bluuebunny/crossref_metadata_2025 huggingface dataset:
Influence of Post-Processing on the Properties of Multi-Material Parts Obtained by Material Projection AM
Authors: Zapico Pablo , Rodríguez-González Pablo , Robles-Valero Pablo , Fernández-Abia Ana Isabel , Barreiro Joaquín
Published: April 2023

The great geometric complexity that additive manufacturing allows in parts, together with the possibility of combining several materials in the same part, establishes a new design and manufacturing paradigm. Despite the interest of many leading sectors, the lack of standardization still makes it necessary to carry out characterization work to enjoy these advantages in functional parts. In many of these techniques, the process does not end with the end of the machine cycle, but different post-processing must be carried out to consider the part finished. It has been found that the type of post process applied can have a similar effect on part quality as other further studied process parameters. In this work, the material projection technique was used to manufacture multi-material parts combining resins with different mechanical properties. The influence of different post-processing on the tensile behavior of these parts was analyzed. The results show the detrimental effect of ultrasonic treatment with isopropyl alcohol in the case of the more flexible resin mixtures, being advisable to use ultrasonic with mineral oil or furnace treatment. For more rigid mixtures, the furnace is the best option, although the other post-processing techniques do not significantly deteriorate their performance.
One-Step Bulk-Suspension Polymerization of Polyethylene Glycol-Based Copolymer Microspheres for Phase Change Textiles
Authors: Zhou Guohang , Zeng Jiexiang , Tang Song , Bai Zijian , Jiang Jianyu , Zhang Hong , Wang Yan
Published: April 2023

The paper presents a feasible strategy through one-step bulk-suspension polymerization, grafting PEG onto an in situ synthesized copolymer. In more detail, PEG was grafted onto a homemade polystyrene/maleic anhydride copolymer (SMA) via bulk-suspension polymerization with poly(vinyl alcohol) as a suspending agent. According to the optimal reaction conditions, the grafting rate of PEG was 56.2% through chemical titration experiments. At the same time, the quantitative relationship between the grafting rate and enthalpy was demonstrated for the first time in a PEG-based solid–solid phase change material (S-SPCM). Morphology observation revealed that the obtained S-SPCM is made up of white microspheres of approximately 100–150 μm. The powdery product polystyrene/maleic anhydride grafted polyethylene glycol (SMA-g-PEG) obtained through bulk-suspension polymerization endowed that the whole product could be used directly as a phase change material without postprocessing. The melting enthalpy and crystallization enthalpy of SMA-g-PEG were 79.3 J/g and 76.9 J/g, respectively. Based on the effective fixed load of PEG, the macrostructure of SMA-g-PEG was almost unchanged at 70 °C compared with the macrostructures at 20 °C, and the latent heat of SMA-g-PEG was decreased slightly after 1000 thermal cycles. Overall, the obtained SMA-g-PEG can be used as a filler in insulation materials and composited with fibers to obtain phase change thermoregulated smart textiles.
Impact of ZnS/Mn on the Structure, Optical, and Electric Properties of PVC Polymer
Authors: El-Naggar A. M. , Heiba Zein K. , Kamal A. M. , Abd-Elkader Omar H. , Mohamed Mohamed Bakr
Published: April 2023

The most efficient way to create novel materials that may be used in a variety of optoelectronic applications is thought to be doped mixed polymers with appropriate fillers. Undoped and doped PVC polymers with ZnS/Mn formed at different temperatures were fabricated using the casting method. The Rietveld method was used to discover the structure and microstructure of Zn0.95Mn0.05S prepared at T = 300, 400, and 500 °C. The distribution and existence of the nanofiller over the PVC matrix were determined via XRD, FTIR, EDS, and SEM techniques. The effect of the preparation temperatures of the ZnS/Mn nanofiller on the absorption, transmittance, reflectance, refractive index, extinction coefficient, dielectric constant, AC conductivity, electrical modulus, and DC conductivity activation energy data of the PVC polymer was studied using the diffused reflectance technique. Doping PVC with ZnS/Mn (prepared at 300 °C) lowered the direct and indirect optical band gaps from 5.4 and 4.52 eV to minimum values of 4.55 and 3.63 eV. The fluorescence intensity of pure PVC is greatly enhanced upon loading with ZnS/Mn. The PVC exhibited two near UV peaks, one violet and one blue color, while, in addition, the doped polymers exhibited green and orange colors. The corresponding CIE diagram for all the samples was also determined.
Polymers and the Environment: Some Current Feature Trends
Authors: García-Martínez Jesús-María , Collar Emilia P.
Published: April 2023

In the early 1980s, the first global environmental crisis occurred with an emphasis on the role of plastics in big cities’ massive solid waste streams [...]
The Stability and Properties of Polystyrene/Kaolinite Nanocomposites during Synthesis via Emulsion Polymerization
Authors: de Macêdo Neto José Costa , de Freitas Bruno Mello , de Miranda Adalberto Gomes , de Almeida Rodrigues Reinaldo , Del Pino Gilberto Garcia , Kieling Antônio Claudio , dos Santos Marcos Dantas , Duvoisin Junior Sergio , Sanches Antônio Estanislau , Gondres Torné Israel , Silva Cláudia Cândida , da Costa João Carlos Martins , Bello Roger Hoel
Published: April 2023

