bluuebunny/crossref_metadata_2025 huggingface dataset:
The Medicinal Species of the Lycium Genus (Goji Berries) in East Asia: A Review of Its Effect on Cell Signal Transduction Pathways
Authors: Jiang Chenyu , Chen Ziyu , Liao Weilin , Zhang Ren , Chen Geer , Ma Lijuan , Yu Haijie
Published: May 2024

Natural plants contain numerous chemical compounds that are beneficial to human health. The berries from the Lycium genus are widely consumed and are highly nutritious. Moreover, their chemical constituents have attracted attention for their health-promoting properties. In East Asia, there are three varieties of the Lycium genus (Lycium barbarum L., Lycium chinense Miller, and L. ruthenicum Murray) that possess medicinal value and are commonly used for treating chronic diseases and improving metabolic disorders. These varieties are locally referred to as “red Goji berries” or “black Goji berries” due to their distinct colors, and they differ in their chemical compositions, primarily in terms of carotenoid and anthocyanin content. The pharmacological functions of these berries include anti-aging, antioxidant, anti-inflammatory, and anti-exercise fatigue effects. This review aims to analyze previous and recent studies on the active ingredients and pharmacological activities of these Lycium varieties, elucidating their signaling pathways and assessing their impact on the gut microbiota. Furthermore, the potential prospects for using these active ingredients in the treatment of COVID-19 are evaluated. This review explores the potential targets of these Lycium varieties in the treatment of relevant diseases, highlighting their potential value in drug development.
Phospholipid Signaling in Crop Plants: A Field to Explore
Authors: Amokrane Lucas , Pokotylo Igor , Acket Sébastien , Ducloy Amélie , Troncoso-Ponce Adrian , Cacas Jean-Luc , Ruelland Eric
Published: May 2024

In plant models such as Arabidopsis thaliana, phosphatidic acid (PA), a key molecule of lipid signaling, was shown not only to be involved in stress responses, but also in plant development and nutrition. In this article, we highlight lipid signaling existing in crop species. Based on open access databases, we update the list of sequences encoding phospholipases D, phosphoinositide-dependent phospholipases C, and diacylglycerol-kinases, enzymes that lead to the production of PA. We show that structural features of these enzymes from model plants are conserved in equivalent proteins from selected crop species. We then present an in-depth discussion of the structural characteristics of these proteins before focusing on PA binding proteins. For the purpose of this article, we consider RESPIRATORY BURST OXIDASE HOMOLOGUEs (RBOHs), the most documented PA target proteins. Finally, we present pioneering experiments that show, by different approaches such as monitoring of gene expression, use of pharmacological agents, ectopic over-expression of genes, and the creation of silenced mutants, that lipid signaling plays major roles in crop species. Finally, we present major open questions that require attention since we have only a perception of the peak of the iceberg when it comes to the exciting field of phospholipid signaling in plants.
Molding Process Retaining Gold Nanoparticle Assembly Structures during Transfer to a Polycarbonate Surface
Authors: Zimmermann Philipp , Schletz Daniel , Hoffmann Marisa , Probst Patrick T. , Fery Andreas , Nagel Jürgen
Published: May 2024

The immobilization of gold nanoparticle (AuNP) linear surface assemblies on polycarbonate (PC) melt surface via molding is investigated. The order of the particle assemblies is preserved during the molding process. The assemblies on PC exhibit plasmonic coupling features and dichroic properties. The structure of the assemblies is quantified based on Scanning Electron Microscopy (SEM) and image analysis data using an orientational order parameter. The transfer process from mold to melt shows high structural fidelity. The order parameter of around 0.98 reflects the orientation of the lines and remains unaffected, independent of the injection direction of the melt relative to the particle lines. This is discussed in the frame of fountain flow during injection molding. The particles were permanently fixed and withstood the injection molding process, detachment of the substrate, and extraction in boiling ethanol. The plasmonic particles coupled strongly within the dense nanoparticle lines to produce anisotropic optical properties, as quantified by dichroic ratios of 0.28 and 0.52 using ultraviolet–visible–near-infrared (UV–Vis–NIR) spectroscopy. AuNP line assemblies on a polymer surface may be a basis for plasmonic devices like surface-enhanced Raman scattering (SERS) sensors or a precursor for nanowires. Their embedding via injection molding constitutes an important link between particle-self-assembly approaches for optically functional surfaces and polymer processing techniques.
Preparation of a Flame-Retardant Curing Agent Based on Phytic Acid–Melamine Ion Crosslinking and Its Application in Wood Coatings
Authors: Wei An , Wang Shunxiang , Zou Yongjin , Xiang Cuili , Xu Fen , Sun Lixian
Published: May 2024

