bluuebunny/crossref_metadata_2025 huggingface dataset:
3D-Printed Chitosan-Based Scaffolds with Scutellariae baicalensis Extract for Dental Applications
Authors: Paczkowska-Walendowska Magdalena , Koumentakou Ioanna , Lazaridou Maria , Bikiaris Dimitrios , Miklaszewski Andrzej , Plech Tomasz , Cielecka-Piontek Judyta
Published: March 2024

The plant material Scutellariae baicalensis radix, which is rich in flavones (baicalin), possesses antibacterial, antifungal, antioxidant, and anti-inflammatory properties. This work aimed to develop a 3D-printed chitosan-based hydrogel rich in Scutellariae baicalensis extract as an innovative approach for the personalized treatment of periodontal diseases. Chitosan-based hydrogels were prepared, and the printability of the prepared hydrogels was determined. The hydrogel with 2.5% w/v of high molecular-weight chitosan (CS), 2% w/v gelatin (Gel), and 10% w/w of extract (Ex) presented the best printability, producing smooth and uniform scaffolds. It was proved that the CS/Gel/Ex hydrogel was stabilized by hydrogen bonds and remained in amorphous dispersion in the 3D-printed structures (confirmed by ATR-FTIR and XRPD). Due to the amorphization of the active substance, a significant increase in the release of baicalin in vitro was observed. It was demonstrated that there was an initial burst release and a continuous release profile (n = 3). Higuchi kinetic was the most likely baicalin release kinetic. The second fit, the Korsmeyer–Peppas kinetics model, showed coupled diffusion of the active ingredient in the hydrated matrix and polymer relaxation regulated release, with n values ranging from 0.45 to 0.89. The anti-inflammatory properties of 3D-printed scaffolds were assessed as the ability to inhibit the activity of the hyaluronidase enzyme. Activity was assessed as IC50 = 63.57 ± 4.98 mg hydrogel/mL (n = 6). Cytotoxicity tests demonstrated the biocompatibility of the material. After 24 h of exposure to the 2.5CS/2Gel/10Ex scaffold, fibroblasts migrated toward the scratch, closed the “wound” by 97.1%, and significantly accelerated the wound healing process. The results render the 3D-printed CS/Gel/extract scaffolds as potential candidates for treating periodontal diseases.
An Edible Antibacterial Coating Integrating Lytic Bacteriophage Particles for the Potential Biocontrol of Salmonella enterica in Ripened Cheese
Authors: Vila Marta M. D. C. , Cinto Edjane C. , Pereira Arthur O. , Baldo Denicezar Â. , Oliveira Jr. José M. , Balcão Victor M.
Published: March 2024

The goal of this research was to create an antibacterial biopolymeric coating integrating lytic bacteriophages against Salmonella enterica for use in ripened cheese. Salmonella enterica is the main pathogen that contaminates food products and the food industry. The food sector still uses costly and non-selective decontamination and disease control methods. Therefore, it is necessary to look for novel pathogen biocontrol technologies. Bacteriophage-based biocontrol seems like a viable option in this situation. The results obtained show promise for food applications since the edible packaging developed (EdiPhage) was successful in maintaining lytic phage viability while preventing the contamination of foodstuff with the aforementioned bacterial pathogen.
Crosslinked Polyesters as Fully Biobased Coatings with Cutin Monomer from Tomato Peel Wastes
Authors: Ruffini Eleonora , Bianchi Oltolini Andrea , Magni Mirko , Beretta Giangiacomo , Cavallaro Marco , Suriano Raffaella , Turri Stefano
Published: March 2024

Cutin, one of the main structural components of tomato peels, is a waxy biopolymer rich in hydroxylated fatty acids. In this study, 10,16-dihydroxyhexadecanoic acid (10,16-diHHDA) was extracted and isolated from tomato peels and exploited to develop fully crosslinked polyesters as potential candidates for replacing fossil-based metal protective coatings. A preliminary screening was conducted to select the base formulation, and then a design of experiments (DoE) was used as a methodology to identify the optimal composition to develop a suitable coating material. Different formulations containing 10,16-diHHDA and other biorefinery monomers, including 2,5-furandicarboxylic acid, were considered. To this end, all polyesters were characterized through differential scanning calorimetry (DSC) and gel content measurements to determine their Tg value and crosslinking efficiency. Compositions exhibiting the best trade-off between Tg value, chemical resistance, and sufficiently high 10,16-diHHDA content between 39 and 48 wt.% were used to prepare model coatings that were characterized for assessing their wettability, scratch hardness, chemical resistance, and adhesion to metal substrates. These polyester coatings showed a Tg in the range of 45–55 °C, a hydrophobic behavior with a water contact angle of around 100°, a good solvent resistance (>100 MEK double rubs), and an adhesion strength to steel higher than 2 MPa. The results obtained confirmed the potential of cutin-based resins as coatings for metal protection, meeting the requirements for ensuring physicochemical properties of the final product, as well as for optimizing the valorization of such an abundant agri-food waste as tomato peels.
Electrochemical Sensing of Cadmium and Lead Ions in Water by MOF-5/PANI Composites
Authors: Milikić Jadranka , Savić Marjetka , Janošević Ležaić Aleksandra , Šljukić Biljana , Ćirić-Marjanović Gordana
Published: March 2024

