bluuebunny/crossref_metadata_2025 huggingface dataset:
Formation of Potential Heterotic Groups of Oat Using Variation at Microsatellite Loci
Authors: Havrlentová Michaela , Ondreičková Katarína , Hozlár Peter , Gregusová Veronika , Mihálik Daniel , Kraic Ján
Published: November 2021

An evaluation of polymorphism at the microsatellite loci was applied in distinguishing 85 oat (Avena sativa L.) genotypes selected from the collection of genetic resources. The set of genotypes included oats with white, yellow, and brown seeds as well as a subgroup of naked oat (Avena sativa var. nuda Koern). Variation at these loci was used to form potential heterotic groups potentially used in the oat breeding program. Seven from 20 analyzed microsatellite loci revealed polymorphism. Altogether, 35 microsatellite alleles were detected (2–10 per locus). Polymorphic patterns completely differentiated all genotypes within the subgroups of white, brown, and naked oats, respectively. Only within the greatest subgroup of yellow genotypes, four pairs of genotypes remained unseparated. Genetic differentiation between the oat subgroups allowed the formation of seven potential heterotic groups using the STRUCTURE analysis. The overall value of the fixation index (Fst) suggested a high genetic differentiation between the subgroups and validated a heterotic grouping. This approach can be implemented as a simple predictor of heterosis in parental crosses prior to extensive field testing or development and implementation of more accurate genomic selection.
Deciphering Reserve Mobilization, Antioxidant Potential, and Expression Analysis of Starch Synthesis in Sorghum Seedlings under Salt Stress
Authors: Punia Himani , Tokas Jayanti , Mor Virender Singh , Bhuker Axay , Malik Anurag , Singh Nirmal , Satpal  , Alsahli Abdulaziz Abdullah , Hefft Daniel Ingo
Published: November 2021

Salt stress is one of the major constraints affecting plant growth and agricultural productivity worldwide. Sorghum is a valuable food source and a potential model for studying and better understanding the salt stress mechanics in the cereals and obtaining a more comprehensive knowledge of their cellular responses. Herein, we examined the effects of salinity on reserve mobilization, antioxidant potential, and expression analysis of starch synthesis genes. Our findings show that germination percentage is adversely affected by all salinity levels, more remarkably at 120 mM (36% reduction) and 140 mM NaCl (46% reduction) than in the control. Lipid peroxidation increased in salt-susceptible genotypes (PC-5: 2.88 and CSV 44F: 2.93 nmloe/g.FW), but not in tolerant genotypes. SSG 59-3 increased activities of α-amylase, and protease enzymes corroborated decreased starch and protein content, respectively. SSG 59-3 alleviated adverse effects of salinity by suppressing oxidative stress (H2O2) and stimulating enzymatic and non-enzymatic antioxidant activities (SOD, APX, CAT, POD, GR, and GPX), as well as protecting cell membrane integrity (MDA, electrolyte leakage). A significant increase (p ≤ 0.05) was also observed in SSG 59-3 with proline, ascorbic acid, and total carbohydrates. Among inorganic cations and anions, Na+, Cl−, and SO42− increased, whereas K+, Mg2+, and Ca2+ decreased significantly. SSG 59-3 had a less pronounced effect of excess Na+ ions on the gene expression of starch synthesis. Salinity also influenced Na+ ion efflux and maintained a lower cytosolic Na+/K+ ratio via concomitant upregulation of SbNHX-1 and SbVPPase-I ion transporter genes. Thus, we have highlighted that salinity physiologically and biochemically affect sorghum seedling growth. Based on these findings, we highlighted that SSG 59-3 performed better by retaining higher plant water status, antioxidant potential, and upregulation of ion transporter genes and starch synthesis, thereby alleviating stress, which may be augmented as genetic resources to establish sorghum cultivars with improved quality in saline soils.
A Metabolic Profiling Analysis Revealed a Primary Metabolism Reprogramming in Arabidopsis glyI4 Loss-of-Function Mutant
Authors: Proietti Silvia , Bertini Laura , Falconieri Gaia Salvatore , Baccelli Ivan , Timperio Anna Maria , Caruso Carla
Published: November 2021

Methylglyoxal (MG) is a cytotoxic compound often produced as a side product of metabolic processes such as glycolysis, lipid peroxidation, and photosynthesis. MG is mainly scavenged by the glyoxalase system, a two-step pathway, in which the coordinate activity of GLYI and GLYII transforms it into D-lactate, releasing GSH. In Arabidopsis thaliana, a member of the GLYI family named GLYI4 has been recently characterized. In glyI4 mutant plants, a general stress phenotype characterized by compromised MG scavenging, accumulation of reactive oxygen species (ROS), stomatal closure, and reduced fitness was observed. In order to shed some light on the impact of gly4 loss-of-function on plant metabolism, we applied a high resolution mass spectrometry-based metabolomic approach to Arabidopsis Col-8 wild type and glyI4 mutant plants. A compound library containing a total of 70 metabolites, differentially synthesized in glyI4 compared to Col-8, was obtained. Pathway analysis of the identified compounds showed that the upregulated pathways are mainly involved in redox reactions and cellular energy maintenance, and those downregulated in plant defense and growth. These results improved our understanding of the impacts of glyI4 loss-of-function on the general reprogramming of the plant’s metabolic landscape as a strategy for surviving under adverse physiological conditions.
Biomimetics Design Optimization and Drag Reduction Analysis for Indonesia N219 Seaplanes Catamaran Float
Authors: Utomo Allessandro , Gunawan  , Yanuar
Published: November 2021

