bluuebunny/crossref_metadata_2025 huggingface dataset:
Development of Plug Joint with Polymer-Modified Rubber Asphalt as Filling Material
Authors: Kim Kyung-Nam , Kim Yeong-Min , Le Tri Ho Minh
Published: October 2023

Rising traffic volume, heavy loads, and construction activities have raised concerns about expansion joint device damage. This study focuses on developing an innovative expansion joint using polymer-modified rubber asphalt as the filling material to enhance its service life. Styrene–butadiene–styrene (SBS) emerged as a suitable modifier for rubber-modified asphalt, significantly improving elasticity and adhesion. Through the strategic combination of 3- and 2-block linear SBS, the elasticity and adhesion properties were significantly improved, resulting in the formulation of a well-suited polymer-modified rubber asphalt binder. The developed asphalt binder exhibits impressive elastic recovery (61.1% to 66.1%), surpassing commercial products, with enhanced constructability and workability (15% to 21% viscosity reduction). The carefully engineered mastic asphalt mixture showcases self-leveling characteristics at a moderate 210 °C, addressing historical constructability challenges. Settlement is 40% less than traditional hot mix asphalt for surface layers, with improved moisture and stripping resistance, enhancing existing asphalt plug joint durability and workability. Collectively, this novel mixture, comprising polymer-modified rubber and mastic asphalt, showcases the potential to enhance the durability of existing asphalt plug joints while ensuring superior constructability and workability.
Geant4 Simulation of Photon- and Neutron-Shielding Capabilities of Biopolymer Blends of Poly(lactic acid) and Poly(hydroxybutyrate)
Authors: Akhdar Hanan , Alshehri Maryam
Published: October 2023

Simulation is used by scientists to imitate a real-life experimental setup in order to save time, costs and effort. Geant4, a toolkit based on the Monte Carlo method, has been widely used in investigating the radiation-shielding properties of different materials. In many recent studies, researchers have focused on polymers and their shielding capabilities. Poly(lactic acid) (PLA) is a widely used biopolymer in many applications due to its excellent mechanical properties. However, it has limitations related to its degree of crystallinity and molecular characteristics, which could be improved through blending with other biodegradable polymers such as poly(hydroxybutyrate) (PHB). Previous published studies have shown that the mechanical properties of such blends can be improved further. In this work, the effect of blending PHB with PLA on the photon- and neutron-shielding capabilities will be investigated using Geant4 over a wide energy range, as well as the effect of doping those blends with metal oxides. The results show that the shielding properties of the polymers are affected by blending with other polymers and by doping the polymer blends with different metal oxides, and they confirm that Geant4 is a very reliable tool that can simulate any material’s shielding properties against photons and neutrons.
New Horizons in Nanofiller-Based Polymer Composites II
Authors: Kumar Vineet , Tang Xiaowu
Published: October 2023

Nanofiller-based polymer composites are a hot-topic research area with significant industrial potential [...]
Boosted Chemical Protective Properties Using Interface Constructed between Ti3C2Tx MXene and Natural Rubber
Authors: Chen Qinyu , Zhang Min , Li Xiaopeng , Zhou Chuan , Yang Guang , Li Heguo , Zheng Xiaohui
Published: October 2023

Rubbers are extensively applied in chemical protective clothing (CPC) due to their eye-catching anti-penetration of chemicals. However, their impermeability, particularly that of natural rubber (NR), is unsatisfactory. In this work, we demonstrate the facile construction of Ti3C2Tx MXene/NR interface using a plant-scale and feasible method combining latex mixing, emulsion flocculation, and flat-plate vulcanisation. The above crafts achieved a homogeneous dispersion of Ti3C2Tx MXene in the NR matrix in a single layer, thereby constructing a strong interfacial interaction between Ti3C2Tx MXene and NR, which induced the formation of a robust three-dimensional (3D) network in the composite. The anti-swelling capacity of the 3D cross-linked network structure and the layered structure of Ti3C2Tx MXene effectively prolonged the permeation path of toxic chemicals. Compared with pure NR, the nanocomposite with 1 wt% of Ti3C2Tx MXene showed substantially enhanced breakthrough times of toluene, dichloromethane, and concentrated sulfuric acid (increased by 140%, 178.6%, and 92.5%, respectively). Furthermore, its tensile strength, elongation at break, and shore hardness increased by 7.847 MPa, 194%, and 12 HA, respectively. Taken together with the satisfactory anti-permeability, tensile strength, elongation at break, and shore hardness, the resulting Ti3C2Tx MXene/NR nanocomposites hold promise for application to long-term and high-strength CPC in the chemical industry and military fields.
Homogeneity Enhancement of Mixtures Containing Epoxy Polymer and 100% Reclaimed Asphalt Pavement
Authors: Yang Jun , Yi Xingyu , Chen Huimin , Wong Yiik Diew , Fan Yulou , Huang Wei
Published: October 2023

