A
matching cut
is a partition of the vertex set of a graph into two sets
A
and
B
such that each vertex has at most one neighbor in the other side of the cut. The
Matching Cut
problem asks whether a ...graph has a matching cut, and has been intensively studied in the literature. Motivated by a question posed by Komusiewicz et al. Discrete Applied Mathematics, 2020, we introduce a natural generalization of this problem, which we call
d
-Cut
: for a positive integer
d
, a
d
-
cut
is a bipartition of the vertex set of a graph into two sets
A
and
B
such that each vertex has at most
d
neighbors across the cut. We generalize (and in some cases, improve) a number of results for the
Matching Cut
problem. Namely, we begin with an NP-hardness reduction for
d
-Cut
on
(
2
d
+
2
)
-regular graphs and a polynomial algorithm for graphs of maximum degree at most
d
+
2
. The degree bound in the hardness result is unlikely to be improved, as it would disprove a long-standing conjecture in the context of internal partitions. We then give FPT algorithms for several parameters: the maximum number of edges crossing the cut, treewidth, distance to cluster, and distance to co-cluster. In particular, the treewidth algorithm improves upon the running time of the best known algorithm for
Matching Cut
. Our main technical contribution, building on the techniques of Komusiewicz et al. DAM, 2020, is a polynomial kernel for
d
-Cut
for every positive integer
d
, parameterized by the vertex deletion distance of the input graph to a cluster graph. We also rule out the existence of polynomial kernels when parameterizing simultaneously by the number of edges crossing the cut, the treewidth, and the maximum degree. Finally, we provide an exact exponential algorithm slightly faster than the naive brute force approach running in time
O
∗
2
n
.
Triple negative breast cancer: A thorough review of biomarkers da Silva, Jesse Lopes; Cardoso Nunes, Natalia Cristina; Izetti, Patricia ...
Critical reviews in oncology/hematology,
January 2020, 2020-Jan, 2020-01-00, 20200101, Volume:
145
Journal Article
Peer reviewed
•TNBC is a heterogeneous disease with several proposed molecular subclassifications.•A complete review of predictive and prognostic biomarkers was performed.•New molecular target therapy is presented ...in the context of TNBC in favor of personalized therapy.•Immunogenetic signatures and ct-DNA have shown promising results to better guide the treatment of the disease.
Triple-negative breast cancer (TNBC) is defined as a type of breast cancer with lack of expression of estrogen receptor (ER), progesterone receptor (PR) and HER2 protein. The tumorigenesis is not likely to be driven by hormonal or HER2 pathway. In comparison to other types of breast cancer, TNBC stands out for its aggressive behavior, more prone to early recurrence. Historically, TNBC has been considered a disease with poor response to molecular target therapy, requiring better validation of biomarkers. Recent issues related to tumor heterogeneity have been widely discussed suggesting the subdivision of TNBC into different molecular subtypes. Through a complete research on the main published trials databases and platforms of ongoing clinical studies, the current manuscript was carried out in order to present a critical view of the role of immunohistochemical and molecular biomarkers for the prognosis and response prediction of TNBC to traditional therapy and new molecular target agents.
Xanthine oxidase inhibitors (XOI), classified as purine-like (allopurinol and oxypurinol) and non-purine (febuxostat and topiroxostat) XOI, present antioxidant properties by reducing the production ...of reactive oxygen species derived from purine metabolism. Oxidative stress is an important factor related to endothelial dysfunction and ischemia-reperfusion injury, and may be implicated in the pathogenesis of heart failure, hypertension, and ischemic heart disease. However, there is contradictory evidence regarding the possible cardiovascular (CV) protective effect exerted by XOI. Our objective is to compare the incidence of major adverse cardiovascular events (MACE), mortality, total (TCE) and specific CV events in randomized controlled trials (RCTs) testing XOI against placebo or no treatment.
PubMed, EMBASE, Web of Science, Cochrane Central, Lilacs databases were searched from inception to Dec 30 2016, along with hand searching. RCTs including exclusively adult individuals, lasting ≥ 4 weeks, with no language restriction, were eligible. Independent paired researchers selected studies and extracted data. Considering the expected rarity of events, Peto and DerSimonian/Laird odds ratios (OR), the latter in case of heterogeneity, were used for analysis. Random-effects meta-regression was used to explore heterogeneity.
The analysis of MACE included 81 articles (10,684 patients, 6434 patient-years). XOI did not significantly reduce risk of MACE (OR
= 0.71, 95% CI 0.46-1.09) and death (0.89, 0.59-1.33), but reduced risk of TCE (0.60, 0.44-0.82; serious TCE: 0.64, 0.46 to 0.89), and hypertension (0.54, 0.37 to 0.80). There was protection for MACE in patients with previous ischemic events (0.42, 0.23-0.76). Allopurinol protected for myocardial infarction (0.38, 0.17-0.83), hypertension (0.32, 0.18-0.58), TCE (0.48, 0.31 to 0.75, I
= 55%) and serious TCE (0.56, 0.36 to 0.86, I
= 44%). Meta-regression associated increasing dose of allopurinol with higher risk of TCE and serious TCE (P < 0.05). Accordingly, lower doses (≤ 300 mg/day) of allopurinol reduced the risk of TCE, unlike higher doses. Non-purine-like XOI did not significantly reduce or increase the risk of adverse CV events, but confidence intervals were wide. Quality of evidence was generally low to moderate.
