bluuebunny/crossref_metadata_2025 huggingface dataset:
Mechanical Properties of Protein-Based Food Packaging Materials
Authors: Shah Yasir Abbas , Bhatia Saurabh , Al-Harrasi Ahmed , Afzaal Muhammad , Saeed Farhan , Anwer Md Khalid , Khan Mahbubur Rahman , Jawad Muhammad , Akram Noor , Faisal Zargham
Published: March 2023

The quality and safety of food products greatly depend on the physiochemical properties of the food packaging material. There is an increasing trend in the utilization of protein-based biopolymers for the preparation of edible films and coating due to their film-forming properties. Various studies have reported the preparation of protein-based edible films with desirable mechanical and barrier properties. The mechanical attributes of the protein-based food packaging materials can be enhanced by incorporating various components in the film composition such as plasticizers, surfactants, crosslinkers, and various bioactive compounds, including antimicrobial and antioxidant compounds. This review article summarizes the recent updates and perspective on the mechanical attributes such as Tensile Strength (TS), Elongation at Break (EAB), and Young’s Modulus (YM) of edible films based on different proteins from plants and animal sources. Moreover, the effects of composite materials such as other biopolymers, bioactive compounds, essential oils, and plasticizers on the mechanical properties of protein-based edible films are also discussed.
Enhancing the Anti-Migration Performance and Mechanical Properties of EPDM Insulation through Functionalized GO
Authors: Lu Zhehong , Zhu Ziqiang , Zhang Yulong , Wang Chenyang , Bai Haoran , Zhang Guangpu , Hu Yubing , Jiang Wei
Published: March 2023

The excessive migration of small molecular plasticizers in solid propellants may lead to debonding and changes in combustion characteristics, affecting the safety of solid rocket motors. Herein, two functionalized graphene oxides (GO) were used to enhance the anti-migration performance of EPDM insulation. GO, 3-Aminopropyltriethoxysilane-modified GO (AGO) and octadecylamine-modified GO (HGO) were filled into EPDM to fabricate EPDM insulation. The anti-migration properties and migration kinetics of EPDM insulations were studied using immersion tests. Moreover, the mechanical properties, including the tensile properties, crosslink density, hardness, and aging resistance of different EPDM insulations, were also explored. Compared with GO, AGO, and HGO obviously enhanced the anti-migration and mechanical properties of the EPDM insulations. This study shows that the anti-migration performance of EPDM insulation can be enhanced by functionalized GO.
Antifungal Activity of Water-Based Adhesives Derived from Pineapple Stem Flour with Apple Cider Vinegar as an Additive
Authors: Ninsuwan Kamonlak , Nimnuan Jaturavit , Watcharakitti Jidapa , Siriwong Chomsri , Amornsakchai Taweechai , Smith Siwaporn Meejoo
Published: March 2023

As a byproduct of bromelain extraction procedures, pineapple stem flour is underutilized. Since water glues derived from gelatinization typically have poor mold resistance, this study aims to produce flour-based value-added products, such as mold-resistant water-based adhesives. To address this issue, this study explored the use of apple cider vinegar (ACV) as a low-cost, non-toxic, commercially available antifungal agent to improve the mold resistance of adhesives. Furthermore, laurate flour was produced via a transesterification of the flour and methyl laurate using a K2CO3 catalyst. Both the unmodified flour and the functionalized flour were employed to prepare water-based adhesives. For both flour systems, adding ACV at concentrations of at least 2.0% v/v enhanced the mold resistance of the adhesives and completely inhibited the development of A. niger mycelia for up to 90 days of storage. The adhesives made from the transesterified flour exhibited a higher shear strength for the paper bonding (ca. 8%) than the unmodified ones. Additionally, the ACV additive had no negative effects on the shear strengths of the water-based adhesives. All of the flour-based adhesives developed in this study had a higher shear strength for paper substrates than two locally available commercial water glues.
Understanding the Effect of Triazole on Crosslinked PPO–SEBS-Based Anion Exchange Membranes for Water Electrolysis
Authors: Choi Jiyong , Min Kyungwhan , Mo Yong-Hwan , Han Sang-Beom , Kim Tae-Hyun
Published: March 2023

For anion exchange membrane water electrolysis (AEMWE), two types of anion exchange membranes (AEMs) containing crosslinked poly(phenylene oxide) (PPO) and poly(styrene ethylene butylene styrene) (SEBS) were prepared with and without triazole. The impact of triazole was carefully examined. In this work, the PPO was crosslinked with the non-aryl ether-type SEBS to take advantage of its enhanced chemical stability and phase separation under alkaline conditions. Compared to their triazole-free counterpart, the crosslinked membranes made with triazole had better hydroxide-ion conductivity because of the increased phase separation, which was confirmed by X-ray diffraction (XRD) and atomic force microscopy (AFM). Moreover, they displayed improved mechanical and alkaline stability. Under water electrolysis (WE) conditions, a triazole-containing crosslinked PPO–SEBS membrane electrode assembly (MEA) was created using IrO2 as the anode and a Pt/C catalyst as the cathode. This MEA displayed a current density of 0.7 A/cm2 at 1.8 V, which was higher than that of the MEA created with the triazole-free counterpart. Our study indicated that the crosslinked PPO–SEBS membrane containing triazoles had improved chemo-physical and electrical capabilities for WE because of the strong hydrogen bonding between triazole and water/OH−.
Structural Variations in Biobased Polyfurfuryl Alcohol Induced by Polymerization in Water
Authors: Delliere Pierre , Pizzi Antonio , Guigo Nathanael
Published: March 2023

