bluuebunny/crossref_metadata_2025 huggingface dataset:
A Fresh Look at Grape Powdery Mildew (Erysiphe necator) A and B Genotypes Revealed Frequent Mixed Infections and Only B Genotypes in Flag Shoot Samples
Authors: Csikós Anett , Németh Márk Z. , Frenkel Omer , Kiss Levente , Váczy Kálmán Zoltán
Published: September 2020

Erysiphe necator populations, causing powdery mildew of grapes, have a complex genetic structure. Two genotypes, A and B, were identified in most vineyards across the world on the basis of fixed single nucleotide polymorphisms (SNPs) in several DNA regions. It was hypothesized that A populations overwinter as mycelia in grapevine buds, giving rise to so-called flag shoots in spring, and are more sensitive to fungicides than B populations, which overwinter as ascospores and become widespread later in the season. Other studies concluded that the biological significance of these genotypes is unclear. In the spring of 2015, there was a unique opportunity to collect E. necator samples from flag shoots in Hungary. The same grapevines were sampled in summer and autumn as well. A total of 182 samples were genotyped on the basis of β-tubulin (TUB2), nuclear ribosomal DNA (nrDNA) intergenic spacer (IGS), and internal transcribed spacer (ITS) sequences. Genotypes of 56 samples collected in 2009–2011 were used for comparison. Genotype A was not detected at all in spring, and was present in only 19 samples in total, mixed with genotype B, and sometimes with another frequently found genotype, designated as B2. These results did not support the hypothesis about temporal isolation of the two genotypes and indicated that these are randomly distributed in vineyards.
Influence of Aerobic Pretreatment of Poultry Manure on the Biogas Production Process
Authors: Rubežius Mantas , Venslauskas Kęstutis , Navickas Kęstutis , Bleizgys Rolandas
Published: September 2020

Anaerobic digestion of poultry manure is a potentially-sustainable means of stabilizing this waste while generating biogas. However, technical, and environmental protection challenges remain, including high concentrations of ammonia, low C/N ratios, limited digestibility of bedding, and questions about transformation of nutrients during digestion. This study evaluated the effect of primary biological treatment of poultry manure on the biogas production process and reduction of ammonia emissions. Biogas yield from organic matter content in the aerobic pretreatment groups was 13.96% higher than that of the control group. Biogas production analysis showed that aerobic pretreatment of poultry manure has a positive effect on biogas composition; methane concentration increases by 6.94–7.97% after pretreatment. In comparison with the control group, NH3 emissions after aerobic pretreatment decreased from 3.37% (aerobic pretreatment without biological additives) to 33.89% (aerobic pretreatment with biological additives), depending on treatment method.
Coal-Degrading Bacteria Display Characteristics Typical of Plant Growth Promoting Rhizobacteria
Authors: Titilawo Yinka , Masudi Wiya L. , Olawale Jacob T. , Sekhohola-Dlamini Lerato M. , Cowan A. Keith
Published: September 2020

Coal mining produces large quantities of discard that is stockpiled in large dumps. This stockpiled material, termed coal discard, poses an environmental threat emphasising the need for appropriate bioremediation. Here, metagenomic analysis of the 16S rRNA from ten coal-degrading strains previously isolated from coal slurry from discard dumps and from the rhizosphere of diesel-contaminated sites was used to establish genetic relatedness to known plant growth-promoting (PGP) bacteria in the NCBI database. Measurement of indole and ammonium production and solubilisation of P and K were used to screen bacteria for PGP characteristics. BLAST analysis revealed ≥ 99% homology of six isolates with reference PGP strains of Bacillus, Escherichia, Citrobacter, Serratia, Exiguobacterium and Microbacterium, while two strains showed 94% and 91% homology with Proteus. The most competent PGP strains were Proteus strain ECCN 20b, Proteus strain ECCN 23b and Serratia strain ECCN 24b isolated from diesel-contaminated soil. In response to L-trp supplementation, the concentration of indolic compounds (measured as indole-3-acetic acid) increased. Production of ammonium and solubilisation of insoluble P by these strains was also apparent. Only Serratia strain ECCN 24b was capable of solubilising insoluble K. Production of indoles increased following exposure to increasing aliquots of coal discard, suggesting no negative effect of this material on indole production by these coal-degrading bacterial isolates and that these bacteria may indeed possess PGP characteristics.
Preliminary Study on the Assessment of the Marginal Fit of Three-Dimensional Methacrylate Oligomer Phosphine Oxide Provisional Fixed Dental Prostheses Made by Digital Light Processing
Authors: Molinero-Mourelle Pedro , Gómez-Polo Miguel , Gómez-Polo Cristina , Ortega Rocio , del Río Highsmith Jaime , Celemín-Viñuela Alicia
Published: September 2020

