bluuebunny/crossref_metadata_2025 huggingface dataset:
Differential Circuit Mechanisms of Young and Aged Visual Cortex in the Mammalian Brain
Authors: Danka Mohammed Chand Parvez
Published: January 2021

The main goal of this review is to summarize and discuss (1) age-dependent structural reorganization of mammalian visual cortical circuits underlying complex visual behavior functions in primary visual cortex (V1) and multiple extrastriate visual areas, and (2) current evidence supporting the notion of compensatory mechanisms in aged visual circuits as well as the use of rehabilitative therapy for the recovery of neural plasticity in normal and diseased aging visual circuit mechanisms in different species. It is well known that aging significantly modulates both the structural and physiological properties of visual cortical neurons in V1 and other visual cortical areas in various species. Compensatory aged neural mechanisms correlate with the complexity of visual functions; however, they do not always result in major circuit alterations resulting in age-dependent decline in performance of a visual task or neurodegenerative disorders. Computational load and neural processing gradually increase with age, and the complexity of compensatory mechanisms correlates with the intricacy of higher form visual perceptions that are more evident in higher-order visual areas. It is particularly interesting to note that the visual perceptual processing of certain visual behavior functions does not change with age. This review aims to comprehensively discuss the effect of normal aging on neuroanatomical alterations that underlie critical visual functions and more importantly to highlight differences between compensatory mechanisms in aged neural circuits and neural processes related to visual disorders. This type of approach will further enhance our understanding of inter-areal and cortico-cortical connectivity of visual circuits in normal aging and identify major circuit alterations that occur in different visual deficits, thus facilitating the design and evaluation of potential rehabilitation therapies as well as the assessment of the extent of their rejuvenation.
Role of Gut Microbiota and Their Metabolites on Atherosclerosis, Hypertension and Human Blood Platelet Function: A Review
Authors: Duttaroy Asim K.
Published: January 2021

Emerging data have demonstrated a strong association between the gut microbiota and the development of cardiovascular disease (CVD) risk factors such as atherosclerosis, inflammation, obesity, insulin resistance, platelet hyperactivity, and plasma lipid abnormalities. Several studies in humans and animal models have demonstrated an association between gut microbial metabolites such as trimethylamine-N-oxide (TMAO), short-chain fatty acids, and bile acid metabolites (amino acid breakdown products) with CVD. Human blood platelets are a critical contributor to the hemostatic process. Besides, these blood cells play a crucial role in developing atherosclerosis and, finally, contribute to cardiac events. Since the TMAO, and other metabolites of the gut microbiota, are asociated with platelet hyperactivity, lipid disorders, and oxidative stress, the diet-gut microbiota interactions have become an important research area in the cardiovascular field. The gut microbiota and their metabolites may be targeted for the therapeutic benefit of CVD from a clinical perspective. This review’s main aim is to highlight the complex interactions between microbiota, their metabolites, and several CVD risk factors.
Effects of Breastfeeding on Maternal Body Composition in Moroccan Lactating Women during Twelve Months after Birth Using Stable Isotopic Dilution Technique
Authors: Rabi Baha , Benjeddou Kaoutar , Idrissi Mohamed , Rami Anass , Mekkaoui Bouchera , El Hamdouchi Asmaa , Benkirane Hasnae , Barkat Amina , Saeid Naima , El Kari Khalid , Aguenaou Hassan
Published: January 2021

Background: Exclusive breastfeeding during the first six months of an infant’s life is an important factor for their optimal growth and health. Breastfeeding also has maternal benefits and can assist with postpartum weight loss. As shown by previous studies, postpartum weight retention can contribute to obesity. Objective: To quantify the human milk and evaluate the effect of breastfeeding on maternal weight loss during the 12 months postpartum. Method: This study included 70-mother–baby pairs. Infants’ intake of human milk and water from other sources, as well as the body composition of the mothers, were measured at the 1st, 3rd, 6th, 9th and 12th month postpartum by using the deuterium oxide dose-to-mother technique. Results: There was a significant change in the mothers’ body composition between the first and twelfth months in exclusive breastfeeding women compared to not-exclusive ones. Similarly, the difference between the quantities of human milk intake was highly significant in exclusive breastfeeding women compared to women who were not exclusively breastfeeding. Conclusion: Our results showed that exclusive breastfeeding for twelve months has a significant effect on postpartum weight loss among Moroccan women and that it is an effective way to control overweight and obesity among lactating women.
Obesities: A New Peer-Reviewed Open-Access Interdisciplinary Journal Fostering Research on Multifactorial Obesity and Associated Cardiometabolic Disorders
Authors: Guigas Bruno
Published: January 2021

Obesity prevalence has increased continuously over the past 50 years, a dramatic worldwide expansion not only limited to industrialized countries but also observed in a large number of low- and middle-income countries experiencing rapid rural–urban transition [...]
SARS-CoV-2-Cholesterol Interaction: A Lot of Food for Thought
Authors: Di Guardo Giovanni
Published: January 2021

