bluuebunny/crossref_metadata_2025 huggingface dataset:
Investigation of Biocidal Effect of Microfiltration Membranes Impregnated with Silver Nanoparticles by Sputtering Technique
Authors: Linhares Aline M. F. , Borges Cristiano P. , Fonseca Fabiana V.
Published: July 2020

Silver nanoparticles were loaded in microfiltration membranes by sputtering technique for the development of biocidal properties and biofouling resistance. This technology allows good adhesion between silver nanoparticles and the membranes, and fast deposition rate. The microfiltration membranes (15 wt.% polyethersulfone and 7.5 wt.% polyvinylpyrrolidone in N,N-dimethylacetamide) were prepared by phase inversion method, and silver nanoparticles were deposited on their surface by the physical technique of vapor deposition in a sputtering chamber. The membranes were characterized by Field Emission Scanning Electron Microscopy, and the presence of silver was investigated by Energy-Dispersive Spectroscopy and X-ray Diffraction. Experiments of silver leaching were carried out through immersion and filtration tests. After 10 months of immersion in water, the membranes still presented ~90% of the initial silver, which confirms the efficiency of the sputtering technique. Moreover, convective experiments indicated that 98.8% of silver remained in the membrane after 24 h of operation. Biocidal analyses (disc diffusion method and biofouling resistance) were performed against Pseudomonas aeruginosa and confirmed the antibacterial activity of these membranes with 0.6 and 0.7 log reduction of viable planktonic and sessile cells, respectively. These results indicate the great potential of these new membranes to reduce biofouling effects.
Surface Modification of Carbon Microspheres with Guanidine Phosphate and Its Application as a Flame Retardant in PET
Authors: Jiang Shan , Ji Cheng , Zha Dan , Ding Yonghong , Wu Dun , Yu Qiang
Published: July 2020

Composites based on polyethylene terephthalate (PET) and surface-modified carbon microspheres (CMSs) were prepared by melt mixing. The surface modification of CMSs was conducted with low-temperature plasma technology first, and a phosphorus-nitrogen flame retardant, guanidine phosphate (GDP), was then grafted onto CMSs. The modification of CMSs was done to improve both the filler dispersity in the PET matrix and the flame-retardant performance of composites. The obtained CMSs-GDP was characterized by FTIR spectra and a scanning electron microscope (SEM). The grafting ratio was characterized and calculated by thermal gravimetric analysis (TGA). The grain size analysis was used to describe the dispersity of CMSs. The mechanical properties of the PET/CMSs-GDP composite were measured using a universal testing machine. The PET/CMSs-GDP composite can achieve a limiting oxygen index (LOI) value of 32.4% and a vertical burning test (UL94) V-0 rating at 3% CMSs-GDP loading.
A Review on Citric Acid as Green Modifying Agent and Binder for Wood
Authors: Lee Seng Hua , Md Tahir Paridah , Lum Wei Chen , Tan Li Peng , Bawon Paiman , Park Byung-Dae , Osman Al Edrus Syeed SaifulAzry , Abdullah Ummi Hani
Published: July 2020

Citric acid (CA) can be found naturally in fruits and vegetables, particularly citrus fruit. CA is widely used in many fields but its usage as a green modifying agent and binder for wood is barely addressed. Esterification is one of the most common chemical reactions applied in wood modification. CA contains three carboxyl groups, making it possible to attain at least two esterification reactions that are required for crosslinking when reacting with the hydroxyl groups of the cell wall polymers. In addition, the reaction could form ester linkages to bring adhesivity and good bonding characteristics, and therefore CA could be used as wood binder too. This paper presents a review concerning the usage of CA as a wood modifying agent and binder. For wood modification, the reaction mechanism between wood and CA and the pros and cons of using CA are discussed. CA and its combination with various reactants and their respective optimum parameters are also compiled in this paper. As for the major wood bonding component, the bonding mechanism and types of wood composites bonded with CA are presented. The best working conditions for the CA in the fabrication of wood-based panels are discussed. In addition, the environmental impacts and future outlook of CA-treated wood and bonded composite are also considered.
Copolymerized Natural Fibre from the Mesocarp of Orbignya phalerata (Babassu Fruit) as an Irrigating-Fertilizer for Growing Cactus Pears
Authors: Edvan Ricardo , Sá Mariane , Magalhães Regina , Ratke Rafael , Sousa Heldeney R. , Neri Lucas Mateus Lima , Silva-Filho Edson C. , Pereira Filho Jose , Bezerra Leilson
Published: July 2020

