bluuebunny/crossref_metadata_2025 huggingface dataset:
Seasonal Variation and Mean Degree of Polymerization of Proanthocyanidin in Leaves and Branches of Rabbiteye Blueberry (Vaccinium virgatum Aiton)
Authors: Koga Yasuko , Setoguchi Yuno , Sugamoto Kazuhiro , Goto Yo , Hirano Tomonari , Kunitake Hisato
Published: July 2024

The leaves and branches of rabbiteye blueberry are rich in proanthocyanidins, which are thought to have different physiological activities depending on their structure and degree of polymerization. In this study, we analyzed the constituents of the leaves and branches of rabbiteye blueberry to determine the seasonal variations in polyphenol and proanthocyanidin (PAC) contents as well as their mean degrees of polymerization (mDP). Total PAC content was determined using two methods: The p-dimethylaminocinnamaldehyde (DMACA) method, which measures monomeric PAC, showed an increase from spring to summer in both leaves and branches. On the other hand, using the butanol/HCl method, which measures only polymerized PAC, the PAC content of leaves increased from spring to summer but those of branches remained low throughout the year, showing no significant increase or decrease. Furthermore, analysis of the mDP of PAC showed increases from spring to summer in the leaves of ‘Kunisato 35 gou’. Although the highest value (8.0) was observed in October, values around 4 remained throughout the year in the branches. Since differences in polymerization degree affect absorption in the body and physiological properties such as antioxidant capacity, selecting the appropriate harvest time and plant organs for each purpose is expected to ensure the quality of processed blueberry foods.
A Micro Capacitive Humidity Sensor Based on Al-Mo Electrodes and Polyimide Film
Authors: Zhou Wenhe , Wei Jiafeng , Wang Liangbi
Published: July 2024

Quickly sensing humidity changes is required in some fields, such as in fuel cell vehicles. The micro humidity sensor used for the relative humidity (RH) measurement with fast response characteristics, and its numerical model and method are rare. This paper firstly presents a numerical model and method for a parallel plate capacitor and a numerical analysis of its dynamic characteristics. The fabrication of this sensor was carried out based on the numerical results, and, the main characteristics of its moisture-sensitive element are shown. This parallel plate capacitor is made using complementary metal-oxide semiconductor (CMOS)-compatible technology, with a P-type monocrystalline silicon wafer used as the substrate, a thin polyimide film (PI) between the upper grid electrode and the lower parallel plate electrode, and electrodes with a molybdenum–aluminum bilayer structure. The shape of the micro sensor is square with 3 mm on the side of the source field. The humidity sensor has a linearity of 0.9965, hysteresis at 7.408% RH, and a sensitivity of 0.4264 pF/%RH. The sensor displays an average adsorption time of 1 s and a minimum adsorption time of 850 ms when the relative humidity increases from 33.2% RH to 75.8% RH. The sensor demonstrates very good stability during a 240 h test in a 25 °C environment. The numerical model and method provided by this study are very useful for predicting the performance of a parallel plate capacitor.
Analytical Model to Deduce the Conformational and Dynamical Behavior in Dendrimers: A Review
Authors: Bhardwaj Shelly , Kumar Amit
Published: July 2024

This review utilizes an optimized Rouse–Zimm discrete hydrodynamic model and the preaveraged Oseen tensor, which accurately consider hydrodynamic interactions to study model dendrimers. We report the analytical theories that have been previously developed for the creation of generalized analytical models for dendrimers. These generalized theories were used to assess the conformational and dynamical behavior of the dendrimers. By including stiffness in the bonds, the neglect of excluded volume interactions may be somewhat offset. This is true at least in the case of short spacers. While the topological limitations on the directions and orientations of the individual bond vectors in dendrimers implement semiflexibility, the intensity of these contacts was determined by the potential geometric orientations of the bonds, and later on the excluded volume interactions in dendrimers, which were described in terms of the effective co-volume between nearest non-bonded monomers and modeled using the delta function pseudopotential. With the aid of the models developed, the authors condensed various conformational and dynamic properties of dendrimers that depend on their degree of semiflexibility and the strength of the excluded volume. These analyses came to the conclusion that the flexible dendrimer in one limit and the earlier described freely rotating model of dendrimers in the other constitute a highly generalized way of capturing a wide range of conformations in the developed mathematical model in dendrimers.
Anticorrosion Performance of Waterborne Coatings with Modified Nanoscale Titania under Subtropical Maritime Climate
Authors: Lyu Yang , Sun Weipeng , Feng Tingyou , Li Wenge , Jiang Yong , Zuo Chenglin , Wang Shuangxi
Published: July 2024