With over 80 million cases, and 1,800,000 deaths reported at the end of 2020 by the World Health Organization, the “CoronaVirus Disease-2019” (CoViD-19) pandemic, caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), should be viewed as a global catastrophe [...]
Rapid Increase in the IS26-Mediated cfr Gene in E. coli Isolates with IncP and IncX4 Plasmids and Co-Existing cfr and mcr-1 Genes in a Swine Farm
Authors: Ma Zhenbao , Liu Jiao , Chen Lin , Liu Xiaoqin , Xiong Wenguang , Liu Jian-Hua , Zeng Zhenling
Published: January 2021

This paper aimed to investigate the molecular epidemiological features of the cfr gene in E. coli isolates in a typical swine farm during 2014–2017. A total of 617 E. coli isolates were screened for the cfr gene using PCR amplification. A susceptibility test, pulsed-field gel electrophoresis (PFGE), S1-PFGE, southern blotting hybridization, and the genetic context of the cfr gene were all used for analyzing all cfr-positive E. coli isolates. A conjugation experiment was conducted with the broth mating method using E. coli C600 as the recipient strain and 45 mcr-1-cfr-bearing E. coli isolates as the donor strain. Plasmids pHNEP124 and pHNEP129 were revealed by Illumina Miseq 2500. Eighty-five (13.7%) E. coli isolates were positive for the cfr gene and the prevalence of the cfr gene had significantly increased from 1.6% in 2014 to 29.1% in 2017. The Pulsed-Field Gel Electrophoresis (PFGE) analysis indicated that the spread of the cfr gene among E. coli isolates was mainly due to horizontal transfer. In addition, the cfr gene was primarily located on the plasmids between 28.8-kb to 60-kb in size, and the cfr gene was flanked by two copies of IS26 with the same orientation. Sequence analysis suggested that the plasmids pHNEP124 and pHNEP129 co-harboring the cfr and mcr-1 genes belonged to the plasmids IncP plasmid and IncX4 plasmid, respectively. In conclusion, this is the first study to report the high prevalence of the cfr gene among E. coli isolates and the first report of the complete genome sequence of IncP and IncX4 plasmids carrying the mcr-1 and cfr genes. The occurrence and dissemination of the cfr/mcr-1-carrying plasmids among E. coli isolates need further surveillance.
Au–ZnO Conjugated Black Phosphorus as a Near-Infrared Light-Triggering and Recurrence-Suppressing Nanoantibiotic Platform against Staphylococcus aureus
Authors: Naskar Atanu , Lee Sohee , Kim Kwang-sun
Published: January 2021

Antibiotic therapy is the gold standard for bacterial infections treatment. However, the rapid increase in multidrug-resistant (MDR) bacterial infections and its recent use for secondary bacterial infections in many COVID-19 patients has considerably weakened its treatment efficacy. These shortcomings motivated researchers to develop new antibacterial materials, such as nanoparticle-based antibacterial platform with the ability to increase the chances of killing MDR strains and prevent their drug resistance. Herein, we report a new black phosphorus (BP)-based non-damaging near-infrared light-responsive platform conjugated with ZnO and Au nanoparticles as a synergistic antibacterial agent against Staphylococcus aureus species. First, BP nanosheets containing Au nanoparticles were assembled in situ with the ZnO nanoparticles prepared by a low-temperature solution synthesis method. Subsequently, the antibacterial activities of the resulting Au–ZnO–BP nanocomposite against the non-resistant, methicillin-resistant, and erythromycin-resistant S. aureus species were determined, after its photothermal efficacy was assessed. The synthesized nanocomposite exhibited excellent anti-S. aureus activity and good photothermal characteristics. The non-resistant S. aureus species did not produce drug-resistant bacteria after the treatment of multiple consecutive passages under the pressure of the proposed nanoantibiotic, but rapidly developed resistance to erythromycin. This work clearly demonstrates the excellent photothermal antibacterial properties of Au–ZnO–BP nanocomposite against the MDR S. aureus species.
Crop Adaptation: Weedy and Crop Wild Relatives as an Untapped Resource to Utilize Recent Increases in Atmospheric CO2
Authors: Ziska Lewis H.
Published: January 2021

Adaptation measures are necessary to ensure the stability and performance of the food supply relative to anthropogenic climate change. Although a wide range of measures have been proposed (e.g., planting dates, crop choices, drought resistance), there may be a ubiquitous means to increase productivity relatively quickly. Numerous studies have shown that the projected increase in atmospheric CO2 can stimulate crop growth and seed yield with noted intra-specific differences within crop cultivars, suggesting potential differences to CO2 that could be exploited to enhance seed yield in the future. However, it is worth emphasizing that atmospheric CO2 has already risen substantially (≈27% since 1970) and that, at present, no active effort by breeders has been made to select for the CO2 increase that has already occurred. In contrast, for weedy or crop wild relatives (CWR), there are indications of evolutionary adaptation to these recent increases. While additional steps are needed, the identification and introgression of these CO2-sensitive traits into modern crop cultivars may be a simple and direct means to increase crop growth and seed yield.
Environmental Impacts of Glass- and Carbon-Fiber-Reinforced Polymer Bar-Reinforced Seawater and Sea Sand Concrete Beams Used in Marine Environments: An LCA Case Study
Authors: Dong Shaoce , Li Chenggao , Xian Guijun
Published: January 2021