The utilization of reclaimed asphalt pavement (RAP) could reduce the cost of pavements containing epoxy polymer (EP) materials. This study was aimed at improving the homogeneity of an EP-reclaimed asphalt mixtures (ERAMs) at both the micro- and meso-scale to provide a reference for an ERAM production process. At the microscale, nanoindentation tests were conducted to characterize the diffusion between the EP and aged asphalt mastic. At the mesoscale, computerized tomography (CT) X-ray scanning and MATLAB analysis were employed to investigate the distribution of the aggregate within the ERAM. The results revealed that mixing temperature played a significant role in the diffusion and distribution between the EP and the aged asphalt mastic, thus impacting the mechanical properties of the material. Heating at 180 °C (the recommended mixing temperature of EP) resulted in a wider blending zone between the EP and the aged asphalt mastic compared to heating at 160 °C (the usual mixing temperature of ordinary reclaimed asphalt mixtures). The overall dispersion of the aggregate in the ERAM exhibited greater homogeneity in the vertical direction than in the horizontal direction. Adjusting the gradation of the RAP was found to be effective in reducing horizontal variability in the distribution of the coarse aggregate, fine aggregate, and air voids in the ERAM. Adjusting the RAP gradation further enhanced the vertical homogeneity in the distribution of the fine aggregate, while its impact on the vertical distribution of the coarse aggregate was minimal. Short-term aging led to increased variability in the distribution of the coarse aggregate, fine aggregate, and air voids within the ERAM. However, adjusting the gradation was effective in mitigating the adverse effects of short-term aging on both horizontal and vertical homogeneity in the aggregate distribution.
Antioxidant Edible Films Based on Pear Juice and Pregelatinized Cassava Starch: Effect of the Carbohydrate Profile at Different Degrees of Pear Ripeness
Authors: Quintero Pimiento Carmen Rosa , Fernández Paula Virginia , Ciancia Marina , López-Córdoba Alex , Goyanes Silvia , Bertuzzi María Alejandra , Foresti María Laura
Published: October 2023

Edible films based on fruit and vegetable purees combined with different food-grade biopolymeric binding agents (e.g., pectin, gelatin, starch, sodium alginate) are recognized as interesting packaging materials that benefit from the physical, mechanical, and barrier properties of biopolymers as well as the sensory and nutritional properties of purees. In the current contribution, edible antioxidant films based on pear juice and pregelatinized cassava starch were developed. In particular, the suitability of using pregelatinized cassava starch for the non-thermal production of these novel edible films was evaluated. In addition, the effects on the films’ properties derived from the use of pear juice instead of the complete puree, from the content of juice used, and from the carbohydrate composition associated with the ripening of pears were all studied. The produced films were characterized in terms of their total polyphenol content, water sensitivity, and water barrier, optical, mechanical and antioxidant properties. Results showed that the use of pear juice leads to films with enhanced transparency compared with puree-based films, and that juice concentration and carbohydrate composition associated with the degree of fruit ripeness strongly govern the films’ properties. Furthermore, the addition of pregelatinized cassava starch at room temperature discloses a significant and favorable impact on the cohesiveness, lightness, water resistance, and adhesiveness of the pear-juice-based films, which is mainly attributed to the effective interactions established between the starch macromolecules and the juice components.
Investigation of Cinnamic Acid Derivatives as Alternative Plasticizers for Improved Ductility of Polyvinyl Chloride Films
Authors: Barandiaran Alejandro , Montanes Nestor , Sanchez-Nacher Lourdes , Balart Rafael , Selles Miguel Angel , Moreno Virginia
Published: October 2023

This study investigates the viability of cinnamic acid derivatives as alternative plasticizers for polyvinyl chloride (PVC) films by addressing concerns about conventional phthalate-based options that pose health and environmental risks. By theoretical modeling, this research evaluates the compatibility between various cinnamic acid-based plasticizers and the PVC matrix, which suggests their potential effectiveness. Additionally, the incorporation of these plasticizers notably enhances the tensile properties of PVC films, particularly in terms of ductility and elongation at break by surpassing the neat PVC. Moreover, cinnamic acid-based plasticizers induce a drop in the glass transition temperature and storage modulus by, thereby, enhancing flexibility and reducing brittleness in the material. Although a slight reduction in the onset degradation temperature is observed, it does not impede the industrial processing of PVC plastisols at temperatures up to 190 °C. Optically, plasticized films exhibit high transparency with minimal UV and visible light absorption, which renders them suitable for applications necessitating clarity. The water vapor transmission rate analysis indicates increased permeability, influenced by molecular volumes. Atomic force microscopy reveals a compacted, homogeneous surface structure in most plasticized films, which signifies improved film quality. Thus, utilizing cinnamic acid derivatives as PVC plasticizers offers substantial mechanical and structural benefits, while compatibility ensures effective integration by contributing to environmentally sustainable PVC formulations with enhanced performance.
Numerical Study on the Damage of a Carbon Woven Composite Panel Subjected to Blast Loading
Authors: Vescovini Alessandro , Lomazzi Luca , Giglio Marco , Manes Andrea
Published: October 2023

