bluuebunny/crossref_metadata_2025 huggingface dataset:
Effect of Near-Infrared Blood Photobiomodulation on Red Blood Cell Damage from the Extracorporeal Circuit during Hemodialysis In Vitro
Authors: Walski Tomasz , Grzeszczuk-Kuć Karolina , Berlik Weronika , Synal-Kulczak Izabela , Bohara Raghvendra , Detyna Jerzy , Komorowska Małgorzata
Published: May 2022

The contact of blood with the bioincompatible membranes of the dialyzer, which is part of the extracorporeal circuit during hemodialysis (HD), causes upregulation of various cellular and non-cellular processes, including massive generation and release of reactive oxygen species (ROS), (which is one of the primary causes of anemia in chronic renal failure). We hypothesize that near-infrared (NIR) radiation possesses antioxidant properties and is considered to protect the red blood cell (RBC) membrane by enhancing its resilience to negative pressures. Our experimental setup consisted of an HD machine equipped with a dialyzer with a polyamide membrane; whole bovine blood was examined in vitro in blood-treated circulation. Blood samples were taken at 0, 5, 15, and 30 min during the HD therapy. We also assessed osmotic fragility, hematocrit, hemolysis, and oxidative stress as a concentration of reactive thiobarbituric acid substances (TBARS). Our results have shown that RBC membrane peroxidation increased significantly after 30 min of circulation, whereas the TBARS level in NIR-treated blood remained relatively steady throughout the experiment. The osmotic fragility of NIR-irradiated samples during dialysis was decreased compared to control samples. Our studies confirm that in vitro, blood photobiomodulation using NIR light diminishes oxidative damage during HD and can be considered a simultaneous pretreatment strategy for HD.
Development of a Model for Genomic Prediction of Multiple Traits in Common Bean Germplasm, Based on Population Structure
Authors: Shao Jing , Hao Yangfan , Wang Lanfen , Xie Yuxin , Zhang Hongwei , Bai Jiangping , Wu Jing , Fu Junjie
Published: May 2022

Due to insufficient identification and in-depth investigation of existing common bean germplasm resources, it is difficult for breeders to utilize these valuable genetic resources. This situation limits the breeding and industrial development of the common bean (Phaseolus vulgaris L.) in China. Genomic prediction (GP) is a breeding method that uses whole-genome molecular markers to calculate the genomic estimated breeding value (GEBV) of candidate materials and select breeding materials. This study aimed to use genomic prediction to evaluate 15 traits in a collection of 628 common bean lines (including 484 landraces and 144 breeding lines) to determine a common bean GP model. The GP model constructed by landraces showed a moderate to high predictive ability (ranging from 0.59–0.88). Using all landraces as a training set, the predictive ability of the GP model for most traits was higher than that using the landraces from each of two subgene pools, respectively. Randomly selecting breeding lines as additional training sets together with landrace training sets to predict the remaining breeding lines resulted in a higher predictive ability based on principal components analysis. This study constructed a widely applicable GP model of the common bean based on the population structure, and encouraged the development of GP models to quickly aggregate excellent traits and accelerate utilization of germplasm resources.
Impact of Leaf Removal on Phenolics and Antioxidant Activity of Trebbiano Berries (Vitis vinifera L.)
Authors: Quartacci Mike Frank , Sgherri Cristina , Pinzino Calogero
Published: May 2022

Leaf removal is a canopy management practice widely applied in viticulture to enhance the phenol composition and concentration of grapes, which then results in improved wine quality. Many studies were carried out on red berried varieties, but information on white ones is scanty. The aim of the study was to assess the effect of basal leaf defoliation in post fruit set on the phenol composition, ascorbate level and antioxidant activity of Trebbiano grapes. Electron paramagnetic resonance was also employed to monitor the decay kinetics of 1,1-diphenyl-2-picrylhydrazyl which allowed the identification of antioxidants with different action rates. The results show that defoliation caused an increase in the phenolic acid (hydroxycinnamic and hydroxybenzoic acids) and flavonol concentrations of berries without changes in the composition. Both ascorbate and antioxidant activity were also enhanced in the berries from defoliated vines. Besides increasing the number of fast-rate antioxidants, leaf removal resulted in the formation of intermediate-rate ones. In the Trebbiano variety, leaf removal in the post fruit set may represent an effective strategy to enhance the phenolic composition and the antioxidant defense system of berries.
Investigation of the Relation between Temperature and M13 Phage Production via ATP Expenditure
Authors: Choi Young Kyun , Han Sang Min , Lee Sang Min , Soh Jeong Ook , Lee Seung Kyu , Lee Ju Hun
Published: May 2022

