bluuebunny/crossref_metadata_2025 huggingface dataset:
Optimizing Microwave-Assisted Extraction from Levisticum officinale WDJ Koch Roots Using Pareto Optimal Solutions
Authors: Plawgo Michał , Kocira Sławomir , Bohata Andrea
Published: May 2024

The current research trend is not only focused on advanced techniques to intensify the extraction of bioactive compounds from plants, but also on the optimization process. The objective of this work was the implementation of the multiple criteria analysis using navigation on Pareto sets to determine the optimal parameters for the microwave-assisted extraction of Levisticum officinale WDJ Koch roots in order to obtain the maximum efficiency of the antioxidant potential of the extracts. The optimized parameters were extraction time, microwave power, and plant biomass/solvent ratio, while the evaluation criteria were based on the total phenols, flavonoids, reducing sugars, and antioxidant capacity. It was shown that the process parameters analyzed, i.e., biomass/solvent ratio, process time, and microwave power, determined the extraction efficiency of total phenolic content (TPC). A different observation was made for the analysis of total flavonoid content (TFC) and total antioxidant potential (TAA). Compared to the assessment of TFC and TAA, a completely different trend was observed for the analysis of total reducing sugars (RSC). Sets of Pareto optimal, compromise, and preferred solutions were identified that will maximize the efficiency of the extraction of bioactive compounds from biomass. Due to the determined number of Pareto optimal solutions, an approach related to the introduction of preferences in the optimization procedure was applied. It was shown that for a satisfactory level of bioactive compounds, extraction should be carried out at a maximum microwave power of 750 W. Preferred solutions were obtained for root biomass to water ratios ranging from 0.0536 g/mL to 0.0679 g/mL. The preferred optimal time for microwave-assisted water extraction ranged from 64.2857 to 85.7143 s.
Improving Microalgae Feasibility Cultivation: Preliminary Results on Exhausted Medium Reuse Strategy
Authors: Marra Luigi , Aurino Elena , Raganati Francesca , Pollio Antonino , Marzocchella Antonio
Published: May 2024

Although microalgae exploitation is very promising, process sustainability is undermined by biomass production and harvesting. Among the various bottlenecks of the production process, particular attention should be paid to the water footprint. Indeed, a huge volume of water is required in microalgae production. Water reuse can support both the water footprint and medium cost reduction, saving water and unconverted substrates. The present study reports preliminary results regarding the utilization of a water reuse strategy for two Chlorophyta microalgae under batch conditions. Growth parameters and chlorophyll content are monitored and the optimal amount of reused medium is assessed. The results show that 70% of the medium can be reused with no loss of specific growth rate and chlorophyll fraction for Pseudococcomyxa simplex in three consecutive batch cultivations. By contrast, a significant decline in Chlorella vulgaris growth was observed after the first cultivation in reused medium, across all tested conditions.
Response Surface Methodology: An Optimal Design for Maximising the Efficiency of Microwave-Assisted Extraction of Total Phenolic Compounds from Coriandrum sativum Leaves
Authors: Hihat Soraya , Touati Noureddine , Sellal Abdelhakim , Madani Khodir
Published: May 2024

The optimization of total phenolic compounds (TPC) extraction yield and maximization of total antioxidant capacity (TAC) from coriander leaves were investigated using response surface methodology. The extraction of TPC was carried out using microwave-assisted extraction. A Box-Behnken design was used to study the effects of the three independent variables, solvent concentration (ethanol/water 20–80%), microwave power (100–500 watt) and irradiation time (30–150 s) on the response. A second-order polynomial model was used to predict the reaction. The regression analysis showed that about 99% of the variations could be explained by the models. The predicted values were 50.97 GAE/g dw and 5.75 mg GAE/g dw for TPC and TAC, respectively. The reaction surface analysis showed that the optimum extraction parameters that maximized the extraction of antioxidants yield were 52.62% ethanol, 452.12 watt and 150 s. Under optimal conditions, the experimental values for TPC and TAC were 49.63 ± 0.93 mg GAE/g dw and 5.55 ± 0.07 mg GAE/g dw, respectively. The experimental values are in agreement with the predicted values, indicating the suitability of the model used and the success of the response surface methodology in optimizing the extraction conditions.
Adsorbent Biomaterials Based on Natural Clays and Orange Peel Waste for the Removal of Anionic Dyes from Water
Authors: Mihai Sonia , Bondarev Andreea , Călin Cătalina , Sȋrbu Elena-Emilia
Published: May 2024

