bluuebunny/crossref_metadata_2025 huggingface dataset:
Gas Transport Phenomena and Polymer Dynamics in PHB/PLA Blend Films as Potential Packaging Materials
Authors: Siracusa Valentina , Karpova Svetlana , Olkhov Anatoliy , Zhulkina Anna , Kosenko Regina , Iordanskii Alexey
Published: March 2020

Actually, in order to replace traditional fossil-based polymers, many efforts are devoted to the design and development of new and high-performance bioplastics materials. Poly(hydroxy alkanoates) (PHAS) as well as polylactides are the main candidates as naturally derived polymers. The intention of the present study is to manufacture fully bio-based blends based on two polyesters: poly (3-hydroxybutyrate) (PHB) and polylactic acid (PLA) as real competitors that could be used to replace petrol polymers in packaging industry. Blends in the shape of films have been prepared by chloroform solvent cast solution methodology, at different PHB/PLA ratios: 1/0, 1/9, 3/7, 5/5, 0/1. A series of dynamic explorations have been performed in order to characterize them from a different point of view. Gas permeability to N2, O2, and CO2 gases and probe (TEMPO) electron spin resonance (ESR) analyses were performed. Blend surface morphology has been evaluated by Scanning Electron Microscopy (SEM) while their thermal behavior was analyzed by Differential Scanning Calorimetry (DSC) technique. Special attention was devoted to color and transparency estimation. Both probe rotation mobility and N2, O2, and CO2 permeation have monotonically decreased during the transition from PLA to PHB, for all contents of bio-blends, namely because of transferring from PLA with lower crystallinity to PHB with a higher one. Consequently, the role of the crystallinity was elucidated. The temperature dependences for CO2 permeability and diffusivity as well as for probe correlation time allowed the authors to evaluate the activation energy of both processes. The values of gas transport energy activation and TEMPO rotation mobility are substantially close to each other, which should testify that polymer segmental mobility determines the gas permeability modality.
Yarrowia lipolytica Adhesion and Immobilization onto Residual Plastics
Authors: Botelho Alanna , Penha Adrian , Fraga Jully , Barros-Timmons Ana , Coelho Maria Alice , Lehocky Marian , Štěpánková Kateřina , Amaral Priscilla
Published: March 2020

Research in cell adhesion has important implications in various areas, such as food processing, medicine, environmental engineering, biotechnological processes. Cell surface characterization and immobilization of microorganisms on solid surfaces can be performed by promoting cell adhesion, in a relatively simple, inexpensive, and quick manner. The adhesion of Yarrowia lipolytica IMUFRJ 50682 to different surfaces, especially potential residual plastics (polystyrene, poly(ethylene terephthalate), and poly(tetrafluoroethylene)), and its use as an immobilized biocatalyst were tested. Y. lipolytica IMUFRJ 50682 presented high adhesion to different surfaces such as poly(tetrafluoroethylene) (Teflon), polystyrene, and glass, independent of pH, and low adhesion to poly(ethylene terephthalate) (PET). The adhesion of the cells to polystyrene was probably due to hydrophobic interactions involving proteins or protein complexes. The adhesion of the cells to Teflon might be the result not only of hydrophobic interactions but also of acid–basic forces. Additionally, the present work shows that Y. lipolytica cell extracts previously treated by ultrasound waves (cell debris) maintained their enzymatic activity (lipase) and could be attached to polystyrene and PET and used successfully as immobilized biocatalysts in hydrolysis reactions.
3D-VAR Data Assimilation of SEVIRI Radiances for the Prediction of Solar Irradiance in Italy Using WRF Solar Mesoscale Model—Preliminary Results
Authors: Gentile Sabrina , Di Paola Francesco , Cimini Domenico , Gallucci Donatello , Geraldi Edoardo , Larosa Salvatore , Nilo Saverio T. , Ricciardelli Elisabetta , Ripepi Ermann , Viggiano Mariassunta , Romano Filomena
Published: March 2020

Solar power generation is highly fluctuating due to its dependence on atmospheric conditions. The integration of this variable resource into the energy supply system requires reliable predictions of the expected power production as a basis for management and operation strategies. This is one of the goals of the Solar Cloud project, funded by the Italian Ministry of Economic Development (MISE)—to provide detailed forecasts of solar irradiance variables to operators and organizations operating in the solar energy industry. The Institute of Methodologies for Environmental Analysis of the National Research Council (IMAA-CNR), participating to the project, implemented an operational chain that provides forecasts of all the solar irradiance variables at high temporal and horizontal resolution using the numerical weather prediction Advanced Research Weather Research and Forecasting (WRF-ARW) Solar version 3.8.1 released by the National Center for Atmospheric Research (NCAR) in August 2016. With the aim of improving the forecast of solar irradiance, the three-dimensional (3D-Var) data assimilation was tested to assimilate radiances from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) aboard the Meteosat Second Generation (MSG) geostationary satellite into WRF Solar. To quantify the impact, the model output is compared against observational data. Hourly Global Horizontal Irradiance (GHI) is compared with ground-based observations from Regional Agency for the Protection of the Environment (ARPA) and with MSG Shortwave Solar Irradiance estimations, while WRF Solar cloud coverage is compared with Cloud Mask by MSG. A preliminary test has been performed in clear sky conditions to assess the capability of the model to reproduce the diurnal cycle of the solar irradiance. The statistical scores for clear sky conditions show a positive performance of the model with values comparable to the instrument uncertainty and a correlation of 0.995. For cloudy sky, the solar irradiance and the cloud cover are better simulated when the SEVIRI radiances are assimilated, especially in the short range of the simulation. For the cloud cover, the Mean Bias Error one hour after the assimilation time is reduced from 41.62 to 20.29 W/m2 when the assimilation is activated. Although only two case studies are considered here, the results indicate that the assimilation of SEVIRI radiance improves the performance of WRF Solar especially in the first 3 hour forecast.
Phenology-Based Mapping of an Alien Invasive Species Using Time Series of Multispectral Satellite Data: A Case-Study with Glossy Buckthorn in Québec, Canada
Authors: Labonté Joanie , Drolet Guillaume , Sylvain Jean-Daniel , Thiffault Nelson , Hébert Francois , Girard Francois
Published: March 2020