For the first time, composites of metal-organic framework MOF-5 and conjugated polymer polyaniline (PANI), (MOF-5/PANI), prepared using PANI in its conducting (emeraldine salt, ES) or nonconducting form (emeraldine base, EB) at various MOF-5 and PANI mass ratios, were evaluated as electrode materials for the electrochemical detection of cadmium (Cd2+) and lead (Pb2+) ions in aqueous solutions. Testing of individual components of composites, PANI-ES, PANI-EB, and MOF-5, was also performed for comparison. Materials are characterized by Raman spectroscopy, scanning electron microscopy (SEM) and dynamic light scattering (DLS), and their electrochemical behavior was discussed in terms of their zeta potential, structural, morphology, and textural properties. All examined composites showed high electrocatalytic activity for the oxidation of Cd and Pb to Cd2+ and Pb2+, respectively. The MOF/EB-1 composite (71.0 wt.% MOF-5) gave the highest oxidation currents during both individual and simultaneous detection of two heavy metal ions. Current densities recorded with MOF/EB-1 were also higher than those of its individual components, reflecting the synergistic effect where MOF-5 offers high surface area for two heavy metals adsorption and PANI offers a network for electron transfer during metals’ subsequent oxidation. Limits of detection using MOF/EB-1 electrode for Cd2+ and Pb2+ sensing were found to be as low as 0.077 ppm and 0.033 ppm, respectively. Moreover, the well-defined and intense peaks of Cd oxidation to Cd2+ and somewhat lower peaks of Pb oxidation to Pb2+ were observed at voltammograms obtained for the Danube River as a real sample with no pretreatment, which implies that herein tested MOF-5/PANI electrodes could be used as electrochemical sensors for the detection of heavy metal ions in the real water samples.
Intranasal Ion-Triggered In Situ Delivery System of Virus-like Particles: Development Using the Quality by Design Approach
Authors: Bakhrushina Elena O. , Mikhel Iosif B. , Kondratieva Valeriya M. , Zubareva Irina M. , Kosenkova Svetlana I. , Belyatskaya Anastasiya V. , Stepanova Olga I. , Krasnyuk Ivan I. , Grebennikova Tatyana V. , Krasnyuk Ivan I.
Published: March 2024

The rapid growth in the prevalence of infectious diseases requires timely action from drug developers. In recent years, the COVID-19 pandemic has demonstrated the unpreparedness of the population for such emergencies. The introduction of modern methods of Design of Experiments (DoE) is required to accelerate the process of drug development and bring a drug to market. The main objective of this study was to develop an ion-triggered in situ system for intranasal delivery of VLP using a Quality by Design approach. Based on a literature review and initial studies, the key QTPP, CQA, CPP, and CMA were identified to develop a novel delivery system for virus-like particles. As a result of the studies on the quality attributes of the developed delivery system, an ion-triggered in situ gel meeting all the specified parameters was obtained using the Quality by Design method.
Recent Progress in Covalent Organic Frameworks for Cathode Materials
Authors: Wang Chi , Tian Yuchao , Chen Wuhong , Lin Xiaochun , Zou Jizhao , Fu Dongju , Yu Xiao , Qiu Ruling , Qiu Junwei , Zeng Shaozhong
Published: March 2024

Covalent organic frameworks (COFs) are constructed from small organic molecules through reversible covalent bonds, and are therefore considered a special type of polymer. Small organic molecules are divided into nodes and connectors based on their roles in the COF’s structure. The connector generally forms reversible covalent bonds with the node through two reactive end groups. The adjustment of the length of the connector facilitates the adjustment of pore size. Due to the diversity of organic small molecules and reversible covalent bonds, COFs have formed a large family since their synthesis in 2005. Among them, a type of COF containing redox active groups such as –C=O–, –C=N–, and –N=N– has received widespread attention in the field of energy storage. The ordered crystal structure of COFs ensures the ordered arrangement and consistent size of pores, which is conducive to the formation of unobstructed ion channels, giving these COFs a high-rate performance and a long cycle life. The voltage and specific capacity jointly determine the energy density of cathode materials. For the COFs’ cathode materials, the voltage plateau of their active sites’ VS metallic lithium is mostly between 2 and 3 V, which has great room for improvement. However, there is currently no feasible strategy for this. Therefore, previous studies mainly improved the theoretical specific capacity of the COFs’ cathode materials by increasing the number of active sites. We have summarized the progress in the research on these types of COFs in recent years and found that the redox active functional groups of these COFs can be divided into six subcategories. According to the different active functional groups, these COFs are also divided into six subcategories. Here, we summarize the structure, synthesis unit, specific surface area, specific capacity, and voltage range of these cathode COFs.
Stabilization of Black Locust Flower Extract via Encapsulation Using Alginate and Alginate–Chitosan Microparticles
Authors: Boškov Ivana A. , Savić Ivan M. , Grozdanić Stanisavljević Nađa Đ. , Kundaković-Vasović Tatjana D. , Radović Selgrad Jelena S. , Savić Gajić Ivana M.
Published: March 2024

