bluuebunny/crossref_metadata_2025 huggingface dataset:
Optimization of Friction Welding Parameters to Maximize the Tensile Strength of Magnesium Alloy with Aluminum Alloy Dissimilar Joints Using Genetic Algorithm
Authors: Winiczenko Radosław , Skibicki Andrzej , Skoczylas Paweł
Published: September 2021

The friction rotary welding (FRW) of magnesium alloy to aluminum alloy was presented in a paper due to significant interest in the manufacturing industry. A genetic algorithm (GA) method for optimizing FRW process parameters of dissimilar light alloys was presented. After obtaining the welding parameters by GA method, it was possible to determine the best tensile strength of the friction joint. The obtained joints were subjected to tensile strength. The highest tensile strength TS = 178 MPa was found using a genetic algorithm for the following friction welding parameters: friction force FF = 16 kN, friction time FT = 4 s, and upsetting force UF = 44 kN. The optimized values were compared with the experimental results. The application of the genetic algorithm method allowed increasing the tensile strength joint from 88 to 180 MPa. The maximum tensile strength of the friction welded magnesium alloy-aluminum alloy joints was 73% of the base AZ31B metal. The relationship between welding parameters and strenght of welds was also demonstrated in this study.
Optimization Study of Guide Vanes for the Intake Fan-Duct Connection Using CFD
Authors: Hurtado Juan Pablo , Villegas Bryan , Pérez Sebastián , Acuña Enrique
Published: September 2021

The connection between an intake fan and a ventilation shaft must be designed in such a way that it minimizes the energy waste due to singularity losses. As a result, the questions of which radius of curvature to use and if guide vanes have to be included need to be answered. In that case, the variables such as the number, upstream and downstream penetration length, radius of curvature, and width of the vanes, need to be defined. Although this work is oriented to mine ventilation, these questions are usually valid in other engineering applications as well. The objective of this study is to define the previously mentioned variables to determine the optimal design combination for the radius/diameter relationship (r/D). Computational fluid dynamics was used to determine the shock loss factor of seven elbow curvature ratios for a 3 m diameter duct and fan, with and without guide vanes to estimate the best performing configuration and, therefore, to maximize the fan airflow volume. The methodology used consisted of initially developing models in 2D geometries, to optimize the meshing and the CPU use, and studying separately the number of vanes, upstream and downstream penetration, radius of curvature, and width of the vanes for each curvature ratio (r/D). Then, the best-performing variable combinations for each curvature ratio were selected to be simulated and studied with the 3D geometries. The application of the guide vane designs for three-dimensional simulated geometries is presented, first without and then with guide vanes, including the shock loss factors obtained. The methodology and obtained results allowed quantifying the energy savings and to reduce the CFD simulations steps required to optimize the design of the elbow and guide vanes. The results obtained cannot be used with elbows in exhaust fans, because fluid dynamics phenomena are different.
Reactor Selection for Upgrading Hemicelluloses: Conventional and Miniaturised Reactors for Hydrogenations
Authors: Haase Stefan , de Araujo Filho Cesar A. , Wärnå Johan , Murzin Dmitry Yu. , Salmi Tapio
Published: September 2021

This work presents an advanced reactor selection strategy that combines elements of a knowledge-based expert system to reduce the number of feasible reactor configurations with elaborated and automatised process simulations to identify reactor performance parameters. Special focus was given to identify optimal catalyst loadings and favourable conditions for each configuration to enable a fair comparison. The workflow was exemplarily illustrated for the Ru/C-catalysed hydrogenation of arabinose and galactose to the corresponding sugar alcohols. The simulations were performed by using pseudo-2D reactor models implemented in Aspen Custom Modeler® and automatised by using the MS-Excel interface and VBA. The minichannel packings, namely wall-coated minichannel reactor (MCWR), minichannel reactor packed with catalytic particles (MCPR), and minichannel reactor packed with a catalytic open-celled foam (MCFR), outperform the conventional and miniaturised trickle-bed reactors (TBR and MTBR) in terms of space-time yield and catalyst use. However, longer reactor lengths are required to achieve 99% conversion of the sugars in MCWR and MCPR. Considering further technical challenges such as liquid distribution, packing the reactor, as well as the robustness and manufacture of catalysts in a biorefinery environment, miniaturised trickle beds are the most favourable design for a production scenario of 5000 t/a galactitol. However, the minichannel configurations will be more advantageous for reaction systems involving consecutive and parallel reactions and highly exothermic systems.
Nano Iron Oxide-PCL Composite as an Improved Soft Tissue Scaffold
Authors: Rezaei Vahid , Mirzaei Esmaeil , Taghizadeh Seyedeh-Masoumeh , Berenjian Aydin , Ebrahiminezhad Alireza
Published: September 2021