This article aimed to assess the marginal fit of methacrylate-oligomer-phosphine-oxide curable-resin provisional-fixed dental prostheses made by digital-light-processing (DLP) three-dimensional (3D) printing. A stainless-steel master model with two abutments was scanned, and five three-unit provisional bridges were designed and printed in VITA shade A3.5 curable resin in 50 μm-thick layers. The marginal fit of each abutment was measured at six points using a profile projector. A descriptive data analysis of the fit measurements was performed by descriptive and explorative processes with the SPSS software. The curable-resin provisional restorations made by DLP 3D printing reached values of 46.37 μm (SD: 29.58 μm), which were considered clinically acceptable, with values similar to polyethylene-methacrylate and polyether-ether-ketone provisional restorations.
Modeling Incipient Use of Neolithic Cultigens by Taiwanese Foragers: Perspectives from Niche Variation Theory, the Prey Choice Model, and the Ideal Free Distribution
Authors: Yu Pei-Lin
Published: September 2020

The earliest evidence for agriculture in Taiwan dates to about 6000 years BP and indicates that farmer-gardeners from Southeast China migrated across the Taiwan Strait. However, little is known about the adaptive interactions between Taiwanese foragers and Neolithic Chinese farmers during the transition. This paper considers theoretical expectations from human behavioral ecology based models and macroecological patterning from Binford’s hunter-gatherer database to scope the range of responses of native populations to invasive dispersal. Niche variation theory and invasion theory predict that the foraging niche breadths will narrow for native populations and morphologically similar dispersing populations. The encounter contingent prey choice model indicates that groups under resource depression from depleted high-ranked resources will increasingly take low-ranked resources upon encounter. The ideal free distribution with Allee effects categorizes settlement into highly ranked habitats selected on the basis of encounter rates with preferred prey, with niche construction potentially contributing to an upswing in some highly ranked prey species. In coastal plain habitats preferred by farming immigrants, interactions and competition either reduced encounter rates with high ranked prey or were offset by benefits to habitat from the creation of a mosaic of succession ecozones by cultivation. Aquatic-focused foragers were eventually constrained to broaden subsistence by increasing the harvest of low ranked resources, then mobility-compatible Neolithic cultigens were added as a niche-broadening tactic. In locations less suitable for farming, fishing and hunting continued as primary foraging tactics for centuries after Neolithic arrivals. The paper concludes with a set of evidence-based archaeological expectations derived from these models.
Religion and Socioeconomic Wellbeing—Empirical Study of the Impact of Religion on Socioeconomic Rights in the Pluralistic and Democratic Context of Tamil Nadu, India
Authors: Anthony Francis-Vincent , Sterkens Carl
Published: September 2020

There is no gainsaying that in a globalized world, economic and technological development greatly determine human wellbeing. In the Indian context, the dialectics between socialist and capitalist economy, while giving way to the latter since 1991, has progressively led to the enlargement of the middle class, yet widened the gap between the rich and the poor. Such a situation points to the importance of socioeconomic rights for guaranteeing human flourishing. The question that we pose is whether religions can play a significant role in favoring these human rights, given their own specific vision of human life and of its socioeconomic facets, such as work, wealth, leisure, health, and education. In other words, can personal and contextual religious attitudes and religious socialization contribute to socioeconomic wellbeing? The empirical research undertaken in the pluralistic and democratic context of Tamil Nadu, India, seeks to verify among 1215 Christian, Muslim, and Hindu students, the impact of religion on their attitude towards socioeconomic rights. The emerging results reveal that some aspects of religious attitudes and socialization have a significant impact on students’ agreement with socioeconomic rights, particularly in the case of Christians and Muslims. We conclude with a discussion on the salient findings and their implications.
Statistical Approach to the Problem of Selecting the Most Appropriate Model for Managing Stormwater in Newly Designed Multi-Family Housing Estates
Authors: Kordana-Obuch Sabina , Starzec Mariusz
Published: September 2020

Stormwater is a valuable resource, whose management in harmony with nature is one of the main challenges of modern water management. The problems encountered are additionally exacerbated by the lack of space for the development of sustainable drainage systems. For that reason, new housing estates should be designed considering efficient stormwater management. This paper assesses five stormwater management models to determine the statistically most appropriate model for managing stormwater in newly designed multi-family housing estates using multi-criteria analysis. Various options were assessed by means of the scoring method based on six groups of criteria (political, economic, social, technological, legal, and environmental). The research considered the different views and priorities of the experts involved in stormwater management in Poland. A survey conducted among them showed that the statistically most suitable way of managing stormwater is its infiltration into the ground with the use of infiltration basins or tanks. Only if the possibility of their application is excluded, should the application of other models of stormwater management, especially its retention, be considered. It is expected that the research results presented in this paper will be a guide for investors and developers, and their use will allow people who are not experts in the field of stormwater management to make appropriate decisions.
Fast Total Variation Method Based on Iterative Reweighted Norm for Airborne Scanning Radar Super-Resolution Imaging
Authors: Tuo Xingyu , Zhang Yin , Huang Yulin , Yang Jianyu
Published: September 2020

