bluuebunny/crossref_metadata_2025 huggingface dataset:
Association between Telomere Length and Pediatric Obesity: A Systematic Review
Authors: Raftopoulou Christina , Paltoglou George , Charmandari Evangelia
Published: March 2022

Objective: Telomere length (TL) is a robust marker of biological aging, and increased telomere attrition is noted in adults with obesity. The primary objective of this systematic review was to summarize current knowledge on the effects of childhood obesity in TL. The secondary objective was to assess the effect of weight management interventions in TL. Methods: The following databases were searched: PubMed, Scopus, Web of Science and Heal-link.gr from inception to September 2021. The search was performed using the following combinations of terms: “telomer*” [All Fields] AND (“length” [All Fields] OR “lengths” [All Fields]) AND “obes*” [All Fields] AND (“child*” [All Fields] OR “adolescen*” [All Fields]). Results: A total of 16 original articles were included in this systematic review. Eleven of them were cross-sectional and five were lifestyle interventions. Conclusions: There was a tendency towards a negative association between childhood obesity and TL. Life-style interventions in children have been associated with increased TL peripherally, indicating a possible association of the redistribution of younger cells in the periphery with the favorable effect of these interventions. Further prospective studies with larger sample sizes that employ other markers of cell aging would potentially elucidate this important mechanistic relation.
Effect of Camel Milk on Glucose Homeostasis in Patients with Diabetes: A Systematic Review and Meta-Analysis of Randomized Controlled Trials
Authors: AlKurd Refat , Hanash Nivine , Khalid Narmin , Abdelrahim Dana N. , Khan Moien A. B. , Mahrous Lana , Radwan Hadia , Naja Farah , Madkour Mohamed , Obaideen Khaled , Abu Shihab Katia , Faris MoezAlIslam
Published: March 2022

The effects of camel milk (CM) intake on glycemic control in patients with diabetes are controversial. This systematic review and meta-analysis of randomized controlled trials (RCTs) was conducted to summarize the effect of CM intake on glucose homeostasis parameters in patients with both types of diabetes mellitus; T1DM and T2DM. We searched Google Scholar, PubMed/MEDLINE, EBSCO host, CINAHL, ScienceDirect, Cochrane, ProQuest Medical, Web of Science, and Scopus databases from inception until the end of November 2021. Relevant RCTs were identified, and the effect size was reported as mean difference (MD) and standard deviation (SD). Parameters of glycosylated hemoglobin (HbA1c), fasting blood glucose (FBG), postprandial blood glucose (PBG), fasting serum insulin (FI), insulin resistance (expressed in terms of HOMA-IR), insulin dose (ID) received, serum insulin antibody (IA), and C-peptide (CP) were tested. Out of 4054 collected articles, 14 RCTs (total 663 subjects) were eligible for inclusion. The pooled results obtained using a random-effects model showed a statistically significant decrease in HbA1c levels (MD, −1.24, 95% confidence interval (CI): −2.00, −0.48, p < 0.001 heterogeneity (I2) = 94%) and ID received (MD, −16.72, 95% CI: −22.09, −11.35 p < 0.00001, I2 = 90%), with a clear tendency was shown, but non-significant, to decrease FBG (MD, −23.32, 95% CI: −47.33, 0.70, p = 0.06, I2 = 98%) in patients with diabetes who consumed CM in comparison to those on usual care. Conversely, the consumption of CM did not show significant reductions in the rest of the glucose homeostasis parameters. Subgroup analysis revealed that patients with T2DM were more beneficially affected by CM intake than those with T1DM in lowering FBG, while patients with T1DM were more beneficially affected by CM intake than those with T2DM in lowering HbA1c. Both fresh and treated (pasteurized/fermented) CM gave similar beneficial effects in lowering HbA1c. Lastly, a relatively superior effect for longer duration on shorter duration (>6 months, ≤6 months, respectively) of CM intake is found in lowering HbA1c. To conclude, long-term consumption of CM by patients with diabetes could be a useful adjuvant therapy alongside classical medications, especially in lowering the required insulin dose and HbA1c. Due to the high heterogeneity observed in the included studies, more controlled trials with a larger sample size are warranted to confirm our results and to control some confounders and interfering factors existing in the analyzed articles.
Dietary Supplements and the Skin: Focus on Photoprotection and Antioxidant Activity—A Review
Authors: Marcílio Cândido Thalita , Bueno Ariede Maíra , Vieira Lima Fabiana , de Souza Guedes Luciana , Robles Velasco Maria Valéria , Rolim Baby André , Rosado Catarina
Published: March 2022

Skin health is not only significantly affected by ageing, but also by other lifestyle-related factors, such as sun exposure, exercise and eating habits, smoking or alcohol intake. It is known that the cutaneous tissue can exhibit visible signs of senescence, in the form of, for example, dull complexion, loss of firmness, or changes in pigmentation. Consumers attempt to improve skin health and appearance not only by cosmetic products, but also with the consumption of food supplements. Recently, there has been an increase in the amount of food supplements with claims that are related to skin and hair health. Nevertheless, the literature is still scarce in evidence of the efficacy of this type of products. Considering this scenario, we aim in this review to assemble studies and methodologies that are directed at the substantiation of the cutaneous health claims of food supplements. For example, we reviewed those that were indicative of antioxidant properties, improvement in pigmentation disorders, increased hydration or protection against the damages caused by ultraviolet radiation.
A Modular Hepatitis E Virus Replicon System for Studies on the Role of ORF1-Encoded Polyprotein Domains
Authors: Cierniak Filip , Ulrich Rainer G. , Groschup Martin H. , Eiden Martin
Published: March 2022