M13 bacteriophage is a promising biomolecule capable of various bionano and material science applications. The biomaterial can self-assemble into matrices to fabricate bioscaffolds using high phage concentration and high phage purity. Previous studies aimed to acquire these conditions in large-scale phage production and have identified the optimal culture temperature range at 28–31 °C. However, explanations as to why this temperature range was optimal for phage production is absent from the work. Therefore, in this study, we identified the relation between culture temperature and M13 phage production using ATP expenditure calculations to comprehend the high yield phage production at the optimal temperature range. We extended a coarse-grained model for the evaluation of phage protein and ribosomal protein synthesis with the premise that phage proteins (a ribosomal protein) are translated by bacterial ribosomes in E. coli through expenditure of ATP energy. By comparing the ATP energy for ribosomal protein synthesis estimated using the coarse-grained model and the experimentally calculated ATP expenditure for phage production, we interpreted the high phage yield at the optimal temperature range and recognized ATP analysis as a reasonable method that can be used to evaluate other parameters for phage production optimization.
Influence of Chromatographic Conditions on LOD and LOQ of Fluoxetine and Sertraline Analyzed by TLC-Densitometric Method
Authors: Parys Wioletta , Pyka-Pająk Alina
Published: May 2022

This research introduces the analysis of fluoxetine and sertraline by means of the TLC-densitometric method. They provide information on LOD and LOQ under various chromatographic conditions. The study used adsorption (NPTLC) and partition (RPTLC) thin-layer chromatography in combination with a densitometric analysis. Four types of chromatographic plates precoated with: silica gel 60 F254, silica gel 60, silanized silica gel 60 F254 (RP-2), and a mixture of silica gel 60 and kieselguhr F254, as well as three mobile phases: chloroform + methanol + ammonia (9:1:0.4, v/v/v), chloroform + methanol + glacial acetic acid (5:4:1, v/v/v), and acetone + toluene + ammonia (10:9:1, v/v/v), were used in NPTLC. RP-18F254 and silanized silica gel 60 F254 (RP-2) plates and four mobile phases: methanol + water (10:0 and 9:1, v/v), acetone + water (10:0 and 9:1, v/v), were used in RPTLC. The lowest LOD and LOQ values for fluoxetine were obtained using a silanized silica gel 60 F254 (RP-2) with acetone + toluene + ammonia (10:9:1, v/v/v) in NPTLC, and with a silanized silica gel 60 F254 (RP-2) in combination with methanol + water (10:0, v/v) in RPTLC. The lowest LOD and LOQ values of sertraline were obtained using a silica gel 60 with acetone + toluene + ammonia (10:9:1; v/v/v) in NPTLC. The smallest amount of sertraline was detected on the silanized silica gel 60 F254 plate in combination with methanol + water (9:1, v/v) in RPTLC. The obtained results provide important information that can give a good basis and set the direction for further, more detailed research; the results can also benefit other researchers who analyze fluoxetine and sertraline with the TLC technique in model systems (testing standards) as well as in drug and biological samples.
Geothermal Potential of the Brenner Base Tunnel—Initial Evaluations
Authors: Geisler Thomas , Voit Klaus , Burger Ulrich , Cordes Tobias , Lehner Florian , Götzl Gregor , Wolf Magdalena , Marcher Thomas
Published: May 2022

Increasing demands on mobility and transport, but limited space above ground, lead to new traffic routes being built, even more underground in the form of tunnels. In addition to improving the traffic situation, tunnels offer the possibility of contributing to climate-friendly heating by indirectly serving as geothermal power plants. In this study, the geothermal potential of the future longest railway tunnel in the world, the Brenner Base Tunnel, was evaluated. At the Brenner Base Tunnel, warm water naturally flows from the apex of the tunnel towards the city of Innsbruck, Austria. In order to estimate its geothermal potential, hydrological data of discharge rates and temperatures were investigated and analyzed. The investigations indicated the highest geothermal potential in the summertime, while the lowest occurs during winter. It could be shown that these variations were a result of cooling during discharge through areas of low overburden (mid mountain range), where the tunnel atmosphere is increasingly influenced by the air temperatures outside the tunnel. Nevertheless, the calculations showed that there will be a usable potential after completion of the tunnel.
Catalytic Performances of Sn-Beta Catalysts Prepared from Different Heteroatom-Containing Beta Zeolites for the Retro-Aldol Fragmentation of Glucose
Authors: Navar Ricardo , Botti Luca , Tarantino Giulia , Hammond Ceri
Published: May 2022

Beta zeolites with different heteroatoms incorporated into the lattice at two loadings (Si/M = 100 or 200, where M = Al, Fe, Ga, B) were hydrothermally synthesised and used as starting materials for the preparation of Sn-Beta using Solid-State Incorporation. 119Sn CPMG MAS NMR showed that various Sn species were formed, the distribution of which depended on the identity of the initial heteroatom and the original Si/M ratio. The final Sn-Beta materials (with Si/Sn = 200) were explored as catalysts for the retro-aldol fragmentation of glucose to α-hydroxy-esters in the continuous regime. Amongst these materials, B-derived Sn-Beta was found to exhibit improved levels of selectivity and stability, particularly compared to Sn-Beta catalysts synthesised from commercially available Al-Beta materials, achieving a combined yield of methyl lactate and methyl vinyl glycolate > 80% at short times on the stream. Given that B atoms can be removed from the Beta lattice in mild conditions without the use of highly concentrated acidic media, this discovery demonstrates that B-Beta is an attractive starting material for the future post-synthetic preparation of Lewis acidic zeolites.
Unexpected Regional Zonal Structures in Low Latitude Ionosphere Call for a High Longitudinal Resolution of the Global Ionospheric Maps
Authors: Liu Libo , Yang Yuyan , Le Huijun , Chen Yiding , Zhang Ruilong , Zhang Hui , Sun Wenjie , Li Guozhu
Published: May 2022

