Popular Problem Research Articles

Protective impact of Spirulina platensis against γ-irradiation and thioacetamide-induced nephrotoxicity in rats mediated by regulation of micro-RNA 1 and micro-RNA 146a.

Chronic kidney disease develops popular and medical health problems, especially in developing countries. The objective of this study is to investigate the protective mechanism of Spirulina platensis against γ-irradiation (R) and/or thioacetamide (TAA)-induced nephrotoxicity in rats. Rats intoxicated with R or TAA showed alterations in kidney function markers (urea, creatinine, albumin, and total protein contents), oxidative stress markers (malondialdehyde, reduced glutathione), antioxidant enzymes (superoxide dismutase, catalase), and several inflammatory markers (including, the high-sensitivity C-reactive protein, hypoxia-inducible factor-1 alpha, tumor necrosis factor-alpha, interferon-gamma, some interleukins, and nuclear factor-kappa B). Rats also acquired apoptosis, evinced by high caspase-3 efficacy. This nephrotoxicity mediated by upregulation of the messenger RNA (mRNA) gene expression of the autophagy markers: Beclin-1, microtubule-associated protein LC3, p62 binding protein, immunoglobulin G receptor Fcγ receptor (FcγR), micro-RNA-1 (miR-1), protein expression of phospho-adenosine monophosphate-activated protein kinase, and phospho-mammalian target of rapamycin, along with downregulation of miR-146a mRNA gene expression and alteration of calcium and iron levels. The combined treatment R/TAA enhanced the observed oxidative stress, inflammation, apoptosis, and autophagy that mediated by higher upregulation of miR-1 and downregulation of miR-146a mRNA gene expression. Spirulina platensis administration exhibited a nephroprotective impact on R, TAA, and R/TAA toxicities via regulating miR-1 and miR-146a mRNA gene expression that monitored adenosine monophosphate-activated protein kinase/mammalian target of rapamycin signaling.

Review of Recent Developments in Human Pose Estimation

Pose estimation is a popular computer vision problem that involves identifying and estimating human poses. It has found many use cases in games, animation, virtual trainers, posture analysis, driver assistance systems, etc. There has been significant progress in this field over the years. To cover the recent advances, we have analysed and summarized some of the influential methods to approach the problem of Human body pose detection. These include the traditional Convolution Neural Networks, Normalization, Deep Neural Network or the DeepPose, Adversarial PoseNet, and OpenPose. We start our study with a deep neural network that could yield results comparable to the traditional complicated architectures. Then we discuss how normalization can help in more precise pose estimations and make the relative distribution of joints more compact. We also talk about the estimation of pose using Deep Neural Networks, Part Affinity Fields and multichannel heatmaps.

A parallel memetic algorithm with explicit management of diversity for the job shop scheduling problem

The job shop scheduling problem (JSSP) is a very popular NP-hard optimization problem that involves assigning jobs to resources. Recent advances in the field of memetic algorithms show that explicitly managing the diversity of the population by taking into account the stopping criterion with the aim of dynamically adapting the balance between exploration and exploitation is key to their success. This is especially the case in long-term executions. However, this design principle has not yet been applied to the JSSP. This paper proposes a novel memetic algorithm that integrates some of the most advanced components devised in the literature for the JSSP with a replacement strategy that explicitly manages the diversity by considering a novel dissimilarity measure. To properly address large instances, a parallel master-worker model is used. Experimental validation shows the important advances attained by our proposal when compared to two state-of-the-art optimizers. The advantages are clear in both sequential and parallel cases, with more impressive achievements appearing in the parallel case. The parallel proposal has yielded new best-known solutions in 30 well-known JSSP instances, matching the lower bound in two of them, meaning that at least two new optimal solutions have been discovered.

