bluuebunny/crossref_metadata_2025 huggingface dataset:
Rice Crop Detection Using LSTM, Bi-LSTM, and Machine Learning Models from Sentinel-1 Time Series
Authors: Crisóstomo de Castro Filho Hugo , Abílio de Carvalho Júnior Osmar , Ferreira de Carvalho Osmar Luiz , Pozzobon de Bem Pablo , dos Santos de Moura Rebeca , Olino de Albuquerque Anesmar , Rosa Silva Cristiano , Guimarães Ferreira Pedro Henrique , Fontes Guimarães Renato , Trancoso Gomes Roberto Arnaldo
Published: August 2020

The Synthetic Aperture Radar (SAR) time series allows describing the rice phenological cycle by the backscattering time signature. Therefore, the advent of the Copernicus Sentinel-1 program expands studies of radar data (C-band) for rice monitoring at regional scales, due to the high temporal resolution and free data distribution. Recurrent Neural Network (RNN) model has reached state-of-the-art in the pattern recognition of time-sequenced data, obtaining a significant advantage at crop classification on the remote sensing images. One of the most used approaches in the RNN model is the Long Short-Term Memory (LSTM) model and its improvements, such as Bidirectional LSTM (Bi-LSTM). Bi-LSTM models are more effective as their output depends on the previous and the next segment, in contrast to the unidirectional LSTM models. The present research aims to map rice crops from Sentinel-1 time series (band C) using LSTM and Bi-LSTM models in West Rio Grande do Sul (Brazil). We compared the results with traditional Machine Learning techniques: Support Vector Machines (SVM), Random Forest (RF), k-Nearest Neighbors (k-NN), and Normal Bayes (NB). The developed methodology can be subdivided into the following steps: (a) acquisition of the Sentinel time series over two years; (b) data pre-processing and minimizing noise from 3D spatial-temporal filters and smoothing with Savitzky-Golay filter; (c) time series classification procedures; (d) accuracy analysis and comparison among the methods. The results show high overall accuracy and Kappa (>97% for all methods and metrics). Bi-LSTM was the best model, presenting statistical differences in the McNemar test with a significance of 0.05. However, LSTM and Traditional Machine Learning models also achieved high accuracy values. The study establishes an adequate methodology for mapping the rice crops in West Rio Grande do Sul.
ALS-Based Detection of Past Human Activities in the Białowieża Forest—New Evidence of Unknown Remains of Past Agricultural Systems
Authors: Stereńczak Krzysztof , Zapłata Rafał , Wójcik Jarosław , Kraszewski Bartłomiej , Mielcarek Miłosz , Mitelsztedt Krzysztof , Białczak Małgorzata , Krok Grzegorz , Kuberski Łukasz , Markiewicz Anna , Modzelewska Aneta , Parkitna Karolina , Piasecka Żaneta , Pilch Kamil , Rzeczycki Karol , Sadkowski Rafał , Wietecha Martyna , Rysiak Piotr , von Gadow Klaus , Cieszewski Chris J.
Published: August 2020

The Białowieża Forest (BF), a unique ecosystem of historical significance in central Europe, has a long history of assumed human settlement, with at least 200 known archaeological sites (until 2016). This study uncovers new evidence of the cultural heritage of this unique forest area using Airborne Laser Scanning (ALS) technology combined with traditional archaeological field assessment methods to verify the ALS data interpretations and to provide additional evidence about the function and origin of the newly detected archaeological sites. The results of this study include (1) a scientific approach for an improved identification of archaeological resources in forest areas; (2) new evidence about the history of the human use of the BF based on ALS data, covering the entire Polish part of the BF; and (3) an improved remote sensing infrastructure, supporting existing GIS (Geographic Information System) systems for the BF, a famous UNESCO Heritage site. Our study identified numerous locations with evidence of past human agricultural activities known in the literature as “field systems”, “lynchets” and “Celtic fields”. The initial identification included more than 300 km of possible field boundaries and plough headlands, many of which we have verified on the ground. Various past human activities creating those boundaries have existed since the (pre-) Roman Period up to the 13th century AD. The results of this study demonstrate that past human activities in the Polish part of the Białowieża Forest had been more prevalent than previously believed. As a practical result of the described activities, a geodatabase was created; this has practical applications for the system of monument protection in Poland, as well as for local communities and the BF’s management and conservation. The more widely achieved results are in line with the implementation of the concept of a cultural heritage inventory in forested and protected areas—the actions taken specify (built globally) the forms of protection and management of cultural and environmental goods.
Probe Request Based Device Identification Attack and Defense
Authors: Gu Xiaolin , Wu Wenjia , Gu Xiaodan , Ling Zhen , Yang Ming , Song Aibo
Published: August 2020

