bluuebunny/crossref_metadata_2025 huggingface dataset:
Chemical Composition, Bioactivity and Safety Aspects of Kuding Tea—From Beverage to Herbal Extract
Authors: Wüpper Svenja , Lüersen Kai , Rimbach Gerald
Published: September 2020

Kuding tea (KT) is a bitter-tasting herbal tea that has been commonly used in traditional Chinese medicine (TCM). The large-leaved Ku-Ding-Cha (Aquifoliaceae) is composed of its representative species Ilex latifolia Thunb and Ilex kudingcha C.J. Tseng. Because of its potential lipid-lowering, body weight-reducing and blood-glucose-lowering properties, KT has increasingly been recognised for its importance over the past several decades. KT is no longer used only as a beverage, and various extraction methods have been applied to obtain aqueous and ethanolic KT extracts (KTE) or their fractions, which could potentially be used as dietary supplements. The major bioactive components of KT are triterpene saponins and polyphenols, but the composition of KT differs substantially between and among the different KT species. This in turn might affect the physiological effects of KT. KT exhibits antiobesity properties, possibly partly by affecting the intestinal microbiota. In addition, KT may mediate putative antioxidative, anti-inflammatory and anticancer activities. However, there is evidence that high KTE supplementation can adversely affect liver metabolism. The physiological relevance of KT in humans remains rather unclear since the potential health benefits of KT and its constituents reviewed here are mainly derived on the basis of in vitro and animal studies.
Whole Blueberry and Isolated Polyphenol-Rich Fractions Modulate Specific Gut Microbes in an In Vitro Colon Model and in a Pilot Study in Human Consumers
Authors: Ntemiri Alexandra , Ghosh Tarini S. , Gheller Molly E. , Tran Tam T. T. , Blum Jamie E. , Pellanda Paola , Vlckova Klara , Neto Marta C. , Howell Amy , Thalacker-Mercer Anna , O’Toole Paul W.
Published: September 2020

Blueberry (BB) consumption is linked to improved health. The bioconversion of the polyphenolic content of BB by fermentative bacteria in the large intestine may be a necessary step for the health benefits attributed to BB consumption. The identification of specific gut microbiota taxa that respond to BB consumption and that mediate the bioconversion of consumed polyphenolic compounds into bioactive forms is required to improve our understanding of how polyphenols impact human health. We tested the ability of polyphenol-rich fractions purified from whole BB—namely, anthocyanins/flavonol glycosides (ANTH/FLAV), proanthocyanidins (PACs), the sugar/acid fraction (S/A), and total polyphenols (TPP)—to modulate the fecal microbiota composition of healthy adults in an in vitro colon system. In a parallel pilot study, we tested the effect of consuming 38 g of freeze-dried BB powder per day for 6 weeks on the fecal microbiota of 17 women in two age groups (i.e., young and older). The BB ingredients had a distinct effect on the fecal microbiota composition in the artificial colon model. The ANTH/FLAV and PAC fractions were more effective in promoting microbiome alpha diversity compared to S/A and TPP, and these effects were attributed to differentially responsive taxa. Dietary enrichment with BB resulted in a moderate increase in the diversity of the microbiota of the older subjects but not in younger subjects, and certain health-relevant taxa were significantly associated with BB consumption. Alterations in the abundance of some gut bacteria correlated not only with BB consumption but also with increased antioxidant activity in blood. Collectively, these pilot data support the notion that BB consumption is associated with gut microbiota changes and health benefits.
Krill Oil Has Different Effects on the Plasma Lipidome Compared with Fish Oil Following 30 Days of Supplementation in Healthy Women: A Randomized Controlled and Crossover Study
Authors: Sung Hyunsin H. , Sinclair Andrew J. , Huynh Kevin , Smith Adam A. T. , Mellett Natalie A. , Meikle Peter J. , Su Xiao Q.
Published: September 2020

This is a follow-up of our previous postprandial study and it focused on the plasma lipidomic responses to 30 days of krill oil (KO) versus fish oil (FO) supplementations in healthy women. Eleven women (aged 18–50 years) consumed KO or FO for 30 days in a randomized, cross-over study, with at least a four-week washout period between supplementations. The daily supplements provided 1.27 g/day of long-chain (LC) omega-3 polyunsaturated fatty acids (PUFA) from KO (containing 0.76 g eicosapentaenoic acid (EPA), 0.42 g docosahexaenoic acid (DHA)) and 1.44 g/day from FO (containing 0.79 g EPA, 0.47 g DHA). Fasting plasma samples at days 0, 15, and 30 were analyzed using gas chromatography and liquid chromatography electrospray ionisation-tandem mass spectrometry. KO resulted in a significantly greater relative area under the curve (relAUC) for plasma EPA after 30 days. Lipidomic analysis showed that 26 of 43 lipid molecular species had a significantly greater relAUC in the KO group, while 17/43 showed a significantly lower relAUC compared with the FO group. More than 38% of the lipids species which increased more following KO contained omega-3 PUFA, while where FO was greater than KO, only 12% contained omega-3 PUFA. These data show that KO and FO do not have equivalent effects on the plasma lipidome.
Lifestyle Risk Factors for Type 2 Diabetes Mellitus and National Diabetes Care Systems in European Countries
Authors: Altobelli Emma , Angeletti Paolo Matteo , Profeta Valerio F. , Petrocelli Reimondo
Published: September 2020