Steel structures located in subtropical marine climates face harsh conditions such as strong sunlight and heavy rain, and they are extremely corroded. In this study, a waterborne coating with excellent corrosion resistance, hydrophobic ability, high-temperature resistance and high density was successfully prepared by using modified nanoscale titania powders and grafted polymers. The effects of three modifiers on titania nanoparticles and waterborne coatings’ properties were studied independently. The experimental results showed that the activation index of the modification employing methacryloxy silane reached 97.5%, which achieved the best modification effect at 64.4 °C for 43.3 min. The waterborne coating with nanoscale titania modified by methacryloxy silane exhibited the best hydrophobic effect, with a drop contact angle of 115.4° and excellent heat resistance of up to 317.2 °C. The application of the waterborne modified coating in steel structures under subtropical maritime climates showed that the waterborne titania coatings demonstrated excellent resistance to corrosion, high temperatures and harsh sunlight, with a maximum service life of up to five years. Economic analysis indicated that, considering a conservative three-year effective lifespan, this coating could save more than 50% in cost compared with conventional industrial coatings. Finally, the strengthening mechanism of the polymer coatings with modified nanoscale titania was analyzed.
Biological and Physicochemical Analysis of Sr-Doped Hydroxyapatite/Chitosan Composite Layers
Authors: Zarif Maria Elena , Bita Bogdan , Yehia-Alexe Sasa Alexandra , Negut Irina , Gradisteanu Pircalabioru Gratiela , Andronescu Ecaterina , Groza Andreea
Published: July 2024

In this work results are presented on the evaluation of HAp, HApSr, HAp_CS, and HApSr_CS layers deposited on Ti substrates regarding L929 cell viability and cytotoxicity as well as antimicrobial activity against Staphylococcus aureus, in connection with their physicochemical properties. The HAp and HApSr layers generated by radio-frequency magnetron sputtering technique were further covered with chitosan by a matrix-assisted pulsed laser evaporation technique. During the plasma depositions, the Ti substrates were heated externally by a home-made oven above 100 °C. The HApSr_CS layers generated on the unpolished Ti substrates at 100 °C and 400 °C showed the highest biocompatibility properties and antimicrobial activity against Staphylococcus aureus. The morphology of the layer surfaces, revealed by scanning electron microscopy, is dependent on substrate temperature and substrate surface roughness. The optically polished surfaces of Ti substrates revealed grain-like and microchannel structure morphologies of the layers deposited at 25 °C substrate temperature and 400 °C, respectively. Chitosan has no major influence on HAp and HApSr layer surface morphologies. X-ray photoelectron spectroscopy indicated the presence of Ca 2p3/2 peak characteristic of the HAp structure even in the case of the HApSr_CS samples generated at a 400 °C substrate temperature. Fourier transform infrared spectroscopy investigations showed shifts in the wavenumber positions of the P-O absorption bands as a function of Sr or chitosan presence in the HAp layers generated at 25, 100, and 400 °C substrate temperatures.
Drone-Based Ground-Penetrating Radar with Manual Transects for Improved Field Surveys of Buried Ice
Authors: Tjoelker Adam R. , Baraër Michel , Valence Eole , Charonnat Bastien , Masse-Dufresne Janie , Mark Bryan G. , McKenzie Jeffrey M.
Published: July 2024

The steep and unstable terrain found on debris-covered glaciers, rock glaciers, talus slopes, moraines and other proglacial features often make terrestrial ground-penetrating radar (GPR) surveys unsafe or cost-prohibitive. To address these challenges, this research introduces a novel approach for studying buried ice using multi-low-frequency drone-based GPR. Monostatic antennas of 50, 100, and 200 MHz were flown along a transect spanning a debris-covered glacier and an ice–debris complex at Shár Shaw Tagà (Grizzly Creek) in southwest Yukon, Canada. The drone-based results were compared to manual GPR at two locations along the transect. The two manual segments were conducted using the same radar system in a bi-static mode and included common mid-point (CMP) surveys. Overall, the drone-based radar successfully identified buried ice and enabled estimation of ice body thickness. Notably, CMP results confirmed layer characteristics and enabled depths to be measured across the entire drone-based transect. Discrimination of detail across a range of depths was made possible by comparing the three low frequencies, highlighting the possibility of using this method for future investigations of debris thickness in addition to quantifying buried ice. This study confirms the effectiveness of drone-based GPR combined with manual CMP for surveying ice beneath previously inaccessible terrain.
Hierarchical Prototype-Aligned Graph Neural Network for Cross-Scene Hyperspectral Image Classification
Authors: Shen Danyao , Hu Haojie , He Fang , Zhang Fenggan , Zhao Jianwei , Shen Xiaowei
Published: July 2024

The objective of cross-scene hyperspectral image (HSI) classification is to develop models capable of adapting to the “domain gap” that exists between different scenes, enabling accurate object classification in previously unseen scenes. Many researchers have devised various domain adaptation techniques aimed at aligning the statistical or spectral distributions of data from diverse scenes. However, many previous studies have overlooked the potential benefits of incorporating spatial topological information from hyperspectral imagery, which could provide a more accurate representation of the inherent data structure in HSIs. To overcome this issue, we introduce an innovative approach for cross-scene HSI classification, founded on hierarchical prototype graph alignment. Specifically, this method leverages prototypes as representative embedded representations of all samples within the same class. By employing multiple graph convolution and pooling operations, multi-scale domain alignment is attained. Beyond statistical distribution alignment, we integrate graph matching to effectively reconcile semantic and topological information. Experimental results on several datasets achieve significantly improved accuracy and generalization capabilities for cross-scene HSI classification tasks.
Application of Fast MEEMD–ConvLSTM in Sea Surface Temperature Predictions
Authors: Wanigasekara R. W. W. M. U. P. , Zhang Zhenqiu , Wang Weiqiang , Luo Yao , Pan Gang
Published: July 2024