To broaden the applications of wood, it is necessary to prepare flame-retardant coatings that can protect wood substrates during combustion. In this study, a bio-based, intumescent, flame-retardant phytic acid–melamine polyelectrolyte (PM) was prepared using phosphorus-rich biomass phytic acid and nitrogen-rich melamine as raw materials through an ion crosslinking reaction. Subsequently, a series of bio-based, flame-retardant wood coatings were prepared by optimizing the structure of urea–formaldehyde resin with the addition of melamine, sodium lignosulfonate, and PM as a flame-retardant curing agent. Woods coated with PM-containing coatings displayed significantly improved flame-retardant performances in comparison to uncoated woods. For PM-cured woods, the measured values of total heat release and total smoke production were 91.51% and 57.80% lower, respectively, compared with those of uncoated wood. Furthermore, the fire growth index decreased by 97.32%, indicating a lower fire hazard. This increase in flame retardancy and smoke suppression performance is due to the dense expanded carbon layer formed during the combustion of the coating, which isolates oxygen and heat. In addition, the mechanical properties of the flame-retardant coatings cured with PM are similar to those cured with a commercial curing agent, NH4Cl. In addition, the prepared flame-retardant coating can also stain the wood. This study proves the excellent flame-retarding and curing effect of ammonium phytate in urea–formaldehyde resin coatings and provides a new approach for the application of bio-based flame retardants in wood coatings.
Assessment of the Micro-Tensile Bond Strength of a Novel Bioactive Dental Restorative Material (Surefil One)
Authors: Alghamdi Abdulrahman A. , Athamh Smaher , Alzhrani Reham , Filemban Hanan
Published: May 2024

Objectives: The aim of this study is to assess the micro-tensile bond strength and the mode of failure of a bioactive hybrid self-adhesive composite (Surefil one) under various dentin conditions. Methods: Thirty-two extracted human molar teeth were used to test the micro-tensile bond strength of Surefil one under different dentine conditions (no treatment, 37% phosphoric acid etching, and universal adhesive) in comparison with a resin-modified glass ionomer (RIVA). All restorations were light cure-bonded onto flat dentine and then sectioned into beams. Then, fractured specimens were observed under a light microscope to evaluate the mode of failure. Results: The Surefil one no-treatment group (NTG) exhibited the highest micro-tensile bond strength. Furthermore, there was no statistically significant difference observed between the Surefil one adhesive group (EAG) and the Surefil one acid etch group (EG). However, compared to other groups, the resin-modified glass ionomer (RIVA) produced the lowest results, which are statistically significant. Conclusion: Surefil one offers superior bond strength values when compared to resin-modified glass ionomers. Furthermore, Surefil one requires no dentin condition and has more straightforward clinical steps.
Stability and Biaxial Behavior of Fresh Cheese Coated with Nanoliposomes Encapsulating Grape Seed Tannins and Polysaccharides Using Immersion and Spray Methods
Authors: Monasterio Angela , Núñez Emerson , Verdugo Valeria , Osorio Fernando A.
Published: May 2024

In the food industry context, where fresh cheese stands out as a highly perishable product with a short shelf life, this study aimed to extend its preservation through multi-layer edible coatings. The overall objective was to analyze the biaxial behavior and texture of fresh cheese coated with nanoliposomes encapsulating grape seed tannins (NTs) and polysaccharides (hydroxypropyl methylcellulose; HPMC and kappa carrageenan; KC) using immersion and spray methods, establishing comparisons with uncoated cheeses and commercial samples, including an accelerated shelf-life study. NT, HPMC, and KC were employed as primary components in the multi-layer edible coatings, which were applied through immersion and spray. The results revealed significant improvements, such as a 20% reduction in weight loss and increased stability against oxidation, evidenced by a 30% lower peroxide index than the uncoated samples. These findings underscore the effectiveness of edible coatings in enhancing the quality and extending the shelf life of fresh cheese, highlighting the innovative application of nanoliposomes and polysaccharide blends and the relevance of applying this strategy in the food industry. In conclusion, this study provides a promising perspective for developing dairy products with improved properties, opening opportunities to meet market demands and enhance consumer acceptance.
Experimental Study of the Tensile Behavior of Structures Obtained by FDM 3D Printing Process
Authors: Ben hadj Hassine Salem , Chatti Sami , Louhichi Borhen , Seibi Abdennour
Published: May 2024

Fused Deposition Modelling (FDM) is one of the layer-based technologies that fall under the umbrella term “Additive Manufacturing”, where the desired part is created through the successive layer-by-layer addition process with high accuracy using computer-aided design data. Additive manufacturing technology, or as it is commonly known, 3D (three-dimensional) printing, is a rapidly growing sector of manufacturing that is incorporated in automotive, aerospace, biomedical, and many other fields. This work explores the impact of the Additive Manufacturing process on the mechanical proprieties of the fabricated part. To conduct this study, the 3D printed tensile specimens are designed according to the ASTM D638 standards and printed from a digital template file using the FDM 3D printer Raise3D N2. The material chosen for this 3D printing parameter optimization is Polylactic acid (PLA). The FDM process parameters that were studied in this work are the infill pattern, the infill density, and the infill cell orientation. These factors’ effects on the tensile behavior of printed parts were analyzed by the design of experiments method, using the statistical software MINITAB2020.
Efficient Alcoholysis of Poly(ethylene terephthalate) by Using Supercritical Carbon Dioxide as a Green Solvent
Authors: Xu Yiwei , Cui Ran , Han Yuqing , Jiang Jie , Hu Dongdong , Zhao Ling , Xi Zhenhao
Published: May 2024