The aim of this work was to study the stability and morphological properties of polystyrene latex containing kaolinite as a filler during the process of synthesis of nanocomposites viaemulsion polymerization. Nanocomposites with 1, 3, and 5 wt% of kaolinite were prepared. Latexes with 1 to 3 wt% of kaolinite were stable during the polymerization reaction. Hydrodynamic diameters of 93.68 and 82.11 nm were found for latexes with 1 and 3 wt% of kaolinite, respectively. The quantities of 1 to 3 wt% of kaolinite added during the reaction did not influence the reaction conversion curves or the number of particles. X-ray diffraction (XRD) and unconventional techniques of scanning electron microscopy (SEM) and high-resolution transmission electron microscopy (HRTEM) showed the presence of exfoliated and intercalated structures of the kaolinite.
Effect of Anionic Polyacrylamide Polymer on Frost Heave Mitigation and Its Implication for Frost-Susceptible Soil
Authors: Ji Yukun , Wang Haihang , Li Xiaozhao , Zhao Peng , Wang Qinke , Li Ruilin , Vandeginste Veerle
Published: April 2023

Seasonally frozen ground regions occupy approximately 55% of the exposed land surface in the Northern Hemisphere, and frost heave is the common global problem in seasonally frozen soil areas. Frost heave induces uneven deformation of ground and damages railways, road paving, and buildings. How to mitigate frost heave is the most important technical issue in this field that has provoked great interest. Here, using freezing experiments, we investigate the effect of anionic polyacrylamide (APAM) polymer on frost susceptible soil. The results demonstrate a so-far undocumented inhibition of frost heave by APAM in freezing soil, namely APAM (tested at concentrations from 0.0 wt% to 0.60 wt%) slows down the frost heave by a factor of up to 2.1 (since 0.60 wt% APAM can decrease frost heave from 8.56 mm to 4.14 mm in comparison to the control experiment). Moreover, it can be observed that the maximum water content near the frozen fringe decreased from 53.4% to 31.4% as the APAM content increased from 0.0 wt% to 0.60 wt%, implying a mitigated ice lens growth. Hydrogen bonding between APAM and soil particles triggers an adsorption mechanism that accumulates soil particles, and thus can potentially inhibit the separation and growth of the ice lens. Moreover, the residue of APAM due to hydrogen bonding-induced adsorption in the pores of granular media may narrow seepage channels (capillary barriers) and provide an unfavourable condition for water migration. The use of APAM can also increase the viscosity of the solution, which causes a greater water migration resistance. This research provides new insights into APAM-influenced frost heave (introducing APAM into the soil can induce bridging adsorption between APAM polymer segments and a particle surface), can enable engineers and researchers to utilise chemical improvement design and to consider suitable actions (e.g., by injecting APAM solution into a frost susceptible soil or using APAM-modified soil to replace the frost susceptible soil) to prevent frost heave from having a negative impact on traffic roads and buildings in cold regions.
Application of Atmospheric-Pressure Plasma Treatment in Anti-Hairfalling of Polyester–Cotton Fleece Knitted Fabrics
Authors: Chen Zhipeng , Zhao Zhong , Wu Jihong
Published: April 2023

In this study, atmospheric-pressure plasma (APP) was used to modify the surface of polyester–cotton fleece knitted fabrics to improve their anti-hairfalling properties. A series of treated samples were obtained by changing the power of plasma and treatment time. Scanning electron microscopy (SEM) and the surface roughness results reveal that the APP treatment can increase the roughness of fibers. The withdraw force and hairiness length of fibers results indicate that increasing withdraw force and decreasing hairiness length of fabrics can reduce hairfalling of the fibers. The values of weight loss rate confirm APP-treated powers and times can influence anti-hairfalling properties of fabrics. In addition, the best APP-treated time and electric power for the anti-hairfalling properties of the treated fabrics are respectively 15 s and 1.0 kW. Under this condition, the anti-hairfalling properties of the treated fabrics are improved by 48.3%, the contact angle decreased by 39.7%, and the wicking height increased by 18.3% compared with the untreated fabrics. It is notable that APP treatment does not affect the handle and tensile properties of fabrics.
Reducing Interface Defects and Porosity of Adhesive Bonded Aluminum Alloy Joints via Ultrasonic Vibration
Authors: Wang Hui , Kang Guodong , Chen Yizhe , Liu Zhaoyi , Hua Lin
Published: April 2023

The surface microstructure formed by physical or chemical modification is essential for the desired joint strength. However, defects in the bonding interface and adhesive can be found. Such defects decrease shear strength and durability. In this study, ultrasonic vibration was applied to liquid adhesive on the sandblasted aluminum alloy plates. With ultrasonic treatment, the joints obtained the compact bonding interfaces and lower porosity of the adhesive layer. The treatment improved the shear strength by 9.1%. After two weeks of hydrothermal aging, the shear strength of joints only sandblasted decreased drastically by 48.9%, while it was 14% for the joints with ultrasonic vibration. The cavitation effect in the adhesive was detected by the aluminum foil erosion method. The result showed that a great number of micro-jets generated by the cavitation effect have intensive impact on the bonding interface which provide the adhesive with powerful force to fill the micro-grooves. Another finding in this work is that bubbles were gathered in the adhesive away from the vibration area. This mechanism was successfully used to reduce the porosity of the adhesive layer of joints.
Electrospinning Novel Sodium Alginate/MXene Nanofiber Membranes for Effective Adsorption of Methylene Blue
Authors: Li Meng , Zhang Pingxiu , Wang Qianfang , Yu Ningya , Zhang Xiaomin , Su Shengpei
Published: April 2023

