bluuebunny/crossref_metadata_2025 huggingface dataset:
Mechanistic Insights on Salicylic Acid Mediated Enhancement of Photosystem II Function in Oregano Seedlings Subjected to Moderate Drought Stress
Authors: Moustakas Michael , Sperdouli Ilektra , Moustaka Julietta , Şaş Begüm , İşgören Sumrunaz , Morales Fermín
Published: January 2023

Dramatic climate change has led to an increase in the intensity and frequency of drought episodes and, together with the high light conditions of the Mediterranean area, detrimentally influences crop production. Salicylic acid (SA) has been shown to supress phototoxicity, offering photosystem II (PSII) photoprotection. In the current study, we attempted to reveal the mechanism by which SA is improving PSII efficiency in oregano seedlings under moderate drought stress (MoDS). Foliar application of SA decreased chlorophyll content under normal growth conditions, but under MoDS increased chlorophyll content, compared to H2O-sprayed oregano seedlings. SA improved the PSII efficiency of oregano seedlings under normal growth conditions at high light (HL), and under MoDS, at both low light (LL) and HL. The mechanism by which, under normal growth conditions and HL, SA sprayed oregano seedlings compared to H2O-sprayed exhibited a more efficient PSII photochemistry, was the increased (17%) fraction of open PSII reaction centers (qp), and the increased (7%) efficiency of these open reaction centers (Fv′/Fm′), which resulted in an enhanced (24%) electron transport rate (ETR). SA application under MoDS, by modulating chlorophyll content, resulted in optimized antenna size and enhanced effective quantum yield of PSII photochemistry (ΦPSII) under both LL (7%) and HL (25%), compared to non-SA-sprayed oregano seedlings. This increased effective quantum yield of PSII photochemistry (ΦPSII) was due to the enhanced efficiency of the oxygen evolving complex (OEC), and the increased fraction of open PSII reaction centers (qp), which resulted in an increased electron transport rate (ETR) and a lower amount of singlet oxygen (1O2) production with less excess excitation energy (EXC).
Phytochemical and Biological Study of Trophic Interaction between Pseudosphinx Tetrio L. Larvae and Allamanda Cathartica L.
Authors: Matignon Linda , Lo Mame Marietou , Monpierre Magneric , Correia Mauro Vicentini , Valencia Drochss Pettry , Palmeira-Mello Marcos V. , Sylvestre Marie-Noëlle , Pruneau Ludovic , Sylvestre Muriel , Domenech Antonio , Benfodda Zohra , Meffre Patrick , Cebrián-Torrejón Gerardo
Published: January 2023

In this article, we propose to explore the chemical interaction between Pseudosphinx tetrio L. and Allamanda cathartica L. using different analytical methods, including an innovative electrochemical approach (called electrochemical ecology) and multivariate analysis, and we investigate the potential antimicrobial effects (antibacterial and antifungal activities) of this interaction in order to gain a better understanding of their specific interaction. The analytical study presents a similar chemical profile between the leaves of healthy and herbivorous A. cathartica and the excretions of the caterpillars. The similar analytical profile of the leaves of A. cathartica and the excretions of P. tetrio, and the difference with the caterpillar bodies, suggests a selective excretion of compounds by the caterpillar. The measured antimicrobial activities support the physicochemical tests. The natural products found selectively in the excretions (rather than in the body) could explain the ability of P. tetrio to feed on this toxic Apocynaceae species.
Collagen Derived from Fish Industry Waste: Progresses and Challenges
Authors: Rajabimashhadi Zahra , Gallo Nunzia , Salvatore Luca , Lionetto Francesca
Published: January 2023

Fish collagen garnered significant academic and commercial focus in the last decades featuring prospective applications in a variety of health-related industries, including food, medicine, pharmaceutics, and cosmetics. Due to its distinct advantages over mammalian-based collagen, including the reduced zoonosis transmission risk, the absence of cultural-religious limitations, the cost-effectiveness of manufacturing process, and its superior bioavailability, the use of collagen derived from fish wastes (i.e., skin, scales) quickly expanded. Moreover, by-products are low cost and the need to minimize fish industry waste’s environmental impact paved the way for the use of discards in the development of collagen-based products with remarkable added value. This review summarizes the recent advances in the valorization of fish industry wastes for the extraction of collagen used in several applications. Issues related to processing and characterization of collagen were presented. Moreover, an overview of the most relevant applications in food industry, nutraceutical, cosmetics, tissue engineering, and food packaging of the last three years was introduced. Lastly, the fish-collagen market and the open technological challenges to a reliable recovery and exploitation of this biopolymer were discussed.
Self-Aware Artificial Coiled Yarn Muscles with Enhanced Electrical Conductivity and Durability via a Two-Step Process
Authors: Gong Yongqi , Chen Wanyi , Li Jianyang , Zhao Shun , Ren Luquan , Wang Kunyang , Li Bingqian
Published: January 2023

