Lady Gaga, allt handlar om strategi? : En studie kring vem eller vilka personer och vilka faktorer som skapade superstjärnan Lady Gaga

De frågeställningar som jag har utgått från är vem eller vilka personer, och vilka andra faktorer skapade superstjärnan Lady Gaga? Har Lady Gaga blivit framgångsrik på egen hand eller huvudsakligen som ett resultat av andra personer och deras inverkan? Syftet är att analysera bakgrunden till Lady Gagas enorma succé och utforska möjliga orsaker och förklaringar till den, för att sedan kunna använda och föra vidare kunskapen om dessa framgångsstrategier i musikbranschen. Jag har använt mig av kvalitativa intervjuer med fallstudien som metod. Intervjuerna är gjorda i New York, USA och omfattar inflytelserika personer från musik-, konst- och modevärlden, med inriktning på vilka faktorer och personer som varit viktiga och avgörande för Lady Gagas karriär. Analysen är baserad på en teori om Lady Gagas strategier kallad 4E efter ledorden emotions (känslor), erfarenhet, engagemang och exklusivitet. Intervjuerna och fallstudien bekräftar 4E-teorin på alla fyra punkter. Analysen visar att två ytterligare viktiga faktorer är vikten av stödjande grupper runt en ny okänd artist och New Yorks mångfald av artister, musiker och konstnärer.

När det inte blir som man har tänkt sig : - En fallstudie av undervisningssituationer med elever med behov av särskilt stöd

Fallstudien beskriver och diskuterar några lektioner med elever som har specifika inlärnings-svårigheter, utifrån skolverkets Allmänna råd, och belyser vilka pedagogiskt svårhanterliga problem och dilemman som kan uppstå när det inte blir som läraren har planerat. Teorin om den proximala utvecklingszonen åskådliggörs. Aktivitetens betydelse för inlärning belyses. Komplexiteten kring lärarskap och ledarskap illustreras. Skolverkets teori om kunskap genom progression, enligt givna årskurser med givet innehåll, stämmer inte alltid med erfarenheterna hos några av verklighetens elever. Samspelet mellan lärare och elever belyses i studien. Be-skrivningar av utvecklingsrelaterade funktionsnedsättningar och psykiskt sårbara barn visar ytterligare hur elevers individuella förutsättningar kan ge konsekvenser för inlärning.

Elevers läsvanor : En kvalitativ studie av fyra fordonselevers läsvanor

The following study was conducted at an upper secondary school in Sweden and attempts to explore the question of what influences male pupils’ reading habits. Many quantitative international studies, including PISA, PIRLS and IEA Reading Literacy, have sought to answer this question, but only partially succeeded due to the limitations of their methods. Therefore, this study seeks to explore this question in more depth using qualitative methods, including interviews and classroom observations, but also minor tests. Two facts which the previously mentioned international studies have found is that boys and particularly immigrant boys tend to have worse reading results than their counterparts. It is therefore the aim of this study to study four male students in upper secondary school; of which two are native Swedes and the other two are unaccompanied refugee children; one from Afghanistan and the other from Morocco. The findings of this study are as follows. Firstly, necessity was found to be the single most important factor for the reading habits of these four pupils; especially the two refugees. Both refugees learnt to read under harsh circumstances in madrassas in their respective home countries. Moreover, the Moroccan pupil learnt to speak and read Spanish fluently during his seven years as a homeless child. Furthermore, in the absence of necessity, interest was found to be decisive in determining the pupils’ reading habits. In addition to this, the study theorizes that an interest in reading generally arises before the ability to read and not vice versa. However, teachers can in fact affect their pupils’ reading habits even in upper secondary school.