Design optimization on the Indonesia N219 seaplane catamaran is necessary to provide better service to rural islands of Indonesia. This research aims at decreasing drag using a design based on biomimicry by imitating the hydrodynamic characteristics of sailfish (Istiophorus platypterus) for pontoon floats. The design is then validated using a numerical fluid test using ANSYS Fluent to see the reduction in drag due to the change from a conventional or Wipeline® 13000 design to a biomimetics adaptation design. Next, further optimization was carried out based on the adaptation design based on trim tests, clearance tests, and deadrise angle dimensions suitable for biomimicry designs at Froude number speeds of 0.4 to 0.7. The design results with the adaptation of biomimicry show that a change in the design with this optimization affects a drag reduction that reaches 30% of the total drag generated by the conventional design.
Effects of Adding Laccase to Bacterial Consortia Degrading Heavy Oil
Authors: Dai Xiaoli , Lv Jing , Wei Wenxia , Guo Shaohui
Published: November 2021

High-efficiency bioremediation technology for heavy oil pollution has been a popular research topic in recent years. Laccase is very promising for the remediation of heavy oil pollution because it can not only convert bio-refractory hydrocarbons into less toxic or completely harmless compounds, but also accelerate the biodegradation efficiency of heavy oil. However, there are few reports on the use of laccase to enhance the biodegradation of heavy oil. In this study, we investigated the effect of laccase on the bacterial consortia degradation of heavy oil. The degradation efficiencies of bacterial consortia and the laccase-bacterial consortia were 60.6 ± 0.1% and 68.2 ± 0.6%, respectively, and the corresponding heavy oil degradation rate constants were 0.112 day−1 and 0.198 day−1, respectively. The addition of laccase increased the heavy oil biodegradation efficiency (p < 0.05) and biodegradation rate of the bacterial consortia. Moreover, gas chromatography–mass spectrometry analysis showed that the biodegradation efficiencies of the laccase-bacterial consortia for saturated hydrocarbons and aromatic hydrocarbons were 82.5 ± 0.7% and 76.2 ± 0.9%, respectively, which were 16.0 ± 0.3% and 13.0 ± 1.8% higher than those of the bacterial consortia, respectively. In addition, the degradation rate constants of the laccase-bacterial consortia for saturated hydrocarbons and aromatic hydrocarbons were 0.267 day−1 and 0.226 day−1, respectively, which were 1.07 and 1.15 times higher than those of the bacterial consortia, respectively. The degradation of C15 to C35 n-alkanes and 2 to 5-ring polycyclic aromatic hydrocarbons by laccase-bacterial consortia was higher than individual bacterial consortia. It is further seen that the addition of laccase significantly improved the biodegradation of long-chain n-alkanes of C22–C35 (p < 0.05). Overall, this study shows that the combination of laccase and bacterial consortia is an effective remediation technology for heavy oil pollution. Adding laccase can significantly improve the heavy oil biodegradation efficiency and biodegradation rate of the bacterial consortia.
Measuring the Flow Functions of Pharmaceutical Powders Using the Brookfield Powder Flow Tester and Freeman FT4
Authors: Leaper Mark
Published: November 2021

This study examined the feasibility of combining data from different powder flow testers to determine the flow function characteristics of pharmaceutical powders. The Brookfield PFT and Freeman FT4 can measure flow function over different scales of consolidation load but were found to be most complementary with CRM limestone powder and lactose. The brittle behaviour of Easytab particles at higher loads made obtaining repeatable results with the FT4 challenging. By using the method of Wang et al., where the flow function coefficient ffc is plotted against the dimensionless cohesion C* (measured cohesion Ta divided by the initial compaction I), a plot was formed which could be used to predict the behaviour of other systems, which compared well with previous studies.
Diazotrophic Behaviour in a Non-Sterile Bioreactor: The Effect of O2-Availability
Authors: de Zoete Amber Yasemin Shirin , Brink Hendrik Gideon , Beukes Joshua Cornelus , van Rooyen Ignatius Leopoldus , Nicol Willie
Published: November 2021

The behaviour of a locally isolated diazotrophic consortium was investigated with the prospect of agricultural applications. A repeatable culture was obtained in a non-sterile bioreactor. Metagenomic analysis indicated Chryseobacterium ssp. and Flavobacterium ssp. were the dominant species, making up approximately 50% of the microbial community. The oxygen supply was varied and mass-transfer limited growth was attained under all experimental conditions. Negligible amounts of aqueous metabolites were formed, indicating a high selectivity towards biomass production. High oxygen availability resulted in decreased growth efficiencies i.e., the specific energy requirements for biomass synthesis. This was attributed to reduced electron transport chain efficiencies and nitrogenase protection mechanisms. Mass and energy balances indicated that sessile biomass with a high C:N served as a carbon sink. The most efficient growth was measured at an aeration feed composition of 21% oxygen and 79% nitrogen. The study presents one of the only known investigations of operational conditions on diazotrophic growth in a non-sterile bioreactor. In addition, it provides a strong foundation for the development of a Biological Nitrogen Fixation process with scaling potential.
Albert the Great, the Albert Legend, and the Legitimation of the Dominicans
Authors: Hendrix Scott E.
Published: November 2021