Black locust flower extract contains various polyphenols and their glucosides contribute to the potential health benefits. After intake of these bioactive compounds and passage through the gastrointestinal tract, their degradation can occur and lead to a loss of biological activity. To overcome this problem, the bioactive compounds should be protected from environmental conditions. This study aimed to encapsulate the black flower extract in the microparticles based on biodegradable polysaccharides, alginate, and chitosan. In the extract, the total antioxidant content was found to be 3.18 ± 0.01 g gallic acid equivalent per 100 g of dry weight. Also, the presence of lipids (16), phenolics (27), organic acids (4), L-aspartic acid derivative, questinol, gibberellic acid, sterol, and saponins (2) was confirmed using the UHPLC–ESI–MS analysis. In vitro assays showed that the extract has weak anti-α-glucosidase activity and moderate antioxidant and cytotoxic activity against the HeLa cell line. The extrusion method with secondary air flow enabled the preparation of microparticles (about 270 μm) encapsulated with extract. An encapsulation efficiency of over 92% was achieved in the alginate and alginate–chitosan microparticles. The swelling study confirmed a lower permeability of alginate–chitosan microparticles compared with alginate microparticles. For both types of microparticles, the release profile of antioxidants in the simulated gastrointestinal fluids at 37 °C followed the Korsmeyer–Peppas model. A lower diffusion coefficient than 0.5 indicated the simple Fick diffusion of antioxidants. The alginate–chitosan microparticles enabled a more sustained release of antioxidants from extract compared to the alginate microparticles. The obtained results indicated an improvement in the antioxidant activity of bioactive compounds from the extract and their protection from degradation in the simulated gastric conditions via encapsulation in the polymer matrixes. Alginate–chitosan showed slightly slower cumulative antioxidant release from microparticles and better antioxidant activity of the extract compared to the alginate system. According to these results, alginate–chitosan microparticles are more suitable for further application in the encapsulation of black locust flower extract. Also, the proposed polymer matrix as a drug delivery system is safe for human use due to its biodegradability and non-toxicity.
The Aging of Polymers under Electromagnetic Radiation
Authors: Maraveas Chrysanthos , Kyrtopoulos Ioannis Vasileios , Arvanitis Konstantinos G. , Bartzanas Thomas
Published: March 2024

Polymeric materials degrade as they react with environmental conditions such as temperature, light, and humidity. Electromagnetic radiation from the Sun’s ultraviolet rays weakens the mechanical properties of polymers, causing them to degrade. This study examined the phenomenon of polymer aging due to exposure to ultraviolet radiation. The study examined three specific objectives, including the key theories explaining ultraviolet (UV) radiation’s impact on polymer decomposition, the underlying testing procedures for determining the aging properties of polymeric materials, and appraising the current technical methods for enhancing the UV resistance of polymers. The study utilized a literature review methodology to understand the aging effect of electromagnetic radiation on polymers. Thus, the study concluded that using additives and UV absorbers on polymers and polymer composites can elongate the lifespan of polymers by shielding them from the aging effects of UV radiation. The findings from the study suggest that thermal conditions contribute to polymer degradation by breaking down their physical and chemical bonds. Thermal oxidative environments accelerate aging due to the presence of UV radiation and temperatures that foster a quicker degradation of plastics.
Investigation of Physical Properties of Polymer Composites Filled with Sheep Wool
Authors: Vasina Martin , Straznicky Premysl , Hrbacek Pavel , Rusnakova Sona , Bosak Ondrej , Kubliha Marian
Published: March 2024

Sheep farmers are currently facing an oversupply of wool and a lack of willing buyers. Due to low prices, sheep wool is often either dumped, burned, or sent to landfills, which are unsustainable and environmentally unfriendly practices. One potential solution is the utilization of sheep wool fibers in polymer composites. This paper focuses on the study of mechanical vibration damping properties, sound absorption, light transmission, electrical conductivity of epoxy (EP), polyurethane (PU), and polyester (PES) resins, each filled with three different concentrations of sheep wool (i.e., 0%, 3%, and 5% by weight). It can be concluded that the sheep wool content in the polymer composites significantly influenced their physical properties. The impact of light transmission through the tested sheep wool fiber-filled polymer composites on the quality of daylight in a reference room was also mathematically simulated using Wdls 5.0 software.
Compound Microalgae-Type Biofunctional Hydrogel for Wound Repair during Full-Thickness Skin Injuries
Authors: Mao Yi , Sun Yajuan , Yang Cheng
Published: March 2024

A dual biofunctional hydrogel (HQCS-SP) wound dressing, offering antibacterial properties and a biological response, was innovatively designed and developed to repair full-layer skin defects. The HQCS-SP hydrogel creates an artificial matrix that facilitates cell recruitment, extracellular matrix deposition, exhibiting exceptional tissue affinity, robust self-healing, effective hemostatic capabilities and accelerates wound healing. It is synthesized by crosslinking modified chitosan (HQCS) with spirulina protein (SP) and Fe3+. The HQCS provides antibacterial, antioxidant, good tissue affinity and excellent hemostasis performance. The incorporation of SP not only reinforces the antioxidant, antibacterial, anti-inflammatory, and pro-angiogenesis effects but also participates in the regulation of signal pathways and promotes wound healing. Therefore, this study offers a new visual angle for the design of advanced functional trauma dressings with great application potential in the bio-medical field.
An Eco-Friendly Manner to Prepare Superwetting Melamine Sponges with Switchable Wettability for the Separation of Oil/Water Mixtures and Emulsions
Authors: Belachew Guyita Berako , Hu Chien-Chieh , Chang Yan-Yu , Wang Chih-Feng , Hung Wei-Song , Chen Jem-Kun , Lai Juin-Yih
Published: March 2024

