Wind Generation Impact on Symmetrical Fault Level at Grid Buses

This paper mainly aims at evaluating quantitatively the impact of wind turbine generators (WTGs) on fault level (FL) in case of a balanced fault occurring in the host grid (HG). This impact is not generic but it depends on the grid configuration, operation mode, and load profile; the impact may be positive for a network while it is negative for another one. Therefore, the impact will be estimated for a specific distribution network (DN). The grid faults and wind generations (WGs) are simulated by the simulation tool Power Factory DigSilent 14.0.506. The paper addresses the influence on FL of grid buses in general and particularly on FL of the point of common coupling (PCC). The effect of both penetration and dispersion levels of embedded WTGs on fault response is also investigated. Moreover, the influence of WG type on FL is assessed. It is concluded, among other points, that the FL at PCC could rise by about 150% and 17% due to embedded WG of type 1 and type 2 respectively, what it leads to the recommendation to avoid installing type 1 wind systems for new wind farm

Learning Robust Model Predictive Control for Voltage Control of Islanded Microgrid

This paper proposes a novel control design for voltage tracking of an islanded AC microgrid in the presence of {nonlinear} loads and parametric uncertainties at the primary level of control. The proposed method is based on the Tube-Based Robust Model Predictive Control (RMPC), an online optimization-based method which can handle the constraints and uncertainties as well. The challenge with this method is the conservativeness imposed by designing the tube based on the worst-case scenario of the uncertainties. This weakness is amended in this paper by employing a combination of a learning-based Gaussian Process (GP) regression and RMPC. The advantage of using GP is that both the mean and variance of the loads are predicted at each iteration based on the real data, and the resulted values of mean and the bound of confidence are utilized to design the tube in RMPC. The theoretical results are also provided to prove the recursive feasibility and stability of the proposed learning based RMPC. Finally, the simulation results are carried out on both single and multiple DG (Distributed Generation) units

A Mathematical Model for Minimizing Add-On Operational Cost in Electrical Power Systems Using Design of Experiments Approach

One of the key functions of the Distribution System Operators (DSOs) ofelectrical power systems (EPS) is to minimize the transmission anddistribution power losses and consequently the operational cost. Thisobjective can be reached by operating the system in an optimal mode which is performed by adjusting control parameters such as on-load tap changer (OLTC) settings of transformers, generator excitation levels, and VAR compensators switching. The deviation from operation optimality will result in additional losses and additional operational cost of the power system. Reduction of the operational cost increases the power system efficiency and provides a significant reduction in total energy consumption. This paper proposes a mathematical model for minimizing the additional (add-on) costs based on Design of Experiments (DOE). The relation between add-on operational costs and OLTC settings is established by means of regression statistical analysis. The developed model is applied to a 20-bustest network. The regression curve fitting procedure requires simulation experiments which have been carried out by the DigSilent PowerFactory 13.2 Program for performing network power flow. The results show the effectiveness of the model. The research work raises the importance the power system operation management of the EPS where the Distribution System Operator can avoid the add-on operational costs by continuous correction to get an operation mode close to optimality

Orchestration in the Cloud-to-Things Compute Continuum: Taxonomy, Survey and Future Directions

IoT systems are becoming an essential part of our environment. Smart cities, smart manufacturing, augmented reality, and self-driving cars are just some examples of the wide range of domains, where the applicability of such systems has been increasing rapidly. These IoT use cases often require simultaneous access to geographically distributed arrays of sensors, and heterogeneous remote, local as well as multi-cloud computational resources. This gives birth to the extended Cloud-to-Things computing paradigm. The emergence of this new paradigm raised the quintessential need to extend the orchestration requirements i.e., the automated deployment and run-time management) of applications from the centralised cloud-only environment to the entire spectrum of resources in the Cloud-to-Things continuum. In order to cope with this requirement, in the last few years, there has been a lot of attention to the development of orchestration systems in both industry and academic environments. This paper is an attempt to gather the research conducted in the orchestration for the Cloud-to-Things continuum landscape and to propose a detailed taxonomy, which is then used to critically review the landscape of existing research work. We finally discuss the key challenges that require further attention and also present a conceptual framework based on the conducted analysis.Comment: Journal of Cloud Computing Pages: 2

