Numerical Study of Air-Flow Phenomena Through a Baffled Rectangular Micro-Channel

The aim of this study is to investigate the heat transfer characteristics of turbulent airflow phenomena in a rectangular micro-channel in presence of two plane shaped (type-1) and diamond shaped (type-2) baffles which will help to develop various heat exchanger models. Finite volume method has been used to solve the governing equations and the FLUENT software has been employed to visualize the simulation results. For both the baffles, the profile of flow structure, normalized velocity profile, normalized friction factor and average Nusselt number have been investigated with the variations of Reynolds number ranges between [10,000-50,000]. In terms of fluid flow and heat transfer phenomena, it has been found that in the presence of diamond shaped baffles (type-2) are more convenient than plane shaped baffles.

Supervised Learning for Binary Classification on US Adult Income

In this project, various binary classification methods have been used to make predictions about US adult income level in relation to social factors including age, gender, education, and marital status. We first explore descriptive statistics for the dataset and deal with missing values. After that, we examine some widely used classification methods, including logistic regression, discriminant analysis, support vector machine, random forest, and boosting. Meanwhile, we also provide suitable R functions to demonstrate applications. Various metrics such as ROC curves, accuracy, recall and F-measure are calculated to compare the performance of these models. We find the boosting is the best method in our data analysis due to its highest AUC value and the highest prediction accuracy. In addition, among all predictor variables, we also find three variables that have the largest impact on the US adult income level.

Closed-Loop Drive Detection and Diagnosis of Multiple Combined Faults in Induction Motor Through Model-Based and Neuro-Fuzzy Network Techniques

In this paper, a fault detection and diagnosis approach adopted for an input-output feedback linearization (IOFL) control of induction motor (IM) drive is proposed. This approach has been employed to detect and identify the simple and mixed broken rotor bars and static air-gap eccentricity faults right from the start its operation by utilizing advanced techniques. Therefore, two techniques are applied: the model-based strategy, which is an online method used to generate residual stator current signal in order to indicate the presence of possible failures by means of the sliding mode observer (SMO) in the closed-loop drive. However, this strategy is not able to recognise the fault types and it can be affected by the other disturbances. Therefore, the offline method using the multi-adaptive neuro-fuzzy inference system (MANAFIS) technique is proposed to identify the faults and distinguish them. However, the MANAFIS required a relevant database to achieve satisfactory results. Hence, the stator current analysis based on the HFFT combination of the Hilbert transform (HT) and Fast Fourier transform (FFT) is applied to extract the amplitude of harmonics due to defects occur and used them as an input data set for the MANFIS under different loads and fault severities. The simulation results show the efficiency of the proposed techniques and its ability to detect and diagnose any minor faults in a closed-loop drive of IM.

Effect of Meteorological Conditions and Anthropogenic Factors on Air Concentrations of PM2.5 and PM10 Particulates on the Examples of the City of Kielce, Poland

The paper analyzes the influence of meteorological conditions (air temperature, wind speed, humidity, visibility) and anthropogenic factors (population in cities and in rural areas, road length, number of vehicles, emission of dusts and gases, coal consumption in industrial plants, number of air purification devices installed in industrial plants) on the concentration of PM2.5 and PM10 dusts in the air in the region of Kielce city in Poland. Spearman correlation coefficient was used to evaluate the relationship between the mentioned independent variables and air quality indicators. The calculated values of the correlation coefficient showed statistically significant relationships between air quality and the amount of installed air purification equipment in industrial plants. A statistically significant effect of the population in rural settlement units on the increase in air concentrations of PM2.5 and PM10 was also found, which proves the influence of the so-called low emission of pollutants on the air quality in the studied region. The analyses also revealed a statistically significant effect of road length on the decrease in PM2.5 and PM10 air content. This result indicates that a decrease in traffic intensity on particular road sections leads to an improvement in air quality. The analyses showed that despite the progressing anthropopression in the Kielce city region the air quality with respect to PM2.5 and PM10 content is improving. To verify the results obtained from statistical calculations, parametric models were also determined to predict PM2.5 and PM10 concentrations in the air, using the methods of Random Forests (RF), Boosted Trees (BT) and Support Vector Machines (SVM) for comparison purposes. The modelling results confirmed the conclusions that had been made based on previous statistical calculations.

Binary Beetle Antennae Search Algorithm for Tangency Portfolio Diversification

The tangency portfolio, also known as the market portfolio, is the most efficient portfolio and arises from the intercept point of the Capital Market Line (CML) and the efficient frontier. In this paper, a binary optimal tangency portfolio under cardinality constraint (BOTPCC) problem is defined and studied as a nonlinear programming (NLP) problem. Because such NLP problems are widely approached by heuristic, a binary beetle antennae search algorithm is employed to provide a solution to the BTPSCC problem. Our method proved to be a magnificent substitute to other evolutionary algorithms in real-world datasets, based on numerical applications and computer simulations.