Zoonotic hepatitis E virus (HEV) infection is an emerging cause of acute viral hepatitis in developed countries. Known reservoirs of zoonotic genotype 3 (HEV-3) are mainly pigs and wild boar, and to a lesser extent rabbits and deer. Rabbit hepatitis E virus (HEV-3ra) is prevalent in rabbits worldwide and represents a particular risk for zoonotic infection. Current understanding of the molecular mechanisms of HEV pathogenesis is incomplete, particularly due to the limited availability of efficient and reliable cell culture systems. In order to identify genomic regions responsible for HEV propagation in cell culture, we developed a modular chimeric reporter replicon system based on cell culture-adapted (Kernow-C1/p6 and 47832mc) and rabbit-derived HEV strains. Replication in HepG2 cells was monitored on the basis of a Gaussia luciferase reporter gene that was inserted in place of the open reading frame (ORF) 2 of the HEV genome. Luciferase activity of rabbit HEV-derived replicons was significantly lower than that of Kernow-C1/p6 and 47832mc replicons. Serial exchanges of defined ORF1 segments within the Kernow-C1/p6 replicon backbone indicated that HEV replication in HepG2 cells is not determined by a single domain but rather by an interplay of longer segments of the ORF1-derived nonstructural polyprotein. This implies that a specific combination of viral factors is required for efficient HEV propagation in cell culture.
The Virome of ‘Lamon Bean’: Application of MinION Sequencing to Investigate the Virus Population Associated with Symptomatic Beans in the Lamon Area, Italy
Authors: Tarquini Giulia , Martini Marta , Maestri Simone , Firrao Giuseppe , Ermacora Paolo
Published: March 2022

‘Lamon bean’ is a protected geographical indication (PGI) for a product of four varieties of bean (Phaseolus vulgaris L.) grown in a specific area of production, which is located in the Belluno district, Veneto region (N.E. of Italy). In the last decade, the ‘Lamon bean’ has been threatened by severe virus epidemics that have compromised its profitability. In this work, the full virome of seven bean samples showing different foliar symptoms was obtained by MinION sequencing. Evidence that emerged from sequencing was validated through RT-PCR and ELISA in a large number of plants, including different ecotypes of Lamon bean and wild herbaceous hosts that may represent a virus reservoir in the field. Results revealed the presence of bean common mosaic virus (BCMV), cucumber mosaic virus (CMV), peanut stunt virus (PSV), and bean yellow mosaic virus (BYMV), which often occurred as mixed infections. Moreover, both CMV and PSV were reported in association with strain-specific satellite RNAs (satRNAs). In conclusion, this work sheds light on the cause of the severe diseases affecting the ‘Lamon bean’ by exploitation of MinION sequencing.
Algae: Study of Edible and Biologically Active Fractions, Their Properties and Applications
Authors: Babich Olga , Sukhikh Stanislav , Larina Viktoria , Kalashnikova Olga , Kashirskikh Egor , Prosekov Alexander , Noskova Svetlana , Ivanova Svetlana , Fendri Imen , Smaoui Slim , Abdelkafi Slim , Michaud Philippe , Dolganyuk Vyacheslav
Published: March 2022

The beneficial properties of algae make them perfect functional ingredients for food products. Algae have a high energy value and are a source of biologically active substances, proteins, fats, carbohydrates, vitamins, and macro- and microelements. They are also rich in polyunsaturated fatty acids, proteins, mycosporine-like amino acids, polysaccharides, polyphenols, carotenoids, sterols, steroids, lectins, halogenated compounds, polyketides, alkaloids, and carrageenans. Different extraction parameters are used depending on the purpose and the substances to be isolated. In this study, the following parameters were used: hydromodule 1:10 and an extraction duration of 1–2 h at the extraction temperature of 25–40 °C. A 30–50% solution of ethanol in water was used as an extractant. Algae extracts can be considered as potential natural sources of biologically active compounds with antimicrobial activity and antiviral properties. The content of crude protein, crude fat, and carbohydrates in U. Prolifera, C. racemosa var. peltata (Chlorophyta), S. oligocystum and S. fusiforme (SF-1) was studied. It was found that C. muelleri (Bacillariophyta), I. galbana (Haptophyta), and T. weissflogii (Bacillariophyta) contain about 1.9 times more omega-3 than omega-6 fatty acids. N. gaditana (Ochrophyta), D. salina (Chlorophyta), P. tricornutum (Bacillaryophyta) and I. galbana (Haptophyta) extracts showed inhibitory activity of varying intensities against E. coli or P. aeruginosa. In addition, algae and algae-derived compounds have been proposed to offer attractive possibilities in the food industry, especially in the meat sector, to evolve functional foods with myriad functionalities. Algae can increase the biological activity of food products, while the further study of the structure of compounds found in algae can broaden their future application possibilities.
Polymer Translocation through Nanometer Pores
Authors: Paun Maria-Alexandra , Paun Vladimir-Alexandru , Paun Viorel-Puiu
Published: March 2022

