Fourth-generation Programming Language Research Articles

MATLAB APPLICATIONS FOR MATHEMATICS STUDENTS

One of the oldest professions in human society is mathematics. It is closely tied to all stages of the development of human society's cognitive system. An essential tool for researching and resolving these real-world issues is mathematical modeling. A mathematical model is a structure that can be created mathematically by using the right tools and making the necessary, simplified assumptions about the underlying laws of a specific object and purpose in the real world. A mathematical model is an abstraction of real-world issues, to put it simply. Computer application technology has greatly influenced many different businesses as a result of computer progress. Among these, MATLAB is a piece of commercial math software that excels in applications for math in science and technology. One of the most widely used fourth-generation programming languages worldwide is MATLAB. It is one of the greatest environments for numerical analysis. Additionally, the most potent and effective language utilized in technical computing is MATLAB. It is designed to address mathematically notated challenges. It is employed in algorithm development and matrix calculations. Additionally, it enables us to run programmers written in any other programming language. It implies that MATLAB can make use of the advantages of other programming languages. It is extensively utilized in robotics, communications, control systems, signal processing, and image processing. This paper first examines MATLAB's benefits and discusses how to utilize it for mathematical operations and modeling.

Profit Maximization of Magdalena Dairy Raisers Association’s Dairy Products in Magdalena, Laguna

This research demonstrates the use of linear programming in maximizing the profit. It applies the concept of simplex method; a method in linear programming to solve industrial problems that aims to maximize the profit. The Magdalena Dairy Raisers Association specializes in production of dairy products. Three different varieties of dairy products were observed. The data was simulated using MATLAB; a fourth-generation programming language and numerical analysis environment that creates data visualization and user interfaces (UI), calculates matrices, and develops and runs algorithms. The results show that using the Linear Programming Model, we obtain the total revenue of each dairy product with an increase of 108.17% for Fresh Milk, 12.10% for Choco Milk, and 35.95% for Milk O Jel from their previous revenue. As a result, we achieved our goal to maximize the net profit with a total increase of 151.16%..

VISUAL SYMMETRIC SPEECH CRYPTOGRAPHY

Today Communication is mainly depends upon digital data communication. Data is sent across internet using sequence of bits. Data integrity is major issue in such a communication. To preserve data integrity of the message sent over the internet various method of cryptography is used. Though there are two types of algorithm for encryption namely symmetric [1] and asymmetric [1], This paper is mainly concern with digital symmetric cryptography using image to replace some bits and at the receiver side this encrypted message is decrypted at the receiver side For Encryption block cipher mode [2] is used which is data is encrypted in blocks rather than individual bits. Digital encryption of data is the approach, in which the original data, say x, is first digitized into a sequence of bits, x (k) which are then embedded digitally with the chosen segments of the image file, y (k) before transmission. For this, we have utilized the MATLAB (Matrix-Laboratory) which is a multi-paradigm numerical computing environment and fourth generation programming language. Encryption is a much stronger method of protecting speech communications than any form of scrambling. The ability to securely store and transfer sensitive information has proved to be a critical factor of success in today's day and age. The secrecy as well as integrity of data during storage and transmission has been the motivating factor for us in the build up to this paper.

Modelling the Mass Transfer Kinetics of the Grafting of 4-Chloromethylstyrene onto Polyethylene Substrate

The behaviours and performances of an ideal grafting of 4-chloromethylstyrene onto polyethylene substrates were predicted/discussed using designed models. (MATLAB) (matrix laboratory) amulti-paradigm numerical computing environment and fourth-generation programming language. was used as the model simulator due to its very broad and all-encompassing functions and capabilities. The models designed were compared and contrasted with one another and also with the experimental data for validation. Inferences were drawn on the basis of the findings and within the confines of the simplifying assumptions made. Conventional adsorption models developed and tested include Lagergren pseudo first-order model, Langmuir–Hinshelwood models, intra-particle diffusion model and pseudo second-order diffusion model. The validity of these models was tested on the basis of the R2 of linearised plots of the models. R2> 0.9 was the criterion for validity. The Lagergren pseudo first-order model had the highest R2 values of 0.9034, 0.9400, 0.9798 and 0.9884 for the polyethylene samples at a reaction temperature of 60°C. The Langmuir–Hinshelwood model was also satisfactory as it gave R2 values of 0.9282 at a temperature of 60°C The valid models were applied for process conditions other than those experimented upon and were found to give valid and reasonable results.

