bluuebunny/crossref_metadata_2025 huggingface dataset:
Quinoa (Chenopodium quinoa Willd.): Genetic Diversity According to ISSR and SCoT Markers, Relative Gene Expression, and Morpho-Physiological Variation under Salinity Stress
Authors: Abd El-Moneim Diaa , ELsarag Eman , Aloufi Salman , El-Azraq Asmaa , ALshamrani Salha , Safhi Fatmah , Ibrahim Amira
Published: December 2021

Quinoa (Chenopodium quinoa Willd.) is a halophytic crop that can withstand a variety of abiotic stresses, including salt. The present research examined the mechanisms of salt tolerance in five different quinoa genotypes at four different salinity levels (control (60), 80, 120, and 160 mM NaCl). ISSR and SCoT analysis revealed high polymorphism percentages of 90.91% and 85.26%, respectively. Furthermore, ISSR 1 and SCoT 7 attained the greatest number of polymorphic amplicons (27 and 26), respectively. Notably, LINE-6 and M-28 genotypes demonstrated the greatest number of unique positive and negative amplicons (50 and 42) generated from ISSR and SCoT, respectively. Protein pattern analysis detected 11 bands with a polymorphism percentage 27.27% among the quinoa genotypes, with three unique bands distinguishable for the M-28 genotype. Similarity correlation indicated that the highest similarity was between S-10 and Regeolone-3 (0.657), while the lowest similarity was between M-28 and LINE-6 (0.44). Significant variations existed among the studied salinity treatments, genotypes, and the interactions between them. The highest and lowest values for all the studied morpho-physiological and biochemical traits were recorded at 60 and 160 mM NaCl concentrations, respectively, except for the Na and proline contents, which exhibited the opposite relationship. The M-28 genotype demonstrated the highest values for all studied characteristics, while the LINE-6 genotype represented the lowest in both seasons. On the other hand, mRNA transcript levels for CqSOS1 did not exhibit differential expression in roots and leaf tissues, while the expression of CqNHX1 was upregulated more in both tissues for the M-28 genotype than for the LINE-6 genotype, and its maximum induction was seen in the leaves. Overall, the genotypes M-28 and LINE-6 were identified as the most and least salinity-tolerant, respectively.
Chemical Composition and Content of Biologically Active Substances Found in Cotinus coggygria, Dactylorhiza maculata, Platanthera chlorantha Growing in Various Territories
Authors: Sukhikh Stanislav , Asyakina Lyudmila , Korobenkov Maxim , Skrypnik Liubov , Pungin Artem , Ivanova Svetlana , Larichev Timothy , Larina Viktoria , Krol Olesia , Ulrikh Elena , Chupakhin Evgeny , Babich Olga
Published: December 2021

Medicinal plants (Cotinus coggygria, Dactylorhiza maculata, Platanthera chlorantha) growing in various territories (Kaliningrad, Moscow, and Minsk regions) were the objects of research. This paper presents a study of the chemical composition of these plants. To analyze the qualitative and quantitative composition of biologically active substances, the method of high-performance liquid chromatography was used. Atomic absorption spectrometry was used to study the content of trace elements. The content of organic acids and vitamins was determined by capillary electrophoresis using the Kapel-105/105M capillary electrophoresis system with high negative polarity. Extracts of medicinal plants were obtained on a Soxhlet apparatus using 70% ethanol as an extractant. It was found that among the biologically active substances in the plants under discussion, hyperoside, rutin (C. coggygria), Ferulic acid and Gallic acid (D. maculata), triene hydrocarbon (3,7-Dimethyl-1,3,6-octatriene), unsaturated alcohol (3,7-Dimethyl-2,6-octadien-1-ol), and benzyl acetate (P. chlorantha) prevailed. Samples of these medicinal plants contained trace elements (phosphorus, potassium, calcium, sodium, magnesium, and sulfur) and many aliphatic organic acids (succinic acid, benzoic acid, fumaric acid, citric acid, oxalic acid, and tartaric acid). The largest amount of biologically active substances and secondary metabolites of the studied plants from the Eastern Baltic is associated with climatic and ecological differences from other regions. The composition of these plants determines the potential of their use in feed additives for livestock and poultry as part of measures to improve the quality of livestock products. The use of medicinal plants for the production of feed additives is relevant in terms of improving regional economies, as well as improving the quality of life and nation’s health by providing ecologically clean livestock products.
Boosted Antioxidant Effect Using a Combinatory Approach with Essential Oils from Origanum compactum, Origanum majorana, Thymus serpyllum, Mentha spicata, Myrtus communis, and Artemisia herba-alba: Mixture Design Optimization
Authors: Ouedrhiri Wessal , Mechchate Hamza , Moja Sandrine , Mothana Ramzi A. , Noman Omar M. , Grafov Andriy , Greche Hassane
Published: December 2021