By the time of his death in 1280 Albert the Great was respected not only as a theologian and philosopher, but also as one of the greatest authorities on astrology in the West. Such expertise rarely gains plaudits today, but in late medieval Europe knowledge of esoteric pursuits was held in high regard. This is why Dominicans not only did nothing to challenge the growth of the “Albert Legend,” that Albert had mastered all magical and esoteric topics, but also promoted this myth. By promoting this legend, they bolstered and legitimized the reputation of the Order of Preachers.
North to South Variations in the Suspended Sediment Transport Budget within Large Siberian River Deltas Revealed by Remote Sensing Data
Authors: Chalov Sergey , Prokopeva Kristina , Habel Michał
Published: November 2021

This study presents detailed suspended sediment budget for the four Siberian river deltas, representing contrasting conditions between Northern and Southern environments. Two of the studied rivers empty their water and sediments into the marine located in the permafrost zone in the Arctic region (Lena and Kolyma), and the other two (Selenga and Upper Angara) flow into Lake Baikal located in the steppe and forest-steppe zone of Southern Siberia. For the first time, these poorly monitored areas are analyzed in terms of the long-term and seasonal changes of spatial patterns of suspended sediment concentrations (SSC) over distributaries systems. Remote sensing reflectance is derived from continuous time series of Landsat images and calibrated with the onsite field measurements of SSC. Seasonal variability of suspended sediment changes over deltas was captured for the period from 1989 to 2020. We identify significant variability in the sedimentation processes between different deltas, which is explained by particularities of deltas networks and geomorphology and the existence of specific drivers—continuous permafrost impact in the North and abundant aquatic vegetation and wetland-dominated areas in the South. The study emphasizes that differences exist between Northern and Southern deltas regarding suspended sediments transport conditions. Mostly retention of suspended sediment is observed for Southern deltas due to sediment storage at submerged banks and marshlands located in the backwater zone of the delta during high discharges. In the Northern (arctic) deltas due to permafrost impacts (melting of the permafrost), the absence of sub-aquatic banks and river to ocean interactions of suspended sediment transport is mostly increased downwards, predominantly under higher discharges and along main distributary channels. These results shine light on the geochemical functions of the deltas and patterns of sequestering various metals bound to river sediments.
Detection of Particulate Matter Changes Caused by 2020 California Wildfires Based on GNSS and Radiosonde Station
Authors: Guo Jinyun , Hou Rui , Zhou Maosheng , Jin Xin , Li Guowei
Published: November 2021

From August to October 2020, a serious wildfire occurred in California, USA, which produced a large number of particulate matter and harmful gases, resulting in huge economic losses and environmental pollution. Particulate matter delays the GNSS signal, which affects the like precipitable water vapor (LPWV) derived by the GNSS non-hydrostatic delay. Most of the information of GNSS-derived LPWV is caused by water vapor, and a small part of the information is caused by particulate matter. A new method based on the difference (ΔPWV) between the PWV of virtual radiosonde stations network and GNSS-derived LPWV is proposed to detect the changes of particulate matter in the atmosphere during the 2020 California wildfires. There are few radiosonde stations in the experimental area and they are far away from the GNSS station. In order to solve this problem, we propose to use the multilayer perceptron (MLP) neural network method to establish the virtual radiosonde network in the experimental area. The PWV derived by the fifth-generation European center for medium-range weather forecasts reanalysis model (PWVERA5) is used as the input data of machine learning. The PWV derived by radiosonde data (PWVRAD) is used as the training target data of machine learning. The ΔPWV is obtained based on PWV derived by the virtual radiosonde station network and GNSS in the experimental area. In order to further reduce the influence of noise and other factors on ΔPWV, this paper attempts to decompose ΔPWV time series by using the singular spectrum analysis method, and obtain its principal components, subsequently, analyzing the relationship between the principal components of ΔPWV with particulate matter. The results indicate that the accuracy of PWV predicted by the virtual radiosonde network is significantly better than the fifth-generation European center for the medium-range weather forecast reanalysis model, and the change trend of ΔPWV is basically consistent with the change law of particulate matter in which the value of ΔPWV in the case of fire is significantly higher than that before and after the fire. The mean of correlation coefficients between ΔPWV and PM10 at each GNSS station before, during and after wildfires are 0.068, 0.397 and 0.065, respectively, which show the evident enhancement of the correlation between ΔPWV and particulate matter during wildfires. It is concluded that because of the high sensitiveness of ΔPWV to the change of particulate matter, the GNSS technique can be used as an effective new approach to detect the change of particulate matter and, then, to detect wildfires effectively.
Correlation Analysis between Land-Use/Cover Change and Coastal Subsidence in the Yellow River Delta, China: Reviewing the Past and Prospecting the Future
Authors: Zhang Yi , Liu Yilin , Zhang Xinyuan , Huang Haijun , Qin Keyu , Bai Zechao , Zhou Xinghua
Published: November 2021

