bluuebunny/crossref_metadata_2025 huggingface dataset:
Microsatellite Content in 397 Nuclear Exons and Their Flanking Regions in the Fern Family Ophioglossaceae
Authors: Koubínová Darina ,   GoFlag Consortium, Grant Jason R.
Published: March 2024

Microsatellites or SSRs are small tandem repeats that are 1–6 bp long. They are usually highly polymorphic and form important portions of genomes. They have been extensively analyzed in humans, animals and model plants; however, information from non-flowering plants is generally lacking. Here, we examined 29 samples of Ophioglossaceae ferns, mainly from the genera Botrychium and Sceptridium. We analyzed the SSR distribution, density and composition in almost 400 nuclear exons and their flanking regions. We detected 45 SSRs in exons and 1475 SSRs in the flanking regions. In the exons, only di-, tri- and tetranucleotides were found, and all of them were 12 bp long. The annotation of the exons containing SSRs showed that they were related to various processes, such as metabolism, catalysis, transportation or plant growth. The flanking regions contained SSRs from all categories, with the most numerous being dinucleotides, followed by tetranucleotides. More than one-third of all the SSRs in the flanking regions were 12 bp long. The SSR densities in the exons were very low, ranging from 0 to 0.07 SSRs/kb, while those in the flanking regions ranged from 0.24 to 0.81 SSRs/kb; and those in the combined dataset ranged from 0.2 to 0.81 SSRs/kb. The majority of the detected SSRs in the flanking regions were polymorphic and present at the same loci across two or more samples but differing in the number of repeats. The SSRs detected here may serve as a basis for further population genetic, phylogenetic or evolutionary genetic studies, as well as for further studies focusing on SSRs in the genomes and their roles in adaptation, evolution and diseases.
Transcriptome Profiling Reveals Molecular Responses to Salt Stress in Common Vetch (Vicia sativa L.)
Authors: Sun Yanmei , Zhao Na , Sun Hongjian , Xu Shan , Lu Yiwen , Xi Haojie , Guo Zhenfei , Shi Haifan
Published: March 2024

Common vetch (Vicia sativa L.) is an important annual diploid leguminous forage. In the present study, transcriptomic profiling in common vetch in response to salt stress was conducted using a salt-tolerant line (460) and a salt-sensitive line (429). The common responses in common vetch and the specific responses associated with salt tolerance in 460 were analyzed. Several KEGG (Kyoto Encyclopedia of Genes and Genomes) pathways, including plant hormone and MAPK (mitogen-activated protein kinase) signaling, galactose metabolism, and phenylpropanoid phenylpropane biosynthesis, were enriched in both lines, though some differentially expressed genes (DEGs) showed distinct expression patterns. The roots in 460 showed higher levels of lignin than in 429. α-linolenic acid metabolism, carotenoid biosynthesis, the photosynthesis-antenna pathway, and starch and sucrose metabolism pathways were specifically enriched in salt-tolerant line 460, with higher levels of accumulated soluble sugars in the leaves. In addition, higher transcript levels of genes involved in ion homeostasis and reactive oxygen species (ROS) scavenging were observed in 460 than in 429 in response to salt stress. The transcriptomic analysis in common vetch in response to salt stress provides useful clues for further investigations on salt tolerance mechanism in the future.
Melatonin Mitigates Water Deficit Stress in Cenchrus alopecuroides (L.) Thunb through Up-Regulating Gene Expression Related to the Photosynthetic Rate, Flavonoid Synthesis, and the Assimilatory Sulfate Reduction Pathway
Authors: Jiang Li , Yun Minqiang , Ma Yinxi , Qu Tongbao
Published: March 2024

Melatonin can improve plant adaptability to water deficit stress by regulating the biosynthesis of flavonoids and improving the reactive oxygen species-scavenging enzyme system. However, it remains unclear whether melatonin mitigates the effects and causes of water deficit stress in Cenchrus alopecuroides. We conducted a PEG-simulated water stress pot experiment to determine whether and how exogenous melatonin alleviates water deficit in C. alopecuroides. The experiment was divided into four treatments: (1) normal watering (Control), (2) 40% PEG-6000 treatment (D), (3) 100 μmol·L−1 melatonin treatment (MT), and (4) both melatonin and PEG-6000 treatment (DMT). The results showed that melatonin can alleviate water deficit in C. alopecuroides by effectively inhibiting plant chlorophyll degradation and MDA accumulation while increasing antioxidant enzyme activities and photosynthetic rates under water deficit stress. The transcriptome results indicated that melatonin regulates the expression of genes with the biosynthesis pathway of flavonoids (by increasing the expression of PAL, 4CL, HCT, and CHS), photosynthesis-antenna proteins (by increasing the expression of LHC), and sulfur metabolism (the expression of PAPSS and CysC is up-regulated in the assimilatory sulfate reduction pathway), while up-regulating the transcription factors (AP2/ERF-ERF-, C2H2-, WRKY-, Tify-, bHLH-, NAC-, and MYB-related). These findings revealed the possible causes by which melatonin mitigates water deficit stress in C. alopecuroides, which provided novel insights into the role of melatonin in water deficit stress.
Advanced Biotechnological Interventions in Mitigating Drought Stress in Plants
Authors: Şimşek Özhan , Isak Musab A. , Dönmez Dicle , Dalda Şekerci Akife , İzgü Tolga , Kaçar Yıldız Aka
Published: March 2024

