A genetic algorithm for project scheduling with multi-modes and renewable resources

This paper presents a genetic algorithm-based approach for project scheduling with multi-modes and renewable resources. In this problem activities of the project may be executed in more than one operating mode and renewable resource constraints are imposed. The objective function is the minimization of the project completion time. The idea of this approach is integrating a genetic algorithm with a schedule generation scheme. This study also proposes applying a local search procedure trying to yield a better solution when the genetic algorithm and the schedule generation scheme obtain a solution. The experimental results show that this algorithm is an effective method for solving this problem.

Evolving Distributed Algorithms with Genetic Programming

Distributed systems are one of the most vital components of the economy. The most prominent example is probably the internet, a constituent element of our knowledge society. During the recent years, the number of novel network types has steadily increased. Amongst others, sensor networks, distributed systems composed of tiny computational devices with scarce resources, have emerged. The further development and heterogeneous connection of such systems imposes new requirements on the software development process. Mobile and wireless networks, for instance, have to organize themselves autonomously and must be able to react to changes in the environment and to failing nodes alike. Researching new approaches for the design of distributed algorithms may lead to methods with which these requirements can be met efficiently. In this thesis, one such method is developed, tested, and discussed in respect of its practical utility. Our new design approach for distributed algorithms is based on Genetic Programming, a member of the family of evolutionary algorithms. Evolutionary algorithms are metaheuristic optimization methods which copy principles from natural evolution. They use a population of solution candidates which they try to refine step by step in order to attain optimal values for predefined objective functions. The synthesis of an algorithm with our approach starts with an analysis step in which the wanted global behavior of the distributed system is specified. From this specification, objective functions are derived which steer a Genetic Programming process where the solution candidates are distributed programs. The objective functions rate how close these programs approximate the goal behavior in multiple randomized network simulations. The evolutionary process step by step selects the most promising solution candidates and modifies and combines them with mutation and crossover operators. This way, a description of the global behavior of a distributed system is translated automatically to programs which, if executed locally on the nodes of the system, exhibit this behavior. In our work, we test six different ways for representing distributed programs, comprising adaptations and extensions of well-known Genetic Programming methods (SGP, eSGP, and LGP), one bio-inspired approach (Fraglets), and two new program representations called Rule-based Genetic Programming (RBGP, eRBGP) designed by us. We breed programs in these representations for three well-known example problems in distributed systems: election algorithms, the distributed mutual exclusion at a critical section, and the distributed computation of the greatest common divisor of a set of numbers. Synthesizing distributed programs the evolutionary way does not necessarily lead to the envisaged results. In a detailed analysis, we discuss the problematic features which make this form of Genetic Programming particularly hard. The two Rule-based Genetic Programming approaches have been developed especially in order to mitigate these difficulties. In our experiments, at least one of them (eRBGP) turned out to be a very efficient approach and in most cases, was superior to the other representations.

A neural network enhanced generalized minimum variance self-tuning proportional, integral and derivative control algorithm for complex dynamic systems

A neural network enhanced proportional, integral and derivative (PID) controller is presented that combines the attributes of neural network learning with a generalized minimum-variance self-tuning control (STC) strategy. The neuro PID controller is structured with plant model identification and PID parameter tuning. The plants to be controlled are approximated by an equivalent model composed of a simple linear submodel to approximate plant dynamics around operating points, plus an error agent to accommodate the errors induced by linear submodel inaccuracy due to non-linearities and other complexities. A generalized recursive least-squares algorithm is used to identify the linear submodel, and a layered neural network is used to detect the error agent in which the weights are updated on the basis of the error between the plant output and the output from the linear submodel. The procedure for controller design is based on the equivalent model, and therefore the error agent is naturally functioned within the control law. In this way the controller can deal not only with a wide range of linear dynamic plants but also with those complex plants characterized by severe non-linearity, uncertainties and non-minimum phase behaviours. Two simulation studies are provided to demonstrate the effectiveness of the controller design procedure.