Iron oxide nanoparticles were employed to fabricate a soft tissue scaffold with enhanced physicochemical and biological characteristics. Growth promotion effect of L-lysine coated magnetite (Lys@Fe3O4) nanoparticles on the liver cell lines was proved previously. So, in the current experiment these nanoparticles were employed to fabricate a soft tissue scaffold with growth promoting effect on the liver cells. Lys@Fe3O4 nanoparticles were synthesized via co-precipitation reaction. Resulted particles were ~7 nm in diameter and various concentrations (3, 5, and 10 wt%) of these nanoparticles were used to fabricate nanocomposite PCL fibers. Electrospinning technique was employed and physicochemical characteristics of the resulted nanofibers were evaluated. Electron micrographs and EDX-mapping analysis showed that nanoparticles were well dispersed in the PCL fibers and no bead structure were formed. As expected, incorporation of Lys@Fe3O4 to the PCL nanofibers resulted in a reduction in hydrophobicity of the scaffold. Nanocomposite scaffolds were shown increased tensile strength with increasing concentration of employed nanoparticles. In contrast to PCL scaffold, nearly 150% increase in the cell viability was observed after 3-days exposure to the nanocomposite scaffolds. This study indicates that incorporation of magnetite nanoparticles in the PCL fibers make them more prone to cell attachment. However, incorporated nanoparticles can provide the attached cells with valuable iron element and consequently promote the cells growth rate. Based on the results, magnetite enriched PCL nanofibers could be introduced as a scaffold to enhance the biological performance for liver tissue engineering purposes.
Antioxidant Activity of Spiranthes sinensis and Its Protective Effect against UVB-Induced Skin Fibroblast Damage
Authors: Huang Shang-Ming , Shieh Chwen-Jen , Wu Yu-Lin , Pan Yang-Zing , Yu Chi-Yang
Published: September 2021

Spiranthes sinensis (S. sinensis), a species of orchid, is a well-known herb medicine used to treat disorders such as stomachache, diabetes, shingles, and certain inflammatory diseases. Presently, the antioxidant activity of S. sinensis as well as its protective effect on UVB-induced skin injury are unclear. In this study, S. sinensis was extracted with boiling water or 75% (v/v) ethanol, and then its antioxidant composition and antioxidant activity were determined. The protective effects of S. sinensis against UVB-induced damage in human skin fibroblasts (CCD-966SK) were also investigated. Our data showed that the extraction yield of boiling water was higher than that of 75% ethanol. However, compared to the aqueous extracts, the ethanol extracts not only had higher phenolic, flavonoid, and condensed tannin contents, but also exhibited higher free radical scavenging activity, higher reducing power, and higher ferrous ion-chelating capacity. When fibroblasts were pre-cultured with the water or ethanol extracts of S. sinensis (1500 μg/mL) for 24 h before applying UVB irradiation, the S. sinensis extracts restored 17% to 27% of cell viability compared to a control only irradiated with UVB. Overall, our study suggests that S. sinensis extracts can be used as effective antioxidants and have the potential to protect skin fibroblasts from UVB irradiation.
Proteomes Are of Proteoforms: Embracing the Complexity
Authors: Carbonara Katrina , Andonovski Martin , Coorssen Jens R.
Published: September 2021

Proteomes are complex—much more so than genomes or transcriptomes. Thus, simplifying their analysis does not simplify the issue. Proteomes are of proteoforms, not canonical proteins. While having a catalogue of amino acid sequences provides invaluable information, this is the Proteome-lite. To dissect biological mechanisms and identify critical biomarkers/drug targets, we must assess the myriad of proteoforms that arise at any point before, after, and between translation and transcription (e.g., isoforms, splice variants, and post-translational modifications [PTM]), as well as newly defined species. There are numerous analytical methods currently used to address proteome depth and here we critically evaluate these in terms of the current ‘state-of-the-field’. We thus discuss both pros and cons of available approaches and where improvements or refinements are needed to quantitatively characterize proteomes. To enable a next-generation approach, we suggest that advances lie in transdisciplinarity via integration of current proteomic methods to yield a unified discipline that capitalizes on the strongest qualities of each. Such a necessary (if not revolutionary) shift cannot be accomplished by a continued primary focus on proteo-genomics/-transcriptomics. We must embrace the complexity. Yes, these are the hard questions, and this will not be easy…but where is the fun in easy?
Estimating the Stability of Psychological Dimensions via Bootstrap Exploratory Graph Analysis: A Monte Carlo Simulation and Tutorial
Authors: Christensen Alexander P. , Golino Hudson
Published: September 2021

Exploratory Graph Analysis (EGA) has emerged as a popular approach for estimating the dimensionality of multivariate data using psychometric networks. Sampling variability, however, has made reproducibility and generalizability a key issue in network psychometrics. To address this issue, we have developed a novel bootstrap approach called Bootstrap Exploratory Graph Analysis (bootEGA). bootEGA generates a sampling distribution of EGA results where several statistics can be computed. Descriptive statistics (median, standard error, and dimension frequency) provide researchers with a general sense of the stability of their empirical EGA dimensions. Structural consistency estimates how often dimensions are replicated exactly across the bootstrap replicates. Item stability statistics provide information about whether dimensions are unstable due to misallocation (e.g., item placed in the wrong dimension), multidimensionality (e.g., item belonging to more than one dimension), and item redundancy (e.g., similar semantic content). Using a Monte Carlo simulation, we determine guidelines for acceptable item stability. After, we provide an empirical example that demonstrates how bootEGA can be used to identify structural consistency issues (including a fully reproducible R tutorial). In sum, we demonstrate that bootEGA is a robust approach for identifying the stability and robustness of dimensionality in multivariate data.
Model Driven Causal Factors of Panic Buying and Their Implications for Prevention: A Systematic Review
Authors: Rajkumar Ravi Philip , Arafat S M Yasir
Published: September 2021

