bluuebunny/crossref_metadata_2025 huggingface dataset:
Efflux MexAB-Mediated Resistance in P. aeruginosa Isolated from Patients with Healthcare Associated Infections
Authors: Kishk Rania M. , Abdalla Mohamed O. , Hashish Abdullah A. , Nemr Nader A. , El Nahhas Nihal , Alkahtani Saad , Abdel-Daim Mohamed M. , Kishk Safaa M.
Published: June 2020

Today, one of the most important challenges for physicians is the adequate treatment of infections due to multidrug resistant organism (MDR). Pseudomonas aeruginosa is considered an opportunistic organism causing different types of healthcare associated infections (HAIs). We aimed to investigate the MDR and pandrug resistance (PDR) rate in P. aeruginosa in our region and detect efflux-pump mexAB genes and the proposed binding interactions of five different categories of antimicrobial agents with the mexB pump. A total of 180 non-duplicated P. aeruginosa strains were isolated from patients with HAIs in the Suez Canal University Hospital. Phenotypically, minimum inhibitory concentration (MIC) was done for all MDR and PDR strains before and after addition of efflux pump inhibitor carbonyl cyanide m-chlorophenyl hydrazone (CCCP). Molecular detection of mexA and mexB genes was done by using polymerase chain reaction (PCR). Most of the isolated strains (126 strains) were MDR (70%); only 10 samples (5.5%) were PDR. MexA and mexB genes were detected in 88.2% (120 strains) and 70.5% (96 strains) of stains, respectively. All PDR strains (10 stains) carried both mexA and mexB genes. Efflux mexAB genes were detected in all MDR and PDR strains (136 strains). Molecular modeling studies were performed to investigate the modes of intermolecular binding interactions between the antimicrobial agents and mexB key amino acids that resulted in MDR and PDR. The current study reported high prevalence of MDR and PDR P. aeruginosa in patients with HAIs in the Suez Canal University Hospitals.
Genetic Diversity of Babesia bovis MSA-1, MSA-2b and MSA-2c in China
Authors: Wang Jinming , Yang Jifei , Gao Shandian , Wang Xiaoxing , Sun Hao , Lv Zhaoyong , Li Youquan , Liu Aihong , Liu Junlong , Luo Jianxun , Guan Guiquan , Yin Hong
Published: June 2020

The apicomplexan parasite Babesia bovis is a tick-borne intracellular hemoprotozoan parasite that is widespread across China. Genetic diversity is an important strategy used by parasites to escape the immune responses of their hosts. In our present study, 575 blood samples, collected from cattle in 10 provinces, were initially screened using a nested PCR (polymerase chain reaction) for detection of B. bovis infection. To perform genetic diversity analyses, positive samples were further amplified to obtain sequences of three B. bovis merozoite surface antigen genes (MSA-1, MSA-2b, MSA-2c). The results of the nested PCR approach showed that an average of 8.9% (51/575) of cattle were positive for B. bovis infection. Phylogenetic analyses of the predicted amino acid sequences revealed that unique antigen variants were formed only by Chinese isolates. Our findings provide vital information for understanding the genetic diversity of B. bovis in China.
Travel Medicine Curricula across Canadian Pharmacy Programs and Alignment with Scope of Practice
Authors: Fernandes Heidi V.J. , Cook Brittany , Houle Sherilyn K.D.
Published: June 2020

Limited research exists on pharmacy students’ training in travel medicine, and how this aligns with scope of practice. This research aimed to detail travel medicine education across pharmacy programs in Canada and map this against the scope of practice for pharmacists in each university’s jurisdiction. A survey based on the International Society of Travel Medicine’s Body of Knowledge was developed and distributed to all Canadian undergraduate pharmacy schools to identify topic areas taught, teaching modalities utilized, and knowledge assessment performed. Educational data was collected and analyzed descriptively, and compared to pharmacists’ scope of practice in the province in which each university is located. Training provided to students varied significantly across universities and topic areas, with topics amenable to self-care (e.g., traveller’s diarrhea and insect bite prevention) or also encountered outside of the travel context (e.g., sexually transmitted infections) taught more regularly than travel-specific topics (e.g., dengue and altitude illness). No apparent relationship was observed between a program’s curriculum and their provincial scope of practice. For example, training in vaccine-preventable diseases did not necessarily align with scope related to vaccine administration. Alignment of education to current and future scope will best equip new practitioners to provide care to travelling patients.
The Power of Three in Cannabis Shotgun Proteomics: Proteases, Databases and Search Engines
Authors: Vincent Delphine , Savin Keith , Rochfort Simone , Spangenberg German
Published: June 2020