Blast loading represents a critical dynamic condition for engineering structures. While the response of metal materials to such a condition has been studied in detail, the behavior of composites has not been properly addressed yet. In this context, this work leverages numerical methods to assess the damage that occurs in a carbon-fiber-reinforced polymer plate subjected to close-range blast loading. Numerical analyses were carried out using two methods, i.e., the pure Lagrangian and hybrid coupled Eulerian–Lagrangian approaches. The simulations were validated against observations from a benchmark experimental test taken from the literature. The results showed that (i) the hybrid approach seems to be the most promising solution in terms of efficiency and accuracy; (ii) the Lagrangian approach can accurately reproduce the experimental observations, even though it comes with strong limitations; and (iii) the numerically predicted damage adheres to the experimentally observed damage, although the simulation outcome is influenced by the modeling technique used to describe the behavior of the composite material. We consider the approaches presented in this paper promising for investigation of blast-loaded composite structures, and further improvements can be achieved by (i) refining the description of the material behavior, e.g., by including the strain rate sensitivity; and (ii) better modeling the boundary conditions.
Use of Pyrolysis–Gas Chromatography/Mass Spectrometry as a Tool to Study the Natural Variation in Biopolymers in Different Tissues of Economically Important European Softwood Species
Authors: Hentges David , Gérardin Philippe , Vinchelin Pierre , Dumarçay Stéphane
Published: October 2023

Intraspecific macromolecule variation in stemwood, knotwood, and branchwood was studied using analytical pyrolysis with the intention of introducing a rapid working method to assess the variance in lignin content using analytical pyrolysis and highlight variability markers. The study was performed on Picea abies, Abies alba, and Pseudotsuga menziesii. Lignin determined via analytical pyrolysis–GC/MS (Py-lignin) can be used to identify variations in lignin content, compared to using classical Klason lignin values as a reference method for lignin determination, which requires a correction factor. Principal component analysis (PCA) was performed to identify biopolymer pyrolysis product markers for different species, tissues, or heights that could help highlight structural differences. Douglas fir was differentiated from spruce and silver fir in the levoglucosan amount. Guaiacol was more present in spruce wood, and creosol was more present in Douglas fir. Knotwood was structurally close to stemwood in spruce and silver fir, but there was a clear transition between stemwood and branchwood tissue in Douglas fir. Knotwood was differentiated by higher furan compounds. Branchwood was clearly separate from stemwood and knotwood and presented the same markers as compression wood in the form of phenylpropanoid lignins (H-lignin) as well as isoeugenol and vinyl guaiacol, the two most produced lignin pyrolysis products.
Photoaligning Polymeric Command Surfaces: Bind, or Mix?
Authors: Nassrah Ameer R. K. , Batkova Marianna , Tomašovičová Natália , Tóth-Katona Tibor
Published: October 2023

We compare photoaligning properties of polymer layers fabricated from the same constituents: polymethyl-methacrylate (PMMA) and azo-dye Disperse Red 1 (DR1), either chemically attached to the PMMA main-chain, or physically mixed with it. Photoaligning properties depend on the preparation method drastically. Photoalignment was found to be far more efficient when PMMA is functionalized with DR1 compared to the case of physically mixing the constituents. This finding is supported by atomic force microscope (AFM) scans monitoring the light-induced changes at the polymer–air interface, and revealing a photoinduced mass transfer, especially in the case of functionalized PMMA.
Study on Mushy Zone Coefficient in Solidification Heat Transfer Mathematical Model of Thin Slab with High Casting Speed
Authors: Ding Zhijun , Xue Yuekai , Zhang Limin , Li Chenxiao , Wang Shuhuan , Ni Guolong
Published: October 2023

When the casting speed of the thin slab continuous caster is increased, the ratios of the solid and liquid phases in the solidification front of the molten steel in the mold change, which affects the thickness of the solidified shell. In order to accurately calculate the thickness of the solidified shell and determine the value range of the mushy zone coefficient suitable for the mathematical model of solidification heat transfer at high casting speed, this paper established the solidification heat transfer mathematical model in thin slab funnel mold, and the influence of different mushy zone coefficients on the accuracy of solidification heat transfer mathematical model was analyzed and compared with the actual solidified shell thickness. The results showed that, when the casting speed was increased to 4~6 m/min and the coefficient of the mush zone coefficient was 3 × 108~9 × 108 kg/(m3⋅s), the thickness of solidified shell calculated by the solidification heat transfer model was in good agreement with that measured in practice. The research in this paper provides an important reference for the establishment of the solidification heat transfer mathematical model at high casting speed in the future.
The Ethics of Contingency: An Alternative (to) Morality in the Analects
Authors: D’Ambrosio Paul
Published: October 2023

An appreciation of the emphasis on flexibility in the Analects is one of the most consistent motifs in the various interpretations of this text. When applied specifically to normative readings, embracing adaptability, having malleable standards, and taking particulars into account are both promising and challenging ways to think about whether something might be moral or ethical. This paper attempts to deconstruct this familiar discussion along novel lines that can reveal new ways to reflect on the importance of flexibility in the Analects, while, at the same time, it reconstrues ways in which comparative projects might be oriented. The main argument is this: the Analects does not recognize a categorical distinction between a moral and non-moral realm. Whereas, in much modern academic thought, there is a distinction between what counts as contingencies and what is up to the individual, which, in turn, loosely corresponds to the non-moral and moral realms, the Analects does not make such clear distinctions. Everything, including core moral issues such as the individual’s own choice, moral guidelines, and the contextualization of (moral) models, is all ultimately grounded in contingencies. Nevertheless, the Analects provides ways of reflecting on choices and actions that aim to steer people in specific directions. We may understand this as a type of morality, but one that is significantly distinct from those wherein the difference between the moral (non-contingent) and non-moral (contingent) is foundational.
Africa Independent Churches as Amabandla Omoya and Syncretism in South Africa
Authors: Mashabela James Kenokeno
Published: October 2023