Several studies have demonstrated the possible synergistic effect as an effective strategy to boost the bioactivity of essential oils. Using this framework, this study was conducted to effectively establish the ideal combination of six essential oils from different plants (Origanum compactum, Origanum majorana, Thymus serpyllum, Mentha spicata, Myrtus communis, and Artemisia herba-alba) that would express the best antioxidant activity. Each mixture was optimized using a mixture design approach to generate the most effective blend. The 2,2-diphenyl-1-picrylhydrazyl radical scavenging method was used as a reference method to assess the antioxidant activity. Each essential oil’s composition was identified using the GC/MS method. The single essential oil activities demonstrated variable antioxidant effects, and following the mixture design approach, the optimal antioxidant blend was revealed, as two mixtures demonstrated the best antiradical activity with 79.46% obtained with the mixture of O. majorana (28%) and M. spicata (71%) and 78.8% obtained with the mixture O. compactum (64%), O. majorana (13%), and T. serpyllum (21%). This study proposes a practical way to elaborate mixtures in the search for a boosting effect that can be oriented for the food or pharmaceutical industry.
High-Frequency Plant Regeneration, Genetic Uniformity, and Flow Cytometric Analysis of Regenerants in Rutachalepensis L.
Authors: Qahtan Ahmed A. , Faisal Mohamad , Alatar Abdulrahman A. , Abdel-Salam Eslam M.
Published: December 2021

Ruta chalepensis L., an evergreen shrub in the citrus family, is well-known around the world for its essential oils and variety of bioactivities, indicating its potential medicinal applications. In this study, we investigated the effect of different culture conditions, including plant growth regulators, media types, pH of the medium, and carbon sources, on in vitro regeneration from nodal explants of R. chalepensis. Following 8 weeks of culture, the highest percentage of regeneration (96.3%) and maximum number of shoots (40.3 shoot/explant) with a length of 4.8 cm were obtained with Murashige and Skoog (MS) medium at pH 5.8, supplemented with 3.0% sucrose and 5.0 µM 6-Benzyladenine (BA) in combination with 1.0 µM 1-naphthaleneacetic acid (NAA). For rooting, individually harvested shootlets were transferred on ½ MS (half-strength) supplemented with IAA (indole-3-acetic acid), IBA (indole 3-butyric acid), or NAA, and the best response in terms of root induction (91.6%), number of roots (5.3), and root mean length (4.9 cm) was achieved with 0.5 µM IBA after 6 weeks. An average of 95.2 percent of healthy, in vitro regenerated plantlets survived after being transplanted into potting soil, indicating that they were effectively hardened. DNA assays (PCR-based markers) such as random amplification of polymorphic DNA (RAPD) and directed amplification of minisatellite-region (DAMD) were employed to assess in vitro cultivated R. chalepensis plantlets that produced a monomorphic banding pattern confirming the genetic stability. Additionally, no changes in the flow cytometric profile of ploidy between regenerated plantlets and donor plants were detected. Regeneration of this valuable medicinal plant in vitro will open up new avenues in pharmaceutical biotechnology by providing an unconventional steadfast system for mass multiplication and might be effectively used in genetic manipulation for enhanced bioactive constituents.
Copolymerization of Ethylene with Selected Vinyl Monomers Catalyzed by Group 4 Metal and Vanadium Complexes with Multidentate Ligands: A Short Review
Authors: Białek Marzena , Fryga Julia
Published: December 2021

This paper gives a short overview of homogeneous post-metallocene catalysts based on group 4 metal and vanadium complexes bearing multidentate ligands. It summarizes the catalytic behavior of those catalysts in copolymerization of ethylene with 1-olefins, with styrenic monomers and with α,ω-alkenols. The review is focused on finding correlations between the structure of a complex, its catalyst activity and comonomer incorporation ability, as well as the microstructure of the copolymer chains.
Continuous Crystallization Using Ultrasound Assisted Nucleation, Cubic Cooling Profiles and Oscillatory Flow
Authors: Vancleef Arne , De Beuckeleer Ward , Van Gerven Tom , Thomassen Leen C. J. , Braeken Leen
Published: December 2021

Continuous tubular crystallizers have the potential to reduce manufacturing costs and increase product quality. However, designing tubular crystallizers is a complex and challenging task as crystallization is a complex, multiphase process with a propensity for fouling and clogging. While several designs have been proposed to overcome these issues, these designs are either unproven or poorly scalable and complex. In this work a continuous crystallizer is designed and evaluated to mitigate these issues. The tubular crystallizer combines a novel method to obtain a cubic cooling profile to control the supersaturation, ultrasound to induce nucleation and oscillatory flow to improve mixing and minimize fouling and sedimentation. The results show that the crystallizer was able to operate for more than 4 h without clogging, with high yields and a narrow particle size distribution. The design proposed here is therefore considered a viable approach for continuous crystallizers.
Design of a Bench-Scale Tubular Reactor Similar to Plug Flow Reactor for Gas-Phase Kinetic Data Generation-Illustration with the Pyrolysis of Octanoic Acid
Authors: Gornay Julien , Plasari Edouard , Portha Jean-François , Glaude Pierre-Alexandre , Billaud Francis , Coniglio Lucie
Published: December 2021

