bluuebunny/crossref_metadata_2025 huggingface dataset:
An Experimental Study on the Influence of Different Cooling Methods on the Mechanical Properties of PVA Fiber-Reinforced High-Strength Concrete after High-Temperature Action
Authors: Wu Jian , Hu Chaoqun , Wang Yuxi , Hu Liangjie , Zhang Lidan , Wang Jianhui , Ding Weigao
Published: July 2024

High-strength concrete (HSC) has a high compressive strength, high density, excellent durability, and seepage resistance, but its deformation ability is weak. Adding fibers can improve the physical and mechanical properties of HSC. Additionally, the HSC structure may face the threat of fire. In the process of fire extinguishing, the damage mechanism of high-temperature-resistant concrete is complicated due to the different contact conditions with water at different locations. Hence, it is essential to conduct pertinent research on the behavior of fiber-reinforced HSC with different cooling methods after high-temperature action. In this paper, polyvinyl alcohol fiber (PVA fiber) was selected to be added into the HSC to carry out high-temperature experimental research, so as to explore the apparent changes, failure pattern, and mass loss rate of the fiber-reinforced HSC using different cooling methods and analyze the influence of its residual compressive strength and flexural strength. The test results suggest that, with the increase in heating temperature, the color of the specimen’s surface transitions from dark blue-gray to white, and the quantity of surface cracks on the specimen gradually rises. The mechanical strength gradually decreases as the heating temperature increases. At a consistent heating temperature, the mechanical strength initially rises, and then falls with an increase in fiber content. The maximum compressive strength and flexural strength were achieved at PVA fiber contents of 0.2% and 0.3%, respectively. For different temperatures and fiber contents, the mechanical strength after natural cooling is generally higher than that after immersion cooling. In addition, X-ray polycrystalline diffractometry (XRD) and scanning electron microscopy (SEM) tests were conducted to analyze the compositional alterations and microstructure of the fiber-reinforced HSC following high-temperature exposure, accompanied by an explanation of the factors influencing the alterations in the physical and mechanical properties. Therefore, the findings of this study can serve as a valuable reference for the utilization of HSC in engineering structures and contribute to the advancement of HSC technology.
Polymer-Based Hydroxyapatite–Silver Composite Resin with Enhanced Antibacterial Activity for Dental Applications
Authors: Garibay-Alvarado Jesús Alberto , Garcia-Zamarron Diana Juana , Silva-Holguín Pamela Nair , Donohue-Cornejo Alejandro , Cuevas-González Juan Carlos , Espinosa-Cristóbal León Francisco , Ruíz-Baltazar Álvaro de Jesús , Reyes-López Simón Yobanny
Published: July 2024

The primary objective of this investigation was to synthesize a resin incorporating nanoparticles of hydroxyapatite and silver (HA-NpsAg) to enhance biocompatibility and antimicrobial efficacy, thereby facilitating potential implementation within the dental industry. These enhancements aim to ensure reliable, durable, functional, and aesthetically pleasing restorations while concurrently reducing susceptibility to bacterial colonization within the oral cavity. Hydroxyapatite powders were prepared using the sol–gel method and doped with silver nanoparticles obtained by chemical reduction. The crystalline amorphous calcium phosphate powder had a particle size of 279 nm, and the silver nanoparticles had an average diameter of 26.5 nm. Resin spheres containing HA-NpsAg (RHN) were then synthesized at two concentrations (0.5% and 1%) by dissolving the initial monomer mixture in tetrahydrofuran. Subsequent antimicrobial evaluations were conducted via agar diffusion and turbidimetry, employing three strains of Gram-negative bacteria (E. coli, K. oxytoca, and P. aeruginosa) and three strains of Gram-positive bacteria (S. mutans, S. aureus, and B. subtilis). The findings revealed that P. aeruginosa exhibited maximum susceptibility to RHN powder at a concentration of 0.5%, while RHN powder at 1% concentration demonstrated maximal inhibition against S. aureus and S. mutans. Overall, our study highlights the successful synthesis of a dental resin with hydroxyapatite and silver nanoparticles, exhibiting bactericidal properties at low silver concentrations. These findings hold promise for enhancing dental materials with improved antimicrobial efficacy and clinical performance.
Behavior of a Precast Bridge Pier with Basalt Fiber-Reinforced Polymer (BFRP)-Strengthened Segments under Seismic Loading
Authors: Li Chao , Peng Yaowei , Yang Pengyu , Zhou Hao , Wang Ningbo
Published: July 2024

