Popular Problem Research Articles

Accurate through-the-thickness stress distributions in thin-walled metallic structures subjected to large displacements and large rotations

The present paper presents the evaluation of three-dimensional (3D) stress distributions of shell structures in the large displacement and rotation fields. The proposed geometrical nonlinear model is based on a combination of the Carrera Unified Formulation (CUF) and the Finite Element Method (FEM). Besides, a Newton-Raphson linearization scheme is adopted to compute the geometrical nonlinear equations, which are constrained using the arc-length path-following method. Static analyses are performed using refined models and the full Green-Lagrange strain-displacement relations. The Second Piola-Kirchhoff (PK2) stress distributions are evaluated, and lower- to higher-order expansions are employed. Popular benchmarks problems are analyzed, including cylindrical isotropic shell structure with various boundary and loading conditions. Various numerical assessments for different equilibrium conditions in the moderate and large displacement fields are proposed. Results show the distribution of axial and shear stresses, varying the refinement of the proposed two-dimensional (2D) shell model. It is shown that for axial components, a lower-order expansion is sufficient, whereas a higher-order one is needed to accurately predict shear stresses.

Problems of science popularization exemplified by modern Russian practice

Science popularization is a specific public activity of scientists, the purpose of which is education and self-realization. The article emphasizes the fact that this activity is self-organized in Russia, and the key role in its development is played by technical and technological improvement and the Internet. Media development contributes to the uncontrollable spread of pseudoscientific and unscientific theories as well as their criticism. The study is focused on the showization of science, which is presented to the public in the form of spectacle and entertainment. The forms of popularization of science in sociocultural practice begin to fulfill the function of intellectual leisure for the public. Popularization in the context of media development enables scientific knowledge to become public knowledge by means of publishing popular science literature, organizing online and offline lecture halls, web portals with scientific and educational content, which is evidence of the democratization of science in general and the activities of the scientist in particular. The activities of popularizers in modern Russia are consolidating, traditions are being developed, shaped by stable rituals, and recognizable authoritative “stars” are appearing. Thus, the popularization of science seems to be an ambiguous phenomenon of modern Russian culture, which reveals the features of a centralizing sociocultural institution.

Robust high-dimensional semiparametric regression using optimized differencing method applied to the vitamin B2 production data

Background and purpose: By evolving science, knowledge, and technology, we deal with high-dimensional data in which the number of predictors may considerably exceed the sample size. The main problems with high-dimensional data are the estimation of the coefficients and interpretation. For high-dimension problems, classical methods are not reliable because of a large number of predictor variables. In addition, classical methods are affected by the presence of outliers and collinearity.Methods: Nowadays, many real-world data sets carry structures of high-dimensional problems. To handle this problem, we used the least absolute shrinkage and selection operator (LASSO). Also, due to the flexibility and applicability of the semiparametric model in medical data, it can be used for modeling the genomic data. Motivated by these, here an improved robust approach in a high-dimensional data set was developed for the analysis of gene expression and prediction in the presence of outliers.Results: Among the common problems in regression analysis, there was the problem of outliers. In the regression concept, an outlier is a point that fails to follow the main linear pattern of the data. The ordinary least-squares estimator was found potentially sensitive to the outliers; this fact provided necessary motivations to investigate robust estimations. Generally, the robust regression is among the most popular problems in the statistics community. In the present study, the least trimmed squares (LTS) estimation was applied to overcome the outlier problem.Conclusions: We have proposed an optimization approach for semiparametric models to combat outliers in the data set. Especially, based on a penalization LASSO scheme, we have suggested a nonlinear integer programming problem as the semiparametric model which can be effectively solved by any evolutionary algorithm. We have also studied a real-world application related to the riboflavin production. The results showed that the proposed method was reasonably efficient in contrast to the LTS Method.