Integrated Methodology for Physical and Economic Assessment of Coastal Interventions Impacts

Due to economic, environmental, and social interest of coastal areas, together with their erosion problems, different coastal management strategies can be considered, with different physical (shoreline evolution) and economic (net present value, ratio benefit-cost, break-even point) consequences and impacts. Therefore, this work presents an integrated methodology that aims to compare and discuss the most promising coastal intervention scenarios to mitigate erosion problems and climate change effects, considering costs and benefits related to each intervention. The proposed methodology takes a step forward in assessing the coastal erosion mitigation strategies, incorporating three well-defined and sequential stages: shoreline evolution in a medium-term perspective; structures pre-design; and a cost-benefit assessment. To show the relevance of the methodology, a hypothetic case study and several intervention scenarios were assessed. In order to mitigate costal erosion two different situations were analyzed: the reference scenario and the intervention scenarios. 34 intervention scenarios were proposed and evaluated to mitigate the erosion verified. Depending on the parameter considered (reduce erosion areas, protect the full extension of urban waterfronts, improve the economic performance of the intervention by increasing the net present value, the benefit-cost ratio or decreasing the break-even time), best results are obtained for different scenarios. The definition of the best option for coastal erosion mitigation is complex and depends on the main goal defined for the intervention. In conclusion, costs and benefits analysis are demanded and it is considered that the proposed methodology allows choosing better physical and economic options for future coastal interventions, helping decision-making processes related to coastal management.

Control of Electrodialysis Desalination Systems as Smart Loads in Microgrids with High Penetration of Renewable Generation

Water desalination systems connected to microgrids with high penetration of renewable energy generation are frequently used to promote the development of remote areas. These microgrids often have power quality and even stability problems. This work shows that electrodialysis desalination systems can be managed as smart loads, that is, they can contribute to the power balance and voltage regulation of the microgrid without neglecting its main function of water desalination. For this, a model of multiple inputs and multiple outputs for a desalination system is proposed where the variables to control are the treated water concentration and the active and reactive powers demanded by the desalination system. Based on this model, a control law is proposed that allows to face the complexity of the non-linear system in a simple and precise way. The proposed control guarantees the low salt concentration of the drinking water and favors the energy balance of the microgrid, allowing better control of the power quality and greater penetration of renewable energy generation.

Using Machine Learning Algorithms on Prediction of Stock Price

In this paper, we investigate analysis and prediction of the time-dependent data. We focus our attention on four different stocks are selected from Yahoo Finance historical database. To build up models and predict the future stock price, we consider three different machine learning techniques including Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN) and Support Vector Regression (SVR). By treating close price, open price, daily low, daily high, adjusted close price, and volume of trades as predictors in machine learning methods, it can be shown that the prediction accuracy is improved.

Bottleneck Detection and Reduction Using Simulation Modeling to Reduce Overcrowding of Hospital Emergency Department

Overcrowding is a common problem in hospital emergency departments (EDs) where the ED service cannot meet care demands within reasonable time frames. This paper introduces a quantitative approach using computer simulation modeling for hospital decision makers to explore trade-offs between efficiency, workload and capacity of EDs. A computer simulation model is built based on the ED of a local hospital to improvement efficiency of the ED patient flow. Bottlenecks of the emergency care process are detected using the built model. The ED performance is examined by applying alternative strategies to reduce patient waiting time and length of stay. The proposed method can be applied to improve the operation efficiency of healthcare systems in the current pandemic, COVID -19.

2D Analytical Model of Armature Reaction Magnetic Field Distribution in Slotless Permanent-Magnet Linear Tubular Machines

This paper presents a 2D analytical model for predicting the magnetic flux density distribution in slotless permanent-magnet (PM) linear tubular (PMLT) motors due to armature reaction effects based on the sub-domain method. According to this method, the machine cross-section is divided into the six sub-regions and Maxwell partial differential equations (PDEs) are formed in each sub-region. Solving these PDEs leads to defining the magnetic vector potential in each sub-region and applying curl on the calculated magnetic vector potential results in determining the magnetic flux density components. Eventually, the extracted results are compared with those of the finite-element method (FEM) to confirm the accuracy of the described analytical model. The results reveal that the presented analytical model is a suitable candidate for predicting the magnetic flux density components of the slotless PMLT motors in a shorter time.