Glossy buckthorn (Frangula alnus Mill.) is an alien species in Canada that is invading many forested areas. Glossy buckthorn has impacts on the biodiversity and productivity of invaded forests. Currently, we do not know much about the species’ ecology and no thorough study of its distribution in temperate forests has been performed yet. As is often the case with invasive plant species, the phenology of glossy buckthorn differs from that of other indigenous plant species found in invaded communities. In the forests of eastern Canada, the main phenological difference is a delay in the shedding of glossy buckthorn leaves, which occurs later in the fall than for other indigenous tree species found in that region. Therefore, our objective was to use that phenological characteristic to map the spatial distribution of glossy buckthorn over a portion of southern Québec, Canada, using remote sensing-based approaches. We achieved this by applying a linear temporal unmixing model to a time series of the normalized difference vegetation index (NDVI) derived from Landsat 8 Operational Land Imager (OLI) images to create a map of the probability of the occurrence of glossy buckthorn for the study area. The map resulting from the temporal unmixing model shows an agreement of 69% with field estimates of glossy buckthorn occurrence measured in 121 plots distributed over the study area. Glossy buckthorn mapping accuracy was limited by evergreen species and by the spectral and spatial resolution of the Landsat 8 OLI.
Deep Relation Network for Hyperspectral Image Few-Shot Classification
Authors: Gao Kuiliang , Liu Bing , Yu Xuchu , Qin Jinchun , Zhang Pengqiang , Tan Xiong
Published: March 2020

Deep learning has achieved great success in hyperspectral image classification. However, when processing new hyperspectral images, the existing deep learning models must be retrained from scratch with sufficient samples, which is inefficient and undesirable in practical tasks. This paper aims to explore how to accurately classify new hyperspectral images with only a few labeled samples, i.e., the hyperspectral images few-shot classification. Specifically, we design a new deep classification model based on relational network and train it with the idea of meta-learning. Firstly, the feature learning module and the relation learning module of the model can make full use of the spatial–spectral information in hyperspectral images and carry out relation learning by comparing the similarity between samples. Secondly, the task-based learning strategy can enable the model to continuously enhance its ability to learn how to learn with a large number of tasks randomly generated from different data sets. Benefitting from the above two points, the proposed method has excellent generalization ability and can obtain satisfactory classification results with only a few labeled samples. In order to verify the performance of the proposed method, experiments were carried out on three public data sets. The results indicate that the proposed method can achieve better classification results than the traditional semisupervised support vector machine and semisupervised deep learning models.
A Precise Multi-Exposure Image Fusion Method Based on Low-level Features
Authors: Qi Guanqiu , Chang Liang , Luo Yaqin , Chen Yinong , Zhu Zhiqin , Wang Shujuan
Published: March 2020

Multi exposure image fusion (MEF) provides a concise way to generate high-dynamic-range (HDR) images. Although the precise fusion can be achieved by existing MEF methods in different static scenes, the corresponding performance of ghost removal varies in different dynamic scenes. This paper proposes a precise MEF method based on feature patches (FPM) to improve the robustness of ghost removal in a dynamic scene. A reference image is selected by a priori exposure quality first and then used in the structure consistency test to solve the image ghosting issues existing in the dynamic scene MEF. Source images are decomposed into spatial-domain structures by a guided filter. Both the base and detail layer of the decomposed images are fused to achieve the MEF. The structure decomposition of the image patch and the appropriate exposure evaluation are integrated into the proposed solution. Both global and local exposures are optimized to improve the fusion performance. Compared with six existing MEF methods, the proposed FPM not only improves the robustness of ghost removal in a dynamic scene, but also performs well in color saturation, image sharpness, and local detail processing.
Skin Lesion Segmentation from Dermoscopic Images Using Convolutional Neural Network
Authors: Zafar Kashan , Gilani Syed Omer , Waris Asim , Ahmed Ali , Jamil Mohsin , Khan Muhammad Nasir , Sohail Kashif Amer
Published: March 2020

