bluuebunny/crossref_metadata_2025 huggingface dataset:
Structural Characterization of Exopolysaccharide Produced by Leuconostoccitreum B-2 Cultured in Molasses Medium and Its Application in Set Yogurt
Authors: Liang Lu , Xu Min , Pan Lei , Zhou Zhijiang , Han Ye
Published: May 2022

Sugarcane molasses is an agricultural by-product containing sucrose. In this study, the exopolysaccharide (M-EPS) produced by Leuconostoc citreum B-2 in molasses-based medium was characterized, optimized, and its application in set yogurt was investigated. The structure analysis, including gel permeation chromatography, Fourier transform infrared spectroscopy, and nuclear magnetic resonance, revealed that the M-EPS was a linear dextran composed of D-glucose units, which were linked by α-(1→6) glycosidic bonds with 19.3% α-(1→3) branches. The M-EPS showed a lower molecular weight than that produced from sucrose. The M-EPS was added into the set yogurt, and then the water holding capacity, pH, and microstructure of set yogurt were evaluated. Compared with the controls, the addition of M-EPS improved the water holding capacity and reduced the pH of set yogurt. Meanwhile, the structure of the three-dimensional network was also observed in the set yogurt containing M-EPS, indicating that M-EPS had a positive effect on the stability of set yogurt. The results provide a theoretical basis for the cost-effective utilization of sugarcane molasses.
An Unsupervised Clustering Method for Selection of the Fracturing Stage Design Based on the Gaussian Mixture Model
Authors: Wang Xin , Yang Lifeng , Fan Meng , Zou Yushi , Wang Wenchao
Published: May 2022

In order to further improve the efficiency and economic benefits of multi-stage fracturing of unconventional oil and gas horizontal wells, it is urgently needed to conduct comprehensive reservoir quality evaluation research on the whole horizontal well section. Firstly, based on logging data, focusing on reservoir quality and completion quality, and comprehensively considering key factors such as reservoir physical property indexes and fracability indexes, a subjective and objective coupled evaluation model of the entropy weight method (EWM) and the analytic hierarchy process (AHP) without bias is established to obtain the composite reservoir quality index. Then, unsupervised gaussian mixture model (GMM) clustering algorithms are used to classify the reservoir comprehensive quality index and finally four grades of fracturing stages are established. Taking shale oil well A and B of the Permian Lucaogou Formation in Jimsar Sag, Junggar Basin, as examples, the comprehensive reservoir quality evaluation and clustering model training, testing, and prediction were carried out. By comparing the clustering results with the actual fracturing stages and oil production, it is found that the evaluation results obtained by the GMM clustering algorithms based on the coupled evaluation model of EWM and AHP can identify the good fracturing grades. The algorithm can also predict the fracturing grades of other wells in the same block. It proves the accuracy of the method proposed in this paper and provides a favorable technical basis for determining the placement of multi-cluster fracturing perforation.
Production of Bivalent Subunit Vaccine for Porcine via 2A-Like Sequence in Baculovirus Expression Vector System
Authors: Chen Ming-Hsiang , Varikkodan Muhammed Muhsin , Lin Ting-Hui , Chiang Chien-Min , Sari Indah Permata , Perng Ming-Der , Wu Tzong-Yuan
Published: May 2022

Classical swine fever virus (CSFV) and porcine circovirus type 2 (PCV2) have caused severe diseases in swine populations worldwide. Here, a polycistronic baculovirus vector was developed to express a bivalent vaccine, consisting of the CSFV-E2 and PCV2-Cap protein, and an immunomodulator protein derived from the Flammulina velutipes, FVE-FIP, as well as the selection marker, green fluorescent protein. The simultaneous expression of the CSFV-E2 and PCV2-Cap protein was mediated by the 2A-like sequence derived from the Perina nuda virus (PnV), while the expression of the FVE-FIP was driven by the internal ribosome entry site (IRES) element derived from the Rhophalosipum padi virus (RhPV). The Western blot analysis result suggested that the CSFV-E2, PCV2-Cap, and FVE-FIP protein were successfully co-expressed by the infected Spodoptera frugiperda IPBL-Sf21 (Sf21) cell line. The extracted cell lysate containing all three recombinant proteins was administered to Balb/C mice with or without the supplementation of Freund’s adjuvant. The ELISA analysis of the serum collected from all the immunized groups showed detectable antibodies against CSFV-E2 and PCV2-Cap. Furthermore, the immunized group without the adjuvant supplementation demonstrated a similar level of antibodies to the group with adjuvant supplementation, suggesting the efficiency of the FVE-FIP in enhancing the immune response. These results demonstrated the polycistronic baculovirus vector could be employed to develop bivalent vaccines for pigs.
Fracture Interference and Refracturing of Horizontal Wells
Authors: Lin Hai , Tian Yakai , Sun Zhenwei , Zhou Fujian
Published: May 2022

