bluuebunny/crossref_metadata_2025 huggingface dataset:
Effect of Fermented Milk Supplemented with Nisin or Plantaricin Q7 on Inflammatory Factors and Gut Microbiota in Mice
Authors: Liu Yisuo , Bu Yushan , Cao Jiayuan , Liu Yinxue , Zhang Tai , Hao Linlin , Yi Huaxi
Published: February 2024

Lactic-acid-bacteria-derived bacteriocins are used as food biological preservatives widely. Little information is available on the impact of bacteriocin intake with food on gut microbiota in vivo. In this study, the effects of fermented milk supplemented with nisin (FM-nisin) or plantaricin Q7 (FM-Q7) from Lactiplantibacillus plantarum Q7 on inflammatory factors and the gut microbiota of mice were investigated. The results showed that FM-nisin or FM-Q7 up-regulated IFN-γ and down-regulated IL-17 and IL-12 in serum significantly. FM-nisin down-regulated TNF-α and IL-10 while FM-Q7 up-regulated them. The results of 16S rRNA gene sequence analysis suggested that the gut microbiome in mice was changed by FM-nisin or FM-Q7. The Firmicutes/Bacteroides ratio was reduced significantly in both groups. It was observed that the volume of Akkermansia_Muciniphila was significantly reduced whereas those of Lachnospiraceae and Ruminococcaceae were increased. The total number of short-chain fatty acids (SCFAs) in the mouse feces of the FM-nisin group and FM-Q7 group was increased. The content of acetic acid was increased while the butyric acid content was decreased significantly. These findings indicated that FM-nisin or FM-Q7 could stimulate the inflammation response and alter gut microbiota and metabolic components in mice. Further in-depth study is needed to determine the impact of FM-nisin or FM-Q7 on the host’s health.
What Is New about Parsley, a Potential Source of Cardioprotective Therapeutic Substances?
Authors: Casanova Livia Marques , dos Santos Nascimento Luana Beatriz , Costa Sônia Soares
Published: February 2024

Petroselinum crispum (Mill.) Fuss (Apiaceae), popularly known as parsley, is a ubiquitous aromatic herb used for culinary and medicinal purposes worldwide. In addition to the richness in nutrients, several bioactive secondary metabolites, especially the flavone apigenin and its glycosides, have been described in this species. Parsley has already been shown to have several health-promoting activities, such as antithrombotic, antihypertensive, and hypolipidemic properties. The multiple studies conducted in animal models so far suggest this species is a potential source of cardioprotective agents. This review provides up-to-date information and perspectives on the potential of parsley and its bioactive compounds for the development of nutraceutical products and drugs for promoting cardiovascular health. It includes not only a discussion of parsley’s reported bioactivities but also the knowledge of supplements and food additives developed as innovative parsley-based products.
Effect of Botulinum Toxin Injections in the Treatment of Spasticity of Different Etiologies: An Umbrella Review
Authors: Otero-Luis Iris , Martinez-Rodrigo Arturo , Cavero-Redondo Iván , Moreno-Herráiz Nerea , Martínez-García Irene , Saz-Lara Alicia
Published: February 2024

Background: Spasticity is a very common neurological sequelae that significantly impacts the quality of life of patients, affecting more than 12 million people worldwide. Botulinum toxin is considered a reversible treatment for spasticity, but due to the large amount of available evidence, synthesis seems necessary. Therefore, we conducted an overview of existing systematic reviews and meta-analyses to evaluate the effect of botulinum toxin injections in the treatment of spasticity of different etiologies. Methods: A systematic search of different databases, including Pubmed, Scopus, the Cochrane Library, and Web of Science, was performed from inception to February 2024. Standardized mean differences (SMDs) and their respective 95% confidence intervals (CIs) were calculated to assess the effect of botulinum toxin compared to that of the control treatment using the Modified Ashworth Scale (MAS). All the statistical analyses were performed using STATA 15 software. Results: 28 studies were included in the umbrella review. The effect of botulinum toxin injections on spasticity, as measured by the MAS, was significantly lower in all but three studies, although these studies also supported the intervention. The SMDs reported by the meta-analyses ranged from −0.98 to −0.01. Conclusion: Botulinum toxin injections were effective at treating spasticity of different etiologies, as indicated by the measurements on the MAS. This implies an improvement in muscle tone and, consequently, in the patient’s mobility and quality of life.
Readthrough Activators and Nonsense-Mediated mRNA Decay Inhibitor Molecules: Real Potential in Many Genetic Diseases Harboring Premature Termination Codons
Authors: Benslimane Nesrine , Loret Camille , Chazelas Pauline , Favreau Frédéric , Faye Pierre-Antoine , Lejeune Fabrice , Lia Anne-Sophie
Published: February 2024

