Heart Diseases Prediction Using Block-chain and Machine Learning

Most people around the globe are dying due to heart disease. The main reason behind the rapid increase in the death rate due to heart disease is that there is no infrastructure developed for the healthcare department that can provide a secure way of data storage and transmission. Due to redundancy in the patient data, it is difficult for cardiac Professionals to predict the disease early on. This rapid increase in the death rate due to heart disease can be controlled by monitoring and eliminating some of the key attributes in the early stages such as blood pressure, cholesterol level, body weight, and addiction to smoking. Patient data can be monitored by cardiac Professionals (Cp) by using the advanced framework in the healthcare departments. Blockchain is the world's most reliable provider. The use of advanced systems in the healthcare departments providing new ways of dealing with diseases has been developed as well. In this article Machine Learning (ML) algorithm known as a sine-cosine weighted k-nearest neighbor (SCA-WKNN) is used for predicting the Hearth disease with the maximum accuracy among the existing approaches. Blockchain technology has been used in the research to secure the data throughout the session and can give more accurate results using this technology. The performance of the system can be improved by using this algorithm and the dataset proposed has been improved by using different resources as well.Comment: page 23, figurse 1

Data-Driven Reduced-Order Modeling of Unsteady Nonlinear Shock Wave using Physics-Informed Neural Network (PINN) Based Solution

This article presents a preliminary study on data-driven reduced-order modeling (ROM) of unsteady nonlinear shock wave. A basic form of such problem can be modeled using the Burgers’ equation. The physics-informed neural networks (PINN) approach is used to obtain numerical solutions to the problem at certain time steps. PINN is a cutting-edge computational framework that seamlessly integrates deep neural networks with the governing physics of the problem and is turning out to be promising for enhancing the accuracy and efficiency of numerical solutions in a wide array of scientific and engineering applications. Next, extraction of the Proper Orthogonal Decomposition (POD) modes from the solution field is carried out, providing a compact representation of the system’s dominant spatial patterns. Subsequently, temporal coefficients are computed at specific time intervals, allowing for a reduced-order representation of the temporal evolution of the system. These temporal coefficients are then employed as input data to train a deep neural network (DNN) model designed to predict the temporal coefficient at various time steps. The predicted coefficient can be used to form the solution. The synergy between the POD-based spatial decomposition and the data-driven capabilities of DNN results in an efficient and accurate model for approximating the solution. The trained ANN subsequently takes the value of the Reynolds number and historical POD coefficients as inputs, generating predictions for future temporal coefficients. The study demonstrates the potential of combining model reduction techniques with machine learning approaches for solving complex partial differential equations. It showcases the use of physics-informed deep learning for obtaining numerical solutions. The idea presented can be extended to solve more complicated problems involving Navier-Stokes equations

Numerical Recipes in Python

Numerical Recipes in Python is to serve as Laboratory Manual of Simplified Numerical Analysis (Python Version): A companion book of the principal book: Simplified Numerical Analysis (Fourth Edition) by Dr. Amjad Ali

DEVELOPMENT OF AN EFFECTIVE ENERGY MANAGEMENT SYSTEM IN POWER PLANTS OF PAKISTAN

In many regions of the world the management of energy conserves is a challenging task. Numerous factors inclusive of economic, environmental and political are having substantial effects on energy management practices, leading to a variety of reservations in appropriate decision making. Energy Management System (EnMS) provides a standardized roadmap for organization efficiency, effectiveness and profitability. By using the EnMS techniques, energy losses could be reduced, and there is substantial saving of fuel which could be used for further power generation. ISO50001:2011 is the standard that deals with EnMS. The objective of the current research work is to pinpoint the optimal approaches in the development of Energy Management Systems (EnMS) of a Combined Cycle Power Plant (CCPP). The CCPP was analyzed for a period of six months for the development of EnMS. Results showed that there were saving of 8.13 × 106 BTUs of energy which means a saving of Rs.480000 per day, which will keep on increasing as a result of the implementation of the developed EnMS, hence improving the overall efficiency of the system. The results obtained from the current research could be utilized as a guide for the further design and operation of the industrial energy management system

Inheritance pattern of yield attributes in spring wheat at grain filling stage under different temperature regimes