Muscles are capable of modulating the body and adapting to environmental changes with a highly integrated sensing and actuation. Inspired by biological muscles, coiled/twisted fibers are adopted that can convert volume expansion into axial contraction and offer the advantages of flexibility and light weight. However, the sensing-actuation integrated fish line/yarn-based artificial muscles are still barely reported due to the poor actuation-sensing interface with off-the-shelf fibers. We report herein artificial coiled yarn muscles with self-sensing and actuation functions using the commercially available yarns. Via a two-step process, the artificial coiled yarn muscles are proved to obtain enhanced electrical conductivity and durability, which facilitates the long-term application in human-robot interfaces. The resistivity is successfully reduced from 172.39 Ω·cm (first step) to 1.27 Ω·cm (second step). The multimode sense of stretch strain, pressure, and actuation-sensing are analyzed and proved to have good linearity, stability and durability. The muscles could achieve a sensitivity (gauge factor, GF) of the contraction strain perception up to 1.5. We further demonstrate this self-aware artificial coiled yarn muscles could empower non-active objects with actuation and real-time monitoring capabilities without causing damage to the objects. Overall, this work provides a facile and versatile tool in improving the actuation-sensing performances of the artificial coiled yarn muscles and has the potential in building smart and interactive soft actuation systems.
Characterization of PLA/PCL/Nano-Hydroxyapatite (nHA) Biocomposites Prepared via Cold Isostatic Pressing
Authors: Solechan Solechan , Suprihanto Agus , Widyanto Susilo Adi , Triyono Joko , Fitriyana Deni Fajar , Siregar Januar Parlaungan , Cionita Tezara
Published: January 2023

Hydroxyapatite has the closest chemical composition to human bone. Despite this, the use of nano-hydroxyapatite (nHA) to produce biocomposite scaffolds from a mixture of polylactic acid (PLA) and polycaprolactone (PCL) using cold isostatic pressing has not been studied intensively. In this study, biocomposites were created employing nHA as an osteoconductive filler and a polymeric blend of PLA and PCL as a polymer matrix for prospective usage in the medical field. Cold isostatic pressing and subsequent sintering were used to create composites with different nHA concentrations that ranged from 0 to 30 weight percent. Using physical and mechanical characterization techniques such as Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), scanning electron microscopy (SEM), and density, porosity, tensile, and flexural standard tests, it was determined how the nHA concentrations affected the biocomposite’s general properties. In this study, the presence of PLA, PCL, and nHA was well identified using FTIR, XRD, and SEM methods. The biocomposites with high nHA content showed intense bands for symmetric stretching and the asymmetric bending vibration of PO43−. The incorporation of nHA into the polymeric blend matrix resulted in a rather irregular structure and the crystallization became more difficult. The addition of nHA improved the density and tensile and flexural strength of the PLA/PCL matrix (0% nHA). However, with increasing nHA content, the PLA/PCL/nHA biocomposites became more porous. In addition, the density, flexural strength, and tensile strength of the PLA/PCL/nHA biocomposites decreased with increasing nHA concentration. The PLA/PCL/nHA biocomposites with 10% nHA had the highest mechanical properties with a density of 1.39 g/cm3, a porosity of 1.93%, a flexural strength of 55.35 MPa, and a tensile strength of 30.68 MPa.
Thin Films of Polyaniline-Based Nanocomposites with CeO2 and WO3 Metal Oxides Applied to the Impedimetric and Capacitive Transducer Stages in Chemical Sensors
Authors: Rossignatti Beatriz Cotting , Vieira Amanda Portes , Barbosa Martin Schwellberger , Abegão Luís Miguel Gomes , Mello Hugo José Nogueira Pedroza Dias
Published: January 2023

There is a recognized need for the development of cost-effective, stable, fast, and optimized novel materials for technological applications. Substantial research has been undertaken on the role of polymeric nanocomposites in sensing applications. However, the use of PANI-based nanocomposites in impedimetric and capacitive electrochemical sensors has yet to be understood. The present study aimed to explore the relationship between the sensitivity and linearity of electrochemical pH sensors and the composition of nanocomposites. Thin films of PANI/CeO2 and PANI/WO3 were deposited via spin coating for characterization and application during the electrochemical impedance and capacitance spectroscopy (EIS and ECS) transduction stages. The findings showed that the optimized performance of the devices was extended not only to the sensitivity but also to the linearity. An increase of 213% in the ECS sensitivity of the PANI/CeO2 compared to the metal oxide and an increase of 64% in the ECS linearity of the PANI/WO3 compared to the polymeric sensitivity were reported. This study identified the structure–property relationship of nanocomposite thin films of PANI with metal oxides for use in electrochemical sensors. The developed materials could be applied in devices to be used in different fields, such as food, environment, and biomedical monitoring.
Simulated Environmental Conditioning of PHB Composites Reinforced with Barley Fibres to Determine the Viability of Their Use as Plastics for the Agriculture Sector
Authors: Oliver-Ortega Helena , Julián Fernando , Espinach Francesc Xavier , Méndez José Alberto
Published: January 2023

