bluuebunny/crossref_metadata_2025 huggingface dataset:
Understanding Macroalgae: A Comprehensive Exploration of Nutraceutical, Pharmaceutical, and Omics Dimensions
Authors: Adarshan Sivakumar , Sree Vairavel Sivaranjani Sivani , Muthuramalingam Pandiyan , Nambiar Krishnanjana S , Sevanan Murugan , Satish Lakkakula , Venkidasamy Baskar , Jeelani Peerzada Gh , Shin Hyunsuk
Published: December 2023

Driven by a surge in global interest in natural products, macroalgae or seaweed, has emerged as a prime source for nutraceuticals and pharmaceutical applications. Characterized by remarkable genetic diversity and a crucial role in marine ecosystems, these organisms offer not only substantial nutritional value in proteins, fibers, vitamins, and minerals, but also a diverse array of bioactive molecules with promising pharmaceutical properties. Furthermore, macroalgae produce approximately 80% of the oxygen in the atmosphere, highlighting their ecological significance. The unique combination of nutritional and bioactive attributes positions macroalgae as an ideal resource for food and medicine in various regions worldwide. This comprehensive review consolidates the latest advancements in the field, elucidating the potential applications of macroalgae in developing nutraceuticals and therapeutics. The review emphasizes the pivotal role of omics approaches in deepening our understanding of macroalgae’s physiological and molecular characteristics. By highlighting the importance of omics, this review also advocates for continued exploration and utilization of these extraordinary marine organisms in diverse domains, including drug discovery, functional foods, and other industrial applications. The multifaceted potential of macroalgae warrants further research and development to unlock their full benefits and contribute to advancing global health and sustainable industries.
Magic Blue Light: A Versatile Mediator of Plant Elongation
Authors: Kong Yun , Zheng Youbin
Published: December 2023

Blue light plays an important role in regulating plant elongation. However, due to the limitations of older lighting technologies, the responses of plants to pure blue light have not been fully studied, and some of our understandings of the functions of blue light in the literature need to be revisited. This review consolidates and analyzes the diverse findings from previous studies on blue-light-mediated plant elongation. By synthesizing the contrasting results, we uncover the underlying mechanisms and explanations proposed in recent research. Moreover, we delve into the exploration of blue light-emitting diodes (LEDs) as a tool for manipulating plant elongation in controlled-environment plant production, highlighting the latest advancements in this area. Finally, we acknowledge the challenges faced and outline future directions for research in this promising field. This review provides valuable insights into the pivotal role of blue light in plant growth and offers a foundation for further investigations to optimize plant elongation using blue light technology.
Holographic Grating Enhancement Induced by a Dual-Photo-Initiator System in PMMA Substrate Polymers
Authors: Wang Peiyao , Sun Xiudong , Liu Peng
Published: December 2023

Polymer systems induced by the reaction between monomers and photo-initiators play a crucial role in the formation of volume-phase gratings. In this paper, we fabricated a dual-photo-initiator photopolymer by doping EY (Eosin Yellow) molecules into a TI (Titanocene, Irgacure 784@BASF) dispersed PMMA (poly-[methyl methacrylate]) substrate system, with the aim of promoting the diffusion and polymerization processes in volume holographic storage. The two-wave interference system is adopted to record a permanent grating structure in our materials. The temporal diffraction variations of photopolymerization (during the interference exposure) and dark diffusion (after the interference exposure) processes have been investigated and analyzed. Aiming to analyze the influence of EY doping ratios on holographic performances, some key parameters were examined in the experiment. We first measured the temporal evolution of diffraction efficiency, then an exponential fitting was adopted to obtain the response time. Finally, the angular selectivity was evaluated by the Bragg condition after holographic recording. Also, the temporal evolution of each component is described by the nonlocal polymerization-driven diffusion model with a dual-photo-initiator composition, theoretically. Furthermore, we experimentally achieved the holographic grating enhancement in both the dark diffusion and photopolymerization processes by doping appropriate EY concentrations, respectively. This work provides a foundation for the acceptability of TI&EY/PMMA polymers in further holographic storage research.
A Novel Approach for Glycero-(9,10-trioxolane)-Trialeate Incorporation into Poly(lactic acid)/Poly(ɛ-caprolactone) Blends for Biomedicine and Packaging
Authors: Alexeeva Olga V. , Olkhov Anatoliy A. , Konstantinova Marina L. , Podmasterev Vyacheslav V. , Petrova Tuyara V. , Martirosyan Levon Yu. , Karyagina Olga K. , Kozlov Sergey S. , Lomakin Sergey M. , Tretyakov Ilya V. , Siracusa Valentina , Iordanskii Alexey L.
Published: December 2023