Background. Diabetes is increasing by 3.09% per year in males and 1.92% in females. Lifestyle risk factors are related to diabetes. The aim of this work is to highlight within EU-28 countries the distribution percentages of some lifestyle risk factors and some components of diabetes health care. Methods. A literature search was conducted to highlight the presence of diabetes registries, which are fundamental tools for disease surveillance and health planning; the presence of a national diabetes plan (NDP); the care setting; and methods used for reimbursement of drugs, devices, and coverage of any comorbidities associated with diabetes. A multiple correspondence analysis (MCA) was carried out to evaluate the possible associations between the variables considered. Results. The highest percentages of diabetes (>10%) are registered in Bulgaria, Malta, and Hungary. Concerning the prevalence of overweight, no European country shows overall percentages of less than 50%. Regarding obesity, 57% of countries show prevalence rates of 25%. The record for physical inactivity belongs to Malta, with 45% of individuals being inactive. The percentage of physical inactivity for females is higher than for males across Europe. In total, 57% of the countries have an insurance-based health system, while 12 countries have public national health systems. Further, 57% of countries have an NDP, while 42% of the EU countries have established a prevalence register for diabetes. Conclusions. Prevalence rates for type 2 DM in the range of 8–9% are noted in 50% of EU-28 countries. In total, 21 out of EU countries show a high prevalence rate for overweight, while 7% of EU-28 countries have an obesity prevalence rate of 25%. Diabetes treatment is entrusted to general practitioners in most countries. The results of this work highlight the differences between countries, but also between genders.
Protein-Energy Wasting Assessment and Clinical Outcomes in Patients with Acute Kidney Injury: A Systematic Review with Meta-Analysis
Authors: Khor Ban-Hock , Tiong Hui-Ci , Tan Shing Cheng , Abdul Rahman Raha , Abdul Gafor Abdul Halim
Published: September 2020

Nutritional assessment is essential to identify patients with acute kidney injury (AKI) who are protein-energy wasting (PEW) and at risk of poor clinical outcomes. This systematic review aimed to investigate the relationship of nutritional assessments for PEW with clinical outcomes in patients with AKI. A systematic search was performed in PubMed, Scopus, and Cochrane Library databases using search terms related to PEW, nutrition assessment, and AKI to identify prospective cohort studies that involved AKI adult patients with at least one nutritional assessment performed and reported relevant clinical outcomes, such as mortality, length of stay, and renal outcomes associated with the nutritional parameters. Seventeen studies reporting eight nutritional parameters for PEW assessment were identified and mortality was the main clinical outcome reported. A meta-analysis showed that PEW assessed using subjective global assessment (SGA) was associated with greater mortality risk (RR: 1.99, 95% CI: 1.36–2.91). Individual nutrition parameters, such as serum chemistry, body mass, muscle mass, and dietary intakes, were not consistently associated with mortality. In conclusion, SGA is a valid tool for PEW assessment in patients with AKI, while other nutrition parameters in isolation had limited validity for PEW assessment.
Cavity Ring-Down Spectroscopy for Molecular Trace Gas Detection Using A Pulsed DFB QCL Emitting at 6.8 µm
Authors: Gadedjisso-Tossou Komlan S. , Stoychev Lyubomir I. , Mohou Messanh A. , Cabrera Humberto , Niemela Joseph , Danailov Miltcho B. , Vacchi Andrea
Published: September 2020

A trace gas sensor based on pulsed cavity ring-down spectroscopy (CRDS) was developed for measurement of the ν4 fundamental vibrational band of ammonia (NH3) centered at 1468.898 cm−1. A pulsed distributed feedback quantum cascade laser (DFB-QCL) operating at 6.8 µm (1470.58 cm−1) quite well covered the absorption band of the ammonia and strong fundamental vibrational absorption bands of different molecular gases in this unexplored region. The cavity was partially evacuated down to 0.4 Atm by a turbo-molecular pump to reduce the partial interference between the NH3 spectra and water near the absorption peak of ammonia. A sensitivity of nine parts per billion was reached for a measurement time of 120 s as well as an optical path length of 226 m. The device demonstrated high spectral performance and versatility due to its wide tuning range, narrow linewidth, and comparatively high-energy mid-IR radiation in the relatively unexplored 6.8 µm region, which is very important for high-resolution spectroscopy of a variety of gases.
Spatiotemporal Trends in Wildfires across the Western United States (1950–2019)
Authors: Weber Keith T. , Yadav Rituraj
Published: September 2020