Understanding how to develop highly efficient and robust adsorbents for the removal of organic dyes in wastewater is crucial in the face of the rapid development of industrialization. Herein, d-Ti3C2Tx nanosheets (MXene) were combined with sodium alginate (SA), followed by electrospinning and successive Ca2+-mediated crosslinking, giving rise to a series of SA/MXene nanofiber membranes (NMs). The effects of the MXene content of the NMs on the adsorption performance for methylene blue (MB) were investigated systemically. Under the optimum MXene content of 0.74 wt.%, SA/MXene NMs possessed an MB adsorption capacity of 440 mg/g, which is much higher than SA/MXene beads with the same MXene content, pristine MXene, or electrospinning SA NMs. Furthermore, the optimum SA/MXene NMs showed excellent reusability. After the adsorbent was reused ten times, both the MB adsorption capacity and removal rate could remain at 95% of the levels found in the fresh samples, which indicates that the electrospinning technique has great potential for developing biomass-based adsorbents with high efficiency.
Preparation of Surgical Thread from a Bioplastic Based on Nopal Mucilage
Authors: Herrera-Ibarra Evelyn , Salazar-Hernández Mercedes , Talavera-López Alfonso , Solis-Marcial O. J. , Hernandez-Soto Rosa , Ruelas-Leyva Jose P. , Hernández José A.
Published: April 2023

Currently, natural materials represent a sustainable option for the manufacture of biopolymers with numerous industrial applications and characteristics comparable with synthetic materials. Nopal mucilage (NM) is an excellent natural resource for the synthesis of bioplastics (BPs). In the present research, the fabrication of biopolymers by using NM is addressed. Changes in the plasticizer (sorbitol and cellulose) concentration, in addition to the implementation of two sources of starch (corn starch (CS) and potato starch (PS)) to obtain the surgical thread, were analyzed. The NM extracted was close to 14% with ethanol. During the characterization of the extract, properties such as moisture, humidity, viscosity, and functional groups, among others, were determined. In the CS and PS analysis, different structures of the polymeric chains were observed. BP degradation with different solvents was performed. Additionally, the addition of sorbitol and cellulose for the BP mixtures presenting the highest resistance to solvent degradation and less solubility to water was conducted. The obtained thread had a uniform diameter, good elasticity, and low capillarity compared to other prototypes reported in the literature.
Study on the Flow, Foaming Characteristics and Structural Strength of Polypropylene Structural Foam Injection Molding by Innovative Nitrogen and Molten Plastic Mixing Mechanism
Authors: Huang Po-Wei , Peng Hsin-Shu , Hwang Sheng-Jye , Huang Chao-Tsai
Published: April 2023

Plastic foam molding methods include thermoforming, extrusion and injection molding. Injection foam molding is a one-time molding method with high production efficiency and good product quality. It is suitable for foamed plastic products with complex shapes and strict size requirements. It is also the main method for producing structural bubbles. In this investigation, we developed a structural foam injection molding technology using the gas supply equipment connected to the unique plasticizing mechanism of the injection machine and studied its influence on the specimens’ melt rheology quality and foam structures. In the experiment, the forming material was polypropylene (PP), and the gas for mixing/forming foaming characteristics was nitrogen (N2). Additionally, in order to observe the rheological properties of N2/melt mixing, a melt flow specimen mold cavity was designed and the change in the melt viscosity index was observed using a melt pressure sensing element installed at the nozzle position. With the nitrogen supply equipment connected to a unique plasticizing mechanism, the mixing of gas and molten plastic can be achieved at the screw plasticizing stage, where the foaming effect is realized during the melt-filling process due to the thermodynamic instability of the gas. It was also found that an increase in N2 fill content increased melt fluidity, and the trend of melt pressure and melt viscosity index showed that the higher the gas content, the lower the trend. The foaming characteristic depends on the gas thermodynamic instability and the pressure release, so it can be seen from the melt fill path that, the greater the pressure near the gate, the lower the foaming amount and the internal structure (SEM) after molding; the farther from the gate, the greater the relative increase in the foaming growth/amount. This phenomenon will be more obvious when the N2 fill content is increased.
Surface Functionalization of 4D Printed Substrates Using Polymeric and Metallic Wrinkles
Authors: Agyapong Johnson N. , Van Durme Bo , Van Vlierberghe Sandra , Henderson James H.
Published: April 2023