Nowadays, the search for new materials with a sustainable character to reduce the production of residues is under continuous research. In this sense, fully biodegradable composites based on polyhydroxybutyrate and different pretreated fibres coming from barley straw have been fabricated, and their resistance to environmental controlled conditions have been characterized. The materials were already compounded in a kinetic mixer and injection-moulded as specimens for tensile assay to be aged in a Xenotest chamber so as to simulate environmental conditioning. The samples, after accelerated aging, were characterized thus: mechanical characterization (tensile assay), water uptake (immersion and contact angle), and surface observation (optical and SEM microscopy). The incorporation of the fibres helps the composite to keep its structure for a longer time. On the other hand, the presence of the fibres increases the water uptake capacity to allow water permeation in the composite, which allows final degradation, characterised by a significant drop in properties after one month of exposure to simulated environmental conditions.
Eco-Friendly Fibers Embedded Yarn Structure in High-Performance Fabrics to Improve Moisture Absorption and Drying Properties
Authors: Kim Hyun-Ah
Published: January 2023

This study examined the perspiration absorption and drying characteristics of eco-friendly fiber-embedded fabrics with different yarn structures. The wicking and drying rates of fifteen fabrics made from quadrilobal PET, Lyocell, and bamboo fibers were measured using two evaluation methods and compared with the pore diameter and hygroscopic characteristics of the constituent fibers in the yarns. The sheath/core yarn structure played a vital role in improving the moisture absorption and drying properties of the eco-friendly fibers embedded in high-performance fabrics, which was partly affected by the hygroscopicity and non-circular cross-section of constituent fibers in the yarns. Superior perspiration absorption and drying properties among the various eco-friendly high-performance fabrics were observed in the quadrilobal PET/Lyocell sheath/core and quadrilobal PET/bamboo spun yarn fabrics. By contrast, the PET/Lyocell Siro-fil, bamboo spun, and hi-multi PET yarn fabrics exhibited inferior moisture absorption and drying properties. In particular, the evaluated results between transverse and vertical wicking measuring methods in absorption property showed a similar trend. In contrast, the drying property measured between the drying rate (min) at a steady state and the drying rate (g) at a transient state showed a different trend. Multiple regression analysis showed that the wicking property of the eco-friendly fiber-embedded fabrics was mainly related to the pore diameter, cross-sectional shape, and absorption property of the fibers in the yarns, and it was also highly associated with the drying characteristics of the fabrics. The market application of the sheath/core yarn structure using Lyocell and bamboo fibers with quadrilobal PET is available for producing eco-friendly fabrics that can contribute to environmental improvement and wear comfort related to the moisture absorption and fast-drying properties of the woven fabrics.
Molecular Dynamics Simulation of the Synergistic Effect of Alkali/Surfactant/Polymer on the Formation and Stabilization of Water-Based Foam Systems
Authors: Wang Yong , Le Xinpeng , Wang Xingwang , Liu Wenbo , Wang Zhihua
Published: January 2023

The stable maintenance effect of a chemical oil displacement agent on a foam liquid film usually creates problems with the oilfields surface system. To achieve comprehensive insights into the influence mechanism of these chemical agent components on the foam liquid film, an “SDBS/HPAM/OH−” water-based foam simulation system and corresponding control systems were constructed by adjusting the categories and quantities of component molecules by molecular dynamics (MD) simulation. The simulated results indicated that the foam stability follows the order of “SDBS/HPAM/OH−” system > “SDBS/HPAM” system > “SDBS” system. The smaller the inclination angle of the SDBS molecular tail chain, the greater the tendency of the SDBS molecular configuration to be “upright” at the gas−liquid interface, which is not conducive to preventing the aggregation and penetration of gas molecules at the gas−liquid interface. Although the presence of HPAM molecules can significantly enhance the stability of the liquid film by restricting the liquid film’s drainage and the diffusion of gas molecules, the addition of HPAM molecules would weaken the formation ability of the foam liquid film. Through decreasing the aggregation of cations around the co-adsorption layer, OH− not only enhances the interfacial activity of SDBS molecules, but also reduces the electrostatic repulsion between –COO− groups on the HPAM molecular chain, which makes the foam more stable. With an increase in the pH, SDBS concentration, and HPAM concentration, the stability of foam liquid film was strengthened. These results are helpful in facilitating new insights into the formation and stabilization mechanism of water-based foams. In particular, they provide support for the development and application of new defoaming technologies.
Production and Recovery of Ectoine: A Review of Current State and Future Prospects
Authors: Ng Hui Suan , Wan Pik-Ki , Kondo Akihiko , Chang Jo-Shu , Lan John Chi-Wei
Published: January 2023

Ectoine (1,4,5,6-tetrahydro-2-methyl-4-pyrimidinecarboxylic acid) is a revolutionizing substance with vast applications in the cosmetic and food industries. Ectoine is often sourced from halobacteria. The increasing market demand for ectoine has urged the development of cost-effective and sustainable large-scale production of ectoine from microbial sources. This review describes the existing and potential microbial sources of ectoine and its derivatives, as well as microbial production and fermentation approaches for ectoine recovery. In addition, conventional methods and emerging technologies for enhanced production and recovery of ectoine from microbial fermentation with a focus on the aqueous biphasic system (ABS) are discussed. The ABS is a practically feasible approach for the integration of fermentation, cell disruption, bioconversion, and clarification of various biomolecules in a single-step operation. Nonetheless, the implementation of the ABS on an industrial-scale basis for the enhanced production and recovery of ectoine is yet to be exploited. Therefore, the feasibility of the ABS to integrate the production and direct recovery of ectoine from microbial sources is also highlighted in this review.
Multi-Time Scale Optimal Scheduling Model of Wind and Hydrogen Integrated Energy System Based on Carbon Trading
Authors: Wen Xuan , Sun Bo , Gu Bing , Lv Yan
Published: January 2023