Nonsense mutations that generate a premature termination codon (PTC) can induce both the accelerated degradation of mutated mRNA compared with the wild type version of the mRNA or the production of a truncated protein. One of the considered therapeutic strategies to bypass PTCs is their “readthrough” based on small-molecule drugs. These molecules promote the incorporation of a near-cognate tRNA at the PTC position through the native polypeptide chain. In this review, we detailed the various existing strategies organized according to pharmacological molecule types through their different mechanisms. The positive results that followed readthrough molecule testing in multiple neuromuscular disorder models indicate the potential of this approach in peripheral neuropathies.
Nanomedicines: Emerging Platforms in Smart Chemotherapy Treatment—A Recent Review
Authors: Arafat Mosab , Sakkal Molham , Beiram Rami , AbuRuz Salahdein
Published: February 2024

Cancer continues to pose one of the most critical challenges in global healthcare. Despite the wide array of existing cancer drugs, the primary obstacle remains in selectively targeting and eliminating cancer cells while minimizing damage to healthy ones, thereby reducing treatment side effects. The revolutionary approach of utilizing nanomaterials for delivering cancer therapeutic agents has significantly enhanced the efficacy and safety of chemotherapeutic drugs. This crucial shift is attributed to the unique properties of nanomaterials, enabling nanocarriers to transport therapeutic agents to tumor sites in both passive and active modes, while minimizing drug elimination from delivery systems. Furthermore, these nanocarriers can be designed to respond to internal or external stimuli, thus facilitating controlled drug release. However, the production of nanomedications for cancer therapy encounters various challenges that can impede progress in this field. This review aims to provide a comprehensive overview of the current state of nanomedication in cancer treatment. It explores a variety of nanomaterials, focusing on their unique properties that are crucial for overcoming the limitations of conventional chemotherapy. Additionally, the review delves into the properties and functionalities of nanocarriers, highlighting their significant impact on the evolution of nanomedicine. It also critically assesses recent advancements in drug delivery systems, covering a range of innovative delivery methodologies. Finally, the review succinctly addresses the challenges encountered in developing nanomedications, offering insightful perspectives to guide future research in this field.
A Comprehensive Computational Insight into the PD-L1 Binding to PD-1 and Small Molecules
Authors: Fantacuzzi Marialuigia , Paciotti Roberto , Agamennone Mariangela
Published: February 2024

Immunotherapy has marked a revolution in cancer therapy. The most extensively studied target in this field is represented by the protein–protein interaction between PD-1 and its ligand, PD-L1. The promising results obtained with the clinical use of monoclonal antibodies (mAbs) directed against both PD-1 and PD-L1 have prompted the search for small-molecule binders capable of disrupting the protein–protein contact and overcoming the limitations presented by mAbs. The disclosure of the first X-ray complexes of PD-L1 with BMS ligands showed the protein in dimeric form, with the ligand in a symmetrical hydrophobic tunnel. These findings paved the way for the discovery of new ligands. To this end, and to understand the binding mechanism of small molecules to PD-L1 along with the dimerization process, many structure-based computational studies have been applied. In the present review, we examined the most relevant articles presenting computational analyses aimed at elucidating the binding mechanism of PD-L1 with PD-1 and small molecule ligands. Additionally, virtual screening studies that identified validated PD-L1 ligands were included. The relevance of the reported studies highlights the increasingly prominent role that these techniques can play in chemical biology and drug discovery.
Flooding Length Mediates Fencing and Grazing Effects on Soil Respiration in Meadow Steppe
Authors: Qu Yan , Wang Deping , Jin Sanling , Zheng Zhirong , Diao Zhaoyan , Rong Yuping
Published: February 2024

Grassland management affects soil respiration (Rs, consists of heterotrophic respiration and autotrophic respiration) through soil micro-ecological processes, such as hydrothermal, plant root, organic carbon decomposition and microbial activity. Flooding, an irregular phenomenon in grasslands, may strongly regulate the response of soil respiration and its components to grassland management, but the regulatory mechanism remains unclear. We conducted a 3-year experiment by grassland management (fencing and grazing) and flooding conditions (no flooding (NF), short-term flooding (STF) and long-term flooding (LTF)) to study their effects on Rs and its components in a meadow steppe in the Hui River basin of Hulunbuir. We found differences in the patterns of Rs and its components under grassland management and flooding conditions. In 2021–2023, the temporal trends of Rs, heterotrophic respiration (Rh) and autotrophic respiration (Ra) were generally consistent, with peaks occurring on days 190–220, and the peaks of grazing were higher than that of fencing. In NF, Rs of grazed grassland was significantly higher than that of fenced grassland in 2021–2022 (p < 0.05). In STF and LTF, there was no significant difference in Rs between fenced and grazed grassland (p > 0.05). The dependence of Rs on soil temperature (ST) decreased with increasing flooding duration, and the dependence of Rs on ST of grazed grassland was higher than fenced grassland under NF and STF, but there was no difference between fenced grassland and grazed grassland under LTF. In addition, Rh was more sensitive to ST than Ra. This may be due to the different pathways of ST effects on Rs under grazing in different flooding conditions. Our study indicates that the effect of flooding on Rs is the key to the rational use of grassland under future climate change. To reduce regional carbon emissions, we recommend grazing on flooding grassland and fencing on no-flooding grassland.
Eco-Geographical and Botanical Patterns of Resistance to Lepidoptera Insects in Brassica rapa L.
Authors: Artemyeva Anna M. , Kurina Anastasia B.
Published: February 2024