Poly(furfuryl alcohol) is a thermostable biobased thermoset. The polymerization of furfuryl alcohol (FA) is sensitive to a number of side reactions, mainly the opening of the furan ring into carbonyl species. Such carbonyls can be used to introduce new properties into the PFA materials through derivatization. Hence, better understanding of the furan ring opening is required to develop new applications for PFA. This article studies the structural discrepancies between a PFA prepared in neat conditions versus a PFA prepared in aqueous conditions, i.e., with more carbonyls, through NMR and MALDI ToF. Overall, the PFA prepared in water exhibited a structure more heterogeneous than the PFA prepared in neat conditions. The presence of ketonic derivatives such as enols and ketals were highlighted in the case of the aqueous PFA. In this line, the addition of water at the beginning of the polymerization stimulated the production of aldehydes by a factor two. Finally, the PFA prepared in neat conditions showed terminal lactones instead of aldehydes.
Nucleus-Targeting Nanoplatform Based on Dendritic Peptide for Precise Photothermal Therapy
Authors: Wang Wen-Song , Ma Xiao-Yu , Zheng Si-Yao , Chen Si , Fan Jin-Xuan , Liu Fan , Yan Guo-Ping
Published: March 2023

Photothermal therapy directly acting on the nucleus is a potential anti-tumor treatment with higher killing efficiency. However, in practical applications, it is often difficult to achieve precise nuclear photothermal therapy because agents are difficult to accurately anchor to the nucleus. Therefore, it is urgent to develop a nanoheater that can accurately locate the nucleus. Here, we designed an amphiphilic arginine-rich dendritic peptide (RDP) with the sequence CRRK(RRCG(Fmoc))2, and prepared a nucleus-targeting nanoplatform RDP/I by encapsulating the photothermal agent IR780 in RDP for precise photothermal therapy of the tumor nucleus. The hydrophobic group Fmoc of the dendritic peptide provides strong hydrophobic force to firmly encapsulate IR780, which improves the solubility and stability of IR780. Moreover, the arginine-rich structure facilitates cellular uptake of RDP/I and endows it with the ability to quickly anchor to the nucleus. The nucleus-targeting nanoplatform RDP/I showed efficient nuclear enrichment ability and a significant tumor inhibition effect.
Modeling and Operating Time Optimization of Layer Melt Crystallization and Sweating Processes
Authors: Bai Yunhe , Qi Luguang , Sun Ying , Zhu Zhenxing , Xie Chuang
Published: March 2023

Improving the separation efficiency of the layer melt crystallization process is a key but difficult task. Herein, a comprehensive model involving both crystallization and sweating was proposed and used to optimize the operating time of crystallization and sweating processes. The crystallization process was modeled based on the relationship between differential and integral distribution coefficients under a constant layer growth rate. For the sweating process, an empirical sweating equation was employed to govern the sweating model, the parameters of which were determined experimentally using P-xylene as the model substance. The separation efficiency was then optimized by minimizing the operating time at a given product purity and yield. A sensitivity analysis showed that the crystallization and sweating times nonlinearly increase with increasing yield. After the yield exceeds 0.65, an increasing crystallization time is the dominant factor in improving the separation efficiency, while the sweating time and ratio even slightly decrease. The total operating time at low yield is U-shaped with the layer growth rate. The optimal layer growth rate decreases with increasing yield. This model provides guidance for determining the optimal operating parameters of layer melt crystallization and sweating processes.
Storage Stability of Spray- and Freeze-Dried Chitosan-Based Pickering Emulsions Containing Roasted Coffee Oil: Color Evaluation, Lipid Oxidation, and Volatile Compounds
Authors: Ribeiro Elisa Franco , Polachini Tiago Carregari , Locali-Pereira Adilson Roberto , Janzantti Natália Soares , Quiles Amparo , Hernando Isabel , Nicoletti Vânia Regina
Published: March 2023

Drying Pickering o/w emulsions has been considered as a promising strategy to produce oil microcapsules, as long as their quality parameters can be preserved over storage. In this sense, it is shown as an interesting alternative to preserve the quality of roasted coffee oil, a valuable agroindustrial byproduct. Thus, freeze- and spray-dried chitosan-based Pickering emulsions of roasted coffee oil were evaluated over 30 days of storage at 25 °C together with the non-encapsulated oil as a control. Water sorption isotherms were determined, whereas color, oxidative stability (peroxide value and conjugated dienes) and volatile compounds were assessed over the storage period. Type II isotherms and Guggenheim–Anderson–Boer (GAB) model parameters showed that water binding was impaired by the surface oil in freeze-dried samples. Oxidation was maintained under acceptable values over the storage for all samples, with slightly higher protection also observed for volatile compounds in the spray-dried particles. The powdered emulsions were able to suitably preserve the oil’s quality over 30 days of storage, enabling its commercialization and application as a food ingredient and potential flavoring.
Synthesis of CaWO4 as a Photocatalyst for Degradation of Methylene Blue and Carmine under Ultraviolet Light Irradiation
Authors: Yuan Cunyang , Sun Fuwei , Liu Haibo , Chen Tianhu , Chu Ziyang , Wang Hanlin , Zou Xuehua , Zhai Peixun , Chen Dong
Published: March 2023