In recent years, noticeable subsidence depressions have occurred along the coastal zone of the Yellow River Delta. Consistent with these changes, dramatic human modifications within the coastal zone stand out, and the coastline is altered from an undisturbed natural area to an artificial coastline. However, very few studies have attempted to quantitatively analyze the relationship between subsidence depression and human activities. Here, the subsidence characteristics of the different land-use types in the Yellow River Delta are examined, and their spatiotemporal trends are quantified using a long-term satellite-observed time series of 30 years (1984–2017) regarding the land use map in combination with the InSAR-derived vertical ground deformations during three typical periods (P1: 1992–2000, P2: 2007–2010, and P3: 2016–2017). Noticeably, the highest subsidence rates were observed in areas where substantial human activities were observed, such as the subsidence in the salt fields ranging from 13 mm/year to 32 mm/year to 453 mm/year, respectively. Moreover, through the land-use prediction of Land Change Modeler (LCM), it is found that the salt field area will be further expanded in the future. The ecological vulnerability of the Yellow River Delta coastal zone should receive more attention in the future in terms of planning environmental protection strategies.
A Robust InSAR Phase Unwrapping Method via Phase Gradient Estimation Network
Authors: Pu Liming , Zhang Xiaoling , Zhou Zenan , Li Liang , Zhou Liming , Shi Jun , Wei Shunjun
Published: November 2021

Phase unwrapping is a critical step in synthetic aperture radar interferometry (InSAR) data processing chains. In almost all phase unwrapping methods, estimating the phase gradient according to the phase continuity assumption (PGE-PCA) is an essential step. The phase continuity assumption is not always satisfied due to the presence of noise and abrupt terrain changes; therefore, it is difficult to get the correct phase gradient. In this paper, we propose a robust least squares phase unwrapping method that works via a phase gradient estimation network based on the encoder–decoder architecture (PGENet) for InSAR. In this method, from a large number of wrapped phase images with topography features and different levels of noise, the deep convolutional neural network can learn global phase features and the phase gradient between adjacent pixels, so a more accurate and robust phase gradient can be predicted than that obtained by PGE-PCA. To get the phase unwrapping result, we use the traditional least squares solver to minimize the difference between the gradient obtained by PGENet and the gradient of the unwrapped phase. Experiments on simulated and real InSAR data demonstrated that the proposed method outperforms the other five well-established phase unwrapping methods and is robust to noise.
Memory-Based Pruning of Deep Neural Networks for IoT Devices Applied to Flood Detection
Authors: Fernandes Junior Francisco Erivaldo , Nonato Luis Gustavo , Ranieri Caetano Mazzoni , Ueyama Jó
Published: November 2021

Automatic flood detection may be an important component for triggering damage control systems and minimizing the risk of social or economic impacts caused by flooding. Riverside images from regular cameras are a widely available resource that can be used for tackling this problem. Nevertheless, state-of-the-art neural networks, the most suitable approach for this type of computer vision task, are usually resource-consuming, which poses a challenge for deploying these models within low-capability Internet of Things (IoT) devices with unstable internet connections. In this work, we propose a deep neural network (DNN) architecture pruning algorithm capable of finding a pruned version of a given DNN within a user-specified memory footprint. Our results demonstrate that our proposed algorithm can find a pruned DNN model with the specified memory footprint with little to no degradation of its segmentation performance. Finally, we show that our algorithm can be used in a memory-constraint wireless sensor network (WSN) employed to detect flooding events of urban rivers, and the resulting pruned models have competitive results compared with the original models.
Abnormal Detection of Cash-Out Groups in IoT Based Payment
Authors: Zhou Hao , Zhang Ming , Pang Lei , Li Jian-Hua
Published: November 2021

With the rise of online/mobile transactions, the cost of cash-out has decreased and the cost of detection has increased. In the world of online/mobile payment in IoT, merchants and credit cards can be applied and approved online and used in the form of a QR code but not a physical card or Point of Sale equipment, making it easy for these systems to be controlled by a group of fraudsters. In mainland China, where the credit card transaction fee is, on average, lower than a retail loan rate, the credit card cash-out option is attractive for people for an investment or business operation, which, after investigation, can be considered unlawful if over a certain amount is used. Because cash-out will incur fees for the merchants, while bringing money to the credit cards’ owners, it is difficult to confirm, as nobody will declare or admit it. Furthermore, it is more difficult to detect cash-out groups than individuals, because cash-out groups are more hidden, which leads to bigger transaction amounts. We propose a new method for the detection of cash-out groups. First, the seed cards are mined and the seed cards’ diffusion is then performed through the local graph clustering algorithm (Approximate PageRank, APR). Second, a merchant association network in IoT is constructed based on the suspicious cards, using the graph embedding algorithm (Node2Vec). Third, we use the clustering algorithm (DBSCAN) to cluster the nodes in the Euclidean space, which divides the merchants into groups. Finally, we design a method to classify the severity of the groups to facilitate the following risk investigation. The proposed method covers 145 merchants from 195 known risky merchants in groups that acquire cash-out from four banks, which shows that this method can identify most (74.4%) cash-out groups. In addition, the proposed method identifies a further 178 cash-out merchants in the group within the same four acquirers, resulting in a total of 30,586 merchants. The results and framework are already adopted and absorbed into the design for a cash-out group detection system in IoT by the Chinese payment processor.
Deep Learning Approaches for Continuous Authentication Based on Activity Patterns Using Mobile Sensing
Authors: Mekruksavanich Sakorn , Jitpattanakul Anuchit
Published: November 2021

