evaluating the behavior of drivers and their physiological or psychological state. This review paper investigates the potential of wearable sensors in VR driving simulators. Methods: A literature search was performed on four databases (Scopus, Web of Science, Science Direct, and IEEE Xplore) using appropriate search terms to retrieve scientific articles from a period of eleven years, from 2013 to 2023. Results: After removing duplicates and irrelevant papers, 44 studies were selected for analysis. Some important aspects were extracted and presented: the number of publications per year, countries of publication, the source of publications, study aims, characteristics of the participants, and types of wearable sensors. Moreover, an analysis and discussion of different aspects are provided. To improve car simulators that use virtual reality technologies and boost the effectiveness of particular driver training programs, data from the studies included in this systematic review and those scheduled for the upcoming years may be of interest.
A Study on Graph Optimization Method for GNSS/IMU Integrated Navigation System Based on Virtual Constraints
Authors: Qiu Haiyang , Zhao Yun , Wang Hui , Wang Lei
Published: July 2024

In GNSS/IMU integrated navigation systems, factors like satellite occlusion and non-line-of-sight can degrade satellite positioning accuracy, thereby impacting overall navigation system results. To tackle this challenge and leverage historical pseudorange information effectively, this paper proposes a graph optimization-based GNSS/IMU model with virtual constraints. These virtual constraints in the graph model are derived from the satellite’s position from the previous time step, the rate of change of pseudoranges, and ephemeris data. This virtual constraint serves as an alternative solution for individual satellites in cases of signal anomalies, thereby ensuring the integrity and continuity of the graph optimization model. Additionally, this paper conducts an analysis of the graph optimization model based on these virtual constraints, comparing it with traditional graph models of GNSS/IMU and SLAM. The marginalization of the graph model involving virtual constraints is analyzed next. The experiment was conducted on a set of real-world data, and the results of the proposed method were compared with tightly coupled Kalman filtering and the original graph optimization method. In instantaneous performance testing, the method maintains an RMSE error within 5% compared with real pseudorange measurement, while in a continuous performance testing scenario with no available GNSS signal, the method shows approximately a 30% improvement in horizontal RMSE accuracy over the traditional graph optimization method during a 10-second period. This demonstrates the method’s potential for practical applications.
Utilizing Deep Feature Fusion for Automatic Leukemia Classification: An Internet of Medical Things-Enabled Deep Learning Framework
Authors: Islam Md Manowarul , Rifat Habibur Rahman , Shahid Md. Shamim Bin , Akhter Arnisha , Uddin Md Ashraf
Published: July 2024

Acute lymphoblastic leukemia, commonly referred to as ALL, is a type of cancer that can affect both the blood and the bone marrow. The process of diagnosis is a difficult one since it often calls for specialist testing, such as blood tests, bone marrow aspiration, and biopsy, all of which are highly time-consuming and expensive. It is essential to obtain an early diagnosis of ALL in order to start therapy in a timely and suitable manner. In recent medical diagnostics, substantial progress has been achieved through the integration of artificial intelligence (AI) and Internet of Things (IoT) devices. Our proposal introduces a new AI-based Internet of Medical Things (IoMT) framework designed to automatically identify leukemia from peripheral blood smear (PBS) images. In this study, we present a novel deep learning-based fusion model to detect ALL types of leukemia. The system seamlessly delivers the diagnostic reports to the centralized database, inclusive of patient-specific devices. After collecting blood samples from the hospital, the PBS images are transmitted to the cloud server through a WiFi-enabled microscopic device. In the cloud server, a new fusion model that is capable of classifying ALL from PBS images is configured. The fusion model is trained using a dataset including 6512 original and segmented images from 89 individuals. Two input channels are used for the purpose of feature extraction in the fusion model. These channels include both the original and the segmented images. VGG16 is responsible for extracting features from the original images, whereas DenseNet-121 is responsible for extracting features from the segmented images. The two output features are merged together, and dense layers are used for the categorization of leukemia. The fusion model that has been suggested obtains an accuracy of 99.89%, a precision of 99.80%, and a recall of 99.72%, which places it in an excellent position for the categorization of leukemia. The proposed model outperformed several state-of-the-art Convolutional Neural Network (CNN) models in terms of performance. Consequently, this proposed model has the potential to save lives and effort. For a more comprehensive simulation of the entire methodology, a web application (Beta Version) has been developed in this study. This application is designed to determine the presence or absence of leukemia in individuals. The findings of this study hold significant potential for application in biomedical research, particularly in enhancing the accuracy of computer-aided leukemia detection.
Tackling Few-Shot Challenges in Automatic Modulation Recognition: A Multi-Level Comparative Relation Network Combining Class Reconstruction Strategy
Authors: Ma Zhao , Fang Shengliang , Fan Youchen , Hou Shunhu , Xu Zhaojing
Published: July 2024