This study demonstrates the efficient removal of Alizarin Yellow R anionic dye (AY) from aqueous solutions using green adsorbents. Natural kaolin clay (A1), acid-modified natural clay (A2), chemically treated orange peel (C1) and biochar produced by the thermal treatment of orange peel (C2) were tested for the adsorption of AY. The characteristics of the sorbents were determined by instrumental methods: SEM, EDS, FTIR, BET and TGA. The adsorption experiments were performed under different conditions, including the initial AY dye concentration, adsorbent weight, pH, temperature and contact time. The maximum adsorption capacities had values between 15.72 and 74.62 mg/g at 298 K and the optimal pH of 6.5 at initial concentrations ranging from 30 to 70 mg/L for all adsorbents. The equilibrium data were used for the adsorption isotherm models: Freundlich, Langmuir and Temkin. The Freundlich model fit best for the adsorbents A2, C1 and C2, and the Langmuir isotherm had the highest regression value for the adsorbent A1 (R2 = 0.9935). Thermodynamic parameters indicated the spontaneous and favorable adsorption process of AY. A study of the adsorption kinetics proved that they best fit the pseudo-second-order model, with the highest coefficients of determination (R2), outperforming the pseudo-first-order model. The results of this study indicate the potential for the valorization of locally available clays and orange peel waste in the purification processes of water.
Integration of Chemical Looping Combustion to a Gasified Stream with Low Hydrogen Content
Authors: Fraga-Cruz Guadalupe S. , Pérez-Méndez Mario A. , Jiménez-García Gladys , Huirache-Acuña Rafael , Nápoles-Rivera Fabricio , Espino-Valencia Jaime , Maya-Yescas Rafael
Published: May 2024

Global population growth requires the use of various natural resources to satisfy the basic needs of humanity. Fossil fuels are mainly used to produce electricity, transportation and the artificial air conditioning of habitats. Nevertheless, countries around the world are looking for alternative energy sources due to the decrease in the availability of these fuels and their high environmental impact. The mixture of hydrogen and carbon monoxide (H2 + CO), commonly called syngas, is a high-value feedstock for various industrial applications. By varying the composition of syngas, especially the H2/CO molar ratio, it can be used to produce methanol, fuels or synthetic natural gas. However, when this ratio is very low, the separation of this gas usually represents a great problem when making the energy balance, which is why it is proposed to adapt a combustion process in chemical cycles, taking advantage of the energy of this gas, reducing the energy impact of the process. During the present project, mass and energy balances were developed for combustion in chemical cycles, using ilmenite as a carrier, integrating heat exchangers to take advantage of the residual energy at the output of the process, to preheat the inlet current in the regenerator. Here, a comparative was made at different temperatures of the air stream and evaluating the mechanism of the ilmenite when a syngas stream is used as fuel.
Oil and Gas Well Engineering Measurement and Control
Authors: Ma Tianshou , Xu Yuqiang
Published: May 2024

Oil and gas wells represent a unique channel in regard to oil and gas exploration and production [...]
Optimizing Accumulator Performance in Hydraulic Systems through Support Vector Regression and Rotational Factors
Authors: Xu Zilong , Zhou Juan , Chen Hu , Xu Bo , Shen Zhengxiang
Published: May 2024

The piston-type accumulator is an energy storage device in hydraulic–pneumatic systems, playing a significant role in industries such as petrochemicals, heavy machinery, and steel metallurgy. The displacement parameters of the piston-type accumulator are vitally important for fault diagnosis and early warning in hydraulic systems. Traditional displacement measurement methods cannot meet the requirements of the internal testing environment of the accumulator. Therefore, this paper proposes an accumulator piston displacement signal compensation method based on rotational factors and support vector regression. Firstly, empirical mode decomposition is utilized to denoise the signal. Then, rotational factors are used to generate a delay compensation module to compensate for the signal attenuation and time delay caused by metallic reflection and scattering within the cylinder of the radar signal. The support vector regression model is improved based on a hash table to enhance its computational efficiency and achieve radar displacement signal compensation. Finally, a simulation experiment is designed to verify the effectiveness of the proposed method.
Design Method of Cam Steering Mechanism Based on Path Fitting
Authors: Zheng Xiaofei , Zhao Hantao , Zhang Songhui , Liu Dan , Wang Binrui
Published: May 2024

In order to improve the accuracy of a solar-powered punch card car’s movement on a designated route and reduce positional deviations during its operation, a solar-powered punch card car with a single cam as the steering guidance mechanism was designed. The car adopts a three-wheel structure. The transmission mechanism, steering mechanism, driving mechanism, and regulating mechanism of the car were analyzed. The kinematics model of the car was established and the motion characteristics of the car were obtained. By analyzing the relationship between the steering angle of the car and the curvature radius of its travel route, the front wheel angle of the car at each position was calculated using MATLAB R2020a. This allowed us to establish the relationship between the front wheel angle and the displacement of the steering push rod, which was further converted into the theoretical contour line of the cam. Subsequently, the theoretical contour line of the cam was completed and envelope correction was performed. Finally, through mechanical analysis and experimental verification using a prototype, the results indicated that the single-cam steering guidance mechanism calculated using this fast path fitting method exhibited excellent mechanical performance and a smooth and accurate trajectory, and the traveling path of the theoretical cam contour curve was basically consistent with the actual trajectory route.
Structural Analysis of Thermal Diffusion and Non-Uniform Temperature Distribution along the Sidewall Thickness of STS316L during Gas Tungsten Arc Butt Welding
Authors: Na Taehyung , Jeong Gwang-Ho , Kim Kiyoung , Kim Yongdeog , Bae Junsung , Kim Seonmin , Ahn Sang-Hyun , Bae Seung-Hoon , Kim Sang-Kyo , Cho Dae-Won
Published: May 2024