Cannabis research has taken off since the relaxation of legislation, yet proteomics is still lagging. In 2019, we published three proteomics methods aimed at optimizing protein extraction, protein digestion for bottom-up and middle-down proteomics, as well as the analysis of intact proteins for top-down proteomics. The database of Cannabis sativa proteins used in these studies was retrieved from UniProt, the reference repositories for proteins, which is incomplete and therefore underrepresents the genetic diversity of this non-model species. In this fourth study, we remedy this shortcoming by searching larger databases from various sources. We also compare two search engines, the oldest, SEQUEST, and the most popular, Mascot. This shotgun proteomics experiment also utilizes the power of parallel digestions with orthogonal proteases of increasing selectivity, namely chymotrypsin, trypsin/Lys-C and Asp-N. Our results show that the larger the database the greater the list of accessions identified but the longer the duration of the search. Using orthogonal proteases and different search algorithms increases the total number of proteins identified, most of them common despite differing proteases and algorithms, but many of them unique as well.
Narrative and the Politics of Identity: Patterns of the Spread and Acceptance of Radicalism and Terrorism in Indonesia
Authors: Syam Firdaus , Mangunjaya Fachruddin Majeri , Rahmanillah Ajeng Rizqi , Nurhadi Robi
Published: June 2020

This study aims to examine Islamic narratives heard at mosques and in study groups in the greater metropolitan area of Jakarta, Indonesia. The article asks if youth and leaders of youth organizations in Jakarta are receptive to radical/terrorist discourse or if they deliberate and weigh what certain narratives mean. Qualitative in-depth interviews were conducted with 24 subjects. These included Rohani Islamic group leaders who hold extracurricular study groups after middle and high school classes, as well as Islamic Mission organizations or Lembaga Da’wah Kampus (LDK—literally translates as Campus Mission Organization; they are some senior students and may invite Islamic scholars or themselves teach Islam and preach to students who are willing to learn Islam specially only at the university as an extracurricular activity; in this article, we translate it as Islamic Mission organization.). which exist on Jakarta’s university campuses where radical narratives are discussed. Other organizations and their leaders were also included. Questions posed to members of these organizations by the authors of this article asked if they accepted, rejected, or negotiated certain ideas regarded as radical by the Indonesian government. Respondents were asked if they believed violent acts against non-reform Muslims and non-Muslims were justified. Respondents were also asked if the Indonesian constitution, Pancasila, should continue its secular democratic legal format, or if it should be replaced by sharia law. Ultimately, most informants took more moderate stances, somewhere in between pure secularism and pure radical terrorism. In this way, this study disproves scholars such as Martin van Bruinessen (2013) who claim that Indonesian Islam is becoming more conservative, and others such as Harsono who claim Indonesian Islam is becoming more violent. While violence was condoned by some respondents, this article reveals that a majority of respondents rejected the view that sharia law should prevail. Ultimately most respondents in this study decided a balanced viewpoint was the best. Thus, this article reveals the degree of moderation of most Jakarta residents, and the nuance and depth of consideration that devout individuals give to a range of contemporary ideas as they negotiate their stance on religion, the state, and their local identities.
Archaeology of Ancient Israelite Religion(s): An Introduction
Authors: Faust Avraham
Published: June 2020

Israelite religion has always fascinated scholars [...]
The New Visibility of Religion and Its Impact on Populist Politics
Authors: Hoelzl Michael
Published: June 2020

The marginalised research field of populism and religion has mainly focused on the positive aspects of how religion and populism can be combined with mutual benefits for both parties, whereas the critical potential and limitations that religion and theology pose to populist politics has often been overlooked. The following essay intends to contribute to the complex research area of religion and populism, by focusing on the negative side, that is, the incompatibilities of religion and theology with populism. It is suggested that the very nature of religious belief and theological convictions impose limits on their use in populist politics.
Identification of the Roles of Climate Factors, Engineering Construction, and Agricultural Practices in Vegetation Dynamics in the Lhasa River Basin, Tibetan Plateau
Authors: Li Dan , Luo Hongying , Hu Tiesong , Shao Dongguo , Cui Yuanlai , Khan Shahbaz , Luo Yufeng
Published: June 2020

Understanding vegetation dynamics is necessary to address potential ecological threats and develop sustainable ecosystem management at high altitudes. In this study, we revealed the spatiotemporal characteristics of vegetation growth in the Lhasa River Basin using net primary productivity (NPP) and normalized difference vegetation index (NDVI) during the period of 2000–2005. The roles of climatic factors and specific anthropogenic activities in vegetation dynamics were also identified, including positive or negative effects and the degree of impact. The results indicated that the interannual series of NPP and NDVI in the whole basin both had a continuous increasing trend from 102 to 128 gC m−2 yr−1 and from 0.417 to 0.489 (p < 0.05), respectively. The strongest advanced trends (>2 gC m−2 yr−1 or >0.005 yr−1) were detected in mainly the southeastern and northeastern regions. Vegetation dynamics were not detected in 10% of the basin. Only 20% of vegetation dynamics were driven by climatic conditions, and precipitation was the controlling climatic factor determining vegetation growth. Accordingly, anthropogenic activities made a great difference in vegetation coverage, accounting for about 70%. The construction of urbanization and reservoir led to vegetation degradation, but the farmland practices contributed the vegetation growth. Reservoir construction had an adverse impact on vegetation within 6 km of the river, and the direct damage to vegetation was within 1 km. The impacts of urbanization were more serious than that of reservoir construction. Urban sprawl had an adverse impact on vegetation within a 6 km distance from the surrounding river and resulted in the degradation of vegetation, especially within a 3 km range. Intensive fertilization and guaranteed irrigation improved the cropland ecosystem conditions, creating a favorable effect on the accumulation of crop organic matter in a range of 5 km, with an NPP trend value of 1.2 gC m−2 yr−1. The highly intensive grazing activity forced ecological environmental pressures such that the correlation between livestock numbers and vegetation growth trend was significantly linear negative.
Identifying Ecosystem Function Shifts in Africa Using Breakpoint Analysis of Long-Term NDVI and RUE Data
Authors: Higginbottom Thomas P. , Symeonakis Elias
Published: June 2020

