CORRELATION OF MYOFASCIAL TRIGGER POINTS WITH UPPER LIMB DISABILITY IN POST MASTECTOMY FEMALES IN PAKISTAN

Aims of Study: The objective was to correlate myofascial trigger points and upper limb disability in post-mastectomy females. Methodology: This six-month duration study included 45 participants aged 18 or older, using non-probability convenience sampling, who had undergone mastectomy at least six months prior. Numeric pain rating scale, Simon’s trigger point criteria and a disability questionnaire were used to assess disease complications. Results: A study involving 45 female participants (mean age 42.8±6.754) found that 33.3% had mild pain, 55.6% had moderate pain, and 11.1% had severe pain. Additionally, 46.7% had mild disability and 53.3% had moderate disability. There was a significant correlation between myofascial trigger points and upper extremity disability. Limitations and Future Implication: The limitations include the small sample size used in study and limited generalization of findings due to cultural and contextual factors. Further research is needed to explore interventions and understand the long-term impact of myofascial trigger points on upper limb function. Originality: This research is original in its focus on the correlation between myofascial trigger points and post-mastectomy upper limb disability. Conclusion: This study concluded that there is a highly significant relation between trigger points in muscles and disability of upper extremity in female patients after mastectomy.

Sla-Aware Energy Efficient Resource Management for Cloud Environments

Cloud Computing has emerged as one of the leading computational paradigms in recent times. It provides online services to customers using pay-as-you-go model and enables customers to outsource large and complex tasks to cloud data centers for remote execution and storage. As these large data centers provide basic resources to hosted tasks, they also consume a huge amount of energy, which leads to not only higher operating cost but also a large carbon footprint. Consequently, researchers proposed number of solutions to handle the aforementioned issues, and majority of these solutions are based on resource consolidation approach. Resource consolidation based techniques attempt to place the incoming tasks on minimum possible servers, thereby increasing the resource utilization and decreasing energy consumption. However, in case of fluctuating workloads, which is encountered regularly in cloud computing, aggressive consolidation increases the risks of Service Level Agreement (SLA) violations due to nonavailability of resources. Therefore, the focus of research has shifted towards SLA-aware energy-efficient solutions that attempt to reduce energy consumption and SLA violations simultaneously. In this work, improved resource management solutions are presented that attempt to reduce energy consumption while keeping down the SLA violations at the minimum. This research improved the existing energy-efficient techniques to further enhance their performance while introducing SLA-awareness. In the proposed solutions, lower and upper thresholds are used to identify the under-utilized and overutilized servers, respectively. In this research, five existing techniques are modified, namely; Best Fit Decreasing (BFD), Enhanced-Conscious Task Consolidation (ECTC), Maximum Utilization (MaxUtil), Power and Computing Capacity-Aware BFD (PCABFD), and Energy-aware and Performance per watt Oriented Best Fit (EPOBF). Moreover, the work also presents four novel SLA-aware energy-efficient resource management and workload consolidation techniques, namely; (1) Minimum Energy BFD (MEBFD), (2) Maximum Capacity BFD (MCBFD), (3) Available Capacity and Power (ACP) based technique, and (4) Required Capacity and Power (RCP) based technique. These techniques attempt to reduce energy consumption by using workload consolidation approach, wherein; thresholds are used to keep some of the resources free for handling workload fluctuations to avoid SLA violations. Moreover, a Pareto-Efficient Technique (PET) is proposed that explores the solution space in two dimensions (energy consumption and SLA violations). Further, a Behavior based Energy and SLA-aware Technique (BEST) is proposed that monitors the behavior of VMs and optimizes VM placement accordingly. In addition to resource allocation techniques, an SLA and Power-Aware VM (SPAVM) migration technique is proposed that does not migrate the VM instantly. Instead, SPAVM waits for a given period of time, and the VM is migrated to another server only if during that period VM resource demand doesn’t lower further. Alternatively, if the demand lowers then the VM is not migrated. Consequently, along with the number of migrations, power consumed on VM migrations is also reduced and SLA violations are dealt with by using the thresholds. In addition, this research presents two dynamic threshold mechanisms: (1) Exponential Smoothing based Threshold (EST) mechanism and (2) Moving Average based Threshold (MAT). Formal modeling and verification of the proposed techniques using Petri nets have been conducted. The extensive evaluation process is followed to analyze the performance of proposed techniques. Experimental results indicate that the proposed techniques improve both the energy efficiency and SLA-awareness as compared to recent techniques in literature. Techniques presented in this thesis can be used by the IT companies that have large data centers to process user''s data and tasks. The proposed solutions can help large cloud service providers in reducing energy expenditure while avoiding SLA violations leading to an increase in profitability.