Clinical treatment of skin lesion is primarily dependent on timely detection and delimitation of lesion boundaries for accurate cancerous region localization. Prevalence of skin cancer is on the higher side, especially that of melanoma, which is aggressive in nature due to its high metastasis rate. Therefore, timely diagnosis is critical for its treatment before the onset of malignancy. To address this problem, medical imaging is used for the analysis and segmentation of lesion boundaries from dermoscopic images. Various methods have been used, ranging from visual inspection to the textural analysis of the images. However, accuracy of these methods is low for proper clinical treatment because of the sensitivity involved in surgical procedures or drug application. This presents an opportunity to develop an automated model with good accuracy so that it may be used in a clinical setting. This paper proposes an automated method for segmenting lesion boundaries that combines two architectures, the U-Net and the ResNet, collectively called Res-Unet. Moreover, we also used image inpainting for hair removal, which improved the segmentation results significantly. We trained our model on the ISIC 2017 dataset and validated it on the ISIC 2017 test set as well as the PH2 dataset. Our proposed model attained a Jaccard Index of 0.772 on the ISIC 2017 test set and 0.854 on the PH2 dataset, which are comparable results to the current available state-of-the-art techniques.
Plutonium (IV) Quantification in Technologically Relevant Media Using Potentiometric Sensor Array
Authors: Savosina Julia , Agafonova-Moroz Marina , Yaroshenko Irina , Ashina Julia , Babain Vasily , Lumpov Alexander , Legin Andrey , Kirsanov Dmitry
Published: March 2020

The quantification of plutonium in technological streams during spent nuclear fuel (SNF) reprocessing is an important practical task that has to be solved to ensure the safety of the process. Currently applied methods are tedious, time-consuming and can hardly be implemented in on-line mode. A fast and simple quantitative plutonium (IV) analysis using a potentiometric sensor array based on extracting agents is suggested in this study. The response of the set of specially designed PVC-plasticized membrane sensors can be related to plutonium content in solutions simulating real SNF-reprocessing media through multivariate regression modeling, providing 30% higher precision of plutonium quantification than optical spectroscopy.
The Potential of Food Packaging Attributes to Influence Consumers’ Decisions to Sort Waste
Authors: Nemat Babak , Razzaghi Mohammad , Bolton Kim , Rousta Kamran
Published: March 2020

Food packaging waste is a valuable resource for material recovery, if it is properly separated and sorted by consumers. The packaging itself may have the potential to assist consumer sorting by, for example, communicating a correct sorting practice. This is partly due to the fact that the sorting of packaging waste, which is a habitual behavior of consumers, can be perceived as being confusing and inconvenient. Consumers can, therefore, choose not to sort. It is argued that material recovery could be enhanced if packaging can afford easy and proper sorting and separation. To investigate the potential of packaging to support proper sorting, six types of yogurt and cream packaging were examined across 15 households in Karlskrona (a medium-sized Swedish city). The aim is to investigate the effect of selected packaging attributes on consumer decisions regarding waste sorting. The results reveal that some of the selected packaging waste is not properly separated and sorted. It is concluded that the design of food packaging based on user-centered needs could affect consumer decisions for the proper sorting of packaging waste, which enables improved material recovery. The design should focus especially on the package’s visual attributes, the material selection, and the package´s waste sorting related functions.
Analysis of the Risk Factors Affecting the Severity of Traffic Accidents on Spanish Crosstown Roads: The Driver’s Perspective
Authors: Casado-Sanz Natalia , Guirao Begoña , Attard Maria
Published: March 2020

Globally, road traffic accidents are an important public health concern which needs to be tackled. A multidisciplinary approach is required to understand what causes them and to provide the evidence for policy support. In Spain, one of the roads with the highest fatality rate is the crosstown road, a particular type of rural road in which urban and interurban traffic meet, producing conflicts and interference with the population. This paper contributes to the previous existing research on the Spanish crosstown roads, providing a new vision that had not been analyzed so far: the driver’s perspective. The main purpose of the investigation is to identify the contributing factors that increment the likelihood of a fatal outcome based on single-vehicle crashes, which occurred on Spanish crosstown roads in the period 2006-2016. In order to achieve this aim, 1064 accidents have been analyzed, applying a latent cluster analysis as an initial tool for the fragmentation of crashes. Next, a multinomial logit (MNL) model was applied to find the most important factors involved in driver injury severity. The statistical analysis reveals that factors such as lateral crosstown roads, low traffic volumes, higher percentages of heavy vehicles, wider lanes, the non-existence of road markings, and finally, infractions, increase the severity of the drivers’ injuries.
The Loss of Landscape Ecological Functionality in the Barcelona Province (1956–2009): Could Land-Use History Involve a Legacy for Current Biodiversity?
Authors: Tello Enric , Marull Joan , Padró Roc , Cattaneo Claudio , Coll Francesc
Published: March 2020

