bluuebunny/crossref_metadata_2025 huggingface dataset:
Effects of Ketamine vs. Midazolam in Adolescent Treatment Resistant Depression
Authors: Macejova Andrea , Kovacova Veronika , Tonhajzerova Ingrid , Visnovcova Zuzana , Ferencova Nikola , Mlyncekova Zuzana , Kukucka Tomas , Ondrejka Igor
Published: December 2024

Background: Adolescent treatment resistant depression (TRD) is increasing in recent years. While ketamine showed rapid antidepressant effects in adult TRD studies, research on its effectiveness in adolescents is limited. Methods: This study examines the effects of intravenous ketamine vs. midazolam on depressive and anxiety symptomatology assessed by the Montgomery–Åsberg Depression Rating Scale (MADRS), Hamilton Anxiety Rating Scale (HAM-A), and Children’s Depression Inventory (CDI) at two time points—2 h after initial infusion (T0+2h) and 24 h after the end of the treatment (Te+24h) in a sample of 55 adolescent TRD females (27 receiving ketamine, 28 midazolam). Results: At T0+2h, within-group comparisons revealed a significant reduction in MADRS and HAM-A scores compared to baseline in the ketamine and midazolam groups. At Te+24h, both groups demonstrated similar significant reductions in MADRS, HAM-A, and CDI scores compared to baseline. The MADRS assessment in the ketamine group showed 33% and 59% responders, and in the midazolam group, 14% and 46% responders at T0+2h and Te+24h, respectively. HAM-A evaluation in the ketamine group revealed 33% and 56% responders, and in the midazolam group, 11% and 39% responders at T0+2h and at Te+24h, respectively. CDI rating discovered 11% and 44% responders in the ketamine group and 4% and 21% responders in the midazolam group at T0+2h and Te+24h, respectively. Moreover, inner tension significantly decreased in ketamine compared to the midazolam group at Te+24h. Conclusions: Ketamine showed a reduction in depressive and anxiety symptoms during a short-term period with particular efficacy in alleviating inner tension over midazolam, suggesting its potential advantages in specific symptom relief in rarely studied adolescent TRD.
miR-195-5p Inhibits Colon Cancer Progression via KRT23 Regulation
Authors: Piccinno Emanuele , Scalavino Viviana , Labarile Nicoletta , Armentano Raffaele , Giannelli Gianluigi , Serino Grazia
Published: December 2024

Background/Objectives: KRT23 was recently discovered as an epithelial-specific intermediate filament protein in the type I keratin family. Many studies have underlined keratin’s involvement in several biological processes as well as in the pathogenesis of different diseases. Specifically, KRT23 was reported to affect the structural integrity of epithelial cells and to trigger cellular signaling leading to the onset of cancer. The aim of this study is to characterize a novel mechanism based on miR-195-5p/KRT23 in colorectal cancer. Methods: KRT23 mRNA and protein expression were characterized in FFPE sections from patients with CRC. The effects of miR-195-5p on KRT23 expression at the mRNA and protein levels were assessed by transient transfection experiments with mimic and inhibitor molecules. Cell attachment/detachment, migration, invasion, clone formation, and apoptosis were evaluated in human CRC cell lines after miR-195-5p mimic transfection. Results: We identified KRT23 as a putative target of miR-195-5p, a microRNA that we previously demonstrated to be reduced in CRC. We have proved the KRT23 expression deregulation in the tumoral section compared to adjacent normal mucosa in patients with CRC, according to the data derived from the public repository. We proved that the gain of miR-195-5p decreased the KRT23 expression. Conversely, we demonstrated that the inhibition of miR-195-5p led to an increase in KRT23 expression levels. We have demonstrated the in vitro effectiveness of miR-195-5p on CRC progression and that the in vivo intraperitoneal delivery of miR-195-5p mimic lowered colonic KRT23 mRNA and protein expression. Conclusions: These findings highlight a new regulatory mechanism by miR-195-5p in CRC affecting the keratin intermediate filaments and underline the miR-195-5p potential clinical properties.
Synergistic Enhancement of the High-Temperature Friction and Wear Behavior of PTFE Matrix Composites with the Addition of CF, PEEK, and TiC
Authors: Yuan Lin , Han Yunxiang , Zhen Jinming , Jia Zhengfeng , Zhang Ran
Published: December 2024

With the rapid development of the aerospace, automobile, and ocean industries, there is an urgent need for the fabrication of high-performance polymer matrix composites with low friction and wear in wide temperature ranges. In this paper, polytetrafluoroethylenes (PTFEs) doped with polyether-ether-ketone (PEEK), carbon fiber (CF), and TiC were prepared, and the effects of testing temperatures from room temperature to 250 °C in air conditions were investigated. The results showed that the friction coefficient of the PTFE matrix composites had no obviously changing trend, while the wear resistance properties were significantly improved. Due to the synergistic lubrication and enhancement of CF, PEEK, and TiC, the wear rate for composites with these particles decreased from (2.04–2.72) × 10−3 mm3/Nm for pure PTFE to (0.67–1.96) × 10−4 mm3/Nm. Moreover, the SEM analysis results showed that the main wear mechanisms are fatigue and abrasive wear for the PTFE matrix composites. The results obtained in this study will provide data and technical support for the development of high-performance polymer matrix composites with low friction and wear that can be used over a wide temperature range.
Using the Burning of Polymer Compounds to Determine the Applicability of the Acoustic Method in Fire Extinguishing
Authors: Loboichenko Valentyna , Wilk-Jakubowski Jacek Lukasz , Levterov Alexander , Wilk-Jakubowski Grzegorz , Statyvka Yevhenii , Shevchenko Olga
Published: December 2024