The material described in this article deals with waste conversion into energy vectors by pyrolysis, steam cracking, or oxidation of liquid biomass, carried out at small to medium scale. The design of a bench-scale experimental setup devoted to gas phase kinetic data generation in a tubular reactor under laminar regime close to plug flow is detailed based on a very simple approach. Validation of the designed bench-scale setup was successfully carried out within the context of octanoic acid pyrolysis by generating kinetic data with satisfactory measurement repeatability and material balances. The key to this positive result is that axial dispersion coefficient is much smaller in gas-phase than in liquid-phase, thus allowing the designed small sized tubular reactor to be close to the plug flow reactor. Such a feature of the axial dispersion coefficient is not well known by the wider public. Besides, octanoic acid was selected as surrogate for carboxylic acids because of their key role in various industrial applications (combustion of ethyl biodiesel; production of biofuel and biosourced chemicals).
Research on Measurement and Application of China’s Regional Logistics Development Level under Low Carbon Environment
Authors: Guo Zixue , Tian Yu , Guo Xinmei , He Zefang
Published: December 2021

To solve the problem of fuzziness and randomness in regional logistics decarbonization evaluation and accurately assess regional logistics decarbonization development, an evaluation model of regional logistics decarbonization development is established. First, the evaluation index of regional logistics decarbonization development is constructed from three dimensions: low-carbon logistics environment support, low-carbon logistics strength and low-carbon logistics potential. Second, the evaluation indexes are used as cloud model variables, and the cloud numerical characteristic values and cloud affiliation degrees are determined according to the cloud model theory. The entropy weight method is used to determine the index weights, and the comprehensive determination degree of the research object affiliated to the logistics decarbonization level is calculated comprehensively. Finally, Beijing-Tianjin-Hebei region is used as an example for empirical evidence, analyzing the development logistics decarbonization and its and temporal variability in Beijing, Tianjin and Hebei provinces and cities. The results of the study show that the development logistics decarbonization in Beijing, Tianjin and Hebei Province has been improved to different degrees during 2013–2019, but the development is uneven. Developing to 2019, the three provinces and cities of Beijing, Tianjin and Hebei still have significant differences in terms of economic environment, logistics industry scale, logistics industry inputs and outputs, and technical support.
Migration-Based Moth-Flame Optimization Algorithm
Authors: Nadimi-Shahraki Mohammad H. , Fatahi Ali , Zamani Hoda , Mirjalili Seyedali , Abualigah Laith , Abd Elaziz Mohamed
Published: December 2021

Moth–flame optimization (MFO) is a prominent swarm intelligence algorithm that demonstrates sufficient efficiency in tackling various optimization tasks. However, MFO cannot provide competitive results for complex optimization problems. The algorithm sinks into the local optimum due to the rapid dropping of population diversity and poor exploration. Hence, in this article, a migration-based moth–flame optimization (M-MFO) algorithm is proposed to address the mentioned issues. In M-MFO, the main focus is on improving the position of unlucky moths by migrating them stochastically in the early iterations using a random migration (RM) operator, maintaining the solution diversification by storing new qualified solutions separately in a guiding archive, and, finally, exploiting around the positions saved in the guiding archive using a guided migration (GM) operator. The dimensionally aware switch between these two operators guarantees the convergence of the population toward the promising zones. The proposed M-MFO was evaluated on the CEC 2018 benchmark suite on dimension 30 and compared against seven well-known variants of MFO, including LMFO, WCMFO, CMFO, CLSGMFO, LGCMFO, SMFO, and ODSFMFO. Then, the top four latest high-performing variants were considered for the main experiments with different dimensions, 30, 50, and 100. The experimental evaluations proved that the M-MFO provides sufficient exploration ability and population diversity maintenance by employing migration strategy and guiding archive. In addition, the statistical results analyzed by the Friedman test proved that the M-MFO demonstrates competitive performance compared to the contender algorithms used in the experiments.
Quality of Milled Rice from Large-Scale Dried Paddy Rice by Hot Air Combined with Radio Frequency Heating
Authors: Chitsuthipakorn Karn , Thanapornpoonpong Sa-nguansak
Published: December 2021

A scaled-up process for paddy drying was developed using hot air (HA) combined with radio frequency (RF) heating. The study was conducted using hot air (control treatment) arranged in descending order in four temperature levels, namely 80 °C at moisture content of 25–26%, 70 °C at moisture content of 20–25%, 60 °C at moisture content of 17–20%, and 50 °C at moisture content of 13–17%, as well as with hot air combined with radio frequency (HA/RF) at different paddy temperatures (45–60 °C) by adjusting the appropriate RF energy when passing through RF heating chamber, namely HA/RF45, HA/RF50, HA/RF55, and HA/RF60. Each treatment was performed in three replicates and data were statistically analyzed in a randomized complete block design. The quality attributes of paddies affected by the drying process were assessed: fissure percentage, color, milling quality, and sensory evaluation. The drying efficiency showed that the drying time and the specific energy consumption could be decreased by up to 54.44% and 23.17% at HA/RF60 and HA/RF45, respectively. As the RF heating temperature increased, the fissure percentage of brown rice kernels at HA/RF45 and HA was not significantly impacted. Regarding color evaluation, combining RF heating and convective drying at all given conditions could be statistically applied in terms of the b*, WI, and ΔE* value. Considering the milling yield of HA as the baseline, head rice yield was maximized at HA/RF45, while bran yield reached the maximum at HA/RF60. The liking score of cooked rice after it was dried using the HA method was the highest. This study concludes that the HA/RF45 was the most appropriate drying condition, and this may provide preliminary exposure to the industrial drying of paddies.
CFD Modeling of Flame Jump across Air Gap between Evasé and Capture Duct for Ventilation Air Methane Abatement
Authors: Peng Zhengbiao , Zanganeh Jafar , Moghtaderi Behdad
Published: December 2021