Could past land uses, and the land cover changes carried out, affect the current landscape capacity to maintain biodiversity? If so, knowledge of historical landscapes and their socio-ecological transitions would be useful for sustainable land use planning. We constructed a GIS dataset in 10 × 10 km UTM cells of the province of Barcelona (Catalonia, Spain) for 1956 and 2009 with the changing levels of farming disturbance exerted through the human appropriation of photosynthetic net primary production (HANPP), and a set of landscape ecology metrics to assess the impacts of the corresponding land-use changes. Then, we correlated them with the spatial distribution of total species richness (including vascular plants, amphibians, reptiles, birds and mammals). The results allow us to characterize the main trends in changing landscape patterns and processes, and explore whether a land-use legacy of many complex agroforest mosaics maintained by the intermediate farming disturbance managed in 1956 could still exist, despite the decrease or disappearance of those mosaics before 2009 due to the combined impacts of agroindustrial intensification (meaning higher HANPP levels), forest transition (meaning lower HANPP levels) and urban sprawl. Statistical analysis reveals a positive impact of the number of larger, less disturbed forest patches, where many protected natural sites have been created in 1956–2009. However, it also confirms that this result has not only been driven by conservation policies and that the distribution of species richness is currently correlated with the maintenance of intermediate levels of HANPP. This suggests that both land-sharing and land-sparing approaches to biodiversity conservation may have played a synergistic role owing to the legacy of complex land cover mosaics of former agricultural landscapes that are now under a serious threat.
Probabilistic Generation Model of Solar Irradiance for Grid Connected Photovoltaic Systems Using Weibull Distribution
Authors: Afzaal Muhammad Umar , Sajjad Intisar Ali , Awan Ahmed Bilal , Paracha Kashif Nisar , Khan Muhammad Faisal Nadeem , Bhatti Abdul Rauf , Zubair Muhammad , Rehman Waqas ur , Amin Salman , Haroon Shaikh Saaqib , Liaqat Rehan , Hdidi Walid , Tlili Iskander
Published: March 2020

Around the world, countries are integrating photovoltaic generating systems to the grid to support climate change initiatives. However, solar power generation is highly uncertain due to variations in solar irradiance level during different hours of the day. Inaccurate modelling of this variability can lead to non-optimal dispatch of system resources. Therefore, accurate characterization of solar irradiance patterns is essential for effective management of renewable energy resources in an electrical power grid. In this paper, the Weibull distribution based probabilistic model is presented for characterization of solar irradiance patterns. Firstly, Weibull distribution is utilized to model inter-temporal variations associated with reference solar irradiance data through moving window averaging technique, and then the proposed model is used for irradiance pattern generation. To achieve continuity of discrete Weibull distribution parameters calculated at different steps of moving window, Generalized Regression Neural Network (GRNN) is employed. Goodness of Fit (GOF) techniques are used to calculate the error between mean and standard deviation of generated and reference patterns. The comparison of GOF results with the literature shows that the proposed model has improved performance. The presented model can be used for power system planning studies where the uncertainty of different resources such as generation, load, network, etc., needs to be considered for their better management.
Supplier Selection for Photovoltaic Module Installation Utilizing Fuzzy Inference and the VIKOR Method: A Green Approach
Authors: Pérez-Velázquez Alexei , Oro-Carralero Leandro Leysdian , Moya-Rodríguez Jorge Laureano
Published: March 2020

The necessary transformation of the world’s energy matrices has led to a growth in developing technologies based on renewable sources. In this context, photovoltaic panels and their components count in Brazil with a production and commercialization chain that has accumulated a sustained growth of more than 100% of its generation capacities between the years 2018 and 2019, and that can fulfill or overcome this rate in 2019 to 2020. However, the conditions of a competitive market and the availability of a significant number of middle and small companies for the distribution and installation of photovoltaic technology may represent a scenario where multiple indicators must be considered. The purpose of this study is to apply a combined method to aid decision-making that corresponds to the supplier selection of the technology in the context of Northeast Brazil. The method is composed by the combination of a diffuse inference technique together with a multicriteria decision-making method, VIKOR, and the weight assignment to the indicators using the entropy method, according to the values of the decision matrix resulting from the diffuse inference technique that allows to develop the calculus. The results show that data collection from multiple sources and based on input variables can offer metrics about the suppliers for the selection criteria. Restrictions derived from data collection can be a barrier, and the method relies on an adapted script that facilitates application.
Psychological Capital Protects Social Workers from Burnout and Secondary Traumatic Stress
Authors: Vîrgă Delia , Baciu Elena-Loreni , Lazăr Theofild-Andrei , Lupșa Daria
Published: March 2020

Summary: To counteract the negative consequences inherent to the emotionally demanding professions like social work, we need to advance the understanding of the resources that preserve the employees’ well-being. This study investigated the role of Psychological Capital (PsyCap) in protecting social workers from developing burnout and Secondary Traumatic Stress (STS). The design of the study builds on the job demands-resources model and the conservation of resources theory. A national sample of 193 Romanian social workers participated in the study. We used the structural equation modeling framework for data analysis. We tested two structural models that had burnout as a mediator for the relationship between PsyCap and STS: A partial mediation model and a total mediation model. Findings: The total mediation model was supported by our data suggesting that PsyCap has a protective role against burnout, and subsequently, STS. Moreover, the results indicate that burnout is the critical link between personal resources and STS. Applications: The results of the study contribute to enhancing the protection of the social workers’ well-being in their professional settings, by advancing the knowledge about the resources that need to be developed in order to prevent or reduce the negative job consequences associated with helping professions. As such, increasing PsyCap levels of employees enhance the sustainability of their working conditions.
Distribution of Urban Blue and Green Space in Beijing and Its Influence Factors
Authors: Wang Haoying , Hu Yunfeng , Tang Li , Zhuo Qi
Published: March 2020