Due to fracture interference, not all perforations can be fractured, resulting in 20% of fractures contributing to 80% of the total production. The extraction of oil and gas also reduces production, necessitating refracturing. In this study, the finite element method was used to simulate multiple fractures fracturing simultaneously and the stress field distribution was then extracted and applied to a new geological model. This paper explains the effect of stress around the horizontal wellbore on new fractures during the refracturing of old wells using a temporary plugging technique. The results show that initial breaking pressures are the same, but as fractures extend, inter-fracture interference increases, resulting in different fracture extension pressures and widths. The fracturing fluid is filtered into the reservoir matrix after fracturing, reducing formation stress. Compared with fracturing at the initial fracture site, reperforating fracturing has a lower fracture extension pressure and a longer fracture length. According to this study, hydraulic fractures have a 15 m effective influence radius on the external formation. Stress relief is beneficial for fracture initiation prior to refracturing. Reperforating and fracturing, in combination with temporary plugging technology, can assist in increasing the effective stimulated reservoir volume and achieving high and stable production.
Mindfulness and Modern Mindfulness: Considering Buddhist Communities and Personal Salvation from Depression
Authors: Somers Brian D.
Published: May 2022

This thesis aims to consider some of the differences between mindfulness as a Buddhist practice and mindfulness-based programs. The primary difference considered is the individualistic perspectives taken by mindfulness-based programs. If modern mindfulness-based techniques are meant as a treatment for depression, and depression is in part caused by isolation then these programs must also consider mindfulness as a project, which does not accentuate the self as distinct from others. Personal salvation from deficits of the mind is a regular theme of modern mindfulness. This initial goal-oriented, self-interested perspective is potentially threatening to a depressed person who secludes her- or himself in a private world of the “fix it” self-project. With interdependent origination (緣起) as a tenet and the sangha (僧) as one of the three jewels (三寶), Buddhism emphasizes community where salvation is defined as the liberation of all beings from suffering. Therefore, this thesis suggests that mindfulness practices initiated from a self-help perspective are troubled to the extent that they isolate the practitioner. Therefore, a Buddhist interpretation of modern mindfulness, especially regarding individualism and isolation as a cause of depression, is desirable.
Assessing Changes in Mountain Treeline Ecotones over 30 Years Using CNNs and Historical Aerial Images
Authors: Wang Zuyuan , Ginzler Christian , Eben Birgit , Rehush Nataliia , Waser Lars T.
Published: May 2022

Historical black-and-white (B&W) aerial images have been recognized as an important source of information for assessing vegetation dynamics. However, the use of these images is limited by the lack of multispectral information, as well as by their varying quality. It is therefore important to study and develop methods that are capable of automatic and accurate classification of these B&W images while reducing the need for tedious manual work. The goal of this study was to assess changes over 30 years in woody vegetation cover along alpine treeline ecotones using B&W aerial images from two time points. A convolutional neural networks model was firstly set up based on three structure classes calculated from Airborne Laser Scanning data using the B&W aerial images from 2010. Then, the model was improved by active addition of training samples of those that were wrongly predicted from historical B&W aerial images from 1980. A comparison with visual image interpretation revealed generally high agreement for the class “dense forest” and lower agreement for the class “group of trees”. The study illustrates that vegetation changes at the treeline ecotone can be detected in order to assess areawide long-term vegetation dynamics at a fine spatial resolution.
A Comparison of the Barrick and Backscatter Ionogram Methods of Calculating Sea Surface Backscatter Coefficients
Authors: Edwards Danielle , Cervera Manuel , MacKinnon Andrew
Published: May 2022

An understanding of land and sea surface backscatter coefficients at high frequencies (HF) is required to accurately assess ionospheric propagation conditions for over-the-horizon radar. In this paper, two methods of calculating sea surface backscatter coefficients are compared. The first method is a theoretical method developed by Barrick in 1972, which treats the sea as a slightly rough surface defined by a wave height spectrum and uses a perturbation approach. The second method compares the difference between observed and modelled backscatter ionograms in which all other losses are accounted for to obtain a measurement of the sea surface backscatter coefficient. Similar trends and values for the sea surface backscatter coefficients from each method were found despite the many models and assumptions required for both these methods. However, it was noted that there was a larger range of values for the sea surface backscatter coefficients when the Barrick method was used.
Estimation of Seaweed Biomass Based on Multispectral UAV in the Intertidal Zone of Gouqi Island
Authors: Chen Jianqu , Li Xunmeng , Wang Kai , Zhang Shouyu , Li Jun
Published: May 2022

UAV remote sensing inversion is an efficient and accurate method for obtaining information on vegetation coverage, biomass and other parameters. It is widely used on forest, grassland and other terrestrial vegetation. However, it is rarely used on aquatic vegetation, especially in intertidal zones and other complex environments. Additionally, it is mainly used for inversion of coverage, and there have been few studies thus far on biomass assessment. In this paper, we applied multispectral UAV aerial photography data to evaluate the biomass of seaweed in an intertidal zone. During the ebb tide, UAV aerial photography and in situ sampling data were collected in the study area. After optimizing the spectral index and performing a multiple linearity test, the spectral parameters were selected as the input of the evaluation model. Combined with two machine learning algorithms, namely random forest (RF) and gradient boosting decision tree (GBDT), the biomasses of three species of seaweed (Ulva pertusa, Sargassum thunbergii and Sargassum fusiforme) in the intertidal zone were assessed. In addition, the input parameters of the machine learning algorithms were optimized by one-way ANOVA and Pearson’s correlation analysis. We propose a method to assess the biomass of intertidal seaweed based on multispectral UAV data combined with statistics and machine learning. The results show that the two machine learning algorithms have different accuracies in terms of biomass evaluation using multispectral images; the gradient boosting decision tree can evaluate the biomass of seaweed in the intertidal zone more accurately.
Spatial Structure of an Urban Park System Based on Fractal Theory: A Case Study of Fuzhou, China
Authors: You Meizi , Guan Chenghe , Lai Riwen
Published: May 2022