Panic buying is a commonly observed response to disasters, and has been widely observed during the COVID-19 pandemic. However, little is known about the variables influencing this behavior. This review summarizes the existing research in this field and examines its implications for the prevention and control of panic buying. Methodology: All papers published prior to or during the pandemic, providing an empirically tested model of panic buying behavior (Group A) or a theoretical model supported by literature (Group B), were retrieved through a literature search. For papers in Group A, specific risk or protective factors were extracted and tabulated. Overlaps between Group A and Group B models were identified. Study results were analyzed to identify potential strategies which could limit panic buying behavior. Results: It was found that a wide variety of primary (crisis/disease-related), secondary (psychological, informational and sociopolitical), and tertiary (supply chain-related) factors were significantly associated with panic buying, while a single variable–reflective functioning was identified as protective. Conclusions: These results provide valuable leads for strategies aimed at preventing or reducing panic buying, particularly in countries still affected by the pandemic. It is hoped that these findings will be useful from both health administration and academic perspectives.
Disinformation and Responsibility in Young People in Spain during the COVID-19 Era
Authors: Farfán Juana , Mazo María Elena
Published: September 2021

This paper analyzes the main variables that determine the relationship between disinformation and youth responsibility during the latest stage of the COVID-19 pandemic in Spain (from April to June 2021). Are young people keeping well informed during the COVID-19 pandemic in Spain? Are the youth behaving responsibly? This document presents several results regarding these questions. In our introduction, we establish a theoretical framework for the following concepts: disinformation, responsibility, credibility, and youth responsibility variables. Our primary interest is in communication factors. The applied methods are a reference review of the national and international literature surrounding this subject and qualitative opinion research conducted through discussion groups with young university students from private and public communication schools in Madrid. A recent study, held in June 2021, provides valuable material for this paper. The main results and findings are as follows: not being satisfied with the information received about COVID-19; knowledge about the most credible news sources; the connection between information and responsibility; and the solutions that are claimed to more responsible in this context by various youth participants. In conclusion, this paper confirms the first hypothesis of considering disinformation as a variable that causes a lack of personal responsibility among youths in complying with public health expectations. Regarding the second hypothesis, we verify that young people consider communication one of the main solutions for being more responsible. In other words, when presented with more information about COVID-19 they feel more aware of the disease.
Support Vector Machines with Quantum State Discrimination
Authors: Leporini Roberto , Pastorello Davide
Published: September 2021

We analyze possible connections between quantum-inspired classifications and support vector machines. Quantum state discrimination and optimal quantum measurement are useful tools for classification problems. In order to use these tools, feature vectors have to be encoded in quantum states represented by density operators. Classification algorithms inspired by quantum state discrimination and implemented on classic computers have been recently proposed. We focus on the implementation of a known quantum-inspired classifier based on Helstrom state discrimination showing its connection with support vector machines and how to make the classification more efficient in terms of space and time acting on quantum encoding. In some cases, traditional methods provide better results. Moreover, we discuss the quantum-inspired nearest mean classification.
Climate on the Blanca Massif, Sangre de Cristo Mountains, Colorado, USA, during the Last Glacial Maximum
Authors: Brugger Keith A. , Leonard Eric M. , Refsnider Kurt A. , Dolan Peter
Published: September 2021

Temperature-index modeling is used to determine the magnitude of temperature depression on the Blanca Massif, Colorado, required to maintain steady-state mass balances of nine reconstructed glaciers at their extent during the Last Glacial Maximum (LGM). The mean temperature depression thus determined is ~8.6 +0.7/−0.9 °C where the uncertainties account for those inherent in the glacier reconstructions, in model parameters (e.g., melt factors), and possible modest changes in LGM precipitation. Associated equilibrium-line altitudes (ELAs) exhibit a statistically significant directional dependency being lower toward the north and east. Under the assumption that regional temperature change was uniform, required changes in precipitation vary systematically—also exhibiting a directional dependency coinciding with that in ELAs—and indicate increases (over modern) occurred on the eastern side of the massif while decreases occurred on the western side. This disparity represents a strengthening of a precipitation asymmetry, particularly winter precipitation, which exists today. The modern precipitation asymmetry may be a consequence of snow being blown over to the eastern side of the massif (advective transport) by southwesterly flow. Intensification of this flow during the LGM would have enhanced advection, and augmented snow accumulation on glaciers, thus explaining the lower ELAs and increased precipitation on that side of the massif.
The Translation of Buddhism in the Funeral Architecture of Medieval China
Authors: Yu Shuishan
Published: September 2021