Efficient bulk-loading on dynamic metric access methods

This paper presents a new technique and two algorithms to bulk-load data into multi-way dynamic metric access methods, based on the covering radius of representative elements employed to organize data in hierarchical data structures. The proposed algorithms are sample-based, and they always build a valid and height-balanced tree. We compare the proposed algorithm with existing ones, showing the behavior to bulk-load data into the Slim-tree metric access method. After having identified the worst case of our first algorithm, we describe adequate counteractions in an elegant way creating the second algorithm. Experiments performed to evaluate their performance show that our bulk-loading methods build trees faster than the sequential insertion method regarding construction time, and that it also significantly improves search performance. (C) 2009 Elsevier B.V. All rights reserved.

Electroencephalographic changes and seizures in familial hemiplegic migraine patients with the CACNA1A gene S218L mutation

The S218L CACNA1A mutation has been previously described in two families with familial hemiplegic migraine. We present three siblings with the mutation with the novel association of childhood seizures, and highlight the dynamic changes seen on electroencephalography during hemiplegic migraine attacks. Depressed activity contralateral to the hemiparesis was seen on electroencephalography during acute hemiplegic migraine attacks, which may be due to changes to calcium channels caused by the S218L mutation. Both parents were asymptomatic and did not carry the S218L mutation in their blood. This suggests the presence of mosaicism in the transmitting parent.

AUTOFLY-Aid: Flight Deck Automation Support with Dynamic 4D Trajectory Management for Responsive and Adaptive Airborne Collision Avoidance

AUTOFLY-Aid Project aims to develop and demonstrate novel automation support algorithms and tools to the flight crew for flight critical collision avoidance using “dynamic 4D trajectory management”. The automation support system is envisioned to improve the primary shortcomings of TCAS, and to aid the pilot through add-on avionics/head-up displays and reality augmentation devices in dynamically evolving collision avoidance scenarios. The main theoretical innovative and novel concepts to be developed by AUTOFLY-Aid project are a) design and development of the mathematical models of the full composite airspace picture from the flight deck’s perspective, as seen/measured/informed by the aircraft flying in SESAR 2020, b) design and development of a dynamic trajectory planning algorithm that can generate at real-time (on the order of seconds) flyable (i.e. dynamically and performance-wise feasible) alternative trajectories across the evolving stochastic composite airspace picture (which includes new conflicts, blunder risks, terrain and weather limitations) and c) development and testing of the Collision Avoidance Automation Support System on a Boeing 737 NG FNPT II Flight Simulator with synthetic vision and reality augmentation while providing the flight crew with quantified and visual understanding of collision risks in terms of time and directions and countermeasures.

Nested Sampling and its Applications in Stable Compressive Covariance Estimation and Phase Retrieval with Near-Minimal Measurements

Compressed covariance sensing using quadratic samplers is gaining increasing interest in recent literature. Covariance matrix often plays the role of a sufficient statistic in many signal and information processing tasks. However, owing to the large dimension of the data, it may become necessary to obtain a compressed sketch of the high dimensional covariance matrix to reduce the associated storage and communication costs. Nested sampling has been proposed in the past as an efficient sub-Nyquist sampling strategy that enables perfect reconstruction of the autocorrelation sequence of Wide-Sense Stationary (WSS) signals, as though it was sampled at the Nyquist rate. The key idea behind nested sampling is to exploit properties of the difference set that naturally arises in quadratic measurement model associated with covariance compression. In this thesis, we will focus on developing novel versions of nested sampling for low rank Toeplitz covariance estimation, and phase retrieval, where the latter problem finds many applications in high resolution optical imaging, X-ray crystallography and molecular imaging. The problem of low rank compressive Toeplitz covariance estimation is first shown to be fundamentally related to that of line spectrum recovery. In absence if noise, this connection can be exploited to develop a particular kind of sampler called the Generalized Nested Sampler (GNS), that can achieve optimal compression rates. In presence of bounded noise, we develop a regularization-free algorithm that provably leads to stable recovery of the high dimensional Toeplitz matrix from its order-wise minimal sketch acquired using a GNS. Contrary to existing TV-norm and nuclear norm based reconstruction algorithms, our technique does not use any tuning parameters, which can be of great practical value. The idea of nested sampling idea also finds a surprising use in the problem of phase retrieval, which has been of great interest in recent times for its convex formulation via PhaseLift, By using another modified version of nested sampling, namely the Partial Nested Fourier Sampler (PNFS), we show that with probability one, it is possible to achieve a certain conjectured lower bound on the necessary measurement size. Moreover, for sparse data, an l1 minimization based algorithm is proposed that can lead to stable phase retrieval using order-wise minimal number of measurements.