The total variation (TV) method has been applied to realizing airborne scanning radar super-resolution imaging while maintaining the outline of the target. The iterative reweighted norm (IRN) approach is an algorithm for addressing the minimum Lp norm problem by solving a sequence of minimum weighted L2 norm problems, and has been applied to solving the TV norm. However, during the solving process, the IRN method is required to update the weight term and result term in each iteration, involving multiplications and the inversion of large matrices. Consequently, it suffers from a huge calculation load, which seriously restricts the application of the TV imaging method. In this work, by analyzing the structural characteristics of the matrix involved in iteration, an efficient method based on suitable matrix blocking is proposed. It transforms multiplications and the inversion of large matrices into the computation of multiple small matrices, thereby accelerating the algorithm. The proposed method, called IRN-FTV method, is more time economical than the IRN-TV method, especially for high dimensional observation scenarios. Numerical results illustrate that the proposed IRN-FTV method enjoys preferable computational efficiency without performance degradation.
Fully Automated Countrywide Monitoring of Fuel Break Maintenance Operations
Authors: Aubard Valentine , Pereira-Pires João E. , Campagnolo Manuel L. , Pereira José M. C. , Mora André , Silva João M. N.
Published: September 2020

Fuel break (FB) networks are strategic locations for fire control and suppression. In order to be effective for wildfire control, they need to be maintained through regular interventions to reduce fuel loads. In this paper, we describe a monitoring system relying on Earth observations to detect fuel reduction inside the FB network being implemented in Portugal. Two fast automated pixel-based methodologies for monthly monitoring of fuel removals in FB are developed and compared. The first method (M1) is a classical supervised classification using the difference and postdisturbance image of monthly image composites. To take into account the impact of different land cover and phenology in the detection of fuel treatments, a second method (M2) based on an innovative statistical change detection approach was developed. M2 explores time series of vegetation indices and does not require training data or user-defined thresholds. The two algorithms were applied to Sentinel-2 10 m bands and fully processed in the cloud-based platform Google Earth Engine. Overall, the unsupervised M2, which is based on a Welch t-test of two moving window averages, gives better results than the supervised M1 and is suitable for an automated countrywide fuel treatment detection. For both methods, two vegetation indices, the Modified Excess of Green and the Normalized Difference Vegetation Index, were compared and exhibited similar performances.
Deep Nonnegative Dictionary Factorization for Hyperspectral Unmixing
Authors: Wang Wenhong , Liu Hongfu
Published: September 2020

As a powerful blind source separation tool, Nonnegative Matrix Factorization (NMF) with effective regularizations has shown significant superiority in spectral unmixing of hyperspectral remote sensing images (HSIs) owing to its good physical interpretability and data adaptability. However, the majority of existing NMF-based spectral unmixing methods only adopt the single layer factorization, which is not favorable for exploiting the complex and structured representation relationship of endmembers implied in HSIs. In order to overcome such an issue, we propose a novel two-stage Deep Nonnegative Dictionary Factorization (DNDF) approach with a sparseness constraint and self-supervised regularization for HSI unmixing. Beyond simply extending one-layer factorization to multi-layer, DNDF conducts fuzzy clustering to tackle the mixed endmembers of HSIs. Moreover, self-supervised regularization is integrated into our DNDF model to impose an effective constraint on the endmember matrix. Experimental results on three real HSIs demonstrate the superiority of DNDF over several state-of-the-art methods.
Analysis of the Spatial Differences in Canopy Height Models from UAV LiDAR and Photogrammetry
Authors: Liu Qingwang , Fu Liyong , Chen Qiao , Wang Guangxing , Luo Peng , Sharma Ram P. , He Peng , Li Mei , Wang Mengxi , Duan Guangshuang
Published: September 2020

Forest canopy height is one of the most important spatial characteristics for forest resource inventories and forest ecosystem modeling. Light detection and ranging (LiDAR) can be used to accurately detect canopy surface and terrain information from the backscattering signals of laser pulses, while photogrammetry tends to accurately depict the canopy surface envelope. The spatial differences between the canopy surfaces estimated by LiDAR and photogrammetry have not been investigated in depth. Thus, this study aims to assess LiDAR and photogrammetry point clouds and analyze the spatial differences in canopy heights. The study site is located in the Jigongshan National Nature Reserve of Henan Province, Central China. Six data sets, including one LiDAR data set and five photogrammetry data sets captured from an unmanned aerial vehicle (UAV), were used to estimate the forest canopy heights. Three spatial distribution descriptors, namely, the effective cell ratio (ECR), point cloud homogeneity (PCH) and point cloud redundancy (PCR), were developed to assess the LiDAR and photogrammetry point clouds in the grid. The ordinary neighbor (ON) and constrained neighbor (CN) interpolation algorithms were used to fill void cells in digital surface models (DSMs) and canopy height models (CHMs). The CN algorithm could be used to distinguish small and large holes in the CHMs. The optimal spatial resolution was analyzed according to the ECR changes of DSMs or CHMs resulting from the CN algorithms. Large negative and positive variations were observed between the LiDAR and photogrammetry canopy heights. The stratified mean difference in canopy heights increased gradually from negative to positive when the canopy heights were greater than 3 m, which means that photogrammetry tends to overestimate low canopy heights and underestimate high canopy heights. The CN interpolation algorithm achieved smaller relative root mean square errors than the ON interpolation algorithm. This article provides an operational method for the spatial assessment of point clouds and suggests that the variations between LiDAR and photogrammetry CHMs should be considered when modeling forest parameters.
Mapping Carbon Monoxide Pollution of Residential Areas in a Polish City
Authors: Kwiecień Janusz , Szopińska Kinga
Published: September 2020

