Parallel machine scheduling with tool loading: a constraint programming approach

This paper presents constraint programming models that aim to solve scheduling and tool assignment problems in parallel machine environments. There are a number of jobs to be processed on parallel machines. Each job requires a set of tools, but limited number of tools are available in the system due to economic restrictions. The problem is to assign the jobs and the required tools to machines and to determine the schedule so that the makespan is minimised. Three constraint programming models are developed and compared with existing methods described in the literature.     

Fused and Modified Evolutionary Optimization of Multiple Intelligent Systems Using ANN,SVM Approaches

The Fused Modified Grasshopper Optimization Algorithm has been proposed, which selects the most specific feature sets from images of the disease of plant leaves. The Proposed algorithm ensures the detection of diseases during the early stages of the diagnosis of leaf disease by farmers and, finally, the crop needed to be controlled by farmers to ensure the survival and protection of plants. In this study, a novel approach has been suggested based on the standard optimization algorithm for grasshopper and the selection of features. Leaf conditions in plants are a major factor in reducing crop yield and quality. Any delay or errors in the diagnosis of the disease can lead to delays in the management of plant disease spreading and damage and related material losses. Comparative new heuristic optimization of swarm intelligence, Grasshopper Optimization Algorithm was inspired by grasshopper movements for their feeding strategy. It simulates the attitude and social interaction of grasshopper swarm in terms of gravity and wind advection. In the decision on features extracted by an accelerated feature selection algorithm, popular approaches such as ANN and SVM classifiers had been used. For the evaluation of the proposed model, different data sets of plant leaves were used. The proposed model was successful in the diagnosis of the diseases of leaves the plant with an accuracy of 99.41 percent (average). The proposed biologically inspired model was sufficiently satisfied, and the best or most desirable characteristics were established. Finally, the results of the research for these data sets were estimated by the proposed Fused Modified Grasshopper Optimization Algorithm (FMGOA). The results of that experiment were demonstrated to allow classification models to reduce input features and thus to increase the precision with the presented Modified Grasshopper Optimization Algorithm. Measurement and analysis were performed to prove the model validity through model parameters such as precision, recall, f-measure, and precision.     

Stock Price Prediction Using Predictive Error Compensation Wavelet Neural Networks

Machine Learning (ML) algorithms have been widely used for financial time series prediction and trading through bots. In this work, we propose a Predictive Error Compensated Wavelet Neural Network (PEC-WNN) ML model that improves the prediction of next day closing prices. In the proposed model we use multiple neural networks where the first one uses the closing stock prices from multiple-scale time-domain inputs. An additional network is used for error estimation to compensate and reduce the prediction error of the main network instead of using recurrence. The performance of the proposed model is evaluated using six different stock data samples in the New York stock exchange. The results have demonstrated significant improvement in forecasting accuracy in all cases when the second network is used in accordance with the first one by adding the outputs. The RMSE error is 33% improved when the proposed PEC-WNN model is used compared to the Long Short-Term Memory (LSTM) model. Furthermore, through the analysis of training mechanisms, we found that using the updated training the performance of the proposed model is improved. The contribution of this study is the applicability of simultaneously different time frames as inputs. Cascading the predictive error compensation not only reduces the error rate but also helps in avoiding overfitting problems.     

Synthesis and investigation of new cholesteryl carbamate-based liquid crystals

Journal of Materials Science: Materials in Electronics - Cholesteryl chloroformate which is known liquid crystal material was modified to give the new molecules a long-lasting LC phases with the...     

FixMyStreet Brussels: Socio-Demographic Inequality in Crowdsourced Civic Participation

FixMyStreet (FMS) is a web-based civic participation platform that allows inhabitants to report environmental defects like potholes and damaged pavements to the government. In this paper, we examine the use of FMS in Brussels, the capital city of Belgium. Analyzing a total of 30,041 reports since its inception in 2013, we demonstrate how civic participation on FMS varies between the ethnically diverse districts in Brussels. We compare FMS use to a range of sociodemographic indicators derived from official city statistics as well as geotagged social media data from Twitter. Our statistical analysis revealed several significant differences between the districts that suggested that crowdsourced civic participation platforms tend to marginalize low-income and ethnically diverse communities. In this respect, our findings provide timely evidence to inform the design of more inclusive crowdsourced, civic participation platforms in the future.     

Tunnelling spectra of individual magnetic endofullerene molecules

The manipulation of single magnetic molecules may enable new strategies for high-density information storage and quantum-state control. However, progress in these areas depends on developing techniques for addressing individual molecules and controlling their spin. Here, we report success in making electrical contact to individual magnetic N@C(60) molecules and measuring spin excitations in their electron tunnelling spectra. We verify that the molecules remain magnetic by observing a transition as a function of magnetic field that changes the spin quantum number and also the existence of non-equilibrium tunnelling originating from low-energy excited states. From the tunnelling spectra, we identify the charge and spin states of the molecule. The measured spectra can be reproduced theoretically by accounting for the exchange interaction between the nitrogen spin and electron(s) on the C(60) cage.     

Prediction of Heat Transfer Coefficient in Saturated Flow Boiling Heat Transfer in Parallel Rectangular Microchannel Heat Sinks: An Experimental Study

This study focuses mainly on the prediction of saturated flow boiling heat transfer in microchannels. A wide range of experiments has been carried out with de-ionized water to obtain a comprehensive data set. Experiments of mass fluxes of 51–728.7 kg/m²s, wall heat fluxes of 36–221.7 kW/m², vapor qualities of 0.01–0.69, liquid Reynolds number of 7.72–190, aspect ratios of 0.37–5.00 (with a constant hydraulic diameter of 100 µm) and hydraulic diameters of 100–250 µm (for constant aspect ratio = 1). A new correlation including the aspect ratio effect is proposed to predict the heat transfer coefficient for saturated flow boiling in microchannels. The proposed correlation shows very good predictions with an overall mean absolute error of 16.9% and 86.4%, 96.2% and 99.5% of the predicted data falling within ±30, ±40 and ±50% error bands, respectively.     

Photocatalytic properties of ZnO nanoparticle coating on porous ceramic substrates with varying porosities produced from fly ash and red mud

In order to improve filtering efficacy, nanoparticles are often deposited as photocatalytic degrading agents onto porous ceramics. This study aimed to deposit ZnO nanoparticles on ceramic substrates produced from fly ash and red mud with adjustable porosity and investigate their photocatalytic properties. To achieve this goal, at first porous ceramics were produced and sintered at various temperature/time intervals. It was observed that sintering at 800°C for 120 min provided a proper structure and porosity. In addition, MgO replacement with MgCO₃ lowered the water absorption of the samples from 25.63% to 11.45%. The samples were then coated with ZnO nanoparticles using the sol–gel method and the ZnO structures obtained were micron-sized plates. It was observed that increasing porosity increased the ZnO amount and accordingly the photocatalytic properties of the products. During the adsorption tests conducted in the dark, the coated ceramic samples were stained with MB with a maximum MB adsorption ratio of ∼14%. On the other hand, no visible MB stain was observed on the samples that were exposed to UV irradiation, and the MB removal after the UV irradiation was 93.6%; therefore, it was concluded that the dominant MB removal mechanism was photocatalytic.