Sequential Construction Research Articles

ESTIMATING WITH A GIVEN ACCURACY OF THE COEFFICIENTS AT NONLINEAR TERMS OF UNIVARIATE POLYNOMIAL REGRESSION USING A SMALL NUMBER OF TESTS IN AN ARBITRARY LIMITED ACTIVE EXPERIMENT

We substantiate the structure of the efficient numerical axis segment an active experiment on which allows finding estimates of the coefficients fornonlinear terms of univariate polynomial regression with high accuracy using normalized orthogonal Forsyth polynomials with a sufficiently smallnumber of experiments. For the case when an active experiment can be executed on a numerical axis segment that does not satisfy these conditions, wesubstantiate the possibility of conducting a virtual active experiment on an efficient interval of the numerical axis. According to the results of the experiment, we find estimates for nonlinear terms of the univariate polynomial regression under research as a solution of a linear equalities system withan upper non-degenerate triangular matrix of constraints. Thus, to solve the problem of estimating the coefficients for nonlinear terms of univariatepolynomial regression, it is necessary to choose an efficient interval of the numerical axis, set the minimum required number of values of the scalarvariable which belong to this segment and guarantee a given value of the variance of estimates for nonlinear terms of univariate polynomial regressionusing normalized orthogonal polynomials of Forsythe. Next, it is necessary to find with sufficient accuracy all the coefficients of the normalized orthogonal polynomials of Forsythe for the given values of the scalar variable. The resulting set of normalized orthogonal polynomials of Forsythe allows us to estimate with a given accuracy the coefficients of nonlinear terms of univariate polynomial regression in an arbitrary limited active experiment: the range of the scalar variable values can be an arbitrary segment of the numerical axis. We propose to find an estimate of the constant and ofthe coefficient at the linear term of univariate polynomial regression by solving the linear univariate regression problem using ordinary least squaresmethod in active experiment conditions. Author and his students shown in previous publications that the estimation of the coefficients for nonlinearterms of multivariate polynomial regression is reduced to the sequential construction of univariate regressions and the solution of the correspondingsystems of linear equalities. Thus, the results of the paper qualitatively increase the efficiency of finding estimates of the coefficients for nonlinearterms of multivariate polynomial regression given by a redundant representation.

Probabilistic Analysis of Tunnel Roof Deflection under Sequential Excavation Using ANN-Based Monte Carlo Simulation and Simplified Reliability Approach

Tunnel roof deflection is an important measure to control the safety of excavation activity, especially for large-section tunnels using the sequential construction method. A three-dimensional (3D) model of a sequential excavation project in the Lijiaping metro tunnel, Chongqing, China, was developed and the maximum tunnel roof deflection was calculated deterministically at the end of each excavation stage. By defining the characteristics of the soft rock layers surrounding the tunnel section as the random variables, a probabilistic analysis was conducted and the problem was formulated as a reliability model. Two different approaches were used to solve the established reliability problem. One entails an artificial neural network (ANN) trained by a large data set obtained from numerical simulations and then accompanied by the Monte Carlo (MC) sampling method to calculate the probability. The other is a simplified reliability approach using a small data set to approximate the exceedance probability via a regression-based algorithm. The proposed ANN-based metamodel used in the first method could accurately predict tunnel roof deflection and replace the software simulator. Subsequently, the probabilistic results obtained from this method following MC sampling could well converge and provide the probability with enough accuracy. Interestingly, the simplified approach with more than 40 random samples can also provide acceptable results, which provides an economical approach to estimate the exceedance probability.

Palladium-Catalyzed Remote Diborylative Cyclization of Dienes with Diborons via Chain Walking.

A novel method for catalytic remote bismetalation of alkene substrates by the addition of dimetal reagents is accomplished by using chain walking. In the presence of a palladium catalyst, the reaction of various 1,n-dienes and diborons were converted into cyclopentane derivatives with two boryl groups at remote positions via facile regioselective transformation of an unactivated sp3 C-H bond to a C-B bond. Sequential construction of three distant bonds, which is difficult to achieve by any method, was accomplished for the reactions of 1,n-dienes (n ≥ 7).