In order to reduce the environmental impact of poly(ethylene terephthalate) (PET) plastic waste, supercritical fluids were used to facilitate effective recovery via improved solvent effects. This work focuses on the mechanisms of supercritical CO2 (ScCO2) during the alcoholysis processing of PET using systematic experiments and molecular dynamics (MD) simulations. The results of the alcoholysis experiment indicated that PET chips can be completely depolymerized within only an hour at 473 K assisted with ScCO2 at an optimal molar ratio of CO2/ethanol of 0.2. Random scission of PET dominates the early stage of the depolymerization reaction process, while specific scission dominates the following stage. Correspondingly, molecular dynamics (MD) simulations revealed that the solubilization and self-diffusion properties of ScCO2 facilitate the transportation of alcohol molecules into the bulk phase of PET, which leads to an accelerated diffusion of both oligomers and small molecules in the system. However, the presence of excessive CO2 has a negative impact on depolymerization by weakening the hydrogen bonding between polyester chain segments and ethanol, as well as decreasing the swelling degree of PET. These data provide a deep understanding of PET degradation by alcohols and the enhancement of ScCO2. It should be expected to achieve an efficient and high-yield depolymerization process of wasted polyesters assisted with ScCO2 at a relatively low temperature.
Recovery of High-Value Compounds from Yarrowia lipolytica IMUFRJ 50682 Using Autolysis and Acid Hydrolysis
Authors: da Silva Rhonyele Maciel , Ribeiro Bernardo Dias , Lemes Ailton Cesar , Coelho Maria Alice Zarur
Published: May 2024

This study aimed to evaluate the sequential hydrolysis of the biomass from unconventional and versatile Y. lipolytica to recover mannoproteins, carbohydrates, and other compounds as well as to determine the antioxidant activity of ultrafiltered fractions. The crude biomass underwent autolysis, and the resulting supernatant fraction was used for mannoprotein recovery via precipitation with ethanol. The precipitate obtained after autolysis underwent acid hydrolysis, and the resulting supernatant was ultrafiltered, precipitated, and characterized. The process yields were 55.5% and 46.14% for the crude biomass grown in glucose and glycerol, respectively. The mannoprotein with higher carbohydrate content (from crude biomass grown in glycerol) exhibited a higher emulsification index of 47.35% and thermal stability (60% weight loss). In contrast, the mannoprotein with higher protein content (from crude biomass grown in glucose) showed a better surface tension reduction of 44.50 mN/m. The technological properties showed that the crude biomass and the food ingredients are feasible to apply in food processing. The fractionation of the acid hydrolysis portion allowed the evaluation of the antioxidant power synergism among the components present in the hydrolysate, mostly the protein peptide chain. The sequential hydrolysis method is viable for extracting valuable products from Y. lipolytica.
Synthesis of Silver-Decorated Magnetite Nanoparticles Using Self-Assembly Methods
Authors: An Gye Seok
Published: May 2024

This study investigated the synthesis and functional characteristics of Fe3O4@Ag core–shell nanoparticles, focusing on the impact of amino functionalization on their structural and chemical properties. Utilizing self-assembly methods driven by electrostatic interactions, we achieved the effective adsorption of Ag nanoparticles into Fe3O4 cores previously modified with silane (APTES) or polymer (PEI) precursors. Our results elucidate how the type of amino precursor affects the surface charge and subsequent adsorption dynamics, revealing that PEI-modified Fe3O4 nanoparticles exhibit more substantial Ag nanoparticle adsorption than those modified with APTES. This enhanced adsorption was attributed to the higher density of the amine groups introduced by PEI, which also affected the electrostatic properties of the nanoparticles, as evidenced by their zeta-potential values. Moreover, this study highlighted the role of electrostatic attraction in the self-assembly process, facilitating a controlled synthesis environment that enhances the stability and functionality of nanoparticles for potential biomedical and catalytic applications. This research not only advances our understanding of nanoparticle behavior under different surface chemistries but also demonstrates the importance of surface engineering in optimizing nanoparticle performance for targeted applications.
Comparison of Tetraselmis suecica Cell Disruption Techniques: Kinetic Study and Extraction of Hydrosoluble Compounds
Authors: Rida Hussein , Peydecastaing Jérôme , Takache Hosni , Ismail Ali , Pontalier Pierre-Yves
Published: May 2024

The optimization of cell disruption is a critical step in microalgal biorefineries. We used the same batch of Tetraselmis suecica culture to compare two mechanical cell disruption techniques, focusing on the extraction yield of water-soluble molecules. The conditions for high-pressure homogenization (HPH) studied were two passes at a moderate pressure of 300 bars. For ultrasound (US) treatment, we used an amplitude of 20% (equivalent to 100 W) for 25 min. These conditions were chosen on the basis of a preliminary screen of extraction conditions. HPH extracted proteins and pigments more efficiently than US, whereas US was superior for uronic acid extraction. Interestingly, the two methods had similar extraction yields for carbohydrates under the studied conditions. We also analyzed the kinetics of molecule release by considering the centrifugation time lag for HPH and applying a first-order kinetic model for US. HPH outperformed US in terms of the immediate extraction and release of molecules.
Expanding the Application of Sentinel-2 Chlorophyll Monitoring across United States Lakes
Authors: Salls Wilson B. , Schaeffer Blake A. , Pahlevan Nima , Coffer Megan M. , Seegers Bridget N. , Werdell P. Jeremy , Ferriby Hannah , Stumpf Richard P. , Binding Caren E. , Keith Darryl J.
Published: May 2024