Cactus pears face challenges due to global climate change, which is leading to in-depth research to monitor and increase their water activity. The objective of this study was to evaluate the use of the natural test hydrogel (TH) from Orbignya phalerata fibre as nutrients and water for growing cactus pear genotypes (“Baiana” and “Doce” [Nopalea cochenillifera], ‘Gigante’ [Opuntia fícus-indica], and “Mexican Elephant Ear” [Opuntia stricta]) compared to the use of commercial hydrogel (CH), which is based on polymers composed of polyacrylamide, and a treatment without the use of hydrogel (WH). A completely randomized design was used, in a factorial scheme (4 × 3), with four genotypes of cactus pear and three forms of hydration, with five replications. The number and area of cladode was greatest (p < 0.01) in plants with CH and TH irrigation-fertilization in the ‘Doce’ cactus genotype. The dry biomass of the cladode and root in the ‘Gigante’ cactus genotype was greatest (p < 0.01) in the treatments with CH and TH irrigation-fertilisation. The ‘Baiana’, ‘Doce’, and ‘Gigante’ cactus genotypes exhibited more (p < 0.01) dry matter content with the TH irrigation-fertilisation. The highest (p < 0.01) neutral detergent fibre content was observed in the ‘Baiana’ and ‘Doce’ cactus genotypes when irrigation occurred with WH treatment, and the highest acid detergent fibre content in the ‘Gigante’ genotype. The copolymerized natural fibre from the mesocarp of Orbignya phalerata (babassu fruit) induced a better growth and chemical composition of cactus pear genotypes than the hydrogel based on polymers composed of polyacrylamide.
Erratum: Synthesis, Characterization, and Analysis of Hybrid Carbon Nanotubes by Chemical Vapor Deposition: Application for Aluminum Removal. Polymers 2020, 12, 1305
Authors: Basheer Alfarooq O. , M. Hanafiah Marlia , Abdulhakim Alsaadi Mohammed , Wan Yaacob Wan Zuhairi , Al-Douri Y.
Published: July 2020

The authors wish to make a change to the published paper [...]
Macromolecular Engineering by Applying Concurrent Reactions with ATRP
Authors: Wang Yu , Nguyen Mary , Gildersleeve Amanda J.
Published: July 2020

Modern polymeric material design often involves precise tailoring of molecular/supramolecular structures which is also called macromolecular engineering. The available tools for molecular structure tailoring are controlled/living polymerization methods, click chemistry, supramolecular polymerization, self-assembly, among others. When polymeric materials with complex molecular architectures are targeted, it usually takes several steps of reactions to obtain the aimed product. Concurrent polymerization methods, i.e., two or more reaction mechanisms, steps, or procedures take place simultaneously instead of sequentially, can significantly reduce the complexity of the reaction procedure or provide special molecular architectures that would be otherwise very difficult to synthesize. Atom transfer radical polymerization, ATRP, has been widely applied in concurrent polymerization reactions and resulted in improved efficiency in macromolecular engineering. This perspective summarizes reported studies employing concurrent polymerization methods with ATRP as one of the reaction components and highlights future research directions in this area.
In Situ Preparation of Crosslinked Polymer Electrolytes for Lithium Ion Batteries: A Comparison of Monomer Systems
Authors: Röchow Eike T. , Coeler Matthias , Pospiech Doris , Kobsch Oliver , Mechtaeva Elizaveta , Vogel Roland , Voit Brigitte , Nikolowski Kristian , Wolter Mareike
Published: July 2020

Solid polymer electrolytes for bipolar lithium ion batteries requiring electrochemical stability of 4.5 V vs. Li/Li+ are presented. Thus, imidazolium-containing poly(ionic liquid) (PIL) networks were prepared by crosslinking UV-photopolymerization in an in situ approach (i.e., to allow preparation directly on the electrodes used). The crosslinks in the network improve the mechanical stability of the samples, as indicated by the free-standing nature of the materials and temperature-dependent rheology measurements. The averaged mesh size calculated from rheologoical measurements varied between 1.66 nm with 10 mol% crosslinker and 4.35 nm without crosslinker. The chemical structure of the ionic liquid (IL) monomers in the network was varied to achieve the highest possible ionic conductivity. The systematic variation in three series with a number of new IL monomers offers a direct comparison of samples obtained under comparable conditions. The ionic conductivity of generation II and III PIL networks was improved by three orders of magnitude, to the range of 7.1 × 10−6 S·cm−1 at 20 °C and 2.3 × 10−4 S·cm−1 at 80 °C, compared to known poly(vinylimidazolium·TFSI) materials (generation I). The transition from linear homopolymers to networks reduces the ionic conductivity by about one order of magnitude, but allows free-standing films instead of sticky materials. The PIL networks have a much higher voltage stability than PEO with the same amount and type of conducting salt, lithium bis(trifluoromethane sulfonyl)imide (LiTFSI). GII-PIL networks are electrochemically stable up to a potential of 4.7 V vs. Li/Li+, which is crucial for a potential application as a solid electrolyte. Cycling (cyclovoltammetry and lithium plating-stripping) experiments revealed that it is possible to conduct lithium ions through the GII-polymer networks at low currents. We concluded that the synthesized PIL networks represent suitable candidates for solid-state electrolytes in lithium ion batteries or solid-state batteries.
Phase Stress Measurement of Centrifugally Cast Duplex Stainless Steel by Neutron Diffraction
Authors: Wang Yun
Published: July 2020