The ventilation air–methane (VAM) released from underground mines is often transported into regenerative thermal oxidizer (RTO) devices and burnt into heat energy. This study numerically investigates the scenarios where explosion occurs inside the RTO and the flame and pressure waves propagate back quickly towards the VAM discharge duct. Possibilities of secondary explosion in the discharge duct, hence in the downstream underground mines, are examined. The results critically showed that when the methane concentration accumulated in the RTO reached 7.5% or above, the flame generated from the explosion jumped to the evasé of the discharge section (over a distance of 29.4 m) and could induce explosions in underground mines.
The Analysis of the Spatial Production Mechanism and the Coupling Coordination Degree of the Danwei Compound Based on the Spatial Ternary Dialectics
Authors: Yang Zihan , Yang Jianqiang , Ren Kai
Published: December 2021

With the gradual deepening of the development of high-quality urban transformation, the “Danwei Compound” urban space production method constitutes the basis of Chinese current urban spatial transformation. The transformation plan of the original danwei compound “stock” to promote the healthy development of urban society has become the focus of research. First, with the help of Lefebvre’s space production theory, combined with the spatial transformation characteristics of its own structural form experienced by the Chinese urban danwei compound, the space production is divided into three stages, namely, the diversity-orderly type average space of the danwei compound system period, dispersed type abstract space of the commercial enclosed community period, and the integrated differential space of a livable community undergoing regeneration and transformation. At each stage, the government, market, and residents have different influences on time-space production. Secondly, using Hefei’s typical danwei compound as the research carrier, according to the space ternary dialectics, a multi-level analysis of “representations of space-representational space-spatial practice” is carried out on the production mechanism, and the logic of different types of spaces in different periods are described. Among them, the representations of space of the change of the danwei compound are the interrelationship of multiple governance subjects in different periods, such as changes in the implementation degree of governance strategies, the degree of residents’ community governance participation, residents’ satisfaction with community governance, etc. The representational space is the residents’ community perception and interpersonal relationship at different transition stages, Interpersonal trust, and other social relations’ changes. Spatial practice is manifested in changes in the support of public service facilities, public space, per capita living area, building quality, architectural style, and illegal building area. Finally, the three-dimensional space dialectical coupling coordination degree model is used to analyze and compare the representations of space of typical settlements in the three stages and the coupling characteristics of the representational space and the practice of space. On this basis, we provide innovative ideas and put forward relevant measures and suggestions for the regeneration, transformation, and development of livable areas.
Wearable Devices, Smartphones, and Interpretable Artificial Intelligence in Combating COVID-19
Authors: Hijazi Haytham , Abu Talib Manar , Hasasneh Ahmad , Bou Nassif Ali , Ahmed Nafisa , Nasir Qassim
Published: December 2021

Physiological measures, such as heart rate variability (HRV) and beats per minute (BPM), can be powerful health indicators of respiratory infections. HRV and BPM can be acquired through widely available wrist-worn biometric wearables and smartphones. Successive abnormal changes in these indicators could potentially be an early sign of respiratory infections such as COVID-19. Thus, wearables and smartphones should play a significant role in combating COVID-19 through the early detection supported by other contextual data and artificial intelligence (AI) techniques. In this paper, we investigate the role of the heart measurements (i.e., HRV and BPM) collected from wearables and smartphones in demonstrating early onsets of the inflammatory response to the COVID-19. The AI framework consists of two blocks: an interpretable prediction model to classify the HRV measurements status (as normal or affected by inflammation) and a recurrent neural network (RNN) to analyze users’ daily status (i.e., textual logs in a mobile application). Both classification decisions are integrated to generate the final decision as either “potentially COVID-19 infected” or “no evident signs of infection”. We used a publicly available dataset, which comprises 186 patients with more than 3200 HRV readings and numerous user textual logs. The first evaluation of the approach showed an accuracy of 83.34 ± 1.68% with 0.91, 0.88, 0.89 precision, recall, and F1-Score, respectively, in predicting the infection two days before the onset of the symptoms supported by a model interpretation using the local interpretable model-agnostic explanations (LIME).
Reflected Wave Reduction Based on Time-Delay Separation for the Plane Array of Multilayer Acoustic Absorbers
Authors: Park Hwijin , Won Yeong Bae , Jeong Sehyeong , Pyun Joo Young , Park Kwan Kyu , Lee Jeong-Min , Seo Hee-Seon , Yi Hak
Published: December 2021