In the context of carbon trading, energy conservation and emissions reduction are the development directions of integrated energy systems. In order to meet the development requirements of energy conservation and emissions reduction in the power grid, considering the different responses of the system in different time periods, a wind-hydrogen integrated multi-time scale energy scheduling model was established to optimize the energy-consumption scheduling problem of the system. As the scheduling model is a multiobjective nonlinear problem, the artificial fish swarm algorithm–shuffled frog leaping algorithm (AFS-SFLA) was used to solve the scheduling model to achieve system optimization. In the experimental test process, the Griewank benchmark function and the Rosenbrock function were selected to test the performance of the proposed AFS-SFL algorithm. In the Griewank environment, compared to the SFLA algorithm, the AFS-SFL algorithm was able to find a feasible solution at an early stage, and tended to converge after 110 iterations. The optimal solution was −4.83. In the test of total electric power deviation results at different time scales, the maximum deviation of early dispatching was 14.58 MW, and the minimum deviation was 0.56 MW. The overall deviation of real-time scheduling was the minimum, and the minimum deviation was 0 and the maximum deviation was 1.89 WM. The integrated energy system adopted real-time scale dispatching, with good system stability and low-energy consumption. Power system dispatching optimization belongs to the objective optimization problem. The artificial fish swarm algorithm and frog algorithm were innovatively combined to solve the dispatching model, which improved the accuracy of power grid dispatching. The research content provides an effective reference for the efficient use of clean and renewable energy.
Masking or Unmasking the Evil? Polish Opinion-Forming Weeklies vis-à-vis the Crisis in the Roman Catholic Church in Poland
Authors: Paszenda Joanna , Rogoż Michał Mateusz
Published: January 2023

Almost twenty years have passed since the end of John Paul II’s pontificate. The Roman Catholic Church in Poland faces a serious crisis, stimulated not only by secularisation processes, which are characteristic for Western Europe, but also by a whole series of adverse events that unfolded at the end of the year 2020; they resembled a disaster marathon and had a considerable potential to attract media attention. A wide spectrum of Polish opinion-forming weeklies, ranging from left-liberal to ultra-conservative and far-right ones, as well as those associated with the Polish Episcopate, published accounts of the situation within the Catholic Church. In this manner, press discourse shaped the public perception of Catholicism in Poland. An analysis of the periodicals’ content disclosed a strong polarisation of opinions and a variety of interpretations. Differences between the particular weeklies were identified at the level of agenda-setting and prioritisation, framing, persuasion techniques employed, and additional contexts that were evoked, including the legal, moral, historical, philosophical, and religious ones.
A Texture Feature Removal Network for Sonar Image Classification and Detection
Authors: Li Chuanlong , Ye Xiufen , Xi Jier , Jia Yunpeng
Published: January 2023

Deep neural network (DNN) was applied in sonar image target recognition tasks, but it is very difficult to obtain enough sonar images that contain a target; as a result, the direct use of a small amount of data to train a DNN will cause overfitting and other problems. Transfer learning is the most effective way to address such scenarios. However, there is a large domain gap between optical images and sonar images, and common transfer learning methods may not be able to effectively handle it. In this paper, we propose a transfer learning method for sonar image classification and object detection called the texture feature removal network. We regard the texture features of an image as domain-specific features, and we narrow the domain gap by discarding the domain-specific features, and hence, make it easier to complete knowledge transfer. Our method can be easily embedded into other transfer learning methods, which makes it easier to apply to different application scenarios. Experimental results show that our method is effective in side-scan sonar image classification tasks and forward-looking sonar image detection tasks. For side-scan sonar image classification tasks, the classification accuracy of our method is enhanced by 4.5% in a supervised learning experiment, and for forward-looking sonar detection tasks, the average precision (AP) is also significantly improved.
Attention to Both Global and Local Features: A Novel Temporal Encoder for Satellite Image Time Series Classification
Authors: Zhang Weixiong , Zhang Hao , Zhao Zhitao , Tang Ping , Zhang Zheng
Published: January 2023

Satellite image time series (SITS) classification is a challenging application concurrently driven by long-term, large-scale, and high spatial-resolution observations acquired by remote sensing satellites. The focus of current SITS classification research is to exploit the richness of temporal information in SITS data. In the literature, self-attention mechanism-based networks, which are capable of capturing global temporal attention, have achieved state-of-the-art results in SITS classification. However, these methods lack attention to local temporal information, which is also significant for SITS classification tasks. To explore the potential of different scales of temporal information in SITS data, a global–local temporal attention encoder (GL-TAE) is proposed in this paper. GL-TAE has two submodules set up in parallel, one of which is a lightweight temporal attention encoder (LTAE) for extracting global temporal attention and the other is lightweight convolution (LConv) for extracting local temporal attention. Compared with methods exploring global-only or local-only temporal features, the proposed GL-TAE can achieve better performance on two public SITS datasets, which proves the effectiveness of hybrid global–local temporal attention features. The experiments also demonstrate that GL-TAE is a lightweight model, which achieves the same performance as other models but with fewer parameters.
TSCNet: Topological Structure Coupling Network for Change Detection of Heterogeneous Remote Sensing Images
Authors: Wang Xianghai , Cheng Wei , Feng Yining , Song Ruoxi
Published: January 2023