Road traffic is among the main sources of atmospheric pollution in cities. Maps of pollutants are based on geostatistical models using a digital model of the city along with traffic parameters allowing for ongoing analyses and prediction of the condition of the environment. The aim of the work was to determine the size of areas at risk of carbon monoxide pollution derived from road traffic along with determining the number of inhabitants exposed to excessive CO levels using geostatistical modeling on the example of the city of Bydgoszcz, a city in the northern part of Poland. The COPERT STREET LEVEL program was used to calculate CO emissions. Next, based on geostatistical modelling, a prediction map of CO pollution (kg/year) was generated, along with determining the level of CO concentration (mg/m3/year). The studies accounted for the variability of road sources as well as the spatial structure of the terrain. The results are presented for the city as well as divided into individual housing estates. The level of total carbon monoxide concentration for the city was 5.18 mg/m3/year, indicating good air quality. Detailed calculation analyses showed that the level of air pollution with CO varies in the individual housing estates, ranging from 0.08 to 35.70 mg/m3/year. Out of the 51 studied residential estates, the limit value was exceeded in 10, with 45% of the population at risk of poor air quality. The obtained results indicate that only detailed monitoring of the level of pollution can provide us with reliable information on air quality. The results also show in what way geostatistical tools can be used to map the spatial variability of air pollution in a city. The obtained spatial details can be used to improve estimated concentration based on interpolation between direct observation and prediction models.
Applicability Evaluation of Multisource Satellite Precipitation Data for Hydrological Research in Arid Mountainous Areas
Authors: Wang Xiangzhen , Li Baofu , Chen Yaning , Guo Hao , Wang Yunqian , Lian Lishu
Published: September 2020

Global Satellite Mapping of Precipitation (GSMaP), Climate Hazards Group InfraRed Preconception with Station data (CHIRPS), Tropical Rain Measurement Mission Multisatellite Precipitation Analysis (TRMM 3B42 V7) and Rainfall Estimation from Soil Moisture Observations (SM2RAIN) are satellite precipitation products with high applicability, but their applicability in hydrological research in arid mountainous areas is not clear. Based on precipitation and runoff data, this study evaluated the applicability of each product to hydrological research in a typical mountainous basin (the Qaraqash River basin) in an arid region by using two methods: a statistical index and a hydrological model (Soil and Water Assessment Tool, SWAT). Simulation results were evaluated by Nash efficiency coefficient (NS), relative error (PBIAS) and determination coefficient (R2). The results show that: (1) The spatial distributions of precipitation estimated by these four products in the Qaraqash River basin are significantly different, and the multi-year average annual precipitation of GSMaP is 97.11 mm, which is the closest to the weather station interpolation results. (2) On the annual and monthly scales, GSMaP has the highest correlation (R ≥ 0.82) with the observed precipitation and the smallest relative error (BIAS < 6%). On the seasonal scale, the inversion accuracy of GSMaP in spring, summer and autumn is significantly higher than other products. In winter, all four sets of products perform poorly in estimating the actual precipitation. (3) Monthly runoff simulations based on SM2RAIN and GSMaP show good fitting (R2 > 0.6). In daily runoff simulation, GSMaP has the greatest ability to reproduce runoff changes. The study provides a reference for the optimization of precipitation image data and hydrological simulation in data-scarce areas.
Albedo Impacts of Changing Agricultural Practices in the United States through Space-Borne Analysis
Authors: Starr Jon , Zhang Jianglong , Reid Jeffrey S. , Roberts David C.
Published: September 2020

Using the collocated Moderate Resolution Imaging Spectroradiometer (MODIS)-derived Bidirectional Reflectance Distribution Function (BRDF) with the U.S. Department of Agriculture’s National Agricultural Statistics Service’s Cropland Data Layer (CDL), the daily albedo of homogenous agricultural fields was derived for 51 common United States field crops by wavelength, sky-type, day of year, crop, and hardiness zone from 2015–2018. This study suggests that crop growth can result in changes in reflectivity up to a factor of 2 at most wavelengths and is unique per crop type in timing and range. Additionally, broadband impacts were studied and found to be less conspicuous than the individual wavelengths, but still significant. The results were used to evaluate a common method of cropland albedo estimation, normalized difference vegetation index (NDVI) as a proxy for albedo, and this method was found to have some significant limitations dependent on wavelength and date. Finally, a database of surface albedo variations as a function of growing period is constructed for common field crops to the United States (as well as additional land-cover types). This database can be used to aid both satellite remote-sensing applications and long-term weather modeling efforts by providing a method for parameter adjustments based on crop driven albedo changes, including changes in cropland composition related to commodity markets and other external factors.
An Image Matching Method for SAR Orthophotos from Adjacent Orbits in Large Area Based on SAR-Moravec
Authors: Han Chunming , Luo Wei , Guo Huadong , Ding Yixing
Published: September 2020