Smartphones as ubiquitous gadgets are rapidly becoming more intelligent and context-aware as sensing, networking, and processing capabilities advance. These devices provide users with a comprehensive platform to undertake activities such as socializing, communicating, sending and receiving e-mails, and storing and accessing personal data at any time and from any location. Nowadays, smartphones are used to store a multitude of private and sensitive data including bank account information, personal identifiers, account passwords and credit card information. Many users remain permanently signed in and, as a result, their mobile devices are vulnerable to security and privacy risks through assaults by criminals. Passcodes, PINs, pattern locks, facial verification, and fingerprint scans are all susceptible to various assaults including smudge attacks, side-channel attacks, and shoulder-surfing attacks. To solve these issues, this research introduces a new continuous authentication framework called DeepAuthen, which identifies smartphone users based on their physical activity patterns as measured by the accelerometer, gyroscope, and magnetometer sensors on their smartphone. We conducted a series of tests on user authentication using several deep learning classifiers, including our proposed deep learning network termed DeepConvLSTM on the three benchmark datasets UCI-HAR, WISDM-HARB and HMOG. Results demonstrated that combining various motion sensor data obtained the highest accuracy and energy efficiency ratio (EER) values for binary classification. We also conducted a thorough examination of the continuous authentication outcomes, and the results supported the efficacy of our framework.
Radioluminescence Response of Ce-, Cu-, and Gd-Doped Silica Glasses for Dosimetry of Pulsed Electron Beams
Authors: Söderström Daniel , Kettunen Heikki , Morana Adriana , Javanainen Arto , Ouerdane Youcef , El Hamzaoui Hicham , Capoen Bruno , Bouwmans Géraud , Bouazaoui Mohamed , Girard Sylvain
Published: November 2021

Radiation-induced emission of doped sol-gel silica glass samples was investigated under a pulsed 20-MeV electron beam. The studied samples were drawn rods doped with cerium, copper, or gadolinium ions, which were connected to multimode pure-silica core fibers to transport the induced luminescence from the irradiation area to a signal readout system. The luminescence pulses in the samples induced by the electron bunches were studied as a function of deposited dose per electron bunch. All the investigated samples were found to have a linear response in terms of luminescence as a function of electron bunch sizes between 10−5 Gy/bunch and 1.5×10−2 Gy/bunch. The presented results show that these types of doped silica rods can be used for monitoring a pulsed electron beam, as well as to evaluate the dose deposited by the individual electron bunches. The electron accelerator used in the experiment was a medical type used for radiation therapy treatments, and these silica rod samples show high potential for dosimetry in radiotherapy contexts.
Development of an Optical Method to Monitor Nitrification in Drinking Water
Authors: Hossain Sharif , Cook David , Chow Christopher , Hewa Guna
Published: November 2021

Nitrification is a common issue observed in chloraminated drinking water distribution systems, resulting in the undesirable loss of monochloramine (NH2Cl) residual. The decay of monochloramine releases ammonia (NH3), which is converted to nitrite (NO2−) and nitrate (NO3−) through a biological oxidation process. During the course of monochloramine decay and the production of nitrite and nitrate, the spectral fingerprint is observed to change within the wavelength region sensitive to these species. In addition, chloraminated drinking water will contain natural organic matter (NOM), which also has a spectral fingerprint. To assess the nitrification status, the combined nitrate and nitrite absorbance fingerprint was isolated from the total spectra. A novel method is proposed here to isolate their spectra and estimate their combined concentration. The spectral fingerprint of pure monochloramine solution at different concentrations indicated that the absorbance difference between two concentrations at a specific wavelength can be related to other wavelengths by a linear function. It is assumed that the absorbance reduction in drinking water spectra due to monochloramine decay will follow a similar pattern as in ultrapure water. Based on this criteria, combined nitrate and nitrite spectra were isolated from the total spectrum. A machine learning model was developed using the support vector regression (SVR) algorithm to relate the spectral features of pure nitrate and nitrite with their concentrations. The model was used to predict the combined nitrate and nitrite concentration for a number of test samples. Out of these samples, the nitrified sample showed an increasing trend of combined nitrate and nitrite productions. The predicted values were matched with the observed concentrations, and the level of precision by the method was ± 0.01 mg-N L−1. This method can be implemented in chloraminated distribution systems to monitor and manage nitrification.
Methods of Projecting Mode Amplitude Changes on the Wavelength Axis in Order to Determine the Bending Radius on the Basis of TFBG Grating Spectra
Authors: Cięszczyk Sławomir , Harasim Damian , Ormanbekova Ainur , Skorupski Krzysztof , Wawrzyk Martyna
Published: November 2021