This study investigated how welding affects the thermal deformation of square cells produced for casks, which are dry storage containers for spent nuclear fuel. We aimed to minimize structural deformation by utilizing STS316L as the material for the square cells. We explored a method of subdividing the square cells and joining them through butt welding. Keeping the upper plate thickness constant, GTA butt welding was conducted while varying the column’s wall thickness, followed by measurement with a laser vision sensor. The heat conduction and thermal strain were then calculated using a finite element analysis (FEM). Both experimental and analytical results confirmed that there was significant thermal deformation in the cases of thick-walled columns due to variations in heat conduction distribution, with the resulting deformation patterns depending on thickness.
Effect of Recycled Concrete Aggregate Addition on the Asphalt Mixtures Performance: ITZ Area, Microstructure, and Chemical Analysis Perspectives
Authors: Al-Bayati Hanaa Khaleel Alwan , Jadaa Waleed , Tighe Susan L.
Published: May 2024

The importance of environmental consciousness and sustainability is increasing among transportation governing bodies worldwide. Many government bodies are concerned with maximizing the usage of recycled substances in road construction. Therefore, assessing the effect of recycled materials consumption is essential, mainly when designing new ‘green’ pavement types. The primary objective of this study is to investigate the impact of different treatments on improving the interfacial transition zone (ITZ) of coarse recycled concrete aggregate (CRCA) and its application in asphalt mixes. Such an aim is accomplished by enhancing its physical and mechanical characteristics, as well as its microstructure. The surface morphology, chemical composition, and intermix phases of the ITZ area and calcium silicate hydrate (CSH) compounds for CRCA were evaluated using scanning electron microscopy (SEM), an energy-dispersive X-ray analyzer (EDAX), and X-ray diffraction analysis (XRD). The performance of asphalt mixtures that included treated and untreated CRCA was also examined using different tests. It was found that heat treatment is an effective technique for enhancing the ITZ. However, cracks were seen in the mortar of CRCA when exposed to high temperatures (500 °C), which adversely affects the characteristics of the mortar. Acid treatment appeared to be an effective approach for improving the ITZ area. Nevertheless, the treatment that used acetic acid, a weak acid, was more effective than HCl acid, a strong acid. The outcomes revealed that the ITZ microstructure is significantly enhanced under different treatment types; however, microstructure improvements mainly included increased surface homogeneity and CSH compounds and a reduced Ca/Si ratio. It was also found that the asphalt mixtures with different proportions of untreated CRCA exhibited enhanced resistance to rutting. Furthermore, their tensile strength ratio (TSR) values were above the minimal level requirements. Moreover, the asphalt mixture with 30% CRCA, which was treated with various treatment methods, demonstrated a significant improvement in the mixtures’ mechanical properties; therefore, its application is highly successful and an environmentally friendly solution.
Sacred Resurgence: Revitalizing Buddhist Temples in Modern China
Authors: Liu Yifeng
Published: May 2024

This paper examines the construction and maintenance of Chinese Han Buddhist temples in modern China against the backdrop of societal transformation. Initially, it analyzes the profound impact of social changes since the mid-19th century on Buddhist monasteries, including political turmoil, economic development, and urbanization. Furthermore, the paper explores how temples were reconstructed and revitalized within this historical context, highlighting the monastic community’s unwavering commitment to protecting the Dharma and ensuring its enduring presence. Additionally, this paper also explores the role of charismatic monks in enhancing the sanctity of temples and the influence of Buddhist institutional frameworks on the dynamics of state and society. The study employs a multifaceted analysis to understand the complex interplay between temple construction, economic development, and the cultural heritage of Buddhism in China.
Constrained Iterative Adaptive Algorithm for the Detection and Localization of RFI Sources Based on the SMAP L-Band Microwave Radiometer
Authors: Wang Xinxin , Wang Xiang , Wang Lin , Fan Jianchao , Wei Enbo
Published: May 2024

The Soil Moisture Active Passive (SMAP) satellite carries an L-band microwave radiometer. This sensor can be used to observe global soil moisture (SM) and sea surface salinity (SSS) within the protected L-band spectrum (1400–1427 MHz). Owing to the complex effects of radio frequency interference (RFI), the SM and SSS data are missing or have low accuracy. In this paper, a constrained iterative adaptive algorithm for the detection, identification, and localization of RFI sources is designed, named MICA-BEID. The algorithm synthesizes antenna temperatures for the third and fourth Stokes parameters before RFI filtering, creating a new polarization parameter called WSPDA, designed to approximate the level of RFI interference on the L-band microwave radiometer. The algorithm then utilizes the WSPDA intensity and distribution density of RFI detection samples to enhance the identification and classification of RFI sources across various intensity levels. By utilizing statistical methods such as the probability density function (PDF) and the cumulative distribution function (CDF), the algorithm dynamically adjusts adaptive parameters, including the RFI detection threshold and the maximum effective radius of RFI sources. Through the application of multiple iterative clustering methods, the algorithm can adaptively detect and identify RFI sources at various satellite orbits and intensity levels. Through extensive comparative analysis with other localization results and known RFI sources, the MICA-BEID algorithm can achieve optimal localization accuracy of approximately 1.2 km. The localization of RFI sources provides important guidance for identifying and turning off illegal RFI sources. Moreover, the localization and long-time-series characteristic analysis of RFI sources that cannot be turned off is of significant value for simulating the spatial distribution characteristics of localized RFI source intensity in local areas.
An Evaluation of Optimization Algorithms for the Optimal Selection of GNSS Satellite Subsets
Authors: Alluhaybi Abdulaziz , Psimoulis Panos , Remenyte-Prescott Rasa
Published: May 2024