Photocatalysis is considered a promising method for wastewater treatment; however, most synthesized photocatalysts have complex structures and are costly. Thus, in this study, a novel CaWO4 sample was synthesized by a co-precipitation method in one step. The characteristic results show that CaWO4 has good dispersibility, a large specific surface area, and good photoresponse under UV light. The synthesized CaWO4 can be used to degrade methylene blue (MB) and carmine (CR) under UV light without the addition of oxidants. The effects of a water matrix, including pH value, solid–liquid ratio, light intensity, and initial concentration of pollutants on photocatalytic degradation were studied. According to the optimization of these factors, the optimal photocatalytic degradation condition was found under the catalyst concentration of 1.0 g/L and ultraviolet light intensity of 80 W. The optimal pH is 8.2 for the MB system and 6.0 for the CR system. The optimal photocatalytic degradation of MB and CR at 100 mg/L can be achieved as 100%. According to the results of scavenger experiments, holes and hydroxyl radicals dominate the degradation of MB while hydroxyl radicals and superoxide anions are mainly responsible for the degradation of CR. Further analyses showed that photogenerated electrons generated on the surface of the CaWO4 can form electron–hole pairs, thereby producing hydroxyl radicals and superoxide anions to degrade dyes. In addition, the CaWO4 has a good cycling performance in the process of degrading MB (more than 80% after five cycles). It provides a new idea for the photocatalytic degradation of dyes using mineral-like materials.
Reasonable Support Technology of Full-Stress Anchoring Technology of Advance Roadway: A Case Study
Authors: Guo Xiaowei , Zheng Xigui , Li Peng , Liu Cancan , Wang Jiyu , Shahani Niaz Muhammad , Xu Wenjie , Li Boyang , Lai Guowei , Wang Yonghui , Xin Wei , Xu Hongxiang
Published: March 2023

Based on the engineering background of providing advance support for the working face of mining roadways, this paper studies the reasonable support technology of advance roadway roofs by combining theoretical analysis, numerical simulation, and field tests. Based on the geological conditions of the 1304 working face of Yineng Coal Mine, the FLAC3D numerical simulation software was used to compare and analyze the effects of the original single hydraulic prop advance support and the bolt-mesh-cable support without the single hydraulic prop. The results show that although the deformation of the surrounding rock is reduced under the support of the single hydraulic prop, the convergence of the roof and floor of the roadway and the left and right sides are still as high as 288 mm and 308 mm, respectively, which does not meet the requirements for safe production. Based on this problem, this study proposes full-stress anchoring technology. FLAC3D numerical simulation software is used to simulate and analyze the supporting effect of the full-stress anchoring support technology in advanced mining roadways. The results of numerical simulation experiments show that the convergence of the roof and floor and the convergence of the left and right sides of the roadway surrounding rock are 33 mm and 52 mm, respectively, which have a good control effect on the roadway surrounding rock. The field test of bolt full-stress anchoring support technology was carried out in the return air roadway of the 1304 working face. The deformation of the surrounding rock of the roadway was monitored by setting up stations. The measured results show that the maximum roof and floor convergence of the roadway is 42 mm, and the maximum convergence of the two sides of the roadway is 69 mm, which meets the requirements for safe mining on site. In this study, by comparing with the advance support effect of the original single hydraulic prop, the rationality of the full-stress anchoring technology of the mining roadway in the advance section of the working panel is determined. The use of bolt full-stress anchoring instead of the traditional single hydraulic prop for advanced support has a better surrounding rock control effect and a lower support cost. This is a new technology for advanced support of surrounding rock in mining roadways, which enriches the control technology of roadway surrounding rock and also provides technical reference for other similar engineering cases.
M-E-AWA: A Novel Task Scheduling Approach Based on Weight Vector Adaptive Updating for Fog Computing
Authors: Dai Zhiming , Ding Weichao , Min Qi , Gu Chunhua , Yao Baohua , Shen Xiaohan
Published: March 2023

Task offloading and real-time scheduling are hot topics in fog computing. This paper aims to address the challenges of complex modeling and solving multi-objective task scheduling in fog computing environments caused by widely distributed resources and strong load uncertainties. Firstly, a task unloading model based on dynamic priority adjustment is proposed. Secondly, a multi-objective optimization model is constructed for task scheduling based on the task unloading model, which optimizes time delay and energy consumption. The experimental results show that M-E-AWA (MOEA/D with adaptive weight adjustment based on external archives) can effectively handle multi-objective optimization problems with complex Pareto fronts and reduce the response time and energy consumption costs of task scheduling.
Experimental Study on the Forcible Imbibition Law of Water in Shale Gas Reservoirs
Authors: Zhao Zhihong , He Yanyan , Guo Jianchun , Zheng Xiaoqiang , Tao Liang , Deng Xianan
Published: March 2023