Eutrophication of inland lakes poses various societal and ecological threats, making water quality monitoring crucial. Satellites provide a comprehensive and cost-effective supplement to traditional in situ sampling. The Sentinel-2 MultiSpectral Instrument (S2 MSI) offers unique spectral bands positioned to quantify chlorophyll a, a water-quality and trophic-state indicator, along with fine spatial resolution, enabling the monitoring of small waterbodies. In this study, two algorithms—the Maximum Chlorophyll Index (MCI) and the Normalized Difference Chlorophyll Index (NDCI)—were applied to S2 MSI data. They were calibrated and validated using in situ chlorophyll a measurements for 103 lakes across the contiguous U.S. Both algorithms were tested using top-of-atmosphere reflectances (ρt), Rayleigh-corrected reflectances (ρs), and remote sensing reflectances (Rrs). MCI slightly outperformed NDCI across all reflectance products. MCI using ρt showed the best overall performance, with a mean absolute error factor of 2.08 and a mean bias factor of 1.15. Conversion of derived chlorophyll a to trophic state improved the potential for management applications, with 82% accuracy using a binary classification. We report algorithm-to-chlorophyll-a conversions that show potential for application across the U.S., demonstrating that S2 can serve as a monitoring tool for inland lakes across broad spatial scales.
A Low-Cost 3D SLAM System Integration of Autonomous Exploration Based on Fast-ICP Enhanced LiDAR-Inertial Odometry
Authors: Pang Conglin , Zhou Liqing , Huang Xianfeng
Published: May 2024

Advancements in robotics and mapping technology have spotlighted the development of Simultaneous Localization and Mapping (SLAM) systems as a key research area. However, the high cost of advanced SLAM systems poses a significant barrier to research and development in the field, while many low-cost SLAM systems, operating under resource constraints, fail to achieve high-precision real-time mapping and localization, rendering them unsuitable for practical applications. This paper introduces a cost-effective SLAM system design that maintains high performance while significantly reducing costs. Our approach utilizes economical components and efficient algorithms, addressing the high-cost barrier in the field. First, we developed a robust robotic platform based on a traditional four-wheeled vehicle structure, enhancing flexibility and load capacity. Then, we adapted the SLAM algorithm using the LiDAR-inertial Odometry framework coupled with the Fast Iterative Closest Point (ICP) algorithm to balance accuracy and real-time performance. Finally, we integrated the 3D multi-goal Rapidly exploring Random Tree (RRT) algorithm with Nonlinear Model Predictive Control (NMPC) for autonomous exploration in complex environments. Comprehensive experimental results confirm the system’s capability for real-time, autonomous navigation and mapping in intricate indoor settings, rivaling more expensive SLAM systems in accuracy and efficiency at a lower cost. Our research results are published as open access, facilitating greater accessibility and collaboration.
An Ontology for Describing Wind Lidar Concepts
Authors: Costa Francisco , Giyanani Ashim , Liu Dexing , Keane Aidan , Ratti Carlo Alberto , Clifton Andrew
Published: May 2024

This article reports on an open-source ontology that has been developed to establish an industry-wide consensus on wind lidar concepts and terminology. The article provides an introduction to wind lidar ontology, provides an overview of its development, and provides a summary of its aims and achievements. The ontology serves both reference and educational purposes for wind energy applications and lidar technology. The article provides an overview of the creation process, the outcomes of the project, and the proposed uses of the ontology. The ontology is available online and provides standardisation of terminology within the lidar knowledge domain. The open-source framework provides the basis for information sharing and integration within remote sensing science and fields of application.
The Performance of Landsat-8 and Landsat-9 Data for Water Body Extraction Based on Various Water Indices: A Comparative Analysis
Authors: Chen Jie , Wang Yankun , Wang Jingzhe , Zhang Yinghui , Xu Yue , Yang Ou , Zhang Rui , Wang Jing , Wang Zhensheng , Lu Feidong , Hu Zhongwen
Published: May 2024

