bluuebunny/crossref_metadata_2025 huggingface dataset:
Effect of Polydispersity on the Structural and Magnetic Properties of a Magnetopolymer Composite
Authors: Radushnov Dmitry I. , Solovyova Anna Yu. , Elfimova Ekaterina A.
Published: June 2023

When using magnetopolymer composites in high-precision industrial and biomedical technologies, the problem of predicting their properties in an external magnetic field arises. In this work, we study theoretically the influence of the polydispersity of a magnetic filler on a composite’s equilibrium magnetization and on the orientational texturing of magnetic particles formed during polymerization. The results are obtained using rigorous methods of statistical mechanics and Monte Carlo computer simulations in the framework the bidisperse approximation. It is shown that by adjusting the dispersione composition of the magnetic filler and the intensity of the magnetic field at which the sample’s polymerization occurs, it is possible to control the composite’s structure and magnetization. The derived analytical expressions determine these regularities. The developed theory takes into account dipole–dipole interparticle interactions and therefore can be applied to predict the properties of concentrated composites. The obtained results are a theoretical basis for the synthesis of magnetopolymer composites with a predetermined structure and magnetic properties.
Oxidized Natural Biopolymer for Enhanced Surface, Physical and Mechanical Properties of Glass Ionomer Luting Cement
Authors: Khan Aftab Ahmed , Bari Ahmed , Abdullah Al-Kheraif Abdulaziz , Alsunbul Hanan , Alhaidry Hind , Alharthi Rasha , Aldegheishem Alhanoof
Published: June 2023

This laboratory investigation aimed to synthesize and characterize micron-sized Gum Arabic (GA) powder and incorporate it in commercially available GIC luting formulation for enhanced physical and mechanical properties of GIC composite. Oxidation of GA was performed and GA-reinforced GIC in 0.5, 1.0, 2.0, 4.0 & 8.0 wt.% formulations were prepared in disc-shaped using two commercially available GIC luting materials (Medicem and Ketac Cem Radiopaque). While the control groups of both materials were prepared as such. The effect of reinforcement was evaluated in terms of nano hardness, elastic modulus, diametral tensile strength (DTS), compressive strength (CS), water solubility and sorption. Two-way ANOVA and post hoc tests were used to analyze data for statistical significance (p < 0.05). FTIR spectrum confirmed the formation of acid groups in the backbone of polysaccharide chain of GA while XRD peaks confirmed that crystallinity of oxidized GA. The experimental group with 0.5 wt.% GA in GIC enhanced the nano hardness while 0.5 wt.% and 1.0 wt.% GA in GIC increased the elastic modulus compared to the control. The CS of 0.5 wt.% GA in GIC and DTS of 0.5 wt.% and 1.0 wt.% GA in GIC demonstrated elevation. In contrast, the water solubility and sorption of all the experimental groups increased compared to the control groups. The incorporation of lower weight ratios of oxidized GA powder in GIC formulation helps in enhancing the mechanical properties with a slight increase in water solubility and sorption parameters. The addition of micron-sized oxidized GA in GIC formulation is promising and needs further research for improved performance of GIC luting composition.
Unusual Aspects of Charge Regulation in Flexible Weak Polyelectrolytes
Authors: Blanco Pablo M. , Narambuena Claudio F. , Madurga Sergio , Mas Francesc , Garcés Josep L.
Published: June 2023

This article reviews the state of the art of the studies on charge regulation (CR) effects in flexible weak polyelectrolytes (FWPE). The characteristic of FWPE is the strong coupling of ionization and conformational degrees of freedom. After introducing the necessary fundamental concepts, some unconventional aspects of the the physical chemistry of FWPE are discussed. These aspects are: (i) the extension of statistical mechanics techniques to include ionization equilibria and, in particular, the use of the recently proposed Site Binding-Rotational Isomeric State (SBRIS) model, which allows the calculation of ionization and conformational properties on the same foot; (ii) the recent progresses in the inclusion of proton equilibria in computer simulations; (iii) the possibility of mechanically induced CR in the stretching of FWPE; (iv) the non-trivial adsorption of FWPE on ionized surfaces with the same charge sign as the PE (the so-called “wrong side” of the isoelectric point); (v) the influence of macromolecular crowding on CR.
pH-Sensitive Hybrid System Based on Eu3+/Gd3+ Co-Doped Hydroxyapatite and Mesoporous Silica Designed for Theranostic Applications
Authors: dos Apostolos Rafaela Caroline Rodrigues , Andrada Andreza de Sousa , Oliveira André Felipe , Neto Ernesto Soares Freitas , de Sousa Edésia Martins Barros
Published: June 2023

