bluuebunny/crossref_metadata_2025 huggingface dataset:
Eating Vegetables First at Start of Meal and Food Intake among Preschool Children in Japan
Authors: Yang Jiaxi , Tani Yukako , Tobias Deirdre K. , Ochi Manami , Fujiwara Takeo
Published: June 2020

Eating behavior is an important aspect for dietary quality and long-term health. This study examined associations between eating vegetables first at a meal and food intakes among preschool children in Tokyo, Japan. We used cross-sectional data of 135 preschool children from seven nursery schools in Adachi City, Tokyo, Japan. Caregivers completed a survey on child’s eating behaviors and a diet questionnaire. Linear regression was used to examine frequency of eating vegetables first at a meal and food intakes; percent difference and the corresponding 95% confidence interval (95% CI) were presented. Overall, 25.2% of children reported eating vegetables first at a meal every time, 52.6% sometimes, and 22.2% not often or never. In the multivariate analysis, higher vegetable intake remained significant after adjusting for other covariates (compared with the group of eating vegetables first not often or never, the group reported sometimes: 27%, 95% CI: 0–63%; the group reported every time: 93%, 95% CI: 43–159%). No significant difference in intake by frequency categories of eating vegetables first was observed for other food groups, including fruits, meat, fish, cereals, and sweets. Children eating vegetables first at a meal more was associated with higher total intake of vegetables compared with children who did not eat vegetables first, among Japanese preschool children.
Intermittent Fasting and High-Intensity Exercise Elicit Sexual-Dimorphic and Tissue-Specific Adaptations in Diet-Induced Obese Mice
Authors: Wilson Robin A. , Stathis Christos G. , Hayes Alan , Cooke Matthew B.
Published: June 2020

The molecular adaptations that underpin body composition changes and health benefits of intermittent fasting (IF) and high-intensity interval training (HIIT) are unclear. The present study investigated these adaptations within the hypothalamus, white adipose and skeletal muscle tissue following 12 weeks of IF and/or HIIT in diet-induced obese mice. Mice (C57BL/6, 8-week-old, males/females) were fed high-fat (59%) and sugar (30%) water (HF/S) for 12 weeks followed by an additional 12 weeks of HF/S plus either IF, HIIT, combination (IF+HIIT) or HF/S only control (CON). Tissues were harvested at 12 and 24 weeks and analysed for various molecular markers. Hypothalamic NPY expression was significantly lower following IF+HIIT compared to CON in females. In adipose tissue, leptin expression was significantly lower following IF and IF+HIIT compared to CON in males and females. Males demonstrated increased markers of fat oxidation (HADH, FABP4) following IF+HIIT, whereas females demonstrated reduced markers of adipocyte differentiation/storage (CIDEC and FOXO1) following IF and/or IF+HIIT. In muscle, SIRT1, UCP3, PGC1α, and AS160 expression was significantly lower following IF compared to CON in males and/or females. This investigation suggests that males and females undertaking IF and HIIT may prevent weight gain via different mechanisms within the same tissue.
Lipotoxic Injury Differentially Regulates Brain Microvascular Gene Expression in Male Mice
Authors: Nuthikattu Saivageethi , Milenkovic Dragan , Rutledge John C. , Villablanca Amparo C.
Published: June 2020

The Western diet (WD) and hyperlipidemia are risk factors for vascular disease, dementia, and cognitive impairment. However, the molecular mechanisms are poorly understood. This pilot study investigated the genomic pathways by which the WD and hyperlipidemia regulate gene expression in brain microvessels. Five-week-old C57BL/6J wild type (WT) control and low-density lipoprotein receptor deficient (LDL-R−/−) male mice were fed the WD for eight weeks. Differential gene expression, gene networks and pathways, transcription factors, and non-protein coding RNAs were evaluated by a genome-wide microarray and bioinformatics analysis of laser-captured hippocampal microvessels. The WD resulted in the differential expression of 1972 genes. Much of the differentially expressed gene (DEG) was attributable to the differential regulation of cell signaling proteins and their transcription factors, approximately 4% was attributable to the differential expression of miRNAs, and 10% was due to other non-protein coding RNAs, primarily long non-coding RNAs (lncRNAs) and small nucleolar RNAs (snoRNAs) not previously described to be modified by the WD. Lipotoxic injury resulted in complex and multilevel molecular regulation of the hippocampal microvasculature involving transcriptional and post-transcriptional regulation and may provide a molecular basis for a better understanding of hyperlipidemia-associated dementia risk.
Vitamin K Status in Adherent and Non-Adherent Patients with Phenylketonuria: A Cross-Sectional Study
Authors: Mozrzymas Renata , Walkowiak Dariusz , Drzymała-Czyż Sławomira , Krzyżanowska-Jankowska Patrycja , Duś-Żuchowska Monika , Kałużny Łukasz , Walkowiak Jarosław
Published: June 2020