Computational aspects of the inverse single facility location problem on trees under lk-norm

We consider in this paper the inverse versions of the two popular problems in location theory, say the 1-median and the 1-center problems on trees. The cost for modifying vertex weights is measured under lk-norm for a positive integer k and hence the cost function is nonlinear. For the inverse 1-median problem, we develop a linear time algorithm based on the optimal solution of the induced unconstrained problem. For the inverse 1-center problem, we prove that the problem can be decomposed into linearly many subproblems and the objective function in each subproblem is piecewise-convex. Furthermore, we also discuss an O(nlog⁡n) time algorithm for the inverse 1-center problem under l2-norm based on the convexity of the cost function of each subproblem.

1D waveform inversion of GPR trace by particle swarm optimization

This study investigates the identification of buried materials from ground-penetrating radar (GPR) signals by applying Particle Swarm Optimization (PSO) method for the inversion of individual traces. Interpretation of GPR data can be carried out either by applying some basic processing steps to raw data or by inverting GPR data. However, for obtaining more specific information about the buried object, some more advanced algorithms are required. In this study, the PSO, which has been used to solve many engineering problems, is utilized to invert the GPR traces for detecting various types of buried plastic pipe fillings. Firstly, we confirmed the applicability of PSO to GPR traces using numerical modeling. Then, the data acquired over buried pipe filling materials (air, water ice) were evaluated experimentally by the proposed method. Determination of the filling material is one of the most popular problems of shallow pipe exploration by GPR. The inversion process resulted in good data fit between observed and calculated traces for air-filled, water-filled, and ice-filled pipe. The estimated dielectric properties including relative dielectric permittivity, conductivity, and relative magnetic permeability values were in the range of the corresponding parameter values in the literature at the end of inversion for each model. These results show that the PSO method applied to GPR data can distinguish plastic pipe contents. Moreover, the results suggest that PSO can be used for the quantitative interpretation of GPR data.

A Subspace Based Transfer Joint Matching with Laplacian Regularization for Visual Domain Adaptation.

In a real-world application, the images taken by different cameras with different conditions often incur illumination variation, low-resolution, different poses, blur, etc., which leads to a large distribution difference or gap between training (source) and test (target) images. This distribution gap is challenging for many primitive machine learning classification and clustering algorithms such as k-Nearest Neighbor (k-NN) and k-means. In order to minimize this distribution gap, we propose a novel Subspace based Transfer Joint Matching with Laplacian Regularization (STJML) method for visual domain adaptation by jointly matching the features and re-weighting the instances across different domains. Specifically, the proposed STJML-based method includes four key components: (1) considering subspaces of both domains; (2) instance re-weighting; (3) it simultaneously reduces the domain shift in both marginal distribution and conditional distribution between the source domain and the target domain; (4) preserving the original similarity of data points by using Laplacian regularization. Experiments on three popular real-world domain adaptation problem datasets demonstrate a significant performance improvement of our proposed method over published state-of-the-art primitive and domain adaptation methods.

Popular Matching for Security-Enhanced Resource Allocation in Social Internet of Flying Things

As the Internet of Things (IoT) is maturing and acquires its social flavor, the Social IoT enables smart devices to build inter-thing social networks without human intervention. As a new form of smart devices, unmanned aerial vehicles (UAVs) are finding their way into IoT applications. The integrated Social Internet of Flying Things (SIoFT) can provide the social-aware UAV-assisted services. However, the broadcast nature of air-to-ground (A2G) channels makes them vulnerable to being eavesdropped by terrestrial malicious users due to their strong line-of-sight (LoS) links. In this paper, we investigate to ensure the security of A2G communications when the location information of multiple potential eavesdroppers cannot be perfectly estimated. Following the “no pain no gain” principle, the terrestrial users who reuse the UAV cellular spectrum will act as friendly jammers to realize “win-win” situation. Hence, joint trajectory design, power control, and channel allocation optimization problem is formulated to maximize the average secrecy rate of UAVs in worst case. In the first stage, we utilize the block coordinate descent method and successive convex optimization method to solve the trajectory design and power control problems in an iterative manner. In the second stage, we convert the user pairing problem into a popular matching problem with externalities. Two distributed algorithms are proposed to maintain the popular matching under dynamics. Moreover, we conduct detailed analysis of the popularity, convergence, and computational complexity. Simulation results demonstrate the superiority of our proposed method in terms of different performance metrics.