Wi-Fi network has an open nature so that it needs to face greater security risks compared to wired network. The MAC address represents the unique identifier of the device, and is easily obtained by an attacker. Therefore MAC address randomization is proposed to protect the privacy of devices in a Wi-Fi network. However, implicit identifiers are used by attackers to identify user’s device, which can cause the leakage of user’s privacy. We propose device identification based on 802.11ac probe request frames. Here, a detailed analysis on the effectiveness of 802.11ac fields is given and a novel device identification method based on deep learning whose average f1-score exceeds 99% is presented. With a purpose of preventing attackers from obtaining relevant information by the device identification method above, we design a novel defense mechanism based on stream cipher. In that case, the original content of probe request frame is hidden by encrypting probe request frames and construction of probe request is reserved to avoid the finding of attackers. This defense mechanism can effectively reduce the performance of the proposed device identification method whose average f1-score is below 30%. In general, our research on attack and defense mechanism can preserve device privacy better.
Real-Time Impedance Detection of Intra-Articular Space in a Porcine Model Using a Monopolar Injection Needle
Authors: Abbasi Muhammad Aitzaz , Kim Hwijung , Chinnadayyala Somasekhar R. , Park Ki Deok , Cho Sungbo
Published: August 2020

Rheumatoid arthritis and osteoarthritis can be treated through specific drug injection into the intra-articular space. Several failures during drug injection attempts with conventional fluoroscopy and ultrasonography in a small area of the intra-articular space have been reported. In this work we present an innovative impedance measurement-based method/algorithm for needle tip positioning to enhance image-guided intra-articular vaccination treatment. A novel algorithm for detecting the intra-articular space in the elbow and knee joints of a live porcine model is reported. An impedance measurement system was developed for biological tissue measurement. The electrical impedance in the intra-articular space was monitored and the needle tip was examined by ultrasonography. The contrast dye was vaccinated and checked using fluoroscopy to confirm that the dye was properly inoculated in the cavity. The electrical impedance was estimated for various needle inclusion profundity levels in saline solution, which were broadly used to evaluate the proposed device for in vivo examinations. Good efficiency was observed in the impedance-based measurements using a monopolar injection needle for intra-articular therapy. To enhance the needle tip positioning for intra-articular therapy, the intended impedance measurement device with a monopolar injection needle can be used as a complement to existing modalities.
Modeling of Stochastic Wind Based on Operational Flight Data Using Karhunen–Loève Expansion Method
Authors: Wang Xiaolong , Beller Lukas , Czado Claudia , Holzapfel Florian
Published: August 2020

Wind has a significant influence on the operational flight safety. To quantify the influence of the wind characteristics, a wind series generator is required in simulations. This paper presents a method to model the stochastic wind based on operational flight data using the Karhunen–Loève expansion. The proposed wind model allows us to generate new realizations of wind series, which follow the original statistical characteristics. To improve the accuracy of this wind model, a vine copula is used in this paper to capture the high dimensional dependence among the random variables in the expansions. Besides, the proposed stochastic model based on the Karhunen–Loève expansion is compared with the well-known von Karman turbulence model based on the spectral representation in this paper. Modeling results of turbulence data validate that the Karhunen–Loève expansion and the spectral representation coincide in the stationary process. Furthermore, construction results of the non-stationary wind process from operational flights show that the generated wind series have a good match in the statistical characteristics with the raw data. The proposed stochastic wind model allows us to integrate the new wind series into the Monte Carlo Simulation for quantitative assessments.
BlockSIEM: Protecting Smart City Services through a Blockchain-based and Distributed SIEM
Authors: Botello Juan Velandia , Mesa Andrés Pardo , Rodríguez Fabián Ardila , Díaz-López Daniel , Nespoli Pantaleone , Mármol Félix Gómez
Published: August 2020

The Internet of Things (IoT) paradigm has revolutionized several industries (e.g., manufacturing, health, transport, education, among others) by allowing objects to connect to the Internet and, thus, enabling a variety of novel applications. In this sense, IoT devices have become an essential component of smart cities, allowing many novel and useful services, but, at the same time, bringing numerous cybersecurity threats. The paper at hand proposes BlockSIEM, a blockchain-based and distributed Security Information and Event Management (SIEM) solution framework for the protection of the aforementioned smart city services. The proposed SIEM relies on blockchain technology to securely store and access security events. Such security events are generated by IoT sentinels that are in charge of shielding groups of IoT devices. The IoT sentinels may be deployed in smart city scenarios, such as smart hospitals, smart transport systems, smart airports, among others, ensuring a satisfactory level of protection. The blockchain guarantees the non-repudiation and traceability of the registry of security events due to its features. To demonstrate the feasibility of the proposed approach, our proposal is implemented using Ethereum and validated through different use cases and experiments.
Graph Eigen Decomposition-Based Feature-Selection Method for Epileptic Seizure Detection Using Electroencephalography
Authors: Molla Md. Khademul Islam , Hassan Kazi Mahmudul , Islam Md. Rabiul , Tanaka Toshihisa
Published: August 2020