This is the first study to evaluate vitamin K status in relation to dietary intake and phenylalanine dietary compliance in patients with phenylketonuria (PKU). The dietary and PKU formula intake of vitamin K was calculated in 34 PKU patients, with vitamin K status determined by the measurement of prothrombin induced by vitamin K absence (PIVKA-II). Blood phenylalanine concentrations in the preceding 12 months were considered. There were significantly more phenylalanine results exceeding 6 mg/dL in patients with normal PIVKA-II concentrations than in those with abnormal PIVKA-II levels (p = 0.035). Similarly, a higher total intake of vitamin K and dietary vitamin intake expressed as μg/day (p = 0.033 for both) and %RDA (p = 0.0002 and p = 0.003, respectively) was observed in patients with normal PIVKA-II levels. Abnormal PIVKA-II concentrations were associated with a lower OR (0.1607; 95%CI: 0.0273–0.9445, p = 0.043) of having a median phenylalanine concentration higher than 6 mg/dL. In conclusion, vitamin K deficiency is not uncommon in phenylketonuria and may also occur in patients with adequate vitamin K intake. PKU patients with better dietary compliance have a higher risk of vitamin K deficiency. The present findings highlight the need for further studies to re-evaluate dietary recommendations regarding vitamin K intake, both concerning formula-based and dietary consumption of natural products.
Hemodialysis—Nutritional Flaws in Diagnosis and Prescriptions. Could Amino Acid Losses Be the Sharpest “Sword of Damocles”?
Authors: Bolasco Piergiorgio
Published: June 2020

This review aims to highlight the strengths and weaknesses emerging from diagnostic evaluations and prescriptions in an intent to prevent progression over time of malnutrition and/or protein-energy wasting (PEW) in hemodialysis (HD) patients. In particular, indications of the most effective pathway to follow in diagnosing a state of malnutrition are provided based on a range of appropriate chemical-clinical, anthropometric and instrumental analyses and monitoring of the nutritional status of HD patients. Finally, based on the findings of recent studies, therapeutic options to be adopted for the purpose of preventing or slowing down malnutrition have been reviewed, with particular focus on protein-calorie intake, the role of oral and/or intravenous supplements and efficacy of some classes of amino acids. A new determining factor that may lead inexorably to PEW in hemodialysis patients is represented by severe amino acid loss during hemodialysis sessions, for which mandatory compensation should be introduced.
Fish Cooking Methods and Impaired Glucose Metabolism Among Japanese Workers: The Furukawa Nutrition and Health Study
Authors: Nanri Akiko , Takazaki Ayane , Kochi Takeshi , Eguchi Masafumi , Kabe Isamu , Mizoue Tetsuya
Published: June 2020

The aim of this study was to examine the cross-sectional association between fish and shellfish intake and impaired glucose metabolism with consideration for cooking methods in a Japanese working population. Participants were 1774 workers aged 18–69 years. Dietary intake was assessed using a validated self-administered diet history questionnaire. Participants were asked about their most frequently used cooking method for fish, and the method was classified as either “raw and stewing” or “broiling, deep-frying, and stir-frying”. Impaired glucose metabolism was defined by a history of diabetes, current use of anti-diabetic drugs, fasting blood glucose ≥110 mg/dl, or HbA1c ≥6.0%. Logistic regression analysis was used to estimate the odds ratios of impaired glucose metabolism for fish intake by cooking method. Fish intake was not associated with impaired glucose metabolism in either group. When the outcome was defined as diabetes, the odds of diabetes increased with fish intake among participants who most frequently used broiling, deep-frying, or stir-frying methods, albeit they were not statistically significant; the multivariable-adjusted odds ratio for the highest versus the lowest tertiles of fish intake was 1.95 (95%CI, 0.71–5.41). Cooking methods for fish may not modify the association between fish intake and impaired glucose metabolism among Japanese populations.
Microbiota and Lifestyle: A Special Focus on Diet
Authors: Redondo-Useros Noemí , Nova Esther , González-Zancada Natalia , Díaz Ligia E. , Gómez-Martínez Sonia , Marcos Ascensión
Published: June 2020

It is widely known that a good balance and healthy function for bacteria groups in the colon are necessary to maintain homeostasis and preserve health. However, the lack of consensus on what defines a healthy gut microbiota and the multitude of factors that influence human gut microbiota composition complicate the development of appropriate dietary recommendations for our gut microbiota. Furthermore, the varied response to the intake of probiotics and prebiotics observed in healthy adults suggests the existence of potential inter- and intra-individual factors, which might account for gut microbiota changes to a greater extent than diet. The changing dietary habits worldwide involving consumption of processed foods containing artificial ingredients, such as sweeteners; the coincident rise in emotional disorders; and the worsening of other lifestyle habits, such as smoking habits, drug consumption, and sleep, can together contribute to gut dysbiosis and health impairment, as well as the development of chronic diseases. This review summarizes the current literature on the effects of specific dietary ingredients (probiotics, prebiotics, alcohol, refined sugars and sweeteners, fats) in the gut microbiota of healthy adults and the potential inter- and intra-individual factors involved, as well as the influence of other potential lifestyle factors that are dramatically increasing nowadays.
Kinetic Equation Approach to Graphene in Strong External Fields
Authors: Smolyansky Stanislav A. , Panferov Anatolii D. , Blaschke David B. , Gevorgyan Narine T.
Published: June 2020