The precast segmental column (PSC) has been proposed for reducing onsite construction time and minimizing impacts on traffic and the environment. It has been proven to have good seismic performance according to previous studies. However, due to the rocking behavior of the column, the toe of the bottom segment could experience excessive compressive damage. In addition, the commonly used steel rebars in the PSC could experience corrosion problems during the service life of the structure. Moreover, ordinary Portland cement concrete (OPC) is normally used in the construction of the PSC, but the manufacturing processes of the OPC could emit a lot of carbon dioxide. This paper investigates the seismic performance of PSCs incorporating Basalt Fiber Reinforced Polymer (BFRP) bars and geopolymer concrete (GPC) segments. To mitigate the concrete crushing damage of the segment, the BFRP sheet was used to wrap the bottom segment of one of the specimens. The results revealed that the BFRP-reinforced geopolymer concrete PSC exhibited good seismic performance with minimal damage and small residual displacement. Strengthening the bottom segment with BFRP wrapping proved to be effective in reducing concrete damage. As a result, the column with BFRP wrap demonstrated the ability to withstand ground motions with higher Peak Ground Acceleration (PGA) compared to the column without strengthening.
Polyamidoamine Dendrimers: Brain-Targeted Drug Delivery Systems in Glioma Therapy
Authors: Yan Xinyi , Chen Qi
Published: July 2024

Glioma is the most common primary intracranial tumor, which is formed by the malignant transformation of glial cells in the brain and spinal cord. It has the characteristics of high incidence, high recurrence rate, high mortality and low cure rate. The treatments for glioma include surgical removal, chemotherapy and radiotherapy. Due to the obstruction of the biological barrier of brain tissue, it is difficult to achieve the desired therapeutic effects. To address the limitations imposed by the brain’s natural barriers and enhance the treatment efficacy, researchers have effectively used brain-targeted drug delivery systems (DDSs) in glioma therapy. Polyamidoamine (PAMAM) dendrimers, as branched macromolecular architectures, represent promising candidates for studies in glioma therapy. This review focuses on PAMAM-based DDSs in the treatment of glioma, highlighting their physicochemical characteristics, structural properties as well as an overview of the toxicity and safety profiles.
Experimental Study on Factors Affecting Fracture Conductivity
Authors: Tian Fuchun , Jia Yunpeng , Yang Liyong , Liu Xuewei , Guo Xinhui , Martyushev Dmitriy A.
Published: July 2024

The conductivity of propped fractures following hydraulic fracturing is crucial in determining the success of the fracturing process. Understanding the primary factors affecting fracture conductivity and uncovering their impact patterns are essential for guiding the selection of fracturing engineering parameters. We conducted experiments to test fracture conductivity and analyzed the effects of proppant particle size, closure pressure, and fracture surface properties on conductivity. Using the orthogonal experimental method, we clarified the primary and secondary relationships of the influencing factors on conductivity. The results indicate that proppant particle size, formation closure pressure, and fracture surface properties significantly affect fracture conductivity, with the order of influence being closure pressure > fracture surface properties > proppant particle size. Using large-particle-size proppants effectively increases interparticle porosity and enhances fracture conductivity. However, large-particle-size proppants reduce the number of contact points between particles, increasing the pressure on individual particles and making them more prone to crushing, which decreases fracture conductivity. Proppants become compacted under closure pressure, leading to a reduction in fracture conductivity. Proppant particles can embed into the fracture surface under closure pressure, further impacting fracture conductivity. Compared to non-laminated fracture surfaces, proppant particles are more likely to embed into laminated fracture surfaces under closure pressure, resulting in a greater embedding depth and reduced conductivity.
Global Optimization and Quantitative Assessment of Large-Scale Renewables-Based Hydrogen System Considering Various Transportation Modes and Multi-Field Hydrogen Loads
Authors: Hong Liu , Liu Deqi , Shi Lei , Tan Yuhua , Xiang Yujin , Zhang Qian , Li Tianle
Published: July 2024

In the past, hydrogen was mostly produced from fossil fuels, causing a certain degree of energy and environmental problems. With the development of low-carbon energy systems, renewable energy hydrogen production technology has developed rapidly and become one of the focuses of research in recent years. However, the existing work is still limited by small-scale hydrogen production systems, and there is a lack of comprehensive research on the whole production-storage-transportation-utilization hydrogen system (PSTUH2S), especially on the modeling of different hydrogen transportation modes and various hydrogen loads in different fields. To make up for these deficiencies, the specific physical and mathematical models of the PSTUH2S are firstly described in this paper, with a full account of large-scale water-electrolytic hydrogen production from renewable power curtailment and grid power, various hydrogen storage and transportation modes, and multi-field hydrogen consumption paths. Furthermore, to achieve the maximum economic, energy, and environmental benefits from the PSTUH2S, a multi-objective nonlinear optimization model is also presented herein and then solved by the hybrid method combining the nonlinear processing method, the CPLEX solver and the piecewise time series production simulation method. Lastly, case studies are conducted against the background of a region in northwest China, where hydrogen consumption capacity in various years is accurately assessed and the potential advantages of the PSTUH2S are demonstrated. As the simulation results show, the power curtailment of renewable energy generation can be reduced by 3.61/11.87/14.72 billion kW·h in 2025/2030/2035, respectively, thus contributing to a 4.98%~10.09% increase in the renewable energy consumption rate and millions of tons of carbon emission reduction in these years. In terms of the total equivalent economic benefits, the proposed method is able to bring about a cost saving of USD 190.44 million, USD 634.66 million, and USD 865.87 million for 2025, 2030, and 2035, respectively.
Candida parapsilosis CMGB-YT Biosurfactant for Treatment of Heavy Metal- and Microbial-Contaminated Wastewater
Authors: Csutak Ortansa Elisabeta , Nicula Nicoleta-Oana , Lungulescu Eduard-Marius , Marinescu Virgil Emanuel , Gifu Ioana Catalina , Corbu Viorica Maria
Published: July 2024