Wildfire regimes are changing across the globe with several ecosystems witnessing more frequent fires across longer fire seasons. The western United States is one such region. The NASA RECOVER Historic Fires Database (HFD) contains all documented wildfires across the western United States occurring between 1950 and 2019 (n = 55,566). This study analyzed the spatiotemporal patterns of these wildfires using ArcGIS Pro Geographic Information System (GIS) software to characterize changes in fire frequency, size, and severity over time. Analysis of annual fire frequency and acres burned reveals a near exponential growth in fire frequency (R2 = 0.71, P < 0.001) and size (R2 = 0.67, P < 0.001) since 1950. A comparison of mean and median acres burned annually suggests the occurrence of mega-fires (wildfires burning more than 100,000 acres) is also increasing. To illustrate this, this study found the mean size of fires occurring in the decade of the 1950s was 1204 acres while in the most recent decade (2010–2019) mean fire size has more than doubled, reaching an average of 3474 acres. The trend in fire severity between 2001 and 2017 used 365 Differenced Normalized Burn Ratio (dNBR) layers calculated using Landsat or Sentinel-2 satellite imagery. Results suggest fire severity has remained relatively stable in light of increasing fire frequency and size, however more research is required to more fully understand changes in fire severity. The results of this study and other related studies are important as they provide useful information to land managers and policy makers regarding the changing wildfire regime currently being witnessed across the western United States.
Evaluating Post-Fire Vegetation Recovery in Cajander Larch Forests in Northeastern Siberia Using UAV Derived Vegetation Indices
Authors: Talucci Anna C. , Forbath Elena , Kropp Heather , Alexander Heather D. , DeMarco Jennie , Paulson Alison K. , Zimov Nikita S. , Zimov Sergei , Loranty Michael M.
Published: September 2020

The ability to monitor post-fire ecological responses and associated vegetation cover change is crucial to understanding how boreal forests respond to wildfire under changing climate conditions. Uncrewed aerial vehicles (UAVs) offer an affordable means of monitoring post-fire vegetation recovery for boreal ecosystems where field campaigns are spatially limited, and available satellite data are reduced by short growing seasons and frequent cloud cover. UAV data could be particularly useful across data-limited regions like the Cajander larch (Larix cajanderi Mayr.) forests of northeastern Siberia that are susceptible to amplified climate warming. Cajander larch forests require fire for regeneration but are also slow to accumulate biomass post-fire; thus, tall shrubs and other understory vegetation including grasses, mosses, and lichens dominate for several decades post-fire. Here we aim to evaluate the ability of two vegetation indices, one based on the visible spectrum (GCC; Green Chromatic Coordinate) and one using multispectral data (NDVI; Normalized Difference Vegetation Index), to predict field-based vegetation measures collected across post-fire landscapes of high-latitude Cajander larch forests. GCC and NDVI showed stronger linkages with each other at coarser spatial resolutions e.g., pixel aggregated means with 3-m, 5-m and 10-m radii compared to finer resolutions (e.g., 1-m or less). NDVI was a stronger predictor of aboveground carbon biomass and tree basal area than GCC. NDVI showed a stronger decline with increasing distance from the unburned edge into the burned forest. Our results show NDVI tended to be a stronger predictor of some field-based measures and while GCC showed similar relationships with the data, it was generally a weaker predictor of field-based measures for this region. Our findings show distinguishable edge effects and differentiation between burned and unburned forests several decades post-fire, which corresponds to the relatively slow accumulation of biomass for this ecosystem post-fire. These findings show the utility of UAV data for NDVI in this region as a tool for quantifying and monitoring the post-fire vegetation dynamics in Cajander larch forests.
Studying Land Cover Changes in a Malaria-Endemic Cambodian District: Considerations and Constraints
Authors: Pepey Anaïs , Souris Marc , Vantaux Amélie , Morand Serge , Lek Dysoley , Mueller Ivo , Witkowski Benoit , Herbreteau Vincent
Published: September 2020

Malaria control is an evolving public health concern, especially in times of resistance to insecticides and to antimalarial drugs, as well as changing environmental conditions that are influencing its epidemiology. Most literature demonstrates an increased risk of malaria transmission in areas of active deforestation, but knowledge about the link between land cover evolution and malaria risk is still limited in some parts of the world. In this study, we discuss different methods used for analysing the interaction between deforestation and malaria, then highlight the constraints that can arise in areas where data is lacking. For instance, there is a gap in knowledge in Cambodia about components of transmission, notably missing detailed vector ecology or epidemiology data, in addition to incomplete prevalence data over time. Still, we illustrate the situation by investigating the evolution of land cover and the progression of deforestation within a malaria-endemic area of Cambodia. To do so, we investigated the area by processing high-resolution satellite imagery from 2018 (1.5 m in panchromatic mode and 6 m in multispectral mode) and produced a land use/land cover map, to complete and homogenise existing data from 1988 and from 1998 to 2008 (land use/land cover from high-resolution satellite imagery). From these classifications, we calculated different landscapes metrics to quantify evolution of deforestation, forest fragmentation and landscape diversity. Over the 30-year period, we observed that deforestation keeps expanding, as diversity and fragmentation indices globally increase. Based on these results and the available literature, we question the mechanisms that could be influencing the relationship between land cover and malaria incidence and suggest further analyses to help elucidate how deforestation can affect malaria dynamics.
Detection of Italian Ryegrass in Wheat and Prediction of Competitive Interactions Using Remote-Sensing and Machine-Learning Techniques
Authors: Sapkota Bishwa , Singh Vijay , Neely Clark , Rajan Nithya , Bagavathiannan Muthukumar
Published: September 2020