Stereoscopic zoom for visual optimization based on grid deformation

Stereoscopic image zoom optimization is a popular basic research problem in the field of image processing and computer vision in recent years. The zoom visual enhancement technology of 3D images has attracted more and more attention. To this end, this paper proposes a method of stereoscopic zoom vision optimization based on grid deformation from the model of camera zoom shooting, and strives to improve the experience of 3D stereo-scopic vision. Firstly, use the digital zoom method to simulate the camera model to properly zoom in on the target area, and then establish the mapping relationship between the reference image and the target image according to the camera zoom distance. Secondly, extract the foreground target object and use the modified just noticeable depth difference (JNDiD) model to guide the adaptive depth adjustment of the target object. Finally, combined with the seven grid-optimized energy terms designed in this paper, the image grid is optimized to improve the visual perception of the target object and ensure a good visual experience for the entire stereoscopic image. Compared with the existing digital zoom method, the proposed method has better effects on the size control of the image target object and the depth adjustment of the target object.

Adaptive Robust Kernels for Non-Linear Least Squares Problems

State estimation is a key ingredient in most robotic systems. Often, state estimation is performed using some form of least squares minimization. Basically, all error minimization procedures that work on real-world data use robust kernels as the standard way for dealing with outliers in the data. These kernels, however, are often hand-picked, sometimes in different combinations, and their parameters need to be tuned manually for a particular problem. In this letter, we propose the use of a generalized robust kernel family, which is automatically tuned based on the distribution of the residuals and includes the common m-estimators. We tested our adaptive kernel with two popular estimation problems in robotics, namely ICP and bundle adjustment. The experiments presented in this letter suggest that our approach provides higher robustness while avoiding a manual tuning of the kernel parameters.

The problems of popularization of science in the Russian Federation in the context of amendments to the legal regulation of educational activity

The subject of this research is the relevant issues pertaining to popularization of science in the Russian Federation. The goal of this article lies in determination of the currently existing key issues of popularization of science in the Russian Federation: 1) the absence of effective response to the transformation of the traditional field of scientific promotion activity (distribution of new forms and methods of such activity); 2) imperfection of the new legal regulation of educational and scientific promotion activity, which may implicitly lead to negative consequences. The conducted research involves in theoretical conceptualization of scientific promotion activity in the digital age the novelties of legislative regulation of educational activity adopted in April 2021 (the so-called Law on Educational Activity). Unlike other works dedicated to the analysis of this law, this article assesses its basic provisions from the perspective of the effectiveness of achieving the goals set in the context of its impact upon the state of popularization of science in the Russian society. It is established that the model for preventing deterioration of the quality of educational content proposed in the Law on Educational Activity is not effective enough and may further reduce the quality of scientific promotion activity. Therefore, the purpose of imposed restrictions may fail to be achieved. The author believes that the more effective way would lie in intensification of the activity of traditional actors of popularization of science, rather than restriction of the activity of nontraditional actors of popularization of science.

FUSION OF SELECTED MULTI-MODAL FEATURES FOR ACCURATE FACE RECOGNITION

Yüz tanıma çok sayıda uygulama alanı olan popüler bir bilgisayarla görü problemidir. Farklı ışık koşulları ve değişen yüz ifadeleri yüz tanıma problemini zorlaştıran etkenlerdir. Yüz tanıma işlemi için çeşitli yöntemlerle elde edilen öznitelikler yüze ait farklı karakteristik özellikleri yansıtır. Bu karakteristik özelliklerden faydalanılarak yüz tanıma işlemi gerçekleştirilir. Bu çalışmada, örüntü ve doku tanımada sıklıkla kullanılan Yerel İkili Örüntü ve Felzenszwalb Yönelimli Gradyan Histogram özniteliklerinin birleştirilmesi ile yüz tanıma problemine çok kipli bir çözüm sunulmuştur. Yüz imgesi bölgelere ayrılarak, her iki yöntem ile bölgelerden öznitelik vektörleri elde edilmiştir. Bununla birlikte elde edilen vektörlere öznitelik seçim yöntemleri uygulanarak hem vektör boyutu azaltılmış hem de başarım arttırılmıştır. Öznitelik seçimi sonucu her iki yöntem için seçilen öznitelik alt kümeleri birleştirilerek uzamsal ve spektral öznitelikleri içeren tek bir öznitelik vektörü haline getirilmiştir. Seçilen öznitelikler Ki-kare sınıflandırıcısı kullanılarak sınıflandırılmıştır. Önerilen yöntemin başarımı FERET veri setinde ölçülmüş, %89.45 yüz doğrulama ve %94.55 yüz tanıma başarımı elde edilmiştir.