Nanomaterials such as pH-responsive polymers are promising for targeted drug delivery systems, due to the difference in pH between tumor and healthy regions. However, there is a significant concern about the application of these materials in this field due to their low mechanical resistance, which can be attenuated by combining these polymers with mechanically resistant inorganic materials such as mesoporous silica nanoparticles (MSN) and hydroxyapatite (HA). Mesoporous silica has interesting properties such as high surface area and hydroxyapatite has been widely studied to aid in bone regeneration, providing special properties adding multifunctionality to the system. Furthermore, fields of medicine involving luminescent elements such as rare earth elements are an interesting option in cancer treatment. The present work aims to obtain a pH-sensitive hybrid system based on silica and hydroxyapatite with photoluminescent and magnetic properties. The nanocomposites were characterized by X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), nitrogen adsorption methods, CHN elemental analysis, Zeta Potential, scanning electron microscopy (SEM), and transmission electron microscopy (TEM), vibrational sample magnetometry (VSM), and photoluminescence analysis. Incorporation and release studies of the antitumor drug doxorubicin were performed to evaluate the potential use of these systems in targeted drug delivery. The results showed the luminescent and magnetic properties of the materials and showed suitable characteristics for application in the release of pH-sensitive drugs.
Material Extrusion of Multi-Polymer Structures Utilizing Design and Shrinkage Behaviors: A Design of Experiment Study
Authors: Al-Tamimi Abdulsalam Abdulaziz , Tlija Mehdi , Abidi Mustufa Haider , Anis Arfat , Abd Elgawad Abd Elaty E.
Published: June 2023

Material extrusion (ME) is an additive manufacturing technique capable of producing functional parts, and its use in multi-material fabrication requires further exploration and expansion. The effectiveness of material bonding is one of the main challenges in multi-material fabrication using ME due to its processing capabilities. Various procedures for improving the adherence of multi-material ME parts have been explored, such as the use of adhesives or the post-processing of parts. In this study, different processing conditions and designs were investigated with the aim of optimizing polylactic acid (PLA) and acrylonitrile–butadiene–styrene (ABS) composite parts without the need for pre- or post-processing procedures. The PLA-ABS composite parts were characterized based on their mechanical properties (bonding modulus, compression modulus, and strength), surface roughness (Ra, Rku, Rsk, and Rz), and normalized shrinkage. All process parameters were statistically significant except for the layer composition parameter in terms of Rsk. The results show that it is possible to create a composite structure with good mechanical properties and acceptable surface roughness values without the need for costly post-processing procedures. Furthermore, the normalized shrinkage and the bonding modulus were correlated, indicating the ability to utilize shrinkage in 3D printing to improve material bonding.
Agro-Industrial Plant Proteins in Electrospun Materials for Biomedical Application
Authors: Zdraveva Emilija , Gaurina Srček Višnja , Kraljić Klara , Škevin Dubravka , Slivac Igor , Obranović Marko
Published: June 2023

Plant proteins are receiving a lot of attention due to their abundance in nature, customizable properties, biodegradability, biocompatibility, and bioactivity. As a result of global sustainability concerns, the availability of novel plant protein sources is rapidly growing, while the extensively studied ones are derived from byproducts of major agro-industrial crops. Owing to their beneficial properties, a significant effort is being made to investigate plant proteins’ application in biomedicine, such as making fibrous materials for wound healing, controlled drug release, and tissue regeneration. Electrospinning technology is a versatile platform for creating nanofibrous materials fabricated from biopolymers that can be modified and functionalized for various purposes. This review focuses on recent advancements and promising directions for further research of an electrospun plant protein-based system. The article highlights examples of zein, soy, and wheat proteins to illustrate their electrospinning feasibility and biomedical potential. Similar assessments with proteins from less-represented plant sources, such as canola, pea, taro, and amaranth, are also described.
From Bottle Caps to Frisbee—A Case Study on Mechanical Recycling of Plastic Waste towards a Circular Economy
Authors: Akhras Mohamad Hassan , Freudenthaler Paul J. , Straka Klaus , Fischer Joerg
Published: June 2023

This study demonstrates an open-loop recycling process of a specific post-consumer plastic waste stream. The targeted input waste material was defined as high-density polyethylene beverage bottle caps. Two methods of waste collection, informal and formal, were employed. Thereafter, materials were hand-sorted, shredded, regranulated, and then injection-molded into a flying disc (i.e., frisbee) as a pilot product. To observe the potential changes in the material throughout the entire recycling process, eight different test methods including melt mass-flow rate (MFR), differential scanning calorimetry (DSC), and mechanical tests were carried out on the various material states. The study showed that the informal collection led to a relatively higher purity in the input stream, which also appeared to have a 23% lower MFR value compared to that of the formally collected materials. The DSC measurements revealed a cross-contamination by polypropylene, which clearly affected the properties of all investigated materials. The cross-contamination led to a slightly higher tensile modulus in the recyclate, while the Charpy notched impact strength declined after processing by approximately 15% and 8% compared to those of the informal and formal input materials, respectively. All materials and the processing data were documented and stored online as a practical implementation of a digital product passport as a potential digital traceability tool. Furthermore, the suitability of the resulting recyclate to be used in transport packaging applications was also investigated. It was found that a direct replacement of virgin materials for this specific application is not possible without proper material modification.
Effect of High-Entropy Spinel Ferrite (Mn0.2Zr0.2Cu0.2Ca0.2Ni0.2)Fe2O4 Doping Concentration on the Ferroelectric Properties of PVDF-Based Polymers
Authors: Qiao Jiale , Liu Zhaoting , Mu Haiwei , Liu Chao
Published: June 2023