In the context of the widespread expansion of damage by herbivorous pests of Brassica crops, taking into account the requirements for minimizing pesticide pollution of the environment, it is important to have fundamental knowledge of the geographical features of the distribution of pests and about the botanical confinement of plant resistance in order to develop a strategy for creating new Brassica cultivars with complex resistance to insects. The relevance of our work is related to the study of the variability in the degree of resistance of the extensive genetic diversity of Brassica rapa accessions to the main herbivorous pests of Brassica crops in contrasting ecological and geographical zones of the Russian Federation (Arctic, northwestern, and southern zones). We have studied the distribution and food preferences of Lepidoptera insects (diamondback moth Plutella xylostella and cabbage moth Mamestra brassicae) on a set of 100 accessions from the VIR B. rapa collection (Chinese cabbage, pakchoi, wutacai, zicaitai, mizuna, and leaf and root turnips) in the field in three zones of the Russian Federation. We have found that the diamondback moth and cabbage moth are largely harmful in three zones of the European part of the Russian Federation, although the degree of damage to plants by these insects varies by year of cultivation. On average, for the set studied during the two years of the experiment, the degree of plant damage by both pests in the Arctic zone was low and almost low, and in the northwestern and southern zones, it was medium. It was noted that diamondback moth damage was greater in the northwestern zone in both years and in the southern and Arctic zones in 2021, while in 2022, the degree of cabbage moth damage was slightly higher in the southern and Arctic zones. Under the conditions of field diamondback moth damage, the accessions of Chinese cabbage, wutacai, and mizuna turned out to be the most resistant (the damage score was 1.92–1.99), whereas the accessions of wutacai and pakchoi were the most resistant to the cabbage moth (the damage score was 1.62–1.78). A high variability in the degree of resistance of Brassica crops to Lepidoptera insects from complete resistance to susceptibility was revealed. We have identified sources of resistance to insects, including complex resistance in all study areas, among landraces and some modern cultivars of Chinese cabbage, pakchoi, wutacai, and mizuna from Japan and China, as well as European turnips. The highest susceptibility to pests in the studied set was noted in the accession of root turnip “Hinona” (k-1422, USA) (average damage score of 3.24–3.53 points). We were not able to establish the morphological features of resistant plants or the geographical confinement of the origin of resistance of B. rapa crop accessions.
The Impact of Rootstock on “Big Top” Nectarine Postharvest Concerning Chilling Injury, Biochemical and Molecular Parameters
Authors: Navarro Aimar , Giménez Rosa , Val Jesús , Moreno María Ángeles
Published: February 2024

Peaches and nectarines have a short shelf life even when harvested at appropriate physiological maturity. Market life is increased by storage at low temperatures. However, chilling injury symptoms can appear, causing physiological disorders and limiting shipping potential. The rootstock effect on the post-harvest quality has hardly been explored. Thus, the principal aim of this work was to study the influence of seven different Prunus rootstocks on the “Big Top” nectarine cv, considering harvest and post-harvest quality parameters and their correlation with chilling injury disorders. Basic fruit quality traits, individual sugars and organic acids analyzed by HPLC and other biochemical compounds such as relative antioxidant capacity, total phenolics content, flavonoids, anthocyanins, vitamin C and related enzyme activities (PAL, POD, PPO) were considered. In addition, correlations with possible candidate genes for chilling injury (CI) tolerance were searched by qPCR. Although a low susceptibility to CI symptoms has been found in “Big Top”, rootstocks “PADAC 9902-01”, “PADAC 99-05” and “ReplantPAC” exhibited lower CI symptoms. A statistically significant influence of the evaluated rootstocks was found concerning the parameters of this study. Phenols and anthocyanins seem to be important parameters to be considered in the prevention of chilling injury disorders. Moreover, PAL1, PPO4, PG2 and LDOX genes relative expressions were positively associated with chilling injury susceptibility. This study opens new perspectives for understanding peach fruit adaptation and response to cold storage temperatures during the post-harvest period.
Overprinting of TPU onto PA6 Substrates: The Influences of the Interfacial Area, Surface Roughness and Processing Parameters on the Adhesion between Components
Authors: Slapnik Janez , Lorber Rebeka , Pulko Irena , Huskić Miroslav , Črešnar Klementina Pušnik
Published: February 2024