Continuous advancements in GNSS systems have led, apart from the broadly used GPS, to the development of other satellite systems (Galileo, BeiDou, GLONASS), which have significantly increased the number of available satellites for GNSS positioning applications. However, despite GNSS satellites’ redundancy, a potential poor GNSS satellite signal (i.e., low signal-to-noise ratio) can negatively affect the GNSS’s performance and positioning accuracy. On the other hand, selecting high-quality GNSS satellite signals by retaining a sufficient number of GNSS satellites can enhance the GNSS’s positioning performance. Various methods, including optimization algorithms, which are also commonly adopted in artificial intelligence (AI) methods, have been applied for satellite selection. In this study, five optimization algorithms were investigated and assessed in terms of their ability to determine the optimal GNSS satellite constellation, such as Artificial Bee Colony optimization (ABC), Ant Colony Optimization (ACO), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Simulated Annealing (SA). The assessment of the optimization algorithms was based on two criteria, such as the robustness of the solution for the optimal satellite constellation and the time required to find the solution. The selection of the GNSS satellites was based on the weighted geometric dilution of precision (WGDOP) parameter, where the geometric dilution of precision (GDOP) is modified by applying weights based on the quality of the satellites’ signal. The optimization algorithms were tested on the basis of 24 h of tracking data gathered from a permanent GNSS station, for GPS-only and multi-GNSS data (GPS, GLONASS, and Galileo). According to the comparison results, the ABC, ACO, and PSO algorithms were equivalent in terms of selection accuracy and speed. However, ABC was determined to be the most suitable algorithm due it requiring the fewest number of parameters to be set. To further investigate ABC’s performance, the method was applied for the selection of an optimal GNSS satellite subset according to the number of total available tracked GNSS satellites (up to 31 satellites), leading to more than 300 million possible combinations of 15 GNSS satellites. ABC was able to select the optimal satellite subsets with 100% accuracy.
Modeling and Locating the Wind Erosion at the Dry Bottom of the Aral Sea Based on an InSAR Temporal Decorrelation Decomposition Model
Authors: Song Yubin , Xun Xuelian , Zheng Hongwei , Chen Xi , Bao Anming , Liu Ying , Luo Geping , Lei Jiaqiang , Xu Wenqiang , Liu Tie , Hellwich Olaf , Guan Qing
Published: May 2024

The dust originating from the extinct lake of the Aral Sea poses a considerable threat to the surrounding communities and ecosystems. The accurate location of these wind erosion areas is an essential prerequisite for controlling sand and dust activity. However, few relevant indicators reported in this current study can accurately describe and measure wind erosion intensity. A novel wind erosion intensity (WEI) of a pixel resolution unit was defined in this paper based on deformation due to the wind erosion in this pixel resolution unit. We also derived the relationship between WEI and soil InSAR temporal decorrelation (ITD). ITD is usually caused by the surface change over time, which is very suitable for describing wind erosion. However, within a pixel resolution unit, the ITD signal usually includes soil and vegetation contributions, and extant studies concerning this issue are considerably limited. Therefore, we proposed an ITD decomposition model (ITDDM) to decompose the ITD signal of a pixel resolution unit. The least-square method (LSM) based on singular value decomposition (SVD) is used to estimate the ITD of soil (SITD) within a pixel resolution unit. We verified the results qualitatively by the landscape photos, which can reflect the actual conditions of the soil. At last, the WEI of the Aral Sea from 23 June 2020, to 5 July 2020 was mapped. The results confirmed that (1) based on the ITDDM model, the SITD can be accurately estimated by the LSM; (2) the Aral Sea is experiencing severe wind erosion; and (3) the middle, northeast, and southeast bare areas of the South Aral Sea are where salt dust storms may occur.
Greenness and Actual Evapotranspiration in the Unrestored Riparian Corridor of the Colorado River Delta in Response to In-Channel Water Deliveries in 2021 and 2022
Authors: Nagler Pamela L. , Sall Ibrahima , Gomez-Sapiens Martha , Barreto-Muñoz Armando , Jarchow Christopher J. , Flessa Karl , Didan Kamel
Published: May 2024