H-FfMRA: A multi resource fully fair resources allocation algorithm in heterogeneous cloud computing

The allocation of multiple types of resources fairly and efficiently has become a substantial concern in state-of-the-art computing systems. Accordingly, the rapid growth of cloud computing has highlighted the importance of resource management as a complicated and NP-hard problem. Unlike traditional frameworks, in modern data centers, incoming jobs pose demand profiles, including diverse sets of resources such as CPU, memory, and bandwidth across multiple servers. Accordingly, the fair distribution of resources, respecting such heterogeneity appears to be a challenging issue. Furthermore, the efficient use of resources as well as fairness, establish trade-off that renders a higher degree of satisfaction for both users and providers. Dominant Resource Fairness (DRF) has been introduced as an initial attempt to address fair resource allocation in multi-resource cloud computing infrastructures. Dozens of approaches have been proposed to overcome existing shortcomings associated with DRF. Although all those developments have satisfied several desirable fairness features, there are still substantial gaps. Firstly, it is not clear how to measure the fair allocation of resources among users. Secondly, no particular trade-off considers non-dominant resources in allocation decisions. Thirdly, those allocations are not intuitively fair as some users are not able to maximize their allocations. In particular, the recent approaches have not considered the aggregate resource demands concerning dominant and non-dominant resources across multiple servers. These issues lead to an uneven allocation of resources over numerous servers which is an obstacle against utility maximization for some users with dominant resources. Correspondingly, in this paper, a resource allocation algorithm called H-FFMRA is proposed to distribute resources with fairness across servers and users, considering dominant and non-dominant resources. The experiments show that H-FFMRA achieves approximately %20 improvements on fairness as well as full utilization of resources compared to DRF in multi-server settings

Comparison of Honey versus Polylactide Anti-Adhesion Barrier on Peritoneal Adhesion and Healing of Colon Anastomosis in Rabbits

BACKGROUND: Postoperative adhesion is still a consequence of intra-abdominal surgeries, which results in bowel obstruction and abdominopelvic pain. Bowel anastomosis as a common abdominal surgery has the incidence of leakage in up to 30% of patients that increase morbidity and mortality. Due to similar pathways of adhesion formation and wound healing, it is important to find a way to reduce adhesions and anastomosis leakage. AIM: This study was designed to compare antiadhesive as well as anastomosis healing improvement effect of honey and polylactide anti-adhesive barrier film. METHODS: Forty-five rabbits divided into three groups of honey, adhesion barrier film, and control group in an animal study. Under a similar condition, rabbits underwent resection and anastomosis of cecum under general anaesthesia. In the first group, honey was used at the anastomosis site, in the second one polylactide adhesion barrier film utilised, and the third one was the control group. Adhesion, as well as anastomosis leakage, was assessed after 21 days. Data were analysed using the Statistical Package for Social Scientists (SPSS) for Windows version 25. RESULTS: Three groups of 15 rabbits were studied. The results showed that mean peritoneal adhesion score (PAS) was lower in the honey group (1.67) in comparison to the adhesion barrier film group (3.40) and the control group (6.33). CONCLUSION: Bio-absorbable polylactide barrier has an anti-adhesion effect but is not suitable for intestinal anastomosis in rabbits. Further studies needed to evaluate these effects on human beings

Orchestration in the Cloud-to-Things compute continuum: taxonomy, survey and future directions

IoT systems are becoming an essential part of our environment. Smart cities, smart manufacturing, augmented reality, and self-driving cars are just some examples of the wide range of domains, where the applicability of such systems have been increasing rapidly. These IoT use cases often require simultaneous access to geographically distributed arrays of sensors, heterogeneous remote, local as well as multi-cloud computational resources. This gives birth to the extended Cloud-to-Things computing paradigm. The emergence of this new paradigm raised the quintessential need to extend the orchestration requirements (i.e., the automated deployment and run-time management) of applications from the centralised cloud-only environment to the entire spectrum of resources in the Cloud-to-Things continuum. In order to cope with this requirement, in the last few years, there has been a lot of attention to the development of orchestration systems in both industry and academic environments. This paper is an attempt to gather the research conducted in the orchestration for the Cloud-to-Things continuum landscape and to propose a detailed taxonomy, which is then used to critically review the landscape of existing research work. We finally discuss the key challenges that require further attention and also present a conceptual framework based on the conducted analysis