This article presents a historical contribution of African Independent Churches in South Africa, particularly with references to the Ibandla lamaNazaretha of Isaiah Shembe, Zion Christian Church (ZCC) of Engenas Lekganyane and the St John’s Apostolic Faith Mission (AFM) of Christina Nku. There is no doubt that these churches continue to be attractive and relevant to the South African context in the 21st century. They form an integrated part of African Christianity in South Africa. The article investigates ways in which these churches respond to the spiritual and cultural needs of Africans. It is interesting to look at how the Holy Spirit, healing, prayer and prophecy manifest in these churches. The concept of African Spirituality is discussed and contextualised within these churches.
Kierkegaard, “the Public”, and the Vices of Virtue-Signaling: The Dangers of Social Comparison
Authors: Lippitt John
Published: October 2023

Concerns about the dangers of social comparison emerge in multiples places in Kierkegaard’s authorship. I argue that these concerns—and his critique of the role of “the public”—take on a new relevance in the digital age. In this article, I focus on one area where concerns about the risks of social comparison are paramount: the contemporary debate about moral grandstanding or “virtue-signaling”. Neil Levy and Evan Westra have recently attempted to defend virtue-signaling against Justin Tosi and Brandon Warmke’s critique. I argue that these defences fail and that a consideration of epistemic bubbles and echo chambers is critical to seeing why. The over-confidence to which they give rise exacerbates certain vices with the potential to do moral, social and epistemic harm: I focus in particular on self-righteousness (complementing Kierkegaard’s discussion of envy). I then argue that Kierkegaard’s contrast between the religious category of the “single individual”—the genuine person of “character”—and the person who effectively appeals to the authority of some version of “the public” deepens our understanding of why we should reject defences of virtue-signaling. It helps us to distinguish between two kinds of virtue-signaler (“superficial enthusiasts” and “clear-eyed cynics”), both of whom contribute, in different ways, to the negative impacts of the vice of self-righteousness. Contrary to Levy’s claim that virtue-signaling is virtuous, I conclude that typically it is closer to vice than to virtue.
A Learning Strategy for Amazon Deforestation Estimations Using Multi-Modal Satellite Imagery
Authors: Lee Dongoo , Choi Yeonju
Published: October 2023

Estimations of deforestation are crucial as increased levels of deforestation induce serious environmental problems. However, it is challenging to perform investigations over extensive areas, such as the Amazon rainforest, due to the vast size of the region and the difficulty of direct human access. Satellite imagery can be used as an effective solution to this problem; combining optical images with synthetic aperture radar (SAR) images enables deforestation monitoring over large areas irrespective of weather conditions. In this study, we propose a learning strategy for multi-modal deforestation estimations on this basis. Images from three different satellites, Sentinel-1, Sentinel-2, and Landsat 8, were utilized to this end. The proposed algorithm overcomes visibility limitations due to a long rainy season of the Amazon by creating a multi-modal dataset using supplementary SAR images, achieving high estimation accuracy. The dataset is composed of satellite data taken on a daily basis with relatively less monthly generated, ground truth masking data, which is called the many-to-one-mask condition. The Normalized Difference Vegetation Index and Normalized Difference Soil Index bands are selected to comprise the datasets. This yields better detection performance and a shorter training time than datasets consisting of RGB or all bands. Multiple deep neural networks are independently trained for each modality and an appropriate fusion method is developed to detect deforestation. The proposed method utilizes the distance similarity of the predicted deforestation rate to filter prediction results. The elements with high degrees of similarity are merged into the final result with average and denoising operations. The performances of five network variants of the U-Net family are compared, with Attention U-Net observed to exhibit the best prediction results. Finally, the proposed method is utilized to estimate the deforestation status of novel queries with high accuracy.
Phase Characteristics and Angle Deception of Frequency-Diversity-Array-Transmitted Signals Based on Time Index within Pulse
Authors: Zhou Changlin , Wang Chunyang , Gong Jian , Tan Ming , Bao Lei , Liu Mingjie
Published: October 2023

The transmitted beam of frequency diversity array (FDA) has the range–angle–time coupling property, which has essential applicative potential in angle deception and active anti-jamming. In this paper, the concept of time index within pulse is introduced. Firstly, the phase characteristics of FDA-transmitted signals based on the time index within pulse concept are studied. Then, the deceptive angle performance of FDA-transmitted signals is discussed. The theoretical analysis and simulation results show that the phase characteristics of the FDA signal are not related to the range, but to the time index within pulse. With the phase center as the reference point, the phase is equal as long as the time index within the pulse is the same. Angle deception and active anti-jamming can be achieved using the optimized frequency increment of each FDA.
A Neural Network for Hyperspectral Image Denoising by Combining Spatial–Spectral Information
Authors: Lian Xiaoying , Yin Zhonghai , Zhao Siwei , Li Dandan , Lv Shuai , Pang Boyu , Sun Dexin
Published: October 2023