Natural resource managers may utilize remotely sensed data to monitor vegetation within their decision-making frameworks for improving habitats. Under binational agreements between the United States and Mexico, seven reaches were targeted for riparian habitat enhancement. Monitoring was carried out using Landsat 8 16-day intervals of the two-band enhanced vegetation index 2 (EVI2) for greenness and actual evapotranspiration (ETa). In-channel water was delivered in 2021 and 2022 at four places in Reach 4. Three reaches (Reaches 4, 5 and 7) showed no discernable difference in EVI2 from reaches that did not receive in-channel water (Reaches 1, 2, 3 and 6). EVI2 in 2021 was higher than 2020 in all reaches except Reach 3, and EVI2 in 2022 was lower than 2021 in all reaches except Reach 7. ET(EVI2) was higher in 2020 than in 2021 and 2022 in all seven reaches; it was highest in Reach 4 (containing restoration sites) in all years. Excluding restoration sites, compared with 2020, unrestored reaches showed that EVI2 minimally increased in 2021 and 2022, while ET(EVI2) minimally decreased despite added water in 2021–2022. Difference maps comparing 2020 (no-flow year) to 2021 and 2022 (in-channel flows) reveal areas in Reaches 5 and 7 where the in-channel flows increased greenness and ET(EVI2).
A Geometry-Compensated Sensitivity Study of Polarimetric Bistatic Scattering for Rough Surface Observation
Authors: Wang Yanting , Ainsworth Thomas L.
Published: May 2024

The use of bistatic polarimetric SAR for rough surface observation has attracted increasing interest in recent years, with its acquisition of additional polarimetric information. In this paper, we investigate the sensitivity of polarimetric variables to soil moisture and surface roughness, with the intention of locating favorable bistatic geometries for soil moisture retrieval. However, in the bistatic setting, the expanded imaging geometry is convolved with the polarimetric scattering response along with the in-scene variations in the soil moisture and surface roughness. The probing polarization states continuously evolve with the bistatic geometry, incurring varying polarization orientation angles. In this investigation, we propose to first compensate the bistatic polarimetric observations for the geometry-induced polarization rotation. Simulations based on a two-scale rough surface scattering model are then used to evaluate the optimal imaging geometry for the best sensitivity to the soil moisture content. We show the different sensing geometries associated with a full list of common polarimetric variables, as we seek favorable bistatic geometries in non-specular directions. The influences of both surface roughness scales are evaluated, with the small-scale roughness parameter imposing the greatest limitation on our results.
Deep Learning-Based Automatic River Flow Estimation Using RADARSAT Imagery
Authors: Ziadi Samar , Chokmani Karem , Chaabani Chayma , El Alem Anas
Published: May 2024

Estimating river flow is a key parameter for effective water resource management, flood risk prevention, and hydroelectric facilities planning. Yet, traditional gauging methods are not reliable under very high flows or extreme events. Hydrometric network stations are often sparse, and their spatial distribution is not optimal. Therefore, many river sections cannot be monitored using traditional flow measurements and observations. In the last few decades, satellite sensors have been considered as complementary observation sources to traditional water level and flow measurements. This kind of approach has provided a way to maintain and expand the hydrometric observation network. Remote sensing data can be used to estimate flow from rating curves that relate instantaneous flow (Q) to channel cross-section geometry (effective width or depth of the water surface). Yet, remote sensing has limitations, notably its dependence on rating curves. Due to their empirical nature, rating curves are limited to specific river sections (reaches) and cannot be applied to other watercourses. Recently, deep-learning techniques have been successfully applied to hydrology. The primary goal of this study is to develop a deep-learning approach for estimating river flow in the Boreal Shield ecozone of Eastern Canada using RADARSAT-1 and -2 imagery and convolutional neural networks (CNN). Data from 39 hydrographic sites in this region were used in modeling. A new CNN architecture was developed to provide a straightforward estimation of the instantaneous river flow rate. Our results yielded a coefficient of determination (R2) and a Nash–Sutcliffe value of 0.91 and a root mean square error of 33 m3/s. Notably, the model performs exceptionally well for rivers wider than 40 m, reflecting its capability to adapt to varied hydrological contexts. These results underscore the potential of integrating advanced satellite imagery with deep learning to enhance hydrological monitoring across vast and remote areas.
Effects of Land Use/Cover Change on Terrestrial Carbon Stocks in the Yellow River Basin of China from 2000 to 2030
Authors: Zhang Jiejun , Yang Jie , Liu Pengfei , Liu Yi , Zheng Yiwen , Shen Xiaoyu , Li Bingchen , Song Hongquan , Liang Zongzheng
Published: May 2024

Accurately assessing and predicting the impacts of land use changes on ecosystem carbon stocks in the Yellow River Basin (YRB) and exploring the optimization of land use structure to increase ecosystem carbon stocks are of great practical significance for China to achieve the goal of “double carbon”. In this study, we used multi-year remote sensing data, meteorological data and statistical data to measure the ecosystem carbon stock in the YRB from 2000 to 2020 based on the InVEST model, and then simulated and measured the ecosystem carbon stock under four different land use scenarios coupled with the FLUS model in 2030. The results show that, from 2000 to 2020, urban expansion in the YRB continued, but woodland and grassland grew more slowly. Carbon stock showed an increasing trend during the first 20 years, with an overall increase of 7.2 megatons, or 0.23%. Simulating the four land use scenarios in 2030, carbon stock will decrease the most under the cropland protection scenario, with a decrease of 17.7 megatons compared with 2020. However, carbon stock increases the most under the ecological protection scenario, with a maximum increase of 9.1 megatons. Furthermore, distinct trends in carbon storage were observed across different regions, with significant increases in the upstream under the natural development scenario, in the midstream under the ecological protection scenario and in the downstream under the cropland protection scenario. We suggest that the upstream should maintain the existing development mode, with ecological protection prioritized in the middle reaches and farmland protection prioritized in the lower reaches. This study provides a scientific basis for the carbon balance, land use structure adjustment and land management decision-making in the YRB.
Global Inversion of Lunar Surface Oxides by Adding Chang’e-5 Samples
Authors: Wu Shuangshuang , Chen Jianping , Xue Chenli , Pan Yiwen , Zhang Cheng
Published: May 2024

