EISSN : 2076-3417
Current Publisher: MDPI AG (10.3390)
Total articles ≅ 24,883
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Applied Sciences, Volume 11; doi:10.3390/app11125645
The purpose of this study was to assess the physical and mechanical properties of pavement concrete for rural roads of South Korea made with air-cooled slag aggregate, which is an industrial byproduct. This study assessed the physical and chemical properties according to the following performance requirements based on the design criteria of the Korea Ministry of Agriculture’s Agricultural Production Infrastructure Maintenance Business Plan and the Korea Expressway Corporation’s Highway Construction Specialized Specifications: slump of 80 mm or greater, air content of 4.5 ± 1.5%, compressive strength of at least 21 MPa, splitting tensile strength of at least 4.2 MPa, and a chloride penetration resistance of less than 4000 C. The slump, air content, compressive strength, splitting tensile strength, flexural strength, and chloride ion permeability of the aggregate-containing concretes were measured. The air-cooled slag aggregates provided the necessary physical and chemical properties and presented no environmental issues. Furthermore, the slump and air content of concrete made with the aggregates met the target values. The slump decreased and the air content increased with increasing amounts of air-cooled slag aggregate. Mechanical testing of the concretes containing air-cooled slag aggregate established that they met the performance requirements for rural road pavement.
Applied Sciences, Volume 11; doi:10.3390/app11125656
Graph neural networks (GNNs) have been very successful at solving fraud detection tasks. The GNN-based detection algorithms learn node embeddings by aggregating neighboring information. Recently, CAmouflage-REsistant GNN (CARE-GNN) is proposed, and this algorithm achieves state-of-the-art results on fraud detection tasks by dealing with relation camouflages and feature camouflages. However, stacking multiple layers in a traditional way defined by hop leads to a rapid performance drop. As the single-layer CARE-GNN cannot extract more information to fix the potential mistakes, the performance heavily relies on the only one layer. In order to avoid the case of single-layer learning, in this paper, we consider a multi-layer architecture which can form a complementary relationship with residual structure. We propose an improved algorithm named Residual Layered CARE-GNN (RLC-GNN). The new algorithm learns layer by layer progressively and corrects mistakes continuously. We choose three metrics—recall, AUC, and F1-score—to evaluate proposed algorithm. Numerical experiments are conducted. We obtain up to 5.66%, 7.72%, and 9.09% improvements in recall, AUC, and F1-score, respectively, on Yelp dataset. Moreover, we also obtain up to 3.66%, 4.27%, and 3.25% improvements in the same three metrics on the Amazon dataset.
Applied Sciences, Volume 11; doi:10.3390/app11125631
Feta is a Greek protected designation of origin (PDO) brined curd white cheese made from small ruminants’ milk. In the present research, Greek Feta cheese bacterial diversity was evaluated via matrix-assisted laser desorption ionization–time of flight mass spectrometry (MALDI-TOF MS). Analysis of 23 cheese samples, produced in different regions of the country, was performed in two ripening times (three or six months post-production). The identified microbiota were primarily constituted of lactic acid bacteria. A total of 13 different genera were obtained. The dominant species in both ripening times were Lactobacillus plantarum (100.0% and 87.0%, at three or six months post-production, respectively), Lactobacillus brevis (56.5% and 73.9%), Lactobacillus paracasei (56.5% and 39.1%), Lactobacillus rhamnosus (13.0% and 17.4%), Lactobacillus paraplantarum (4.3% and 26.1%), Lactobacillus curvatus (8.7% and 8.7%). Other species included Enterococcus faecalis (47.8% and 43.5%), Enterococcus faecium (34.8% and 17.4%), Enterococcus durans (13.0% and 17.4%), Enterococcus malodoratus (4.3% and 4.3%), and Streptococcus salivarius subsp. thermophilus (21.7% and 30.4%). The increased ripening time was found to be correlated to decreased total solids (r = 0.616; p = 0.002), protein (r = 0.683; p< 0.001), and PH (r = 0.780; p< 0.001). The results of this study contribute to a better understanding of the core microbiota of Feta cheese.