Hyperspectral imaging often suffers from various types of noise, including sensor non-uniformity and atmospheric disturbances. Removing multiple types of complex noise in hyperspectral images (HSIs) while preserving high fidelity in spectral dimensions is a challenging task in hyperspectral data processing. Existing methods typically focus on specific types of noise, resulting in limited applicability and an inadequate ability to handle complex noise scenarios. This paper proposes a denoising method based on a network that considers both the spatial structure and spectral differences of noise in an image data cube. The proposed network takes into account the DN value of the current band, as well as the horizontal, vertical, and spectral gradients as inputs. A multi-resolution convolutional module is employed to accurately extract spatial and spectral noise features, which are then aggregated through residual connections at different levels. Finally, the residual mixed noise is approximated. Both simulated and real case studies confirm the effectiveness of the proposed denoising method. In the simulation experiment, the average PSNR value of the denoised results reached 31.47 at a signal-to-noise ratio of 8 dB, and the experimental results on the real data set Indian Pines show that the classification accuracy of the denoised hyperspectral image (HSI) is improved by 16.31% compared to the original noisy version.
A Method for Detecting Ionospheric TEC Anomalies before Earthquake: The Case Study of Ms 7.8 Earthquake, February 06, 2023, Türkiye
Authors: Feng Jiandi , Xiao Yuan , Chen Jianghe , Sun Shuyi , Ke Fuyang
Published: October 2023

The ionospheric anomalies before an earthquake may be related to earthquake preparation. The study of the ionospheric anomalies before an earthquake provides potential value for earthquake prediction. This paper proposes a method for detecting ionospheric total electron content (TEC) anomalies before an earthquake, taking the MS 7.8 earthquake in Türkiye on 6 February 2023 as an example. First, the data of four ground-based GNSS stations close to the epicenter were processed by using the sliding interquartile range method and the long short-term memory (LSTM) network. The anomaly dates detected by the two methods were identified as potential pre-earthquake TEC anomaly dates after eliminating solar and geomagnetic interference. Then, by using the sliding interquartile range method to process and analyze the CODE GIM (Center for Orbit Determination in Europe, Global Ionospheric Map) data from a global perspective, we further verified the existence of TEC anomalies before the earthquake on the above TEC anomaly days. Finally, the influence of the equatorial ionospheric anomaly (EIA) on the TEC anomaly disturbance was excluded. The results show that the ionospheric TEC anomalies on January 20, January 27, February 4, and February 5 before the Türkiye earthquake may be correlated with the earthquake.
Reconstruction of Land Surface Temperature Derived from FY-4A AGRI Data Based on Two-Point Machine Learning Method
Authors: Li Yueli , Zhu Shanyou , Luo Yumei , Zhang Guixin , Xu Yongming
Published: October 2023

Land surface temperature (LST) is one of the most important parameters of the interface between the earth surface and the atmosphere, and it plays a significant role in many research fields, such as agriculture, climate, hydrology, and the environment. However, the thermal infrared band of remote sensors is easily affected by clouds and aerosols, leading to many data gaps in LST products, which restricts the subsequent application of these products. In this paper, Beijing, China, is selected as the study area, and the LST data retrieved from Fengyun 4A (FY-4A) Advanced Geosynchronous Radiation Imager (AGRI) are reconstructed based on the two-point machine learning method. Firstly, the two-point machine learning model is built to reconstruct the theoretical clear-sky LST from simulated and actual images, and the accuracy of the reconstruction results is evaluated compared with the random forest algorithm and the inverse distance weighted method. Secondly, the actual LST under the influence of clouds is reconstructed by using the ERA5 reanalysis LST data as the auxiliary data, and the reconstruction accuracy is then evaluated by the field measurement LST data. The experimental results show that (1) the prediction accuracy of the two-point machine learning method is higher than that of the random forest method in both simulated data and actual data experiments; (2) the R2 of reconstructed LST under theoretical clear-sky conditions is 0.6860 and the root mean square error (RMSE) is 2.9 K, while the R2 of the reconstructed accuracy of actual LST under clouds is 0.7275 and the RMSE is 2.6 K, i.e., the RMSE decreases by 10.34%; (3) the two-point machine method combined with the auxiliary ERA5 LST data can well reconstruct LST under cloudy conditions and present a reasonable LST distribution.
Assessment of Extreme Ocean Winds within Intense Wintertime Windstorms over the North Pacific Using SMAP L-Band Radiometer Observations
Authors: Pichugin Mikhail , Gurvich Irina , Baranyuk Anastasiya
Published: October 2023