Residual stress can be easily generated during material processing and affect the performance of structural components. Phase stress distribution in austenitic-ferritic duplex stainless steels (DSSs) is complicated due to the different material properties between the two phases. In this study, residual phase stress distribution along the thickness direction of centrifugally cast DSS hollow cylinder was measured by pulsed neutron diffraction with the time-of-flight (TOF) method. The triaxial phase stress distribution along the thickness direction shows that the phase stress of austenitic phase is generally in tension and higher than that of ferrite phase. From the outer surface to the inner surface, the macro-stress distributes from −400 MPa to 200 MPa. The mechanism of macro-stress formation was deduced by taking into consideration the thermal shrinkage behavior during the cooling process of water quench after the solution heat treatment. Furthermore, the lattice strain and phase stress evolution under the uniaxial tensile loading was evaluated by in-situ neutron diffraction measurement. The results indicated that the magnitude of phase stress could be affected by plastic working as well. All these measurements were conducted at Japan Proton Accelerator Research Complex (J-PARC).
Urban Tree Health Classification Across Tree Species by Combining Airborne Laser Scanning and Imaging Spectroscopy
Authors: Chi Dengkai , Degerickx Jeroen , Yu Kang , Somers Ben
Published: July 2020

Declining urban tree health can affect critical ecosystem services, such as air quality improvement, temperature moderation, carbon storage, and biodiversity conservation. The application of state-of-the-art remote sensing data to characterize tree health has been widely examined in forest ecosystems. However, such application to urban trees has not yet been fully explored—due to the presence of heterogeneous tree species and backgrounds, severely complicating the classification of tree health using remote sensing information. In this study, tree health was represented by a set of field-assessed tree health indicators (defoliation, discoloration, and a combination thereof), which were classified using airborne laser scanning (ALS) and hyperspectral imagery (HSI) with a Random Forest classifier. Different classification scenarios were established aiming at: (i) Comparing the performance of ALS data, HSI and their combination, and (ii) examining to what extent tree species mixtures affect classification accuracy. Our results show that although the predictive power of ALS and HSI indices varied between tree species and tree health indicators, overall ALS indices performed better. The combined use of both ALS and HSI indices results in the highest accuracy, with weighted kappa coefficients (Kc) ranging from 0.53 to 0.79 and overall accuracy ranging from 0.81 to 0.89. Overall, the most informative remote sensing indices indicating urban tree health are ALS indices related to point density, tree size, and shape, and HSI indices associated with chlorophyll absorption. Our results further indicate that a species-specific modelling approach is advisable (Kc points improved by 0.07 on average compared with a mixed species modelling approach). Our study constitutes a basis for future urban tree health monitoring, which will enable managers to guide early remediation management.
Using Satellite Thermal-Based Evapotranspiration Time Series for Defining Management Zones and Spatial Association to Local Attributes in a Vineyard
Authors: Ohana-Levi Noa , Knipper Kyle , Kustas William P. , Anderson Martha C. , Netzer Yishai , Gao Feng , Alsina Maria del Mar , Sanchez Luis A. , Karnieli Arnon
Published: July 2020

A well-planned irrigation management strategy is crucial for successful wine grape production and is highly dependent on accurate assessments of water stress. Precision irrigation practices may benefit from the quantification of within-field spatial variability and temporal patterns of evapotranspiration (ET). A spatiotemporal modeling framework is proposed to delineate the vineyard into homogeneous areas (i.e., management zones) according to their ET patterns. The dataset for this study relied on ET retrievals from multiple satellite platforms, generating estimates at high spatial (30 m) and temporal (daily) resolutions for a Vitis vinifera Pinot noir vineyard in the Central Valley of California during the growing seasons of 2015-2018. Time-series decomposition was used to deconstruct the time series of each pixel into three components: long-term trend, seasonality, and remainder, which indicates daily fluctuations. For each time-series component, a time-series clustering (TSC) algorithm was applied to partition the time series of all pixels into homogeneous groups and generate TSC maps. The TSC maps were compared for spatial similarities using the V-measure statistic. A random forest (RF) classification algorithm was used for each TSC map against six environmental variables (elevation, slope, northness, lithology, topographic wetness index, and soil type) to check for spatial association between ET-TSC maps and the local characteristics in the vineyard. Finally, the TSC maps were used as independent variables against yield (ton ha-1) using analysis of variance (ANOVA) to assess whether the TSC maps explained yield variability. The trend and seasonality TSC maps had a moderate spatial association (V = 0.49), while the remainder showed dissimilar spatial patterns to seasonality and trend. The RF model showed high error matrix-based prediction accuracy levels ranging between 86% and 90%. For the trend and seasonality models, the most important predictor was soil type, followed by elevation, while the remainder TSC was strongly linked with northness spatial variability. The yield levels corresponding to the two clusters in all TSC were significantly different. These findings enabled spatial quantification of ET time series at different temporal scales that may benefit within-season decision-making regarding the amounts, timing, intervals, and location of irrigation. The proposed framework may be applicable to other cases in both agricultural systems and environmental modeling.
Evaluation of Sentinel-2 and Landsat 8 Images for Estimating Chlorophyll-a Concentrations in Lake Chad, Africa
Authors: Buma Willibroad Gabila , Lee Sang-Il
Published: July 2020