Time-series of vegetation greenness data, derived from Earth-observation imagery, have become a key source of information for studying large-scale environmental change. The ever increasing length of such series allows for a range of indicators to be derived and for increasingly complex analyses to be applied. This study presents an analysis of trends in vegetation productivity—measured using the Global Inventory Monitoring and Modelling System third generation (GIMMS3g) Normalised Difference Vegetation Index (NDVI) data—for African savannahs, over the 1982–2015 period. Two annual metrics were derived from the 34 year dataset: the monthly, smoothed NDVI (the aggregated growth season NDVI) and the associated Rain Use Efficiency (growth season NDVI divided by annual rainfall). These indicators were then used in a BFAST-based change-point analysis, allowing the direction of change over time to change and the detection of one major break in the time-series. We also analysed the role of land cover type and climate zone as associations of the observed changes. Both methods agree that vegetation greening was pervasive across African savannahs, although RUE displayed less significant changes than NDVI. Monotonically increasing trends were the most common trend type for both indicators. The continental scale of the greening may suggest global processes as key drivers, such as carbon fertilization. That NDVI trends were more dynamic than RUE suggests that a large component of vegetation trends is driven by precipitation variability. Areas of negative trends were conspicuous by their minimalism. However, some patterns were apparent. In the southern Sahel and West Africa, declining NDVI and RUE overlapped with intensive population and agricultural regions. Dynamic trend reversals, in RUE and NDVI, located in Angola, Zambia and Tanzania, coincide with areas where a long-term trend of forest degradation and agricultural expansion has recently given way to increases in woody biomass. Meanwhile in southern Africa, monotonic increases in RUE with varying NDVI trend types may be indicative of shrub encroachment. However, all these processes are small-scale relative to the GIMMS NDVI data, and reconciling these conflicting drivers is not a trivial task. Our study highlights the importance of considering multiple options when undertaking trend analyses, as different inputs and methods can reveal divergent patterns.
Assessing Forest/Non-Forest Separability Using Sentinel-1 C-Band Synthetic Aperture Radar
Authors: Hansen Johannes N. , Mitchard Edward T. A. , King Stuart
Published: June 2020

Synthetic Aperture Radar has a unique potential for continuous forest mapping as it is not affected by cloud cover. While longer wavelengths, such as L-band, are commonly used for forest applications, in this paper we assess the aptitude of C-band Sentinel-1 data for this purpose, for which there is much interest due to its high temporal resolution (five days) and “free, full, and open” data policy. We tested its ability to distinguish forest from non-forest in six study sites, located in Alaska, Colombia, Finland, Florida, Indonesia, and the UK. Using the time series for a full year significantly increases the classification accuracy compared to a single scene (a mean of 85 % compared to 77 % across the study sites for the best classifier). Our results show that we can further improve the mean accuracy to 87 % when only considering the annual mean and standard deviation of co-polarized (VV) and cross-polarized (VH) backscatter. In this case, separation accuracies of up to 93 % (in Finland) are possible, though in the worst case (Alaska), the highest possible accuracy using these variables was 80 % . The best overall performance was observed when using a Support Vector Machine classifier, outperforming random forest, k-Nearest-Neighbors, and Quadratic Discriminant Analysis. We further show that the small information content we found in the phase data is an artifact of terrain slope orientation and has a negligible impact on classifier performance. We conclude that for the purposes of forest mapping the smaller file size and easier to process GRD products are sufficient, unless the SLC products are used to compute the temporal coherence which was not tested in this study.
Thematic Classification Accuracy Assessment with Inherently Uncertain Boundaries: An Argument for Center-Weighted Accuracy Assessment Metrics
Authors: Maxwell Aaron E. , Warner Timothy A.
Published: June 2020

Accuracy assessment is one of the most important components of both applied and research-oriented remote sensing projects. For mapped classes that have sharp and easily identified boundaries, a broad array of accuracy assessment methods has been developed. However, accuracy assessment is in many cases complicated by classes that have fuzzy, indeterminate, or gradational boundaries, a condition which is common in real landscapes; for example, the boundaries of wetlands, many soil map units, and tree crowns. In such circumstances, the conventional approach of treating all reference pixels as equally important, whether located on the map close to the boundary of a class, or in the class center, can lead to misleading results. We therefore propose an accuracy assessment approach that relies on center-weighting map segment area to calculate a variety of common classification metrics including overall accuracy, class user’s and producer’s accuracy, precision, recall, specificity, and the F1 score. This method offers an augmentation of traditional assessment methods, can be used for both binary and multiclass assessment, allows for the calculation of count- and area-based measures, and permits the user to define the impact of distance from map segment edges based on a distance weighting exponent and a saturation threshold distance, after which the weighting ceases to grow. The method is demonstrated using synthetic and real examples, highlighting its use when the accuracy of maps with inherently uncertain class boundaries is evaluated.
Assessment of Fire Fuel Load Dynamics in Shrubland Ecosystems in the Western United States Using MODIS Products
Authors: Li Zhengpeng , Shi Hua , Vogelmann James E. , Hawbaker Todd J. , Peterson Birgit
Published: June 2020

