Search

Resource management efficiency can be a beneficial step toward optimizing power consumption in software-hardware integrated systems. Languages such as C, C++, and Fortran have been extremely popular for dealing with optimization, memory management, and other resource management. We investigate novel algorithmic architectures capable of optimizing resource requirements and increasing energy efficiency. The experimental results obtained with C++ can be extended to other programming languages as well. We emphasize the inherent drawbacks of memory management operators. These operators are intended to be extremely generic in their application, just as the concept of dynamic memory is. As a result, they are unable to take advantage of the various optimization techniques and opportunities that specific use cases present. Each source code file is modeled after its own distinct memory usage pattern, which can be used to speed up memory management routines. Such concepts are frequently time-consuming and costly to implement; consequently, they are not the primary concern of application developers, as they require manual development and integration. We intend to address this gap by providing a suite of memory management algorithms that enable dramatic performance improvements at the source code level while allowing for seamless integration across multiple use cases. The techniques have been evaluated on several performance parameters, and results have been presented. In this paper, we have compared a variety of memory allocation techniques and compared their space and energy efficiency requirements. Three variants of SSDAM, SSDAM-E, and DLLOM strategies have been evaluated and compared against the base performance of new and delete operators. SSDAM-E, SSDAM with new delete operators, and DDLOM improve the memory consumption by the factors of 8.01, 7.0, and 4.0, respectively. In the worst case, SSDAM-E gave an average running time of 5.650 sec faster than the DLLOM average time of 7.496 sec. As far as energy efficiency is considered, SSDAM-Original and SSDAM-E-Original attain 100%, in comparison with the base efficiency of 12.48% characterized by new/delete operators.

This study aims to determine economic burden or cost of human immunodeficiency virus (HIV) and its consequences from patient perspective and to examine factors influencing the cost. This is a prevalence-based cost-of-illness (COI) study. A cross-sectional survey was carried out in Yangon, Myanmar during March and April 2018. Face-to-face structured interview was conducted among 264 people living with HIV (PLHIV) who were members of the selected four self-help groups (SHGs) that provided HIV peer support. Micro-costing approach was adopted for the cost calculation. Direct medical cost, direct non-medical cost and indirect cost were considered. Cost of HIV per patient in the year 2017 was estimated at 228.2 international dollar (Int$). Direct medical cost was only small portion of the total cost (5.6%). Indirect cost or cost of time loss was the largest contributor among the cost components, accounted for 61.2% of the total cost. First year of the antiretroviral therapy (ART) initiation and second line drug regimen have positive effect on the costs. HIV resulted in substantial economic burden for the patients. Effective interventions aim at reducing the economic burden on patients by providing compensation on transportation cost and providing job opportunity are essential. Further researchers examining cost from provider and societal perspectives are warranted to provide complete picture of the economic burden of HIV in Myanmar.