The rapid and accurate extraction of water information from satellite imagery has been a crucial topic in remote sensing applications and has important value in water resources management, water environment monitoring, and disaster emergency management. Although the OLI-2 sensor onboard Landsat-9 is similar to the well-known OLI onboard Landsat-8, there were significant differences in the average absolute percentage change in the bands for water detection. Additionally, the performance of Landsat-9 in water body extraction is yet to be fully understood. Therefore, it is crucial to conduct comparative studies to evaluate the water extraction performance of Landsat-9 with Landsat-8. In this study, we analyze the performance of simultaneous Landsat-8 and Landsat-9 data for water body extraction based on eight common water indices (Normalized Difference Water Index (NDWI) and Modified Normalized Difference Water Index (MNDWI), Augmented Normalized Difference Water Index (ANDWI), Water Index 2015 (WI2015), tasseled cap wetness index (TCW), Automated Water Extraction Index for scenes with shadows (AWEIsh) and without shadows (AWEInsh) and Multi-Band Water Index (MBWI)) to extract water bodies in seven study sites worldwide. The Otsu algorithm is utilized to automatically determine the optimal segmentation threshold for water body extraction. The results showed that (1) Landsat-9 satellite data can be used for water body extraction effectively, with results consistent with those from Landsat-8. The eight selected water indices in this study are applicable to both Landsat-8 and Landsat-9 satellites. (2) The NDWI index shows a larger variability in accuracy compared to other indices when used on Landsat-8 and Landsat-9 imagery. Therefore, additional caution should be exercised when using the NDWI for water body analysis with both Landsat-8 and Landsat-9 satellites simultaneously. (3) For Landsat-8 and Landsat-9 imagery, ratio-based water indices tend to have more omission errors, while difference-based indices are more prone to commission errors. Overall, ratio-based indices exhibit greater variability in overall accuracy, whereas difference-based indices demonstrate lower sensitivity to variations in the study area, showing smaller overall accuracy fluctuations and higher robustness. This study can provide necessary references for the selection of water indices based on the newest Landsat-9 data. The results are crucial for guiding the combined use of Landsat-8 and Landsat-9 for global surface water mapping and understanding its long-term changes.
Geocomplexity Statistical Indicator to Enhance Multiclass Semantic Segmentation of Remotely Sensed Data with Less Sampling Bias
Authors: He Wei , Li Lianfa , Gao Xilin
Published: May 2024

Challenges in enhancing the multiclass segmentation of remotely sensed data include expensive and scarce labeled samples, complex geo-surface scenes, and resulting biases. The intricate nature of geographical surfaces, comprising varying elements and features, introduces significant complexity to the task of segmentation. The limited label data used to train segmentation models may exhibit biases due to imbalances or the inadequate representation of certain surface types or features. For applications like land use/cover monitoring, the assumption of evenly distributed simple random sampling may be not satisfied due to spatial stratified heterogeneity, introducing biases that can adversely impact the model’s ability to generalize effectively across diverse geographical areas. We introduced two statistical indicators to encode the complexity of geo-features under multiclass scenes and designed a corresponding optimal sampling scheme to select representative samples to reduce sampling bias during machine learning model training, especially that of deep learning models. The results of the complexity scores showed that the entropy-based and gray-based indicators effectively detected the complexity from geo-surface scenes: the entropy-based indicator was sensitive to the boundaries of different classes and the contours of geographical objects, while the Moran’s I indicator had a better performance in identifying the spatial structure information of geographical objects in remote sensing images. According to the complexity scores, the optimal sampling methods appropriately adapted the distribution of the training samples to the geo-context and enhanced their representativeness relative to the population. The single-score optimal sampling method achieved the highest improvement in DeepLab-V3 (increasing pixel accuracy by 0.3% and MIoU by 5.5%), and the multi-score optimal sampling method achieved the highest improvement in SegFormer (increasing ACC by 0.2% and MIoU by 2.4%). These findings carry significant implications for quantifying the complexity of geo-surface scenes and hence can enhance the semantic segmentation of high-resolution remote sensing images with less sampling bias.
Blind Edge-Retention Indicator for Assessing the Quality of Filtered (Pol)SAR Images Based on a Ratio Gradient Operator and Confidence Interval Estimation
Authors: Ma Xiaoshuang , Li Le , Wang Gang
Published: May 2024

Speckle reduction is a key preprocessing approach for the applications of Synthetic Aperture Radar (SAR) data. For many interpretation tasks, high-quality SAR images with a rich texture and structure information are useful. Therefore, a satisfactory SAR image filter should retain this information well after processing. Some quantitative assessment indicators have been presented to evaluate the edge-preservation capability of single-polarization SAR filters, among which the non-clean-reference-based (i.e., blind) ones are attractive. However, most of these indicators are derived based only on the basic fact that the speckle is a kind of multiplicative noise, and they do not take into account the detailed statistical distribution traits of SAR data, making the assessment not robust enough. Moreover, to our knowledge, there are no specific blind assessment indicators for fully Polarimetric SAR (PolSAR) filters up to now. In this paper, a blind assessment indicator based on an SAR Ratio Gradient Operator (RGO) and Confidence Interval Estimation (CIE) is proposed. The RGO is employed to quantify the edge gradient between two neighboring image patches in both the speckled and filtered data. A decision is then made as to whether the ratio gradient value in the filtered image is close to that in the unobserved clean image by considering the statistical traits of speckle and a CIE method. The proposed indicator is also extended to assess the PolSAR filters by transforming the polarimetric scattering matrix into a scalar which follows a Gamma distribution. Experiments on the simulated SAR dataset and three real-world SAR images acquired by ALOS-PALSAR, AirSAR, and TerraSAR-X validate the robustness and reliability of the proposed indicator.
DAMF-Net: Unsupervised Domain-Adaptive Multimodal Feature Fusion Method for Partial Point Cloud Registration
Authors: Zhao Haixia , Sun Jiaqi , Dong Bin
Published: May 2024

