TIME VALUE OF MONEY: APPLICATION AND RATIONALITY- AN APPROACH USING DIFFERENTIAL EQUATIONS AND DEFINITE INTEGRALS

The time value of money is one of the most important concepts in finance. Money that a firm has in its possession today is more valuable than future payments because today’s money can be invested to earn positive returns in future. The principles of time value analysis have many applications, ranging from setting up schedules for paying off loans to decisions about whether to acquire new equipment. Problems concerning Time Value of Money, which involves calculation of these concepts, are usually solved by algebraic formulae. This paper attempts to solve such problems using differential equations & definite integral techniques and makes a comparison with the results obtained by the traditional method

TIME VALUE OF MONEY: APPLICATION AND RATIONALITY- AN APPROACH USING DIFFERENTIAL EQUATIONS AND DEFINITE INTEGRALS

The time value of money is one of the most important concepts in finance. Money that a firm has in its possession today is more valuable than future payments because today’s money can be invested to earn positive returns in future. The principles of time value analysis have many applications, ranging from setting up schedules for paying off loans to decisions about whether to acquire new equipment. Problems concerning Time Value of Money, which involves calculation of these concepts, are usually solved by algebraic formulae. This paper attempts to solve such problems using differential equations & definite integral techniques and makes a comparison with the results obtained by the traditional method

Life cycle costing: an alternative selection tool

In today’s complex business environment, both raising and application of fund becomes so costly. Thus, business needs to take a wise decision of investing funds in fixed facilities, which at one side consumes a lot of costly fund and on the other, set the value of the business. Net present value (NPV), pay back period (PBP), internal rate of return (IRR) are some widely used and customary tools in such situation most of which are based on projected revenues. In this paper, we have tried to use life cycle costing as a strong alternative, which considers every cost category throughout the life of the assets, from cradle to grave, to represent the effective use of funds in its totality. The theoretical foundation of LCC as a tool comes from literature review but the application of LCC in alternative choosing areas are the development of the authors. The use of mathematical tools and equations is an exemplary one that may be changed or modified to fit it with the typical context, if necessary. The paper can be a guideline which finally concludes that the use of life cycle costing as an alternative selection tool results a better cost structure analysis than others.Cost Breakdown Structure (CBS), Invisible Costs, Iceberg Effect, Affinity Diagram

Explaining Software Bugs Leveraging Code Structures in Neural Machine Translation

Software bugs claim approximately 50% of development time and cost the global economy billions of dollars. Once a bug is reported, the assigned developer attempts to identify and understand the source code responsible for the bug and then corrects the code. Over the last five decades, there has been significant research on automatically finding or correcting software bugs. However, there has been little research on automatically explaining the bugs to the developers, which is essential but a highly challenging task. In this paper, we propose Bugsplainer, a transformer-based generative model, that generates natural language explanations for software bugs by learning from a large corpus of bug-fix commits. Bugsplainer can leverage structural information and buggy patterns from the source code to generate an explanation for a bug. Our evaluation using three performance metrics shows that Bugsplainer can generate understandable and good explanations according to Google's standard, and can outperform multiple baselines from the literature. We also conduct a developer study involving 20 participants where the explanations from Bugsplainer were found to be more accurate, more precise, more concise and more useful than the baselines

Defectors: A Large, Diverse Python Dataset for Defect Prediction

Defect prediction has been a popular research topic where machine learning (ML) and deep learning (DL) have found numerous applications. However, these ML/DL-based defect prediction models are often limited by the quality and size of their datasets. In this paper, we present Defectors, a large dataset for just-in-time and line-level defect prediction. Defectors consists of $\approx$ 213K source code files ($\approx$ 93K defective and $\approx$ 120K defect-free) that span across 24 popular Python projects. These projects come from 18 different domains, including machine learning, automation, and internet-of-things. Such a scale and diversity make Defectors a suitable dataset for training ML/DL models, especially transformer models that require large and diverse datasets. We also foresee several application areas of our dataset including defect prediction and defect explanation. Dataset link: https://doi.org/10.5281/zenodo.770898

Prospect of Hydroxyl Radical Exposure during Seawater Bathing to Treat Amoebic Gill Disease in Atlantic Salmon