In order to achieve the objective of the work—an experimental study of the capabilities of the acoustic method for extinguishing organic compounds and for ensuring environmental monitoring—the effect of the combustion of various polymers on the acoustic parameters of the medium is considered. The negative effect of the combustion of organic substances on the medium is noted. The features of the use of fire extinguishing agents are analyzed, and it is noted that the acoustic method is a promising, inexpensive, and environmentally friendly approach for use in extinguishing fires. The ideas about the capabilities of this method using the combustion of various polymers were further developed, which is the novelty of the work. As the main results, it is proposed to use the angle of incidence, concentration of suspended particles, temperature, and wave resistance of the environment as special correction factors for acoustic sensors when monitoring in a smoky space. The possibility of using the combustion parameters of organic compounds to determine the properties of acoustic waves in a smoke-filled area is shown. The perspective of implementing the results obtained in the practice of fire prevention and liquidation was observed to increase the efficiency of fire extinguishing and increase the safety of the population and personnel of special services. The proposed approach can become part of the ecological and economic innovations of municipal communities and national strategies to achieve the goals of sustainable development.
Re-Study of the Gilt Bronze Buddha Statuettes Unearthed from the Eastern Han Dynasty Tomb in Chengren Village, Xianyang City, China
Authors: Zhang Liming
Published: December 2024

In 2021, two small gilt bronze Buddha statuettes were unearthed from a tomb from the Eastern Han Dynasty in Chengren Village, Xianyang City, Shanxi Province. The excavation team believe that these statuettes are from the late Eastern Han Dynasty and represent the earliest independent gilt bronze Buddha statuettes ever discovered in China through archaeological excavations, a belief that has attracted widespread interest and debate among scholars worldwide. However, because the tomb had been looted in the past, the publication of these findings immediately sparked considerable debate, particularly over the dating of the statuettes. The main controversy revolves around two dating proposals: the “Late Eastern Han Dynasty” and the “Period of the Sixteen Kingdoms”. This paper proposes a third viewpoint by examining previously overlooked aspects and materials regarding the statuettes and by placing them within the context of the Guanlong region’s tradition of small gilt bronze Buddha statuettes. We contend that the two statuettes were not created at the same time: we believe that the standing Buddha statuette dates from the end of the “Period of the Sixteen Kingdoms”, whereas the flat five-Buddha statuette was likely crafted between the Yanxing 延興 era and the early Taihe 太和 era of Emperor Xiaowen 孝文帝 of the Northern Wei Dynasty. The styles, combinations of forms, and themes in these statuettes are not distinctive and are, in fact, typical of small gilt bronze Buddha statuettes from the late “Period of the Sixteen Kingdoms” to the mid-Northern Wei Dynasty in the Guanlong region.
Acute Fatty Liver of Pregnancy
Authors: White Alesha , Nelson David B. , Cunningham F. Gary
Published: December 2024

Acute fatty liver of pregnancy (AFLP), although rare, is a potentially fatal obstetrical disorder. It is characterized by acute liver failure of varying degrees with associated coagulopathy that occurs often in the third trimester of pregnancy. The following review discusses the history, etiopathogenesis, and subsequent clinical and laboratory findings that are associated with this disorder. It additionally attempts to aid in differentiating AFLP from other common obstetrical and non-obstetrical diseases with special emphasis on its differentiation from hemolysis, elevated liver enzymes, and low platelets (HELLP) syndrome. This review also discusses recommendations for delivery and postpartum care with focus on return to baseline and additional life-saving measures that may need to be pursued in the most difficult and refractory cases. Finally, it addresses maternal and perinatal outcomes associated with the disorder and the effect that this disorder can have on future pregnancies.
Determination of Chimney Non-Verticality from TLS Data Using RANSAC Method
Authors: Pleterski Žan , Rak Gašper , Kregar Klemen
Published: December 2024