Urban blue and green space is a key element supporting the normal operation of urban landscape ecosystems and guaranteeing and improving people's lives. In this paper, 97.1k photos of Beijing were captured by using web crawler technology, and the blue sky and green vegetation objects in the photos were extracted by using the Image Cascade Network (ICNet) neural network model. We analyzed the distribution characteristics of the blue–green space area proportion index and its relationships with the background economic and social factors. The results showed the following. (1) The spatial distribution of Beijing's blue–green space area proportion index showed a pattern of being higher in the west and lower in the middle and east. (2) There was a positive correlation between the satellite remote sensing normalized difference vegetation index (NDVI) and the proportion index of green space area, but the fitting degree of geospatial weighted regression decreased with an increasing analysis scale. (3) There were differences in the relationship between the housing prices in different regions and the proportion index of blue–green space, but the spatial fitting degree of the two increased with the increase of study scale. (4) There was a negative correlation between the proportion index of blue–green space and population density, and the low-population areas per unit blue–green space were mainly distributed in the south of the city and the urban fringe areas beyond the Third Ring Road. The urban blue–green space analysis that was constructed by this study provides new aspect for urban landscape ecology study, and the results proposed here also provide support for government decision-makers to optimize urban ecological layouts.
Determination of the Peak Hour Ridership of Metro Stations in Xi’an, China Using Geographically-Weighted Regression
Authors: Yu Lijie , Cong Yarong , Chen Kuanmin
Published: March 2020

The ridership of a metro station during a city’s peak hour is not always the same as that during the station’s own peak hour. To investigate this inconsistency, this study introduces the peak deviation coefficient to describe this phenomenon. Data from 88 metro stations in Xi’an, China, are used to analyze the peak deviation coefficient based on the geographically weighted regression model. The results demonstrate that when the land around a metro station is mainly land for work, primary and middle schools, and residences, its station’s peak hour is consistent with the city’s peak hour. Additionally, the station’s peak hour is more likely to deviate from the city’s peak hour for suburban stations. There are two ridership options when designing stations, namely the extra peak hour ridership during a city’s peak hour and that during a station’s peak hour, and the larger of the two is used to design metro stations. The mixed land use ratio must be considered in urban land use planning, because although non-commuting land can mitigate the traffic pressure of a city’s peak hour, it may cause the deviation of the station’s peak hours from that of the city.
Material Symmetries in Homogenized Hexagonal-Shaped Composites as Cosserat Continua
Authors: Fantuzzi Nicholas , Trovalusci Patrizia , Luciano Raimondo
Published: March 2020

In this work, material symmetries in homogenized composites are analyzed. Composite materials are described as materials made of rigid particles and elastic interfaces. Rigid particles of arbitrary hexagonal shape are considered and their geometry described by a limited set of parameters. The purpose of this study is to analyze different geometrical configurations of the assemblies corresponding to various material symmetries such as orthotetragonal, auxetic and chiral. The problem is investigated through a homogenization technique which is able to carry out constitutive parameters using a principle of energetic equivalence. The constitutive law of the homogenized continuum has been derived within the framework of Cosserat elasticity, wherein the continuum has additional degrees of freedom with respect to classical elasticity. A panel composed of material with various symmetries, corresponding to some particular hexagonal geometries defined, is analyzed under the effect of localized loads. The results obtained show the difference of the micropolar response for the considered material symmetries, which depends on the non-symmetries of the strain and stress tensor as well as on the additional kinematical and work-conjugated statical descriptors. This work underlines the importance of resorting to the Cosserat theory when analyzing anisotropic materials.
Failure Prediction Model Using Iterative Feature Selection for Industrial Internet of Things
Authors: Kwon Jung-Hyok , Kim Eui-Jik
Published: March 2020

This paper presents a failure prediction model using iterative feature selection, which aims to accurately predict the failure occurrences in industrial Internet of Things (IIoT) environments. In general, vast amounts of data are collected from various sensors in an IIoT environment, and they are analyzed to prevent failures by predicting their occurrence. However, the collected data may include data irrelevant to failures and thereby decrease the prediction accuracy. To address this problem, we propose a failure prediction model using iterative feature selection. To build the model, the relevancy between each feature (i.e., each sensor) and the failure was analyzed using the random forest algorithm, to obtain the importance of the features. Then, feature selection and model building were conducted iteratively. In each iteration, a new feature was selected considering the importance and added to the selected feature set. The failure prediction model was built for each iteration via the support vector machine (SVM). Finally, the failure prediction model having the highest prediction accuracy was selected. The experimental implementation was conducted using open-source R. The results showed that the proposed failure prediction model achieved high prediction accuracy.
A Smart, Sensible Agriculture System Using the Exponential Moving Average Model
Authors: Kim Tai-hoon , Solanki Virendra Singh , Baraiya Hardik J. , Mitra Anirban , Shah Hirav , Roy Sudipta
Published: March 2020