Water imbibition is a key factor affecting the flowback regime of shale gas wells after volume fracturing. In this study, a set of experimental apparatus and corresponding test and evaluation methods were developed to analyze the laws of forcible imbibition of water in a shale reservoir, characterize the initiation time of microfractures induced by shale hydration quantitatively, and optimize the shut-in time of shale gas wells; the imbibition depths of different pore types are quantitatively calculated based on the multiple pore imbibition analytical model. The experimental results show that: according to imbibition saturation growth rate, the shale forcible imbibition can be divided into three periods, imbibition diffusion, imbibition transition, and imbibition balance. Among them, the imbibition diffusion period is the main period for imbibition capacity rise. The reason for this phenomenon is that due to the fluid pressure difference effect, the shale fills its large pores and microfractures rapidly in the early stage, and in the percolation transition period, the percolation rate decreases continuously due to the gradual increase of fluid saturation. Due to the Jamin effect, it is difficult for the fluid to enter the small pores and the fluid fills the pore roar channel, the seepage saturation tends to stabilize, and the seepage equilibrium period appears. In the early period of shut-in, the imbibition capacity of shale increases significantly under the action of fluid pressure, providing a large amount of imbibition fluid for the spontaneous imbibition later. The imbibition depth of a clay pore was much greater than that of a brittle mineral pore and an organic pore. The reservoir confining pressure has prohibition on shale imbibition, but even under reservoir confining pressure, imbibition can still improve the fracturing effect of the reservoir, resulting in an increase in porosity of 0.42–1.63 times and an increase in permeability of 17.6–67.3 times. Under the experimental conditions, the initiation time of induced microfractures is 98.5 h on average and is in negative correlation with imbibition capacity. On this basis, the optimized shortest shut-in time of a shale gas well is 5 days. The study results can provide a scientific basis for the optimization of the flowback regime of shale gas reservoirs.
Automotive Paint Sludge: A Review of Pretreatments and Recovery Options
Authors: Ruffino Barbara , Campo Giuseppe , Idris Siti Shawalliah , Salihoğlu Güray , Zanetti Mariachiara
Published: March 2023

Automotive paint sludge (PS) is the waste product generated in the painting process of vehicle bodies. Although automotive spray painting is an automated operation, its efficiency is still quite low, since approximately 40–50% of the employed paint does not reach the target and, after being mixed with the collecting water, becomes PS. PS is a very complex material that contains several organic and inorganic components, in addition to 90% water immediately after production. Italian automotive factories produce from 2.5 to 5.0 kg of PS per painted car. If that figure is related to the number of vehicles produced worldwide every year, in the order of 100 million, it determines an annual PS amount in the order of 200,000–500,000 t. Consequently, a proper final destination for PS must be found. The waste management hierarchy and the principles of the circular economy require that we privilege solutions that foresee the recovery of valuable products or energy. This paper first reviews the processes and the machines that have been recently developed to obtain an enhanced mechanical dewatering of PS. The pretreatment of PS dewatering is often crucial in order to obtain high efficiency in the subsequent recovery process. Afterwards, the paper presents and discusses the recovery options that have been proposed and tested, at different scales, by several authors in the last thirty years. The processes for PS management can be grouped as follows: (i) direct employment of physically/chemically treated PS in the production of primers and sealants; (ii) utilization of PS for the production of building materials, as supplementary components of cement concrete, mortar, or bituminous mixtures; (iii) extraction of valuable organic and inorganic products by using thermal processes (pyrolysis, gasification); (iv) biological processes for PS detoxification, metal recovery, and stabilization before landfilling.
Use of a Convolutional Neural Network for Predicting Fuel Consumption of an Agricultural Tractor
Authors: Jalilnezhad Hamid , Abbaspour-Gilandeh Yousef , Rasooli-Sharabiani Vali , Mardani Aref , Hernández-Hernández José Luis , Montero-Valverde José Antonio , Hernández-Hernández Mario
Published: March 2023