With the development of deep learning, convolutional neural networks (CNNs) have been successfully applied in the field of change detection in heterogeneous remote sensing (RS) images and achieved remarkable results. However, most of the existing methods of heterogeneous RS image change detection only extract deep features to realize the whole image transformation and ignore the description of the topological structure composed of the image texture, edge, and direction information. The occurrence of change often means that the topological structure of the ground object has changed. As a result, these algorithms severely limit the performance of change detection. To solve these problems, this paper proposes a new topology-coupling-based heterogeneous RS image change detection network (TSCNet). TSCNet transforms the feature space of heterogeneous images using an encoder–decoder structure and introduces wavelet transform, channel, and spatial attention mechanisms. The wavelet transform can obtain the details of each direction of the image and effectively capture the image’s texture features. Unnecessary features are suppressed by allocating more weight to areas of interest via channels and spatial attention mechanisms. As a result of the organic combination of a wavelet, channel attention mechanism, and spatial attention mechanism, the network can focus on the texture information of interest while suppressing the difference of images from different domains. On this basis, a bitemporal heterogeneous RS image change detection method based on the TSCNet framework is proposed. The experimental results on three public heterogeneous RS image change detection datasets demonstrate that the proposed change detection framework achieves significant improvements over the state-of-the-art methods.
Leveraging Self-Paced Semi-Supervised Learning with Prior Knowledge for 3D Object Detection on a LiDAR-Camera System
Authors: An Pei , Liang Junxiong , Hong Xing , Quan Siwen , Ma Tao , Chen Yanfei , Wang Liheng , Ma Jie
Published: January 2023

Three dimensional (3D) object detection with an optical camera and light detection and ranging (LiDAR) is an essential task in the field of mobile robot and autonomous driving. The current 3D object detection method is based on deep learning and is data-hungry. Recently, semi-supervised 3D object detection (SSOD-3D) has emerged as a technique to alleviate the shortage of labeled samples. However, it is still a challenging problem for SSOD-3D to learn 3D object detection from noisy pseudo labels. In this paper, to dynamically filter the unreliable pseudo labels, we first introduce a self-paced SSOD-3D method SPSL-3D. It exploits self-paced learning to automatically adjust the reliability weight of the pseudo label based on its 3D object detection loss. To evaluate the reliability of the pseudo label in accuracy, we present prior knowledge based SPSL-3D (named as PSPSL-3D) to enhance the SPSL-3D with the semantic and structure information provided by a LiDAR-camera system. Extensive experimental results in the public KITTI dataset demonstrate the efficiency of the proposed SPSL-3D and PSPSL-3D.
A Novel Framework for Correcting Satellite-Based Precipitation Products for Watersheds with Discontinuous Observed Data, Case Study in Mekong River Basin
Authors: Lee Giha , Nguyen Duc Hai , Le Xuan-Hien
Published: January 2023

Satellite-based precipitation (SP) data are gaining scientific interest due to their advantage in producing high-resolution products with quasi-global coverage. However, since the major reliance of precipitation data is on the distinctive geographical features of each location, they remain at a considerable distance from station-based data. This paper examines the effectiveness of a convolutional autoencoder (CAE) architecture in pixel-by-pixel bias correction of SP products for the Mekong River Basin (MRB). Two satellite-based products (TRMM and PERSIANN-CDR) and a gauge-based product (APHRODITE) are gridded rainfall products mined in this experiment. According to the estimated statistical criteria, the CAE model was effective in reducing the gap between SP products and benchmark data both in terms of spatial and temporal correlations. The two corrected SP products (CAE_TRMM and CAE_CDR) performed competitively, with CAE TRMM appearing to have a slight advantage over CAE CDR, however, the difference was minor. This study’s findings proved the effectiveness of deep learning-based models (here CAE) for bias correction of SP products. We believe that this technique will be a feasible alternative for delivering an up-to-current and reliable dataset for MRB studies, given that the sole available gauge-based dataset for this area has been out of date for a long time.
GNSS-R Observations of Marine Plastic Litter in a Water Flume: An Experimental Study
Authors: Gonga Amadeu , Pérez-Portero Adrián , Camps Adriano , Pascual Daniel , de Fockert Anton , de Maagt Peter
Published: January 2023