This study reports unexpected strong longitudinal structures from Global Navigation Satellite System (GNSS) derived total electron content (TEC) observations in the low-latitude ionosphere over Asia. The observations during 2019–2020 show diverse patterns in the zonal difference of regional TEC, even under geomagnetically quiet conditions. The TEC in the northern hemisphere occasionally exhibits drastic zonal gradients. The intense regional gradients in TEC span a longitudinal extent of about 20°. The higher values may appear on the east or the west side. Strong zonal gradients may appear in all seasons, regardless of geomagnetically quiet or active conditions. The 15 December 2019 and 16 March 2020 cases depict an intense zonal differences cluster in the narrow latitudinal band of 16°N to 28°N, spanning a regional scale smaller than the normal longitudinal structures. In contrast, the Global Ionospheric Maps (GIMs) with a longitudinal resolution of 5° show a much flatter zonal picture. Such intense and regional-scale zonal structures in the low-latitude ionosphere call for a high zonal resolution of GIMs in terms of better geographically distributed observations. Notably, no counterpart regional structures are found at the conjugated points in the southern hemisphere during the two cases. Although the physical drivers are not certain, the appearance only in the northern hemisphere possibly excludes the dominant contribution to forming the regional structures from the equatorial electric field.
Lunar Terrestrial Analog Experiment on the Spectral Interpretations of Rocks Observed by the Yutu-2 Rover
Authors: Chang Rui , Yang Wei , Lin Honglei , Xu Rui , Gou Sheng , Wang Rong , Lin Yangting
Published: May 2022

A visible and near-infrared imaging spectrometer (VNIS) loaded by the Chang’e-4 rover is the primary method for detecting the mineral composition of the lunar surface in the landing region. However, different data processing methods yield inconsistent mineral modes in measured lunar soil and rocks. To better constrain the mineral modes of the soil and rocks measured by Chang’e-4 VNIS, a noritic-gabbroic rock with a mineral composition similar to that of the lunar highland rocks is measured by scanning electron microscopy (SEM), the spare flight model of Chang’e-4 VNIS and TerraSpec-4 of ASD. Backscattered electron and energy dispersive spectrometry show that olivine, pyroxene, and plagioclase modal mineral abundances are 12.9, 35.0, and 52.2%, respectively. The estimated results of the spectrum by the Hapke radiative transfer model are 7.5, 39.3, and 53.2% for olivine, pyroxene, and plagioclase, respectively, which is consistent with to those of SEM mapping within error. In contrast, the estimated results of the modified Gaussian model are 29 and 71% for olivine and pyroxene, respectively, indicating the absence of plagioclase. Based on our implemented Hapke model, we decode the data of the two rocks detected by the rover on the 3rd and 26th lunar days of mission operations. The obtained results suggest that both rocks are norite or gabbro with noticeable differences. The first rock, with more olivine and pyroxene, may have been excavated from the Finsen crater. The second rock, with more plagioclase, may have been ejected from the southwestern edge of the Von Kármán crater, indicating the initial lunar crust.
A Method for Classifying Complex Features in Urban Areas Using Video Satellite Remote Sensing Data
Authors: Ye Fanghong , Ai Tinghua , Wang Jiaming , Yao Yuan , Zhou Zheng
Published: May 2022

The classification of optical satellite-derived remote sensing images is an important satellite remote sensing application. Due to the wide variety of artificial features and complex ground situations in urban areas, the classification of complex urban features has always been a focus of and challenge in the field of remote sensing image classification. Given the limited information that can be obtained from traditional optical satellite-derived remote sensing data of a classification area, it is difficult to classify artificial features in detail at the pixel level. With the development of technologies, such as satellite platforms and sensors, the data types acquired by remote sensing satellites have evolved from static images to dynamic videos. Compared with traditional satellite-derived images, satellite-derived videos contain increased ground object reflection information, especially information obtained from different observation angles, and can thus provide more information for classifying complex urban features and improving the corresponding classification accuracies. In this paper, first, we analyze urban-area, ground feature characteristics and satellite-derived video remote sensing data. Second, according to these characteristics, we design a pixel-level classification method based on the application of machine learning techniques to video remote sensing data that represents complex, urban-area ground features. Last, we conduct experiments on real data. The test results show that applying the method designed in this paper to classify dynamic, satellite-derived video remote sensing data can improve the classification accuracy of complex features in urban areas compared with the classification results obtained using static, satellite-derived remote sensing image data at the same resolution.
The Impact of Central Heating on the Urban Thermal Environment Based on Multi-Temporal Remote Sensing Images
Authors: Chen Xinran , Gu Xingfa , Zhan Yulin , Wang Dakang , Zhang Yazhou , Mumtaz Faisal , Shi Shuaiyi , Liu Qixin
Published: May 2022

