bluuebunny/crossref_metadata_2025 huggingface dataset:
Identification of Angiogenic Cargoes in Human Fibroblasts-Derived Extracellular Vesicles and Induction of Wound Healing
Authors: Gangadaran Prakash , Oh Eun Jung , Rajendran Ramya Lakshmi , Kim Hyun Mi , Oh Ji Min , Kwak Suin , Hong Chae Moon , Choi Kang Young , Chung Ho Yun , Ahn Byeong-Cheol
Published: June 2022

A complete redevelopment of the skin remains a challenge in the management of acute and chronic wounds. Recently, the application of extracellular vesicles (EVs) for soft tissue wound healing has received much attention. As fibroblasts are fundamental cells for soft tissues and skin, we investigate the proangiogenic factors in human normal fibroblast-derived EVs (hNF-EVs) and their effects on wound healing. Normal fibroblasts were isolated from human skin tissues and characterized by immunofluorescence (IF) and Western blotting (WB). hNF-EVs were isolated by ultracentrifugation and characterized using transmission electron microscopy and WB. The proangiogenic cargos in hNF-EVs were identified by a TaqMan assay and a protein array. Other in vitro assays, including internalization assays, cell counting kit-8 analysis, scratch wound assays, WBs, and tube formation assays were conducted to assess the effects of hNF-EVs on fibroblasts and endothelial cells. A novel scaffold-free noninvasive delivery of hNF-EVs with or without fibrin glue was applied onto full-thickness skin wounds in mice. The wound healing therapeutical effect of hNF-EVs was assessed by calculating the rate of wound closure and through histological analysis. Isolated hNF was confirmed by verifying the expression of the fibroblast markers vimentin, αSMA, Hsp70, and S100A4. Isolated hNF-EVs showed intact EVs with round morphology, enriched in CD81 and CD63, and devoid of the cell markers GM130, Calnexin, and Cytochrome C. Our TaqMan assay showed that hNF-EVs were enriched in miR130a and miR210, and protein arrays showed enriched levels of the proangiogenic proteins’ vascular endothelial growth factor (VEGF)-D and CXCL8. Next, we found that the internalization of hNF-EVs into hNF increased the proliferation and migration of hNF, in addition to increasing the expression of bFGF, MMP2, and αSMA. The internalization of hNF-EVs into the endothelial cells increased their proliferation and tube formation. A scaffold-free noninvasive delivery of hNF-EVs with or without fibrin glue accelerated the wound healing rate in full-thickness skin wounds in mice, and the treatments increased the cellular density, deposition, and maturation of collagens in the wounds. Moreover, the scaffold-free noninvasive delivery of hNF-EVs with or without fibrin glue increased the VEGF and CD31 expression in the wounds, indicating that hNF-EVs have an angiogenic ability to achieve complete skin regeneration. These findings open up for new treatment strategies to be developed for wound healing. Further, we offer a new approach to the efficient, scaffold-free noninvasive delivery of hNF-EVs to wounds.
Synthesis and Biological Evaluation of Highly Active 7-Anilino Triazolopyrimidines as Potent Antimicrotubule Agents
Authors: Oliva Paola , Romagnoli Romeo , Cacciari Barbara , Manfredini Stefano , Padroni Chiara , Brancale Andrea , Ferla Salvatore , Hamel Ernest , Corallo Diana , Aveic Sanja , Milan Noemi , Mariotto Elena , Viola Giampietro , Bortolozzi Roberta
Published: June 2022

Two different series of fifty-two compounds, based on 3′,4′,5′-trimethoxyaniline (7a–ad) and variably substituted anilines (8a–v) at the 7-position of the 2-substituted-[1,2,4]triazolo [1,5-a]pyrimidine nucleus, had moderate to potent antiproliferative activity against A549, MDA-MB-231, HeLa, HT-29 and Jurkat cancer cell lines. All derivatives with a common 3-phenylpropylamino moiety at the 2-position of the triazolopyrimidine scaffold and different halogen-substituted anilines at its 7-position, corresponding to 4′-fluoroaniline (8q), 4′-fluoro-3′-chloroaniline (8r), 4′-chloroaniline (8s) and 4′-bromoaniline (8u), displayed the greatest antiproliferative activity with mean IC50′s of 83, 101, 91 and 83 nM, respectively. These four compounds inhibited tubulin polymerization about 2-fold more potently than combretastatin A-4 (CA-4), and their activities as inhibitors of [3H]colchicine binding to tubulin were similar to that of CA-4. These data underlined that the 3′,4′,5′-trimethoxyanilino moiety at the 7-position of the [1,2,4]triazolo [1,5-a]pyrimidine system, which characterized compounds 7a–ad, was not essential for maintaining potent antiproliferative and antitubulin activities. Compounds 8q and 8r had high selectivity against cancer cells, and their interaction with tubulin led to the accumulation of HeLa cells in the G2/M phase of the cell cycle and to apoptotic cell death through the mitochondrial pathway. Finally, compound 8q significantly inhibited HeLa cell growth in zebrafish embryos.
Ginsenoside Compound K Protects against Obesity through Pharmacological Targeting of Glucocorticoid Receptor to Activate Lipophagy and Lipid Metabolism
Authors: Yang Siwen , Liu Ting , Hu Chenxing , Li Weili , Meng Yuhan , Li Haiyang , Song Chengcheng , He Congcong , Zhou Yifa , Fan Yuying
Published: June 2022

