bluuebunny/crossref_metadata_2025 huggingface dataset:
Eco(toxicological) Assessment of the Neonicotinoid Formulation Actara® Using Planarian Girardia tigrina as Model Organism
Authors: Barbosa Rone S. , Rotili Eliane Aparecida , Santos Amanda Magda Almeida , Ribeiro Fabianne , Dornelas Aline Silvestre Pereira , Pereira Douglas Henrique , Cavallini Grasiele Soares , Soares Amadeu M. V. M. , Sarmento Renato Almeida , Gravato Carlos
Published: December 2022

Neonicotinoid pesticides are one of the most commercialized groups worldwide. Their application in agriculture aims to control pests through a systemic mode of action which is not specific to target species. Our study aimed to evaluate the effects of the insecticide Actara® [active ingredient thiamethoxam (TMX)] on a non-target species, Girardia tigrina. Therefore, acute and sublethal endpoints, such as mortality, feeding activity, locomotion and behavioral biomarkers were assessed. Actara® exerted low toxicity towards the planarian Girardia tigrina, showing a 96 h LC50 value of 77.6 mg TMX·L−1 (95% C.I: 74.1–81.2 mg TMX·L−1; R2 = 0.85). At the sublethal level, Actara® exerted no effect on regeneration of photoreceptors and auricles of planarians after 24 and 48 h post-exposure (NOEC > 7.8 mg TMX·L−1). The feeding rate of planarians was significantly increased by Actara®, but only at the highest tested concentration (LOEC = 7.8 mg TMX·L−1). Planarians showed to be less sensitive to the active ingredient TMX compared to other freshwater species. This might be explained by the presence of a high proportion of sub-types of acetylcholine receptors in planarians, exhibiting low binding affinity sites for TMX, an acetylcholine partial agonist. The comparison between effects induced by Actara® with the ones caused by other formulations, in planarians, might support our understanding of how other unknown ingredients can modify the uptake, and bioavailability of such substances, as well as the detoxification capacity of planarians, all of which influences toxicity.
Bio-Inspired Muco-Adhesive Polymers for Drug Delivery Applications
Authors: Jawadi Zina , Yang Christine , Haidar Ziyad S. , Santa Maria Peter L. , Massa Solange
Published: December 2022

Muco-adhesive drug delivery systems continue to be one of the most studied for controlled pharmacokinetics and pharmacodynamics. Briefly, muco-adhesive polymers, can be described as bio-polymers that adhere to the mucosal (mucus) surface layer, for an extended residency period of time at the site of application, by the help of interfacial forces resulting in improved drug delivery. When compared to traditional drug delivery systems, muco-adhesive carriers have the potential to enhance therapeutic performance and efficacy, locally and systematically, in oral, rectal, vaginal, amongst other routes. Yet, the achieving successful muco-adhesion in a novel polymeric drug delivery solution is a complex process involving key physico-chemico-mechanical parameters such as adsorption, wettability, polymer chain length, inter-penetration and cross-linking, to list a few. Hence, and in light of accruing progress, evidence and interest, during the last decade, this review aims to provide the reader with an overview of the theories, principles, properties, and underlying mechanisms of muco-adhesive polymers for pharmaceutics; from basics to design to characterization to optimization to evaluation to market. A special focus is devoted to recent advances incorporating bio-inspired polymers for designing controlled muco-adhesive drug delivery systems.
Additive Manufacturing of Polymer/Mg-Based Composites for Porous Tissue Scaffolds
Authors: Ali Fawad , Kalva Sumama , Koç Muammer
Published: December 2022

Due to their commercial availability, superior processability, and biocompatibility, polymers are frequently used to build three-dimensional (3D) porous scaffolds. The main issues limiting the widespread clinical use of monophasic polymer scaffolds in the bone healing process are their inadequate mechanical strength and inappropriate biodegradation. Due to their mechanical strength and biocompatibility, metal-based scaffolds have been used for various bone regenerative applications. However, due to the mismatch in mechanical properties and nondegradability, they lack integration with the host tissues, resulting in the production of fiber tissue and the release of toxic ions, posing a risk to the durability of scaffolds. Due to their natural degradability in the body, Mg and its alloys increasingly attract attention for orthopedic and cardiovascular applications. Incorporating Mg micro-nano-scale particles into biodegradable polymers dramatically improves scaffolds and implants’ strength, biocompatibility, and biodegradability. Polymer biodegradable implants also improve the quality of life, particularly for an aging society, by eliminating the secondary surgery often needed to remove permanent implants and significantly reducing healthcare costs. This paper reviews the suitability of various biodegradable polymer/Mg composites for bone tissue scaffolds and then summarizes the current status and challenges of polymer/magnesium composite scaffolds. In addition, this paper reviews the potential use of 3D printing, which has a unique design capability for developing complex structures with fewer material waste at a faster rate, and with a personalized and on-site fabrication possibility.
Linear and Nonlinear Elastic Properties of Polystyrene-Based Nanocomposites with Allotropic Carbon Fillers and Binary Mixtures
Authors: Belashov Andrey V. , Zhikhoreva Anna A. , Moskalyuk Olga A. , Beltukov Yaroslav M. , Semenova Irina V.
Published: December 2022

