bluuebunny/crossref_metadata_2025 huggingface dataset:
Thermo-Sensitive Genic Male Sterile Lines of Neo-Tetraploid Rice Developed through Gene Editing Technology Revealed High Levels of Hybrid Vigor
Authors: Chen Yang , Shahid Muhammad Qasim , Wu Jinwen , Deng Ruilian , Chen Zhixiong , Wang Lan , Liu Guoqiang , Zhou Hai , Liu Xiangdong
Published: May 2022

Neo-tetraploid rice, which developed from the progenies of autotetraploid hybrid by our research group, is a useful germplasm with high fertility and strong heterosis when they crossed with other autotetraploid rice lines. The CRISPR/Cas9-mediated TMS5 gene editing system has been widely used in diploid rice, but there are few reports in tetraploid rice. Here, we used CRISPR/Cas9 technology to edit the TMS5 gene, which is a temperature sensitive gene controlling the fertility in diploid rice, in neo-tetraploid rice to develop male sterile lines. Two mutant lines, H2s and H3s, were developed from the gene editing and displayed characteristics of thermo-sensitive genic male sterility. The daily mean temperatures of 23 °C to 26 °C were found to be critical for sterility (restrictive temperature) in H2s and H3s under both controlled (growth chambers) and natural growing conditions (field). Cytological observation showed the anther dysplasia appeared later in H2s and H3s than that of the TMS5 mutant of diploid rice (E285s) under the same conditions. Then these mutant lines, H2s and H3s, were crossed with tetraploid rice to generate F1 hybrids, which exhibited obvious advantages for effective number of panicles, total grains and seed setting. The high levels of hybrids heterosis were maintained for several generations that can save seed cost. Our research provides an effective way of developing thermo-sensitive genic male sterility (TGMS) lines of tetraploid rice using gene editing, which will accelerate the utilization of polyploid heterosis.
Wound Healing and Antioxidant Capabilities of Zizyphus mauritiana Fruits: In-Vitro, In-Vivo, and Molecular Modeling Study
Authors: Shady Nourhan Hisham , Soltane Raya , Maher Sherif A. , Saber Entesar Ali , Elrehany Mahmoud A. , Mostafa Yaser A. , Sayed Ahmed M. , Abdelmohsen Usama Ramadan
Published: May 2022

LC-HRMS-assisted chemical profiling of Zizyphus mauritiana fruit extract (ZFE) led to the dereplication of 28 metabolites. Furthermore, wound healing activity of ZFE in 24 adult male New Zealand Dutch strain albino rabbits was investigated in-vivo supported by histopathological investigation. Additionally, the molecular mechanism was studied through different in-vitro investigations as well as, studying both relative gene expression and relative protein expression patterns. Moreover, the antioxidant activity of ZFE extract was examined using two in-vitro assays including hydrogen peroxide and superoxide radical scavenging activities that showed promising antioxidant potential. Topical application of the extract on excision wounds showed a significant increase in the wound healing rate (p < 0.001) in comparison to the untreated and MEBO®-treated groups, enhancing TGF-β1, VEGF, Type I collagen expression, and suppressing inflammatory markers (TNF-α and IL-1β). Moreover, an in silico molecular docking against TNFα, TGFBR1, and IL-1β showed that some of the molecules identified in ZFE can bind to the three wound-healing related protein actives sites. Additionally, PASS computational calculation of antioxidant activity revealed potential activity of three phenolic compounds (Pa score > 0.5). Consequently, ZFE may be a potential alternative medication helping wound healing owing to its antioxidant and anti-inflammatory activities.
Oil Palm Breeding in the Modern Era: Challenges and Opportunities
Authors: John Martin Jerome Jeyakumar , Yarra Rajesh , Wei Lu , Cao Hongxing
Published: May 2022

Oil palm, a cross-pollinated crop with long generation time, poses a lot of challenges in achieving sustainable oil palm with high yield and quality. The African oil palm (Elaeis guineensis Jacq.) is the most productive and versatile oil-yielding crop in the world, producing more than any other oil-yielding crop. Despite recent challenges, such as stress tolerance, superior oil quality, disease tolerance, and the need for new market niches, there is a growing need to explore and develop new varieties with high yield potential and the genetic diversity required to maintain oil palm yield stability. Breeding is an indispensable part of producing high-quality planting materials to increase oil palm yield. Biotechnological technologies have transformed conventional plant breeding approaches by introducing novel genotypes for breeding. Innovative pre-breeding and breeding approaches, such as identifying candidate genes in wild or land races using genomics tools, can pave the way for genetic improvement in oil palm. In this review, we highlighted the modern breeding tools, including genomics, marker-assisted breeding, genetic engineering, and genome editing techniques in oil palm crops, and we explored certain concerns connected to the techniques and their applications in practical breeding.
Functional Characterization of the Lysine-Specific Histone Demethylases Family in Soybean
Authors: Liu Mengshi , Jiang Jiacan , Han Yapeng , Shi Mengying , Li Xianli , Wang Yingxiang , Dong Zhicheng , Yang Cunyi
Published: May 2022