Research on the impact of anthropogenic heat discharge in a thermal environment is significant in climate change research. Central heating is more common in the winter in Northeast China as an anthropogenic heat. This study investigates the impact of central heating on the thermal environment in Shenyang, Changchun, and Harbin based on multi-temporal land surface temperature retrieval from remote sensing. An equivalent heat island index method was proposed to overcome the problem of the method based on a single-phase image, which cannot evaluate all the central heating season changes. The method improves the comprehensiveness of a thermal environment evaluation by considering the long-term heat accumulation. The results indicated a significant increase in equivalent heat island areas at night with 22.1%, 17.3%, and 19.5% over Shenyang, Changchun, and Harbin. The increase was significantly positively correlated with the central heating supply (with an R-value of 0.89 for Shenyang, 0.93 for Changchun, and 0.86 for Harbin; p < 0.05). The impact of central heating has a more significant effect than the air temperature. The results provide a reference for future studies of urban thermal environment changes.
The Post-Failure Spatiotemporal Deformation of Certain Translational Landslides May Follow the Pre-Failure Pattern
Authors: Wang Luyao , Qiu Haijun , Zhou Wenqi , Zhu Yaru , Liu Zijing , Ma Shuyue , Yang Dongdong , Tang Bingzhe
Published: May 2022

Investigating landslide deformation patterns in different evolution stages is important for understanding landslide movement. Translational landslides generally slide along a relatively straight surface of rupture. Whether the post-failure spatiotemporal deformation for certain translational landslides follows the pre-failure pattern remains untested. Here, the pre- and post-failure spatiotemporal deformations of the Simencun landslide along the Yellow River in 2018 were analyzed through multi-temporal remote sensing image analysis, Interferometric Synthetic Aperture Radar (InSAR) deformation monitoring and intensive field investigations. The results show that the pre- and post-failure spatial deformations both follow a retrogressive failure pattern. The long time series of the displacement before and after failure is characterized by obvious seasonal and periodic stage acceleration movements. Effective rainfall played an important role in the increase of the displacement acceleration, and the change in temperature might have accelerated the displacement. Finally, there is a possibility that the post-failure spatiotemporal deformation pattern of translational landslides does follow the pre-failure pattern when certain conditions are satisfied. The results are of great significance to improving our understanding of the spatiotemporal deformation pattern of landslides and to post-failure risk prevention and control.
High Wind Geophysical Model Function Modeling for the HY-2A Scatterometer Using Neural Network
Authors: Xie Xuetong , Wang Jing , Lin Mingsen
Published: May 2022

Under low to medium wind speeds and no rainfall, the retrieved vector wind from a scatterometer is accurate and reliable. However, under high wind conditions, the currently used geophysical model function (GMF), such as NSCAT-2, for wind vector retrieval has the disadvantage of overestimating the backscattering coefficient, which leads to a decrease in the quality of the retrieved ocean surface winds. To enhance the wind retrieval precision of the HY-2A scatterometer under high wind conditions, a new GMF for high wind (HW-GMF) is established by using the neural network method based on the backscattering coefficient data of the HY-2A scatterometer combined with the wind speed data of the Special Sensor Microwave Imager (SSM/I) and the Final (FNL) operational global analysis wind direction data from the National Centers for Environmental Prediction (NCEP). The absolute value of the mean deviation between the predicted σ0 by the HW-GMF and the measured σ0 by the HY-2A scatterometer is less than 0.1 dB, indicating that the HW-GMF has high accuracy. To verify the HW-GMF performance, the wind field inversion accuracy of the HW-GMF is compared with that of the NSCAT-2 GMF, a GMF currently used in the data processing of the HY-2A scatterometer. The experimental results show that the deviation between the HW-GMF retrieved wind speed and the SSM/I wind speed is within 2 m/s in the high wind speed range of 15–35 m/s, indicating that the HW-GMF improves the precision of the wind speed inversion of the HY-2A scatterometer under high wind speed conditions.
Numerical Weather Predictions and Re-Analysis as Input for Lidar Inversions: Assessment of the Impact on Optical Products
Authors: Wang Yuanzu , Amodeo Aldo , O’Connor Ewan J. , Baars Holger , Bortoli Daniele , Hu Qiaoyun , Sun Dongsong , D’Amico Giuseppe
Published: May 2022

