Comparing the reliability of accounting-based and market-based prediction models

Recently developed financial distress prediction models adopt a market-based approach. It gained its popularity in the academic world due to its theoretical appeal. However, the comparison of market-based with traditional accountingratio- based models is limited in the literature. Therefore, this paper humbly attempts to add finding to the literature by comparing the accounting-based model with market-based model in order to present a comprehensive computational comparison of methodologies to fulfil the strategic information needs of investors and other stakeholders. Our accountingbased model employed multivariate discriminant analysis (MDA) and logistic regression analysis (LRA) and for marketbased model, we adopted Merton technique. Our sample consists of one hundred and fifty eight public listed companies in Malaysia. Sixteen financial ratios with five-feature groups including activity ratio, cash flow ratio, solvency ratio, liquidity ratio and profitability ratio are selected as variables for our accounting-based model. For the market-based model, we generate the logarithm by adopting the information from the market such as stock price and interest rate. The result of one year prior to financial distress classification indicates that LRA has the highest accuracy compared to other methodologies and both the accounting-based models (LRA and MDA) outperformed market-based (Merton) model

Scheduling divisible jobs to optimize the computation and energy costs

The important challenge in cloud computing environment is to design a scheduling strategy to handle jobs, and to process them in a heterogeneous environment with shared data centers. In this paper, we attempt to investigate a new analytical framework model that enables an existing private cloud data-center for scheduling jobs and minimizing the overall computation and energy cost together. Our model is based on Divisible Load Theory (DLT) model to derive closed-form solution for the load fractions to be assigned to each machines considering computation and energy cost. Our analysis also attempts to schedule the jobs such a way that cloud provider can gain maximum benefit for his service and Quality of Service (QoS) requirement user’s job. Finally, we quantify the performance of the strategies via rigorous simulation studies

Load-Balancing Models for Scheduling Divisible Load on Large Scale Data Grids

In many data grid applications, data can be decomposed into multiple independent sub datasets and distributed for parallel execution. This property has been successfully employed using Divisible Load Theory (DLT) , which has been proven to be a powerful tool for modeling divisible load problems in large scale data grid. Load balancing in such environment plays a critical role in achieving high utilization of resources to schedule the applications efficiently through join consideration of communication and computation time. There are some scheduling models, which have been studied, such as Constraint DLT (CDLT), Task Data Present (TDP) and Genetic Algorithm (GA). However, there has been no optimal solution reached. At the same time, effective schedulers are not only required to minimize the maximum completion time (makespan) of the jobs, but also the execution time of the schedulers.This thesis proposes several load balancing models for scheduling divisible load on large scale data grids, when both processor and communication link speed are heterogeneous. The proposed models can be decomposed into three stages. The first stage is to develop new DLT based models for multiple sources scheduling. Closed form solutions for the load allocation are derived. The new models are called Adaptive DLT (ADLT) and A2DLT models. In the second stage, an Iterative DLT (IDLT) model is proposed. Recursive numerical equations are derived to find the optimal workload assigned to the grid node. The closed form solutions are derived for the optimal load allocation. Although the IDLT model is proposed for single source, it has been applied in the case of multiple sources. The third stage integrates the proposed DLT based models with GA algorithm to solve the time consuming problem. In addition, the integration of the proposed DLT model with Simulated Annealing (SA) algorithm has been also developed. The experimental results have proven that the proposed models yield better perform ance than previous models in terms of makespan and scheduler execution time. The ADLT and A2DLT models have reduced the makespan by 21% and 37% respectively compared to CDLT model. The IDLT model is capable of producing almost optimal solution for single source scheduling with low time complexity. In addition, the integration of the proposed DLT model with GA and SA algorithms has also significantly improved the performance. The SA is 64.70% better than GA in terms of makespan. Thus, the proposed models can balance the processing loads efficiently so that they can be integrated in the existing data grid schedulers to improve the performance

An improved simulated annealing algorithm to avoid crosstalk in optical omega network

A major problem called crosstalk is introduced by Optical Omega Network (OON), which is caused by coupling two signals within a Switching Element (SE). It is important to focus on an efficient solution to avoid crosstalk, which is routing the traffic through an N times N optical network to avoid coupling two signals within each SE. Optimal routing in OON is an NP-hard problem. Many heuristic algorithms were designed by many researchers to perform this routing. Routing the messages in degree-decreasing of the message conflicts gave best performance among them. When Simulated Annealing (SA) algorithm was used to solve the problem, it gave good results. It is a good idea to use these two algorithms to improve the performance. This paper presents an Improved SA (ISA) for message routing in OON that combines SA algorithm with the best heuristic algorithms. Simulation Results show that the proposed ISA can be a competitive choice for solving the crosstalk problem

Parallel Optical Window Algorithm Applied to Optical Multistage Interconnection Network

The crosstalk problem is introduced in an optical multistage interconnection network caused by coupling two signals within a switching element. To avoid this crosstalk, a time domain approach is used, which is to partition the set of permutation connections into several subsets such that the connections in each subset can be established simultaneously in the network without crosstalk. Since we want to partition the messages to be sent to the network into several groups, we have to use the window method that is used for finding the conflicts among all the messages to be sent. In this paper, a new parallel algorithm of the window method is developed called the Balanced Parallel Window Method (BPWM) algorithm. The BPMW algorithm reduces the execution time by a percentage of 83% of the time compared to the sequential algorithm with seven processors