Polyvinylidene fluoride (PVDF)-based dielectric energy storage materials have the advantages of environmental friendliness, high power density, high operating voltage, flexibility, and being light weight, and have enormous research value in the energy, aerospace, environmental protection, and medical fields. To investigate the magnetic field and the effect of high-entropy spinel ferrite (Mn0.2Zr0.2Cu0.2Ca0.2Ni0.2)Fe2O4 nanofibers (NFs) on the structural, dielectric, and energy storage properties of PVDF-based polymers, (Mn0.2Zr0.2Cu0.2Ca0.2Ni0.2)Fe2O4 NFs were prepared via the use of electrostatic spinning methods, and (Mn0.2Zr0.2Cu0.2Ca0.2Ni0.2)Fe2O4/PVDF composite films were prepared via the use of the coating method. The effects of a 0.8 T parallel magnetic field, induced for 3 min, and the content of high-entropy spinel ferrite on the relevant electrical properties of the composite films are discussed. The experimental results show that, structurally, the magnetic field treatment causes the originally agglomerated nanofibers in the PVDF polymer matrix to form a linear fiber chain with different fiber chains parallel to each other along the magnetic field direction. Electrically, the introduction of the magnetic field enhanced the interfacial polarization, and the (Mn0.2Zr0.2Cu0.2Ca0.2Ni0.2)Fe2O4/PVDF composite film with a doping concentration of 10 vol% had a maximum dielectric constant of 13.9, as well as a low energy loss of 0.068. The high-entropy spinel ferrite (Mn0.2Zr0.2Cu0.2Ca0.2Ni0.2)Fe2O4 NFs and the magnetic field influenced the phase composition of the PVDF-based polymer. The α-phase and γ-phase of the cohybrid-phase B1 vol% composite films had a maximum discharge energy density of 4.85 J/cm3 and a charge/discharge efficiency of 43%.
Sustainability Assessment of the End-of-Life Technologies for Biocomposite Waste in the Aviation Industry
Authors: Ferjan Špela , Jovičić Milkica , Lardiés Miazza Nora , Ligthart Tom , Harvey Clare , Fita Sergio , Mehta Rajesh , Samani Pouya
Published: June 2023

Biocomposites have emerged as promising alternative materials for the aviation industry. However, there is a limited body of scientific literature addressing the end-of-life management of biocomposites. This article evaluated different end-of-life technologies for biocomposite recycling in a structured, five-step approach applying the innovation funnel principle. First, ten end-of-life (EoL) technologies were compared in terms of their circularity potential and technology readiness levels (TRL). Second, a multi-criteria decision analysis (MCDA) was carried out to find out the top four most promising technologies. Afterwards, experimental tests were conducted at a laboratory scale to evaluate the top three technologies for recycling biocomposites by analysing (1) three types of fibres (basalt, flax, carbon) and (2) two types of resins (bioepoxy and Polyfurfuryl Alcohol (PFA) resins). Subsequently, further experimental tests were performed to identify the top two recycling technologies for the EoL treatment of biocomposite waste from the aviation industry. Finally, the sustainability and economic performance of the top two identified EoL recycling technologies were evaluated through life cycle assessment (LCA) and techno-economic analysis (TEA). The experimental results, performed via the LCA and TEA assessments, demonstrated that both solvolysis and pyrolysis are technically, economically, and environmentally viable options for the EoL treatment of biocomposite waste from the aviation industry.
Steel Slag Decorated with Calcium Oxide and Cerium Oxide as a Solid Base for Effective Transesterification of Palm Oil
Authors: Sun Jichao , Yu Hewei , Zhang Peisen , Qi Gaoyu , Chen Xiuxiu , Liang Xiaohui , Si Hongyu
Published: June 2023

For further resource utilization of solid waste steel slag and the reduction in biodiesel production costs, this study used steel slag as a carrier to synthesize a CaO-CeO2/slag solid base catalyst for the effective transesterification of palm oil into fatty acid methyl esters (FAMEs). The synthesis involved a two-step impregnation of steel slag with nitrate of calcium and cerium and thermal activation at 800 °C for 180 min. Then, the catalysts’ textural, chemical, and CO2 temperature-programmed desorption properties were characterized. The catalytic activity depended highly on the ratio of Ca-Ce to steel slag mass; the CaO-CeO2/slag-0.8 catalyst showed outstanding performance. Characterization showed that the surface area and total basicity of the Ca-Ce/slag-0.8 catalyst were 3.66 m2/g and 1.289 mmol/g, respectively. The reactivity results showed that FAMEs obtained using 7 wt.% catalyst, 9:1 of methanol-to-palm-oil molar ratio, 180 min reaction duration, and 70 °C reaction temperature was optimum (i.e., 95.3% yield). In addition, the CaO-CeO2/slag-0.8 catalyst could be reused for at least three cycles, retaining 91.2% of FAMEs yield after n-hexane washing. Hence, the catalyst exhibits an excellent potential for cost-effective and environmentally friendly biodiesel production.
Fermentation of Menaquinone-7: The Influence of Environmental Factors and Storage Conditions on the Isomer Profile
Authors: Lal Neha , Seifan Mostafa , Berenjian Aydin
Published: June 2023