Histone modifications, such as methylation and demethylation, have crucial roles in regulating chromatin structure and gene expression. Lysine-specific histone demethylases (LSDs) belong to the amine oxidase family, which is an important family of histone lysine demethylases (KDMs), and functions in maintaining homeostasis of histone methylation. Here, we identified six LSD-like (LDL) genes from the important leguminous soybean. Phylogenetic analyses divided the six GmLDLs into four clusters with two highly conserved SWRIM and amine oxidase domains. Indeed, demethylase activity assay using recombinant GmLDL proteins in vitro demonstrated that GmLDLs have demethylase activity toward mono- and dimethylated Lys4 but not trimethylated histone 3, similar to their orthologs previously reported in animals. Using real-time PCR experiments in combination with public transcriptome data, we found that these six GmLDL genes exhibit comparable expressions in multiple tissues or in response to different abiotic stresses. Moreover, our genetic variation investigation of GmLDL genes among 761 resequenced soybean accessions indicates that GmLDLs are well conserved during soybean domestication and improvement. Taken together, these findings demonstrate that GmFLD, GmLDL1a, and GmLDL1b are bona fide H3K4 demethylases towards H4K4me1/2 and GmLDLs exist in various members with likely conserved and divergent roles in soybeans.
Enrichment of Grapes with Zinc-Efficiency of Foliar Fertilization with ZnSO4 and ZnO and Implications on Winemaking
Authors: Daccak Diana , Lidon Fernando C. , Pessoa Cláudia Campos , Luís Inês Carmo , Coelho Ana Rita F. , Marques Ana Coelho , Ramalho José C. , Silva Maria José , Rodrigues Ana Paula , Guerra Mauro , Leitão Roberta G. , Campos Paula Scotti , Pais Isabel P. , Semedo José N. , Silva Maria Manuela , Kullberg José Carlos , Brito Maria , Galhano Carlos , Legoinha Paulo , Pessoa Maria Fernanda , Simões Manuela , Reboredo Fernando H.
Published: May 2022

Grapes and wine are widely consumed in the world, yet their mineral content can be influenced by many factors such as the mineral composition of soils, viticulture practices and environmental conditions. In this context, considering the importance of Zn in the human physiology, the enrichment of Moscatel and Castelão grapes (white and red variety, respectively) with this nutrient prompted this study; further assessment of tissue deposition and some implications for wine production. Using two foliar fertilizers (ZnO or ZnSO4, at 150, 450 and 900 g ha−1), decreases in net photosynthesis and stomatal conductance occurred in both varieties, suggesting that the physiological threshold of Zn toxicity was reached without visible symptoms. Following foliar spraying with both fertilizers, the content of Zn in leaves of the Castelão and Moscatel varieties showed higher values in all treatments relative to the control. Moreover, in grapes this tendency occurred only in Castelão. Concerning Cu, Fe, Ca, K, S and P, some significant differences also happened in leaves and grapes among treatments. At harvest, the indexes of Zn enrichment in grapes increased between 2.14- and 8.38-fold and between 1.02- and 1.44-fold in Castelão and Moscatel varieties, respectively. Zinc in the dried skin of Castelão only increased with ZnO and ZnSO4 sprayed at 900 g ha−1 (ca. 2.71- and 1.5-fold relative to the control, respectively), but in Moscatel a clear accumulation trend could not be found. The dry weight of grapes ranged (in %) between 16 and 23 (but did not vary significantly among treatments of each variety or in each treatment between varieties), and total soluble solids (e.g., mainly soluble sugars and proteins) and color parameters showed some significant variations. Through winemaking, the contents of Zn increased in both varieties (1.34- and 3.57-fold, in Castelão and Moscatel, respectively) and in all treatments, although non-significantly in Castelão. It is concluded that, to increase the contents of Zn in grapes without reaching the threshold of toxicity, ZnO or ZnSO4 can be used for foliar spraying of Castelão and Moscatel varieties until 900 g ha−1 and that winemaking augments the level of this nutrient.
Genetic Diversity Assessment of the International Maize and Wheat Improvement Center and Chinese Wheat Core Germplasms by Non-Denaturing Fluorescence In Situ Hybridization
Authors: Yang Manyu , Yang Zujun , Yang Wuyun , Yang Ennian
Published: May 2022

Germplasm is the material basis for crop genetic improvement and related basic research. Knowledge of genetic diversity present in wheat is the prerequisite for wheat breeding and improvement. Non-denaturing fluorescence in situ hybridization (ND-FISH) is a powerful tool to distinguish chromosomal polymorphisms and evaluate genetic diversity in wheat. In this study, ND-FISH using Oligo-pSc119.2-1, Oligo-pTa535-1, and Oligo-(GAA)7 as probes were used to analyze the genetic diversity among 60 International Maize and Wheat Improvement Center (CIMMYT) derived wheat lines, and 93 cultivated wheat and landraces from the Chinese wheat core germplasm. A total of 137 polymorphic FISH patterns were obtained, in which 41, 65, and 31 were from A-, B-, and D-genome chromosomes, respectively, indicating polymorphism of B-genome > A-genome > D-genome. In addition, 22 and 51 specific FISH types were observed in the two germplasm resource lines. Twelve types of rearrangements, including seven new translocations, were detected in all 153 wheat lines. Genetic relationships among 153 wheat lines were clustered into six groups. Our research provides cytological information for rational utilization of wheat germplasm resources.
Experimental Study on the Flexural Behavior of Lap-Spliced Ultra-High-Performance Fiber-Reinforced Concrete Beams
Authors: Bae Baek-Il , Choi Hyun-Ki
Published: May 2022