Arquitectura moderna: puente para la dignidad de la vivienda popular en Colombia

El presente artículo es resultado de la investigación adelantada como alternativa de grado como aporte al semillero de APQUA del grupo de investigación PAME de la Universidad de Boyacá.
 La arquitectura moderna tuvo una valiosa participación dentro del proceso de dignificación de la vivienda popular en Colombia. A raíz de la industrialización del país entre las décadas de 1830 y 1850 (Mayor Mora, 2002), el problema de escasez y baja calidad de la vivienda para el sector popular se intensificó debido a la migración de campesinos a la ciudad en busca de oportunidades laborales y a la alta demanda de mano de obra. Hasta antes de 1918, el Estado colombiano no prestó la atención requerida a esta situación; las condiciones precarias de las viviendas y la carencia de higiene afectaban directamente a las familias y a la ciudad
 En el transcurso de la investigación, al indagar sobre la arquitectura moderna en Colombia y sobre el desarrollo y evolución de la vivienda popular, se encontró que, inicialmente, fueron las comunidades religiosas católicas y las entidades de beneficencia las que se preocuparon ante el desalentador panorama, por lo que comenzaron las construcción de viviendas con un esquema social participativo, de manera conjunta con los beneficiados, con diseños que respondían a los parámetros básicos de la arquitectura moderna en cuanto a iluminación, ventilación, amplitud y flexibilidad de los espacios; estas fueron las primeras viviendas dignas para clases populares en el país.
 A inicios de la década de 1920, el Gobierno expidió leyes tendientes a promover la construcción de vivienda popular bajo lineamientos que aplicaban los parámetros básicos modernos, los cuales garantizaban el bienestar de las familias de ingresos bajos. Estos nuevos proyectos de vivienda fueron diseñados por arquitectos colombianos llegados al país desde Europa después de terminar allí sus estudios, influenciados por las nuevas tendencias de la arquitectura moderna. Fue un momento en el que el arquitecto comenzó a tener un rol fundamental en el desarrollo de las ciudades colombianas.
 La aplicación de los conceptos de la arquitectura moderna en construcciones populares dio como resultado espacios higiénicos, confortables, flexibles y progresivos, brindando una vivienda digna a la clase obrera, víctima hasta entonces del olvido del Estado, que por mucho tiempo pasó por alto sus necesidades y derechos. La vivienda popular, construida con materiales económicos y aplicando los principios de iluminación, ventilación y amplitud de espacios, dio nueva esperanza y mejoró las condiciones de una clase social afectada por el déficit de vivienda; esto da un giro de 180° a la situación, pues a partir de este momento, la arquitectura moderna ya no es exclusivamente para clases altas, sino que se convierte en una solución para diversas problemáticas de las clases media y baja.
 A finales de la década de 1960, el Estado empezó a involucrar compañías inmobiliarias en estos proyectos de vivienda popular, al resultarle costoso su diseño por parte de arquitectos reconocidos, que cobraban altas sumas de dinero por su trabajo. Adicionalmente, los presupuestos para estas viviendas disminuyeron, lo cual desalentó a muchos arquitectos que querían generar calidad, pero para ello se requería de una inversión justa. En conclusión, aquella calidad de las viviendas populares que se había alcanzado gracias a la aplicación de los parámetros de la arquitectura moderna durante la primera mitad del siglo XX, comenzó a disminuir progresivamente, perdiéndose poco a poco los logros alcanzados.
 Lo que ocurrió en años posteriores fue el camino a la crítica situación actual que presenta la vivienda de interés social. A partir de la creación del Sistema Nacional de Vivienda de Interés Social en 1991, integrado por entidades públicas y privadas, se estableció el subsidio familiar de vivienda, lo que significó un cambio sustancial en el papel del Estado en la solución de la problemática de vivienda popular, pues pasó de ser promotor, a ser simplemente un facilitador del mercado.
 La investigación contempla una comparación entre la vivienda popular influida por la arquitectura moderna y la vivienda de interés social de hoy. Actualmente, las normativas sobre vivienda de interés social se centran en la exigencia de dimensiones mínimas de los espacios con una destinación definida; dado que la norma no contempla aspectos fundamentales de una vivienda digna como la espacialidad y las áreas libres interiores, la iluminación y ventilación, el tratamiento paisajístico en las urbanizaciones, los equipamientos comunales, el acceso a transporte público, entre otros, estos aspectos no son ya tenidos en cuenta en los diseños. Lo que debería ser un derecho, ahora es un privilegio que, lamentablemente, la mayoría de las personas “beneficiadas” con Viviendas de Interés Social, no tienen.