Italian ryegrass (Lolium perenne ssp. multiflorum (Lam) Husnot) is a troublesome weed species in wheat (Triticum aestivum) production in the United States, severely affecting grain yields. Spatial mapping of ryegrass infestation in wheat fields and early prediction of its impact on yield can assist management decision making. In this study, unmanned aerial systems (UAS)-based red, green and blue (RGB) imageries acquired at an early wheat growth stage in two different experimental sites were used for developing predictive models. Deep neural networks (DNNs) coupled with an extensive feature selection method were used to detect ryegrass in wheat and estimate ryegrass canopy coverage. Predictive models were developed by regressing early-season ryegrass canopy coverage (%) with end-of-season (at wheat maturity) biomass and seed yield of ryegrass, as well as biomass and grain yield reduction (%) of wheat. Italian ryegrass was detected with high accuracy (precision = 95.44 ± 4.27%, recall = 95.48 ± 5.05%, F-score = 95.56 ± 4.11%) using the best model which included four features: hue, saturation, excess green index, and visible atmospheric resistant index. End-of-season ryegrass biomass was predicted with high accuracy (R2 = 0.87), whereas the other variables had moderate to high accuracy levels (R2 values of 0.74 for ryegrass seed yield, 0.73 for wheat biomass reduction, and 0.69 for wheat grain yield reduction). The methodology demonstrated in the current study shows great potential for mapping and quantifying ryegrass infestation and predicting its competitive response in wheat, allowing for timely management decisions.
SLRL4D: Joint Restoration of Subspace Low-Rank Learning and Non-Local 4-D Transform Filtering for Hyperspectral Image
Authors: Sun Le , He Chengxun , Zheng Yuhui , Tang Songze
Published: September 2020

During the process of signal sampling and digital imaging, hyperspectral images (HSI) inevitably suffer from the contamination of mixed noises. The fidelity and efficiency of subsequent applications are considerably reduced along with this degradation. Recently, as a formidable implement for image processing, low-rank regularization has been widely extended to the restoration of HSI. Meanwhile, further exploration of the non-local self-similarity of low-rank images are proven useful in exploiting the spatial redundancy of HSI. Better preservation of spatial-spectral features is achieved under both low-rank and non-local regularizations. However, existing methods generally regularize the original space of HSI, the exploration of the intrinsic properties in subspace, which leads to better denoising performance, is relatively rare. To address these challenges, a joint method of subspace low-rank learning and non-local 4-d transform filtering, named SLRL4D, is put forward for HSI restoration. Technically, the original HSI is projected into a low-dimensional subspace. Then, both spectral and spatial correlations are explored simultaneously by imposing low-rank learning and non-local 4-d transform filtering on the subspace. The alternating direction method of multipliers-based algorithm is designed to solve the formulated convex signal-noise isolation problem. Finally, experiments on multiple datasets are conducted to illustrate the accuracy and efficiency of SLRL4D.
Evaluation the Performance of Several Gridded Precipitation Products over the Highland Region of Yemen for Water Resources Management
Authors: AL-Falahi Ali Hamoud , Saddique Naeem , Spank Uwe , Gebrechorkos Solomon H. , Bernhofer Christian
Published: September 2020

Management of water resources under climate change is one of the most challenging tasks in many arid and semiarid regions. A major challenge in countries, such as Yemen, is the lack of sufficient and long-term climate data required to drive hydrological models for better management of water resources. In this study, we evaluated the accuracy of accessible satellite and reanalysis-based precipitation products against observed data from Al Mahwit governorate (highland region, Yemen) during 1998–2007. Here, we evaluated the accuracy of the Climate Hazards Group Infrared Precipitation with Station (CHIRPS) data, National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR), Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), Tropical Rainfall Measuring Mission (TRMM 3B42), Unified Gauge-Based Analysis of Global Daily Precipitation (CPC), and European Atmospheric Reanalysis (ERA-5). The evaluation was performed on daily, monthly, and annual time steps by directly comparing the data from each single station with the data from the nearest grid box for each product. At a daily timescale, CHIRPS captures the daily rainfall characteristics best, such as the number of wet days, with average deviation from wet durations around 11.53%. TRMM 3B42 is the second-best performing product for a daily estimate with an average deviation of around 34.7%. However, CFSR (85.3%) and PERSIANN-CDR (103%) and ERA-5 (−81.13%) show an overestimation and underestimation of wet days and do not reflect rainfall variability of the study area. Moreover, CHIRPS is the most accurate gridded product on a monthly basis with high correlation and lower bias. The average monthly correlation between the observed and CHIRPS, TRMM 3B42, PERSIANN-CDR, CPC, ERA-5, and CFSR is 0.78, 0.56, 0.53, 0.15, 0.20, and 0.51, respectively. The average monthly bias is −2.9, −5.25, 7.35, −25.29, −24.96, and 16.68 mm for CHIRPS, TRMM 3B42, PERSIANN-CDR, CPC, ERA-5, and CFSR, respectively. CHIRPS displays the spatial distribution of annual rainfall pattern well with percent bias (Pbias) of around −8.68% at the five validation points, whereas TRMM 3B42, PERSIANN-CDR, and CFSR show a deviation of greater than 15.30, 22.90, and 66.21%, respectively. CPC and ERA-5 show Pbias of about −88.6% from observed data. Overall, in absence of better data, CHIRPS data can be used for hydrological and climate change studies on the highland region of Yemen where precipitation is often episodical and measurement records are spatially and temporally limited.
Recent Abnormal Hydrologic Behavior of Tibetan Lakes Observed by Multi-Mission Altimeters
Authors: Zhan Pengfei , Song Chunqiao , Wang Jida , Li Wenkai , Ke Linghong , Liu Kai , Chen Tan
Published: September 2020