In this paper the loaded polymer transport and its escape via a nanometer size aperture, virtually by nanomembrane, the polymer being moved by an exterior electrostatic field, has been studied. Assuming a linear dependency of the friction coefficient on the number of segments m and a parabolic behavior for the open-free (Gibbs) energy, in attendance of a present electrical potential across nanopore, an explicit flux formula for the polymers passed over a dimensional restricted pore, was derived. In addition, the linear polymers transport through a nanometer-sized pore under the action of a constant force is presented. The important mechanical effects of superimposed steady force and the monomers number of macromolecule chain on the polymer translocation process by nanomembranes, in a 2D diffusion model, have been demonstrated. The escape time by a three-dimensional graph as a function of the electric field intensity and monomers number of polymer was represented.
Effect of Additives and Print Orientation on the Properties of Laser Sintering-Printed Polyamide 12 Components
Authors: Zárybnická Lucie , Petrů Jana , Krpec Pavel , Pagáč Marek
Published: March 2022

3D printing, also known as additive manufacturing, is becoming increasingly popular for prototype processing in industrial practice. Laser sintering, which is a laser powder bed fusion technique, is a versatile and common 3D printing technology, which enables compact and high-quality products. Polyamide 12, a popular 3D printing material, provides reliable mechanical and thermal properties. Weaknesses in applying this technology for polyamide 12 include incomplete information regarding the application of various types of additives and different printing orientations with respect to the properties. This study aimed to investigate the influence of various additives (including carbon fiber, glass fiber, flame retardant, and aluminum powder) combined with polyamide 12, using processing of predefined powder refreshing mixture on the properties of a finished product. The thermal, surface, and mechanical properties of samples printed with five different polyamides 12-based powders at three different print orientations were investigated. It was found that the inclusion of additives decreases the tensile strength and increases the surface roughness of printed components—however, the toughness increases. The results can assist designers in selecting an appropriate material that will produce a finished part with the required properties for a given application.
Analysis of the Time Step Influence in the Yearly Simulation of Integrated Seawater Multi-Effect Distillation and Parabolic trough Concentrating Solar Thermal Power Plants
Authors: Ortega-Delgado Bartolomé , Palenzuela Patricia , Alarcón-Padilla Diego-César
Published: March 2022

The joint demand for power and freshwater is continuously increasing due to population growth, the rise of economic activity, and climate change. Integrated concentrating solar thermal power and desalination (CSP+D) plants may provide a key solution for the pressing freshwater deficit and energy problems in many regions of the world. Simulation tools with an accurate prediction of the yearly electric energy and freshwater production are needed. This paper analyzed the influence of the time step in the annual simulation of a CSP+D plant composed of a seawater multi-effect distillation unit and a parabolic trough concentrating solar thermal power plant, considering the location of Tabernas (Spain). A dynamic simulation tool of this system was developed, implementing the models in Engineering Equation Solver. The annual electricity and water productions obtained for the study case considered were 154 GWh and 3.45 hm3, respectively, using 5 min time steps, and 94 GWh and 2.1 hm3, respectively, with 1 h time steps. The results obtained show that a short time step interval (5 min) is recommended when using the detailed CSP model considered, which is prepared for simulation with short time steps. Step times of 1 h lead to excessive errors (about 30% in summer and 100% in winter), which underestimate the actual production.
Research on Adsorption and Desorption Performance of Gas-Phase Naphthalene on Hydrophobic Modified FDU-15
Authors: Zhao Chunyu , Liu Yingshu , Meng Miaomiao , Li Ziyi , Wang Haihong , Liu Wenhai , Yang Xiong
Published: March 2022

Naphthalene (NAP) is a typical gaseous polycyclic aromatic hydrocarbons (PAHs) pollutant that displays toxicological effects on biosystems. Ordered mesoporous carbon has relatively adequate adsorption capacity; however, the attached hydrophilic functional groups were proven to affect the adsorption performance in the presence of moisture. In this paper, trimethylchlorosilane (TMCS) is used to carry out the hydrophobic modification of ordered mesoporous carbon FDU-15, and the adsorption and desorption properties of FDU-15 were studied. Furthermore, the adsorption isotherms of naphthalene on FDU-15 and modified FDU-15 were fitted by L-F equation, and the kinetic parameters of desorption of naphthalene on modified FDU-15 were analyzed based on the method of temperature programming desorption (TPD). The results showed that the micropore volume and specific surface area of FDU-15 were significantly increased after hydrophobically modified by TMCS, and the polar functional groups of the hydrophobically modified FDU-15 were significantly reduced. Furthermore, the adsorption of naphthalene by FDU-15 before and after modification conformed to the L-F equation (R2 > 99%), and the adsorption of naphthalene by modified FDU-5 at low concentration was significantly improved due to the increase of micropores. Based on desorption kinetic performance study of modified FDU-15, it can be seen that the adsorption kinetic characteristics of naphthalene on the modified FDU-15 conform to the mechanical function of the JMA equation. When the mass ratio of TMCs to FDU-15 is 1:10 in the modification process, the pore structure and surface hydrophobicity of the modified FDU-15 reach an excellent balance. At this time, the adsorbent had the optimum desorption performance under experimental conditions, and the desorption activation energy was decreased from 60.98 kJ/mol of FDU-15 to 50.28 kJ/mol.
Fast Electrochemical Measurement of Laccase Activity for Monitoring Grapes’ Infection with Botrytis cinerea
Authors: Lulea Andreea Catalina , Ruginescu Robert , Banciu Roberta Maria , Pantazi Catalina , Brinduse Elena , Ion Marian , Quintela Silvia , Elejalde Edurne , Fernández-de-Castro Laura , Villarán Maria Carmen , Ruiz-de-Vergara Zuria , Ruíz Cristobal , Epure Petru , Purcarea Cristina , Vasilescu Alina
Published: March 2022