Menaquinone-7 (MK-7) provides significant health gains due to its excellent pharmacokinetic properties. However, MK-7 occurs at low concentrations in mainstream foods, heightening the demand for nutritional supplements. MK-7 exists as geometric isomers, and only all-trans MK-7 is bioactive. Exposure to certain environments impacts the isomer profile. Knowledge of these factors and their influence on the isomer composition is important, as the efficacy of fermented MK-7 end products is solely determined by the all-trans isomer. This investigation aimed to evaluate the short- and long-term effect of atmospheric oxygen, common temperatures, and light on the isomer profile. From the short-term study, it was ascertained that MK-7 is moderately heat-stable but extremely light-sensitive. The stability of all-trans MK-7 was then examined during 8 weeks of storage at a low temperature with minimal oxygen exposure in the absence of light. Negligible change in the all-trans MK-7 concentration occurred, suggesting it is reasonably stable during prolonged storage in this environment. These findings will aid the development of optimal storage conditions to preserve bioactive MK-7 in fermented nutritional supplements, the large-scale availability and consumption of which will help compensate for the dietary deficit of this essential vitamin and provide consumers with better health outcomes.
Performance Improvement of Multi-Robot Data Transmission in Aggregated Robot Processing Architecture with Caches and QoS Balancing Optimization
Authors: Jalil Abdul , Kobayashi Jun , Saitoh Takeshi
Published: June 2023

Robot Operating System 2 (ROS 2) is a robotic software that uses a set of Quality of Service (QoS) policies to manage the quality of robot data transmissions in a network, such as the RELIABLE and KEEP_LAST options. In ROS 2 node communication, the RELIABLE connection guarantees that all message data can be properly sent from the publisher to the subscriber. However, strict reliability is not guaranteed if the RELIABLE connection uses the KEEP_LAST option to transmit the robot data in the publish–subscribe communication. This study aims to analyze the efficiency of local cache, cache control, and QoS balancing optimization to improve ROS 2 node communication when using the RELIABLE and KEEP_LAST options to transmit multi-robot data in Aggregated Robot Processing (ARP) architecture. Our idea in local cache and cache control is to streamline the sensor data output before processing it when the sensor device produces the data with the same value in a row. Furthermore, QoS balancing optimization aims to balance the DEPTH and DEADLINE QoS configuration to determine the rates and buffer size in ROS 2 node communication. This study shows that combining local cache and QoS balancing optimization improves multi-robot data transmission and cooperation in ARP architecture.
Exploring Spatiotemporal Variations in Land Surface Temperature Based on Local Climate Zones in Shanghai from 2008 to 2020
Authors: Hou Xinyan , Xie Xuan , Bagan Hasi , Chen Chaomin , Wang Qinxue , Yoshida Takahiro
Published: June 2023

Understanding changes in urban internal structure and land surface temperature (LST) is essential. The local climate zone (LCZ) scheme has been extensively applied to characterize urban spatial structure, which has potential for urban climate research. We combined optical imagery and synthetic aperture radar (SAR) data (Landsat-5 and PALSAR for 2008; Sentinel-2 and PALSAR-2 for 2020) to map the LCZs in Shanghai, China. The results showed that the areas of open high-rise and open mid-rise buildings significantly increased from 2008 to 2020. Then, we investigated the spatiotemporal variations in LST based on the LCZ data from 2008 to 2020 using the grid method. The mean daytime LST (obtained from Landsat-5 and Landsat-8) was higher in 2020 than in 2008 for each LCZ type in spring. The mean daytime LSTs of compact mid-rise, compact low-rise, large low-rise and heavy industry zones were higher than those of other LCZ types in spring and summer. The mean nighttime LST (obtained from ASTER) in the downtown area was higher than that in the suburbs in summer. Furthermore, the mean nighttime LST of the built types was also generally higher than that of the natural types in summer. A comparison of the mean daytime LSTs in 2008 and 2020 revealed that the expansion trend of the higher LST areas in spring and summer is consistent with the expansion areas of the mid-rise and high-rise built types.
Developing Comprehensive Local Climate Zone Land Use Datasets for Advanced High-Resolution Urban Climate and Environmental Modeling
Authors: Wang Yongwei , Zhao Danmeng , Ma Qian
Published: June 2023