This comprehensive article critically analyzes the advanced biotechnological strategies to mitigate plant drought stress. It encompasses an in-depth exploration of the latest developments in plant genomics, proteomics, and metabolomics, shedding light on the complex molecular mechanisms that plants employ to combat drought stress. The study also emphasizes the significant advancements in genetic engineering techniques, particularly CRISPR-Cas9 genome editing, which have revolutionized the creation of drought-resistant crop varieties. Furthermore, the article explores microbial biotechnology’s pivotal role, such as plant growth-promoting rhizobacteria (PGPR) and mycorrhizae, in enhancing plant resilience against drought conditions. The integration of these cutting-edge biotechnological interventions with traditional breeding methods is presented as a holistic approach for fortifying crops against drought stress. This integration addresses immediate agricultural needs and contributes significantly to sustainable agriculture, ensuring food security in the face of escalating climate change challenges.
Exogenous Sodium and Calcium Alleviate Drought Stress by Promoting the Succulence of Suaeda salsa
Authors: Zhang Dong , Tian Changyan , Mai Wenxuan
Published: March 2024

Succulence is a key trait involved in the response of Suaeda salsa to salt stress. However, few studies have investigated the effects of the interaction between salt and drought stress on S. salsa growth and succulence. In this study, the morphology and physiology of S. salsa were examined under different salt ions (Na+, Ca2+, Mg2+, Cl−, and SO42−) and simulated drought conditions using different polyethylene glycol concentrations (PEG; 0%, 5%, 10%, and 15%). The results demonstrate that Na+ and Ca2+ significantly increased leaf succulence by increasing leaf water content and enlarging epidermal cell size compared to Mg2+, Cl−, and SO42−. Under drought (PEG) stress, with an increase in drought stress, the biomass, degree of leaf succulence, and water content of S. salsa decreased significantly in the non-salt treatment. However, with salt treatment, the results indicated that Na+ and Ca2+ could reduce water stress due to drought by stimulating the succulence of S. salsa. In addition, Na+ and Ca2+ promoted the activity of superoxide dismutase (SOD), catalase (CAT), and peroxidase (POD), which could reduce oxidative stress. In conclusion, Na+ and Ca2+ are the main factors promoting succulence and can effectively alleviate drought stress in S. salsa.
Climate Factors Influence Above- and Belowground Biomass Allocations in Alpine Meadows and Desert Steppes through Alterations in Soil Nutrient Availability
Authors: Wang Jiangfeng , Zhang Xing , Wang Ru , Yu Mengyao , Chen Xiaohong , Zhu Chenghao , Shang Jinlong , Gao Jie
Published: March 2024

Biomass is a direct reflection of community productivity, and the allocation of aboveground and belowground biomass is a survival strategy formed by the long-term adaptation of plants to environmental changes. However, under global changes, the patterns of aboveground–belowground biomass allocations and their controlling factors in different types of grasslands are still unclear. Based on the biomass data of 182 grasslands, including 17 alpine meadows (AMs) and 21 desert steppes (DSs), this study investigates the spatial distribution of the belowground biomass allocation proportion (BGBP) in different types of grasslands and their main controlling factors. The research results show that the BGBP of AMs is significantly higher than that of DSs (p < 0.05). The BGBP of AMs significantly decreases with increasing mean annual temperature (MAT) and mean annual precipitation (MAP) (p < 0.05), while it significantly increases with increasing soil nitrogen content (N), soil phosphorus content (P), and soil pH (p < 0.05). The BGBP of DSs significantly decreases with increasing MAP (p < 0.05), while it significantly increases with increasing soil phosphorus content (P) and soil pH (p < 0.05). The random forest model indicates that soil pH is the most important factor affecting the BGBP of both AMs and DSs. Climate-related factors were identified as key drivers shaping the spatial distribution patterns of BGBP by exerting an influence on soil nutrient availability. Climate and soil factors exert influences not only on grassland biomass allocation directly, but also indirectly by impacting the availability of soil nutrients.
Multilocus Molecular Phylogeny of the Umbilicaria aprina Group (Umbilicariaceae, Lichenized Ascomycota) Supports Species Level and Neo-Endemic Status of Umbilicaria krascheninnikovii
Authors: Davydov Evgeny A. , Himelbrant Dmitry E. , Kuznetsova Ekaterina S. , Stepanchikova Irina S. , Yakovchenko Lidia S.
Published: March 2024

The Northeast Asian endemic species of lichen-forming fungus Umbilicaria krascheninnikovii is herein discussed in the global context of biogeography and phylogeny of the U. aprina group. The name U. krascheninnikovii has been erroneously used by lichenologists for Umbilicaria spp. from high latitudes or altitudes worldwide, as there are omphalodisc apothecia and rough “crystals” of a necral layer on the upper surface. To test the monophyly and phylogenetic relationships within the U. aprina group, four independent DNA regions (nrITS/5.8S, RPB2, mtLSU, and mtSSU) were used for six rare species, including a dozen specimens of U. krascheninnikovii from its locus classicus in Kamchatka. The study is based on the phylograms obtained using maximum likelihood and a Bayesian phylogenetic inference framework. As a result of phylogenetic and biogeographic analyses, it was shown that U. krascheninnikovii is a neo-endemic of the areas of modern volcanism in Kamchatka, Japan, as well as in the Kurile Islands, where this species was recorded for the first time. The morphology of U. krascheninnikovii is herein described and illustrated. Increasing the role of the sexual process and reducing asexual thalloconidiogenesis are shown to be apomorphic traits in the U. aprina group. The combination of sexual and asexual reproduction provides adaptive advantages in changing environmental conditions.
Two-Level Self-Thickening Mechanism of a Novel Acid Thickener with a Hydrophobic-Associated Structure during High-Temperature Acidification Processes
Authors: Li Peng , Wang Lei , Lai Xiaojuan , Gao Jinhao , Dang Zhiqiang , Wang Rong , Mao Fan , Li Yemin , Jia Guangliang
Published: March 2024