Smart agriculture systems with combinations of advanced technologies are used in an attempt to increase the competence of certain farming activities and the standard of living for farm employees by reducing significant labor and tedious tasks. Internet-of-things-based sensors are capable of providing such information about smart agriculture and then acting upon predictions using data analysis. The proposed methodology works alongside a cloud-based server and a mobile-based device (ideally an Android/iOS device) to assist the user in regulating the standing of the plant as monitored by a mix of software packages and hardware devices. Our system detects changes in the moisture, temperature, and light intensity conditions in and around the plant and performs a learning-based call to supply necessary irrigation and illumination to plants. It permits the user to update, manage, and monitor using wireless sensing element networks. The sensors measure the aforementioned parameters and store the data within the cloud, which users can access at any time from anywhere. Farmers will have access to the most up-to-date knowledge so that they can act accordingly and make modifications as needed. This smart planting has become a core tool associated with cost-effective technology in agricultural modernization technologies. The proposed smart modern agriculture tool can be used to monitor climatic factors such as temperature, moisture, and virtually all environmental parameters relevant to the growth of plants.
The Analysis of Electronic Circuit Fault Diagnosis Based on Neural Network Data Fusion Algorithm
Authors: Wang Nana
Published: March 2020

Symmetries play very important roles in the dynamics of electrical systems. The relevant electronic circuits with fault diagnostics, including the optimized neural network algorithm model, are designed on the basis of symmetry principles. In order to improve the efficiency of the circuit pressure test, a circuit pressure function equivalent compression test method based on the parallel neural network algorithm is proposed. For the implementation stage of the circuit pressure test, the improved modified node algorithm (MNA) is used to build an optimization model, and the circuit network is converted into an ordinary differential equation for the circuit pressure function equivalent compression test. The test aims to minimize flux. Then, backpropagation (BP) neural network algorithm data fusion is introduced to optimize the minimum flux model of the cyclic pressure functional equivalent compression test. Finally, a simulation experiment is carried out to verify the effectiveness of the algorithm in the accuracy and efficiency of the pressure test. The results show that the improved BP neural network improves the data fusion accuracy and shortens the sample training time; compared with the uncompressed algorithm, the running time of the proposed algorithm is greatly reduced and the execution efficiency is high; compared with the vascular pressure test method, there is no significant difference in the convergence accuracy and it is at a level of 10−5. Since the parallel computing problem is not considered in either of the two-pulse tube pressure test methods, the convergence time of the algorithm increases exponentially with the increase in the number of parallel threads. However, the algorithm in this research considers the problem of parallel execution and uses a quad-core processor, with no significant change in computing time and high computing efficiency. Therefore, BP neural network data fusion can be used for the fault diagnosis of electronic circuits, with a high operating efficiency and good development prospects.
Engineering of Ribosome-inactivating Proteins for Improving Pharmacological Properties
Authors: Lu Jia-Qi , Zhu Zhen-Ning , Zheng Yong-Tang , Shaw Pang-Chui
Published: March 2020

Ribosome-inactivating proteins (RIPs) are N-glycosidases, which depurinate a specific adenine residue in the conserved α-sarcin/ricin loop (α-SRL) of rRNA. This loop is important for anchoring elongation factor (EF-G for prokaryote or eEF2 for eukaryote) in mRNA translocation. Translation is inhibited after the attack. RIPs therefore may have been applied for anti-cancer, and anti-virus and other therapeutic applications. The main obstacles of treatment with RIPs include short plasma half-life, non-selective cytotoxicity and antigenicity. This review focuses on the strategies used to improve the pharmacological properties of RIPs on human immunodeficiency virus (HIV) and cancers. Coupling with polyethylene glycol (PEG) increases plasma time and reduces antigenicity. RIPs conjugated with antibodies to form immunotoxins increase the selective toxicity to target cells. The prospects for future development on the engineering of RIPs for improving their pharmacological properties are also discussed.
Membrane Activity and Channel Formation of the Adenylate Cyclase Toxin (CyaA) of Bordetella pertussis in Lipid Bilayer Membranes
Authors: Knapp Oliver , Benz Roland
Published: March 2020

The Gram-negative bacterium Bordetella pertussis is the cause of whooping cough. One of its pathogenicity factors is the adenylate cyclase toxin (CyaA) secreted by a Type I export system. The 1706 amino acid long CyaA (177 kDa) belongs to the continuously increasing family of repeat in toxin (RTX) toxins because it contains in its C-terminal half a high number of nine-residue tandem repeats. The protein exhibits cytotoxic and hemolytic activities that target primarily myeloid phagocytic cells expressing the αMβ2 integrin receptor (CD11b/CD18). CyaA represents an exception among RTX cytolysins because the first 400 amino acids from its N-terminal end possess a calmodulin-activated adenylate cyclase (AC) activity. The entry of the AC into target cells is not dependent on the receptor-mediated endocytosis pathway and penetrates directly across the cytoplasmic membrane of a variety of epithelial and immune effector cells. The hemolytic activity of CyaA is rather low, which may have to do with its rather low induced permeability change of target cells and its low conductance in lipid bilayer membranes. CyaA forms highly cation-selective channels in lipid bilayers that show a strong dependence on aqueous pH. The pore-forming activity of CyaA but not its single channel conductance is highly dependent on Ca2+ concentration with a half saturation constant of about 2 to 4 mM.
The Nuclear Remodeling Induced by Helicobacter Cytolethal Distending Toxin Involves MAFB Oncoprotein
Authors: Péré-Védrenne Christelle , He Wencan , Azzi-Martin Lamia , Prouzet-Mauléon Valérie , Buissonnière Alice , Cardinaud Bruno , Lehours Philippe , Mégraud Francis , Grosset Christophe F. , Ménard Armelle
Published: March 2020