The Local Climate Zone (LCZ) classification scheme is a vital method of building a category dataset for high-resolution urban land. For the development of urban meteorology, air pollution and related disciplines, the high-resolution classification data of urban buildings are very important. This study aims to create LCZ datasets with detailed architectural characteristics for major cities and urban agglomerations in China, and obtain more accurate results. We constructed 120 m resolution land use datasets for 63 cities (mainly provincial capitals, municipalities directly under the Central Government, important prefecture-level cities and special administrative regions) and 4 urban agglomerations in China based on the local climate zone (LCZ) classification scheme using the World Urban Database and Access Portal Tools method (WUDAPT). Nearly 100,000 samples were used, of which 76,000 training samples were used to provide spectral signatures and 23,000 validation samples were used to ensure accuracy assessments. Compared with similar studies, the LCZ datasets in this paper were generally of good quality, with an overall accuracy of 71–93% (mean 82%), an accuracy for built classifications of 57–83% (mean 72%), and an accuracy for natural classifications of 70–99% (mean 90%). In addition, 35% of 63 Chinese cities have construction areas of more than 5%, and the plateaus northwest of Chengdu and Chongqing are covered with snow all year round. Therefore, based on the original LCZ classification system, the construction area (LZC H) and the snow cover (LCZ I) were newly added as the basic classifications of urban LCZ classification in China. Detailed architectural features of cities and urban agglomerations in China are provided by the LCZ datasets in this study. It can be applied to fine numerical models of the meteorological and atmospheric environment and improve the prediction accuracy.
Urban Flood Dynamic Risk Assessment Based on Typhoon Rainfall Process: A Case Study of Typhoon “Lupit” (2109) in Fuzhou, China
Authors: Qin Xiaochen , Wu Yilong , Lin Tianshu , Gao Lu
Published: June 2023

Flood disasters caused by typhoon rainfall seriously threaten regional social and economic development. Accurately assessing the risk of typhoons and their secondary disasters is a great challenge in disaster prevention and reduction. To address this, the city of Fuzhou, Fujian Province, which was severely affected by Typhoon “Lupit” (2109), was selected as a case study. A typhoon rainfall flood disaster system including four components (the disaster-causing factor, disaster-pregnant environment, disaster-bearing body, and disaster prevention and reduction capacity) was constructed. A typhoon-rainfall process comprehensive intensity index (TPCI) based on different time scales within the typhoon process was developed to accurately evaluate the flood risk. The TPCI represented the disaster-causing factors of rainfall intensity, duration, and concentration features. Geographical similarity and random forest (RF) were applied to screen 23 typical indices for an urban flood disaster risk assessment model. The results indicated that the TPCI based on a 6 h precipitation simulation at a 24 h time scale was highly effective in highlighting the role of short-term precipitation in the typhoon process. A total of 66.5% of the floodplain area had a medium-grade or higher TPCI value, while 32.5% of the area had a low-grade TPCI. Only 1% of the flooded areas were not identified, which indicated that the TPCI could accurately capture the risk of typhoon rainfall. The urban flood disaster risk assessment model comprehensively considered socioeconomic and natural environment conditions. High-risk areas were identified as regions with extreme precipitation and dense populations. The dynamic evaluation results accurately described the spatiotemporal differences in the flood disaster risk. A period of extreme precipitation lagged the landfall time of Typhoon “Lupit”, causing the proportion of areas above the medium–high-risk threshold of flood disasters to rapidly increase from 8.29% before the landfall of the typhoon to 23.57% before its demise. The high-risk areas of flood disasters were mainly distributed in the towns of Shangjie, Nanyu, and Gaishan, which was consistent with the observed disasters. These study findings could contribute to the development of effective measures for disaster prevention and reduction, and improve the resilience of urban areas to typhoon disasters.
Estimation of the Two-Dimensional Direction of Arrival for Low-Elevation and Non-Low-Elevation Targets Based on Dilated Convolutional Networks
Authors: Hu Guoping , Zhao Fangzheng , Liu Bingqi
Published: June 2023

This paper addresses the problem of the two-dimensional direction-of-arrival (2D DOA) estimation of low-elevation or non-low-elevation targets using L-shaped uniform and sparse arrays by analyzing the signal models’ features and their mapping to 2D DOA. This paper proposes a 2D DOA estimation algorithm based on the dilated convolutional network model, which consists of two components: a dilated convolutional autoencoder and a dilated convolutional neural network. If there are targets at low elevation, the dilated convolutional autoencoder suppresses the multipath signal and outputs a new signal covariance matrix as the input of the dilated convolutional neural network to directly perform 2D DOA estimation in the absence of a low-elevation target. The algorithm employs 3D convolution to fully retain and extract features. The simulation experiments and the analysis of their results revealed that for both L-shaped uniform and L-shaped sparse arrays, the dilated convolutional autoencoder could effectively suppress the multipath signals without affecting the direct wave and non-low-elevation targets, whereas the dilated convolutional neural network could effectively achieve 2D DOA estimation with a matching rate and an effective ratio of pitch and azimuth angles close to 100% without the need for additional parameter matching. Under the condition of a low signal-to-noise ratio, the estimation accuracy of the proposed algorithm was significantly higher than that of the traditional DOA estimation.
MSAFNet: Multiscale Successive Attention Fusion Network for Water Body Extraction of Remote Sensing Images
Authors: Lyu Xin , Jiang Wenxuan , Li Xin , Fang Yiwei , Xu Zhennan , Wang Xinyuan
Published: June 2023