Much effort has been applied in estimating the concentrations of chlorophyll-a (Chl a) in lakes. The optical complexity and lack of in situ data complicate estimating Chl a in such water bodies. We compared four established satellite reflectance algorithms—the two-band and three-band algorithms (2BDA, 3BDA), fluorescence line height (FLH), and normalized difference chlorophyll index (NDCI)—to estimate Chl a concentration in Lake Chad. We evaluated the performance and applicability of Landsat-8 (L8) and Sentinel-2 (S2) images with the four Chl a estimation algorithms. For accuracy, we compared the concentration levels from the four algorithms to those from Worldview-3 (WV3) images. We identified two promising algorithms that could be used alongside L8 and S2 satellite images to monitor Chl a concentrations in Lake Chad. With an averaged R2 of 0.8, the 3BDA and NDCI Chl a algorithms performed accurately with S2 and L8 images. For the S2 and L8 images, 3BDA had the highest performance when compared to the WV3 estimates. We demonstrate the usefulness of sensor images in improving water quality information for areas that are difficult to access or when conventional data are limited.
Assessment of Intra-Annual and Inter-Annual Variabilities of Soil Erosion in Crete Island (Greece) by Incorporating the Dynamic “Nature” of R and C-Factors in RUSLE Modeling
Authors: Polykretis Christos , Alexakis Dimitrios D. , Grillakis Manolis G. , Manoudakis Stelios
Published: July 2020

Under the continuously changing conditions of the environment, the exploration of spatial variability of soil erosion at a sub-annual temporal resolution, as well as the identification of high-soil loss time periods and areas, are crucial for implementing mitigation and land management interventions. The main objective of this study was to estimate the monthly and seasonal soil loss rates by water-induced soil erosion in Greek island of Crete for two recent hydrologically contrasting years, 2016 (dry) and 2019 (wet), as a result of Revised Universal Soil Loss Equation (RUSLE) modeling. The impact of temporal variability of the two dynamic RUSLE factors, namely rainfall erosivity (R) and cover management (C), was explored by using rainfall and remotely sensed vegetation data time-series of high temporal resolution. Soil, topographical, and land use/cover data were exploited to represent the other three static RUSLE factors, namely soil erodibility (K), slope length and steepness (LS) and support practice (P). The estimated rates were mapped presenting the spatio-temporal distribution of soil loss for the study area on a both intra-annual and inter-annual basis. The identification of high-loss months/seasons and areas in the island was achieved by these maps. Autumn (about 35 t ha−1) with October (about 61 t ha−1) in 2016, and winter (about 96 t ha−1) with February (146 t ha−1) in 2019 presented the highest mean soil loss rates on a seasonal and monthly, respectively, basis. Summer (0.22–0.25 t ha−1), with its including months, showed the lowest rates in both examined years. The intense monthly fluctuations of R-factor were found to be more influential on water-induced soil erosion than the more stabilized tendency of C-factor. In both years, olive groves in terms of agricultural land use and Chania prefecture in terms of administrative division, were detected as the most prone spatial units to erosion.
Vision System for Automatic On-Tree Kiwifruit Counting and Yield Estimation
Authors: Mekhalfi Mohamed Lamine , Nicolò Carlo , Ianniello Ivan , Calamita Federico , Goller Rino , Barazzuol Maurizio , Melgani Farid
Published: July 2020

Yield estimation is an essential preharvest practice among most large-scale farming companies, since it enables the predetermination of essential logistics to be allocated (i.e., transportation means, supplies, labor force, among others). An overestimation may thus incur further costs, whereas an underestimation entails potential crop waste. More interestingly, an accurate yield estimation enables stakeholders to better place themselves in the market. Yet, computer-aided precision farming is set to play a pivotal role in this respect. Kiwifruit represents a major produce in several countries (e.g., Italy, China, New and Zealand). However, up to date, the relevant literature remains short of a complete as well as automatic system for kiwifruit yield estimation. In this paper, we present a fully automatic and noninvasive computer vision system for kiwifruit yield estimation across a given orchard. It consists mainly of an optical sensor mounted on a minitractor that surveys the orchard of interest at a low pace. Afterwards, the acquired images are fed to a pipeline that incorporates image preprocessing, stitching, and fruit counting stages and outputs an estimated fruit count and yield estimation. Experimental results conducted on two large kiwifruit orchards confirm a high plausibility (i.e., errors of 6% and 15%) of the proposed system. The proposed yield estimation solution has been in commercial use for about 2 years. With respect to the traditional manual yield estimation carried out by kiwifruit companies, it was demonstrated to save a significant amount of time and cut down on estimation errors, especially when speaking of large-scale farming.
Machine Learning Modelling and Feature Engineering in Seismology Experiment
Authors: Brykov Michail Nikolaevich , Petryshynets Ivan , Pruncu Catalin Iulian , Efremenko Vasily Georgievich , Pimenov Danil Yurievich , Giasin Khaled , Sylenko Serhii Anatolievich , Wojciechowski Szymon
Published: July 2020