Oil/water separation processes have garnered significant global attention due to the quick growth in industrial development, recurring chemical leakages, and oil spills. Hence, there is a significant demand for the development of inexpensive superwetting materials in an eco-friendly manner to separate oil/water mixtures and emulsions. In this study, a superwetting melamine sponge (SMS) with switchable wettabilities was prepared by modifying melamine sponge (MS) with sodium dodecanoate. The as-prepared SMS exhibited superhydrophobicity, superoleophilicity, underwater superoleophobicity, and underoil superhydrophobicity. The SMS can be utilized in treating both light and heavy oil/water mixtures through the prewetting process. It demonstrated fast permeation fluxes (reaching 108,600 L m−2 h−1 for a light oil/water mixture and 147,700 L m−2 h−1 for a heavy oil/water mixture) and exhibited good separation efficiency (exceeding 99.56%). The compressed SMS was employed in separating surfactant-stabilized water-in-oil emulsions (SWOEs), as well as surfactant-stabilized oil-in-water emulsions (SOWEs), giving high permeation fluxes (reaching 7210 and 5054 L m−2 h−1, respectively). The oil purity for SWOEs’ filtrates surpassed 99.98 wt% and the separation efficiencies of SOWEs exceeded 98.84%. Owing to their remarkable capability for separating oil/water mixtures and emulsions, eco-friendly fabrication method, and feasibility for large-scale production, our SMS has a promising potential for practical applications.
Fabrication and Characterization of Electrospun Cu-Doped TiO2 Nanofibers and Enhancement of Photocatalytic Performance Depending on Cu Content and Electron Beam Irradiation
Authors: Lee So-Hyeon , Na Kyeong-Han , Kim Jae-Yoon , Yoon Han-Sol , Han HyukSu , Choi Won-Youl
Published: March 2024

Titanium dioxide (TiO₂) is a widely studied material with many attractive properties such as its photocatalytic features. However, its commercial use is limited due to issues such as deactivation in the visible spectrum caused by its wide bandgap and the short lifetime of photo-excited charge carriers. To overcome these challenges, various modifications could be considered. In this study, we investigated copper doping and electron beam treatment. As-spun TiO2 nanofibers were fabricated by electrospinning a TiO2 sol, which obtained viscosity through a polyvinylpyrrolidone (PVP) matrix. Cu-doped TiO2 nanofibers with varying dopant concentrations were synthesized by adding copper salts. Then, the as-spun nanofibers were calcined for crystallization. To evaluate photocatalytic performance, a photodegradation test of methylene blue aqueous solution was performed for 6 h. Methylene blue concentration was measured over time using UV-Vis spectroscopy. The results showed that Cu doping at an appropriate concentration and electron-beam irradiation showed improved photocatalytic efficiency compared to bare TiO2 nanofibers. When the molar ratio of Cu/Ti was 0.05%, photodegradation rate was highest, which was 10.39% higher than that of bare TiO2. As a result of additional electron-beam treatment of this sample, photocatalytic efficiency improved up to 8.93% compared to samples without electron-beam treatment.
Rapid DNA Detection of Salmonella enterica Typhimurium and Heidelberg from Poultry Samples
Authors: Mathias Joana Bittencourt , Souza Margarida Neves , Kipper Diéssy , Fonseca André Salvador Kazantzi , Lunge Vagner Ricardo , Ikuta Nilo
Published: March 2024

The Salmonella enterica serovars Typhimurium (S. Typhimurium), Heidelberg (S. Heidelberg), and their monophasic variants (S. 1,4,[5],12:i:-, S. 1,4,[5],12:r:- and S. 1,4,[5],12:-:1,2) are highly disseminated in poultry farming and can contaminate chicken meat, eggs, and other foods of avian origin. A time-consuming bacteriological and serological analysis is usually required to identify serovars by traditional methods. Incomplete and inconclusive serological results are frequent in routine analysis, mainly due to the occurrence of bacterial isolates presenting similar antigenic profiles. Molecular biology assays have been developed to improve the detection of specific Salmonella serovars and strains. This study aimed to develop a multiplex real-time PCR (SHTAmp) for the rapid DNA detection of S. Typhimurium, S. Heidelberg, and their monophasic variants from poultry samples. The methodology was used in the analysis of 147 field isolates from Brazilian poultry flocks previously evaluated with serological analysis. The results demonstrated that it was able to specifically and rapidly detect 21 S. Typhimurium and 57 S. Heidelberg isolates with complete antigenic formulae. Furthermore, SHTAmp was able to differentiate nine S. Typhimurium and 44 S. Heidelberg isolates with incomplete serological formulae (monophasic and aphasic variants). The complete methodology was also successfully used to detect these bacteria directly from 34 poultry samples after pre-enrichment in buffered peptone water (BPW). In conclusion, SHTAmp is a fast and accurate method to detect the two frequent and concerning serovars S. Typhimurium and S. Heidelberg directly from poultry samples.
COVID-19 and Christian Faith-Based Organizations in Great Britain: A Research and Resource Review of Organizational, Financial and Human Contributions and Impacts in the Context of a Wider “Christian Ecology”
Authors: Weller Paul Gareth
Published: March 2024

The COVID-19 pandemic presented what, on both global and national levels, has arguably so far been the most extensive health, economic and social challenge of the 21st century. Responding to this challenge, it soon became clear that, while having a vital role in the contextual provision of necessary services, public authorities also needed to engage with organizations in the voluntary, community sector, including Faith-Based Organizations (FBOs). This article presents and discusses and analyzes a digest of research and resource evidence from the beginning of 2020–to the end of 2023 concerning the organizational, financial and human contributions of and impacts of the COVID-19 pandemic on, in particular, Christian FBOs in Great Britain. It goes on to analyze these contributions and impacts within the analytical framework of what it calls a wider “Christian ecology” as the context within which such FBOs live, to which they contribute, and, as argued by this review, apart from which they cannot be properly understood. Finally, in exploring some of the lessons to be learned from the evidence and analysis presented, the article identifies and considers some key opportunities and issues that arise at the interface between the work of (especially, but not only, Christian) FBOs and the structures and processes of the governmental powers-that-be.
End-to-End Edge-Guided Multi-Scale Matching Network for Optical Satellite Stereo Image Pairs
Authors: Luo Yixin , Wang Hao , Lv Xiaolei
Published: March 2024