During the last few decades, water pollution has become a growing concern at international level. To date, only a few Candida parapsilosis strains were successfully used in environmental remediation. In the present article, the strain C. parapsilosis CMGB-YT was studied for its ability to assimilate hydrophobic substrates and to produce biosurfactants with antimicrobial activity and positive effects on heavy metal removal from contaminated wastewaters. The strain C. parapsilosis CMGB-YT was grown on yeast peptone (YP) media with 1% n-decane, n-dodecane, n-tetradecane, n-hexadecane, as well as commercial sunflower and olive oils. The production of the biosurfactant was evaluated using the emulsification index (E24%). The surface properties and emulsifying stability of the biosurfactant were determined. The effect of the biosurfactant on the cell growth of two strains of Rhodotorula mucilaginosa and on their removal capacity of lead (0.032 g/L) and cadmium (0.030 g/L) ions from synthetic wastewater were also studied. The antimicrobial potential of 20 mg/mL and 40 mg/mL biosurfactant was established in the presence of pathogenic Candida krusei strains. C. parapsilosis CMGB-YT assimilated n-hexadecane with good rates over 216 h and produced an anionic glycolipidic biosurfactant with stable E24% towards long-chain carbon compounds at different temperatures, with an alkaline pH and high salinity (10% NaCl). The biosurfactant reduced the surface tension to 53.58 ± 0.42 mN/m, while the critical micellar concentration (CMC) was reached at 4.2% biosurfactant. The crude biosurfactant (5%) enhanced R. mucilaginosa growth in heavy metal-contaminated wastewater, increased chemical oxygen demand (COD) removal of up to 80%, and improved Cd2+ removal by 10%. Additionally, the concentrated biosurfactant effectively prevented Candida krusei biofilm formation. In conclusion, the biosurfactant produced by C. parapsilosis CMGB-YT demonstrates promising potential for the efficient treatment of wastewater contaminated with heavy metals and microbial pathogens.
Evaluation of the Synergistic Oil Displacement Effect of a CO2 Low Interfacial Tension Viscosity-Increasing System in Ultra-Low Permeability Reservoirs
Authors: Chen Zequn , Dong Yuanwu , Hu Hao , Zhang Xinyue , Tang Shanfa
Published: July 2024

In addressing the issue of poor control over gas permeability during the CO2 flooding process in ultra-low permeability reservoirs, this study proposes the use of a low interfacial tension viscosity-increasing system as a substitute for water in CO2–water alternating flooding to enhance CO2 mobility control and increase oil recovery. The performance of the system was evaluated through tests of viscosity, interfacial tension, wettability, and emulsification properties, and the injection behavior and gas channeling prevention effect of the viscosity-increasing system with CO2 alternate flooding were investigated. The results indicate that the low interfacial tension viscosity-increasing fluid exhibits good thickening properties, interfacial activity, hydrophilic wettability, and oil–water emulsification performance, also demonstrating strong environmental adaptability. The CO2–low interfacial tension viscosity-increasing fluid alternate flooding shows good injectivity in ultra-low permeability cores (1.085 mD). Following water flooding in heterogeneous ultra-low permeability cores, the implementation of CO2 low interfacial tension viscosity-increasing fluid alternate flooding can lead to a 15.91% increase in overall recovery compared to water flooding, outperforming CO2 flooding and CO2–water alternating flooding. The mechanisms by which the CO2 low interfacial tension viscosity-increasing fluid enhances oil recovery include reducing interfacial tension, improving mobility ratio, altering rock surface wettability, and emulsification effects. The low interfacial tension viscosity-increasing systems demonstrate effective mobility control and oil displacement capabilities and synergistically enhance the efficiency of CO2, presenting potential application prospects in the development of CO2 flooding in ultra-low permeability reservoirs.
Development and Application of a Constitutive Equation for 25CrMo4 Steel
Authors: Zheng Shuhua , Ren Zihao , Sun Xiaohui , Li Guofang , Wang Jun , Zhang Song
Published: July 2024

The material constitutive equation of 25CrMo4 steel was established through an isothermal compression experiment. First, a thermal compression experiment was carried out with a Gleeble-3500 thermal simulator to study the thermoplastic deformation behaviour of 25CrMo4 steel at various temperatures (850, 950, 1050, and 1150 °C) and strain rates (0.01, 0.1, 1.0, and 10 s−1). The measured true stress–strain curve showed that when the temperature is constant, the flow stress increases with the strain rate, whereas when the strain rate is constant, the flow stress decreases with the temperature. Then, the constitutive model of peak stress of 25CrMo4 was established after analyzing the stress and strain statistics. The model parameters were optimized. The accuracy of the flow stress constitutive model was verified by comparing the flow stress prediction model with the experimental results. The hot forging process of the inner core wheel was numerically simulated based on DEFORM-3D v11 software, and the parameters of this process were formulated by analyzing the metal flow rate and equivalent stress and strain fields.
Wavelet Cross-Correlation Signal Processing for Two-Phase Flow Control System in Oil Well Production
Authors: Arseniev Dmitry , Malykhina Galina , Kratirov Dmitry
Published: July 2024