This article aims to discusses machine learning modelling using a dataset provided by the LANL (Los Alamos National Laboratory) earthquake prediction competition hosted by Kaggle. The data were obtained from a laboratory stick-slip friction experiment that mimics real earthquakes. Digitized acoustic signals were recorded against time to failure of a granular layer compressed between steel plates. In this work, machine learning was employed to develop models that could predict earthquakes. The aim is to highlight the importance and potential applicability of machine learning in seismology The XGBoost algorithm was used for modelling combined with 6-fold cross-validation and the mean absolute error (MAE) metric for model quality estimation. The backward feature elimination technique was used followed by the forward feature construction approach to find the best combination of features. The advantage of this feature engineering method is that it enables the best subset to be found from a relatively large set of features in a relatively short time. It was confirmed that the proper combination of statistical characteristics describing acoustic data can be used for effective prediction of time to failure. Additionally, statistical features based on the autocorrelation of acoustic data can also be used for further improvement of model quality. A total of 48 statistical features were considered. The best subset was determined as having 10 features. Its corresponding MAE was 1.913 s, which was stable to the third decimal point. The presented results can be used to develop artificial intelligence algorithms devoted to earthquake prediction.
IoT Platform for Seafood Farmers and Consumers
Authors: Jæger Bjørn , Mishra Alok
Published: July 2020

There has been a strong growth in aquatic products supported by the global seafood industry. Consumers demand information transparency to support informed decisions and to verify nutrition, food safety, and sustainable operations. Supporting these needs rests on the existence of interoperable Internet of Things (IoT) platforms for traceability that goes beyond the minimum “one up, one down” scheme required by regulators. Seafood farmers, being the source of both food and food-information, are critical to achieving the needed transparency. Traditionally, seafood farmers carry the costs of providing information, while downstream actors reap the benefits, causing limited provision of information. Now, global standards for labelling, data from IoT devices, and the reciprocity of utility from collecting data while sharing them represent great potential for farmers to generate value from traceability systems. To enable this, farmers need an IoT platform integrated with other IoT platforms in the value network. This paper presents a case study of an enterprise-level IoT platform for seafood farmers that satisfies consumers’ end-to-end traceability needs while extracting data from requests for information from downstream actors.
Microfluidics by Additive Manufacturing for Wearable Biosensors: A Review
Authors: Padash Mahshid , Enz Christian , Carrara Sandro
Published: July 2020

Wearable devices are nowadays at the edge-front in both academic research as well as in industry, and several wearable devices have been already introduced in the market. One of the most recent advancements in wearable technologies for biosensing is in the area of the remote monitoring of human health by detection on-the-skin. However, almost all the wearable devices present in the market nowadays are still providing information not related to human ‘metabolites and/or disease’ biomarkers, excluding the well-known case of the continuous monitoring of glucose in diabetic patients. Moreover, even in this last case, the glycaemic level is acquired under-the-skin and not on-the-skin. On the other hand, it has been proven that human sweat is very rich in molecules and other biomarkers (e.g., ions), which makes sweat a quite interesting human liquid with regards to gathering medical information at the molecular level in a totally non-invasive manner. Of course, a proper collection of sweat as it is emerging on top of the skin is required to correctly convey such liquid to the molecular biosensors on board of the wearable system. Microfluidic systems have efficiently come to the aid of wearable sensors, in this case. These devices were originally built using methods such as photolithographic and chemical etching techniques with rigid materials. Nowadays, fabrication methods of microfluidic systems are moving towards three-dimensional (3D) printing methods. These methods overcome some of the limitations of the previous method, including expensiveness and non-flexibility. The 3D printing methods have a high speed and according to the application, can control the textures and mechanical properties of an object by using multiple materials in a cheaper way. Therefore, the aim of this paper is to review all the most recent advancements in the methods for 3D printing to fabricate wearable fluidics and provide a critical frame for the future developments of a wearable device for the remote monitoring of the human metabolism directly on-the-skin.
Milk Source Identification and Milk Quality Estimation Using an Electronic Nose and Machine Learning Techniques
Authors: Mu Fanglin , Gu Yu , Zhang Jie , Zhang Lei
Published: July 2020

In this study, an electronic nose (E-nose) consisting of seven metal oxide semiconductor sensors is developed to identify milk sources (dairy farms) and to estimate the content of milk fat and protein which are the indicators of milk quality. The developed E-nose is a low cost and non-destructive device. For milk source identification, the features based on milk odor features from E-nose, composition features (Dairy Herd Improvement, DHI analytical data) from DHI analysis and fusion features are analyzed by principal component analysis (PCA) and linear discriminant analysis (LDA) for dimension reduction and then three machine learning algorithms, logistic regression (LR), support vector machine (SVM), and random forest (RF), are used to construct the classification model of milk source (dairy farm) identification. The results show that the SVM model based on the fusion features after LDA has the best performance with the accuracy of 95%. Estimation model of the content of milk fat and protein from E-nose features using gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), and random forest (RF) are constructed. The results show that the RF models give the best performance (R2 = 0.9399 for milk fat; R2 = 0.9301 for milk protein) and indicate that the proposed method in this study can improve the estimation accuracy of milk fat and protein, which provides a technical basis for predicting the quality of milk.
Fault-Tolerant Model Predictive Control Algorithm for Path Tracking of Autonomous Vehicle
Authors: Geng Keke , Chulin Nikolai Alexandrovich , Wang Ziwei
Published: July 2020