In this paper, the structural performance of lap-spliced beams were investigated by the testing of 18-UHPFRC beams with and without a lap-spliced region and steel fiber. All test specimens were subjected to flexural load by using the four-point bending test. It was shown that test steel fiber inclusion in the ultra-high-strength matrix significantly increased the strength of the lap-spliced beams. The maximum strength of the specimen increased linearly with the increase in steel fiber volume fraction. The hybrid-type UHPFRC, with lower steel fiber volume fraction, using two types of steel fiber was effective in improving the bond strength. In order to verify the safety of lap splice length design methods by current code provisions and UHPFRC design recommendations, the average bond stress at the ultimate state was used to calculate the lap splice length, which makes reinforcement yielding, and this value, compared with design lap splice length. Most of the design equations were under the estimated bond stress of UHPFRC. However, the AFGC recommendation, which considers the tensile strength of UHPFRC, overestimates the bond stress of UHPFRC because this recommendation was developed from the direct pull-out test results.
Long-Term Landsat-Based Monthly Burned Area Dataset for the Brazilian Biomes Using Deep Learning
Authors: Alencar Ane A. C. , Arruda Vera L. S. , Silva Wallace Vieira da , Conciani Dhemerson E. , Costa Diego Pereira , Crusco Natalia , Duverger Soltan Galano , Ferreira Nilson Clementino , Franca-Rocha Washington , Hasenack Heinrich , Martenexen Luiz Felipe Morais , Piontekowski Valderli J. , Ribeiro Noely Vicente , Rosa Eduardo Reis , Rosa Marcos Reis , dos Santos Sarah Moura B. , Shimbo Julia Z. , Vélez-Martin Eduardo
Published: May 2022

Fire is a significant agent of landscape transformation on Earth, and a dynamic and ephemeral process that is challenging to map. Difficulties include the seasonality of native vegetation in areas affected by fire, the high levels of spectral heterogeneity due to the spatial and temporal variability of the burned areas, distinct persistence of the fire signal, increase in cloud and smoke cover surrounding burned areas, and difficulty in detecting understory fire signals. To produce a large-scale time-series of burned area, a robust number of observations and a more efficient sampling strategy is needed. In order to overcome these challenges, we used a novel strategy based on a machine-learning algorithm to map monthly burned areas from 1985 to 2020 using Landsat-based annual quality mosaics retrieved from minimum NBR values. The annual mosaics integrated year-round observations of burned and unburned spectral data (i.e., RED, NIR, SWIR-1, and SWIR-2), and used them to train a Deep Neural Network model, which resulted in annual maps of areas burned by land use type for all six Brazilian biomes. The annual dataset was used to retrieve the frequency of the burned area, while the date on which the minimum NBR was captured in a year, was used to reconstruct 36 years of monthly burned area. Results of this effort indicated that 19.6% (1.6 million km2) of the Brazilian territory was burned from 1985 to 2020, with 61% of this area burned at least once. Most of the burning (83%) occurred between July and October. The Amazon and Cerrado, together, accounted for 85% of the area burned at least once in Brazil. Native vegetation was the land cover most affected by fire, representing 65% of the burned area, while the remaining 35% burned in areas dominated by anthropogenic land uses, mainly pasture. This novel dataset is crucial for understanding the spatial and long-term temporal dynamics of fire regimes that are fundamental for designing appropriate public policies for reducing and controlling fires in Brazil.
Using Machine-Learning for the Damage Detection of Harbour Structures
Authors: Hake Frederic , Göttert Leonard , Neumann Ingo , Alkhatib Hamza
Published: May 2022

The ageing infrastructure in ports requires regular inspection. This inspection is currently carried out manually by divers who sense the entire below-water infrastructure by hand. This process is cost-intensive as it involves a lot of time and human resources. To overcome these difficulties, we propose scanning the above and below-water port structure with a multi-sensor system, and by a fully automated process to classify the point cloud obtained into damaged and undamaged zones. We make use of simulated training data to test our approach because not enough training data with corresponding class labels are available yet. Accordingly, we build a rasterised height field of a point cloud of a sheet pile wall by subtracting a computer-aided design model. The latter is propagated through a convolutional neural network, which detects anomalies. We make use of two methods: the VGG19 deep neural network and local outlier factors. We showed that our approach can achieve a fully automated, reproducible, quality-controlled damage detection, which can analyse the whole structure instead of the sample-wise manual method with divers. We were able to achieve valuable results for our application. The accuracy of the proposed method is 98.8% following a desired recall of 95%. The proposed strategy is also applicable to other infrastructure objects, such as bridges and high-rise buildings.
Reference-Based Multi-Level Features Fusion Deblurring Network for Optical Remote Sensing Images
Authors: Li Zhiyuan , Guo Jiayi , Zhang Yueting , Li Jie , Wu Yirong
Published: May 2022