An algorithm based on continuous measurement of multiphase flows of oil well production has been designed to improve the efficiency of the technical control of oil production processes in the field. Separation-free, non-contact measurement of multiphase flows of oil well products allows increasing the efficiency of managing oil production processes in the field. Monitoring the current density using radioisotope measuring transducers (RMTs) allows obtaining information about the structure of the flow in the form of the distribution of gas inclusions and the speed of movement of liquid and gas in a two-phase flow. Fluid velocity measurement is based on digital processing of RMT signals, applying a continuous or discrete undecimated wavelet transform to them, and assessing the cross-correlation of wavelet coefficients in individual subspaces of the wavelet decomposition. The cross-correlation coefficients of two RMT signals located at a base distance, calculated in the subspaces of the wavelet decomposition, characterize the speed of movement of gas bubbles of different sizes in a vertical pipe. The measurement assumes that the velocity of the liquid phase of the oil flow in a vertical pipe mainly corresponds to the velocity of small bubbles. This speed should be determined by the maximum cross-correlation of wavelet coefficients in the corresponding decomposition subspace. Computer modeling made it possible to evaluate the characteristics of the algorithm for controlling the speed of liquid movement in the gas–liquid flow of oil well products and determine the mass flow rate of the liquid and the relative value of the gas content. The implementation of the algorithm in a multi-channel version of the device allows monitoring an entire cluster of wells in the field.
Concurrent Onset of Central Retinal Vein Occlusion and Inflammation of a Large Maxillary Odontogenic Cyst: Case Report and Analysis
Authors: Kopić Vlatko , Kopić Andrijana , Mišir Mihael , Petrović Sanjin
Published: July 2024

Central retinal vein occlusion typically manifests in older individuals with underlying systemic pathology, leading to a spectrum of symptoms ranging from blurred vision to complete vision loss. While odontogenic infections are recognized for causing complications affecting the eye and vision, their potential role as an etiological factor in cases of sudden vision impairment merits consideration. This article presents a case involving central retinal vein thrombosis, wherein resolution was achieved through a combination of ophthalmic therapy and the surgical removal of a concurrently existing large inflamed odontogenic cyst located in the ipsilateral hemimaxilla. This case underscores the importance of recognizing odontogenic factors in the assessment of sudden vision impairment and the efficacy of a multidisciplinary therapeutic approach.
Critical Raw Materials Supply: Challenges and Potentialities to Exploit Rare Earth Elements from Siliceous Stones and Extractive Waste
Authors: Zhao Xinyuan , Khelifi Faten , Casale Marco , Cavallo Alessandro , Padoan Elio , Yang Ke , Dino Giovanna Antonella
Published: July 2024

Critical raw materials (CRMs) supply is a challenge that EU countries have to face, with many thinking about domestic procurement from natural ore deposits and anthropogenic deposits (landfills and extractive waste facilities). The present research focuses on the possibilities linked to the supply of CRMs and the potential for exploiting rare earth elements (REEs), investigating a large variety of extractive waste and siliceous rocks in the Piedmont region (Northern Italy). Indeed, the recovery of REEs from the extractive waste (EW) of siliceous quarries and other siliceous ore deposits can be a valuable way to reduce supply chain risks. Starting with a review of the literature on mining activities in Piedmont and continuing with the sampling and geochemical, mineralogical, petrographic, and environmental characterization of EW facilities connected to siliceous dimension stones, of kaolinitic gneiss ore deposits, and of soils present near the investigated areas, this study shows that the degree of REEs enrichment differs depending on the sampling area (soil or EW) and lithology. The concentration of REEs in the EW at some sampling sites fulfils the indicators of industrial-grade and industrial recovery; the high cumulative production and potential market values of EW and the positive recovery effects through proven methodologies indicate a viable prospect of REE recovery from EW. However, REE recovery industrialization faces challenges such as the difficulty in achieving efficient large-scale recovery due to large regional differences in REE abundance, the mismatch between potential market value and waste annual production, etc. Nonetheless, in the future, EW from dimension stone quarries could be differentially studied and reused based on the enrichment and distribution characteristics of trace elements. The present paper shows investigation procedures undertaken to determine both CRMs potentialities and environmental issues (on the basis of literature data employed to select the more-promising areas and on sampling and characterization activities in the selected areas), together with procedures to determine the waste quantities and tentative economic values of REEs present in the investigated areas. This approach, tested on a large area (Piedmont region), is replicable and applicable to other similar case studies (at EU and non-EU levels) and offers decision makers the possibility to acquire a general overview of the potential available resources in order to decide whether and where to concentrate efforts (including economic ones) in a more detailed study to evaluate the exploitable anthropogenic deposits.
ANN Enhanced Hybrid Force/Position Controller of Robot Manipulators for Fiber Placement
Authors: Villa-Tiburcio José Francisco , Estrada-Torres José Antonio , Hernández-Alvarado Rodrigo , Montes-Martínez Josue Rafael , Bringas-Posadas Darío , Franco-Urquiza Edgar Adrián
Published: July 2024