The energy crisis and depleting fossil fuel resources have always been the focus of researchers. Fuel consumption of agricultural tractors is not an exception. Researchers have used different methods to predict fuel consumption. With the development of artificial intelligence in the last decade, all re-searchers’ attention has been directed towards it. Deep learning is a subset of machine learning, which was inspired by the data processing patterns in the human brain. The deep learning method has been used in research due to the advantages of high accuracy and generalization. So far, no research has used this method to predict fuel consumption. In this research, field experiments were carried out in sandy clay loam and clay soils to model the temporal fuel consumption and specific fuel consumption of an agricultural tractor using a convolutional neural network (CNN), while having some parameters such as the soil type, soil conditions, tool parameters, and operation pa-rameters. The experiments were conducted within each soil texture in a factorial manner based on the randomized complete block design (RCBD) with three replicates. For each soil texture, various moisture levels (8–17% for dry and 18–40% for moist soils), tractor forward speeds (1.2, 1.6, 1.8, and 2.2 km h−1), working depths (30 and 50 cm), the number of passes (2 and 6), and tire inflation pressure (20 and 25 psi) were selected, and cone index, dynamic load, and moisture content were measured in each experimental section. The designed networks used to predict the instant fuel consumption were of a CNN type. The results indicated that the network developed based on the Sgdm algorithm outperformed the others, and thus it was selected for modeling purposes. The network was evaluated based on R2 and MSE criteria. For the temporal fuel consumption, the best results were obtained while using 8-510-510-1 architecture with R2 = 0.9729 and MSE = 0.0049. The 8-100-95-1 architecture also led to the best prediction of the specific fuel consumption with R2 of 0.9737 and MSE of 0.0054. The high prediction accuracy and low error in this research compared to previous studies indicate the superiority of this method in order to predict fuel consumption. It was also observed from the results that the input parameters, which include soil, tool, and operational parameters, are all effective on fuel consumption. Proper management of some parameters, such as working depth, tire inflation pressure, and forward speed, can help to optimize fuel consumption.
Improving the Spatial Prediction of Soil Organic Carbon Content Using Phenological Factors: A Case Study in the Middle and Upper Reaches of Heihe River Basin, China
Authors: Liu Xinyu , Wang Jian , Song Xiaodong
Published: March 2023

The accurate mapping of soil organic carbon (SOC) distribution is important for carbon sequestration and land management strategies, contributing to mitigating climate change and ensuring agricultural productivity. The Heihe River Basin in China is an important region that has immense potential for SOC storage. Phenological variables are effective indicators of vegetation growth, and hence are closely related to SOC. However, few studies have incorporated phenological variables in SOC prediction, especially in alpine areas such as the Heihe River Basin. This study used random forest (RF) and extreme gradient boosting (XGBoost) to study the effects of phenological variables (e.g., Greenup, Dormancy, etc.) obtained from MODIS (i.e., Moderate Resolution Imaging Spectroradiometer) product (MCD12Q2) on SOC content prediction in the middle and upper reaches of Heihe River Basin. The current study also identified the dominating variables in SOC prediction and compared model performance using a cross validation procedure. The results indicate that: (1) when phenological variables were considered, the R2 (coefficient of determination) of RF and XGBoost were 0.68 and 0.56, respectively, and RF consistently outperforms XGBoost in various cross validation experiments; (2) the environmental variables MAT, MAP, DEM and NDVI play the most important roles in SOC prediction; (3) the phenological variables can account for 32–39% of the spatial variability of SOC in both the RF and XGBoost models, and hence were the most important factor among the five categories of predictive variables. This study proved that the introduction of phenological variables can significantly improve the performance of SOC prediction. They should be used as indispensable variables for accurately modeling SOC in related studies.
Lunar Procellarum KREEP Terrane (PKT) Stratigraphy and Structure with Depth: Evidence for Significantly Decreased Th Concentrations and Thermal Evolution Consequences
Authors: Zhang Jingyi , Head James , Liu Jianzhong , Potter Ross
Published: March 2023

Dating from the lunar magma ocean solidification period, the Procellarum KREEP Terrane (PKT) occupies 16% of the surface but has a much higher thorium abundance compared to the rest of the Moon and is thus interpreted to carry 40% of the radioactive elements by volume in the form of an anomalously thick KREEP-rich layer. Subsequent research has focused on the processes responsible for PKT concentration and localization (e.g., degree-1 convection, farside impact basin effects, etc.), and the effect of PKT high-radioactivity localization on lunar thermal evolution (e.g., topography relaxation, mantle heating, late-stage mare basalt generation, etc.). Here we use a stratigraphic approach and new crustal thickness data to probe the nature of the PKT with depth. We find that most PKT characteristics can be explained by sequential impact cratering events that excavated and redistributed to the surface/near-surface a much thinner Th-rich KREEP layer at depth, implying that no anomalous conditions of PKT thickness, radioactive abundances, geodynamics, thermal effects or magma generation are likely to be required as in the previous studies.
Underwater 3D Scanning System for Cultural Heritage Documentation
Authors: Bräuer-Burchardt Christian , Munkelt Christoph , Bleier Michael , Heinze Matthias , Gebhart Ingo , Kühmstedt Peter , Notni Gunther
Published: March 2023

Three-dimensional capturing of underwater archeological sites or sunken shipwrecks can support important documentation purposes. In this study, a novel 3D scanning system based on structured illumination is introduced, which supports cultural heritage documentation and measurement tasks in underwater environments. The newly developed system consists of two monochrome measurement cameras, a projection unit that produces aperiodic sinusoidal fringe patterns, two flashlights, a color camera, an inertial measurement unit (IMU), and an electronic control box. The opportunities and limitations of the measurement principles of the 3D scanning system are discussed and compared to other 3D recording methods such as laser scanning, ultrasound, and photogrammetry, in the context of underwater applications. Some possible operational scenarios concerning cultural heritage documentation are introduced and discussed. A report on application activities in water basins and offshore environments including measurement examples and results of the accuracy measurements is given. The study shows that the new 3D scanning system can be used for both the topographic documentation of underwater sites and to generate detailed true-scale 3D models including the texture and color information of objects that must remain under water.
Unsupervised Transformer Boundary Autoencoder Network for Hyperspectral Image Change Detection
Authors: Liu Song , Li Haiwei , Wang Feifei , Chen Junyu , Zhang Geng , Song Liyao , Hu Bingliang
Published: March 2023