Sea Surface Temperature (SST) is of great importance to study several major phenomena due to ocean interactions with other earth systems. Previous studies on SST based on statistical inference methods were less accurate for longer prediction lengths. A considerable number of studies in recent years involve machine learning for SST modeling. These models were able to mitigate this problem to some length by modeling SST patterns and trends. Sequence analysis by decomposition is used for SST forecasting in several studies. Ensemble Empirical Mode Decomposition (EEMD) has been proven in previous studies as a useful method for this. The application of EEMD in spatiotemporal modeling has been introduced as Multidimensional EEMD (MEEMD). The aim of this study is to employ fast MEEMD methods to decompose the SST spatiotemporal dataset and apply a Convolutional Long Short-Term Memory (ConvLSTM)-based model to model and forecast SST. The results show that the fast MEEMD method is capable of enhancing spatiotemporal SST modeling compared to the Linear Inverse Model (LIM) and ConvLSTM model without decomposition. The model was further validated by making predictions from April to May 2023 and comparing them to original SST values. There was a high consistency between predicted and real SST values.
Integrated Multi-Scale Aircraft Detection and Recognition with Scattering Point Intensity Adaptiveness in Complex Background Clutter SAR Images
Authors: Ye Xuyuan , Du Chuan
Published: July 2024

Detecting aircraft targets in Synthetic Aperture Radar (SAR) images is critical for military and civilian applications. However, due to SAR’s special imaging mechanism, aircraft targets often consist of scattering points with large fluctuations in intensity. This often leads to the detector failing to detect weak scattering points. Not only that, previous SAR image aircraft-object-detection models have focused more on detecting and locating targets, with little emphasis on target recognition. This paper proposes a scattering-point-intensity-adaptive detection and recognition network (SADRN). In order to correctly detect the target area, we propose a Self-adaptive Bell-shaped Kernel (SBK) within the detector, which constructs a bell-shaped two-dimensional distribution centered on the target center, making the detection “threshold” for the target decrease from the center towards the periphery, reducing the missed alarms of weak scattering points at the edges of the target. To help the model adapt to multi-scale targets, we propose the FADLA-34 backbone network, aggregating information from feature maps across different scales. We also embed CBAM into the detector, which enhances the attention to the target area in the spatial dimension and strengthens the extraction of useful features in the channel dimension, reducing interference from the complex background clutter on object detection. Furthermore, to integrate detection and recognition, we introduce the multi-task head, which utilizes the three feature maps from the backbone network to generate the detection boxes and categories of the targets. Finally, the SADRN achieves superior detection and recognition performance on the SAR-AIRcraft-1.0, exceeding other mainstream methods. Visualization and analysis further confirm the effectiveness and superiority of the SADRN.
Context-Aggregated and SAM-Guided Network for ViT-Based Instance Segmentation in Remote Sensing Images
Authors: Liu Shuangzhou , Wang Feng , You Hongjian , Jiao Niangang , Zhou Guangyao , Zhang Tingtao
Published: July 2024

Instance segmentation of remote sensing images can not only provide object-level positioning information but also provide pixel-level positioning information. This pixel-level information annotation has a wide range of uses in the field of remote sensing, and it is of great value for environmental detection and resource management. Because optical images generally have complex terrain environments and changeable object shapes, SAR images are affected by complex scattering phenomena, and the mask quality obtained by the traditional instance segmentation method used in remote sensing images is not high. Therefore, it is a challenging task to improve the mask quality of instance segmentation in remote sensing images. Since the traditional two-stage instance segmentation method consists of backbone, neck, bbox head, and mask head, the final mask quality depends on the product of all front-end work quality. Therefore, we consider the difficulty of optical and SAR images to bring instance segmentation to the targeted improvement of the neck, bbox head, and mask head, and we propose the Context-Aggregated and SAM-Guided Network (CSNet). In this network, the plain feature fusion pyramid network (PFFPN) can generate a pyramid for the plain feature and provide a feature map of the appropriate instance scale for detection and segmentation. The network also includes a context aggregation bbox head (CABH), which uses the context information and instance information around the instance to solve the problem of missed detection and false detection in detection. The network also has a SAM-Guided mask head (SGMH), which learns by using SAM as a teacher, and uses the knowledge learned to improve the edge of the mask. Experimental results show that CSNet significantly improves the quality of masks generated under optical and SAR images, and CSNet achieves 5.1% and 3.2% AP increments compared with other SOTA models.
An Optimal Denoising Method for Spaceborne Photon-Counting LiDAR Based on a Multiscale Quadtree
Authors: Zhang Baichuan , Liu Yanxiong , Dong Zhipeng , Li Jie , Chen Yilan , Tang Qiuhua , Huang Guoan , Tao Junlin
Published: July 2024

Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) has excellent potential for obtaining water depth information around islands and reefs. Combining the density-based spatial clustering of applications with noise algorithm (DBSCAN) and multiscale quadtree analysis, we propose a new photon-counting lidar denoising method to discard the large amount of noise in ICESat-2 data. First, the kernel density estimation (KDE) is used to preprocess the point cloud data, and a threshold is set to remove the noise photons on the sea surface. Next, the DBSCAN algorithm is used to preliminarily remove underwater noise photons. Then, the quadtree segmentation and Otsu algorithm are used for fine denoising to extract accurate bottom signal photons. Based on ICESat-2 pho-ton-counting data from six typical islands and reefs worldwide, the proposed method outperforms other algorithms in terms of denoising effect. Compared to in situ data, the determination coefficient (R2) reaches 94.59%, and the root mean square error (RMSE) is 1.01 m. The proposed method can extract accurate underwater terrain information, laying a foundation for offshore bathymetry.
Probabilistic Analysis of Critical Speed Values of a Rotating Machine as a Function of the Change of Dynamic Parameters
Authors: Šavrnoch Zdenko , Sapieta Milan , Dekýš Vladimír , Ferfecki Petr , Zapoměl Jaroslav , Sapietová Alžbeta , Molčan Michal , Fusek Martin
Published: July 2024

Real-world rotordynamic systems exhibit inherent uncertainties in manufacturing tolerances, material properties, and operating conditions. This study presents a Monte Carlo simulation approach using MSC Adams View and Adams Insight to investigate the impact of these uncertainties on the performance of a Laval/Jeffcott rotor model. Key uncertainties in bearing damping, bearing clearance, and mass imbalance were modeled with probabilistic distributions. The Monte Carlo analysis revealed the probabilistic nature of critical speeds, vibration amplitudes, and overall system stability. The findings highlight the importance of probabilistic methods in robust rotordynamic design and provide insights for establishing manufacturing tolerances and operational limits.
Implementation of Sound Direction Detection and Mixed Source Separation in Embedded Systems
Authors: Wang Jian-Hong , Le Phuong Thi , Bee Weng-Sheng , Putri Wenny Ramadha , Su Ming-Hsiang , Li Kuo-Chen , Chen Shih-Lun , He Ji-Long , Pham Tuan , Li Yung-Hui , Wang Jia-Ching
Published: July 2024

In recent years, embedded system technologies and products for sensor networks and wearable devices used for monitoring people’s activities and health have become the focus of the global IT industry. In order to enhance the speech recognition capabilities of wearable devices, this article discusses the implementation of audio positioning and enhancement in embedded systems using embedded algorithms for direction detection and mixed source separation. The two algorithms are implemented using different embedded systems: direction detection developed using TI TMS320C6713 DSK and mixed source separation developed using Raspberry Pi 2. For mixed source separation, in the first experiment, the average signal-to-interference ratio (SIR) at 1 m and 2 m distances was 16.72 and 15.76, respectively. In the second experiment, when evaluated using speech recognition, the algorithm improved speech recognition accuracy to 95%.
Comprehensive Review: Machine and Deep Learning in Brain Stroke Diagnosis
Authors: Fernandes João N. D. , Cardoso Vitor E. M. , Comesaña-Campos Alberto , Pinheira Alberto
Published: July 2024

Brain stroke, or a cerebrovascular accident, is a devastating medical condition that disrupts the blood supply to the brain, depriving it of oxygen and nutrients. Each year, according to the World Health Organization, 15 million people worldwide experience a stroke. This results in approximately 5 million deaths and another 5 million individuals suffering permanent disabilities. The complex interplay of various risk factors highlights the urgent need for sophisticated analytical methods to more accurately predict stroke risks and manage their outcomes. Machine learning and deep learning technologies offer promising solutions by analyzing extensive datasets including patient demographics, health records, and lifestyle choices to uncover patterns and predictors not easily discernible by humans. These technologies enable advanced data processing, analysis, and fusion techniques for a comprehensive health assessment. We conducted a comprehensive review of 25 review papers published between 2020 and 2024 on machine learning and deep learning applications in brain stroke diagnosis, focusing on classification, segmentation, and object detection. Furthermore, all these reviews explore the performance evaluation and validation of advanced sensor systems in these areas, enhancing predictive health monitoring and personalized care recommendations. Moreover, we also provide a collection of the most relevant datasets used in brain stroke analysis. The selection of the papers was conducted according to PRISMA guidelines. Furthermore, this review critically examines each domain, identifies current challenges, and proposes future research directions, emphasizing the potential of AI methods in transforming health monitoring and patient care.
Comment on Martínez-Delgado et al. Using Absorption Models for Insulin and Carbohydrates and Deep Leaning to Improve Glucose Level Predictions. Sensors 2021, 21, 5273
Authors: Misplon Josiah Z. R. , Saini Varun , Sloves Brianna P. , Meerts Sarah H. , Musicant David R.
Published: July 2024