Grapes’ infection with the fungi Botrytis cinerea is one of the major causes of economic loss in the winemaking sector worldwide. The laccase activity of grapes is considered an appropriate indicator of this type of fungal infection, and enzymatic activity higher than 3 U/mL indicates a high risk of irreversibly damaged grape must due to enzymatic browning. This work describes a fast test for the measurement of laccase activity based on a dual optical and electrochemical detection method. A paper sensor impregnated with the enzymatic substrate dye 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) provides a semi-quantitative optical measurement. While the paper sensor can be used independently, when combined with a screen-printed electrode and amperometry measurements, it enables the quantitative detection of laccase activities down to 0.4 U/mL in only 5 min. The method was applied for monitoring the artificial infection of white, rosé, and red grapes with different strains of Botrytis cinerea. The results were confirmed by parallel analysis using the spectrophotometric method of laccase activity determination based on syringaldazine. The influence of the fungal strain and type of grape on laccase activity levels is reported. The demonstrated robustness, simplicity, and versatility of the developed method make it ideal for application on-site in the vineyard or at grape processing points.
Automatic Building Detection for Multi-Aspect SAR Images Based on the Variation Features
Authors: Liu Qi , Li Qiang , Yu Weidong , Hong Wen
Published: March 2022

Multi-aspect synthetic aperture radar (SAR) images contain more information available for automatic target recognition (ATR) than images from a single view. However, the sensitivity to aspect angles also makes it hard to extract and integrate information from multi-aspect images. In this paper, we propose a novel method based on the variations features to realize automatic building detection in the image level. First, to get a comprehensive description of target variation patterns, statistical characteristic variances are derived from three representative and complementary categories. Then, these obtained features are fused and put in the K-means classifier for prescreening, whose results are used as the training sets in supervised classification later to avoid manual labeling. Second, for more precise detection performance, finer features in vector forms are obtained by principal component analysis (PCA). The variation patterns of these feature vectors are explored in two different manners of correlation and fluctuation analyses and processed by separate support vector machines (SVMs) after fusion. Finally, the independent SVM detection results are fused according to a maximum probability rule. Experiments conducted on two different airborne data sets demonstrate the robustness and effectiveness of the proposed method, in spite of significant target signature variabilities and cluttered background.
An Algorithm to Bias-Correct and Transform Arctic SMAP-Derived Skin Salinities into Bulk Surface Salinities
Authors: Trossman David , Bayler Eric
Published: March 2022

An algorithmic approach, based on satellite-derived sea-surface (“skin”) salinities (SSS), is proposed to correct for errors in SSS retrievals and convert these skin salinities into comparable in-situ (“bulk”) salinities for the top-5 m of the subpolar and Arctic Oceans. In preparation for routine assimilation into operational ocean forecast models, Soil Moisture Active Passive (SMAP) satellite Level-2 SSS observations are transformed using Argo float data from the top-5 m of the ocean to address the mismatch between the skin depth of satellite L-band SSS measurements (∼1 cm) and the thickness of top model layers (typically at least 1 m). Separate from the challenge of Argo float availability in most of the subpolar and Arctic Oceans, satellite-derived SSS products for these regions currently are not suitable for assimilation for a myriad of other reasons, including erroneous ancillary air-sea forcing/flux products. In the subpolar and Arctic Oceans, the root-mean-square error (RMSE) between the SMAP SSS product and several in-situ salinity observational data sets for the top-5 m is greater than 1.5 pss (Practical Salinity Scale), which can be larger than their temporal variability. Thus, we train a machine-learning algorithm (called a Generalized Additive Model) on in-situ salinities from the top-5 m and an independent air-sea forcing/flux product to convert the SMAP SSS into bulk-salinities, correct biases, and quantify their standard errors. The RMSE between these corrected bulk-salinities and in-situ measurements is less than 1 pss in open ocean regions. Barring persistently problematic data near coasts and ice-pack edges, the corrected bulk-salinity data are in better agreement with in-situ data than their SMAP SSS equivalent.
From Phase Transition to Interdecadal Changes of ENSO, Altered by the Lower Stratospheric Ozone
Authors: Kilifarska Natalya Andreeva , Velichkova Tsvetelina Plamenova , Batchvarova Ekaterina Anguelova
Published: March 2022