The hybridisation of injection moulding (IM) and additive manufacturing (AM) offers the opportunity to combine the high productivity of IM and the high flexibility of AM into a single process. IM parts can be overprinted through fused filament fabrication (FFF) to allow for the customisation of parts or to add new functionalities. However, the right material pair must be chosen, and processing parameters must be optimised to achieve suitable adhesion between the components. The present study dealt with the investigation of the influence of the interfacial area, substrate surface roughness and overprinting processing parameters on the adhesion between the polyamide 6 (PA6) substrate and thermoplastic polyurethane (TPU) rib overprinted via FFF. PA6 substrates were produced through the IM of plates into a mould with different textures to obtain substrates with three different surface roughnesses. The ribs with varied interfacial areas were overprinted onto produced substrates using a desktop FFF 3D printer. To study the effect of overprinting processing parameters, the ribs were overprinted under varying printing and substrate temperatures and printing speeds according to the Box–Behnken design of experiments (DoE). The chemical composition and thermal properties of used materials were determined via attenuated total reflectance–Fourier transform infrared spectroscopy (ATR-FTIR), thermogravimetric analysis (TGA), differential scanning calorimetry (DSC) and dynamic mechanical analysis (DMA). The surface properties of prepared substrates were studied via digital optical microscopy (OM), through surface roughness measurements using a confocal microscope, through contact angle (CA) measurements and through the determination of free surface energy (SFE). The adhesion between the components was determined by evaluating the tear-off strength using a universal testing machine (UTM). With an increasing interfacial area, the tear-off strength decreased, while substrate surface roughness had no statistically significant effect. Overprinting parameters influenced the tear-off strength in the order of printing speed > printing temperature > substrate temperature. High values of tear-off strength were found for the lowest printing speed, while there were no important differences found between the middle and upper values. With increasing printing and substrate temperatures, the tear-off strength increased linearly. The highest value of tear-off strength (0.84 MPa) was observed at a printing temperature, substrate temperature and printing speed of 250 °C, 80 °C and 2 mm/s, respectively.
Use of Hydrogel Electrolyte in Zn-MnO2 Rechargeable Batteries: Characterization of Safety, Performance, and Cu2+ Ion Diffusion
Authors: Cho Jungsang , Turney Damon E. , Yadav Gautam Ganapati , Nyce Michael , Wygant Bryan R. , Lambert Timothy N. , Banerjee Sanjoy
Published: February 2024

Achieving commercially acceptable Zn-MnO2 rechargeable batteries depends on the reversibility of active zinc and manganese materials, and avoiding side reactions during the second electron reaction of MnO2. Typically, liquid electrolytes such as potassium hydroxide (KOH) are used for Zn-MnO2 rechargeable batteries. However, it is known that using liquid electrolytes causes the formation of electrochemically inactive materials, such as precipitation Mn3O4 or ZnMn2O4 resulting from the uncontrollable reaction of Mn3+ dissolved species with zincate ions. In this paper, hydrogel electrolytes are tested for MnO2 electrodes undergoing two-electron cycling. Improved cell safety is achieved because the hydrogel electrolyte is non-spillable, according to standards from the US Department of Transportation (DOT). The cycling of “half cells” with advanced-formulation MnO2 cathodes paired with commercial NiOOH electrodes is tested with hydrogel and a normal electrolyte, to detect changes to the zincate crossover and reaction from anode to cathode. These half cells achieved ≥700 cycles with 99% coulombic efficiency and 63% energy efficiency at C/3 rates based on the second electron capacity of MnO2. Other cycling tests with “full cells” of Zn anodes with the same MnO2 cathodes achieved ~300 cycles until reaching 50% capacity fade, a comparable performance to cells using liquid electrolyte. Electrodes dissected after cycling showed that the liquid electrolyte allowed Cu ions to migrate more than the hydrogel electrolyte. However, measurements of the Cu diffusion coefficient showed no difference between liquid and gel electrolytes; thus, it was hypothesized that the gel electrolytes reduced the occurrence of Cu short circuits by either (a) reducing electrode physical contact to the separator or (b) reducing electro-convective electrolyte transport that may be as important as diffusive transport.
Research on Temperature Sensing Method for Three-Core Cable Intermediate Joint Considering Three-Phase Load Imbalance
Authors: Li Xinhai , Zhang Zhifang , Yang Ting , Ruan Jiangjun , Niu Borui
Published: February 2024

Temperature is a key factor affecting the insulation performance and operation safety of cable joints. Accurate acquisition of hot spot temperatures of cable joints is a difficult issue in cable operation and maintenance. Three-core cables may have unbalanced three-phase loads in actual operation. This paper takes a 10 kV three-core cable joint as the research object; based on the temperature field numerical simulation method, it analyzes the diffusion path of the main heat flow inside the joint and establishes an inversion model that fits the hot spot temperature of the joint through the surface temperature of the cable body. At the same time, considering the special situation of the unbalanced three-phase load of a three-core cable, the joint hot spot temperature inversion model of a three-core cable under an unbalanced three-phase load is further established. This paper further uses the cable joint multi-step unbalanced load temperature rise test to verify the accuracy of the cable joint hot spot temperature inversion model. The maximum error of the transient hot spot temperature inversion does not exceed 4.5 °C, and the steady-state error is within 3 °C. A higher inversion accuracy was achieved. The research results of this article can provide a reference for real-time monitoring of the hot spot temperature of cable joints.
Climate Change-Related Disaster Risk Mitigation through Innovative Insurance Mechanism: A System Dynamics Model Application for a Case Study in Latvia
Authors: Feofilovs Maksims , Pagano Andrea Jonathan , Vannucci Emanuele , Spiotta Marina , Romagnoli Francesco
Published: February 2024