Assessing fire behavior in shrubland/grassland ecosystems of the western United States has proven especially problematic, in part due to the complex nature of the vegetation and its relationships with prior fire history events. Our goals in this study were (1) to determine if we can effectively leverage the high temporal resolution capabilities of current remote sensing systems such as the Moderate Resolution Imaging Spectroradiometer (MODIS) to improve upon shrub and grassland mapping and (2) to determine if these improvements alter and improve fire behavior model results in these grass- and shrub-dominated systems. The study focused on the shrublands and grasslands of the Owyhee Basin, which is located primarily in southern Idaho. Shrubland and grassland fuel load dynamics were characterized using Normalized Difference Vegetation Index (NDVI) and Net Primary Production (NPP) datasets (both derived from MODIS). NDVI shrub and grassland values were converted to biomass, and custom fire behavior fuel models were then developed to evaluate the impacts of surface fuel changes on fire behaviors. Results from the study include the following: (1) high intra- and interannual spectral variability characterized these shrubland/grassland ecosystems, and this spectral variability was highly correlated with climate variables, most notably precipitation; (2) fire activity had a higher likelihood of occurring in areas where the NDVI (and biomass) differential between spring and summer values was especially high; (3) the annual fuel loads estimated from MODIS NPP showed that live herbaceous fuel loads were closely correlated with annual precipitation; (4) estimated fuel load accumulation was higher on shrublands than grasslands with the same vegetation productivity; (5) the total fuel load on shrublands was impacted by shrubland age, and live woody fuel load was over 66% of the total fuel load; and (6) comparisons of simulated fire behavior and spread between dynamic and static fuel loads, the latter estimates being obtained from the operational and nationwide LANDFIRE program, showed clear differences in fire indices and fire burn areas between the dynamic fuel loads and the static fuel loads. Current standard fuel models appear to have bias in underestimating the fire spread and total burnable area.
Change Detection in UWB SAR Images Based on Robust Principal Component Analysis
Authors: Schwartz Christofer , Ramos Lucas P. , Duarte Leonardo T. , Pinho Marcelo da S. , Pettersson Mats I. , Vu Viet T. , Machado Renato
Published: June 2020

This paper addresses the use of a data analysis tool, known as robust principal component analysis (RPCA), in the context of change detection (CD) in ultrawideband (UWB) very high-frequency (VHF) synthetic aperture radar (SAR) images. The method considers image pairs of the same scene acquired at different time instants. The CD method aims to maximize the probability of detection (PD) and minimize the false alarm rate (FAR). Such aim fits into a multiobjective optimization problem, since maximizing the probability of detection generally implies an increase in the number of false alarms. In that sense, varying the RPCA regularization parameter leads to PD variation with respect to FAR, which is known as receiver operating characteristic (ROC) curve. To evaluate the proposed method, the CARABAS-II data set was considered. The experimental results show that RPCA via principal component pursuit (PCP) can provide a good trade-off between PD and FAR. A comparison between the results obtained with the proposed method and a classical CD algorithm based on the likelihood ratio test provides the pros and cons of the proposed method.
Non-Destructive Early Detection and Quantitative Severity Stage Classification of Tomato Chlorosis Virus (ToCV) Infection in Young Tomato Plants Using Vis–NIR Spectroscopy
Authors: Morellos Antonios , Tziotzios Georgios , Orfanidou Chrysoula , Pantazi Xanthoula Eirini , Sarantaris Christos , Maliogka Varvara , Alexandridis Thomas K. , Moshou Dimitrios
Published: June 2020

Tomato chlorosis virus (ToCV) is a serious, emerging tomato pathogen that has a significant impact on the quality and quantity of tomato production worldwide. Detecting ToCV via means of spectral measurements in an early pre-symptomatic stage offers an alternative to the existing laboratory methods, leading to better disease management in the field. In this study, leaf spectra from healthy and diseased leaves were measured with a spectrometer. The diseased leaves were subjected to RT-qPCR for the detection and quantification of the titer of ToCV. Neighborhood component analysis (NCA) algorithm was employed for the feature selection of the effective wavelengths and the most important vegetation indices out of the 24 that were tested. Two machine learning methods, namely XY-fusion network (XY-F) and multilayer perceptron with automated relevance determination (MLP–ARD), were employed for the estimation of the disease existence and viral load in the tomato leaves. The results showed that before outlier elimination, the MLP–ARD classifier generally outperformed the XY-F network with an overall accuracy of 92.1% against 88.3% for the XY-F. Outlier elimination contributed to the performance of the classifiers as the overall accuracy for both XY-F and MLP–ARD reached 100%.
Evaluating the Performance of Sentinel-3A OLCI Land Products for Gross Primary Productivity Estimation Using AmeriFlux Data
Authors: Zhang Zhijiang , Zhao Lin , Lin Aiwen
Published: June 2020