This article explores the Buddhist ritual and architectural conventions that were incorporated into the Chinese funeral architecture during the medieval period from the 3rd to the 13th centuries. A careful observation of some key types of sacred architectural forms from ancient East Asia, for instance, pagoda, lingtai, and hunping, reviews fundamental similarities in their form and structure. Applying translation theory rather than the influence and Sinicization model to analyze the impact of Buddhism on Chinese funeral architecture, this article offers a comparative study of the historical contexts from which certain architectural types and imageries were produced. It argues that there was an intertwined mutual translation of formal and ritual conventions between Buddhist and Chinese funeral architecture, which had played a significant role in the formations of both architectural traditions in Medieval China.
An Analysis of Bank Financial Strength Ratings and Credit Rating Data
Authors: Ruddy John A.
Published: September 2021

In this study, data from two credit rating agencies are analyzed to consider how different Bank Financial Strength Ratings and Credit Ratings from two rating agencies compare. To my knowledge, prior research has not analyzed Bank Financial Strength Ratings from different rating agencies, nor has it compared Bank Financial Strength Ratings to general credit ratings. These facts make this research unique. Univariate analyses are utilized to show relationships in the ratings data, along with parametric and non-parametric tests to make statistical inferences about the ratings data. There are five findings. First, ratings from different rating agencies are highly correlated. Second, different types of ratings from the same rating agency are highly correlated. Third, bank financial strength ratings are more conservative than credit ratings. Fourth, bank financial strength ratings declined in rating more quickly at the start of the financial crisis. Fifth, bank financial strength ratings from the Kroll Bond Rating Agency were more conservative than ratings from Moody’s Investors Service. The research findings and results are important for investors who consider ratings agency data to determine the risk of banking institutions. The results are also important to businesses that rely on bank credit rating data and policy makers who regulate banking institutions.
An Empirical Study on the Financial Preparation for Retirement of the Independent Workers for Profit in Poland
Authors: Bednarczyk Teresa H. , Skibińska-Fabrowska Ilona , Szymańska Anna
Published: September 2021

Modern pension schemes are based on the delegation of responsibility for pension provision from state institutions to individuals, which implies voluntary retirement saving. Workers for profit (independent workers in household market enterprises) hold much greater personal responsibility for financing their pensions than workers for pay. The main aim of this study was to provide an empirical identification of economic and social factors that would determine the propensity toward long-term saving for pensions by independent, for-profit workers in Poland. Additionally, the study recognizes the level of saving accumulated by them as well as preferred forms in which this saving is made.In order to select determinants of pension saving, a logistic regression model was used. The data come from the direct survey conducted in 2020 by CAWI method (Computer-Assisted Web Interview) on a random nationwide sample of Poles. The analysis of the data also used other methods of descriptive and mathematical statistics. The conducted research showed that the respondents’ individual decisions concerning saving for retirement are affected by such factors as gender, age, family situation, amount of revenue, share of revenue from business activity in total revenue, and subjective assessment of the respondents’ financial situation. The respondents declared holding various, though not high, savings. Moreover, it turned out that independent workers for profit in Poland opt for non-conventional forms of gathering pension savings.
Matrix Pencil Method for Vital Sign Detection from Signals Acquired by Microwave Sensors
Authors: Chamaani Somayyeh , Akbarpour Alireza , Helbig Marko , Sachs Jürgen
Published: September 2021

Microwave sensors have recently been introduced as high-temporal resolution sensors, which could be used in the contactless monitoring of artery pulsation and breathing. However, accurate and efficient signal processing methods are still required. In this paper, the matrix pencil method (MPM), as an efficient method with good frequency resolution, is applied to back-reflected microwave signals to extract vital signs. It is shown that decomposing of the signal to its damping exponentials fulfilled by MPM gives the opportunity to separate signals, e.g., breathing and heartbeat, with high precision. A publicly online dataset (GUARDIAN), obtained by a continuous wave microwave sensor, is applied to evaluate the performance of MPM. Two methods of bandpass filtering (BPF) and variational mode decomposition (VMD) are also implemented. In addition to the GUARDIAN dataset, these methods are also applied to signals acquired by an ultra-wideband (UWB) sensor. It is concluded that when the vital sign is sufficiently strong and pure, all methods, e.g., MPM, VMD, and BPF, are appropriate for vital sign monitoring. However, in noisy cases, MPM has better performance. Therefore, for non-contact microwave vital sign monitoring, which is usually subject to noisy situations, MPM is a powerful method.
Wheelchair-Mounted Upper Limb Robotic Exoskeleton with Adaptive Controller for Activities of Daily Living
Authors: Schabron Bridget , Desai Jaydip , Yihun Yimesker
Published: September 2021