This paper presents a control technique for reducing the reflection of acoustic signals for the plane array of multilayer acoustic absorbers underwater. In order to achieve this, a plane array of multilayer acoustic absorbers is proposed to attenuate low-frequency noise, with each unit consisting of a piezoelectric transducer, two layers of polyvinylidene fluorides and three layers of the acoustic window. Time-delay separation is used to find the incident and reflected acoustic signals to achieve reflected sound reduction. Experimental comparison of the attenuation rate of the reflected acoustic signal when performing passive and active controls is considered to verify the effectiveness of the time-delay separation technique applied plane array absorbers. Experiments on the plane array of smart skin absorbers confirmed that the reduction of reflected acoustic signals makes it suitable for a wide range of underwater applications.
Energy Efficient UAV Flight Path Model for Cluster Head Selection in Next-Generation Wireless Sensor Networks
Authors: Haider Syed Kamran , Jiang Aimin , Almogren Ahmad , Rehman Ateeq Ur , Ahmed Abbas , Khan Wali Ullah , Hamam Habib
Published: December 2021

Wireless sensor networks (WSNs) are one of the fundamental infrastructures for Internet of Things (IoTs) technology. Efficient energy consumption is one of the greatest challenges in WSNs because of its resource-constrained sensor nodes (SNs). Clustering techniques can significantly help resolve this issue and extend the network’s lifespan. In clustering, WSN is divided into various clusters, and a cluster head (CH) is selected in each cluster. The selection of appropriate CHs highly influences the clustering technique, and poor cluster structures lead toward the early death of WSNs. In this paper, we propose an energy-efficient clustering and cluster head selection technique for next-generation wireless sensor networks (NG-WSNs). The proposed clustering approach is based on the midpoint technique, considering residual energy and distance among nodes. It distributes the sensors uniformly creating balanced clusters, and uses multihop communication for distant CHs to the base station (BS). We consider a four-layer hierarchical network composed of SNs, CHs, unmanned aerial vehicle (UAV), and BS. The UAV brings the advantage of flexibility and mobility; it shortens the communication range of sensors, which leads to an extended lifetime. Finally, a simulated annealing algorithm is applied for the optimal trajectory of the UAV according to the ground sensor network. The experimental results show that the proposed approach outperforms with respect to energy efficiency and network lifetime when compared with state-of-the-art techniques from recent literature.
A Novel, Low Computational Complexity, Parallel Swarm Algorithm for Application in Low-Energy Devices
Authors: Długosz Zofia , Rajewski Michał , Długosz Rafał , Talaśka Tomasz
Published: December 2021

In this work, we propose a novel metaheuristic algorithm that evolved from a conventional particle swarm optimization (PSO) algorithm for application in miniaturized devices and systems that require low energy consumption. The modifications allowed us to substantially reduce the computational complexity of the PSO algorithm, translating to reduced energy consumption in hardware implementation. This is a paramount feature in the devices used, for example, in wireless sensor networks (WSNs) or wireless body area sensors (WBANs), in which particular devices have limited access to a power source. Various swarm algorithms are widely used in solving problems that require searching for an optimal solution, with simultaneous occurrence of a different number of sub-optimal solutions. This makes the hardware implementation worthy of consideration. However, hardware implementation of the conventional PSO algorithm is challenging task. One of the issues is an efficient implementation of the randomization function. In this work, we propose novel methods to work around this problem. In the proposed approach, we replaced the block responsible for generating random values using deterministic methods, which differentiate the trajectories of particular particles in the swarm. Comprehensive investigations in the software model of the modified algorithm have shown that its performance is comparable with or even surpasses the conventional PSO algorithm in a multitude of scenarios. The proposed algorithm was tested with numerous fitness functions to verify its flexibility and adaptiveness to different problems. The paper also presents the hardware implementation of the selected blocks that modify the algorithm. In particular, we focused on reducing the hardware complexity, achieving high-speed operation, while reducing energy consumption.
Implementation of Resonant Electric Based Metamaterials for Electromagnetic Wave Manipulation at Microwave Frequencies
Authors: Assimonis Stylianos D. , Chandravanshi Sandhya , Yurduseven Okan , Zelenchuk Dmitry , Malyuskin Oleksandr , Abbasi Muhammad Ali Babar , Fusco Vincent , Cotton Simon L.
Published: December 2021

In this paper, we present the application of a resonant electric based metamaterial element and its two-dimensional metasurface implementation for a variety of emerging wireless applications. Metasurface apertures developed in this work are synthesized using sub-wavelength sampled resonant electric-based unit-cell structures and can achieve electromagnetic wave manipulation at microwave frequencies. The presented surfaces are implemented in a variety of forms, from absorption surfaces for energy harvesting and wireless power transfer to wave-chaotic surfaces for compressive sensing based single-pixel direction of arrival estimation and reflecting surfaces. It is shown that the resonant electric-synthesized metasurface concept offers a significant potential for these applications with high fidelity absorption, transmission and reflection characteristics within the microwave frequency spectrum.
A Deep Autoencoder-Based Convolution Neural Network Framework for Bearing Fault Classification in Induction Motors
Authors: Toma Rafia Nishat , Piltan Farzin , Kim Jong-Myon
Published: December 2021