The paper “Using Absorption Models for Insulin and Carbohydrates and Deep Leaning to Improve Glucose Level Predictions” (Sensors 2021, 21, 5273) proposes a novel approach to predicting blood glucose levels for people with type 1 diabetes mellitus (T1DM). By building exponential models from raw carbohydrate and insulin data to simulate the absorption in the body, the authors reported a reduction in their model’s root-mean-square error (RMSE) from 15.5 mg/dL (raw) to 9.2 mg/dL (exponential) when predicting blood glucose levels one hour into the future. In this comment, we demonstrate that the experimental techniques used in that paper are flawed, which invalidates its results and conclusions. Specifically, after reviewing the authors’ code, we found that the model validation scheme was malformed, namely, the training and test data from the same time intervals were mixed. This means that the reported RMSE numbers in the referenced paper did not accurately measure the predictive capabilities of the approaches that were presented. We repaired the measurement technique by appropriately isolating the training and test data, and we discovered that their models actually performed dramatically worse than was reported in the paper. In fact, the models presented in the that paper do not appear to perform any better than a naive model that predicts future glucose levels to be the same as the current ones.
Dynamic Cooperative Communications with Mutual Information Accumulation for Mobile Robots in Industrial Internet of Things
Authors: Sun Daoyuan , Liu Zefan , Zhang Xinming
Published: July 2024

Mobile robots play an important role in the industrial Internet of Things (IIoT); they need effective mutual communication between the cloud and themselves when they move in a factory. By using the sensor nodes existing in the IIoT environment as relays, mobile robots and the cloud can communicate through multiple hops. However, the mobility and delay sensitivity of mobile robots bring new challenges. In this paper, we propose a dynamic cooperative transmission algorithm with mutual information accumulation to cope with these two challenges. By using rateless coding, nodes can reduce the delay caused by retransmission under poor channel conditions. With the help of mutual information accumulation, nodes can accumulate information faster and reduce delay. We propose a two-step dynamic algorithm, which can obtain the current routing path with low time complexity. The simulation results show that our algorithm is better than the existing heuristic algorithm in terms of delay.
Cure State Sensing of Polymethylmethacrylate Using a Vibrating Axial Probe
Authors: Nguyen Avonley , Nguyen Quang V. , Funk Daniel
Published: July 2024

A new axially vibrating sensor based on an audio voice coil transducer and a lead zirconate titanate (PZT) piezoelectric disc microphone was developed as a probe for the measurement of in vitro rheological fluid properties, including curing progress for polymethylmethacrylate (PMMA) mixtures with important uses as bone cement in the field of orthopedics. The measurement of the vibrating axial sensor’s acoustic spectra in PMMA undergoing curing can be described by a damped harmonic oscillator formalism and resonant frequency (ca. 180 Hz) shift can be used as an indicator of curing progress, with shifts to the blue by as much as 14 Hz. The resonant frequency peak was measured in 19 different 4.0 g PMMA samples to have a rate of shift of 0.0462 ± 0.00624 Hz·s−1 over a period of 400 s while the PMMA was in a dough state and before the PMMA transitioned to a hard-setting phase. This transition is unambiguously indicated by this sensor technology through the generation of a distinct circa 5 kHz high-Q under-damped ring-down response.
Building Vulnerability to Landslides: Broad-Scale Assessment in Xinxing County, China
Authors: Shi Fengting , Li Ling , Wu Xueling , Wang Yueyue , Niu Ruiqing
Published: July 2024

This study develops a model to assess building vulnerability across Xinxing County by integrating quantitative derivation with machine learning techniques. Building vulnerability is characterized as a function of landslide hazard risk and building resistance, wherein landslide hazard risk is derived using CNN (1D) for nine hazard-causing factors (elevation, slope, slope shape, geotechnical body type, geological structure, vegetation cover, watershed, and land-use type) and landslide sites; building resistance is determined through quantitative derivation. After evaluating the building susceptibility of all the structures, the susceptibility of each village is then calculated through subvillage statistics, which are aimed at identifying the specific needs of each area. Simultaneously, different landslide hazard classes are categorized, and an analysis of the correlation between building resistance and susceptibility reveals that building susceptibility exhibits a positive correlation with landslide hazard and a negative correlation with building resistance. Following a comprehensive assessment of building susceptibility in Xinxing County, a sample encompassing different landslide intensity areas and susceptibility classes of buildings was chosen for on-site validation, thus yielding an accuracy rate of the results as high as 94.5%.
Research on a Recognition Algorithm for Traffic Signs in Foggy Environments Based on Image Defogging and Transformer
Authors: Liu Zhaohui , Yan Jun , Zhang Jinzhao
Published: July 2024

The efficient and accurate identification of traffic signs is crucial to the safety and reliability of active driving assistance and driverless vehicles. However, the accurate detection of traffic signs under extreme cases remains challenging. Aiming at the problems of missing detection and false detection in traffic sign recognition in fog traffic scenes, this paper proposes a recognition algorithm for traffic signs based on pix2pixHD+YOLOv5-T. Firstly, the defogging model is generated by training the pix2pixHD network to meet the advanced visual task. Secondly, in order to better match the defogging algorithm with the target detection algorithm, the algorithm YOLOv5-Transformer is proposed by introducing a transformer module into the backbone of YOLOv5. Finally, the defogging algorithm pix2pixHD is combined with the improved YOLOv5 detection algorithm to complete the recognition of traffic signs in foggy environments. Comparative experiments proved that the traffic sign recognition algorithm proposed in this paper can effectively reduce the impact of a foggy environment on traffic sign recognition. Compared with the YOLOv5-T and YOLOv5 algorithms in moderate fog environments, the overall improvement of this algorithm is achieved. The precision of traffic sign recognition of the algorithm in the fog traffic scene reached 78.5%, the recall rate was 72.2%, and mAP@0.5 was 82.8%.
Enhancing Badminton Game Analysis: An Approach to Shot Refinement via a Fusion of Shuttlecock Tracking and Hit Detection from Monocular Camera
Authors: Hsu Yi-Hua , Yu Chih-Chang , Cheng Hsu-Yung
Published: July 2024

