bluuebunny/crossref_metadata_2025 huggingface dataset:
Anticancer Activity of Lesbicoumestan in Jurkat Cells via Inhibition of Oxidative Stress-Mediated Apoptosis and MALT1 Protease
Authors: Lee Joo-Eun , Bo Fang , Thuy Nguyen Thi Thanh , Hong Jaewoo , Lee Ji Shin , Cho Namki , Yoo Hee Min
Published: January 2021

This study explores the potential anticancer effects of lesbicoumestan from Lespedeza bicolor against human leukemia cancer cells. Flow cytometry and fluorescence microscopy were used to investigate antiproliferative effects. The degradation of mucosa-associated lymphoid tissue lymphoma translocation protein 1 (MALT1) was evaluated using immunoprecipitation, Western blotting, and confocal microscopy. Apoptosis was investigated using three-dimensional (3D) Jurkat cell resistance models. Lesbicoumestan induced potent mitochondrial depolarization on the Jurkat cells via upregulated expression levels of mitochondrial reactive oxygen species. Furthermore, the underlying apoptotic mechanisms of lesbicoumestan through the MALT1/NF-κB pathway were comprehensively elucidated. The analysis showed that lesbicoumestan significantly induced MALT1 degradation, which led to the inhibition of the NF-κB pathway. In addition, molecular docking results illustrate how lesbicoumestan could effectively bind with MALT1 protease at the latter’s active pocket. Similar to traditional 2D cultures, apoptosis was markedly induced upon lesbicoumestan treatment in 3D Jurkat cell resistance models. Our data support the hypothesis that lesbicoumestan is a novel inhibitor of MALT1, as it exhibited potent antiapoptotic effects in Jurkat cells.
Characterization and Design of Photovoltaic Solar Cells That Absorb Ultraviolet, Visible and Infrared Light
Authors: Bernardes Sara , Lameirinhas Ricardo A. Marques , Torres João Paulo N. , Fernandes Carlos A. F.
Published: January 2021

The world is witnessing a tide of change in the photovoltaic industry like never before; we are far from the solar cells of ten years ago that only had 15–18% efficiency. More and more, multi-junction technologies seem to be the future for photovoltaics, with these technologies already hitting the mark of 30% under 1-sun. This work focuses especially on a state-of-the-art triple-junction solar cell, the GaInP/GaInAs/Ge lattice-matched, that is currently being used in most satellites and concentrator photovoltaic systems. The three subcells are first analyzed individually and then the whole cell is put together and simulated. The typical figures-of-merit are extracted; all the I−V curves obtained are presented, along with the external quantum efficiencies. A study on how temperature affects the cell was done, given its relevance when talking about space applications. An overall optimization of the cell is also elaborated; the cell’s thickness and doping are changed so that maximum efficiency can be reached. For a better understanding of how varying both these properties affect efficiency, graphic 3D plots were computed based on the obtained results. Considering this optimization, an improvement of 0.2343% on the cell’s efficiency is obtained.
The Relationship between Nurses’ Training and Perceptions of Electronic Documentation Systems
Authors: Zaman Nohel , Goldberg David M. , Kelly Stephanie , Russell Roberta S. , Drye Sherrie L.
Published: January 2021

Electronic documentation systems have been widely implemented in the healthcare field. These systems have become a critical part of the nursing profession. This research examines how nurses’ general computer skills, training, and self-efficacy affect their perceptions of using these systems. A sample of 248 nurses was surveyed to examine their general computer skills, self-efficacy, and training in electronic documentation systems in nursing programs. We propose a model to investigate the extent to which nurses’ computer skills, self-efficacy, and training in electronic documentation influence perceptions of using electronic documentation systems in hospitals. The data supports a mediated model in which general computer skills, self-efficacy, and training influence perceived usefulness through perceived ease of use. The significance of these findings was confirmed through structural equation modeling. As the electronic documentation systems are customized for every organization, our findings suggest value in nurses receiving training to learn these specific systems in the workplace or during their internships. Doing so may improve patient outcomes by ensuring that nurses use the systems consistently and effectively.
Haemophilin-Producing Strains of Haemophilus haemolyticus Protect Respiratory Epithelia from NTHi Colonisation and Internalisation
Authors: Atto Brianna , Kunde Dale , Gell David , Tristram Stephen
Published: January 2021

Nontypeable Haemophilus influenzae (NTHi) is a significant respiratory tract pathogen responsible for infections that collectively pose a substantial health and socioeconomic burden. The clinical course of these infections is largely dictated by NTHi interactions with host respiratory epithelia, and thus, approaches that disrupt colonisation and invasion may have significant therapeutic potential. Survival, successful host–cell interactions, and pathogenesis are reliant on NTHi’s ability to sequester host-derived haem. Previously, we demonstrated the therapeutic potential of exploiting this haem-dependence using a closely related competitor bacterium, Haemophilus haemolyticus (Hh). Hh strains capable of producing the novel haem-binding protein haemophilin (Hpl) possessed potent inhibitory activity by restricting NTHi access to haem in a broth co-culture environment. Here, we extend this work to cell culture models that more closely represent the human respiratory epithelium and show that Hh strains with high levels of hpl expression protect epithelial cell line monolayers against adhesion and invasion by NTHi. Inhibitory activity was dependent on the level of Hpl production, which was stimulated by NTHi challenge and nasopharyngeal cell exposure. Provided these protective benefits translate to in vivo applications, Hpl-producing Hh may have probiotic utility against NTHi infections by inhibiting requisite nasopharyngeal colonisation.
Noli Timere: The Role of Reassuring Adults in Dealing with COVID-19 Anxiety in Pediatric Age
Authors: Smirni Daniela
Published: January 2021