Water body extraction is a typical task in the semantic segmentation of remote sensing images (RSIs). Deep convolutional neural networks (DCNNs) outperform traditional methods in mining visual features; however, due to the inherent convolutional mechanism of the network, spatial details and abstract semantic representations at different levels are difficult to capture accurately at the same time, and then the extraction results decline to become suboptimal, especially on narrow areas and boundaries. To address the above-mentioned problem, a multiscale successive attention fusion network, named MSAFNet, is proposed to efficiently aggregate the multiscale features from two aspects. A successive attention fusion module (SAFM) is first devised to extract multiscale and fine-grained features of water bodies, while a joint attention module (JAM) is proposed to further mine salient semantic information by jointly modeling contextual dependencies. Furthermore, the multi-level features extracted by the above-mentioned modules are aggregated by a feature fusion module (FFM) so that the edges of water bodies are well mapped, directly improving the segmentation of various water bodies. Extensive experiments were conducted on the Qinghai-Tibet Plateau Lake (QTPL) and the Land-cOVEr Domain Adaptive semantic segmentation (LoveDA) datasets. Numerically, MSAFNet reached the highest accuracy on both QTPL and LoveDA datasets, including Kappa, MIoU, FWIoU, F1, and OA, outperforming several mainstream methods. Regarding the QTPL dataset, MSAFNet peaked at 99.14% and 98.97% in terms of F1 and OA. Although the LoveDA dataset is more challenging, MSAFNet retained the best performance, with F1 and OA being 97.69% and 95.87%. Additionally, visual inspections exhibited consistency with numerical evaluations.
Influence of On-Site Camera Calibration with Sub-Block of Images on the Accuracy of Spatial Data Obtained by PPK-Based UAS Photogrammetry
Authors: Pitombeira Kalima , Mitishita Edson
Published: June 2023

Unmanned Aerial Systems (UAS) Photogrammetry has become widely used for spatial data acquisition. Nowadays, RTK (Real Time Kinematic) and PPK (Post Processed Kinematic) are the main correction methods for accurate positioning used for direct measurements of camera station coordinates in UAS imagery. Thus, 3D camera coordinates are commonly used as additional observations in Bundle Block Adjustment to perform Global Navigation Satellite System-Assisted Aerial Triangulation (GNSS-AAT). This process requires accurate Interior Orientation Parameters to ensure the quality of photogrammetric intersection. Therefore, this study investigates the influence of on-site camera calibration with a sub-block of images on the accuracy of spatial data obtained by PPK-based UAS Photogrammetry. For this purpose, experiments of on-the-job camera self-calibration in the Metashape software with the SfM approach were performed. Afterward, experiments of GNSS-Assisted Aerial Triangulation with on-site calibration in the Erdas Imagine software were performed. The outcomes show that only the experiment of GNSS-AAT with three Ground Control Points yielded horizontal and vertical accuracies close to nominal precisions of the camera station positions by GNSS-PPK measurements adopted in this study, showing horizontal RMSE (Root-Mean Square Error) of 0.222 m and vertical RMSE of 0.154 m. Furthermore, the on-site camera calibration with a sub-block of images significantly improved the vertical accuracy of the spatial information extraction.
Study on the Damage Mechanism of Sandstone under Different Water Content States
Authors: Wang Hongjian , Cui Zhendong , Xu Chong , Yong Rui , Zhao Fei , Chen Shangyuan
Published: June 2023

Understanding the failure mechanisms of rocks that are exposed to different water contents is important for rock stability in rock engineering applications, and the quantitative analysis of rock behavior is necessary for predicting and preventing the occurrence of rock failure due to water effects. Mechanical tests using real-time acoustic emission (AE) technology were carried out to reveal the damage evolution in sandstone rocks in a dried state, natural state, and saturated state, which includes a quantitative analysis of AE characteristics and cracking properties. The testing results indicate that with the growth of water content, sandstone rocks show a decreasing trend in strength and tend to experience gentle damage with relatively fewer fractures. The crack morphology of the main fracture surfaces is quantitatively described, including a fractal dimension calculation and cracking length measurements. As the water content rises, when rock failure occurs, a higher AE b-value can be obtained, revealing an increasing proportion of large-scale cracks. The fractal dimension of the acoustic emission hit rate shows that the evolution of rock damage and deformation has self-similarity, that is, the transformation from order to disorder to order, and it is affected by different water contents. The AE waveforms of the sandstone have two dominant frequency bands (0~75 kHz and 75~150 kHz) no matter which water-bearing state they are in. The increase in rock water content has resulted in the decline of AE waveforms located in the range of 200–300 kHz, whereas the rise of AE waveforms is located in the range of 0–50 kHz. The findings of this study deepen our understanding of the mechanism behind rock failure and provide a meaningful reference for disaster assessment and control.
Fusion of Multi-Modal Features to Enhance Dense Video Caption
Authors: Huang Xuefei , Chan Ka-Hou , Wu Weifan , Sheng Hao , Ke Wei
Published: June 2023

Dense video caption is a task that aims to help computers analyze the content of a video by generating abstract captions for a sequence of video frames. However, most of the existing methods only use visual features in the video and ignore the audio features that are also essential for understanding the video. In this paper, we propose a fusion model that combines the Transformer framework to integrate both visual and audio features in the video for captioning. We use multi-head attention to deal with the variations in sequence lengths between the models involved in our approach. We also introduce a Common Pool to store the generated features and align them with the time steps, thus filtering the information and eliminating redundancy based on the confidence scores. Moreover, we use LSTM as a decoder to generate the description sentences, which reduces the memory size of the entire network. Experiments show that our method is competitive on the ActivityNet Captions dataset.
Development of DMPS-EMAT for Long-Distance Monitoring of Broken Rail
Authors: Guo Wujun , Yu Zhiyang , Chui Hsiang-Chen , Chen Xiaoming
Published: June 2023