Wrinkle topographies have been studied as simple, versatile, and in some cases biomimetic surface functionalization strategies. To fabricate surface wrinkles, one material phenomenon employed is the mechanical-instability-driven wrinkling of thin films, which occurs when a deforming substrate produces sufficient compressive strain to buckle a surface thin film. Although thin-film wrinkling has been studied on shape-changing functional materials, including shape-memory polymers (SMPs), work to date has been primarily limited to simple geometries, such as flat, uniaxially-contracting substrates. Thus, there is a need for a strategy that would allow deformation of complex substrates or 3D parts to generate wrinkles on surfaces throughout that complex substrate or part. Here, 4D printing of SMPs is combined with polymeric and metallic thin films to develop and study an approach for fiber-level topographic functionalization suitable for use in printing of arbitrarily complex shape-changing substrates or parts. The effect of nozzle temperature, substrate architecture, and film thickness on wrinkles has been characterized, as well as wrinkle topography on nuclear alignment using scanning electron microscopy, atomic force microscopy, and fluorescent imaging. As nozzle temperature increased, wrinkle wavelength increased while strain trapping and nuclear alignment decreased. Moreover, with increasing film thickness, the wavelength increased as well.
Implementation of Ground-Based Lightning Locating System Using Particle Swarm Optimization Algorithm for Lightning Mapping and Monitoring
Authors: Mehranzamir Kamyar , Pour Amin Beiranvand , Abdul-Malek Zulkurnain , Afrouzi Hadi Nabipour , Alizadeh Seyed Morteza , Hashim Mazlan
Published: April 2023

Cloud-to-ground (CG) lightning is a natural phenomenon that poses significant threats to human safety, infrastructure, and equipment. The destructive impacts of lightning strikes on humans and their property have been a longstanding concern for both society and industry. Countries with high thunderstorm frequencies, such as Malaysia, experience significant fatalities and damage due to lightning strikes. To this end, a lightning locating system (LLS) was developed and deployed in a 400 km2 study area at the University Technology Malaysia (UTM), Johor, Malaysia for detecting cloud-to-ground lightning discharges. The study utilized a particle swarm optimization (PSO) algorithm as a mediator to identify the best location for a lightning strike. The algorithm was initiated with 30 particles, considering the outcomes of the MDF and TDOA techniques. The effectiveness of the PSO algorithm was found to be dependent on how the search process was arranged. The results of the detected lightning strikes by the PSO-based LLS were compared with an industrial lightning detection system installed in Malaysia. From the experimental data, the mean distance differences between the PSO-based LLS and the industrial LLS inside the study area was up to 573 m. Therefore, the proposed PSO-based LLS would be efficient and accurate to detect and map the lightning discharges occurring within the coverage area. This study is significant for researchers, insurance companies, and the public seeking to be informed about the impacts of lightning discharges.
Different Characteristics and Drivers of the Extraordinary Pakistan Rainfall in July and August 2022
Authors: Ma Yuanyuan , Hu Xiaoxue , Chen Yiting , Hu Zhiyuan , Feng Taichen , Feng Guolin
Published: April 2023

The unprecedented and long-lasting abnormal monsoon rainfall attacked Pakistan in the summer of 2022, causing severe flooding. This study investigated the sub-seasonal characteristics and mechanisms of this distinctively extreme precipitation event. The historical rainfall in July and August and extreme precipitation mainly occurred in northern Pakistan. Both the monthly rainfall in July and August 2022 and the extreme precipitation during the summer were far exceeding the historical record and involved unique spatial distribution. The rainfall in July 2022 is nationwide and mainly located in northern Pakistan, while the rainfall in August and extreme precipitation occurred in southern Pakistan. Different physical processes are responsible for the precipitation in July and August 2022. In July, the South Asian high (SAH) and Iranian high extended eastward. Meanwhile, the anticyclonic circulation anomalies occurred in northwestern Pakistan and the easterly winds enhanced in the south side of the Tibetan Plateau (TP), which strengthened water vapor transporting from the Bay of Bengal and cooperated with the cyclonic system over the Arabian Sea to enhance the precipitation over Pakistan. In August, the SAH further extended eastward and the Western Pacific Subtropical High extended westward to the TP. Meanwhile, the European blocking (EB) developed, and a deep trough appeared over northwestern Pakistan. This weakened the easterly flow along southern TP but enhanced the southerly flow accompanying the cyclone over the Bay of Bengal and the Arabian Sea, and thus guided the water vapor transporting to southern Pakistan and enhanced the precipitation. The extreme precipitation in July was mainly attributed to the unusually strong Indian monsoon, while the extreme precipitation in August was the result of a combination of the Indian monsoon and EB. The study provided important information about extreme precipitation in Pakistan, which will help policymakers take measures to deal with the effects of flooding.
D-Net: A Density-Based Convolutional Neural Network for Mobile LiDAR Point Clouds Classification in Urban Areas
Authors: Zaboli Mahdiye , Rastiveis Heidar , Hosseiny Benyamin , Shokri Danesh , Sarasua Wayne A. , Homayouni Saeid
Published: April 2023