Since the earliest stages of the Corona Virus Disease-19 (COVID-19) spread, the elderly has been identified as the most vulnerable and health authorities have rightly focused on that population. Minor attention was paid to pediatric populations and their emotional reactions. Actually, children and adolescents faced severe anxiety, fear and stress conditions. An efficient management of the pandemic, therefore, must take into account the pediatric population which cannot be neglected as a minor matter compared to the elderly, the economy and health care. Since the lockdown time is over, children and adolescents must recover sociality, return to living in the open air, rediscover playing, free time, aiming for the beauty of their everyday life. In order to mitigate the long-term impact of COVID-19, the key response is the reassuring presence of the adult as ‘a secure base’. The current study aimed to collect an overview of the recent references that report evidence on the role of adults in containing pandemic anxiety COVID-19 in pediatric populations, suggesting the need to ensure a reassuring presence of the adult, an effective child-parent communication, a child-friendly day and a long-lasting shared time with parents.
Exogenous Application of Foliar Salicylic Acid and Propolis Enhances Antioxidant Defenses and Growth Parameters in Tomato Plants
Authors: El-Hady Nouran Ahmed Abdo Abd , ElSayed Abdelaleim Ismail , El-saadany Sayed Soliman , Deligios Paola A. , Ledda Luigi
Published: January 2021

Salicylic acid (SA) and propolis (PR) are known to regulate the physiological process and to have a relevant role in bioactive compounds content. Our experiment was designed to evaluate the effect of SA and PR application on the growth, yield, and quality parameters of tomato grown for the fresh market in field conditions in Egypt. We studied the effect of twelve treatments where SA (0.50, 1.00, 1.50, 2.00, and 2.50 mM) and PR (1, 2, 10, 20, and 100 mg propolis mL−1) were applied at increasing doses as a sole agent or combined each other (1.50 mM + 10 mg mL−1 for SA and PR, respectively). An untreated control was also considered. Tomato plants treated with SA (0.50, 1.00, and 1.50 mM) showed a significant effect in all traits especially SA1 (0.50 mM) in growth parameters and SA2 (1.00 mM) in pigment and antioxidant content. Propolis foliar application was more effective than SA as it revealed that raising the concentration of aqueous extract enhanced the growth parameters and pigment in tomato. The best result was obtained by the 10 mg mL−1 treatment. The effect of propolis on antioxidant enzymes varied as the 10 mg mL−1 treatment was effective on peroxidases and superoxide dismutase, while 100 mg mL−1 was more effective on catalase. Salicylic acid and propolis have a positive effect on both preserving tomato plants and on nutrient supply, so the mixed intermediate concentration (1.50 mM + 10 mg mL−1) is considered very effective and results in an improvement of all plant traits.
Low Molecular Weight Oligomers of Poly(alkylene succinate) Polyesters as Plasticizers in Poly(vinyl alcohol) Based Pharmaceutical Applications
Authors: Palamidi Artemis , Kapourani Afroditi , Christodoulou Evi , Klonos Panagiotis A. , Kontogiannopoulos Konstantinos N. , Kyritsis Apostolos , Bikiaris Dimitrios N. , Barmpalexis Panagiotis
Published: January 2021

The plasticizing effect of three low molecular weight oligomers of aliphatic poly(alkylene succinate) polyesters, namely poly(butylene succinate) (PBSu), poly(ethylene succinate) (PESu), and poly(propylene succinate) (PPSu), on partially hydrolyzed poly(vinyl alcohol) (PVA) used in melt-based pharmaceutical applications, was evaluated for the first time. Initially, the three aliphatic polyesters were prepared by the melt polycondensation process and characterized by differential scanning calorimetry (DSC), 1H NMR, intrinsic viscosity, and size exclusion chromatography (SEC). Subsequently, their effect on the thermophysical and physicochemical properties of PVA was thoroughly evaluated. According to the obtained results, PVA was completely miscible with all three polyesters, while PESu induced PVA’s thermal degradation, with the phenomenon starting from ~220 °C, in contrast to PBSu and PPSu, where a thermal profile similar to PVA was observed. Furthermore, molecular interactions between PVA and the prepared poly(alkylene succinate) polyesters were revealed by DSC, ATR-FTIR, and molecular dynamics simulations. Finally, melt flow index (MFI) measurements showed that, in contrast to PBSu, the use of PESu or PPSu significantly improved PVA’s melt flow properties. Hence, according to findings of the present work, only the use of low molecular weight PPSu is suitable in order to reduce processing temperature of PVA and improve its melt flow properties (plasticizing ability) without affecting its thermal decomposition.
Thermo-Responsive Behavior of Mixed Aqueous Solution of Hydrophilic Polymer with Pendant Phosphorylcholine Group and Poly(Acrylic Acid)
Authors: Fukumoto Hirokazu , Ishihara Kazuhiko , Yusa Shin-Ichi
Published: January 2021