Neuro-muscular disorders and diseases such as cerebral palsy and Duchenne Muscular Dystrophy can severely limit a person’s ability to perform activities of daily living (ADL). Exoskeletons can provide an active or passive support solution to assist these groups of people to perform ADL. This study presents an artificial neural network-trained adaptive controller mechanism that uses surface electromyography (sEMG) signals from the human forearm to detect hand gestures and navigate an in-house-built wheelchair-mounted upper limb robotic exoskeleton based on the user’s intent while ensuring safety. To achieve the desired position of the exoskeleton based on human intent, 10 hand gestures were recorded from 8 participants without upper limb movement disabilities. Participants were tasked to perform water bottle pick and place activities while using the exoskeleton, and sEMG signals were collected from the forearm and processed through root mean square, median filter, and mean feature extractors prior to training a scaled conjugate gradient backpropagation artificial neural network. The trained network achieved an average of more than 93% accuracy, while all 8 participants who did not have any prior experience of using an exoskeleton were successfully able to perform the task in less than 20 s using the proposed artificial neural network-trained adaptive controller mechanism. These results are significant and promising thus could be tested on people with muscular dystrophy and neuro-degenerative diseases.
Brain-Computer Interface: Advancement and Challenges
Authors: Mridha M. F. , Das Sujoy Chandra , Kabir Muhammad Mohsin , Lima Aklima Akter , Islam Md. Rashedul , Watanobe Yutaka
Published: September 2021

Brain-Computer Interface (BCI) is an advanced and multidisciplinary active research domain based on neuroscience, signal processing, biomedical sensors, hardware, etc. Since the last decades, several groundbreaking research has been conducted in this domain. Still, no comprehensive review that covers the BCI domain completely has been conducted yet. Hence, a comprehensive overview of the BCI domain is presented in this study. This study covers several applications of BCI and upholds the significance of this domain. Then, each element of BCI systems, including techniques, datasets, feature extraction methods, evaluation measurement matrices, existing BCI algorithms, and classifiers, are explained concisely. In addition, a brief overview of the technologies or hardware, mostly sensors used in BCI, is appended. Finally, the paper investigates several unsolved challenges of the BCI and explains them with possible solutions.
Functional Living Skills: A Non-Immersive Virtual Reality Training for Individuals with Major Neurocognitive Disorders
Authors: Panerai Simonetta , Gelardi Donatella , Catania Valentina , Rundo Francesco , Tasca Domenica , Musso Sabrina , Prestianni Giuseppina , Muratore Stefano , Babiloni Claudio , Ferri Raffaele
Published: September 2021

The loss of functional living skills (FLS) is an essential feature of major neurocognitive disorders (M-NCD); virtual reality training (VRT) offers many possibilities for improving FLS in people with M-NCD. The aim of our study was to verify the effectiveness of a non-immersive VRT on FLS for patients with M-NCD. VRT was carried out for 10 to 20 sessions, by means of four 3D apps developed in our institute and installed on a large touch screen. The experimental group (EG) and the control group (CG) included 24 and 18 patients with M-NCD, respectively. They were administered the in vivo test (in specific hospital places reproducing the natural environments) at T1 (pre-training) and T3 (post-training); at T2, only EG was administered VRT. Statistically significant differences between EG and CG in all the in vivo tests were found in the number of correct responses; during VRT, the number of correct responses increased, while the execution times and the number of clues decreased. The improvement in the in vivo tests appeared to be related to the specific VRT applied. The satisfaction of participants with the VRT was moderate to high.
Post-Operative Remote Monitoring for Same-Day Discharge Elective Orthopedic Surgery: A Pilot Study
Authors: Mouli Vibav H. , Carrera Christopher X. , Schudrowitz Natalie , Flanagan Jay Jean , Shah Vivek , Fitz Wolfgang
Published: September 2021

The purposes of this pilot study are to utilize digital remote monitoring to (a) evaluate the usability and satisfaction of a wireless blood pressure (BP) and heart rate (HR) monitor and (b) determine whether these data can enable safe mobilization at home after same-day discharge (SDD) joint replacement. A population of 23 SDD patients undergoing unicompartmental knee arthroplasty (UKA), total knee arthroplasty (TKA), or total hip arthroplasty (THA) were given a cellular BP/HR monitor, with real-time data capture. Patients took three readings after surgery, observing for specific blood pressure decreases, HR increases, or hypotensive symptoms. If any criteria applied, patients followed a hydration protocol and delayed ambulation. Home coaching was also provided to each patient. Patient experience was surveyed, and responses were assessed using descriptive statistics. Of 18 patients discharged (78%), 17 returned surveys, of which 100% reported successful device operation. The mean “ease of use” rating was 8.9/10; satisfaction with home coaching was 9.7/10; and belief that the protocol improved patient safety was 8.4/10. A total of 27.8% (n = 5) had hypotensive readings and appropriately delayed ambulation. Our pilot findings support the feasibility of and confirm the satisfaction with remote monitoring after SDD arthroplasty. All patients with symptoms of hypotension were successfully remotely managed using a standardized hydration protocol prior to safe mobilization.
Synthesis Methods of Obtaining Materials for Hydrogen Sensors
Authors: Constantinoiu Izabela , Viespe Cristian
Published: September 2021