The continuous monitoring of tall industrial buildings is necessary to ensure safe operation. With technological advances in terrestrial laser scanning and other non-contact measurement methods, the methods and techniques for assessing the stability of tall industrial chimneys are evolving. This paper presents a method for determining the non-verticality and straightness of chimneys that offers significant advantages over existing methods. Narrow bands of scanned point clouds are processed at selected height intervals. Using the RANSAC method, points that do not belong to the chimney shell are filtered and the centre of the circle or ellipse is adjusted using the least squares method. The proposed method enables the efficient filtering of point clouds due to frequent obstructions on the chimney shell, the determination of the regularity of the chimney shell shape, a mathematical analysis of the chimney axis curvature, and an intuitive graphical representation of chimney non-verticality. The comparison of the results with other studies confirms the efficiency of the method.
Machine Learning-Based Summer Crops Mapping Using Sentinel-1 and Sentinel-2 Images
Authors: Maleki Saeideh , Baghdadi Nicolas , Bazzi Hassan , Dantas Cassio Fraga , Ienco Dino , Nasrallah Yasser , Najem Sami
Published: December 2024

Accurate crop type mapping using satellite imagery is crucial for food security, yet accurately distinguishing between crops with similar spectral signatures is challenging. This study assessed the performance of Sentinel-2 (S2) time series (spectral bands and vegetation indices), Sentinel-1 (S1) time series (backscattering coefficients and polarimetric parameters), alongside phenological features derived from both S1 and S2 time series (harmonic coefficients and median features), for classifying sunflower, soybean, and maize. Random Forest (RF), Multi-Layer Perceptron (MLP), and XGBoost classifiers were applied across various dataset configurations and train-test splits over two study sites and years in France. Additionally, the InceptionTime classifier, specifically designed for time series data, was tested exclusively with time series datasets to compare its performance against the three general machine learning algorithms (RF, XGBoost, and MLP). The results showed that XGBoost outperformed RF and MLP in classifying the three crops. The optimal dataset for mapping all three crops combined S1 backscattering coefficients with S2 vegetation indices, with comparable results between phenological features and time series data (mean F1 scores of 89.9% for sunflower, 76.6% for soybean, and 91.1% for maize). However, when using individual satellite sensors, S1 phenological features and time series outperformed S2 for sunflower, while S2 was superior for soybean and maize. Both phenological features and time series data produced close mean F1 scores across spatial, temporal, and spatiotemporal transfer scenarios, though median features dataset was the best choice for spatiotemporal transfer. Polarimetric S1 data did not yield effective results. The InceptionTime classifier further improved classification accuracy over XGBoost for all crops, with the degree of improvement varying by crop and dataset (the highest mean F1 scores of 90.6% for sunflower, 86.0% for soybean, and 93.5% for maize).
An Automatic Modulation Recognition Algorithm Based on Time–Frequency Features and Deep Learning with Fading Channels
Authors: Zuo Xiaoya , Yang Yuan , Yao Rugui , Fan Ye , Li Lu
Published: December 2024

Automatic modulation recognition (AMR) stands as a crucial core technology within the realm of signal processing and perception, playing a significant part in harsh electromagnetic environments. The time–frequency image (TFI) of communication signals can manifest modulation characteristics and serve as a foundation for signal modulation recognition and classification. However, under the influence of the electromagnetic environment, communication signals are exposed to varying degrees of interference, which poses a challenge to the recognition of modulation types. Taking into account the effects of interference and channel fading, this paper introduces a communication signal modulation recognition algorithm based on deep learning (DL) and time–frequency analysis. This approach employs short-time Fourier transform (STFT) to generate time–frequency diagrams from time-domain signals. Subsequently, it binarizes the image and feeds it as input data to the neural network. Our research presents a composite deep convolutional neural network (CNN) architecture known as the composite dense-residual neural network (CDRNN). This architecture focuses on enhancing the feature extraction and identification, aiming to achieve accurate recognition of modulation types in harsh electromagnetic environments. Finally, simulation results validate that the proposed deep learning algorithm holds remarkable advantages in boosting the accuracy of modulation type recognition with better adaptability. The algorithm shows better performance even in harsh electromagnetic environments. When the signal-to-noise ratio (SNR) is 18 dB, the recognition accuracy can reach 92.1%.
Evaluation of In Situ FAPAR Measurement Protocols Using 3D Radiative Transfer Simulations
Authors: Lanconelli Christian , Cappucci Fabrizio , Adams Jennifer Susan , Gobron Nadine
Published: December 2024

The fraction of absorbed photosynthetically active radiation (FAPAR) is one of the bio-geophysical Essential Climate Variables assessed through remote sensing observations and distributed globally by space and environmental agencies. Any reliable remote sensing product should be benchmarked against a reference, which is normally determined by means of ground-based measurements. They should generally be aggregated spatially to be compared with remote sensing products at different resolutions. In this work, the effectiveness of various in situ sampling methods proposed to assess FAPAR from flux measurements was evaluated using a three-dimensional radiative transfer framework over eight virtual vegetated landscapes, including dense forests (leaf-on and leaf-off models), open canopies, sparse vegetation, and agricultural fields with a nominal extension of 1 hectare. The reference FAPAR value was determined by summing the absorbed PAR-equivalent photons by either all canopy components, both branches and leaves, or by only the leaves. The incoming and upwelling PAR fluxes were simulated in different illumination conditions and at a high spatial resolution (50 cm). They served to replicate in situ virtual FAPAR measurements, which were carried out using either stationary sensor networks or transects. The focus was on examining the inherent advantages and drawbacks of in situ measurement protocols against GCOS requirements. Consequently, the proficiency of each sampling technique in reflecting the distribution of incident and reflected PAR fluxes—essential for calculating FAPAR—was assessed. This study aims to support activities related to the validation of remote sensing FAPAR products by assessing the potential uncertainty associated with in situ determination of the reference values. Among the sampling schemes considered in our work, the cross shaped sampling schemes showed a particular efficiency in properly representing the pixel scale FAPAR over most of the scenario considered.
Development of Methods for Satellite Shoreline Detection and Monitoring of Megacusp Undulations
Authors: Angelini Riccardo , Angelats Eduard , Luzi Guido , Masiero Andrea , Simarro Gonzalo , Ribas Francesca
Published: December 2024