(1) Background: The glucocorticoid receptor (GR) plays a key role in lipid metabolism, but investigations of GR activation as a potential therapeutic approach have been hampered by a lack of selective agonists. Ginsenoside compound K (CK) is natural small molecule with a steroid-like structure that offers a variety of therapeutic benefits. Our study validates CK as a novel GR agonist for the treatment of obesity. (2) Methods: By using pulldown and RNA interference, we determined that CK binds to GR. The anti-obesity potential effects of CK were investigated in obese mice, including through whole-body energy homeostasis, glucose and insulin tolerance, and biochemical and proteomic analysis. Using chromatin immunoprecipitation, we identified GR binding sites upstream of lipase ATGL. (3) Results: We demonstrated that CK reduced the weight and blood lipids of mice more significantly than the drug Orlistat. Proteomics data showed that CK up-regulated autophagy regulatory proteins, enhanced fatty acid oxidation proteins, and decreased fatty acid synthesis proteins. CK induced lipophagy with the initial formation of the phagophore via AMPK/ULK1 activation. However, a blockade of autophagy did not disturb the increase in CK on lipase expression, suggesting that autophagy and lipase are independent pathways in the function of CK. The pulldown and siRNA experiments showed that GR is the critical target. After binding to GR, CK not only activated lipophagy, but also promoted the binding of GR to the ATGL promoter. (4) Conclusions: Our findings indicate that CK is a natural food candidate for reducing fat content and weight.
Inhaled siRNA Formulations for Respiratory Diseases: From Basic Research to Clinical Application
Authors: Fan Yulin , Yang Zhijun
Published: June 2022

The development of siRNA technology has provided new opportunities for gene-specific inhibition and knockdown, as well as new ideas for the treatment of disease. Four siRNA drugs have already been approved for marketing. However, the instability of siRNA in vivo makes systemic delivery ineffective. Inhaled siRNA formulations can deliver drugs directly to the lung, showing great potential for treating respiratory diseases. The clinical applications of inhaled siRNA formulations still face challenges because effective delivery of siRNA to the lung requires overcoming the pulmonary and cellular barriers. This paper reviews the research progress for siRNA inhalation formulations for the treatment of various respiratory diseases and summarizes the chemical structural modifications and the various delivery systems for siRNA. Finally, we conclude the latest clinical application research for inhaled siRNA formulations and discuss the potential difficulty in efficient clinical application.
A Note from the Editor-in-Chief of Physiologia
Authors: Atherton Philip J.
Published: June 2022

I am proud to call myself a physiologist, and, as such, it was my pleasure and honour (over a year ago now) to take on the role of founding Editor-in-Chief of Physiologia [...]
Endophytes and Halophytes to Remediate Industrial Wastewater and Saline Soils: Perspectives from Qatar
Authors: Yasseen Bassam T. , Al-Thani Roda F.
Published: June 2022

Many halophytes are considered to be salt hyperaccumulators, adopting ion extrusion and inclusion mechanisms. Such plants, with high aboveground biomass, may play crucial roles in saline habitats, including soil desalination and phytoremediation of polluted soils and waters. These plants cause significant changes in some of the soil’s physical and chemical properties; and have proven efficient in removing heavy metals and metabolizing organic compounds from oil and gas activities. Halophytes in Qatar, such as Halopeplis perfoliata, Salicornia europaea, Salsola soda, and Tetraena qatarensis, are shown here to play significant roles in the phytoremediation of polluted soils and waters. Microorganisms associated with these halophytes (such as endophytic bacteria) might boost these plants to remediate saline and polluted soils. A significant number of these bacteria, such as Bacillus spp. and Pseudomonas spp., are reported here to play important roles in many sectors of life. We explore the mechanisms adopted by the endophytic bacteria to promote and support these halophytes in the desalination of saline soils and phytoremediation of polluted soils. The possible roles played by endophytes in different parts of native plants are given to elucidate the mechanisms of cooperation between these native plants and the associated microorganisms.
Viscoelastic and Properties of Amphiphilic Chitin in Plasticised Polylactic Acid/Starch Biocomposite
Authors: Olaiya N. G. , Maraveas C. , Salem Mohamed A. , Raja S. , Rashedi Ahmad , Alzahrani Abdullah Y. , El-Bahy Zeinhom M. , Olaiya Funmilayo G.
Published: June 2022

The enhancement of the PLA thermomechanical properties is significant due to its suitability as a replacement for primary synthetic polymer use in diverse industrial production. The amphiphilic chitin was used as a compatibilizer in PLA/starch biocomposite. The properties of plasticised polylactic acid blended with starch, and amphiphilic chitin was studied for enhanced thermomechanical and viscoelastic properties. Chitin was modified using acetylated substitution reaction and blended with plasticised PLA/starch biocomposite. The biocomposite was prepared with combined compression and melt extrusion techniques. The biocomposite’s thermomechanical, thermal, mechanical, and morphological properties were studied using dynamic mechanical analysis, TGA-DSC, tensile test, and scanning electron microscopy. The storage and loss modulus were significantly enhanced with increased amphiphilic chitin content. Similarly, the single peak of tan delta showed good miscibility of the polymeric blend. Additionally, the modulus increases with frequency change from 1 Hz to 10 Hz. The thermal stability of the biocomposite was observed to be lower than the neat PLA. The tensile properties of the biocomposite increased significantly more than the neat PLA, with P4S4C having the highest tensile strength and modulus of 87 MPa and 7600 MPa. The SEM images show good miscibility with no significant void in the fractured surface. The viscoelastic properties of PLA were enhanced considerably with plasticizer and amphiphilic chitin with improved biodegradability. The properties of the biocomposite can be adapted for various industrial applications.
Estimating Flexural Strength of FRP Reinforced Beam Using Artificial Neural Network and Random Forest Prediction Models
Authors: Khan Kaffayatullah , Iqbal Mudassir , Salami Babatunde Abiodun , Amin Muhammad Nasir , Ahamd Izaz , Alabdullah Anas Abdulalim , Arab Abdullah Mohammad Abu , Jalal Fazal E.
Published: June 2022