Acquiring disparity maps by dense stereo matching is one of the most important methods for producing digital surface models. However, the characteristics of optical satellite imagery, including significant occlusions and long baselines, increase the challenges of dense matching. In this study, we propose an end-to-end edge-guided multi-scale matching network (EGMS-Net) tailored for optical satellite stereo image pairs. Using small convolutional filters and residual blocks, the EGMS-Net captures rich high-frequency signals during the initial feature extraction phase. Subsequently, pyramid features are derived through efficient down-sampling and consolidated into cost volumes. To regularize these cost volumes, we design a top–down multi-scale fusion network that integrates an attention mechanism. Finally, we innovate the use of trainable guided filter layers in disparity refinement to improve edge detail recovery. The network is trained and evaluated using the Urban Semantic 3D and WHU-Stereo datasets, with subsequent analysis of the disparity maps. The results show that the EGMS-Net provides superior results, achieving endpoint errors of 1.515 and 2.459 pixels, respectively. In challenging scenarios, particularly in regions with textureless surfaces and dense buildings, our network consistently delivers satisfactory matching performance. In addition, EGMS-Net reduces training time and increases network efficiency, improving overall results.
Improving the Detection Effect of Long-Baseline Lightning Location Networks Using PCA and Waveform Cross-Correlation Methods
Authors: Zhang Ting , Wang Jiaquan , Ma Qiming , Fu Liping
Published: March 2024

Ultra-long-distance and high-precision lightning location technology is an important means to realize low-cost and wide-area lightning detection. This paper carried out research on the high-precision location technology of very-low-frequency (VLF) lightning electromagnetic pulse based on the Asia-Pacific Lightning Location Network (APLLN) deployed in 2018. Two key technologies are proposed in this paper: one is the calculation method of signal arrival time using very-low-frequency lightning electromagnetic pulse waveform, and the other is the compression transmission technology of lightning electromagnetic pulse waveform based on a signal principal component analysis. The results of a comparison and evaluation of the improved APLLN with the ADTD system show that the APLLN has a relative location efficiency of 69.1% and an average location error within the network of 4.5 km.
Performance of Algorithms for Retrieving Chlorophyll a Concentrations in the Arctic Ocean: Impact on Primary Production Estimates
Authors: Li Juan , Matsuoka Atsushi , Pang Xiaoping , Massicotte Philippe , Babin Marcel
Published: March 2024

Chlorophyll a concentration (Chl) is a key variable for estimating primary production (PP) through ocean-color remote sensing (OCRS). Accurate Chl estimates are crucial for better understanding of the spatio-temporal trends in PP in recent decades as a consequence of climate change. However, a number of studies have reported that currently operational chlorophyll a algorithms perform poorly in the Arctic Ocean (AO), largely due to the interference of colored and detrital material (CDM) with the phytoplankton signal in the visible part of the spectrum. To determine how and to what extent CDM biases the estimation of Chl, we evaluated the performances of eight currently available ocean-color algorithms: OC4v6, OC3Mv6, OC3V, OC4L, OC4P, AO.emp, GSM01 and AO.GSM. Our results suggest that the empirical AO.emp algorithm performs the best overall, but, for waters with high CDM acdm(443) > 0.067 m−1), a common scenario in the Arctic, the two semi-analytical GSM models yield better performance. In addition, sensitivity analyses using a spectrally and vertically resolved Arctic primary-production model show that errors in Chl mostly propagate proportionally to PP estimates, with amplification of up to 7%. We also demonstrate that, the higher level of CDM in relation to Chl in the water column, the larger the bias in both Chl and PP estimates. Lastly, although the AO.GSM is the best overall performer among the algorithms tested, it tends to fail for a significant number of pixels (16.2% according to the present study), particularly for waters with high CDM. Our results therefore suggest the ongoing need to develop an algorithm that provides reasonable Chl estimates for a wide range of optically complex Arctic waters.
An Overlay Accelerator of DeepLab CNN for Spacecraft Image Segmentation on FPGA
Authors: Guo Zibo , Liu Kai , Liu Wei , Sun Xiaoyao , Ding Chongyang , Li Shangrong
Published: March 2024