Fault diagnosis and classification for machines are integral to condition monitoring in the industrial sector. However, in recent times, as sensor technology and artificial intelligence have developed, data-driven fault diagnosis and classification have been more widely investigated. The data-driven approach requires good-quality features to attain good fault classification accuracy, yet domain expertise and a fair amount of labeled data are important for better features. This paper proposes a deep auto-encoder (DAE) and convolutional neural network (CNN)-based bearing fault classification model using motor current signals of an induction motor (IM). Motor current signals can be easily and non-invasively collected from the motor. However, the current signal collected from industrial sources is highly contaminated with noise; feature calculation thus becomes very challenging. The DAE is utilized for estimating the nonlinear function of the system with the normal state data, and later, the residual signal is obtained. The subsequent CNN model then successfully classified the types of faults from the residual signals. Our proposed semi-supervised approach achieved very high classification accuracy (more than 99%). The inclusion of DAE was found to not only improve the accuracy significantly but also to be potentially useful when the amount of labeled data is small. The experimental outcomes are compared with some existing works on the same dataset, and the performance of this proposed combined approach is found to be comparable with them. In terms of the classification accuracy and other evaluation parameters, the overall method can be considered as an effective approach for bearing fault classification using the motor current signal.
Batteryless, Miniaturized Implantable Glucose Sensor Using a Fluorescent Hydrogel
Authors: Lee Hyeonkeon , Lee Jongheon , Park Honghyeon , Nam Mi Song , Heo Yun Jung , Kim Sanghoek
Published: December 2021

We propose a biomedical sensor system for continuous monitoring of glucose concentration. Despite recent advances in implantable biomedical devices, mm sized devices have yet to be developed due to the power limitation of the device in a tissue. We here present a mm sized wireless system with backscattered frequency-modulation communication that enables a low-power operation to read the glucose level from a fluorescent hydrogel sensor. The configuration of the reader structure is optimized for an efficient wireless power transfer and data communication, miniaturizing the entire implantable device to 3 × 6 mm 2 size. The operation distance between the reader and the implantable device reaches 2 mm with a transmission power of 33 dBm. We demonstrate that the frequency of backscattered signals changes according to the light intensity of the fluorescent glucose sensor. We envision that the present wireless interface can be applied to other fluorescence-based biosensors to make them highly comfortable, biocompatible, and stable within a body.
Hand Pose Recognition Using Parallel Multi Stream CNN
Authors: Noreen Iram , Hamid Muhammad , Akram Uzma , Malik Saadia , Saleem Muhammad
Published: December 2021

Recently, several computer applications provided operating mode through pointing fingers, waving hands, and with body movement instead of a mouse, keyboard, audio, or touch input such as sign language recognition, robot control, games, appliances control, and smart surveillance. With the increase of hand-pose-based applications, new challenges in this domain have also emerged. Support vector machines and neural networks have been extensively used in this domain using conventional RGB data, which are not very effective for adequate performance. Recently, depth data have become popular due to better understating of posture attributes. In this study, a multiple parallel stream 2D CNN (two-dimensional convolution neural network) model is proposed to recognize the hand postures. The proposed model comprises multiple steps and layers to detect hand poses from image maps obtained from depth data. The hyper parameters of the proposed model are tuned through experimental analysis. Three publicly available benchmark datasets: Kaggle, First Person, and Dexter, are used independently to train and test the proposed approach. The accuracy of the proposed method is 99.99%, 99.48%, and 98% using the Kaggle hand posture dataset, First Person hand posture dataset, and Dexter dataset, respectively. Further, the results obtained for F1 and AUC scores are also near-optimal. Comparative analysis with state-of-the-art shows that the proposed model outperforms the previous methods.
A Semi-Supervised Transfer Learning with Dynamic Associate Domain Adaptation for Human Activity Recognition Using WiFi Signals
Authors: Chen Yuh-Shyan , Chang Yu-Chi , Li Chun-Yu
Published: December 2021

Human activity recognition without equipment plays a vital role in smart home applications, freeing humans from the shackles of wearable devices. In this paper, by using the channel state information (CSI) of the WiFi signal, semi-supervised transfer learning with dynamic associate domain adaptation is proposed for human activity recognition. In order to improve the CSI quality and denoising of CSI, we carried out missing packet filling, burst noise removal, background estimation, feature extraction, feature enhancement, and data augmentation in the data pre-processing stage. This paper considers the problem of environment-independent human activity recognition, also known as domain adaptation. The pre-trained model is trained from the source domain by collecting a complete labeled dataset of all of the CSI of human activity patterns. Then, the pre-trained model is transferred to the target environment through the semi-supervised transfer learning stage. Therefore, when humans move to different target domains, a partial labeled dataset of the target domain is required for fine-tuning. In this paper, we propose a dynamic associate domain adaptation called DADA. By modifying the existing associate domain adaptation algorithm, the target domain can provide a dynamic ratio of labeled dataset/unlabeled dataset, while the existing associate domain adaptation algorithm only allows target domains with the unlabeled dataset. The advantage of DADA is that it provides a dynamic strategy to eliminate different effects on different environments. In addition, we further designed an attention-based DenseNet model, or AD, as our training network, which is modified by an existing DenseNet by adding the attention function. The solution we proposed was simplified to DADA-AD throughout the paper. The experimental results show that for domain adaptation in different domains, the accuracy of human activity recognition of the DADA-AD scheme is 97.4%. It also shows that DADA-AD has advantages over existing semi-supervised learning schemes.
Estimation of Fluor Emission Spectrum through Digital Photo Image Analysis with a Water-Based Liquid Scintillator
Authors: Choi Ji-Won , Choi Ji-Young , Joo Kyung-Kwang
Published: December 2021

