1,222 research outputs found

    The concepts of Smart cities, Smart Tourism Destination and Smart Tourism Cities and their interrelationship

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    Because of the dramatic urbanization processes and increasing number of the population, cities are required to develop complex strategies and innovative plans for their future. Advancing technologies are causing the transformation of cities into smart cities and the recent trend of tourism research shows the potential relationship of smart cities with tourism. In this article, the content of the concepts of smartness, smart tourism destination (STD), smart city, smart tourism cities, their interdependence and importance are studied. Furthermore, the purpose of this study is to explore what STDs provide for tourists and the chances that smart cities offer for local people, analysing the potential benefits of STDs for tourists, stakeholders and destinations, and their importance in urban development based on current scholar research

    Resource-aware scheduling for 2D/3D multi-/many-core processor-memory systems

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    This dissertation addresses the complexities of 2D/3D multi-/many-core processor-memory systems, focusing on two key areas: enhancing timing predictability in real-time multi-core processors and optimizing performance within thermal constraints. The integration of an increasing number of transistors into compact chip designs, while boosting computational capacity, presents challenges in resource contention and thermal management. The first part of the thesis improves timing predictability. We enhance shared cache interference analysis for set-associative caches, advancing the calculation of Worst-Case Execution Time (WCET). This development enables accurate assessment of cache interference and the effectiveness of partitioned schedulers in real-world scenarios. We introduce TCPS, a novel task and cache-aware partitioned scheduler that optimizes cache partitioning based on task-specific WCET sensitivity, leading to improved schedulability and predictability. Our research explores various cache and scheduling configurations, providing insights into their performance trade-offs. The second part focuses on thermal management in 2D/3D many-core systems. Recognizing the limitations of Dynamic Voltage and Frequency Scaling (DVFS) in S-NUCA many-core processors, we propose synchronous thread migrations as a thermal management strategy. This approach culminates in the HotPotato scheduler, which balances performance and thermal safety. We also introduce 3D-TTP, a transient temperature-aware power budgeting strategy for 3D-stacked systems, reducing the need for Dynamic Thermal Management (DTM) activation. Finally, we present 3QUTM, a novel method for 3D-stacked systems that combines core DVFS and memory bank Low Power Modes with a learning algorithm, optimizing response times within thermal limits. This research contributes significantly to enhancing performance and thermal management in advanced processor-memory systems

    Modern computing: Vision and challenges

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    Over the past six decades, the computing systems field has experienced significant transformations, profoundly impacting society with transformational developments, such as the Internet and the commodification of computing. Underpinned by technological advancements, computer systems, far from being static, have been continuously evolving and adapting to cover multifaceted societal niches. This has led to new paradigms such as cloud, fog, edge computing, and the Internet of Things (IoT), which offer fresh economic and creative opportunities. Nevertheless, this rapid change poses complex research challenges, especially in maximizing potential and enhancing functionality. As such, to maintain an economical level of performance that meets ever-tighter requirements, one must understand the drivers of new model emergence and expansion, and how contemporary challenges differ from past ones. To that end, this article investigates and assesses the factors influencing the evolution of computing systems, covering established systems and architectures as well as newer developments, such as serverless computing, quantum computing, and on-device AI on edge devices. Trends emerge when one traces technological trajectory, which includes the rapid obsolescence of frameworks due to business and technical constraints, a move towards specialized systems and models, and varying approaches to centralized and decentralized control. This comprehensive review of modern computing systems looks ahead to the future of research in the field, highlighting key challenges and emerging trends, and underscoring their importance in cost-effectively driving technological progress

    Potential of machine learning/Artificial Intelligence (ML/AI) for verifying configurations of 5G multi Radio Access Technology (RAT) base station