The 3D semantic segmentation of a LiDAR point cloud is essential for various complex infrastructure analyses such as roadway monitoring, digital twin, or even smart city development. Different geometric and radiometric descriptors or diverse combinations of point descriptors can extract objects from LiDAR data through classification. However, the irregular structure of the point cloud is a typical descriptor learning problem—how to consider each point and its surroundings in an appropriate structure for descriptor extraction? In recent years, convolutional neural networks (CNNs) have received much attention for automatic segmentation and classification. Previous studies demonstrated deep learning models’ high potential and robust performance for classifying complicated point clouds and permutation invariance. Nevertheless, such algorithms still extract descriptors from independent points without investigating the deep descriptor relationship between the center point and its neighbors. This paper proposes a robust and efficient CNN-based framework named D-Net for automatically classifying a mobile laser scanning (MLS) point cloud in urban areas. Initially, the point cloud is converted into a regular voxelized structure during a preprocessing step. This helps to overcome the challenge of irregularity and inhomogeneity. A density value is assigned to each voxel that describes the point distribution within the voxel’s location. Then, by training the designed CNN classifier, each point will receive the label of its corresponding voxel. The performance of the proposed D-Net method was tested using a point cloud dataset in an urban area. Our results demonstrated a relatively high level of performance with an overall accuracy (OA) of about 98% and precision, recall, and F1 scores of over 92%.
SaltISNet3D: Interactive Salt Segmentation from 3D Seismic Images Using Deep Learning
Authors: Zhang Hao , Zhu Peimin , Liao Zhiying
Published: April 2023

Salt interpretation using seismic data is essential for structural interpretation and oil and gas exploration. Although deep learning has made great progress in automatic salt image segmentation, it is often difficult to obtain satisfactory results in complex situations. Thus, interactive segmentation with human intervention can effectively replace the fully automatic method. However, the current interactive segmentation cannot be directly applied to 3D seismic data and requires a lot of human interaction. Because it is difficult to collect 3D seismic data containing salt, we propose a workflow to simulate salt data and use a large amount of 3D synthetic salt data for training and testing. We use a 3D U-net model with skip connections to improve the accuracy and efficiency of salt interpretation. This model takes 3D seismic data volume with a specific size as an input and generates a salt probability volume of the same size as an output. To obtain more detailed salt results, we utilize a 3D graph-cut to ameliorate the results predicted by the 3D U-net model. The experimental results indicate that our method can achieve more efficient and accurate segmentation of 3D salt bodies than fully automatic methods.
Comparing Machine Learning Algorithms for Soil Salinity Mapping Using Topographic Factors and Sentinel-1/2 Data: A Case Study in the Yellow River Delta of China
Authors: Li Jie , Zhang Tingting , Shao Yun , Ju Zhengshan
Published: April 2023

Soil salinization is a critical and global environmental problem. Effectively mapping and monitoring the spatial distribution of soil salinity is essential. The main aim of this work was to map soil salinity in Shandong Province located on the Yellow River Delta of China using Sentinel-1/2 remote sensing data and digital elevation model (DEM) data, coupled with soil sampling data, and combined with four regression models: support vector regression (SVR), stepwise multi-regression (SMR), partial least squares regression (PLSR) and random forest regression (RFR). For these purposes, 60 soil samples were collected during the field survey conducted from 9 to 14 October 2019, corresponding to the Sentinel-1/2 and DEM data. Then we established a soil salinity and feature dataset based on the sampled data and the features extracted from Sentinel-1/2 and DEM data. This study adopted the feature importance of the RF model to screen all features. The results showed that the CRSI index made the greatest contribution in retrieving soil salinity in this region. In this paper, 18 sampling points were used to validate and compare the performance of the four models. The results reveal that, compared with the other regression models, the PLSR model has the best performance (R2 = 0.66, and RMSE = 1.30). Finally, the PLSR method was used to predict the spatial distribution of soil salinity in the Yellow River Delta. We concluded that the model can be used effectively for the quantitative estimation of soil salinity and provides a useful tool for ecological construction.
Meteorological Satellite Observations Reveal Diurnal Exceedance of Water Quality Guideline Thresholds in the Coastal Great Barrier Reef
Authors: Patricio-Valerio Larissa , Schroeder Thomas , Devlin Michelle J. , Qin Yi , Smithers Scott
Published: April 2023

The Great Barrier Reef (GBR) is a marine protected area subject to natural and anthropogenic disturbances. Water quality is critical for the health and protecting resilience of GBR coral ecosystems against the synergistic and cumulative pressures of tropical cyclones, marine heat waves, and outbreaks of crown-of-thorns starfish. The concentration of Total Suspended Solids (TSS) is a key water quality parameter measured at multiple spatio-temporal scales from in situ probes to satellite observations. High TSS concentrations can adversely impact coral and seagrasses on the inshore GBR. We present diurnal TSS derived from Himawari-8 Geostationary satellite observations at 10 min frequency and demonstrate its applicability for improved monitoring of GBR water quality. Diurnal TSS obtained from Himawari-8 observations were compared to TSS computed from in situ bio-optical measurements at the Lucinda Jetty Coastal Observatory (LJCO). The coastal waters at LJCO experience diurnal variability of TSS (~7 mg L−1), where magnitude peaks followed the slack tides, and the largest diurnal changes were associated with freshwater discharge residuals from the wet season. Exceedance maps revealed that TSS is above guideline thresholds in the open coastal and mid-shelf waters for ~60% of the valid monthly observations, including during dry season months.
SQNet: Simple and Fast Model for Ocean Front Identification
Authors: Niu Rui , Tan Yiyang , Ye Feng , Gong Fang , Huang Haiqing , Zhu Qiankun , Hao Zengzhou
Published: April 2023