Blind image deblurring is a long-standing challenge in remote sensing image restoration tasks. It aims to recover a latent sharp image from a blurry image while the blur kernel is unknown. To solve this problem, many image priors-based algorithms and learning-based algorithms have been proposed. However, most of these methods are based on a single blurry image. Due to the lack of high frequency information, the images restored by these algorithms still have some deficiencies in edge and texture details. In this work, we propose a novel deep learning model named Reference-Based Multi-Level Features Fusion Deblurring Network (Ref-MFFDN), which registers the reference image and the blurry image in the multi-level feature space and transfers the high-quality textures from registered reference features to assist image deblurring. Comparative experiments on the testing set prove that our Ref-MFFDN outperforms many state-of-the-art single image deblurring approaches in both quantitative evaluation and visual results, which indicates the effectiveness of using reference images in remote sensing image deblurring tasks. More ablation experiments demonstrates the robustness of Ref-MFFDN to the input image size, the effectiveness of multi-level features fusion network (MFFN) and the effect of different feature levels in multi-feature extractor (MFE) on algorithm performance.
Online Extrinsic Calibration on LiDAR-Camera System with LiDAR Intensity Attention and Structural Consistency Loss
Authors: An Pei , Gao Yingshuo , Wang Liheng , Chen Yanfei , Ma Jie
Published: May 2022

Extrinsic calibration on a LiDAR-camera system is an essential task for the advanced perception application for the intelligent vehicle. In the offline situation, a calibration object based method can estimate the extrinsic parameters in high precision. However, during the long time application of LiDAR-camera system in the actual scenario, the relative pose of LiDAR and camera has small and accumulated drift, so that the offline calibration result is not accurate. To correct the extrinsic parameter conveniently, we present a deep learning based online extrinsic calibration method in this paper. From Lambertian reflection model, it is found that an object with higher LiDAR intensity has the higher possibility to have salient RGB features. Based on this fact, we present a LiDAR intensity attention based backbone network (LIA-Net) to extract the significant co-observed calibration features from LiDAR data and RGB image. In the later stage of training, the loss of extrinsic parameters changes slowly, causing the risk of vanishing gradient and limiting the training efficiency. To deal with this issue, we present the structural consistency (SC) loss to minimize the difference between projected LiDAR image (i.e., LiDAR depth image, LiDAR intensity image) and its ground truth (GT) LiDAR image. It aims to accurately align the LiDAR point and RGB pixel. With LIA-Net and SC loss, we present the convolution neural network (CNN) based calibration network LIA-SC-Net. Comparison experiments on a KITTI dataset demonstrate that LIA-SC-Net has achieved more accurate calibration results than state-of-the-art learning based methods. The proposed method has both accurate and real-time performance. Ablation studies also show the effectiveness of proposed modules.
An Improved L2Net for Repetitive Texture Image Registration with Intensity Difference Heterogeneous SAR Images
Authors: Men Peng , Guo Hao , An Jubai , Li Guanyu
Published: May 2022

Heterogeneous synthetic aperture radar (SAR) images contain more complementary information compared with homologous SAR images; thus, the comprehensive utilization of heterogeneous SAR images could potentially improve performance for the monitoring of sea surface objects, such as sea ice and enteromorpha. Image registration is key to the application of monitoring sea surface objects. Heterogeneous SAR images have intensity differences and resolution differences, and after the uniform resolution, intensity differences are one of the most important factors affecting the image registration accuracy. In addition, sea surface objects have numerous repetitive and confusing features for feature extraction, which also limits the image registration accuracy. In this paper, we propose an improved L2Net network for image registration with intensity differences and repetitive texture features, using sea ice as the research object. The deep learning network can capture feature correlations between image patch pairs, and can obtain the correct matching from a large number of features with repetitive texture. In the SAR image pair, four patches of different sizes centered on the corner points are proposed as inputs. Thus, local features and more global features are fused to obtain excellent structural features, to distinguish between different repetitive textural features, add contextual information, further improve the feature correlation, and improve the accuracy of image registration. An outlier removal strategy is proposed to remove false matches due to repetitive textures. Finally, the effectiveness of our method was verified by comparative experiments.
Special Issue “EUV and X-ray Wavefront Sensing”
Authors: Idir Mourad , Cocco Daniele , Huang Lei
Published: May 2022

X-ray optics are extensively used in synchrotron radiation and free-electron laser facilities, as well as in table-top laboratory sources [...]
A Single Subject, Feasibility Study of Using a Non-Contact Measurement to “Visualize” Temperature at Body-Seat Interface
Authors: Liu Zhuofu , Cascioli Vincenzo , McCarthy Peter W.
Published: May 2022