An accurate calculation of the flexural capacity of flexural members is vital for the safe and economical design of FRP reinforced structures. The existing empirical models are not accurately calculating the flexural capacity of beams and columns. This study investigated the estimation of the flexural capacity of beams using non-linear capabilities of two Artificial Intelligence (AI) models, namely Artificial neural network (ANN) and Random Forest (RF) Regression. The models were trained using optimized hyperparameters obtained from the trial-and-error method. The coefficient of correlation (R), Mean Absolute Error, and Root Mean Square Error (RMSE) were observed as 0.99, 5.67 kN-m, and 7.37 kN-m, for ANN, while 0.97, 7.63 kN-m, and 8.02 kN-m for RF regression model, respectively. Both models showed close agreement between experimental and predicted results; however, the ANN model showed superior accuracy and flexural strength performance. The parametric and sensitivity analysis of the ANN models showed that an increase in bottom reinforcement, width and depth of the beam, and increase in compressive strength increased the bending moment capacity of the beam, which shows the predictions by the model are corroborated with the literature. The sensitivity analysis showed that variation in bottom flexural reinforcement is the most influential parameter in yielding flexural capacity, followed by the overall depth and width of the beam. The change in elastic modulus and ultimate strength of FRP manifested the least importance in contributing flexural capacity.
Feasibility Study on the Fused Filaments of Injection-Molding-Grade Poly(Ethylene Terephthalate) for 3D Printing
Authors: Tsai Hsi-Hsun , Wu Shao-Jung , Wu Yu-De , Hong Wei-Zheng
Published: June 2022

Unlike that of glycol-modified Poly(ethylene terephthalate) (PETG), the crystallinity of PET can be post-adjusted to enhance the mechanical properties of 3D-printed parts such as food-contact tableware and bio-implants. The aforementioned PET material could be 3D printed to produce the desired parts for performance evaluation before mass production by injection molding. In this study, using differential scanning calorimetry (DSC), we examined the pellets, extruded filament, and printed specimen to identify variations in melting and crystalline temperatures, as well as crystallinity. It was also shown by Thermogravimetric Analyzer (TGA) that the addition of talcum powder increased the thermal stability of filament and resulted in an interaction between the fillers and polymer matrix. The crystallinities of the filament and printed specimen were then compared with the yield strengths and Young’s moduli to confirm the effects of the decreased molecular weight of the extruded PET filament. The talcum powder effectively improved the viscosity of the PET melted during the extrusion process for the filament and then enhanced the crystallinity of the PET, thereby achieving a significantly higher Young’s modulus. The printed PET specimen presented an excellent yield strength of 25 MPa and ductile properties with strain-at-break values of 30%, successfully indicating potential applications in food-contact tableware and bio-implants.
Strain Field Features and Three-Dimensional Crustal Deformations Constrained by Dense GRACE and GPS Measurements in NE Tibet
Authors: Zhang Tengxu , Shen Ziyu , He Lin , Shen Wenbin , Li Wei
Published: June 2022

The continuing impact between the Eurasia Plate and India results in the thickening and shortening of the N-S Tibetan Plateau. There has been strong tectonic movement along the boundary of the zones of deformation of the NE corner of the Tibetan plateau (NET) since the new tectonic period, with its dynamic mechanisms remaining controversial. Here, we use observations of 39 Continuous Global Positioning System (CGPS) gauges and 451 Crustal Movement Observation Network of China (CMONOC) campaign-mode stations to detect the three-dimensional deformation of the crust in the NET. Improved processing procedures are implemented to strengthen the patterns of strain throughout the NET. The principal component analysis (PCA) technique is introduced to decompose the time series into spatial eigenvectors and principal components (PCs), and the first three PCs are used to estimate and rectify common mode errors (CMEs). In addition, GRACE observations are used to detect deformation changes that account for non-tidal oceanic mass loading, hydrological loading, and surface pressure. The rectified deformation of the crust indicates the anisotropic nature of both the subsidence and uplift, and that the highest uplift rate of the Longmen Shan fault uplift reaches 7.13 ± 0.53 mm/yr. Finally, the horizontal velocity is further used to enumerate the strain rates throughout the NET. The results show that the shear band retained property in line with the strike-slip fault along the Altyn Tagh fault, the Qilian Shan faults, the Haiyuan fault, the West Qinling fault, the East Kunlun fault, and the Longmen Shan fault. In addition, the results further indicate that the whole NET shows a strong relationship with the mean principal rates of horizontal shortening strain. Extension and compression of the crust reasonably describe its sinking and uplifting.
Monitoring Raptor Movements with Satellite Telemetry and Avian Radar Systems: An Evaluation for Synchronicity
Authors: Washburn Brian E. , Maher David , Beckerman Scott F. , Majumdar Siddhartha , Pullins Craig K. , Guerrant Travis L.
Published: June 2022