The safety of railway transportation is crucial to social and economic development. Therefore, real-time monitoring of the rail is particularly necessary. The current track circuit structure is complex and costly, posing challenges to monitoring broken tracks using alternative methods. As a non-contact detection technology with a lower environmental impact, electromagnetic ultrasonic transducers (EMATs) have become a concern. However, traditional EMATs have problems such as low conversion efficiency and complex modes, which can limit their effectiveness for long-distance monitoring. Therefore, this study introduces a novel dual-magnet phase-stacked EMAT (DMPS-EMAT) design comprising two magnets and a dual-layer winding coil arrangement. The magnets are positioned at a distance equal to the wavelength of the A0 wave from each other, while the center distance between the two sets of coils beneath the transducer is also equal to the wavelength. After analyzing the dispersion curves of the rail waist, it was determined that the optimal frequency for long-distance rail monitoring is 35 kHz. At this frequency, adjusting the relative positions of the two magnets and the coil directly underneath to be one A0 wavelength can effectively excite a constructive interference A0 wave in the rail waist. The simulation and experimental results show that DMPS-EMAT excited a single-mode A0 wave, resulting in a 1.35-times increase in amplitude.
Conductor Losses in Radiofrequency Coils for Magnetic Resonance below 3T: Estimation Methods and Minimization Strategies
Authors: Giovannetti Giulio , Flori Alessandra , Frijia Francesca
Published: June 2023

The design of optimized radiofrequency (RF) coils is a fundamental task for maximizing the signal-to-noise ratio (SNR) in Magnetic Resonance Imaging (MRI) and Magnetic Resonance Spectroscopy (MRS) applications. An efficient coil should be designed by minimizing the coil noise with respect to the sample noise, since coil conductor resistance affects data quality by reducing the SNR, especially for coils tuned to a low frequency. Such conductor losses strongly depend on the frequency (due to the skin effect) and on the conductor cross-sectional shape (strip or wire). This paper reviews the different methods for estimating conductor losses in RF coils for MRI/MRS applications, comprising analytical formulations, theoretical/experimental hybrid approaches and full-wave simulations. Moreover, the different strategies for minimizing such losses, including the use of Litz wire, cooled and superconducting coils, are described. Finally, recent emerging technologies in RF coil design are briefly reviewed.
Assessment of a Person’s Emotional State Based on His or Her Posture Parameters
Authors: Shichkina Yulia , Bureneva Olga , Salaurov Evgenii , Syrtsova Ekaterina
Published: June 2023

This article is devoted to the study of the correlation between the emotional state of a person and the posture of his or her body in the sitting position. In order to carry out the study, we developed the first version of the hardware-software system based on a posturometric armchair, allowing the characteristics of the posture of a sitting person to be evaluated using strain gauges. Using this system, we revealed the correlation between sensor readings and human emotional states. We showed that certain readings of a sensor group are formed for a certain emotional state of a person. We also found that the groups of triggered sensors, their composition, their number, and their location are related to the states of a particular person, which led to the need to build personalized digital pose models for each person. The intellectual component of our hardware–software complex is based on the concept of co-evolutionary hybrid intelligence. The system can be used during medical diagnostic procedures and rehabilitation processes, as well as in controlling people whose professional activity is connected with increased psycho-emotional load and can cause cognitive disorders, fatigue, and professional burnout and can lead to the development of diseases.
Long-Acting Real-Time Microscopic Monitoring Inside the Proton Exchange Membrane Water Electrolyzer
Authors: Lee Chi-Yuan , Chen Chia-Hung , Chuang Hsian-Chun , Hsieh Hsiao-Te , Chiu Yen-Chen
Published: June 2023

The proton exchange membrane water electrolyzer (PEMWE) requires a high operating voltage for hydrogen production to accelerate the decomposition of hydrogen molecules so that the PEMWE ages or fails. According to the prior findings of this R&D team, temperature and voltage can influence the performance or aging of PEMWE. As the PEMWE ages inside, the nonuniform flow distribution results in large temperature differences, current density drops, and runner plate corrosion. The mechanical stress and thermal stress resulting from pressure distribution nonuniformity will induce the local aging or failure of PEMWE. The authors of this study used gold etchant for etching, and acetone was used for the lift-off part. The wet etching method has the risk of over-etching, and the cost of the etching solution is also higher than that of acetone. Therefore, the authors of this experiment adopted a lift-off process. Using the flexible seven-in-one (voltage, current, temperature, humidity, flow, pressure, oxygen) microsensor developed by our team, after optimized design, fabrication, and reliability testing, it was embedded in PEMWE for 200 h. The results of our accelerated aging test prove that these physical factors affect the aging of PEMWE.
Single Modality vs. Multimodality: What Works Best for Lung Cancer Screening?
Authors: Sousa Joana Vale , Matos Pedro , Silva Francisco , Freitas Pedro , Oliveira Hélder P. , Pereira Tania
Published: June 2023

