A probabilistic multi-objective approach for FACTS devices allocation with different levels of wind penetration under uncertainties and load correlation

This study presents a probabilistic multi-objective optimization approach to obtain the optimal locations and sizes of static var compensator (SVC) and thyristor-controlled series capacitor (TCSC) in a power transmission network with large level of wind generation. In this study, the uncertainties of the wind power generation and correlated load demand are considered. The uncertainties are modeled in this work using the points estimation method (PEM). The optimization problem is solved using the Multi-objective particle swarm optimization (MOPSO) algorithm to find the best position and rating of the flexible AC transmission system (FACTS) devices. The objective of the problem is to maximize the system loadability while minimizing the power losses and FACTS devices installation cost. Additionally, a technique based on fuzzy decision-making approach is employed to extract one of the Pareto optimal solutions as the best compromise one. The proposed approach is applied on the modified IEEE 30-bus system. The numerical results evince the effectiveness of the proposed approach and shows the economic benefits that can be achieved when considering the FACTS controller

Mismatch Repair Proteins (MLH1, MSH2, MSH6, and PMS2) Immunohistochemical Expression and Microsatellite Instability in Endometrial Carcinoma

BACKGROUND: Endometrial cancer (EC) is the fourth most common female cancer worldwide constituting 7% of cancer in women. It is a disease of older, postmenopausal women. The most of these patients have an identifiable source of excess estrogen, while in a small subset the pathogenesis is related to mismatch repair abnormality and lynch syndrome (LC). Mismatch repair behave as tumor suppressors and the most clinically relevant include MLH1, MSH2, MSH6, and PMS2. mutations in mismatch repair (MMR) results in a strong mutator phenotype known as microsatellite instability, which is a hallmark of LC-associated cancers. AIM: The aim of the study was to study microsatellite instability in endometrial cancer using the immunohistochemical expression of mismatch repair proteins (MLH1, MSH2, MSH6 and PMS2). MATERIAL AND METHODS: Sixty EC cases were studied using MLH-1, MSH-2, MSH-6, and PMS-2 immunohistochemistry and their expression was correlated with different clinicopathologic parameters. RESULTS: A statistically significant relationship exists between MMR immunohistochemistry (IHC) proteins and tumor grade. Intact MMR proteins profile was associated with the lower tumor grade (31.3% were Grade 1 and 46.9% were Grade 2). Combined loss of MLH1/PMS2, combined loss of MSH2/MSH6, and isolated loss of PMS2 were also associated with the lower tumor grade while isolated loss of MSH6 was associated with the high tumor grade. However, no statistically significant correlation was found between MMR IHC proteins expression and the age of patients; tumor histopathological types, or FIGO stage. CONCLUSION: A statistically significant correlation between the tumor grade of EC cases and the MMR IHC proteins was found. Further studies are recommended to assess correlation between MMR proteins defect and different clinicopathological parameters of endometrial carcinoma

FACTS allocation considering loads uncertainty, steady state operation constraints, and dynamic operation constraints

This study proposes an algorithm to allocate different types of flexible AC transmission system (FACTS) in power systems. The main objective of this study is to maximize profit by minimizing the system’s operating cost including FACTS devices (FDs) installation cost. Dynamic and steady state operating restrictions with loads uncertainty are included in the problem formulation. The overall problem is solved using both teaching learning based optimization (TLBO) technique for attaining the optimal allocation of the FDs as main-optimization problem and matpower interior point solver (MIPS) for optimal power flow (OPF) as the sub-optimization problem. The validation of the proposed approach is verified by applying it to test system of 59-bus; Simplified 14-Generator model of the South East Australian power system

Population based optimization algorithms improvement using the predictive particles

A new efficient improvement, called Predictive Particle Modification (PPM), is proposed in this paper. This modification makes the particle look to the near area before moving toward the best solution of the group. This modification can be applied to any population algorithm. The basic philosophy of PPM is explained in detail. To evaluate the performance of PPM, it is applied to Particle Swarm Optimization (PSO) algorithm and Teaching Learning Based Optimization (TLBO) algorithm then tested using 23 standard benchmark functions. The effectiveness of these modifications are compared with the other unmodified population optimization algorithms based on the best solution, average solution, and convergence rate

Generalized optimal placement of PMUs considering power system observability, communication infrastructure, and quality of service requirements