A mixed aqueous solution of hydrophilic poly(2-methacryloyloxyethyl phosphorylcholine) (PMPC) and poly(acrylic acid) (PAAc) becomes cloudy under acidic conditions at room temperature. The pendant carboxylic acid groups in PAAc form hydrogen bonds with the ester and phosphate groups in PMPC. While the polymers aggregate under acidic conditions, neither one associate under basic conditions because of the deprotonation of the pendant carboxy groups in PAAc. We observed that the interpolymer complex formed from PMPC, and PAAc was dissociated in aqueous solutions with increasing temperature, which is an upper critical solution temperature behavior. With increasing temperature, the molecular motion increased to dissociate the interpolymer complex. The phase transition temperature increased with increasing polymer and salt concentrations, and with decreasing pH.
Wave Dispersion Analysis of Fluid Conveying Nanocomposite Shell Reinforced by MWCNTs Considering the Effect of Waviness and Agglomeration Efficiency
Authors: Alkhedher Mohammad , Talebizadehsardari Pouyan , Eyvazian Arameh , Khan Afrasyab , Farouk Naeim
Published: January 2021

The current paper is aimed to investigate the effects of waviness, random orientation, and agglomeration factor of nanoreinforcements on wave propagation in fluid-conveying multi-walled carbon nanotubes (MWCNTs)-reinforced nanocomposite cylindrical shell based on first-order shear deformable theory (FSDT). The effective mechanical properties of the nanocomposite cylindrical shell are estimated employing a combination of a novel form of Halpin-Tsai homogenization model and rule of mixture. Utilized fluid flow obeys Newtonian fluid law and it is axially symmetric and laminar flow and it is considered to be fully developed. The effect of flow velocity is explored by implementing Navier-Stokes equation. The kinetic relations of nanocomposite shell are calculated via FSDT. Moreover, the governing equations are derived using the Hamiltonian approach. Afterward, a method which solves problems analytically is applied to solve the obtained governing equations. Effects of a wide range of variants such as volume fraction of MWCNTs, radius to thickness ratio, flow velocity, waviness factor, random orientation factor, and agglomeration factor on the phase velocity and wave frequency of a fluid conveying MWCNTs-reinforced nanocomposite cylindrical shell were comparatively illustrated and the results were discussed in detail.
Experimental and Numerical Analysis of the Mechanical Properties of a Pretreated Shape Memory Alloy Wire in a Self-Centering Steel Brace
Authors: Zhang Bo , Zeng Sizhi , Tang Fenghua , Hu Shujun , Zhou Qiang , Jia Yigang
Published: January 2021

As a stimulus-sensitive material, the difference in composition, fabrication process, and influencing factors will have a great effect on the mechanical properties of a superelastic Ni-Ti shape memory alloy (SMA) wire, so the seismic performance of the self-centering steel brace with SMA wires may not be accurately obtained. In this paper, the cyclic tensile tests of a kind of SMA wire with a 1 mm diameter and special element composition were tested under multi-working conditions, which were pretreated by first tensioning to the 0.06 strain amplitude for 40 cycles, so the mechanical properties of the pretreated SMA wires can be simulated in detail. The accuracy of the numerical results with the improved model of Graesser’s theory was verified by a comparison to the experimental results. The experimental results show that the number of cycles has no significant effect on the mechanical properties of SMA wires after a certain number of cyclic tensile training. With the loading rate increasing, the pinch effect of the hysteresis curves will be enlarged, while the effective elastic modulus and slope of the transformation stresses in the process of loading and unloading are also increased, and the maximum energy dissipation capacity of the SMA wires appears at a loading rate of 0.675 mm/s. Moreover, with the initial strain increasing, the slope of the transformation stresses in the process of loading is increased, while the effective elastic modulus and slope of the transformation stresses in the process of unloading are decreased, and the maximum energy dissipation capacity appears at the initial strain of 0.0075. In addition, a good agreement between the test and numerical results is obtained by comparing with the hysteresis curves and energy dissipation values, so the numerical model is useful to predict the stress–strain relations at different stages. The test and numerical results will also provide a basis for the design of corresponding self-centering steel dampers.
Films and Materials Derived from Aminomalononitrile
Authors: Thissen Helmut , Evans Richard A. , Ball Vincent
Published: January 2021