This study aims to undertake hydroxyl (.OH) radical-based preliminary investigations with a view to utilising seawater as a viable alternative to freshwater for the treatment of amoebic gill disease (AGD) in aquaculture industries. The study presents in vitro viability studies of clonal amoebae species to examine the effects of .OH radicals on both parasites and hosts. The study also assesses the toxicity to Chinook salmon cell lines (CHSE-214) in freshwater and 35 ppt seawater via continuous dosing of 35 mM .OH radicals and hydrogen peroxide (H2O2) for 1.5 to 4 hr, separately at 18°C and 15°C. Comparatively high viability of CHSE-214 (60% in .OH and 50% in H2O2) for a prolonged treatment of up to 4 hr in seawater at 15°C indicates suitability of low seawater temperature while using either .OH or H2O2 during bathing. The viability of CHSE-214 remained relatively stable in seawater (55%–60% in .OH and 50%–60% in H2O2), at both temperatures of 18°C and 15°C. However, at 15°C, a drastic reduction of viability of CHSE-214 in freshwater (from 80% to 48% in .OH and from 70% to 58% in H2O2) has indicated high variations in toxicity levels in freshwater at low temperature. Using DNA staining agents in flow cytometry, the in vitro viability study results in >22.5% mortality of clonal Neoparamoeba perurans (NP), an AGD causative agent, in 35 ppt seawater containing 35 mM .OH radicals via one-off dosing for 1 hour at 15°C. In addition, fast radical consumption is more pronounced in the case of .OH radicals as compared to H2O2 in both freshwater and seawater due to extreme reactivity of the former. Hence, this study suggests that .OH radicals are detrimental to the viability of NP in seawater, and thereby, establishes grounds for further in vivo investigations of using seawater supplemented with continuous dosing of .OH radicals for Atlantic salmon bathing as a treatment of AGD

Scalability of advanced oxidation processes (AOPs) in industrial applications: a review

Disinfection and decontamination of water by application of oxidisers is an essential treatment step across numerous industrial sectors including potable supply and industry waste management, however, could be greatly enhanced if operated as advanced oxidation processes (AOPs). AOPs destroy contaminants including pathogens by uniquely harnessing radical chemistry. Despite AOPs offer great practical opportunities, no reviews to date have highlighted the critical AOP virtues that facilitate AOPs’ scale up under growing industrial demand. Hence, this review analyses the critical AOP parameters such as oxidant conversion efficiency, batch mode vs continuous-flow systems, location of radical production, radical delivery by advanced micro-/mesoporous structures and AOP process costs to assist the translation of progressing developments of AOPs into their large-scale applications. Additionally, the state of the art is analysed for various AOP inducing radical/oxidiser measurement techniques and their half-lives with a view to identify radicals/oxidisers that are suitable for in-situ production. It is concluded that radicals with short half-lives such as hydroxyl (10−4 μsec) and sulfate (30–40 μsec) need to be produced in-situ via continuous-flow reactors for their effective transport and dosing. Meanwhile, radicals/oxidisers with longer half-lives such as ozone (7–10 min), hydrogen peroxide (stable for several hours), and hypochlorous acid (10 min −17 h) need to be applied through batch reactor systems due to their relatively longer stability during transportation and dosing. Complex and costly synthesis as well as cytotoxicity of many micro-/mesoporous structures limit their use in scaling up AOPs, particularly to immobilising and delivering the short-lived hydroxyl and sulfate radicals to their point of applications. Overall, radical delivery using safe and advanced biocompatible micro-/mesoporous structures, radical conversion efficiency using advanced reactor design and portability of AOPs are priority areas of development for scaling up to industry

Authorship Identification of Source Code Segments Written by Multiple Authors Using Stacking Ensemble Method

Source code segment authorship identification is the task of identifying the author of a source code segment through supervised learning. It has vast importance in plagiarism detection, digital forensics, and several other law enforcement issues. However, when a source code segment is written by multiple authors, typical author identification methods no longer work. Here, an author identification technique, capable of predicting the authorship of source code segments, even in the case of multiple authors, has been proposed which uses a stacking ensemble classifier. This proposed technique is built upon several deep neural networks, random forests and support vector machine classifiers. It has been shown that for identifying the author group, a single classification technique is no longer sufficient and using a deep neural network-based stacking ensemble method can enhance the accuracy significantly. The performance of the proposed technique has been compared with some existing methods which only deal with the source code segments written precisely by a single author. Despite the harder task of authorship identification for source code segments written by multiple authors, our proposed technique has achieved promising results evidenced by the identification accuracy, compared to the related works which only deal with code segments written by a single author.Comment: 2019 22nd International Conference on Computer and Information Technology (ICCIT