A Harris Hawk Optimisation system for energy and resource efficient virtual machine placement in cloud data centers.

Virtualisation is a major technology in cloud computing for optimising the cloud data centre's power usage. In the current scenario, most of the services are migrated to the cloud, putting more load on the cloud data centres. As a result, the data center's size expands resulting in increased energy usage. To address this problem, a resource allocation optimisation method that is both efficient and effective is necessary. The optimal utilisation of cloud infrastructure and optimisation algorithms plays a vital role. The cloud resources rely on the allocation policy of the virtual machine on cloud resources. A virtual machine placement technique, based on the Harris Hawk Optimisation (HHO) model for the cloud data centre is presented in this paper. The proposed HHO model aims to find the best place for virtual machines on suitable hosts with the least load and power consumption. PlanetLab's real-time workload traces are used for performance evaluation with existing PSO (Particle Swarm Optimisation) and PABFD (Best Fit Decreasing). The performance evaluation of the proposed method is done using power consumption, SLA, CPU utilisation, RAM utilisation, Execution time (ms) and the number of VM migrations. The performance evaluation is done using two simulation scenarios with scaling workload in scenario 1 and increasing resources for the virtual machine to study the performance in underloaded and overloaded conditions. Experimental results show that the proposed HHO algorithm improved execution time(ms) by 4%, had a 27% reduction in power consumption, a 16% reduction in SLA violation and an increase in resource utilisation by 17%. The HHO algorithm is also effective in handling dynamic and uncertain environments, making it suitable for real-world cloud infrastructures.

LAMSkyCam: A low-cost and miniature ground-based sky camera.

Ground-based sky imagers (GSIs) are increasingly becoming popular amongst the remote sensing analysts. This is because such imagers offer fantastic alternatives to satellite measurements for the purpose of earth observations. In this paper, we propose an extremely low-cost and miniature ground-based sky camera for atmospheric study. Built using 3D printed and off-the-shelf components, our sky camera is lightweight and robust for use in diverse climatic conditions. With a 63 ° field of view angle, the camera captures high resolution sky/cloud images for both day and night times at 5 min intervals. The camera is designed to be mounted on a pole-like architecture and with its compact form, it can be installed at any location without requiring any change in the existing infrastructure. For remote areas, the camera also has a local backup facility from which data can be easily accessed manually. We have open-sourced the hardware design of our sky camera, and therefore researchers can easily manufacture and deploy these cameras for their respective use cases.

Targeting stopwords for quality assurance of SNOMED-CT.

OBJECTIVE: We assess the potential of exploiting stopwords in biomedical concept names to complete the logical definitions of concepts that are not sufficiently defined. METHODS: Concepts containing stopwords are selected from the Disorder hierarchy of Systematized NOmenclature of MEDicine (SNOMED-CT). SNOMED-CT consists of two types of concepts: Fully Defined (FD) concepts which are sufficiently defined and Partially Defined (PD) concepts which are not sufficiently defined. In this work, FD concepts containing stopwords are treated as a source of ground truth to complete the definitions of, lexically and semantically similar, PD concepts. FD and PD concepts are lexically and semantically analysed to create sample-sets. Mandatory attribute-relationships are calculated by using an intersection-set logic for each FD sample-set. PD sample-sets are audited against this mandatory attribute-relationship template to identify inconsistencies in modelling styles and potentially missing attribute-relationships. RESULTS: Lexical and semantic patterns around 11 stopwords were analysed. 26 sample-sets were extracted for the 11 stopwords. Mandatory attribute-relationships were identified for 24 of the 26 sample-sets. The method identified 62.5% - 72.22% of the PD concepts, containing the stopwords in and due to, to be inconsistent in their modelling style and potentially missing at least one attribute-relationship according to the created template.

Spatio-temporal variations and contextual factors of the supply of Airbnb in Rome. An initial investigation.

This paper offers an analysis of the supply of Airbnb accommodation in Rome, one of the main tourist destinations in the world, the third-largest city in Europe, by the number of Airbnb listings. The aim is to focus on the recent spatial trend of Airbnb listings, including the period of the COVID-19 pandemic, and highlight the main housing and socioeconomic characteristics of the neighbourhoods associated with a strong presence of Airbnb listings. The study is developed with quantitative methods and spatial regression (spatial lag and spatial error using OLS as a benchmark), based on data collected from the Inside Airbnb and Tomslee websites. In the period 2014-2019, the listing trend in Rome has been increasing in absolute numbers. After the start of the pandemic, the trend became negative, and the decline of Airbnb offerings is more substantial for shared accommodation. Airbnb supply is related to the distance from the city centre, the average income of the area, empty apartments, singles and the share of foreign residents coming from high-income countries. A signal of spatial diffusion of Airbnb listings emerges in the coastal area, even if they are increasingly concentrated in the historic centre, where there is a monoculture of short-term renting.

Emerging paradigm shift in urban indicators: Integration of the vertical dimension.

Since new urbanism strategies encourage higher density and compact city development, it is expected that the height of urban environments will increase in the next few years as a remedy for many urban problems such as urban sprawl, cost of living, and detrimental environmental impacts of horizontal development of cities. Therefore, urban designers and planners should consider the third dimension of cities according to the vertical growth paradigm that is inherently a three-dimensional (3D) socioeconomic and environmental process. While a large body of literature is focusing on horizontal or two-dimensional (2D) urban indicators, it still lacks more research to compare 2D and 3D urban indicators. In this study, urban environment quality indicators, as a prominent example of urban indicators, were measured in two and three dimensions in the central business district of Urmia in Iran. Also, a Pearson's correlation analysis was performed to find a pairwise relationship between indicators. The results of the correlation analysis revealed that most 3D indicators have no significant linear relationship with other indicators, so predicting 3D indicator values based on other indicators is a difficult or even impossible task. Comparing 2D indicators with 3D ones shows that approximately 30 percent of the study area has a different urban environmental quality if it integrates the vertical dimension with 2D indicators. In addition, measuring and modelling 3D indicators provide better locational information on urban conditions and the life of citizens than traditional 2D urban indicators. This study recommends planning for the expansion of 3D information and associated tools that lead to deeper analytical insights into 3D Urban Environmental Quality assessment.