Currently, eight million metric tons of plastic end up in the oceans every year, and microplastics in different forms are present in almost all water systems in the world: streams, rivers, lakes, or oceans, and even in our blood. Detection of marine litter is an urgent task. Some works have recently reported the potential of GNSS-Reflectometry to detect marine plastic litter from space. This study presents the results of a controlled field experiment conducted under the auspices of ESA at the “Atlantic Basin” at the Deltares research institute (Delft, The Netherlands). Several types of wave conditions were created: sinusoidal and with JONSWAP spectrum, with different significant wave heights, and with different types of plastics and marine litter collected from the Dutch coast. Experimental results show the difficulty in detecting marine plastic litter based on a change of the reflected power. However, a statistical analysis of the GNSS-R estimated reflectivities (amplitude and phase) computed with very short integration times (coherent integration time Tcoh = 1 ms, and no incoherent averaging: Nincoh = 1) show that it may be possible to detect large accumulations of some types of marine litter that dampen the water waves, such as nets, bottles in a net, food wraps, and bags.
Construction of an Integrated Drought Monitoring Model Based on Deep Learning Algorithms
Authors: Zhang Yonghong , Xie Donglin , Tian Wei , Zhao Huajun , Geng Sutong , Lu Huanyu , Ma Guangyi , Huang Jie , Choy Lim Kam Sian Kenny Thiam
Published: January 2023

Drought is one of the major global natural disasters, and appropriate monitoring systems are essential to reveal drought trends. In this regard, deep learning is a very promising approach for characterizing the non-linear nature of drought factors. We used multi-source remote sensing data such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and Climate Hazards Group Infrared Precipitation with Station (CHIRPS) data to integrate drought impact factors such as precipitation, vegetation, temperature, and soil moisture. The application of convolutional long short-term memory (ConvLSTM) to construct an integrated drought monitoring model was proposed and tested, using the Xinjiang Uygur Autonomous Region as an example. To better compare the monitoring performance of ConvLSTM models, three other classical deep learning models and three classical machine learning models were also used for comparison. The results show that the composite drought index (CDI) output by the ConvLSTM model had a consistent high correlation with the drought rating of the multi-scale standardized precipitation evapotranspiration index (SPEI). The correlation coefficients between the CDI and the multi-scale standardized precipitation index (SPI) were all above 0.5 (p < 0.01), which was highly significant, and the correlation coefficient between CDI-1 and the monthly soil relative humidity at a 10 cm depth was above 0.45 (p < 0.01), which was well correlated. In addition, the spatial distribution of the CDI-6 simulated by the model was highly correlated with the degree of drought expressed by the SPEI-6 observations at the stations. This study provides a new approach for integrated regional drought monitoring.
Remote Sensing Crop Recognition by Coupling Phenological Features and Off-Center Bayesian Deep Learning
Authors: Wu Yongchuang , Wu Penghai , Wu Yanlan , Yang Hui , Wang Biao
Published: January 2023

Obtaining accurate and timely crop area information is crucial for crop yield estimates and food security. Because most existing crop mapping models based on remote sensing data have poor generalizability, they cannot be rapidly deployed for crop identification tasks in different regions. Based on a priori knowledge of phenology, we designed an off-center Bayesian deep learning remote sensing crop classification method that can highlight phenological features, combined with an attention mechanism and residual connectivity. In this paper, we first optimize the input image and input features based on a phenology analysis. Then, a convolutional neural network (CNN), recurrent neural network (RNN), and random forest classifier (RFC) were built based on farm data in northeastern Inner Mongolia and applied to perform comparisons with the method proposed here. Then, classification tests were performed on soybean, maize, and rice from four measurement areas in northeastern China to verify the accuracy of the above methods. To further explore the reliability of the method proposed in this paper, an uncertainty analysis was conducted by Bayesian deep learning to analyze the model’s learning process and model structure for interpretability. Finally, statistical data collected in Suibin County, Heilongjiang Province, over many years, and Shandong Province in 2020 were used as reference data to verify the applicability of the methods. The experimental results show that the classification accuracy of the three crops reached 90.73% overall and the average F1 and IOU were 89.57% and 81.48%, respectively. Furthermore, the proposed method can be directly applied to crop area estimations in different years in other regions based on its good correlation with official statistics.
An Improved DDPG and Its Application in Spacecraft Fault Knowledge Graph
Authors: Xing Xiaoyu , Wang Shuyi , Liu Wenjing
Published: January 2023

We construct a spacecraft performance-fault relationship graph of the control system, which can help space robots locate and repair spacecraft faults quickly. In order to improve the performance-fault relationship graph, we improve the Deep Deterministic Policy Gradient (DDPG) algorithm, and propose a relationship prediction method that combines representation learning reasoning with deep reinforcement learning reasoning. We take the spacecraft performance-fault relationship graph as the agent learning environment and adopt reinforcement learning to realize the optimal interaction between the agent and the environment. Meanwhile, our model uses a deep neural network to construct a complex value function and strategy function, which makes the agent have excellent perceptual decision-making ability and accurate value judgment ability. We evaluate our model on a performance-fault relationship graph of the control system. The experimental results show that our model has high prediction speed and accuracy, which can completely infer the optimal relationship path between entities to complete the spacecraft performance-fault relationship graph.
Analysis of Gas Turbine Compressor Performance after a Major Maintenance Operation Using an Autoencoder Architecture
Authors: de Castro-Cros Martí , Velasco Manel , Angulo Cecilio
Published: January 2023