The report presents the results of using the nonperturbative kinetic approach to describe the excitation of plasma oscillations in a graphene monolayer. As examples the constant electric field as well as an electric field of short high-frequency pulses are considered. The dependence of the induced conduction and polarization currents characteristics on the pulse intensity, pulse duration, and polarization is investigated. The characteristics of secondary electromagnetic radiation resulting from the alternating currents is investigated. The nonlinear response to the external electric field characterizes graphene as an active medium. Qualitative agreement is obtained with the existing experimental result of measurements of currents in constant electric fields and radiation from graphene in the case of excitation by means of the infrared and optical pulses.
A Cost-Effective Method to Reproduce the Morphology of the Nearshore and Intertidal Zone in Microtidal Environments
Authors: Furlani Stefano , Vaccher Valeria , Macovaz Vanja , Devoto Stefano
Published: June 2020

The photogrammetric method is widely used in coastal areas and in submerged environments. Time-lapse images collected with unmanned aerial vehicles are used to reproduce the emerged areas, while images taken by divers are used to reproduce submerged ones. Conversely, 3D models of natural or human-made objects lying at the water level are severely affected by the difference in refractive index between air and seawater. For this reason, the matching of 3D models of emergent and submerged coasts has been very rarely tested and never used in Earth Sciences. The availability of a large number of time-lapse images, collected at the intertidal zone during previous snorkel surveys, encouraged us to test the merging of 3D models of emerged and submerged environments. Considering the rapid and effective nature of the aforementioned program of swim surveys, photogrammetric targets were not used during image acquisition. This forced us to test the matching of the independent models by recognizing prominent landforms along the waterline. Here we present the approach used to test the method, the instrumentation used for the field tests, and the setting of cameras fixed to a specially built aluminum support console and discuss both its advantages and its limits compared to UAVs. 3D models of sea cliffs were generated by applying structure-from-motion (SfM) photogrammetry. Horizontal time-lapse images, collected with action cameras while swimming parallel to the coastline at nearly constant velocity, were used for the tests. Subsequently, prominent coastal landforms were used to couple the independent models obtained from the emergent and submerged cliffs. The method was pilot tested in two coastal sites in the north-eastern Adriatic (part of the Mediterranean basin). The first site was a 25 m sea wall of sandstone set within a small harbor, while the second site was a 150 m route below plunging limestone cliffs. The data show that inexpensive action cameras provide a sufficient resolution to support and integrate geomorphological field surveys along rocky coastlines.
GIS Data as a Valuable Source of Information for Increasing Resolution of the WRF Model for Warsaw
Authors: Siewert Jolanta , Kroszczynski Krzysztof
Published: June 2020

The Weather Research and Forecasting (WRF) model is commonly associated with meteorological data, but its algorithms may also use geographical data. The objective of this paper is to evaluate the impact of the high resolution CORINE Land Cover (CLC) data and the SRTM topography on the estimation accuracy of the weather model parameters in the WRF microscale simulations (200 × 200 m) for Warsaw. In the presented studies, the authors propose their own method of attaching the CLC data to the WRF microscale modeling for the CLC border areas, where first calculational domains reach beyond areas of CLC coverage. As a part of the research, the adaptation of the proposed method was examined by the assessment of the WRF microscale modeling simulations for Warsaw. The modified IGBP MODIS land use/land cover (LULC) and USGS GMTED2010 terrain elevation geographical data (30 arc seconds) was applied for the WRF simulations as default. As higher resolution geographical data (100 m), the LULC from CORINE Land Cover (CLC) 2018 data, and the SRTM topography were adopted. In this study the forecasts of air temperature and relative humidity at 2 m, and wind (speed and direction) at 10 m above ground level obtained using the WRF model for particular simulations were evaluated against measurements made at the Warsaw airports: Chopin (EPWA) and Babice (EPBC). The research has indicated that for microscale calculation fields there are noticeable changes in the meteorological parameter values when the CLC and the SRTM data are integrated into the WRF model, which in most cases yielded more accurate values of temperature and relative humidity at 2 m. This has also proved the correctness of the proposed methodology of the CLC data adoption. The improvement in the forecasted meteorological parameters is different for the particular locations and depends on the degree of the LULC and topography data change after higher resolution data adoption.
A Novel Privacy Approach of Digital Aerial Images Based on Mersenne Twister Method with DNA Genetic Encoding and Chaos
Authors: Masood Fawad , Boulila Wadii , Ahmad Jawad , Arshad  , Sankar Syam , Rubaiee Saeed , Buchanan William J.
Published: June 2020

Aerial photography involves capturing images from aircraft and other flying objects, including Unmanned Aerial Vehicles (UAV). Aerial images are used in many fields and can contain sensitive information that requires secure processing. We proposed an innovative new cryptosystem for the processing of aerial images utilizing a chaos-based private key block cipher method so that the images are secure even on untrusted cloud servers. The proposed cryptosystem is based on a hybrid technique combining the Mersenne Twister (MT), Deoxyribonucleic Acid (DNA), and Chaotic Dynamical Rossler System (MT-DNA-Chaos) methods. The combination of MT with the four nucleotides and chaos sequencing creates an enhanced level of security for the proposed algorithm. The system is tested at three separate phases. The combined effects of the three levels improve the overall efficiency of the randomness of data. The proposed method is computationally agile, and offered more security than existing cryptosystems. To assess, this new system is examined against different statistical tests such as adjacent pixels correlation analysis, histogram consistency analyses and its variance, visual strength analysis, information randomness and uncertainty analysis, pixel inconsistency analysis, pixels similitude analyses, average difference, and maximum difference. These tests confirmed its validity for real-time communication purposes.
KDA3D: Key-Point Densification and Multi-Attention Guidance for 3D Object Detection
Authors: Wang Jiarong , Zhu Ming , Wang Bo , Sun Deyao , Wei Hua , Liu Changji , Nie Haitao
Published: June 2020