We report measurements of linear and nonlinear elastic properties of polystyrene-based nanocomposites with six types of nanofillers, including single and binary mixtures of allotropic carbon nanoparticles. Composite samples were fabricated by the same technology and contained the same filler concentration (5% wt.), which allowed for a direct comparison of their properties. It was shown that the most significant variations of linear and nonlinear elastic properties occur in different nanocomposites. In particular, the most pronounced enhancements of linear elastic moduli (in about 50%) obtained in tensile and flexural tests and in dynamic mechanical analysis were recorded in the sample filled with spherical fullerene nanoparticles. While the most profound rise of absolute values of nonlinear elastic moduli (tens of times) was obtained in the sample filled with the mixture of carbon nanotubes and graphene. The observed tendencies demonstrated the synergistic effect of fillers of different dimensionality on the elastic properties of nanocomposites.
Effects of Different Delignification and Drying Methods on Fiber Properties of Moso Bamboo
Authors: Bai Yifeng , Wang Wenqing , Zhang Yongyue , Wang Xiangwei , Wang Xinzhou , Shi Jiangtao
Published: December 2022

Bamboo has become an important kind of fibrous raw material in the world due to its fast-growing property and abundance of natural fiber. During the purification and utilization of bamboo fiber, the removal of lignin is vital and it is affected by the chemical treatment system and drying method. In this paper, the effects of three different delignification chemical systems and three drying methods (air drying, drying and freeze drying) on the physical and chemical properties of bamboo fiber were comparatively studied. The results prove that all three delignification techniques can effectively remove lignin from wood, and by utilizing peroxyformic acid and alkaline sodium sulfite, hemicellulose can be removed to a certain extent. With the selective removal of amorphous hemicellulose and lignin and the hydrolysis of cellulose molecular chains in amorphous regions, all three treatments contributed to an increase in the relative crystallinity of cellulose (ranging from 55% to 60%). Moreover, it was found that the drying methods exerted a certain influence on the mechanical properties of fiber. For instance, drying or air drying would improve the tensile strength of fiber significantly, approximately 2–3.5 times that of original bamboo fiber, and the tensile strength of the drying group reached 850–890 MPa. In addition, the alkaline sodium sulfite treatment had little effect on the thermal stability of bamboo fiber, resulting in high thermal stability of the prepared samples, and the residual mass reached 25–37%. On the contrary, the acetic acid/hydrogen peroxide method exerted great influence on the thermal stability of bamboo fiber, giving rise to a relatively poor thermal stability of prepared fibers, and the residual mass was only about 15%. Among the three drying methods, samples under air drying treatment had the highest residual mass, while those under freeze drying had the lowest. To summarize, the alkaline sodium sulfite method is more suitable for preparing bamboo fiber with higher tensile strength and thermal stability.
Self-Assembled TLR7/8 Agonist-Mannose Conjugate as An Effective Vaccine Adjuvant for SARS-CoV-2 RBD Trimer
Authors: Teng Changcai , Meng Xiongyan , Hu Yeqin , Mao Hongzhao , Li Huiting , Yang Jing , Sun Tiantian , Meng Shuai , Zong Chengli
Published: December 2022

Small synthetic TLR7/8-agonists can be used as vaccine adjuvants to enhance cell and humoral-mediated immune responses to specific antigens. Despite their potency, after local injection they can be dispersed to undesired body parts causing high reactogenicity, limiting their clinical applications. Here we describe a vaccination strategy that employs the covalent conjugate of a mannose and TLR7/8 agonist as a vaccine adjuvant to take advantage of mannose binding C-type lectins on dendritic cells to enhance the vaccine’s immunogenicity. The mannose-TLR7/8 agonist conjugate can self-assemble into nanoparticles with the hydrophilic mannose on the outside and hydrophobic TLR7/8 agonist inside. Although its ability to stimulate HEK-BlueTM hTLR7/8 cells dropped, it can efficiently stimulate mouse bone marrow-derived dendritic cells as indicated by the up-regulation of CD80 and CD86, and higher cytokine expression levels of TNF-α, IL6, and IL-12p70 than the native TLR7/8 agonist. In vivo, vaccination using the SARS-CoV-2 RBD trimer as the antigen and the conjugate as the adjuvant induced a significantly higher amount of IgG2a. These results suggest that the mannose-TLR7/8-agonist conjugate can be used as an effective vaccine adjuvant.
Evaluation of the Shape Memory Effect of Random and Aligned Electrospun Polyurethane Mats with Different Fibers Diameter
Authors: Kijeńska-Gawrońska Ewa , Maliszewski Adrian , Bil Monika
Published: December 2022