Extracting the flight trajectory of the shuttlecock in a single turn in badminton games is important for automated sports analytics. This study proposes a novel method to extract shots in badminton games from a monocular camera. First, TrackNet, a deep neural network designed for tracking small objects, is used to extract the flight trajectory of the shuttlecock. Second, the YOLOv7 model is used to identify whether the player is swinging. As both TrackNet and YOLOv7 may have detection misses and false detections, this study proposes a shot refinement algorithm to obtain the correct hitting moment. By doing so, we can extract shots in rallies and classify the type of shots. Our proposed method achieves an accuracy of 89.7%, a recall rate of 91.3%, and an F1 rate of 90.5% in 69 matches, with 1582 rallies of the Badminton World Federation (BWF) match videos. This is a significant improvement compared to the use of TrackNet alone, which yields 58.8% accuracy, 93.6% recall, and 72.3% F1 score. Furthermore, the accuracy of shot type classification at three different thresholds is 72.1%, 65.4%, and 54.1%. These results are superior to those of TrackNet, demonstrating that our method effectively recognizes different shot types. The experimental results demonstrate the feasibility and validity of the proposed method.
Delay-Oriented Roadside Unit Deployment for Highway Intersections in Vehicular Ad Hoc Networks
Authors: Luan Gan , Chen Zhenjia , Yue Chunyi , Guan Shi
Published: July 2024

Optimizing the deployment of roadside units (RSUs) holds great potential for enhancing the delay performance of vehicular ad hoc networks. However, there has been limited focus on devising RSU deployment strategies tailored specifically for highway intersections. In this study, we introduce a novel probabilistic model to characterize events occurring around highway intersections. By leveraging this model, we analytically determine the expected event reporting delays for both highway segments and intersections. Subsequently, we propose an RSU deployment scheme specifically designed for highway intersections, aimed at minimizing the expected event reporting delay. To implement this scheme, we introduce an innovative algorithm named cooperative walking. Through illustrative examples, we demonstrate that our proposed RSU deployment strategy for highway intersections outperforms the commonly employed uniform RSU deployment scheme and the previously proposed balloon method in terms of delay performance.
YOLOv8 Model for Weed Detection in Wheat Fields Based on a Visual Converter and Multi-Scale Feature Fusion
Authors: Liu Yinzeng , Zeng Fandi , Diao Hongwei , Zhu Junke , Ji Dong , Liao Xijie , Zhao Zhihuan
Published: July 2024

Accurate weed detection is essential for the precise control of weeds in wheat fields, but weeds and wheat are sheltered from each other, and there is no clear size specification, making it difficult to accurately detect weeds in wheat. To achieve the precise identification of weeds, wheat weed datasets were constructed, and a wheat field weed detection model, YOLOv8-MBM, based on improved YOLOv8s, was proposed. In this study, a lightweight visual converter (MobileViTv3) was introduced into the C2f module to enhance the detection accuracy of the model by integrating input, local (CNN), and global (ViT) features. Secondly, a bidirectional feature pyramid network (BiFPN) was introduced to enhance the performance of multi-scale feature fusion. Furthermore, to address the weak generalization and slow convergence speed of the CIoU loss function for detection tasks, the bounding box regression loss function (MPDIOU) was used instead of the CIoU loss function to improve the convergence speed of the model and further enhance the detection performance. Finally, the model performance was tested on the wheat weed datasets. The experiments show that the YOLOv8-MBM proposed in this paper is superior to Fast R-CNN, YOLOv3, YOLOv4-tiny, YOLOv5s, YOLOv7, YOLOv9, and other mainstream models in regards to detection performance. The accuracy of the improved model reaches 92.7%. Compared with the original YOLOv8s model, the precision, recall, mAP1, and mAP2 are increased by 10.6%, 8.9%, 9.7%, and 9.3%, respectively. In summary, the YOLOv8-MBM model successfully meets the requirements for accurate weed detection in wheat fields.
Experimental Study on Damage Effect of Mid-Infrared Pulsed Laser on Charge Coupled Device (CCD) and HgCgTe Detectors
Authors: Liu Yang , Zhou Feng , Wang Yunzhe , Zhang Yin , Zhang Yunfeng , Zheng Hanyu , Shao Junfeng
Published: July 2024