Accurate and reliable estimation of gross primary productivity (GPP) is of great significance in monitoring global carbon cycles. The fraction of absorbed photosynthetically active radiation (FAPAR) and vegetation index products of the Moderate Resolution Imaging Spectroradiometer (MODIS) are currently the most widely used data in evaluating GPP. The launch of the Ocean and Land Colour Instrument (OLCI) onboard the Sentinel-3 satellite provides the FAPAR and the OLCI Terrestrial Chlorophyll Index (OTCI) products with higher temporal resolution and smoother spatial distribution than MODIS, having the potential to monitor terrain GPP. OTCI is one of the red-edge indices and is particularly sensitive to canopy chlorophyll content related to GPP. The purpose of the study is to evaluate the performance of OLCI FAPAR and OTCI for the estimation of GPP across seven biomes in 2017–2018. To this end, OLCI FAPAR and OTCI products in combination with insitu meteorological data were first integrated into the MODIS GPP algorithm and in three OTCI-driven models to simulate GPP. The modeled GPP (GPPOLCI-FAPAR and GPPOTCI) were then compared with flux tower GPP (GPPEC) for each site. Furthermore, the GPPOLCI-FAPAR and GPP derived from the MODIS FAPAR (GPPMODIS-FAPAR) were compared. Results showed that the performance of GPPOLCI-FAPAR was varied in different sites, with the highest R2 of 0.76 and lowest R2 of 0.45. The OTCI-driven models that include APAR data exhibited a significant relationship with GPPEC for all sites, and models using only OTCI provided the most varied performance, with the relationship between GPPOTCI and GPPEC from strong to nonsignificant. Moreover, GPPOLCI-FAPAR (R2 = 0.55) performed better than GPPMODIS-FAPAR (R2 = 0.44) across all biomes. These results demonstrate the potential of OLCI FAPAR and OTCI products in GPP estimation, and they also provide the basis for their combination with the soon-to-launch Fluorescence Explorer satellite and their integration with the Sentinel-3 land surface temperature product into light use models for GPP monitoring at regional and global scales.
A Coarse-to-Fine Deep Learning Based Land Use Change Detection Method for High-Resolution Remote Sensing Images
Authors: Wang Mingchang , Zhang Haiming , Sun Weiwei , Li Sheng , Wang Fengyan , Yang Guodong
Published: June 2020

In recent decades, high-resolution (HR) remote sensing images have shown considerable potential for providing detailed information for change detection. The traditional change detection methods based on HR remote sensing images mostly only detect a single land type or only the change range, and cannot simultaneously detect the change of all object types and pixel-level range changes in the area. To overcome this difficulty, we propose a new coarse-to-fine deep learning-based land-use change detection method. We independently created a new scene classification dataset called NS-55, and innovatively considered the adaptation relationship between the convolutional neural network (CNN) and the scene complexity by selecting the CNN that best fit the scene complexity. The CNN trained by NS-55 was used to detect the category of the scene, define the final category of the scene according to the majority voting method, and obtain the changed scene by comparison to obtain the so-called coarse change result. Then, we created a multi-scale threshold (MST) method, which is a new method for obtaining high-quality training samples. We used the high-quality samples selected by MST to train the deep belief network to obtain the pixel-level range change detection results. By mapping coarse scene changes to range changes, we could obtain fine multi-type land-use change detection results. Experiments were conducted on the Multi-temporal Scene Wuhan dataset and aerial images of a particular area of Dapeng New District, Shenzhen, where promising results were achieved by the proposed method. This demonstrates that the proposed method is practical, easy-to-implement, and the NS-55 dataset is physically justified. The proposed method has the potential to be applied in the large scale land use fine change detection problem and qualitative and quantitative research on land use/cover change based on HR remote sensing data.
Predicting Soybean Yield at the Regional Scale Using Remote Sensing and Climatic Data
Authors: Stepanov Alexey , Dubrovin Konstantin , Sorokin Aleksei , Aseeva Tatiana
Published: June 2020