Tilted fibre Bragg grating (TFBG) are used as sensors to determine many quantities such as refractive index, temperature, stress, rotation and bending. The TFBG spectrum contains a lot of information and various algorithms are used for its analysis. However, most of these algorithms are dedicated to the analysis of spectral changes under the influence of the refractive index. The most popular algorithm used for this purpose is to calculate the area occupied by cladding modes. Among the remaining algorithms, there are those that use the determination of the cut-off wavelength as a surrounding refractive index (SRI) indicator. Projection on the wavelength axis can also be used to calculate the bending radius of the fibre. However, this is a more difficult task than with SRI, because the mode decay in bending is not so easy to catch. In this article, we propose a multi-step algorithm that allows to determine the impact of bending on mode leakage. At the same time, the place on the wavelength from the side of the Bragg mode and the ghost mode is determined, which represents the cladding mode radiated from the cladding under the influence of bending. The developed algorithm consists of the following operations carried out on the transmission spectrum: Fourier filtering, calculation of the cumulative value of the spectral length, low-pass filtering of the cumulative curve or its corresponding polynomial approximation, determination of the first and second derivative of the approximated curve, and projection of the second derivative of the curve on the wavelength axis. The shift of the wavelength determined in this way indirectly indicates the bending radius of the optical fibre. Based on multiple measurements, we prove that the presented algorithm provides better results when determining the bending radius compared to other algorithms adopted for this purpose and proposed for SRI measurements. Additionally, we analyse the method of determining the shift of a fragment of the spectrum using the phase of the discrete Fourier transform.
Image-Based Automated Width Measurement of Surface Cracking
Authors: Carrasco Miguel , Araya-Letelier Gerardo , Velázquez Ramiro , Visconti Paolo
Published: November 2021

The detection of cracks is an important monitoring task in civil engineering infrastructure devoted to ensuring durability, structural safety, and integrity. It has been traditionally performed by visual inspection, and the measurement of crack width has been manually obtained with a crack-width comparator gauge (CWCG). Unfortunately, this technique is time-consuming, suffers from subjective judgement, and is error-prone due to the difficulty of ensuring a correct spatial measurement as the CWCG may not be correctly positioned in accordance with the crack orientation. Although algorithms for automatic crack detection have been developed, most of them have specifically focused on solving the segmentation problem through Deep Learning techniques failing to address the underlying problem: crack width evaluation, which is critical for the assessment of civil structures. This paper proposes a novel automated method for surface cracking width measurement based on digital image processing techniques. Our proposal consists of three stages: anisotropic smoothing, segmentation, and stabilized central points by k-means adjustment and allows the characterization of both crack width and curvature-related orientation. The method is validated by assessing the surface cracking of fiber-reinforced earthen construction materials. The preliminary results show that the proposal is robust, efficient, and highly accurate at estimating crack width in digital images. The method effectively discards false cracks and detects real ones as small as 0.15 mm width regardless of the lighting conditions.
A Modular Cooperative Wall-Climbing Robot Based on Internal Soft Bone
Authors: Huang Wenkai , Hu Wei , Zou Tao , Xiao Junlong , Lu Puwei , Li Hongquan
Published: November 2021

Most existing wall-climbing robots have a fixed range of load capacity and a step distance that is small and mostly immutable. It is therefore difficult for them to adapt to a discontinuous wall with particularly large gaps. Based on a modular design and inspired by leech peristalsis and internal soft-bone connection, a bionic crawling modular wall-climbing robot is proposed in this paper. The robot demonstrates the ability to handle variable load characteristics by carrying different numbers of modules. Multiple motion modules are coupled with the internal soft bone so that they work together, giving the robot variable-step-distance functionality. This paper establishes the robotic kinematics model, presents the finite element simulation analysis of the model, and introduces the design of the multi-module cooperative-motion method. Our experiments show that the advantage of variable step distance allows the robot not only to quickly climb and turn on walls, but also to cross discontinuous walls. The maximum climbing step distance of the robot can reach 3.6 times the length of the module and can span a discontinuous wall with a space of 150 mm; the load capacity increases with the number of modules in series. The maximum load that N modules can carry is about 1.3 times the self-weight.
Synthetic Source Universal Domain Adaptation through Contrastive Learning
Authors: Cho Jungchan
Published: November 2021