In practice, most industrial robot manipulators use PID (Proportional + Integral + Derivative) controllers, thanks to their simplicity and adequate performance under certain conditions. Normally, this type of controller has a good performance in tasks where the robot moves freely, performing movements without contact with its environment. However, complications arise in applications such as the AFP (Automated Fiber Placement) process, where a high degree of precision and repeatability is required in the control of parameters such as position and compression force for the production of composite parts. The control of these parameters is a major challenge in terms of quality and productivity of the final product, mainly due to the complex geometry of the part and the type of tooling with which the AFP system is equipped. In the last decades, several control system approaches have been proposed in the literature, such as classical, adaptive or sliding mode control theory based methodologies. Nevertheless, such strategies present difficulties to change their dynamics since their design consider only some set of disturbances. This article presents a novel intelligent type control algorithm based on back-propagation neural networks (BP-NNs) combined with classical PID/PI control schemes for force/position control in manipulator robots. The PID/PI controllers are responsible for the main control action, while the BP-NNs contributes with its ability to estimate and compensate online the dynamic variations of the AFP process. It is proven that the proposed control achieves both, stability in the Lyapunov sense for the desired interaction force between the end-effector and the environment, and position trajectory tracking for the robot tip in Cartesian space. The performance and efficiency of the proposed control is evaluated by numerical simulations in MATLAB-Simulink environment, obtaining as results that the errors for the desired force and the tracking of complex trajectories are reduced to a range below 5% in root mean square error (RMSE).
Generative Adversarial Networks for SAR Automatic Target Recognition and Classification Models Enhanced Explainability: Perspectives and Challenges
Authors: Remusati Héloïse , Le Caillec Jean-Marc , Schneider Jean-Yves , Petit-Frère Jacques , Merlet Thomas
Published: July 2024

Generative adversarial networks (or GANs) are a specific deep learning architecture often used for different usages, such as data generation or image-to-image translation. In recent years, this structure has gained increased popularity and has been used in different fields. One area of expertise currently in vogue is the use of GANs to produce synthetic aperture radar (SAR) data, and especially expand training datasets for SAR automatic target recognition (ATR). In effect, the complex SAR image formation makes these kind of data rich in information, leading to the use of deep networks in deep learning-based methods. Yet, deep networks also require sufficient data for training. However, contrary to optical images, we generally do not have a substantial number of available SAR images because of their acquisition and labelling cost; GANs are then an interesting tool. Concurrently, how to improve explainability for SAR ATR deep neural networks and how to make their reasoning more transparent have been increasingly explored as model opacity deteriorates trust of users. This paper aims at reviewing how GANs are used with SAR images, but also giving perspectives on how GANs could be used to improve interpretability and explainability of SAR classifiers.
DBI-Attack:Dynamic Bi-Level Integrated Attack for Intensive Multi-Scale UAV Object Detection
Authors: Zhao Zhengyang , Wang Buhong , Wang Zhen , Yao Xuan
Published: July 2024

Benefiting from the robust feature representation capability of convolutional neural networks (CNNs), the object detection technology of intelligent high-altitude UAV remote sensing has been developed rapidly. In this field, the adversarial examples (AEs) pose serious security risks and vulnerabilities to deep learning-based systems. Due to the limitation of object size, image degradation, and scene brightness, adding adversarial disturbances to small and dense objects is extremely challenging. To study the threat of AE for UAV object detection, a dynamic bi-level integrated attack (DBI-Attack) is proposed for intensive multi-scale UAV object detection. Firstly, we use the dynamic iterative attack (DIA) method to generate perturbation on the classification level by improving the momentum iterative fast gradient sign method (MIM). Secondly, the bi-level adversarial attack method (BAAM) is constructed to add global perturbation on the decision level for completing the white-box attack. Finally, the integrated black-box attack method (IBAM) is combined to realize the black-box mislabeling and fabrication attacks. We experiment on the real drone traffic vehicle detection datasets to better evaluate the attack effectiveness. The experimental results show that the proposed method can achieve mislabeling and fabrication attacks on the UAV object detectors in black-box conditions. Furthermore, the adversarial training is applied to improve the model robustness. This work aims to call more attention to the adversarial and defensive aspects of UAV target detection models.
Millimeter-Wave Radar Detection and Localization of a Human in Indoor Complex Environments
Authors: Xing Zhixuan , Chen Penghui , Wang Jun , Bai Yujing , Song Jinhao , Tian Liuyang
Published: July 2024