Crop yield modeling at the regional level is one of the most important methods to ensure the profitability of the agro-industrial economy and the solving of the food security problem. Due to a lack of information about crop distribution over large agricultural areas, as well as the crop separation problem (based on remote sensing data) caused by the similarity of phenological cycles, a question arises regarding the relevance of using data obtained from the arable land mask of the region to predict the yield of individual crops. This study aimed to develop a regression model for soybean crop yield monitoring in municipalities and was conducted in the Khabarovsk Territory, located in the Russian Far East. Moderate Resolution Imaging Spectroradiometer (MODIS) data, an arable land mask, the meteorological characteristics obtained using the VEGA-Science web service, and crop yield data for 2010–2019 were used. The structure of crop distribution in the Khabarovsk District was reproduced in experimental fields, and Normalized Difference Vegetation Index (NDVI) seasonal variation approximating functions were constructed (both for total district sown area and different crops). It was found that the approximating function graph for the experimental fields corresponds to a similar graph for arable land. The maximum NDVI forecast error on the 30th week in 2019 using the approximation parameters according to 2014–2018 did not exceed 0.5%. The root-mean-square error (RMSE) was 0.054. The maximum value of the NDVI, as well as the indicators characterizing the temperature regime, soil moisture, and photosynthetically active radiation in the region during the period from the 1st to the 30th calendar weeks of the year, were previously considered as parameters of the regression model for predicting soybean yield. As a result of the experiments, the NDVI and the duration of the growing season were included in the regression model as independent variables. According to 2010–2018, the mean absolute percentage error (MAPE) of the regression model was 6.2%, and the soybean yield prediction absolute percentage error (APE) for 2019 was 6.3%, while RMSE was 0.13 t/ha. This approach was evaluated with a leave-one-year-out cross-validation procedure. When the calculated maximum NDVI value was used in the regression equation for early forecasting, MAPE in the 28th–30th weeks was less than 10%.
Practical Recommendations for Hyperspectral and Thermal Proximal Disease Sensing in Potato and Leek Fields
Authors: Appeltans Simon , Guerrero Angela , Nawar Said , Pieters Jan , Mouazen Abdul M.
Published: June 2020

Thermal and hyperspectral proximal disease sensing are valuable tools towards increasing pesticide use efficiency. However, some practical aspects of the implementation of these sensors remain poorly understood. We studied an optimal measurement setup combining both sensors for disease detection in leek and potato. This was achieved by optimising the signal-to-noise ratio (SNR) based on the height of measurement above the crop canopy, off-zenith camera angle and exposure time (ET) of the sensor. Our results indicated a clear increase in SNR with increasing ET for potato. Taking into account practical constraints, the suggested setup for a hyperspectral sensor in our experiment involves (for both leek and potato) an off-zenith angle of 17°, height of 30 cm above crop canopy and ET of 1 ms, which differs from the optimal setup of the same sensor for wheat. Artificial light proved important to counteract the effect of cloud cover on hyperspectral measurements. The interference of these lamps with thermal measurements was minimal for a young leek crop but increased in older leek and after long exposure. These results indicate the importance of optimising the setup before measurements, for each type of crop.
Modeling and Evaluation of Piezoelectric Transducer (PZT)-Based Through-Metal Energy and Data Transfer
Authors: Ding Lianghui , Chen Kehong , Huang Falong , Yang Feng , Qian Liang
Published: June 2020

Through-metal transfer of energy and data using piezoelectric transduce can avoid the potential leakage problem caused by physical penetrations and wired feed-through. The through-metal transfer efficiency of energy or data is determined by the relative pressure on the receiving PZT (piezoelectric transducer). Hence, in this paper, we first propose the Spatial Equivalent Plane Acoustic Pressure (SEPAP), which is defined as the integration of the acoustic pressure over the receiving area, to model the pressure on the receiving PZT. Then we analyze the features of SEPAP and the factors impacting it by utilizing COMSOL. Furthermore, we propose a low-cost and small-size prototype for simultaneous transfer of energy and bidirectional communication through metal by using two pairs of PZTs working on different resonant frequencies. Extensive experiment has been done on evaluating the match between SEPAP transfer efficiency and the power transfer efficiency and analyzing the achievable data rate for bi-directional communication. Test through a 20 mm aluminum alloy plate shows that power transmission with efficiency 20.3% and data communication rate up to 38.4 Kbps can be achieved simultaneously.
Computationally Efficient Cooperative Dynamic Range-Only SLAM Based on Sum of Gaussian Filter
Authors: Kim Jung-Hee , Kim Doik
Published: June 2020

A cooperative dynamic range-only simultaneous localization and mapping (CDRO-SLAM) algorithm based on the sum of Gaussian (SoG) filter was recently introduced. The main characteristics of the CDRO-SLAM are (i) the integration of inter-node ranges as well as usual direct robot-node ranges to improve the convergence rate and localization accuracy and (ii) the tracking of any moving nodes under dynamic environments by resetting and updating the SoG variables. In this paper, an efficient implementation of the CDRO-SLAM (eCDRO-SLAM) is proposed to mitigate the high computational burden of the CDRO-SLAM due to the inter-node measurements. Furthermore, a thorough computational analysis is presented, which reveals that the computational efficiency of the eCDRO-SLAM is significantly improved over the CDRO-SLAM. The performance of the proposed eCDRO-SLAM is compared with those of several conventional RO-SLAM algorithms and the results show that the proposed efficient algorithm has a faster convergence rate and a similar map estimation error regardless of the map size. Accordingly, the proposed eCDRO-SLAM can be utilized in various RO-SLAM applications.
Statistical Analysis of Bistatic Radar Ground Clutter for Different German Rural Environments
Authors: Kohler Michael , O’Hagan Daniel W. , Weiss Matthias , Wegner David , Worms Josef , Bringmann Oliver
Published: June 2020