QCD analysis of pion fragmentation functions in the xFitter framework

We present the first open-source analysis of fragmentation functions (FFs) of charged pions (entitled IPM-xFitter) computed at next-to-leading order (NLO) and next-to-next-to-leading order (NNLO) accuracy in perturbative QCD using the xFitter framework. This study incorporates a comprehensive and up-to-date set of pion production data from single-inclusive annihilation (SIA) processes, as well as the most recent measurements of inclusive cross-sections of single pion by the BELLE collaboration. The determination of pion FFs along with their theoretical uncertainties is performed in the Zero-Mass Variable-Flavor Number Scheme (ZM-VFNS). We also present comparisons of our FFs set with recent fits from the literature. The resulting NLO and NNLO pion FFs provide valuable insights for applications in present and future high-energy analysis of pion final state processes

Height and body-mass index trajectories of school-aged children and adolescents from 1985 to 2019 in 200 countries and territories: a pooled analysis of 2181 population-based studies with 65 million participants

Summary Background Comparable global data on health and nutrition of school-aged children and adolescents are scarce. We aimed to estimate age trajectories and time trends in mean height and mean body-mass index (BMI), which measures weight gain beyond what is expected from height gain, for school-aged children and adolescents. Methods For this pooled analysis, we used a database of cardiometabolic risk factors collated by the Non-Communicable Disease Risk Factor Collaboration. We applied a Bayesian hierarchical model to estimate trends from 1985 to 2019 in mean height and mean BMI in 1-year age groups for ages 5–19 years. The model allowed for non-linear changes over time in mean height and mean BMI and for non-linear changes with age of children and adolescents, including periods of rapid growth during adolescence. Findings We pooled data from 2181 population-based studies, with measurements of height and weight in 65 million participants in 200 countries and territories. In 2019, we estimated a difference of 20 cm or higher in mean height of 19-year-old adolescents between countries with the tallest populations (the Netherlands, Montenegro, Estonia, and Bosnia and Herzegovina for boys; and the Netherlands, Montenegro, Denmark, and Iceland for girls) and those with the shortest populations (Timor-Leste, Laos, Solomon Islands, and Papua New Guinea for boys; and Guatemala, Bangladesh, Nepal, and Timor-Leste for girls). In the same year, the difference between the highest mean BMI (in Pacific island countries, Kuwait, Bahrain, The Bahamas, Chile, the USA, and New Zealand for both boys and girls and in South Africa for girls) and lowest mean BMI (in India, Bangladesh, Timor-Leste, Ethiopia, and Chad for boys and girls; and in Japan and Romania for girls) was approximately 9–10 kg/m2. In some countries, children aged 5 years started with healthier height or BMI than the global median and, in some cases, as healthy as the best performing countries, but they became progressively less healthy compared with their comparators as they grew older by not growing as tall (eg, boys in Austria and Barbados, and girls in Belgium and Puerto Rico) or gaining too much weight for their height (eg, girls and boys in Kuwait, Bahrain, Fiji, Jamaica, and Mexico; and girls in South Africa and New Zealand). In other countries, growing children overtook the height of their comparators (eg, Latvia, Czech Republic, Morocco, and Iran) or curbed their weight gain (eg, Italy, France, and Croatia) in late childhood and adolescence. When changes in both height and BMI were considered, girls in South Korea, Vietnam, Saudi Arabia, Turkey, and some central Asian countries (eg, Armenia and Azerbaijan), and boys in central and western Europe (eg, Portugal, Denmark, Poland, and Montenegro) had the healthiest changes in anthropometric status over the past 3·5 decades because, compared with children and adolescents in other countries, they had a much larger gain in height than they did in BMI. The unhealthiest changes—gaining too little height, too much weight for their height compared with children in other countries, or both—occurred in many countries in sub-Saharan Africa, New Zealand, and the USA for boys and girls; in Malaysia and some Pacific island nations for boys; and in Mexico for girls. Interpretation The height and BMI trajectories over age and time of school-aged children and adolescents are highly variable across countries, which indicates heterogeneous nutritional quality and lifelong health advantages and risks