Universal domain adaptation (UDA) is a crucial research topic for efficient deep learning model training using data from various imaging sensors. However, its development is affected by unlabeled target data. Moreover, the nonexistence of prior knowledge of the source and target domain makes it more challenging for UDA to train models. I hypothesize that the degradation of trained models in the target domain is caused by the lack of direct training loss to improve the discriminative power of the target domain data. As a result, the target data adapted to the source representations is biased toward the source domain. I found that the degradation was more pronounced when I used synthetic data for the source domain and real data for the target domain. In this paper, I propose a UDA method with target domain contrastive learning. The proposed method enables models to leverage synthetic data for the source domain and train the discriminativeness of target features in an unsupervised manner. In addition, the target domain feature extraction network is shared with the source domain classification task, preventing unnecessary computational growth. Extensive experimental results on VisDa-2017 and MNIST to SVHN demonstrated that the proposed method significantly outperforms the baseline by 2.7% and 5.1%, respectively.
Hip Lift Transfer Assistive System for Reducing Burden on Caregiver’s Waist
Authors: Wu Jiang , Shino Motoki
Published: November 2021

In Japan, the aging population is expected to increase the number of elderly people in the future. The purpose of this study was to develop a hip lift transfer assistive system to improve the QOL of elderly living and to prevent back pain for caregivers. We extracted the impediment factor and the necessary scene for the assistance, decided on the transfer process from the wheelchair in the toilet, and considered the reduction method of the burden based on the quantitative evaluation of the caregiver’s lumbar burden and developed the device. Then we proposed the algorithm of the system by grasping the behavior and lumbar burden characteristics at the time of using the hip lift transfer assistive system by the developed device in which the proposed support algorithm of standing seating assistance operation is implemented in the actual use environment. Through the assistive movement evaluation experiment and the actual operation in the toilet, we have verified that the use of this device can reduce the caregivers’ lumbar burden below the standard value (3400 N) and have proved the effectiveness of the proposed transfer assistive system.
A Power-Efficient Sensing Approach for Pulse Wave Palpation-Based Heart Rate Measurement
Authors: Bravo Gabriel , Silva Jesús M. , Noriega Salvador A. , Martínez Erwin A. , Enríquez Francisco J. , Sifuentes Ernesto
Published: November 2021

Heart rate (HR) is an essential indicator of health in the human body. It measures the number of times per minute that the heart contracts or beats. An irregular heartbeat can signify a severe health condition, so monitoring heart rate periodically can help prevent heart complications. This paper presents a novel wearable sensing approach for remote HR measurement by a compact resistance-to-microcontroller interface circuit. A heartbeat’s signal can be detected by a Force Sensing Resistor (FSR) attached to the body near large arteries (such as the carotid or radial), which expand their area each time the heart expels blood to the body. Depending on how the sensor interfaces with the subject, the FSR changes its electrical resistance every time a pulse is detected. By placing the FSR in a direct interface circuit, those resistance variations can be measured directly by a microcontroller without using either analog processing stages or an analog-to-digital converter. In this kind of interface, the self-heating of the sensor is avoided, since the FSR does not require any voltage or bias current. The proposed system has a sampling rate of 50 Sa/s, and an effective resolution of 10 bits (200 mΩ), enough for obtaining well-shaped cardiac signals and heart rate estimations in real time by the microcontroller. With this approach, the implementation of wearable systems in health monitoring applications is more feasible.
Hole Repairing Algorithm for 3D Point Cloud Model of Symmetrical Objects Grasped by the Manipulator
Authors: Cui Linyan , Zhang Guolong , Wang Jinshen
Published: November 2021

For the engineering application of manipulator grasping objects, mechanical arm occlusion and limited imaging angle produce various holes in the reconstructed 3D point clouds of objects. Acquiring a complete point cloud model of the grasped object plays a very important role in the subsequent task planning of the manipulator. This paper proposes a method with which to automatically detect and repair the holes in the 3D point cloud model of symmetrical objects grasped by the manipulator. With the established virtual camera coordinate system and boundary detection, repair and classification of holes, the closed boundaries for the nested holes were detected and classified into two kinds, which correspond to the mechanical claw holes caused by mechanical arm occlusion and the missing surface produced by limited imaging angle. These two kinds of holes were repaired based on surface reconstruction and object symmetry. Experiments on simulated and real point cloud models demonstrate that our approach outperforms the other state-of-the-art 3D point cloud hole repair algorithms.
A Novel Occupancy Mapping Framework for Risk-Aware Path Planning in Unstructured Environments
Authors: Laconte Johann , Kasmi Abderrahim , Pomerleau François , Chapuis Roland , Malaterre Laurent , Debain Christophe , Aufrère Romuald
Published: November 2021

In the context of autonomous robots, one of the most important tasks is to prevent potential damage to the robot during navigation. For this purpose, it is often assumed that one must deal with known probabilistic obstacles, then compute the probability of collision with each obstacle. However, in complex scenarios or unstructured environments, it might be difficult to detect such obstacles. In these cases, a metric map is used, where each position stores the information of occupancy. The most common type of metric map is the Bayesian occupancy map. However, this type of map is not well suited for computing risk assessments for continuous paths due to its discrete nature. Hence, we introduce a novel type of map called the Lambda Field, which is specially designed for risk assessment. We first propose a way to compute such a map and the expectation of a generic risk over a path. Then, we demonstrate the benefits of our generic formulation with a use case defining the risk as the expected collision force over a path. Using this risk definition and the Lambda Field, we show that our framework is capable of doing classical path planning while having a physical-based metric. Furthermore, the Lambda Field gives a natural way to deal with unstructured environments, such as tall grass. Where standard environment representations would always generate trajectories going around such obstacles, our framework allows the robot to go through the grass while being aware of the risk taken.
Advanced Optical Sensing of Phenolic Compounds for Environmental Applications
Authors: Delfino Ines , Diano Nadia , Lepore Maria
Published: November 2021