This paper shows more evidence for the existing spatial–temporal synchronization of the air surface temperature and pressure within the lower stratospheric ozone, which is unevenly distributed over the globe. The focus of this study is put on the region of formation and manifestation of El Niño Southern Oscillation (ENSO). Statistical analysis of data (covering the period 1900–2019) displays a well pronounced covariance of ozone at 70 hPa with (i) Nino3.4 index, (ii) air surface temperature, and (iii) sea level pressure, in each grid-point with spatial resolution of 5° in latitude and longitude. The ozone impact could be found at different time scales—from interannual (altering the ENSO phase transition) to interdecadal. Moreover, the centers of action of ozone on the sea level temperature and pressure are positioned at different places, depending on the temporal scale of variability—from the tropical Central Pacific—at interannual and interdecadal, to extratropics—at subdecadal time scales. We show also that positive ozone anomalies at 70 hPa trigger a cooling of the sea surface, with a delay of 9 record’s time intervals. The ozone depletion, on the other side, is followed by a sea level warming with a delay of 1–2 record’s time intervals.
The Effect of Signal Duration on the Classification of Heart Sounds: A Deep Learning Approach
Authors: Bao Xinqi , Xu Yujia , Kamavuako Ernest Nlandu
Published: March 2022

Deep learning techniques are the future trend for designing heart sound classification methods, making conventional heart sound segmentation dispensable. However, despite using fixed signal duration for training, no study has assessed its effect on the final performance in detail. Therefore, this study aims at analysing the duration effect on the commonly used deep learning methods to provide insight for future studies in data processing, classifier, and feature selection. The results of this study revealed that (1) very short heart sound signal duration (1 s) weakens the performance of Recurrent Neural Networks (RNNs), whereas no apparent decrease in the tested Convolutional Neural Network (CNN) model was found. (2) RNN outperformed CNN using Mel-frequency cepstrum coefficients (MFCCs) as features. There was no difference between RNN models (LSTM, BiLSTM, GRU, or BiGRU). (3) Adding dynamic information (∆ and ∆²MFCCs) of the heart sound as a feature did not improve the RNNs’ performance, and the improvement on CNN was also minimal (≤2.5% in MAcc). The findings provided a theoretical basis for further heart sound classification using deep learning techniques when selecting the input length.
Simultaneous EEG-fMRI: What Have We Learned and What Does the Future Hold?
Authors: Warbrick Tracy
Published: March 2022

Simultaneous EEG-fMRI has developed into a mature measurement technique in the past 25 years. During this time considerable technical and analytical advances have been made, enabling valuable scientific contributions to a range of research fields. This review will begin with an introduction to the measurement principles involved in EEG and fMRI and the advantages of combining these methods. The challenges faced when combining the two techniques will then be considered. An overview of the leading application fields where EEG-fMRI has made a significant contribution to the scientific literature and emerging applications in EEG-fMRI research trends is then presented.
Use of a Wearable Biosensor to Study Heart Rate Variability in Chronic Obstructive Pulmonary Disease and Its Relationship to Disease Severity
Authors: Park Seon-Cheol , Saiphoklang Narongkorn , Jung Donghyun , Gomez David , Phillips Jonathan E. , Dolezal Brett A. , Tashkin Donald P. , Barjaktarevic Igor , Cooper Christopher B.
Published: March 2022

The purpose of this study was to explore the relationships between heart rate variability (HRV) and various phenotypic measures that relate to health and functional status in chronic obstructive pulmonary disease (COPD), and secondly, to demonstrate the feasibility of ascertaining HRV via a chest-worn wearable biosensor in COPD patients. HRV analysis was performed using SDNN (standard deviation of the mean of all normal R-R intervals), low frequency (LF), high frequency (HF), and LF/HF ratio. We evaluated the associations between HRV and COPD severity, class of bronchodilator therapy prescribed, and patient reported outcomes. Seventy-nine participants with COPD were enrolled. There were no differences in SDNN, HF, and LF/HF ratio according to COPD severity. The SDNN in participants treated with concurrent beta-agonists and muscarinic antagonists was lower than that in other participants after adjusting heart rate (beta coefficient −3.980, p = 0.019). The SDNN was positively correlated with Veterans Specific Activity Questionnaire (VSAQ) score (r = 0.308, p = 0.006) and handgrip strength (r = 0.285, p = 0.011), and negatively correlated with dyspnea by modified Medical Research Council (mMRC) questionnaire (r = −0.234, p = 0.039), health status by Saint George’s Respiratory Questionnaire (SGRQ) (r = −0.298, p = 0.008), symptoms by COPD Assessment Test (CAT) (r = −0.280, p = 0.012), and BODE index (r = −0.269, p = 0.020). When measured by a chest-worn wearable device, reduced HRV was observed in COPD participants receiving inhaled beta-sympathomimetic agonist and muscarinic antagonists. HRV was also correlated with various health status and performance measures.
A Field Calibration Solution to Achieve High-Grade-Level Performance for Low-Cost Dual-Frequency GNSS Receiver and Antennas
Authors: Krietemeyer Andreas , van der Marel Hans , van de Giesen Nick , ten Veldhuis Marie-Claire
Published: March 2022