The rationality and efficiency of the spatial structure of an urban park system are critical in building a livable urban environment. Fractal theory is currently treated as the frontier theory for exploring the law of complex systems; however, it has rarely been applied to urban park systems. This study applied the aggregation, grid and correlation dimension models of fractal theory in Fuzhou, China. The spatial structure and driving factors of the urban park system were analyzed and an innovative model was proposed. The evidence shows that the spatial structure of the park system has fractal characteristics, although self-organization and optimization have not yet been fully formed, revealing a multi-core nesting pattern. Moreover, the core is cluster of four popular parks with weakening adsorption, and the emerging Baima River Park is located at the geometric center, which is likely to be further developed. The system structure is primarily driven by geographical conditions, planning policies, and transportation networks. Against this backdrop, an innovative model for the park system was proposed. The central park has heterogeneity and synergistic development, relying on the kinds of flow which can lead to the formation of a park city, a variation of a garden city. At the regional scale, relying on the geographical lines, the formation of a regional park zone could be realized. These findings provide new perspectives to reveal the spatial structure of urban park systems. The information derived can assist policy makers and planners in formulating more scientific plans, and may contribute to building a balanced and efficient urban park system.
Estimating Aboveground Biomass of Two Different Forest Types in Myanmar from Sentinel-2 Data with Machine Learning and Geostatistical Algorithms
Authors: Wai Phyo , Su Huiyi , Li Mingshi
Published: May 2022

The accurate estimation of spatially explicit forest aboveground biomass (AGB) provides an essential basis for sustainable forest management and carbon sequestration accounting, especially in Myanmar, where there is a lack of data for forest conservation due to operational limitations. This study mapped the forest AGB using Sentinel-2 (S-2) images and Shuttle Radar Topographic Mission (SRTM) based on random forest (RF), stochastic gradient boosting (SGB) and Kriging algorithms in two forest reserves (Namhton and Yinmar) in Myanmar, and compared their performance against AGB measured by the traditional methods. Specifically, a suite of forest sample plots were deployed in the two forest reserves, and forest attributes were measured to calculate the plot-level AGB based on allometric equations. The spectral bands, vegetation indices (VIs) and textures derived from processed S-2 data and topographic parameters from SRTM were utilized to statistically link with field-based AGB by implementing random forest (RF) and stochastic gradient boosting (SGB) algorithms. Followed by an evaluation of the algorithmic performances, RF-based Kriging (RFK) models were employed to determine the spatial distribution of AGB as an improvement of accuracy against RF models. The study’s results showed that textural measures produced from wavelet analysis (WA) and vegetation indices (VIs) from Sentinel-2 were the strongest predictors for evergreen forest reserve (Namhton) AGB prediction and spectral bands and vegetation indices (VIs) showed the highest sensitivity to the deciduous forest reserve (Yinmar) AGB prediction. The fitted models were RF-based ordinary Kriging (RFOK) for Namhton forest reserve and RF-based co-Kriging (RFCK) for Yinmar forest reserve because their respective R2, whilst the RMSE values were validated as 0.47 and 24.91 AGB t/ha and 0.52 and 34.72 AGB t/ha, respectively. The proposed random forest Kriging framework provides robust AGB maps, which are essential to estimate the carbon sequestration potential in the context of REDD+. From this particular study, we suggest that the protection/disturbance status of forests affects AGB values directly in the study area; thus, community-participated or engaged forest utilization and conservation initiatives are recommended to promote sustainable forest management.
An Innovative Synthetic Aperture Radar Design Method for Lunar Water Ice Exploration
Authors: Zhang Yanyan , Zhao Fei , Chang Sheng , Liu Mingliang , Wang Robert
Published: May 2022

Owing to the Moon’s rough surface, there is a growing controversy over the conclusion that water ice exists in the lunar permanently shadowed regions (PSRs) with a high circular polarization ratio (CPR). To further detect water ice on the Moon, an innovative design method for spaceborne synthetic aperture radar (SAR) system is proposed, to obtain radar data that can be used to distinguish water ice from lunar regolith with a small difference in the dielectric constants. According to Campbell’s dielectric constant model and the requirement that SAR radiometric resolution is smaller than the contrast of targets in images, a newly defined SAR system function involved in the method is presented to evaluate the influence of some system parameters on the water ice detection capability of SAR. In addition, several simulation experiments are performed, and the results demonstrate that the presented SAR design method may be helpful for lunar water ice exploration.
A Distributed N-FINDR Cloud Computing-Based Solution for Endmembers Extraction on Large-Scale Hyperspectral Remote Sensing Data
Authors: Ayma Quirita Victor Andres , da Costa Gilson Alexandre Ostwald Pedro , Beltrán César
Published: May 2022