Two acid thickeners, ADMC and ADOM, were prepared by aqueous solution polymerization using acrylamide (AM) and methacryloyloxyethyl trimethyl ammonium chloride (DMC) as raw materials, with or without the introduction of octadecyl polyoxyethylene ether methacrylate (OEMA). It was characterized by FTIR, 1H NMR, and the fluorescence spectra of pyrene. The double-layer thickening mechanism of ADOM was proved by comparing the thickening and rheological properties of ADMC and ADOM tested by a six-speed rotary viscometer and a HAKKE MARSIV rheometer during the acidification process. The results showed that the synthetic product was the target product; the first stage of the self-thickening ADOM fresh acid solution during high-temperature acidification was mainly affected by Ca2+ concentration, and the second stage of self-thickening was mainly affected by temperature. The residual viscosity of the 0.8 wt% ADOM residual acid solution was 250, 201.5, and 61.3 mPa·s, respectively, after shearing at 90, 120, and 150 °C for 60 min at a shear rate of 170 s−1. The thickening acid ADOM with a hydrophobic association structure has good temperature resistance and shear resistance, which can be used for high-temperature deep-well acid fracturing. In addition, no metal crosslinking agent was introduced in the system to avoid damage to its formation, and ADOM exhibited good resistance to Ca2+, which could provide ideas for the reinjection of the acidizing flowback fluid. It also has certain advantages for environmental protection.
Design and Optimization of 3D-Printed Variable Cross-Section I-Beams Reinforced with Continuous and Short Fibers
Authors: Zhang Xin , Sun Peijie , Zhang Yu , Wang Fei , Tu Yun , Ma Yunsheng , Zhang Chun
Published: March 2024

By integrating fiber-reinforced composites (FRCs) with Three-dimensional (3D) printing, the flexibility of lightweight structures was promoted while eliminating the mold’s limitations. The design of the I-beam configuration was performed according to the equal-strength philosophy. Then, a multi-objective optimization analysis was conducted based on the NSGA-II algorithm. 3D printing was utilized to fabricate I-beams in three kinds of configurations and seven distinct materials. The flexural properties of the primitive (P-type), the designed (D-type), and the optimized (O-type) configurations were verified via three-point bending testing at a speed of 2 mm/min. Further, by combining different reinforcements, including continuous carbon fibers (CCFs), short carbon fibers (SCFs), and short glass fibers (SGFs) and distinct matrices, including polyamides (PAs), and polylactides (PLAs), the 3D-printed I-beams were studied experimentally. The results indicate that designed and optimized I-beams exhibit a 14.46% and 30.05% increase in the stiffness-to-mass ratio and a 7.83% and 40.59% increment in the load-to-mass ratio, respectively. The CCFs and SCFs result in an outstanding accretion in the flexural properties of 3D-printed I-beams, while the accretion is 2926% and 1070% in the stiffness-to-mass ratio and 656.7% and 344.4% in the load-to-mass ratio, respectively. For the matrix, PAs are a superior choice compared to PLAs for enhancing the positive impact of reinforcements.
Nanocrystalline Cellulose-Supported Iron Oxide Composite Materials for High-Performance Lithium-Ion Batteries
Authors: Tran Quang Nhat , Park Chan Ho , Le Thi Hoa
Published: March 2024

Nanocrystalline cellulose (NCC) can be converted into carbon materials for the fabrication of lithium-ion batteries (LIBs) as well as serve as a substrate for the incorporation of transition metal oxides (TMOs) to restrain the volume expansion, one of the most significant challenges of TMO-based LIBs. To improve the electrochemical performance and enhance the longer cycling stability of LIBs, a nanocrystalline cellulose-supported iron oxide (Fe2O3) composite (denoted as NCC–Fe2O3) is synthesized and utilized as electrodes in LIBs. The obtained NCC–Fe2O3 electrode exhibited stable cycling performance, better capacity, and high-rate capacity, and delivered a specific discharge capacity of 576.70 mAh g−1 at 100 mA g−1 after 1000 cycles. Moreover, the NCC–Fe2O3 electrode was restored and showed an upward trend of capacity after working at high current densities, indicating the fabricated composite is a promising approach to designing next-generation high-energy density lithium-ion batteries.
Preparation, Structure and Properties of Epoxy/Carbonyl Iron Powder Wave-Absorbing Foam for Electromagnetic Shielding
Authors: Liu Xiaoli , Huang Hao , Lu Haijun
Published: March 2024