The atmospheric molecular number density can be obtained from atmospheric temperature and pressure profiles and is a significant input parameter for the inversion of lidar measurements. When measurements of vertical profiles of temperature and pressure are not available, atmospheric models are typically considered a valid alternative option. This paper investigates the influence of different atmospheric models (forecast and reanalysis) on the retrieval of aerosol optical properties (extinction and backscatter coefficients) by applying Raman and elastic-only methods to lidar measurements, to assess their use in lidar data processing. In general, reanalyzes are more accurate than forecasts, but, typically, they are not delivered in time for allowing near-real-time lidar data analysis. However, near-real-time observation is crucial for real-time monitoring of the environment and meteorological studies. The forecast models used in the paper are provided by the Integrated Forecasting System operated by the European Centre for Medium-Range Weather Forecasts (IFS_ECMWF) and the Global Data Assimilation System (GDAS), whereas the reanalysis model is obtained from the fifth-generation European Centre for Medium-Range Weather Forecasts ReAnalysis v5 (ERA5). The lidar dataset consists of measurements collected from four European Aerosol Research Lidar Network (EARLINET) stations during two intensive measurement campaigns and includes more than 200 cases at wavelengths of 355 nm, 532 nm, and 1064 nm. We present and discuss the results and influence of the forecast and reanalysis models in terms of deviations of the derived aerosol optical properties. The results show that the mean relative deviation in molecular number density is always below ±3%, while larger deviations are shown in the derived aerosol optical properties, and the size of the deviation depends on the retrieval method together with the different wavelengths. In general, the aerosol extinction coefficient retrieval is more dependent on the model used than the aerosol backscatter retrievals are. The larger influence on the extinction retrieval is mainly related to the deviation in the gradient of the temperature profile provided by forecast and reanalysis models rather than the absolute deviation of the molecular number density. We found that deviations in extinction were within ±5%, with a probability of 83% at 355 nm and 60% at 532 nm. Moreover, for aerosol backscatter coefficient retrievals, different models can have a larger impact when the backscatter coefficient is retrieved with the elastic method than when the backscatter coefficient is calculated using the Raman method at both 355 nm and 532 nm. In addition, the atmospheric aerosol load can also influence the deviations in the aerosol extinction and backscatter coefficients, showing a larger impact under low aerosol loading scenarios.
0-Dimensional Persistent Homology Analysis Implementation in Resource-Scarce Embedded Systems
Authors: Branco Sérgio , Carvalho João G. , Reis Marco S. , Lopes Nuno V. , Cabral Jorge
Published: May 2022

Persistent Homology (PH) analysis is a powerful tool for understanding many relevant topological features from a given dataset. PH allows finding clusters, noise, and relevant connections in the dataset. Therefore, it can provide a better view of the problem and a way of perceiving if a given dataset is equal to another, if a given sample is relevant, and how the samples occupy the feature space. However, PH involves reducing the problem to its simplicial complex space, which is computationally expensive and implementing PH in such Resource-Scarce Embedded Systems (RSES) is an essential add-on for them. However, due to its complexity, implementing PH in such tiny devices is considerably complicated due to the lack of memory and processing power. The following paper shows the implementation of 0-Dimensional Persistent Homology Analysis in a set of well-known RSES, using a technique that reduces the memory footprint and processing power needs of the 0-Dimensional PH algorithm. The results are positive and show that RSES can be equipped with this real-time data analysis tool.
CSAC-Net: Fast Adaptive sEMG Recognition through Attention Convolution Network and Model-Agnostic Meta-Learning
Authors: Fan Xinchen , Zou Lancheng , Liu Ziwu , He Yanru , Zou Lian , Chi Ruan
Published: May 2022

Gesture recognition through surface electromyography (sEMG) provides a new method for the control algorithm of bionic limbs, which is a promising technology in the field of human–computer interaction. However, subject specificity of sEMG along with the offset of the electrode makes it challenging to develop a model that can quickly adapt to new subjects. In view of this, we introduce a new deep neural network called CSAC-Net. Firstly, we extract the time-frequency feature from the raw signal, which contains rich information. Secondly, we design a convolutional neural network supplemented by an attention mechanism for further feature extraction. Additionally, we propose to utilize model-agnostic meta-learning to adapt to new subjects and this learning strategy achieves better results than the state-of-the-art methods. By the basic experiment on CapgMyo and three ablation studies, we demonstrate the advancement of CSAC-Net.
The Hybrid Position/Force Walking Robot Control Using Extenics Theory and Neutrosophic Logic Decision
Authors: Gal Ionel-Alexandru , Ciocîrlan Alexandra-Cătălina , Vlădăreanu Luige
Published: May 2022

This paper presents a hybrid force/position control. We developed it for a hexapod walking robot that combines multiple bipedal robots to increase its load. The control method integrated Extenics theory with neutrosophic logic to obtain a two-stage decision-making algorithm. The first stage was an offline qualitative decision-applying Extenics theory, and the second was a real-time decision process using neutrosophic logic and DSmT theory. The two-stage algorithm separated the control phases into a kinematic control method that used a PID regulator and a dynamic control method developed with the help of sliding mode control (SMC). By integrating both control methods separated by a dynamic switching algorithm, we obtained a hybrid force/position control that took advantage of both kinematic and dynamic control properties to drive a mobile walking robot. The experimental and predicted results were in good agreement. They indicated that the proposed hybrid control is efficient in using the two-stage decision algorithm to drive the hexapod robot motors using kinematic and dynamic control methods. The experiment presents the robot’s foot positioning error while walking. The results show how the switching method alters the system precision during the pendulum phase compared to the weight support phase, which can better compensate for the robot’s dynamic parameters. The proposed switching algorithm directly influences the overall control precision, while we aimed to obtain a fast switch with a lower impact on the control parameters. The results show the error on all axes and break it down into walking stages to better understand the control behavior and precision.
Design and Analysis of High-Capacity MIMO System in Line-of-Sight Communication
Authors: Jiang Kuangming , Wang Xiaoyong , Jin Yanliang , Saleem Asad , Zheng Guoxin
Published: May 2022