In this paper, we propose a novel 3D object detector KDA3D, which achieves high-precision and robust classification, segmentation, and localization with the help of key-point densification and multi-attention guidance. The proposed end-to-end neural network architecture takes LIDAR point clouds as the main inputs that can be optionally complemented by RGB images. It consists of three parts: part-1 segments 3D foreground points and generates reliable proposals; part-2 (optional) enhances point cloud density and reconstructs the more compact full-point feature map; part-3 refines 3D bounding boxes and adds semantic segmentation as extra supervision. Our designed lightweight point-wise and channel-wise attention modules can adaptively strengthen the “skeleton” and “distinctiveness” point-features to help feature learning networks capture more representative or finer patterns. The proposed key-point densification component can generate pseudo-point clouds containing target information from monocular images through the distance preference strategy and K-means clustering so as to balance the density distribution and enrich sparse features. Extensive experiments on the KITTI and nuScenes 3D object detection benchmarks show that our KDA3D produces state-of-the-art results while running in near real-time with a low memory footprint.
Regularization of Building Roof Boundaries from Airborne LiDAR Data Using an Iterative CD-Spline
Authors: dos Santos Renato César , Galo Mauricio , Habib Ayman F.
Published: June 2020

Building boundaries play an essential role in many applications such as urban planning and production of 3D realistic views. In this context, airborne LiDAR data have been explored for the generation of digital building models. Despite the many developed strategies, there is no method capable of encompassing all the complexities in an urban environment. In general, the vast majority of existing regularization methods are based on building boundaries that are made up of straight lines. Therefore, the development of a strategy able to model building boundaries, regardless of their degree of complexity is of high importance. To overcome the limitations of existing strategies, an iterative CD-spline (changeable degree spline) regularization method is proposed. The main contribution is the automated selection of the polynomial function that best models each segment of the building roof boundaries. Conducted experiments with real data verified the ability of the proposed approach in modeling boundaries with different levels of complexities, including buildings composed of complex curved segments and point cloud with different densities, presenting Fscore and PoLiS around 95% and 0.30 m, respectively.
Accelerating a Geometrical Approximated PCA Algorithm Using AVX2 and CUDA
Authors: Machidon Alina , Machidon Octavian , Ciobanu Cătălin , Ogrutan Petre
Published: June 2020

Remote sensing data has known an explosive growth in the past decade. This has led to the need for efficient dimensionality reduction techniques, mathematical procedures that transform the high-dimensional data into a meaningful, reduced representation. Projection Pursuit (PP) based algorithms were shown to be efficient solutions for performing dimensionality reduction on large datasets by searching low-dimensional projections of the data where meaningful structures are exposed. However, PP faces computational difficulties in dealing with very large datasets—which are common in hyperspectral imaging, thus raising the challenge for implementing such algorithms using the latest High Performance Computing approaches. In this paper, a PP-based geometrical approximated Principal Component Analysis algorithm (gaPCA) for hyperspectral image analysis is implemented and assessed on multi-core Central Processing Units (CPUs), Graphics Processing Units (GPUs) and multi-core CPUs using Single Instruction, Multiple Data (SIMD) AVX2 (Advanced Vector eXtensions) intrinsics, which provide significant improvements in performance and energy usage over the single-core implementation. Thus, this paper presents a cross-platform and cross-language perspective, having several implementations of the gaPCA algorithm in Matlab, Python, C++ and GPU implementations based on NVIDIA Compute Unified Device Architecture (CUDA). The evaluation of the proposed solutions is performed with respect to the execution time and energy consumption. The experimental evaluation has shown not only the advantage of using CUDA programming in implementing the gaPCA algorithm on a GPU in terms of performance and energy consumption, but also significant benefits in implementing it on the multi-core CPU using AVX2 intrinsics.
CoMask: Corresponding Mask-Based End-to-End Extrinsic Calibration of the Camera and LiDAR
Authors: Yin Lu , Luo Bin , Wang Wei , Yu Huan , Wang Chenjie , Li Chengyuan
Published: June 2020