A particle swarm based hybrid system for imbalances medical data sampling

Background
Medical and biological data are commonly with small sample size, missing values, and most importantly, imbalanced class distribution. In this study we propose a particle swarm based hybrid system for remedying the class imbalance problem in medical and biological data mining. This hybrid system combines the particle swarm optimization (PSO) algorithm with multiple classifiers and evaluation metrics for evaluation fusion. Samples from the majority class are ranked using multiple objectives according to their merit in compensating the class imbalance, and then combined with the minority class to form a balanced dataset.

Results
One important finding of this study is that different classifiers and metrics often provide different evaluation results. Nevertheless, the proposed hybrid system demonstrates consistent improvements over several alternative methods with three different metrics. The sampling results also demonstrate good generalization on different types of classification algorithms, indicating the advantage of information fusion applied in the hybrid system.

Conclusion
The experimental results demonstrate that unlike many currently available methods which often perform unevenly with different datasets the proposed hybrid system has a better generalization property which alleviates the method-data dependency problem. From the biological perspective, the system provides indication for further investigation of the highly ranked samples, which may result in the discovery of new conditions or disease subtypes.

A layered-encoding cascade optimization approach to product-mix planning in high-mix-low-volume manufacturing

High-mix-low-volume (HMLV) production is currently a worldwide manufacturing trend. It requires a high degree of customization in the manufacturing process to produce a wide range of products in low quantity in order to meet customers' demand for more variety and choices of products. Such a kind of business environment has increased the conversion time and decreased the production efficiency due to frequent production changeover. In this paper, a layered-encoding cascade optimization (LECO) approach is proposed to develop an HMLV product-mix optimizer that exhibits the benefits of low conversion time, high productivity, and high equipment efficiency. Specifically, the genetic algorithm (GA) and particle swarm optimization (PSO) techniques are employed as optimizers for different decision layers in different LECO models. Each GA and PSO optimizer is studied and compared. A number of hypothetical and real data sets from a manufacturing plant are used to evaluate the performance of the proposed GA and PSO optimizers. The results indicate that, with a proper selection of the GA and PSO optimizers, the LECO approach is able to generate high-quality product-mix plans to meet the production demands in HMLV manufacturing environments.

An interactive evolutionary algorithm for MMIC low noise amplifier design

A layer-encoded interactive evolutionary algorithm (IEA) for optimization of design parameters of a monolithic microwave integrated circuit (MMIC) low noise amplifier is presented. The IEA comprises a combination of the genetic algorithm (GA) and the particle swarm optimization (PSO) technique. The layer-encoding structure allows human intervention in order to accelerate the process of evolution, whereas the GA and PSO technique are incorporated to enhance both global and local searches. With this combination of features, the proposed IEA has shown to be efficient in meeting all requirements and constraints of the MMIC. In addition, the IEA is able to optimize noise figure, current, and power gain of the MMIC amplifier design.

Construction of neural network-based prediction intervals using particle swarm optimization

Prediction intervals (PIs) are excellent tools for quantification of uncertainties associated with point forecasts and predictions. This paper adopts and develops the lower upper bound estimation (LUBE) method for construction of PIs using neural network (NN) models. This method is fast and simple and does not require calculation of heavy matrices, as required by traditional methods. Besides, it makes no assumption about the data distribution. A new width-based index is proposed to quantitatively check how much PIs are informative. Using this measure and the coverage probability of PIs, a multi-objective optimization problem is formulated to train NN models in the LUBE method. The optimization problem is then transformed into a training problem through definition of a PI-based cost function. Particle swarm optimization (PSO) with the mutation operator is used to minimize the cost function. Experiments with synthetic and real-world case studies indicate that the proposed PSO-based LUBE method can construct higher quality PIs in a simpler and faster manner.

A cascade genetic algorithm and particle swarm approach for solving unit commitment and economic dispatch problems

Positive Unit commitment and economic dispatch are two important decisions in thermal power generation scheduling. The tasks involve determination and allocation of power generation to thermal units that minimize the total power generation cost and satisfy the production constraints.This paper presents a cascade Genetic Algorithm and Particle Swarm Optimization (GA-PSO) approach for solving thermal power generation scheduling based on a layered matrix encoding structure.The proposed hybrid method is compared to layered matrix encoding GA using the thermal power generation problem given in Williams [1] to demonstrate its effectiveness in generating an optimal, cost-effective power generation schedule.The results showed that cascade GA-PSO outperformed the layered matrix encoding GA in minimizing the total power production cost for unit commitment and power dispatch problems.