The ocean front has a non-negligible role in global ocean–atmosphere interactions, marine fishery production, and the marine military. Hence, obtaining the positions of the ocean front is crucial in oceanic research. At present, the positioning method of recognizing an ocean front has achieved a breakthrough in the mean dice similarity coefficient (mDSC) of above 90%, but it is difficult to use to achieve rapid extraction in emergency scenarios, including marine fisheries and search and rescue. To reduce the its dependence on machines and apply it to more requirements, according to the characteristics of an ocean front, a multi-scale model SQNet (Simple and Quick Net) dedicated to ocean front position recognition is designed, and its perception domain is expanded while obtaining current scale data. In experiments along the coast of China and the waters of the Gulf of Mexico, it was not difficult to find that SQNet exceedingly reduced running time while ensuring high-precision results (mDSC of higher than 90%). Then, after conducting intra-model self-comparison, it was determined that expanding the perceptual domain and changing the weight ratio of the loss function could improve the accuracy and operational efficiency of the model, which could be better applied in ocean front recognition.
Fault-Diagnosis and Fault-Recovery System of Hall Sensors in Brushless DC Motor Based on Neural Networks
Authors: Chu Kenny Sau Kang , Chew Kuew Wai , Chang Yoong Choon
Published: April 2023

This paper proposes a neural-network-based framework using Convolutional Neural Network and Long-Short Term Memory (CNN-LSTM) for detecting faults and recovering signals from Hall sensors in brushless DC motors. Hall sensors are critical components in determining the position and speed of motors, and faults in these sensors can disrupt their normal operation. Traditional fault-diagnosis methods, such as state-sensitive and transition-sensitive approaches, and fault-recovery methods, such as vector tracking observer, have been widely used in the industry but can be inflexible when applied to different models. The proposed fault diagnosis using the CNN-LSTM model was trained on the signal sequences of Hall sensors and can effectively distinguish between normal and faulty signals, achieving an accuracy of the fault-diagnosis system of around 99.3% for identifying the type of fault. Additionally, the proposed fault recovery using the CNN-LSTM model was trained on the signal sequences of Hall sensors and the output of the fault-detection system, achieving an efficiency of determining the position of the phase in the sequence of the Hall sensor signal at around 97%. This work has three main contributions: (1) a CNN-LSTM neural network structure is proposed to be implemented in both the fault-diagnosis and fault-recovery systems for efficient learning and feature extraction from the Hall sensor data. (2) The proposed fault-diagnosis system is equipped with a sensitive and accurate fault-diagnosis system that can achieve an accuracy exceeding 98%. (3) The proposed fault-recovery system is capable of recovering the position in the sequence states of the Hall sensors, achieving an accuracy of 95% or higher.
Fast Helmet and License Plate Detection Based on Lightweight YOLOv5
Authors: Wei Chenyang , Tan Zhao , Qing Qixiang , Zeng Rong , Wen Guilin
Published: April 2023

The integrated fast detection technology for electric bikes, riders, helmets, and license plates is of great significance for maintaining traffic safety. YOLOv5 is one of the most advanced single-stage object detection algorithms. However, it is difficult to deploy on embedded systems, such as unmanned aerial vehicles (UAV), with limited memory and computing resources because of high computational load and high memory requirements. In this paper, a lightweight YOLOv5 model (SG-YOLOv5) is proposed for the fast detection of the helmet and license plate of electric bikes, by introducing two mechanisms to improve the original YOLOv5. Firstly, the YOLOv5s backbone network and the Neck part are lightened by combining the two lightweight networks, ShuffleNetv2 and GhostNet, included. Secondly, by adopting an Add-based feature fusion method, the number of parameters and the floating-point operations (FLOPs) are effectively reduced. On this basis, a scene-based non-truth suppression method is proposed to eliminate the interference of pedestrian heads and license plates on parked vehicles, and then the license plates of the riders without helmets can be located through the inclusion relation of the target boxes and can be extracted. To verify the performance of the SG-YOLOv5, the experiments are conducted on a homemade RHNP dataset, which contains four categories: rider, helmet, no-helmet, and license plate. The results show that, the SG-YOLOv5 has the same mean average precision (mAP0.5) as the original; the number of model parameters, the FLOPs, and the model file size are reduced by 90.8%, 80.5%, and 88.8%, respectively. Additionally, the number of frames per second (FPS) is 2.7 times higher than that of the original. Therefore, the proposed SG-YOLOv5 can effectively achieve the purpose of lightweight and improve the detection speed while maintaining great detection accuracy.
Analysis of Co-Channel Coexistence Mitigation Methods Applied to IEEE 802.11p and 5G NR-V2X Sidelink
Authors: Wu Zhuofei , Bartoletti Stefania , Martinez Vincent , Todisco Vittorio , Bazzi Alessandro
Published: April 2023