In a clinical context, physicians usually take into account information from more than one data modality when making decisions regarding cancer diagnosis and treatment planning. Artificial intelligence-based methods should mimic the clinical method and take into consideration different sources of data that allow a more comprehensive analysis of the patient and, as a consequence, a more accurate diagnosis. Lung cancer evaluation, in particular, can benefit from this approach since this pathology presents high mortality rates due to its late diagnosis. However, many related works make use of a single data source, namely imaging data. Therefore, this work aims to study the prediction of lung cancer when using more than one data modality. The National Lung Screening Trial dataset that contains data from different sources, specifically, computed tomography (CT) scans and clinical data, was used for the study, the development and comparison of single-modality and multimodality models, that may explore the predictive capability of these two types of data to their full potential. A ResNet18 network was trained to classify 3D CT nodule regions of interest (ROI), whereas a random forest algorithm was used to classify the clinical data, with the former achieving an area under the ROC curve (AUC) of 0.7897 and the latter 0.5241. Regarding the multimodality approaches, three strategies, based on intermediate and late fusion, were implemented to combine the information from the 3D CT nodule ROIs and the clinical data. From those, the best model—a fully connected layer that receives as input a combination of clinical data and deep imaging features, given by a ResNet18 inference model—presented an AUC of 0.8021. Lung cancer is a complex disease, characterized by a multitude of biological and physiological phenomena and influenced by multiple factors. It is thus imperative that the models are capable of responding to that need. The results obtained showed that the combination of different types may have the potential to produce more comprehensive analyses of the disease by the models.
Principles and Applications of Seismic Monitoring Based on Submarine Optical Cable
Authors: Yu Junzhe , Xu Pengbai , Yu Zhangjun , Wen Kunhua , Yang Jun , Wang Yuncai , Qin Yuwen
Published: June 2023

Submarine optical cables, utilized as fiber-optic sensors for seismic monitoring, are gaining increasing interest because of their advantages of extending the detection coverage, improving the detection quality, and enhancing long-term stability. The fiber-optic seismic monitoring sensors are mainly composed of the optical interferometer, fiber Bragg grating, optical polarimeter, and distributed acoustic sensing, respectively. This paper reviews the principles of the four optical seismic sensors, as well as their applications of submarine seismology over submarine optical cables. The advantages and disadvantages are discussed, and the current technical requirements are concluded, respectively. This review can provide a reference for studying submarine cable-based seismic monitoring.
Extended Kalman Filter Design for Tracking Time-of-Flight and Clock Offsets in a Two-Way Ranging System
Authors: Srinivas Sharanya , Herschfelt Andrew , Bliss Daniel W.
Published: June 2023

As radio frequency (RF) hardware continues to improve, two-way ranging (TWR) has become a viable approach for high-precision ranging applications. The precision of a TWR system is fundamentally limited by estimates of the time offset T between two platforms and the time delay τ of a signal propagating between them. In previous work, we derived a family of optimal “one-shot” joint delay–offset estimators and demonstrated that they reduce to a system of linear equations under reasonable assumptions. These estimators are simple and computationally efficient but are also susceptible to channel impairments that obstruct one or more measurements. In this work, we formulate an extended Kalman filter (EKF) for this class of estimators that specifically addresses this limitation. Unlike a generic KF approach, the proposed solution specifically integrates the estimation process to minimize the computational complexity. We benchmark the proposed first- and second-order EKF solutions against the existing one-shot estimators in a MATLAB Monte Carlo simulation environment. We demonstrate that the proposed solution achieves comparable estimation performance and, in the case of the second-order solution, reduces the computation time by an order of magnitude.
Small-Scale Ship Detection for SAR Remote Sensing Images Based on Coordinate-Aware Mixed Attention and Spatial Semantic Joint Context
Authors: Jiang Zhengjie , Wang Yupei , Zhou Xiaoqi , Chen Liang , Chang Yuan , Song Dongsheng , Shi Hao
Published: June 2023

With the rapid development of deep learning technology in recent years, convolutional neural networks have gained remarkable progress in SAR ship detection tasks. However, noise interference of the background and inadequate appearance features of small-scale objects still pose challenges. To tackle these issues, we propose a small ship detection algorithm for SAR images by means of a coordinate-aware mixed attention mechanism and spatial semantic joint context method. First, the coordinate-aware mixed attention mechanism innovatively combines coordinate-aware channel attention and spatial attention to achieve coordinate alignment of mixed attention features. In this way, attention with finer spatial granularity is conducive to strengthening the focusing ability on small-scale objects, thereby suppressing the background clutters accurately. In addition, the spatial semantic joint context method exploits the local and global environmental information jointly. The detailed spatial cues contained in the multi-scale local context and the generalized semantic information encoded in the global context are used to enhance the feature expression and distinctiveness of small-scale ship objects. Extensive experiments are conducted on the LS-SSDD-v1.0 and the HRSID dataset. The results with an average precision of 77.23% and 90.85% on the two datasets show the effectiveness of the proposed methods.