Coastal zones, particularly sandy beaches, are highly dynamic environments subject to a variety of natural and anthropogenic forcings. Instantaneous shoreline is a widely used indicator of beach changes in image-based applications, and it can display undulations at different spatial and temporal scales. Megacusps, periodic seaward and landward shoreline perturbations, are an example of such undulations that can significantly modify beach width and impact its usability. Traditionally, the study of these phenomena relied on video monitoring systems, which provide high-frequency imagery but limited spatial coverage. Instead, this study explored the potential of employing multispectral satellite-derived shorelines, specifically from Sentinel-2 (S2) and PlanetScope (PLN) platforms, for characterizing and monitoring megacusps’ formation and their dynamics over time. First, a tool was developed and validated to guarantee accurate shoreline detection, based on a combination of spectral indices, along with both thresholding and unsupervised clustering techniques. Validation of this shoreline detection phase was performed on three micro-tidal Mediterranean beaches, comparing with high-resolution orthomosaics and in-situ GNSS data, obtaining a good subpixel accuracy (with a mean absolute deviation of 1.5–5.5 m depending on the satellite type). Second, a tool for megacusp characterization was implemented and subsequent validation with reference data proved that satellite-derived shorelines could be used to robustly and accurately describe megacusps. The methodology could not only capture their amplitude and wavelength (of the order of 10 and 100 m, respectively) but also monitor their weekly–daily evolution using different potential metrics, thanks to combining S2 and PLN imagery. Our findings demonstrate that multispectral satellite imagery provides a viable and scalable solution for monitoring shoreline megacusp undulations, enhancing our understanding and offering an interesting option for coastal management.
Pixel-Based Long-Wave Infrared Spectral Image Reconstruction Using a Hierarchical Spectral Transformer
Authors: Wang Zi , Yang Yang , Yuan Liyin , Li Chunlai , Wang Jianyu
Published: December 2024

Long-wave infrared (LWIR) spectral imaging plays a critical role in various applications such as gas monitoring, mineral exploration, and fire detection. Recent advancements in computational spectral imaging, powered by advanced algorithms, have enabled the acquisition of high-quality spectral images in real time, such as with the Uncooled Snapshot Infrared Spectrometer (USIRS). However, the USIRS system faces challenges, particularly a low spectral resolution and large amount of data noise, which can degrade the image quality. Deep learning has emerged as a promising solution to these challenges, as it is particularly effective at handling noisy data and has demonstrated significant success in hyperspectral imaging tasks. Nevertheless, the application of deep learning in LWIR imaging is hindered by the severe scarcity of long-wave hyperspectral image data, which limits the training of robust models. Moreover, existing networks that rely on convolutional layers or attention mechanisms struggle to effectively capture both local and global spectral correlations. To address these limitations, we propose the pixel-based Hierarchical Spectral Transformer (HST), a novel deep learning architecture that learns from publicly available single-pixel long-wave infrared spectral databases. The HST is designed to achieve a high spectral resolution for LWIR spectral image reconstruction, enhancing both the local and global contextual understanding of the spectral data. We evaluated the performance of the proposed method on both simulated and real-world LWIR data, demonstrating the robustness and effectiveness of the HST in improving the spectral resolution and mitigating noise, even with limited data.
Development, Verification and Assessment of a Laser Profilometer and Analysis Algorithm for Microtexture Assessment of Runway Surfaces
Authors: Baimukhametov Gadel , White Greg
Published: December 2024

Runway surface friction is critically important to safe aircraft operations and mostly depends on the surface texture, which provides grip in the presence of contamination and directly affects the friction coefficient in general. Microtexture assessment is the most challenging part of texture assessment since there is no standardised pavement microtexture control method in runway maintenance and management practice. The purpose of this study was to develop a simple laser profilometer and analysis model and subsequent validation for use in runway friction surveys. To that end, a simple laser profilometer was developed, and a profile picture analysis and macrotexture filtration method were designed. Test results were compared to the stylus-based roughness tester and the British Pendulum Tester. The proposed profile picture analysis and profile smothering and filtration methodology, based on linear approximation, is simpler and more effective for the case of macrotexture filtration for the friction survey. The laser profilometer model results were highly correlated with the stylus-based roughness tester results (R2 = 0.99). The average roughness of the microtexture profile, after smothering and macrotexture filtration, also showed good correlation with the British Pendulum results (R2 = 0.78). The results from this study confirm the possibility of texture assessment for routine runway friction surveys using a simple and economical laser profilometer, which is not routinely available in current airport surface friction management.
Estimation of Crack Tip Position in Adhesively Bonded Joints Subjected to Mode II Fatigue Loading
Authors: Mehrabi M. , Martulli L. M. , Bernasconi A. , Carboni M.
Published: December 2024