Nowadays, it is still a great challenge to detect and locate indoor humans using a frequency-modulated continuous-wave radar accurately. Due to the interference of the indoor environment and complex objects such as green plants, the radar signal may penetrate, reflect, refract, and scatter, and the echo signals will contain noise, clutter, and multipath of different characteristics. Therefore, a method combined with comprehensive non-target signal removal and human localization is proposed to achieve position estimation of a human target. Time-variant clutter is innovatively mitigated through time accumulation using point clustering. Ghost targets are reduced according to propagation path matching. The experimental results show that the method can locate the real target human within an average error of 0.195 m in multiple complex environments with green plants, curtains, or furniture using a 77 GHz millimeter-wave radar. Meanwhile, the proposed method performs better than conventional methods. The detection probability is 81.250% when the human is behind a potted plant and is 90.286% when beside it.
A CNN- and Transformer-Based Dual-Branch Network for Change Detection with Cross-Layer Feature Fusion and Edge Constraints
Authors: Wang Xiaofeng , Guo Zhongyu , Feng Ruyi
Published: July 2024

Change detection aims to identify the difference between dual-temporal images and has garnered considerable attention over the past decade. Recently, deep learning methods have shown robust feature extraction capabilities and have achieved improved detection results; however, they exhibit limitations in preserving clear boundaries for the identified regions, which is attributed to the inadequate contextual information aggregation capabilities of feature extraction, and fail to adequately constrain the delineation of boundaries. To address this issue, a novel dual-branch feature interaction backbone network integrating the CNN and Transformer architectures to extract pixel-level change information was developed. With our method, contextual feature aggregation can be achieved by using a cross-layer feature fusion module, and a dual-branch upsampling module is employed to incorporate both spatial and channel information, enhancing the precision of the identified change areas. In addition, a boundary constraint is incorporated, leveraging an MLP module to consolidate fragmented edge information, which increases the boundary constraints within the change areas and minimizes boundary blurring effectively. Quantitative and qualitative experiments were conducted on three benchmarks, including LEVIR-CD, WHU Building, and the xBD natural disaster dataset. The comprehensive results show the superiority of the proposed method compared with previous approaches.
Spatial Scale Effect on Fractional Vegetation Coverage Changes and Driving Factors in the Henan Section of the Yellow River Basin
Authors: Wang Rongxi , Wang Hongtao , Wang Cheng , Duan Jingjing , Zhang Shuting
Published: July 2024

Vegetation plays a crucial role in terrestrial ecosystems, and the FVC (Fractional Vegetation Coverage) is a key indicator reflecting the growth status of vegetation. The accurate quantification of FVC dynamics and underlying driving factors has become a hot topic. However, the scale effect on FVC changes and driving factors has received less attention in previous studies. In this study, the changes and driving factors of FVC at multiple scales were analyzed to reveal the spatial and temporal change in vegetation in the Henan section of the Yellow River basin. Firstly, based on the pixel dichotomy model, the FVC at different times and spatial scales was calculated using Landsat-8 data. Then, the characteristics of spatial and temporal FVC changes were analyzed using simple linear regression and CV (Coefficient of Variation). Finally, a GD (Geographic Detector) was used to quantitatively analyze the driving factors of FVC at different scales. The results of this study revealed that (1) FVC showed an upward trend at all spatial scales, increasing by an average of 0.55% yr−1 from 2014 to 2022. The areas with an increasing trend in FVC were 10.83% more than those with a decreasing trend. (2) As the spatial scale decreased, the explanatory power of the topography factors (aspect, elevation, and slope) for changes in FVC was gradually strengthened, while the explanatory power of climate factors (evapotranspiration, temperature, and rainfall) and anthropogenic activities (night light) for changes in FVC decreased. (3) The q value of evapotranspiration was always the highest across different scales, peaking notably at a spatial scale of 1000 m (q = 0.48).
A Semi-Automatic Approach for Tree Crown Competition Indices Assessment from UAV LiDAR
Authors: Puletti Nicola , Guasti Matteo , Innocenti Simone , Cesaretti Lorenzo , Chiavetta Ugo
Published: July 2024

Understanding the spatial heterogeneity of forest structure is crucial for comprehending ecosystem dynamics and promoting sustainable forest management. Unmanned aerial vehicle (UAV) LiDAR technology provides a promising method to capture detailed three-dimensional (3D) information about forest canopies, aiding in management and silvicultural practices. This study investigates the heterogeneity of forest structure in broadleaf forests using UAV LiDAR data, with a particular focus on tree crown features and their different information content compared to diameters. We explored a non-conventionally used method that emphasizes crown competition by employing a nearest neighbor selection technique based on metrics derived from UAV point cloud profiles at the tree level, rather than traditional DBH (diameter at breast height) spatial arrangement. About 300 vegetation elements within 10 plots collected in a managed Beech forest were used as reference data. We demonstrate that crown-based approaches, which are feasible with UAV LiDAR data at a reasonable cost and time, significantly enhances the understanding of forest heterogeneity, adding new information content for managers. Our findings underscore the utility of UAV LiDAR in characterizing the complexity and variability of forest structure at high resolution, offering valuable insights for carbon accounting and sustainable forest management.
The Evolution of Meteorological Satellite Cloud-Detection Methodologies for Atmospheric Parameter Retrievals
Authors: Romano Filomena , Cimini Domenico , Di Paola Francesco , Gallucci Donatello , Larosa Salvatore , Nilo Saverio Teodosio , Ricciardelli Elisabetta , Iisager Barbara D. , Hutchison Keith
Published: July 2024