The fault detection and isolation are very important for the driving safety of autonomous vehicles. At present, scholars have conducted extensive research on model-based fault detection and isolation algorithms in vehicle systems, but few of them have been applied for path tracking control. This paper determines the conditions for model establishment of a single-track 3-DOF vehicle dynamics model and then performs Taylor expansion for modeling linearization. On the basis of that, a novel fault-tolerant model predictive control algorithm (FTMPC) is proposed for robust path tracking control of autonomous vehicle. First, the linear time-varying model predictive control algorithm for lateral motion control of vehicle is designed by constructing the objective function and considering the front wheel declination and dynamic constraint of tire cornering. Then, the motion state information obtained by multi-sensory perception systems of vision, GPS, and LIDAR is fused by using an improved weighted fusion algorithm based on the output error variance. A novel fault signal detection algorithm based on Kalman filtering and Chi-square detector is also designed in our work. The output of the fault signal detector is a fault detection matrix. Finally, the fault signals are isolated by multiplication of signal matrix, fault detection matrix, and weight matrix in the process of data fusion. The effectiveness of the proposed method is validated with simulation experiment of lane changing path tracking control. The comparative analysis of simulation results shows that the proposed method can achieve the expected fault-tolerant performance and much better path tracking control performance in case of sensor failure.
Smart Contract Centric Inference Engine For Intelligent Electric Vehicle Transportation System
Authors: Khan Prince Waqas , Byun Yung-Cheol
Published: July 2020

The provision of electric vehicles (EVs) is increasing due to the need for ecological green energy. The increment in EVs leads to an intelligent electric vehicle transportation system’s need instead of cloud-based systems to manage privacy and security issues. Collecting and delivering the data to current transportation systems means disclosing personal information about vehicles and drivers. We have proposed a secure and intelligent electric vehicle transportation system based on blockchain and machine learning. The proposed method utilizes the state of the art smart contract module of blockchain to build an inference engine. This system takes the sensors’ data from the vehicle control unit of EV, stores it in the blockchain, makes decisions using an inference engine, and executes those decisions using actuators and user interface. We have utilized a double-layer optimized long short term memory (LSTM) algorithm to predict EV’s stator temperature. We have also performed an informal analysis to demonstrate the proposed system’s robustness and reliability. This system will resolve the security issues for both information and energy interactions in EVs.
Critical Factors for In Vivo Measurements of Human Skin by Terahertz Attenuated Total Reflection Spectroscopy
Authors: Wang Lixia , Guilavogui Sayon , Yin Henghui , Wu Yiping , Zang Xiaofei , Xie Jingya , Ding Li , Chen Lin
Published: July 2020

Attenuated total reflection (ATR) geometry is a suitable choice for in vivo measurements of human skin due to the deep penetration of the field into the sample and since it makes it easy to measure the reference spectrum. On the other hand, there are several critical factors that may affect the terahertz (THz) response in these kinds of experiments. Here, we analyse in detail the influence of the following factors: the contact positions between the thumb and the prism, the contact pressure, the contact duration, and the materials of the prism. Furthermore, we use the THz-ATR technology to evaluate different types of handcream and also establish the theoretical model to investigate the reflectivity after interacting with the skin. The results agree well with experimental ones. Our analysis makes it clear the importance of controlling the above factors during measurements to enable reliable THz response and results which, in turn, may be used to monitor water motion in human skin and to predict possible diseases.
MeshLifter: Weakly Supervised Approach for 3D Human Mesh Reconstruction from a Single 2D Pose Based on Loop Structure
Authors: Jeong Sunwon , Chang Ju Yong
Published: July 2020

In this paper, we address the problem of 3D human mesh reconstruction from a single 2D human pose based on deep learning. We propose MeshLifter, a network that estimates a 3D human mesh from an input 2D human pose. Unlike most existing 3D human mesh reconstruction studies that train models using paired 2D and 3D data, we propose a weakly supervised learning method based on a loop structure to train the MeshLifter. The proposed method alleviates the difficulty of obtaining ground-truth 3D data to ensure that the MeshLifter can be trained successfully from a 2D human pose dataset and an unpaired 3D motion capture dataset. We compare the proposed method with recent state-of-the-art studies through various experiments and show that the proposed method achieves effective 3D human mesh reconstruction performance. Notably, our proposed method achieves a reconstruction error of 59.1 mm without using the 3D ground-truth data of Human3.6M, the standard dataset for 3D human mesh reconstruction.
Development of a Hybrid Path Planning Algorithm and a Bio-Inspired Control for an Omni-Wheel Mobile Robot
Authors: Kim Changwon , Suh Junho , Han Je-Heon
Published: July 2020