Low-cost dual-frequency receivers and antennas have created opportunities for a wide range of new applications, in regions and disciplines where traditional GNSS equipment is unaffordable. However, the major drawback of using low-cost antenna equipment is that antenna phase patterns are typically poorly defined. Therefore, the noise in tropospheric zenith delay and coordinate time series is increased and systematic errors may occur. Here, we present a field calibration method that fully relies on low-cost solutions. It does not require costly software, uses low-cost equipment (~500 Euros), requires limited specialist expertise, and takes complex processing steps into the cloud. The application is more than just a relative antenna calibration: it is also a means to assess the quality and performance of the antenna, whether this is at a calibration site or directly in the field. We cover PCV calibrations, important for deformation monitoring, GNSS meteorology and positioning, and the computation of PCOs when the absolute position is of interest. The method is made available as an online web service. The performance of the calibration method is presented for a range of antennas of different quality and price in combination with a low-cost dual-frequency receiver. Carrier phase residuals of the low-cost antennas are reduced by 11–34% on L1 and 19–39% on L2, depending on the antenna type and ground plane used. For the cheapest antenna, when using a circular ground plane, the L1 residual is reduced from 3.85 mm before to 3.41 mm after calibration, and for L2 from 5.34 mm to 4.3 mm. The calibration reduces the Median Absolute Deviations (MADs) of the low-cost antennas in the vertical direction using Post Processed Kinematic (PPK) by 20–24%. For the cheapest antenna, the MAD is reduced from 5.6 to 3.8 mm, comparable to a geodetic-grade antenna (3.5 mm MAD). The calibration also has a positive impact on the Precise Point Positioning (PPP) results, delivering more precise results and reducing height biases.
Artificial Intelligence Algorithms for Malware Detection in Android-Operated Mobile Devices
Authors: Alkahtani Hasan , Aldhyani Theyazn H. H.
Published: March 2022

With the rapid expansion of the use of smartphone devices, malicious attacks against Android mobile devices have increased. The Android system adopted a wide range of sensitive applications such as banking applications; therefore, it is becoming the target of malware that exploits the vulnerabilities of the security system. A few studies proposed models for the detection of mobile malware. Nevertheless, improvements are required to achieve maximum efficiency and performance. Hence, we implemented machine learning and deep learning approaches to detect Android-directed malicious attacks. The support vector machine (SVM), k-nearest neighbors (KNN), linear discriminant analysis (LDA), long short-term memory (LSTM), convolution neural network-long short-term memory (CNN-LSTM), and autoencoder algorithms were applied to identify malware in mobile environments. The cybersecurity system was tested with two Android mobile benchmark datasets. The correlation was calculated to find the high-percentage significant features of these systems in the protection against attacks. The machine learning and deep learning algorithms successfully detected the malware on Android applications. The SVM algorithm achieved the highest accuracy (100%) using the CICAndMal2017 dataset. The LSTM model also achieved a high percentage accuracy (99.40%) using the Drebin dataset. Additionally, by calculating the mean error, mean square error, root mean square error, and Pearson correlation, we found a strong relationship between the predicted values and the target values in the validation phase. The correlation coefficient for the SVM method was R2 = 100% using the CICAndMal2017 dataset, and LSTM achieved R2 = 97.39% in the Drebin dataset. Our results were compared with existing security systems, showing that the SVM, LSTM, and CNN-LSTM algorithms are of high efficiency in the detection of malware in the Android environment.
Variational Model for Single-Image Reflection Suppression Based on Multiscale Thresholding
Authors: Shao Pei-Chiang
Published: March 2022

Reflections often cause degradation in image quality for pictures taken through glass medium. Removing the undesired reflections is becoming increasingly important. For human vision, it can produce much more pleasing results for multimedia applications. For machine vision, it can benefit various applications such as image segmentation and classification. Reflection removal is itself a highly illposed inverse problem that is very difficult to solve, especially for a single input image. Existing methods mainly rely on various prior information and assumptions to alleviate the ill-posedness. In this paper, we design a variational model based on multiscale hard thresholding to both effectively and efficiently suppress image reflections. A direct solver using the discrete cosine transform for implementing the proposed variational model is also provided. Both synthetic and real glass images are used in the numerical experiments to compare the performance of the proposed algorithm with other representative algorithms. The experimental results show the superiority of our algorithm over the previous ones.
The Effects of Speed-Modulated Visual Stimuli Seen through Smart Glasses on Work Efficiency after Viewing
Authors: Hasegawa Eiichi , Isoyama Naoya , Monteiro Diego Vilela , Sakata Nobuchika , Kiyokawa Kiyoshi
Published: March 2022

It is known that subjective time and work efficiency are affected by visual stimuli. However, existing studies only consider the effects of visual information on the user during viewing and ignore the after effects. Using smart glasses lets users see visual information while moving until just before arriving at the office or school. We hypothesize that the user’s effects from the visual information they were looking at just before working or studying affects the subsequent work. Through two user studies, we investigated whether information presented on smart glasses affected subsequent work efficiency. In the first experiment, participants were presented with avatars running at two levels of speed, or no avatars, through simulated smart glasses in a virtual environment. They then solved a dot-clicking task on a desktop monitor. In the second experiment, we investigated whether the same effect could be shown while walking in the real environment, with a running and a fast-walking avatar both at the same speed in order to see the difference in the effects of the different movements. In the first experiment, we confirmed that the speed of later work tended to improve when presenting the running human-shaped avatar. From the results of the second experiment, which was conducted in the real environment, we did not confirm that the subsequent work speed varied depending on the type of avatar being displayed. As a reason for the trend of improvement in the task efficiency in the first experiment, observation of fast human motion may have unconsciously accelerated the observers’ body movement speed due to the mirror neuron mechanism. As a reason for why the work speed did not improve in the second experiment, the participants may be affected by other pedestrians and running cars. Additionally, it was difficult to see the images on the smart glasses while walking in the real environment.
A 0.617–2.7 GHz Highly Linear High-Power Dual Port 15 Throws Antenna Switch Module (DP15T-ASM) with Branched-Antenna Technique and Termination Mode
Authors: Rad Reza E. , Choi Kyung-Duk , Kim Sung-Jin , Pu Young-Gun , Jung Yeon-Jae , Huh Hyung-Ki , Yoo Joon-Mo , Kim Seok-Kee , Lee Kang-Yoon
Published: March 2022