The integration of the camera and LiDAR has played an important role in the field of autonomous driving, for example in visual–LiDAR SLAM and 3D environment fusion perception, which rely on precise geometrical extrinsic calibration. In this paper, we proposed a fully automatic end-to-end method based on the 3D–2D corresponding mask (CoMask) to directly estimate the extrinsic parameters with high precision. Simple subtraction was applied to extract the candidate point cluster from the complex background, and then 3D LiDAR points located on checkerboard were selected and refined by spatial growth clustering. Once the distance transform of 2D checkerboard mask was generated, the extrinsic calibration of the two sensors could be converted to 3D–2D mask correspondence alignment. A simple but efficient strategy combining the genetic algorithm with the Levenberg–Marquardt method was used to solve the optimization problem globally without any initial estimates. Both simulated and realistic experiments showed that the proposed method could obtain accurate results without manual intervention, special environment setups, or prior initial parameters. Compared with the state of the art, our method has obvious advantages in accuracy, robustness, and noise resistance. Our code is open-source on GitHub.
Precipitation Dominates Long-Term Water Storage Changes in Nam Co Lake (Tibetan Plateau) Accompanied by Intensified Cryosphere Melts Revealed by a Basin-Wide Hydrological Modelling
Authors: Zhong Xiaoyang , Wang Lei , Zhou Jing , Li Xiuping , Qi Jia , Song Lei , Wang Yuanwei
Published: June 2020

Lakes on the Tibetan Plateau (TP) have changed dramatically as a result of climate change during recent decades. Studying the changes in long-term lake water storage (LWS) is of great importance for regional water security and ecosystems. Nam Co Lake is the second largest lake in the central TP. To investigate the long-term changes in LWS, a distributed cryosphere-hydrology model (WEB-DHM) driven by multi-source data was evaluated and then applied to simulate hydrological processes across the whole Nam Co Lake basin from 1980 to 2016. Firstly, a comparison of runoff (lake inflow), land surface temperature, and snow depth between the model simulations and observations or remote sensing products showed that WEB-DHM could accurately simulate hydrological processes in the basin. Meanwhile, the simulated daily LWS was in good agreement with satellite-derived data during 2000–2016. Secondly, long-term simulations showed that LWS increased by 9.26 km3 during 1980–2016, reaching a maximum in 2010 that was 10.25 km3 greater than that in 1980. During this period, LWS firstly decreased (1980–1987), then increased (1988–2008), and decreased again (2009–2016). Thirdly, the contributions of precipitation runoff, melt-water runoff, lake surface precipitation, and lake evaporation to Nam Co LWS were 71%, 33%, 24%, and -28%, respectively. Snow and glacier melting have significantly intensified during recent decades (2.96 m3 s−1/decade on average), contributing a mean proportion of 22% of lake inflows. These findings are consistent with the significant increasing trends of annual precipitation and temperature in the lake basin (25 mm/decade and 0.4 K/decade, respectively). We conclude that long-term variations in Nam Co LWS during 1980–2016 were largely controlled by precipitation; however, the contribution of precipitation runoff to total lake inflow has decreased while the contribution from warming-induced snow and glacier melting has significantly increased.
Clear-Sky Surface Solar Radiation and the Radiative Effect of Aerosol and Water Vapor Based on Simulations and Satellite Observations over Northern China
Authors: Zhang Guang , Ma Yingying
Published: June 2020

The distribution and trend of clear-sky surface solar radiation (SSR) and the quantitative effects of aerosol and water vapor are investigated in northern China during 2001–2015 using radiation simulations and satellite observations. Clear-sky SSR in northern China is high in summer and low in winter, which is dominated by astronomical factors and strongly modulated by the seasonal variations of radiative effects of aerosol (ARE) and water vapor (WVRE). The larger variation of WVRE than ARE indicates that water vapor plays a more important role in moderating the seasonal variation of clear-sky SSR. Clear-sky SSR shows an overall decreasing trend of –0.12 W/m2 per year, with decrease more strongly than –0.60 W/m2 per year in west-central Shandong and increase (about 0.40 W/m2) in south-central Inner Mongolia. The consistency of spatial distribution and high correlation between clear-sky SSR and ARE trend indicate that the clear-sky SSR trend is mainly determined by aerosol variation. Dust mass concentration decreases about 16% in south-central Inner Mongolia from 2001 to 2015, resulting in the increase in clear-sky SSR. In contrast, sulfate aerosol increases about 92% in west-central Shandong, leading to the decreasing trend of clear-sky SSR.
Using RGISTools to Estimate Water Levels in Reservoirs and Lakes
Authors: Militino Ana F. , Montesino-SanMartin Manuel , Pérez-Goya Unai , Ugarte M. Dolores
Published: June 2020

The combination of freely accessible satellite imagery from multiple programs improves the spatio-temporal coverage of remote sensing data, but it exhibits barriers regarding the variety of web services, file formats, and data standards. Ris an open-source software environment with state-of-the-art statistical packages for the analysis of optical imagery. However, it lacks the tools for providing unified access to multi-program archives to customize and process the time series of images. This manuscript introduces RGISTools, a new software that solves these issues, and provides a working example on water mapping, which is a socially and environmentally relevant research field. The case study uses a digital elevation model and a rarely assessed combination of Landsat-8 and Sentinel-2 imagery to determine the water level of a reservoir in Northern Spain. The case study demonstrates how to acquire and process time series of surface reflectance data in an efficient manner. Our method achieves reasonably accurate results, with a root mean squared error of 0.90 m. Future improvements of the package involve the expansion of the workflow to cover the processing of radar images. This should counteract the limitation of the cloud coverage with multi-spectral images.
Achieving Higher Resolution Lake Area from Remote Sensing Images Through an Unsupervised Deep Learning Super-Resolution Method
Authors: Qin Mengjiao , Hu Linshu , Du Zhenhong , Gao Yi , Qin Lianjie , Zhang Feng , Liu Renyi
Published: June 2020