This paper presents a generalized optimal placement of Phasor Measurement Units (PMUs) considering power system observability, reliability, Communication Infrastructure (CI), and latency time associated with this CI. Moreover, the economic study for additional new data transmission paths is considered as well as the availability of predefined locations of some PMUs and the preexisting communication devices (CDs) in some buses. Two cases for the location of the Control Center Base Station (CCBS) are considered; predefined case and free selected case. The PMUs placement and their required communication network topology and channel capacity are co-optimized simultaneously. In this study, two different approaches are applied to optimize the objective function; the first approach is combined from Binary Particle Swarm Optimization-Gravitational Search Algorithm (BPSOGSA) and the Minimum Spanning Tree (MST) algorithm, while the second approach is based only on BPSOGSA. The feasibility of the proposed approaches are examined by applying it to IEEE 14-bus and IEEE 118-bus systems

Alleviation of drought and salt stress in vegetables : crop responses and mitigation strategies

In recent decades, the demand for vegetables has increased signifcantly due to the blooming global population. Climate change has afected vegetable production by increasing the frequencies and severity of abiotic and biotic stresses. Among the abiotic stresses, drought and salinity are the major issues that possess severe threats on vegetable production. Many vegetables (e.g., carrot, tomato, okra, pea, eggplant, lettuce, potato) are usually sensitive to drought and salt stress. The defence mechanisms of plants against salt and drought stress have been extensively studied in model plant species and feld crops. Better understanding of the mechanisms of susceptibility of vegetables to drought and salt stresses will help towards the development of more tolerant genotypes as a long-term strategy against these stresses. However, the intensity of the challenges also warrants more immediate approaches to mitigate these stresses and enhance vegetable production in the short term. Therefore, this review enlightens the updated knowledge of responses (physiological and molecular) against drought and salinity in vegetables and potentially efective strategies to enhance production. Moreover, we summarized diferent technologies such as seed priming, genetic transformation, biostimulants, nanotechnology, and cultural practices adopted to enhance vegetable production under drought and salinity stress. We propose that approaches of conventional breeding, genetic engineering, and crop management should be combined to generate drought and salt resistance cultivars and adopt smart cultivation practices for sustainable vegetable production in a changing climate

Contribution of proton and electron precipitation to the observed electron concentration in October–November 2003 and September 2005

Understanding the altitude distribution of particle precipitation forcing is vital for the assessment of its atmospheric and climate impacts. However, the proportion of electron and proton forcing around the mesopause region during solar proton events is not always clear due to uncertainties in satellite-based flux observations. Here we use electron concentration observations of the European Incoherent Scatter Scientific Association (EISCAT) incoherent scatter radars located at Tromsø (69.58° N, 19.23° E) to investigate the contribution of proton and electron precipitation to the changes taking place during two solar proton events. The EISCAT measurements are compared to the results from the Sodankylä Ion and Neutral Chemistry Model (SIC). The proton ionization rates are calculated by two different methods – a simple energy deposition calculation and the Atmospheric Ionization Model Osnabrück (AIMOS v1.2), the latter providing also the electron ionization rates. Our results show that in general the combination of AIMOS and SIC is able to reproduce the observed electron concentration within ± 50% when both electron and proton forcing is included. Electron contribution is dominant above 90 km, and can contribute significantly also in the upper mesosphere especially during low or moderate proton forcing. In the case of strong proton forcing, the AIMOS electron ionization rates seem to suffer from proton contamination of satellite-based flux data. This leads to overestimation of modelled electron concentrations by up to 90% between 75–90 km and up to 100–150% at 70–75 km. Above 90 km, the model bias varies significantly between the events. Although we cannot completely rule out EISCAT data issues, the difference is most likely a result of the spatio-temporal fine structure of electron precipitation during individual events that cannot be fully captured by sparse in situ flux (point) measurements, nor by the statistical AIMOS model which is based upon these observations

Anthracnose Disease (Colletotrichum sp.) Affecting Olive Fruit Quality and Its Control in Egypt