SMIM: A unified framework of survival sensitivity analysis using multiple imputation and martingale.

Censored survival data are common in clinical trial studies. We propose a unified framework for sensitivity analysis to censoring at random in survival data using multiple imputation and martingale, called SMIM. The proposed framework adopts the δ-adjusted and control-based models, indexed by the sensitivity parameter, entailing censoring at random and a wide collection of censoring not at random assumptions. Also, it targets a broad class of treatment effect estimands defined as functionals of treatment-specific survival functions, taking into account missing data due to censoring. Multiple imputation facilitates the use of simple full-sample estimation; however, the standard Rubin's combining rule may overestimate the variance for inference in the sensitivity analysis framework. We decompose the multiple imputation estimator into a martingale series based on the sequential construction of the estimator and propose the wild bootstrap inference by resampling the martingale series. The new bootstrap inference has a theoretical guarantee for consistency and is computationally efficient compared to the nonparametric bootstrap counterpart. We evaluate the finite-sample performance of the proposed SMIM through simulation and an application on an HIV clinicaltrial.

Variance-based adaptive sequential sampling for Polynomial Chaos Expansion

This paper presents a novel adaptive sequential sampling method for building Polynomial Chaos Expansion surrogate models. The technique enables one-by-one extension of an experimental design while trying to obtain an optimal sample at each stage of the adaptive sequential surrogate model construction process. The proposed sequential sampling strategy selects from a pool of candidate points by trying to cover the design domain proportionally to their local variance contribution. The proposed criterion for the sample selection balances both exploitation of the surrogate model and exploration of the design domain. The adaptive sequential sampling technique can be used in tandem with any user-defined sampling method, and here was coupled with commonly used Latin Hypercube Sampling and advanced Coherence D-optimal sampling in order to present its general performance. The obtained numerical results confirm its superiority over standard non-sequential approaches in terms of surrogate model accuracy and estimation of the output variance.

Fusion 360 gallery

Parametric computer-aided design (CAD) is a standard paradigm used to design manufactured objects, where a 3D shape is represented as a program supported by the CAD software. Despite the pervasiveness of parametric CAD and a growing interest from the research community, currently there does not exist a dataset of realistic CAD models in a concise programmatic form. In this paper we present the Fusion 360 Gallery , consisting of a simple language with just the sketch and extrude modeling operations, and a dataset of 8,625 human design sequences expressed in this language. We also present an interactive environment called the Fusion 360 Gym , which exposes the sequential construction of a CAD program as a Markov decision process, making it amendable to machine learning approaches. As a use case for our dataset and environment, we define the CAD reconstruction task of recovering a CAD program from a target geometry. We report results of applying state-of-the-art methods of program synthesis with neurally guided search on this task.

Autophagy Modulation and Synergistic Therapy to Combat Multidrug Resistance Breast Cancer Using Hybrid Cell Membrane Nanoparticles.

The development of multidrug resistance (MDR) is a commonly observed phenomenon in many cancer types. It contributed significantly to the poor outcome of many currently available chemotherapies. Considering autophagy as one of the most important physiological process in cancer progression, we thereby proposed an anti-autophagy siRNA and doxorubicin (Dox) co-delivery system (MC/D-siR) to combat MDR breast cancer using sequential construction. Our results demonstrated the potential of MC/D-siR to effectively transfect the loaded siRNA to result in significant downregulation of intracellular autophagy level in MCF-7/Adr (Dox resistance MCF-7 cell line) cells, which in turn cut off the ATP supply and to reverse the MDR and potentiated accumulated drug retention in cells. As a result, MC/D-siR showed much elevated anticancer benefits than single loaded platforms (MC/Dox or MC/siRNA), indicating the ability for effective MDR cancer treatment through the combination of autophagy regulation and chemotherapy.