Application of glass- or carbon-fiber-reinforced polymer (GFRP/CFRP) bars makes the direct use of seawater and sea sand concrete (SWSSC) in construction feasible, which is of high interest in order to conserve the limited resources of fresh water and river sand. The present paper performed the life cycle assessment (LCA) of constructing three kinds of beams (GFRP/CFRP bar-reinforced SWSSC beams, and steel bar-reinforced common concrete (SRC) beam) in marine environments to show the environmental benefits of using FRP bar-reinforced SWSSC beams in marine environments. According to ISO 14040 and ISO 14044, stages including production, transportation, construction, use and end-of-life are within the LCA’s boundary. The ReCiPe method and eight main environmental impact categories were used to characterize the environmental impacts of those beams. LCA results indicate that one cubic meter SWSSC possesses much lower environmental impacts in terms of all eight categories compared with common concrete with the same volume when used in marine environments, with reduction rates from 26.3% to 48.6%. When the two transportation distances were set as 50 and 20 km and without considering the difference in service life, compared to SRC beam, GFRP-SWSSC beam performs better in six categories and CFRP-SWSSC beam performs better in four categories. When considering increased transportation distance and the higher durability performance, the advantageous categories for GFRP-SWSSC and CFRP-SWSSC beams increase to seven and six, respectively. The environmental impacts of all the three beams are mainly affected by the production stages.
Development of Poly(L-Lactic Acid)/Chitosan/Basil Oil Active Packaging Films via a Melt-Extrusion Process Using Novel Chitosan/Basil Oil Blends
Authors: Salmas Constantinos E. , Giannakas Aris E. , Baikousi Maria , Leontiou Areti , Siasou Zoe , Karakassides Michael A.
Published: January 2021

Following the global trend toward a cyclic economy, the development of a fully biodegradable active packaging film is the target of this work. An innovative process to improve the mechanical, antioxidant, and barrier properties of Poly(L-Lactic Acid)/Chitosan films is presented using essential basil oil extract. A Chitosan/Basil oil blend was prepared via a green evaporation/adsorption method as a precursor for the development of the Poly(L-Lactic Acid)/Chitosan/Basil Oil active packaging film. This Chitosan/Basil Oil blend was incorporated directly in the Poly(L-Lactic Acid) matrix with various concentrations. Modification of the chitosan with the Basil Oil improves the blending with the Poly(L-Lactic Acid) matrix via a melt-extrusion process. The obtained Poly(L-Lactic Acid)/Chitosan/Basil Oil composite films exhibited advanced food packaging properties compared to those of the Poly(L-Lactic Acid)/Chitosan films without Basil Oil addition. The films with 5%wt and 10%wt Chitosan/Basil Oil loadings exhibited better thermal, mechanical, and barrier behavior and significant antioxidant activity. Thus, PLLA/CS/BO5 and PLLA/CS/BO10 are the most promising films to potentially be used for active packaging applications.
Improving Transactional Data System Based on an Edge Computing–Blockchain–Machine Learning Integrated Framework
Authors: Shahbazi Zeinab , Byun Yung-Cheol
Published: January 2021

The modern industry, production, and manufacturing core is developing based on smart manufacturing (SM) systems and digitalization. Smart manufacturing’s practical and meaningful design follows data, information, and operational technology through the blockchain, edge computing, and machine learning to develop and facilitate the smart manufacturing system. This process’s proposed smart manufacturing system considers the integration of blockchain, edge computing, and machine learning approaches. Edge computing makes the computational workload balanced and similarly provides a timely response for the devices. Blockchain technology utilizes the data transmission and the manufacturing system’s transactions, and the machine learning approach provides advanced data analysis for a huge manufacturing dataset. Regarding smart manufacturing systems’ computational environments, the model solves the problems using a swarm intelligence-based approach. The experimental results present the edge computing mechanism and similarly improve the processing time of a large number of tasks in the manufacturing system.
Superradiance in Quantum Vacuum
Authors: Mendonça José Tito
Published: January 2021

A new process associated with the nonlinear optical properties of the electromagnetic quantum vacuum is described. It corresponds to the superradiant emission of photons, resulting from the interaction of an intense laser pulse with frequency ω0 with a counter-propagating high-harmonic signal with a spectrum of frequencies nω1, for n integer, in the absence of matter. Under certain conditions, photon emission from vacuum will be enhanced by the square of the number of intense spikes associated with the high-harmonic pulse. This occurs when the field created by the successive spikes is coherently emitted, as in typical superradiant processes involving atoms. Subradiant conditions, where the nonlinearity of quantum vacuum is entirely suppressed, can equally be defined.
A Study on Link Functions for Modelling and Forecasting Old-Age Survival Probabilities of Australia and New Zealand
Authors: Liu Jacie Jia
Published: January 2021