Phenolic compounds are particularly dangerous due to their ability to remain in the environment for a long period of time and their toxic effects. They enter in the environment in different ways, such as waste from paper manufacturing, agriculture (pesticides, insecticides, herbicides), pharmaceuticals, the petrochemical industry, and coal processing. Conventional methods for phenolic compounds detection present some disadvantages, such as cumbersome sample preparation, complex and time-consuming procedures, and need of expensive equipment. Therefore, there is a very large interest in developing sensors and new sensing schemes for fast and easy-to-use methods for detecting and monitoring the phenolic compound concentration in the environment, with special attention to water. Good analytical properties, reliability, and adaptability are required for the developed sensors. The present paper aims at revising the most generally used optical methods for designing and fabricating biosensors and sensors for phenolic compounds. Some selected examples of the most interesting applications of these techniques are also proposed.
The Porous Border Woven with Prejudices and Economic Interests. Polish Border Admission Practices in the Time of COVID-19
Authors: Klaus Witold
Published: November 2021

The COVID-19 pandemic has severely restricted global movement, thus affecting migration processes and immigrants themselves. The paper focuses on the evaluation of bordering procedures and practices introduced by the Polish government in the time of the pandemic. The aim is to highlight the duality in the admission processes at Polish borders between labour and forced migrants, which have been driven, as I argue, by economic interests and the xenophobic attitudes of the government. The paper is based on interviews with experts assisting migrants during the pandemic in Poland, whose direct contact with thousands of clients has allowed them to acquire broad knowledge of how the new legal provisions have affected different groups of immigrants. The data confirms that the Polish border is very porous. It has been almost completely closed to asylum seekers, especially those fleeing from Muslim countries, for whom the only option is to cross the border illegally. Only one exception was made for Belarusians, who were cordially welcomed at the border while escaping persecution in their home country in the wake of their protests against Lukashenko’s regime. Economic migrants, on the other hand, exist on the other side of the spectrum. For immigrant workers, borders have remained open throughout the whole pandemic. Moreover, some further measures facilitating their arrival were introduced, such as de facto lifting of quarantine for seasonal farm workers.
Tactics of a general practitioner in case of suspected acute cerebrovascular accident and measures to prevent it
Authors: Kovalev Dmitriy Sergeevich
Published: November 2021

Acute cerebrovascular accident (stroke) is a fairly common severe pathology, which is one of the main causes of death in most countries. Every year in the world, a stroke occurs in 15 million people, of whom about 5 million die, and the same number become disabled. The disease is based on a sudden sharp failure of the normal blood supply to the brain substance due to a rupture or blockage of a blood vessel. Mortality in the first weeks after a stroke can reach 30-35%. Depending on the pathogenesis of the lesion, stroke can be ischemic and hemorrhagic (ischemic occurs about 6 times more often); clinical symptoms will depend on which area of the brain is involved in the pathological process. Predisposing factors include heredity, gender (more often observed in men), age (in elderly age, the risk of stroke increases every year), arterial hypertension, diabetes mellitus, obesity, hypodynamia. Since in 80% of cases after a stroke people become disabled, often unable to take care of themselves, the problem of acute cerebrovascular accident has long passed from the category of purely medical issues to the category of socio-economic ones. The tasks of the general practitioner include the timely identification of factors predisposing to the development of a stroke and minimization of the possible risks of this formidable condition, and in the case of a stroke, active participation in the patient's rehabilitation process.
History of caesarean section surgery
Authors: Klimov Vladimir Anatolievich
Published: November 2021

Caesarean section is one of the oldest operations in the history of mankind and is the most common surgical intervention in obstetric practice. Translated from Latin, «caesarean section» means «royal cut», and in the days of the Roman Empire, only people marked with a special gift could be born in this way. According to one of the existing legends, this is how Gaius Julius Caesar was born, and it was the incision in his mother's womb that gave him the name «Caesar». However, given the fact that his mother died when Caesar was 46 years old, the legend still has the right to remain a legend. It is also believed that by removing the baby from the womb of the deceased mother, the ancient Greek god Asclepius, the doctor, was born — he was also Aesculapius among the Romans. His mother was struck by Zeus the Thunderer for her infidelity to her husband Apollo, but the child was taken out alive after the woman's death. Be that as it may, the first mentions of a caesarean section do not at all date back to the 16th century, as was commonly believed, but have much deeper roots. It is believed that even some famous people were born by removing them from their mother's womb, and this gave them a chance at life. The beginning of the scientific version of the appearance of the caesarean section operation can be considered the publication of the monograph by F. Rousset in 1581, in which a sufficiently detailed description of the technique of the cesarean section operation was provided and a list of indications for it was given [2]. Today, this operation is a fairly common surgical intervention that can significantly alleviate the suffering of a woman in labor, avoid a number of complications, and sometimes save the life of a mother and child.