Interest in adhesively bonded joints has significantly increased due to their numerous advantages over other joining techniques. However, they are frequently used in structures subjected to fatigue loading, which might cause defects such as cracks within the bondline. Thus, timely detection, localization, and size estimation of such defects are crucial for ensuring structural safety. This study focused on experimentally investigating crack length estimation in adhesively bonded joints under mode II fatigue loading. To analyze the crack growth, a comprehensive comparison was conducted between various techniques, such as visual testing, digital image correlation, optical backscatter reflectometry, and the analytical compliance-based beam method. In interrupted fatigue tests (static acquisition), digital image correlation and optical backscatter reflectometry exhibited consistent damage sensitivity, estimating larger crack lengths compared to visual testing by approximately 3 mm and 5 mm, respectively. The optical backscatter reflectometry in uninterrupted tests (dynamic acquisition) showed significantly larger estimations, approximately double those of static ones. This demonstrated its potential to detect possible damage within the adhesive that might not be detected by other methods, as shown previously for quasi-static loading conditions. Its capability in early damage detection under the dynamic regime makes it a valuable tool for continuous monitoring. Furthermore, a comparison of optical backscatter reflectometry’s performance in quasi-static, static, and dynamic acquisitions indicated a potentially larger process zone under quasi-static loading, a finding confirmed by the compliance-based beam method.
Progressive and Asymmetrical Deadlift Loads Captured by Wearable Motion Tape Sensors
Authors: Wyckoff Elijah , Sten David , Wareham Regan , Loh Kenneth J.
Published: December 2024

Weight training is widely adopted and highly effective for enhancing both muscular strength and endurance. A popular weightlifting exercise is the deadlift, which targets multiple muscle groups including the lower back, glutes, and hamstrings. However, incorrect technique (i.e., poor form) can slow training progress, result in asymmetrical muscle development, and cause serious injuries. The objective of this study was to validate that a self-adhesive, elastic fabric, wearable, skin-strain sensor called Motion Tape (MT) could monitor a person’s posture while performing deadlift exercises. Two pairs of Motion Tape were attached on the front and back sides of the pelvis at each posterior superior iliac spine to record muscle engagement during deadlift exercises. The results of this preliminary study confirmed that the MT identified asymmetry in muscle engagement during deadlifting repetitions. In addition, the sensors could quantify the different levels of effort exerted according to the deadlift weight load.
Early Technology Readiness Level (TRL) Development of the Microfluidic Inorganic Conductivity Detector for Europa and the Solenoid-Based Actuator Assembly for Impact Penetrators
Authors: Govinda Raj Chinmayee , Odeh Mohamed , Salyards Cambrie , Stockton Amanda
Published: December 2024

This study introduces an innovative in situ lander/impact-penetrator design tailored for Discovery-class missions to Europa, specifically focused on conducting astrobiological analyses. The platform integrates a microfluidic capacitively coupled contactless conductivity detector (C4D), optimized for the detection of low-concentration salts potentially indicative of biological activity. Our microfluidic system allows for automated sample routing and precise conductivity-based detection, making it suitable for the harsh environmental and logistical demands of Europa’s icy surface. This technology provides a robust toolset for exploring extraterrestrial habitability by enabling in situ chemical analyses with minimal operational intervention, paving the way for advanced astrobiological investigations on Europa.
Re-Parameterization After Pruning: Lightweight Algorithm Based on UAV Remote Sensing Target Detection
Authors: Yang Yang , Song Pinde , Wang Yongchao , Cao Lijia
Published: December 2024