Avian radar technologies have the potential to serve an important role in the quantification of bird movements and determining patterns of bird use in areas where human–wildlife conflicts might occur (e.g., airports, wind energy facilities). Ground-truthing studies are needed to help wildlife managers understand the biological meaning of radar information, as the capabilities and limitations of these technologies are relatively unknown. We conducted a study to evaluate the efficacy of three X-band marine radar sensors for tracking red-tailed hawks (Buteo jamaicensis) on or near the airfield at Chicago’s O’Hare International Airport from September 2010 to May 2014. Specific information regarding red-tailed hawk locations derived from satellite telemetry was used to determine how frequently the three radar sensors provided corresponding tracks of these avian targets (i.e., synchronized monitoring). We examined various factors (e.g., bird altitude and distance to the radar) to determine if they had any influence on the frequency of synchronicity between satellite telemetry locations and radar tracks. We found evidence that as the distance between a hawk and the radars increased, the radars’ ability to detect and track known avian targets decreased. Overall, the frequency of synchronization events for red-tailed hawks was low. Of the 1977 red-tailed hawk locations that should have been visible to the radar sensors, 51 of these bird movements were tracked by at least one of the radar sensors (2.6%). This study provides a new methodology for evaluating the performance of radar systems for tracking birds and determining what factors might influence overall performance.
Prediction of the Nitrogen, Phosphorus and Potassium Contents in Grape Leaves at Different Growth Stages Based on UAV Multispectral Remote Sensing
Authors: Peng Xuelian , Chen Dianyu , Zhou Zhenjiang , Zhang Zhitao , Xu Can , Zha Qing , Wang Fang , Hu Xiaotao
Published: June 2022

The rapid and accurate acquisition of nitrogen, phosphorus and potassium nutrient contents in grape leaves is critical for improving grape yields and quality and for industrial development. In this study, crop growth was non-destructively monitored based on unmanned aerial vehicle (UAV) remote sensing technology. Three irrigation levels (W1, W2 and W3) and four fertilization levels (F3, F2, F1 and F0) were set in this study, and drip irrigation fertilization treatments adopted a complete block design. A correlation analysis was conducted using UAV multispectral image data obtained from 2019 to 2021 and the field-measured leaf nitrogen content (LNC), leaf potassium content (LKC) and leaf phosphorus content (LPC) values; from the results, the vegetation indices (VIs) that were sensitive to LNC, LKC and LPC were determined. By combining spectral indices with partial least squares (PLS), random forest (RF), support vector machine (SVM) and extreme learning machine (ELM) machine-learning algorithms, prediction models were established. Finally, the optimal combinations of spectral variables and machine learning models for predicting LNC, LPC and LKC in each grape growth period were determined. The results showed that: (1) there were high demands for nitrogen during the new shoot growth and flowering periods, potassium was the main nutrient absorbed in the fruit expansion period, and phosphorus was the main nutrient absorbed in the veraison and maturity periods; (2) combining multiple spectral variables with the RF, SVM and ELM models could result in improved LNC, LPC and LKC predictions. The optimal prediction model determination coefficient (R2) derived during the new shoot growth period was above 0.65, and that obtained during the other growth periods was above 0.75. The relative root mean square error (RRMSE) of the above models was below 0.20, and the Willmott consistency index (WIA) was above 0.88. In conclusion, UAV multispectral images have good application effects when predicting nutrient contents in grape leaves. This study can provide technical support for accurate vineyard nutrient management using UAV platforms.
Design of Intelligent Monitoring System in Galloping Power Transmission Line
Authors: Wang Lijun , Li Hao , Lu Xu , Li Xiangyang , Zhang Jianyong , Wang Xinxin , Chen Changxin
Published: June 2022

To prevent the frequent occurrence of transmission line galloping accidents, many scholars have carried out studies. However, there are still many difficulties that have not been solved. To address the issues that have arisen during the installation of the monitoring system, a new installation technique for the galloping monitoring terminal structure has been developed, and structural design and transmission line impact have been taken into account. A method combining Kalman and Mahony complementary filtering has been shown to solve the problem of wire twisting when galloping is taken into account. The displacement is derived by double-integrating the acceleration, although the trend term has a significant impact on the integration result. To handle the trend term issue and other error effects, a method combining the least-squares method, the adaptive smoothing method, and the time-frequency domain hybrid integration approach is used. Finally, the monitoring terminal’s structural design is simulated and evaluated, and the measured amplitude is assessed on a galloping standard test bench. The difference between the measured amplitude and the laboratory standard value is less than 10%, meeting the engineering design criteria. And the galloping trajectory is identical to the test bench trajectory, which is critical for user end monitoring.
Printed and Flexible ECG Electrodes Attached to the Steering Wheel for Continuous Health Monitoring during Driving
Authors: Warnecke Joana M. , Ganapathy Nagarajan , Koch Eugen , Dietzel Andreas , Flormann Maximilian , Henze Roman , Deserno Thomas M.
Published: June 2022