An efficient meta-heuristic algorithm for grid computing

A grid computing system consists of a group of programs and resources that are spread across machines in the grid. A grid system has a dynamic environment and decentralized distributed resources, so it is important to provide efficient scheduling for applications. Task scheduling is an NP-hard problem and deterministic algorithms are inadequate and heuristic algorithms such as particle swarm optimization (PSO) are needed to solve the problem. PSO is a simple parallel algorithm that can be applied in different ways to resolve optimization problems. PSO searches the problem space globally and needs to be combined with other methods to search locally as well. In this paper, we propose a hybrid-scheduling algorithm to solve the independent task- scheduling problem in grid computing. We have combined PSO with the gravitational emulation local search (GELS) algorithm to form a new method, PSO–GELS. Our experimental results demonstrate the effectiveness of PSO–GELS compared to other algorithms.

Mixing Matrix Estimation From Sparse Mixtures With Unknown Number of Sources

In blind source separation, many methods have been proposed to estimate the mixing matrix by exploiting sparsity. However, they often need to know the source number a priori, which is very inconvenient in practice. In this paper, a new method, namely nonlinear projection and column masking (NPCM), is proposed to estimate the mixing matrix. A major advantage of NPCM is that it does not need any knowledge of the source number. In NPCM, the objective function is based on a nonlinear projection and its maxima just correspond to the columns of the mixing matrix. Thus a column can be estimated first by locating a maximum and then deflated by a masking operation. This procedure is repeated until the evaluation of the objective function decreases to zero dramatically. Thus the mixing matrix and the number of sources are estimated simultaneously. Because the masking procedure may result in some small and useless local maxima, particle swarm optimization (PSO) is introduced to optimize the objective function. Feasibility and efficiency of PSO are also discussed. Comparative experimental results show the efficiency of NPCM, especially in the cases where the number of sources is unknown and the sources are relatively less sparse.

Short-term load and wind power forecasting using neural network-based prediction intervals

Electrical power systems are evolving from today's centralized bulk systems to more decentralized systems. Penetrations of renewable energies, such as wind and solar power, significantly increase the level of uncertainty in power systems. Accurate load forecasting becomes more complex, yet more important for management of power systems. Traditional methods for generating point forecasts of load demands cannot properly handle uncertainties in system operations. To quantify potential uncertainties associated with forecasts, this paper implements a neural network (NN)-based method for the construction of prediction intervals (PIs). A newly introduced method, called lower upper bound estimation (LUBE), is applied and extended to develop PIs using NN models. A new problem formulation is proposed, which translates the primary multiobjective problem into a constrained single-objective problem. Compared with the cost function, this new formulation is closer to the primary problem and has fewer parameters. Particle swarm optimization (PSO) integrated with the mutation operator is used to solve the problem. Electrical demands from Singapore and New South Wales (Australia), as well as wind power generation from Capital Wind Farm, are used to validate the PSO-based LUBE method. Comparative results show that the proposed method can construct higher quality PIs for load and wind power generation forecasts in a short time.

Construction of neural network-based prediction intervals for short-term electrical load forecasting

Short-term load forecasting (STLF) is of great importance for control and scheduling of electrical power systems. The uncertainty of power systems increases due to the random nature of climate and the penetration of the renewable energies such as wind and solar power. Traditional methods for generating point forecasts of load demands cannot properly handle uncertainties in datasets. To quantify these potential uncertainties associated with forecasts, this paper implements a neural network (NN)-based method for construction of prediction intervals (PIs). A newly proposed method, called lower upper bound estimation (LUBE), is applied to develop PIs using NN models. The primary multi-objective problem is firstly transformed into a constrained single-objective problem. This new problem formulation is closer to the original problem and has fewer parameters than the cost function. Particle swarm optimization (PSO) integrated with the mutation operator is used to solve the problem. Two case studies from Singapore and New South Wales (Australia) historical load datasets are used to validate the PSO-based LUBE method. Demonstrated results show that the proposed method can construct high quality PIs for load forecasting applications.