The phase of the channel matrix elements has a significant impact on channel capacity in a mobile multiple-input multiple-output (MIMO) communication system, notably in line-of-sight (LoS) communication. In this paper, the general expression for the phase of the channel matrix at maximum channel capacity is determined. Moreover, the optimal antenna configuration of the 2 × 2 and 3 × 3 transceiver antenna array is realized for LoS communication, providing methods for n×n optimal antenna placement, which can be used in short-range LoS communication and non-scattering environment communication, such as coupling train communication and inter-satellite communication. Simulation results show that the 2 × 2 rectangular antenna array is more suitable for the communication of coupling trains, while the 3 × 3 circular arc antenna array is more suitable for virtual coupling trains according to antenna configurations. Moreover, the 2 × 2 antenna rectangular configuration proposed in this paper has reached the optimal channel in inter-satellite communication, which lays a foundation for the deployment of communication systems.
Zonotopic Linear Parameter Varying SLAM Applied to Autonomous Vehicles
Authors: Facerias Marc , Puig Vicenç , Alcala Eugenio
Published: May 2022

This article presents an approach to address the problem of localisation within the autonomous driving framework. In particular, this work takes advantage of the properties of polytopic Linear Parameter Varying (LPV) systems and set-based methodologies applied to Kalman filters to precisely locate both a set of landmarks and the vehicle itself. Using these techniques, we present an alternative approach to localisation algorithms that relies on the use of zonotopes to provide a guaranteed estimation of the states of the vehicle and its surroundings, which does not depend on any assumption of the noise nature other than its limits. LPV theory is used to model the dynamics of the vehicle and implement both an LPV-model predictive controller and a Zonotopic Kalman filter that allow localisation and navigation of the robot. The control and estimation scheme is validated in simulation using the Robotic Operating System (ROS) framework, where its effectiveness is demonstrated.
Low-Power Lossless Data Compression for Wireless Brain Electrophysiology
Authors: Cuevas-López Aarón , Pérez-Montoyo Elena , López-Madrona Víctor J. , Canals Santiago , Moratal David
Published: May 2022

Wireless electrophysiology opens important possibilities for neuroscience, especially for recording brain activity in more natural contexts, where exploration and interaction are not restricted by the usual tethered devices. The limiting factor is transmission power and, by extension, battery life required for acquiring large amounts of neural electrophysiological data. We present a digital compression algorithm capable of reducing electrophysiological data to less than 65.5% of its original size without distorting the signals, which we tested in vivo in experimental animals. The algorithm is based on a combination of delta compression and Huffman codes with optimizations for neural signals, which allow it to run in small, low-power Field-Programmable Gate Arrays (FPGAs), requiring few hardware resources. With this algorithm, a hardware prototype was created for wireless data transmission using commercially available devices. The power required by the algorithm itself was less than 3 mW, negligible compared to the power saved by reducing the transmission bandwidth requirements. The compression algorithm and its implementation were designed to be device-agnostic. These developments can be used to create a variety of wired and wireless neural electrophysiology acquisition systems with low power and space requirements without the need for complex or expensive specialized hardware.
Experimental Investigation on the Influence of Depth on Rockburst Characteristics in Circular Tunnels
Authors: Si Xuefeng , Peng Kang , Luo Song
Published: May 2022

To investigate the influence of depth on the rockburst of surrounding rock in a circular tunnel, true-triaxial tests at different depths were carried out on cubic granite specimens with a circular through-going hole. A micro camera was used to monitor the rockburst process of the circular hole sidewall in real time. The test results show that the failure process at different depths can be divided into four periods: the calm period, the particle ejection period, the rock fragment exfoliation period, and the rock bursting period. With an increase in depth, the three-dimensional unequal stress state gradually increased; the failure range and the size of rock fragments increased, the initial failure vertical stress linearly increased, and the strength and stability of the surrounding rock were enhanced. Therefore, the support range of surrounding rock should be increased as the depth increased to improve the overall stability of surrounding rock and reduce the failure range.
Electrochemical Aptasensors for Antibiotics Detection: Recent Achievements and Applications for Monitoring Food Safety
Authors: Evtugyn Gennady , Porfireva Anna , Tsekenis George , Oravczova Veronika , Hianik Tibor
Published: May 2022