Unsupervised Multistep Deformable Registration of Remote Sensing Imagery Based on Deep Learning

Image registration is among the most popular and important problems of remote sensing. In this paper we propose a fully unsupervised, deep learning based multistep deformable registration scheme for aligning pairs of satellite imagery. The presented method is based on the expression power of deep fully convolutional networks, regressing directly the spatial gradients of the deformation and employing a 2D transformer layer to efficiently warp one image to the other, in an end-to-end fashion. The displacements are calculated with an iterative way, utilizing different time steps to refine and regress them. Our formulation can be integrated into any kind of fully convolutional architecture, providing at the same time fast inference performances. The developed methodology has been evaluated in two different datasets depicting urban and periurban areas; i.e., the very high-resolution dataset of the East Prefecture of Attica, Greece, as well as the high resolution ISPRS Ikonos dataset. Quantitative and qualitative results demonstrated the high potentials of our method.

A novel cloud workflow scheduling algorithm based on stable matching game theory

Workflow scheduling is one of the most popular and challenging problems in cloud computing. However, among the studies on cloud workflow scheduling, very few consider the fairness among workflow tasks which could significantly delay the workflows and hence deteriorates user satisfaction. In this paper, we propose a workflow scheduling algorithm based on stable matching game theory to minimize workflow makespan and ensure the fairness among the tasks. The local optimization methods based on critical path and task duplication are developed to improve the performance of the algorithm. In addition, a novel evaluation metric is proposed to measure the fairness among workflow tasks. Comprehensive experiments are conducted to compare the performance of the proposed algorithm with other four representative algorithms. Experimental results demonstrate that our algorithm outperforms the other compared algorithms in terms of all three performance metrics under different workflow applications.

Regularization of Autoencoders for Bank Client Profiling Based on Financial Transactions

Predicting if a client is worth giving a loan—credit scoring—is one of the most essential and popular problems in banking. Predictive models for this goal are built on the assumption that there is a dependency between the client’s profile before the loan approval and their future behavior. However, circumstances that cause changes in the client’s behavior may not depend on their will and cannot be predicted by their profile. Such clients may be considered “noisy” as their eventual belonging to the defaulters class results rather from random factors than from some predictable rules. Excluding such clients from the dataset may be helpful in building more accurate predictive models. In this paper, we report on primary results on testing the hypothesis that a client can become a defaulter in two scenarios: intentionally and unintentionally. We verify our hypothesis applying data driven regularized classification using an autoencoder to client profiles. To model an intention as a hidden variable, we propose an especially designed regularizer for the autoencoder. The regularizer aims to obtain a representation of defaulters that includes a cluster of intentional defaulters and unintentional defaulters as outliers. The outliers were detected by our model and excluded from the dataset. This improved the credit scoring model and confirmed our hypothesis.