As the weak link in electro-optical imaging systems, photodetectors have always faced the threat of laser damage. In this paper, we experimentally investigated the damage mechanism of the photodetector induced by the out-of-band laser. The damage thresholds of the mid-infrared pulsed laser for Charge Coupled Device (CCD) and HgCdTe detectors were determined through damage experiments. The analysis of the damage phenomena and data for both CCD and HgCdTe detectors clearly demonstrated that out-of-band mid-infrared pulsed lasers could entirely incapacitate CCD and HgCdTe detectors. Our analysis of the damage process and data revealed that the primary mechanism of damage to CCD and HgCdTe detectors by mid-infrared pulsed lasers was primarily thermal. This study serves as a reference for further research on the mid-infrared pulsed laser damage mechanisms of CCD and HgCdTe detectors, as well as for laser protection and performance optimization in imaging systems.
Distributed State Observer for Systems with Multiple Sensors under Time-Delay Information Exchange
Authors: Fang Wen , Zhu Fanglai
Published: July 2024

The issues of state estimations based on distributed observers for linear time-invariant (LTI) systems with multiple sensors are discussed in this paper. We deal with the scenario when the information exchange has known time delays, and aim at designing a distributed observer for each subsystem such that each distributed observer can estimate the system state asymptotically by rejecting the time delay. To begin with, by rewriting the target system in a connecting form, a subsystem which is affected by the time-delay states of other nodes is established. And then, for this subsystem, a distributed observer with time delay is constructed. Moreover, an equivalent state transformation is made for the observer error dynamic system based on the observable canonic decomposition theorem. Further, in order to ensure that the distributed observer error dynamic system is asymptotically stable even if there exists a time delay, a linear matrix inequality (LMI) which is relative to the Laplace matrix is elaborately set up, and a special Lyapunov function candidate based on the LMI is considered. Next, based on the Lyapunov function and Lyapunov stability theory, we prove that the error dynamic system of the distributed observer is asymptotically stable, and the observer gain is determined by a feasible solution of the LMI. Finally, a simulation example is given to illustrate the effectiveness of the proposed method.
Deep Low-Carbon Economic Optimization Using CCUS and Two-Stage P2G with Multiple Hydrogen Utilizations for an Integrated Energy System with a High Penetration Level of Renewables
Authors: Fan Junqiu , Zhang Jing , Yuan Long , Yan Rujing , He Yu , Zhao Weixing , Nin Nang
Published: July 2024

Integrating carbon capture and storage (CCS) technology into an integrated energy system (IES) can reduce its carbon emissions and enhance its low-carbon performance. However, the full CCS of flue gas displays a strong coupling between lean and rich liquor as carbon dioxide liquid absorbents. Its integration into IESs with a high penetration level of renewables results in insufficient flexibility and renewable curtailment. In addition, integrating split-flow CCS of flue gas facilitates a short capture time, giving priority to renewable energy. To address these limitations, this paper develops a carbon capture, utilization, and storage (CCUS) method, into which storage tanks for lean and rich liquor and a two-stage power-to-gas (P2G) system with multiple utilizations of hydrogen including a fuel cell and a hydrogen-blended CHP unit are introduced. The CCUS is integrated into an IES to build an electricity–heat–hydrogen–gas IES. Accordingly, a deep low-carbon economic optimization strategy for this IES, which considers stepwise carbon trading, coal consumption, renewable curtailment penalties, and gas purchasing costs, is proposed. The effects of CCUS, the two-stage P2G system, and stepwise carbon trading on the performance of this IES are analyzed through a case-comparative analysis. The results show that the proposed method allows for a significant reduction in both carbon emissions and total operational costs. It outperforms the IES without CCUS with an 8.8% cost reduction and a 70.11% reduction in carbon emissions. Compared to the IES integrating full CCS, the proposed method yields reductions of 6.5% in costs and 24.7% in emissions. Furthermore, the addition of a two-stage P2G system with multiple utilizations of hydrogen further amplifies these benefits, cutting costs by 13.97% and emissions by 12.32%. In addition, integrating CCUS into IESs enables the full consumption of renewables and expands hydrogen utilization, and the renewable consumption proportion in IESs can reach 69.23%.
Prediction on Demand for Regional Online Car-Hailing Travel Based on Self-Attention Memory and ConvLSTM
Authors: Li Jianqi , Zeng Wenbao , Liu Weiqi , Cheng Rongjun
Published: July 2024

High precision in forecasting travel demand for online car-hailing is crucial for traffic management to schedule vehicles, hence reducing energy consumption and achieving sustainable development. Netflix demand forecasting relies on the capture of spatiotemporal correlations. To extract the spatiotemporal information more fully, this study designs and develops a novel spatiotemporal prediction model with multidimensional inputs (MSACL) by embedding a self-attention memory (SAM) module into a convolutional long short-term memory neural network (ConvLSTM). The SAM module can extract features with long-range spatiotemporal dependencies. The experimental data are derived from the Chengdu City online car-hailing trajectory data set and the external factors data set. Comparative experiments demonstrate that the proposed model has higher accuracy. The proposed model outperforms the Sa-ConvLSTM model and has the highest prediction accuracy, shows a reduction in the mean absolute error (MAE) by 1.72, a reduction in the mean squared error (MSE) by 0.43, and an increase in the R-squared (R2) by 4%. In addition, ablation experiments illustrate the effectiveness of each component, where the external factor inputs have the least impact on the model accuracy, but the removal of the SAM module results in the most significant decrease in model accuracy.
Assessing the Role of Air Nanobubble-Saturated Water in Enhancing Soil Moisture, Nutrient Retention, and Plant Growth
Authors: Arablousabet Yeganeh , Povilaitis Arvydas
Published: July 2024