Epileptic seizure is a sudden alteration of behavior owing to a temporary change in the electrical functioning of the brain. There is an urgent demand for an automatic epilepsy detection system using electroencephalography (EEG) for clinical application. In this paper, the EEG signal is divided into short time frames. Discrete wavelet transform is used to decompose each frame into a number of subbands. Different entropies as well as a group of features with which to characterize the spike events are extracted from each subband signal of an EEG frame. The features extracted from individual subbands are concatenated, yielding a high-dimensional feature vector. A discriminative subset of features is selected from the feature vector using a graph eigen decomposition (GED)-based approach. Thus, the reduced number of features obtained is effective for differentiating the underlying characteristics of EEG signals that indicate seizure events and those that indicate nonseizure events. The GED method ranks the features according to their contribution to correct classification. The selected features are used to classify seizure and nonseizure EEG signals using a feedforward neural network (FfNN). The performance of the proposed method is evaluated by conducting various experiments with a standard dataset obtained from the University of Bonn. The experimental results show that the proposed seizure-detection scheme achieves a classification accuracy of 99.55%, which is higher than that of state-of-the-art methods. The efficiency of FfNN is compared with linear discriminant analysis and support vector machine classifiers, which have classification accuracies of 98.72% and 99.39%, respectively. Hence, the proposed method is confirmed as a potential marker for EEG-based seizure detection.
A Novel Sensor Prototype with Enhanced and Adaptive Sensitivity Based on Negative Stiffness Mechanism
Authors: Liu Lijun , Nie Yongzhong , Lei Ying
Published: August 2020

Loess–mudstone/soil-rock interfacial landslide is one of the prominent landslide hazards that occurs in soil rock contacting zones. It is necessary to develop sensors with high sensitivity to weak and low frequency vibrations for the early warning of such interfacial landslides. In this paper, a novel monitoring sensor prototype with enhanced and adaptive sensitivity is developed for this purpose. The novelty of the sensitive sensor is based on the variable capacitances and negative stiffness mechanism due to the electric filed forces on the vibrating plate. Owing to the feedback control of adjustable electrostatic field by an embedded micro controller, the sensor has adaptive amplification characteristics with high sensitivity to weak and low frequency input and low sensitivity to high input. The design and manufacture of the proposed sensor prototype by Micro-Electro-Mechanical Systems (MEMS) with proper packaging are introduced. Post-signal processing is also presented. Some preliminary testing of the prototype and experimental monitoring of sand interfacial slide which mimics soil–rock interfacial landslide were performed to demonstrate the performance of the developed sensor prototype with adaptive amplification and enhanced sensitivity.
Wavelength Selection FOR Rapid Identification of Different Particle Size Fractions of Milk Powder Using Hyperspectral Imaging
Authors: Khan Asma , Munir Muhammad Tajammal , Yu Wei , Young Brent
Published: August 2020

Hyperspectral imaging (HSI) in the spectral range of 400–1000 nm was tested to differentiate three different particle size fractions of milk powder. Partial least squares discriminant analysis (PLS-DA) was performed to observe the relationship of spectral data and particle size information for various samples of instant milk powder. The PLS-DA model on full wavelengths successfully classified the three fractions of milk powder with a coefficient of prediction 0.943. Principal component analysis (PCA) identified each of the milk powder fractions as separate clusters across the first two principal components (PC1 and PC2) and five characteristic wavelengths were recognised by the loading plot of the first three principal components. Weighted regression coefficient (WRC) analysis of the partial least squares model identified 11 important wavelengths. Simplified PLS-DA models were developed from two sets of reduced wavelengths selected by PCA and WRC and showed better performance with predictive correlation coefficients (Rp2) of 0.962 and 0.979, respectively, while PLS-DA with complete spectrum had Rp2 of 0.943. Similarly, classification accuracy of PLS-DA was improved to 92.2% for WRC based predictive model. Calculation time was also reduced to 2.1 and 2.8 s for PCA and WRC based simplified PLS-DA models in comparison to the complete spectrum model that was taking 32.2 s on average to predict the classification of milk powder samples. These results demonstrated that HSI with appropriate data analysis methods could become a potential analyser for non-invasive testing of milk powder in the future.
Weber Texture Local Descriptor for Identification of Group-Housed Pigs
Authors: Huang Weijia , Zhu Weixing , Ma Changhua , Guo Yizheng
Published: August 2020

The individual identification of group-housed pigs plays an important role in breeding process management and individual behavior analysis. Recently, livestock identification methods based on the side view or face image have strict requirements on the position and posture of livestock, which poses a challenge for the application of the monitoring scene of group-housed pigs. To address the issue above, a Weber texture local descriptor (WTLD) is proposed for the identification of group-housed pigs by extracting the local features of back hair, skin texture, spots, and so on. By calculating the differential excitation and multi-directional information of pixels, the local structure features of the main direction are fused to enhance the description ability of features. The experimental results show that the proposed WTLD achieves higher recognition rates with a lower feature dimension. This method can identify pig individuals with different positions and postures in the pig house. Without limitations on pig movement, this method can facilitate the identification of individual pigs with greater convenience and universality.
A New Optical Sensor Based on Laser Speckle and Chemometrics for Precision Agriculture: Application to Sunflower Plant-Breeding
Authors: Ryckewaert Maxime , Héran Daphné , Faur Emma , George Pierre , Grèzes-Besset Bruno , Chazallet Frédéric , Abautret Yannick , Zerrad Myriam , Amra Claude , Bendoula Ryad
Published: August 2020