Adaptive surrogate-based harmony search algorithm for design optimization of variable stiffness composite materials

Having the advancement in automated fiber placement (AFP) and three-dimensional (3D) printing, fabricating novel composites reinforced with curvilinear fibers, namely variable stiffness composites (VSCs), have gradually become feasible. Contrasted to the traditional straight fiber-reinforced composites, VSCs possess significantly enhanced mechanical properties and increased mechanical performance. However, the optimization of a VSC panel is still a challenging problem, which can be attributed to the high non-convexity of the feasible design space and highly involved computation resources. To accelerate the design and optimization of VSC structures, in this study, we propose an effective and efficient adaptive surrogate-based harmony search algorithm (ASBHSA). The VSC is modeled using the fiber path methodology through which the fiber trajectory is generated by a mathematical formulation. In addition, the manufacturability and quality of the product are guaranteed by considering the curvature constraint. For the ASBHSA, a distance-based infill criterion in combination with a radial basis function (RBF) is proposed to guide the adaptive sampling. The global best harmony search (GHS) with a good ability to find the global minimum is integrated into the developed optimization framework. After solving some popular benchmark optimization problems as well as making comparisons of the optimal designs of VSCs with those in published works, the proposed optimization framework is demonstrated being able to effectively and efficiently find the global minimum. Then, a series of designs and optimizations of laminated VSC plates under various boundary conditions and different loading conditions are carried out systematically. The reported interesting findings may provide some insights and guidance in the design of laminated VSC composites. Moreover, our proposed optimization model provides a useful tool for solving various kinds of optimization problems.

Multistart solution-based tabu search for the Set-Union Knapsack Problem

The NP-hard Set-Union Knapsack Problem is a general model able to formulate a number of practical problems. As a variant of the popular knapsack problem, SUKP is to find a subset of candidate items (an item is composed of several distinct weighted elements) such that a profit function is maximized while a knapsack capacity constraint is satisfied. We investigate for the first time a multistart solution-based tabu search algorithm for solving the problem. The proposed algorithm combines a solution-based tabu search procedure with a multistart strategy to ensure an effective examination of candidate solutions. We report computational results on 60 benchmark instances from the literature, including new best results (improved lower bounds) for 7 large instances. We show additional experiments to shed lights on the roles of the key composing ingredients of the algorithm. The code of the algorithm will be publicly available.

Self-assembly of amphiphilic Janus spheres using the lattice Boltzmann method

We discuss the relevance of the lattice Boltzmann method (LBM) for soft matter alongside other simulation methods. We set up a popular problem of self-assembly of Janus spheres by combining native fluid and particle models in LBM with a simple amphiphilic pair potential. Thermal fluctuations and close contact corrections are also incorporated along with a novel periodic boundary condition for finite-sized 3D particles. Preliminary results of Janus sphere self-assembly are presented and compared with similar works in the literature. We also comment on the difficulties that LBM faces tackling such problems.

Finding Effective Item Assignment Plans with Weighted Item Associations Using A Hybrid Genetic Algorithm

By identifying useful relationships between massive datasets, association rule mining can provide new insights to decision-makers. Item assignment models based on association between items are used to place items in a retail or e-commerce environment to increase sales. However, existing models fail to combine these associations with item-specific information, such as profit and purchasing frequency. To find effective assignments with item-specific information, we propose a new hybrid genetic algorithm that incorporates a robust tabu search with a novel rectangular partially matched crossover, focusing on rectangular layouts. Interestingly, we show that our item assignment model is equivalent to popular quadratic assignment NP-hard problems. We show the effectiveness of the proposed algorithm, using benchmark instances from QAPLIB and synthetic databases that represent real-life retail situations, and compare our algorithm with other existing algorithms. We also show that the proposed crossover operator outperforms a few existing ones in both fitness values and search times. The experimental results show that not only does the proposed item assignment model generates a more profitable assignment plan than the other tested models based on association alone but it also obtains better solutions than the other tested algorithms.

An Effective Improved Multi-objective Evolutionary Algorithm (IMOEA) for Solving Constraint Civil Engineering Optimization Problems

This paper introduces a new metaheuristic optimization method based on evolutionary algorithms to solve single-objective engineering optimization problems faster and more efficient. By considering constraints as a new objective function, problems turned to multi objective optimization problems. To avoid regular local optimum, different mutations and crossovers are studied and the best operators due their performances are selected as main operators of algorithm. Moreover, certain infeasible solutions can provide useful information about the direction which lead to best solution, so these infeasible solutions are defined on basic concepts of optimization and uses their feature to guide convergence of algorithm to global optimum. Dynamic interference of mutation and crossover are considered to prevent unnecessary calculation and also a selection strategy for choosing optimal solution is introduced. To verify the performance of the proposed algorithm, some CEC 2006 optimization problems which prevalently used in the literatures, are inspected. After satisfaction of acquired result by proposed algorithm on mathematical problems, four popular engineering optimization problems are solved. Comparison of results obtained by proposed algorithm with other optimization algorithms show that the suggested method has a powerful approach in finding the optimal solutions and exhibits significance accuracy and appropriate convergence in reaching the global optimum.