Direct communication between vehicles and surrounding objects, called vehicle-to-everything (V2X), is ready for the market and promises to raise the level of safety and comfort while driving. To this aim, specific bands have been reserved in some countries worldwide and different wireless technologies have been developed; however, these are not interoperable. Recently, the issue of co-channel coexistence has been raised, leading the European Telecommunications Standards Institute (ETSI) to propose a number of solutions, called mitigation methods, for the coexistence of the IEEE 802.11p based ITS-G5 and the 3GPP fourth generation (4G) long term evolution (LTE)-V2X sidelink. In this work, several of the envisioned alternatives are investigated when adapted to the coexistence of the IEEE 802.11p with its enhancement IEEE 802.11bd and the latest 3GPP standards, i.e., the fifth generation (5G) new radio (NR)-V2X. The results, obtained through an open-source simulator that is shared with the research community for the evaluation of additional proposals, show that the methods called A and C, which require modifications to the standards, improve the transmission range of one or both systems without affecting the other, at least in low-density scenarios.
Synchronous Phase-Shifting Interference for High Precision Phase Imaging of Objects Using Common Optics
Authors: Zhao Jiaxi , Liu Lin , Wang Tianhe , Wang Xiangzhou , Du Xiaohui , Hao Ruqian , Liu Juanxiu , Zhang Jing
Published: April 2023

Quantitative phase imaging and measurement of surface topography and fluid dynamics for objects, especially for moving objects, is critical in various fields. Although effective, existing synchronous phase-shifting methods may introduce additional phase changes in the light field due to differences in optical paths or need specific optics to implement synchronous phase-shifting, such as the beamsplitter with additional anti-reflective coating and a micro-polarizer array. Therefore, we propose a synchronous phase-shifting method based on the Mach–Zehnder interferometer to tackle these issues in existing methods. The proposed method uses common optics to simultaneously acquire four phase-shifted digital holograms with equal optical paths for object and reference waves. Therefore, it can be used to reconstruct the phase distribution of static and dynamic objects with high precision and high resolution. In the experiment, the theoretical resolution of the proposed system was 1.064 µm while the actual resolution could achieve 1.381 µm, which was confirmed by measuring a phase-only resolution chart. Besides, the dynamic phase imaging of a moving standard object was completed to verify the proposed system’s effectiveness. The experimental results show that our proposed method is suitable and promising in dynamic phase imaging and measurement of moving objects using phase-shifting digital holography.
Simplified Markerless Stride Detection Pipeline (sMaSDP) for Surface EMG Segmentation
Authors: Castro Aguiar Rafael , Sam Jeeva Raj Edward Jero , Chakrabarty Samit
Published: April 2023

To diagnose mobility impairments and select appropriate physiotherapy, gait assessment studies are often recommended. These studies are usually conducted in confined clinical settings, which may feel foreign to a subject and affect their motivation, coordination, and overall mobility. Conducting gait studies in unconstrained natural settings instead, such as the subject’s Activities of Daily Life (ADL), could provide a more accurate assessment. To appropriately diagnose gait deficiencies, muscle activity should be recorded in parallel with typical kinematic studies. To achieve this, Electromyography (EMG) and kinematic are collected synchronously. Our protocol sMaSDP introduces a simplified markerless gait event detection pipeline for the segmentation of EMG signals via Inertial Measurement Unit (IMU) data, based on a publicly available dataset. This methodology intends to provide a simple, detailed sequence of processing steps for gait event detection via IMU and EMG, and serves as tutorial for beginners in unconstrained gait assessment studies. In an unconstrained gait experiment, 10 healthy subjects walk through a course designed to mimic everyday walking, with their kinematic and EMG data recorded, for a total of 20 trials. Five different walking modalities, such as level walking, ramp up/down, and staircase up/down are included. By segmenting and filtering the data, we generate an algorithm that detects heel-strike events, using a single IMU, and isolates EMG activity of gait cycles. Applicable to different datasets, sMaSDP was tested in healthy gait and gait data of Parkinson’s Disease (PD) patients. Using sMaSDP, we extracted muscle activity in healthy walking and identified heel-strike events in PD patient data. The algorithm parameters, such as expected velocity and cadence, are adjustable and can further improve the detection accuracy, and our emphasis on the wearable technologies makes this solution ideal for ADL gait studies.
Comparative Study on Distributed Lightweight Deep Learning Models for Road Pothole Detection
Authors: Tahir Hassam , Jung Eun-Sung
Published: April 2023

This paper delves into image detection based on distributed deep-learning techniques for intelligent traffic systems or self-driving cars. The accuracy and precision of neural networks deployed on edge devices (e.g., CCTV (closed-circuit television) for road surveillance) with small datasets may be compromised, leading to the misjudgment of targets. To address this challenge, TensorFlow and PyTorch were used to initialize various distributed model parallel and data parallel techniques. Despite the success of these techniques, communication constraints were observed along with certain speed issues. As a result, a hybrid pipeline was proposed, combining both dataset and model distribution through an all-reduced algorithm and NVlinks to prevent miscommunication among gradients. The proposed approach was tested on both an edge cluster and Google cluster environment, demonstrating superior performance compared to other test settings, with the quality of the bounding box detection system meeting expectations with increased reliability. Performance metrics, including total training time, images/second, cross-entropy loss, and total loss against the number of the epoch, were evaluated, revealing a robust competition between TensorFlow and PyTorch. The PyTorch environment’s hybrid pipeline outperformed other test settings.
A Low-Cost Hardware/Software Platform for Lossless Real-Time Data Acquisition from Imaging Spectrometers
Authors: Fernández-Conde Jesús
Published: April 2023