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    Abstract. The enhancements in mobile networks from 1G to 5G have greatly increased data transmission reliability and speed. However, concerns with 5G must be addressed. As system performance and reliability improve, ML and AI integration in products and services become more common. The integration teams in cellular network equipment creation test devices from beginning to end to ensure hardware and software parts function correctly. Radio unit integration is typically the first integration phase, where the radio is tested independently without additional network components like the BBU and UE. 5G architecture and the technology that it is using are explained further. The architecture defined by 3GPP for 5G differs from previous generations, using Network Functions (NFs) instead of network entities. This service-based architecture offers NF reusability to reduce costs and modularity, allowing for the best vendor options for customer radio products. 5G introduced the O-RAN concept to decompose the RAN architecture, allowing for increased speed, flexibility, and innovation. NG-RAN provided this solution to speed up the development and implementation process of 5G. The O-RAN concept aims to improve the efficiency of RAN by breaking it down into components, allowing for more agility and customization. The four protocols, the eCPRI interface, and the functionalities of fronthaul that NGRAN follows are expressed further. Additionally, the significance of NR is described with an explanation of its benefits. Some benefits are high data rates, lower latency, improved spectral efficiency, increased network flexibility, and improved energy efficiency. The timeline for 5G development is provided along with different 3GPP releases. Stand-alone and non-stand-alone architecture is integral while developing the 5G architecture; hence, it is also defined with illustrations. The two frequency bands that NR utilizes, FR1 and FR2, are expressed further. FR1 is a sub-6 GHz frequency band. It contains frequencies of low and high values; on the other hand, FR2 contains frequencies above 6GHz, comprising high frequencies. FR2 is commonly known as the mmWave band. Data collection for implementing the ML approaches is expressed that contains the test setup, data collection, data description, and data visualization part of the thesis work. The Test PC runs tests, executes test cases using test libraries, and collects data from various logs to analyze the system’s performance. The logs contain information about the test results, which can be used to identify issues and evaluate the system’s performance. The data collection part describes that the data was initially present in JSON files and extracted from there. The extraction took place using the Python code script and was then fed into an Excel sheet for further analysis. The data description explains the parameters that are taken while training the models. Jupyter notebook has been used for visualizing the data, and the visualization is carried out with the help of graphs. Moreover, the ML techniques used for analyzing the data are described. In total, three methods are used here. All the techniques come under the category of supervised learning. The explained models are random forest, XG Boost, and LSTM. These three models form the basis of ML techniques applied in the thesis. The results and discussion section explains the outcomes of the ML models and discusses how the thesis will be used in the future. The results include the parameters that are considered to apply the ML models to them. SINR, noise power, rxPower, and RSSI are the metrics that are being monitored. These parameters have variance, which is essential in evaluating the quality of the product test setup, the quality of the software being tested, and the state of the test environment. The discussion section of the thesis explains why the following parameters are taken, which ML model is most appropriate for the data being analyzed, and what the next steps are in implementation

    Pulmonary arterial hypertension in repaired congenital heart disease: a multicentre study

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    This doctoral thesis aims to investigate the demographics, treatment patterns and prognosis of paediatric pulmonary hypertension (PH) and the emerging group of children and adults with PH in the setting of repaired congenital heart disease (CHD). I have conducted three studies, each with a distinct focus. The first is a retrospective, longitudinal study of ten CHD centres across the UK that assesses the clinical characteristics and treatment patterns of adults with a Fontan-type circulation receiving pulmonary vasodilators. I have compared treated patients with a matched cohort of Fontan patients who are not receiving pulmonary vasodilator therapy, complemented by an expert survey to determine current practice and the goals of therapy. In the second study, I created a 20-year national UK registry of paediatric PH and derived estimates of incidence and prevalence for all groups of paediatric PH in different age categories. I determined the natural history of paediatric PH and performed survival analysis. I then focused on patients with CHD and described the changes in demographics, with a substantial increase in patients with previously repaired CHD, who now form the largest PAH-CHD subgroup. The third study focuses on this latter group of repaired PAH-CHD. I highlighted the heterogeneity in terms of severity and onset of PAH. I identified prognostic markers and variables associated with PH resolution and developed a novel risk score for predicting adverse clinical outcomes in this group. This score will form the basis for the risk stratification of this high-risk population, to inform prognostication and guide treatment.Open Acces

    Fundamental Valuation of NOV

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    The purpose of this thesis is to conduct a fundamental valuation of NOV to provide an equity value and share price as of 17 January 2023. To support the fundamental valuation of the company, I have also performed a relative valuation, using the EV/EBITDA and P/S multiples. I conduct a comprehensive examination of macroeconomic, industry-, and company-specific factors that drive value in the oilfield services and equipment industry. These analyses are utilized to make necessary assumptions, forecast NOV’s future performance, and ultimately estimate the company’s equity value and final price target. Acknowledging the accelerating global energy transition and growing public concern about climate change, companies that offer equipment and technologies supportive of cleaner energy sources have experienced considerable demand growth in recent years. Driven by regulatory changes, subsidies, volatile oil and gas prices, and the ongoing shift towards sustainable energy sources, the industry is continuously nudged to adapt and innovate. With its long history as a market leader in the global oilfield services and equipment industry, an extensive product portfolio, and a global customer base comprising several large upstream oil and gas companies, NOV is solidly positioned within the global energy markets. Amid a moderately competitive situation, NOV is poised to continue to grow the upcoming years, particularly as it ventures into the booming renewable energy market. Considering these factors, the fundamental valuation yields an estimated share price of 11,6forNOV.SupportedbyarelativevaluationusingtheEV/EBITDAmultiple,thisanalysissuggestsapotentialdownsiderelativetothecurrentstockprice.ThefinalestimatedpricetargetofNOV’sstockisadjustedto11,6 for NOV. Supported by a relative valuation using the EV/EBITDA multiple, this analysis suggests a potential downside relative to the current stock price. The final estimated price target of NOV’s stock is adjusted to 12,5, derived from a weighted average of the estimations from both fundamental and relative valuation methods, allocated with a 70/30 weight, respectively. While the estimates are characterized by a high degree of uncertainty, as investigated through a sensitivity analysis, the conclusion suggests a potential overvaluation of NOV. Hence, as of January 17, 2023, I would propose a sell recommendation.nhhma
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