Hamiltonian path problem solution using DNA computing

In this article we study DNA computing, a method which is based on working with DNA molecules in a laboratory. That approach is implemented in solving one of the most popular combinatorial problem — the Hamiltonian path problem. Related to recent improvements in the biophysics methods, which are needed for DNA computing, we propose to change some steps in the classical algorithm to increase accuracy of this method. The branch-and-bound method, the most popular method which is realized on a computer, is also shown in this paper to compare its performance with the time consumption of DNA computing. The results of that comparison prove that it becomes inefficient to use the branch-and-bound method from the counted number of vertices because of its exponentially growing complexity, while DNA computing works parallel and has linearly growing time consumption.

Aesthetic dermatology procedures in coronavirus days.

BackgroundSevere acute respiratory syndrome‐coronavirus‐2 (SARS‐CoV‐2; COVID‐19), which causes coronavirus disease 2019, is highly contagious and a particularly popular problem in all around the world and also in all departments of every hospital.AimsProtecting the well‐being of the aesthetic dermatologists while providing a sufficient workforce is vital for pandemic planning.Results and ConclusionsIn this article, we will discuss this problem from an aesthetic dermatology aspect and we will review whether these procedures are safe or not.

Deep convolutional neural networks for multi-scale time-series classification and application to tokamak disruption prediction using raw, high temporal resolution diagnostic data

In this paper, we discuss recent advances in deep convolutional neural networks (CNNs) for sequence learning, which allow identifying long-range, multi-scale phenomena in long sequences, such as those found in fusion plasmas. We point out several benefits of these deep CNN architectures, such as not requiring experts such as physicists to hand-craft input data features, the ability to capture longer range dependencies compared to the more common sequence neural networks (recurrent neural networks like long short-term memory networks), and the comparative computational efficiency. We apply this neural network architecture to the popular problem of disruption prediction in fusion energy tokamaks, utilizing raw data from a single diagnostic, the Electron Cyclotron Emission imaging (ECEi) diagnostic from the DIII-D tokamak. Initial results trained on a large ECEi dataset show promise, achieving an F1-score of ∼91% on individual time-slices using only the ECEi data. This indicates that the ECEi diagnostic by itself can be sensitive to a number of pre-disruption markers useful for predicting disruptions on timescales for not only mitigation but also avoidance. Future opportunities for utilizing these deep CNN architectures with fusion data are outlined, including the impact of recent upgrades to the ECEi diagnostic.

IoT based Garbage and Waste Collection Bin Overflow Indicator

Waste the board is the fundamental problem that the world deals self reliant in case of creating country. The troubles within the waste corporation are that the waste holder at open spots advances beyond time before the begin of the partner cleaning process. It consequently precise numerous dangers, as an instance, lousy aroma and repulsiveness to that spot which can be the basic driving force for unfold of numerous illnesses. To avoid all such unsafe circumstance and maintain up open neatness and prosperity this work is mounted on an astute refuse device. The popular problem of the work is to expand a sagacious shrewd rubbish prepared gadget for a proper refuse the board .This paper introduces a pointy prepared shape for junk opportunity by using offering a caution hint to the huge town server for minute cleaning of waste with valid check reliant on level of waste filling. This technique is helped via the ultrasonic sensor that's interfaced with Arduino UNO to test the diploma of refuse crammed inside the dustbin and sends the caution to the everyday net server once if trash is crammed . The fuel sensor and the fire sensor are applied to take a look at the spoil of fire and it take a gander at the closeness of any risky fuel over the development holder. The fuel sensor and the fire sensor are used to take a gander at the wreck of fireplace and it look at the closeness of any risky fuel over the accumulation holder. The complete gadget is stored up by way of an embedded module combined with IOT Assistance and sensor. The non-stop status of ways waste is accumulated that might be watched and stuck up by means of the location authority with the guide. Despite this the crucial change measures will be balanced.