Self-organizing fuzzy inference ensemble system for big streaming data classification

An evolving intelligent system (EIS) is able to self-update its system structure and meta-parameters from streaming data. However, since the majority of EISs are implemented on a single-model architecture, their performances on large-scale, complex data streams are often limited. To address this deficiency, a novel self-organizing fuzzy inference ensemble framework is proposed in this paper. As the base learner of the proposed ensemble system, the self-organizing fuzzy inference system is capable of self-learning a highly transparent predictive model from streaming data on a chunk-by-chunk basis through a human-interpretable process. Very importantly, the base learner can continuously self-adjust its decision boundaries based on the inter-class and intra-class distances between prototypes identified from successive data chunks for higher classification precision. Thanks to its parallel distributed computing architecture, the proposed ensemble framework can achieve great classification precision while maintain high computational efficiency on large-scale problems. Numerical examples based on popular benchmark big data problems demonstrate the superior performance of the proposed approach over the state-of-the-art alternatives in terms of both classification accuracy and computational efficiency.

Popular Matchings in Complete Graphs

Our input is a complete graph G on n vertices where each vertex has a strict ranking of all other vertices in G. The goal is to construct a matching in G that is popular. A matching M is popular if M does not lose a head-to-head election against any matching M': here each vertex casts a vote for the matching in {M,M'} in which it gets a better assignment. Popular matchings need not exist in the given instance G and the popular matching problem is to decide whether one exists or not. The popular matching problem in G is easy to solve for odd n. Surprisingly, the problem becomes texttt {NP}-complete for even n, as we show here. This is one of the few graph theoretic problems efficiently solvable when n has one parity and texttt {NP}-complete when n has the other parity.

Automated parameter tuning as a bilevel optimization problem solved by a surrogate-assisted population-based approach

This work presents a proposal for the automated parameter tuning problem (APTP) modeled as a bilevel optimization problem. Different definitions and theoretical results are given in order to formalize the APTP in the context of this hierarchical optimization problem. The obtained bilevel optimization problem is solved via a population-based algorithm added with surrogate models to identify promising regions in the parameter search space. The approach is tested by configuring four representative metaheuristics for numerical optimization on a set of well-known and recent test problems; also a competitive algorithm for a popular combinatorial optimization problem was configured (considering a large benchmark suite). The experimental results are compared against those of a state-of-the-art parameter tuning method called IRACE. The results, validated by the Bayesian signed-rank statistical test, indicate that BCAP, even it is based on an usually costly model (i.e. a bilevel optimization problem), with only half of the calls to the target algorithm, is able to find better configurations than those obtained by the compared approach.

Weakly symmetric stress equilibration and a posteriori error estimation for linear elasticity

AbstractThis paper proposes and analyzes a posteriori error estimator based on stress equilibration for linear elasticity with emphasis on the behavior for (nearly) incompressible materials. It is based on an H(div)‐conforming, weakly symmetric stress reconstruction from the displacement‐pressure approximation computed with a stable finite element pair. Our focus is on the Taylor‐Hood combination of continuous finite element spaces of polynomial degrees k + 1 and k for the displacement and the pressure, respectively. This weak symmetry allows us to prove that the resulting error estimator constitutes a guaranteed upper bound for the error with a constant that depends only on local constants associated with the patches and thus on the shape regularity of the triangulation. It does not involve global constants like those from Korn's in equality which may become very large depending on the location and type of the boundary conditions. Local efficiency, also uniformly in the incompressible limit, is deduced from the upper bound by the residual error estimator. Numerical results for the popular Cook's membrane test problem confirm the theoretical predictions.