This study explores how the System Dynamics modeling approach can help deal with the problem of conventional insurance mechanisms by studying the feedback loops governing complex systems connected to the disaster insurance mechanism. Instead of addressing the disaster’s underlying risk, the traditional disaster insurance strategy largely focuses on providing financial security for asset recovery after a disaster. This constraint becomes especially concerning as the threat of climate-related disasters grows since it may result in rising long-term damage expenditures. A new insurance mechanism is suggested as a solution to this problem to lower damage costs while safeguarding insured assets and luring new assets to be protected. A local case study utilizing a System Dynamics stock and flow model is created and validated by examining the model’s structure, sensitivity analysis, and extreme value test. The results of the case study performed on a city in Latvia highlight the significance of effective disaster risk reduction strategies applied within the innovative insurance mechanism in lowering overall disaster costs. The logical coherence seen throughout the analysis of simulated scenario results strengthens the established model’s plausibility. The case study’s findings support the innovative insurance mechanism’s dynamic hypothesis and show the main influencing factors on the dynamics within the proposed innovative insurance mechanism. The information this study can help insurance firms, policy planners, and disaster risk managers make decisions that will benefit local communities and other stakeholders regarding climate-related disaster risk mitigation.
A Control Interface for Autonomous Positioning of Magnetically Actuated Spheres Using an Artificial Neural Network
Authors: Huynh Victor , Mutawak Basam , Do Minh Quan , Ankrah Elizabeth A. , Kassaeiyan Pouya , Weinberg Irving N. , Peixoto Nathalia , Wei Qi , Mair Lamar O.
Published: February 2024

Electromagnet arrays show significant potential in the untethered guidance of particles, devices, and eventually robots. However, complications in obtaining accurate models of electromagnetic fields pose challenges for precision control. Manipulation often requires the reduced-order modeling of physical systems, which may be computationally complex and may still not account for all possible system dynamics. Additionally, control schemes capable of being applied to electromagnet arrays of any configuration may significantly expand the usefulness of any control approach. In this study, we developed a data-driven approach to the magnetic control of a neodymium magnets (NdFeB magnetic sphere) using a simple, highly constrained magnetic actuation architecture. We developed and compared two regression-based schemes for controlling the NdFeB sphere in the workspace of a four-coil array of electromagnets. We obtained averaged submillimeter positional control (0.85 mm) of a NdFeB hard magnetic sphere in a 2D plane using a controller trained using a single-layer, five-input regression neural network with a single hidden layer.
Monitoring Spatial–Temporal Variations in River Width in the Aral Sea Basin with Sentinel-2 Imagery
Authors: Zhou Jingjing , Ke Linghong , Ding Xin , Wang Ruizhe , Zeng Fanxuan
Published: February 2024

Rivers in arid regions serve as crucial freshwater resources for local communities and play an essential role in global hydrological and biogeochemical cycles. The Aral Sea Basin (ASB) in Central Asia is characterized by an arid climate and river dynamics that are sensitive to climate change and human activities. Monitoring the spatiotemporal variations in river water extent in the ASB is essential to maintain an ecological balance and ensure water security. In this study, we extracted data regarding monthly river water bodies in the ASB from 2017 to 2022 by synthesizing monthly Sentinel-2 images. The water extents on the Sentinel images were automatically mapped using the Otsu method, and the river widths for all river channels were calculated using the RivWidth algorithm. We investigated the relationships between the river dynamics and the geomorphology, climatic change, human activities, and the annual and interannual variations in the river width in different reaches of the basin. The results show a seasonal variability in the river width, with most rivers reaching the largest width in the warm season and a few rivers in the middle and lower areas reaching the valley value in the warm season. Compared to their tributaries, the mainstem in the middle/lower regions showed less seasonal variability. According to interannual analysis, most of the rivers in the ASB significantly narrowed between 2017 and 2022, a phenomenon which is generally impacted by temperature and evapotranspiration variations. Comparisons show that our results provide improved information about the narrow river reaches and denser river networks compared to the previous global dataset, demonstrating the advantageous properties of high spatial resolution in Sentinel-2 imagery.
Farmland Segmentation in Landsat 8 Satellite Images Using Deep Learning and Conditional Generative Adversarial Networks
Authors: Nair Shruti , Sharifzadeh Sara , Palade Vasile
Published: February 2024

Leveraging mid-resolution satellite images such as Landsat 8 for accurate farmland segmentation and land change monitoring is crucial for agricultural management, yet is hindered by the scarcity of labelled data for the training of supervised deep learning pipelines. The particular focus of this study is on addressing the scarcity of labelled images. This paper introduces several contributions, including a systematic satellite image data augmentation approach that aims to maintain data population consistency during model training, thus mitigating performance degradation. To alleviate the labour-intensive task of pixel-wise image labelling, we present a novel application of a modified conditional generative adversarial network (CGAN) to generate artificial satellite images and corresponding farm labels. Additionally, we scrutinize the role of spectral bands in satellite image segmentation and compare two prominent semantic segmentation models, U-Net and DeepLabV3+, with diverse backbone structures. Our empirical findings demonstrate that augmenting the dataset with up to 22.85% artificial samples significantly enhances the model performance. Notably, the U-Net model, employing standard convolution, outperforms the DeepLabV3+ models with atrous convolution, achieving a segmentation accuracy of 86.92% on the test data.
Water Ice Resources on the Shallow Subsurface of Mars: Indications to Rover-Mounted Radar Observation
Authors: Zheng Naihuan , Ding Chunyu , Su Yan , Orosei Roberto
Published: February 2024