Abstract One hundred spring wheat accessions were assessed for heat tolerance under plastic sheet tunnel resulting in seven parents with diverse heat tolerance. Tolerant and susceptible genotypes were graded on the bases of their relative cell injury percentages and relative values for different yield components. The analysis of variance for relative cell injury % revealed highly significant differences among the genotypes with a range from 28 to 98 %. These 7 parents were crossed in a full diallel system to evaluate the inheritance pattern of some spike related yield attributes (spike length, spikelets per spike, spike density, spike weight, grains per spike and grain yield per plant) under different temperature regimes at grain filling stage. Preliminary ANOVA revealed significant genotypic variation (P<0.01) for all the traits studied under both environments. Spike length, spikelets per spike and spike density revealed partial fitness of data for additive dominance model under normal conditions while other traits like spike weight, grains per spike and grain yield per plant showed full adequacy. Under heat stress all traits showed partial adequacy except for grains per spike which showed full adequacy. Formal ANOVA displayed significant effects for both additive and dominance effects in most characters under both regimes. Grain yield per plant showed significancy for only 'a' item under normal conditions and both 'a' and 'b' under high temperature regime. The additive component of variance (D) was significant and more than dominance variance H 1 and H 2 for spike length, spike density and grain yield per plant under both temperature regimes showing preponderance of additive effects. Grains per spike showed prevalence of dominant gene action under both conditions. Spikelets per spike and spike weight showed dominance effects under normal conditions and additive ones under heat stress. Estimates of narrow sense heritability were moderate to high in almost all the traits except for spikelets per spike under normal conditions in which it was low. Predominance of additive genetic effects in majority of traits under heat stress suggested early generation selection through pedigree method while presence of non-additive effects may respond to heterosis breeding. Keywords: Spring wheat, Additive-dominance model, Inheritance pattern, Heat tolerance. Abbreviations: RCI%= Relative cell injury percentage, a = additive, b= over all dominance effects, c= maternal effects, and d= reciprocal affects, b 1 = directional dominance effects, b 2 = effects due to parents contributing varying degree of dominant alleles, b 3 = specific gene interaction, D= estimate of additive effects, H 1 and H 2 = variation due to dominance effects of genes, F= estimate of the relative frequency of dominant to recessive alleles in the parental lines. F= positive when-ever the dominant alleles are more frequent than the recessive alleles, h 2 = direction of dominance, (H 1 /D) 0.5 =mean degree of dominance, H 2 /4H 1 = proportion of genes with positive and negative effects in the parents, [(4DH1) 0.5 +F]/[(4DH1) 0.5 -F]= proportion of dominant and recessive genes in the parents

MODELLING OF OPTIMIZED STAND-ALONE PV SYSTEM FOR BASIC DOMESTIC ENERGY USE IN PAKISTAN

Energy is a basic and vital for sustainable economic development of any developing country including Pakistan. A renewable energy solution for the energy crisis in a densely populated city of Lahore, Pakistan is presented in this article. PVsyst software is used to design a stand-alone PV system to tailor the basic energy demand for household requirements. Geographical and climatic study exposes that Pakistan has enormous potential of solar energy with average value of 5-6 kWh/m2/day or 1800-2200 kWh/ m2/annum. Accordingly, present research proved that this technology is a viable clean energy source. The modelled system has met the maximum demand using nominal space on roof top along with battery storage, leaving sufficient space for further expansion of the system to meet increase energy demand. As a result, the system has shown a promising sign and proves its feasibility for the region with energy loss consideration.&nbsp

Effect of Heat Sink Configuration on the COP of Thermoelectric Vaccine Refrigerator

Polio vaccines are significantly sensitive to temperature and environment. During polio vaccination campaigns in far off villages of Pakistan, it is one of the biggest challenges to store and transport the vaccine without affecting its quality. Various refrigeration techniques are used to keep the vaccine below ambient temperature. The energy consumption of conventional refrigeration systems is too high and the refrigerants having CFCs are hazardous to environment, contributing to global warming by depleting ozone layer. These are also slightly difficult to be developed into a lightweight and portable solar devices used outside.  Solar Powered Thermoelectric Refrigerator (SPTR) is a distinct type of refrigeration system which runs on solar energy rather than that of conventional source of electrical energy, based upon the peltier effect to create hot and cold sides. The current research work was carried out at the Mechanical Engineering Department, U.E.T. Lahore (KSK Campus). The experimental results indicated the unit is capable to maintain a temperature of 6 0C at an ambient temperature below that of 50 0C. The maximum coefficient of performance was recorded as 0.78. Special configuration of heat sink was used to get maximum heat dissipation with minimum cost. An optimal value of solar irradiance let the cooling rate and coefficient of performance to attain maximum value. The designed SPTR would be of a great potential for cold storage in the areas where electricity supply is absent. It has the advantages of being small, lightweight, low running cost, noiseless, portable, reliable, and also low initial cost in mass production. &nbsp