Fibrous shape memory scaffolds composed of thermoplastic polyurethane based on a mixture of polycaprolactone diols were fabricated. The effect of the fiber diameter and arrangement– random (rPU) or aligned (aPU), on crystallinity, mechanical properties, and shape memory was analyzed. The diameters of the fibers were controlled by changing the concentration of polyurethane (PU) solutions in the range of 5% to 16% and fibers alignment by utilization of different collectors. The chemical structure was confirmed by Fourier Transformed Infrared spectroscopy (FTIR), crystallinity was evaluated based on differential scanning calorimetry (DSC,) and mechanical properties were measured by the tensile test. Additionally, shape memory programming was performed using a dynamic mechanical analyzer (DMA), and shape recovery was evaluated in the air and in the water environment. DSC results showed that the electrospinning process did not change the crystallinity or melting temperature of synthesized thermoplastic polyurethanes. The melting temperature of the crystalline switching segments was around 26–27 °C, and the crystalline phase of hard segments was around 130 °C. Shape memory properties were analyzed in the contest of the fiber diameter and alignment of the fibers, while changes in the fibers’ diameters from 360 nm to 1760 nm did not result in significant changes in shape recovery coefficient (Rr) especially evaluated in the air. The longitudinal fiber alignment enhanced mechanical and shape recovery to up to 96% for aPU, with the highest fiber diameter when evaluated in water.
Effects of Cavity Thickness on the Replication of Micro Injection Molded Parts with Microstructure Array
Authors: Jiang Shaofei , Zhang Yuansong , Ma Haowei , Zha Xiaoqiang , Peng Xiang , Li Jiquan , Lu Chunfu
Published: December 2022

Parts with microstructure arrays have been widely used in biotechnologies and optical technologies, and their performances are affected by replication uniformity. The uniformity of the microstructure is still a challenge in micro-injection molded parts and is greatly affected by the cavity thickness and process parameters. In this study, the replication uniformity of microstructures is experimentally investigated. The relationship between the replication uniformity and cavity thickness was explored through single-factor experiments. Additionally, the impacts of the process parameters on the replication uniformity were also studied through uniform design experiments. A regression equation was established to describe the quantitative relationship between the important parameters and replication uniformity. The results showed that the replication uniformity of microstructures increases by 39.82% between the cavity with the thickness of 0.5 mm and a cavity of 0.7 mm. In addition, holding time is the most significant factor influencing the replication uniformity, followed by mold temperature, melt temperature, and injection speed. It is concluded that the thickness of cavity and the process parameters have significant influence on the replication uniformity. The experimental results provide important data on how to improve the replication uniformity of parts with microstructure arrays.
Investigations on Adhesion Characteristics between High-Content Rubberized Asphalt and Aggregates
Authors: Wang Xiaofeng , Liu Jianan , Wang Zhenjun , Jing Haosen , Yang Bo
Published: December 2022

The use of waste tires to prepare rubberized asphalt has been a hot trend in recent years, and the characteristics of adhesion between rubberized asphalt and aggregates are important factors affecting the performance of asphalt pavement. However, there is a lack of uniform results on the adhesion characteristics of rubberized asphalt. Therefore, crumb-rubber-modified asphalt (CRMA) with 15%, 20%, and 25% rubber contents was prepared in this work, and the basic rheological parameters and cohesive energy of the rubberized asphalt were characterized by DSR. The adhesion properties between rubberized asphalt and aggregates were characterized based on macroscopic binder bond strength (BBS), surface free energy (SFE) theory, and nanoscale atomic force microscopy (AFM) tests. The results show that crumb rubber (CR) can improve the high-temperature elastic properties of asphalt; secondly, CR can have a negative impact on the maximum tensile strength of asphalt and aggregates. CR can improve the SFE parameter of asphalt. The work of adhesion of rubberized asphalt and limestone is the highest, followed by basalt and, finally, granite. Finally, CR can cause the catanaphase in asphalt to gradually break down and become smaller, and the adhesion of rubberized asphalt can be reduced. Overall, CR can reduce the adhesion performance of asphalt, and this work provides a reference for the application of rubberized asphalt.
Unsteady Mass Transfer in Bubble Wakes Analyzed by Lagrangian Coherent Structures in a Flat-Bed Reactor
Authors: Kursula Lotta , Kexel Felix , Fitschen Jürgen , Hoffmann Marko , Schlüter Michael , von Kameke Alexandra
Published: December 2022

To increase the yield and selectivity in reactive bubbly flows, the gas-liquid interactions have to be understood in depth. In the current fundamental study, flow and concentration data of the wakes of two-dimensional bubbles in an organic solvent are obtained experimentally in a flat-bed reactor. The unsteady mass transport phenomena in these turbulent wakes of two freely rising, two-dimensional bubbles with bubble Reynolds numbers Re=949 and Re=388 are evaluated by analyzing Lagrangian Coherent Structures (LCS). To reveal how LCS govern the transport of dissolved gas in bubble wakes, and therefore affect gas-liquid reactions, LCS in two-dimensional velocity fields are computed and compared with concentration fields of dissolved gas. The analysis of backward Finite Time Lyapunov Exponent (bFTLE) fields reveals coherent fluid dynamic structures for both bubble Reynolds numbers studied. In the higher bubble Reynolds number case, two types of coherent structures are found, which hinder the mixing of the dissolved gas and the liquid bulk. Repelling LCS are found to enclose parcels transported into the vortices, and indicate thus, which fluid parcels can possibly take part in chemical reactions. Due to higher mixing, unveiled by details from the LCS and FTLE analyses, and therefore increased contact area between dissolved gas and fresh liquid, higher yields of reaction products are suggested for the lower bubble Reynolds number case in this two-dimensional study. This is contradicting the rule of thumb that mixing increases for higher bubble Reynolds numbers.
Insight into the Acidity and Catalytic Performance on Butane Isomerization of Thermal Stable Sulfated Monoclinic Zirconia
Authors: Huang Daofeng , Feng Wenhua , Zhang Li , Yue Bin , He Heyong
Published: December 2022