Measuring temperature changes at the body-seat interface has been drawing increased attention from both industrial and scientific fields, due to the increasingly sedentary nature from daily leisure activity to routine work. Although contact measurement is considered the gold standard, it can affect the local micro-environment and the perception of sitting comfort. A non-contact temperature measurement system was developed to determine the interface temperature using data gathered unobtrusively and continuously from an infrared sensor (IRs). System performance was evaluated regarding linearity, hysteresis, reliability and accuracy. Then a healthy participant sat for an hour on low/intermediate density foams with thickness varying from 0.5–8 cm while body-seat interface temperature was measured simultaneously using a temperature sensor (contact) and an IRs (non-contact). IRs data were filtered with empirical mode decomposition and fractal scaling indices before a data-driven artificial neural network was utilized to estimate the contact surface temperature. A strong correlation existed between non-contact and contact temperature measurement (ρ > 0.85) and the estimation results showed a low root mean square error (RMSE) (<0.07 for low density foam and <0.16 for intermediate density foam) and high Nash-Sutcliff efficiency (NSE) values (≈1 for both types of foam materials).
Application of the Differential Evolutionary Algorithm to the Estimation of Pipe Embedding Parameters
Authors: Lu Ping , Chen Shuang , Sheng Xiaozhen , Gao Yan
Published: May 2022

The time-delay estimation (TDE) method is the primary method for predicting leakage locations in buried water distribution pipelines. The accuracy of TDE depends on the acoustic speed and attenuation of the leakage signal propagating along the pipeline. The analytical prediction model is the typical approach for obtaining the propagation speed and attenuation of leakage waves. However, the embedding parameters of the buried pipe in this model must be measured using soil tests, which are very difficult, costly, and time-consuming. These factors restrict the application of the TDE method in pinpointing pipeline leakage. A method for inverse identification of pipe embedding parameters using discrete wavenumbers obtained in field testing is presented in this paper, and the differential evolution algorithm is introduced as an optimization solution. A field experiment is conducted to validate the method, and the test wavenumbers are measured in a cast-iron pipeline. The estimated sensitive parameters in the analytical model using the method are soil elastic modulus, Poisson’s ratio, and pipe–soil contact coefficient, while the conventional soil test is used to measure the soil density due to the character of the optimization algorithm and the soil properties. The application effects show that the estimated parameters are close to those measured from a conventional soil test. The wave speed based on the estimated parameters was an excellent match for the on-site test in the engineering application. This work provides a less costly and more straightforward way to apply the TDE method for leak localization in buried pipelines.
Multi-Domain Neumann Network with Sensitivity Maps for Parallel MRI Reconstruction
Authors: Lee Jun-Hyeok , Kang Junghwa , Oh Se-Hong , Ye Dong Hye
Published: May 2022

MRI is an imaging technology that non-invasively obtains high-quality medical images for diagnosis. However, MRI has the major disadvantage of long scan times which cause patient discomfort and image artifacts. As one of the methods for reducing the long scan time of MRI, the parallel MRI method for reconstructing a high-fidelity MR image from under-sampled multi-coil k-space data is widely used. In this study, we propose a method to reconstruct a high-fidelity MR image from under-sampled multi-coil k-space data using deep-learning. The proposed multi-domain Neumann network with sensitivity maps (MDNNSM) is based on the Neumann network and uses a forward model including coil sensitivity maps for parallel MRI reconstruction. The MDNNSM consists of three main structures: the CNN-based sensitivity reconstruction block estimates coil sensitivity maps from multi-coil under-sampled k-space data; the recursive MR image reconstruction block reconstructs the MR image; and the skip connection accumulates each output and produces the final result. Experiments using the fastMRI T1-weighted brain image dataset were conducted at acceleration factors of 2, 4, and 8. Qualitative and quantitative experimental results show that the proposed MDNNSM method reconstructs MR images more accurately than other methods, including the generalized autocalibrating partially parallel acquisitions (GRAPPA) method and the original Neumann network.
Surface Properties of CVD-Grown Graphene Transferred by Wet and Dry Transfer Processes
Authors: Yoon Min-Ah , Kim Chan , Kim Jae-Hyun , Lee Hak-Joo , Kim Kwang-Seop
Published: May 2022

Graphene, an atomically thin material, has unique electrical, mechanical, and optical properties that can enhance the performance of thin film-based flexible and transparent devices, including gas sensors. Graphene synthesized on a metallic catalyst must first be transferred onto a target substrate using wet or dry transfer processes; however, the graphene surface is susceptible to chemical modification and mechanical damage during the transfer. Defects on the graphene surface deteriorate its excellent intrinsic properties, thus reducing device performance. In this study, the surface properties of transferred graphene were investigated according to the transfer method (wet vs. dry) and characterized using atomic force microscopy, Raman spectroscopy, and contact angle measurements. After the wet transfer process, the surface properties of graphene exhibited tendencies similar to the poly(methyl methacrylate) residue remaining after solvent etching. The dry-transferred graphene revealed a surface closer to that of pristine graphene, regardless of substrates. These results provide insight into the utilization of wet and dry transfer processes for various graphene applications.
Metamaterial Vivaldi Antenna Array for Breast Cancer Detection
Authors: Slimi Marwa , Jmai Bassem , Dinis Hugo , Gharsallah Ali , Mendes Paulo Mateus
Published: May 2022