In recent years major advances in surface chemistry and surface functionalization have been performed through the development, most often inspired by living organisms, of versatile methodologies. Among those, the contact of substrates with aminomalononitrile (AMN) containing solutions at pH = 8.5 allows a conformal coating to be deposited on the surface of all known classes of material. Since AMN is a molecule probably formed in the early atmosphere of our planet and since HCN-based compounds have been detected on many comets and Titan (Saturn’s largest moon) it is likely that such molecules will open a large avenue in surface functionalization mostly for bio-applications. This mini review describes the state of the art of AMN-based coatings from their deposition kinetics, composition, chemical reactivity, hypothetical structure to their first applications as biomaterials. Finally, the AMN-based versatile coatings are compared to other kinds of versatile coating based on catecholamines and polyphenols.
Lighting Candles in the Darkness: An Exploration of Commemorative Acts with British Teenagers at the Auschwitz-Birkenau State Museum
Authors: Richardson Alasdair
Published: January 2021

Every year around 3000 British school pupils and teachers visit the Auschwitz-Birkenau State Museum as participants on a Lessons from Auschwitz Project organized by the Holocaust Educational Trust. Each visit ends with a memorial ceremony held at the end of the railway tracks at Birkenau. This article analyses interview and survey data from participating students and educators to explore their experiences of these ceremonies. The research findings indicate that the context and content of the ceremony are significant for both groups, with a general consensus that the ceremony is an important and appropriate way to end the day visit to Poland and the museum. The students’ responses also particularly raise issues around their emotional engagement with the ceremony and the impact it had on them in this way. In conclusion, this article suggests how similar reflective spaces might be created in other educational contexts at similar sites of memory.
A Method for Assessing Threats to the Economic Security of a Region: A Case Study of Public Procurement in Russia
Authors: Kravchenko Valentina , Kudryavtseva Tatiana , Kuporov Yuriy
Published: January 2021

The issue of economic security is becoming an increasingly urgent one. The purpose of this article is to develop a method for assessing threats to the economic security of the Russian region. This method is based on step-by-step actions: first of all, choosing an element of the region’s economic security system and collecting its descriptive indicators; then grouping indicators by admittance-process-result categories and building hypotheses about their influence; testing hypotheses using a statistical package and choosing the most significant connections, which can pose a threat to the economic security of the region; thereafter ranking regions by the level of threats and developing further recommendations. The importance of this method is that with the help of grouping regions (territory of a country) based on proposed method, it is possible to develop individual economic security monitoring tools. As a result, the efficiency of that country’s region can be higher. In this work, the proposed method was tested in the framework of public procurement in Russia. A total of 14 indicators of procurement activity were collected for each region of the Russian Federation for the period from 2014 to 2018. Regression models were built on the basis of the grouped indicators. Ordinary Least Squares (OLS) Estimation was used. As a result of pairwise regression models analysis, we have defined four significant relationships between public procurement indicators. There are positive connections between contracts that require collateral and the percentage of tolerances, between the number of bidders and the number of regular suppliers, between the number of bidders and the average price drop, and between the number of purchases made from a single supplier and the number of contracts concluded without reduction. It was determined that the greatest risks for the system were associated with the connection between competition and budget savings. It was proposed to rank analyzed regions into four groups: ineffective government procurement, effective government procurement, and government procurement that threatens the system of economic security of the region, that is, high competition with low savings and low competition with high savings. Based on these groups, individual economic security monitoring tools can be developed for each region.
Kinematic Optimization for the Design of a Collaborative Robot End-Effector for Tele-Echography
Authors: Filippeschi Alessandro , Griffa Pietro , Avizzano Carlo Alberto
Published: January 2021

Tele-examination based on robotic technologies is a promising solution to solve the current worsening shortage of physicians. Echocardiography is among the examinations that would benefit more from robotic solutions. However, most of the state-of-the-art solutions are based on the development of specific robotic arms, instead of exploiting COTS (commercial-off-the-shelf) arms to reduce costs and make such systems affordable. In this paper, we address this problem by studying the design of an end-effector for tele-echography to be mounted on two popular and low-cost collaborative robots, i.e., the Universal Robot UR5, and the Franka Emika Panda. In the case of the UR5 robot, we investigate the possibility of adding a seventh rotational degree of freedom. The design is obtained by kinematic optimization, in which a manipulability measure is an objective function. The optimization domain includes the position of the patient with regards to the robot base and the pose of the end-effector frame. Constraints include the full coverage of the examination area, the possibility to orient the probe correctly, have the base of the robot far enough from the patient’s head, and a suitable distance from singularities. The results show that adding a degree of freedom improves manipulability by 65% and that adding a custom-designed actuated joint is better than adopting a native seven-degrees-freedom robot.
Enhanced Estimation of Significant Wave Height with Dual-Polarization Sentinel-1 SAR Imagery
Authors: Pramudya Fabian Surya , Pan Jiayi , Devlin Adam Thomas , Lin Hui
Published: January 2021