The application of absorbing materials for electromagnetic shielding is becoming extensive, and the use of absorbents is one of the most important points of preparing absorbing foam materials. In this work, epoxy resin was used as the matrix and carbonyl iron powder (CIP) was used as the absorbent, and the structural absorbing foam materials were prepared by the ball mill dispersion method. Scanning electron microscopy showed that the CIP was evenly dispersed in the resin matrix. The foam structures formed at pre-polymerization times of 10 min, 30 min and 50 min were analyzed, and it was found that the cell diameter decreased from 0.47 mm to 0.31 mm with the increase in the pre-polymerization time. The reflectivity of the frontal and reverse sides of the foam gradually tends to be unified at frequencies of 2–18 GHz. When the CIP content increased from 30 wt% to 70 wt%, the cell diameter increased from 0.32 mm to 0.4 mm, and the uniformity of CIP distribution deteriorated. However, with the increase in the CIP content, the absorption properties of the composite materials were enhanced, and the absorption frequency band broadened. When the CIP content reached 70 wt%, the compression strength and modulus of the foam increased to 1.32 MPa and 139.0 MPa, respectively, indicating a strong ability to resist deformation.
The Reversible Transformation of a Vesicular Aggregate in Response to a pH Oscillation
Authors: Shimada Moeka , Someya Risa , Okamoto Yasunao , Yamamoto Daigo , Shioi Akihisa
Published: March 2024

The transformation of amphiphilic molecular assemblies in response to chemical oscillations is fundamental in biological systems. The reversible transformation of a vesicular aggregate (VA) in response to a pH oscillation is presented in this study. A VA composed of the cationic surfactant didodecyldimethylammonium bromide is transformed using a pH oscillation ranging between 3 and 7. When the VA attains a stable structure at extreme pH values, the transformation reaches the irreversible stage. However, the addition of a phosphate buffer to the VA suspension changes the pH oscillation pattern from being rectangular to triangular and decreases the oscillation amplitude, successfully achieving the reversible transformation of the VA. Maintaining the non-equilibrium (transient) structures throughout the transformation and not falling into the equilibrium state with a varying pH are essential for the reversible transformation. This may be common and essential for dynamics in biological cells.
Thermosonication Processing of Purple Onion Juice (Allium cepa L.): Anticancer, Antibacterial, Antihypertensive, and Antidiabetic Effects
Authors: Yıkmış Seydi , Erdal Berna , Doguer Caglar , Levent Okan , Türkol Melikenur , Tokatlı Demirok Nazan
Published: March 2024

Onion (Allium cepa L.) juice is an important product used in gastronomy and food formulations. The first objective of this study was to optimize the content of bioactive compounds in purple onion juice (POJ) after the thermosonication process using response surface methodology (RSM) and artificial neural network (ANN) application models. Second, the anticancer, antibacterial, antihypertensive, and antidiabetic effects of POJ obtained after thermal pasteurization (P-POJ) or thermosonication (TS-POJ) were investigated after obtaining the ANN and RSM analysis reports. The optimization process for TS-POJ was carried out at 44 °C, for 13 min, with a 68% amplitude. The findings demonstrated that the angiotensin-converting enzyme (ACE) inhibition level was greater in TS-POJ samples than in the untreated control (C-POJ) sample (p > 0.05). C-POJ, TS-POJ, and P-POJ exhibited the inhibition of cell proliferation in vitro in a dose-dependent manner in lung (A549), cervical (HeLa), and colon cancer cells following 24 h incubation. Thermosonication or thermal pasteurization did not markedly affect the cell proliferation of the examined cancer cells compared to the untreated control group. While no antibacterial effect was observed with low concentrations of samples, they showed an antibacterial effect at pure concentrations (100%). The thermosonication treatment for processing purple onion juice was successful in this study’s results.
Well-Being Technologies and Positive Psychology Strategies for Training Metacognition, Emotional Intelligence and Motivation Meta-Skills in Clinical Populations: A Systematic Review
Authors: Mitsea Eleni , Drigas Athanasios , Skianis Charalabos
Published: March 2024

The holistic growth and psychological well-being of people with special needs and disabilities remain high on the priority agenda for sustainable and inclusive education. Digital well-being technologies and especially “smart technologies”, are ready to revolutionize mental health interventions by meeting trainees’ needs and providing them with more positive and transformative mental, emotional, and social experiences. Μeta-skills refer to a set of consciousness-raising competences that incorporate meta-cognitive, social–emotional, and motivational attributes, allowing individuals to intentionally achieve a state of optimal functioning. Although positive psychology and well-being technologies are considered promising intervention approaches, there is less knowledge regarding the effectiveness of such interventions among people with special needs and disabilities, especially in the crucial domain of meta-skills development. Thus, the current systematic review aims to examine positive psychology strategies as well as the synergy with well-being technologies in the development of metacognition, emotional intelligence, and motivation meta-skills in populations with special training needs and disabilities. The PRISMA methodology was utilized to answer the research questions. A total of forty-nine studies met the inclusion criteria. The results indicated that positive psychology strategies improved a wide range of meta-skills, including self-regulation, emotional control, behavioral control, inhibition control, self-awareness, intrapersonal skills, interpersonal skills, adaptation, goal setting, and self-compassion. Artificial intelligence tools, wearables, smart applications, immersive technologies (virtual and augmented reality), neurofeedback and biofeedback technologies, as well as digital games were found to effectively assist such training programs. The results of the current review may provide positive feedback in the discussion about digitally-aided mental health interventions for training the meta-skills of mental and emotional health.
Quantized Approach to Damped Transversal Mechanical Waves
Authors: Márkus Ferenc , Gambár Katalin
Published: March 2024