The product of ozonolysis, glycero-(9,10-trioxolane)-trioleate (ozonide of oleic acid triglyceride, [OTOA]), was incorporated into polylactic acid/polycaprolactone (PLA/PCL) blend films in the amount of 1, 5, 10, 20, 30 and 40% w/w. The morphological, mechanical, thermal and antibacterial properties of the biodegradable PLA/PCL films after the OTOA addition were studied. According to DSC and XRD data, the degree of crystallinity of the PLA/PCL + OTOA films showed a general decreasing trend with an increase in OTOA content. Thus, a significant decrease from 34.0% for the reference PLA/PCL film to 15.7% for the PLA/PCL + 40% OTOA film was established using DSC. Observed results could be explained by the plasticizing effect of OTOA. On the other hand, the PLA/PCL film with 20% OTOA does not follow this trend, showing an increase in crystallinity both via DSC (20.3%) and XRD (34.6%). OTOA molecules, acting as a plasticizer, reduce the entropic barrier for nuclei formation, leading to large number of PLA spherulites in the plasticized PLA/PCL matrix. In addition, OTOA molecules could decrease the local melt viscosity at the vicinity of the growing lamellae, leading to faster crystal growth. Morphological analysis showed that the structure of the films with an OTOA concentration above 20% drastically changed. Specifically, an interface between the PLA/PCL matrix and OTOA was formed, thereby forming a capsule with the embedded antibacterial agent. The moisture permeability of the resulting PLA/PCL + OTOA films decreased due to the formation of uniformly distributed hydrophobic amorphous zones that prevented water penetration. This architecture affects the tensile characteristics of the films: strength decreases to 5.6 MPa, elastic modulus E by 40%. The behavior of film elasticity is associated with the redistribution of amorphous regions in the matrix. Additionally, PLA/PCL + OTOA films with 20, 30 and 40% of OTOA showed good antibacterial properties on Pseudomonas aeruginosa, Raoultella terrigena (Klebsiella terrigena) and Agrobacterium tumefaciens, making the developed films potentially promising materials for wound-dressing applications.
Effect of Gelatin Coating and GO Incorporation on the Properties and Degradability of Electrospun PCL Scaffolds for Bone Tissue Regeneration
Authors: Loyo Carlos , Cordoba Alexander , Palza Humberto , Canales Daniel , Melo Francisco , Vivanco Juan F. , Baier Raúl Vallejos , Millán Carola , Corrales Teresa , Zapata Paula A.
Published: December 2023

Polymer-based nanocomposites such as polycaprolactone/graphene oxide (PCL/GO) have emerged as alternatives for bone tissue engineering (BTE) applications. The objective of this research was to investigate the impact of a gelatin (Gt) coating on the degradability and different properties of PCL nanofibrous scaffolds fabricated by an electrospinning technique with 1 and 2 wt% GO. Uniform PCL/GO fibers were obtained with a beadless structure and rough surface. PCL/GO scaffolds exhibited an increase in their crystallization temperature (Tc), attributed to GO, which acted as a nucleation agent. Young’s modulus increased by 32 and 63% for the incorporation of 1 and 2 wt% GO, respectively, in comparison with neat PCL. A homogeneous Gt coating was further applied to these fibers, with incorporations as high as 24.7 wt%. The introduction of the Gt coating improved the hydrophilicity and degradability of the scaffolds. Bioactivity analysis revealed that the hydroxyapatite crystals were deposited on the Gt-coated scaffolds, which made them different from their uncoated counterparts. Our results showed the synergic effect of Gt and GO in enhancing the multifunctionality of the PCL, in particular the degradability rate, bioactivity, and cell adhesion and proliferation of hGMSC cells, making it an interesting biomaterial for BTE.
Phase Equilibria and Interdiffusion in the Ternary System Epoxy Oligomer–Polysulfone–Alkyl Glycidyl Ether
Authors: Ponomarenko Artem D. , Nikulova Uliana V. , Shapagin Aleksey V.
Published: December 2023

Phase equilibria, interdiffusion and structure in the initial uncured mixtures of epoxy oligomer–polysulfone–alkyl glycidyl ether were studied. Binodal curves were constructed on isothermal sections of the ternary phase diagram. Thermodynamic mixing parameters were calculated and spinodal curves were plotted. The interdiffusion coefficients of components, establishing the technological modes of mixing the components, were determined. To validate the phase diagram, the phase structure of mixtures, the composition of which at a temperature of 40 °C corresponds to heterogeneous and homogeneous regions, was studied.
Effects of Rare Earth Oxides on the Mechanical and Tribological Properties of Phenolic-Based Hybrid Nanocomposites
Authors: Wang Shenglian , Chen Shuang , Sun Jiachen , Liu Zimo , He Dingxiang , Xu Shaofeng
Published: December 2023

The incorporation of rare earth oxides and nano-silica has been found to significantly enhance the mechanical and tribological characteristics of phenolic-based hybrid nanocomposites. In this work, the impact of these additives was investigated through single-factor experiments. The study revealed that cerium oxide and yttrium oxide were the primary factors influencing changes in the impact strength, shear strength, coefficient of friction, and wear rate. Additionally, the content of nano-silica exerted the most substantial influence on the hardness and compressive strength of the specimens. Furthermore, the material ratios of the phenolic-based hybrid nanocomposites were optimized using an orthogonal experimental design and a fuzzy comprehensive evaluation method. The optimal material ratio for these nanocomposites was determined to be 2% cerium oxide, 2.5% yttrium oxide, and 3% nano-silica, based on their mechanical, frictional, and wear properties. This research provides valuable insights for the development of new brake friction materials with low friction and high wear resistance and contributes to meeting the demand for polymer composites with superior mechanical performance in diverse applications.
Characterization of Moringa oleifera Seed Oil for the Development of a Biopackage Applied to Maintain the Quality of Turkey Ham
Authors: Cueto Covarrubias Lesly Adamari , Valdez Solana Mónica Andrea , Avitia Domínguez Claudia , Téllez Valencia Alfredo , Meza Velázquez Jorge Armando , Sierra Campos Erick
Published: December 2023