Continuous health monitoring in a vehicle enables the earlier detection of symptoms of cardiovascular diseases. In this work, we designed flexible and thin electrodes made of polyurethane for long-term electrocardiogram (ECG) monitoring while driving. We determined the time for reliable ECG recording to evaluate the effectiveness of the electrodes. We recorded data from 19 subjects under four scenarios: rest, city, highway, and rural. The recording time was five min for rest and 15 min for the other scenarios. The total recording (950 min) is publicly available under a CC BY-ND 4.0 license. We used the simultaneous truth and performance level estimation (STAPLE) algorithm to detect the position of R-waves. Then, we derived the RR intervals to compare the estimated heart rate with the ground truth, which we obtained from ECG electrodes on the chest. We calculated the signal-to-noise ratio (SNR) and averaged it for the different scenarios. Highway had the lowest SNR (−6.69 dB) and rural had the highest (−6.80 dB). The usable time of the steering wheel was 42.46% (city), 46.67% (highway), and 47.72% (rural). This indicates that steering-wheel-based ECG recording is feasible and delivers reliable recordings from about 45.62% of the driving time. In summary, the developed electrodes allow continuous in-vehicle heart rate monitoring, and our publicly available recordings provide the opportunity to apply more sophisticated data analytics.
Effect of Pd-Sensitization on Poisonous Chlorine Gas Detection Ability of TiO2: Green Synthesis and Low-Temperature Operation
Authors: Ekar Satish , Nakate Umesh T. , Khollam Yogesh B. , Shaikh Shoyebmohamad F. , Mane Rajaram S. , Rana Abu ul Hassan S. , Palaniswami Marimuthu
Published: June 2022

Ganoderma lucidum mushroom-mediated green synthesis of nanocrystalline titanium dioxide (TiO2) is explored via a low-temperature (≤70 °C) wet chemical method. The role of Ganoderma lucidum mushroom extract in the reaction is to release the ganoderic acid molecules that tend to bind to the Ti4+ metal ions to form a titanium-ganoderic acid intermediate complex for obtaining TiO2 nanocrystallites (NCs), which is quite novel, considering the recent advances in fabricated gas sensing materials. The X-ray powder diffraction, field emission scanning electron microscopy, Raman spectroscopy, and Brunauer–Emmett–Teller measurements etc., are used to characterize the crystal structure, surface morphology, and surface area of as-synthesized TiO2 and Pd-TiO2 sensors, respectively. The chlorine (Cl2) gas sensing properties are investigated from a lower range of 5 ppm to a higher range of 400 ppm. In addition to excellent response–recovery time, good selectivity, constant repeatability, as well as chemical stability, the gas sensor efficiency of the as-synthesized Pd-TiO2 NC sensor is better (136% response at 150 °C operating temperature) than the TiO2 NC sensor (57% at 250 °C operating temperature) measured at 100 ppm (Cl2) gas concentration, suggesting that the green synthesized Pd-TiO2 sensor demonstrates efficient Cl2 gas sensing properties at low operating temperatures over pristine ones.
Benchmarking Object Detection Deep Learning Models in Embedded Devices
Authors: Cantero David , Esnaola-Gonzalez Iker , Miguel-Alonso Jose , Jauregi Ekaitz
Published: June 2022

Object detection is an essential capability for performing complex tasks in robotic applications. Today, deep learning (DL) approaches are the basis of state-of-the-art solutions in computer vision, where they provide very high accuracy albeit with high computational costs. Due to the physical limitations of robotic platforms, embedded devices are not as powerful as desktop computers, and adjustments have to be made to deep learning models before transferring them to robotic applications. This work benchmarks deep learning object detection models in embedded devices. Furthermore, some hardware selection guidelines are included, together with a description of the most relevant features of the two boards selected for this benchmark. Embedded electronic devices integrate a powerful AI co-processor to accelerate DL applications. To take advantage of these co-processors, models must be converted to a specific embedded runtime format. Five quantization levels applied to a collection of DL models are considered; two of them allow the execution of models in the embedded general-purpose CPU and are used as the baseline to assess the improvements obtained when running the same models with the three remaining quantization levels in the AI co-processors. The benchmark procedure is explained in detail, and a comprehensive analysis of the collected data is presented. Finally, the feasibility and challenges of the implementation of embedded object detection applications are discussed.
Towards Deep Radar Perception for Autonomous Driving: Datasets, Methods, and Challenges
Authors: Zhou Yi , Liu Lulu , Zhao Haocheng , López-Benítez Miguel , Yu Limin , Yue Yutao
Published: June 2022

With recent developments, the performance of automotive radar has improved significantly. The next generation of 4D radar can achieve imaging capability in the form of high-resolution point clouds. In this context, we believe that the era of deep learning for radar perception has arrived. However, studies on radar deep learning are spread across different tasks, and a holistic overview is lacking. This review paper attempts to provide a big picture of the deep radar perception stack, including signal processing, datasets, labelling, data augmentation, and downstream tasks such as depth and velocity estimation, object detection, and sensor fusion. For these tasks, we focus on explaining how the network structure is adapted to radar domain knowledge. In particular, we summarise three overlooked challenges in deep radar perception, including multi-path effects, uncertainty problems, and adverse weather effects, and present some attempts to solve them.
Real-Valued Direct Position Determination of Quasi-Stationary Signals for Nested Arrays: Khatri–Rao Subspace and Unitary Transformation
Authors: Zeng Haowei , Yue Heng , Cao Jinke , Zhang Xiaofei
Published: June 2022