In information transfer, the dissipation of a signal is of crucial importance. The feasibility of reconstructing the distorted signal depends on the related permanent loss. Therefore, understanding the quantized dissipative transversal mechanical waves might result in deep insights. In particular, it may be valid on the nanoscale in the case of signal distortion, loss, or even restoration. Based on the description of the damped quantum oscillator, we generalize the canonical quantization procedure for the case of the transversal waves. Then, we deduce the related damped wave equation and the state function. We point out the two possible solutions of the propagating-damping wave equation. One involves the well-known Gaussian spreading solution superposed with the damping oscillation, in which the loss of information is complete. The other is the Airy function solution, which is non-spreading–propagating, so the information loss is only due to oscillation damping. However, the structure of the wave shape remains unchanged for the latter. Consequently, this fact may allow signal reconstruction, resulting in the capability of restoring the lost information.
Unmanned Aerial Vehicle (UAV)-Based Vegetation Restoration Monitoring in Coal Waste Dumps after Reclamation
Authors: Ren He , Zhao Yanling , Xiao Wu , Zhang Lifan
Published: March 2024

Frequent spontaneous combustion activities restrict ecological restoration of coal waste dumps after reclamation. Effective monitoring of vegetation restoration is important for ensuring land reclamation success and preserving the ecological environment in mining areas. Development of unmanned aerial vehicle (UAV) technology has enabled fine-scale vegetation monitoring. In this study, we focused on Medicago sativa L. (alfalfa), a representative herbaceous vegetation type, in a coal waste dump after reclamation in Shanxi province, China. The alfalfa aboveground biomass (AGB) was used as an indicator for assessing vegetation restoration. The objective of this study was to evaluate the capacity of UAV-based fusion of RGB, multispectral, and thermal infrared information for estimating alfalfa AGB using various regression models, including random forest regression (RFR), gradient boosting decision tree (GBDT), K-nearest neighbor (KNN), support vector regression (SVR), and stacking models. The main results are as follows: (i) UAV multi-source data fusion improved alfalfa AGB estimation accuracy, although the enhancement diminished with the increasing number of sensor types. (ii) The stacking model consistently outperformed RFR, GBDT, KNN, and SVR regression models across all feature fusion combinations. It achieved high accuracy with R2 of 0.86–0.88, RMSE of 80.06–86.87 g/m2, and MAE of 60.24–62.69 g/m2. Notably, the stacking model based on only RGB imagery features mitigated the accuracy loss from limited types of features, potentially reducing equipment costs. This study demonstrated the potential of UAV in improving vegetation restoration management of coal waste dumps after reclamation.
Spatio-Temporal Variation of Cyanobacteria Blooms in Taihu Lake Using Multiple Remote Sensing Indices and Machine Learning
Authors: Pan Xin , Yuan Jie , Yang Zi , Tansey Kevin , Xie Wenying , Song Hao , Wu Yuhang , Yang Yingbao
Published: March 2024

In view of the ecological threat posed by cyanobacteria blooms in Taihu Lake (China), this paper presents a study on the area of cyanobacteria extent based on MODIS data using the quantum particle swarm optimization–random forest (QPSO-RF) machine learning algorithm. This paper selects multiple remote sensing input indices that can represent the characteristics of the primary underlying type in Taihu Lake. The proposed method performs best, with an F1 score of 0.91–0.98. Based on this method, the spatio-temporal variation of cyanobacteria blooms in the Taihu Lake complex was analyzed. During 2010–2022, the average area of cyanobacteria blooms in Taihu Lake increased slightly. Severe-scale cyanobacteria blooms occurred in 2015–2019. Cyanobacteria blooms were normally concentrated from May to November. However, the most prolonged extended duration occurred in 2017, lasting for eight months. Spatially, cyanobacteria blooms were mainly identified in the northwestern part of Taihu Lake, with an average occurrence frequency of about 10.0%. The cyanobacteria blooms often began to grow in the northwestern part of the lake and then spread to the Center of the Lake, and also dissipated earliest in the northwestern part of the lake. Our study is also beneficial for monitoring the growth of cyanobacteria blooms in other similar large lakes in long time series.
Freshwater Aquaculture Mapping in “Home of Chinese Crawfish” by Using a Hierarchical Classification Framework and Sentinel-1/2 Data
Authors: Wang Chen , Wang Genhou , Zhang Geli , Cui Yifeng , Zhang Xi , He Yingli , Zhou Yan
Published: March 2024

The escalating evolution of aquaculture has wielded a profound and far-reaching impact on regional sustainable development, ecological equilibrium, and food security. Currently, most aquaculture mapping efforts mainly focus on coastal aquaculture ponds rather than diverse inland aquaculture areas. Recognizing all types of aquaculture areas and accurately classifying different types of aquaculture areas remains a challenge. Here, on the basis of the Google Earth Engine (GEE) and the time-series Sentinel-1 and -2 data, we developed a novel hierarchical framework extraction method for mapping fine inland aquaculture areas (aquaculture ponds + rice-crawfish fields) by employing distinct phenological disparities within two temporal windows (T1 and T2) in Qianjiang, so-called “Home of Chinese Crawfish”. Simultaneously, we evaluated the classification performance of four distinct machine learning classifiers, namely Random Forest (RF), Support Vector Machine (SVM), Classification and Regression Trees (CART), and Gradient Boosting (GTB), as well as 11 feature combinations. Following an exhaustive comparative analysis, we selected the optimal machine learning classifier (i.e., the RF classifier) and the optimal feature combination (i.e., feature combination after an automated feature selection method) to classify the aquaculture areas with high accuracy. The results underscore the robustness of the proposed methodology, achieving an outstanding overall accuracy of 93.8%, with an F1 score of 0.94 for aquaculture. The result indicates that an area of 214.6 ± 10.5 km2 of rice-crawfish fields, constituting approximately 83% of the entire aquaculture area in Qianjiang, followed by aquaculture ponds (44.3 ± 10.7 km2, 17%). The proposed hierarchical framework, based on significant phenological characteristics of varied aquaculture types, provides a new approach to monitoring inland freshwater aquaculture in China and other regions of the world.
Modeling Land Use Transformations and Flood Hazard on Ibaraki’s Coastal in 2030: A Scenario-Based Approach Amid Population Fluctuations
Authors: Safabakhshpachehkenari Mohammadreza , Tonooka Hideyuki
Published: March 2024