Machine learning algorithms and the increasing availability of data have radically changed the way how decisions are made in today’s Industry. A wide range of algorithms are being used to monitor industrial processes and predict process variables that are difficult to be measured. Maintenance operations are mandatory to tackle in all industrial equipment. It is well known that a huge amount of money is invested in operational and maintenance actions in industrial gas turbines (IGTs). In this paper, two variations of autoencoders were used to analyse the performance of an IGT after major maintenance. The data used to analyse IGT conditions were ambient factors, and measurements were performed using several sensors located along the compressor. The condition assessment of the industrial gas turbine compressor revealed significant changes in its operation point after major maintenance; thus, this indicates the need to update the internal operating models to suit the new operational mode as well as the effectiveness of autoencoder-based models in feature extraction. Even though the processing performance was not compromised, the results showed how this autoencoder approach can help to define an indicator of the compressor behaviour in long-term performance.
Enhancing System Performance through Objective Feature Scoring of Multiple Persons’ Breathing Using Non-Contact RF Approach
Authors: Rehman Mubashir , Shah Raza Ali , Ali Najah Abed Abu , Khan Muhammad Bilal , Shah Syed Aziz , Alomainy Akram , Hayajneh Mohammad , Yang Xiaodong , Imran Muhammad Ali , Abbasi Qammer H.
Published: January 2023

Breathing monitoring is an efficient way of human health sensing and predicting numerous diseases. Various contact and non-contact-based methods are discussed in the literature for breathing monitoring. Radio frequency (RF)-based breathing monitoring has recently gained enormous popularity among non-contact methods. This method eliminates privacy concerns and the need for users to carry a device. In addition, such methods can reduce stress on healthcare facilities by providing intelligent digital health technologies. These intelligent digital technologies utilize a machine learning (ML)-based system for classifying breathing abnormalities. Despite advances in ML-based systems, the increasing dimensionality of data poses a significant challenge, as unrelated features can significantly impact the developed system’s performance. Optimal feature scoring may appear to be a viable solution to this problem, as it has the potential to improve system performance significantly. Initially, in this study, software-defined radio (SDR) and RF sensing techniques were used to develop a breathing monitoring system. Minute variations in wireless channel state information (CSI) due to breathing movement were used to detect breathing abnormalities in breathing patterns. Furthermore, ML algorithms intelligently classified breathing abnormalities in single and multiple-person scenarios. The results were validated by referencing a wearable sensor. Finally, optimal feature scoring was used to improve the developed system’s performance in terms of accuracy, training time, and prediction speed. The results showed that optimal feature scoring can help achieve maximum accuracy of up to 93.8% and 91.7% for single-person and multi-person scenarios, respectively.
Lightweight Helmet Detection Algorithm Using an Improved YOLOv4
Authors: Chen Junhua , Deng Sihao , Wang Ping , Huang Xueda , Liu Yanfei
Published: January 2023

Safety helmet wearing plays a major role in protecting the safety of workers in industry and construction, so a real-time helmet wearing detection technology is very necessary. This paper proposes an improved YOLOv4 algorithm to achieve real-time and efficient safety helmet wearing detection. The improved YOLOv4 algorithm adopts a lightweight network PP-LCNet as the backbone network and uses deepwise separable convolution to decrease the model parameters. Besides, the coordinate attention mechanism module is embedded in the three output feature layers of the backbone network to enhance the feature information, and an improved feature fusion structure is designed to fuse the target information. In terms of the loss function, we use a new SIoU loss function that fuses directional information to increase detection precision. The experimental findings demonstrate that the improved YOLOv4 algorithm achieves an accuracy of 92.98%, a model size of 41.88 M, and a detection speed of 43.23 pictures/s. Compared with the original YOLOv4, the accuracy increases by 0.52%, the model size decreases by about 83%, and the detection speed increases by 88%. Compared with other existing methods, it performs better in terms of precision and speed.
Multiple Attention Mechanism Enhanced YOLOX for Remote Sensing Object Detection
Authors: Shen Chao , Ma Caiwen , Gao Wei
Published: January 2023

The object detection technologies of remote sensing are widely used in various fields, such as environmental monitoring, geological disaster investigation, urban planning, and military defense. However, the detection algorithms lack the robustness to detect tiny objects against complex backgrounds. In this paper, we propose a Multiple Attention Mechanism Enhanced YOLOX (MAME-YOLOX) algorithm to address the above problem. Firstly, the CBAM attention mechanism is introduced into the backbone of the YOLOX, so that the detection network can focus on the saliency information. Secondly, to identify the high-level semantic information and enhance the perception of local geometric feature information, the Swin Transformer is integrated into the YOLOX’s neck module. Finally, instead of GIOU loss, CIoU loss is adopted to measure the bounding box regression loss, which can prevent the GIoU from degenerating into IoU. The experimental results of three publicly available remote sensing datasets, namely, AIBD, HRRSD, and DIOR, show that the algorithm proposed possesses better performance, both in relation to quantitative and qualitative aspects.
Design of Laboratory Stand for Displacement Measurement of IPMC Actuators
Authors: Koślik Karina , Kowol Paweł , Brociek Rafał , Wajda Agata , Lo Sciuto Grazia
Published: January 2023