This paper presents a Dual-Port-15-Throw (DP15T) antenna switch module (ASM) Radio Frequency (RF) switch implemented by a branched antenna technique which has a high linearity for wireless communications and various frequency bands, including a low- frequency band of 617–960 MHz, a mid-frequency band of 1.4–2.2 GHz, and a high-frequency band of 2.3–2.7 GHz. To obtain an acceptable Insertion Loss (IL) and provide a consistent input for each throw, a branched antenna technique is proposed that distributes a unified magnetic field at the inputs of the throws. The other role of the proposed antenna is to increase the inductance effects for the closer ports to the antenna pad in order to decrease IL at higher frequencies. The module is enhanced by two termination modes for each antenna path to terminate the antenna when the switch is not operating. The module is fabricated in the silicon-on-insulator CMOS process. The measurement results show a maximum IMD2 and IMD3 of −100 dBm, while for the second and third harmonics the maximum value is −89 dBc. The module operates with a maximum power handling of 35 dBm. Experimental results show a maximum IL of 0.34 and 0.92 dB and a minimum isolation of 49 dB and 35.5 dB at 0.617 GHz and 2.7 GHz frequencies, respectively. The module is implemented in a compact way to occupy an area of 0.74 mm2. The termination modes show a second harmonic of 75 dBc, which is desirable.
Adaptive Recognition of Bioacoustic Signals in Smart Aquaculture Engineering Based on r-Sigmoid and Higher-Order Cumulants
Authors: Cao Tianyu , Zhao Xiaoqun , Yang Yichen , Zhu Caiyun , Xu Zhongwei
Published: March 2022

In recent years, interest in aquaculture acoustic signal has risen since the development of precision agriculture technology. Underwater acoustic signals are known to be noisy, especially as they are inevitably mixed with a large amount of environmental background noise, causing severe interference in the extraction of signal features and the revelation of internal laws. Furthermore, interference adds a considerable burden on the transmission, storage, and processing of data. A signal recognition curve (SRC) algorithm is proposed based on higher-order cumulants (HOC) and a recognition-sigmoid function for feature extraction of target signals. The signal data of interest can be accurately identified using the SRC. The analysis and verification of the algorithm are carried out in this study. The results show that when the SNR is greater than 7 dB, the SRC algorithm is effective, and the performance improvement is maximized when the SNR is 11 dB. Furthermore, the SRC algorithm has shown better flexibility and robustness in application.
Deep Learning-Based Computer-Aided Pneumothorax Detection Using Chest X-ray Images
Authors: Malhotra Priyanka , Gupta Sheifali , Koundal Deepika , Zaguia Atef , Kaur Manjit , Lee Heung-No
Published: March 2022

Pneumothorax is a thoracic disease leading to failure of the respiratory system, cardiac arrest, or in extreme cases, death. Chest X-ray (CXR) imaging is the primary diagnostic imaging technique for the diagnosis of pneumothorax. A computerized diagnosis system can detect pneumothorax in chest radiographic images, which provide substantial benefits in disease diagnosis. In the present work, a deep learning neural network model is proposed to detect the regions of pneumothoraces in the chest X-ray images. The model incorporates a Mask Regional Convolutional Neural Network (Mask RCNN) framework and transfer learning with ResNet101 as a backbone feature pyramid network (FPN). The proposed model was trained on a pneumothorax dataset prepared by the Society for Imaging Informatics in Medicine in association with American college of Radiology (SIIM-ACR). The present work compares the operation of the proposed MRCNN model based on ResNet101 as an FPN with the conventional model based on ResNet50 as an FPN. The proposed model had lower class loss, bounding box loss, and mask loss as compared to the conventional model based on ResNet50 as an FPN. Both models were simulated with a learning rate of 0.0004 and 0.0006 with 10 and 12 epochs, respectively.
PlatypOUs—A Mobile Robot Platform and Demonstration Tool Supporting STEM Education
Authors: Rácz Melinda , Noboa Erick , Détár Borsa , Nemes Ádám , Galambos Péter , Szűcs László , Márton Gergely , Eigner György , Haidegger Tamás
Published: March 2022

Given the rising popularity of robotics, student-driven robot development projects are playing a key role in attracting more people towards engineering and science studies. This article presents the early development process of an open-source mobile robot platform—named PlatypOUs—which can be remotely controlled via an electromyography (EMG) appliance using the MindRove brain–computer interface (BCI) headset as a sensor for the purpose of signal acquisition. The gathered bio-signals are classified by a Support Vector Machine (SVM) whose results are translated into motion commands for the mobile platform. Along with the physical mobile robot platform, a virtual environment was implemented using Gazebo (an open-source 3D robotic simulator) inside the Robot Operating System (ROS) framework, which has the same capabilities as the real-world device. This can be used for development and test purposes. The main goal of the PlatypOUs project is to create a tool for STEM education and extracurricular activities, particularly laboratory practices and demonstrations. With the physical robot, the aim is to improve awareness of STEM outside and beyond the scope of regular education programmes. It implies several disciplines, including system design, control engineering, mobile robotics and machine learning with several application aspects in each. Using the PlatypOUs platform and the simulator provides students and self-learners with a firsthand exercise, and teaches them to deal with complex engineering problems in a professional, yet intriguing way.
Data Fusion in Agriculture: Resolving Ambiguities and Closing Data Gaps
Authors: Barbedo Jayme Garcia Arnal
Published: March 2022