Sulfated monoclinic zirconia (M-SZ) with high thermal stability and high catalytic performance on butane isomerization were obtained by hydrothermal method followed with sulfation treatment. The acidity of M-SZ was studied by 31P MAS NMR, with trimethylphosphine (TMP) as the probe molecule, and the catalytic performance of 1-13C-n-butane over M-SZ was monitored by 13C MAS NMR spectroscopy. Both Brønsted and Lewis acids were observed on M-SZ. Only Brønsted acid strength shows close relation to the activation energy of butane isomerization, and the M-SZ catalyst with the strongest Brønsted acid strength shows the lowest activation energy of 46.4 kJ·mol−1. The catalytic stability tests were evaluated at 673 K for 240 h, which shows that sulfated monoclinic zirconia has higher thermal stability than sulfated tetragonal zirconia.
Simulated Handling to Investigate the Effect of Mechanical Damage on Stored Pomegranate Fruit
Authors: Pathare Pankaj , Al-Dairi Mai , Al-Yahyai Rashid , Al-Mahdouri Adil
Published: December 2022

Mechanical damage is a threat to both food security and sustainability. Bruising is the most common type of mechanical damage, and it causes a huge economic loss due to rejection of fresh produce and downgrading of the appearance quality by consumers. Therefore, this study aims to examine the effect of bruising during postharvest handling using a pendulum test technique. Pomegranate fruit were bruised once at two impact levels (1.189 ± 0.109 and 2.298 ± 0.239 J) and then stored (at 5 °C ± 1 °C and 22 °C ± 1 °C) for 28 days. The study evaluated the effect of impact bruising, storage temperature, and duration on the bruise magnitude and quality attributes of the bruised and non-bruised pomegranates. The results showed that the investigated factors affect the bruise size of bruised pomegranates. Increasing storage temperature from 5 to 22 °C and impact level from 1.189 to 2.298 J increased the bruise area, bruise volume, and bruise susceptibility over time. Alterations in total soluble solids (TSS) and titratable acidity (TA%) were statistically (p < 0.05) induced by bruising, particularly at a high impact. The total soluble solids (TSS) content was reduced in all tested pomegranate fruit (bruised and non-bruised) and recorded the highest percentage decline in those impacted at a high level and stored at 22 °C, at 16.81%. The combination of both studied factors did not affect the water activity (Aw) of aril or the mesocarp of bruised or non-bruised fruit. Bruising parameters and quality attributes were strongly correlated in this study, excluding water activity (Aw). The regression models showed a good determination coefficient (R2) between the predicted and measured values of bruise susceptibility (BS), total soluble solids (TSS), titratable acidity (TA%), and sugar: acid ratio (TSS:TA). The study demonstrates that bruising at a high impact level and long-term storage both affected the susceptibility of pomegranates to bruise, and altered fruit quality. Thus, these factors need to be considered during the postharvest supply chain.
Effects of Reynolds Number on the Overall Characteristics of Flow and Heat Transfer in the Long Micro-Tube with Dimples
Authors: Qi Ruibai , Chen Fangfang , Xu Linjie , Yu Jiawen , Chen Xiaoping
Published: December 2022

The flow and heat transfer characteristics in micro-tube are very important research fields. In order to study the influence of the Reynolds number on the overall characteristics of flow and heat transfer, a 510 mm long micro-tube with dimples was constructed. Thirty-five different working conditions are considered, and the Reynolds number is between 1500 and 8100. At the same time, the change of physical properties of coolant with temperature is considered. The reliability of numerical simulation results is verified by the grid independence verification and the comparison of experimental data. The results show that the dimple will promote the instability of the flow state and improve the heat transfer efficiency. The flow state evolution formed in the stream-wise direction is basically similar, and its fully developed position remains at 76 mm, which is independent of the Reynolds number. For the fully developed region, the partial derivation of static pressure in the stream-wise component has a quasi-linear relationship with the stream-wise direction. With the development in the stream-wise direction, the partial derivation of static temperature in the steam-wise component decreases. With the increase of Reynolds number, the friction factor f and heat transfer factor j decrease. The ratio of factor heat transfer f to Reynolds number relative collapses for the different cases, especially when the Reynolds number is larger than 3800. The heat transfer factor j is an exponential function of the Reynolds number and decreases with the increased Reynolds number.
Forecasting Bitcoin Volatility Using Hybrid GARCH Models with Machine Learning
Authors: Zahid Mamoona , Iqbal Farhat , Koutmos Dimitrios
Published: December 2022