The accurate detection of clouds is an important first step in the processing of remotely sensed satellite data analyses and subsequent cloud model predictions. While initial cloud retrieval technology began with the exploitation of one or two bands of satellite imagery, it has accelerated rapidly in recent years as sensor and retrieval technology, creating a new era in space observation exploration. Additionally, the initial emphasis in satellite retrieval technology focused on cloud detection for cloud forecast models, but more recently, cloud screening in satellite-acquired data is playing an increasingly critical role in the investigation of cloud-free data for the retrieval of soil moisture, vegetation cover, ocean color concentration and sea surface temperatures, as well as the environmental monitoring of a host of products, e.g., atmospheric aerosol data, to study the Earth’s atmospheric and climatic systems. With about 60% of the Earth covered by clouds, on average, it is necessary to accurately detect clouds in remote sensing data to screen cloud contaminate data in remote sensing analyses. In this review, the evolution of cloud-detection methodologies is highlighted with advancement in sensor hardware technology and machine learning algorithmic advances. The review takes into consideration the meteorological sensors usually used for atmospheric parameters estimation (thermodynamic profiles, aerosols, cloud microphysical parameters). Moreover, a discussion is presented on methods for obtaining the cloud-truth data needed to determine the accuracy of these cloud-detection approaches.
Construction of a High-Resolution Waterlogging Disaster Monitoring Framework Based on the APSIM Model: A Case Study of Jingzhou and Bengbu
Authors: Zhang Jian , Pan Bin , Shi Wenxuan , Zhang Yu , Gu Shixiang , Chen Jinming , Xia Quanbin
Published: July 2024

This study investigates waterlogging disasters in winter wheat using the Agricultural Production Systems Simulator (APSIM) model. This research explores the effects of soil hypoxia on wheat root systems and the tolerance of wheat at different growth stages to waterlogging, proposing a model to quantify the degree of waterlogging in wheat. Remote sensing data on soil moisture and wheat distribution are utilized to establish a monitoring system for waterlogging disasters specific to winter wheat. The analysis focused on affected areas in Bengbu and Jingzhou. Experimental results from 2017 to 2022 indicate that the predominant levels of waterlogging disasters in Bengbu and Jingzhou were moderate and mild, with the proportion of mild waterlogging ranging from 30.1% to 39.3% and moderate waterlogging from 14.8% to 25.6%. A combined analysis of multi-source remote sensing data reveals the key roles of precipitation, evapotranspiration, and altitude in waterlogging disasters. This study highlights regional disparities in the distribution of waterlogging disaster risks, providing new strategies and tools for precise assessment of waterlogging disasters.
A New Angle-Calibration Method for Precise Ultra-Short Baseline Underwater Positioning
Authors: Sun Zhen , Wang Zhenjie , Nie Zhixi , Jia Chun , Shan Rui
Published: July 2024

Ultra-short baseline (USBL) underwater positioning systems are widely used in marine scientific research and ocean engineering. Angle misalignment is a main error that reduces the accuracy of USBL underwater positioning. The conventional angle-calibration method assumes that the transponder position obtained by USBL positioning is an errorless coefficient matrix. However, errors inevitably exist in the estimation of the transponder’s position via USBL positioning, and the precision varies at different epochs. Ignoring the error in the transponder’s position will significantly reduce the precision of the angle misalignment estimation. In this paper, a new angle-calibration method is proposed for precise USBL underwater positioning. The angle alignment model is derived by treating the transponder’s position obtained by USBL positioning as an observation, and the stochastic model is then established according to the bearing angles. Robust estimation is likewise applied to further improve the precision of the angle misalignment estimation. To verify the performance of the proposed method, a sea experiment was performed. The results show that the new method has high calibration accuracy and robustness. The estimation precision of this method is improved by 0.0457°~0.6896° in heading, 0.0125°~0.8072° in roll, and 0.0077°~0.9436° in pitch, compared with that of the conventional angle alignment method.
Spatiotemporal Evolution Disparities of Vegetation Trends over the Tibetan Plateau under Climate Change
Authors: Ma Jieru , Ren Hong-Li , Mao Xin , Liu Minghong , Wang Tao , Ma Xudong
Published: July 2024

The Tibetan Plateau has experienced profound climate change with significant implication for spatial vegetation greenness. However, the spatiotemporal disparities of long-term vegetation trends in response to observed climate change remain unclear. Based on remote-sensing vegetation images indicated by the normalized difference vegetation index (NDVI) from two long-term combined datasets, GIMMS and MODIS, we identified two spatiotemporal evolution patterns (SEPs) in long-term vegetation anomalies across the Tibetan Plateau. This new perspective integrates spatial and temporal NDVI changes during the growing seasons over the past four decades. Notably, the dipole evolution pattern that rotates counterclockwise from May to September accounted for 62.8% of the spatial mean amplitude of vegetation trends, dominating the spatiotemporal disparities. This dominant pattern trend is attributed to simultaneous effects of spatial warming and rising CO2, which accounted for 75% and 15%, respectively, along with a lagged effect of dipole precipitation, accounting for 6%. Overall, wetting and warming promote greening evolution in the northern Tibetan Plateau, while slight drying and warming favor browning evolution in the southern Tibetan Plateau. These findings provide insights into the combined effects of climate change on spatiotemporal vegetation trends and inform future adaptive strategies in fragile regions.
Unmanned Aerial Vehicle Object Detection Based on Information-Preserving and Fine-Grained Feature Aggregation
Authors: Zhang Jiangfan , Zhang Yan , Shi Zhiguang , Zhang Yu , Gao Ruobin
Published: July 2024