Here, we examine extreme ocean winds associated with intense wintertime extratropical windstorms over the North Pacific. The study was mainly based on NASA Soil Moisture Active Passive (SMAP) L-band radiometer observations allowing the retrieval of ocean wind speeds up to 70 m/s regardless of precipitation intensity. Additionally, we assessed the ability of atmospheric reanalysis ERA5 and the Climate Forecast System Version 2 (CFSv2) to reproduce high-wind features within severe windstorms, particularly those associated with “explosive” cyclogenesis. The analysis identified 145 windstorm events with hurricane-force (HF) wind zones within the SMAP L-band radiometer swath from 2015 to 2023. These windstorms develop most frequently over two areas: southeast of Kamchatka and south of Alaska, spanning 40–47°N latitudes. Both reanalysis datasets significantly underestimated HF wind speeds compared to SMAP measurements, but CFSv2 tends to reproduce more-intense windstorms than ERA5. Among the notable new findings is that the SMAP data revealed two distinct groups in maximum wind speed distribution, indicating the existence of a separate class of severe windstorm events with a distinct mechanism for extreme wind formation related probably to a Shapiro–Keyser cyclogenesis and the presence of sting jet (SJ) feature. The study highlights the potential of SMAP measurements to study wind extremes and underscores the need for improvements in operational predictive models to better reproduce the formation of SJ windstorms.
MuA-SAR Fast Imaging Based on UCFFBP Algorithm with Multi-Level Regional Attention Strategy
Authors: Xu Fanyun , Wang Rufei , Huang Yulin , Mao Deqing , Yang Jianyu , Zhang Yongchao , Zhang Yin
Published: October 2023

Multistatic airborne SAR (MuA-SAR) benefits from the ability to flexibly adjust the positions of multiple transmitters and receivers in space, which can shorten the synthetic aperture time to achieve the required resolution. To ensure both imaging efficiency and quality of different system spatial configurations and trajectories, the fast factorized back projection (FFBP) algorithm is proposed. However, if the FFBP algorithm based on polar coordinates is directly applied to the MuA-SAR system, the interpolation in the recursive fusion process will bring the problem of redundant calculations and error accumulation, leading to a sharp decrease in imaging efficiency and quality. In this paper, a unified Cartesian fast factorized back projection (UCFFBP) algorithm with a multi-level regional attention strategy is proposed for MuA-SAR fast imaging. First, a global Cartesian coordinate system (GCCS) is established. Through designing the rotation mapping matrix and phase compensation factor, data from different bistatic radar pairs can be processed coherently and efficiently. In addition, a multi-level regional attention strategy based on maximally stable extremal regions (MSER) is proposed. In the recursive fusion process, only the suspected target regions are paid more attention and segmented for coherent fusion at each fusion level, which further improves efficiency. The proposed UCFFBP algorithm ensures both the quality and efficiency of MuA-SAR imaging. Simulation experiments verified the effectiveness of the proposed algorithm.
An Enhanced RIME Optimizer with Horizontal and Vertical Crossover for Discriminating Microseismic and Blasting Signals in Deep Mines
Authors: Zhu Wei , Li Zhihui , Heidari Ali Asghar , Wang Shuihua , Chen Huiling , Zhang Yudong
Published: October 2023

Real-time monitoring of rock stability during the mining process is critical. This paper first proposed a RIME algorithm (CCRIME) based on vertical and horizontal crossover search strategies to improve the quality of the solutions obtained by the RIME algorithm and further enhance its search capabilities. Then, by constructing a binary version of CCRIME, the key parameters of FKNN were optimized using a binary conversion method. Finally, a discrete CCRIME-based BCCRIME was developed, which uses an S-shaped function transformation approach to address the feature selection issue by converting the search result into a real number that can only be zero or one. The performance of CCRIME was examined in this study from various perspectives, utilizing 30 benchmark functions from IEEE CEC2017. Basic algorithm comparison tests and sophisticated variant algorithm comparison experiments were also carried out. In addition, this paper also used collected microseismic and blasting data for classification prediction to verify the ability of the BCCRIME-FKNN model to process real data. This paper provides new ideas and methods for real-time monitoring of rock mass stability during deep well mineral resource mining.
Artificial Neural Network Modeling of a CMOS Differential Low-Noise Amplifier Using the Bayesian Regularization Algorithm
Authors: Subburaman Bhuvaneshwari , Thangaraj Vignesh , Balu Vadivel , Pandyan Uma Maheswari , Kulkarni Jayshri
Published: October 2023

The purpose of this communication is to present the modeling of an Artificial Neural Network (ANN) for a differential Complementary Metal Oxide Semiconductor (CMOS) Low-Noise Amplifier (LNA) designed for wireless applications. For satellite transponder applications employing differential LNAs, various techniques, such as gain boosting, linearity improvement, and body bias, have been individually documented in the literature. The proposed LNA combines all three of these techniques differentially, aiming to achieve a high gain, a low noise figure, excellent linearity, and reduced power consumption. Under simulation conditions at 5 GHz using Cadence, the proposed LNA demonstrates a high gain (S21) of 29.5 dB and a low noise figure (NF) of 1.2 dB, with a reduced supply voltage of only 0.9 V. Additionally, it exhibits a reflection coefficient (S11) of less than −10 dB, a power dissipation (Pdc) of 19.3 mW, and a third-order input intercept point (IIP3) of 0.2 dBm. The performance results of the proposed LNA, combining all three techniques, outperform those of LNAs employing only two of the above techniques. The proposed LNA is modeled using PatternNet BR, and the simulation results closely align with the results of the developed ANN. In comparison to the Cadence simulation method, the proposed approach also offers accurate circuit solutions.
Vehicle Operation Status Monitoring Based on Distributed Acoustic Sensor
Authors: Chen Mengmeng , Ding Haotian , Liu Mingming , Zhu Zhigao , Rui Dongdong , Chen Ye , Xu Fei
Published: October 2023