The chemical distribution on the lunar surface results from the combined effects of both endogenic and exogenic geological processes. Exploring global maps of chemical composition helps to gain insights into the compositional variation among three major geological units, unraveling the geological evolution of the Moon. The existing oxide abundance maps were obtained from spectral images of remote sensing and geochemical data from samples returned by Apollo and Luna, missing the chemical characteristics of the Moon’s late critical period. In this study, by adding geochemical data from Chang’e (CE)-5 lunar samples, we construct inversion models between the Christiansen feature (CF) and oxide abundance of lunar samples using the particle swarm optimization–extreme gradient boosting (PSO-XGBoost) algorithm. Then, new global oxide maps (Al2O3, CaO, FeO, and MgO) and Mg# with the resolution of 32 pixels/degree (ppd) were produced, which reduced the space weathering effect to some extent. The PSO-XGBoost models were compared with partial least square regression (PLSR) models and four previous results, indicating that PSO-XGBoost models possess the capability to effectively describe nonlinear relationships between CF and oxide abundance. Furthermore, the average contents of our results and the Diviner results for 21 major maria demonstrate high correlations, with R2 of 0.95, 0.82, 0.95, and 0.86, respectively. In addition, a new Mg# map was generated, which reveals different magmatic evolutionary processes in the three geologic units.
Coastline Automatic Extraction from Medium-Resolution Satellite Images Using Principal Component Analysis (PCA)-Based Approach
Authors: Parente Claudio , Alcaras Emanuele , Figliomeni Francesco Giuseppe
Published: May 2024

In recent decades several methods have been developed to extract coastlines from remotely sensed images. In fact, this is one of the principal fields of remote sensing research that continues to receive attention, as testified by the thousands of scientific articles present in the main databases, such as SCOPUS, WoS, etc. The main issue is to automatize the whole process or at least a great part of it, so as to minimize the human error connected to photointerpretation and identification of training sites to support the classification of objects (basically soil and water) present in the observed scene. This article proposes a new fully automatic methodological approach for coastline extraction: it is based on the unsupervised classification of the most decorrelated fictitious band derived from Principal Component Analysis (PCA) applied to the satellite images. The experiments are carried out on datasets characterized by images with different geometric resolution, i.e., Landsat 9 Operational Land Imager (OLI) multispectral images (pixel size: 30 m), a Sentinel-2 dataset including blue, green, red and Near Infrared (NIR) bands (pixel size: 10 m) and a Sentinel-2 dataset including red edge, narrow NIR and Short-Wave Infrared (SWIR) bands (pixel size: 20 m). The results are very encouraging, given that the comparison between each extracted coastline and the corresponding real one generates, in all cases, residues that present a Root Mean Squared Error (RMSE) lower than the pixel size of the considered dataset. In addition, the PCA results are better than those achieved with Normalized Difference Water Index (NDWI) and Modified NDWI (MNDWI) applications.
Revealing the Hidden Consequences of Increased Soil Moisture Storage in Greening Drylands
Authors: Wang Yu , Han Tian , Yang Yuze , Hai Yue , Wen Zhi , Li Ruonan , Zheng Hua
Published: May 2024

Vegetation primarily draws water from soil moisture (SM), with restoration in drylands often reducing SM storage (SMS). However, anomalies have been detected in the Beijing–Tianjin Sand Source Region (BTSSR) of China via the Global Land Data Assimilation System (GLDAS) and Gravity Recovery and Climate Experiment (GRACE). This study quantified the sources of increased SMS in drylands to elucidate the effects of vegetation restoration on SMS. The results indicated the following: (1) In vegetated drylands, 46.2% experienced a significant increase in SMS while 53.8% remained stable; both were positively correlated with the normalised difference vegetation index (NDVI). (2) The increase in SMS was accompanied by a decrease in groundwater storage (GWS), as indicated by the significant correlation coefficients of −0.710 and −0.569 for SMS and GWS, respectively. Furthermore, GWS served as the primary source of water for vegetation. (3) The results of the redundancy analysis (RDA) indicated that the initial vegetation, the driver of the observed trend of increased SMS and decreased GWS, accounted for 50.3% of the variability in water storage. Therefore, to sustain dryland ecosystems, we recommend that future vegetation restoration projects give due consideration to the water balance while concurrently strengthening the dynamic monitoring of SMS and GWS.
A Visual–Inertial Pressure Fusion-Based Underwater Simultaneous Localization and Mapping System
Authors: Lu Zhufei , Xu Xing , Luo Yihao , Ding Lianghui , Zhou Chao , Wang Jiarong
Published: May 2024

