A Bi-Layer Multi-Objective Techno-Economical Optimization Model for Optimal Integration of Distributed Energy Resources into Smart/Micro Grids

The energy management system is executed in microgrids for optimal integration of distributed energy resources (DERs) into the power distribution grids. To this end, various strategies have been more focused on cost reduction, whereas effectively both economic and technical indices/factors have to be considered simultaneously. Therefore, in this paper, a two-layer optimization model is proposed to minimize the operation costs, voltage fluctuations, and power losses of smart microgrids. In the outer-layer, the size and capacity of DERs including renewable energy sources (RES), electric vehicles (EV) charging stations and energy storage systems (ESS), are obtained simultaneously. The inner-layer corresponds to the scheduled operation of EVs and ESSs using an integrated coordination model (ICM). The ICM is a fuzzy interface that has been adopted to address the multi-objectivity of the cost function developed based on hourly demand response, state of charges of EVs and ESS, and electricity price. Demand response is implemented in the ICM to investigate the effect of time-of-use electricity prices on optimal energy management. To solve the optimization problem and load-flow equations, hybrid genetic algorithm (GA)-particle swarm optimization (PSO) and backward-forward sweep algorithms are deployed, respectively. One-day simulation results confirm that the proposed model can reduce the power loss, voltage fluctuations and electricity supply cost by 51%, 40.77%, and 55.21%, respectively, which can considerably improve power system stability and energy efficiency.</jats:p

An Introduction to Advanced Machine Learning : Meta Learning Algorithms, Applications and Promises

In [1, 2], we have explored the theoretical aspects of feature extraction optimization processes for solving largescale problems and overcoming machine learning limitations. Majority of optimization algorithms that have been introduced in [1, 2] guarantee the optimal performance of supervised learning, given offline and discrete data, to deal with curse of dimensionality (CoD) problem. These algorithms, however, are not tailored for solving emerging learning problems. One of the important issues caused by online data is lack of sufficient samples per class. Further, traditional machine learning algorithms cannot achieve accurate training based on limited distributed data, as data has proliferated and dispersed significantly. Machine learning employs a strict model or embedded engine to train and predict which still fails to learn unseen classes and sufficiently use online data. In this chapter, we introduce these challenges elaborately. We further investigate Meta-Learning (MTL) algorithm, and their application and promises to solve the emerging problems by answering how autonomous agents can learn to learn?

Applications of Nature-Inspired Algorithms for Dimension Reduction: Enabling Efficient Data Analytics

In [1], we have explored the theoretical aspects of feature selection and evolutionary algorithms. In this chapter, we focus on optimization algorithms for enhancing data analytic process, i.e., we propose to explore applications of nature-inspired algorithms in data science. Feature selection optimization is a hybrid approach leveraging feature selection techniques and evolutionary algorithms process to optimize the selected features. Prior works solve this problem iteratively to converge to an optimal feature subset. Feature selection optimization is a non-specific domain approach. Data scientists mainly attempt to find an advanced way to analyze data n with high computational efficiency and low time complexity, leading to efficient data analytics. Thus, by increasing generated/measured/sensed data from various sources, analysis, manipulation and illustration of data grow exponentially. Due to the large scale data sets, Curse of dimensionality (CoD) is one of the NP-hard problems in data science. Hence, several efforts have been focused on leveraging evolutionary algorithms (EAs) to address the complex issues in large scale data analytics problems. Dimension reduction, together with EAs, lends itself to solve CoD and solve complex problems, in terms of time complexity, efficiently. In this chapter, we first provide a brief overview of previous studies that focused on solving CoD using feature extraction optimization process. We then discuss practical examples of research studies are successfully tackled some application domains, such as image processing, sentiment analysis, network traffics / anomalies analysis, credit score analysis and other benchmark functions/data sets analysis

Evolutionary Computation, Optimization and Learning Algorithms for Data Science

A large number of engineering, science and computational problems have yet to be solved in a computationally efficient way. One of the emerging challenges is how evolving technologies grow towards autonomy and intelligent decision making. This leads to collection of large amounts of data from various sensing and measurement technologies, e.g., cameras, smart phones, health sensors, smart electricity meters, and environment sensors. Hence, it is imperative to develop efficient algorithms for generation, analysis, classification, and illustration of data. Meanwhile, data is structured purposefully through different representations, such as large-scale networks and graphs. We focus on data science as a crucial area, specifically focusing on a curse of dimensionality (CoD) which is due to the large amount of generated/sensed/collected data. This motivates researchers to think about optimization and to apply nature-inspired algorithms, such as evolutionary algorithms (EAs) to solve optimization problems. Although these algorithms look un-deterministic, they are robust enough to reach an optimal solution. Researchers do not adopt evolutionary algorithms unless they face a problem which is suffering from placement in local optimal solution, rather than global optimal solution. In this chapter, we first develop a clear and formal definition of the CoD problem, next we focus on feature extraction techniques and categories, then we provide a general overview of meta-heuristic algorithms, its terminology, and desirable properties of evolutionary algorithms