Exploring the Potential of Deep Learning Segmentation for Mountain Roads Generalisation

Among cartographic generalisation problems, the generalisation of sinuous bends in mountain roads has always been a popular one due to its difficulty. Recent research showed the potential of deep learning techniques to overcome some remaining research problems regarding the automation of cartographic generalisation. This paper explores this potential on the popular mountain road generalisation problem, which requires smoothing the road, enlarging the bend summits, and schematising the bend series by removing some of the bends. We modelled the mountain road generalisation as a deep learning problem by generating an image from input vector road data, and tried to generate it as an output of the model a new image of the generalised roads. Similarly to previous studies on building generalisation, we used a U-Net architecture to generate the generalised image from the ungeneralised image. The deep learning model was trained and evaluated on a dataset composed of roads in the Alps extracted from IGN (the French national mapping agency) maps at 1:250,000 (output) and 1:25,000 (input) scale. The results are encouraging as the output image looks like a generalised version of the roads and the accuracy of pixel segmentation is around 65%. The model learns how to smooth the output roads, and that it needs to displace and enlarge symbols but does not always correctly achieve these operations. This article shows the ability of deep learning to understand and manage the geographic information for generalisation, but also highlights challenges to come.

Cutaneous sıde-effects of the potential COVID-19 drugs.

COVID‐19 disease is a highly contagious and particularly popular problem in all countries. A variety of repurposed drugs and investigational drugs such as remdesivir, chloroquine, hydroxychloroquine, ritonavir, lopinavir, interferon‐beta, and other potential drugs have been studied for COVID19 treatment. We reviewed the potential dermatological side‐effects of these drugs.

Autolanding control system design with deep learning based fault estimation

Developing a control system that can recover the aircraft under actuator failures and severe disturbances is a popular problem in flight control system design. Majority of existing controllers cannot adapt to changes in aircraft dynamics that occur due to severe actuator failures. We propose a novel, data-driven fault estimation method for estimating actuator faults from aircraft state trajectories, which utilizes a deep neural network trained offline on gathered simulation data of fault injected aircraft. Proposed novel deep fault estimation model coupled with an existing nonlinear dynamic inversion based autolanding controller, reacts immediately to a wide range of actuator failures and is able to land the aircraft under many different combinations of actuator failures and severe wind conditions. Performance of the proposed approach is compared to existing state-of-the-art fault tolerant controllers through fault tolerance maps. It is observed that the developed approach is superior both in terms of fault tolerance map coverage and smoothness of the computed controller signals.

Hamiltonian path problem: the performance comparison deoxyribonucleic acid computing and the branch-and-bound method

In this article different approaches to one of the most popular combinatorial problem — the Hamiltonian path problem – are illustrated and compared between each other. It is shown that it becomes inefficient to use branch-and-bound method, the most popular method which is realized on a computer, from the counted number of vertices because of its exponentially growing complexity, one more algorithm which is based on working with deoxyribonucleic acid (DNA) molecules in a laboratory is analysed. That method works parallel and has linearly growing time consumption. Due to the improvements in the biophysics methods, which are needed for DNA computing, that algorithm became much faster than it was several years ago and it is now possible to add some new stages in DNA computing, which are shown in this paper.

AutoSight: Distributed Edge Caching in Short Video Network

In recent years there has been a rapid increase of short video traffic in CDN. While the video contributors change from large video studios to distributed ordinary end users, edge computing naturally matches the cache requirements from short video network. But distributed edge caching exposes some unique characteristics: non-stationary user access pattern and temporal and spatial video popularity pattern, which severely challenge the edge caching performance. While the QoE in traditional CDN has been much improved, prior solutions become invalid in solving the above challenges. In this article, we present AutoSight, a distributed edge caching system for short video network, which significantly boosts cache performance. AutoSight consists of two main components, solving the above two challenges respectively: the CoStore predictor, which solves the non-stationary and unpredictability of local access pattern, by analyzing the complex video correlations; and a caching engine Viewfinder, which solves the temporal and spatial video popularity problem by automatically adjusting future horizon according to video life span. All these inspirations and experiments are based on the real traces of more than 28 million videos with 100 million accesses from 488 servers located in 33 cities. Experiment results show that AutoSight brings significant boosts on distributed edge caching in short video network.

