Generalized Regression Neural Networks and Feed Forward Neural Networks for prediction of scour depth around bridge piers

In this study, Generalized Regression Neural Networks (GRNN) and Feed Forward Neural Networks (FFNN) approaches are used to predict the scour depth around circular bridge piers. Hundred and sixty five data collected from various experimental studies, are used to predict equilibrium scour depth. The model consisting of the combination of dimensional data involving the input variables is constructed. The performance of the models in training and testing sets are compared with observations. Then, the model is also tested by Multiple Linear Regression (MLR) and empirical formula. The results of all approaches are compared in order to get more reliable comparison. The results indicated that GRNN can be applied successfully for prediction of scour depth around circular bridge piers.     

Performance evaluation of cloud computing platforms using statistical methods

Cloud computing is a very attractive research topic. Many studies have examined the infrastructure as a service and software as a service aspects of cloud computing; however, few studies have focused on platform as a service (PaaS). According to recent reports, demand for enterprise PaaS solutions will increase continuously. However, different sectors require different types of PaaS applications and computing resources. Therefore, an evaluation and ranking framework for PaaS solutions according to application needs is required. To address this need, this study presents the most essential aspects of PaaS solutions and provides a framework for evaluating the performance of PaaS providers. It also proposes a suitable set of benchmarking algorithms that can help determine the most appropriate PaaS provider based on different resource needs and application requirements. Performance evaluations of three well-known cloud computing PaaS providers were conducted using the analytic hierarchy process and the logic scoring of preference methods.     

Antioxidant Activity,Total Phenolic Content and Selected Physicochemical Properties of White Mulberry (Morus Alba L.) Fruits

In this study, total phenolic content, antioxidant activity, mineral content and selected physicochemical properties of three white mulberry phenotypes (Morus alba L.) grown in Turkey were measured. Moisture, total soluble solids, total sugar, sucrose, reducing sugar, protein, ash, pH, titratable acidity, color (L, a, b), vitamin C, total phenolic, antioxidant activity of fruits were determined between 72.85–79.75 g/100 g fresh fruit, 21.25–28.50 g/100 g fresh fruit, 12.18–17.02 g/100 g fresh fruit, 1.57–4.36 g/100 g fresh fruit, 9.42–15.46 g/100 g fresh fruit, 0.82–0.89 g/100 g fresh fruit, 2.20–2.65 g/100 g fresh fruit, 5.70–5.86, 0.25–0.28 g/100 g fresh fruit, 31.24–68.69, ?2.46 to 15.68, 4.58–21.74, 10.15–21.50 mg/100 g fresh fruit, 18.16–19.24 μg gallic acid equivalent/mg of sample, and 33.96–38.96%, of sample respectively. Fruits were generally found high in P and low in Ca.     

Computational Markets to Regulate Mobile-Agent Systems

Mobile-agent systems allow applications to distribute their resource consumption across the network. By prioritizing applications and publishing the cost of actions, it is possible for applications to achieve faster performance than in an environment where resources are evenly shared. We enforce the costs of actions through markets, where user applications bid for computation from host machines.We represent applications as collections of mobile agents and introduce a distributed mechanism for allocating general computational priority to mobile agents. We derive a bidding strategy for an agent that plans expenditures given a budget, and a series of tasks to complete. We also show that a unique Nash equilibrium exists between the agents under our allocation policy. We present simulation results to show that the use of our resource-allocation mechanism and expenditure-planning algorithm results in shorter mean job completion times compared to traditional mobile-agent resource allocation. We also observe that our resource-allocation policy adapts favorably to allocate overloaded resources to higher priority agents, and that agents are able to effectively plan expenditures, even when faced with network delay and job-size estimation error.     

Fabrication of oriented electrospun cellulose nanocrystals–polystyrene composite fibers on a rotating drum

Nitrobenzene (CNC-1), trifluoromethyl benzene (CNC-2) modified and polystyrene-grafted (CNC-g) cellulose nanocrystals in polystyrene (PS)-N,N dimethylformamide (DMF) solutions were electrospun and collected as stretched and aligned fibers on a rotating drum. Scanning electron microscope pictures showed significant alignment in the case of unmodified and nitrobenzene-modified CNC-1/PS nanocomposite fibers once the linear speed of rotor reached to 15 m s⁻¹. Fiber diameter decrease was more strong with rotor speed increase in the case of trifluoromethyl benzene modified (CNC-2) and polystyrene-grafted (CNC-g) cellulose nanocrystals/PS systems. Dynamic mechanical analysis including storage and elastic modulus of electrospun-oriented fibers were performed on surface-modified and polymer-grafted CNC/PS samples. According to α transition peak, the increase in the glass-transition temperature with filler concentration was the highest in polymer-grafted CNC-g/PS composite fibers. It was due to the interpenetration of grafted polymer brushes and free polymer chains in continuous phase and resulted in restrictions of motions of polymer chains in the PS matrix. The elastic moduli of nitrobenzene (CNC-1) and trifluoromethyl benzene (CNC-2)-modified CNC-filled PS composite fibers agreed well with percolation model, which indicates the CNC–CNC interactions and network formation with an increase in concentration. Magnitude of the elastic modulus of polymer grafted CNC-g at 0.33 vol % in PS was significantly higher than the prediction from percolation theory. It was due the immobilized polymer chains around CNC-g particles. However, grafted polymer chains, at higher CNC concentrations acted like stickers among CNC particles and caused CNC agglomerates with entrapped free polystyrene from the matrix, thus caused a decrease in the elastic modulus. © 2020 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2020 , 137, 48942.     