In this paper, we performed a feasibility study of using a water-based liquid scintillator (WbLS) for conducting imaging analysis with a digital camera. The liquid scintillator (LS) dissolves a scintillating fluor in an organic base solvent to emit light. We synthesized a liquid scintillator using water as a solvent. In a WbLS, a suitable surfactant is needed to mix water and oil together. As an application of the WbLS, we introduced a digital photo image analysis in color space. A demosaicing process to reconstruct and decode color is briefly described. We were able to estimate the emission spectrum of the fluor dissolved in the WbLS by analyzing the pixel information stored in the digital image. This technique provides the potential to estimate fluor components in the visible region without using an expensive spectrophotometer. In addition, sinogram analysis was performed with Radon transformation to reconstruct transverse images with longitudinal photo images of the WbLS sample.
Plasma Generator with Dielectric Rim and FSS Electrode for Enhanced RCS Reduction Effect
Authors: Oh Taejoo , Cho Changseok , Ahn Wookhyun , Yook Jong-Gwan , Lee Jangjae , You Shinjae , Yim Jinwoo , Ha Jungje , Bae Gihun , You Heung-Cheol , Lee Yongshik
Published: December 2021

In this study, a method was experimentally verified for further reducing the radar cross-section (RCS) of a two-dimensional planar target by using a dielectric rim in a dielectric barrier discharge (DBD) plasma generator using a frequency selective surface (FSS) as an electrode. By designing the frequency selective surface such that the passbands of the radar signal match, it is possible to minimize the effect of the conductor electrode, in order to maximize the RCS reduction effect due to the plasma. By designing the FSS to be independent of the polarization, the effect of RCS reduction can be insensitive to the polarization of the incoming wave. Furthermore, by introducing a dielectric rim between the FSS electrode and the target, an additional RCS reduction effect is achieved. By fabricating the proposed plasma generator, an RCS reduction effect of up to 6.4 dB in X-band was experimentally verified.
Autonomous UAV System for Cleaning Insulators in Power Line Inspection and Maintenance
Authors: Lopez Lopez Ricardo , Batista Sanchez Manuel Jesus , Perez Jimenez Manuel , Arrue Begoña C. , Ollero Anibal
Published: December 2021

The inspection and maintenance tasks of electrical installations are very demanding. Nowadays, insulator cleaning is carried out manually by operators using scaffolds, ropes, or even helicopters. However, these operations involve potential risks for humans and the electrical structure. The use of Unmanned Aerial Vehicles (UAV) to reduce the risk of these tasks is rising. This paper presents an UAV to autonomously clean insulators on power lines. First, an insulator detection and tracking algorithm has been implemented to control the UAV in operation. Second, a cleaning tool has been designed consisting of a pump, a tank, and an arm to direct the flow of cleaning liquid. Third, a vision system has been developed that is capable of detecting soiled areas using a semantic segmentation neuronal network, calculating the trajectory for cleaning in the image plane, and generating arm trajectories to efficiently clean the insulator. Fourth, an autonomous system has been developed to land on a charging pad to charge the batteries and potentially fill the tank with cleaning liquid. Finally, the autonomous system has been validated in a controlled outdoor environment.
Relative Pose Determination of Uncooperative Spacecraft Based on Circle Feature
Authors: Liu Yue , Zhang Shijie , Zhao Xiangtian
Published: December 2021

This paper investigates the problem of spacecraft relative navigation with respect to an unknown target during the close-proximity operations in the on-orbit service system. The serving spacecraft is equipped with a Time-of-Flight (ToF) camera for object recognition and feature detection. A fast and robust relative navigation strategy for acquisition is presented without any extra information about the target by using the natural circle features. The architecture of the proposed relative navigation strategy consists of three ingredients. First, a point cloud segmentation method based on the auxiliary gray image is developed for fast extraction of the circle feature point cloud of the target. Secondly, a new parameter fitting method of circle features is proposed including circle feature calculation by two different geometric models and results’ fusion. Finally, a specific definition of the coordinate frame system is introduced to solve the relative pose with respect to the uncooperative target. In order to validate the efficiency of the segmentation, an experimental test is conducted based on real-time image data acquired by the ToF camera. The total time consumption is saved by 94%. In addition, numerical simulations are carried out to evaluate the proposed navigation algorithm. It shows good robustness under the different levels of noises.
Hyperspectral Estimation of Winter Wheat Leaf Area Index Based on Continuous Wavelet Transform and Fractional Order Differentiation
Authors: Li Changchun , Wang Yilin , Ma Chunyan , Ding Fan , Li Yacong , Chen Weinan , Li Jingbo , Xiao Zhen
Published: December 2021