Moringa oleifera has a high level of active chemicals that are useful in the food industry, and they have antibacterial and food preservation properties. The characterization of M. oleifera seed oil (MOS) may vary due to agronomic and environmental factors. Therefore, it was necessary to know the composition of lipids present in our oil extracted under pressing at 180 °C and thus determine if it is suitable to produce a biopackaging. Within the characterization of the oil, it was obtained that MOS presented high-quality fatty acids (71% oleic acid) with low values of acidity (0.71 mg KOH/g) and peroxide (1.74 meq O2/kg). Furthermore, MOS was not very sensitive to lipoperoxidation by tert-butyl hydroperoxide (tBuOOH) and its phenolic components, oleic acid and tocopherols, allowed MOS to present a recovery of 70% after 30 min of treatment. Subsequently, a biopackaging was developed using a multiple emulsion containing corn starch/carboxymethylcellulose/glycerol/MOS, which presented good mechanical properties (strength and flexibility), transparency, and a barrier that prevents the transfer of UV light by 30% and UV-C by 98%, as well as a flux with the atmosphere of 5.12 × 10−8 g/ m.s. Pa that prevents moisture loss and protects the turkey ham from O2. Hence, the turkey ham suffered less weight loss and less hardness due to its preservation in the biopackaging.
Supercritical Impregnation of Mangifera indica Leaves Extracts into Porous Conductive PLGA-PEDOT Scaffolds
Authors: Valor Diego , García-Casas Ignacio , Montes Antonio , Danese Ella , Pereyra Clara , de la Ossa Enrique Martínez
Published: December 2023

Plant leaves, such as those from Mangifera indica, represent a potential utilization of waste due to their richness in bioactive compounds. Supercritical CO2 allows these compounds to be incorporated into various matrices by impregnation. Combined with its ability to generate polymeric scaffolds, it represents an attractive strategy for the production of biomedical devices. For this purpose, conjugated polymeric scaffolds of biodegradable PLGA (poly(lactic-co-glycolic acid)) and PEDOT:PSS (poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate)), generated in situ by foaming, were employed for the supercritical impregnation of ethanolic mango leaves extract (MLE) in tissue engineering as a potential application. The extraction of MLE was performed by Enhanced Solvent Extraction. The effects of pressure (120–300 bar), temperature (35–55 °C), and depressurization rate (1–50 bar/min) on the physical/conductive properties and the impregnation of MLE were studied. The scaffolds have been characterized by liquid displacement, scanning electron microscope, resistance to conductivity techniques, measurements of impregnated load, antioxidant capacity and antimicrobial activity. Porosity values ranging 9–46% and conductivity values between 10−4–10−5 S/cm were obtained. High pressures, low temperatures and rapid depressurization favored the impregnation of bioactive compounds. Scaffolds with remarkable antioxidant activity were obtained (75.2–87.3% oxidation inhibition), demonstrating the ability to inhibit S. aureus bacterial growth (60.1 to 71.4%).
Experimental Study on Mechanical Properties of Nano-Alumina-Modified Cement Composites Exposed to High Temperatures
Authors: Wang Man , Jiang Yu , Liu Shuaitao , Chen Zhaofan , Su Haijian
Published: December 2023

Due to improved mechanical properties, nano-alumina (NA) addition has been considered as an interesting method to promote the performance of cement composites. To investigate the enhancing effect of NA on the fire resistance of cement-based composites, the physical and mechanical properties of NA-modified cement composites (NAMCCs) were experimentally measured after exposure to high temperatures (up to 800 °C). The variation mechanism of the physical properties of NAMCCs with increasing temperature was explored using X-ray diffraction (XRD) and scanning electron microscopy (SEM) methods. Increasing temperature leads to a gradual decrease in density and ultrasonic wave velocity but an overall increase in mass loss. The addition of NA particles effectively improved the mechanical properties of hardened cement pastes after heat treatment at all the temperatures studied. The compressive strength, elastic modulus and flexural strength all gradually decrease with increasing temperature. Combined with the changes in XRD and SEM, three regions are identified for the variation in mechanical performance in the temperature range of 20~400 °C, 400~600 °C and 600~800 °C. Finally, the relation between the physical and mechanical parameters of these regions was evaluated.
Abstract or Concrete Utopia? Concerning the Ideal Society in Chinese Philosophy and Culture
Authors: Boer Roland
Published: December 2023