Coastal areas, influenced by human activity and natural factors, face major environmental shifts, including climate-induced flood risks. This highlights the importance of forecasting coastal land use for effective flood defense and ecological conservation. Japan’s distinct demographic path necessitates flexible strategies for managing its urban development. The study examines the Ibaraki Coastal region to analyze the impacts of land-use changes in 2030, predicting and evaluating future floods from intensified high tides and waves in scenario-based forecasts. The future roughness map is derived from projected land-use changes, and we utilize this information in DioVISTA 3.5.0 software to simulate flood scenarios. Finally, we analyzed the overlap between simulated floods and each land-use category. The results indicate since 2020, built-up areas have increased by 52.37 sq. km (39%). In scenarios of constant or shrinking urban areas, grassland increased by 28.54 sq. km (42%), and urban land cover decreased by 7.47 sq. km (5.6%) over ten years. Our research examines two separate peaks in water levels associated with urban flooding. Using 2030 land use maps and a peak height of 4 m, which is the lower limit of the maximum run-up height due to storm surge expected in the study area, 4.71 sq. km of residential areas flooded in the urban growth scenario, compared to 4.01 sq. km in the stagnant scenario and 3.96 sq. km in the shrinkage scenario. With the upper limit of 7.2 m, which is the extreme case in most of the study area, these areas increased to 49.91 sq. km, 42.52 sq. km, and 42.31 sq. km, respectively. The simulation highlights future flood-prone urban areas for each scenario, guiding targeted flood prevention efforts.
Indoor Environment Reconstruction for Unmanned System Using Multiple Low-Cost Sensors
Authors: Wang Yunshu , Ding Bin , Wang Haiqing , Meng Qian , Zhuang Yuan , Jia Haonan
Published: March 2024

Considering that laser scan stations are expensive and heavy for the indoor environment reconstruction of unmanned systems, a low-cost and light reconstruction system is proposed in this research. The system consists of a cross-structured light visual (SLV) sensor and an inertial navigation system (INS). The cross-SLV sensor is used to scan the surroundings and to estimate the angle change between two adjacent measurements. To improve the robustness and accuracy of the angle measurement, a Kalman Filter (KF) with simple construction is designed to fuse the inertial information from the INS. The factors which influence ranging accuracy are analyzed and ranging experiments show that the SLV sensor has an accuracy of higher than 2% when the distance is less than 4 m. Then the reconstruction results of a kitchen and corridor show that the error of most points is less than 50 mm for either kitchen (94%) or corridor (85%), and the mean errors and standard deviations of kitchen and corridor are less than 20 mm and 30 mm, respectively.
Stationary Detection for Zero Velocity Update of IMU Based on the Vibrational FFT Feature of Land Vehicle
Authors: Li Mowen , Nie Wenfeng , Suvorkin Vladimir , Rovira-Garcia Adria , Zhang Wei , Xu Tianhe , Xu Guochang
Published: March 2024

The inertial navigation system (INS) and global satellite navigation system (GNSS) are two of the most significant systems for land navigation applications. The inertial measurement unit (IMU) is a kind of INS sensor that measures three-dimensional acceleration and angular velocity measurements. IMUs based on micro-electromechanical systems (MEMSs) are widely employed in vehicular navigation thanks to their low cost and small size, but their magnitude and noisy biases make navigation errors diverge very fast without external constraint. The zero-velocity update (ZVU) function is one of the efficient functions that constrain the divergence of IMUs for a stopped vehicle, and the key of the ZVU is the correct stationary detection for the vehicle. When a land vehicle is stopped, the idling engine produces a very stable vibration, which allows us to perform frequency analysis and a comparison based on the fast Fourier transform (FFT) and IMU measurements. Hence, we propose a stationary detection method based on the FFT for a stopped land vehicle with an idling engine in this study. An urban vehicular navigation experiment was carried out with our GNSS/IMU integration platform. Three stops for 10 to 20 min were set to analyze, generate and evaluate the FFT-based stationary detection method. The FFT spectra showed clearly idling vibrational peaks during the three stop periods. Through the comparison of FFT spectral features with decelerating and accelerating periods, the amplitudes of vibrational peaks were put forward as the key factors of stationary detection. For the consecutive stationary detection in the GNSS/IMU integration process, a three-second sliding window with a one-second updating rate of the FFT was applied to check the amplitudes of peaks. For the assessment of the proposed stationary detection method, GNSS observations were removed to simulate outages during the three stop periods, and the proposed detection method was conducted together with the ZVU. The results showed that the proposed method achieved a 99.7% correct detection rate, and the divergence of the positioning error constrained via the ZVU was within 2 cm for the experimental stop periods, which indicates the effectiveness of the proposed method.
Automated Two-Step Seamline Detection for Generating Large-Scale Orthophoto Mosaics from Drone Images
Authors: Varshosaz Masood , Sajadian Maryam , Pirasteh Saied , Moghimi Armin
Published: March 2024