Diversification In Crude Oil And Other Commodities: A Comparative Analysis

An understanding of how volatilities of and correlations between commodity returns change over time including their directions (positive or negative) and size (stronger or weaker) is of crucial importance for both the domestic and international investors with a view to diversifying their portfolios for hedging against unforeseen risks. This paper is an humble attempt to add value to the existing literature by empirically testing the ‘timevarying’ and ‘scale dependent’ volatilities of and correlations of the sample commodities. Particularly, by incorporating scale dependence, it is able to identify unique portfolio diversification opportunities for different set of investors bearing different investment horizons or holding periods. In order to address the research objectives, we have applied the vector error-correction test and several recently introduced econometric techniques such as the Maximum Overlap Discrete Wavelet Transform (MODWT), Continuous Wavelet Transform (CWT) and Multivariate GARCH – Dynamic Conditional Correlation. The data used in this paper is the daily data of seven commodities (crude oil, gas, gold, silver, copper, soybean and corn) prices from 1 January 2007 until 31 December 2013

Load allocation model for scheduling divisible data grid applications.

Problem statement: In many data grid applications, data can be decomposed into multiple independent sub-datasets and distributed for parallel execution and analysis. Approach: This property had been successfully employed by using Divisible Load Theory (DLT), which had been proved as a powerful tool for modeling divisible load problems in data-intensive grid. Results: There were some scheduling models had been studied but no optimal solution has been reached due to the heterogeneity of the grids. This study proposed a new optimal load allocation based on DLT model recursive numerical closed form solutions are derived to find the optimal workload assigned to the processing nodes. Conclusion/Recommendations: Experimental results showed that the proposed model obtained better solution than other models (almost optimal) in terms of Makespan

Optimal workload allocation model for scheduling divisible data grid applications

In many data grid applications, data can be decomposed into multiple independent sub-datasets and distributed for parallel execution and analysis. This property has been successfully employed using Divisible Load Theory (DLT), which has been proved a powerful tool for modeling divisible load problems in data-intensive grids. There are some scheduling models that have been studied but no optimal solution has been reached due to the heterogeneity of the grids. This paper proposes a new model called the Iterative DLT (IDLT) for scheduling divisible data grid applications. Recursive numerical closed form solutions are derived to find the optimal workload assigned to the processing nodes. Experimental results show that the proposed IDLT model leads to a better solution than other models (almost optimal) in terms of makespan

Efficient Sequential and Parallel Routing Algorithms in Optical Multistage Interconnection Network

As optical technology advances, there is a considerable interest in using this technology to implement interconnection networks and switches. Optical multistage interconnection network is popular in switching and communication applications. It has been used in telecommunication and parallel computing systems for many years. A major problem known as crosstalk is introduced by optical multistage interconnection network, which is caused by coupling two signals within a switching element. It is important to focus on an efficient solution to ,avoid crosstalk, which is routing traffic through an N x N optical network to avoid coupling two signals within each switching element.Under the constraint of avoiding crosstalk, we are interested in realising a permutation that will use the minimum number of passes to send all messages. This routing problem is an NPhard problem. Many algorithms are designed by many researchers to perform this routing such as window method, sequential algorithm, degree-descending algorithm, simulated annealing algorithm, genetic algorithm and ant colony algorithm.This thesis explores two approaches, sequential and parallel approaches. The first approach is to develop an efficient sequential algorithm for the window method. Reduction of the execution time of the algorithm in sequential platform, led to a massive improvement of the algorithm speed. Also an improved simulated annealing is proposed to solve the routing problem. The efficient combination of simulated annealing algorithm with the best heuristic algorithms gave much better result in a very minimal time. Parallelisation is another approach in our research. Three parallel strategies of the window method are developed in this research. The parallel window method with low communication overhead decreased 86% of the time compared to sequential window method. The parallel simulated annealing algorithm is also developed and it reduces 64% of the time compared to sequential simulated annealing

The use of arthroscopy in diagnosing and treating sports-related cartilage lesions

Background: Sports-related cartilage lesions pose challenges for athletes. Cartilage, vital for smooth joint movement, can be damaged. Arthroscopy, a minimally invasive procedure, allows precise diagnosis and treatment of joint issues, offering quicker recovery and minimal scarring, enhancing orthopedic interventions. This study aimed to assess the use of arthroscopy in diagnosing and treating sports-related cartilage lesions. Methods: This prospective observational study was conducted at the department of orthopaedics and traumatology, Life Line Hospital Moulovibazar, Mount Adora Hospital Sylhet, MAG Osmani Medical College Hospital Sylhet, Bangladesh from January 2022 to December 2023. As the study subjects, a total of 58 patients with non-surgically treated acute or chronic sports-related cartilage lesions were enrolled by using a purposive sampling technique. After 6 months, a follow-up report was recorded. Data were analyzed by using Microsoft Office tools. Results: In this study, 72% of participants underwent cuff repair, with the remaining 28% opting for loop repair. The arthroscopic assessment revealed anterior medial cartilage lesions in 34% and anterior lateral lesions in 28%. Posterior medial, posterior lateral, and mid-talus dome cartilage lesions were observed in 17%, 5%, and 16%, respectively. Capsule repair was employed in 86% of cases. Significant improvement in hip range of motion, as well as radiological parameters like lateral center-edge angle, alpha angle (anteroposterior), and alpha angle (Dunn), was observed 6 months postoperatively (p<0.001). Conclusions: In detecting and treating sports-related cartilage lesions, arthroscopy is an effective method. This minimally invasive less painful treatment approach contributes to faster rehabilitation and a quicker return to normal activities