Lightweight object detection algorithms play a paramount role in unmanned aerial vehicles (UAVs) remote sensing. However, UAV remote sensing requires target detection algorithms to have higher inference speeds and greater accuracy in detection. At present, most lightweight object detection algorithms have achieved fast inference speed, but their detection precision is not satisfactory. Consequently, this paper presents a refined iteration of the lightweight object detection algorithm to address the above issues. The MobileNetV3 based on the efficient channel attention (ECA) module is used as the backbone network of the model. In addition, the focal and efficient intersection over union (FocalEIoU) is used to improve the regression performance of the algorithm and reduce the false-negative rate. Furthermore, the entire model is pruned using the convolution kernel pruning method. After pruning, model parameters and floating-point operations (FLOPs) on VisDrone and DIOR datasets are reduced to 1.2 M and 1.5 M and 6.2 G and 6.5 G, respectively. The pruned model achieves 49 frames per second (FPS) and 44 FPS inference speeds on Jetson AGX Xavier for VisDrone and DIOR datasets, respectively. To fully exploit the performance of the pruned model, a plug-and-play structural re-parameterization fine-tuning method is proposed. The experimental results show that this fine-tuned method improves mAP@0.5 and mAP@0.5:0.95 by 0.4% on the VisDrone dataset and increases mAP@0.5:0.95 by 0.5% on the DIOR dataset. The proposed algorithm outperforms other mainstream lightweight object detection algorithms (except for FLOPs higher than SSDLite and mAP@0.5 Below YOLOv7 Tiny) in terms of parameters, FLOPs, mAP@0.5, and mAP@0.5:0.95. Furthermore, practical validation tests have also demonstrated that the proposed algorithm significantly reduces instances of missed detection and duplicate detection.
An Improved Generative Adversarial Network-Based and U-Shaped Transformer Method for Glass Curtain Crack Deblurring Using UAVs
Authors: Huang Jiaxi , Liu Guixiong
Published: December 2024

Drones have emerged as a critical tool for the detection of high-altitude glass curtain cracks. However, their utility is often compromised by vibrations and other environmental factors that can induce motion blur, compromising image quality and the accuracy of crack detection. This paper presents a novel GAN-based and enhanced U-shaped Transformer network, named GlassCurtainCrackDeblurNet, designed specifically for the deblurring of drone-captured images of glass curtain cracks. To optimize the performance of our proposed method for this application, we have meticulously created the GlassCurtainCrackDeblur Dataset. Our method demonstrates superior qualitative and quantitative outcomes when compared to other established deblurring techniques on both the GoPro Dataset and the GlassCurtainCrackDeblur Dataset.
Aspherical Surface Wavefront Testing Based on Multi-Directional Orthogonal Lateral Shearing Interferometry
Authors: Zhu Yahui , Tian Ailing , Wang Hongjun , Liu Bingcai
Published: December 2024

To overcome the limitations of phase sampling points in testing aspherical surface wavefronts using traditional interferometers, we propose a high-spatial-resolution method based on multi-directional orthogonal lateral shearing interferometry. In this study, we provide a detailed description of the methodology, which includes the theoretical foundations and experimental setup, along with the results from simulations and experiments. By establishing a relational model between the multi-directional differential wavefront and differential Zernike polynomials, we demonstrate high-spatial-resolution wavefront reconstruction using multi-directional orthogonal lateral shearing interferometry. Theoretical calculations and simulations of aspherical surface wavefront testing are followed by experimental verification on an aspherical surface with a known asphericity. Comparing the measurement results with those from the LuphoScan profilometer, we achieve a relative measurement error with an RMS precision better than λ/100.
Investigating Influence Factors on Traffic Safety Based on a Hybrid Approach: Taking Pedestrians as an Example
Authors: Li Yue , Shi Yuanyuan , Xiong Huiyuan , Jian Feng , Yu Xinxin , Sun Shuo , Meng Yunlong
Published: December 2024

Road traffic safety is an essential component of public safety and a globally significant issue. Pedestrians, as crucial participants in traffic activities, have always been a primary focus with regard to traffic safety. In the context of the rapid advancement of intelligent transportation systems (ITS), it is crucial to explore effective strategies for preventing pedestrian fatalities in pedestrian–vehicle crashes. This paper aims to investigate the factors that influence pedestrian injury severity based on pedestrian-involved crash data collected from several sensor-based sources. To achieve this, a hybrid approach of a random parameters logit model and random forest based on the SHAP method is proposed. Specifically, the random parameters logit model is utilized to uncover significant factors and the random variability of parameters, while the random forest based on SHAP is employed to identify important influencing factors and feature contributions. The results indicate that the hybrid approach can not only verify itself but also complement more conclusions. Eight significant influencing factors were identified, with seven of the factors identified as important by the random forest analysis. However, it was found that the factors “Workday or not” (Not), “Signal control mode” (No signal and Other security facilities), and “Road safety attribute” (Normal Road) are not considered significant. It is important to note that focusing solely on either significant or important factors may lead to overlooking certain conclusions. The proposed strategies for ITS have the potential to significantly improve pedestrian safety levels.
Developing a New Method of Transformation for Obtaining XYZ Color Values from RGB Images for Agricultural Applications
Authors: Mohammadi Vahid , Ansari Keivan , Gouton Pierre , Attig Houda
Published: December 2024

The extraction of device-independent color values from affordable and accessible digital images based on a standard color space system is crucially necessary for agricultural applications, where color information for plant monitoring or diagnostics is required. This study aimed to develop a transformation matrix for obtaining XYZ color coordinates from the RGB values of digital images for agricultural applications. The calibration procedure was based on Munsell and Macbeth color charts. The color coordinates of eight color charts were measured, and the transformation matrices were built. Leaf samples of six different plants were used and compared based on the proposed transformation technique. The actual XYZ values of plant leaves were measured, and the RGB values were derived from the digital images. The results indicate that the Macbeth color chart with 24 colors had the best performance, with an average ∆ELAB and CIEDE2000 of less than 1.77 and 1.97, respectively. The findings demonstrate that the proposed transformation matrix was successful in converting RGB values to XYZ values and can be employed as a quick, easy, and inexpensive technique for obtaining standard color information.
Effective Detection of Cloud Masks in Remote Sensing Images
Authors: Cui Yichen , Shen Hong , Lam Chan-Tong
Published: December 2024