Current point cloud registration methods predominantly focus on extracting geometric information from point clouds. In certain scenarios, i.e., when the target objects to be registered contain a large number of repetitive planar structures, the point-only based methods struggle to extract distinctive features from the similar structures, which greatly limits the accuracy of registration. Moreover, the deep learning-based approaches achieve commendable results on public datasets, but they face challenges in generalizing to unseen few-shot datasets with significant domain differences from the training data, and that is especially common in industrial applications where samples are generally scarce. Moreover, existing registration methods can achieve high accuracy on complete point clouds. However, for partial point cloud registration, many methods are incapable of accurately identifying correspondences, making it challenging to estimate precise rigid transformations. This paper introduces a domain-adaptive multimodal feature fusion method for partial point cloud registration in an unsupervised manner, named DAMF-Net, that significantly addresses registration challenges in scenes dominated by repetitive planar structures, and it can generalize well-trained networks on public datasets to unseen few-shot datasets. Specifically, we first introduce a point-guided two-stage multimodal feature fusion module that utilizes the geometric information contained in point clouds to guide the texture information in images for preliminary and supplementary feature fusion. Secondly, we incorporate a gradient-inverse domain-aware module to construct a domain classifier in a generative adversarial manner, weakening the feature extractor’s ability to distinguish between source and target domain samples, thereby achieving generalization across different domains. Experiments on a public dataset and our industrial components dataset demonstrate that our method improves the registration accuracy in specific scenarios with numerous repetitive planar structures and achieves high accuracy on unseen few-shot datasets, compared with the results of state-of-the-art traditional and deep learning-based point cloud registration methods.
Time Phase Selection and Accuracy Analysis for Predicting Winter Wheat Yield Based on Time Series Vegetation Index
Authors: Wang Ziwen , Zhang Chuanmao , Gao Lixin , Fan Chengzhi , Xu Xuexin , Zhang Fangzhao , Zhou Yiming , Niu Fangpeng , Li Zhenhai
Published: May 2024

Winter wheat is one of the major cereal crops globally and one of the top three cereal crops in China. The precise forecasting of the yield of winter wheat holds significant importance in the realms of agricultural management and ensuring food security. The use of multi-temporal remote sensing data for crop yield prediction has gained increasing attention. Previous research primarily focused on utilizing remote sensing data from individual or a few growth stages as input parameters or integrated data across the entire growth period. However, a detailed analysis of the impact of different temporal combinations on the accuracy of yield prediction has not been extensively reported. In this study, we optimized the temporal sequence of growth stages using interpolation methods, constructed a yield prediction model incorporating the enhanced vegetation index (EVI) at different growth stages as input parameters, and employed a random forest (RF) algorithm. The results indicated that the RF model utilizing the EVI from all the temporal combinations throughout the growth period as input parameters accurately predicted the winter wheat yield with an R2 of the calibrated dataset exceeding 0.58 and an RMSE less than 1284 kg/ha. Among the 1023 yield models tested in this study with ten different growth stage combinations, the most accurate temporal combination comprised five stages corresponding to the regreening, erecting, jointing, heading, and filling stages, with an R2 of 0.81 and an RMSE of 1250 kg/ha and an NRMSE of 15%. We also observed a significant decrease in estimation accuracy when the number of growth stages was fewer than five and a certain degree of decline when the number exceeded five. Our findings confirmed the optimal number and combination of growth stages for the best yield prediction, providing substantial insights for winter wheat yield forecasting.
Ultrasound and Photoacoustic Imaging for the Guidance of Laser Ablation Procedures
Authors: John Samuel , Yan Yan , Abbasi Shirin , Mehrmohammadi Mohammad
Published: May 2024

The accuracy and efficacy of laser ablation procedures depend on the accurate placement of the laser applicator within the diseased tissue, monitoring the real-time temperature during the ablation procedure, and mapping the extent of the ablated region. Ultrasound (US) imaging has been widely used to guide ablation procedures. While US imaging offers significant advantages for guiding ablation procedures, its limitations include low imaging contrast, angular dependency, and limited ability to monitor the temperature. Photoacoustic (PA) imaging is a relatively new imaging modality that inherits the advantages of US imaging and offers enhanced capabilities for laser-guided ablations, such as accurate, angle-independent tracking of ablation catheters, the potential for quantitative thermometry, and monitoring thermal lesion formation. This work provides an overview of ultrasound-guided procedures and how different US-related artifacts limit their utility, followed by introducing PA as complementary to US as a solution to address the existing limitations and improve ablation outcomes. Furthermore, we highlight the integration of PA-driven features into existing US-guided laser ablation systems, along with their limitations and future outlooks. Integrated US/PA-guided laser ablation procedures can lead to safer and more precise treatment outcomes.
Prediction Accuracy of Soil Chemical Parameters by Field- and Laboratory-Obtained vis-NIR Spectra after External Parameter Orthogonalization
Authors: Metzger Konrad , Liebisch Frank , Herrera Juan M. , Guillaume Thomas , Bragazza Luca
Published: May 2024