The features of quasi-stationary signals (QSS) are considered to be in a direct position determination (DPD) framework, and a real-valued DPD algorithm of QSS for nested arrays is proposed. By stacking the vectorization form of the signal’s covariance for different frames and further eliminating noise, a new noise-eliminated received signal matrix is obtained first. Then, the combination of the Khatri–Rao subspace method and subspace data fusion method was performed to form the cost function. High complexity can be reduced by matrix reconstruction, including the modification of the dimension-reduced matrix and unitary transformation. Ultimately, the advantage of lower complexity, compared with the previous algorithm, is verified by complexity analysis, and the superiority over the existing algorithms, in terms of the maximum number of identifiable sources, estimation accuracy, and resolution, are corroborated by some simulation results.
Highly Linear and Wide Non-Resonant Two-Degree-of-Freedom Piezoelectric Laser Scanner
Authors: Ozaki Takashi , Ohta Norikazu , Fujiyoshi Motohiro
Published: June 2022

Laser scanners with mechanically driven mirrors have exhibited increasing potential for various applications, such as displays and laser radar. Resonant scanners are the predominantly used scanners; however, non-resonant scanners are required for applications where point-to-point driving is desirable. Because a non-resonant drive cannot amplify the drive angle owing to the resonance phenomenon, high values are difficult to achieve for the main performance metrics of the scanners: mirror area, drive angle, and operating frequency. In this paper, we present a two-axis scanner with a piezoelectric actuator made of a piezoelectric single-crystal Pb(In1/2Nb1/2)O3-Pb(Mg1/3Nb2/3)O3-PbTiO3 as the actuation force source. The scanner contains a circular mirror with a diameter of 7 mm and achieves an average static mechanical deflection angle amplitude of 20.8° in two axes with a resonant frequency of 559 Hz. It is equipped with a transmission mechanism that can decouple each axis to achieve high linearity; in our study, the nonlinearity error was less than 1°.
A Faster and More Accurate Iterative Threshold Algorithm for Signal Reconstruction in Compressed Sensing
Authors: Wei Jianxiang , Mao Shumin , Dai Jiming , Wang Ziren , Huang Weidong , Yu Yonghong
Published: June 2022

Fast iterative soft threshold algorithm (FISTA) is one of the algorithms for the reconstruction part of compressed sensing (CS). However, FISTA cannot meet the increasing demands for accuracy and efficiency in the signal reconstruction. Thus, an improved algorithm (FIPITA, fast iterative parametric improved threshold algorithm) based on mended threshold function, restart adjustment mechanism and parameter adjustment is proposed. The three parameters used to generate the gradient in the FISTA are carefully selected by assessing the impact of them on the performance of the algorithm. The developed threshold function is used to replace the soft threshold function to reduce the reconstruction error and a restart mechanism is added at the end of each iteration to speed up the algorithm. The simulation experiment is carried out on one-dimensional signal and the FISTA, RadaFISTA and RestartFISTA are used as the comparison objects, with the result that in one case, for example, the residual rate of FIPITA is about 6.35% lower than those three and the number of iterations required to achieve the minimum error is also about 102 less than that of FISTA.
Multi-Scale Deep Neural Network Based on Dilated Convolution for Spacecraft Image Segmentation
Authors: Liu Yuan , Zhu Ming , Wang Jing , Guo Xiangji , Yang Yifan , Wang Jiarong
Published: June 2022

In recent years, image segmentation techniques based on deep learning have achieved many applications in remote sensing, medical, and autonomous driving fields. In space exploration, the segmentation of spacecraft objects by monocular images can support space station on-orbit assembly tasks and space target position and attitude estimation tasks, which has essential research value and broad application prospects. However, there is no segmentation network designed for spacecraft targets. This paper proposes an end-to-end spacecraft image segmentation network using the semantic segmentation network DeepLabv3+ as the basic framework. We develop a multi-scale neural network based on sparse convolution. First, the feature extraction capability is improved by the dilated convolutional network. Second, we introduce the channel attention mechanism into the network to recalibrate the feature responses. Finally, we design a parallel atrous spatial pyramid pooling (ASPP) structure that enhances the contextual information of the network. To verify the effectiveness of the method, we built a spacecraft segmentation dataset on which we conduct experiments on the segmentation algorithm. The experimental results show that the encoder+ attention+ decoder structure proposed in this paper, which focuses on high-level and low-level features, can obtain clear and complete masks of spacecraft targets with high segmentation accuracy. Compared with DeepLabv3+, our method is a significant improvement. We also conduct an ablation study to research the effectiveness of our network framework.
An Improved ID-Based Data Storage Scheme for Fog-Enabled IoT Environments
Authors: Lin Han-Yu , Tsai Tung-Tso , Ting Pei-Yih , Chen Ching-Chung
Published: June 2022