The time series movements of Bitcoin prices are commonly characterized as highly nonlinear and volatile in nature across economic periods, when compared to the characteristics of traditional asset classes, such as equities and commodities. From a risk management perspective, such behaviors pose challenges, given the difficulty in quantifying and modeling Bitcoin’s price volatility. In this study, we propose hybrid analytical techniques that combine the strengths of the non-stationary properties of Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models with the nonlinear modeling capabilities of deep learning algorithms, such as Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Bidirectional LSTM (BiLSTM) algorithms with single, double, and triple layer network architectures to forecast Bitcoin’s realized price volatility. Our findings, both in-sample and out-of-sample, show that such hybrid models can generate accurate forecasts of Bitcoin’s price volatility.
PatchMask: A Data Augmentation Strategy with Gaussian Noise in Hyperspectral Images
Authors: Dou Hong-Xia , Lu Xing-Shun , Wang Chao , Shen Hao-Zhen , Zhuo Yu-Wei , Deng Liang-Jian
Published: December 2022

Data augmentation (DA) is an effective way to enrich the richness of data and improve a model’s generalization ability. It has been widely used in many advanced vision tasks (e.g., classification, recognition, etc.), while it can hardly be seen in hyperspectral image (HSI) tasks. In this paper, we analyze whether existing augmentation methods are suitable for the task of HSI denoising and find that the biggest challenge lies in neither losing the spatial information of the original image nor destroying the correlation between the various bands for HSI denoising. Based on this, a new data augmentation method named PatchMask is proposed, which makes the training samples as diverse as possible while preserving the spatial and spectral information. The training data augmented by this method are somewhere between clear and noisy, which can make the network learn more effectively and generalize. Experiments demonstrate that our method outperforms other data augmentation methods, such as the benchmark CutBlur, in enhancing HSI denoising. In addition, the given DA method was used on several popular denoising networks, such as QRNN3D, DnCNN, MPRnet, CBDNet, and HSID-CNN, to verify the effectiveness of the proposed method. The results show that the given DA could increase the value of the PSNR by 0.2∼0.5 dB in various examples.
Slip Models of the 2016 and 2022 Menyuan, China, Earthquakes, Illustrating Regional Tectonic Structures
Authors: Wu Donglin , Qu Chunyan , Zhao Dezheng , Shan Xinjian , Chen Han
Published: December 2022

As one of the large-scale block-bounding faults in the northeastern Tibetan Plateau, the Qilian-Haiyuan fault system accommodates a large portion of north-eastward motion of the Tibetan Plateau. In 2016 and 2022, two strong earthquakes of Mw6.0 and Mw6.6 occurred in the Menyuan area near the Lenglongling fault (LLLF) at the western segment of the Qilian-Haiyuan fault. These two adjoining events, only 40 km apart, exhibited notable differences in focal mechanisms and rupture kinematics, indicating complex fault geometries and tectonic structures in the region, which are still poorly known. Here, we obtained an interseismic velocity map spanning 2014–2020 in the Menyuan region using Sentinel-1 InSAR data to probe strain accumulation across the LLLF. We obtained the coseismic deformation fields of the two Menyuan earthquakes using InSAR data and inverted out their slip distributions. We calculated the Coulomb stress changes to examine the interactions and triggering relationship between two ruptures and to access regional seismic potential. We found that the 2016 earthquake was a buried thrust event that occurred on the northern LLLF, whilst the 2022 earthquake was a left-lateral strike-slip event that occurred on the western end of the LLLF. We indicated there may be no direct triggering relationship between two spatiotemporally adjacent earthquakes. However, the 2022 earthquake caused a remarkable stress perturbation to the surrounding area. Particularly, a large area with notable stress increase stands out along the Tuolaishan fault and the LLLF, likely posing a high seismic hazard in the region.
CARNet: Context-Aware Residual Learning for JPEG-LS Compressed Remote Sensing Image Restoration
Authors: Liu Maomei , Tang Lei , Fan Lijia , Zhong Sheng , Luo Hangzai , Peng Jinye
Published: December 2022

JPEG-LS (a lossless (LS) compression standard developed by the Joint Photographic Expert Group) compressed image restoration is a significant problem in remote sensing applications. It faces the following two challenges: first, bridging small pixel-value gaps from wide numerical ranges; and second, removing banding artifacts in the condition of lacking available context information. As far as we know, there is currently no research dealing with the above issues. Hence, we develop this initial line of work on JPEG-LS compressed remote sensing image restoration. We propose a novel CNN model called CARNet. Its core idea is a context-aware residual learning mechanism. Specifically, it realizes residual learning for accurate restoration by adopting a scale-invariant baseline. It enables large receptive fields for banding artifact removal through a context-aware scheme. Additionally, it eases the information flow among stages by utilizing a prior-guided feature-fusion mechanism. Alternatively, we design novel R IQA models to provide a better restoration performance assessment for our study by utilizing gradient priors of JPEG-LS banding artifacts. Furthermore, we prepare a new dataset of JPEG-LS compressed remote sensing images to supplement existing benchmark data. Experiments show that our method sets the state-of-the-art for JPEG-LS compressed remote sensing image restoration.
Near-Field 3D Sparse SAR Direct Imaging with Irregular Samples
Authors: Xing Shiqi , Song Shaoqiu , Quan Sinong , Sun Dou , Wang Junpeng , Li Yongzhen
Published: December 2022