The objective of this work is the design and validation of a directional Vivaldi antenna to detect tumor cells’ electromagnetic waves with a frequency of around 5 GHz. The proposed antenna is 33% smaller than a traditional Vivaldi antenna due to the use of metamaterials in its design. It has an excellent return loss of 25 dB at 5 GHz and adequate radiation characteristics as its gain is 6.2 dB at 5 GHz. The unit cell size of the proposed metamaterial is 0.058λ × 0.054λ at the operation frequency of 5 GHz. The proposed antenna was designed and optimized in CST microwave software, and the measured and simulated results were in good agreement. The experimental study demonstrates that an array composed with the presented antennas can detect the existence of tumors in a liquid breast phantom with positional accuracy through the analysis of the minimum amplitude of Sii.
Automatic Personality Assessment through Movement Analysis
Authors: Delgado-Gómez David , Masó-Besga Antonio Eduardo , Aguado David , Rubio Victor J. , Sujar Aaron , Bayona Sofia
Published: May 2022

Obtaining accurate and objective assessments of an individual’s personality is vital in many areas including education, medicine, sports and management. Currently, most personality assessments are conducted using scales and questionnaires. Unfortunately, it has been observed that both scales and questionnaires present various drawbacks. Their limitations include the lack of veracity in the answers, limitations in the number of times they can be administered, or cultural biases. To solve these problems, several articles have been published in recent years proposing the use of movements that participants make during their evaluation as personality predictors. In this work, a multiple linear regression model was developed to assess the examinee’s personality based on their movements. Movements were captured with the low-cost Microsoft Kinect camera, which facilitates its acceptance and implementation. To evaluate the performance of the proposed system, a pilot study was conducted aimed at assessing the personality traits defined by the Big-Five Personality Model. It was observed that the traits that best fit the model are Extroversion and Conscientiousness. In addition, several patterns that characterize the five personality traits were identified. These results show that it is feasible to assess an individual’s personality through his or her movements and open up pathways for several research.
Solar Energy Harvesting to Improve Capabilities of Wearable Devices
Authors: Páez-Montoro Alba , García-Valderas Mario , Olías-Ruíz Emilio , López-Ongil Celia
Published: May 2022

The market of wearable devices has been growing over the past decades. Smart wearables are usually part of IoT (Internet of things) systems and include many functionalities such as physiological sensors, processing units and wireless communications, that are useful in fields like healthcare, activity tracking and sports, among others. The number of functions that wearables have are increasing all the time. This result in an increase in power consumption and more frequent recharges of the battery. A good option to solve this problem is using energy harvesting so that the energy available in the environment is used as a backup power source. In this paper, an energy harvesting system for solar energy with a flexible battery, a semi-flexible solar harvester module and a BLE (Bluetooth® Low Energy) microprocessor module is presented as a proof-of-concept for the future integration of solar energy harvesting in a real wearable smart device. The designed device was tested under different circumstances to estimate the increase in battery lifetime during common daily routines. For this purpose, a procedure for testing energy harvesting solutions, based on solar energy, in wearable devices has been proposed. The main result obtained is that the device could permanently work if the solar cells received a significant amount of direct sunlight for 6 h every day. Moreover, in real-life scenarios, the device was able to generate a minimum and a maximum power of 27.8 mW and 159.1 mW, respectively. For the wearable system selected, Bindi, the dynamic tests emulating daily routines has provided increases in the state of charge from 19% (winter cloudy days, 4 solar cells) to 53% (spring sunny days, 2 solar cells).
Graph Layer Security: Encrypting Information via Common Networked Physics
Authors: Wei Zhuangkun , Wang Liang , Sun Schyler Chengyao , Li Bin , Guo Weisi
Published: May 2022

The proliferation of low-cost Internet of Things (IoT) devices has led to a race between wireless security and channel attacks. Traditional cryptography requires high computational power and is not suitable for low-power IoT scenarios. Whilst recently developed physical layer security (PLS) can exploit common wireless channel state information (CSI), its sensitivity to channel estimation makes them vulnerable to attacks. In this work, we exploit an alternative common physics shared between IoT transceivers: the monitored channel-irrelevant physical networked dynamics (e.g., water/oil/gas/electrical signal-flows). Leveraging this, we propose, for the first time, graph layer security (GLS), by exploiting the dependency in physical dynamics among network nodes for information encryption and decryption. A graph Fourier transform (GFT) operator is used to characterise such dependency into a graph-bandlimited subspace, which allows the generation of channel-irrelevant cipher keys by maximising the secrecy rate. We evaluate our GLS against designed active and passive attackers, using IEEE 39-Bus system. Results demonstrate that GLS is not reliant on wireless CSI, and can combat attackers that have partial networked dynamic knowledge (realistic access to full dynamic and critical nodes remains challenging). We believe this novel GLS has widespread applicability in secure health monitoring and for digital twins in adversarial radio environments.
Distributed Fiber-Optic Strain Sensing of an Innovative Reinforced Concrete Beam–Column Connection
Authors: Zhang Shenghan , Liu Han , Darwish Esam , Mosalam Khalid M. , DeJong Matthew J.
Published: May 2022