Purine-like XOI may reduce the incidence of adverse CV outcomes. However, higher doses of allopurinol (> 300 mg/day) may be associated with loss of CV protection.
Delamination is the principal mode of failure of laminated composites. It is caused by the rupture of the fiber–matrix interface and results in the separation of the layers. This failure is induced ...by interlaminate tension and shear that is developed due to a variety of factors such as fatigue. Composites are known for excellent structural performance, which can be significantly affected by delamination. Such damages are not always visible on the surface and can lead to sudden catastrophic failures. To ensure a structural performance and integrity, accurate methods to monitor damages are required. This work presents a methodology for damage detection and identification on laminated composite plates using artificial neural networks fed with modal data obtained by finite element analysis. The proposed neural network to quantify damage severity achieved up to 95% success rate. A comparative study was done to evaluate the effect of boundary conditions on damage location. With the comparative study results, another neural network was proposed to locate damage position, achieving excellent results by successfully locating or by significantly reducing the search area. Both proposed ANNs use only frequency variation values as inputs, an easily obtainable quantity that requires few equipment to be acquired. The obtained results from these numerical examples indicate that the proposed approach can detect true damage locations and estimate damage magnitudes with satisfactory accuracy for this particular geometry, even under high measurement noise.
•A Brief Review of Multi-Objective Optimization with Algorithms and Trends.•A new multi-objective algorithm inspired by lightning and Lichtenberg Figures.•The first hybrid multi-objective ...meta-heuristic: trajectory and population - based.•Lichtenberg patterns presents advantageous convergence-coverage balance.•Tested on complex functions-problems and compared with renowned MO meta-heuristics.
With the advancement of computing and inspired by optimal phenomena found in nature, several algorithms capable of solving complex engineering problems have been developed. This work details the development of the Multi-objective Lichtenberg Algorithm, the version capable of dealing with more than one objective of a newly created meta-heuristic inspired by the propagation of radial intra-cloud lightning and Lichtenberg figures. The algorithm considers in its optimization routine a hybrid system based on both the population and the trajectory, demonstrating a great capacity for exploration and exploitation since it distributes points to be evaluated in the objective function through a Lichtenberg figure that is shot in sizes and different rotations at each iteration. The Multi-objective Lichtenberg Algorithm (MOLA) is the first hybrid multi-objective meta-heuristic and was tested against traditional and recent meta-heuristics using famous and complex test function groups and also constrained complex engineering problems. Regarding important metrics for convergence and coverage assessment, the Multi-objective Lichtenberg Algorithm proved to be a promising multi-objective algorithm surpassing others traditional and recent algorithms such as NSGA-II, MOPSO, MOEA/D, MOGOA and MOGWO with expressive values of convergence and maximum spread.
•Multi-objective design optimization using meta-modelling by FEM-RSM.•A New meta-heuristic of optimization (Lichtenberg Algorithm) was applied.•Five responses of a sandwich tube with auxetic core ...were analyzed.•A statistical analysis was performed.•The Poisson’s ratio was reduced in more than 56%•All the responses have been improved.
In this paper, the multi-objective optimization of a sandwich composite tube with an auxetic reentrant core was done. Five different responses of the model were considered: mass; critical buckling load; natural frequency; Poisson’s ratio; and failure load under compression effort. The Response Surface Methodology was applied, and a new meta-heuristic of optimization called the Multi-objective Lichtenberg Algorithm was used to find the optimized configuration of the structure. In addition, a parametric analysis was performed to verify how each of the design factors influenced each of the responses. It was found that the unit cell height is the most significant factor for failure load, natural frequency, and mass. On the other hand, unit cell height has no significance for Poisson's ratio. Furthermore, it was also verified that the Poisson's ratio has no correlation with any of the other responses studied (failure load, critical buckling load, mass, and natural frequency). Finally, it was possible to improve (reduce) the Poisson’s ratio by more than 56% in the optimization of the modal performance and improve the mass, the Poisson’s ratio, the failure load, and the critical buckling load in the static performance. Thus, this paper shows something unprecedented in the literature to date when evaluating the multi-objective design optimization of a tubular sandwich structure with an auxetic core.