Sparse imaging is widely used in synthetic aperture radar (SAR) imaging. Compared with the traditional matched filtering (MF) methods, sparse SAR imaging can directly image the scattered points of a target and effectively reduce the sidelobes and clutter in irregular samples. However, in view of the large-scale computational complexity of sparse reconstruction with raw echo data, traditional sparse reconstruction algorithms often require huge computational expense. To solve the above problems, in this paper, we propose a 3D near-field sparse SAR direct imaging algorithm for irregular trajectories, adopting a piece of preliminary information in the SAR image to update the dictionary matrix dimension, using the Gaussian iterative method, and optimizing the signal-processing techniques, which can achieve 3D sparse reconstruction in a more direct and rapid manner. The proposed algorithm was validated through simulations and empirical study of irregular scanning scenarios and compared with traditional MF and sparse reconstruction methods, and was shown to significantly reduce the computation time and effectively preserve the complex information of the scenes to achieve high-resolution image reconstruction.
Glacial Outburst Floods Responsible for Major Environmental Shift in Arctic Coastal Catchment, Rekvedbukta, Albert I Land, Svalbard
Authors: Wołoszyn Aleksandra , Owczarek Zofia , Wieczorek Iwo , Kasprzak Marek , Strzelecki Mateusz C.
Published: December 2022

Small Arctic coastal catchments and coastal lagoon systems are some of the most vulnerable to climate change. Glacial retreat and the development of glacial lakes and drainage systems provide opportunities for hazardous events such as GLOFs. We observe that the stability of lagoons and their associated barriers are controlled by the frequency and magnitude of storms approaching the coasts, access to sediment supplies and resilience to sea-level rise. Based on multidecadal remote sensing data, we were able to identify the rate of glacial recession, the development of glacial lakes, vegetation response to climate change and a GLOF event, and shoreline and lagoon responses to the environmental shifts within the small catchment. Here we present an example of lagoon system evolution where a glacial outburst flood exerted significant control over lagoon drainage and coastal barrier stability.
What the Upper Atmospheres of Giant Planets Reveal
Authors: O’Donoghue James , Stallard Tom
Published: December 2022

The upper atmospheres of the Giant Planets, Jupiter, Saturn, Uranus and Neptune are transition regions between meteorological layers and outer space. As a result of their exceptionally rarefied nature, they are highly sensitive and therefore revealing probes of the forcing exerted both from above and below. This review provides an overview of these upper atmospheres and the major processes that take place within them, including their powerful auroras, the giant planet ‘energy crisis’ and the decay of Saturn’s rings into the planet. We discuss the many remote-sensing tools that have been used to understand them, for example, large ground-based observatories such as the Keck telescope, space-based observatories such as the Hubble Space Telescope and orbiters such as the Cassini spacecraft. Looking into the future, we discuss the possibilities afforded by the latest and next generation of observatories and space missions, such as the James Webb Space Telescope.
Identifying Potential Landslides on Giant Niexia Slope (China) Based on Integrated Multi-Remote Sensing Technologies
Authors: Dong Xiujun , Yin Tao , Dai Keren , Pirasteh Saied , Zhuo Guanchen , Li Zhiyu , Yu Bing , Xu Qiang
Published: December 2022

The Niexia slope, located in Danba County, Sichuan Province, China, with steep slope terrain and dense vegetation coverage, has a height difference of about 3002 m. A traditional manual survey cannot be performed here, and single remote sensing technology is not comprehensive enough to identify potential landslides on such high and steep slopes. In this paper, an integrated approach with multi-remote sensing techniques was proposed to identify potential landslides of the Niexia slope, which combined Interferometry Synthetic Aperture Radar (InSAR), airborne Light Detection and Ranging (LiDAR), and optical remote sensing technologies. InSAR technology was used to monitor the small displacements of the whole slope, and three potential landslides on Niexia slope were identified. The maximum cumulative displacement reached up to 11.9 cm over 1 year. Subsequently, high-resolution optical remote sensing images acquired by remote sensing satellites and a Digital Elevation Model (DEM) without vegetation influence obtained by LiDAR were used to finely interpret the sign of landslide micro-geomorphology and to determine the potential landslide geometry boundaries. As a result, four and nine potential landslides with landslide micro-geomorphic features were identified, respectively. Finally, the identification results of the three techniques were fused and analyzed to assess the potential landslides on the Niexia slope. We compared the results from multi-remote sensing technologies, showing that the three techniques have advantages and disadvantages in terms of monitoring objects, monitoring range, and monitoring accuracy. The integrated use of these three technologies can identify and monitor potential landslides more comprehensively, which could play an important role in the future.
Three-Dimensional Digital Image Correlation Based on Speckle Pattern Projection for Non-Invasive Vibrational Analysis
Authors: Souto Janeiro Alvaro , Fernández López Antonio , Chimeno Manguan Marcos , Pérez-Merino Pablo
Published: December 2022