New instruments to characterize vegetation must meet cost constraints while providing accurate information. In this paper, we study the potential of a laser speckle system as a low-cost solution for non-destructive phenotyping. The objective is to assess an original approach combining laser speckle with chemometrics to describe scattering and absorption properties of sunflower leaves, related to their chemical composition or internal structure. A laser diode system at two wavelengths 660 nm and 785 nm combined with polarization has been set up to differentiate four sunflower genotypes. REP-ASCA was used as a method to analyze parameters extracted from speckle patterns by reducing sources of measurement error. First findings have shown that measurement errors are mostly due to unwilling residual specular reflections. Moreover, results outlined that the genotype significantly impacts measurements. The variables involved in genotype dissociation are mainly related to scattering properties within the leaf. Moreover, an example of genotype classification using REP-ASCA outcomes is given and classify genotypes with an average error of about 20%. These encouraging results indicate that a laser speckle system is a promising tool to compare sunflower genotypes. Furthermore, an autonomous low-cost sensor based on this approach could be used directly in the field.
Extraction and Analysis of Blue Steel Roofs Information Based on CNN Using Gaofen-2 Imageries
Authors: Sun Meiwei , Deng Yingbin , Li Miao , Jiang Hao , Huang Haoling , Liao Wenyue , Liu Yangxiaoyue , Yang Ji , Li Yong
Published: August 2020

Blue steel roof is advantageous for its low cost, durability, and ease of installation. It is generally used by industrial areas. The accurate and rapid mapping of blue steel roof is important for the preliminary assessment of inefficient industrial areas and is one of the key elements for quantifying environmental issues like urban heat islands. Here, the DeeplabV3+ semantic segmentation neural network based on GaoFen-2 images was used to analyze the quantity and spatial distribution of blue steel roofs in the Nanhai district, Foshan (including the towns of Shishan, Guicheng, Dali, and Lishui), which is the important manufacturing industry base of China. We found that: (1) the DeeplabV3+ performs well with an overall accuracy of 92%, higher than the maximum likelihood classification; (2) the distribution of blue steel roofs was not even across the whole study area, but they were evenly distributed within the town scale; and (3) strong positive correlation was observed between blue steel roofs area and industrial gross output. These results not only can be used to detect the inefficient industrial areas for regional planning but also provide fundamental data for studies of urban environmental issues.
Speciation of Phosphorus from Suspended Sediment Studied by Bulk and Micro-XANES
Authors: Zhang Qingxin , Wieler Mackenzie , O’Connell David , Gill Laurence , Xiao Qunfeng , Hu Yongfeng
Published: August 2020

Mobilization, transformation, and bioavailability of fluvial suspended sediment-associated particulate phosphorus (PP) plays a key role in governing the surface water quality of agricultural catchment streams. Knowledge on the molecular P speciation of suspended sediment is valuable in understanding in-stream PP cycling processes. Such information enables the design of appropriate catchment management strategies in order to protect surface water quality and mitigate eutrophication. In this study, we investigated P speciation associated with fluvial suspended sediments from two geologically contrasting agricultural catchments. Sequential chemical P extractions revealed the operationally defined P fractions for the fluvial suspended sediments, with Tintern Abbey (TA) dominated by redox-sensitive P (PCBD), Al, and Fe oxyhydroxides P (PNaOH) and organic P (POrg) while Ballyboughal (BB) primarily composed of acid soluble P (PDetr), redox-sensitive P (PCBD), and loosely sorbed P (PNH4Cl). The dominant calcareous (Ca) elemental characteristic of BB suspended sediment with some concurrent iron (Fe) influences was confirmed by XRF which is consistent with the catchment soil types. Ca-P sedimentary compounds were not detected using bulk P K-edge XANES, and only P K-edge µ-XANES could confirm their presence in BB sediment. Bulk P K-edge XANES is only capable of probing the average speciation and unable to resolve Ca-P as BB spectra is dominated by organic P, which may suggest the underestimation of this P fraction by sequential chemical P extractions. Notably, µ-XANES of Ca K-edge showed consistent results with P K-edge and soil geochemical characteristics of both catchments where Ca-P bonds were detected, together with calcite in BB, while in TA, Ca-P bonds were detected but mostly as organic complexed Ca. For the TA site, Fe-P is detected using bulk P K-edge, which corresponds with its soil geochemical characteristics and sequential chemical P extraction data. Overall, P concentrations were generally lower in TA, which led to difficulties in Fe-P compound detection using µ-XANES of TA. Overall, our study showed that coupling sequential chemical P extractions with progressively more advanced spectroscopic techniques provided more detailed information on P speciation, which can play a role in mobilization, transformation, and bioavailability of fluvial sediment-associated P.
Empirical Research in Colombian Services Sector: Relation between Transformational Leadership, Climate and Commitment
Authors: Mañas-Rodríguez Miguel A. , Enciso-Forero Eleonora , Salvador-Ferrer Carmen M. , Trigueros Rubén , Aguilar-Parra José M.
Published: August 2020