Effective detection of the contours of cloud masks and estimation of their distribution can be of practical help in studying weather changes and natural disasters. Existing deep learning methods are unable to extract the edges of clouds and backgrounds in a refined manner when detecting cloud masks (shadows) due to their unpredictable patterns, and they are also unable to accurately identify small targets such as thin and broken clouds. For these problems, we propose MDU-Net, a multiscale dual up-sampling segmentation network based on an encoder–decoder–decoder. The model uses an improved residual module to capture the multi-scale features of clouds more effectively. MDU-Net first extracts the feature maps using four residual modules at different scales, and then sends them to the context information full flow module for the first up-sampling. This operation refines the edges of clouds and shadows, enhancing the detection performance. Subsequently, the second up-sampling module concatenates feature map channels to fuse contextual spatial information, which effectively reduces the false detection rate of unpredictable targets hidden in cloud shadows. On a self-made cloud and cloud shadow dataset based on the Landsat8 satellite, MDU-Net achieves scores of 95.61% in PA and 84.97% in MIOU, outperforming other models in both metrics and result images. Additionally, we conduct experiments to test the model’s generalization capability on the landcover.ai dataset to show that it also achieves excellent performance in the visualization results.
Extension of the Side Distance Measurement Aspect Ratio in the Measurement of a Slot or Bore Using a Commercial Laser Triangulation Sensor
Authors: Hošek Jan
Published: December 2024

We propose a new commercial laser triangulation sensor modification to enable the measurement of slots or bores side distance. The study showed the possibility of extending the sensor depth range for a slot or bore side distance measurement using a bypass of the illumination laser beam compared to a simple single mirror attachment to the sensor probe. We derived relations allowing for evaluation of the modified sensor side measurement range in desired depth based on the sensor parameters and the reflective mirror size and position. We demonstrated the functionality of the proposed measurement arrangement with an attachment to the commercial laser triangulation sensor and assessed the side-wall distance measurement. The results show the correct measurement depth and range prediction and the ability to perform side surface distance measurements at depths of more than 3.5 times the slot size.
Geostress-Adaptive Charge Structure Design and Field Validation for Machinery Room Excavation
Authors: Chen Xiaocui , Mi Yuan , Shuai Xinru , Zheng Yuan , Zhao Wenhu
Published: December 2024

The application of blasting in modern engineering construction is prized for its speed, efficiency, and cost-effectiveness. However, the resultant vibrations can have significant adverse effects on surrounding buildings and residents. The challenge of optimizing blasting procedures to satisfy excavation needs while minimizing vibration impacts is a critical concern in blasting excavation. This research addresses this challenge through the development of a 3D simulation and analysis model for an underground pumped storage power plant in East China, utilizing the LS-DYNA finite element analysis software. To explore the influence of charging structures on rock fragmentation and vibration propagation, three distinct blasting programs were formulated, each featuring varied configurations within the machinery room. The analysis revealed that the adoption of an optimized charging structure can significantly decrease damage to the protective layer by approximately 40%, while also reducing the impact on the upstream and downstream side walls by 27.25% and 12.03%, respectively, without compromising the efficacy of the main blast zone. Moreover, the vibration velocities at the remote measurement point were found to be reduced across multiple directions, indicating effective control of the vibration effects. The post-implementation of the optimized blasting strategy at the site, the assessment of the retained surrounding rock integrity, and the impact on protected structures demonstrated that the proposed solution met satisfactory outcomes. This study underscores the potential of simulation-based optimization in managing vibration risks during blasting operations, offering a valuable tool for engineers and practitioners in the field of underground construction.
Z-Score Experience Replay in Off-Policy Deep Reinforcement Learning
Authors: Yang Yana , Xi Meng , Dai Huiao , Wen Jiabao , Yang Jiachen
Published: December 2024

Reinforcement learning, as a machine learning method that does not require pre-training data, seeks the optimal policy through the continuous interaction between an agent and its environment. It is an important approach to solving sequential decision-making problems. By combining it with deep learning, deep reinforcement learning possesses powerful perception and decision-making capabilities and has been widely applied to various domains to tackle complex decision problems. Off-policy reinforcement learning separates exploration and exploitation by storing and replaying interaction experiences, making it easier to find global optimal solutions. Understanding how to utilize experiences is crucial for improving the efficiency of off-policy reinforcement learning algorithms. To address this problem, this paper proposes Z-Score Prioritized Experience Replay, which enhances the utilization of experiences and improves the performance and convergence speed of the algorithm. A series of ablation experiments demonstrate that the proposed method significantly improves the effectiveness of deep reinforcement learning algorithms.
Corrupted Point Cloud Classification Through Deep Learning with Local Feature Descriptor
Authors: Wu Xian , Guo Xueyi , Peng Hang , Su Bin , Ahamod Sabbir , Han Fenglin
Published: December 2024