This research presents a control structure for an omni-wheel mobile robot (OWMR). The control structure includes the path planning module and the motion control module. In order to secure the robustness and fast control performance required in the operating environment of OWMR, a bio-inspired control method, brain limbic system (BLS)-based control, was applied. Based on the derived OWMR kinematic model, a motion controller was designed. Additionally, an optimal path planning module is suggested by combining the advantages of A* algorithm and the fuzzy analytic hierarchy process (FAHP). In order to verify the performance of the proposed motion control strategy and path planning algorithm, numerical simulations were conducted. Through a point-to-point movement task, circular path tracking task, and randomly moving target tracking task, it was confirmed that the suggesting motion controller is superior to the existing controllers, such as PID. In addition, A*–FAHP was applied to the OWMR to verify the performance of the proposed path planning algorithm, and it was simulated based on the static warehouse environment, dynamic warehouse environment, and autonomous ballet parking scenarios. The simulation results demonstrated that the proposed algorithm generates the optimal path in a short time without collision with stop and moving obstacles.
Effect of Inappropriate Binder Grade Selection on Initiation of Asphalt Pavement Cracking
Authors: Mugume Rodgers , Kakoto Denis
Published: July 2020

This paper is aimed at assessing in-service asphalt pavement cracking in order to fully understand its causes as well as reviewing the possible impact of implementing a new mix design method on failures observed. Field and laboratory investigations were conducted as well as a review of design and construction records. Substitution of a Performance Grade (PG) binder with straight run pen grade binder without performing independent Superpave PG verification tests greatly contributed to cracking. A 20/30 pen grade binder which was utilised had already undergone some change in its properties since its manufacture and therefore did not provide the required workability and crack resistance that it would otherwise have been expected to provide. Target mixing and compaction temperature ranges during construction were higher than those recommended confirming that the binder used had already undergone a change in its physical and chemical characteristics between the time of its manufacture and use. Additionally, a lapse in quality control and assurance during asphalt production and laying resulted in a highly voided wearing course which exacerbated the situation.
Adapting Universities for Sustainability Education in Industry 4.0: Channel of Challenges and Opportunities
Authors: Mian Syed Hammad , Salah Bashir , Ameen Wadea , Moiduddin Khaja , Alkhalefah Hisham
Published: July 2020

The emergence of Industry 4.0, also referred to as the fourth industrial revolution, has entirely transformed how the industry or business functions and evolves. It can be attributed to its broadening focus on automation, decentralization, system integration, cyber-physical systems, etc. Its implementation promises numerous benefits in terms of higher productivity, greater volatility, better control and streamlining of processes, accelerated enterprise growth, sustainable development, etc. Despite the worldwide recognition and realization of Industry 4.0, its holistic adoption is constrained by the requirements of specific skills among the workforce. The personnel are expected to acquire adaptive thinking, cognitive and computational skills, predominantly in the area of information technology, data analytics, etc. Thus, the universities that laid the foundation for future talents or trends in society have to adapt and modernize the existing programs, facilities, and infrastructure. This reshaping of higher education in consonance with the vision of Industry 4.0 possesses its opportunities and challenges. There are, of course, a multitude of factors involved and they need a reasonable assessment to strategically plan this metamorphosis. Therefore, this work aims to explore and analyze the different factors that influence the progression and enactment of Industry 4.0 in universities for sustainable education. For this purpose, a systematic approach based on a questionnaire as well as a SWOT (strengths (S), weaknesses (W), opportunities (O), and threats (T)) integrated with the analytic hierarchy process (AHP) is adopted. The questionnaires are administered to university employees and students (or stakeholders) to assess their viewpoint, as well as to estimate the priority values for individual factors to be included in SWOT. The AHP is implemented to quantify the different factors in terms of weights using a pairwise comparison matrix. Finally, the SWOT matrix is established depending on the questionnaire assessment and the AHP weights to figure out stakeholders’ perspectives, in addition to the needed strategic scheme. The SWOT implementation of this research proposes an aggressive approach for universities, where they must make full use of their strengths to take advantage of the emerging opportunities in Industry 4.0. The results also indicate that there are fundamental requirements for universities in Industry 4.0, including effective financial planning, skilled staff, increased industrial partnerships, advanced infrastructure, revised curricula, and insightful workshops. This investigation undoubtedly underlines the importance of practical expertise and the implementation of digital technologies at the university level to empower novices with the requisite skills and a competitive advantage for Industry 4.0.
Odor Annoyance Assessment by Using Logistic Regression on an Example of the Municipal Sector
Authors: Wroniszewska Alicja , Zwoździak Jerzy
Published: July 2020