Nanobubble-saturated water (NBSW) has received significant attention in water management in recent years. Therefore, three parallel experiments (E1, E2, and E3) were conducted on two silty loam soils (one with 12.11% higher clay) and sandy loam soil, with additional biochar amendments in each soil type, to assess air NBSW’s impact on soil moisture, nutrient retention, and plant growth. The results revealed increased soil moisture retention in the sandy loam and silty loam soils with a lower clay content. It reduced the K+ input compared to conventional watering without highly affecting the amount of leached-out substances. Biochar amendment significantly reduced the TDS losses from silty loam with a higher clay content and reduced the leaching of NO3−, Ca2+, and K+ from sandy loam soil. Air NBSW enhanced the stomatal conductance in California pepper plants in silty loam and sandy loam soils but had no effect on silty loam with a higher clay content. A decrease in chlorophyll concentrations and stomatal conductance was observed when air NBSW was combined with biochar in sandy loam soil. The study highlighted that air NBSW alone does not significantly affect water and nutrient retention or key plant parameters. However, its combination with biochar can enhance agricultural water management and sustainability by increasing soil moisture retention and reducing nutrient leaching.
Geological Insights from Porosity Analysis for Sustainable Development of Santos Basin’s Presalt Carbonate Reservoir
Authors: Vásconez Garcia Richard Guillermo , Mohammadizadeh SeyedMehdi , Avansi Michelle Chaves Kuroda , Basilici Giorgio , Bomfim Leticia da Silva , Cunha Oton Rubio , Soares Marcus Vinícius Theodoro , Mesquita Áquila Ferreira , Mahjour Seyed Kourosh , Vidal Alexandre Campane
Published: July 2024

Carbonate reservoirs, influenced by depositional and diagenetic processes and characterized by features like faults and vugs that impact storage capacity, require more than traditional Borehole Imaging logs (BHIs) for accurate porosity data. These data are essential for geological assessments, production forecasting, and reservoir simulations. This work aims to address this limitation by developing methods to measure and monitor the sustainability of carbonate reservoirs and exploring the application of sustainability principles to their management. The study integrates BHIs and conventional logs from two wells to classify porosity-based facies within the Barra Velha Formation (BVF) in the Santos Basin. The methodology involves four steps: (i) analyzing conventional logs; (ii) segmenting BHI logs; (iii) integrating conventional and segmented BHI logs using Self-Organizing Maps (SOM); and (iv) interpreting the resulting classes. Matrix porosity values and non-matrix pore sizes categorize the porosity into four facies: (A to D). The results of this research indicate the following: Facies A has high non-matrix porosity with 14,560 small megapores, 5419 large megapores, and 271 gigapores (71.9%, 26.76%, and 1.34% of the 20,250 pores, respectively). Facies B shows moderate non-matrix porosity with 8,669 small megapores, 2642 large megapores, and 33 gigapores (76.42%, 23.29%, and 0.29% of the 11,344 pores, respectively) and medium matrix porosity. Facies C exhibits low non-matrix porosity with 7749 small megapores, 2132 large megapores, and 20 gigapores (78.27%, 21.53%, and 0.20% of the 9901 pores, respectively) and medium matrix porosity. Facies D has low non-matrix porosity with 9355 small megapores, 2346 large megapores, and 19 gigapores (79.82%, 20.02%, and 0.16% of the 11,720 pores, respectively) and low matrix porosity. The results of this research reveal the effectiveness of a semiautomatic methodology that combines BHI and conventional well logs to distinguish between matrix and non-matrix-related pore spaces, thus enabling a preliminary classification of reservoir facies based on porosity. This study advances our understanding of carbonate reservoir sustainability and heterogeneity, thus offering valuable insights for robust, sustainable reservoir characterization and management in the context of global environmental and geological changes. The novelty of this work lies in integrating data from two sources to classify porosity across the presalt reservoir interval, thus serving as a proxy for preliminary lithofacies identification without core data.
Barriers of and Possibilities for Recycling of Single-Use Take-Away Food and Beverage Packaging: Evidence from Lithuanian Market
Authors: Daugėlaitė Valdonė , Kruopienė Jolita
Published: July 2024

The use of single-use take-away packaging for food and beverages is steadily growing globally. The consumption habits of ordering food online and the very short time of use of single-use take-away packaging suggest that high amounts of potential resources are continuously being wasted. Since there is a lack of studies that could represent the status of packaging materials used within the take-away delivery industry, it is difficult to predict the potential for material recycling and reuse. This research aimed at identifying (1) the predominant packaging materials used by the take-away food and beverage industry according to the food category, (2) packaging weight in order to understand the potential for material recovery, (3) labelling in order to understand communication with consumers about what is relevant for choosing end-of-life scenarios, and (4) the residues of the food and beverage within packaging after use, which potentially affects the recyclability of packaging. The research identified at which stages of the value chain there was a potential for the better circulation of single-use packaging materials and provided insights for decision makers (businesses, institutions, etc.) to improve the sustainability of take-away packaging.