Inland lakes in the Tibetan Plateau (TP) with closed catchments and minimal human disturbance are an important indicator of climate change. However, the examination of changes in the spatiotemporal patterns of Tibetan lakes, especially water level variations, is limited due to inadequate access to measurements. This obstacle has been improved by the development of satellite altimetry observations. The more recent studies revealed that the trend of central TP to grow decreased or reversed between 2010 and 2016. However, thus far, this trend has not been investigated to determine whether this pattern would last for the following years. This study aims to combine the traditional (launched before 2010, e.g., TOPEX/POSEIDON, ERS-1, ERS-2, Jason-1/-2, and Envisat) and recently advanced (launched after 2010, e.g., SARAL and Sentinel-3) altimetry observations to understand the Tibetan lake changes further in recent years. Therefore, we acquired information on the continuous lake level changes in Tibetan lakes using the lake level sequence integration method based on multisource altimetry satellites. The results revealed that water level changes in 22 examined lakes showed abrupt rises in 2016–2018, but the onsets and magnitudes of the rises varied among the lakes. During the study period, the water levels of the lakes (except Nam Co) revealed a drastic rising tendency with a mean rate of 0.74 m/a, which was remarkably higher than the average rate of water level rise over the period 2010–2015 (approximately 0.28 m/a). Specifically, the water level of the nine lakes in the Northern TP (NTP) displayed a significant rising trend, with an average rate of 0.82 m/a. In the Central TP (CTP), the lake level changes were generally divided into two categories. The water levels for the lakes in the Western CTP rose rapidly, while, in the Eastern CTP, the lake water levels rose slowly, with an average rising rate less than 0.40 m/a. The water levels for the lakes in the Northeastern TP (NETP) and Northwestern TP (NWTP) kept a stable rising tendency. According to the results of the climate analysis, the spatial differences of the lake level rise rates were primarily caused by the spatial and temporal changes of precipitation over the TP.
Sensor-Based Extraction Approaches of In-Vehicle Information for Driver Behavior Analysis
Authors: Kim Beomjun , Baek Yunju
Published: September 2020

Advances in vehicle technology have resulted in the development of vehicles equipped with sensors to acquire standardized information such as engine speed and vehicle speed from the in-vehicle controller area network (CAN) system. However, there are challenges in acquiring proprietary information from CAN frames, such as the brake pedal and steering wheel operation, which are essential for driver behavior analysis. Such information extraction requires electronic control unit identifier analysis and accompanying data interpretation. In this paper, we present a system for the automatic extraction of proprietary in-vehicle information using sensor data correlated with the desired information. First, the proposed system estimates the vehicle’s driving status through threshold-, random forest-, and long short-term memory-based techniques using inertial measurement unit and global positioning system values. Then, the system segments in-vehicle CAN frames using the estimation and evaluates each segment with our scoring method to select suitable candidates by examining the similarity between each candidate and its estimation through the suggested distance matching technique. We conduct comprehensive experiments of the proposed system using real vehicles in an urban environment. Performance evaluation shows that the estimation accuracy of the driving condition is 84.20%, and the extraction accuracy of the in-vehicle information is 82.31%, which implies that the presented approaches are quite feasible for automatic extraction of proprietary in-vehicle information.
Experimental Characterization of Single-Color Power LEDs Used as Photodetectors
Authors: Sticklus Jan , Hoeher Peter Adam , Hieronymi Martin
Published: September 2020

Semiconductor-based light emitting diodes can be used for photon emission as well as for detection of photons. In this paper, we present a fair comparison between off-the-shelf power Light emitting diodes (LEDs) and a silicon photodetector with respect to their spectral, temporal, and spatial properties. The examined LED series features unexpected good sensitivity and distinct optical bandpass characteristic suitable for daylight filtering or color selectivity. Primary application is short range optical underwater communication, but results are generally applicable.
Helping the Blind to Get through COVID-19: Social Distancing Assistant Using Real-Time Semantic Segmentation on RGB-D Video
Authors: Martinez Manuel , Yang Kailun , Constantinescu Angela , Stiefelhagen Rainer
Published: September 2020

The current COVID-19 pandemic is having a major impact on our daily lives. Social distancing is one of the measures that has been implemented with the aim of slowing the spread of the disease, but it is difficult for blind people to comply with this. In this paper, we present a system that helps blind people to maintain physical distance to other persons using a combination of RGB and depth cameras. We use a real-time semantic segmentation algorithm on the RGB camera to detect where persons are and use the depth camera to assess the distance to them; then, we provide audio feedback through bone-conducting headphones if a person is closer than 1.5 m. Our system warns the user only if persons are nearby but does not react to non-person objects such as walls, trees or doors; thus, it is not intrusive, and it is possible to use it in combination with other assistive devices. We have tested our prototype system on one blind and four blindfolded persons, and found that the system is precise, easy to use, and amounts to low cognitive load.
Ultra-Narrow-Band Filter Based on High Q Factor in Metallic Nanoslit Arrays
Authors: Guo Ling , Guo Mengran , Yang Hongyan , Ma Jun , Chen Shouhong
Published: September 2020