The objective of this study is to analyze the relationship between the variables of transformational leadership, climate and commitment in a sample of 319 workers of a multinational organization in the Colombian Services Sector. For data collection, we used the Multifactor Leadership Questionnaire (MLQ), the climate/culture questionnaire FOCUS-93 and the Intellectual, Social and Affective Commitment Scale (ISA). Data were processed with SPSS 23 and AMOS 22 for modeling with structural equations. In the path diagram, calculated according to the indicators of structural adjustments, variances were obtained for the dimensions of organizational commitment. The resulting model presented favorable adjustment indicators as evidenced in the results, and the relationship between commitment and climate was significant (β = 4.61; p = 0.001), as well as between climate and commitment (β = 0.018; p = 0.001). However, the relationship between transformational leadership and commitment was not direct but mediated through organizational climate.
Unveiling the Effect of Mean and Volatility Spillover between the United States Economic Policy Uncertainty and WTI Crude Oil Price
Authors: Su Ruixin , Du Jianguo , Shahzad Fakhar , Long Xingle
Published: August 2020

Grounded in the Granger causality test, vector autoregression (VAR) model, and BEKK-GARCH model, our current study aims to examine the effect of mean and volatility spillover between the United States (US) economic policy uncertainty (EPU) and West Texas Intermediate (WTI) crude oil price. Using the US EPU monthly index and WTI spot price data from 1996 to 2019, we revealed that there is a one-way Granger causality link between the US EPU and spot price of WTI crude oil. The VAR model not only illustrated that there is a mean spillover effect between WTI oil price and US EPU, but they will also be affected by its memory, as well as the other’s past. At the same time, it also pointed out that this correlation has positive and negative directions. The BEKK-GARCH model test yielded similar conclusions to the VAR model and, importantly, proved a two-way volatility spillover effect between the US EPU and WTI spot price fluctuations. In conclusion, US economic policy has a substantial influence on the variation of global crude oil prices, as an essential strategic reserve resource and will also influence the government’s economic policy formulation. Understanding the association between WTI crude oil price and policy uncertainty not only helps investors to manage assets allocations and mitigate losses but also guides US policymakers to adjust the energy structure for economic sustainability.
Governmental Ownership of Voluntary Sustainability Information Disclosure in an Emerging Economy: Evidence from Vietnam
Authors: Pham Hang Thi Thuy , Jung Sung-Chang , Lee Su-Yol
Published: August 2020

Emerging economies have increasingly paid attention to sustainability issues in the business circle. However, few studies have explored what facilitates sustainability information disclosure. This study examines how corporate governance mechanisms, particularly government ownership, affect sustainability disclosure in an emerging economy—Vietnam. By combining related research streams, including stakeholder theory, institutional perspective, and principal–agent theory, we present a hypothesis on the effect of corporate governance on sustainability reporting. The logistic regression analysis and analysis of variance on 2678 Vietnamese sample firm-years from 2010 through 2016 indicate that government ownership is negatively associated with voluntary environmental and social information disclosure. Additionally, they demonstrate that ownership concentration tends to lower non-financial information disclosure, while individual largest shareholder has a positive effect. These findings provide managers and policymakers with theoretical and practical implications to encourage firms in emerging Asian economies such as Vietnam to adopt sustainability activities and disclose social information.
Human Biomonitoring of Glyphosate Exposures: State-of-the-Art and Future Research Challenges
Authors: Connolly Alison , Coggins Marie A. , Koch Holger M.
Published: August 2020

Glyphosate continues to attract controversial debate following the International Agency for Research on Cancer carcinogenicity classification in 2015. Despite its ubiquitous presence in our environment, there remains a dearth of data on human exposure to both glyphosate and its main biodegradation product aminomethylphosphonic (AMPA). Herein, we reviewed and compared results from 21 studies that use human biomonitoring (HBM) to measure urinary glyphosate and AMPA. Elucidation of the level and range of exposure was complicated by differences in sampling strategy, analytical methods, and data presentation. Exposure data is required to enable a more robust regulatory risk assessment, and these studies included higher occupational exposures, environmental exposures, and vulnerable groups such as children. There was also considerable uncertainty regarding the absorption and excretion pattern of glyphosate and AMPA in humans. This information is required to back-calculate exposure doses from urinary levels and thus, then compare these levels with health-based guidance values. Back-calculations based on animal-derived excretion rates suggested that there were no health concerns in relation to glyphosate exposure (when compared with EFSA acceptable daily intake (ADI)). However, recent human metabolism data has reported as low as a 1% urinary excretion rate of glyphosate. Human exposures extrapolated from urinary glyphosate concentrations found that upper-bound levels may be much closer to the ADI than previously reported.
Seaweed Essential Oils as a New Source of Bioactive Compounds for Cyanobacteria Growth Control: Innovative Ecological Biocontrol Approach
Authors: El Amrani Zerrifi Soukaina , El Khalloufi Fatima , Mugani Richard , El Mahdi Redouane , Kasrati Ayoub , Soulaimani Bouchra , Barros Lillian , Ferreira Isabel C. F. R. , Amaral Joana S. , Finimundy Tiane Cristine , Abbad Abdelaziz , Oudra Brahim , Campos Alexandre , Vasconcelos Vitor
Published: August 2020