In seeking an appropriate approach to the ideal society in Chinese thought, the present study comprises two main parts. The first part deals with a debate in Chinese philosophy concerning the possibility of an inner or immanent transcendence as a way of defining Chinese culture. As this debate unfolded, it became clear that Chinese philosophers—especially on the mainland—do not regard the transcendent–immanent distinction as applicable to Chinese culture and philosophy. In short, this culture and its philosophy simply has no need for transcendence. Instead, other terms are needed, especially those drawn from a tradition that “secularised” them many millennia ago: moral cultivation, regeneration, home, and intimacy. In this light, the second part of the study deals with two approaches to the ideal society: the Confucian “Great Harmony [大同 datong]” and the short story “Peach Blossom Spring [桃花源 taohuayuan]”. These terms are mediated by a treatment of the “Three Worlds Theory [三世说 sanshishuo]”, developed most fully by He Xiu (129–82 CE). The outcome of this investigation is that the ideal society is very much part of this world. It can be known only through direct observation, empirical investigation, and it is achievable only by detailed planning. It is nothing less than home.
Stain Detection Based on Unmanned Aerial Vehicle Hyperspectral Photovoltaic Module
Authors: Li Da , Li Lan , Cui Mingyang , Shi Pengliang , Shi Yintong , Zhu Jian , Dai Sui , Song Meiping
Published: December 2023

Solar power generation has great development potential as an abundant and clean energy source. However, many factors affect the efficiency of the photovoltaic (PV) module; among these factors, outdoor PV modules are inevitably affected by stains, thus reducing the power generation efficiency of the PV panel. This paper proposes a framework for PV module stain detection based on UAV hyperspectral images (HSIs). The framework consists of two stain detection methods: constrained energy minimization (CEM)-based and orthogonal subspace projection (OSP)-based stain detection methods. Firstly, the contaminated PV modules are analyzed and processed to enhance the data’s analytical capability. Secondly, based on the known spectral signature of the PV module, stain detection methods are proposed, including CEM-based stain detection and OSP-based stain detection for PV modules. The experimental results on real data illustrate that, in comparison with contrasting methods, the proposed method achieves stain detection results that closely align with known stain percentages. Additionally, it exhibits a fitting curve similar to the more maturely developed electroluminescence-based methods currently in use.
Oilfield Reservoir Parameter Inversion Based on 2D Ground Deformation Measurements Acquired by a Time-Series MSBAS-InSAR Method
Authors: Liu Anmengyun , Zhang Rui , Yang Yunjie , Wang Tianyu , Wang Ting , Shama Age , Zhan Runqing , Bao Xin
Published: December 2023

Time-series ground deformation monitoring and reservoir parameter inversion are crucial for the dynamic assessment of oilfield resources and sustainable exploitation in oilfields. As some of the regions with the richest oil reserves in China, the oilfield areas in the western Qaidam Basin were selected as a typical study area. Firstly, we used SAR images collected by the Sentinel-1A satellite from January 2021 to December 2022 and applied the multidimensional small baseline subset (MSBAS) method to obtain vertical and east–west deformation measurements. On this basis, a nonlinear Bayesian inversion method was applied to model the shallow reservoir in a series of complex deformation areas, based on a single-source model and a multi-source model, respectively. As a result, the ground deformation monitoring results obtained by long time-series InSAR clearly reflect the uneven ground deformation caused by the oil extraction and water injection operation processes. There was slight subsidence in the Huatugou oilfield, while significant uplift deformation occurred in the Ganchaigou oilfield and the Youshashan oilfield, with a maximum uplift rate of 48 mm/year. Further analysis indicated that the introduction of the 2D deformation field helps to improve the robustness of oilfield reservoir parameter inversion. Moreover, the dual-source model is more suitable than the single-source model for inverting reservoir parameters of complex deformation. This study not only fills the gap of InSAR deformation monitoring for the oilfields in the western Qaidam Basin but also provides a theoretical reference for the model and method selection of reservoir parameter inversion in other oilfields.
Early Detection of Drought Stress in Durum Wheat Using Hyperspectral Imaging and Photosystem Sensing
Authors: Roy Bishal , Sagan Vasit , Haireti Alifu , Newcomb Maria , Tuberosa Roberto , LeBauer David , Shakoor Nadia
Published: December 2023

Wheat, being the third largest U.S. crop and the principal food grain, faces significant risks from climate extremes such as drought. This necessitates identifying and developing methods for early water-stress detection to prevent yield loss and improve water-use efficiency. This study investigates the potential of hyperspectral imaging to detect the early stages of drought stress in wheat. The goal is to utilize this technology as a tool for screening and selecting drought-tolerant wheat genotypes in breeding programs. Additionally, this research aims to systematically evaluate the effectiveness of various existing sensors and methods for detecting early stages of water stress. The experiment was conducted in a durum wheat experimental field trial in Maricopa, Arizona, in the spring of 2019 and included well-watered and water-limited treatments of a panel of 224 replicated durum wheat genotypes. Spectral indices derived from hyperspectral imagery were compared against other plant-level indicators of water stress such as Photosystem II (PSII) and relative water content (RWC) data derived from proximal sensors. Our findings showed a 12% drop in photosynthetic activity in the most affected genotypes when compared to the least affected. The Leaf Water Vegetation Index 1 (LWVI1) highlighted differences between drought-resistant and drought-susceptible genotypes. Drought-resistant genotypes retained 43.36% more water in leaves under well-watered conditions compared to water-limited conditions, while drought-susceptible genotypes retained only 15.69% more. The LWVI1 and LWVI2 indices, aligned with the RWC measurements, revealed a strong inverse correlation in the susceptible genotypes, underscoring their heightened sensitivity to water stress in earlier stages. Several genotypes previously classified based on their drought resistance showed spectral indices deviating from expectations. Results from this research can aid farmers in improving crop yields by informing early management practices. Moreover, this research offers wheat breeders insights into the selection of drought-tolerant genotypes, a requirement that is becoming increasingly important as weather patterns continue to change.
Greening the Urban Landscape: Assessing the Impact of Tree-Planting Initiatives and Climate Influences on Miami-Dade County’s Greenness
Authors: Dewald Julius R. , Southworth Jane , Szapocznik Jose , Lombard Joanna L. , Brown Scott C.
Published: December 2023