This article presents the statistical analysis of bistatic radar rural ground clutter for different terrain types under low grazing angles. Compared to most state-of-the-art analysis, we present country-specific clutter analysis for subgroups of rural environments rather than for the rural environment as a whole. Therefore, the rural environment analysis is divided into four dominant subgroup terrain types, namely fields with low vegetation, fields with high vegetation, plantations of small trees and forest environments representing a typical rural German environment. We will present the results for both the summer and the winter vegetation. Therefore, bistatic measurement campaigns have been carried out during the summer 2019 and the winter of 2019/20 in the aforementioned four different rural terrain types. The measurements were performed in the radar relevant X-band at a center frequency of 8.85 GHz and over a bandwidth of 100 MHz according to available transmit permission. The distinction of the rural terrain into different subgroups enables a more precise and accurate clutter analysis and modeling of the statistical properties as will be shown in the presented results. The statistical properties are derived from the calculated clutter amplitudes probability density functions and corresponding cumulative distribution functions for each of the four terrain types and the corresponding season. The data basis for the clutter analysis are the processed range-Doppler maps from the bistatic radar measurements. According to the authors’ current knowledge, a similar investigation based on real bistatic radar measurement data with the division into terrain subgroups has not yet been carried out and published for a German rural environment.
Marker-Based Movement Analysis of Human Body Parts in Therapeutic Procedure
Authors: Khan Muhammad Hassan , Zöller Martin , Farid Muhammad Shahid , Grzegorzek Marcin
Published: June 2020

Movement analysis of human body parts is momentous in several applications including clinical diagnosis and rehabilitation programs. The objective of this research is to present a low-cost 3D visual tracking system to analyze the movement of various body parts during therapeutic procedures. Specifically, a marker based motion tracking system is proposed in this paper to capture the movement information in home-based rehabilitation. Different color markers are attached to the desired joints’ locations and they are detected and tracked in the video to encode their motion information. The availability of this motion information of different body parts during the therapy can be exploited to achieve more accurate results with better clinical insight, which in turn can help improve the therapeutic decision making. The proposed framework is an automated and inexpensive motion tracking system with execution speed close to real time. The performance of the proposed method is evaluated on a dataset of 10 patients using two challenging matrices that measure the average accuracy by estimating the joints’ locations and rotations. The experimental evaluation and its comparison with the existing state-of-the-art techniques reveals the efficiency of the proposed method.
Connectivity on Underwater MI-Assisted Acoustic Cooperative MIMO Networks
Authors: Ren Qingyan , Sun Yanjing , Huo Yu , Zhang Liang , Li Song
Published: June 2020

In traditional underwater wireless sensor networks (UWSNs), it is difficult to establish reliable communication links as the acoustic wave experiences severe multipath effect, channel fading, and ambient noise. Recently, with the assistance of magnetic induction (MI) technique, cooperative multi-input-multi-output (MIMO) is utilized in UWSNs to enable the reliable long range underwater communication. Compared with the acoustic-based UWSNs, the UWSNs adopting MI-assisted acoustic cooperative MIMO are referred to as heterogeneous UWSNs, which are able to significantly improve the effective cover space and network throughput. Due to the complex channel characteristics and the heterogeneous architecture, the connectivity of underwater MI-assisted acoustic cooperative MIMO networks is much more complicated than that of acoustic-based UWSNs. In this paper, a mathematical model is proposed to analyze the connectivity of the networks, which considers the effects of channel characteristics, system parameters, and synchronization errors. The lower and upper bounds of the connectivity probability are also derived, which provide guidelines for the design and deployment of underwater MI-assisted acoustic cooperative MIMO networks. Monte Carlo simulations were performed, and the results validate the accuracy of the proposed model.
Creating the Internet of Augmented Things: An Open-Source Framework to Make IoT Devices and Augmented and Mixed Reality Systems Talk to Each Other
Authors: Blanco-Novoa Óscar , Fraga-Lamas Paula , A. Vilar-Montesinos Miguel , Fernández-Caramés Tiago M.
Published: June 2020

Augmented Reality (AR) and Mixed Reality (MR) devices have evolved significantly in the last years, providing immersive AR/MR experiences that allow users to interact with virtual elements placed on the real-world. However, to make AR/MR devices reach their full potential, it is necessary to go further and let them collaborate with the physical elements around them, including the objects that belong to the Internet of Things (IoT). Unfortunately, AR/MR and IoT devices usually make use of heterogeneous technologies that complicate their intercommunication. Moreover, the implementation of the intercommunication mechanisms requires involving specialized developers with have experience on the necessary technologies. To tackle such problems, this article proposes the use of a framework that makes it easy to integrate AR/MR and IoT devices, allowing them to communicate dynamically and in real time. The presented AR/MR-IoT framework makes use of standard and open-source protocols and tools like MQTT, HTTPS or Node-RED. After detailing the inner workings of the framework, it is illustrated its potential through a practical use case: a smart power socket that can be monitored and controlled through Microsoft HoloLens AR/MR glasses. The performance of such a practical use case is evaluated and it is demonstrated that the proposed framework, under normal operation conditions, enables to respond in less than 100 ms to interaction and data update requests.
Floating-Gate MOS Transistor with Dynamic Biasing as a Radiation Sensor
Authors: Ilić Stefan , Jevtić Aleksandar , Stanković Srboljub , Ristić Goran
Published: June 2020