Due to the absence of communication and coordination with external spacecraft, non-cooperative spacecraft present challenges for the servicing spacecraft in acquiring information about their pose and location. The accurate segmentation of non-cooperative spacecraft components in images is a crucial step in autonomously sensing the pose of non-cooperative spacecraft. This paper presents a novel overlay accelerator of DeepLab Convolutional Neural Networks (CNNs) for spacecraft image segmentation on a FPGA. First, several software–hardware co-design aspects are investigated: (1) A CNNs-domain COD instruction set (Control, Operation, Data Transfer) is presented based on a Load–Store architecture to enable the implementation of accelerator overlays. (2) An RTL-based prototype accelerator is developed for the COD instruction set. The accelerator incorporates dedicated units for instruction decoding and dispatch, scheduling, memory management, and operation execution. (3) A compiler is designed that leverages tiling and operation fusion techniques to optimize the execution of CNNs, generating binary instructions for the optimized operations. Our accelerator is implemented on a Xilinx Virtex-7 XC7VX690T FPGA at 200 MHz. Experiments demonstrate that with INT16 quantization our accelerator achieves an accuracy (mIoU) of 77.84%, experiencing only a 0.2% degradation compared to that of the original fully precision model, in accelerating the segmentation model of DeepLabv3+ ResNet18 on the spacecraft component images (SCIs) dataset. The accelerator boasts a performance of 184.19 GOPS/s and a computational efficiency (Runtime Throughput/Theoretical Roof Throughput) of 88.72%. Compared to previous work, our accelerator improves performance by 1.5× and computational efficiency by 43.93%, all while consuming similar hardware resources. Additionally, in terms of instruction encoding, our instructions reduce the size by 1.5× to 49× when compiling the same model compared to previous work.
Spectral–Spatial Graph Convolutional Network with Dynamic-Synchronized Multiscale Features for Few-Shot Hyperspectral Image Classification
Authors: Liu Shuai , Li Hongfei , Jiang Chengji , Feng Jie
Published: March 2024

The classifiers based on the convolutional neural network (CNN) and graph convolutional network (GCN) have demonstrated their effectiveness in hyperspectral image (HSI) classification. However, their performance is limited by the high time complexity of CNN, spatial complexity of GCN, and insufficient labeled samples. To ease these limitations, the spectral–spatial graph convolutional network with dynamic-synchronized multiscale features is proposed for few-shot HSI classification. Firstly, multiscale patches are generated to enrich training samples in the feature space. A weighted spectral optimization module is explored to evaluate the discriminate information among different bands of patches. Then, the adaptive dynamic graph convolutional module is proposed to extract local and long-range spatial–spectral features of patches at each scale. Considering that features of different scales can be regarded as sequential data due to intrinsic correlations, the bidirectional LSTM is adopted to synchronously extract the spectral–spatial characteristics from all scales. Finally, auxiliary classifiers are utilized to predict labels of samples at each scale and enhance the training stability. Label smoothing is introduced into the classification loss to reduce the influence of misclassified samples and imbalance of classes. Extensive experiments demonstrate the superiority of the proposed method over other state-of-the-art methods, obtaining overall accuracies of 87.25%, 92.72%, and 93.36% on the Indian Pines, Pavia University, and Salinas datasets, respectively.
Optimizing the Atmospheric CO2 Retrieval Based on the NDACC-Type FTIR Mid-Infrared Spectra at Xianghe, China
Authors: Wang Jiaxin , Zhou Minqiang , Langerock Bavo , Nan Weidong , Wang Ting , Wang Pucai
Published: March 2024

Carbon dioxide (CO2) is the most important long-lived greenhouse gas and can be retrieved using solar absorption spectra recorded by a ground-based Fourier-transform infrared spectrometer (FTIR). In this study, we investigate the CO2 retrieval strategy using the Network for the Detection of Atmospheric Composition Change–Infrared Working Group (NDACC–IRWG) type spectra between August 2018 and April 2022 (~4 years) at Xianghe, China, aiming to find the optimal observed spectra, retrieval window, and spectroscopy. Two spectral regions, near 2600 and 4800 cm−1, are analyzed. The differences in column-averaged dry-air mole fraction of CO2 (XCO2) derived from spectroscopies (ATM18, ATM20, HITRAN2016, and HITRAN2020) can be up to 1.65 ± 0.95 ppm and 7.96 ± 2.02 ppm for NDACC-type 2600 cm−1 and 4800 cm−1 retrievals, respectively, which is mainly due to the CO2 differences in air-broadened Lorentzian HWHM coefficient (γair) and line intensity (S). HITRAN2020 provides the best fitting, and the retrieved CO2 columns and profiles from both 2600 and 4800 cm−1 are compared to the co-located Total Column Carbon Observing Network (TCCON) measurements and the greenhouse gas reanalysis dataset from the Copernicus Atmosphere Monitoring Service (CAMS). The amplitude of XCO2 seasonal variation derived from the NDACC-type (4800 cm−1) is closer to the TCCON measurements than that from the NDACC-type (2600 cm−1). Moreover, the NDACC-type (2600 cm−1) retrievals are strongly affected by the a priori profile. For tropospheric XCO2, the correlation coefficient between NDACC-type (4800 cm−1) and CAMS model is 0.73, which is higher than that between NDACC-type (2600 cm−1) and CAMS model (R = 0.56).
A Coarse-to-Fine Transformer-Based Network for 3D Reconstruction from Non-Overlapping Multi-View Images
Authors: Shan Yue , Xiao Jun , Liu Lupeng , Wang Yunbiao , Yu Dongbo , Zhang Wenniu
Published: March 2024

Reconstructing 3D structures from non-overlapping multi-view images is a crucial task in the field of 3D computer vision, since it is difficult to establish feature correspondences and infer depth from overlapping parts of views. Previous methods, whether generating the surface mesh or volume of an object, face challenges in simultaneously ensuring the accuracy of detailed topology and the integrity of the overall structure. In this paper, we introduce a novel coarse-to-fine Transformer-based reconstruction network to generate precise point clouds from multiple input images at sparse and non-overlapping viewpoints. Specifically, we firstly employ a general point cloud generation architecture enhanced by the concept of adaptive centroid constraint for the coarse point cloud corresponding to the object. Subsequently, a Transformer-based refinement module applies deformation to each point. We design an attention-based encoder to encode both image projection features and point cloud geometric features, along with a decoder to calculate deformation residuals. Experiments on ShapeNet demonstrate that our proposed method outperforms other competing methods.
A Convolution with Transformer Attention Module Integrating Local and Global Features for Object Detection in Remote Sensing Based on YOLOv8n
Authors: Lang Kaiqi , Cui Jie , Yang Mingyu , Wang Hanyu , Wang Zilong , Shen Honghai
Published: March 2024