Automatic Modulation Recognition (AMR) is a key technology in the field of cognitive communication, playing a core role in many applications, especially in wireless security issues. Currently, deep learning (DL)-based AMR technology has achieved many research results, greatly promoting the development of AMR technology. However, the few-shot dilemma faced by DL-based AMR methods greatly limits their application in practical scenarios. Therefore, this paper endeavored to address the challenge of AMR with limited data and proposed a novel meta-learning method, the Multi-Level Comparison Relation Network with Class Reconstruction (MCRN-CR). Firstly, the method designs a structure of a multi-level comparison relation network, which involves embedding functions to output their feature maps hierarchically, comprehensively calculating the relation scores between query samples and support samples to determine the modulation category. Secondly, the embedding function integrates a reconstruction module, leveraging an autoencoder for support sample reconstruction, wherein the encoder serves dual purposes as the embedding mechanism. The training regimen incorporates a meta-learning paradigm, harmoniously combining classification and reconstruction losses to refine the model’s performance. The experimental results on the RadioML2018 dataset show that our designed method can greatly alleviate the small sample problem in AMR and is superior to existing methods.
Learning Temporal–Spatial Contextual Adaptation for Three-Dimensional Human Pose Estimation
Authors: Wang Hexin , Quan Wei , Zhao Runjing , Zhang Miaomiao , Jiang Na
Published: July 2024

Three-dimensional human pose estimation focuses on generating 3D pose sequences from 2D videos. It has enormous potential in the fields of human–robot interaction, remote sensing, virtual reality, and computer vision. Existing excellent methods primarily focus on exploring spatial or temporal encoding to achieve 3D pose inference. However, various architectures exploit the independent effects of spatial and temporal cues on 3D pose estimation, while neglecting the spatial–temporal synergistic influence. To address this issue, this paper proposes a novel 3D pose estimation method with a dual-adaptive spatial–temporal former (DASTFormer) and additional supervised training. The DASTFormer contains attention-adaptive (AtA) and pure-adaptive (PuA) modes, which will enhance pose inference from 2D to 3D by adaptively learning spatial–temporal effects, considering both their cooperative and independent influences. In addition, an additional supervised training with batch variance loss is proposed in this work. Different from common training strategy, a two-round parameter update is conducted on the same batch data. Not only can it better explore the potential relationship between spatial–temporal encoding and 3D poses, but it can also alleviate the batch size limitations imposed by graphics cards on transformer-based frameworks. Extensive experimental results show that the proposed method significantly outperforms most state-of-the-art approaches on Human3.6 and HumanEVA datasets.
Automatic Monitoring Methods for Greenhouse and Hazardous Gases Emitted from Ruminant Production Systems: A Review
Authors: Ma Weihong , Ji Xintong , Ding Luyu , Yang Simon X. , Guo Kaijun , Li Qifeng
Published: July 2024

The research on automatic monitoring methods for greenhouse gases and hazardous gas emissions is currently a focal point in the fields of environmental science and climatology. Until 2023, the amount of greenhouse gases emitted by the livestock sector accounts for about 11–17% of total global emissions, with enteric fermentation in ruminants being the main source of the gases. With the escalating problem of global climate change, accurate and effective monitoring of gas emissions has become a top priority. Presently, the determination of gas emission indices relies on specialized instrumentation such as breathing chambers, greenfeed systems, methane laser detectors, etc., each characterized by distinct principles, applicability, and accuracy levels. This paper first explains the mechanisms and effects of gas production by ruminant production systems, focusing on the monitoring methods, principles, advantages, and disadvantages of monitoring gas concentrations, and a summary of existing methods reveals their shortcomings, such as limited applicability, low accuracy, and high cost. In response to the current challenges in the field of equipment for monitoring greenhouse and hazardous gas emissions from ruminant production systems, this paper outlines future perspectives with the aim of developing more efficient, user-friendly, and cost-effective monitoring instruments.