In real-time data-intensive applications, achieving real-time data acquisition from sensors and simultaneous storage with the necessary performance is challenging, especially if “no-data-lost” requirements are present. Ad hoc solutions are generally expensive and suffer from a lack of modularity and scalability. In this work, we present a hardware/software platform built using commercial off-the-shelf elements, designed to acquire and store digitized signals captured from imaging spectrometers capable of supporting real-time data acquisition with stringent throughput requirements (sustained rates in the boundaries of 100 MBytes/s) and simultaneous information storage in a lossless fashion. The correct combination of commercial hardware components with a properly configured and optimized multithreaded software application has satisfied the requirements in determinism and capacity for processing and storing large amounts of information in real time, keeping the economic cost of the system low. This real-time data acquisition and storage system has been tested in different conditions and scenarios, being able to successfully capture 100,000 1 Mpx-sized images generated at a nominal speed of 23.5 MHz (input throughput of 94 Mbytes/s, 4 bytes acquired per pixel) and store the corresponding data (300 GBytes of data, 3 bytes stored per pixel) concurrently without any single byte of information lost or altered. The results indicate that, in terms of throughput and storage capacity, the proposed system delivers similar performance to data acquisition systems based on specialized hardware, but at a lower cost, and provides more flexibility and adaptation to changing requirements.
Flamingo-Optimization-Based Deep Convolutional Neural Network for IoT-Based Arrhythmia Classification
Authors: Kumar Ashwani , Kumar Mohit , Mahapatra Rajendra Prasad , Bhattacharya Pronaya , Le Thi-Thu-Huong , Verma Sahil , Kavita  , Mohiuddin Khalid
Published: April 2023

Cardiac arrhythmia is a deadly disease that threatens the lives of millions of people, which shows the need for earlier detection and classification. An abnormal signal in the heart causing arrhythmia can be detected at an earlier stage when the health data from the patient are monitored using IoT technology. Arrhythmias may suddenly lead to death and the classification of arrhythmias is considered a complicated process. In this research, an effective classification model for the classification of heart disease is developed using flamingo optimization. Initially, the ECG signal from the heart is collected and then it is subjected to the preprocessing stage; to detect and control the electrical activity of the heart, the electrocardiogram (ECG) is used. The input signals collected using IoT nodes are collectively presented in the base station for the classification using flamingo-optimization-based deep convolutional networks, which effectively predict the disease. With the aid of communication technologies and the contribution of IoT, medical professionals can easily monitor the health condition of patients. The performance is analyzed in terms of accuracy, sensitivity, and specificity.
On-Orbit Modulation Transfer Function Estimation Based on the Refined Image Kernel
Authors: Wang Yuanhang , Zhong Xing , Qu Zheng , Li Lei , Wu Sipeng , Zeng Chaoli
Published: April 2023

To overcome the limitations of traditional on-orbit modulation function transfer (MTF) measurement methods that are heavily dependent on natural features, scenery, artificial edges, and point source targets, this paper presents an on-orbit MTF measurement method of remote sensing imager based on the refined image kernel (RIK) acquired directly from remote sensing images. First, the kernel is estimated from some remote sensing sub-images with rich texture details by using an iterative support detection (ISD) algorithm; then, it is refined by central pixel energy concentration (EC) to obtain the RIK. Secondly, the MTF curves are calculated by interpolating RIK and Fourier transform. Finally, the final MTF is the average value of MTFs at Nyquist frequency acquired by each RIK. To demonstrate the feasibility and validity of this method, the MTFs were compared to the result of the ISO12233 edge method with an error of no more than 7%. The relative error of the measured results does not exceed 5% for image signal-to-noise ratio (SNR) above 20dB. The results obtained from the on-orbit MTF measurement using remote sensing images of the Jilin-1 satellite have a maximum error of less than 2% compared with the ISO12233 edge method. These demonstrate that the method proposed in this paper supplies highly accurate and robust results and can successfully increase the efficiency of on-orbit MTF measurement, providing a reference for high-frequency monitoring of satellite on-orbit stability and their optical imaging quality.
A Novel Ranging and IMU-Based Method for Relative Positioning of Two-MAV Formation in GNSS-Denied Environments
Authors: Cheng Jia , Ren Peng , Deng Tingxiang
Published: April 2023

Global Navigation Satellite Systems (GNSS) with weak anti-jamming capability are vulnerable to intentional or unintentional interference, resulting in difficulty providing continuous, reliable, and accurate positioning information in complex environments. Especially in GNSS-denied environments, relying solely on the onboard Inertial Measurement Unit (IMU) of the Micro Aerial Vehicles (MAVs) for positioning is not practical. In this paper, we propose a novel cooperative relative positioning method for MAVs in GNSS-denied scenarios. Specifically, the system model framework is first constructed, and then the Extended Kalman Filter (EKF) algorithm, which is introduced for its ability to handle nonlinear systems, is employed to fuse inter-vehicle ranging and onboard IMU information, achieving joint position estimation of the MAVs. The proposed method mainly addresses the problem of error accumulation in the IMU and exhibits high accuracy and robustness. Additionally, the method is capable of achieving relative positioning without requiring an accurate reference anchor. The system observability conditions are theoretically derived, which means the system positioning accuracy can be guaranteed when the system satisfies the observability conditions. The results further demonstrate the validity of the system observability conditions and investigate the impact of varying ranging errors on the positioning accuracy and stability. The proposed method achieves a positioning accuracy of approximately 0.55 m, which is about 3.89 times higher than that of an existing positioning method.