Leaf area index (LAI) is highly related to crop growth, and the traditional LAI measurement methods are field destructive and unable to be acquired by large-scale, continuous, and real-time means. In this study, fractional order differential and continuous wavelet transform were used to process the canopy hyperspectral reflectance data of winter wheat, the fractional order differential spectral bands and wavelet energy coefficients with more sensitive to LAI changes were screened by correlation analysis, and the optimal subset regression and support vector machine were used to construct the LAI estimation models for different growth stages. The precision evaluation results showed that the LAI estimation models constructed by using wavelet energy coefficients combined with a support vector machine at the jointing stage, fractional order differential combined with support vector machine at the booting stage, and wavelet energy coefficients combined with optimal subset regression at the flowering and filling stages had the best prediction performance. Among these, both flowering and filling stages could be used as the best growth stages for LAI estimation with modeling and validation R2 of 0.87 and 0.71, 0.84 and 0.77, respectively. This study can provide technical reference for LAI estimation of crops based on remote sensing technology.
Lateral and Longitudinal Driving Behavior Prediction Based on Improved Deep Belief Network
Authors: Yang Lei , Zhao Chunqing , Lu Chao , Wei Lianzhen , Gong Jianwei
Published: December 2021

Accurately predicting driving behavior can help to avoid potential improper maneuvers of human drivers, thus guaranteeing safe driving for intelligent vehicles. In this paper, we propose a novel deep belief network (DBN), called MSR-DBN, by integrating a multi-target sigmoid regression (MSR) layer with DBN to predict the front wheel angle and speed of the ego vehicle. Precisely, the MSR-DBN consists of two sub-networks: one is for the front wheel angle, and the other one is for speed. This MSR-DBN model allows ones to optimize lateral and longitudinal behavior predictions through a systematic testing method. In addition, we consider the historical states of the ego vehicle and surrounding vehicles and the driver’s operations as inputs to predict driving behaviors in a real-world environment. Comparison of the prediction results of MSR-DBN with a general DBN model, back propagation (BP) neural network, support vector regression (SVR), and radical basis function (RBF) neural network, demonstrates that the proposed MSR-DBN outperforms the others in terms of accuracy and robustness.
Strength Development Monitoring of Cemented Paste Backfill Using Guided Waves
Authors: He Wen , Zheng Changsong , Li Shenhai , Shi Wenfang , Zhao Kui
Published: December 2021

The strength of cemented paste backfill (CPB) directly affects mining safety and progress. At present, in-situ backfill strength is obtained by conducting uniaxial compression tests on backfill core samples. At the same time, it is time-consuming, and the integrity of samples cannot be guaranteed. Therefore guided wave technique as a nondestructive inspection method is proposed for the strength development monitoring of cemented paste backfill. In this paper, the acoustic parameters of guided wave propagation in the different cement-tailings ratios (1:4, 1:8) and different curing times (within 42 d) of CPBs were measured. Combined with the uniaxial compression strength of CPB, relationships between CPB strength and the guided wave acoustic parameters were established. Results indicate that with the increase of backfill curing time, the guided wave velocity decreases sharply at first; on the contrary, attenuation of guided waves increases dramatically. Finally, both velocity and attenuation tend to be stable. When the CPB strength increases with curing time, guided wave velocity shows an exponentially decreasing trend, while the guided wave attenuation shows an exponentially increasing trend with the increase of the CPB strength. Based on the relationship curves between CPB strength and guided wave velocity and attenuation, the guided wave technique in monitoring the strength development of CPB proves feasible.
Subjective and Objective User Behavior Disparity: Towards Balanced Visual Design and Color Adjustment
Authors: Lewandowska Anna , Olejnik-Krugly Agnieszka , Jankowski Jarosław , Dziśko Malwina
Published: December 2021

Interactive environments create endless possibilities for the design of websites, games, online platforms, and mobile applications. Their visual aspects and functional characteristics influence the user experience. Depending on the project, the purpose of the environment can be oriented toward marketing targets, user experience, or accessibility. Often, these conflicting aspects should be integrated within a single project, and a search for trade-offs is needed. One of these conflicts involves a disparity in user behavior concerning declared preferences and real observed activity in terms of visual attention. Taking into account accessibility guidelines (WCAG) further complicates the problem. In our study, we focused on the analysis of color combinations and their contrast in terms of user-friendliness; visual intensity, which is important for attracting user attention; and recommendations from the Web Accessibility Guidelines (WCAG). We took up the challenge to reduce the disparity between user preferences and WCAG contrast, on one hand, and user natural behavior registered with an eye-tracker, on the other. However, we left the choice of what is more important—human conscious reaction or objective user behavior results—to the designer. The former corresponds to user-friendliness, while the latter, visual intensity, is consistent with marketing expectations. The results show that the ranking of visual objects characterized by different levels of contrast differs when considering the perspectives of user experience, commercial goals, and objective recording. We also propose an interactive tool with the possibility of assigning weights to each criterion to generate a ranking of objects.