Distributed fiber-optic sensing (DFOS) technologies have been used for decades to detect damage in infrastructure. One recent DFOS technology, Optical Frequency Domain Reflectometry (OFDR), has attracted attention from the structural engineering community because its high spatial resolution and refined accuracy could enable new monitoring possibilities and new insight regarding the behavior of reinforced concrete (RC) structures. The current research project explores the ability and potential of OFDR to measure distributed strain in RC structures through laboratory tests on an innovative beam–column connection, in which a partial slot joint was introduced between the beam and the column to control damage. In the test specimen, fiber-optic cables were embedded in both the steel reinforcement and concrete. The specimen was tested under quasi-static cyclic loading with increasing displacement demand at the structural laboratory of the Pacific Earthquake Engineering Research (PEER) Center of UC Berkeley. Different types of fiber-optic cables were embedded both in the concrete and the rebar. The influence of the cable coating and cable position are discussed. The DFOS results are compared with traditional measurements (DIC and LVDT). The high resolution of DFOS at small deformations provides new insights regarding the mechanical behavior of the slotted RC beam–column connection, including direct measurement of beam curvature, rebar deformation, and slot opening and closing. A major contribution of this work is the quantification of the performance and limitations of the DFOS system under large cyclic strains. Performance is quantified in terms of non-valid points (which occur in large strains when the DFOS analyzer does not return a strain value), maximum strain that can be reliably measured, crack width that causes cable rupture, and the effect of the cable coating on the measurements. Structural damage indices are also proposed based on the DFOS results. These damage indices correlate reasonably well with the maximum sustained drift, indicating the potential of using DFOS for RC structural damage assessment. The experimental data set is made publicly available.
A Theoretical Study of the Sensing Mechanism of a Schiff-Based Sensor for Fluoride
Authors: Ding Sha , Xia Yong , Lin Xiaoqi , Sun Aokui , Li Xianggang , Liu Yuejun
Published: May 2022

In the current work, we studied the sensing process of the sensor (E)-2-((quinolin-8ylimino) methyl) phenol (QP) for fluoride anion (F–) with a “turn on” fluorescent response by density functional theory (DFT) and time-dependent density functional theory (TDDFT) calculations. The proton transfer process and the twisted intramolecular charge transfer (TICT) process of QP have been explored by using potential energy curves as functions of the distance of N-H and dihedral angle C-N=C-C both in the ground and the excited states. According to the calculated results, the fluorescence quenching mechanism of QP and the fluorescent response for F– have been fully explored. These results indicate that the current calculations completely reproduce the experimental results and provide compelling evidence for the sensing mechanism of QP for F–.
A Hybrid Leak Localization Approach Using Acoustic Emission for Industrial Pipelines
Authors: Gao Yangde , Piltan Farzin , Kim Jong-Myon
Published: May 2022

Acoustic emission techniques are widely used to monitor industrial pipelines. Intelligent methods using acoustic emission signals can analyze acoustic waves and provide important information for leak detection and localization. To address safety and protect the operation of industrial pipelines, a novel hybrid approach based on acoustic emission signals is proposed to achieve reliable leak localization. The proposed method employs minimum entropy deconvolution using the maximization kurtosis norm of acoustic emission signals to remove noise and identify important feature signals. In addition, the damping frequency energy based on the dynamic differential equation with damping term is designed to extract important energy information, and a smooth envelope for the feature signals over time is generated. The zero crossing tracks the arrival time via the envelope changes and identifies the time difference of the acoustic waves from the two channels, each of which is installed at the end of a pipeline. Finally, the time data are combined with the velocity data to localize the leak. The proposed approach has better performance than the existing generalized cross-correlation and empirical mode decomposition combined with the generalized cross-correlation methods, providing proper leak localization in the industrial pipeline.
Sampling Trade-Offs in Duty-Cycled Systems for Air Quality Low-Cost Sensors
Authors: Ferrer-Cid Pau , Garcia-Calvete Julio , Main-Nadal Aina , Ye Zhe , Barcelo-Ordinas Jose M. , Garcia-Vidal Jorge
Published: May 2022

The use of low-cost sensors in conjunction with high-precision instrumentation for air pollution monitoring has shown promising results in recent years. One of the main challenges for these sensors has been the quality of their data, which is why the main efforts have focused on calibrating the sensors using machine learning techniques to improve the data quality. However, there is one aspect that has been overlooked, that is, these sensors are mounted on nodes that may have energy consumption restrictions if they are battery-powered. In this paper, we show the usual sensor data gathering process and we study the existing trade-offs between the sampling of such sensors, the quality of the sensor calibration, and the power consumption involved. To this end, we conduct experiments on prototype nodes measuring tropospheric ozone, nitrogen dioxide, and nitrogen monoxide at high frequency. The results show that the sensor sampling strategy directly affects the quality of the air pollution estimation and that each type of sensor may require different sampling strategies. In addition, duty cycles of 0.1 can be achieved when the sensors have response times in the order of two minutes, and duty cycles between 0.01 and 0.02 can be achieved when the sensor response times are negligible, calibrating with hourly reference values and maintaining a quality of calibrated data similar to when the node is connected to an uninterruptible power supply.
Security and Privacy Analysis of Youth-Oriented Connected Devices
Authors: Solera-Cotanilla Sonia , Vega-Barbas Mario , Pérez Jaime , López Gregorio , Matanza Javier , Álvarez-Campana Manuel
Published: May 2022