Lakes have been identified as an important indicator of climate change and a finer lake area can better reflect the changes. In this paper, we propose an effective unsupervised deep gradient network (UDGN) to generate a higher resolution lake area from remote sensing images. By exploiting the power of deep learning, UDGN models the internal recurrence of information inside the single image and its corresponding gradient map to generate images with higher spatial resolution. The gradient map is derived from the input image to provide important geographical information. Since the training samples are only extracted from the input image, UDGN can adapt to different settings per image. Based on the superior adaptability of the UDGN model, two strategies are proposed for super-resolution (SR) mapping of lakes from multispectral remote sensing images. Finally, Landsat 8 and MODIS (moderate-resolution imaging spectroradiometer) images from two study areas on the Tibetan Plateau in China were used to evaluate the performance of UDGN. Compared with four unsupervised SR methods, UDGN obtained the best SR results as well as lake extraction results in terms of both quantitative and visual aspects. The experiments prove that our approach provides a promising way to break through the limitations of median-low resolution remote sensing images in lake change monitoring, and ultimately support finer lake applications.
Source–Detector Spectral Pairing-Related Inaccuracies in Pulse Oximetry: Evaluation of the Wavelength Shift
Authors: Tsiakaka Olivier , Gosselin Benoit , Feruglio Sylvain
Published: June 2020

Pulse oximetry enables oxygen saturation estimation ( S p O 2) non-invasively in real time with few components and modest processing power. With the advent of affordable development kits dedicated to the monitoring of biosignals, capabilities once reserved to hospitals and high-end research laboratories are becoming accessible for rapid prototyping. While one may think that medical-grade equipment differs greatly in quality, surprisingly, we found that the performance requirements are not widely different from available consumer-grade components, especially regarding the photodetection module in pulse oximetry. This study investigates how the use of candidate light sources and photodetectors for the development of a custom S p O 2 monitoring system can lead to inaccuracies when using the standard computational model for oxygen saturation without calibration. Following the optical characterization of selected light sources, we compare the extracted parameters to the key features in their respective datasheet. We then quantify the wavelength shift caused by spectral pairing of light sources in association with photodetectors. Finally, using the widely used approximation, we report the resulting absolute error in S p O 2 estimation and show that it can lead up to 8% of the critical 90–100% saturation window.
A 1-nS 1-V Sub-1-µW Linear CMOS OTA with Rail-to-Rail Input for Hz-Band Sensory Interfaces
Authors: Jakusz Jacek , Jendernalik Waldemar , Blakiewicz Grzegorz , Kłosowski Miron , Szczepański Stanisław
Published: June 2020

The paper presents an operational transconductance amplifier (OTA) with low transconductance (0.62–6.28 nS) and low power consumption (28–270 nW) for the low-frequency analog front-ends in biomedical sensor interfaces. The proposed OTA implements an innovative, highly linear voltage-to-current converter based on the channel-length-modulation effect, which can be rail-to-rail driven. At 1-V supply and 1-Vpp asymmetrical input driving, the linearity error in the current-voltage characteristics is 1.5%, while the total harmonic distortion (THD) of the output current is 0.8%. For a symmetrical 2-Vpp input drive, the linearity error is 0.3%, whereas THD reaches 0.2%. The linearity is robust for the mismatch and the process-voltage-and-temperature (PVT) variations. The temperature drift of transconductance is 10 pS/°C. The prototype circuit was fabricated in 180-nanometer CMOS technology.
Sensitivity Analysis of Sensors in a Hydraulic Condition Monitoring System Using CNN Models
Authors: König Caroline , Helmi Ahmed Mohamed
Published: June 2020

Condition monitoring (CM) is a useful application in industry 4.0, where the machine’s health is controlled by computational intelligence methods. Data-driven models, especially from the field of deep learning, are efficient solutions for the analysis of time series sensor data due to their ability to recognize patterns in high dimensional data and to track the temporal evolution of the signal. Despite the excellent performance of deep learning models in many applications, additional requirements regarding the interpretability of machine learning models are getting relevant. In this work, we present a study on the sensitivity of sensors in a deep learning based CM system providing high-level information about the relevance of the sensors. Several convolutional neural networks (CNN) have been constructed from a multisensory dataset for the prediction of different degradation states in a hydraulic system. An attribution analysis of the input features provided insights about the contribution of each sensor in the prediction of the classifier. Relevant sensors were identified, and CNN models built on the selected sensors resulted equal in prediction quality to the original models. The information about the relevance of sensors is useful for the system’s design to decide timely on the required sensors.
Low-Profile Slotted Metamaterial Antenna Based on Bi Slot Microstrip Patch for 5G Application
Authors: Hoque Ahasanul , Islam Mohammad Tariqul , Almutairi Ali F.
Published: June 2020