Antibiotics are often used in human and veterinary medicine for the treatment of bacterial diseases. However, extensive use of antibiotics in agriculture can result in the contamination of common food staples such as milk. Consumption of contaminated products can cause serious illness and a rise in antibiotic resistance. Conventional methods of antibiotics detection such are microbiological assays chromatographic and mass spectroscopy methods are sensitive; however, they require qualified personnel, expensive instruments, and sample pretreatment. Biosensor technology can overcome these drawbacks. This review is focused on the recent achievements in the electrochemical biosensors based on nucleic acid aptamers for antibiotic detection. A brief explanation of conventional methods of antibiotic detection is also provided. The methods of the aptamer selection are explained, together with the approach used for the improvement of aptamer affinity by post-SELEX modification and computer modeling. The substantial focus of this review is on the explanation of the principles of the electrochemical detection of antibiotics by aptasensors and on recent achievements in the development of electrochemical aptasensors. The current trends and problems in practical applications of aptasensors are also discussed.
A Deep-Learning-Based Health Indicator Constructor Using Kullback–Leibler Divergence for Predicting the Remaining Useful Life of Concrete Structures
Authors: Nguyen Tuan-Khai , Ahmad Zahoor , Kim Jong-Myon
Published: May 2022

This paper proposes a new technique for the construction of a concrete-beam health indicator based on the Kullback–Leibler divergence (KLD) and deep learning. Health indicator (HI) construction is a vital part of remaining useful lifetime (RUL) approaches for monitoring the health of concrete structures. Through the construction of a HI, the deterioration process can be processed and portrayed so that it can be forwarded to a prediction module for RUL prognosis. The degradation progression and failure can be identified by predicting the RUL based on the situation of the current specimen; as a result, maintenance can be planned to reduce safety risks, reduce financial costs, and prolong the specimen’s useful lifetime. The portrayal of deterioration through HI construction from raw acoustic emission (AE) data is performed using a deep neural network (DNN), whose parameters are obtained by pretraining and fine tuning using a stack autoencoder (SAE). Kullback–Leibler divergence, which is calculated between a reference normal-conditioned signal and a current unknown signal, was used to represent the deterioration process of concrete structures, which has not been investigated for the concrete beams so far. The DNN-based constructor then learns to generate HI from raw data with KLD values as the training label. The HI construction result was evaluated with run-to-fail test data of concrete specimens with two measurements: fitness analysis of the construction result and RUL prognosis. The results confirm the reliability of KLD in portraying the deterioration process, showing a large improvement in comparison to other methods. In addition, this method requires no adept knowledge of the nature of the AE or the system fault, which is more favorable than model-based approaches where this level of expertise is compulsory. Furthermore, AE offers in-service monitoring, allowing the RUL prognosis task to be performed without disrupting the specimen’s work.
Multi-LiDAR Mapping for Scene Segmentation in Indoor Environments for Mobile Robots
Authors: Gonzalez Pavel , Mora Alicia , Garrido Santiago , Barber Ramon , Moreno Luis
Published: May 2022

Nowadays, most mobile robot applications use two-dimensional LiDAR for indoor mapping, navigation, and low-level scene segmentation. However, single data type maps are not enough in a six degree of freedom world. Multi-LiDAR sensor fusion increments the capability of robots to map on different levels the surrounding environment. It exploits the benefits of several data types, counteracting the cons of each of the sensors. This research introduces several techniques to achieve mapping and navigation through indoor environments. First, a scan matching algorithm based on ICP with distance threshold association counter is used as a multi-objective-like fitness function. Then, with Harmony Search, results are optimized without any previous initial guess or odometry. A global map is then built during SLAM, reducing the accumulated error and demonstrating better results than solo odometry LiDAR matching. As a novelty, both algorithms are implemented in 2D and 3D mapping, overlapping the resulting maps to fuse geometrical information at different heights. Finally, a room segmentation procedure is proposed by analyzing this information, avoiding occlusions that appear in 2D maps, and proving the benefits by implementing a door recognition system. Experiments are conducted in both simulated and real scenarios, proving the performance of the proposed algorithms.
Fuzzy-Logic-Based Recommendation System for Processing in Condition Monitoring
Authors: Gorski Jakub , Heesch Mateusz , Dziendzikowski Michal , Dworakowski Ziemowit
Published: May 2022

The development of a machine’s condition monitoring system is often a challenging task. This process requires the collection of a sufficiently large dataset on signals from machine operation, context information related to the operation conditions, and the diagnosis experience. The two referred problems are today relatively easy to solve. The hardest to describe is the diagnosis experience because it is based on imprecise and non-numerical information. However, it is essential to process acquired data to develop a robust monitoring system. This article presents a framework for a system dedicated to recommending processing algorithms for condition monitoring. It includes a database and fuzzy-logic-based modules composed within the system. Based on the contextual knowledge provided by the user, the procedure suggests processing algorithms. This paper presents the evaluation of the proposed agent on two different parallel gearboxes. The results of the system are processing algorithms with assigned model types. The obtained results show that the algorithms recommended by the system achieve a higher accuracy than those selected arbitrarily. The results obtained allow for an average of 5 to 14.5% higher accuracy.
EEG-Based Alzheimer’s Disease Recognition Using Robust-PCA and LSTM Recurrent Neural Network
Authors: Alessandrini Michele , Biagetti Giorgio , Crippa Paolo , Falaschetti Laura , Luzzi Simona , Turchetti Claudio
Published: May 2022