The development of hydrogen sensors has acquired a great interest from researchers for safety in fields such as chemical industry, metallurgy, pharmaceutics or power generation, as well as due to hydrogen’s introduction as fuel in vehicles. Several types of sensors have been developed for hydrogen detection, including resistive, surface acoustic wave, optical or conductometric sensors. The properties of the material of the sensitive area of the sensor are of great importance for establishing its performance. Besides the nature of the material, an important role for its final properties is played by the synthesis method used and the parameters used during the synthesis. The present paper highlights recent results in the field of hydrogen detection, obtained using four of the well-known synthesis and deposition methods: sol-gel, co-precipitation, spin-coating and pulsed laser deposition (PLD). Sensors with very good results have been achieved by these methods, which gives an encouraging perspective for their use in obtaining commercial hydrogen sensors and their application in common areas for society.
Skin Strain Analysis of the Scapular Region and Wearables Design
Authors: Carnevale Arianna , Schena Emiliano , Formica Domenico , Massaroni Carlo , Longo Umile Giuseppe , Denaro Vincenzo
Published: September 2021

Monitoring scapular movements is of relevance in the contexts of rehabilitation and clinical research. Among many technologies, wearable systems instrumented by strain sensors are emerging in these applications. An open challenge for the design of these systems is the optimal positioning of the sensing elements, since their response is related to the strain of the underlying substrates. This study aimed to provide a method to analyze the human skin strain of the scapular region. Experiments were conducted on five healthy volunteers to assess the skin strain during upper limb movements in the frontal, sagittal, and scapular planes at different degrees of elevation. A 6 × 5 grid of passive markers was placed posteriorly to cover the entire anatomic region of interest. Results showed that the maximum strain values, in percentage, were 28.26%, and 52.95%, 60.12% and 60.87%, 40.89%, and 48.20%, for elevation up to 90° and maximum elevation in the frontal, sagittal, and scapular planes, respectively. In all cases, the maximum extension is referred to the pair of markers placed horizontally near the axillary fold. Accordingly, this study suggests interesting insights for designing and positioning textile-based strain sensors in wearable systems for scapular movements monitoring.
Traffic Accident Data Generation Based on Improved Generative Adversarial Networks
Authors: Chen Zhijun , Zhang Jingming , Zhang Yishi , Huang Zihao
Published: September 2021

For urban traffic, traffic accidents are the most direct and serious risk to people’s lives, and rapid recognition and warning of traffic accidents is an important remedy to reduce their harmful effects. However, research scholars are often confronted with the problem of scarce and difficult-to-collect accident data resources for traffic accident scenarios. Therefore, in this paper, a traffic data generation model based on Generative Adversarial Networks (GAN) is developed. To make GAN applicable to non-graphical data, we improve the generator network structure of the model and used the generated model to resample the original data to obtain new traffic accident data. By constructing an adversarial neural network model, we generate a large number of data samples that are similar to the original traffic accident data. Results of the statistical test indicate that the generated samples are not significantly different from the original data. Furthermore, the experiments of traffic accident recognition with several representative classifiers demonstrate that the augmented data can effectively enhance the performance of accident recognition, with a maximum increase in accuracy of 3.05% and a maximum decrease in the false positive rate of 2.95%. Experimental results verify that the proposed method can provide reliable mass data support for the recognition of traffic accidents and road traffic safety.
Deep-Learning-Based Wi-Fi Indoor Positioning System Using Continuous CSI of Trajectories
Authors: Zhang Zhongfeng , Lee Minjae , Choi Seungwon
Published: September 2021

In a Wi-Fi indoor positioning system (IPS), the performance of the IPS depends on the channel state information (CSI), which is often limited due to the multipath fading effect, especially in indoor environments involving multiple non-line-of-sight propagation paths. In this paper, we propose a novel IPS utilizing trajectory CSI observed from predetermined trajectories instead of the CSI collected at each stationary location; thus, the proposed method enables all the CSI along each route to be continuously encountered in the observation. Further, by using a generative adversarial network (GAN), which helps enlarge the training dataset, the cost of trajectory CSI collection can be significantly reduced. To fully exploit the trajectory CSI’s spatial and temporal information, the proposed IPS employs a deep learning network of a one-dimensional convolutional neural network–long short-term memory (1DCNN-LSTM). The proposed IPS was hardware-implemented, where digital signal processors and a universal software radio peripheral were used as a modem and radio frequency transceiver, respectively, for both access point and mobile device of Wi-Fi. We verified that the proposed IPS based on the trajectory CSI far outperforms the state-of-the-art IPS based on the CSI collected from stationary locations through extensive experimental tests and computer simulations.
TIF-Reg: Point Cloud Registration with Transform-Invariant Features in SE(3)
Authors: Chen Baifan , Chen Hong , Song Baojun , Gong Grace
Published: September 2021