Forecasting survival probabilities and life expectancies is an important exercise for actuaries, demographers, and social planners. In this paper, we examine extensively a number of link functions on survival probabilities and model the evolution of period survival curves of lives aged 60 over time for the elderly populations in Australasia. The link functions under examination include the newly proposed gevit and gevmin, which are compared against the traditional ones like probit, complementary log-log, and logit. We project the model parameters and so the survival probabilities into the future, from which life expectancies at old ages can be forecasted. We find that some of these models on survival probabilities, particularly those based on the new links, can provide superior fitting results and forecasting performances when compared to the more conventional approach of modelling mortality rates. Furthermore, we demonstrate how these survival probability models can be extended to incorporate extra explanatory variables such as macroeconomic factors in order to further improve the forecasting performance.
Bayesian Predictive Analysis of Natural Disaster Losses
Authors: Deng Min , Aminzadeh Mostafa , Ji Min
Published: January 2021

Different types of natural events hit the United States every year. The data of natural hazards from 1900 to 2016 in the US shows that there is an increasing trend in annul natural disaster losses after 1980. Climate change is recognized as one of the factors causing this trend, and predictive analysis of natural losses becomes important in loss prediction and risk prevention as this trend continues. In this paper, we convert natural disaster losses to the year 2016 dollars using yearly average Consumers Price Index (CPI), and conduct several tests to verify that the CPI adjusted amounts of loss from individual natural disasters are independent and identically distributed. Based on these test results, we use various model selection quantities to find the best model for the natural loss severity among three composite distributions, namely Exponential-Pareto, Inverse Gamma-Pareto, and Lognormal-Pareto. These composite distributions model piecewise small losses with high frequency and large losses with low frequency. Remarkably, we make the first attempt to derive analytical Bayesian estimate of the Lognormal-Pareto distribution based on the selected priors, and show that the Lognormal-Pareto distribution outperforms the other two composite distributions in modeling natural disaster losses. Important risk measures for natural disasters are thereafter derived and discussed.
Quantifying the Model Risk Inherent in the Calibration and Recalibration of Option Pricing Models
Authors: Feng Yu , Rudd Ralph , Baker Christopher , Mashalaba Qaphela , Mavuso Melusi , Schlögl Erik
Published: January 2021

We focus on two particular aspects of model risk: the inability of a chosen model to fit observed market prices at a given point in time (calibration error) and the model risk due to the recalibration of model parameters (in contradiction to the model assumptions). In this context, we use relative entropy as a pre-metric in order to quantify these two sources of model risk in a common framework, and consider the trade-offs between them when choosing a model and the frequency with which to recalibrate to the market. We illustrate this approach by applying it to the seminal Black/Scholes model and its extension to stochastic volatility, while using option data for Apple (AAPL) and Google (GOOG). We find that recalibrating a model more frequently simply shifts model risk from one type to another, without any substantial reduction of aggregate model risk. Furthermore, moving to a more complicated stochastic model is seen to be counterproductive if one requires a high degree of robustness, for example, as quantified by a 99% quantile of aggregate model risk.
Reliability and Safety of Autonomous Systems Based on Semantic Modelling for Self-Certification
Authors: Zaki Osama , Dunnigan Matthew , Robu Valentin , Flynn David
Published: January 2021

A novel modelling paradigm for online diagnostics and prognostics for autonomous systems is presented. A model for the autonomous system being diagnosed is designed using a logic-based formalism. The model supports the run-time ability to verify that the autonomous system is safe and reliable for operation within a dynamic environment. The paradigm is based on the philosophy that there are different types of semantic relationships between the states of different parts of the system. A finite state automaton is devised for each sensed component and some of the non-sensed components. To capture the interdependencies of components within such a complex robotic platform, automatons were related to each other by semantic relationships. Modality was utilised by the formalism to abstract the relationships and to add measures for the possibility and uncertainty of the relationships. The complexity of the model was analysed to evaluate its scalability and applicability to other systems. The results demonstrate that the complexity is not linear and a computational time of 10 ms was required to achieve run-time diagnostics for 2200 KB of knowledge for complex system interdependences. The ability to detect and mitigate hardware related failures was demonstrated within a confined space autonomous operation. Our findings provide evidence of the applicability of our approach for the significant challenge of run-time safety compliance and reliability in autonomous systems.
Evaluating Downscaling Factors of Microwave Satellite Soil Moisture Based on Machine Learning Method
Authors: Sun Hao , Cui Yajing
Published: January 2021

Downscaling microwave remotely sensed soil moisture (SM) is an effective way to obtain spatial continuous SM with fine resolution for hydrological and agricultural applications on a regional scale. Downscaling factors and functions are two basic components of SM downscaling where the former is particularly important in the era of big data. Based on machine learning method, this study evaluated Land Surface Temperature (LST), Land surface Evaporative Efficiency (LEE), and geographical factors from Moderate Resolution Imaging Spectroradiometer (MODIS) products for downscaling SMAP (Soil Moisture Active and Passive) SM products. This study spans from 2015 to the end of 2018 and locates in the central United States. Original SMAP SM and in-situ SM at sparse networks and core validation sites were used as reference. Experiment results indicated that (1) LEE presented comparative performance with LST as downscaling factors; (2) adding geographical factors can significantly improve the performance of SM downscaling; (3) integrating LST, LEE, and geographical factors got the best performance; (4) using Z-score normalization or hyperbolic-tangent normalization methods did not change the above conclusions, neither did using support vector regression nor feed forward neural network methods. This study demonstrates the possibility of LEE as an alternative of LST for downscaling SM when there is no available LST due to cloud contamination. It also provides experimental evidence for adding geographical factors in the downscaling process.
Earth Observation from KOMPSAT Optical, Thermal, and Radar Satellite Images
Authors: Lee Kwang-Jae , Chae Tae-Byeong , Jung Hyung-Sup
Published: January 2021