High-speed detection of emergent market clustering via an unsupervised parallel genetic algorithm

We implement a master-slave parallel genetic algorithm with a bespoke log-likelihood fitness function to identify emergent clusters within price evolutions. We use graphics processing units (GPUs) to implement a parallel genetic algorithm and visualise the results using disjoint minimal spanning trees. We demonstrate that our GPU parallel genetic algorithm, implemented on a commercially available general purpose GPU, is able to recover stock clusters in sub-second speed, based on a subset of stocks in the South African market. This approach represents a pragmatic choice for low-cost, scalable parallel computing and is significantly faster than a prototype serial implementation in an optimised C-based fourth-generation programming language, although the results are not directly comparable because of compiler differences. Combined with fast online intraday correlation matrix estimation from high frequency data for cluster identification, the proposed implementation offers cost-effective, near-real-time risk assessment for financial practitioners.

Ray tracing technique for shaping a dual reflector antenna system

In radio astronomy applications, antennas having the highest aperture efficiencies are requested. In order to meet this request, a reector shaping program for dual reector antennas has been developed. The design procedure is based on the geometrical optics where differential equations have been formulated to dene the ray trace of the antenna system. Recently, the fourth generation programming language MATLAB has become well known for its numerical computing environment and very useful three-dimensiona data visualization functions. The authors wished to make a dual reector shaping program by a ray tracing method in MATLAB. In this paper, details of the program are explained, and antenna ray tracing results are shown visually by three-dimensiona viewgraphs. The numerical results are compared with the theoretical ones to conrm the correctness of the developed shaping program for a Cassegrain dual reector antenna system. For uniform aperture distribution therst side lobe level is numerically obtained as {17.59 dB, which can be further reduced through shaping to {24.67 dB and {30.65 dB for parabolic and squared parabolic, respectively.

K-plus-nearest neighbor method development for credit scoring machine learning tasks

The pace of development of modern risk management and data mining technologies causes the relevance of searching for new or improved effective methods for statistical and non-statistical forecasting, as well as forming the problems of deep study of existing methods and characteristics of their application conditions. Machine learning, namely memory-based learning is one of the most practically useful, broad and insufficiently studied areas. Also, the development of modern information technologies and ways to improve readability and simplicity of code causes the relevance of the study support with the implementation of the fourth-generation programming language. The research deals with developing basic and advanced k-plus-nearest neighbor method as significantly improved classical k-nearest neighbor method with eliminated shortcomings and inaccuracies of practical realization: the problem of selecting a metric space and the metrics itself, problem of using categorical (including sampled) variables on the set, the issue of probabilistic classification, problem of taking into account equally spaced groups of elements relative to the element to be classified, the model optimality criterion based on the method and the method of its use for selecting the optimal parameter, ways to accelerate application. The main work is focused on using the methodology and indicators of credit scoring in machine learning problems. The full code for the basic proposed method in the SQL language - MS SQL (T-SQL) dialect was given. As a result of the study, efficiency was determined at the stage of applying the basic proposed method in terms of the optimality criterion - Gini index relative to probabilistic forecasts compared to logistic regression in terms of two factors: the quality of forecasts and number of parameters to be optimized. The practical value of the results obtained on the example of simulation using mass consumer credit data lies in the simplicity and effectiveness of the proposed method by means only of the server part of the DBMS.