In producing orthophoto mosaic in a large area from spaceborne synthetic aperture radar (SAR) images, SAR image matching from adjacent orbits is a technical difficulty due to the speckle noise and different imaging mechanism between azimuth and range direction. In this paper, an area-based method, SAR-Moravec, is proposed for SAR orthophoto matching from adjacent orbits in a large area. Compared with the classical area-based Moravec, the template of SAR-Moravec is characterized by more directions for speckle noise restraint and a main direction consistent with the azimuth. In order to get evenly distributed matching points with high accuracy, the grid control mechanism and Gaussian pyramid from coarse to fine are introduced in matching. The whole process contains three steps. First, the pyramid images are constructed by the down-sampling process. Second, under grid control, the feature points are evenly extracted by the modified template. Third, the transformation model is iteratively calculated from the first to the last layer of the pyramid. After the matching process layer-by-layer, the final matching points and transformation model can be obtained. In the experiments, we compare the SAR-Moravec with three widely used methods, including the Moravec, the SAR-scale invariant feature transform (SAR-SIFT), and the SAR-features from an accelerated segment test (SAR-FAST). The results indicate that the proposed method has the best global matching accuracy among these methods and the matching efficiency is better than SAR-SIFT and SAR-FAST methods in large area.
Prediction of Winter Wheat Maturity Dates through Assimilating Remotely Sensed Leaf Area Index into Crop Growth Model
Authors: Zhuo Wen , Huang Jianxi , Gao Xinran , Ma Hongyuan , Huang Hai , Su Wei , Meng Jihua , Li Ying , Chen Huailiang , Yin Dongqin
Published: September 2020

Predicting crop maturity dates is important for improving crop harvest planning and grain quality. The prediction of crop maturity dates by assimilating remote sensing information into crop growth model has not been fully explored. In this study, a data assimilation framework incorporating the leaf area index (LAI) product from Moderate Resolution Imaging Spectroradiometer (MODIS) into a World Food Studies (WOFOST) model was proposed to predict the maturity dates of winter wheat in Henan province, China. Minimization of normalized cost function was used to obtain the input parameters of the WOFOST model. The WOFOST model was run with the re-initialized parameter to forecast the maturity dates of winter wheat grid by grid, and THORPEX Interactive Grand Global Ensemble (TIGGE) was used as forecasting period weather input in the future 15 days (d) for the WOFOST model. The results demonstrated a promising regional maturity date prediction with determination coefficient (R2) of 0.94 and the root mean square error (RMSE) of 1.86 d. The outcomes also showed that the optimal forecasting starting time for Henan was 30 April, corresponding to a stage from anthesis to grain filling. Our study indicated great potential of using data assimilation approaches in winter wheat maturity date prediction.
Sensing Images for Assessing the Minimum Ecological Flux by Automatically Extracting River Surface Width
Authors: Xu Wenting , Shen Qian , Wang Xuelei , Wang Qian , Yao Yue , Huang Wei , Wang Mingxiu , Li Junsheng , Zhang Fangfang , Chen Xiaoyong
Published: September 2020

Global warming and economic development have intensified the evaporation and exploitation of river waters, resulting in reduced global river runoff. In minimum ecological flux management, objective determination of the minimum ecological flux and evaluation of whether a river complies with standards are urgently required. Satellite remote sensing allows for rapid, large-scale, and dynamic monitoring. Herein, the Tangmazhai cross-section of the Taizi River was analyzed using the Chinese Gaofen (GF) series satellite that comprises panchromatic multi-spectral sensors and the Sentinel-2 multi-spectral images to automatically extract the water surface width. We applied the Normalized Difference Water Index (NDWI)-Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA) to 225 cloudless scenes from January 2015 to November 2019. We proposed a method to evaluate the minimum ecological flux using water surface width. The river surface width at this location increased from January 2015 to November 2019, and all widths exceeded the minimum river surface water width for the month. The degree of the minimum ecological flux guarantee was determined to be satisfactory. Because there are less clouds and rain in the North China than South China, our results may be used for evaluating the degree of minimum ecological flux guarantee of many river sections in the north China through monthly monitoring.
A Survey on Adaptive Data Rate Optimization in LoRaWAN: Recent Solutions and Major Challenges
Authors: Kufakunesu Rachel , Hancke Gerhard P. , Abu-Mahfouz Adnan M.
Published: September 2020

Long-Range Wide Area Network (LoRaWAN) is a fast-growing communication system for Low Power Wide Area Networks (LPWAN) in the Internet of Things (IoTs) deployments. LoRaWAN is built to optimize LPWANs for battery lifetime, capacity, range, and cost. LoRaWAN employs an Adaptive Data Rate (ADR) scheme that dynamically optimizes data rate, airtime, and energy consumption. The major challenge in LoRaWAN is that the LoRa specification does not state how the network server must command end nodes pertaining rate adaptation. As a result, numerous ADR schemes have been proposed to cater for the many applications of IoT technology, the quality of service requirements, different metrics, and radio frequency (RF) conditions. This offers a challenge for the reliability and suitability of these schemes. This paper presents a comprehensive review of the research on ADR algorithms for LoRaWAN technology. First, we provide an overview of LoRaWAN network performance that has been explored and documented in the literature and then focus on recent solutions for ADR as an optimization approach to improve throughput, energy efficiency and scalability. We then distinguish the approaches used, highlight their strengths and drawbacks, and provide a comparison of these approaches. Finally, we identify some research gaps and future directions.
Quarantining Malicious IoT Devices in Intelligent Sliced Mobile Networks
Authors: Candal-Ventureira David , Fondo-Ferreiro Pablo , Gil-Castiñeira Felipe , González-Castaño Francisco Javier
Published: September 2020