Object detection in remote sensing scenarios plays an indispensable and significant role in civilian, commercial, and military areas, leveraging the power of convolutional neural networks (CNNs). Remote sensing images, captured by crafts and satellites, exhibit unique characteristics including complicated backgrounds, limited features, distinct density, and varied scales. The contextual and comprehensive information in an image can make a detector precisely localize and classify targets, which is extremely valuable for object detection in remote sensing scenarios. However, CNNs, restricted by the essence of the convolution operation, possess local receptive fields and scarce contextual information, even in large models. To address this limitation and improve detection performance by extracting global contextual information, we propose a novel plug-and-play attention module, named Convolution with Transformer Attention Module (CTAM). CTAM is composed of a convolutional bottleneck block and a simplified Transformer layer, which can facilitate the integration of local features and position information with long-range dependency. YOLOv8n, a superior and faster variant of the YOLO series, is selected as the baseline. To demonstrate the effectiveness and efficiency of CTAM, we incorporated CTAM into YOLOv8n and conducted extensive experiments on the DIOR dataset. YOLOv8n-CTAM achieves an impressive 54.2 mAP@50-95, surpassing YOLOv8n (51.4) by a large margin. Notably, it outperforms the baseline by 2.7 mAP@70 and 4.4 mAP@90, showcasing its superiority with stricter IoU thresholds. Furthermore, the experiments conducted on the TGRS-HRRSD dataset validate the excellent generalization ability of CTAM.
Object Detection in Remote Sensing Images Based on Adaptive Multi-Scale Feature Fusion Method
Authors: Liu Chun , Zhang Sixuan , Hu Mengjie , Song Qing
Published: March 2024

Multi-scale object detection is critical for analyzing remote sensing images. Traditional feature pyramid networks, which are aimed at accommodating objects of varying sizes through multi-level feature extraction, face significant challenges due to the diverse scale variations present in remote sensing images. This situation often forces single-level features to span a broad spectrum of object sizes, complicating accurate localization and classification. To tackle these challenges, this paper proposes an innovative algorithm that incorporates an adaptive multi-scale feature enhancement and fusion module (ASEM), which enhances remote sensing image object detection through sophisticated multi-scale feature fusion. Our method begins by employing a feature pyramid to gather coarse multi-scale features. Subsequently, it integrates a fine-grained feature extraction module at each level, utilizing atrous convolutions with varied dilation rates to refine multi-scale features, which markedly improves the information capture from widely varied object scales. Furthermore, an adaptive enhancement module is applied to the features of each level by employing an attention mechanism for feature fusion. This strategy concentrates on the features of critical scale, which significantly enhance the effectiveness of capturing essential feature information. Compared with the baseline method, namely, Rotated FasterRCNN, our method achieved an mAP of 74.21% ( 0.81%) on the DOTA-v1.0 dataset and an mAP of 84.90% (+9.2%) on the HRSC2016 dataset. These results validated the effectiveness and practicality of our method and demonstrated its significant application value in multi-scale remote sensing object detection tasks.
A Multi-Source Data Fusion Network for Wood Surface Broken Defect Segmentation
Authors: Zhu Yuhang , Xu Zhezhuang , Lin Ye , Chen Dan , Ai Zhijie , Zhang Hongchuan
Published: March 2024

Wood surface broken defects seriously damage the structure of wooden products, these defects have to be detected and eliminated. However, current defect detection methods based on machine vision have difficulty distinguishing the interference, similar to the broken defects, such as stains and mineral lines, and can result in frequent false detections. To address this issue, a multi-source data fusion network based on U-Net is proposed for wood broken defect detection, combining image and depth data, to suppress the interference and achieve complete segmentation of the defects. To efficiently extract various semantic information of defects, an improved ResNet34 is designed to, respectively, generate multi-level features of the image and depth data, in which the depthwise separable convolution (DSC) and dilated convolution (DC) are introduced to decrease the computational expense and feature redundancy. To take full advantages of two types of data, an adaptive interacting fusion module (AIF) is designed to adaptively integrate them, thereby generating accurate feature representation of the broken defects. The experiments demonstrate that the multi-source data fusion network can effectively improve the detection accuracy of wood broken defects and reduce the false detections of interference, such as stains and mineral lines.
Kinematic Analysis and Application to Control Logic Development for RHex Robot Locomotion
Authors: Burzyński Piotr , Pawłuszewicz Ewa , Ambroziak Leszek , Sharma Suryansh
Published: March 2024

This study explores the kinematic model of the popular RHex hexapod robots which have garnered considerable interest for their locomotion capabilities. We study the influence of tripod trajectory parameters on the RHex robot’s movement, aiming to craft a precise kinematic model that enhances walking mechanisms. This model serves as a cornerstone for refining robot control strategies, enabling tailored performance enhancements or specific motion patterns. Validation conducted on a bespoke test bed confirms the model’s efficacy in predicting spatial movements, albeit with minor deviations due to motor load variations and control system dynamics. In particular, the derived kinematic framework offers valuable insights for advancing control logic, particularly navigating in flat terrains, thereby broadening the RHex robot’s application spectrum.
Visual Detection of Road Cracks for Autonomous Vehicles Based on Deep Learning
Authors: Meftah Ibrahim , Hu Junping , Asham Mohammed A. , Meftah Asma , Zhen Li , Wu Ruihuan
Published: March 2024