The planet Mars is the most probable among the terrestrial planets in our solar system to support human settlement or colonization in the future. The detection of water ice or liquid water on the shallow subsurface of Mars is a crucial scientific objective for both the Chinese Tianwen-1 and United States Mars 2020 missions, which were launched in 2020. Both missions were equipped with Rover-mounted ground-penetrating radar (GPR) instruments, specifically the RoPeR on the Zhurong rover and the RIMFAX radar on the Perseverance rover. The in situ radar provides unprecedented opportunities to study the distribution of shallow subsurface water ice on Mars with its unique penetrating capability. The presence of water ice on the shallow surface layers of Mars is one of the most significant indicators of habitability on the extraterrestrial planet. A considerable amount of evidence pointing to the existence of water ice on Mars has been gathered by previous researchers through remote sensing photography, radar, measurements by gamma ray spectroscopy and neutron spectrometers, soil analysis, etc. This paper aims to review the various approaches utilized in detecting shallow subsurface water ice on Mars to date and to sort out the past and current evidence for its presence. This paper also provides a comprehensive overview of the possible clues of shallow subsurface water ice in the landing area of the Perseverance rover, serving as a reference for the RIMFAX radar to detect water ice on Mars in the future. Finally, this paper proposes the future emphasis and direction of rover-mounted radar for water ice exploration on the Martian shallow subsurface.
A Hyperspectral Change Detection (HCD-Net) Framework Based on Double Stream Convolutional Neural Networks and an Attention Module
Authors: Seydi Seyd Teymoor , Boueshagh Mahboubeh , Namjoo Foad , Minouei Seyed Mohammad , Nikraftar Zahir , Amani Meisam
Published: February 2024

Human activities and natural phenomena continually transform the Earth’s surface, presenting ongoing challenges to the environment. Therefore, the accurate and timely monitoring and prediction of these alterations are essential for devising effective solutions and mitigating environmental impacts in advance. This study introduces a novel framework, called HCD-Net, for detecting changes using bi-temporal hyperspectral images. HCD-Net is built upon a dual-stream deep feature extraction process, complemented by an attention mechanism. The first stream employs 3D convolution layers and 3D Squeeze-and-Excitation (SE) blocks to extract deep features, while the second stream utilizes 2D convolution and 2D SE blocks for the same purpose. The deep features from both streams are then concatenated and processed through dense layers for decision-making. The performance of HCD-Net is evaluated against existing state-of-the-art change detection methods. For this purpose, the bi-temporal Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral dataset was utilized to assess the change detection performance. The findings indicate that HCD-Net achieves superior accuracy and the lowest false alarm rate among the compared methods, with an overall classification accuracy exceeding 96%, and a kappa coefficient greater than 0.9.
Spatiotemporal Protein Variations Based on VIIRS-Derived Regional Protein Algorithm in the Northern East China Sea
Authors: Kim Myeongseop , Kim Sungjun , Lee Dabin , Jang Hyo-Keun , Park Sanghoon , Kim Yejin , Kim Jaesoon , Youn Seok-Hyun , Joo Huitae , Son Seunghyun , Lee Sang-Heon
Published: February 2024

Over the past two decades, the environmental characteristics of the northern East China Sea (NECS) that make it a crucial spawning ground for commercially significant species have faced substantial impacts due to climate change. Protein (PRT) within phytoplankton, serving as a nitrogen-rich food for organisms of higher trophic levels, is a sensitive indicator to environmental shifts. This study aims to develop a regional PRT algorithm to characterize spatial and temporal variations in the NECS from 2012 to 2022. Employing switching chlorophyll-a and particulate organic nitrogen algorithms, the developed regional PRT algorithm demonstrates enhanced accuracy. Satellite-estimated PRT concentrations, utilizing data from the Visible Infrared Imaging Radiometer Suite (VIIRS), generally align with the 1:1 line when compared to in situ data. Seasonal patterns and spatial distributions of PRT in both the western and eastern parts of the NECS from 2012 to 2022 were discerned, revealing notable differences in the spatial distribution and major controlling factors between these two areas. In conclusion, the regional PRT algorithm significantly improves estimation precision, advancing our understanding of PRT dynamics in the NECS concerning PRT concentration and environmental changes. This research underscores the importance of tailored algorithms in elucidating the intricate relationships between environmental variables and PRT variations in the NECS.
Integrating SAR and Optical Data for Aboveground Biomass Estimation of Coastal Wetlands Using Machine Learning: Multi-Scale Approach
Authors: Hemati Mohammadali , Mahdianpari Masoud , Shiri Hodjat , Mohammadimanesh Fariba
Published: February 2024