General deep learning methods achieve high-level semantic feature representation by aggregating hierarchical features, which performs well in object detection tasks. However, issues arise with general deep learning methods in UAV-based remote sensing image object detection tasks. Firstly, general feature aggregation methods such as stride convolution may lead to information loss in input samples. Secondly, common FPN methods introduce conflicting information by directly fusing feature maps from different levels. These shortcomings limit the model’s detection performance on small and weak targets in remote sensing images. In response to these concerns, we propose an unmanned aerial vehicle (UAV) object detection algorithm, IF-YOLO. Specifically, our algorithm leverages the Information-Preserving Feature Aggregation (IPFA) module to construct semantic feature representations while preserving the intrinsic features of small objects. Furthermore, to filter out irrelevant information introduced by direct fusion, we introduce the Conflict Information Suppression Feature Fusion Module (CSFM) to improve the feature fusion approach. Additionally, the Fine-Grained Aggregation Feature Pyramid Network (FGAFPN) facilitates interaction between feature maps at different levels, reducing the generation of conflicting information during multi-scale feature fusion. The experimental results on the VisDrone2019 dataset demonstrate that in contrast to the standard YOLOv8-s, our enhanced algorithm achieves a mean average precision (mAP) of 47.3%, with precision and recall rates enhanced by 6.3% and 5.6%, respectively.
Multi-Core Fiber Bragg Grating and Its Sensing Application
Authors: Zhang Xiaotong , Wang Hongye , Yuan Tingting , Yuan Libo
Published: July 2024

With the increase in the demand for large-capacity optical communication capacity, multi-core optical fiber (MCF) communication technology has developed, and both the types of MCFs and related devices have become increasingly mature. The application of MCFs in the field of sensing has also received more and more attention, among which MCF fiber Bragg grating (FBG) devices have received more and more attention and have been widely used in various fields. In this paper, the main writing methods of MCF FBGs and their sensing applications are reviewed. The future development of the MCF FBG is also prospected.
SiamDCFF: Dynamic Cascade Feature Fusion for Vision Tracking
Authors: Lu Jinbo , Wu Na , Hu Shuo
Published: July 2024

Establishing an accurate and robust feature fusion mechanism is key to enhancing the tracking performance of single-object trackers based on a Siamese network. However, the output features of the depth-wise cross-correlation feature fusion module in fully convolutional trackers based on Siamese networks cannot establish global dependencies on the feature maps of a search area. This paper proposes a dynamic cascade feature fusion (DCFF) module by introducing a local feature guidance (LFG) module and dynamic attention modules (DAMs) after the depth-wise cross-correlation module to enhance the global dependency modeling capability during the feature fusion process. In this paper, a set of verification experiments is designed to investigate whether establishing global dependencies for the features output by the depth-wise cross-correlation operation can significantly improve the performance of fully convolutional trackers based on a Siamese network, providing experimental support for rational design of the structure of a dynamic cascade feature fusion module. Secondly, we integrate the dynamic cascade feature fusion module into the tracking framework based on a Siamese network, propose SiamDCFF, and evaluate it using public datasets. Compared with the baseline model, SiamDCFF demonstrated significant improvements.
Enhance Ethanol Sensing Performance of Fe-Doped Tetragonal SnO2 Films on Glass Substrate with a Proposed Mathematical Model for Diffusion in Porous Media
Authors: Sotelo Juan G. , Bonilla-Ríos Jaime , Gordillo José L.
Published: July 2024

This research enhances ethanol sensing with Fe-doped tetragonal SnO2 films on glass, improving gas sensor reliability and sensitivity. The primary objective was to improve the sensitivity and operational efficiency of SnO2 sensors through Fe doping. The SnO2 sensors were synthesized using a flexible and adaptable method that allows for precise doping control, with energy-dispersive X-ray spectroscopy (EDX) confirming homogeneous Fe distribution within the SnO2 matrix. A morphological analysis showed a surface structure ideal for gas sensing. The results demonstrated significant improvement in ethanol response (1 to 20 ppm) and lower temperatures compared to undoped SnO2 sensors. The Fe-doped sensors exhibited higher sensitivity, enabling the detection of low ethanol concentrations and showing rapid response and recovery times. These findings suggest that Fe doping enhances the interaction between ethanol molecules and the sensor surface, improving performance. A mathematical model based on diffusion in porous media was employed to further analyze and optimize sensor performance. The model considers the diffusion of ethanol molecules through the porous SnO2 matrix, considering factors such as surface morphology and doping concentration. Additionally, the choice of electrode material plays a crucial role in extending the sensor’s lifespan, highlighting the importance of material selection in sensor design.