In the field of remote sens., change detection is an important monitoring technology. However, effectively extracting the change feature is still a challenge, especially with an unsupervised method. To solve this problem, we proposed an unsupervised transformer boundary autoencoder network (UTBANet) in this paper. UTBANet consists of a transformer structure and spectral attention in the encoder part. In addition to reconstructing hyperspectral images, UTBANet also adds a decoder branch for reconstructing edge information. The designed encoder module is used to extract features. First, the transformer structure is used for extracting the global features. Then, spectral attention can find important feature maps and reduce feature redundancy. Furthermore, UTBANet reconstructs the hyperspectral image and boundary information simultaneously through two decoders, which can improve the ability of the encoder to extract edge features. Our experiments demonstrate that the proposed structure significantly improves the performance of change detection. Moreover, comparative experiments show that our method is superior to most existing unsupervised methods.
Three-Dimensional Geometry Reconstruction Method from Multi-View ISAR Images Utilizing Deep Learning
Authors: Zhou Zuobang , Jin Xiangguo , Liu Lei , Zhou Feng
Published: March 2023

The three-dimensional (3D) geometry reconstruction method utilizing ISAR image sequence energy accumulation (ISEA) shows great performance on triaxial stabilized space targets but fails when there is unknown motion from the target itself. The orthogonal factorization method (OFM) can solve this problem well under certain assumptions. However, due to the sparsity and anisotropy of ISAR images, the extraction and association of feature points become very complicated, resulting in the reconstructed geometry usually being a relatively sparse point cloud. Therefore, combining the advantages of the above methods, an extended factorization framework (EFF) is proposed. First, the instance segmentation method based on deep learning is used for the extraction and association of a number of key points on multi-view ISAR images. Then, the projection vectors between the 3-D geometry of the space target and the multi-view ISAR images are obtained, using the improved factorization method. Finally, the 3D geometry reconstruction problem is transformed into an unconstrained optimization problem and solved via the quantum-behaved particle swarm optimization (QPSO) method. The proposed framework uses discretely observed multi-view range–Doppler ISAR images as an input, which can make full use of the long-term data of space targets from multiple perspectives and which is non-sensitive to movement. Therefore, the proposed framework shows high feasibility in practical applications. Experiments on simulated and measured data show the effectiveness and robustness of the proposed framework.
Near-Field IPO for Analysis of EM Scattering from Multiple Hybrid Dielectric and Conductor Target and High Resolution Range Profiles
Authors: Wang Qingkuan , Wang Yijin , Tong Chuangming , Wang Zhaolong , Li Ximin , Wang Tong
Published: March 2023

Aiming at improving the accuracy and efficiency of scattering information from multiple targets in near-field regions, this paper proposes a near-field iterative physical optics (IPO) method based on a modified near-field Green’s function for the composite electromagnetic scattering analysis of multiple hybrid dielectric and conductor targets. According to the electric field and magnetic field integral equation, the electric and magnetic current were updated utilizing the Jacobi iteration method. Then, by introducing an expansion center lying in the neighborhood of the source point, Green’s function was modified for near-field scattering between multiple hybrid dielectric and conductor targets. To accelerate the implementation of the procedure, the multilevel fast multipole method, the fast far-field approximation, and parallel multicore programming were introduced. Numerical results indicate that there is good agreement between the results calculated by the near-field IPO method and MLFMM solver in commercial software FEKO while significantly reducing the computational burden. To fully exploit the scattering information, the high resolution range profiles (HRRP) of different targets under different conditions were analyzed, which can be further applied for automatic target detection and recognition.
MSFFAL: Few-Shot Object Detection via Multi-Scale Feature Fusion and Attentive Learning
Authors: Zhang Tianzhao , Sun Ruoxi , Wan Yong , Zhang Fuping , Wei Jianming
Published: March 2023

Few-shot object detection (FSOD) is proposed to solve the application problem of traditional detectors in scenarios lacking training samples. The meta-learning methods have attracted the researchers’ attention for their excellent generalization performance. They usually select the same class of support features according to the query labels to weight the query features. However, the model cannot possess the ability of active identification only by using the same category support features, and feature selection causes difficulties in the testing process without labels. The single-scale feature of the model also leads to poor performance in small object detection. In addition, the hard samples in the support branch impact the backbone’s representation of the support features, thus impacting the feature weighting process. To overcome these problems, we propose a multi-scale feature fusion and attentive learning (MSFFAL) framework for few-shot object detection. We first design the backbone with multi-scale feature fusion and channel attention mechanism to improve the model’s detection accuracy on small objects and the representation of hard support samples. Based on this, we propose an attention loss to replace the feature weighting module. The loss allows the model to consistently represent the objects of the same category in the two branches and realizes the active recognition of the model. The model no longer depends on query labels to select features when testing, optimizing the model testing process. The experiments show that MSFFAL outperforms the state-of-the-art (SOTA) by 0.7–7.8% on the Pascal VOC and exhibits 1.61 times the result of the baseline model in MS COCO’s small objects detection.
Backscattering Analysis at ATR on Rough Surfaces by Ground-Based Polarimetric Radar Using Coherent Decomposition
Authors: Kvasnov Anton V.
Published: March 2023