In this work, we introduce a novel, distributed version of the N-FINDR endmember extraction algorithm, which is able to exploit computer cluster resources in order to efficiently process large volumes of hyperspectral data. The implementation of the distributed algorithm was done by extending the InterCloud Data Mining Package, originally adopted for land cover classification, through the HyperCloud-RS framework, here adapted for endmember extraction, which can be executed on cloud computing environments, allowing users to elastically administer processing power and storage space for adequately handling very large datasets. The framework supports distributed execution, network communication, and fault tolerance, transparently and efficiently to the user. The experimental analysis addresses the performance issues, evaluating both accuracy and execution time, over the processing of different synthetic versions of the AVIRIS Cuprite hyperspectral dataset, with 3.1 Gb, 6.2 Gb, and 15.1Gb respectively, thus addressing the issue of dealing with large-scale hyperspectral data. As a further contribution of this work, we describe in detail how to extend the HyperCloud-RS framework by integrating other endmember extraction algorithms, thus enabling researchers to implement algorithms specifically designed for their own assessment.
Global Mean Sea Level Variation on Interannual–Decadal Timescales: Climatic Connections
Authors: Liao Ting-Juan , Chao Benjamin F.
Published: May 2022

The global mean sea level (GMSL) has been measured precisely by the space geodetic remote-sensing technique of radar altimetry since the 1990s. Aside from the well-studied seasonality and secular sea level rise, here we focus on GMSL variation on the interannual–decadal (ID) timescales (GMSL-ID) and investigate the influences of the climatic oscillations as physical causes. We conduct correlation analyses on the GMSL-ID time series with several climatic oscillations represented by their respective meteorological indices, including El Niño-Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), Atlantic Multidecadal Oscillation (AMO), Arctic Oscillation (AO), and Antarctic Oscillation (AAO). From the time-domain cross-correlation functions and the corresponding frequency-domain cross-coherence spectra, we find the following: (i) high correlation between GMSL-ID and ENSO, primarily befalling on the Central-Pacific (as opposed to the Eastern-Pacific) type of ENSO, on timescales longer than 1.5 years; (ii) moderate correlations of GMSL-ID with PDO on long-period timescales of over 4 years, and with AMO on a timescale of 2–10 years, with AMO leading in phase by 8 months; (iii) weak or practically no correlation of GMSL-ID with either AO or AAO, in the former case given the fact that our GMSL-ID data actually do not cover the Arctic sea. Finally, we least-squares fit the above five indices to GMSL-ID to assess the relative contribution of each oscillation in causing the observed GMSL-ID, for a better understanding of the GMSL under the influences of on-going climate change.
GCBANet: A Global Context Boundary-Aware Network for SAR Ship Instance Segmentation
Authors: Ke Xiao , Zhang Xiaoling , Zhang Tianwen
Published: May 2022

Synthetic aperture radar (SAR) is an advanced microwave sensor, which has been widely used in ocean surveillance, and its operation is not affected by light and weather. SAR ship instance segmentation can provide not only the box-level ship location but also the pixel-level ship contour, which plays an important role in ocean surveillance. However, most existing methods are provided with limited box positioning ability, hence hindering further accuracy improvement of instance segmentation. To solve the problem, we propose a global context boundary-aware network (GCBANet) for better SAR ship instance segmentation. Specifically, we propose two novel blocks to guarantee GCBANet’s excellent performance, i.e., a global context information modeling block (GCIM-Block) which is used to capture spatial global long-range dependences of ship contextual surroundings, enabling larger receptive fields, and a boundary-aware box prediction block (BABP-Block) which is used to estimate ship boundaries, achieving better cross-scale box prediction. We conduct ablation studies to confirm each block’s effectiveness. Ultimately, on two public SSDD and HRSID datasets, GCBANet outperforms the other nine competitive models. On SSDD, it achieves 2.8% higher box average precision (AP) and 3.5% higher mask AP than the existing best model; on HRSID, they are 2.7% and 1.9%, respectively.
A DEM Super-Resolution Reconstruction Network Combining Internal and External Learning
Authors: Lin Xu , Zhang Qingqing , Wang Hongyue , Yao Chaolong , Chen Changxin , Cheng Lin , Li Zhaoxiong
Published: May 2022