To generate an orthophoto mosaic from a collection of aerial images, the original images are first orthorectified individually using a Digital Surface Model (DSM). Then, they are stitched together along some determined seamlines to form the orthophoto mosaic. Determining appropriate seamlines is a critical process, as it affects the visual and geometric quality of the results. The stitching process can usually be done in frame-to-frame or multi-frame modes. Although the latter is more efficient, both still involve a lot of pre-processing, such as creating individual orthophotos, image registration, and overlap extraction. This paper presents a novel coarse-to-fine approach that directly determines the seamline network without such pre-processing. Our method has been specifically applied for UAV photogrammetry projects where, due to the large number of images and the corresponding overlaps, the orthophoto mosaic generation can be very challenging and time-consuming. We established the seamlines simultaneously for all the images through a two-step process. First, a DSM was generated, and a low-resolution grid was overlayed. Then, for each grid point, an optimal image was selected. Then, the grid cells are grouped into polygons based on their corresponding optimal image. Boundaries of these polygons established our seamline network. Thereafter, to generate the orthophoto mosaic, we overlayed a higher/full resolution grid on the top of the DSM, the optimal image of each point of which was quickly identified via our low-resolution polygons. In this approach, not only seamlines were automatically generated, but also were the need for the creation, registration, and overlap extraction of individual orthophotos. Our method was systematically compared with a conventional frame-to-frame (CF) technique from different aspects, including the number of double-mapped areas, discontinuities across the seamlines network, and the amount of processing time. The outcomes revealed a 46% decrease in orthophoto generation time and a notable reduction in the number of double-mapped areas, sawtooth effects, and object discontinuities within the constructed orthophoto mosaic.
Cloud–Aerosol Classification Based on the U-Net Model and Automatic Denoising CALIOP Data
Authors: Zhou Xingzhao , Chen Bin , Ye Qia , Zhao Lin , Song Zhihao , Wang Yixuan , Hu Jiashun , Chen Ruming
Published: March 2024

Precise cloud and aerosol identification hold paramount importance for a thorough comprehension of atmospheric processes, enhancement of meteorological forecasts, and mitigation of climate change. This study devised an automatic denoising cloud–aerosol classification deep learning algorithm, successfully achieving cloud–aerosol identification in atmospheric vertical profiles utilizing CALIPSO L1 data. The algorithm primarily consists of two components: denoising and classification. The denoising task integrates an automatic denoising module that comprehensively assesses various methods, such as Gaussian filtering and bilateral filtering, automatically selecting the optimal denoising approach. The results indicated that bilateral filtering is more suitable for CALIPSO L1 data, yielding SNR, RMSE, and SSIM values of 4.229, 0.031, and 0.995, respectively. The classification task involves constructing the U-Net model, incorporating self-attention mechanisms, residual connections, and pyramid-pooling modules to enhance the model’s expressiveness and applicability. In comparison with various machine learning models, the U-Net model exhibited the best performance, with an accuracy of 0.95. Moreover, it demonstrated outstanding generalization capabilities, evaluated using the harmonic mean F1 value, which accounts for both precision and recall. It achieved F1 values of 0.90 and 0.97 for cloud and aerosol samples from the lidar profiles during the spring of 2019. The study endeavored to predict low-quality data in CALIPSO VFM using the U-Net model, revealing significant differences with a consistency of 0.23 for clouds and 0.28 for aerosols. Utilizing U-Net confidence and a 532 nm attenuated backscatter coefficient to validate medium- and low-quality predictions in two cases from 8 February 2019, the U-Net model was found to align more closely with the CALIPSO observational data and exhibited high confidence. Statistical comparisons of the predicted geographical distribution revealed specific patterns and regional characteristics in the distribution of clouds and aerosols, showcasing the U-Net model’s proficiency in identifying aerosols within cloud layers.
Deep Learning Based Underwater Acoustic Target Recognition: Introduce a Recent Temporal 2D Modeling Method
Authors: Tang Jun , Gao Wenbo , Ma Enxue , Sun Xinmiao , Ma Jinying
Published: March 2024

In recent years, the application of deep learning models for underwater target recognition has become a popular trend. Most of these are pure 1D models used for processing time-domain signals or pure 2D models used for processing time-frequency spectra. In this paper, a recent temporal 2D modeling method is introduced into the construction of ship radiation noise classification models, combining 1D and 2D. This method is based on the periodic characteristics of time-domain signals, shaping them into 2D signals and discovering long-term correlations between sampling points through 2D convolution to compensate for the limitations of 1D convolution. Integrating this method with the current state-of-the-art model structure and using samples from the Deepship database for network training and testing, it was found that this method could further improve the accuracy (0.9%) and reduce the parameter count (30%), providing a new option for model construction and optimization. Meanwhile, the effectiveness of training models using time-domain signals or time-frequency representations has been compared, finding that the model based on time-domain signals is more sensitive and has a smaller storage footprint (reduced to 30%), whereas the model based on time-frequency representation can achieve higher accuracy (1–2%).
Artificial Neural Network-Based Mechanism to Detect Security Threats in Wireless Sensor Networks
Authors: Khan Shafiullah , Khan Muhammad Altaf , Alnazzawi Noha
Published: March 2024