Fast Sequential Feature Extraction for Recurrent Neural Network-Based Hyperspectral Image Classification

Classification is a critical, widely employed type of hyperspectral image (HSI) processing. Recently, deep learning models have been attracting more attention within the hyperspectral remote-sensing community due to their improved classification performance. Among them, recurrent neural networks (RNNs), which were initially used to handle sequential data, have been applied to HSI classification with promising results. The key point for such RNN-based models in a classification context is the extraction of a sequential feature for each individual pixel in a HSI. One popular strategy is to first extract similar pixels compared with a target pixel from the HSI, and then use those similar pixels to encode its sequential feature. However, the computational cost is tremendous, especially if such similarity-calculation search is done on the whole image. In this article, inspired by our previous work regarding similarity measurement-based sequential feature construction, a faster sequential feature extraction framework for long short-term memory (LSTM)-based HSI classification is proposed, where object-based segmentation method is employed for the purpose of imposing spatial constraints and computational acceleration. Within the proposed framework, both the local segment containing the target pixel and nonlocal segments are considered. For a target pixel, similar segments are selected first based on segment-based features, and then similar pixels from selected segments are extracted to construct a sequential feature. During pixel-wise similarity measurement, both spectral and spatial information are considered in such computation. Experimental results on three benchmark HSI data sets illustrate that the proposed methods achieve promising classification performance with markedly lower computation-time cost.

A Reverse Approach to the Total Synthesis of Halichondrin B.

A new strategy is described for the total synthesis of halichondrin B featuring reversal of the sequential construction of a number of its cyclic ethers from the classical approach by instead forming C-O bonds first followed by C-C bond formation. Employing the Nicholas reaction to generate linear ethers as precursors for the total synthesis of halichondrin B and other members of the halichondrin and eribulin families of compounds, this novel approach provides new opportunities for the development of improved syntheses of these complex and valuable compounds. In this Article, we report the syntheses of defined fragments I, MN, EFG, and A. Fragments I and MN were then coupled and elaborated to advanced intermediate IJKLMN, which was joined with fragment EFG to afford, after appropriate elaboration and macrolactonization, the more advanced polycyclic intermediate EFGHIJKLMN. Elaboration of the latter and coupling with fragment A followed by further functionalization completed the total synthesis of halichondrin B through a short and convergent pathway.

Design and Fabrication of Robotic Arm for the assembly of Phase Selector Switch

This paper explores the robot arm where the ability of human to do a project is restrained however now not with the aid of his mental electricity however through his bodily energy. A humanoid mechanical technology is another difficult field. To co-work with people, humanoid robots not just need to include human like structure and structure, yet more significantly, they should arrange human like conduct with respect to the movement, correspondence and intelligence. In environmental factors where human exchange can't be conceivable to do a specific errand, the robots can do. Robots are used for lean industrial processes and have diversified their contributions to meeting lines inside the production international. The main focus of this project is to design and develop the mechanism for robotic arm for lifting. It is a type of mechanical arm, usually programmable, with similar functions to a human arm. The arm might be a unit system or might be an aspect of a more unpredictable mechanical cycle. The end effectors or mechanical hand can be intended to play out any ideal undertaking, for example, welding, holding, turning etc., depending on the application. The mechanical arm is planned and designed with four degrees of freedom and modified to achieve precisely basic light material lifting undertaking to aid the creation line in any industry. 3D printing strategy is utilized in this undertaking to create the parts of the automated arm. Hence, it gave more exact measurements and gigantic time and cost expensive in creation. The automated arm is furnished with 4 servo engines to interface the parts and bring arm development. A sequential construction mechanical system can improve its efficiency through developing the assembling pace and its consistency. They additionally spare individuals from dreary and tedious mechanical production system employments. Many industries are yet to implement such automation in their meeting traces, as they do no longer have the technical recognize how of the changeover, or the concern of failure of investment. It is proposed to layout and increase a low price assembly robotic which could overcome the above drawbacks. We have also included Design Thinking process before starting to do this experiment. The stages of Design Thinking such as Empathize, Define, Ideate, Prototype & test, Evolve are performed in the complete fabrication of the robotic arm. Many industries are yet to implement such automation in their assembly lines, as they do not have the technical knowhow of the changeover, or the fear of failure of investment. To proceed in this direction, a case study was conducted at a phase selector switch making company. Presently the company does manual assembly of the switches, which is a hindrance to the growth of the company. This process required a pilot study to explore the possibility as well as the implementations issues of robots in the assembly. The present system uses a belt conveyor system which has no feedback signals and the speed of the conveyor is not synchronized with the rate of assembly. It is generally not advised to transfer small size components in a conveyor since picking, orienting and inserting the components will be cumbersome. When the size of the component is considerably small, manual assembly is not recommended as per the guidelines of DFMA (Design for manufacture and assembly). Manual error is more prone to assembly of the part in a wrong orientation, especially for those components that are symmetrical.The parts to be assembled are both axi-symmetric as well as prismatic in shape. Constraints in loading and unloading of casing were also reported. In this work, the design and development of a low cost assembly robot is done which can overcome the above drawbacks.