Effective Minimization of Ill-conditioned Functions

The problem of minimization of ill-conditioned functions is considered. These functions are motivated by the limit analysis problem for dielectrics in powerful electric fields. From computing point of view a minimization of these functions is not following by standard methods because they are nonsmooth. Distinct ravine of objective function in combination with a high dimension variables requires a smooth regularisation of finite-dimensional problem. Convergence of heave-ball method in relation to it's internal parameters and optimization parameters are studied in numerical computing environment and fourth-generation programming language Matlab.

Effective Minimization of Nonsmooth Functions in the Limit Analysis Problem for Dielectrics in Powerful Electric Fields

The problem of minimization of ill-conditioned functions is considered. This problem arises as a result of finite-element approximation of the limit analysis problem for dielectrics in powerful electric fields. The objective function is nonsmooth therefore a smooth regularization of finite-dimensional problem is used. As a result distinct ravine of objective function is acquired. Convergence of the gradient and the heave-ball methods in relation to its internal and optimization parameters are studied inside the numerical computing environment and fourth-generation programming language Matlab.

Business Performance Management Platforms Overview Part 6: The SAS Platform

SAS (Statistical Analysis System) is a fourth-generation programming language consisting of a suite of modules designed for business intelligence and customer relationship management. SAS software is the standard used in statistical analysis in the pharmaceutical industry and in the public health, partially due to the powerful data import, handling, and manipulation functions added to the base package over the years, to extend the analytical capabilities to different types and formats of data. Other modules available provide for construction of applications for such tasks as data entry or validation. SAS also provides data mining, data warehousing, business intelligence, sustainability and business performance management software. The spectrum of offerings is so wide that many users are expert in one area of the SAS package, but have little or no experience in another.

Silver Bullets and Other Mysteries

What can developers do when faced with an aged software system? This is where a silver bullet comes in handy. At various times, this silver bullet has been known by names such as structured programming, object-oriented languages, 4GLs (fourth-generation programming languages), CASE (computer-aided software engineering) tools, RDBMSs (relational database management systems), XML, visual programming, n-tier architectures, managed code - the list goes on. What's important is that the move suits everybody perfectly. Software ages and becomes increasingly expensive to maintain. New technologies offer modest but not spectacular improvements in productivity. It's therefore sensible from time to time to rebuild a system from scratch. It might be harmless and politically expedient to claim that we've found a silver bullet, but it's even better to know what we're really doing

Menu organization through block clustering

Block Clustering is proposed as a method to derive the semantic relationships among entities that comprise application programs, operating system environments or programming environments. The procedure was derived from the literature on clustering in the biological sciences. The advantages of the procedure are that it relies on information obtained from design specifications, eliminating difficulties associated with designing through “expert” opinion, and allowing the procedure to be implemented early in the design of a system. Also the procedure works well with systems possessing a small or a large number of entities. Two cases studies of the Block Clustering method are presented for two different fourth generation programming languages. Specificially the 37 HyperTalk 1.5 properties were block clustered and found to have six menu topics. Also the 98 SuperTalk 1.5 properties were Block Clustered and found to have 11 menu topics. The results illustrate the utility of Block Clustering analysis for both small and large systems.

Cost comparison for the development and maintenance of application systems in 3rd and 4th generation languages

Application systems represent considerable assets in many companies today. What they do is essential to the company's activities. This leads to an increasing lifetime of application systems. It may be presupposed that the costs for application systems may be more strongly influenced by management than by any other factor. Using data on costs for applications development and maintenance, this paper compares life cycle curves on the basis of empirical data. The basis for cost comparison is a longitudinal field study of the life cycles of application systems developed in third and fourth generation programming languages. This field study is complemented by a summary of the results of other studies.

The impact of fourth generation programming languages

Fourth generation programming languages are affecting the way in which software is developed. This paper describes what they are and their impact on industry, computer science education and the demand for programmers. The focus of the paper is on whether and/or where fourth generation programming languages should be included in a computer science curriculum.