In urban settings, trees and greenery play a vital role in environmental well-being and community vitality. This study explores the impact of Miami-Dade County’s tree-planting initiative on urban greenness and considers the influence of climate dynamics. Using Landsat data from 2006 to 2019, we find stable overall greenness, with 5.64% of the Census blocks exhibiting significant changes. Seasonal analysis reveals winter as prominent, with 61.47% of Census blocks showing increased greenness. Temperature and precipitation, especially post-2010, correlate with greenness changes. Despite a reported increase in tree cover from 14% to 20%, our findings show only 5–6% of Census blocks with statistically significant changes, highlighting the complexity of achieving substantial improvements in green canopy coverage. The study raises questions about the efficacy of large-scale tree-planting initiatives in densely urbanized areas when human factors are not well understood. Implications for urban planning stress the importance of preserving green spaces and informed decision-making for enhancing vegetation cover in Miami-Dade County, emphasizing the need to consider local conditions, seasonal variations, policies, and human factors in urban greening efforts.
How Do Driving Factors Affect Vegetation Coverage Change in the Shaanxi Region of the Qinling Mountains?
Authors: Wang Shuoyao , Gao Meiling , Li Zhenhong , Ma Jingjing , Peng Jianbing
Published: December 2023

Understanding the effects of natural and human disturbance factors on fractional vegetation coverage (FVC) is significant in the promotion of ecological and environmental protection. However, most of the relevant studies neglect to consider differences in the effect of driving factors on areas with different vegetation change characteristics. In this paper, we have combined Theil-Sen median trend analysis and Mann-Kendall testing to identify degraded and restored areas. Differences in the impact of various factors on FVC in terms of degradation, restoration, and the whole region were distinguished quantitatively using the geodetector model. Additionally, the constraint line approach was used to detect the influence thresholds of factors on FVC. The results are shown as below: (1) FVC showed an overall improving trend, and vegetation restoration and degradation areas accounted for 69.2% and 22.0%, respectively. (2) The two dominant factors affecting FVC were Digital Elevation Model (DEM) and temperature for both degraded and restored regions. However, the explanatory power of precipitation was noticeably different between regions. (3) Most natural factors had a “convex” constraint effect on FVC, which gradually weakened with an increase in the variable below the threshold and vice versa. Human disturbance factors negatively constrained FVC, and the constraint effect increased with increased human activity. This study can help decision-makers optimize specific implementation policies relating to ecological restoration and sustainable development.
Studies on High-Resolution Airborne Synthetic Aperture Radar Image Formation with Pseudo-Random Agility of Interpulse Waveform Parameters
Authors: Ye Zheng , Zhu Daiyin , Niu Shilin , Lv Jiming
Published: December 2023

By means of alteration of the transmitted linear frequency modulation (LFM) signal waveform parameters, such as pulse width or chirp rate, initial phase, pulse repetition interval (PRI), and chirp rate polarity at every position of synthetic apertures, the pseudo-random agility technology of interpulse waveform parameters in airborne Synthetic Aperture Radar (SAR) actively increases the complexity and uncertainty of radar waveforms. This technology confuses jamming interception receivers, thus improving its anti-interference ability for active coherent jamming, which is one of the main research interests of airborne SAR technology. But the pseudo-random agility technology for interpulse waveform parameters faces certain challenges of large computation and complex system design, which need to be further studied and solved. To address these issues, a processing scheme of high-resolution SAR image formation which is appropriate for agile interpulse waveform parameters is proposed in this paper. This method can deal with multiple agile parameters, not only single ones as in most existing literature. Its computation load is nearly comparable to that of traditional SAR image formation with constant waveform parameters. The high-resolution SAR imaging results obtained by processing SAR raw data with agile interpulse waveform parameters demonstrate the effectiveness of the proposed method. In addition, real SAR images with resolutions of 0.5 m and 0.15 m, which are rarely found in the public literature, are shown under the circumstance of randomly changing the transmitted wideband LFM signal pulse parameters one by one.
Uncovering Plastic Litter Spectral Signatures: A Comparative Study of Hyperspectral Band Selection Algorithms
Authors: Olyaei Mohammadali , Ebtehaj Ardeshir
Published: December 2023