Abstract Olive anthracnose is the most important fungal disease of olive fruits worldwide. It occurs in humid olive-growing areas of many countries and causes heavy yield losses and lowering of oil quality. Colletotrichum acutatum was isolated and identified from rotten olive fruits. Pathogenicity test of C. acutatum was confirmed. It was found to be decreased all physical characteristics measured i.e. weight (gm), length (mm), diameter (mm) and volume (ml3). Also, C. acutatum was found to decrease the oil content of the fruits, while increasing their total titratable acidity and moisture content. Physiological studies resulted that, the highest growth rate and sporulation was recorded with PDA medium, PH 6.5 and Light/dark cycle treatments. Hot water treatments at 45, 50 and 55°C were able to decreased spore viability of C. acutatum compared with untreated (control). The best treatment of hot water was recorded with 55°C. in vivo. Also, hot water treatment at 55°C was successful in reducing the percentage of anthracnose disease incidence on olive fruits in vitro. All tested alternative substrates i. e. Ascorbic acid, Benzoic acid, potassium sorbate and citric acid used were able to reduce the linear growth rate of C. acutatum in vitro. Benzoic acid was found to be the best alternative substrate used which gave completely fruit protection (hundred of reduction percent) followed by Ascorbic acid, Potassium sorbate and Citric acid. This is thought to be the first report of anthracnose disease of olive fruits in Egypt

Probing host pathogen cross-talk by transcriptional profiling of both Mycobacterium tuberculosis and infected human dendritic cells and macrophages

This study provides the proof of principle that probing the host and the microbe transcriptomes simultaneously is a valuable means to accessing unique information on host pathogen interactions. Our results also underline the extraordinary plasticity of host cell and pathogen responses to infection, and provide a solid framework to further understand the complex mechanisms involved in immunity to M. tuberculosis and in mycobacterial adaptation to different intracellular environments

Towards a novel biologically-inspired cloud elasticity framework

With the widespread use of the Internet, the popularity of web applications has significantly increased. Such applications are subject to unpredictable workload conditions that vary from time to time. For example, an e-commerce website may face higher workloads than normal during festivals or promotional schemes. Such applications are critical and performance related issues, or service disruption can result in financial losses. Cloud computing with its attractive feature of dynamic resource provisioning (elasticity) is a perfect match to host such applications. The rapid growth in the usage of cloud computing model, as well as the rise in complexity of the web applications poses new challenges regarding the effective monitoring and management of the underlying cloud computational resources. This thesis investigates the state-of-the-art elastic methods including the models and techniques for the dynamic management and provisioning of cloud resources from a service provider perspective. An elastic controller is responsible to determine the optimal number of cloud resources, required at a particular time to achieve the desired performance demands. Researchers and practitioners have proposed many elastic controllers using versatile techniques ranging from simple if-then-else based rules to sophisticated optimisation, control theory and machine learning based methods. However, despite an extensive range of existing elasticity research, the aim of implementing an efficient scaling technique that satisfies the actual demands is still a challenge to achieve. There exist many issues that have not received much attention from a holistic point of view. Some of these issues include: 1) the lack of adaptability and static scaling behaviour whilst considering completely fixed approaches; 2) the burden of additional computational overhead, the inability to cope with the sudden changes in the workload behaviour and the preference of adaptability over reliability at runtime whilst considering the fully dynamic approaches; and 3) the lack of considering uncertainty aspects while designing auto-scaling solutions. This thesis seeks solutions to address these issues altogether using an integrated approach. Moreover, this thesis aims at the provision of qualitative elasticity rules. This thesis proposes a novel biologically-inspired switched feedback control methodology to address the horizontal elasticity problem. The switched methodology utilises multiple controllers simultaneously, whereas the selection of a suitable controller is realised using an intelligent switching mechanism. Each controller itself depicts a different elasticity policy that can be designed using the principles of fixed gain feedback controller approach. The switching mechanism is implemented using a fuzzy system that determines a suitable controller/- policy at runtime based on the current behaviour of the system. Furthermore, to improve the possibility of bumpless transitions and to avoid the oscillatory behaviour, which is a problem commonly associated with switching based control methodologies, this thesis proposes an alternative soft switching approach. This soft switching approach incorporates a biologically-inspired Basal Ganglia based computational model of action selection. In addition, this thesis formulates the problem of designing the membership functions of the switching mechanism as a multi-objective optimisation problem. The key purpose behind this formulation is to obtain the near optimal (or to fine tune) parameter settings for the membership functions of the fuzzy control system in the absence of domain experts’ knowledge. This problem is addressed by using two different techniques including the commonly used Genetic Algorithm and an alternative less known economic approach called the Taguchi method. Lastly, we identify seven different kinds of real workload patterns, each of which reflects a different set of applications. Six real and one synthetic HTTP traces, one for each pattern, are further identified and utilised to evaluate the performance of the proposed methods against the state-of-the-art approaches