Coastal wetlands encompass diverse ecosystems such as tidal marshes, mangroves, and seagrasses, which harbor substantial amounts of carbon (C) within their vegetation and soils. Despite their relatively small global extent, these wetlands exhibit carbon sequestration rates on par with those observed in terrestrial forests. The application of remote sensing technologies offers a promising means of monitoring aboveground biomass (AGB) in wetland environments. However, the scarcity of field data poses a significant challenge to the utilization of spaceborne data for accurate estimation of AGB in coastal wetlands. To address this limitation, this study presents a novel multi-scale approach that integrates field data, aerial imaging, and satellite platforms to generate high-quality biomass maps across varying scales. At the fine scale level, the AVIRIS-NG hyperspectral data were employed to develop a model for estimating AGB with an exceptional spatial resolution of 5 m. Subsequently, at a broader scale, large-scale and multitemporal models were constructed using spaceborne Sentinel-1 and Sentinel-2 data collected in 2021. The Random Forest (RF) algorithm was utilized to train spring, fall and multi-temporal models using 70% of the available reference data. Using the remaining 30% of untouched data for model validation, Root Mean Square Errors (RMSE) of 0.97, 0.98, and 1.61 Mg ha−1 was achieved for the spring, fall, and multi-temporal models, respectively. The highest R-squared value of 0.65 was achieved for the multi-temporal model. Additionally, the analysis highlighted the importance of various features in biomass estimation, indicating the contribution of different bands and indices. By leveraging the wetland inventory classification map, a comprehensive temporal analysis was conducted to examine the average and total AGB dynamics across various wetland classes. This analysis elucidated the patterns and fluctuations in AGB over time, providing valuable insights into the temporal dynamics of these wetland ecosystems.
Real-Time Precise Orbit Determination of Low Earth Orbit Satellites Based on GPS and BDS-3 PPP B2b Service
Authors: Shi Yali , Xu Tianhe , Li Min , Wei Kai , Wang Shuai , Wang Dixing
Published: February 2024

This study investigates and verifies the feasibility of the precise point positioning (PPP)-B2b enhanced real-time (RT) precise orbit determination (POD) of low Earth orbit (LEO) satellites. The principles and characteristics of matching various PPP-B2b corrections are introduced and analyzed. The performance and accuracy of broadcast ephemeris and PPP-B2b signals are compared and evaluated by referring to the precise ephemeris. The root mean square (RMS) errors in the Global Positioning System (GPS) and BeiDou Navigation Satellite System (BDS)-3 broadcast ephemeris orbits in the along direction are larger than those in the other two (radial and cross) directions, and correspondingly, the along component PPP-B2b corrections are greatest. The continuity and smoothness of the GPS and BDS-3 broadcast ephemeris orbits and clock offsets are improved with the PPP-B2b corrections. The availability of PPP-B2b corrections is comprehensively analyzed for the TJU-01 satellite. Several comparative schemes are adopted for the RT POD of the TJU-01 satellite using the broadcast ephemeris and PPP-B2b corrections. The RT POD performance is improved considerably with the broadcast ephemeris corrected by the PPP-B2b signals. The RMS of the RT orbital errors in the radial, along, and cross directions is 0.10, 0.13, and 0.09 m, respectively, using BDS-3 and GPS PPP-B2b corrections, with reference to the solutions calculated with the precise ephemeris. The accuracy is improved by 5.1%, 43.9%, and 28.7% in the three directions, respectively, relative to that achieved with the broadcast ephemeris. It is concluded that a greater proportion of received PPP-B2b satellite signals corresponds to a greater improvement in the accuracy of the RT POD of the LEO satellite.
The Biomass Proxy: Unlocking Global Agricultural Monitoring through Fusion of Sentinel-1 and Sentinel-2
Authors: Burger Rogier , Aouizerats Benjamin , den Besten Nadja , Guillevic Pierre , Catarino Filipe , van der Horst Teije , Jackson Daniel , Koopmans Regan , Ridderikhoff Margot , Robson Greg , Zajdband Ariel , de Jeu Richard
Published: February 2024

The Biomass Proxy is a new cloud-free vegetation monitoring product that offers timely and analysis-ready data indicative of above-ground crop biomass dynamics at 10m spatial resolution. The Biomass Proxy links the consistent and continuous temporal signal of the Sentinel-1 Cross Ratio (CR), a vegetation index derived from Synthetic Aperture Radar backscatter, with the spatial information of the Sentinel-2 Normalized Difference Vegetation Index (NDVI), a vegetation index derived from optical observations. A global scaling relationship between CR and NDVI forms the basis of a novel fusion methodology based on static and dynamic combinations of temporal and spatial responses of CR and NDVI at field level. The fusion process is used to mitigate the impact on product quality of low satellite revisit periods due to acquisition design or persistent cloud coverage, and to respond to rapid changes in a timely manner to detect environmental and management events. The resulting Biomass Proxy provides time series that are continuous, unhindered by clouds, and produced uniformly across all geographical regions and crops. The Biomass Proxy offers opportunities including improved crop growth monitoring, event detection, and phenology stage detection.
Physics-Based Satellite-Derived Bathymetry (SDB) Using Landsat OLI Images
Authors: Kim Minsu , Danielson Jeff , Storlazzi Curt , Park Seonkyung
Published: February 2024