One challenge in predicting soil parameters using in situ visible and near infrared spectroscopy is the distortion of the spectra due to soil moisture. External parameter orthogonalization (EPO) is a mathematical method to remove unwanted variability from spectra. We created two different EPO correction matrices based on the difference between spectra collected in situ and, respectively, spectra collected from the same soil samples after drying and sieving and after drying, sieving and finely grinding. Spectra from 134 soil samples recorded with two different spectrometers were split into calibration and validation sets and the two EPO corrections were applied. Clay, organic carbon and total nitrogen content were predicted by partial least squares regression for uncorrected and EPO-corrected spectra using models based on the same type of spectra (“within domain”) as well as using laboratory-based models to predict in situ collected spectra (“cross-domain”). Our results show that the within-domain prediction of clay is improved with EPO corrections only for the research grade spectrometer, with no improvement for the other parameters. For the cross-domain predictions, there was a positive effect from both EPO corrections on all parameters. Overall, we also found that in situ collected spectra provided an equally successful prediction as laboratory-based spectra.
A Study on the Relationship between Work-Related Health Problems and the Working Conditions of Electronics Industry Workers in South Korea
Authors: Won Sul A. , Choi Jae Wook , Kim Kyung Hee
Published: May 2024

Background: The electronics industry has characteristics, such as the continuous occurrence of new hazards and risk factors due to rapid technological changes, the occurrence of safety and health blind spots due to the outsourcing of work, trade secrets, and a lack of information, so research is needed from a safety and health perspective. This study sought to determine the relationship between work-related health problems and the working conditions of electronics industry workers. Methods: The study subjects were 3354 workers in the electronics industry from the raw data of the 4th to 6th Korean Working Condition Survey (KWCS). The variables were divided into general, occupational, and working environment characteristics, and a logistic regression analysis was conducted to determine the relationship between work-related health problems and working conditions. Results: Among the general and occupational characteristics, gender, education, night work, and working time appropriateness were analyzed as effect factors. In terms of working environment characteristics, physical work risk, musculoskeletal work risk, possibility of risk to work, subjective health impact, and working environment satisfaction were found to have effects on work-related health problems. Conclusions: The results of this study are meaningful in that they identified the effect factors of work-related health problems in the working conditions of workers in the electronics industry, where prior studies are lacking. Therefore, improvements, such as the management of night workers, appropriate working time, reduction in exposure to work risk factors, and improvement in working environment satisfaction, appear to be necessary.
Exploring Students’ and Teachers’ Insights on School-Based Disaster Risk Reduction and Safety: A Case Study of Western Morava Basin, Serbia
Authors: Cvetković Vladimir M. , Nikolić Neda , Lukić Tin
Published: May 2024

Integrated disaster risk reduction in schools represents a key component of safety strategies within the educational sector of every country. The aim of this study is to comprehensively explore the impact of various demographic and socio-economic factors on the perceptions of students and teachers regarding disaster risk reduction and safety in schools. This study is distinguished by its extensive empirical approach, employing a multistage random sampling method to conduct 850 face-to-face interviews (650 with students and 200 with teachers) throughout 2023 in 10 out of the total 18 municipalities in the Western Morava Basin of the Republic of Serbia. Two structured survey instruments were developed, incorporating a mix of qualitative (closed-ended) multiple-choice questions and five-point Likert scales. The research proposes two central hypotheses regarding school-based disaster risk reduction. Firstly, it suggests that gender, age, parent’s employment, academic achievement, living situation, parental education levels, and engagement with social media collectively influence students’ perspectives on this matter (H1–H8). Secondly, it posits that gender, age, marital status, parenthood, and educational background significantly impact teachers’ viewpoints on school-based disaster risk reduction (H1–H5). Multivariate linear regression was used to explore predictors of students’ and teachers’ insights on school-based disaster risk reduction. Various statistical tests, including Chi-square, t-tests, one-way ANOVA, and Pearson’s correlation, were employed to investigate the influence of demographic and socioeconomic factors on these insights. The results of multivariate regression analyses indicate that age, gender, and marital status emerge as the primary predictors across various facets of students’ and teachers’ insights on school-based disaster risk reduction, including awareness of disasters, disaster education activities, attitudes toward disaster risk reduction education, and enhancement of disaster information accessibility. The findings of this study provide comprehensive insights into the key factors influencing students’ and teachers’ perceptions of disaster risk reduction in schools. This research not only contributes to the academic discourse on disaster education but also serves as a foundational basis for improving educational programs, developing policies and strategies, refining normative educational frameworks, guiding teacher training, and informing further research in disaster education.
The Perception of Educators on Gender Equality: A Study in Ecuador
Authors: Díaz Verónica , Vallejos Henry , Oval Carmen , Carrasco Selin , Coloma Carmen , Flores Fabiola , Lozada Carola , Rivera Patricio , Simón María
Published: May 2024