The application of natural compounds extracted from seaweeds is a promising eco-friendly alternative solution for harmful algae control in aquatic ecosystems. In the present study, the anti-cyanobacterial activity of three Moroccan marine macroalgae essential oils (EOs) was tested and evaluated on unicellular Microcystis aeruginosa cyanobacterium. Additionally, the possible anti-cyanobacterial response mechanisms were investigated by analyzing the antioxidant enzyme activities of M. aeruginosa cells. The results of EOs GC–MS analyses revealed a complex chemical composition, allowing the identification of 91 constituents. Palmitic acid, palmitoleic acid, and eicosapentaenoic acid were the most predominant compounds in Cystoseira tamariscifolia, Sargassum muticum, and Ulva lactuca EOs, respectively. The highest anti-cyanobacterial activity was recorded for Cystoseira tamariscifolia EO (ZI = 46.33 mm, MIC = 7.81 μg mL−1, and MBC = 15.62 μg mL−1). The growth, chlorophyll-a and protein content of the tested cyanobacteria were significantly reduced by C. tamariscifolia EO at both used concentrations (inhibition rate >67% during the 6 days test period in liquid media). Furthermore, oxidative stress caused by C. tamariscifolia EO on cyanobacterium cells showed an increase of the activities of superoxide dismutase (SOD) and catalase (CAT), and malondialdehyde (MDA) concentration was significantly elevated after 2 days of exposure. Overall, these experimental findings can open a promising new natural pathway based on the use of seaweed essential oils to the fight against potent toxic harmful cyanobacterial blooms (HCBs).
A Nonparametric Stochastic Approach for Disaggregation of Daily to Hourly Rainfall Using 3-Day Rainfall Patterns
Authors: Park Heeseong , Chung Gunhui
Published: August 2020

As infrastructure and populations are highly condensed in megacities, urban flood management has become a significant issue because of the potentially severe loss of lives and properties. In the megacities, rainfall from the catchment must be discharged throughout the stormwater pipe networks of which the travel time is less than one hour because of the high impervious rate. For a more accurate calculation of runoff from the urban catchment, hourly or even sub-hourly (minute) rainfall data must be applied. However, the available data often fail to meet the hydrologic system requirements. Many studies have been conducted to disaggregate time-series data while preserving distributional statistics from observed data. The K-nearest neighbor resampling (KNNR) method is a useful application of the nonparametric disaggregation technique. However, it is not easy to apply in the disaggregation of daily rainfall data into hourly while preserving statistical properties and boundary continuity. Therefore, in this study, three-day rainfall patterns were proposed to improve reproducible ability of statistics. Disaggregated rainfall was resampled only from a group having the same three-day rainfall patterns. To show the applicability of the proposed disaggregation method, probability distribution and L-moment statistics were compared. The proposed KNNR method with three-day rainfall patterns reproduced better the characteristics of rainfall event such as event duration, inter-event time, and toral amount of rainfall event. To calculate runoff from urban catchment, rainfall event is more important than hourly rainfall depth itself. Therefore, the proposed stochastic disaggregation method is useful to hydrologic analysis, particularly in rainfall disaggregation.
Dam Break Modeling in a Cascade of Small Earthen Dams: Case Study of the Čižina River in the Czech Republic
Authors: Říha Jaromír , Kotaška Stanislav , Petrula Lubomír
Published: August 2020

Failures of small dams can pose a serious threat to people and property even if the size of the schemes is relatively low. In many cases, small dams are situated in a cascade along streams, meaning that the failure of the uppermost dam may cause the dams downstream to fail. In this paper, a cascade of three small reservoirs, Lichnov II (14.6 m high), Lichnov III (10 m high), and Pocheň (8.5 m high), is the subject of the dam break analyses carried out via various methods such as empirical formulae, analogy, and hydraulic modeling. The dam-break flood routing was simulated using a shallow water flow hydraulic model. The simulations confirm that the attenuation effect of the peak discharge is governed by the flood volume, slope, and morphology of the floodplain and increases with the distance from the breached dam following an approximately exponential trend. When estimating peak discharge, empirical formulae derived for a single dam break should be applied carefully as they may underestimate the peak outflow by up to 10% in the case of a dam cascade. The attenuation volume of small reservoirs is small when compared to the flood volume, meaning that the attenuation of the peak discharge usually varies between 5–10%.
Quantifying the Benefits of Residential Greywater Reuse
Authors: Byrne Josh , Dallas Stewart , Anda Martin , Ho Goen
Published: August 2020