Local search multiuser detection

In this work, a wide analysis of local search multiuser detection (LS-MUD) for direct sequence/code division multiple access (DS/CDMA) systems under multipath channels is carried out considering the performance-complexity trade-off. It is verified the robustness of the LS-MUD to variations in loading, E(b)/N(0), near-far effect, number of fingers of the Rake receiver and errors in the channel coefficients estimates. A compared analysis of the bit error rate (BER) and complexity trade-off is accomplished among LS, genetic algorithm (GA) and particle swarm optimization (PSO). Based on the deterministic behavior of the LS algorithm, it is also proposed simplifications over the cost function calculation, obtaining more efficient algorithms (simplified and combined LS-MUD versions) and creating new perspectives for the MUD implementation. The computational complexity is expressed in terms of the number of operations in order to converge. Our conclusion pointed out that the simplified LS (s-LS) method is always more efficient, independent of the system conditions, achieving a better performance with a lower complexity than the others heuristics detectors. Associated to this, the deterministic strategy and absence of input parameters made the s-LS algorithm the most appropriate for the MUD problem. (C) 2008 Elsevier GmbH. All rights reserved.

Association of a homozygous nonsense caveolin-1 mutation with Berardinelli-Seip congenital lipodystrophy

Context: Berardinelli-Seip congenital lipodystrophy (BSCL) is a rare recessive disease characterized by near absence of adipose tissue, resulting in severe dyslipidemia and insulin resistance. In most reported cases, BSCL is due to alterations in either seipin, of unknown function, or 1-acylglycerol-3- phosphate acyltransferase-beta (AGPAT2), which catalyzes the formation of phosphatidic acid. Objective: We sought to determine the genetic origin of the unexplained cases of BSCL. We thus sequenced CAV1, encoding caveolin-1, as a candidate gene involved in insulin signaling and lipid homeostasis. CAV1 is a key structural component of plasma membrane caveolae, and Cav1-deficient mice display progressive loss of adipose tissue and insulin resistance. Design: We undertook phenotyping studies and molecular screening of CAV1 in four patients with BSCL with no mutation in the genes encoding either seipin or AGPAT2. Results: A homozygous nonsense mutation (p.Glu38X) was identified in CAV1 in a patient with BSCL born from a consanguineous union. This mutation affects both the alpha-and beta-CAV1 isoforms and ablates CAV1 expression in skin fibroblasts. Detailed magnetic resonance imaging of the proband confirmed near total absence of both sc and visceral adipose tissue, with only vestigial amounts in the dorsal sc regions. In keeping with the lack of adipose tissue, the proband was also severely insulin resistant and dyslipidemic. In addition, the proband had mild hypocalcemia likely due to vitamin D resistance. Conclusions: These findings identify CAV1 as a new BSCL-related gene and support a critical role for caveolins in human adipocyte function.