Under the Internet of Things paradigm, the emergence and use of a wide variety of connected devices and personalized telematics services have proliferated recently. As a result, along with the penetration of these devices in our daily lives, the users’ security and privacy have been compromised due to some weaknesses in connected devices and underlying applications. This article focuses on analyzing the security and privacy of such devices to promote safe Internet use, especially by young people. First, the connected devices most used by the target group are classified, and an exhaustive analysis of the vulnerabilities that concern the user is performed. As a result, a set of differentiated security and privacy issues existing in the devices is identified. The study reveals that many of these vulnerabilities are related to the fact that device manufacturers often prioritize functionalities and services, leaving security aspects in the background. These companies even exploit the data linked to the use of these devices for various purposes, ignoring users’ privacy rights. This research aims to raise awareness of severe vulnerabilities in devices and to encourage users to use them correctly. Our results help other researchers address these issues with a more global perspective.
Temperature-Compensated Multi-Point Strain Sensing Based on Cascaded FBG and Optical FMCW Interferometry
Authors: Feng Zhiyu , Cheng Yu , Chen Ming , Yuan Libo , Hong Deng , Li Litong
Published: May 2022

We proposed a novel temperature-compensated multi-point strain sensing system based on cascaded FBG and optical FMCW interferometry. The former is used for simultaneous sensing of temperature and strain, and the latter is used for position information reading and multiplexing. In the experiment, a narrow linewidth laser with continuous frequency-sweeping was used as the light source. After demodulating the beat-frequency signal, the link information of the 16 m fiber was obtained, and the measured result was identical to the actual position. The measurement accuracy reached 50.15 mm, and the dynamic range was up to 22.68 dB. Meanwhile, we completed the sensing experiments for temperature range from 20 °C to 90 °C and strain range from 0 με to 7000 με. The sensitivity of the sensing system to temperature was 10.21 pm/°C, the sensitivity and accuracy to strain were as high as 1.163 pm/με and 10 με, respectively. Finally, the measured strain and temperature values were obtained using the sensing matrix. The sensing system has important practical significance in the field of quasi-distributed strain measurement.
Trust-Based Intelligent Routing Protocol with Q-Learning for Mission-Critical Wireless Sensor Networks
Authors: Keum DooHo , Ko Young-Bae
Published: May 2022

Mission-critical wireless sensor networks require a trustworthy and punctual routing protocol to ensure the worst-case end-to-end delay and reliability when transmitting mission-critical data collected by various sensors to gateways. In particular, the trustworthiness of mission-critical data must be guaranteed for decision-making and secure communications. However, it is a challenging issue to meet the requirement of both reliability and QoS in sensor networking environments where cyber-attacks may frequently occur and a lot of mission-critical data is generated. This study proposes a trust-based routing protocol that learns the trust elements using Q-learning to detect various attacks and ensure network performance. The proposed mechanism ensures the prompt detection of cyber threats that may occur in a mission-critical wireless sensor network and guarantees the trustworthy transfer of mission-critical sensor data. This paper introduces a distributed transmission technology that prioritizes the trustworthiness of mission-critical data through Q-learning results considering trustworthiness, QoS, and energy factors. It is a technology suitable for mission-critical wireless sensor network operational environments and can reliably operate resource-constrained devices. We implemented and performed a comprehensive evaluation of our scheme using the OPNET simulator. In addition, we measured packet delivery rates, throughput, survivability, and delay considering the characteristics of mission-critical sensor networks. The simulation results show an enhanced performance when compared with other mechanisms.
Non-Hermitian Sensing in Photonics and Electronics: A Review
Authors: De Carlo Martino , De Leonardis Francesco , Soref Richard A. , Colatorti Luigi , Passaro Vittorio M. N.
Published: May 2022

Recently, non-Hermitian Hamiltonians have gained a lot of interest, especially in optics and electronics. In particular, the existence of real eigenvalues of non-Hermitian systems has opened a wide set of possibilities, especially, but not only, for sensing applications, exploiting the physics of exceptional points. In particular, the square root dependence of the eigenvalue splitting on different design parameters, exhibited by 2 × 2 non-Hermitian Hamiltonian matrices at the exceptional point, paved the way to the integration of high-performance sensors. The square root dependence of the eigenfrequencies on the design parameters is the reason for a theoretically infinite sensitivity in the proximity of the exceptional point. Recently, higher-order exceptional points have demonstrated the possibility of achieving the nth root dependence of the eigenfrequency splitting on perturbations. However, the exceptional sensitivity to external parameters is, at the same time, the major drawback of non-Hermitian configurations, leading to the high influence of noise. In this review, the basic principles of PT-symmetric and anti-PT-symmetric Hamiltonians will be shown, both in photonics and in electronics. The influence of noise on non-Hermitian configurations will be investigated and the newest solutions to overcome these problems will be illustrated. Finally, an overview of the newest outstanding results in sensing applications of non-Hermitian photonics and electronics will be provided.