The study of digital elevation model (DEM) super-resolution reconstruction algorithms has solved the problem of the need for high-resolution DEMs. However, the DEM super-resolution reconstruction algorithm itself is an inverse problem, and making full use of the DEM a priori information is an effective way to solve this problem. In our work, a new DEM super-resolution reconstruction method is proposed based on the complementary relationship between internally learned super-resolution reconstruction methods and externally learned super-resolution reconstruction methods. The method is based on the presence of a large amount of repetitive information within the DEM. Using an internal learning approach to learn the internal prior of the DEM, a low-resolution dataset of the DEM rich in detailed features is generated, and based on this, the training of a constrained external learning network is constructed for the discrepancy data pair. Finally, it introduces residual learning based on the network model to accelerate the operation rate of the network and to solve the model degradation problem brought about by the deepening of the network. This enables the better transfer of learned detailed features in deeper network mappings, which in turn ensures accurate learning of the DEM prior information. The network utilizes the internal prior of the specific DEM as well as the external prior of the DEM dataset and achieves better super-resolution reconstruction results in the experimental results. The results of super-resolution reconstruction by the Bicubic method, Super-Resolution Convolutional Neural Networks (SRCNN), very deep convolutional networks (VDSR), ”Zero-Shot” Super-Resolution networks (ZSSR) and the new method in this paper were compared, and the average RMSE of the super-resolution reconstruction results of the five methods were 8.48 m, 8.30 m, 8.09 m, 7.02 m and 6.65 m, respectively. The mean elevation error at the same resolution is 21.6% better than that of the Bicubic method, 19.9% better than that of the SRCNN, 17.8% better than that of the VDSR method, and 5.3% better than that of the ZSSR method.
A Multi-Temporal Small Baseline Interferometry Procedure Applied to Mining-Induced Deformation Monitoring
Authors: Palamà Riccardo , Crosetto Michele , Rapinski Jacek , Barra Anna , Cuevas-González María , Monserrat Oriol , Crippa Bruno , Kotulak Natalia , Mróz Marek , Mleczko Magdalena
Published: May 2022

This work addresses a methodology based on the interferometric synthetic aperture radar (InSAR) applied to analyze and monitor ground-motion phenomena induced by underground mining activities in the Legnica-Glogow copper district, south-western Poland. The adopted technique employs an InSAR processing chain that exploits a stack of Sentinel-1 synthetic aperture radar (SAR) images using a small baseline multitemporal approach. Interferograms with small temporal baselines are first selected, then their network is optimized and reduced to eliminate noisy data, in order to mitigate the effect of decorrelation sources related to seasonal phenomena, i.e., snow and vegetation growth, and to the radar acquisition geometry. The atmospheric disturbance is mitigated using a spatio-temporal filter based on the nonequispaced fast Fourier transform. The estimated displacement maps and time series show the effect of both linear and impulsive ground motion and are validated against global navigation satellite system (GNSS) measurements. In this context, a significant threat to the built environment is represented by seismic tremors triggered by underground mining activities, which are analyzed using the proposed method to integrate the information gathered by in situ seismometer devices.
Skin Pigmentation Influence on Pulse Oximetry Accuracy: A Systematic Review and Bibliometric Analysis
Authors: Cabanas Ana M. , Fuentes-Guajardo Macarena , Latorre Katina , León Dayneri , Martín-Escudero Pilar
Published: May 2022

Nowadays, pulse oximetry has become the standard in primary and intensive care units, especially as a triage tool during the current COVID-19 pandemic. Hence, a deeper understanding of the measurement errors that can affect precise readings is a key element in clinical decision-making. Several factors may influence the accuracy of pulse oximetry, such as skin color, body temperature, altitude, or patient movement. The skin pigmentation effect on pulse oximetry accuracy has long been studied reporting some contradictory conclusions. Recent studies have shown a positive bias in oxygen saturation measurements in patients with darkly pigmented skin, particularly under low saturation conditions. This review aims to study the literature that assesses the influence of skin pigmentation on the accuracy of these devices. We employed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement to conduct a systematic review retrospectively since February 2022 using WOS, PubMed, and Scopus databases. We found 99 unique references, of which only 41 satisfied the established inclusion criteria. A bibliometric and scientometrics approach was performed to examine the outcomes of an exhaustive survey of the thematic content and trending topics.
Ballistic Deficit Pulse Processing in Cadmium–Zinc–Telluride Pixel Detectors for High-Flux X-ray Measurements
Authors: Buttacavoli Antonino , Principato Fabio , Gerardi Gaetano , Bettelli Manuele , Zappettini Andrea , Seller Paul , Veale Matthew C. , Zanettini Silvia , Abbene Leonardo
Published: May 2022

High-flux X-ray measurements with high-energy resolution and high throughput require the mitigation of pile-up and dead time effects. The reduction of the time width of the shaped pulses is a key approach, taking into account the distortions from the ballistic deficit, non-linearity, and time instabilities. In this work, we will present the performance of cadmium–zinc–telluride (CdZnTe or CZT) pixel detectors equipped with digital shapers faster than the preamplifier peaking times (ballistic deficit pulse processing). The effects on energy resolution, throughput, energy-linearity, time stability, charge sharing, and pile-up are shown. The results highlight the absence of time instabilities and high-energy resolution (<4% FWHM at 122 keV) when ballistic deficit pulse processing (dead time of 90 ns) was used in CZT pixel detectors. These activities are in the framework of an international collaboration on the development of spectroscopic imagers for medical applications (mammography, computed tomography) and non-destructive testing in the food industry.
Deep Neural Network Recognition of Rivet Joint Defects in Aircraft Products
Authors: Amosov Oleg Semenovich , Amosova Svetlana Gennadievna , Iochkov Ilya Olegovich
Published: May 2022