To develop implementation research on distributed optical fiber sensing technology, field tests were conducted on municipal roads and railways using a distributed acoustic sensor (DAS). Data were collected by the DAS during a field test for a long time period (more than 20 min), and we conducted short-term (<10 s) and long-term (≥10 s) analyses on these data separately. In the short-term data analysis, the vehicle type, vehicle length, and working status of the vehicle engine or the compressor were identified. In the long-term data analysis, the traffic flow was monitored, and the running distance, acceleration, speed, and braking distance of the vehicle were obtained. The characteristics of the vehicle operation data obtained in these field tests are important in developing the data processing method of DASs, which will help to promote the implementation of DASs.
Belief Propagation Optimization for Lossy Compression Based on Gaussian Source
Authors: Deng Huan , Song Dan , Xu Zhiping , Sun Yanglong , Wang Lin
Published: October 2023

In the Internet of Things, sensor nodes collect environmental information and utilize lossy compression for saving storage space. To achieve this objective, high-efficiency compression of the continuous source should be studied. Different from existing schemes, lossy source coding is implemented based on the duality principle in this work. Referring to the duality principle between the lossy source coding and the channel decoding, the belief propagation (BP) algorithm is introduced to realize lossy compression based on a Gaussian source. In the BP algorithm, the log-likelihood ratios (LLRs) are iterated, and their iteration paths follow the connecting relation between the check nodes and the variable nodes in the protograph low-density parity-check (P-LDPC) code. During LLR iterations, the trapping set is the main factor that influences compression performance. We propose the optimized BP algorithms to weaken the impact of trapping sets. The simulation results indicate that the optimized BP algorithms obtain better distortion–rate performance.
Evaluation of the TraumaGuard Balloon-in-Balloon Catheter Design for Intra-Abdominal Pressure Monitoring: Insights from Pig and Human Cadaver Studies
Authors: Tayebi Salar , McKinney Tim , McKinney Cynthia , Delvadia Dipak , Levine Marc-Alan , Spofford Edward S. , Malbrain Luca , Stiens Johan , Dabrowski Wojciech , Malbrain Manu L. N. G.
Published: October 2023

Introduction: Intra-abdominal pressure (IAP) monitoring is crucial for the detection and prevention of intra-abdominal hypertension (IAH) and abdominal compartment syndrome (ACS). In the 1970s, air-filled catheters (AFCs) for urodynamic studies were introduced as a solution to overcome the limitations of water-perfused catheters. Recent studies have shown that for correct IAP measurement with traditional AFC, the bladder needs to be primed with 25 mL of saline solution to allow pressure wave transmission to the transducer outside of the body, which limits continuous IAP monitoring. Methods: In this study, a novel triple balloon, air-filled TraumaGuard (TG) catheter system from Sentinel Medical Technologies (Jacksonville, FL, USA) with a unique balloon-in-balloon design was evaluated in a porcine and cadaver model of IAH via laparoscopy (IAPgold). Results: In total, 27 and 86 paired IAP measurements were performed in two pigs and one human cadaver, respectively. The mean IAPTG was 20.7 ± 10.7 mmHg compared to IAPgold of 20.3 ± 10.3 mmHg in the porcine study. In the cadaver investigation, the mean IAPTG was 15.6 ± 10.8 mmHg compared to IAPgold of 14.4 ± 10.4 mmHg. The correlation, concordance, bias, precision, limits of agreement, and percentage error were all in accordance with the WSACS (Abdominal Compartment Society) recommendations and guidelines for research. Conclusions: These findings support the use of the TG catheter for continuous IAP monitoring, providing early detection of elevated IAP, thus enabling the potential for prevention of IAH and ACS. Confirmation studies with the TraumaGuard system in critically ill patients are warranted to further validate these findings.
Research on the Relative Position Detection Method between Orchard Robots and Fruit Tree Rows
Authors: Gu Baoxing , Liu Qin , Gao Yi , Tian Guangzhao , Zhang Baohua , Wang Haiqing , Li He
Published: October 2023

The relative position of the orchard robot to the rows of fruit trees is an important parameter for achieving autonomous navigation. The current methods for estimating the position parameters between rows of orchard robots obtain low parameter accuracy. To address this problem, this paper proposes a machine vision-based method for detecting the relative position of orchard robots and fruit tree rows. First, the fruit tree trunk is identified based on the improved YOLOv4 model; second, the camera coordinates of the tree trunk are calculated using the principle of binocular camera triangulation, and the ground projection coordinates of the tree trunk are obtained through coordinate conversion; finally, the midpoints of the projection coordinates of different sides are combined, the navigation path is obtained by linear fitting with the least squares method, and the position parameters of the orchard robot are obtained through calculation. The experimental results show that the average accuracy and average recall rate of the improved YOLOv4 model for fruit tree trunk detection are 5.92% and 7.91% higher, respectively, than those of the original YOLOv4 model. The average errors of heading angle and lateral deviation estimates obtained based on the method in this paper are 0.57° and 0.02 m. The method can accurately calculate heading angle and lateral deviation values at different positions between rows and provide a reference for the autonomous visual navigation of orchard robots.
OutcropHyBNet: Hybrid Backbone Networks with Data Augmentation for Accurate Stratum Semantic Segmentation of Monocular Outcrop Images in Carbon Capture and Storage Applications
Authors: Madokoro Hirokazu , Sato Kodai , Nix Stephanie , Chiyonobu Shun , Nagayoshi Takeshi , Sato Kazuhito
Published: October 2023