Applied Sciences, Volume 11; doi:10.3390/app11125630
The removal of heavy metals from water has become a global environmental problem. Various materials have been applied as adsorbent to remove metals from water. In this field, nanomaterials have been gaining increasing interest due to their exceptional properties. In this work, we discuss the synthesis of a core-shell structure nanocomposite by the modification of magnetic chitosan (CS) (Fe3O4/CS) with polyethylenimine (PEI) to produce Fe3O4/CS/PEI composite for the adsorption of arsenic ions (As(V) and As(III)) from aqueous solution. The synthesized materials were characterized using X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FT-IR), transmission electron microscope (TEM), and vibrating sample magnetometer (VSM). The results indicated the successful combination of three components of the nanocomposite. The adsorption conditions were optimized by studying the effect of different parameters included pH, contact time, initial concentration, and adsorbent dosage. The optimum adsorption pH was found to be 6.7 while the optimum adsorbent dosage was found to be 2.0 and 1.5 g/L for As(III) and As(V), respectively. The removal efficiency for the uptake of As(III) and As(V) ions over Fe3O4/CS/PEI nanocomposite at optimum conditions was found to be 99.5 and 99.7%, respectively. The experimental results were fitted using Freundlich’s and Langmuir’s isotherms. The data were more fitted to Langmuir isotherm providing a suggestion of monolayer adsorption with maximum adsorption capacity equal to 77.61 and 86.50 mg/g for the removal of As(III) and As(V), respectively. Moreover, linear regression coefficient (R2) indicated that the adsorption of arsenic ions over the synthesized magnetic nanocomposite obeyed pseudo 2nd order suggesting the chemisorption process. The reusability of the nanosorbent for arsenic uptake using sodium hydroxide as eluent was also assessed up to five cycles. Interestingly, Fe3O4/CS/PEI nanocomposite can be considered as a promising adsorbent for As ions’ removal from water and should be tested for the removal of other pollutants.
Applied Sciences, Volume 11; doi:10.3390/app11125643
Elasticity, lattice dynamics, and thermal expansion for uranium and U–6Nb alloy (elastic moduli) are calculated from density functional theory that is extended to include orbital polarization (DFT+OP). Introducing 12.5 at.% of niobium, substitutionally, in uranium softens all the cii elastic moduli, resulting in a significantly softer shear modulus (G). Combined with a nearly invariant bulk modulus (B), the quotient B/G increases dramatically for U–6Nb, suggesting a more ductile material. Lattice dynamics from a harmonic model coupled with a DFT+OP electronic structure is applied for α uranium, and the obtained phonon density of states compares well with inelastic neutron-scattering measurements. The Debye temperature associated with the lattice dynamics falls within the range of experimentally observed Debye temperatures and it also validates our quasi-harmonic (QH) phonon model. The QH Debye–Grüneisen phonon method is combined with a DFT+OP electronic structure and used to explore the anisotropic thermal expansion in α uranium. The anomalous negative thermal expansion (contraction) of the b lattice parameter of the α-phase orthorhombic cell is relatively well reproduced from a free-energy model consisting of QH-phonon and DFT+OP electronic structure contributions.
Applied Sciences, Volume 11; doi:10.3390/app11125644
The global community has recognized an increasing amount of pollutants entering oceans and other water bodies as a severe environmental, economic, and social issue. In addition to prevention, one of the key measures in addressing marine pollution is the cleanup of debris already present in marine environments. Deployment of machine learning (ML) and deep learning (DL) techniques can automate marine waste removal, making the cleanup process more efficient. This study examines the performance of six well-known deep convolutional neural networks (CNNs), namely VGG19, InceptionV3, ResNet50, Inception-ResNetV2, DenseNet121, and MobileNetV2, utilized as feature extractors according to three different extraction schemes for the identification and classification of underwater marine debris. We compare the performance of a neural network (NN) classifier trained on top of deep CNN feature extractors when the feature extractor is (1) fixed; (2) fine-tuned on the given task; (3) fixed during the first phase of training and fine-tuned afterward. In general, fine-tuning resulted in better-performing models but is much more computationally expensive. The overall best NN performance showed the fine-tuned Inception-ResNetV2 feature extractor with an accuracy of
and F1-score , followed by fine-tuned InceptionV3 extractor. Furthermore, we analyze conventional ML classifiers’ performance when trained on features extracted with deep CNNs. Finally, we show that replacing NN with a conventional ML classifier, such as support vector machine (SVM) or logistic regression (LR), can further enhance the classification performance on new data.