This article provides insights into the optical signatures of plastic litter based on a published laboratory-scale reflectance data set (350–2500 nm) of dry and wet plastic debris under clear and turbid waters using different band selection techniques, including sparse variable selection, density peak clustering, and hierarchical clustering. The variable selection method identifies important wavelengths by minimizing a reconstruction error metric, while clustering approaches rely on the strengths of the correlation and local density of the spectra. Analyses of the data reveal three distinct absorption lines at 560, 740, and 980 nm that produce relatively broad reflectance peaks in the measured spectra of wet plastics around 475–490, 635–650, 810–815, and 1070 nm. The results of band selection consistently identify three important regions across 450–470, 650–690, and 1050–1100 nm that are close to the reflectance peaks of the mean of wet plastic spectra over clear and turbid waters. However, as the number of isolated important wavelengths increases, the results of the methodologies diverge. Density peak clustering identifies additional wavelengths in the short-wave infrared (SWIR) region of 1170–1180 nm) as a result of a high local density of the reflectance points. In contrast, hierarchical clustering isolates more wavelengths in the visible range of 365–400 nm due to weak correlations of nearby wavelengths. The results of the clustering methods are not consistent with the visual inspection of the signatures as peaks and valleys in the spectra, which are effectively captured by the variable selection method. It is also found that the presence of suspended sediments can (i) shift the important wavelength towards higher values in the visible part of the spectrum by less than 50 nm, (ii) attenuate the magnitude of wet plastic reflectance by up to 80% across the entire spectrum, and (iii) manifest a similar spectral signature with plastic litter from 1070 to 1100 nm.
Attention Network with Outdoor Illumination Variation Prior for Spectral Reconstruction from RGB Images
Authors: Song Liyao , Li Haiwei , Liu Song , Chen Junyu , Fan Jiancun , Wang Quan , Chanussot Jocelyn
Published: December 2023

Hyperspectral images (HSIs) are widely used to identify and characterize objects in scenes of interest, but they are associated with high acquisition costs and low spatial resolutions. With the development of deep learning, HSI reconstruction from low-cost and high-spatial-resolution RGB images has attracted widespread attention. It is an inexpensive way to obtain HSIs via the spectral reconstruction (SR) of RGB data. However, due to a lack of consideration of outdoor solar illumination variation in existing reconstruction methods, the accuracy of outdoor SR remains limited. In this paper, we present an attention neural network based on an adaptive weighted attention network (AWAN), which considers outdoor solar illumination variation by prior illumination information being introduced into the network through a basic 2D block. To verify our network, we conduct experiments on our Variational Illumination Hyperspectral (VIHS) dataset, which is composed of natural HSIs and corresponding RGB and illumination data. The raw HSIs are taken on a portable HS camera, and RGB images are resampled directly from the corresponding HSIs, which are not affected by illumination under CIE-1964 Standard Illuminant. Illumination data are acquired with an outdoor illumination measuring device (IMD). Compared to other methods and the reconstructed results not considering solar illumination variation, our reconstruction results have higher accuracy and perform well in similarity evaluations and classifications using supervised and unsupervised methods.
BD-SKUNet: Selective-Kernel UNets for Building Damage Assessment in High-Resolution Satellite Images
Authors: Ahmadi Seyed Ali , Mohammadzadeh Ali , Yokoya Naoto , Ghorbanian Arsalan
Published: December 2023

When natural disasters occur, timely and accurate building damage assessment maps are vital for disaster management responders to organize their resources efficiently. Pairs of pre- and post-disaster remote sensing imagery have been recognized as invaluable data sources that provide useful information for building damage identification. Recently, deep learning-based semantic segmentation models have been widely and successfully applied to remote sensing imagery for building damage assessment tasks. In this study, a two-stage, dual-branch, UNet architecture, with shared weights between two branches, is proposed to address the inaccuracies in building footprint localization and per-building damage level classification. A newly introduced selective kernel module improves the performance of the model by enhancing the extracted features and applying adaptive receptive field variations. The xBD dataset is used to train, validate, and test the proposed model based on widely used evaluation metrics such as F1-score and Intersection over Union (IoU). Overall, the experiments and comparisons demonstrate the superior performance of the proposed model. In addition, the results are further confirmed by evaluating the geographical transferability of the proposed model on a completely unseen dataset from a new region (Bam city earthquake in 2003).
Benchmarking of Sensor Configurations and Measurement Sites for Out-of-the-Lab Photoplethysmography
Authors: Supelnic Max Nobre , Ferreira Afonso Fortes , Bota Patrícia Justo , Brás-Rosário Luís , Plácido da Silva Hugo
Published: December 2023

Photoplethysmography (PPG) is used for heart-rate monitoring in a variety of contexts and applications due to its versatility and simplicity. These applications, namely studies involving PPG data acquisition during day-to-day activities, require reliable and continuous measurements, which are often performed at the index finger or wrist. However, some PPG sensors are susceptible to saturation, motion artifacts, and discomfort upon their use. In this paper, an off-the-shelf PPG sensor was benchmarked and modified to improve signal saturation. Moreover, this paper explores the feasibility of using an optimized sensor in the lower limb as an alternative measurement site. Data were collected from 28 subjects with ages ranging from 18 to 59 years. To validate the sensors’ performance, signal saturation and quality, wave morphology, performance of automatic systolic peak detection, and heart-rate estimation, were compared. For the upper and lower limb locations, the index finger and the first toe were used as reference locations, respectively. Lowering the amplification stage of the PPG sensor resulted in a significant reduction in signal saturation, from 18% to 0.5%. Systolic peak detection at rest using an automatic algorithm showed a sensitivity and precision of 0.99 each. The posterior wrist and upper arm showed pulse wave morphology correlations of 0.93 and 0.92, respectively. For these locations, peak detection sensitivity and precision were 0.95, 0.94 and 0.89, 0.89, respectively. Overall, the adjusted PPG sensors are a good alternative for obtaining high-quality signals at the fingertips, and for new measurement sites, the posterior pulse and the upper arm allow for high-quality signal extraction.
Transfer-Learning-Based Temperature Uncertainty Reduction Algorithm for Large Scale Oil Tank Ground Settlement Monitoring
Authors: Liu Tao , Jiang Tao , Liu Gang , Sun Changsen
Published: December 2023