Gender equality is a prerequisite for people-centered human development. This exploratory and descriptive study was conducted to investigate the perception of gender among Ecuadorian primary school teachers. Quantitative data were obtained from teachers in public and urban educational establishments. In order to collect the data, an online survey-type opinion questionnaire was applied with the aim of finding out teachers’ perception of gender equality within their institutions in educational establishments in the Republic of Ecuador, considering the dimensions of personal perceptions of the subject, the organizational culture, and the implementation of gender equality in the internal management of the educational organizations where they work. The results of the questionnaire show that teachers perceive that there is currently more receptiveness to gender issues than in the past. From the answers received, it can be concluded that people identify with gender issues, even though there is a need for elements and support to enable them to be expressed effectively, on a daily basis and sustained over time.
Preventing through Sustainability Education: Training and the Perception of Floods among School Children
Authors: Morote Álvaro-Francisco , Olcina Jorge
Published: May 2024

The global warming process is altering the atmospheric dynamics at mid-latitudes, fostering an increase in the frequency of extreme events. Of these events, floods are those that cause the greatest loss of human life and economic damage in Spain. Education is a key element in preventing these hazards. The objective of this study is to analyze the training, knowledge, and perception that school children (Primary and Secondary education, Baccalaureate) in the Region of Valencia (Spain) have of floods. The research was based on a questionnaire that was administered in different schools and in which 926 students participated. The main results show that only 36.1% have received training in these phenomena, and more than half (54.2%) do not know whether floods are explained in the textbooks. With respect to the perception of these risks, half of the respondents indicate that climate change is increasing the damage caused by floods, and 57.6% believe that it will accelerate them in the future. The study advances the knowledge necessary to establish learning contents and guidelines at the basic levels of education on natural hazards and disaster prevention.
The Sustainability of Island Tourism during Climate Change: The Case of Hawaii, United States
Authors: Craig Christopher A. , Oxarart Richard A.
Published: May 2024

The purpose of this study is to explore the sustainability of island tourism by investigating the increasingly dangerous climate conditions that tourists encounter under climate change. The exploratory case of Hawaii, United States is offered to explore how thermal stress risks have (1) changed as a process of climate change and (2) are associated with airport arrivals of domestic tourists from states other than Hawaii (dependent variable). Hawaii is a destination-based island in the lower-latitudes of the Pacific Ocean that attracts more tourists than many stand-alone countries. Thermal stress—our independent variable—is a climate measure for heat exposure and has medically prescribed levels (°C) for heat-related risks to recreationalists and workers. The findings indicate that (1) thermal stress is increasing as a process of climate change over the past four decades (1984–2023), (2) moderate thermal stress risks are positively related to tourists’ demand (2015–2023), and (3) moderate thermal stress risks are more strongly related to tourists’ demand than lower risk levels. The results suggest that despite increased thermal stress, the demand for tourists’ arrivals remains sustainable for Hawaii. Though not significant, we also observed a decline in tourists’ arrivals with moderately high thermal stress risks. Implications are broken down for each of the sustainable tourism stakeholders: visitors, communities, industry, and the natural environment.
Vertical Takeoff and Landing for Distribution of Parcels to Hospitals: A Case Study about Industry 5.0 Application in Israel’s Healthcare Arena
Authors: Naor Michael , Pinto Gavriel David , Davidov Pini , Cohen Yuval , Izchaki Linor , Hadieh Mukarram , Ghaith Malak
Published: May 2024

To gain a sustained competitive advantage, organizations such as UPS, Fedex, Amazon, etc., began to seek for industry 5.0 innovative autonomous delivery options for the last mile. Autonomous unmanned aerial vehicles are a promising alternative for the logistics industry. The fact that drones are propelled by green renewable energy source fits the companies’ need to become sustainable, replacing their fuel truck fleets, especially for traveling to remote rural locations to deliver small packages, but a major obstacle is the necessity for charging stations which is well documented in the literature. Therefore, the current research embarks on devising a novel yet practical piece of technology adopting the simplicity approach of direct flights to destinations. The analysis showcases the application for a network of warehouses and hospitals in Israel while controlling costs. Given the products in the case study are medical, direct flight has the potential to save lives when every moment counts. Hydrogen cell technology allows long-range flying without refueling, and it is both vibration-free which is essential for sensitive medical equipment and environmentally friendly in terms of air pollution and silence in urban areas. Importantly, hydrogen cells are lighter, with higher energy density than batteries, which makes them ideal for drone usage to reduce weight, maintain a longer life, and enable faster charging, all of which minimize downtime. Also, hydrogen sourcing is low-cost and unlimited compared to lithium-ion material which needs to be mined. The case study investigates an Israeli entrepreneurial company, Gadfin, which builds a vertical takeoff-and-landing-type of drone with folded wings that enable higher speed for the delivery of refrigerated medical cargo, blood, organs for transplant, and more to hospitals in partnership with the Israeli medical logistic conglomerate, SAREL. An analysis of shipping optimization (concerning the number and type of drone) is conducted using a mixed-integer linear programming technique based on various types of constraints such as traveling distance, parcel weight, the amount of flight controllers and daily number of flights allowed in order to not overcrowd the airspace. Importantly, the discussion assesses the ecosystem’s variety of risks and commensurate safety mechanisms for advancing a newly shaped landscape of drones in an Israeli tight airspace to establish a network of national routes for drone traffic. The conclusion of this research cautions limitations to overcome as the utilization of drones expand and offers future research avenues.