Around the sewage treatment plant, in the area affected by a large number of complaints about odor annoyance, field measurements of odor properties and questionnaires were carried out. It was confirmed that the inhabitants of the zone closest to the plant are most exposed to the smell, the most intense smell comes from the sludge dryer building, and smells from primary settling tanks and sediment plots are perceived as unpleasant. The analysis of surveys confirmed the problem of odor nuisance, especially in the immediate vicinity, where over 50% of respondents considered odor annoyance as extreme. A division of respondents was introduced into those experiencing severe nuisance and those for whom the smell was not annoying. Then, to relate the probability of occurrence of odor nuisance with a group of independent variables, logistic regression was used to describe the impact of independent variables on the dichotomous dependent variable. It has been shown that the likelihood of experiencing odor nuisance increases with the increase in the intensity of current odors, the parallel noise, and in people who focus on the existing smell, and decreases with increasing satisfaction with their health and in the case of regularly occurring odor.
Attitudes towards Statistics among Business Students: Do Gender, Mathematical Skills and Personal Traits Matter?
Authors: Opstad Leiv
Published: July 2020

The purpose of this article was to investigate different variables, by combining mathematical skills and personal traits using The Big Five Model, to see which have the most influence on business students’ attitudes towards statistics. The Big Five personality traits make up a model for capturing various personal characteristics. Specifically, we aimed to understand why there is a gender difference in attitudes towards statistics. Statistical skills are a key factor for success in business studies. The chosen methods were pairwise comparisons (t-test) and a linear regression model, using a sample from a Norwegian business school. The finding was that there is a substantial gender gap towards attitudes towards statistics but taking mathematical skills and personal characteristics into consideration then results in this gap becoming much smaller. Furthermore, mathematical skills and personal traits were shown to have an impact on students’ attitudes towards statistics.
Peer-to-Peer Lending and Bank Risks: A Closer Look
Authors: Yeo Eunjung , Jun Jooyong
Published: July 2020

This study examined how the expansion of peer-to-peer (P2P) lending affects bank risks, particularly insolvency and illiquidity risks. We compared a benchmark case wherein banks are the only players in the loan market with a segmented market case wherein the loan market is segmented by borrowers’ creditworthiness, P2P lending platforms operate only in the low-credit market segment, and banks operate in both low- and high-credit segments. For the segmented market case compared with the benchmark one, we find that, while banks’ insolvency risk increases, their illiquidity risk decreases such that their overall risk also decreases. Our results imply that sustainable P2P lending requires an appropriate differentiation of roles between banks and P2P lending platforms—P2P lending platforms operate in the low-credit segment and banks’ involvement in P2P lending is restricted—so that the growth of P2P lending is not adverse for bank stability.
Alpha-Ketoglutaric Acid Production from a Mixture of Glycerol and Rapeseed Oil by Yarrowia lipolytica Using Different Substrate Feeding Strategies
Authors: Rywińska Anita , Tomaszewska-Hetman Ludwika , Rakicka-Pustułka Magdalena , Juszczyk Piotr , Rymowicz Waldemar
Published: July 2020

The microbiological biosynthesis of α-ketoglutaric acid (KGA) has recently captured the attention of many scientists as an alternative to its common chemical synthesis. The present study aimed to evaluate the effect of the feeding strategy of substrates, i.e., glycerol (G = 20 g·dm−3) and rapeseed oil (O = 20 g·dm−3), on yeast growth and the parameters of KGA biosynthesis by a wild strain Yarrowia lipolytica A-8 in fed-batch and repeated-batch cultures. The effectiveness of KGA biosynthesis was demonstrated to depend on thiamine concentration and the substrate feeding method. In the fed-batch culture incubated with 3 µg·dm−3 of thiamine and a substrate feeding variant 2G(_OGO), KGA was produced in the amount of 62.1 g·dm−3 at the volumetric production rate of 0.37 g·dm−3·h−1. These values of KGA production parameters were higher than these obtained in the control culture (with rapeseed oil only). During 10 cycles of the 1788-h repeated-batch culture carried out acc. to the feeding strategy 2G(_OGO), in the last 5 cycles the yeast produced from 55.6 to 58.2 g·dm−3 of KGA and maximally 2.9 g·dm−3 of the pyruvic acid as a by-product.
Applications of Modified Biochar-Based Materials for the Removal of Environment Pollutants: A Mini Review
Authors: Lee Jung Eun , Park Young-Kwon
Published: July 2020

The biochar treated through several processes can be modified and utilized as catalyst or catalyst support due to specific properties with various available functional groups on the surface. The functional groups attached to the biochar surface can initiate active radical species to play an important role, which lead to the destruction of contaminants as a catalyst and the removal of adsorbent by involving electron transfer or redox processes. Centering on the high potential to be developed in field applications, this paper reviews more feasible and sustainable biochar-based materials resulting in efficient removals of environmental pollutants as catalyst or support rather than describing them according to the technology category. This review addresses biochar-based materials for utilization as catalysts, metal catalyst supports of iron/iron oxides, and titanium dioxide because the advanced oxidation process using iron/iron oxides or titanium dioxides is more effective for the removal of contaminants. Biochar-based materials can be used for the removal of inorganic contaminants such as heavy meals and nitrate or phosphate to cause eutrophication of water. The biochar-based materials available for the remediation of eutrophic water by the release of N- or P-containing compounds is also reviewed.