The unstoppable adoption of the Internet of Things (IoT) is driven by the deployment of new services that require continuous capture of information from huge populations of sensors, or actuating over a myriad of “smart” objects. Accordingly, next generation networks are being designed to support such massive numbers of devices and connections. For example, the 3rd Generation Partnership Project (3GPP) is designing the different 5G releases specifically with IoT in mind. Nevertheless, from a security perspective this scenario is a potential nightmare: the attack surface becomes wider and many IoT nodes do not have enough resources to support advanced security protocols. In fact, security is rarely a priority in their design. Thus, including network-level mechanisms for preventing attacks from malware-infected IoT devices is mandatory to avert further damage. In this paper, we propose a novel Software-Defined Networking (SDN)-based architecture to identify suspicious nodes in 4G or 5G networks and redirect their traffic to a secondary network slice where traffic is analyzed in depth before allowing it reaching its destination. The architecture can be easily integrated in any existing deployment due to its interoperability. By following this approach, we can detect potential threats at an early stage and limit the damage by Distributed Denial of Service (DDoS) attacks originated in IoT devices.
Rate Dependent Krasnoselskii-Pokrovskii Modeling and Inverse Compensation Control of Piezoceramic Actuated Stages
Authors: Li Wenjun , Nie Linlin , Liu Ying , Zhou Miaolei
Published: September 2020

The piezoceramic actuated stages have rate-dependent hysteresis nonlinearity, which is not simply related to the current and historical input, but also related to the frequency of the input signal, seriously affects its positioning accuracy. Consider the influence of frequency on hysteresis modeling, a rate-dependent hysteresis nonlinearity model that is based on Krasnoselskii–Pokrovskii (KP) operator is proposed in this paper. A hybrid optimization algorithm of improved particle swarm optimization and cuckoo search is employed in order to identify the density function of rate-dependent KP model, avoiding the blind search process caused by the high randomness of Levy’s flight in the cuckoo search algorithm, and improving the parameter identification performance. For the sake of eliminating the hysteresis characteristics, an inverse feed-forward compensation control that is based on recursive method is proposed without any additional conditions, and a feed-forward compensation controller is designed accordingly. The experimental results show that, under different frequency input signals, as compared with the classic KP model, the proposed rate-dependent KP model can accurately describe the rate-dependent hysteresis characteristics of the piezoceramic actuated stages, and the recursive inverse feed-forward compensation control method can effectively mitigate the hysteresis behaviors.
Molecular Imprinted Polymers Coupled to Photonic Structures in Biosensors: The State of Art
Authors: Chiappini Andrea , Pasquardini Laura , Bossi Alessandra Maria
Published: September 2020

Optical sensing, taking advantage of the variety of available optical structures, is a rapidly expanding area. Over recent years, whispering gallery mode resonators, photonic crystals, optical waveguides, optical fibers and surface plasmon resonance have been exploited to devise different optical sensing configurations. In the present review, we report on the state of the art of optical sensing devices based on the aforementioned optical structures and on synthetic receptors prepared by means of the molecular imprinting technology. Molecularly imprinted polymers (MIPs) are polymeric receptors, cheap and robust, with high affinity and selectivity, prepared by a template assisted synthesis. The state of the art of the MIP functionalized optical structures is critically discussed, highlighting the key progresses that enabled the achievement of improved sensing performances, the merits and the limits both in MIP synthetic strategies and in MIP coupling.
Advances and Trends in Real Time Visual Crowd Analysis
Authors: Khan Khalil , Albattah Waleed , Khan Rehan Ullah , Qamar Ali Mustafa , Nayab Durre
Published: September 2020

Real time crowd analysis represents an active area of research within the computer vision community in general and scene analysis in particular. Over the last 10 years, various methods for crowd management in real time scenario have received immense attention due to large scale applications in people counting, public events management, disaster management, safety monitoring an so on. Although many sophisticated algorithms have been developed to address the task; crowd management in real time conditions is still a challenging problem being completely solved, particularly in wild and unconstrained conditions. In the proposed paper, we present a detailed review of crowd analysis and management, focusing on state-of-the-art methods for both controlled and unconstrained conditions. The paper illustrates both the advantages and disadvantages of state-of-the-art methods. The methods presented comprise the seminal research works on crowd management, and monitoring and then culminating state-of-the-art methods of the newly introduced deep learning methods. Comparison of the previous methods is presented, with a detailed discussion of the direction for future research work. We believe this review article will contribute to various application domains and will also augment the knowledge of the crowd analysis within the research community.
A Novel Change Detection Method for Natural Disaster Detection and Segmentation from Video Sequence
Authors: Qiao Huijiao , Wan Xue , Wan Youchuan , Li Shengyang , Zhang Wanfeng
Published: September 2020