The mathematical statement of the problem of recognizing rivet joint defects in aircraft products is given. A computational method for the recognition of rivet joint defects in aircraft equipment based on video images of aircraft joints has been proposed with the use of neural networks YOLO-V5 for detecting and MobileNet V3 Large for classifying rivet joint states. A novel dataset based on a real physical model of rivet joints has been created for machine learning. The accuracy of the result obtained during modeling was 100% in both binary and multiclass classification.
Colored Petri Net Modelling and Evaluation of Drone Inspection Methods for Distribution Networks
Authors: Fedorova Aleksandra , Beliautsou Viktar , Zimmermann Armin
Published: May 2022

The UAV industry is developing rapidly and drones are increasingly used for monitoring industrial facilities. When designing such systems, operating companies have to find a system configuration of multiple drones that is near-optimal in terms of cost while achieving the required monitoring quality. Stochastic influences such as failures and maintenance have to be taken into account. Model-based systems engineering supplies tools and methods to solve such problems. This paper presents a method to model and evaluate such UAV systems with coloured Petri nets. It supports a modular view on typical setup elements and different types of UAVs and is based on UAV application standards. The model can be easily adapted to the most popular flight tasks and allows for estimating the monitoring frequency and determining the most appropriate grouping and configuration of UAVs, monitoring schemes, air time and maintenance periods. An important advantage is the ability to consider drone maintenance processes. Thus, the methodology will be useful in the conceptual design phase of UAVs, in monitoring planning, and in the selection of UAVs for specific monitoring tasks.
Pattern Recognition of EMG Signals by Machine Learning for the Control of a Manipulator Robot
Authors: Pérez-Reynoso Francisco , Farrera-Vazquez Neín , Capetillo César , Méndez-Lozano Nestor , González-Gutiérrez Carlos , López-Neri Emmanuel
Published: May 2022

Human Machine Interfaces (HMI) principles are for the development of interfaces for assistance or support systems in physiotherapy or rehabilitation processes. One of the main problems is the degree of customization when applying some rehabilitation therapy or when adapting an assistance system to the individual characteristics of the users. To solve this inconvenience, it is proposed to implement a database of surface Electromyography (sEMG) of a channel in healthy individuals for pattern recognition through Neural Networks of contraction in the muscular region of the biceps brachii. Each movement is labeled using the One-Hot Encoding technique, which activates a state machine to control the position of an anthropomorphic manipulator robot and validate the response time of the designed HMI. Preliminary results show that the learning curve decreases when customizing the interface. The developed system uses muscle contraction to direct the position of the end effector of a virtual robot. The classification of Electromyography (EMG) signals is obtained to generate trajectories in real time by designing a test platform in LabVIEW.
Analysis of a Measurement Method and Test System for Pressure Change Rates in Commercial Vehicle Brake Chambers
Authors: Li Gangyan , Shen Rui , Liu Yudong , Yang Fan , Hu Jian
Published: May 2022

The pressure change rate (PCR) of the brake chamber is the key control parameter and evaluation index in the pneumatic braking system for intelligent braking. The PCR threshold value of commercial vehicle brake chambers for braking comfort is analyzed. The PCR measurement method based on a laminar flow resistance tube is proposed, and the PCR test system is designed. The simulation model of a PCR test system for commercial vehicle brake chambers is presented. By analyzing the simulation and experimental results, it is validated that the PCR test system of commercial vehicle brake chambers has the function of measuring PCR in real time. Finally, according to the MSA (Measurement System Analysis) evaluation method, the performance of the PCR test system for commercial vehicle brake chambers is analyzed, and the correctness and applicability of the test system are verified.
Future Wireless Communication Technology towards 6G IoT: An Application-Based Analysis of IoT in Real-Time Location Monitoring of Employees Inside Underground Mines by Using BLE
Authors: Pattnaik Sushant Kumar , Samal Soumya Ranjan , Bandopadhaya Shuvabrata , Swain Kaliprasanna , Choudhury Subhashree , Das Jitendra Kumar , Mihovska Albena , Poulkov Vladimir
Published: May 2022

In recent years, the IoT has emerged as the most promising technology in the key evolution of industry 4.0/industry 5.0, smart home automation (SHA), smart cities, energy savings and many other areas of wireless communication. There is a massively growing number of static and mobile IoT devices with a diversified range of speed and bandwidth, along with a growing demand for high data rates, which makes the network denser and more complicated. In this context, the next-generation communication technology, i.e., sixth generation (6G), is trying to build up the base to meet the imperative need of future network deployment. This article adopts the vision for 6G IoT systems and proposes an IoT-based real-time location monitoring system using Bluetooth Low Energy (BLE) for underground communication applications. An application-based analysis of industrial positioning systems is also presented.
Assessment of the Impact of the Covid-19 Pandemic on the Balance of Payments and the International Investment Position of Russia
Authors: Petrikova E. M.
Published: May 2022

The article analyzes the current situation in the world economy in 2020, caused by the energy crisis due to the failure of OPEC agreements, political conflicts, trade wars, and the COVID-19 pandemic. The pandemic and its socio-economic consequences have become the main cause of the global crisis.