Over the past several decades, as sensor technology has improved, the spatial resolution of satellite images has been steadily improving [...]
Spatiotemporal Variation of Siberian Crane Habitats and the Response to Water Level in Poyang Lake Wetland, China
Authors: Zou Lin , Hu Bisong , Qi Shuhua , Zhang Qianqian , Ning Pan
Published: January 2021

The Poyang Lake wetland in China is the largest wintering destination for Siberian cranes worldwide. Understanding the spatiotemporal characteristics of crane habitats is of great importance for ecological environment governance and biodiversity protection. The shallow water, grassland, and soft mudflat regions of the Poyang Lake wetland are ideal habitats for wintering Siberian cranes. Based on Landsat Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Operational Land Imager (OLI) remote sensing images, habitat areas were extracted and associated with various water levels taken on multiple dates. Landscape metrics were applied to describe the spatial structural characteristics of the crane habitats, and spatial statistics are used to explore the cold and hot spots of their distribution. Moreover, three indicators including sustainability, stability, and variety were applied to evaluate the vulnerability of the crane habitats under different hydrological conditions. Our findings indicate: (a) The main crane habitats exhibit a gradual decreasing degree of fragmentation in time, an obvious uncertainty of shape complexity and a relatively stable connectivity. (b) The crane habitats have a consistent spatial pattern of highly aggregated distributions associated with various water levels. (c) The hot spots of the habitats formed multiple “sheet” belts centered on the “Lake Enclosed in Autumn” regions, while the cold spots indicate a spatial pattern of axial distributions. (d) The majority of the hot spots of the habitats were distributed in sub-lakes found in the southeast part of the Poyang Lake watershed and the Nanjishan and Wucheng nature reserves, while the cold spots were mainly distributed in the main channels of the basins of Poyang Lake. (e) The sustainable habitats were mainly distributed in the “Lake Enclosed in Autumn” regions and intensively aggregated in two national nature reserves. (f) Under conditions of extremely low to average water levels (5.3–11.46 m), an increase of water level causes a decrease of the stability and variety of the crane habitats and weakens the aggregation structure.
Examination of the Daily Cycle Wind Vector Modes of Variability from the Constellation of Microwave Scatterometers and Radiometers
Authors: Turk Francis Joseph , Hristova-Veleva Svetla , Giglio Donata
Published: January 2021

Offshore of many coastal regions, the ocean surface wind varies in speed and direction throughout the day, owing to forcing from land/sea temperature differences and orographic effects. Far offshore, both diurnal and semidiurnal wind vector variability has been noted in the Tropical Atmosphere Ocean-TRIangle Trans-Ocean buoy Network (TAO-TRITON) mooring data in the tropical Pacific Ocean. In this manuscript, the tropical diurnal wind variability is examined with microwave radiometer-derived winds from the Tropical Rainfall Measuring Mission (TRMM) and the Global Precipitation Measurement (GPM), merged with RapidScat and other scatterometer data. Since the relationship between wind speed and its zonal and meridional components is nonlinear, this manuscript describes an observationally based methodology to merge the radiometer and scatterometer-based wind estimates as a function of observation time, to generate a multi-year dataset of diurnal wind variability. Compared to TAO-TRITON mooring array data, the merged satellite-derived wind components fairly well replicate the semidiurnal zonal wind variability over the tropical Pacific but generally show more variability in the meridional wind components. The meridional component agrees with the associated mooring location data in some locations better than others, or it shows no clear dominant diurnal or semidiurnal mode. Similar discrepancies are noted between two forecast model reanalysis products. It is hypothesized that the discrepancies amongst the meridional winds are due to interactions between surface convergence and convective precipitation over tropical ocean basins.
Expert Hypertension Detection System Featuring Pulse Plethysmograph Signals and Hybrid Feature Selection and Reduction Scheme
Authors: Khan Muhammad Umar , Aziz Sumair , Akram Tallha , Amjad Fatima , Iqtidar Khushbakht , Nam Yunyoung , Khan Muhammad Attique
Published: January 2021

Hypertension is an antecedent to cardiac disorders. According to the World Health Organization (WHO), the number of people affected with hypertension will reach around 1.56 billion by 2025. Early detection of hypertension is imperative to prevent the complications caused by cardiac abnormalities. Hypertension usually possesses no apparent detectable symptoms; hence, the control rate is significantly low. Computer-aided diagnosis based on machine learning and signal analysis has recently been applied to identify biomarkers for the accurate prediction of hypertension. This research proposes a new expert hypertension detection system (EHDS) from pulse plethysmograph (PuPG) signals for the categorization of normal and hypertension. The PuPG signal data set, including rich information of cardiac activity, was acquired from healthy and hypertensive subjects. The raw PuPG signals were preprocessed through empirical mode decomposition (EMD) by decomposing a signal into its constituent components. A combination of multi-domain features was extracted from the preprocessed PuPG signal. The features exhibiting high discriminative characteristics were selected and reduced through a proposed hybrid feature selection and reduction (HFSR) scheme. Selected features were subjected to various classification methods in a comparative fashion in which the best performance of 99.4% accuracy, 99.6% sensitivity, and 99.2% specificity was achieved through weighted k-nearest neighbor (KNN-W). The performance of the proposed EHDS was thoroughly assessed by tenfold cross-validation. The proposed EHDS achieved better detection performance in comparison to other electrocardiogram (ECG) and photoplethysmograph (PPG)-based methods.
Preamble Design and Collision Resolution in a Massive Access IoT System
Authors: Zhong Ailing , Li Zhidu , Wang Ruyan , Li Xingjie , Guo Boren
Published: January 2021