Change detection (CD) is critical for natural disaster detection, monitoring and evaluation. Video satellites, new types of satellites being launched recently, are able to record the motion change during natural disasters. This raises a new problem for traditional CD methods, as they can only detect areas with highly changed radiometric and geometric information. Optical flow-based methods are able to detect the pixel-based motion tracking at fast speed; however, they are difficult to determine an optimal threshold for separating the changed from the unchanged part for CD problems. To overcome the above problems, this paper proposed a novel automatic change detection framework: OFATS (optical flow-based adaptive thresholding segmentation). Combining the characteristics of optical flow data, a new objective function based on the ratio of maximum between-class variance and minimum within-class variance has been constructed and two key steps are motion detection based on optical flow estimation using deep learning (DL) method and changed area segmentation based on an adaptive threshold selection. Experiments are carried out using two groups of video sequences, which demonstrated that the proposed method is able to achieve high accuracy with F1 value of 0.98 and 0.94, respectively.
An Approach to Size Sub-Wavelength Surface Crack Measurements Using Rayleigh Waves Based on Laser Ultrasounds
Authors: Li Haiyang , Pan Qianghua , Zhang Xiaotong , An Zhiwu
Published: September 2020

In this paper, the interaction of a broadband Rayleigh wave generated by a laser and an artificial rectangular notch is analyzed theoretically and experimentally. For the theoretical analysis, a Gaussian function is adopted to analyze the modulation of notch depth on the frequency spectrum via reflection and transmission coefficients. By the finite element method, the Rayleigh wave generated by pulsed laser beam irradiation and its scattering waves at cracks are calculated. A curve with a slope close to 4 fitted by crack depth and critical wavelength of the threshold phenomenon is obtained by the wavelet transform and Parseval’s theorem according to simulated and experimental results. Based on this relationship, the critical frequency at which the threshold phenomenon happens due to energy transformation of transmission/reflection Rayleigh waves is adopted to determine the size of sub-wavelength surface crack. The experimental results of artificial notch depth estimation on aluminum alloy specimens consistent with theoretical analysis validates the usefulness of the critical frequency method based on a broadband Rayleigh wave generated by laser ultrasonic.
Improved Diver Communication System by Combining Optical and Electromagnetic Trackers
Authors: Kataria Aman , Ghosh Smarajit , Karar Vinod , Gupta Takshi , Srinivasan Kathiravan , Hu Yuh-Chung
Published: September 2020

The increasing need for observation in seawater or ocean monitoring systems has ignited a considerable amount of interest and the necessity for enabling advancements in technology for underwater wireless tracking and underwater sensor networks for wireless communication. This type of communication can also play an important role in investigating ecological changes in the sea or ocean-like climate change, monitoring of biogeochemical, biological, and evolutionary changes. This can help in controlling and maintaining the production facilities of outer underwater grid blasting by deploying unmanned underwater vehicles (UUVs). Underwater tracking-based wireless networks can also help in maintaining communication between ships and divers, submarines, and between multiple divers. At present, the underwater acoustic communication system is unable to provide the data rate required to monitor and investigate the aquatic environment for various industrial applications like oil facilities or underwater grit blasting. To meet this challenge, an optical and magnetic tracking-based wireless communication system has been proposed as an effective alternative. Either optical or magnetic tracking-based wireless communication can be opted for according to the requirement of the potential application in sea or ocean. However, the hybrid version of optical and wireless tracking-based wireless communication can also be deployed to reduce the latency and improve the data rate for effective communication. It is concluded from the discussion that high data rate optical, magnetic or hybrid mode of wireless communication can be a feasible solution in applications like UUV-to-UUV and networks of aquatic sensors. The range of the proposed wireless communication can be extended using the concept of multihop.
A Miniature Intermittent-Flow Respirometry System with a 3D-Printed, Palm-Sized Zebrafish Treadmill for Measuring Rest and Activity Metabolic Rates
Authors: Huang Shih-Hao , Tsao Chia-Wei , Fang Yan-Hung
Published: September 2020

Zebrafish are a preferred vertebrate model for evaluating metabolism during development, and for toxicity studies. However, commercially available intermittent-flow respirometry systems (IFRS) do not provide a suitable zebrafish-scaled swimming tunnel with a low water volume and proper flow velocities. We developed a miniature IFRS (mIFRS) with a 3D-printed, palm-sized zebrafish treadmill for measuring the swimming ability and metabolic rate of a single one- or three-month-old zebrafish with and without toxicity treatment. The 3D-printed zebrafish treadmill consists of discrete components assembled together which enables the provision of a temporary closed circulating water flow. The results showed that three-month-old zebrafish of normal physiological status had higher energetic efficiency and could swim at a higher critical swimming speed (Ucrit) of 16.79 cm/s with a lower cost of transport (COTopt) of 0.11 μmol g−1m−1. However, for a single three-month-old zebrafish treated with an antibacterial agent, Ucrit decreased to 45% of normal zebrafish and the COTopt increased to 0.24 μmol g−1m−1, due to the impairment of mitochondria. Our mIFRS provides a low-cost, portable, and readily adaptable tool for studying the swimming performance and energetic metabolism of zebrafish.
Marker-Less 3d Object Recognition and 6d Pose Estimation for Homogeneous Textureless Objects: An RGB-D Approach
Authors: Hajari Nasim , Lugo Bustillo Gabriel , Sharma Harsh , Cheng Irene
Published: September 2020

