Experimental and numerical studies on the determination of twist drill temperature in dry drilling: A new approach

In this paper, the influences of the spindle speed and feed rate on the drill temperature responses have been investigated. A new experimental approach was developed to measure drill temperature in drilling process. Drill temperatures were measured by inserting standard thermocouples through the coolant (oil) hole of TiAlN-coated carbide drills. Experimental parameters used in the study has based on Taguchi’s method. Experimental study was conducted by using two different workpiece materials, AISI 1040 steel and Al 7075-T651. The drill bit temperature was predicted using a numerical calculation with Third Wave AdvantEdge^TM Lagrangian based on explicit finite element analysis software. The results obtained from experimental study and finite element analyses (FEA) were compared. Good agreement between the measured and analyzed temperature results was found in dry drilling process.     

Sulfurized Co-Mo Alloy Thin Films as Efficient Electrocatalysts for Hydrogen Evolution Reaction

Catalysis Letters - Here, we successfully fabricated sulfurized Co-Mo alloy thin film electrodes with different compositions and studied their catalytic properties for hydrogen evolution reaction...     

New classification techniques for electroencephalogram (EEG) signals and a real-time EEG control of a robot

This paper studies the state-of-the-art classification techniques for electroencephalogram (EEG) signals. Fuzzy Functions Support Vector Classifier, Improved Fuzzy Functions Support Vector Classifier and a novel technique that has been designed by utilizing Particle Swarm Optimization and Radial Basis Function Networks (PSO-RBFN) have been studied. The classification performances of the techniques are compared on standard EEG datasets that are publicly available and used by brain–computer interface (BCI) researchers. In addition to the standard EEG datasets, the proposed classifier is also tested on non-EEG datasets for thorough comparison. Within the scope of this study, several data clustering algorithms such as Fuzzy C-means, K-means and PSO clustering algorithms are studied and their clustering performances on the same datasets are compared. The results show that PSO-RBFN might reach the classification performance of state-of-the art classifiers and might be a better alternative technique in the classification of EEG signals for real-time application. This has been demonstrated by implementing the proposed classifier in a real-time BCI application for a mobile robot control.     

A direct borohydride-peroxide fuel cell-LiPO hybrid motorcycle prototype: Investigation of short-term behavior of DBPFC stack – I

In the present study, a safer and more performance 270?W Direct Borohydride/Peroxide Fuel Cell (DBPFC) Stack has been constructed for an electrical hybrid motorbike application. Performance tests were carried out with single cell and 5–10–25?cell stacks. Performance loss has been not observed while stacking DBPFC because of the Independent Cell Liquid Distribution Network (ICLDN) system and special bipolar plate design. The power densities have been approximately 120?mWcm^?2 for a single DBPFC and 25-cell DBPFC stack without any stacking loss. Additionally, the stack temperature has been controlled by keeping the oxidant concentration low, and it has been maintained at approximately 52?°C without using a cooling system. The short-term performances of the 25-cell DBPFC stack have been tested over 25?min and 50?min, which showed that the performance and stack security of the DBPFC are highly related to the oxidant properties, such as the concentration, temperature and feed type.     

Wheat starch structure–function relationship in breadmaking: A review

Bread dough and bread are dispersed systems consisting of starch polymers that interact with other flour components and added ingredients during processing. In addition to gluten proteins, starch impacts the quality characteristics of the final baked product. Wheat starch consists of amylose and amylopectin organized into alternating semicrystalline and amorphous layers in granules that vary in size and are embedded in the endosperm protein matrix. Investigation of the molecular movement of protons in the dough system provides a comprehensive insight into granular swelling and amylose leaching. Starch interacts with water, proteins, amylase, lipids, yeast, and salt during various stages of breadmaking. As a result, the starch polymers within the produced crumb and crust, together with the rate of retrogradation and staling due to structural reorganization, moisture migration, storage temperature, and relative humidity determines the final product's textural perception. This review aims to provide insight into wheat starch composition and functionality and critically review recently published research results with reference to starch structure–function relationship and factors affecting it during dough formation, fermentation, baking, cooling, and storage of bread.     

A deep learning-based CEP rule extraction framework for IoT data

The Journal of Supercomputing - With the recent developments in Internet of Things (IoT), the number of sensors that generate raw data with high velocity, variety, and volume is tremendously...     

The Correlation Between Statistically Downscaled Precipitation Data and Groundwater Level Records in North-Western Turkey

Downscaling of atmospheric climate parameters is a sophisticated tool to develop statistical relationships between large-scale atmospheric variables and local-scale meteorological variables. In this study, the variables selected from the National Centre for Environmental Prediction and National Centre for Atmospheric Research (NCEP/NCAR) reanalysis data set were used as predictors for the downscaling of monthly precipitation in a watershed located in north-western Turkey where station records terminated two decades ago. An Artificial Neural Network (ANN) based approach was used to downscale global climate predictors that are positively correlated to the existing time frame of precipitation data in the basin. The downscaled precipitation information were used to extend the non-existing data from the meteorological station, which were later correlated with groundwater level data obtained from automatic pressure transducers that continuously record depth to groundwater. The results of the study showed that, among a large set of NCEP/NCAR parameters, surface precipitation data recorded at the meteorological station was strongly correlated with precipitation rate, air temperature and relative humidity at surface and air temperature at 850, 500, and 200 hPa pressure levels, and geopotential heights at 850 and 200 hPa pressure levels. The gaps in station data were then filled with the correlations obtained from NCEP/NCAR parameters and a complete precipitation data set was obtained that extended to current time line. This extended precipitation time series was later correlated with the existing groundwater level data from an alluvial plain in order to develop a general relationship that can be used in basin-wide water budget estimations. The proposed methodology is believed to serve the needs of engineers and basin planners who try to create a link between related hydrological variables under data-limited conditions.