In a fog-enabled IoT environment, a fog node is regarded as the proxy between end users and cloud servers to reduce the latency of data transmission, so as to fulfill the requirement of more real-time applications. A data storage scheme utilizing fog computing architecture allows a user to share cloud data with other users via the assistance of fog nodes. In particular, a fog node obtaining a re-encryption key of the data owner is able to convert a cloud ciphertext into the one which is decryptable by another designated user. In such a scheme, a proxy should not learn any information about the plaintext during the transmission and re-encryption processes. In 2020, an ID-based data storage scheme utilizing anonymous key generation in fog computing was proposed by some researchers. Although their protocol is provably secure in a proof model of random oracles, we will point out that there are some security flaws inherited in their protocol. On the basis of their work, we further present an improved variant, which not only eliminates their security weaknesses, but also preserves the functionalities of anonymous key generation and user revocation mechanism. Additionally, under the Decisional Bilinear Diffie–Hellman (DBDH) assumption, we demonstrate that our enhanced construction is also provably secure in the security notion of IND-PrID-CPA.
Cross-Medium Photoacoustic Communications: Challenges, and State of the Art
Authors: Mahmud Muntasir , Islam Md Shafiqul , Ahmed Akram , Younis Mohamed , Choa Fow-Sen
Published: June 2022

The current era is notably characterized by the major advances in communication technologies. The increased connectivity has been transformative in terrestrial, space, and undersea applications. Nonetheless, the water medium imposes unique constraints on the signals that can be pursued for establishing wireless links. While numerous studies have been dedicated to tackling the challenges for underwater communication, little attention has been paid to effectively interfacing the underwater networks to remote entities. Particularly it has been conventionally assumed that a surface node will be deployed to act as a relay using acoustic links for underwater nodes and radio links for air-based communication. Yet, such an assumption could be, in fact, a hindrance in practice. The paper discusses alternative means by allowing communication across the air–water interface. Specifically, the optoacoustic effect, also referred to as photoacoustic effect, is being exploited as a means for achieving connectivity between underwater and airborne nodes. The paper provides background, discusses technical challenges, and summarizes progress. Open research problems are also highlighted.
Evaluation of 1D and 2D Deep Convolutional Neural Networks for Driving Event Recognition
Authors: Escottá Álvaro Teixeira , Beccaro Wesley , Ramírez Miguel Arjona
Published: June 2022

Driving event detection and driver behavior recognition have been widely explored for many purposes, including detecting distractions, classifying driver actions, detecting kidnappings, pricing vehicle insurance, evaluating eco-driving, and managing shared and leased vehicles. Some systems can recognize the main driving events (e.g., accelerating, braking, and turning) by using in-vehicle devices, such as inertial measurement unit (IMU) sensors. In general, feature extraction is a commonly used technique to obtain robust and meaningful information from the sensor signals to guarantee the effectiveness of the subsequent classification algorithm. However, a general assessment of deep neural networks merits further investigation, particularly regarding end-to-end models based on Convolutional Neural Networks (CNNs), which combine two components, namely feature extraction and the classification parts. This paper primarily explores supervised deep-learning models based on 1D and 2D CNNs to classify driving events from the signals of linear acceleration and angular velocity obtained with the IMU sensors of a smartphone placed in the instrument panel of the vehicle. Aggressive and non-aggressive behaviors can be recognized by monitoring driving events, such as accelerating, braking, lane changing, and turning. The experimental results obtained are promising since the best classification model achieved accuracy values of up to 82.40%, and macro- and micro-average F1 scores, respectively, equal to 75.36% and 82.40%, thus, demonstrating high performance in the classification of driving events.
The Hemodynamic Parameters Values Prediction on the Non-Invasive Hydrocuff Technology Basis with a Neural Network Applying
Authors: Markuleva Marina , Gerashchenko Mikhail , Gerashchenko Sergey , Khizbullin Robert , Ivshin Igor
Published: June 2022

The task to develop a mechanism for predicting the hemodynamic parameters values based on non-invasive hydrocuff technology of a pulse wave signal fixation is described in this study. The advantages and disadvantages of existing methods of recording the ripple curve are noted in the published materials. This study proposes a new hydrocuff method for hemodynamic parameters and blood pressure values measuring. A block diagram of the device being developed is presented. Algorithms for processing the pulse wave contour are presented. A neural network applying necessity for the multiparametric feature space formation is substantiated. The pulse wave contours obtained using hydrocuff technology of oscillation formation for various age groups are presented. According to preliminary estimates, by the moment of the dicrotic surge formation, it is possible to judge the ratio of the heart and blood vessels work, which makes it possible to form an expanded feature space of significant parameters based on neural network classifiers. This study presents the characteristics accounted for creating a database for training a neural network.
A Novel Driving Noise Analysis Method for On-Road Traffic Detection
Authors: Ma Qinglu , Ma Lian , Liu Fengjie , Sun Daniel (Jian)
Published: June 2022

Effective noise reduction and abnormal feature extraction are important for abnormal sound detection occurring in urban traffic operations. However, to improve the detection accuracy of continuous traffic flow and even overlapping vehicle bodies, effective methods capable to achieve accurate signal-to-noise ratio and appropriate characteristic parameters should be explored. In view of the disadvantages of traditional traffic detection methods, such as Short-Time Energy (STE) and Mel Frequency Cepstral Coefficients (MFCC), this study adopts an improved spectral subtraction method to analyze traffic noise. Through the feature fusion of STE and MFCC coefficients, an innovative feature parameter, E-MFCC, is obtained, assisting to propose a traffic noise detection solution based on Triangular Wave Analysis (TWA). APP Designer in MATLAB was used to establish a traffic detection simulation platform. The experimental results showed that compared with the accuracies of traffic detection using the traditional STE and MFCC methods as 67.77% and 76.01%, respectively, the detection accuracy of the proposed TWA is significantly improved, attaining 91%. The results demonstrated the effectiveness of the traffic detection method proposed in solving the overlapping problem, thus achieving accurate detection of road traffic volume and improving the efficiency of road operation.
A Dynamic Convolution Kernel Generation Method Based on Regularized Pattern for Image Super-Resolution
Authors: Feng Hesen , Ma Lihong , Tian Jing
Published: June 2022