Sentinel-1 synthetic aperture radar (SAR) is one of the most advanced open-access satellite systems available, benefitting from its capability for earth observation under all-weather conditions. In this study, more than 280 Sentinel-1 SAR images are used to derive significant wave heights (Hs) of the sea surface using a polarization-enhanced methodology. Two study areas are selected: one is located near Hawai’i in a deep water region, and the other is in transitional water off the U.S. west coast, where the U.S. National Oceanic and Atmospheric Administration (NOAA) buoy data are available for validations. The enhanced Hs retrieval methodology utilizes dual-polarization SAR image data with strong non-Bragg radar backscattering, resulting in a better estimate of the cut-off wavelength than from those using single-polarization SAR data. The new method to derive Hs is applied to SAR images from 2017 taken from both deep water (near Hawai’i) and coastal water locations (off the U.S. West coast). The assessments of the retrieved Hs from SAR images suggest that the dual-polarization methodology can reduce the estimated Hs RMSE by 24.6% as compared to a single-polarization approach. Long-term reliability of the SAR image-derived Hs products based on the new methodology is also consolidated by large amount of in-situ buoy observations for both the coastal and deep waters.
An Overview of Requirements, Procedures and Current Advances in the Calibration/Validation of Radar Altimeters
Authors: Quartly Graham D. , Chen Ge , Nencioli Francesco , Morrow Rosemary , Picot Nicolas
Published: January 2021

Analysis of the radar echoes from a spaceborne altimeter gives information on sea surface height, wave height and wind speed, as well as other parameters over land and ice. The first spaceborne radar altimeter was pioneered on Skylab in 1974. Since then, there have been about 20 further missions, with several advances in the sophistication of hardware and complexity of processing with the aim of increased accuracy and precision. Because of that, the importance of regular and precise calibration and validation (“cal/val”) remains undiminished, especially with efforts to merge altimetric records from multiple missions spanning different domains and time periods. This special issue brings together 19 papers, with a focus on the recent missions (Jason-2, Jason-3, Sentinel-3A and HY-2B) as well as detailing the issues for anticipated future missions such as SWOT. This editorial provides a brief guide to the approaches and issues for cal/val of the various different derived parameters, including a synopsis of the papers in this special issue.
Comparative Analysis of Edge Information and Polarization on SAR-to-Optical Translation Based on Conditional Generative Adversarial Networks
Authors: Zhang Qian , Liu Xiangnan , Liu Meiling , Zou Xinyu , Zhu Lihong , Ruan Xiaohao
Published: January 2021

To accurately describe dynamic vegetation changes, high temporal and spectral resolution data are urgently required. Optical images contain rich spectral information but are limited by poor weather conditions and cloud contamination. Conversely, synthetic-aperture radar (SAR) is effective under all weather conditions but contains insufficient spectral information to recognize certain vegetation changes. Conditional adversarial networks (cGANs) can be adopted to transform SAR images (Sentinel-1) into optical images (Landsat8), which exploits the advantages of both optical and SAR images. As the features of SAR and optical remote sensing data play a decisive role in the translation process, this study explores the quantitative impact of edge information and polarization (VV, VH, VV&VH) on the peak signal-to-noise ratio, structural similarity index measure, correlation coefficient (r), and root mean squared error. The addition of edge information improves the structural similarity between generated and real images. Moreover, using the VH and VV&VH polarization modes as the input provides the cGANs with more effective information and results in better image quality. The optimal polarization mode with the addition of edge information is VV&VH, whereas that without edge information is VV. Near-infrared and short-wave infrared bands in the generated image exhibit higher accuracy (r > 0.8) than visible light bands. The conclusions of this study could serve as an important reference for selecting cGANs input features, and as a potential reference for the applications of cGANs to the SAR-to-optical translation of other multi-source remote sensing data.
Feature Line Embedding Based on Support Vector Machine for Hyperspectral Image Classification
Authors: Chen Ying-Nong , Thaipisutikul Tipajin , Han Chin-Chuan , Liu Tzu-Jui , Fan Kuo-Chin
Published: January 2021

In this paper, a novel feature line embedding (FLE) algorithm based on support vector machine (SVM), referred to as SVMFLE, is proposed for dimension reduction (DR) and for improving the performance of the generative adversarial network (GAN) in hyperspectral image (HSI) classification. The GAN has successfully shown high discriminative capability in many applications. However, owing to the traditional linear-based principal component analysis (PCA) the pre-processing step in the GAN cannot effectively obtain nonlinear information; to overcome this problem, feature line embedding based on support vector machine (SVMFLE) was proposed. The proposed SVMFLE DR scheme is implemented through two stages. In the first scatter matrix calculation stage, FLE within-class scatter matrix, FLE between-scatter matrix, and support vector-based FLE between-class scatter matrix are obtained. Then in the second weight determination stage, the training sample dispersion indices versus the weight of SVM-based FLE between-class matrix are calculated to determine the best weight between-scatter matrices and obtain the final transformation matrix. Since the reduced feature space obtained by the SVMFLE scheme is much more representative and discriminative than that obtained using conventional schemes, the performance of the GAN in HSI classification is higher. The effectiveness of the proposed SVMFLE scheme with GAN or nearest neighbor (NN) classifiers was evaluated by comparing them with state-of-the-art methods and using three benchmark datasets. According to the experimental results, the performance of the proposed SVMFLE scheme with GAN or NN classifiers was higher than that of the state-of-the-art schemes in three performance indices. Accuracies of 96.3%, 89.2%, and 87.0% were obtained for the Salinas, Pavia University, and Indian Pines Site datasets, respectively. Similarly, this scheme with the NN classifier also achieves 89.8%, 86.0%, and 76.2% accuracy rates for these three datasets.
Inductive Textile Sensor Design and Validation for a Wearable Monitoring Device
Authors: Patiño Astrid García , Menon Carlo
Published: January 2021