A novel STAT5B mutation causing GH insensitivity syndrome associated with hyperprolactinemia and immune dysfunction in two male siblings

Background: GH insensitivity (GHI) syndrome caused by STAT5B mutations was recently reported, and it is characterized by extreme short stature and immune dysfunction. Treatment with recombinant human IGF1 (rhIGF1) is approved for patients with GHI, but the growth response to this therapy in patients with STAT5B mutations has not been reported. Objectives: To report the clinical features, molecular findings, and the short-term growth response to rhIGF1 therapy in patients with STAT5B mutation. Subjects and methods: Hormonal and immunological evaluations were performed in two male siblings with GHI associated with atopic eczema, interstitial lung disease, and thrombocytopenic purpura. STAT5B genes were directly sequenced. The younger sibling was treated with rhIGF1 at a dose of 110 mu g/kg BID. Results: Both siblings had laboratory findings compatible with GHI associated with hyperprolactinemia. Lymphopenia and reduced number of natural killer cells without immunoglobulin abnormalities were observed. STAT5B sequence revealed a homozygous frameshift mutation (p.L142fsX161) in both siblings. The younger sibling (9.9 years of age) was treated with rhIGF1 at appropriate dosage, and he did not present any significant change in his growth velocity (from 2.3 to 3.0 cm/year after 1.5 years of therapy). The presence of a chronic illness could possibly be responsible for the poor result of rhIGF1 treatment. Further studies in patients with STAT5B defects are necessary to define the response to rhIGF1 treatment in this disorder. Conclusion: GHI associated with immune dysfunction, especially interstitial lung disease, and hyperprolactinemia is strongly suggestive of a mutation in STAT5B in both sexes.

Familial corticosteroid-binding globulin deficiency due to a noval null mutation: Association with fatigue and relative hypotension

Corticosteroid-binding globulin is a 383-amino acid glycoprotein that serves a hormone transport role and may have functions related to the stress response and inflammation. We describe a 39-member Italian-Australian family with a novel complete loss of function (null) mutation of the corticosteroid-binding globulin gene. A second, previously described, mutation (Lyon) segregated independently in the same kindred. The novel exon 2 mutation led to a premature termination codon corresponding to residue -12 of the procorticosteroid-binding globulin molecule (c.121G->A). Among 32 family members there were 3 null homozygotes, 19 null heterozygotes, 2 compound heterozygotes, 3 Lyon heterozygotes, and 5 individuals without corticosteroid-binding globulin mutations. Plasma immunoreactive corticosteroid-binding globulin was undetectable in null homozygotes, and mean corticosteroid-binding globulin levels were reduced by approximately 50% at 18.7 ± 1.3 µg/ml (reference range, 30–52 µg/ml) in null heterozygotes. Morning total plasma cortisol levels were less than 1.8 µg/dl in homozygotes and were positively correlated to the plasma corticosteroid-binding globulin level in heterozygotes. Homozygotes and heterozygote null mutation subjects had a high prevalence of hypotension and fatigue. Among 19 adults with the null mutation, the systolic blood pressure z-score was 12.1 ± 3.5; 11 of 19 subjects (54%) had a systolic blood pressure below the third percentile. The mean diastolic blood pressure z-score was 18.1 ± 3.4; 8 of 19 subjects (42%) had a diastolic blood pressure z-score below 10. Idiopathic chronic fatigue was present in 12 of 14 adult null heterozygote subjects (86%) and in 2 of 3 null homozygotes. Five cases met the Centers for Disease Control criteria for chronic fatigue syndrome. Fatigue questionnaires revealed scores of 25.1 ± 2.5 in 18 adults with the mutation vs. 4.2 ± 1.5 in 23 healthy controls (P < 0.0001). Compound heterozygosity for both mutations resulted in plasma cortisol levels comparable to those in null homozygotes. Abnormal corticosteroid-binding globulin concentrations or binding affinity may lead to the misdiagnosis of isolated ACTH deficiency. The mechanism of the association between fatigue and relative hypotension is not established by these studies. As idiopathic fatigue disorders are associated with relatively low plasma cortisol, abnormalities of corticosteroid-binding globulin may be pathogenic.