Enterohepatic Helicobacters, such as Helicobacter hepaticus and Helicobacter pullorum, are associated with several intestinal and hepatic diseases. Their main virulence factor is the cytolethal distending toxin (CDT). In the present study, whole genome microarray-based identification of differentially expressed genes was performed in vitro in HT-29 intestinal cells while following the ectopic expression of the active CdtB subunit of H. hepaticus CDT. A CdtB-dependent upregulation of the V-maf musculoaponeurotic fibrosarcoma oncogene homolog B (MAFB) gene encoding the MAFB oncoprotein was found, as well as the CdtB-dependent regulation of several MAFB target genes. The transduction and coculture experiments confirmed MAFB mRNA and protein induction in response to CDT and its CdtB subunit in intestinal and hepatic cell lines. An analysis of MAFB protein subcellular localization revealed a strong nuclear and perinuclear localization in the CdtB-distended nuclei in intestinal and hepatic cells. MAFB was also detected at the cell periphery of the CdtB-induced lamellipodia in some cells. The silencing of MAFB changed the cellular response to CDT with the formation of narrower lamellipodia, a reduction of the increase in nucleus size, and the formation of less γH2AX foci, the biomarker for DNA double-strand breaks. Taken together, these data show that the CDT of enterohepatic Helicobacters modulates the expression of the MAFB oncoprotein, which is translocated in the nucleus and is associated with the remodeling of the nuclei and actin cytoskeleton.
Cysteine-Rich Secretory Proteins (CRISPs) from Venomous Snakes: An Overview of the Functional Diversity in a Large and Underappreciated Superfamily
Authors: Tadokoro Takashi , M. Modahl Cassandra , Maenaka Katsumi , Aoki-Shioi Narumi
Published: March 2020

The CAP protein superfamily (Cysteine-rich secretory proteins (CRISPs), Antigen 5 (Ag5), and Pathogenesis-related 1 (PR-1) proteins) is widely distributed, but for toxinologists, snake venom CRISPs are the most familiar members. Although CRISPs are found in the majority of venoms, very few of these proteins have been functionally characterized, but those that have been exhibit diverse activities. Snake venom CRISPs (svCRISPs) inhibit ion channels and the growth of new blood vessels (angiogenesis). They also increase vascular permeability and promote inflammatory responses (leukocyte and neutrophil infiltration). Interestingly, CRISPs in lamprey buccal gland secretions also manifest some of these activities, suggesting an evolutionarily conserved function. As we strive to better understand the functions that CRISPs serve in venoms, it is worth considering the broad range of CRISP physiological activities throughout the animal kingdom. In this review, we summarize those activities, known crystal structures and sequence alignments, and we discuss predicted functional sites. CRISPs may not be lethal or major components of venoms, but given their almost ubiquitous occurrence in venoms and the accelerated evolution of svCRISP genes, these venom proteins are likely to have functions worth investigating.
Discovery of the Gene Encoding a Novel Small Serum Protein (SSP) of Protobothrops flavoviridis and the Evolution of SSPs
Authors: Inamaru Kento , Takeuchi Ami , Maeda Marie , Shibata Hiroki , Fukumaki Yasuyuki , Oda-Ueda Naoko , Hattori Shosaku , Ohno Motonori , Chijiwa Takahito
Published: March 2020

Small serum proteins (SSPs) are low-molecular-weight proteins in snake serum with affinities for various venom proteins. Five SSPs, PfSSP-1 through PfSSP-5, have been reported in Protobothrops flavoviridis (“habu”, Pf) serum so far. Recently, we reported that the five genes encoding these PfSSPs are arranged in tandem on a single chromosome. However, the physiological functions and evolutionary origins of the five SSPs remain poorly understood. In a detailed analysis of the habu draft genome, we found a gene encoding a novel SSP, SSP-6. Structural analysis of the genes encoding SSPs and their genomic arrangement revealed the following: (1) SSP-6 forms a third SSP subgroup; (2) SSP-5 and SSP-6 were present in all snake genomes before the divergence of non-venomous and venomous snakes, while SSP-4 was acquired only by venomous snakes; (3) the composition of paralogous SSP genes in snake genomes seems to reflect snake habitat differences; and (4) the evolutionary emergence of SSP genes is probably related to the physiological functions of SSPs, with an initial snake repertoire of SSP-6 and SSP-5. SSP-4 and its derivative, SSP-3, as well as SSP-1 and SSP-2, appear to be venom-related and were acquired later.
Drying Lakes: A Review on the Applied Restoration Strategies and Health Conditions in Contiguous Areas
Authors: Tussupova Kamshat , Anchita Anchita , Hjorth Peder , Moravej Mojtaba
Published: March 2020