Textile sensors have gained attention for wearable devices, in which the most popular are the resistive textile sensor. However, these sensors present high hysteresis and a drift when stretched for long periods of time. Inductive textile sensors have been commonly used as antennas and plethysmographs, and their applications have been extended to measure heartbeat, wireless data transmission, and motion and gesture capturing systems. Inductive textile sensors have shown high reliability, stable readings, low production cost, and an easy manufacturing process. This paper presents the design and validation of an inductive strain textile sensor. The anthropometric dimensions of a healthy participant were used to define the maximum dimensions of the inductive textile sensor. The design of the inductive sensor was studied through theoretical calculations and simulations. Parameters such as height, width, area, perimeter, and number of complete loops were considered to calculate and evaluate the inductance value.
Grain Boundary Control of Organic Semiconductors via Solvent Vapor Annealing for High-Sensitivity NO2 Detection
Authors: Hou Sihui , Zhuang Xinming , Fan Huidong , Yu Junsheng
Published: January 2021

The microstructure of the organic semiconductor (OSC) active layer is one of the crucial topics to improve the sensing performance of gas sensors. Herein, we introduce a simple solvent vapor annealing (SVA) process to control 6,13-bis(triisopropylsilylethynyl)-pentacene (TIPS-pentacene) OSC films morphology and thus yields high-sensitivity nitrogen organic thin-film transistor (OTFT)-based nitrogen dioxide (NO2) sensors. Compared to pristine devices, the toluene SVA-treated devices exhibit an order of magnitude responsivity enhancement to 10 ppm NO2, further with a limit of detection of 148 ppb. Systematic studies on the microstructure of the TIPS-pentacene films reveal the large density grain boundaries formed by the SVA process, improving the capability for the adsorption of gas molecules, thus causing high-sensitivity to NO2. This simple SVA processing strategy provides an effective and reliable access for realizing high-sensitivity OTFT NO2 sensors.
Ear-Bot: Locust Ear-on-a-Chip Bio-Hybrid Platform
Authors: Fishel Idan , Amit Yoni , Shvil Neta , Sheinin Anton , Ayali Amir , Yovel Yossi , Maoz Ben M.
Published: January 2021

During hundreds of millions of years of evolution, insects have evolved some of the most efficient and robust sensing organs, often far more sensitive than their man-made equivalents. In this study, we demonstrate a hybrid bio-technological approach, integrating a locust tympanic ear with a robotic platform. Using an Ear-on-a-Chip method, we manage to create a long-lasting miniature sensory device that operates as part of a bio-hybrid robot. The neural signals recorded from the ear in response to sound pulses, are processed and used to control the robot’s motion. This work is a proof of concept, demonstrating the use of biological ears for robotic sensing and control.
Mobility-Included DNN Partition Offloading from Mobile Devices to Edge Clouds
Authors: Tian Xianzhong , Zhu Juan , Xu Ting , Li Yanjun
Published: January 2021

The latest results in Deep Neural Networks (DNNs) have greatly improved the accuracy and performance of a variety of intelligent applications. However, running such computation-intensive DNN-based applications on resource-constrained mobile devices definitely leads to long latency and huge energy consumption. The traditional way is performing DNNs in the central cloud, but it requires significant amounts of data to be transferred to the cloud over the wireless network and also results in long latency. To solve this problem, offloading partial DNN computation to edge clouds has been proposed, to realize the collaborative execution between mobile devices and edge clouds. In addition, the mobility of mobile devices is easily to cause the computation offloading failure. In this paper, we develop a mobility-included DNN partition offloading algorithm (MDPO) to adapt to user’s mobility. The objective of MDPO is minimizing the total latency of completing a DNN job when the mobile user is moving. The MDPO algorithm is suitable for both DNNs with chain topology and graphic topology. We evaluate the performance of our proposed MDPO compared to local-only execution and edge-only execution, experiments show that MDPO significantly reduces the total latency and improves the performance of DNN, and MDPO can adjust well to different network conditions.
Sensor Fusion-Based Approach to Eliminating Moving Objects for SLAM in Dynamic Environments
Authors: Dang Xiangwei , Rong Zheng , Liang Xingdong
Published: January 2021