Applied Sciences, Volume 11; doi:10.3390/app11125624
Commercially available UV-adsorbent TiO2 nanoparticles were used to assist laser/desorption ionization in the course of matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI). Titanium nano-powders proved extremely stable and efficient for small molecule ionization, with negligible background noise in the low mass region (m/z< 500 Da). Validation steps were carried out, assessing detection limits and comparing the results to those of the established DESI/Orbitrap technique. The new analytical method was used to reveal the molecular distribution of endogenous (lipids) and exogenous (analgesics and antipyretics) compounds in latent finger marks (LFMs). The detection limits of endogenous fatty acids and small molecules such as caffeine were in the range of fmol/mm2 on LFMs. The technique separated overlapping latent finger marks, exploiting the differences in lipid expression of human skin. Finally, the method was used to prove contact between skin and objects contaminated by different substances, such as credit cards and paper clips, with chemical images that maintain the shape of the objects on the LFM.
Applied Sciences, Volume 11; doi:10.3390/app11125667
A polyethylene (PE) liner is the basic element in high-pressure type 4 composite vessels designed for hydrogen or compressed natural gas (CNG) storage systems. Liner defects may result in the elimination of the whole vessel from use, which is very expensive, both at the manufacturing and exploitation stage. The goal is, therefore, the development of efficient non-destructive testing (NDT) methods to test a liner immediately after its manufacturing, before applying a composite reinforcement. It should be noted that the current regulations, codes and standards (RC&S) do not specify liner testing methods after manufacturing. It was considered especially important to find a way of locating and assessing the size of air bubbles and inclusions, and the field of deformations in liner walls. It was also expected that these methods would be easily applicable to mass-produced liners. The paper proposes the use of three optical methods, namely, visual inspection, digital image correlation (DIC), and optical fiber sensing based on Bragg gratings (FBG). Deformation measurements are validated with finite element analysis (FEA). The tested object was a prototype of a hydrogen liner for high-pressure storage (700 bar). The mentioned optical methods were used to identify defects and measure deformations.
Applied Sciences, Volume 11; doi:10.3390/app11125634
Technical drawing (TD) is a subject frequently perceived by engineering students as difficult and even lacking in practical application. Different studies have shown that there is a relationship between studying TD and improvement of spatial ability, and there are precedents of works describing successful educational methodologies based on information and communications technology (ICT), dedicated in some cases to improving spatial ability, and in other cases to facilitating the teaching of TD. Furthermore, interdisciplinary learning (IL) has proven to be effective for the training of science and engineering students. Based on these facts, this paper presents a novel IL educational methodology that, using ICT-based tools, links the teaching of industrial radiology with the teaching of TD, enhancing the spatial ability of students. First, the process of creating the didactic material is described in summary form, and thereafter, the way in which this educational methodology is implemented in the classroom. Finally, we analyze how the use of ICT-based didactic tools such as the one described in this paper can contribute to the achievement of the sustainable development goals set out in the 2030 Agenda of the United Nations.
Applied Sciences, Volume 11; doi:10.3390/app11125652
As a result of rapid urbanization and population movement, flooding in urban areas has become one of the most common types of natural disaster, causing huge losses of both life and property. To mitigate and prevent the damage caused by the recent increase in floods, a number of measures are required, such as installing flood prevention facilities, or specially managing areas vulnerable to flooding. In this study, we presented a technique for determining areas susceptible to flooding using hydrological-topographic characteristics for the purpose of managing flood vulnerable areas. To begin, we collected digital topographic maps and stormwater drainage system data regarding the study area. Using the collected data, surface, locational, and resistant factors were analyzed. In addition, the maximum 1-h rainfall data were collected as an inducing factor and assigned to all grids through spatial interpolation. Next, a logistic regression analysis was performed by inputting hydrological-topographic factors and historical inundation trace maps for each grid as independent and dependent variables, respectively, through which a model for calculating the flood vulnerability of the study area was established. The performance of the model was evaluated by analyzing the receiver operating characteristics (ROC) curve of flood vulnerability and inundation trace maps, and it was found to be improved when the rainfall that changes according to flood events was also considered. The method presented in this study can be used not only to reasonably and efficiently select target sites for flood prevention facilities, but also to pre-detect areas vulnerable to flooding by using real-time rainfall forecasting.