Characterisation of black skin stratum corneum by digital macroscopic images analysis.

Black skin medical images generally show very low contrast. Being in a global initiative of characterisation of black skin horny layer (stratum corneum) by digital images analysis, the authors in this study proposed a four-step approach. The first step consists of differentiation between probable healthy skin regions and those affected. For that, they used an automatic classification system based on multilayer perceptron artificial neural networks. The network has been trained with texture and colour features. Best features selection and network architecture definition were done using sequential network construction algorithm-based method. After classification, selected regions undergo a colour transformation, in order to increase the contrast with the lesion region. Thirdly, created colour information serves as the basis for a modified fuzzy c-mean clustering algorithm to perform segmentation. The proposed method, named neural network-based fuzzy clustering, was applied to many black skin lesion images and they obtained segmentation rates up to 94.67%. The last stage consists in calculating characteristics. Eight parameters are concerned: uniformity, standard deviation, skewness, kurtosis, smoothness, entropy, and average pixel values calculated for red and blue colour channels. All developed methods were tested with a database of 600 images and obtained results were discussed and compared with similar works.

Scalable structural index construction for JSON analytics

JavaScript Object Notation (JSON) and its variants have gained great popularity in recent years. Unfortunately, the performance of their analytics is often dragged down by the expensive JSON parsing. To address this, recent work has shown that building bitwise indices on JSON data, called structural indices , can greatly accelerate querying. Despite its promise, the existing structural index construction does not scale well as records become larger and more complex, due to its (inherently) sequential construction process and the involvement of costly memory copies that grow as the nesting level increases. To address the above issues, this work introduces Pison - a more memory-efficient structural index constructor with supports of intra-record parallelism. First, Pison features a redesign of the bottleneck step in the existing solution. The new design is not only simpler but more memory-efficient. More importantly, Pison is able to build structural indices for a single bulky record in parallel, enabled by a group of customized parallelization techniques. Finally, Pison is also optimized for better data locality, which is especially critical in the scenario of bulky record processing. Our evaluation using real-world JSON datasets shows that Pison achieves 9.8X speedup (on average) over the existing structural index construction solution for bulky records and 4.6X speedup (on average) of end-to-end performance (indexing plus querying) over a state-of-the-art SIMD-based JSON parser on a 16-core machine.

Effect of truss installation sequence on deviation during launching of a box–truss girder

Steel box–truss composite girders (BTCGs) are generally used on cable-stayed railway bridges – the steel box with a truss on top together provide the stiffness and resistance to the applied bending moments and axial forces of the girder. When subjected to strict construction period requirements, the box and truss should be constructed at the same time to shorten the construction period. In order to avoid excessive initial installation stresses caused by auxiliary measures, adjusting the upper chord members of the truss installation sequence was investigated. The results of finite-element analysis and a practical application proved the validity of the method. The assembly error of the upper chords can be reduced to within 3 mm, meeting the requirements of field assembly. At the same time, the method can effectively reduce the free-end deflection of the launching nose during the sequential launching construction of the BTCG. The method shortened the construction period of Yuxi River Bridge by 5–6 months.