Accurate localization and reliable mapping is essential for autonomous navigation of robots. As one of the core technologies for autonomous navigation, Simultaneous Localization and Mapping (SLAM) has attracted widespread attention in recent decades. Based on vision or LiDAR sensors, great efforts have been devoted to achieving real-time SLAM that can support a robot’s state estimation. However, most of the mature SLAM methods generally work under the assumption that the environment is static, while in dynamic environments they will yield degenerate performance or even fail. In this paper, first we quantitatively evaluate the performance of the state-of-the-art LiDAR-based SLAMs taking into account different pattens of moving objects in the environment. Through semi-physical simulation, we observed that the shape, size, and distribution of moving objects all can impact the performance of SLAM significantly, and obtained instructive investigation results by quantitative comparison between LOAM and LeGO-LOAM. Secondly, based on the above investigation, a novel approach named EMO to eliminating the moving objects for SLAM fusing LiDAR and mmW-radar is proposed, towards improving the accuracy and robustness of state estimation. The method fully uses the advantages of different characteristics of two sensors to realize the fusion of sensor information with two different resolutions. The moving objects can be efficiently detected based on Doppler effect by radar, accurately segmented and localized by LiDAR, then filtered out from the point clouds through data association and accurate synchronized in time and space. Finally, the point clouds representing the static environment are used as the input of SLAM. The proposed approach is evaluated through experiments using both semi-physical simulation and real-world datasets. The results demonstrate the effectiveness of the method at improving SLAM performance in accuracy (decrease by 30% at least in absolute position error) and robustness in dynamic environments.
Time–Frequency-Analysis-Based Blind Modulation Classification for Multiple-Antenna Systems
Authors: Jiang Weiheng , Wu Xiaogang , Wang Yimou , Chen Bolin , Feng Wenjiang , Jin Yi
Published: January 2021

Blind modulation classification is an important step in implementing cognitive radio networks. The multiple-input multiple-output (MIMO) technique is widely used in military and civil communication systems. Due to the lack of prior information about channel parameters and the overlapping of signals in MIMO systems, the traditional likelihood-based and feature-based approaches cannot be applied in these scenarios directly. Hence, in this paper, to resolve the problem of blind modulation classification in MIMO systems, the time–frequency analysis method based on the windowed short-time Fourier transform was used to analyze the time–frequency characteristics of time-domain modulated signals. Then, the extracted time–frequency characteristics are converted into red–green–blue (RGB) spectrogram images, and the convolutional neural network based on transfer learning was applied to classify the modulation types according to the RGB spectrogram images. Finally, a decision fusion module was used to fuse the classification results of all the receiving antennas. Through simulations, we analyzed the classification performance at different signal-to-noise ratios (SNRs); the results indicate that, for the single-input single-output (SISO) network, our proposed scheme can achieve 92.37% and 99.12% average classification accuracy at SNRs of −4 and 10 dB, respectively. For the MIMO network, our scheme achieves 80.42% and 87.92% average classification accuracy at −4 and 10 dB, respectively. The proposed method greatly improves the accuracy of modulation classification in MIMO networks.
Generating Road Networks for Old Downtown Areas Based on Crowd-Sourced Vehicle Trajectories
Authors: Zhang Caili , Li Yali , Xiang Longgang , Jiao Fengwei , Wu Chenhao , Li Siyu
Published: January 2021

With the popularity of portable positioning devices, crowd-sourced trajectory data have attracted widespread attention, and led to many research breakthroughs in the field of road network extraction. However, it is still a challenging task to detect the road networks of old downtown areas with complex network layouts from high noise, low frequency, and uneven distribution trajectories. Therefore, this paper focuses on the old downtown area and provides a novel intersection-first approach to generate road networks based on low quality, crowd-sourced vehicle trajectories. For intersection detection, virtual representative points with distance constraints are detected, and the clustering by fast search and find of density peaks (CFDP) algorithm is introduced to overcome low frequency features of trajectories, and improve the positioning accuracy of intersections. For link extraction, an identification strategy based on the Delaunay triangulation network is developed to quickly filter out false links between large-scale intersections. In order to alleviate the curse of sparse and uneven data distribution, an adaptive link-fitting scheme, considering feature differences, is further designed to derive link centerlines. The experiment results show that the method proposed in this paper preforms remarkably better in both intersection detection and road network generation for old downtown areas.
An AI-Application-Oriented In-Class Teaching Evaluation Model by Using Statistical Modeling and Ensemble Learning
Authors: Guo Junqi , Bai Ludi , Yu Zehui , Zhao Ziyun , Wan Boxin
Published: January 2021

In-class teaching evaluation, which is utilized to assess the process and effect of both teachers’ teaching and students’ learning in a classroom environment, plays an increasingly crucial role in supervising and promoting education quality. With the rapid development of artificial intelligence (AI) technology, the concept of smart education has been constantly improved and gradually penetrated into all aspects of education application. Considering the dominant position of classroom teaching in elementary and undergraduate education, the introduction of AI technology into in-class teaching evaluation has become a research hotspot. In this paper, we propose a statistical modeling and ensemble learning-based comprehensive model, which is oriented towards in-class teaching evaluation by using AI technologies such as computer vision (CV) and intelligent speech recognition (ISR). Firstly, we present an index system including a set of teaching evaluation indicators combining traditional assessment scales with new values derived from CV and ISR-based AI analysis. Next, we design a comprehensive in-class teaching evaluation model by using both the analytic hierarchy process-entropy weight (AHP-EW) and AdaBoost-based ensemble learning (AdaBoost-EL) methods. Experiments not only demonstrate that the two modules in the model are respectively applicable to the calculation of indicators with different characteristics, but also verify the performance of the proposed model for AI-based in-class teaching evaluation. In this comprehensive in-class evaluation model, for students’ concentration and participation, ensemble learning module is chosen with less root mean square error (RMSE) of 8.318 and 9.375. In addition, teachers’ media usage and teachers’ type evaluated by statistical modeling module approach higher accuracy with 0.905 and 0.815. Instead, the ensemble learning approaches the accuracy of 0.73 in evaluating teachers’ style, which performs better than the statistical modeling module with the accuracy of 0.69.
Analysis of Flaw Detection Sensitivity of Phased Array Ultrasonics in Austenitic Steel Welds According to Inspection Conditions
Authors: Kim YoungLae , Cho Sungjong , Park Ik Keun
Published: January 2021