Constraint reasoning with local search for continuous optimization

Optimization is a very important field for getting the best possible value for the optimization function. Continuous optimization is optimization over real intervals. There are many global and local search techniques. Global search techniques try to get the global optima of the optimization problem. However, local search techniques are used more since they try to find a local minimal solution within an area of the search space. In Continuous Constraint Satisfaction Problems (CCSP)s, constraints are viewed as relations between variables, and the computations are supported by interval analysis. The continuous constraint programming framework provides branch-and-prune algorithms for covering sets of solutions for the constraints with sets of interval boxes which are the Cartesian product of intervals. These algorithms begin with an initial crude cover of the feasible space (the Cartesian product of the initial variable domains) which is recursively refined by interleaving pruning and branching steps until a stopping criterion is satisfied. In this work, we try to find a convenient way to use the advantages in CCSP branchand- prune with local search of global optimization applied locally over each pruned branch of the CCSP. We apply local search techniques of continuous optimization over the pruned boxes outputted by the CCSP techniques. We mainly use steepest descent technique with different characteristics such as penalty calculation and step length. We implement two main different local search algorithms. We use “Procure”, which is a constraint reasoning and global optimization framework, to implement our techniques, then we produce and introduce our results over a set of benchmarks.

A firefly dynamic penalty approach for solving engineering design problems

Firefly Algorithm is a recent swarm intelligence method, inspired by the social behavior of fireflies, based on their flashing and attraction characteristics [1, 2]. In this paper, we analyze the implementation of a dynamic penalty approach combined with the Firefly algorithm for solving constrained global optimization problems. In order to assess the applicability and performance of the proposed method, some benchmark problems from engineering design optimization are considered.

A search for t(t)over-bar resonances using lepton-plus-jets events in proton-proton collisions at root s=8 TeV with the ATLAS detector

A search for new particles that decay into top quark pairs is reported. The search is performed with the ATLAS experiment at the LHC using an integrated luminosity of 20.3 fb−1 of proton-proton collision data collected at a centre-of-mass energy of s√=8 TeV. The lepton-plus-jets final state is used, where the top pair decays to W+bW−b¯¯, with one W boson decaying leptonically and the other hadronically. The invariant mass spectrum of top quark pairs is examined for local excesses or deficits that are inconsistent with the Standard Model predictions. No evidence for a top quark pair resonance is found, and 95% confidence-level limits on the production rate are determined for massive states in benchmark models. The upper limits on the cross-section times branching ratio of a narrow Z′ boson decaying to top pairs range from 4.2 pb to 0.03 pb for resonance masses from 0.4 TeV to 3.0 TeV. A narrow leptophobic topcolour Z′ boson with mass below 1.8 TeV is excluded. Upper limits are set on the cross-section times branching ratio for a broad colour-octet resonance with Γ/m = 15% decaying to tt¯. These range from 4.8 pb to 0.03 pb for masses from 0.4 TeV to 3.0 TeV. A Kaluza-Klein excitation of the gluon in a Randall-Sundrum model is excluded for masses below 2.2 TeV.

Clinical and genetic investigation of a large Tunisian family with complete achromatopsia: identification of a new nonsense mutation in GNAT2 gene.

Complete achromatopsia is a rare autosomal recessive disease associated with CNGA3, CNGB3, GNAT2 and PDE6C mutations. This retinal disorder is characterized by complete loss of color discrimination due to the absence or alteration of the cones function. The purpose of the present study was the clinical and the genetic characterization of achromatopsia in a large consanguineous Tunisian family. Ophthalmic evaluation included a full clinical examination, color vision testing and electroretinography. Linkage analysis using microsatellite markers flanking CNGA3, CNGB3, GNAT2 and PDE6C genes was performed. Mutations were screened by direct sequencing. A total of 12 individuals were diagnosed with congenital complete achromatopsia. They are members of six nuclear consanguineous families belonging to the same large consanguineous family. Linkage analysis revealed linkage to GNAT2. Mutational screening of GNAT2 revealed three intronic variations c.119-69G>C, c.161+66A>T and c.875-31G>C that co-segregated with a novel mutation p.R313X. An identical GNAT2 haplotype segregating with this mutation was identified, indicating a founder mutation. All patients were homozygous for the p.R313X mutation. This is the first report of the clinical and genetic investigation of complete achromatopsia in North Africa and the largest family with recessive achromatopsia involving GNAT2; thus, providing a unique opportunity for genotype-phenotype correlation for this extremely rare condition.