This paper describes the possibility of using an Electrically Programmable Analog Device (EPAD) as a gamma radiation sensor. Zero-biased EPAD has the lowest fading and the highest sensitivity in the 300 Gy dose range. Dynamic bias of the control gate during irradiation was presented for the first time; this method achieved higher sensitivity compared to static-biased EPADs and better linear dependence. Due to the degradation of the transfer characteristics of EPAD during irradiation, a function of the safe operation area has been found that determines the maximum voltage at the control gate for the desired dose, which will not lead to degradation of the transistor. Using an energy band diagram, it was explained why the zero-biased EPAD has higher sensitivity than the static-biased EPAD.
Anomaly Detection Neural Network with Dual Auto-Encoders GAN and Its Industrial Inspection Applications
Authors: Tang Ta-Wei , Kuo Wei-Han , Lan Jauh-Hsiang , Ding Chien-Fang , Hsu Hakiem , Young Hong-Tsu
Published: June 2020

Recently, researchers have been studying methods to introduce deep learning into automated optical inspection (AOI) systems to reduce labor costs. However, the integration of deep learning in the industry may encounter major challenges such as sample imbalance (defective products that only account for a small proportion). Therefore, in this study, an anomaly detection neural network, dual auto-encoder generative adversarial network (DAGAN), was developed to solve the problem of sample imbalance. With skip-connection and dual auto-encoder architecture, the proposed method exhibited excellent image reconstruction ability and training stability. Three datasets, namely public industrial detection training set, MVTec AD, with mobile phone screen glass and wood defect detection datasets, were used to verify the inspection ability of DAGAN. In addition, training with a limited amount of data was proposed to verify its detection ability. The results demonstrated that the areas under the curve (AUCs) of DAGAN were better than previous generative adversarial network-based anomaly detection models in 13 out of 17 categories in these datasets, especially in categories with high variability or noise. The maximum AUC improvement was 0.250 (toothbrush). Moreover, the proposed method exhibited better detection ability than the U-Net auto-encoder, which indicates the function of discriminator in this application. Furthermore, the proposed method had a high level of AUCs when using only a small amount of training data. DAGAN can significantly reduce the time and cost of collecting and labeling data when it is applied to industrial detection.
mIoT: Metamorphic IoT Platform for On-Demand Hardware Replacement in Large-Scaled IoT Applications
Authors: Lee Dongkyu , Moon Hyeongyun , Oh Sejong , Park Daejin
Published: June 2020

As the Internet of Things (IoT) is becoming more pervasive in our daily lives, the number of devices that connect to IoT edges and data generated at the edges are rapidly increasing. On account of the bottlenecks in servers, due to the increase in data, as well as security and privacy issues, the IoT paradigm has shifted from cloud computing to edge computing. Pursuant to this trend, embedded devices require complex computation capabilities. However, due to various constraints, edge devices cannot equip enough hardware to process data, so the flexibility of operation is reduced, because of the limitations of fixed hardware functions, relative to cloud computing. Recently, as application fields and collected data types diversify, and, in particular, applications requiring complex computation such as artificial intelligence (AI) and signal processing are applied to edges, flexible processing and computation capabilities based on hardware acceleration are required. In this paper, to meet these needs, we propose a new IoT platform, called a metamorphic IoT (mIoT) platform, which can various hardware acceleration with limited hardware platform resources, through on-demand transmission and reconfiguration of required hardware at edges instead of via transference of sensing data to a server. The proposed platform reconfigures the edge’s hardware with minimal overhead, based on a probabilistic value, known as callability. The mIoT consists of reconfigurable edge devices based on RISC-V architecture and a server that manages the reconfiguration of edge devices based on callability. Through various experimental results, we confirmed that the callability-based mIoT platform can provide the hardware required by the edge device in real time. In addition, by performing various functions with small hardware, power consumption, which is a major constraint of IoT, can be reduced.
EEG-Brain Activity Monitoring and Predictive Analysis of Signals Using Artificial Neural Networks
Authors: Aileni Raluca Maria , Pasca Sever , Florescu Adriana
Published: June 2020

Predictive observation and real-time analysis of the values of biomedical signals and automatic detection of epileptic seizures before onset are beneficial for the development of warning systems for patients because the patient, once informed that an epilepsy seizure is about to start, can take safety measures in useful time. In this article, Daubechies discrete wavelet transform (DWT) was used, coupled with analysis of the correlations between biomedical signals that measure the electrical activity in the brain by electroencephalogram (EEG), electrical currents generated in muscles by electromyogram (EMG), and heart rate monitoring by photoplethysmography (PPG). In addition, we used artificial neural networks (ANN) for automatic detection of epileptic seizures before onset. We analyzed 30 EEG recordings 10 min before a seizure and during the seizure for 30 patients with epilepsy. In this work, we investigated the ANN dimensions of 10, 50, 100, and 150 neurons, and we found that using an ANN with 150 neurons generates an excellent performance in comparison to a 10-neuron-based ANN. However, this analyzes requests in an increased amount of time in comparison with an ANN with a lower neuron number. For real-time monitoring, the neurons number should be correlated with the response time and power consumption used in wearable devices.