Windowing queries using Minkowski sum and their extension to MapReduce

Given a set of n segments and a query shape Q, the windowing length query asks for finding the sum of the lengths of the parts of the segments that lie inside Q. The popular places problem of a set of curves asks for the subset of the plane where each query shape centered at a point of that region intersects with at least f distinct curves. For square queries, an optimal $$O(n^2)$$ time algorithm and a matching lower bound exist. We solve the length query problem for convex polygons and disks as query shapes, with $$O(\log n+k)$$ query time and polynomial preprocessing time that depends on the complexity of the query shape. We define a new version of the problem of finding popular places in a set of trajectories where the center of a query is a popular place if the length of the curves inside that query is at least f and use our data structure to solve the original problem as well as this new version. Other than length queries, we solve reporting queries that return the set of intersected segments. For disk queries, we design a point-location data structure for congruent disks with $$O(\log n)$$ query time and $$O(n^3\log n)$$ preprocessing. We also give algorithms for computing the length query for c-packed curves, which are a class of curves for which the length of the curve inside a disk of radius r is upper-bounded by cr, where c is a constant. Also, we use length queries for polygons to approximate the minimum value c for which a curve is c-packed, if such a c exists. Our results extend to MRC and MPC models for MapReduce, where we address these problems on a set of x-monotone curves. The round complexities of our MapReduce algorithms are constant. In addition, we also implemented our popular places algorithms on trajectories on inputs as big as 15K points to evaluate the efficiency of our algorithms in practice.

Coronavirus-days ın dermatology.

Severe acute respiratory syndrome‐corona virus‐2, which causes coronavirus disease 2019 (COVID‐19), is highly contagious and a particularly popular problem in all around the World and also in all departments of every hospital. In order to protect the well‐being of health care providers while providing a sufficient workforce to respond to the COVID‐19 are vital for pandemic planning. In this article, we will discuss this problem from a dermatological aspect.

Audiobook, audio podcast, audio series – modern formats of the media space

The article discusses modern audioformats – audiobook, audio series and audio podcast. Now these formats have gained great popularity and demand, considerable market weight with their own segment, the growth of which experts note. The research material is audio products of the modern market, which reflect the trends in the development of audio literature in the current media environment. The scientific novelty of the work consists in the consideration of an audiobook, an audio series and an audio podcast in the context of a new stage in the development of audio culture. The problem of popularity of the studied audio products is considered in direct dependence on audio reading. It is understood as a modern modification of auditory reading, as a reader’s practice due to the qualitative changes in the material and technical base for creating audio records, for their copying and use. The analysis of audio formats in the informational, sociocultural and cognitive aspect of the modern media consumption allows the determination new editorial approaches to the work with audio texts. Conclusions are as follows: one can observe the tendency to transit from voicing printed texts to creating texts specially for voicing; there is a reduction in the novel form, stories become popular; the editing of audio books and audio series requires support from sound design principles; the editorial and director’s task on preparing podcasts is associated with the compositional alignment of several voices of storytellers, forming a single ideological and thematic field.

Unsupervised Domain Adaptation of High-Resolution Aerial Images via Correlation Alignment and Self Training

Deep learning-based approaches for land cover segmentation rely on supervision with pixel-level ground truth, but may not generalize well to unseen image domains. Due to the tedious and labor-intensive labeling process, transferring the models trained with label-rich source data to nonannotated target data becomes a popular problem in recent years. Because of the domain shift, the difference between the source and target distributions can degrade the accuracy on target data if the training occurs directly in a source domain without proper domain adaptation (DA). In this letter, we propose a U-Net based network for DA in the context of semantic segmentation. The model is trained in the source domain with ground truth and test in the target domain without any annotations. We introduce the layer alignment method and the feature covariance loss function to alleviate the domain shift between different domains. To further enhance the adapted model, we adopt a self-training method to improve segmentation performance. Experimental results on the images from the 2018 IEEE Geoscience and Remote Sensing Society (GRSS) data fusion contest and the International Society for Photogrammetry and Remote Sensing (ISPRS) 2-D semantic labeling contest data set reveal the effectiveness of the proposed model. By reducing the domain distribution difference, our method shows better performance compared with the mainstream unsupervised DA methods.