The polymer technology based on Electroactive polymers and metal composite ionic polymer has great potential and advantages in many engineering fields. In this paper, a laboratory stand for testing Ionic polymer–metal composites (IPMC) is presented. The laboratory station includes a power supply system and a measuring system for the displacement of IPMC composites. Tests and measurements are carried out using a laser transducer and a camera equipped with image analysis software to determine the IPMC strips displacement. The experimental investigation of IPMCs under different voltage supplies and waveforms, environmental working humidity conditions, temperature, and loading conditions has proved the significant influence of geometric dimension and the effect of increased stress on the displacement value. For materials powered by a higher voltage value, an increased deflection value was noted. In case of displacement, longer is the sample, higher is the displacement value. The length of the sample under load, affects adversely its performance, resulting in an increase in the load on the sample. For samples of a thick size, a more stable movement with and without load can be noticed.
The Patellostabilometer: A New Device for Quantification of Mediolateral Patella Displacement
Authors: Lachowski Krzysztof , Hammermeister Florian , Halenz Bastian , Lieckefett Florian , Götze Thomas , Prill Robert , Becker Roland
Published: January 2023

Mediolateral patella displacement is of interest for diagnostics and clinically relevant research questions. Apart from manual testing, no standardized method is currently available. Proper quantification of patella mobility is necessary to better understand pathologies at the patellofemoral joint. Patella mobility was assessed in 25 healthy individuals using a Patellostabilometer, a new prototype instrument for quantification of the mediolateral patella displacement. The participants underwent measurements of the mediolateral displacement three times using the Patellostabilometer. A maximal force of 10 N was applied for patella movement. Additionally, leg length and circumference of the knee, upper- and lower-leg were measured. Lateral patella displacement of 18.27 ± 3.76 mm (range 15.85–20.64 mm, interquartile range (IQR) of 4.79) was measured. The medial patella displacement showed 24.47 ± 6.59 mm (range 19.29–29.76 mm, IQR of 10.47). The test–retest measurement error was 2.32 ± 1.76 mm (IQR of 2.38 mm), with five outliers. There was greater test–retest variability between the measurements of the medial displacement compared to the lateral one. The test–retest variability reached 7% of the patella displacement. Other parameters provided no significant correlations. Based on the natural patellofemoral mobility, a precise and clinically relevant quantification of patella mobility is allowed.
Artificial Intelligence Based Approach for Classification of Human Activities Using MEMS Sensors Data
Authors: Khan Yusuf Ahmed , Imaduddin Syed , Singh Yash Pratap , Wajid Mohd , Usman Mohammed , Abbas Mohamed
Published: January 2023

The integration of Micro Electronic Mechanical Systems (MEMS) sensor technology in smartphones has greatly improved the capability for Human Activity Recognition (HAR). By utilizing Machine Learning (ML) techniques and data from these sensors, various human motion activities can be classified. This study performed experiments and compiled a large dataset of nine daily activities, including Laying Down, Stationary, Walking, Brisk Walking, Running, Stairs-Up, Stairs-Down, Squatting, and Cycling. Several ML models, such as Decision Tree Classifier, Random Forest Classifier, K Neighbors Classifier, Multinomial Logistic Regression, Gaussian Naive Bayes, and Support Vector Machine, were trained on sensor data collected from accelerometer, gyroscope, and magnetometer embedded in smartphones and wearable devices. The highest test accuracy of 95% was achieved using the random forest algorithm. Additionally, a custom-built Bidirectional Long-Short-Term Memory (Bi-LSTM) model, a type of Recurrent Neural Network (RNN), was proposed and yielded an improved test accuracy of 98.1%. This approach differs from traditional algorithmic-based human activity detection used in current wearable technologies, resulting in improved accuracy.
Characterisation and Quantification of Upper Body Surface Motions for Tidal Volume Determination in Lung-Healthy Individuals
Authors: Laufer Bernhard , Hoeflinger Fabian , Docherty Paul D. , Jalal Nour Aldeen , Krueger-Ziolek Sabine , Rupitsch Stefan J. , Reindl Leonhard , Moeller Knut
Published: January 2023

Measurement of accurate tidal volumes based on respiration-induced surface movements of the upper body would be valuable in clinical and sports monitoring applications, but most current methods lack the precision, ease of use, or cost effectiveness required for wide-scale uptake. In this paper, the theoretical ability of different sensors, such as inertial measurement units, strain gauges, or circumference measurement devices to determine tidal volumes were investigated, scrutinised and evaluated. Sixteen subjects performed different breathing patterns of different tidal volumes, while using a motion capture system to record surface motions and a spirometer as a reference to obtain tidal volumes. Subsequently, the motion-capture data were used to determine upper-body circumferences, tilt angles, distance changes, movements and accelerations—such data could potentially be measured using optical encoders, inertial measurement units, or strain gauges. From these parameters, the measurement range and correlation with the volume signal of the spirometer were determined. The highest correlations were found between the spirometer volume and upper body circumferences; surface deflection was also well correlated, while accelerations carried minor respiratory information. The ranges of thorax motion parameters measurable with common sensors and the values and correlations to respiratory volume are presented. This article thus provides a novel tool for sensor selection for a smart shirt analysis of respiration.