There is paucity of data on the quantification of the benefits of residential greywater reuse via direct diversion. While estimates have been made based on modelling the potential mains water savings, it is also recognised that the practicalities of system operation and occupant behaviour introduce substantial variation to these estimates. Three single residential housing projects in Fremantle, Western Australia, undertaken over ten years with a substantial focus on water efficiency and mains water substitution, have provided an opportunity to quantify these benefits. All three dwellings were intensively metered and documented. This paper describes the learnings generated along the way, including the methodology developed to effectively integrate direct diversion greywater reuse into a productive garden, along with other water sources to satisfy landscape water demand. Importantly a robust quantification of actual greywater volumes and associated mains water savings was made. The publication of actual greywater volumes will significantly contribute to this field and go a long way towards validating the merits of residential greywater reuse on mains water savings when systems are properly installed and operated. Brief considerations are also provided for energy efficiency and financial assessment.
Transient Phenomena Generated in Emptying Operations in Large-Scale Hydraulic Pipelines
Authors: Romero Guillermo , Fuertes-Miquel Vicente S. , Coronado-Hernández Óscar E. , Ponz-Carcelén Román , Biel-Sanchis Francisco
Published: August 2020

Air pockets generated during emptying operations in pressurized hydraulic systems cause significant pressure drops inside pipes. To avoid these sudden pressure changes, one of the most widely used methods involves the installation of air valves along the pipeline route. These elements allow air exchange between the exterior and the interior of the pipe, which alleviates the pressure drops produced and thus prevents possible breaks or failures in the structure of the installation. This study uses a mathematical model previously validated by the authors in smaller installations to simulate all hydraulic variables involved in emptying processes over time. The purpose of these simulations is the validation of the mathematical model in real large-scale installations, and to do this, the results obtained with the mathematical model are compared with actual measurements made by the partner company. The hydraulic system selected for the study is a pipeline with a nominal diameter of 400 mm and a total length of 1020 m. The results obtained from the mathematical model show great similarity with the experimental measurements, thus validating the model for emptying large pipes.
The role of chronic conditions in influencing symptom attribution and anticipated help-seeking for potential lung cancer symptoms: a vignette-based study
Authors: Kaushal Aradhna , Waller Jo , von Wagner Christian , Kummer Sonja , Whitaker Katriina , Puri Aishwarya , Lyratzopoulos Georgios , Renzi Cristina
Published: August 2020

Background

Very little is known about the influence of chronic conditions on symptom attribution and help-seeking for potential cancer symptoms.

Aim

To determine if symptom attribution and anticipated help-seeking for potential lung cancer symptoms is influenced by pre-existing respiratory conditions (often referred to as comorbidity), such as asthma or chronic obstructive pulmonary disease (COPD).

Design & setting

A total of 2143 adults (1081 with and 1062 without a respiratory condition) took part in an online vignette survey.

Method

The vignette described potential lung cancer symptoms (persistent cough and breathlessness) after which questions were asked on symptom attribution and anticipated help-seeking.

Results

Attribution of symptoms to cancer was similar in participants with and without respiratory conditions (21.5% and 22.1%, respectively). Participants with respiratory conditions, compared with those without, were more likely to attribute the new or changing cough and breathlessness to asthma or COPD (adjusted odds ratio [OR] = 3.64, 95% confidence interval [CI] = 3.02 to 4.39). Overall, 56.5% of participants reported intention to seek help from a GP within 3 weeks if experiencing the potential lung cancer symptoms. Having a respiratory condition increased the odds of prompt help-seeking (OR = 1.25, 95% CI = 1.04 to 1.49). Regular healthcare appointments were associated with higher odds of anticipated help-seeking.

Conclusion

Only one in five participants identified persistent cough and breathlessness as potential cancer symptoms, and half said they would promptly seek help from a GP, indicating scope for promoting help-seeking for new or changing symptoms. Chronic respiratory conditions did not appear to interfere with anticipated help-seeking, which might be explained by regular appointments to manage chronic conditions.
Improved Streaming Quotient Filter: A Duplicate Detection Approach for Data Streams
Authors: Che Shiwei , Yang Wu , Wang Wei
Published: August 2020

The unprecedented development and popularization of the Internet, combined with the emergence of a variety of modern applications, such as search engines, online transactions, climate warning systems and so on, enables the worldwide storage of data to grow unprecedented. Efficient storage, management and processing of such huge amounts of data has become an important academic research topic. The detection and removal of duplicate and redundant data from such multi-trillion data, while ensuring resource and computational efficiency, has constituted a challenging area of research.Because of the fact that all the data of potentially unbounded data streams can not be stored, and the need to delete duplicated data as accurately as possible, intelligent approximate duplicate data detection algorithms are urgently required. Many well-known methods based on the bitmap structure, Bloom Filter and its variants are listed in the literature.
In this paper, we propose a new data structure, Improved Streaming Quotient Filter (ISQF), to efficiently detect and remove duplicate data in a data stream. ISQF intelligently stores the signatures of elements in a data stream, while using an eviction strategy to provide near zero error rates. We show that ISQF achieves near optimal performance with fairly low memory requirements, making it an ideal and efficient method for repeated data detection. It has a very low error rate. Empirically, we compared ISQF with some existing methods (especially Steaming Quotient Filter (SQF)). The results show that our proposed method outperforms theexisting methods in terms of memory usage and accuracy.We also discuss the parallel implementation of ISQF
A Concept-based Sentiment Analysis Approach for Arabic
Authors: Nasser Ahmed , Sever Hayri
Published: August 2020