The use of electroencephalography (EEG) has recently grown as a means to diagnose neurodegenerative pathologies such as Alzheimer’s disease (AD). AD recognition can benefit from machine learning methods that, compared with traditional manual diagnosis methods, have higher reliability and improved recognition accuracy, being able to manage large amounts of data. Nevertheless, machine learning methods may exhibit lower accuracies when faced with incomplete, corrupted, or otherwise missing data, so it is important do develop robust pre-processing techniques do deal with incomplete data. The aim of this paper is to develop an automatic classification method that can still work well with EEG data affected by artifacts, as can arise during the collection with, e.g., a wireless system that can lose packets. We show that a recurrent neural network (RNN) can operate successfully even in the case of significantly corrupted data, when it is pre-filtered by the robust principal component analysis (RPCA) algorithm. RPCA was selected because of its stated ability to remove outliers from the signal. To demonstrate this idea, we first develop an RNN which operates on EEG data, properly processed through traditional PCA; then, we use corrupted data as input and process them with RPCA to filter outlier components, showing that even with data corruption causing up to 20% erasures, the RPCA was able to increase the detection accuracy by about 5% with respect to the baseline PCA.
Fetal Electrocardiogram Signal Extraction Based on Fast Independent Component Analysis and Singular Value Decomposition
Authors: Hao Jingyu , Yang Yuyao , Zhou Zhuhuang , Wu Shuicai
Published: May 2022

Fetal electrocardiograms (FECGs) provide important clinical information for early diagnosis and intervention. However, FECG signals are extremely weak and are greatly influenced by noises. FECG signal extraction and detection are still challenging. In this work, we combined the fast independent component analysis (FastICA) algorithm with singular value decomposition (SVD) to extract FECG signals. The improved wavelet mode maximum method was applied to detect QRS waves and ST segments of FECG signals. We used the abdominal and direct fetal ECG database (ADFECGDB) and the Cardiology Challenge Database (PhysioNet2013) to verify the proposed algorithm. The signal-to-noise ratio of the best channel signal reached 45.028 dB and the issue of missing waveforms was addressed. The sensitivity, positive predictive value and F1 score of fetal QRS wave detection were 96.90%, 98.23%, and 95.24%, respectively. The proposed algorithm may be used as a new method for FECG signal extraction and detection.
Industry 4.0 Technologies Applied to Inland Waterway Transport: Systematic Literature Review
Authors: Restrepo-Arias Juan Felipe , Branch-Bedoya John William , Zapata-Cortes Julian Andres , Paipa-Sanabria Edwin Giovanny , Garnica-López Miguel Andres
Published: May 2022

The focus of this article is inland waterway transport. Different problems in this domain have been studied due to the increase in waterway traffic globally. Industry 4.0 technologies have become an alternative for the possible solution of these problems. For this reason, this paper aims to answer the following research questions: (1) What are the main problems in transporting cargo by inland waterway? (2) What technological strategies are being studied to solve these problems? (3) What technologies from Industry 4.0 are used within the technological strategies to solve the exposed problems? This study adopts a Systematic Literature Review (SLR) approach. For this work, were recovered 645 articles, 88 of which were eligible, from which we could identify five domains corresponding to (1) traffic monitoring, (2) smart navigation, (3) emission reduction, (4) analytics with big data, and (5) cybersecurity. The strategies currently being considered combine navigation technologies, such as AIS (Automatic Identification System), which offers a large amount of data, with Industry 4.0 tools and mainly machine learning techniques, to take advantage of data collected over a long time. This study is, to our knowledge, one of the first to show how Industry 4.0 technologies are currently being used to tackle inland waterway transport problems and current application trends in the scientific community, which is a first step for the development of future studies and more advanced solutions.
Multi-AP and Test Point Accuracy of the Results in WiFi Indoor Localization
Authors: Li Shuyu , Welsen Sherif , Brusic Vladimir
Published: May 2022

WiFi-based indoor positioning has attracted intensive research activities. While localization accuracy is steadily improving due to the application of advanced algorithms, the factors that affect indoor localization accuracy have not been sufficiently understood. Most localization algorithms used in changing indoor spaces are Angle-of-Arrival (AoA) based, and they deploy the conventional MUSIC algorithm. The localization accuracy can be achieved by algorithm improvements or joint localization that deploys multiple Access Points (APs). We performed an experiment that assessed the Test Point (TP) accuracy and distribution of results in a complex environment. The testing space was a 290 m2 three-room environment with three APs with 38 TPs. The joint localization using three APs was performed in the same test space. We developed and implemented a new algorithm for improved accuracy of joint localization. We analyzed the statistical characteristics of the results based on each TP and show that the local space-dependent factors are the key factors for localization accuracy. The most important factors that cause errors are distance, obstacles, corner locations, the location of APs, and the angular orientation of the antenna array. Compared with the well-known SpotFi algorithm, we achieved a mean accuracy (across all TPs) improvement of 46%. The unbiased joint localization median accuracy improved by 20% as compared to the best individual localization.
Mathematics and Poetry—Epilogue for a Special Issue
Authors: Nichita Florin-Felix
Published: May 2022

The Special Issue “Mathematics and Poetry, with a View towards Machine Learning”, started with three guiding questions from the guest editor:What are the similarities (and differences) between mathematical problems and poetic works [...]