Here we propose a novel high Q ultra-narrow-band filter in the optical regime. Multiple high Q resonances are achieved in ultra-thin metallic nanoslit arrays on stacked low index–high index dielectric (LID–HID) substrate. Based on the cooperative effect of suppressed modes and transmission modes, the high spectral resolution of transmission peaks is obtained. The number and Q factor of transmission peaks can be freely manipulated by a simple combination of the stacked LID–HID. It is demonstrated that the linewidths of the transmission peaks can be reduced down to the extreme limit of 1 nm and the Q factor is up to 700 by optimizing the structure parameter of the three-layer LID–HID. The results provide a theoretical basis to design a multi-band nanophotonic device with a high Q factor and have potential applications in the next generation of high-resolution plasmonic biosensing and filtering.
Analyzing Sensor-Based Individual and Population Behavior Patterns via Inverse Reinforcement Learning
Authors: Lin Beiyu , Cook Diane J.
Published: September 2020

Digital markers of behavior can be continuously created, in everyday settings, using time series data collected by ambient sensors. The goal of this work was to perform individual- and population-level behavior analysis from such time series sensor data. In this paper, we introduce a novel algorithm—Resident Relative Entropy-Inverse Reinforcement Learning (RRE-IRL)—to perform an analysis of a single smart home resident or a group of residents, using inverse reinforcement learning. By employing this method, we learnt an individual’s behavioral routine preferences. We then analyzed daily routines for an individual and for eight smart home residents grouped by health diagnoses. We observed that the behavioral routine preferences changed over time. Specifically, the probability that the observed behavior was the same at the beginning of data collection as it was at the end (months later) was lower for residents experiencing cognitive decline than for cognitively healthy residents. When comparing aggregated behavior between groups of residents from the two diagnosis groups, the behavioral difference was even greater. Furthermore, the behavior preferences were used by a random forest classifier to predict a resident’s cognitive health diagnosis, with an accuracy of 0.84.
Non-Contact Measurement of Human Respiration and Heartbeat Using W-band Doppler Radar Sensor
Authors: Kim Heesoo , Jeong Jinho
Published: September 2020

This paper presents a W-band continuous-wave (CW) Doppler radar sensor for non-contact measurement of human respiration and heartbeat. The very short wavelength of the W-band signal allows a high-precision detection of the displacement of the chest surface by the heartbeat as well as respiration. The CW signal at 94 GHz is transmitted through a high-gain horn antenna to the human chest at a distance of 1 m. The phase-modulated reflection signal is down-converted to the baseband by the quadrature mixer with an excellent amplitude and phase matches between I and Q channels, which makes the IQ mismatch correction in the digital domain unnecessary. The baseband I and Q data are digitized using data acquisition (DAQ) board. The arctangent demodulation with automatic phase unwrapping is applied to the low-pass filtered I and Q data to effectively solve the null point problem. A slow-varying DC component is rejected in the demodulated signal by the trend removal algorithm. Then, the respiration signal with a frequency of 0.27 Hz and a displacement of ~6.1 mm is retrieved by applying a low-pass filter. Finally, the respiration signal is removed by the band-pass filter and the heartbeat signal is extracted, showing a frequency of 1.35 Hz and a displacement of ~0.26 mm. The extracted respiration and heartbeat rates are very close to the manual measurement results. The demonstrated W-band CW radar sensors can be easily applied to find the angular location of the human body by using a phased array under a compact size.
A Smart Band for Automatic Supervision of Restrained Patients in a Hospital Environment
Authors: Muñiz Rubén , Díaz Juan , Martínez Juan A. , Nuño Fernando , Bobes Julio , García-Portilla Mᵃ Paz , Sáiz Pilar A.
Published: September 2020

Mechanical contention (MC) is a restrictive, vital but controversial measure, prescribed in the majority of EU countries to handle patients with psycho-motor agitation that do not respond to other types of intervention, with an imminent risk of physical violence and aggression involved. This last resort approach implies risks for the somatic health of the contained individual that go from trauma injuries to, in some extreme cases, sudden death. Despite these risks, somatic supervision and the monitoring of patients under MC is limited, being periodically and manually carried out by nursing personnel with portable equipment. In this context, ensuring continuous monitoring using fully automated equipment is an uncovered yet urgent need. There are several devices already in the market capable of monitoring vital signs, but they are not specifically designed for these type of patients and they can be expensive and/or difficult to integrate with other systems from a software perspective. The work described in this paper gives answers to these necessities with the introduction of a low-cost system, targeted at psychiatric patients, for the acquisition and wireless transmission in real-time of physiological parameters, making use of micro-controllers for collecting and processing sensor data, and WiFi technology to upload the information to the server where a patient’s profile with all the relevant vital parameters resides. In addition to data collection and processing, an application aimed at use by nursing staff has also been developed to raise alerts in case any critical condition is detected.
Magneto-Impedance Biosensor Sensitivity: Effect and Enhancement
Authors: Sayad Abkar , Skafidas Efstratios , Kwan Patrick
Published: September 2020