Non-contact vibration measurements are relevant for non-invasively characterizing the mechanical behavior of structures. This paper presents a novel methodology for full-field vibrational analysis at high frequencies using the three-dimensional digital image correlation technique combined with the projection of a speckle pattern. The method includes stereo calibration and image processing routines for accurate three-dimensional data acquisition. Quantitative analysis allows the extraction of several deformation parameters, such as the cross-correlation coefficients, shape and intensity, as well as the out-of-plane displacement fields and mode shapes. The potential of the methodology is demonstrated on an Unmanned Aerial Vehicle wing made of composite material, followed by experimental validation with reference accelerometers. The results obtained with the projected three-dimensional digital image correlation show a percentage of error below 5% compared with the measures of accelerometers, achieving, therefore, high sensitivity to detect the dynamic modes in structures made of composite material.
GradFreeBits: Gradient-Free Bit Allocation for Mixed-Precision Neural Networks
Authors: Bodner Benjamin Jacob , Ben-Shalom Gil , Treister Eran
Published: December 2022

Quantized neural networks (QNNs) are among the main approaches for deploying deep neural networks on low-resource edge devices. Training QNNs using different levels of precision throughout the network (mixed-precision quantization) typically achieves superior trade-offs between performance and computational load. However, optimizing the different precision levels of QNNs can be complicated, as the values of the bit allocations are discrete and difficult to differentiate for. Moreover, adequately accounting for the dependencies between the bit allocation of different layers is not straightforward. To meet these challenges, in this work, we propose GradFreeBits: a novel joint optimization scheme for training mixed-precision QNNs, which alternates between gradient-based optimization for the weights and gradient-free optimization for the bit allocation. Our method achieves a better or on par performance with the current state-of-the-art low-precision classification networks on CIFAR10/100 and ImageNet, semantic segmentation networks on Cityscapes, and several graph neural networks benchmarks. Furthermore, our approach can be extended to a variety of other applications involving neural networks used in conjunction with parameters that are difficult to optimize for.
A New Orientation Detection Method for Tilting Insulators Incorporating Angle Regression and Priori Constraints
Authors: Zhao Jianli , Liu Liangshuai , Chen Ze , Ji Yanpeng , Feng Haiyan
Published: December 2022

The accurate detection of insulators is an important prerequisite for insulator fault diagnosis. To solve the problem of background interference and overlap caused by the axis-aligned bounding boxes in the tilting insulator detection tasks, we construct an improved detection architecture according to the scale and tilt features of the insulators from several perspectives, such as bounding box representation, loss function, and anchor box construction. A new orientation detection method for tilting insulators based on angle regression and priori constraints is put forward in this paper. Ablation tests and comparative validation tests were conducted on a self-built aerial insulator image dataset. The results show that the detection accuracy of our model was increased by 7.98% compared with that of the baseline, and the overall detection accuracy reached 82.33%. Moreover, the detection effect of our method was better than that of the YOLOv5 detection model and other orientation detection models. Our model provides a new idea for the accurate orientation detection of insulators.
Determination of the Critical Speed of a Cracked Shaft from Experimental Data
Authors: Muñoz-Abella Belén , Montero Laura , Rubio Patricia , Rubio Lourdes
Published: December 2022

In this work, a procedure to obtain an accurate value of the critical speed of a cracked shaft is presented. The method is based on the transversal displacements of the cracked section when the shaft is rotating at submultiples of the critical speed. The SERR (Strain Energy Ralease Rate) theory and the CCL (Crack Closure Line) approach are used to analyse the proposed methodology for considering the behaviour of the crack. In order to obtain the best information and to define the procedure, the orbits and the frequency spectra at different subcritical rotational speed intervals are analyzed by means of the Fast Fourier Transform. The comparison of the maximum values of the FFT peaks within the intervals allows the subcritical speed to be determined, along with the value of the critical speed. When verified, the proposed procedure is applied to shafts with the same geometry and material and with cracks of increasing depth. The results show that the critical speed diminishes with the severity of the crack, as expected. A comparison is made between the critical speed obtained using the vertical and the horizontal displacements, finding no remarkable differences, meaning that in practical applications only one sensor for one of the displacements (in the vertical or horizontal direction) is needed to determine the critical speed. This is one of the main contributions of the paper, as it means that the orbits of the shaft are not needed. Finally, after this study we can conclude that the best results are achieved when the critical speed is obtained using data displacement in only one direction within the intervals around 12 or 13 of the critical speed.
Utilizing Smartphones for Approachable IoT Education in K-12
Authors: Jean Devin , Broll Brian , Stein Gordon , Lédeczi Ákos
Published: December 2022

Distributed computing, computer networking, and the Internet of Things (IoT) are all around us, yet only computer science and engineering majors learn the technologies that enable our modern lives. This paper introduces PhoneIoT, a mobile app that makes it possible to teach some of the basic concepts of distributed computation and networked sensing to novices. PhoneIoT turns mobile phones and tablets into IoT devices and makes it possible to create highly engaging projects through NetsBlox, an open-source block-based programming environment focused on teaching distributed computing at the high school level. PhoneIoT lets NetsBlox programs—running in the browser on the student’s computer—access available sensors. Since phones have touchscreens, PhoneIoT also allows building a Graphical User Interface (GUI) remotely from NetsBlox, which can be set to trigger custom code written by the student via NetsBlox’s message system. This approach enables students to create quite advanced distributed projects, such as turning their phone into a game controller or tracking their exercise on top of an interactive Google Maps background with just a few blocks of code.
Mechanocardiography-Based Measurement System Indicating Changes in Heart Failure Patients during Hospital Admission and Discharge
Authors: Koivisto Tero , Lahdenoja Olli , Hurnanen Tero , Koskinen Juho , Jafarian Kamal , Vasankari Tuija , Jaakkola Samuli , Kiviniemi Tuomas O. , Airaksinen K. E. Juhani
Published: December 2022