A low-profile high-directivity, and double-negative (DNG) metamaterial-loaded antenna with a slotted patch is proposed for the 5G application. The radiated slotted arm as a V shape has been extended to provide a low-profile feature with a two-isometric view square patch structure, which accelerates the electromagnetic (EM) resonance. Besides, the tapered patch with two vertically split parabolic horns and the unit cell metamaterial expedite achieve more directive radiation. Two adjacent splits with meta units enhance the surface current to modify the actual electric current, which is induced by a substrate-isolated EM field. As a result, the slotted antenna shows a 7.14 dBi realized gain with 80% radiation efficiency, which is quite significant. The operation bandwidth is 4.27–4.40 GHz, and characteristic impedance approximately remains the same (50 Ω) to give a VSWR (voltage Standing wave ratio) of less than 2, which is ideal for the expected application field. The overall size of the antenna is 60 × 40 × 1.52 mm. Hence, it has potential for future 5G applications, like Internet of Things (IoT), healthcare systems, smart homes, etc.
Ultrasensitive Surface Plasmon Resonance Biosensor Using Blue Phosphorus–Graphene Architecture
Authors: Li Keyi , Li Lintong , Xu Nanlin , Peng Xiao , Zhou Yingxin , Yuan Yufeng , Song Jun , Qu Junle
Published: June 2020

This study theoretically proposed a novel surface plasmon resonance biosensor by incorporating emerging two dimensional material blue phosphorus and graphene layers with plasmonic gold film. The excellent performances employed for biosensing can be realized by accurately tuning the thickness of gold film and the number of blue phosphorus interlayer. Our proposed plasmonic biosensor architecture designed by phase modulation is much superior to angular modulation, providing 4 orders of magnitude sensitivity enhancement. In addition, the optimized stacked configuration is 42 nm Au film/2-layer blue phosphorus /4-layer graphene, which can produce the sharpest differential phase of 176.7661 degrees and darkest minimum reflectivity as low as 5.3787 × 10−6. For a tiny variation in local refractive index of 0.0012 RIU (RIU, refractive index unit) due to the binding interactions of aromatic biomolecules, our proposed biosensor can provide an ultrahigh detection sensitivity up to 1.4731 × 105 °/RIU, highly promising for performing ultrasensitive biosensing application.
Multi-Camera Vehicle Tracking Using Edge Computing and Low-Power Communication
Authors: Nikodem Maciej , Słabicki Mariusz , Surmacz Tomasz , Mrówka Paweł , Dołęga Cezary
Published: June 2020

Typical approaches to visual vehicle tracking across large area require several cameras and complex algorithms to detect, identify and track the vehicle route. Due to memory requirements, computational complexity and hardware constrains, the video images are transmitted to a dedicated workstation equipped with powerful graphic processing units. However, this requires large volumes of data to be transmitted and may raise privacy issues. This paper presents a dedicated deep learning detection and tracking algorithms that can be run directly on the camera’s embedded system. This method significantly reduces the stream of data from the cameras, reduces the required communication bandwidth and expands the range of communication technologies to use. Consequently, it allows to use short-range radio communication to transmit vehicle-related information directly between the cameras, and implement the multi-camera tracking directly in the cameras. The proposed solution includes detection and tracking algorithms, and a dedicated low-power short-range communication for multi-target multi-camera tracking systems that can be applied in parking and intersection scenarios. System components were evaluated in various scenarios including different environmental and weather conditions.
Detection of Drechslera avenae (Eidam) Sharif [Helminthosporium avenae (Eidam)] in Black Oat Seeds (Avena strigosa Schreb) Using Multispectral Imaging
Authors: França-Silva Fabiano , Rego Carlos Henrique Queiroz , Gomes-Junior Francisco Guilhien , Moraes Maria Heloisa Duarte de , Medeiros André Dantas de , Silva Clíssia Barboza da
Published: June 2020

Conventional methods for detecting seed-borne fungi are laborious and time-consuming, requiring specialized analysts for characterization of pathogenic fungi on seed. Multispectral imaging (MSI) combined with machine vision was used as an alternative method to detect Drechslera avenae (Eidam) Sharif [Helminthosporium avenae (Eidam)] in black oat seeds (Avena strigosa Schreb). The seeds were inoculated with Drechslera avenae (D. avenae) and then incubated for 24, 72 and 120 h. Multispectral images of non-infested and infested seeds were acquired at 19 wavelengths within the spectral range of 365 to 970 nm. A classification model based on linear discriminant analysis (LDA) was created using reflectance, color, and texture features of the seed images. The model developed showed high performance of MSI in detecting D. avenae in black oat seeds, particularly using color and texture features from seeds incubated for 120 h, with an accuracy of 0.86 in independent validation. The high precision of the classifier showed that the method using images captured in the Ultraviolet A region (365 nm) could be easily used to classify black oat seeds according to their health status, and results can be achieved more rapidly and effectively compared to conventional methods.
Improved Handwritten Digit Recognition Using Convolutional Neural Networks (CNN)
Authors: Ahlawat Savita , Choudhary Amit , Nayyar Anand , Singh Saurabh , Yoon Byungun
Published: June 2020

