Ant Colony Based Hybrid Approach for Optimal Compromise Sum-Difference Patterns Synthesis

Dealing with the synthesis of monopulse array antennas, many stochastic optimization algorithms have been used for the solution of the so-called optimal compromise problem between sum and difference patterns when sub-arrayed feed networks are considered. More recently, hybrid approaches, exploiting the convexity of the functional with respect to a sub-set of the unknowns (i.e., the sub-array excitation coefficients) have demonstrated their effectiveness. In this letter, an hybrid approach based on the Ant Colony Optimization (ACO) is proposed. At the first step, the ACO is used to define the sub-array membership of the array elements, while, at the second step, the sub-array weights are computed by solving a convex programming problem. The definitive version is available at www3.interscience.wiley.co

An Improved Excitation Matching Method based on an Ant Colony Optimization for Suboptimal-Free Clustering in Sum-Difference Compromise Synthesis

Dealing with an excitation matching method, this paper presents a global optimization strategy for the optimal clustering in sum-difference compromise linear arrays. Starting from a combinatorial formulation of the problem at hand, the proposed technique is aimed at determining the sub-array configuration expressed as the optimal path inside a directed acyclic graph structure modelling the solution space. Towards this end, an ant colony metaheuristic is used to benefit of its hill-climbing properties in dealing with the non-convexity of the sub-arraying as well as in managing graph searches. A selected set of numerical experiments are reported to assess the efficiency and current limitations of the ant-based strategy also in comparison with previous local combinatorial search methods. (c) 2009 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works

Computationally-Effective Optimal Excitation Matching for the Synthesis of Large Monopulse Arrays

Antenna arrays able to generate two different patterns are widely used in tracking radar systems [1]. Optimal (in the Dolph�]Chebyshev sense) sum [2] and difference patterns [3] can be generated by using two independent feed networks. Unfortunately, such a situation generally turns out to be impracticable because of its costs, the occupied physical space, the circuit complexity, and electromagnetic interferences. Thus, starting from the optimal sum pattern a sub�]optimal solution for the difference pattern is usually synthesized by means of the sub�]array technique. The array elements are grouped in sub�]arrays properly weighted for matching the constrains of the difference beam. Finding the best elements grouping and the sub�]array weights is a complex and challenging research topic, especially when dealing with large arrays. As far as linear arrays are concerned, McNamara proposed in [4] an analytical method for determining the �gbest compromise�h difference pattern. Unfortunately, when the ratio between the elements of the array and sub�]arrays increases, such a technique exhibits several limitations mainly due to the ill�]conditioning of the problem and the computational costs due to exhaustive evaluations. A non�]negligible saving might be achieved by applying optimization algorithms (see for instance [5] and [6]) aimed at minimizing a suitable cost function. Notwithstanding, optimization�]based approaches still appear computationally expensive when dealing with large arrays because of wide dimension of solution space to be sampled. In order to properly deal with these computational issues, this contribution presents an innovative approach based on an optimal excitation matching procedure. By exploiting the relationship between independently�]optimal sum and difference patterns, the dimension of the solution space is considerably reduced and efficiently sampled by taking into account the presence of array elements more suitable to change sub�]array membership. In the following, the proposed technique is described pointing out, through a representative case, its potentialities and effectiveness in dealing with large arrays. This is the author's version of the final version available at IEEE

Synthesis of a Galile oand Wi-Max Three-Band Fractal-Eroded Patch Antenna

In this letter, the synthesis of a three-band patch antenna working in E5-L1 Galileo and Wi − Max frequency bands is described. The geometry of the antenna is defined by performing a Koch-like erosion in a classical rectangular patch structure according to a Particle Swarm strategy to optimize the values of the electrical parameters within given specifications. In order to assess the effectiveness of the antenna design, some results from the numerical synthesis procedure are described and a comparison between simulations and experimental measurements is reported. (c) 2007 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works

An innovative approach based on a tree-searching algorithm for the optimal matching of independently optimum sum and difference excitations

An innovative approach for the optimal matching of independently optimum sum and difference patterns through sub-arrayed monopulse linear arrays is presented. By exploiting the relationship between the independently optimal sum and difference excitations, the set of possible solutions is considerably reduced and the synthesis problem is recast as the search of the best solution in a non-complete binary tree. Towards this end, a fast resolution algorithm that exploits the presence of elements more suitable to charge sub-array membership is presented. The results of a set of numerical experiments are reported in order to validate the proposed approach pointing out its effectiveness also in comparison with state-of-the-art optimal matching techniques. (c) 2008 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works

Analytic Design Techniques for MPT Antenna Arrays

Solar Power Satellites (SPS) represent one of the most interesting technological opportunities to provide large scale, environmentally clean and renewable energy to the Earth [1]‐[3]. A fundamental and critical component of SPSs is the Microwave Power Transmission (MPT) system, which is responsible for the delivery of the collected solar power to the ground rectenna [2]. Towards this end, the MPT array must exhibit a narrow main beam width (), a high beam efficiency (BWBE), and a low peak sidelobe level (). Moreover, reduced realization costs and weights are also necessary [3]. To reach these contrasting goals, several design techniques have been investigated including random methods [4] and hybrid deterministic‐random approaches [2][3]. On the contrary, well‐established design tools based on stochastic optimizers [5][6] are difficult to be employed, due to their high computational costs when dealing with large arrays as those of interest in SPS [3]

Can Systems Biology Advance Clinical Precision Oncology?