Detecting road cracks is essential for inspecting and assessing the integrity of concrete pavement structures. Traditional image-based methods often require complex preprocessing to extract crack features, making them challenging when dealing with noisy concrete surfaces in diverse real-world scenarios, such as autonomous vehicle road detection. This study introduces an image-based crack detection approach that combines a Random Forest machine learning classifier with a deep convolutional neural network (CNN) to address these challenges. Three state-of-the-art models, namely MobileNet, InceptionV3, and Xception, were employed and trained using a dataset of 30,000 images to build an effective CNN. A systematic comparison of validation accuracy across various base learning rates identified a base learning rate of 0.001 as optimal, achieving a maximum validation accuracy of 99.97%. This optimal learning rate was then applied in the subsequent testing phase. The robustness and flexibility of the trained models were evaluated using 6,000 test photos, each with a resolution of 224 × 224 pixels, which were not part of the training or validation sets. The outstanding results, boasting a remarkable 99.95% accuracy, 99.95% precision, 99.94% recall, and a matching 99.94% F1 Score, unequivocally affirm the efficacy of the proposed technique in precisely identifying road fractures in photographs taken on real concrete surfaces.
Wheat Seed Detection and Counting Method Based on Improved YOLOv8 Model
Authors: Ma Na , Su Yaxin , Yang Lexin , Li Zhongtao , Yan Hongwen
Published: March 2024

Wheat seed detection has important applications in calculating thousand-grain weight and crop breeding. In order to solve the problems of seed accumulation, adhesion, and occlusion that can lead to low counting accuracy, while ensuring fast detection speed with high accuracy, a wheat seed counting method is proposed to provide technical support for the development of the embedded platform of the seed counter. This study proposes a lightweight real-time wheat seed detection model, YOLOv8-HD, based on YOLOv8. Firstly, we introduce the concept of shared convolutional layers to improve the YOLOv8 detection head, reducing the number of parameters and achieving a lightweight design to improve runtime speed. Secondly, we incorporate the Vision Transformer with a Deformable Attention mechanism into the C2f module of the backbone network to enhance the network’s feature extraction capability and improve detection accuracy. The results show that in the stacked scenes with impurities (severe seed adhesion), the YOLOv8-HD model achieves an average detection accuracy (mAP) of 77.6%, which is 9.1% higher than YOLOv8. In all scenes, the YOLOv8-HD model achieves an average detection accuracy (mAP) of 99.3%, which is 16.8% higher than YOLOv8. The memory size of the YOLOv8-HD model is 6.35 MB, approximately 4/5 of YOLOv8. The GFLOPs of YOLOv8-HD decrease by 16%. The inference time of YOLOv8-HD is 2.86 ms (on GPU), which is lower than YOLOv8. Finally, we conducted numerous experiments and the results showed that YOLOv8-HD outperforms other mainstream networks in terms of mAP, speed, and model size. Therefore, our YOLOv8-HD can efficiently detect wheat seeds in various scenarios, providing technical support for the development of seed counting instruments.
Novel Approach to Phase-Sensitive Optical Time-Domain Reflectometry Response Analysis with Machine Learning Methods
Authors: Yatseev Vasily A. , Butov Oleg V. , Pnev Alexey B.
Published: March 2024

This paper is dedicated to the investigation of the metrological properties of phase-sensitive reflectometric measurement systems, with a particular focus on addressing the non-uniformity of responses along optical fibers. The authors highlight challenges associated with the stochastic distribution of Rayleigh reflectors in fiber optic systems and propose a methodology for assessing response non-uniformity using both cross-correlation algorithms and machine learning approaches, using chirped-reflectometry as an example. The experimental process involves simulating deformation impact by altering the light source’s wavelength and utilizing a chirped-reflectometer to estimate response non-uniformity. This paper also includes a comparison of results obtained from cross-correlation and neural network-based algorithms, revealing that the latter offers more than 34% improvement in accuracy when measuring phase differences. In conclusion, the study demonstrates how this methodology effectively evaluates response non-uniformity along different sections of optical fibers.
Three-Axis Vector Magnetometer with a Three-Dimensional Flux Concentrator
Authors: Chen Shih-Jui , Hong Der-Tai , Hsieh Ping-Hsun , Wang Tse-Kang
Published: March 2024

This research proposes a magnetic field sensor with spatial orientation ability. Through the assistance of a magnetic flux concentrator, out-of-plane magnetic flux can be concentrated and guided into the planar magnetic cores of a fluxgate sensor. A printed circuit board is used to construct the basic planar structure, on which the proposed three-dimensional magnetic flux concentrator and magnetic cores are assembled. This reduces the alignment error of the coils and improves the reliability of the sensor. Three-axis sensing is achieved by using the second harmonic signals from selected sensing coil pairs. The magnetometer exhibits a linear range to 130 μT. At an excitation frequency of 50 kHz, the measured sensitivities are 257.1, 468.8, and 258.8 V/T for the X-, Y-, and Z-axis sensing modes, respectively. This sensor utilizes only one sensing mechanism for the vector field, making it suitable for IoT applications, especially for assessing mechanical posture or position.