Human–robot collaboration: a fabrication framework for the sequential design and construction of unplanned spatial structures

ABSTRACT Robots in traditional fabrication applications act as passive participants in the process of creation—simply performing a set of predetermined actions to materialize a completed design. We propose a novel bottom-up design framework in which robots are instead given the opportunity to participate centrally within a creative design process. This paper describes how two 6-axis industrial robotic arms were used to cooperatively aggregate a collection of solid spherical units. The branching spatial structure being constructed is unplanned at the outset of this process, and is instead designed in pseudo-realtime during construction. This ‘design-as-you-build’ approach relies on robotic input, in the form of path-planning constraints, in tandem with human evaluation and decision-making. The resulting structure emerges from a human–robot design collaboration operating within the specified physical domain.

MONITORING MECHANISM OF RESILIENCE OF THE ANTI-CRISIS POTENTIAL SYSTEM OF THE CONSTRUCTION ENTERPRISE IN THE LONG-TERM PERIOD

The anti-crisis potential of enterprises belonging to industries with a long production cycle is unstable, as the time interval between incoming cash flows is much longer than between outgoing ones. This problem is especially actual for construction companies, whose income largely depends on the state of investment demand. Therefore, there is a need to develop an effective mechanism to control the resilience of the anti-crisis system of the construction company to environmental conditions. According to many scientists, anti-crisis potential is associated with the financial sanation. Given the undeniable usefulness of the analyzed developments, there has been noted that they are unsuitable for managing the financial sanation capacity and anti-crisis potential of construction companies. After all, most of them are suggested for agricultural or industrial ones. Given the definition of "financial sanation", the implementation of measures aimed at restoring business is impossible without investment. Investing funds in any business project, including those aimed at strengthening the financial condition of the enterprise, will be efficient only when they provide an economic effect. Thus, the requirement of return on investment is indisputable, but for high-risk activities, including construction, there is an additional condition, to get a return on investment as soon as possible: from 3 to 10 years. After all, this is the duration period of different types of macroeconomic cycles, during which the vast majority of construction companies go through all stages of economic development: from growth to decline. The dependence of the payback period of equity capital on other factors of the internal and external environment of business systems has been studied in order to manage the rehabilitation and anti-crisis potential of a construction company. General scientific methods such as analysis, synthesis, deduction, induction, analogy as means of studying and generalizing theoretical approaches to providing anti-crisis potential; Васkwаrd Stерwisе method (construction of multifactor linear regressions Stаtistіса 8.0), providing the sequential construction of equations in which the set of input factors decreases by removing the least significant to explain the variation of the independent variate. In order to increase the efficiency of decision-making on the feasibility of additional equity attraction by construction companies there has been developed a methodological approach to forecasting the level of financial sanation capacity of the construction company. It is a complex; a multifactor model - a linguistic scale, allows to identify changes in the payback period of equity in the medium term using quantitative and linguistic estimates and can be used as a functional module of digitized economic management of the enterprise.

Cluster-Based Thermodynamics of Interacting Dice in a Lattice

In this paper, a model for two-component systems of six-sided dice in a simple cubic lattice is developed, based on a basic cluster approach previously proposed. The model represents a simplified picture of liquid mixtures of molecules with different interaction sites on their surfaces, where each interaction site can be assigned an individual energetic property to account for cooperative effects. Based on probabilities that characterize the sequential construction of the lattice using clusters, explicit expressions for the Shannon entropy, synonymously used as thermodynamic entropy, and the internal energy of the system are derived. The latter are used to formulate the Helmholtz free energy that is minimized to determine thermodynamic bulk properties of the system in equilibrium. The model is exemplarily applied to mixtures that contain distinct isomeric configurations of molecules, and the results are compared with the Monte-Carlo simulation results as a benchmark. The comparison shows that the model can be applied to distinguish between isomeric configurations, which suggests that it can be further developed towards an excess Gibbs-energy, respectively, activity coefficient model for chemical engineering applications.