Effects of operational parameters on emission performance and combustion efficiency in small-scale CFBCs

A well-designed CFBC can burn coal with high efficiency and within acceptable levels of gaseous emissions. In this theoretical study effects of operational parameters on combustion efficiency and the pollutants emitted have been estimated using a developed dynamic 2D (two-dimensional) model for CFBCs. Model simulations have been carried out to examine the effect of different operational parameters such as excess air and gas inlet pressure and coal particle size on bed temperature, the overall CO, NO_x and SO₂ emissions and combustion efficiency from a small-scale CFBC. It has been observed that increasing excess air ratio causes fluidized bed temperature decrease and CO emission increase. Coal particle size has more significant effect on CO emissions than the gas inlet pressure at the entrance to fluidized bed. Increasing excess air ratio leads to decreasing SO₂ and NO_x emissions. The gas inlet pressure at the entrance to fluidized bed has a more significant effect on NO_x emission than the coal particle size. Increasing excess air causes decreasing combustion efficiency. The gas inlet pressure has more pronounced effect on combustion efficiency than the coal particle size, particularly at higher excess air ratios. The developed model is also validated in terms of combustion efficiency with experimental literature data obtained from 300 kW laboratory scale test unit. The present theoretical study also confirms that CFB combustion allows clean and efficient combustion of coal.     

A deep learning approach with Bayesian optimization and ensemble classifiers for detecting denial of service attacks

Detecting malicious behavior is important for preventing security threats in a computer network. Denial of Service (DoS) is among the popular cyber attacks targeted at web sites of high‐profile organizations and can potentially have high economic and time costs. In this paper, several machine learning methods including ensemble models and autoencoder‐based deep learning classifiers are compared and tuned using Bayesian optimization. The autoencoder framework enables to extract new features by mapping the original input to a new space. The methods are trained and tested both for binary and multi‐class classification on Digiturk and Labris datasets, which were introduced recently for detecting various types of DDoS attacks. The best performing methods are found to be ensembles though deep learning classifiers achieved comparable level of accuracy.     

A photoelastic study of an angle crack specimen subject to mixed mode I–III displacements

A methodology, incorporating the use of an automated polariscope together with three-dimensional photoelasticity, is described and employed to determine mode III stress intensity factors. A series of angled cracks in a modified compact tension specimen were examined. The cracks were at an angle to the face of the specimen so that mode I and III displacements were produced. The results of the experiments were compared with the standard expressions for K_I and K_III, and some recent modifications to the expressions. The results support the findings of recent finite element analyses and fracture work by others that the expressions are not accurate and that the stress intensity factors vary along the crack front.     

Evaluation of greenhouse residues gasification performance in hydrogen production

In this study, a modeling study was carried out to investigate the potential of hydrogen production from greenhouse tomato and pepper residues blending in different rates (0%, 25%, 50%, 75% and 100%) by air-steam gasification. The numerical model developed for the gasification system assumes that all carbon in the mixture is gasified. Air to fuel rate and steam to fuel rates are 0.05 due to high content of O₂ in biomass residues. The gasifier temperature is 877 °C (1150 K) for developed model. According to the result of this study, increasing tomato residues blending rate increases hydrogen content of syngas. It is mainly caused by the content of O₂ in tomato residues being higher than content of O₂ in pepper residues. This study shows that the O₂ content of greenhouse residues is an important factor in syngas production, especially in H₂ production.     

Robust microencapsulated phase change materials in concrete mixes for sustainable buildings

For passive building applications, phase change materials (PCMs) are microencapsulated to avoid leakage of PCM from concrete structure. The primary challenge of using microencapsulated PCM (MPCM) is its weak shell structure. New MPCMs with different shell compositions to prevent breakage during mixing in fresh concrete are needed. In this study, free radical polymerization method to microencapsulate capric acid–myristic acid mixture as PCM with two different methyl methacrylate co‐polymers is proposed to produce robust MPCMs for building applications. Two new microcapsules (MPCM‐1 and MPCM‐2) having latent heats of 91.9 and 97.3 J/g were synthesized. SEM analyses showed the size of microcapsules being in the range of 400–850 nm for MPCM‐1 and 250–475 nm for MPCM‐2. Analyses also reveal that the shells of MPCMs were not harmed, as they were added into concrete mixes. The microsphere's geometry was preserved, and distribution was homogeneous. The MPCMs were also studied under thermal tests of 1000 heating/cooling cycles. No significant changes in thermal properties were observed after thermal cycling tests. Copyright © 2016 John Wiley & Sons, Ltd.