Crystalline phases involved in the hydration of calcium silicate-based cements: Semi-quantitative Rietveld X-ray diffraction analysis

Chemical comparisons of powder and hydrated forms of calcium silicate cements (CSCs) and calculation of alterations in tricalcium silicate (Ca3SiO5) calcium hydroxide (Ca(OH)2) are essential for understanding their hydration processes. This study aimed to evaluate and compare these changes in ProRoot MTA, Biodentine and CEM cement. Powder and hydrated forms of tooth coloured ProRoot MTA, Biodentine and CEM cement were subjected to X-ray diffraction (XRD) analysis with Rietveld refinement to semi-quantitatively identify and quantify the main phases involved in their hydration process. Data were reported descriptively. Reduction in Ca3SiO5 and formation of Ca(OH)2 were seen after the hydration of ProRoot MTA and Biodentine; however, in the case of CEM cement, no reduction of Ca3SiO5 and no formation of Ca(OH)2 were detected. The highest percentages of amorphous phases were seen in Biodentine samples. Ettringite was detected in the hydrated forms of ProRoot MTA and CEM cement but not in Biodentine

Treatment of atypical central neurocytoma in a child with high dose chemotherapy and autologous stem cell rescue

The authors describe a 9 month old female with recurrent atypical central neurocytoma and leptomeningeal spread treated with high dose chemotherapy, autologous stem cell rescue, and adjuvant therapy. She had a complete response to therapy and was disease free at 4 years of age until a recurrence 6 months later. The use of intensive chemotherapy followed by autologous stem cell rescue for atypical neurocytoma may be considered as an adjunct to surgical therapy in young patients with atypical neurocytoma not amenable to radiation therapy

Petrographical and geochemical evidences for paragenetic sequence interpretation of diagenesis in mixed siliciclastic–carbonate sediments: Mozduran Formation (Upper Jurassic), south of Agh-Darband, NE Iran

The Upper Jurassic Mozduran Formation with a thickness of 420 m at the type locality is the most important gas-bearing reservoir in NE Iran. It is mainly composed of limestone, dolostone with shale and gypsum interbeds that grade into coarser siliciclastics in the easternmost part of the basin. Eight stratigraphic sections were studied in detail in south of the Agh-Darband area. These analyses suggest that four carbonate facies associations and three siliciclastic lithofacies were deposited in shallow marine to shoreline environments, respectively. Cementation, compaction, dissolution, micritization, neomorphism, hematitization, dolomitization and fracturing are diagenetic processes that affected these sediments.Stable isotope variations of δ18O and δ13C in carbonate rocks show two different trends. High depletion of δ18O and low variation of δ13C probably reflect increasing temperatures during burial diagenesis, while the higher depletion in carbon isotope values with low variations in oxygen isotopes are related to fresh water flushing during meteoric diagenesis. Negative values of carbon isotopes may have also resulted from organic matter alteration during penetration of meteoric water. Fe and Mn enrichment with depletion of δ18O also supports the contention that alteration associated with higher depletion in carbon isotope values with low variations in oxygen isotopes took place during meteoric diagenesis. The presence of bright luminescence indicates redox conditions during precipitation of calcite cement

Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015

SummaryBackground The Global Burden of Diseases, Injuries, and Risk Factors Study 2015 provides an up-to-date synthesis of the evidence for risk factor exposure and the attributable burden of disease. By providing national and subnational assessments spanning the past 25 years, this study can inform debates on the importance of addressing risks in context. Methods We used the comparative risk assessment framework developed for previous iterations of the Global Burden of Disease Study to estimate attributable deaths, disability-adjusted life-years (DALYs), and trends in exposure by age group, sex, year, and geography for 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks from 1990 to 2015. This study included 388 risk-outcome pairs that met World Cancer Research Fund-defined criteria for convincing or probable evidence. We extracted relative risk and exposure estimates from randomised controlled trials, cohorts, pooled cohorts, household surveys, census data, satellite data, and other sources. We used statistical models to pool data, adjust for bias, and incorporate covariates. We developed a metric that allows comparisons of exposure across risk factors—the summary exposure value. Using the counterfactual scenario of theoretical minimum risk level, we estimated the portion of deaths and DALYs that could be attributed to a given risk. We decomposed trends in attributable burden into contributions from population growth, population age structure, risk exposure, and risk-deleted cause-specific DALY rates. We characterised risk exposure in relation to a Socio-demographic Index (SDI). Findings Between 1990 and 2015, global exposure to unsafe sanitation, household air pollution, childhood underweight, childhood stunting, and smoking each decreased by more than 25%. Global exposure for several occupational risks, high body-mass index (BMI), and drug use increased by more than 25% over the same period. All risks jointly evaluated in 2015 accounted for 57·8% (95% CI 56·6–58·8) of global deaths and 41·2% (39·8–42·8) of DALYs. In 2015, the ten largest contributors to global DALYs among Level 3 risks were high systolic blood pressure (211·8 million [192·7 million to 231·1 million] global DALYs), smoking (148·6 million [134·2 million to 163·1 million]), high fasting plasma glucose (143·1 million [125·1 million to 163·5 million]), high BMI (120·1 million [83·8 million to 158·4 million]), childhood undernutrition (113·3 million [103·9 million to 123·4 million]), ambient particulate matter (103·1 million [90·8 million to 115·1 million]), high total cholesterol (88·7 million [74·6 million to 105·7 million]), household air pollution (85·6 million [66·7 million to 106·1 million]), alcohol use (85·0 million [77·2 million to 93·0 million]), and diets high in sodium (83·0 million [49·3 million to 127·5 million]). From 1990 to 2015, attributable DALYs declined for micronutrient deficiencies, childhood undernutrition, unsafe sanitation and water, and household air pollution; reductions in risk-deleted DALY rates rather than reductions in exposure drove these declines. Rising exposure contributed to notable increases in attributable DALYs from high BMI, high fasting plasma glucose, occupational carcinogens, and drug use. Environmental risks and childhood undernutrition declined steadily with SDI; low physical activity, high BMI, and high fasting plasma glucose increased with SDI. In 119 countries, metabolic risks, such as high BMI and fasting plasma glucose, contributed the most attributable DALYs in 2015. Regionally, smoking still ranked among the leading five risk factors for attributable DALYs in 109 countries; childhood underweight and unsafe sex remained primary drivers of early death and disability in much of sub-Saharan Africa. Interpretation Declines in some key environmental risks have contributed to declines in critical infectious diseases. Some risks appear to be invariant to SDI. Increasing risks, including high BMI, high fasting plasma glucose, drug use, and some occupational exposures, contribute to rising burden from some conditions, but also provide opportunities for intervention. Some highly preventable risks, such as smoking, remain major causes of attributable DALYs, even as exposure is declining. Public policy makers need to pay attention to the risks that are increasingly major contributors to global burden. Funding Bill & Melinda Gates Foundation

N-acetylcysteine does not prevent contrast-induced nephropathy after cardiac catheterization in patients with diabetes mellitus and chronic kidney disease: a randomized clinical trial

<p>Abstract</p> <p>Background</p> <p>Patients with diabetes mellitus (DM) and chronic kidney disease (CKD) constitute to be a high-risk population for the development of contrast-induced nephropathy (CIN), in which the incidence of CIN is estimated to be as high as 50%. We performed this trial to assess the efficacy of <it>N</it>-acetylcysteine (NAC) in the prevention of this complication.</p> <p>Methods</p> <p>In a prospective, double-blind, placebo controlled, randomized clinical trial, we studied 90 patients undergoing elective diagnostic coronary angiography with DM and CKD (serum creatinine ≥ 1.5 mg/dL for men and ≥ 1.4 mg/dL for women). The patients were randomly assigned to receive either oral NAC (600 mg BID, starting 24 h before the procedure) or placebo, in adjunct to hydration. Serum creatinine was measured prior to and 48 h after coronary angiography. The primary end-point was the occurrence of CIN, defined as an increase in serum creatinine ≥ 0.5 mg/dL (44.2 μmol/L) or ≥ 25% above baseline at 48 h after exposure to contrast medium.</p> <p>Results</p> <p>Complete data on the outcomes were available on 87 patients, 45 of whom had received NAC. There were no significant differences between the NAC and placebo groups in baseline characteristics, amount of hydration, or type and volume of contrast used, except in gender (male/female, 20/25 and 34/11, respectively; P = 0.005) and the use of statins (62.2% and 37.8%, respectively; P = 0.034). CIN occurred in 5 out of 45 (11.1%) patients in the NAC group and 6 out of 42 (14.3%) patients in the placebo group (P = 0.656).</p> <p>Conclusion</p> <p>There was no detectable benefit for the prophylactic administration of oral NAC over an aggressive hydration protocol in patients with DM and CKD.</p> <p>Trial registration</p> <p>NCT00808795</p

High power Q-switched thulium doped fibre laser using carbon nanotube polymer composite saturable absorber

We have proposed and demonstrated a Q-switched Thulium doped bre laser (TDFL) with a ‘Yin-Yang’ all- bre cavity scheme based on a combination of nonlinear optical loop mirror (NOLM) and nonlinear ampli ed loop mirror (NALM). Unidirectional lasing operation has been achieved without any intracavity isolator. By using a carbon nanotube polymer composite based saturable absorber (SA), we demonstrated the laser output power of ~197 mW and pulse energy of 1.7 μJ. To the best of our knowledge, this is the highest output power from a nanotube polymer composite SA based Q-switched Thulium doped bre laser