Biosensors based on magneto-impedance (MI) effect are powerful tools for biomedical applications as they are highly sensitive, stable, exhibit fast response, small in size, and have low hysteresis and power consumption. However, the performance of these biosensors is influenced by a variety of factors, including the design, geometry, materials and fabrication procedures. Other less appreciated factors influencing the MI effect include measuring circuit implementation, the material used for construction, geometry of the thin film sensing element, and patterning shapes compatible with the interface microelectronic circuitry. The type magnetic (ferrofluid, Dynabeads, and nanoparticles) and size of the particles, the magnetic particle concentration, magnetic field strength and stray magnetic fields can also affect the sensor sensitivity. Based on these considerations it is proposed that ideal MI biosensor sensitivity could be achieved when the sensor is constructed in sandwich thick magnetic layers with large sensing area in a meander shape, measured with circuitry that provides the lowest possible external inductance at high frequencies, enclosed by a protective layer between magnetic particles and sensing element, and perpendicularly magnetized when detecting high-concentration of magnetic particles.
A Graph Convolutional Network-Based Deep Reinforcement Learning Approach for Resource Allocation in a Cognitive Radio Network
Authors: Zhao Di , Qin Hao , Song Bin , Han Beichen , Du Xiaojiang , Guizani Mohsen
Published: September 2020

Cognitive radio (CR) is a critical technique to solve the conflict between the explosive growth of traffic and severe spectrum scarcity. Reasonable radio resource allocation with CR can effectively achieve spectrum sharing and co-channel interference (CCI) mitigation. In this paper, we propose a joint channel selection and power adaptation scheme for the underlay cognitive radio network (CRN), maximizing the data rate of all secondary users (SUs) while guaranteeing the quality of service (QoS) of primary users (PUs). To exploit the underlying topology of CRNs, we model the communication network as dynamic graphs, and the random walk is used to imitate the users’ movements. Considering the lack of accurate channel state information (CSI), we use the user distance distribution contained in the graph to estimate CSI. Moreover, the graph convolutional network (GCN) is employed to extract the crucial interference features. Further, an end-to-end learning model is designed to implement the following resource allocation task to avoid the split with mismatched features and tasks. Finally, the deep reinforcement learning (DRL) framework is adopted for model learning, to explore the optimal resource allocation strategy. The simulation results verify the feasibility and convergence of the proposed scheme, and prove that its performance is significantly improved.
A Screen-Printed Sensor Coupled with Flow System for Quantitative Determination of a Novel Promising Anticancer Agent Candidate
Authors: Tyszczuk-Rotko Katarzyna , Kozak Jędrzej , Sztanke Małgorzata , Sztanke Krzysztof , Sadok Ilona
Published: September 2020

A carbon nanofibers modified screen-printed carbon sensor (SPCE/CNFs) was applied for the determination of a novel promising anticancer agent candidate (ethyl 8-(4-methoxyphenyl)-4-oxo-4,6,7,8-tetrahydroimidazo[2,1-c][1,2,4]triazine-3-carboxylate, EIMTC) using square-wave voltammetry (SWV). It is the first method for the quantitative determination of EIMTC. The modified screen-printed sensor exhibited excellent electrochemical activity in reducing EIMTC. The peak current of EIMTC was found to be linear in two concentration ranges of 2.0 × 10−9 – 2.0 × 10−8 mol L−1 and 2.0 × 10−8 – 2.0 × 10−7 mol L−1, with a detection limit of 5.0 × 10−10 mol L−1. The connection of flow-cell for the SPCE/CNFs with SWV detection allowed for the successful determination of EIMTC in human serum samples. Ultra-high-performance liquid chromatography coupled to electrospray ionization triple quadrupole mass spectrometry (UHPLC-ESI-MS/MS) acted as a comparative method in the serum samples analysis.
A Novel Spread Spectrum and Clustering Mixed Approach with Network Coding for Enhanced Narrowband IoT (NB-IoT) Scalability
Authors: Migabo Emmanuel , Djouani Karim , Kurien Anish
Published: September 2020

The Narrowband Internet of Things (NB-IoT) is a very promising licensed Internet of things (IoT) technology for accommodating massive device connections in 5G and beyond. To enable network scalability, this study proposes a two-layers novel mixed approach that aims not only to create an efficient spectrum sharing among the many NB-IoT devices but also provides an energy-efficient network. On one layer, the approach uses an Adaptive Frequency Hopping Spread Spectrum (AFHSS) technique that uses a lightweight and secure pseudo-random sequence to exploit the channel diversity, to mitigate inter-link and cross-technology interference. On the second layer, the approach consists of a clustering and network coding (data aggregation) approach based on an energy-signal strength mixed gradient. The second layer contributes to offload the BS, allows for energy-efficient network scalability, helps balance the energy consumption of the network, and enhances the overall network lifetime. The proposed mixed strategy algorithm is modelled and simulated using the Matrix Laboratory (MATLAB) Long Term Evolution (LTE) toolbox. The obtained results reveal that the proposed mixed approach enhances network scalability while improving energy efficiency, transmission reliability, and network lifetime when compared to the existing spread spectrum only, nodes clustering only, and mixed approach with no network coding approaches.
Rail Track Detection and Projection-Based 3D Modeling from UAV Point Cloud
Authors: Sahebdivani Shima , Arefi Hossein , Maboudi Mehdi
Published: September 2020