The author conducted statistical analysis of the effects of the COVID-19 pandemic on the balance of payments and the international investment position of Russia, which in aggregate form reveals the results of the country's foreign economic activity. The author analyzes country's external sector statistics summarizing the results of Russia's activities with the rest of the world in 2020 and identifies the trends of the impact of the coronavirus pandemic on the Russian economy and the results of the foreign economic activity of the Russian state. The paper analyzes in detail all the enlarged items of the balance of payments and the international investment position of the Russian Federation and quantifies the impact of the COVID-19 pandemic in 2020–2021 on them and also provides an overview of the development of the external sector of the Russian economy at the end of the year.

While analyzing the statistical data of the balance of payments and the international investment position of Russia in 2000–2021, a comparison is made with the current situation in the economy and foreign economic sphere in connection with the global crisis of 2008 and the introduction of international financial and economic sanctions after 2014. The author with regard to the effects of the COVID-19 coronavirus pandemic on the balance of payments and the international investment position of the Russian Federation in 2020–2021 substantiates a forecast of further prospects for the development of the foreign economic sector of the Russian economy in the medium term.
Birth Seasonality in Russia: Regional Features
Authors: Rodionova L. A. , Kopnova E. D.
Published: May 2022

In the article based on statistical methods the authors evaluateinterregional differences in seasonality of births in Russia and attempt toexplain the differences in the seasonality of the number of births in the regions of Russia. The relevance of these issues is explained by the need to monitor changes in demographic indicators following the implementation of national projects for the period up to the mid-2020s.

The authors considered the most fundamental results of some domestic and foreign studies on this subject. Some hypotheses regarding the causes of the seasonality of births were tested using econometric tools (based on official birth statistics in the regions of the Russian Federation for the period 2006–2020).The statistically significant factors that form the amplitude of seasonal fluctuations were the degree of urbanization, income and the climate based on the OLS model. The amplitude of fluctuations in births decreased with an increase in the level of urbanization (growth in the share of the urban population) and the population of the region. An increase in January temperature increased the amplitude of birth fluctuations. The positive impact of income on the amplitude of the fluctuation was explained by the active planning of the timing of childbearing in high-income regions, but further research was needed. Nutritional characteristics were found to be statistically insignificant factors. The number of marriages took a special place in the formation of the seasonality of births. On the whole for the Russian Federation, based on time series models, a cointegration relationship between the number of marriages and the number of births was found. This relationship had begun to weaken over the past five years.
Stratification of Patients with Diabetes Using Continuous Glucose Monitoring Profiles and Machine Learning
Authors: Mao Yinan , Tan Kyle Xin Quan , Seng Augustin , Wong Peter , Toh Sue-Anne , Cook Alex R.
Published: May 2022

Background.Continuous glucose monitoring (CGM) offers an opportunity for patients with diabetes to modify their lifestyle to better manage their condition and for clinicians to provide personalized healthcare and lifestyle advice. However, analytic tools are needed to standardize and analyze the rich data that emerge from CGM devices. This would allow glucotypes of patients to be identified to aid clinical decision-making.Methods.In this paper, we develop an analysis pipeline for CGM data and apply it to 148 diabetic patients with a total of 8632 days of follow up. The pipeline projects CGM data to a lower-dimensional space of features representing centrality, spread, size, and duration of glycemic excursions and the circadian cycle. We then use principal components analysis andk-means to cluster patients’ records into one of four glucotypes and analyze cluster membership using multinomial logistic regression.Results.Glucotypes differ in the degree of control, amount of time spent in range, and on the presence and timing of hyper- and hypoglycemia. Patients on the program had statistically significant improvements in their glucose levels.Conclusions.This pipeline provides a fast automatic function to label raw CGM data without manual input.
EFECTO DEL GENOTIPO, AMBIENTE Y ÁCIDO HÚMICO EN EL CULTIVO IN VITRO DE ANTERAS DE TRIGO
Authors: Ramírez-Malagón Rafael , Borodanenko Anatoli , Ochoa-Alejo Neftalí , Pérez-Moreno Luis , Barrera-Guerra José Luis , Núñez-Palenius Héctor Gordon
Published: May 2022

El cultivo in vitro de anteras de trigo (Triticum aestivum L.) es una herramienta útil en el mejoramiento genético de esta especie porque ahorra tiempo y costo para la generación de líneas isogénicas, porque permite aislar homocigotos recesivos de interés agronómico y fijar más rápido las características de cruzas de interés agronómico que por las técnicas tradicionales de mejoramiento genético. Doce líneas de trigo de invierno de origen ruso que no respondían al cultivo in vitro de anteras, fueron cruzadas con dos variedades de trigo de primavera de respuesta positiva. Se encontró que la progenie resultante dio respuesta positiva, lo cual indica que la respuesta al cultivo de anteras in vitro está controlada genéticamente. Se probó el efecto del ambiente en el que se desarrollaron las plantas donadoras de anteras en las respuestas al cultivo in vitro (a cielo abierto con temperaturas de 5 a 25 ºC, e invernadero entre 7 a 35 ºC). La mayor formación de callos embriogénicos y plantas fértiles se duplicó en las anteras aisladas de plantas que fueron cultivadas a cielo abierto. Al evaluar el efecto de tres productos químicos (Q-2000®, Maxi-Grow® y Humigen®) aplicados a las espigas, sobre la regeneración de plantas a partir de anteras cultivadas in vitro, se encontró que el ácido húmico a 3.5 mL L-1 incrementó entre tres y cinco veces la formación de callos embriogénicos, en comparación con las plantas testigo. Se encontró correlación positiva entre el ancho de las hojas de las plántulas regeneradas del cultivo in vitro y su fertilidad, por lo que dicha medida de las hojas podría ser un posible marcador morfológico de la haploidía o dihaploidia, en la mayoría de genotipos.
Evaluation Of Signal Strength And Quality Of A Ku-Band Satellite Downlink During Raining Season In Guinea Savanna Region Of Nigeria
Authors: Yunisa Yahaya , Timothy Zhimwang Jangfa , Enoh Pius Ogherohwo , Aminu Ibrahim , Samuel Shaka Oghenemega , Frank  , Mercy Lagbegha-ebi
Published: May 2022

