Optimisation of the evaporator of a refrigerator employing hydrocarbon as a refrigerant

ABSTRACT

The refrigeration and air conditioning system is a booming research in the research area. In refrigeration, the scientists focus on the new refrigerant in order to reduce the global warming potential (GWP), high coefficient of performance (COP) and meet zero ozone depletion potential (ODP). Not only the new refrigerant as a solution to attain zero ODP, high COP and at the same time to optimise the design of refrigeration system to meet ODP and COP as a required level. This paper aims to optimise the design of a double refrigerator evaporator for a refrigerator employing non ozone-depleting hydrocarbon refrigerant using brine as a secondary solution with a low GWP. It is done by numerical modelling followed by the parametric modelling using CATIA V5.     

Heteroatom‐Mediated Interactions between Ruthenium Single Atoms and an MXene Support for Efficient Hydrogen Evolution

A titanium carbide (Ti₃C₂T_x) MXene is employed as an efficient solid support to host a nitrogen (N) and sulfur (S) coordinated ruthenium single atom (Ru_SA) catalyst, which displays superior activity toward the hydrogen evolution reaction (HER). X‐ray absorption fine structure spectroscopy and aberration corrected scanning transmission electron microscopy reveal the atomic dispersion of Ru on the Ti₃C₂T_x MXene support and the successful coordination of Ru_SA with the N and S species on the Ti₃C₂T_x MXene. The resultant Ru_SA‐N‐S‐Ti₃C₂T_x catalyst exhibits a low overpotential of 76 mV to achieve the current density of 10 mA cm^?2. Furthermore, it is shown that integrating the Ru_SA‐N‐S‐Ti₃C₂T_x catalyst on n⁺np⁺‐Si photocathode enables photoelectrochemical hydrogen production with exceptionally high photocurrent density of 37.6 mA cm^?2 that is higher than the reported precious Pt and other noble metals catalysts coupled to Si photocathodes. Density functional theory calculations suggest that Ru_SA coordinated with N and S sites on the Ti₃C₂T_x MXene support is the origin of this enhanced HER activity. This work would extend the possibility of using the MXene family as a solid support for the rational design of various single atom catalysts.     

New Fuzzy Fractional Epidemic Model Involving Death Population

In this research, we propose a new change in classical epidemic models by including the change in the rate of death in the overall population. The existing models like Susceptible-Infected-Recovered (SIR) and Susceptible-Infected-Recovered-Susceptible (SIRS) include the death rate as one of the parameters to estimate the change in susceptible, infected and recovered populations. Actually, because of the deficiencies in immunity, even the ordinary flu could cause death. If people’s disease resistance is strong, then serious diseases may not result in mortalities. The classical model always assumes a closed system where there is no new birth or death, no immigration or emigration, while in reality, such assumptions are not realistic. Moreover, the classical epidemic model does not report the change in population due to death caused by a disease. With this study, we try to incorporate the rate of change in the population of death caused by a disease, where the model is framed to reduce the curve of death along with the susceptible and infected populations. Since the rate of change turned out to be very small, we have tried to estimate it fractionally. Thus, the model is defined using fuzzy logic and is solved by two different methods: a Laplace Adomian decomposition method (LADM) and a differential transform method (DTM) for an arbitrary order α. To test its accuracy, we compared the results of both DTM and LADM with the fourth-order Runge-Kutta method (RKM-4) at α = 1.     

Magnetofluoro-Immunosensing Platform Based on Binary Nanoparticle-Decorated Graphene for Detection of Cancer Cell-Derived Exosomes

Multi-functionalized carbon nanomaterials have attracted interest owing to their excellent synergic properties, such as plasmon resonance energy transfer and surface-enhanced Raman scattering. Particularly, nanoparticle (NP)-decorated graphene (GRP) has been applied in various fields. In this study, silver NP (AgNP)- and magnetic iron oxide NP (IONP)-decorated GRP were prepared and utilized as biosensing platforms. In this case, AgNPs and GRP exhibit plasmonic properties, whereas IONPs exhibit magnetic properties; therefore, this hybrid nanomaterial could function as a magnetoplasmonic substrate for the magnetofluoro-immunosensing (MFI) system. Conversely, exosomes were recently considered high-potential biomarkers for the diagnosis of diseases. However, exosome diagnostic use requires complex isolation and purification methods. Nevertheless, we successfully detected a prostate-cancer-cell-derived exosome (PC-exosome) from non-purified exosomes in a culture media sample using Ag/IO-GRP and dye-tetraspanin antibodies (Ab). First, the anti-prostate-specific antigen was immobilized on the Ag/IO-GRP and it could isolate the PC-exosome from the sample via an external magnetic force. Dye-tetraspanin Ab was added to the sample to induce the sandwich structure. Based on the number of exosomes, the fluorescence intensity from the dye varied and the system exhibited highly sensitive and selective performance. Consequently, these hybrid materials exhibited excellent potential for biosensing platforms.     