How to support massive access efficiently is one of the challenges in the future Internet of Things (IoT) systems. To address such challenge, this paper proposes an effective preamble collision resolution scheme to sustain massive random access (RA) for an IoT system. Specifically, a new sub-preamble structure is first proposed to reduce the preamble collision probability. To identify different devices that send the same preamble to the gNB on the same physical random access channel (PRACH), a multiple timing advance (TA) capturing scheme is then proposed. Thereafter, an RA scheme is designed to sustain massive access and the performance of the scheme is studied analytically. Finally, the effectiveness of the proposed RA scheme is validated by extensive simulation experiments in terms of preamble detection probability, preamble collision probability, RA success probability, resource efficiency and TA capturing.
Optical Technologies for the Improvement of Skin Cancer Diagnosis: A Review
Authors: Rey-Barroso Laura , Peña-Gutiérrez Sara , Yáñez Carlos , Burgos-Fernández Francisco J. , Vilaseca Meritxell , Royo Santiago
Published: January 2021

The worldwide incidence of skin cancer has risen rapidly in the last decades, becoming one in three cancers nowadays. Currently, a person has a 4% chance of developing melanoma, the most aggressive form of skin cancer, which causes the greatest number of deaths. In the context of increasing incidence and mortality, skin cancer bears a heavy health and economic burden. Nevertheless, the 5-year survival rate for people with skin cancer significantly improves if the disease is detected and treated early. Accordingly, large research efforts have been devoted to achieve early detection and better understanding of the disease, with the aim of reversing the progressive trend of rising incidence and mortality, especially regarding melanoma. This paper reviews a variety of the optical modalities that have been used in the last years in order to improve non-invasive diagnosis of skin cancer, including confocal microscopy, multispectral imaging, three-dimensional topography, optical coherence tomography, polarimetry, self-mixing interferometry, and machine learning algorithms. The basics of each of these technologies together with the most relevant achievements obtained are described, as well as some of the obstacles still to be resolved and milestones to be met.
Piezoelectric Transducer-Based Diagnostic System for Composite Structure Health Monitoring
Authors: Dragašius Egidijus , Eidukynas Darius , Jūrėnas Vytautas , Mažeika Darius , Galdikas Mantas , Mystkowski Arkadiusz , Mystkowska Joanna
Published: January 2021

This paper focuses on the investigation of the diagnostic system for health monitoring and defects, detecting in composite structures using a piezoelectric sensor. A major overview of structural defects in composite materials that have an influence on product performance as well as material strength is presented. Particularly, the proposed diagnostic (health monitoring) system enables to monitor the composite material plate defects during the exploitation in real-time. The investigated health monitoring system can indicate the material structure defects when the periodic test input signal is provided to excite the plate. Especially, the diagnostic system is useful when the defect placement is hard to be identified. In this work, several various numerical and experimental studies were carried out. Particularly, during the first study, the piezoelectric transducer was used to produce mechanical excitation to the composite plate when the impact response is measured with another piezoelectric sensor. The second study focuses on the defect identification algorithms of the raw hologram data consisting of the recorded oscillation modes of the affected composite plate. The main paper results obtained in both studies enable us to determine whether the composite material is characterized by mechanical defects occurring during the response to the periodic excitation. In case of damage, the observed response amplitude was decreased by 70%. Finally, using the time-domain experimental results, the frequency response functions (FRFs) are applied to damage detection assessment and to obtain extra damage information.
Single- and Cross-Modality Near Duplicate Image Pairs Detection via Spatial Transformer Comparing CNN
Authors: Zhang Yi , Zhang Shizhou , Li Ying , Zhang Yanning
Published: January 2021

Recently, both single modality and cross modality near-duplicate image detection tasks have received wide attention in the community of pattern recognition and computer vision. Existing deep neural networks-based methods have achieved remarkable performance in this task. However, most of the methods mainly focus on the learning of each image from the image pair, thus leading to less use of the information between the near duplicate image pairs to some extent. In this paper, to make more use of the correlations between image pairs, we propose a spatial transformer comparing convolutional neural network (CNN) model to compare near-duplicate image pairs. Specifically, we firstly propose a comparing CNN framework, which is equipped with a cross-stream to fully learn the correlation information between image pairs, while considering the features of each image. Furthermore, to deal with the local deformations led by cropping, translation, scaling, and non-rigid transformations, we additionally introduce a spatial transformer comparing CNN model by incorporating a spatial transformer module to the comparing CNN architecture. To demonstrate the effectiveness of the proposed method on both the single-modality and cross-modality (Optical-InfraRed) near-duplicate image pair detection tasks, we conduct extensive experiments on three popular benchmark datasets, namely CaliforniaND (ND means near duplicate), Mir-Flickr Near Duplicate, and TNO Multi-band Image Data Collection. The experimental results show that the proposed method can achieve superior performance compared with many state-of-the-art methods on both tasks.
Iterative Regression Based Hybrid Localization for Wireless Sensor Networks
Authors: Lee Kyunghyun , Kim Sangkyeum , You Kwanho
Published: January 2021

