Relationship between power loss and network topology in power systems

This paper is concerned with studying how the minimum power loss in a power system is related to its network topology. The existing algorithms in the literature all exploit nonlinear, heuristic, or local search algorithms to find the minimum power loss, which make them blind to the network topology. Given certain constraints on power level, bus voltages, etc., a linear-matrix-inequality (LMI) optimization problem is derived, which provides a lower bound on the minimum active loss in the network. The proposed LMI problem has the property that its objective function depends on the loads and its matrix inequality constraint is related to the topology of the power system. This property makes it possible to address many important power problems, such as the optimal network reconfiguration and the optimal placement/sizing of distributed generation units in power systems. Moreover, a condition is provided under which the solution of the given LMI problem is guaranteed to be exactly equal to the minimum power loss. As justified mathematically and verified on IEEE test systems, this condition is expected to hold widely in practice, implying that a practical power loss minimization problem is likely to be solvable using a convex algorithm

Utility Functionals Associated With Available Congestion Control Algorithms

This paper is concerned with understanding the connection between the existing Internet congestion control algorithms and the optimal control theory. The available resource allocation controllers are mainly devised to derive the state of the system to a desired equilibrium point and, therefore, they are oblivious to the transient behavior of the closed-loop system. To take into account the real-time performance of the system, rather than merely its steady-state performance, the congestion control problem should be solved by maximizing a proper utility functional as opposed to a utility function. For this reason, this work aims to investigate what utility functionals the existing congestion control algorithms maximize. In particular, it is shown that there exist meaningful utility functionals whose maximization leads to the celebrated primal, dual and primal/dual algorithms. An implication of this result is that a real network problem may be solved by regarding it as an optimal control problem on which some practical constraints, such as a real-time link capacity constraint, are imposed

Determinant criteria for designing Health benefit package in selected countries

Health benefit package described as primary health interventions that provided with government using general funds for all regardless their financial ability. This study was aimed at determine appropriate pattern for Iran using comparative survey of Health benefit package in various countries. A review exploration was done, scholars was selected population of both developed and developing countries, required information was also extracted by articles, searches and reports of reliable sources and date were analyzed by SPSS, in brief. The vast majority frequencies was respectively allocated to accessibility (40.7%), cost- effectiveness (29.6%), prioritize, efficacy and cost (22.2%). most countries located in WHO African region were selected cost-effectiveness and accessibility, WHO southeast Asia region were selected, coverage, prioritize, efficacy and quality and finally most WHO Europeans region were elected effectiveness and services costs for including services in Health benefit package. According to most Health benefit package designer emphasis on criteria including accessibility and costeffectiveness, to design Health benefit package for Iran, these criteria must be noticed

Congestion control algorithms from optimal control perspective

This paper is concerned with understanding the connection between the existing Internet congestion control algorithms and the optimal control theory. The available resource allocation controllers are mainly devised to derive the state of the system to a desired equilibrium point and, therefore, they are oblivious to the transient behavior of the closed-loop system. This work aims to investigate what dynamical functions the existing algorithms maximize (minimize). In particular, it is shown that there exist meaningful cost functionals whose minimization leads to the celebrated primal and dual congestion algorithms. An implication of this result is that a real network problem may be solved by regarding it as an optimal control problem on which some practical constraints, such as a real-time link capacity constraint, are imposed

Pan-cancer classifications of tumor histological images using deep learning

Histopathological images are essential for the diagnosis of cancer type and selection of optimal treatment. However, the current clinical process of manual inspection of images is time consuming and prone to intra- and inter-observer variability. Here we show that key aspects of cancer image analysis can be performed by deep convolutional neural networks (CNNs) across a wide spectrum of cancer types. In particular, we implement CNN architectures based on Google Inception v3 transfer learning to analyze 27815 H&E slides from 23 cohorts in The Cancer Genome Atlas in studies of tumor/normal status, cancer subtype, and mutation status. For 19 solid cancer types we are able to classify tumor/normal status of whole slide images with extremely high AUCs (0.995±0.008). We are also able to classify cancer subtypes within 10 tissue types with AUC values well above random expectations (micro-average 0.87±0.1). We then perform a cross-classification analysis of tumor/normal status across tumor types. We find that classifiers trained on one type are often effective in distinguishing tumor from normal in other cancer types, with the relationships among classifiers matching known cancer tissue relationships. For the more challenging problem of mutational status, we are able to classify TP53 mutations in three cancer types with AUCs from 0.65-0.80 using a fully-trained CNN, and with similar cross-classification accuracy across tissues. These studies demonstrate the power of CNNs for not only classifying histopathological images in diverse cancer types, but also for revealing shared biology between tumors. We have made software available at: https://github.com/javadnoorb/HistCNNFirst author draf

Distribution-based measures of tumor heterogeneity are sensitive to mutation calling and lack strong clinical predictive power.

Mutant allele frequency distributions in cancer samples have been used to estimate intratumoral heterogeneity and its implications for patient survival. However, mutation calls are sensitive to the calling algorithm. It remains unknown whether the relationship of heterogeneity and clinical outcome is robust to these variations. To resolve this question, we studied the robustness of allele frequency distributions to the mutation callers MuTect, SomaticSniper, and VarScan in 4722 cancer samples from The Cancer Genome Atlas. We observed discrepancies among the results, particularly a pronounced difference between allele frequency distributions called by VarScan and SomaticSniper. Survival analysis showed little robust predictive power for heterogeneity as measured by Mutant-Allele Tumor Heterogeneity (MATH) score, with the exception of uterine corpus endometrial carcinoma. However, we found that variations in mutant allele frequencies were mediated by variations in copy number. Our results indicate that the clinical predictions associated with MATH score are primarily caused by copy number aberrations that alter mutant allele frequencies. Finally, we present a mathematical model of linear tumor evolution demonstrating why MATH score is insufficient for distinguishing different scenarios of tumor growth. Our findings elucidate the importance of allele frequency distributions as a measure for tumor heterogeneity and their prognostic role

Development of fuzzy anti-roll bar controller for improving vehicle stability

The main objective of this paper is to develop active control mechanism based on fuzzy logic controller (FLC) for improving vehicle path following, roll and handling performances simultaneously. At the first stage, 3DOF vehicle model which includes yaw rate, lateral velocity (lateral dynamic) and roll angle (roll dynamic) are developed. The controller produces optimal moment to increase stability and roll margin of vehicle by receiving the steering angle as an input and vehicle variables as a feedback signal. The effectiveness of proposed controller and vehicle model are evaluated during fishhook and single lane-change maneuvers. Simulation results demonstrate that FLC by reducing roll angle, lateral velocity and acceleration, vehicle roll resistance and handling properties are improved. Finally the sensitivity and robustness analysis of developed controller for varying longitudinal speeds are investigated