Strength properties of most sedimentary and metamorphic rocks are known to vary with direction. Knowledge of this so-called rock anisotropy is of utmost importance for reliability analysis and ...engineering design. The purpose of this paper is twofold. First, we propose a formulation of the Hoek–Brown (HB) failure criterion, which calculates strength anisotropy using a non-uniform scaling of the stress tensor. We use two scaling factors, CN and CS, to link the orientation of the anisotropy planes with the loading direction. As we assume isotropic parameters for intact rock, our HB model formulation is relatively easy to use and has the additional advantage that it does not demand any modifications to the HB failure criterion. Second, we embed our HB model formulation in a Bayesian framework and illustrate its power and usefulness using experimental data of anisotropic rock samples published in the literature. Results demonstrate that our HB model formulation predicts accurately measured peak strengths of rocks with different degrees of anisotropy, confining stresses and anisotropy orientations. The uncertainty in peak strength of anisotropic rocks can be quite large, reiterating the need for an explicit treatment of strength anisotropy uncertainty in rock mechanics studies. The Bayesian methodology is general-purpose, and, as such, can help better inform geotechnical engineers, contractors and other professionals about rock conditions and design reliability and assist decision makers in determining the overall risks of engineering structures.
An
n
-vertex graph is equitably
k
-colorable if there is a proper coloring of its vertices such that each color is used either
n
/
k
or
n
/
k
times. While classic
Vertex Coloring
is fixed parameter ...tractable under well established parameters such as pathwidth and feedback vertex set, equitable coloring is W1-hard. We present an extensive study of structural parameterizations of
Equitable Coloring
, tackling both tractability and kernelization questions. We begin by showing that the problem is fixed parameter tractable when parameterized by distance to cluster or by distance to co-cluster—improving on the FPT algorithm of Fiala et al. (Theor Comput Sci 412(23):2513–2523, 2011) parameterized by vertex cover—and also when parameterized by distance to disjoint paths of bounded length. To justify the latter result, we adapt a proof of Fellows et al. (Inf Comput 209(2):143–153, 2011) to show that
Equitable Coloring
is W1-hard when simultaneously parameterized by distance to disjoint paths and number of colors. In terms of kernelization, on the positive side we present a linear kernel for the distance to clique parameter and a cubic kernel when parameterized by the maximum leaf number; on the other hand, we show that, unlike
Vertex Coloring
,
Equitable Coloring
does not admit a polynomial kernel when jointly parameterized by vertex cover and number of colors, unless
NP
⊆
coNP
/
poly
. We also revisit the literature and derive other results on the parameterized complexity of the problem through minor reductions or other observations.
Evolutionary Polynomial Regression (EPR) has found widespread application and use for model structure development in engineering and science. This hybrid evolutionary approach merges real world data ...and explanatory variables to generate well-structured models in the form of polynomial equations. The simple and transparent models produced by this technique enable us to explore, via sensitivity analysis, the robustness of the derived models. Yet, existing EPR frameworks do not make explicit use of sensitivity analysis in the selection of robust and high-fidelity model structures. In this paper, we develop a multi-step sensitivity-driven method which combines the strengths of differential evolution and model selection via Monte Carlo simulation to explore the input–output relationships of model structures. In the first step, our hybrid approach automatically determines the optimum number of terms of the polynomial equations. In a subsequent step, our algorithm explores the mean parametric response of each explanatory variable used in the mathematical formulation to select a final model structure. Finally, in our selection of the most robust mathematical structure, we take explicit consideration of the prediction uncertainty of the simulated output. We illustrate and evaluate our EPR method for different engineering problems involving modeling and prediction of the moisture content and creep index of soils. Altogether, our results demonstrate that the use of sensitivity analysis as an integral part of model structure search and selection will lead to robust models with high predictive ability.
•A robust approach improves model structure selection within the EPR framework.•Sensitivity analysis is embedded in the model selection process.•High-fidelity models are selected via Monte Carlo simulations.•The model parametric responses exhibit good predictive and generalization abilities.
The design of open-pit mines should balance safety and economy. However, safe geotechnical conditions generally involve redesigning the geometry of slopes and groundwater drawdown, significantly ...increasing the costs of mining operations. The use of numerical models to simulate groundwater drawdown and slope stability can be an alternative to assess cost–benefit trade-offs for decision-making. This study documents a mining plan using groundwater drawdown scenarios that illustrate how geotechnical, economic, and environmental indicators can be combined to obtain optimum slope geometry for open-pit mining. The optimization approach analyzed different scenarios of groundwater drawdown for the final pit of a phosphate mine to improve the pit slopes stability. The groundwater simulation scenarios included the combination of deep horizontal drains and pumping wells. Stability analyses using the limit equilibrium method were used to obtain the bench, inter-ramp, and overall factors of safety of different representative sections. The factors of safety obtained, the drawdown costs and the water table elevation of each section were selected as indicators for obtaining the optimal drawdown scenario using a multi-objective tool. The groundwater control system obtained with 11 horizontal drains and 1 pumping well was considered the most adequate from the geotechnical and economic perspectives. Slope geometry optimization obtained with this drawdown scenario led to adequate inter-ramp and overall safety factors for the final pit design, reducing the barren-to-ore ratio to 0.38, much less than the present ratio (≈ 3). The results are important for optimizing the slope geometry of open-pit mines and can be replicated in other regions.
PDF