Among various localization methods, a localization method that uses a radio frequency signal-based wireless sensor network has been widely applied due to its robustness against noise factors and few limits on installation location. In this paper, we focus on an iterative localization scheme for a mobile with a limited number of time difference of arrival (TDOA) and angle of arrival (AOA) data measured from base stations. To acquire the optimal location of a mobile, we propose a recursive solution for localization using an iteratively reweighted-recursive least squares (IR-RLS) algorithm. The proposed IR-RLS scheme can obtain the optimal solution with a fast computational speed when additional TDOA and/or AOA data is measured from base stations. Moreover, while the number of measured TDOA/AOA data was limited, the proposed IR-RLS scheme could obtain the precise location of a mobile. The performance of the proposed IR-RLS method is confirmed through some simulation results.
Stamping Monitoring by Using an Adaptive 1D Convolutional Neural Network
Authors: Huang Chih-Yung , Dzulfikri Zaky
Published: January 2021

Stamping is one of the most widely used processes in the sheet metalworking industry. Because of the increasing demand for a faster process, ensuring that the stamping process is conducted without compromising quality is crucial. The tool used in the stamping process is crucial to the efficiency of the process; therefore, effective monitoring of the tool health condition is essential for detecting stamping defects. In this study, vibration measurement was used to monitor the stamping process and tool health. A system was developed for capturing signals in the stamping process, and each stamping cycle was selected through template matching. A one-dimensional (1D) convolutional neural network (CNN) was developed to classify the tool wear condition. The results revealed that the 1D CNN architecture a yielded a high accuracy (>99%) and fast adaptability among different models.
Study on the Propagation Characteristics of Terahertz Waves in Dusty Plasma with a Ceramic Substrate by the Scattering Matrix Method
Authors: Rao Qingwen , Xu Guanjun , Wang Pengfei , Zheng Zhengqi
Published: January 2021

The propagation characteristics of terahertz (THz) waves incident vertically into inhomogeneous and collisional dusty plasma with a ceramic substrate are studied using the scattering matrix method (SMM). The effects of the incident wave frequency and plasma parameters, such as the maximal electron density, dust particle density, dust particle radius and collision frequency, on the reflectance and transmittance of THz waves in the dusty plasma are discussed. In addition, the differences of the propagation properties in the dusty plasma, with and without ceramic substrate, are analyzed. Meanwhile, the differences of the propagation properties in dusty plasma and common plasma, respectively, with ceramic substrate are also compared. Simulation results show that the substrate and dust particles have significant influence on the propagation characteristics of THz wave in plasma sheath. Finally, the transmission increases with the increase of electron density, dust density, dust particle radius and collision frequency.
Rotation, Strain, and Translation Sensors Performance Tests with Active Seismic Sources
Authors: Bernauer Felix , Behnen Kathrin , Wassermann Joachim , Egdorf Sven , Igel Heiner , Donner Stefanie , Stammler Klaus , Hoffmann Mathias , Edme Pascal , Sollberger David , Schmelzbach Cédric , Robertsson Johan , Paitz Patrick , Igel Jonas , Smolinski Krystyna , Fichtner Andreas , Rossi Yara , Izgi Gizem , Vollmer Daniel , Eibl Eva P. S. , Buske Stefan , Veress Christian , Guattari Frederic , Laudat Theo , Mattio Laurent , Sèbe Olivie , Olivier Serge , Lallemand Charlie , Brunner Basil , Kurzych Anna T. , Dudek Michał , Jaroszewicz Leszek R. , Kowalski Jerzy K. , Bońkowski Piotr A. , Bobra Piotr , Zembaty Zbigniew , Vackář Jiří , Málek Jiří , Brokesova Johana
Published: January 2021

Interest in measuring displacement gradients, such as rotation and strain, is growing in many areas of geophysical research. This results in an urgent demand for reliable and field-deployable instruments measuring these quantities. In order to further establish a high-quality standard for rotation and strain measurements in seismology, we organized a comparative sensor test experiment that took place in November 2019 at the Geophysical Observatory of the Ludwig-Maximilians University Munich in Fürstenfeldbruck, Germany. More than 24 different sensors, including three-component and single-component broadband rotational seismometers, six-component strong-motion sensors and Rotaphone systems, as well as the large ring laser gyroscopes ROMY and a Distributed Acoustic Sensing system, were involved in addition to 14 classical broadband seismometers and a 160 channel, 4.5 Hz geophone chain. The experiment consisted of two parts: during the first part, the sensors were co-located in a huddle test recording self-noise and signals from small, nearby explosions. In a second part, the sensors were distributed into the field in various array configurations recording seismic signals that were generated by small amounts of explosive and a Vibroseis truck. This paper presents details on the experimental setup and a first sensor performance comparison focusing on sensor self-noise, signal-to-noise ratios, and waveform similarities for the rotation rate sensors. Most of the sensors show a high level of coherency and waveform similarity within a narrow frequency range between 10 Hz and 20 Hz for recordings from a nearby explosion signal. Sensor as well as experiment design are critically accessed revealing the great need for reliable reference sensors.
Which Digital-Output MEMS Magnetometer Meets the Requirements of Modern Road Traffic Survey?
Authors: Hodoň Michal , Karpiš Ondrej , Ševčík Peter , Kociánová Andrea
Published: January 2021