This article deals with the analysis of backscattering at automatic target recognition (ATR) by ground-based radar located on rough terrain surfaces, using the properties of wave polarization. The purpose of the study is to examine and compare linear and circular polarized reflected waves, which can be described by decomposition theorems. Coherent decompositions (Pauli, Krogager, Cameron decomposition) are considered in the case of a rough terrain, for which the advantage of the Pauli decomposition has been shown. The article demonstrates an approach to the extraction of polarization signal backscattering data for two types of vehicles with different profiles. It is shown that the measurement results can be calibrated by a corner reflector that takes into account the properties of the ground surface, and further used for ATR based on supervised learning algorithms. The accuracy of object classification was 68.1% and 54.2% for the signal generated by linearly and elliptically polarized waves, respectively. Based on these results, we recommend using a linearly polarized wave as an object recognition mechanism. At the same time, any reflected depolarized wave significantly reshapes the structure due to the rotation of the object profile and the influence of a rough surface (vegetation fluctuations). This explains the low recognition accuracy in general.
Multi-Agent Deep Reinforcement Learning for Multi-Robot Applications: A Survey
Authors: Orr James , Dutta Ayan
Published: March 2023

Deep reinforcement learning has produced many success stories in recent years. Some example fields in which these successes have taken place include mathematics, games, health care, and robotics. In this paper, we are especially interested in multi-agent deep reinforcement learning, where multiple agents present in the environment not only learn from their own experiences but also from each other and its applications in multi-robot systems. In many real-world scenarios, one robot might not be enough to complete the given task on its own, and, therefore, we might need to deploy multiple robots who work together towards a common global objective of finishing the task. Although multi-agent deep reinforcement learning and its applications in multi-robot systems are of tremendous significance from theoretical and applied standpoints, the latest survey in this domain dates to 2004 albeit for traditional learning applications as deep reinforcement learning was not invented. We classify the reviewed papers in our survey primarily based on their multi-robot applications. Our survey also discusses a few challenges that the current research in this domain faces and provides a potential list of future applications involving multi-robot systems that can benefit from advances in multi-agent deep reinforcement learning.
A PCA-Based Approach for Very Early-Age Hydration Monitoring of Self-Compacting Concrete Using Embedded PZT Sensors
Authors: Liu Qunfeng , Mu Yifan , Li Xiaoting , Wu Xing , Ren Xiang
Published: March 2023

This work proposed a novel approach based on principal component analyses (PCAs) to monitor the very early-age hydration of self-compacting concrete (SCC) with varying replacement ratios of fly ash (FA) to cement at 0%, 15%, 30%, 45%, and 60%, respectively. Based on the conductance signatures obtained from electromechanical impedance (EMI) tests, the effect of the FA content on the very early-age hydration of SCCs was indicated by the predominant resonance shifts, the statistical metrics, and the contribution ratios of principal components, quantitatively. Among the three, the PCA-based approach not only provided robust indices to predict the setting times with physical implications but also captured the liquid-solid transition elongation (1.5 h) during the hydration of SCC specimens with increasing FA replacement ratios from 0% to 45%. The results demonstrated that the PCA-based approach was more accurate and robust for quantitative hydration monitoring than the conventional penetration resistance test and the other two counterpart indices based on EMI tests.
A UWB/INS Trajectory Tracking System Application in a Cycling Safety Study
Authors: Zhu Sicong , Yue Hao , Suzuki Tatsuto , Kim Inhi , Yu Lei , Lan Qing
Published: March 2023

This paper focuses on the safety issue for cyclists and pedestrians at unsignalized intersections. The cycling speed needs to be calmed when approaching the intersection. This study proposes and deploys an integrated portable ultra-wideband/inertial navigation system (UWB/INS) to extract cycling trajectories for a cycling safety study. The system is based on open-source hardware and delivers an open-source code for an adaptive Kalman filter to enhance positioning precision for data quality assurance at an outdoor experimental site. The results demonstrate that the system can deliver reliable trajectories for low-mobility objects. To mitigate accident risk and severity, varied cycling speed calming measures are tested at an experimental site. Based on the trajectory data, the statistical features of cycling velocities are evaluated and compared. A new proposed geometric design is found to be most effective when compared with conventional traffic signs.
Estimation of Respiratory Rate during Biking with a Single Sensor Functional Near-Infrared Spectroscopy (fNIRS) System
Authors: Shahbakhti Mohammad , Hakimi Naser , Horschig Jörn M. , Floor-Westerdijk Marianne , Claassen Jurgen , Colier Willy N. J. M.
Published: March 2023