Traditional systems of handwriting recognition have relied on handcrafted features and a large amount of prior knowledge. Training an Optical character recognition (OCR) system based on these prerequisites is a challenging task. Research in the handwriting recognition field is focused around deep learning techniques and has achieved breakthrough performance in the last few years. Still, the rapid growth in the amount of handwritten data and the availability of massive processing power demands improvement in recognition accuracy and deserves further investigation. Convolutional neural networks (CNNs) are very effective in perceiving the structure of handwritten characters/words in ways that help in automatic extraction of distinct features and make CNN the most suitable approach for solving handwriting recognition problems. Our aim in the proposed work is to explore the various design options like number of layers, stride size, receptive field, kernel size, padding and dilution for CNN-based handwritten digit recognition. In addition, we aim to evaluate various SGD optimization algorithms in improving the performance of handwritten digit recognition. A network’s recognition accuracy increases by incorporating ensemble architecture. Here, our objective is to achieve comparable accuracy by using a pure CNN architecture without ensemble architecture, as ensemble architectures introduce increased computational cost and high testing complexity. Thus, a CNN architecture is proposed in order to achieve accuracy even better than that of ensemble architectures, along with reduced operational complexity and cost. Moreover, we also present an appropriate combination of learning parameters in designing a CNN that leads us to reach a new absolute record in classifying MNIST handwritten digits. We carried out extensive experiments and achieved a recognition accuracy of 99.87% for a MNIST dataset.
Hand-Made Embroidered Electromyography: Towards a Solution for Low-Income Countries
Authors: Pitou Samuel , Michael Brendan , Thompson Karina , Howard Matthew
Published: June 2020

Surface electromyography is used for non-invasive evaluations of the neuromuscular system and conventionally involves electrodes placed on the skin to collect electrical signals associated with muscle activity. Recently, embroidered electrodes have been presented as a low-cost alternative to the current commercial solutions. However, the high cost of equipment used in their fabrication forms a barrier to deployment. To address this, this paper presents the first study into the hand-sewing of electrodes for surface electromyography to assess its feasibility as an affordable, alternative means of production. In experiments reported here, batches of hand-sewn electrodes from six novice embroiderers are tested for (i) manufacturing consistency, and (ii) myographic data acquisition against conventional gelled and machine-sewn electrodes. First, the electrical properties of the created electrodes are assessed through simple resistance measurements. Then, linear regression is performed using electromyography data to test if force-variation detection is feasible. The results demonstrate that hand-sewn electrodes provide similar sensitivity to force variation as their machine-sewn counterparts according to the linear regression gradients calculated ( 8.84 using the hand-sewn electrodes and 9.38 using the machine-sewn electrodes, on the flexor muscles of the forearm). This suggests that hand-made, low-cost textile interfaces could be deployed using local production in developing economies.
The Role of Dactylis Glomerata and Diesel Oil in the Formation of Microbiome and Soil Enzyme Activity
Authors: Borowik Agata , Wyszkowska Jadwiga , Kucharski Mirosław , Kucharski Jan
Published: June 2020

The global demand for petroleum contributes to a significant increase in soil pollution with petroleum-based products that pose a severe risk not only to humans but also to plants and the soil microbiome. The increasing pollution of the natural environment urges the search for effective remediation methods. Considering the above, the objective of this study was to determine the usability of Dactylis glomerata for the degradation of hydrocarbons contained in diesel oil (DO), as well as the effects of both the plant tested and DO on the biochemical functionality and changes in the soil microbiome. The experiment was conducted in a greenhouse with non-polluted soil as well as soil polluted with DO and phytoremediated with Dactylis glomerata. Soil pollution with DO increased the numbers of microorganisms and soil enzymes and decreased the value of the ecophysiological diversity index of microorganisms. Besides, it contributed to changes in the bacterial structure at all taxonomic levels. DO was found to increase the abundance of Proteobacteria and to decrease that of Actinobacteria, Acidobacteria, Chloroflexi, Gemmatimonadetes and Firmicutes. In the non-polluted soil, the core microbiome was represented by Kaistobacter and Rhodoplanes, whereas in the DO-polluted soil, it was represented by Parvibaculum and Rhodococcus. In soil sown with Dactylis glomerata, gasoline fraction (C6–C12) degradation was higher by 17%; mineral oil (C12–C35), by 9%; benzene, by 31%; anthracene, by 12%; chrysene, by 38%; benzo(a)anthracene, by 19%; benzo(a)pyrene, by 17%; benzo(b)fluoranthene, by 15%; and benzo(k)fluoranthene, by 18% than in non-sowed soil. To conclude, Dactylis glomerata proved useful in degrading DO hydrocarbons and, therefore, may be recommended for the phytoremediation of soils polluted with petroleum-based products. It has been shown that the microbiological, biochemical and chemical tests are fast and sensitive in the diagnosis of soil contamination with petroleum products, and a combination of all these tests gives a reliable assessment of the state of soils.
An Analysis of a Compact Label-Free Guiding-Wave Biosensor Based on a Semiconductor-Clad Dielectric Strip Waveguide
Authors: Angulo Barrios Carlos
Published: June 2020

In this paper, a compact, integrated, semiconductor-clad strip waveguide label-free biosensor is proposed and analyzed. The device is based on CMOS-compatible materials such as amorphous-Si and silicon oxynitride. The optical sensor performance has been modeled by a three-dimensional beam propagation method. The simulations indicate that a 20-μm-long device can exhibit a surface limit of detection of 3 ng/cm2 for avidin molecules in aqueous solution. The sensor performance compares well to those displayed by other photonic biosensors with much larger footprints. The fabrication tolerances have been also studied in order to analyze the feasibility of the practical implementation of the biosensor.