The task of recognising an object and estimating its 6d pose in a scene has received considerable attention in recent years. The accessibility and low-cost of consumer RGB-D cameras, make object recognition and pose estimation feasible even for small industrial businesses. An example is the industrial assembly line, where a robotic arm should pick a small, textureless and mostly homogeneous object and place it in a designated location. Despite all the recent advancements of object recognition and pose estimation techniques in natural scenes, the problem remains challenging for industrial parts. In this paper, we present a framework to simultaneously recognise the object’s class and estimate its 6d pose from RGB-D data. The proposed model adapts a global approach, where an object and the Region of Interest (ROI) are first recognised from RGB images. The object’s pose is then estimated from the corresponding depth information. We train various classifiers based on extracted Histogram of Oriented Gradient (HOG) features to detect and recognize the objects. We then perform template matching on the point cloud based on surface normal and Fast Point Feature Histograms (FPFH) to estimate the pose of the object. Experimental results show that our system is quite efficient, accurate and robust to illumination and background changes, even for the challenging objects of Tless dataset.
Classification of Smoke Contaminated Cabernet Sauvignon Berries and Leaves Based on Chemical Fingerprinting and Machine Learning Algorithms
Authors: Summerson Vasiliki , Gonzalez Viejo Claudia , Szeto Colleen , Wilkinson Kerry L. , Torrico Damir D. , Pang Alexis , De Bei Roberta , Fuentes Sigfredo
Published: September 2020

Wildfires are an increasing problem worldwide, with their number and intensity predicted to rise due to climate change. When fires occur close to vineyards, this can result in grapevine smoke contamination and, subsequently, the development of smoke taint in wine. Currently, there are no in-field detection systems that growers can use to assess whether their grapevines have been contaminated by smoke. This study evaluated the use of near-infrared (NIR) spectroscopy as a chemical fingerprinting tool, coupled with machine learning, to create a rapid, non-destructive in-field detection system for assessing grapevine smoke contamination. Two artificial neural network models were developed using grapevine leaf spectra (Model 1) and grape spectra (Model 2) as inputs, and smoke treatments as targets. Both models displayed high overall accuracies in classifying the spectral readings according to the smoking treatments (Model 1: 98.00%; Model 2: 97.40%). Ultraviolet to visible spectroscopy was also used to assess the physiological performance and senescence of leaves, and the degree of ripening and anthocyanin content of grapes. The results showed that chemical fingerprinting and machine learning might offer a rapid, in-field detection system for grapevine smoke contamination that will enable growers to make timely decisions following a bushfire event, e.g., avoiding harvest of heavily contaminated grapes for winemaking or assisting with a sample collection of grapes for chemical analysis of smoke taint markers.
The Effects of Gentrification on the Elderly: A Case Study in the City of Cáceres
Authors: Domínguez-Parraga Lidia
Published: September 2020

This study aims to analyze the consequences of the gentrification process as a result of tourism on the elderly inhabitants. Firstly, the concept of gentrification is reviewed, a process that is no longer exclusive to large cities but has spread to smaller and lesser-known municipalities. A clear example of this type of new tourist destination is the city of Cáceres, a World Heritage medium-sized city where tourism is the basis of its economy. The research considers gentrification in medium-sized cities and its effect on active aging. Based on a qualitative methodological approach, a total of 32 in-depth interviews were conducted and analyzed to compare two neighborhoods—one gentrified and one not. The results show a remarkable disparity in the residents’ perceptions of their environment and their city. The findings suggest adverse effects on the quality of aging due to the gentrification process, such as family dependency, social-space disconnection, and a generally pessimistic image of the neighborhood. Consequently, the impact of tourism negatively affects the gentrified neighborhood inhabitants’ psychological, social, and emotional well-being.
Critical Success Factors, Motivations, and Risks in Social Impact Bonds
Authors: Carè Rosella , Rania Francesco , De Lisa Riccardo
Published: September 2020

Social impact bonds (SIBs) have emerged as one of the most innovative financial instruments designed to support the social service sector in the delivery of innovative social programs. Despite the growing interest of academics and practitioners in SIBs, the debate appears polarized around a series of recurrent aspects, and only a limited number of studies have sought to understand the risks and motivations related to similar initiatives. Using an exploratory approach based on a mixed-method grounded theory methodology, this study analyzed the results of 12 questionnaires that asked experts about their experiences and perceptions in SIB project development and implementation. The study identified and assessed three main groups of motivations, critical success factors, and risk factors by focusing on the private-sector SIB actors with the aim of understanding their motivations and their perceived main success drivers and risk factors. This work contributes to the knowledge on the conditions for attracting private sector actors and supporting policymakers in the development of new SIB models. The findings could facilitate the development of risk management practices for the purpose of stimulating the participation of private actors in SIB initiatives.