Present systems for road traffic surveillance largely utilize MEMS magnetometers for the purpose of vehicle detection and classification. Magnetoresistive sensing or LR oscillation circuitry are technologies providing the sensors with the competitive advantage which lies in the energy efficiency and low price. There are several chip suppliers on the market who specialize in the development of these sensors. The aim of this paper is to compare available sensors from the viewpoint of their suitability for traffic measurements. A summary of the achieved results is given in the form of the score for each sensor. The introduced sensor chart should provide the audience with knowledge about pros and cons of sensors, especially if intended for the purposes of road traffic surveillance. The authors in this research focused on the specific situation of road traffic monitoring with magnetometers placed at the roadside.
ResBCDU-Net: A Deep Learning Framework for Lung CT Image Segmentation
Authors: Jalali Yeganeh , Fateh Mansoor , Rezvani Mohsen , Abolghasemi Vahid , Anisi Mohammad Hossein
Published: January 2021

Lung CT image segmentation is a key process in many applications such as lung cancer detection. It is considered a challenging problem due to existing similar image densities in the pulmonary structures, different types of scanners, and scanning protocols. Most of the current semi-automatic segmentation methods rely on human factors therefore it might suffer from lack of accuracy. Another shortcoming of these methods is their high false-positive rate. In recent years, several approaches, based on a deep learning framework, have been effectively applied in medical image segmentation. Among existing deep neural networks, the U-Net has provided great success in this field. In this paper, we propose a deep neural network architecture to perform an automatic lung CT image segmentation process. In the proposed method, several extensive preprocessing techniques are applied to raw CT images. Then, ground truths corresponding to these images are extracted via some morphological operations and manual reforms. Finally, all the prepared images with the corresponding ground truth are fed into a modified U-Net in which the encoder is replaced with a pre-trained ResNet-34 network (referred to as Res BCDU-Net). In the architecture, we employ BConvLSTM (Bidirectional Convolutional Long Short-term Memory)as an advanced integrator module instead of simple traditional concatenators. This is to merge the extracted feature maps of the corresponding contracting path into the previous expansion of the up-convolutional layer. Finally, a densely connected convolutional layer is utilized for the contracting path. The results of our extensive experiments on lung CT images (LIDC-IDRI database) confirm the effectiveness of the proposed method where a dice coefficient index of 97.31% is achieved.
X-Band Radar Cross-Section of Tandem Helicopter Based on Dynamic Analysis Approach
Authors: Zhou Zeyang , Huang Jun
Published: January 2021

In order to study the radar signature of a tandem helicopter in the X-band, a dynamic analysis approach (DAA) is presented to determine its radar cross-section (RCS) under different influence factors. The basic passage time, rotation speed, observation angle, rotor disk inclination, fuselage attitude angle and Doppler feature are studied and discussed in detail. The results show that the dynamic characteristics of the rotor RCS will bring significant changes to the peak and average values of the helicopter RCS. Within a given observation angle range, a larger elevation angle is undesirable for helicopter stealth. The inclination of the rotor disc will affect the many small peaks and local fluctuations of the helicopter RCS. The positively increased attitude angle will have an undesirable effect on the average RCS and dynamic characteristics of the helicopter. The DAA is feasible and effective for studying the radar cross-section of a tandem helicopter.
Deep Learning-Based Acoustic Emission Scheme for Nondestructive Localization of Cracks in Train Rails under a Load
Authors: Suwansin Wara , Phasukkit Pattarapong
Published: January 2021

This research proposes a nondestructive single-sensor acoustic emission (AE) scheme for the detection and localization of cracks in steel rail under loads. In the operation, AE signals were captured by the AE sensor and converted into digital signal data by AE data acquisition module. The digital data were denoised to remove ambient and wheel/rail contact noises, and the denoised data were processed and classified to localize cracks in the steel rail using a deep learning algorithmic model. The AE signals of pencil lead break at the head, web, and foot of steel rail were used to train and test the algorithmic model. In training and testing the algorithm, the AE signals were divided into two groupings (150 and 300 AE signals) and the classification accuracy compared. The deep learning-based AE scheme was also implemented onsite to detect cracks in the steel rail. The total accuracy (average F1 score) under the first and second groupings were 86.6% and 96.6%, and that of the onsite experiment was 77.33%. The novelty of this research lies in the use of a single AE sensor and AE signal-based deep learning algorithm to efficiently detect and localize cracks in the steel rail, unlike existing AE crack-localization technology that relies on two or more sensors and human interpretation.