The anisotropy and inhomogeneity exhibited by austenitic steel in welds poses a challenge to nondestructive testing employing ultrasonic waves, which is predominantly utilized for the inspection of welds in power plants. In this study, we assess the reliability of phased array ultrasonic testing (PAUT) by analyzing the flaw detection sensitivity of ultrasonic beams in anisotropic welds, based on the inspection conditions. First, we simulated the sectorial scan technique, frequently employed for the inspection of actual welds, while taking into account the ultrasonic wave mode, frequency, and shape and position of a flaw. Subsequently, we analyzed the flaw sensitivity by comparing A-scan signals and S-scan results. The sensitivity analysis results confirmed the detection of all flaws by considering at least two inspection methods based on the shape and position of the flaw. Furthermore, we verified our model by performing an experiment under the same conditions as the simulation and found that the results were in agreement. Hence, we find that the simulation modeling technique proposed in this study can be utilized to develop suitable inspection conditions, according to the flaw characteristics or inspection environment.
MOSS—Multi-Modal Best Subset Modeling in Smart Manufacturing
Authors: Wang Lening , Du Pang , Jin Ran
Published: January 2021

Smart manufacturing, which integrates a multi-sensing system with physical manufacturing processes, has been widely adopted in the industry to support online and real-time decision making to improve manufacturing quality. A multi-sensing system for each specific manufacturing process can efficiently collect the in situ process variables from different sensor modalities to reflect the process variations in real-time. However, in practice, we usually do not have enough budget to equip too many sensors in each manufacturing process due to the cost consideration. Moreover, it is also important to better interpret the relationship between the sensing modalities and the quality variables based on the model. Therefore, it is necessary to model the quality-process relationship by selecting the most relevant sensor modalities with the specific quality measurement from the multi-modal sensing system in smart manufacturing. In this research, we adopted the concept of best subset variable selection and proposed a new model called Multi-mOdal beSt Subset modeling (MOSS). The proposed MOSS can effectively select the important sensor modalities and improve the modeling accuracy in quality-process modeling via functional norms that characterize the overall effects of individual modalities. The significance of sensor modalities can be used to determine the sensor placement strategy in smart manufacturing. Moreover, the selected modalities can better interpret the quality-process model by identifying the most correlated root cause of quality variations. The merits of the proposed model are illustrated by both simulations and a real case study in an additive manufacturing (i.e., fused deposition modeling) process.
A Deep Neural Network-Based Feature Fusion for Bearing Fault Diagnosis
Authors: Hoang Duy Tang , Tran Xuan Toa , Van Mien , Kang Hee Jun
Published: January 2021

This paper presents a novel method for fusing information from multiple sensor systems for bearing fault diagnosis. In the proposed method, a convolutional neural network is exploited to handle multiple signal sources simultaneously. The most important finding of this paper is that a deep neural network with wide structure can extract automatically and efficiently discriminant features from multiple sensor signals simultaneously. The feature fusion process is integrated into the deep neural network as a layer of that network. Compared to single sensor cases and other fusion techniques, the proposed method achieves superior performance in experiments with actual bearing data.
Machine-Learning Classification of a Number of Contaminant Sources in an Urban Water Network
Authors: Lučin Ivana , Grbčić Luka , Čarija Zoran , Kranjčević Lado
Published: January 2021

In the case of a contamination event in water distribution networks, several studies have considered different methods to determine contamination scenario information. It would be greatly beneficial to know the exact number of contaminant injection locations since some methods can only be applied in the case of a single injection location and others have greater efficiency. In this work, the Neural Network and Random Forest classifying algorithms are used to predict the number of contaminant injection locations. The prediction model is trained with data obtained from simulated contamination event scenarios with random injection starting time, duration, concentration value, and the number of injection locations which varies from 1 to 4. Classification is made to determine if single or multiple injection locations occurred, and to predict the exact number of injection locations. Data was obtained for two different benchmark networks, medium-sized network Net3 and large-sized Richmond network. Additionally, an investigation of sensor layouts, demand uncertainty, and fuzzy sensors on model accuracy is conducted. The proposed approach shows excellent accuracy in predicting if single or multiple contaminant injections in a water supply network occurred and good accuracy for the exact number of injection locations.