Sensors operating in open-air environments can be affected by various environmental factors. Specifically, ground settlement (GS) monitoring sensors installed in oil tanks are susceptible to non-uniform temperature fields caused by uneven sunshine exposure. This disparity in environmental conditions can lead to errors in sensor readings. To address this issue, this study aimed to analyze the impact of temperature on GS monitoring sensors and establish a mapping relationship between temperature uncertainty (fluctuations of measurement caused by temperature variation) and temperature variation. By collecting the temperature information and inferring the temperature uncertainty being introduced, this interference can be removed. However, it is crucial to note that in real-world complex scenarios, the relationship between temperature uncertainty and temperature variation is not always a constant positive correlation, which limits the data available for certain periods. Moreover, the limited availability of data presents a challenge when analyzing the complex mapping relationship. To overcome these challenges, a transfer-learning-based algorithm was introduced to develop a more accurate model for predicting temperature uncertainty based on temperature variation, even with limited data. Subsequently, a practical test was conducted to validate the proposed algorithm’s performance. The results demonstrated that the algorithm outperformed a simple linear fitting model using the least squares method (LSM), achieving an improvement of up to 21.9%. This outcome highlights the algorithm’s potential for enhancing the performance of GS sensors in daytime monitoring and contributing to the safe operation of oil tank facilities and infrastructure health monitoring.
Job-Deadline-Guarantee-Based Joint Flow Scheduling and Routing Scheme in Data Center Networks
Authors: Suo Long , Ma Han , Jiao Wanguo , Liu Xiaoming
Published: December 2023

Many emerging Internet of Things (IoT) applications deployed on cloud platforms have strict latency requirements or deadline constraints, and thus meeting the deadlines is crucial to ensure the quality of service for users and the revenue for service providers in these delay-stringent IoT applications. Efficient flow scheduling in data center networks (DCNs) plays a major role in reducing the execution time of jobs and has garnered significant attention in recent years. However, only few studies have attempted to combine job-level flow scheduling and routing to guarantee meeting the deadlines of multi-stage jobs. In this paper, an efficient heuristic joint flow scheduling and routing (JFSR) scheme is proposed. First, targeting maximizing the number of jobs for which the deadlines have been met, we formulate the joint flow scheduling and routing optimization problem for multiple multi-stage jobs. Second, due to its mathematical intractability, this problem is decomposed into two sub-problems: inter-coflow scheduling and intra-coflow scheduling. In the first sub-problem, coflows from different jobs are scheduled according to their relative remaining times; in the second sub-problem, an iterative coflow scheduling and routing (ICSR) algorithm is designed to alternately optimize the routing path and bandwidth allocation for each scheduled coflow. Finally, simulation results demonstrate that the proposed JFSR scheme can significantly increase the number of jobs for which the deadlines have been met in DCNs.
Estimation of Biochemical Pigment Content in Poplar Leaves Using Proximal Multispectral Imaging and Regression Modeling Combined with Feature Selection
Authors: Zhang Changsai , Xue Yong
Published: December 2023

Monitoring the biochemical pigment contents in individual plants is crucial for assessing their health statuses and physiological states. Fast, low-cost measurements of plants’ biochemical traits have become feasible due to advances in multispectral imaging sensors in recent years. This study evaluated the field application of proximal multispectral imaging combined with feature selection and regressive analysis to estimate the biochemical pigment contents of poplar leaves. The combination of 6 spectral bands and 26 vegetation indices (VIs) derived from the multispectral bands was taken as the group of initial variables for regression modeling. Three variable selection algorithms, including the forward selection algorithm with correlation analysis (CORR), recursive feature elimination algorithm (RFE), and sequential forward selection algorithm (SFS), were explored as candidate methods for screening combinations of input variables from the 32 spectral-derived initial variables. Partial least square regression (PLSR) and nonlinear support vector machine regression (SVR) were both applied to estimate total chlorophyll content (Chla+b) and carotenoid content (Car) at the leaf scale. The results show that the nonlinear SVR prediction model based on optimal variable combinations, selected by SFS using multiple scatter correction (MSC) preprocessing data, achieved the best estimation accuracy and stable prediction performance for the leaf pigment content. The Chla+b and Car models developed using the optimal model had R2 and RMSE predictive statistics of 0.849 and 0.825 and 5.116 and 0.869, respectively. This study demonstrates the advantages of using a nonlinear SVR model combined with SFS variable selection to obtain a more reliable estimation model for leaf biochemical pigment content.
An Integrated Autonomous Dynamic Navigation Approach toward a Composite Air–Ground Risk Construction Scenario
Authors: Jiang Da , Wang Meijing , Chen Xiaole , Zhang Hongchao , Wang Kang , Li Chengchi , Li Shuhui , Du Ling
Published: December 2023