Three-dimensional point cloud registration (PCReg) has a wide range of applications in computer vision, 3D reconstruction and medical fields. Although numerous advances have been achieved in the field of point cloud registration in recent years, large-scale rigid transformation is a problem that most algorithms still cannot effectively handle. To solve this problem, we propose a point cloud registration method based on learning and transform-invariant features (TIF-Reg). Our algorithm includes four modules, which are the transform-invariant feature extraction module, deep feature embedding module, corresponding point generation module and decoupled singular value decomposition (SVD) module. In the transform-invariant feature extraction module, we design TIF in SE(3) (which means the 3D rigid transformation space) which contains a triangular feature and local density feature for points. It fully exploits the transformation invariance of point clouds, making the algorithm highly robust to rigid transformation. The deep feature embedding module embeds TIF into a high-dimension space using a deep neural network, further improving the expression ability of features. The corresponding point cloud is generated using an attention mechanism in the corresponding point generation module, and the final transformation for registration is calculated in the decoupled SVD module. In an experiment, we first train and evaluate the TIF-Reg method on the ModelNet40 dataset. The results show that our method keeps the root mean squared error (RMSE) of rotation within 0.5∘ and the RMSE of translation error close to 0 m, even when the rotation is up to [−180∘, 180∘] or the translation is up to [−20 m, 20 m]. We also test the generalization of our method on the TUM3D dataset using the model trained on Modelnet40. The results show that our method’s errors are close to the experimental results on Modelnet40, which verifies the good generalization ability of our method. All experiments prove that the proposed method is superior to state-of-the-art PCReg algorithms in terms of accuracy and complexity.
A SLIC-DBSCAN Based Algorithm for Extracting Effective Sky Region from a Single Star Image
Authors: Shi Chenguang , Zhang Rui , Yu Yong , Sun Xingzhe , Lin Xiaodong
Published: September 2021

The star tracker is widely used for high-accuracy missions due to its high accuracy position high autonomy and low power consumption. On the other hand, the ability of interference suppression of the star tracker has always been a hot issue of concern. A SLIC-DBSCAN-based algorithm for extracting effective information from a single image with strong interference has been developed in this paper to remove interferences. Firstly, the restricted LC (luminance-based contrast) transformation is utilized to enhance the contrast between background noise and the large-area interference. Then, SLIC (the simple linear iterative clustering) algorithm is adopted to segment the saliency map and in this process, optimized parameters are harnessed. Finally, from these segments, features are extracted and superpixels with similar features are combined by using DBSCAN (density-based spatial clustering of applications with noise). The proposed algorithm is proved effective by successfully removing large-area interference and extracting star spots from the sky region of the real star image.
Investigation and Mitigation of Noise Contributions in a Compact Heterodyne Interferometer
Authors: Zhang Yanqi , Hines Adam S. , Valdes Guillermo , Guzman Felipe
Published: September 2021

We present a noise estimation and subtraction algorithm capable of increasing the sensitivity of heterodyne laser interferometers by one order of magnitude. The heterodyne interferometer is specially designed for dynamic measurements of a test mass in the application of sub-Hz inertial sensing. A noise floor of 3.31×10−11m/Hz at 100 mHz is achieved after applying our noise subtraction algorithm to a benchtop prototype interferometer that showed a noise level of 2.76×10−10m/Hz at 100 mHz when tested in vacuum at levels of 3×10−5 Torr. Based on the previous results, we investigated noise estimation and subtraction techniques of non-linear optical pathlength noise, laser frequency noise, and temperature fluctuations in heterodyne laser interferometers. For each noise source, we identified its contribution and removed it from the measurement by linear fitting or a spectral analysis algorithm. The noise correction algorithm we present in this article can be generally applied to heterodyne laser interferometers.
Design of Optimized Coded LFM Waveform for Spectrum Shared Radar System
Authors: Kim Dong-Hoon , Kim Hyung-Jung , Lim Jae-Han
Published: September 2021

To meet the increasing demands for remote sensing, a number of radar systems using Linear Frequency Modulation (LFM) waveforms have been deployed, causing the problem of depleting frequency resources. To address this problem, several researchers have proposed the Spectrum Shared Radar System (SSRS) in which multiple radars share the same frequency band to transmit and receive their own signals. To mitigate the interferences caused by the signal transmission by other radars, SSRS employs orthogonal waveforms that inherit the orthogonality of the waveforms from orthogonal codes. However, the inherited orthogonality of the codes is significantly reduced when incorporating LFM waveforms with the codes. To solve this problem, in this paper, we propose a novel but simple scheme for generating a set of optimized coded LFM waveforms via new optimization framework. In the optimization framework, we minimize the weighted sum of autocorrelation sidelobe peaks (ASP) and cross-correlation peaks (CP) of the coded LFM waveforms to maximize the orthogonality of the waveforms. Through computer simulations, we show that the waveforms generated by the proposed scheme outperform the waveforms created by previous proposals in terms of ASP and CP.
Identification of Two Commercial Pesticides by a Nanoparticle Gas-Sensing Array
Authors: Skotadis Evangelos , Kanaris Aris , Aslanidis Evangelos , Kalatzis Nikos , Chatzipapadopoulos Fotis , Marianos Nikolaos , Tsoukalas Dimitris
Published: September 2021

