Hybrid resource optimization strategy in heterogeneous wireless networks

The future generation of heterogeneous wireless networks (HWNs) will combine various radio access technologies for connecting various mobile subscribers (MS) based on the quality of service (QoS) and wireless network parameters, connecting MS to the best possible wireless network (WN) has been a trending research topic in HWNs. Existing resource optimization methods are designed to meet the QoS of network criteria and user preferences are neglected. Very limited work is done for resource optimization considering user preferences. However, these models are designed considering multi-mode terminals (MMTs) running a single service at a time under a low-density network; as a result, cannot be adopted to run multiple services simultaneously and; thus, fail to meet current users’ service dynamics requirement. Further, fails to bring good tradeoffs between reducing interference and improving performance. In addressing the research problem this work introduced a hybrid resource optimization strategy (HROS) to reduce interference by establishing channel availability and enhancing resource utilization through game theory. The HROS proves the existence of nash equilibrium (NE) improves throughput by 16.32% and reduces collision by 26.16% over the existing resource optimization-based network selection (RONS) scheme

Energy and cost-aware workload scheduler for heterogeneous cloud platform

Parallel scientific workloads, often represented as directed acyclic graphs (DAGs), consist of interdependent tasks that require significant data exchange and are executed on distributed clusters. The communication overhead between tasks running on different nodes can lead to substantial increases in makespan, energy usage, and monetary costs. Therefore, there is potential to balance communication and computation to reduce these costs. In this paper, we introduce an energy and cost-aware workload scheduler (ECAWS) tailored for executing parallel scientific workloads, generated by the internet of things (IoT), in a heterogeneous cloud environment. The performance of the proposed ECAWS model is evaluated against existing models using the Inspiral scientific workload. Results indicate that ECAWS outperforms other models in reducing makespan, costs, and energy consumption

Biosocial predictors and blood pressure goal attainment among postmenopausal women with hypertension

ObjectivesIn postmenopausal states, women may not maintain blood pressure (BP) in the same way as men, even though most women follow their treatment plans and prescriptions more consistently than men. Biological and lifestyle factors influence the progression of hypertension in postmenopausal women (PMW). This study aimed to determine biosocial predictors associated with achieving the target BP in PMW with hypertension.MethodsA prospective observational study was conducted in the General Medicine Department at Karuna Medical College Hospital, Kerala, India. The definition of BP goal attainment was established based on the guidelines outlined by the VIII Joint National Committee 2014 (JNC VIII). Multivariate logistic regression analysis was used to analyse biosocial predictors, such as educational status, employment status, body mass index (BMI), number of children, age at menarche, age at menopause, and number of co-morbidities, associated with BP goal achievement.ResultsOf the patients, 56.4% achieved their BP goals on monotherapy and 59.7% achieved it on combination therapy. Level of education [odds ratio (OR) = 1.275, 95% confidence interval (CI): 0.234–7.172], employment status (OR = 0.853, 95% CI: 0.400–1.819), age at menopause (OR = 1.106, 95% CI: 0.881–1.149), number of children (OR = 1.152, 95% CI: 0.771–1.720), BMI (OR = 0.998, 95% CI: 0.929–1.071), and number of co-morbidities (OR = 0.068, 95% CI: 0.088–1.093) did not show a significant relationship, and age at menarche (OR = 1.577, 95% CI: 1.031–2.412) showed a significant association with BP goal attainment among hypertensive postmenopausal women.ConclusionHalf of the hypertensive postmenopausal women did not achieve their BP goals. Interventions are required to expand screening coverage and, under the direction of medical professionals, there should be plans to improve hypertension control and increase awareness of the condition

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model

Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to widespread adoption, most LLMs are developed by resource-rich organizations and are frequently kept from the public. As a step towards democratizing this powerful technology, we present BLOOM, a 176B-parameter open-access language model designed and built thanks to a collaboration of hundreds of researchers. BLOOM is a decoder-only Transformer language model that was trained on the ROOTS corpus, a dataset comprising hundreds of sources in 46 natural and 13 programming languages (59 in total). We find that BLOOM achieves competitive performance on a wide variety of benchmarks, with stronger results after undergoing multitask prompted finetuning. To facilitate future research and applications using LLMs, we publicly release our models and code under the Responsible AI License