Concept-Based Sentiment Analysis (CBSA) methods are considered to be more advanced and more accurate when it compared to ordinary Sentiment Analysis methods, because it has the ability of detecting the emotions that conveyed by multi-word expressions concepts in language. This paper presented a CBSA system for Arabic language which utilizes both of machine learning approaches and concept-based sentiment lexicon. For extracting concepts from Arabic, a rule-based concept extraction algorithm called semantic parser is proposed. Different types of feature extraction and representation techniques are experimented among the building prosses of the sentiment analysis model for the presented Arabic CBSA system. A comprehensive and comparative experiments using different types of classification methods and classifier fusion models, together with different combinations of our proposed feature sets, are used to evaluate and test the presented CBSA system. The experiment results showed that the best performance for the sentiment analysis model is achieved by combined Support Vector Machine-Logistic Regression (SVM-LR) model where it obtained a F-score value of 93.23% using the Concept-Based-Features+Lexicon-Based-Features+Word2vec-Features (CBF+LEX+W2V) features combinations
An Enhanced Corpus for Arabic Newspapers Comments
Authors: Rahab Hichem , Zitouni Abdelhafid , Djoudi Mahieddine
Published: August 2020

In this paper, we propose our enhanced approach to create a dedicated corpus for Algerian Arabic newspapers comments. The developed approach has to enhance an existing approach by the enrichment of the available corpus and the inclusion of the annotation step by following the Model Annotate Train Test Evaluate Revise (MATTER) approach. A corpus is created by collecting comments from web sites of three well know Algerian newspapers. Three classifiers, support vector machines, naïve Bayes, and k-nearest neighbors, were used for classification of comments into positive and negative classes. To identify the influence of the stemming in the obtained results, the classification was tested with and without stemming. Obtained results show that stemming does not enhance considerably the classification due to the nature of Algerian comments tied to Algerian Arabic Dialect. The promising results constitute a motivation for us to improve our approach especially in dealing with non Arabic sentences, especially Dialectal and French ones
The Importance of Breastfeeding Based on Islamic Rules and Qur’an
Authors: Mehrpisheh Shahrokh , Memarian Azadeh , Ameri Maryam , Saberi Isfeedvajani Mohsen
Published: August 2020

The Qur’an, the word of God, is the best book that has been revealed to guide human beings contains a complete plan of human life. However, in addition to educational, ethical, and religious issues, it also contains valuable scientific information. Several verses in the Holy Qur’an discuss the importance of breast milk and its proper planning. Qur’an also orders up parents, especially mothers, for getting this goddamn blessing to their children. Qur’an verses as well as Islamic hadiths emphasize that breast milk is an unparalleled and comprehensive food that plays a valuable and unique role in the improvement of mental health, physical development, and desirable development of children. Based on Islamic sources, such as the Holy Qur’an and the traditions of the Prophet (PBUH) and Islamic hadith, there are some interesting and important points about breastfeeding, its benefits and its provisions. The Qur’an verses and Islamic hadiths emphasize the importance of breastfeeding and the importance of continuing it until 24 months. The description of these hadiths and verses is presented in the text. Considering the religious and Qur’an educations regarding the importance of breastfeeding infants, it can be concluded that breastfeeding contains the most complete immune and growth factors in the infant. In addition to physical needs, breastfeeding provides emotional and mental development and also gives countless benefits to the mother.
Prostate Cancer Screening in Middle-Aged and Older American Men: Combined Effects of Ethnicity and Years of Schooling
Authors: Assari Shervin
Published: August 2020

Background: Prostate cancer screening is more commonly utilized by highly educated people. As shown by marginalization-related diminished returns (MDRs), the effects of socioeconomic status (SES) such as education on the health outcomes are considerably smaller for ethnic minorities than for Whites. The role of MDRs as a source of ethnic health disparities is, however, still unknown. Objectives: The current study had two aims: first, to explore the association between years of schooling and having taken a prostatespecific antigen (PSA) test among men in the US, and second, to explore ethnic differences in this association. Methods: This study was a secondary analysis of data from the National Health Interview Survey (NHIS-2015). The data of 5,053 men aged 55 years or older who were either Latino, non-Latino, African–American, or White were analyzed. Years of schooling was the independent variable. The dependent variable was taking a PSA test sometime during one’s lifetime. Age, region, and employment were the control variables. Ethnicity was the focal moderating variable. Binary logistic regression was used for data analysis. Results: A higher number of years of schooling was associated with higher odds of having taken a PSA test, net of all confounders. Ethnicity showed a significant statistical interaction with years of schooling on having taken a PSA test. This interaction was suggestive of a smaller slope for Latino men than non-Latino men. White and African American men did not show differential effects of years of schooling on having taken a PSA test. Conclusion: Similar to the MDRs patterns in other domains, non-Latino White men show more health gain from their years of schooling than Latino men. Highly educated Latino men still need programs to encourage their use of prostate cancer screening.