This study presents the evaluation of signal strength and quality of a ku-band satellite downlink signals during raining season in Guinea Savanna region of Nigeria. Rain rate data and ku-band signal over Jos were retrieved from the Nigeria Meteorological Agency (NIMET). Rain rate data was measured using Davis Vantage Vue weather station while microwave signal were obtained using spectrum analyzer. The measured data were analyzed using Microsoft office excel. Rainfall is broadly classified into two types: stratiform (drizzle: ≤5mm/hr and widespread: >5≤25mm/hr) and convection (shower: >25≤50mm/hr and thunderstorm: >50mm/hr). Results were calculated based on ITU-Recommendations P.618-12 and P.838. The results obtained revealed that the effect of rain on signal strength and quality on a ku-band link depend on the rain type and duration of rainfall. The effect of drizzle (≤5mm/hr) on the ku-band link is not significant regardless the duration of rainfall. The results also shows that severe signal losses above 60dB were obtained under shower (>25≤50mm/hr) and thunderstorm (>50mm/hr). Also, widespread (>5≤25mm/hr) that prevailed for a longer period causes severe signal losses. This is to say that lower rain rate of about 25mm/hr that prevailed for a very long time have significant effect on Kuband link. The results of this study will serve as crucial design parameters for communication systems engineers for providing fade margins and developing accurate fade mitigation techniques (FMTs) for the Guinea Savanna region of Nigeria. It was observed from this study that the experimental studies within the Ku-band frequency are getting over exhausted. Therefore, experimental studied in the region of higher band like K and Ka are highly recommended.
Smart Home, Support At Old Age And Support For Persons With Disabilities: Speech Processing For Control Of Energy
Authors: Zubair Abdul Rasak , Olu-Flourish Emmanuel Sinmiloluwa , Nnaukwu Martins Obinna
Published: May 2022

Generally, conventional home wiring system use simple latching switch that is being connected to the power supply for controlling electrical appliances such as fan, light, washing machine, air conditioner and television. The switch is usually located at the wall near the electrical appliance. This requires the user to move to the location of the switches to control the appliances. There is rapid increase in the number of people with special needs like the elderly and the disabled. Smart houses are considered a good alternative for the independent life of older persons and persons with disabilities. A smart home is a home that provides its residents the comfort, the convenience and the ease of operation of devices at all times, irrespective of where the resident actually is within the house. Smart Homes include devices that have automatic functions and systems that can be remotely controlled by the user. The primary objective of a smart house is to enhance comfort, energy saving, security for the residents and independent living of people at old age and people with disabilities. A low-cost prototype of a voice controlled smart home system controlling four devices by an Arduino microcontroller via a four-channel relay is presented. Voice control is one of the easiest methods to give input commands and is a more personalized form of control, since it can be adapted and customized to a particular speaker’s voice. Voice recognition is a computer software program embedded in a hardware device with the ability to decode the human voice. Most voice recognition systems require “training” (also called “enrolment”) where an individual speaker reads text or isolated vocabulary into the system. The system analyses the person’s specific voices and uses it to fine-tune the recognition of that person’s command. Upon successful recognition of the voice command, the microcontroller drives the corresponding load with the help of the relay circuit. Voice or Speech Processing has been applied successfully for the control of the supply of energy to home appliances.
Design Of A Secure Virtual File Storage System On Cloud Using Hybrid Cryptography
Authors: Buhari Bello A. , Mubarak Aliyu , Bodinga Bello A. , Sifawa Muazu D.
Published: May 2022

As Security is becoming more and more useful in the field of computing, users would like to be sure of how secure their files are on a system, as security is one of the most crucial fields in networking and file storage. Dependable file storage and access establish several security issues in a cloud computing. This research designed and implemented virtual secure file storage system on cloud using hybrid cryptography. The cryptography method used for file encryption and decryption is AES and SHA-2 hash function. It is implemented using Cloud APIs with REST calls or client libraries in PHP. The system interfaces were developed using HTML, CSS and JAVASCRIPT. Back end development was done using PHP, MYSQL and GCP Cloud Storage Library then the file encryption and decryption was achieved through PHP classes which includes open_ssl_file_encryption and decryption (AES) and also MCRYPT function. The proposed virtual system is also compared with some latest related works.