Precision oncology is perceived as a way forward to treat individual cancer patients. However, knowing particular cancer mutations is not enough for optimal therapeutic treatment, because cancer genotype-phenotype relationships are nonlinear and dynamic. Systems biology studies the biological processes at the systems’ level, using an array of techniques, ranging from statistical methods to network reconstruction and analysis, to mathematical modeling. Its goal is to reconstruct the complex and often counterintuitive dynamic behavior of biological systems and quantitatively predict their responses to environmental perturbations. In this paper, we review the impact of systems biology on precision oncology. We show examples of how the analysis of signal transduction networks allows to dissect resistance to targeted therapies and inform the choice of combinations of targeted drugs based on tumor molecular alterations. Patient-specific biomarkers based on dynamical models of signaling networks can have a greater prognostic value than conventional biomarkers. These examples support systems biology models as valuable tools to advance clinical and translational oncological research

Heat Sensing Receptor TRPV1 Is a Mediator of Thermotaxis in Human Spermatozoa

The molecular bases of sperm thermotaxis, the temperature-oriented cell motility, are currently under investigation. Thermal perception relies on a subclass of the transient receptor potential [TRP] channels, whose member TRPV1 is acknowledged as the heat sensing receptor. Here we investigated the involvement of TRPV1 in human sperm thermotaxis. We obtained semen samples from 16 normozoospermic subjects attending an infertility survey programme, testis biopsies from 6 patients with testicular germ cell cancer and testis fine needle aspirates from 6 patients with obstructive azoospermia undergoing assisted reproductive technologies. Expression of TRPV1 mRNA was assessed by RT-PCR. Protein expression of TRPV1 was determined by western blot, flow cytometry and immunofluorescence. Sperm motility was assessed by Sperm Class Analyser. Acrosome reaction, apoptosis and intracellular-Ca2+ content were assessed by flow cytometry. We found that TRPV1 mRNA and protein were highly expressed in the testis, in both Sertoli cells and germ-line cells. Moreover, compared to no-gradient controls at 31°C or 37°C (Ctrl 31°C and Ctrl 37°C respectively), sperm migration towards a temperature gradient of 31-37°C (T gradient) in non-capacitated conditions selected a higher number of cells (14,9 ± 4,2×106 cells T gradient vs 5,1± 0,3×106 cells Ctrl 31°C and 5,71±0,74×106 cells Ctrl 37°C; P = 0,039). Capacitation amplified the migrating capability towards the T gradient. Sperms migrated towards the T gradient showed enriched levels of both TRPV1 protein and mRNA. In addition, sperm cells were able to migrate toward a gradient of capsaicin, a specific agonist of TRPV1, whilst capsazepine, a specific agonist of TRPV1, blocked this effect. Finally, capsazepine severely blunted migration towards T gradient without abolishing. These results suggest that TRPV1 may represent a facilitating mediator of sperm thermotaxis

On a Cahn–Hilliard–Keller–Segel model with generalized logistic source describing tumor growth

We propose a new type of diffuse interface model describing the evolution of a tumor mass under the effects of a chemical substance (e.g., a nutrient or a drug). The process is described by utilizing the variables $\varphi$, an order parameter representing the local proportion of tumor cells, and $\sigma$, representing the concentration of the chemical. The order parameter $\varphi$ is assumed to satisfy a suitable form of the Cahn-Hilliard equation with mass source and logarithmic potential of Flory-Huggins type (or generalizations of it). The chemical concentration $\sigma$ satisfies a reaction-diffusion equation where the cross-diffusion term has the same expression as in the celebrated Keller-Segel model. In this respect, the model we propose represents a new coupling between the Cahn-Hilliard equation and a subsystem of the Keller-Segel model. We believe that, compared to other models, this choice is more effective in capturing the chemotactic effects that may occur in tumor growth dynamics (chemically induced tumor evolution and consumption of nutrient/drug by tumor cells). Note that, in order to prevent finite time blowup of $\sigma$, we assume a chemical source term of logistic type. Our main mathematical result is devoted to proving existence of weak solutions in a rather general setting that covers both the two- and three- dimensional cases. Under more restrictive assumptions on coefficients and data, and in some cases on the spatial dimension, we prove various regularity results. Finally, in a proper class of smooth solutions we show uniqueness and continuous dependence on the initial data in a number of significant cases.Comment: 38 page