The expansion of the railway industry has increased the demand for the three-dimensional modeling of railway tracks. Due to the increasing development of UAV technology and its application advantages, in this research, the detection and 3D modeling of rail tracks are investigated using dense point clouds obtained from UAV images. Accordingly, a projection-based approach based on the overall direction of the rail track is proposed in order to generate a 3D model of the railway. In order to extract the railway lines, the height jump of points is evaluated in the neighborhood to select the candidate points of rail tracks. Then, using the RANSAC algorithm, line fitting on these candidate points is performed, and the final points related to the rail are identified. In the next step, the pre-specified rail piece model is fitted to the rail points through a projection-based process, and the orientation parameters of the model are determined. These parameters are later improved by fitting the Fourier curve, and finally a continuous 3D model for all of the rail tracks is created. The geometric distance of the final model from rail points is calculated in order to evaluate the modeling accuracy. Moreover, the performance of the proposed method is compared with another approach. A median distance of about 3 cm between the produced model and corresponding point cloud proves the high quality of the proposed 3D modeling algorithm in this study.
Feasibility Study on the Use of Infrared Thermography to Classify Fattening Pigs into Feeding Groups According Their Body Composition
Authors: Lengling Alexandra , Alfert Antonius , Reckels Bernd , Steinhoff-Wagner Julia , Büscher Wolfgang
Published: September 2020

Fattening pig husbandry and associated negative environmental impacts due to nitrogen inputs by ammonia emissions are current issues of social discussion. New resource-efficient feeding systems offer great potential to reduce excess nutrient inputs into the environment. Using ultrasound measurements, fattening pigs can be divided into performance groups based on their backfat/muscle ratio to feed them according to their nutritional needs. Ultrasound measurements are not suitable for practical use, so alternatives have to be found. As a non-invasive, contactless method, infrared thermography offers many advantages. This study investigated whether infrared thermography can be used to differentiate between “fat” and “lean” animals. Two evaluation methods with different measurement spot sizes were compared. During a fattening period, 980 pigs were examined three times with an infrared camera. Both methods showed significant differences. Body surface temperature was influenced by factors like measurement spot size and soiling of the animals. Body surface temperature decreased (−5.5 °C), while backfat thickness increased (+0.7 cm) in the course of the fattening period. Significant correlations (R > |0.5|; p < 0.001) between both parameters were found. Differentiation between “fat” and “lean” animals, based on temperature data, was not possible. Nevertheless, the application of thermography should be investigated further with the aim of resource-efficient feeding. The results of this feasibility study can serve as a basis for this.
AiRobSim: Simulating a Multisensor Aerial Robot for Urban Search and Rescue Operation and Training
Authors: Chen Junjie , Li Shuai , Liu Donghai , Li Xueping
Published: September 2020

Unmanned aerial vehicles (UAVs), equipped with a variety of sensors, are being used to provide actionable information to augment first responders’ situational awareness in disaster areas for urban search and rescue (SaR) operations. However, existing aerial robots are unable to sense the occluded spaces in collapsed structures, and voids buried in disaster rubble that may contain victims. In this study, we developed a framework, AiRobSim, to simulate an aerial robot to acquire both aboveground and underground information for post-disaster SaR. The integration of UAV, ground-penetrating radar (GPR), and other sensors, such as global navigation satellite system (GNSS), inertial measurement unit (IMU), and cameras, enables the aerial robot to provide a holistic view of the complex urban disaster areas. The robot-collected data can help locate critical spaces under the rubble to save trapped victims. The simulation framework can serve as a virtual training platform for novice users to control and operate the robot before actual deployment. Data streams provided by the platform, which include maneuver commands, robot states and environmental information, have potential to facilitate the understanding of the decision-making process in urban SaR and the training of future intelligent SaR robots.
A Novel Surface Recovery Algorithm for Dual Wavelength White LED in Vertical Scanning Interferometry (VSI)
Authors: Zhu Linlin , Dong Yuchu , Li Zexiao , Zhang Xiaodong
Published: September 2020

The two peaks characteristic of yellow and blue light in the spectrum of dual-wavelength white light emitting diodes (LEDs) introduce distinctive features to the interference signal of white light scanning interferometry (WLSI). The distinctive features are defined as discontinuities, so that the fringe contrast function cannot be modeled as a single Gaussian function, and causes the interferogram to have uneven distribution of fringes of different orders in the scanning interferometer. This phenomenon leads to the low accuracy of the zero-order fringe position in the envelope calculation, which affects the repeatability and accuracy of the interferometry. This paper proposes a new surface recovery algorithm based on the Hilbert phase envelope and adjacent reference points calculation, which can effectively overcome the influence of the discontinuous signal of dual-wavelength LED white light interference on the three-dimensional reconstruction of WLSI measurements. The reliability of the algorithm is verified by experiments, and the measurement accuracy of LED WLSI system is evaluated.