Proper Utilization Of Available Resources Research Articles

Current challenges of blood transfusions in patients with thalassemia in India and future perspectives

The introduction of regular red blood cell transfusions transformed thalassemia major from a fatal childhood disease into a chronic disorder. Thalassemia is highly prevalent in South Asia, including the Indian subcontinent, and blood transfusion remains the cornerstone of management for these patients. But safe blood transfusions still remain a major problem in India. Difficulties in maintaining adequate blood inventory, a lack of a national blood act, and fragmented blood transfusion services are some of the major contributing factors for the delay in blood supply. In most of the blood centers, alloantibody detection facilities and extended red cell antigen typing are unavailable. Awareness is the key to reducing alloimmunization, which limits the effectiveness of transfusions and the potential availability of blood. Patients with thalassemia are also at high risk of transfusion-transmitted infections unless appropriate blood screening is in place. Hence, many patients remain under-transfused, resulting in decreased health and quality-of-life outcomes. Facilities such as leucoreduction and immunohematological monitoring following a blood transfusion are often lacking in India, especially at the sub-district level. Continuous efforts to raise community awareness, regular training of health-care workers, and proper utilization of available resources are essential to ensuring safe blood transfusions for patients with thalassemia. Access to the new treatments at an affordable cost may reduce the blood transfusion burden for thalassemia patients in India.

Smart City E-Governance Through Intelligent ICT Framework

The concept of Smart Cities was developed during the first decade of 21st century and the govt. of India has started the smart city mission since June, 2015. Smart city e-governance is an integral part of this paper and the deliverables are quite helpful for citizen centric services. The ‘smartness’ of a city is linked to the provision of city centric services, high quality of life and the proper utilization of available resources in tandem with the government, which helps through investments in citizens, transport and modern communication infrastructure (ICT) for sustainable economic development. This article aims at explaining the Smart City project design architecture, describing how city data is collected, transmitted, stored and processed using smart IoT devices, Machine Learning (ML) and Deep Learning (DL) techniques.

Multidisciplinary approach for improvement of immunotherapy utilization in solid tumors with H MSI /dMMR –An action plan from a single oncology center

Immune checkpoint inhibitors such as nivolumab and pembrolizumab are approved by Food and Drug Administration for patients diagnosed with metastatic solid tumors with high microsatellite instability (MSI) or mismatch repair deficient (dMMR). The prognosis of these patients is generally poor, that is why it is very essential to identify the targeted patients who can benefit from immunotherapy. Pembrolizumab is approved in our institution for this indication. In order to ensure proper utilization of available resources, we decided to make a pilot retrospective review to evaluate pembrolizumab utilization, to identify the problems we are facing then to set an action plan through multidisciplinary approach. Based on the results we set recommendations. This paper is innovative and the first in kind. It could be used as guidance for other centers using immunotherapy and could be applied for other drugs as well to ensure best practice, patient safety and maximize utilization of resources.

Supervised machine learning approach to predict qualitative software product

Software development process (SDP) is a framework imposed on software product development and is a multi-stage process wherein a wide range of tasks and activities pan out in each stage. Each stage requires careful observations to improve productivity, quality, etc. to ease the process of development. During each stage, problems surface likes constraint of on-time completion, proper utilization of available resources and appropriate traceability of work progress, etc. and may lead to reiteration due to the defects spotted during testing and then, results into the negative walk-through due to unsatisfactory outcomes. Working on such defects can help to take a step towards the proper steering of activities and thus to improve the expected performance of the software product. Handpicking the proper notable features of SDP and then analyzing their nature towards the outcome can greatly help in getting a reliable software product by meeting the expected objectives. This paper proposed supervised Machine Learning (ML) models for the predictions of better SDP, particularly focusing on cost estimation, defect prediction, and reusability. The experimental studies were conducted on the primary data, and the evaluation reveals the model suitability in terms of efficiency and effectiveness for SDP prediction (accuracy of cost estimation: 65%, defect prediction: 93% and reusability: 82%).

Application of Viral System Algorithm in Load Balancing of Cloud Environment

As the cloud computing technology is gaining popularity with time, more and more users and applications are shifting towards it. This is why clouds are experiencing high load, which demands for load balancing of user tasks submitted to cloud for execution. This makes load balancing of non-preemptive tasks a key issue in cloud computing. Superior task scheduling leads to balanced loads among cloud nodes, which results in faster execution of tasks. Task scheduling in cloud environment is an instance of NP-hard optimization problem. When few nodes in a cloud are overloaded whereas other nodes are under loaded then in such situation the performance of overloaded VMs is diminished. It demands a task scheduling so that the incoming tasks can be distributed uniformly across virtual machines (VMs) for proper utilization of available resources. In this study, we propose a novel load balancing algorithm named Viral System Based Load Balancing (VSB-LB) algorithm, which is based on bio-inspired viral system algorithm that distributes the tasks uniformly among VMs. The proposed algorithm is compared with basic load balancing algorithms such as First Come First Serve (FCFS), Weighted Round Robin (WRR) as well as newer bio-inspired Load balance Aware Genetic Algorithm (LAGA) to show its effectiveness. Simulation results proved that VSBLB outperforms FCFS and WRR and LAGA for performing load balancing.

Clustered Webbase: A Repository of Web Pages Based on Top Level Domain

The World Wide Web is a huge source of hyperlinked information; it is growing every moment in context of web documents. So it has become an enormous challenge to manage the local repository (storage module of search engine) for to handling the web documents efficiently that leads to less access time of web documents and proper utilization of available resources. This research paper proposes an architecture of search engine with the clustered repository, organized in a better manner to make task easy for user to retrieving the web pages in reasonable amount of time. The research focuses on coordinator module which not only indexes the documents but also uses compression technique to increase the storage capacity of repository.

Cataract surgery outcome at a rural eye care hospital in India

We report the final outcomes, based on final visual acuity, of patients who underwent cataract surgery at an outreach rural ophthalmology unit and document the details of the ophthalmic examination, type of surgery, intra- and postoperative complications and the final visual acuity. Of 208 eyes (198 patients), 81.3% had small incision cataract surgery and 18.7% had phacoemulsification. We believe that, with good planning and the proper utilization of available resources, it is possible to achieve a good visual outcome in the great majority of cases.