Three-dimensional point cloud recognition is a very fundamental work in fields such as autonomous driving and face recognition. However, in real industrial scenarios, input point cloud data are often accompanied by factors such as occlusion, rotation, and noise. These factors make it challenging to apply existing point cloud classification algorithms in real industrial scenarios. Currently, most studies enhance model robustness from the perspective of neural network structure. However, researchers have found that simply adjusting the neural network structure has proven insufficient in addressing the decline in accuracy caused by data corruption. In this article, we use local feature descriptors as a preprocessing method to extract features from point cloud data and propose a new neural network architecture aligned with these local features, effectively enhancing performance even in extreme cases of data corruption. In addition, we conducted data augmentation to the 10 intentionally selected categories in ModelNet40. Finally, we conducted multiple experiments, including testing the robustness of the model to occlusion and coordinate transformation and then comparing the model with existing SOTA models. Furthermore, in actual scene experiments, we used depth cameras to capture objects and input the obtained data into the established model. The experimental results show that our model outperforms existing popular algorithms when dealing with corrupted point cloud data. Even when the input point cloud data are affected by occlusion or coordinate transformation, our proposed model can maintain high accuracy. This suggests that our method can alleviate the problem of decreased model accuracy caused by the aforementioned factors.
Adaptive Step RRT*-Based Method for Path Planning of Tea-Picking Robotic Arm
Authors: Li Xin , Yang Jingwen , Wang Xin , Fu Leiyang , Li Shaowen
Published: December 2024

The Adaptive Step RRT* (AS-RRT*) path planning algorithm for tea-picking robotic arms was proposed as a solution to the autonomy, safety, and efficiency problems inherent to tea-picking robots in tea plantations. The algorithm employs an accumulator-based sampling point selection strategy to enhance the efficiency of path planning and the quality of the resulting path. It combines fast connectivity and pruning optimization methods to identify collision-free paths in a shorter time and to reduce the computational burden. It also incorporates a dynamic step length adjustment mechanism following collision detection, ensuring that the robot arm can avoid obstacles in real time. Furthermore, the generated paths were optimized through the introduction of redundant node removal and curve smoothing techniques. In the robotic arm motion planning experiments, the depth vision sensor was employed to obtain three-dimensional information within the tea plantation as the data source. The experimental results demonstrate that the AS-RRT* algorithm reduces the path length by 14.18% and the path planning time is less than 1 s, indicating that the proposed method enhances the efficiency of path planning and obstacle avoidance performance of the tea-picking robot arm.
Enhancing the Ground Truth Disparity by MAP Estimation for Developing a Neural-Net Based Stereoscopic Camera
Authors: Gil Hanbit , Ryu Sehyun , Woo Sungmin
Published: December 2024

This paper presents a novel method to enhance ground truth disparity maps generated by Semi-Global Matching (SGM) using Maximum a Posteriori (MAP) estimation. SGM, while not producing visually appealing outputs like neural networks, offers high disparity accuracy in valid regions and avoids the generalization issues often encountered with neural network-based disparity estimation. However, SGM struggles with occlusions and textureless areas, leading to invalid disparity values. Our approach, though relatively simple, mitigates these issues by interpolating invalid pixels using surrounding disparity information and Bayesian inference, improving both the visual quality of disparity maps and their usability for training neural network-based commercial depth-sensing devices. Experimental results validate that our enhanced disparity maps preserve SGM’s accuracy in valid regions while improving the overall performance of neural networks on both synthetic and real-world datasets. This method provides a robust framework for advanced stereoscopic camera systems, particularly in autonomous applications.
Enhancing Situational Awareness of Helicopter Pilots in Unmanned Aerial Vehicle-Congested Environments Using an Airborne Visual Artificial Intelligence Approach
Authors: Mugabe John , Wisniewski Mariusz , Perrusquía Adolfo , Guo Weisi
Published: December 2024

The use of drones or Unmanned Aerial Vehicles (UAVs) and other flying vehicles has increased exponentially in the last decade. These devices pose a serious threat to helicopter pilots who constantly seek to maintain situational awareness while flying to avoid objects that might lead to a collision. In this paper, an Airborne Visual Artificial Intelligence System is proposed that seeks to improve helicopter pilots’ situational awareness (SA) under UAV-congested environments. Specifically, the system is capable of detecting UAVs, estimating their distance, predicting the probability of collision, and sending an alert to the pilot accordingly. To this end, we aim to combine the strengths of both spatial and temporal deep learning models and classic computer stereo vision to (1) estimate the depth of UAVs, (2) predict potential collisions with other UAVs in the sky, and (3) provide alerts for the pilot with regards to the drone that is likely to collide. The feasibility of integrating artificial intelligence into a comprehensive SA system is herein illustrated and can potentially contribute to the future of autonomous aircraft applications.