Industrial Centric Node Localization and Pollution Prediction Using Hybrid Swarm Techniques

Major fields such as military applications, medical fields, weather forecasting, and environmental applications use wireless sensor networks for major computing processes. Sensors play a vital role in emerging technologies of the 20th century. Localization of sensors in needed locations is a very serious problem. The environment is home to every living being in the world. The growth of industries after the industrial revolution increased pollution across the environment. Owing to recent uncontrolled growth and development, sensors to measure pollution levels across industries and surroundings are needed. An interesting and challenging task is choosing the place to fit the sensors. Many meta-heuristic techniques have been introduced in node localization. Swarm intelligent algorithms have proven their efficiency in many studies on localization problems. In this article, we introduce an industrial-centric approach to solve the problem of node localization in the sensor network. First, our work aims at selecting industrial areas in the sensed location. We use random forest regression methodology to select the polluted area. Then, the elephant herding algorithm is used in sensor node localization. These two algorithms are combined to produce the best standard result in localizing the sensor nodes. To check the proposed performance, experiments are conducted with data from the KDD Cup 2018, which contain the name of 35 stations with concentrations of air pollutants such as PM, SO₂, CO, NO₂, and O₃. These data are normalized and tested with algorithms. The results are comparatively analyzed with other swarm intelligence algorithms such as the elephant herding algorithm, particle swarm optimization, and machine learning algorithms such as decision tree regression and multi-layer perceptron. Results can indicate our proposed algorithm can suggest more meaningful locations for localizing the sensors in the topology. Our proposed method achieves a lower root mean square value with 0.06 to 0.08 for localizing with Stations 1 to 5.     

Modified Spatial Modulation and Low Complexity Signal Vector Based Minimum Mean Square Error Detection for MIMO Systems under Spatially Correlated Channels

Wireless Personal Communications - Spatial Modulation (SM) is an innovative digital modulation scheme, which is expected to be a competitive candidate for next generation networks. All the variants...     

Investigation of the effects of exhaust and power loss in dual-fuel six-stroke engine with EGR technology

ABSTRACT

In today’s world, the usage of internal combustion engines is inevitable. Particularly the diesel engines find their importance more than the petrol engines due to their operating cost. But diesel engines have their demerits in the area of exhaust and power loss. Necessary steps have to be taken in order to effectively use the fuel available. In this technical presentation, we have discussed about the utilisation of six-stroke engines which run on dual fuel. The six-stroke engine’s principle resembles a double-stage compressor. By this way, effective compression is done and the need for turbocharger is completely neglected. We have also considered cylinder’s position in a six-stroke engine. As the lubrication and cooling system needs special attention in the case of opposing-type cylinders, we have formulated a better and simple arrangement in which same power is produced, eradicating the lubrication problems. Also, the pollution (NOx) emitted by the diesel engines is also taken into account. We found the solution in the form of dual-fuel and exhaust gas recirculation system. The combusting temperature of the diesel engine is above 2000°F and this is the prime reason for NOx emission. So an alternative fuel which can be combusted below the level of diesel should be used. Moreover, the availability and production cost must be taken into consideration. We found ethanol as a better alternative for diesel. The cold starting of the engine is made easier using a glow plug, which is used to preheat the charge coming inside the combustion chamber.     

Hybrid feature vector based detection of Glaucoma

This paper focus on the investigation of the potential in retinal image analysis for the detection of Glaucoma. The computer-based analysis of the parameter involves the use of image processing algorithms for pre-processing, localization and segmentation of the region of interest (ROI), feature extraction from ROI, and classification. The initial step in computer based detection system includes the enhancing scheme for improving the contrast of the fundus image from the three databases, Drishti-GS1, FAU and RIMONE. The optic disc region has been localized from the enhanced image. Structural deformation of the optic disc region, one of the primary indicators of the glaucoma demands more accuracy in segmentation process. As a solution to this problem, non-morphological features are extracted from the enhanced optic disc region. The non-morphological features from spatial domain include Local Binary Pattern, Histogram of Oriented Gradient and Fractal features. The significant feature extracted from the spatial domain are selected using Sequential Floating Forward Selection method and are then fed into the Support Vector Machine, Naive Bayes and Logistic Regression classifiers. Performance of the classifier is analyzed by computing the accuracy, sensitivity, specificity and positive prediction value. The performance of the classifier is also validated using the receiver operating characteristics plot. The hybrid feature from the spatial domain contributes to increase the efficiency of classification.