Unmanned transportation in construction scenarios presents a significant challenge due to the presence of complex dynamic on-ground obstacles and potential airborne falling objects. Consequently, the typical methodology for composite air–ground risk avoidance in construction scenarios holds enormous importance. In this paper, an integrated potential-field-based risk assessment approach is proposed to evaluate the threat severity of the environmental obstacles. Meanwhile, the self-adaptive dynamic window approach is suggested to manage the real-time motion planning solution for air–ground risks. By designing the multi-objective velocity sample window, we constrain the vehicle’s speed planning instructions within reasonable limits. Combined with a hierarchical decision-making mechanism, this approach achieves effective obstacle avoidance with multiple drive modes. Simulation results demonstrate that, in comparison with the traditional dynamic window approach, the proposed method offers enhanced stability and efficiency in risk avoidance, underlining its notable safety and effectiveness.
Fault Knowledge Graph Construction and Platform Development for Aircraft PHM
Authors: Meng Xiangzhen , Jing Bo , Wang Shenglong , Pan Jinxin , Huang Yifeng , Jiao Xiaoxuan
Published: December 2023

To tackle the problems of over-reliance on traditional experience, poor troubleshooting robustness, and slow response by maintenance personnel to changes in faults in the current aircraft health management field, this paper proposes the use of a knowledge graph. The knowledge graph represents troubleshooting in a new way. The aim of the knowledge graph is to improve the correlation between fault data by representing experience. The data source for this study consists of the flight control system manual and typical fault cases of a specific aircraft type. A knowledge graph construction approach is proposed to construct a fault knowledge graph for aircraft health management. Firstly, the data are classified using the ERNIE model-based method. Then, a joint entity relationship extraction model based on ERNIE-BiLSTM-CRF-TreeBiLSTM is introduced to improve entity relationship extraction accuracy and reduce the semantic complexity of the text from a linguistic perspective. Additionally, a knowledge graph platform for aircraft health management is developed. The platform includes modules for text classification, knowledge extraction, knowledge auditing, a Q&A system, and graph visualization. These modules improve the management of aircraft health data and provide a foundation for rapid knowledge graph construction and knowledge graph-based fault diagnosis.
Magnetostatic Simulation and Design of Novel Radiofrequency Coils Based on Transverse Field Current Elements for Magnetic Resonance Applications
Authors: Giovannetti Giulio , Alecci Marcello , Galante Angelo
Published: December 2023

Radiofrequency (RF) coils are key components in Magnetic Resonance (MR) systems and can be categorized into volume and surface coils according to their shapes. Volume RF coils can generate a uniform field in a large central sample’s region, while surface RF coils, usually smaller than volume coils, typically have a higher Signal-to-Noise Ratio (SNR) in a reduced Region Of Interest (ROI) close to the coil plane but a relatively poorer field homogeneity. Circular and square loops are the simplest and most used design for developing axial field surface RF coils. However, for specific MR applications, the use of dedicated transverse field RF coils can be necessary or advantageous. Building on a previously developed and validated RF coil simulator, based on the magnetostatic approach, here we explore the potential applications of novel multiple axial field and transverse field surface RF coils in non-standard configurations. We demonstrate via numerical simulations that simple volume RF coils, matching a Helmholtz-like design, can be built with two identical transverse field RF coils separated by a given distance. Following well-known principles, the SNR of such novel configurations can be improved by a factor of up to √2 by combining two 90° rotated coils, producing, inside a central ROI, a circularly polarized B1 field.
In-Depth Evaluation of Saliency Maps for Interpreting Convolutional Neural Network Decisions in the Diagnosis of Glaucoma Based on Fundus Imaging
Authors: Sigut Jose , Fumero Francisco , Estévez José , Alayón Silvia , Díaz-Alemán Tinguaro
Published: December 2023

Glaucoma, a leading cause of blindness, damages the optic nerve, making early diagnosis challenging due to no initial symptoms. Fundus eye images taken with a non-mydriatic retinograph help diagnose glaucoma by revealing structural changes, including the optic disc and cup. This research aims to thoroughly analyze saliency maps in interpreting convolutional neural network decisions for diagnosing glaucoma from fundus images. These maps highlight the most influential image regions guiding the network’s decisions. Various network architectures were trained and tested on 739 optic nerve head images, with nine saliency methods used. Some other popular datasets were also used for further validation. The results reveal disparities among saliency maps, with some consensus between the folds corresponding to the same architecture. Concerning the significance of optic disc sectors, there is generally a lack of agreement with standard medical criteria. The background, nasal, and temporal sectors emerge as particularly influential for neural network decisions, showing a likelihood of being the most relevant ranging from 14.55% to 28.16% on average across all evaluated datasets. We can conclude that saliency maps are usually difficult to interpret and even the areas indicated as the most relevant can be very unintuitive. Therefore, its usefulness as an explanatory tool may be compromised, at least in problems such as the one addressed in this study, where the features defining the model prediction are generally not consistently reflected in relevant regions of the saliency maps, and they even cannot always be related to those used as medical standards.