Detecting objects, particularly naval mines, on the seafloor is a complex task. In naval mine countermeasures (MCM) operations, sidescan or synthetic aperture sonars have been used to search large areas. However, a single sensor cannot meet the requirements of high-precision autonomous navigation. Based on the ORB-SLAM3-VI framework, we propose ORB-SLAM3-VIP, which integrates a depth sensor, an IMU sensor and an optical sensor. This method integrates the measurements of depth sensors and an IMU sensor into the visual SLAM algorithm through tight coupling, and establishes a multi-sensor fusion SLAM model. Depth constraints are introduced into the process of initialization, scale fine-tuning, tracking and mapping to constrain the position of the sensor in the z-axis and improve the accuracy of pose estimation and map scale estimate. The test on seven sets of underwater multi-sensor sequence data in the AQUALOC dataset shows that, compared with ORB-SLAM3-VI, the ORB-SLAM3-VIP system proposed in this paper reduces the scale error in all sequences by up to 41.2%, and reduces the trajectory error by up to 41.2%. The square root has also been reduced by up to 41.6%.
MRD-YOLO: A Multispectral Object Detection Algorithm for Complex Road Scenes
Authors: Sun Chaoyue , Chen Yajun , Qiu Xiaoyang , Li Rongzhen , You Longxiang
Published: May 2024

Object detection is one of the core technologies for autonomous driving. Current road object detection mainly relies on visible light, which is prone to missed detections and false alarms in rainy, night-time, and foggy scenes. Multispectral object detection based on the fusion of RGB and infrared images can effectively address the challenges of complex and changing road scenes, improving the detection performance of current algorithms in complex scenarios. However, previous multispectral detection algorithms suffer from issues such as poor fusion of dual-mode information, poor detection performance for multi-scale objects, and inadequate utilization of semantic information. To address these challenges and enhance the detection performance in complex road scenes, this paper proposes a novel multispectral object detection algorithm called MRD-YOLO. In MRD-YOLO, we utilize interaction-based feature extraction to effectively fuse information and introduce the BIC-Fusion module with attention guidance to fuse different modal information. We also incorporate the SAConv module to improve the model’s detection performance for multi-scale objects and utilize the AIFI structure to enhance the utilization of semantic information. Finally, we conduct experiments on two major public datasets, FLIR_Aligned and M3FD. The experimental results demonstrate that compared to other algorithms, the proposed algorithm achieves superior detection performance in complex road scenes.
Risk Evaluation and Attack Detection in Heterogeneous IoMT Devices Using Hybrid Fuzzy Logic Analytical Approach
Authors: Pritika  , Shanmugam Bharanidharan , Azam Sami
Published: May 2024

The rapidly expanding Internet of Medical Things (IoMT) landscape fosters enormous opportunities for personalized healthcare, yet it also exposes patients and healthcare systems to diverse security threats. Heterogeneous IoMT devices present challenges that need comprehensive risk assessment due to their varying functionality, protocols, and vulnerabilities. Hence, to achieve the goal of having risk-free IoMT devices, the authors used a hybrid approach using fuzzy logic and the Fuzzy Analytical Hierarchy Process (FAHP) to evaluate risks, providing effective and useful results for developers and researchers. The presented approach specifies qualitative descriptors such as the frequency of occurrence, consequence severity, weight factor, and risk level. A case study with risk events in three different IoMT devices was carried out to illustrate the proposed method. We performed a Bluetooth Low Energy (BLE) attack on an oximeter, smartwatch, and smart peak flow meter to discover their vulnerabilities. Using the FAHP method, we calculated fuzzy weights and risk levels, which helped us to prioritize criteria and alternatives in decision-making. Smartwatches were found to have a risk level of 8.57 for injection attacks, which is of extreme importance and needs immediate attention. Conversely, jamming attacks registered the lowest risk level of 1, with 9 being the maximum risk level and 1 the minimum. Based on this risk assessment, appropriate security measures can be implemented to address the severity of potential threats. The findings will assist healthcare industry decision-makers in evaluating the relative importance of risk factors, aiding informed decisions through weight comparison.
A Rapid Localization Method Based on Super Resolution Magnetic Array Information for Unknown Number Magnetic Sources
Authors: Miao Linliang , Zhang Tianyi , Zuo Chao , Chen Zijie , Yang Xiaofei , Ouyang Jun
Published: May 2024

A rapid method that uses super-resolution magnetic array data is proposed to localize an unknown number of magnets in a magnetic array. A magnetic data super-resolution (SR) neural network was developed to improve the resolution of a magnetic sensor array. The approximate 3D positions of multiple targets were then obtained based on the normalized source strength (NSS) and magnetic gradient tensor (MGT) inversion. Finally, refined inversion of the position and magnetic moment was performed using a trust region reflective algorithm (TRR). The effectiveness of the proposed method was examined using experimental field data collected from a magnetic sensor array. The experimental results showed that all the targets were successfully captured in multiple trials with three to five targets with an average positioning error of less than 3 mm and an average time of less than 300 ms.
Corrosion Monitoring by Plastic Optic Fiber Sensor Using Bi-Directional Light Transmission
Authors: Hou Liang , Akutagawa Shinichi
Published: May 2024