The estimation of depth in optically shallow waters using satellite imagery can be efficient and cost-effective. Active sensors measure the distance traveled by an emitted laser pulse propagating through the water with high precision and accuracy if the bottom peak intensity of the waveform is greater than the noise level. However, passive optical imaging of optically shallow water involves measuring the radiance after the sunlight undergoes downward attenuation on the way to the sea floor, and the reflected light is then attenuated while moving back upward to the water surface. The difficulty of satellite-derived bathymetry (SDB) arises from the fact that the measured radiance is a result of a complex association of physical elements, mainly the optical properties of the water, bottom reflectance, and depth. In this research, we attempt to apply physics-based algorithms to solve this complex problem as accurately as possible to overcome the limitation of having only a few known values from a multispectral sensor. Major analysis components are atmospheric correction, the estimation of water optical properties from optically deep water, and the optimization of bottom reflectance as well as the water depth. Specular reflection of the sky radiance from the water surface is modeled in addition to the typical atmospheric correction. The physical modeling of optically dominant components such as dissolved organic matter, phytoplankton, and suspended particulates allows the inversion of water attenuation coefficients from optically deep pixels. The atmospheric correction and water attenuation results are used in the ocean optical reflectance equation to solve for the bottom reflectance and water depth. At each stage of the solution, physics-based models and a physically valid, constrained Levenberg–Marquardt numerical optimization technique are used. The physics-based algorithm is applied to Landsat Operational Land Imager (OLI) imagery over the shallow coastal zone of Guam, Key West, and Puerto Rico. The SDB depths are compared to airborne lidar depths, and the root mean squared error (RMSE) is mostly less than 2 m over water as deep as 30 m. As the initial choice of bottom reflectance is critical, along with the bottom reflectance library, we describe a pure bottom unmixing method based on eigenvector analysis to estimate unknown site-specific bottom reflectance.
Calculation and Analysis of Key Parameters of Underwater Optical Imaging System
Authors: Zhou Guangpeng , Liu Ying , Dang Boshi , Yu Chen , Li Linhan , Du Jie , Ma Junlin , Liu Xinyue
Published: February 2024

When photographing objects underwater, it is important to utilize an optical window to isolate the imaging device from the water. The properties of the entire imaging system will change, and the imaging quality will decrease due to the refraction impact of the water and the window. The theoretical calculation method for air imaging is no longer relevant in this context. To analyze the unique rule, this research derives the formulas for key parameters of underwater imaging systems under paraxial circumstances. First, the optical window is modeled, then the formula for the optical window’s focal length in the underwater environment is derived, and the change rule for the focal length of various window forms underwater is condensed. For the ideal imaging system using a domed optical window, the equivalent two-optical group model of the imaging system is established, and the formula for calculating the focal length, working distance, and depth of field of the underwater imaging system is derived through paraxial ray tracing. The accuracy of the formula is verified through the comparative analysis of the formula calculation results and the Zemax modeling simulation results. It provides an important theoretical basis for the in-depth study of underwater imaging technology.
Passive Electroluminescence and Photoluminescence Imaging Acquisition of Photovoltaic Modules
Authors: Redondo-Plaza Alberto , Morales-Aragonés José Ignacio , Gallardo-Saavedra Sara , Mateo-Romero Héctor Felipe , Araujo-Rendón Santiago , Zorita-Lamadrid Ángel L. , Alonso-Gómez Víctor , Hernández-Callejo Luis
Published: February 2024

In photovoltaic power plant inspections, techniques for module assessment play a crucial role as they enhance fault detection and module characterization. One valuable technique is luminescence. The present paper introduces a novel technique termed passive luminescence. It enhances both electroluminescence and photoluminescence imaging acquisition in photovoltaic power plants under normal operation in high irradiance conditions. This technique is based on the development of an electronic board, which allows the polarity of the module to be changed, enabling the current generated by the photovoltaic string to be injected into the module and producing electroluminescence effects. Additionally, the board can bypass the module and set an open circuit, inducing photoluminescence emission using sunlight as an excitation source. The proper coordination of the board and an InGaAs camera with a bandpass filter has allowed for the integration of a lock-in technique, which has produced electroluminescence and photoluminescence pictures that can be used for fault detection.
Driver Drowsiness Multi-Method Detection for Vehicles with Autonomous Driving Functions
Authors: Beles Horia , Vesselenyi Tiberiu , Rus Alexandru , Mitran Tudor , Scurt Florin Bogdan , Tolea Bogdan Adrian
Published: February 2024

The article outlines various approaches to developing a fuzzy decision algorithm designed for monitoring and issuing warnings about driver drowsiness. This algorithm is based on analyzing EOG (electrooculography) signals and eye state images with the aim of preventing accidents. The drowsiness warning system comprises key components that learn about, analyze and make decisions regarding the driver’s alertness status. The outcomes of this analysis can then trigger warnings if the driver is identified as being in a drowsy state. Driver drowsiness is characterized by a gradual decline in attention to the road and traffic, diminishing driving skills and an increase in reaction time, all contributing to a higher risk of accidents. In cases where the driver does not respond to the warnings, the ADAS (advanced driver assistance systems) system should intervene, assuming control of the vehicle’s commands.