Objective: The employment of wearable systems for continuous monitoring of vital signs is increasing. However, due to substantial susceptibility of conventional bio-signals recorded by wearable systems to motion artifacts, estimation of the respiratory rate (RR) during physical activities is a challenging task. Alternatively, functional Near-Infrared Spectroscopy (fNIRS) can be used, which has been proven less vulnerable to the subject’s movements. This paper proposes a fusion-based method for estimating RR during bicycling from fNIRS signals recorded by a wearable system. Methods: Firstly, five respiratory modulations are extracted, based on amplitude, frequency, and intensity of the oxygenated hemoglobin concentration (O2Hb) signal. Secondly, the dominant frequency of each modulation is computed using the fast Fourier transform. Finally, dominant frequencies of all modulations are fused, based on averaging, to estimate RR. The performance of the proposed method was validated on 22 young healthy subjects, whose respiratory and fNIRS signals were simultaneously recorded during a bicycling task, and compared against a zero delay Fourier domain band-pass filter. Results: The comparison between results obtained by the proposed method and band-pass filtering indicated the superiority of the former, with a lower mean absolute error (3.66 vs. 11.06 breaths per minute, p<0.05). The proposed fusion strategy also outperformed RR estimations based on the analysis of individual modulation. Significance: This study orients towards the practical limitations of traditional bio-signals for RR estimation during physical activities.
4Gbaud PS-16QAM D-Band Fiber-Wireless Transmission over 4.6 km by Using Balance Complex-Valued NN Equalizer with Random Oversampling
Authors: Xie Tangyao , Yu Jianguo
Published: March 2023

D-band (110–170 GHz) is a promising direction for the future of 6th generation mobile networks (6G) for high-speed mobile communication since it has a large available bandwidth, and it can provide a peak rate of hundreds of Gbit/s. Compared with the traditional electrical approach, photonics millimeter wave (mm-wave) generation in D-band is more practical and effectively overcomes the bottleneck of electrical devices. However, long-distance D-band wireless transmission is still limited by some key factors such as large absorption loss and nonlinear noises. Deep neural network algorithms are regarded as an important technique to model the nonlinear wireless behavior, among which the study on complex-value equalization is critical, especially in coherent detection systems. Moreover, probabilistic shaping is useful to improve the transmission capacity but also causes an imbalanced machine learning issue. In this paper, we propose a novel complex-valued neural network equalizer coupled with balanced random oversampling (ROS). Thanks to the adaptive deep learning method for probabilistic shaping-quadrature amplitude modulation (PS-QAM), we successfully realize a 135 GHz 4Gbaud PS-16QAM with a shaping entropy of 3.56 bit/symbol wireless transmission over 4.6 km. The bit error ratio (BER) of 4Gbaud PS-16QAM can be decreased to a soft-decision forward error correction (SD-FEC) with a 25% overhead of 2 × 10−2. Therefore, we can achieve a net rate of an 11.4 Gbit/s D-band radio-over-fiber (ROF) delivery over 4.6 km air free wireless distance.
Evaluation of Low-Complexity Adaptive Full Direct-State Kalman Filter for Robust GNSS Tracking
Authors: Cortés Iñigo , van der Merwe Johannes Rossouw , Lohan Elena Simona , Nurmi Jari , Felber Wolfgang
Published: March 2023

This paper evaluates the implementation of a low-complexity adaptive full direct-state Kalman filter (DSKF) for robust tracking of global navigation satellite system (GNSS) signals. The full DSKF includes frequency locked loop (FLL), delay locked loop (DLL), and phase locked loop (PLL) tracking schemes. The DSKF implementation in real-time applications requires a high computational cost. Additionally, the DSKF performance decays in time-varying scenarios where the statistical distribution of the measurements changes due to noise, signal dynamics, multi-path, and non-line-of-sight effects. This study derives the full lookup table (LUT)-DSKF: a simplified full DSKF considering the steady-state convergence of the Kalman gain. Moreover, an extended version of the loop-bandwidth control algorithm (LBCA) is presented to adapt the response time of the full LUT-DSKF. This adaptive tracking technique aims to increase the synchronization robustness in time-varying scenarios. The proposed tracking architecture is implemented in an GNSS hardware receiver with an open software interface. Different configurations of the adaptive full LUT-DSKF are evaluated in simulated scenarios with different dynamics and noise cases for each implementation. The results confirm that the LBCA used in the FLL-assisted-PLL (FAP) is essential to maintain a position, velocity, and time (PVT) fix in high dynamics.
Missed Opportunities Due to Gender Bias: A Qualitative Analysis of Microdiscrimination against Female University Students in Spain
Authors: Martínez-Ruiz María A. , Hernández-Amorós María J.
Published: March 2023

This study investigates the microdiscrimination that female students experience on their journey through higher education. Using qualitative methodology we interpret, analyse and categorize gender microinequities. The results show that female students are aware of the social naturalization of gender roles, but they in no way adopt them. Few participants go beyond condemning the fact that stereotypes are deeply rooted in the family and society. They do not generally perceive that the legal, political and economic systems are also structured in such a way as to hinder equal opportunities. Political and institutional measures, therefore, need to be applied to draw attention to the inequalities generated on a macrostructural level by the patriarchal system.