In this paper, a new sensor is proposed to efficiently gather crucial information on corrosion phenomena and their progression within steel components. Fabricated with plastic optical fibers (POF), the sensor can detect corrosion-induced physical changes in the appearance of monitoring points within the steel material. Additionally, the new sensor incorporates an innovative structure that efficiently utilizes bi-directional optical transmission in the POF, simplifying the installation procedure and reducing the total cost of the POF cables by as much as 50% when monitoring multiple points. Furthermore, an extremely compact dummy sensor with the length of 5 mm and a diameter of 2.2 mm for corrosion-depth detection was introduced, and its functionality was validated through experiments. This paper outlines the concept and fundamental structure of the proposed sensor; analyzes the results of various experiments; and discusses its effectiveness, prospects, and economic advantages.
Hydrogen 4.0: A Cyber–Physical System for Renewable Hydrogen Energy Plants
Authors: Yavari Ali , Harrison Christopher J. , Gorji Saman A. , Shafiei Mahnaz
Published: May 2024

The demand for green hydrogen as an energy carrier is projected to exceed 350 million tons per year by 2050, driven by the need for sustainable distribution and storage of energy generated from sources. Despite its potential, hydrogen production currently faces challenges related to cost efficiency, compliance, monitoring, and safety. This work proposes Hydrogen 4.0, a cyber–physical approach that leverages Industry 4.0 technologies—including smart sensing, analytics, and the Internet of Things (IoT)—to address these issues in hydrogen energy plants. Such an approach has the potential to enhance efficiency, safety, and compliance through real-time data analysis, predictive maintenance, and optimised resource allocation, ultimately facilitating the adoption of renewable green hydrogen. The following sections break down conventional hydrogen plants into functional blocks and discusses how Industry 4.0 technologies can be applied to each segment. The components, benefits, and application scenarios of Hydrogen 4.0 are discussed while how digitalisation technologies can contribute to the successful integration of sustainable energy solutions in the global energy sector is also addressed.
Mathematical Physics Analysis of Nozzle Shaping at the Gas Outlet from the Aperture to the Differentially Pumped Chamber in Environmental Scanning Electron Microscopy (ESEM)
Authors: Maxa Jiří , Neděla Vilém , Šabacká Pavla , Binar Tomáš
Published: May 2024

A combination of experimental measurement preparations using pressure and temperature sensors in conjunction with the theory of one-dimensional isentropic flow and mathematical physics analyses is presented as a tool for analysis in this paper. Furthermore, the subsequent development of a nozzle for use in environmental electron microscopy between the specimen chamber and the differentially pumped chamber is described. Based on experimental measurements, an analysis of the impact of the nozzle shaping located behind the aperture on the character of the supersonic flow and the resulting dispersion of the electron beam passing through the differential pumped chamber is carried out on the determined pressure ratio using a combination of theory and mathematical physics analyses. The results show that nozzle shapes causing under-expanded gas outflow from the aperture to the nozzle have a worse impact on the dispersion of the primary electron beam. This is due to the flow velocity control. The controlled reduction in the static pressure curve on the primary electron beam path thus causes a significantly higher course of electron dispersion values than variants with shapes causing over-expanded gas outflow.
RI2AP: Robust and Interpretable 2D Anomaly Prediction in Assembly Pipelines
Authors: Shyalika Chathurangi , Roy Kaushik , Prasad Renjith , Kalach Fadi El , Zi Yuxin , Mittal Priya , Narayanan Vignesh , Harik Ramy , Sheth Amit
Published: May 2024

Predicting anomalies in manufacturing assembly lines is crucial for reducing time and labor costs and improving processes. For instance, in rocket assembly, premature part failures can lead to significant financial losses and labor inefficiencies. With the abundance of sensor data in the Industry 4.0 era, machine learning (ML) offers potential for early anomaly detection. However, current ML methods for anomaly prediction have limitations, with F1 measure scores of only 50% and 66% for prediction and detection, respectively. This is due to challenges like the rarity of anomalous events, scarcity of high-fidelity simulation data (actual data are expensive), and the complex relationships between anomalies not easily captured using traditional ML approaches. Specifically, these challenges relate to two dimensions of anomaly prediction: predicting when anomalies will occur and understanding the dependencies between them. This paper introduces a new method called Robust and Interpretable 2D Anomaly Prediction (RI2AP) designed to address both dimensions effectively. RI2AP is demonstrated on a rocket assembly simulation, showing up to a 30-point improvement in F1 measure compared to current ML methods. This highlights its potential to enhance automated anomaly prediction in manufacturing. Additionally, RI2AP includes a novel interpretation mechanism inspired by a causal-influence framework, providing domain experts with valuable insights into sensor readings and their impact on predictions. Finally, the RI2AP model was deployed in a real manufacturing setting for assembling rocket parts. Results and insights from this deployment demonstrate the promise of RI2AP for anomaly prediction in manufacturing assembly pipelines.