Image super-resolution aims to reconstruct a high-resolution image from its low-resolution counterparts. Conventional image super-resolution approaches share the same spatial convolution kernel for the whole image in the upscaling modules, which neglect the specificity of content information in different positions of the image. In view of this, this paper proposes a regularized pattern method to represent spatially variant structural features in an image and further exploits a dynamic convolution kernel generation method to match the regularized pattern and improve image reconstruction performance. To be more specific, first, the proposed approach extracts features from low-resolution images using a self-organizing feature mapping network to construct regularized patterns (RP), which describe different contents at different locations. Second, the meta-learning mechanism based on the regularized pattern predicts the weights of the convolution kernels that match the regularized pattern for each different location; therefore, it generates different upscaling functions for images with different content. Extensive experiments are conducted using the benchmark datasets Set5, Set14, B100, Urban100, and Manga109 to demonstrate that the proposed approach outperforms the state-of-the-art super-resolution approaches in terms of both PSNR and SSIM performance.
A Fuzzy Similarity-Based Approach to Classify Numerically Simulated and Experimentally Detected Carbon Fiber-Reinforced Polymer Plate Defects
Authors: Versaci Mario , Angiulli Giovanni , Crucitti Paolo , De Carlo Domenico , Laganà Filippo , Pellicanò Diego , Palumbo Annunziata
Published: June 2022

This paper presents an eddy current approach for testing, estimating, and classifying CFRP plate sub-surface defects, mainly due to delamination, through specific 2D magnetic induction field amplitude maps. These maps, showing marked fuzziness content, require the development of a procedure based on a fuzzy approach being efficiently classified. Since similar defects produce similar maps, we propose a method based on innovative fuzzy similarity formulations. This procedure can collect maps similar to each other in particular defect classes. In addition, a low-cost analysis system, including the probe, has been implemented in hardware. The developed tool can detect and evaluate the extent of surface defects with the same performance as a hardware tool of higher specifications, and it could be fruitfully employed by airline companies to maintain aircraft in compliance with safety standards.
Estimation of Combustion Parameters from Engine Vibrations Based on Discrete Wavelet Transform and Gradient Boosting
Authors: Kefalas Achilles , Ofner Andreas B. , Pirker Gerhard , Posch Stefan , Geiger Bernhard C. , Wimmer Andreas
Published: June 2022

An optimal control of the combustion process of an engine ensures lower emissions and fuel consumption plus high efficiencies. Combustion parameters such as the peak firing pressure (PFP) and the crank angle (CA) corresponding to 50% of mass fraction burned (MFB50) are essential for a closed-loop control strategy. These parameters are based on the measured in-cylinder pressure that is typically gained by intrusive pressure sensors (PSs). These are costly and their durability is uncertain. To overcome these issues, the potential of using a virtual sensor based on the vibration signals acquired by a knock sensor (KS) for control of the combustion process is investigated. The present work introduces a data-driven approach where a signal-processing technique, designated as discrete wavelet transform (DWT), will be used as the preprocessing step for extracting informative features to perform regression tasks of the selected combustion parameters with extreme gradient boosting (XGBoost) regression models. The presented methodology will be applied to data from two different spark-ignited, single cylinder gas engines. Finally, an analysis is obtained where the important features based on the model’s decisions are identified.
Decoding Physical and Cognitive Impacts of Particulate Matter Concentrations at Ultra-Fine Scales
Authors: Talebi Shawhin , Lary David J. , Wijeratne Lakitha O. H. , Fernando Bharana , Lary Tatiana , Lary Matthew , Sadler John , Sridhar Arjun , Waczak John , Aker Adam , Zhang Yichao
Published: June 2022

The human body is an incredible and complex sensing system. Environmental factors trigger a wide range of automatic neurophysiological responses. Biometric sensors can capture these responses in real time, providing clues about the underlying biophysical mechanisms. In this prototype study, we demonstrate an experimental paradigm to holistically capture and evaluate the interactions between an environmental context and physiological markers of an individual operating that environment. A cyclist equipped with a biometric sensing suite is followed by an environmental survey vehicle during outdoor bike rides. The interactions between environment and physiology are then evaluated though the development of empirical machine learning models, which estimate particulate matter concentrations from biometric variables alone. Here, we show biometric variables can be used to accurately estimate particulate matter concentrations at ultra-fine spatial scales with high fidelity (r2 = 0.91) and that smaller particles are better estimated than larger ones. Inferring environmental conditions solely from biometric measurements allows us to disentangle key interactions between the environment and the body. This work sets the stage for future investigations of these interactions for a larger number of factors, e.g., black carbon, CO2, NO/NO2/NOx, and ozone. By tapping into our body’s ‘built-in’ sensing abilities, we can gain insights into how our environment influences our physical health and cognitive performance.