Improved particle swarm optimization-based artificial neural network for rainfall-runoff modeling

This paper presents the application of an improved particle swarm optimization (PSO) technique for training an artificial neural network (ANN) to predict water levels for the Heshui watershed, China. Daily values of rainfall and water levels from 1988 to 2000 were first analyzed using ANNs trained with the conjugate-gradient, gradient descent and Levenberg-Marquardt neural network (LM-NN) algorithms. The best results were obtained from LM-NN and these results were then compared with those from PSO-based ANNs, including conventional PSO neural network (CPSONN) and improved PSO neural network (IPSONN) with passive congregation. The IPSONN algorithm improves PSO convergence by using the selfish herd concept in swarm behavior. Our results show that the PSO-based ANNs performed better than LM-NN. For models run using a single parameter (rainfall) as input, the root mean square error (RMSE) of the testing dataset for IPSONN was the lowest (0.152 m) compared to those for CPSONN (0.161 m) and LM-NN (0.205 m). For multi-parameter (rainfall and water level) inputs, the RMSE of the testing dataset for IPSONN was also the lowest (0.089 m) compared to those for CPSONN (0.105 m) and LM-NN (0.145 m). The results also indicate that the LM-NN model performed poorly in predicting the low and peak water levels, in comparison to the PSO-based ANNs. Moreover, the IPSONN model was superior to CPSONN in predicting extreme water levels. Lastly, IPSONN had a quicker convergence rate compared to CPSONN.

Wind-thermal systems operation optimization considering emission problem

This paper proposes a hybrid computational framework based on Sequential Quadratic Programming (SQP) and Particle Swarm Optimization (PSO) to address the Combined Unit Commitment and Emission (CUCE) problem. By considering a model which includes both thermal generators and wind farms, the proposed hybrid computational framework can minimize the scheduling cost and greenhouse gases emission cost. The viability of the proposed hybrid technique is demonstrated using a set of numerical case studies. Moreover, comparisons are performed with other optimization algorithms. The simulation results show that our hybrid method is better in terms of the speed and accuracy. The main contribution of this paper is the development of a emission unit commitment model integrating with wind energy and combining the SQP and PSO methods to achieve faster and better performance optimization

Uncertainty handling using neural network-based prediction intervals for electrical load forecasting

The complexity and level of uncertainty present in operation of power systems have significantly grown due to penetration of renewable resources. These complexities warrant the need for advanced methods for load forecasting and quantifying uncertainties associated with forecasts. The objective of this study is to develop a framework for probabilistic forecasting of electricity load demands. The proposed probabilistic framework allows the analyst to construct PIs (prediction intervals) for uncertainty quantification. A newly introduced method, called LUBE (lower upper bound estimation), is applied and extended to develop PIs using NN (neural network) models. The primary problem for construction of intervals is firstly formulated as a constrained single-objective problem. The sharpness of PIs is treated as the key objective and their calibration is considered as the constraint. PSO (particle swarm optimization) enhanced by the mutation operator is then used to optimally tune NN parameters subject to constraints set on the quality of PIs. Historical load datasets from Singapore, Ottawa (Canada) and Texas (USA) are used to examine performance of the proposed PSO-based LUBE method. According to obtained results, the proposed probabilistic forecasting method generates well-calibrated and informative PIs. Furthermore, comparative results demonstrate that the proposed PI construction method greatly outperforms three widely used benchmark methods. © 2014 Elsevier Ltd.