Decrease of saline lakes, which comprise 44% of all available lake water, is a major concern. It additionally accelerates the desertification process of the region. Thus, various countries have taken different actions in protecting their lake water levels. The aim of this paper is to assess different strategies directed to tackle the decreased lake water levels in Lake Urmia and the Aral Sea, which split into the North Aral Sea and South Aral Sea. These are among the world’s largest and fastest drying saline lakes observed in the past 50 years and have both reduced to 10% of their original size. The paper presents a thorough review of academic reports, official documents, and databases. Although the dry-up of a lake is a natural process, it has been sped up by human interventions in the hydrological cycle. Dust storms (strong winds) cause problems in the surroundings. In the case of the Aral Sea, they transmit the pollutants from the dry lake bed causing severe health issues. Various strategies were implemented to manage the socio-economic conditions caused due to the drying of lakes. The strategy implemented for the North Aral Sea was to restore the lake by reducing the water withdrawals from the Syr Darya river, which lead to increased water inflow to the sea. The suggested strategy for Lake Urmia was to restore the lake by water transfer activities from various water sources. These projects have not yet been realized. The strategy implemented for the South Aral Sea was to use a dry lake bed to diversify the economy by oil and mineral extraction along with developing a tourist industry based on the considerable interest to come and observe an ecological disaster of such monumental proportions. These findings show that there is no common best solution for this type of problem. The best fit depends on the local context and it is strongly path-dependent.
Nonlinear Dynamic Modeling of Urban Water Consumption Using Chaotic Approach (Case Study: City of Kelowna)
Authors: Yousefi Peyman , Courtice Gregory , Naser Gholamreza , Mohammadi Hadi
Published: March 2020

This study investigated urban water consumption complexity using chaos theory to improve forecasting performance to help optimize system management, reduce costs and improve reliability. The objectives of this study were to (1) investigate urban water distribution consumption complexity and its role in forecasting technique performance, (2) evaluate forecasting models by periodicity and lead time, and (3) propose a suitable forecasting technique based on operator applications and performance through various time scales. An urban consumption dataset obtained from the City of Kelowna (British Columbia, Canada) was used as a test case to forecast future consumption values using varying lead times under different temporal scales to identify models which may improve forecasting performance. Chaos theory techniques were employed to inform model optimization. This study attempted to address the paucity of studies on chaos theory applications in water consumption forecasting. This was accomplished by applying non-linear approximation, dynamic investigation, and phase space reconstruction for input variables, to improve the accuracy in various periodicity and lead time. To reconstruct the phase space, lag time was calculated using average mutual information for daily resolution as 17 days to reconstruct the phase space. The optimum embedding dimension and correlation exponent for the phase space were 18 and 3.5, respectively. Comparing the results, the non-linear local approximation model provided the best performance. The forecasting horizon for the models was 122 days. Moreover, phase space reconstruction improved the accuracy of the models for the different lead times. The findings of this study may improve forecasting performance and provide evidence to support further investigation of the chaotic behaviour of water consumption values over different time scales.
Multicomponent Water Effects on Rotating Machines Disk Erosion
Authors: Yang Chunxia , Hou Shanshan , Xu Junhui , Zhang Yuquan , Zheng Yuan , Fernandez-Rodriguez E , Zhou Daqing
Published: March 2020

When sand particles are entrained into carrier flow, such as liquid, a strong interaction occurs with the surface of the metallic material, resulting in serious erosion damage. However, the effect of the physical properties of particles and materials on erosion characteristics has not been well studied. In this paper, the erosion-wear behavior of a rotating disc surface under the action of solid–liquid two-phase flow was studied by using the discrete particle model (DPM). The wear effects on the surface of sample due to particle diameter (d = 0.1 mm, d = 0.2 mm, d = 0.3 mm, d = 0.4 mm), particle volume fraction (CV = 2%, CV = 3%, CV = 4%, CV = 5%), and particle inlet velocity (v = 1.05 m/s, v = 2.05 m/s, v = 3.05 m/s, v = 4.05 m/s) were analyzed using representative values of operating conditions of rotating machines. The results show that the wear amount increases exponentially with the radius, whilst the maximum wear amount increases faster than the average wear amount with the particle volume fraction. The surface wear grows inversely with the particle diameter but slightly with the particle inlet velocity. A case study of stainless steel samples at different radius positions on the surface of rotating disc is carried out using a mixed velocity of sand and water of 2.05 m/s, an average particle size of 0.1 mm, and a concentration of CV = 2.5%. The experiments show the wear amount increases with the radius on the surface of the rotating disc, just as predicted by the numerical simulation. Two important findings emerge from the study: (1) the wear morphology of the specimen surface develops from two to three regions; (2) when the basal body is rotating at high speed, the wear degree is influenced more by the circumferential than particle inlet velocity. The wear morphology was observed by using a scanning electron microscope (SEM). It exhibited a mixture of fine and coarse scratches and pits, and the distribution of these varied according to the radial distance of the disc.