Wireless sensor networks (WSNs) are essential in many areas, from healthcare to environmental monitoring. However, WSNs are vulnerable to routing attacks that might jeopardize network performance and data integrity due to their inherent vulnerabilities. This work suggests a unique method for enhancing WSN security through the detection of routing threats using feed-forward artificial neural networks (ANNs). The proposed solution makes use of ANNs’ learning capabilities to model the network’s dynamic behavior and recognize routing attacks like black-hole, gray-hole, and wormhole attacks. CICIDS2017 is a heterogeneous dataset that was used to train and test the proposed system in order to guarantee its robustness and adaptability. The system’s ability to recognize both known and novel attack patterns enhances its efficacy in real-world deployment. Experimental assessments using an NS2 simulator show how well the proposed method works to improve routing protocol security. The proposed system’s performance was assessed using a confusion matrix. The simulation and analysis demonstrated how much better the proposed system performs compared to the existing methods for routing attack detection. With an average detection rate of 99.21% and a high accuracy of 99.49%, the proposed system minimizes the rate of false positives. The study advances secure communication in WSNs and provides a reliable means of protecting sensitive data in resource-constrained settings.
Research on Ocular Artifacts Removal from Single-Channel Electroencephalogram Signals in Obstructive Sleep Apnea Patients Based on Support Vector Machine, Improved Variational Mode Decomposition, and Second-Order Blind Identification
Authors: Xiong Xin , Sun Zhiran , Wang Aikun , Zhang Jiancong , Zhang Jing , Wang Chunwu , He Jianfeng
Published: March 2024

The electroencephalogram (EEG) has recently emerged as a pivotal tool in brain imaging analysis, playing a crucial role in accurately interpreting brain functions and states. To address the problem that the presence of ocular artifacts in the EEG signals of patients with obstructive sleep apnea syndrome (OSAS) severely affects the accuracy of sleep staging recognition, we propose a method that integrates a support vector machine (SVM) with genetic algorithm (GA)-optimized variational mode decomposition (VMD) and second-order blind identification (SOBI) for the removal of ocular artifacts from single-channel EEG signals. The SVM is utilized to identify artifact-contaminated segments within preprocessed single-channel EEG signals. Subsequently, these signals are decomposed into variational modal components across different frequency bands using the GA-optimized VMD algorithm. These components undergo further decomposition via the SOBI algorithm, followed by the computation of their approximate entropy. An approximate entropy threshold is set to identify and remove components laden with ocular artifacts. Finally, the signal is reconstructed using the inverse SOBI and VMD algorithms. To validate the efficacy of our proposed method, we conducted experiments utilizing both simulated data and real OSAS sleep EEG data. The experimental results demonstrate that our algorithm not only effectively mitigates the presence of ocular artifacts but also minimizes EEG signal distortion, thereby enhancing the precision of sleep staging recognition based on the EEG signals of OSAS patients.
A Framework for Detecting False Data Injection Attacks in Large-Scale Wireless Sensor Networks
Authors: Hu Jiamin , Yang Xiaofan , Yang Lu-Xing
Published: March 2024

False data injection attacks (FDIAs) on sensor networks involve injecting deceptive or malicious data into the sensor readings that cause decision-makers to make incorrect decisions, leading to serious consequences. With the ever-increasing volume of data in large-scale sensor networks, detecting FDIAs in large-scale sensor networks becomes more challenging. In this paper, we propose a framework for the distributed detection of FDIAs in large-scale sensor networks. By extracting the spatiotemporal correlation information from sensor data, the large-scale sensors are categorized into multiple correlation groups. Within each correlation group, an autoregressive integrated moving average (ARIMA) is built to learn the temporal correlation of cross-correlation, and a consistency criterion is established to identify abnormal sensor nodes. The effectiveness of the proposed detection framework is validated based on a real dataset from the U.S. smart grid and simulated under both the simple FDIA and the stealthy FDIA strategies.
Sensor Fusion for Underwater Vehicle Navigation Compensating Misalignment Using Lie Theory
Authors: Jeong Da Bin , Ko Nak Yong
Published: March 2024

This paper presents a sensor fusion method for navigation of unmanned underwater vehicles. The method combines Lie theory into Kalman filter to estimate and compensate for the misalignment between the sensors: inertial navigation system and Doppler Velocity Log (DVL). In the process and measurement model equations, a 3-dimensional Euclidean group (SE(3)) and 3-sphere space (S3) are used to express the pose (position and attitude) and misalignment, respectively. SE(3) contains position and attitude transformation matrices, and S3 comprises unit quaternions. The increments in pose and misalignment are represented in the Lie algebra, which is a linear space. The use of Lie algebra facilitates the application of an extended Kalman filter (EKF). The previous EKF approach without Lie theory is based on the assumption that a non-differentiable space can be approximated as a differentiable space when the increments are sufficiently small. On the contrary, the proposed Lie theory approach enables exact differentiation in a differentiable space, thus enhances the accuracy of the navigation. Furthermore, the convergence and stability of the internal parameters, such as the Kalman gain and measurement innovation, are improved.