Heart failure (HF) is a disease related to impaired performance of the heart and is a significant cause of mortality and treatment costs in the world. During its progression, HF causes worsening (decompensation) periods which generally require hospital care. In order to reduce the suffering of the patients and the treatment cost, avoiding unnecessary hospital visits is essential, as hospitalization can be prevented by medication. We have developed a data-collection device that includes a high-quality 3-axis accelerometer and 3-axis gyroscope and a single-lead ECG. This allows gathering ECG synchronized data utilizing seismo- and gyrocardiography (SCG, GCG, jointly mechanocardiography, MCG) and comparing the signals of HF patients in acute decompensation state (hospital admission) and compensated condition (hospital discharge). In the MECHANO-HF study, we gathered data from 20 patients, who each had admission and discharge measurements. In order to avoid overfitting, we used only features developed beforehand and selected features that were not outliers. As a result, we found three important signs indicating the worsening of the disease: an increase in signal RMS (root-mean-square) strength (across SCG and GCG), an increase in the strength of the third heart sound (S3), and a decrease in signal stability around the first heart sound (S1). The best individual feature (S3) alone was able to separate the recordings, giving 85.0% accuracy and 90.9% accuracy regarding all signals and signals with sinus rhythm only, respectively. These observations pave the way to implement solutions for patient self-screening of the HF using serial measurements.
Iterative Online 3D Reconstruction from RGB Images
Authors: Cardoen Thorsten , Leroux Sam , Simoens Pieter
Published: December 2022

3D reconstruction is the computer vision task of reconstructing the 3D shape of an object from multiple 2D images. Most existing algorithms for this task are designed for offline settings, producing a single reconstruction from a batch of images taken from diverse viewpoints. Alongside reconstruction accuracy, additional considerations arise when 3D reconstructions are used in real-time processing pipelines for applications such as robot navigation or manipulation. In these cases, an accurate 3D reconstruction is already required while the data gathering is still in progress. In this paper, we demonstrate how existing batch-based reconstruction algorithms lead to suboptimal reconstruction quality when used for online, iterative 3D reconstruction and propose appropriate modifications to the existing Pix2Vox++ architecture. When additional viewpoints become available at a high rate, e.g., from a camera mounted on a drone, selecting the most informative viewpoints is important in order to mitigate long term memory loss and to reduce the computational footprint. We present qualitative and quantitative results on the optimal selection of viewpoints and show that state-of-the-art reconstruction quality is already obtained with elementary selection algorithms.
Assessment of Immune Status Using Inexpensive Cytokines: A Literature Review and Learning Approaches
Authors: Jamaludeen Noor , Lehmann Juliane , Beyer Christian , Vogel Katrin , Pierau Mandy , Brunner-Weinzierl Monika , Spiliopoulou Myra
Published: December 2022

The emergence of point-of-care (POC) testing has lately been promoted to deliver rapid, reliable medical tests in critical life-threatening situations, especially in resource-limited settings. Recently, POC tests have witnessed further advances due to the technological revolution in smartphones. Smartphones are integrated as reliable readers to the POC results to improve their quantitative detection. This has enabled the use of more complex medical tests by the patient him/herself at home without the need for professional staff and sophisticated equipment. Cytokines, the important immune system biomarkers, are still measured today using the time-consuming Enzyme-Linked Immunosorbent Assay (ELISA), which can only be performed in specially equipped laboratories. Therefore, in this study, we investigate the current development of POC technologies suitable for the home testing of cytokines by conducting a PRISMA literature review. Then, we classify the collected technologies as inexpensive and expensive depending on whether the cytokines can be measured easily at home or not. Additionally, we propose a machine learning-based solution to even increase the efficiency of the cytokine measurement by leveraging the cytokines that can be inexpensively measured to predict the values of the expensive ones. In total, we identify 12 POCs for cytokine quantification. We find that Interleukin 1β (IL-1β), Interleukin 3 (IL-3), Interleukin 6 (IL-6), Interleukin 8 (IL-8) and Tumor necrosis factor (TNF) can be measured with inexpensive POC technology, namely at home. We build machine-learning models to predict the values of other expensive cytokines such as Interferon-gamma (IFN-γ), IL-10, IL-2, IL-17A, IL-17F, IL-4 and IL-5 by relying on the identified inexpensive ones in addition to the age of the individual. We evaluate to what extent the built machine learning models can use the inexpensive cytokines to predict the expensive ones on 351 healthy subjects from the public dataset 10k Immunomes. The models for IFN-γ show high results for the coefficient of determination: R2 = 0.743. The results for IL-5 and IL-4 are also promising, whereas the predictive model of IL-10 achieves only R2 = 0.126. Lastly, the results demonstrate the vital role of TNF and IL-6 in the immune system due to its high importance in the predictions of all the other expensive cytokines.