This study presents the experimental testing of a gas-sensing array, for the detection of two commercially available pesticides (i.e., Chloract 48 EC and Nimrod), towards its eventual use along a commercial smart-farming system. The array is comprised of four distinctive sensing devices based on nanoparticles, each functionalized with a different gas-absorbing polymeric layer. As discussed herein, the sensing array is able to identify as well as quantify three gas-analytes, two pesticide solutions, and relative humidity, which acts as a reference analyte. All of the evaluation experiments were conducted in close to real-life conditions; specifically, the sensors response towards the three analytes was tested in three relative humidity backgrounds while the effect of temperature was also considered. The unique response patterns generated after the exposure of the sensing-array to the two gas-analytes were analyzed using the common statistical analysis tool Principal Component Analysis (PCA). The sensing array, being compact, low-cost, and highly sensitive, can be easily integrated with pre-existing crop-monitoring solutions. Given that there are limited reports for effective pesticide gas-sensing solutions, the proposed gas-sensing technology would significantly upgrade the added-value of the integrated system, providing it with unique advantages.
Deep and Machine Learning Using SEM, FTIR, and Texture Analysis to Detect Polysaccharide in Raspberry Powders
Authors: Przybył Krzysztof , Koszela Krzysztof , Adamski Franciszek , Samborska Katarzyna , Walkowiak Katarzyna , Polarczyk Mariusz
Published: September 2021

In the paper, an attempt was made to use methods of artificial neural networks (ANN) and Fourier transform infrared spectroscopy (FTIR) to identify raspberry powders that are different from each other in terms of the amount and the type of polysaccharide. Spectra in the absorbance function (FTIR) were prepared as well as training sets, taking into account the structure of microparticles acquired from microscopic images with Scanning Electron Microscopy (SEM). In addition to the above, Multi-Layer Perceptron Networks (MLPNs) with a set of texture descriptors (machine learning) and Convolution Neural Network (CNN) with bitmap (deep learning) were devised, which is an innovative attitude to solving this issue. The aim of the paper was to create MLPN and CNN neural models, which are characterized by a high efficiency of classification. It translates into recognizing microparticles (obtaining their homogeneity) of raspberry powders on the basis of the texture of the image pixel.
Cyber-Physical Vulnerability Assessment in Smart Grids Based on Multilayer Complex Networks
Authors: Alonso Monica , Turanzas Jaime , Amaris Hortensia , Ledo Angel T.
Published: September 2021

In the last decade, the main attacks against smart grids have occurred in communication networks (ITs) causing the disconnection of physical equipment from power networks (OTs) and leading to electricity supply interruptions. To deal with the deficiencies presented in past studies, this paper addresses smart grids vulnerability assessment considering the smart grid as a cyber-physical heterogeneous interconnected system. The model of the cyber-physical system is composed of a physical power network model and the information and communication technology network model (ICT) both are interconnected and are interrelated by means of the communication and control equipment installed in the smart grid. This model highlights the hidden interdependencies between power and ICT networks and contains the interaction between both systems. To mimic the real nature of smart grids, the interconnected heterogeneous model is based on multilayer complex network theory and scale-free graph, where there is a one-to-many relationship between cyber and physical assets. Multilayer complex network theory centrality indexes are used to determine the interconnected heterogeneous system set of nodes criticality. The proposed methodology, which includes measurement, communication, and control equipment, has been tested on a standardized power network that is interconnected to the ICT network. Results demonstrate the model’s effectiveness in detecting vulnerabilities in the interdependent cyber-physical system compared to traditional vulnerability assessments applied to power networks (OT).
A Machine Learning Method for Classification and Identification of Potato Cultivars Based on the Reaction of MOS Type Sensor-Array
Authors: Khorramifar Ali , Rasekh Mansour , Karami Hamed , Malaga-Toboła Urszula , Gancarz Marek
Published: September 2021

In response to one of the most important challenges of the century, i.e., the estimation of the food demands of a growing population, advanced technologies have been employed in agriculture. The potato has the main contribution to people’s diet worldwide. Therefore, its different aspects are worth studying. The large number of potato varieties, lack of awareness about its new cultivars among farmers to cultivate, time-consuming and inaccurate process of identifying different potato cultivars, and the significance of identifying potato cultivars and other agricultural products (in every food industry process) all necessitate new, fast, and accurate methods. The aim of this study was to use an electronic nose, along with chemometrics methods, including PCA, LDA, and ANN as fast, inexpensive, and non-destructive methods for detecting different potato cultivars. In the present study, nine sensors with the best response to VOCs were adopted. VOCs sensors were used at various VOCs concentrations (1 to 10,000 ppm) to detect different gases. The results showed that a PCA with two main components, PC1 and PC2, described 92% of the total samples’ dataset variance. In addition, the accuracy of the LDA and ANN methods were 100 and 96%, respectively.