The rapid advancement of climate change and global warming have widespread impacts on society, including ecosystems, water security, food production, health, and infrastructure. To achieve significant global emission reductions, approximately 74% is expected to come from cutting carbon dioxide (CO2) emissions in energy supply and demand. Carbon Capture and Storage (CCS) has attained global recognition as a preeminent approach for the mitigation of atmospheric carbon dioxide levels, primarily by means of capturing and storing CO2 emissions originating from fossil fuel systems. Currently, geological models for storage location determination in CCS rely on limited sampling data from borehole surveys, which poses accuracy challenges. To tackle this challenge, our research project focuses on analyzing exposed rock formations, known as outcrops, with the goal of identifying the most effective backbone networks for classifying various strata types in outcrop images. We leverage deep learning-based outcrop semantic segmentation techniques using hybrid backbone networks, named OutcropHyBNet, to achieve accurate and efficient lithological classification, while considering texture features and without compromising computational efficiency. We conducted accuracy comparisons using publicly available benchmark datasets, as well as an original dataset expanded through random sampling of 13 outcrop images obtained using a stationary camera, installed on the ground. Additionally, we evaluated the efficacy of data augmentation through image synthesis using Only Adversarial Supervision for Semantic Image Synthesis (OASIS). Evaluation experiments on two public benchmark datasets revealed insights into the classification characteristics of different classes. The results demonstrate the superiority of Convolutional Neural Networks (CNNs), specifically DeepLabv3, and Vision Transformers (ViTs), particularly SegFormer, under specific conditions. These findings contribute to advancing accurate lithological classification in geological studies using deep learning methodologies. In the evaluation experiments conducted on ground-level images obtained using a stationary camera and aerial images captured using a drone, we successfully demonstrated the superior performance of SegFormer across all categories.
Application of Biosensors in Detecting Breast Cancer Metastasis
Authors: Deng Yu , Zhang Yubi , Zhou Meng , Wu Bin , Zhou Jing
Published: October 2023

Breast cancer has garnered global attention due to its high incidence worldwide, and even more noteworthy is that approximately 90% deaths due to breast cancer are attributed to cancer metastasis. Therefore, the early diagnosis of breast cancer metastasis holds significant importance for reducing mortality outcomes. Biosensors play a crucial role in the early detection of metastatic breast cancer due to their advantages, such as ease of use, portability, and real-time analysis capabilities. This review primarily described various types of sensors for detecting breast cancer metastasis based on biomarkers and cell characteristics, including electrochemical, optical, and microfluidic chips. We offered detailed descriptions of the performance of these various biosensors and made comparisons between them. Furthermore, we described the pathology of breast cancer and summarized commonly used biomarkers for metastatic breast cancer. Finally, we discussed the advantages of current-stage biosensors and the challenges that need to be addressed, as well as prospects for their future development.
Atmospheric Turbulence Degraded Video Restoration with Recurrent GAN (ATVR-GAN)
Authors: Ettedgui Bar , Yitzhaky Yitzhak
Published: October 2023

Atmospheric turbulence (AT) can change the path and direction of light during video capturing of a target in space due to the random motion of the turbulent medium, a phenomenon that is most noticeable when shooting videos at long ranges, resulting in severe video dynamic distortion and blur. To mitigate geometric distortion and reduce spatially and temporally varying blur, we propose a novel Atmospheric Turbulence Video Restoration Generative Adversarial Network (ATVR-GAN) with a specialized Recurrent Neural Network (RNN) generator, which is trained to predict the scene’s turbulent optical flow (OF) field and utilizes a recurrent structure to catch both spatial and temporal dependencies. The new architecture is trained using a newly combined loss function that counts for the spatiotemporal distortions, specifically tailored to the AT problem. Our network was tested on synthetic and real imaging data and compared against leading algorithms in the field of AT mitigation and image restoration. The proposed method outperformed these methods for both synthetic and real data examined.
Spacecraft Segment Damage Identification Method Based on Fiber Optic Strain Difference Field Reconstruction and Norm Calculation
Authors: Xu Jihong , Zeng Jie , Chen Binbin , Lu Ruixin , Zhu Yangyang , Qi Lei , Chen Xiangfei
Published: October 2023

Real-time online identification of spacecraft segment damage is of great significance for realizing spacecraft structural health monitoring and life prediction. In this paper, a damage response characteristic field inversion algorithm based on the differential reconstruction of strain response is proposed to solve the problem of not being able to recognize the small damages of spacecraft structure directly by the strain response alone. Four crack damage location identification methods based on vector norm computation are proposed, which realize online identification and precise location of structural damage events without external excitation by means of spacecraft structural working loads only. A spacecraft segment structural damage monitoring system based on fiber optic grating sensors was constructed, and the average error of damage localization based on the curvature vector 2 norm calculation was 2.58 mm, and the root-mean-square error was 1.98 mm. The results show that the method has superior engineering applicability for on-orbit service environments.