Acquiring useful data from agricultural areas has always been somewhat of a challenge, as these are often expansive, remote, and vulnerable to weather events. Despite these challenges, as technologies evolve and prices drop, a surge of new data are being collected. Although a wealth of data are being collected at different scales (i.e., proximal, aerial, satellite, ancillary data), this has been geographically unequal, causing certain areas to be virtually devoid of useful data to help face their specific challenges. However, even in areas with available resources and good infrastructure, data and knowledge gaps are still prevalent, because agricultural environments are mostly uncontrolled and there are vast numbers of factors that need to be taken into account and properly measured for a full characterization of a given area. As a result, data from a single sensor type are frequently unable to provide unambiguous answers, even with very effective algorithms, and even if the problem at hand is well defined and limited in scope. Fusing the information contained in different sensors and in data from different types is one possible solution that has been explored for some decades. The idea behind data fusion involves exploring complementarities and synergies of different kinds of data in order to extract more reliable and useful information about the areas being analyzed. While some success has been achieved, there are still many challenges that prevent a more widespread adoption of this type of approach. This is particularly true for the highly complex environments found in agricultural areas. In this article, we provide a comprehensive overview on the data fusion applied to agricultural problems; we present the main successes, highlight the main challenges that remain, and suggest possible directions for future research.
CM-LSTM Based Spectrum Sensing
Authors: Chen Wantong , Wu Hailong , Ren Shiyu
Published: March 2022

This paper presents spectrum sensing as a classification problem, and uses a spectrum-sensing algorithm based on a signal covariance matrix and long short-term memory network (CM-LSTM). We jointly exploited the spatial cross-correlation of multiple signals received by the antenna array and the temporal autocorrelation of single signals; we used the long short-term memory network (LSTM), which is good at extracting temporal correlation features, as the classification model; we then input the covariance matrix of the signals received by the array into the LSTM classification model to achieve the fusion learning of spatial correlation features and temporal correlation features of the signals, thus significantly improving the performance of spectrum sensing. Simulation analysis shows that the CM-LSTM-based spectrum-sensing algorithm shows better performance compared with support vector machine (SVM), gradient boosting machine (GBM), random forest (RF), and energy detection (ED) algorithm-based spectrum-sensing algorithms for different signal-to-noise ratios (SNRs) and different numbers of secondary users (SUs). Among them, SVM is a classical machine-learning algorithm, GBM and RF are two integrated learning methods with better generalization capability, and ED is a classical, traditional, and spectrum-sensing algorithm.
How Being a Researcher Impacted My Life
Authors: Herer Alix , Schwartz Ariel E.
Published: March 2022

Doing research can help people to learn about a lot of different topics. For example, researchers can learn how to work in a group, collect and analyze data, how to make accessible materials, and get to know their own strengths. In this paper, a researcher shares what she learned while working on two research projects about young adults with intellectual/developmental disabilities and co-occurring mental health conditions. The first project was peer mentoring. In this project, we focused on individual needs for someone who may be struggling with mental health. The second project was about workplace disclosure. In this project, we tried to find out if young adults disclose their mental health conditions at work and in job interviews. We explain how we did the projects, how the researcher learned to do research, and what made it easy to learn. We also share about the impact of doing research on the researcher’s personal life. Finally, we share why doing inclusive research is important and how to help researchers with disabilities feel like they are valued members of the research team.
Home-Field Advantage of Litter Decomposition Faded 8 Years after Spruce Forest Clearcutting in Western Germany
Authors: Zhuang Liyan , Schnepf Andrea , Unger Kirsten , Liang Ziyi , Bol Roland
Published: March 2022

Home-field advantage (HFA) encompasses all the processes leading to faster litter decomposition in the ‘home’ environment compared to that of ‘away’ environments. To determine the occurrence of HFA in a forest and adjacent clear-cut, we set up a reciprocal litter decomposition experiment within the forest and clear-cut for two soil types (Cambisols and Gleysols) in temperate Germany. The forest was dominated by Norway spruce (Picea abies), whereas forest regeneration of European Beech (Fagus sylvatica) after clearcutting was encouraged. Our observation that Norway spruce decomposed faster than European beech in 70-yr-old spruce forest was most likely related to specialized litter-soil interaction under existing spruce, leading to an HFA. Elevated soil moisture and temperature, and promoted litter N release, indicated the rapid change of soil-litter affinity of the original spruce forest even after a short-term regeneration following clearcutting, resulting in faster beech decomposition, particularly in moisture- and nutrient-deficient Cambisols. The divergence between forest and clear-cut in the Cambisol of their litter δ15N values beyond nine months implied litter N decomposition was only initially independent of soil and residual C status. We conclude that clearcutting modifies the litter-field affinity and helps promote the establishment or regeneration of European beech in this and similar forest mountain upland areas.