IMPROVEMENT OF CROSS SECTION OF COMPRESSED ELEMENTS BY TOPOLOGICAL RATIONALIZATION

The article describes the procedure of direct (rational) design of vertical compressed elements (columns, pylons, stiffening diaphragms, retaining walls). This technology is an exclusive modification of topological (bionic) rationalization. It is based on new energy principles and algorithms for sequential construction of geometric and / or physical-mechanical "image" of the structure. The example of the formation of energy-equivalent columns shows the sequence of computational operations of the method. The solution is built analytically in order to show the nuances of the necessary operations. At the same time, these examples show that the introduced optimization criteria in the form of en → const and U → inf U (here en is the value of the normalized density of potential deformation energy, and U is the potential energy of deformation) determine, including the minimum volume of constructs, as well as their minimum deflections. A fundamental element of the approach is the use of a new criterion of the limit state, which provides an estimate of the intensity of the element. In this case, the properties of the material and the type of stress-strain state are taken into account. The obtained analytical solution was used as a control test for the general computational procedure of the method. In this regard, the article presents the results of the analytical and numerical solution. The efficiency of the computational procedure is confirmed by the speed of its convergence and the minimum difference of geometric parameters (topology) of the structure with test examples. The technological sequence of computational operations of the method is completed by the construction of elements (pylons) with a simple external and complex internal geometry. Keywords: deformation, energy, iteration, voids, cavity, rationalization.

Temporality revisited: Dynamicity issues in collaborative digital writing research

This paper explores the feature of dynamicity (a composite of temporal and local properties) in research on collaborative digital writing (CDW) in academic writing assignments. The paper traces the ways in which current research typically approaches CDW and identifies the underlying elements of current and technological inquiry in this field: components of text (the process and products of writing and learning communication), external variables, such as learning orientations and group composition, and an intermediate layer of time management that is related to organizing the assignment. The paper identifies gaps in the current understanding of the phenomenon of collaborative writing for learning and pinpoints some basic weaknesses with concepts used in this research, in particular assumptions related to the time aspect of the collaborative writing process. The prevalent view of current research, which conceptualizes writing processes as predominantly time-ordered intermediate products (texts) at several designated points within the writing assignment, overlooks the sequential dynamics of textual interaction, linearity, and “local” concept construction as influential factors in CDW assignments. Instead, this paper argues for the ideas of sequential concept construction and locally changing sources for the writer at a certain point in the writing. The insights presented in this paper can help address some of these time-related shortcomings of current research. A few selected key aspects of implementing dynamicity in studies of CDW are exemplified in a CDW assignment with information science students that the author conducted back in 2016.

Greedy nonlinear autoregression for multifidelity computer models at different scales

Although the popular multi-fidelity surrogate models, stochastic collocation and nonlinear autoregression have been applied successfully to multiple benchmark problems in different areas of science and engineering, they have certain limitations. We propose a uniform Bayesian framework that connects these two methods allowing us to combine the strengths of both. To this end, we introduce Greedy-NAR, a nonlinear Bayesian autoregressive model that can handle complex between-fidelity correlations and involves a sequential construction that allows for significant improvements in performance given a limited computational budget. The proposed enhanced nonlinear autoregressive method is applied to three benchmark problems that are typical of energy applications, namely molecular dynamics and computational fluid dynamics. The results indicate an increase in both prediction stability and accuracy when compared to those of the standard multi-fidelity autoregression implementations. The results also reveal the advantages over the stochastic collocation approach in terms of accuracy and computational cost. Generally speaking, the proposed enhancement provides a straightforward and easily implemented approach for boosting the accuracy and efficiency of concatenated structure multi-fidelity simulation methods, e.g., the nonlinear autoregressive model, with a negligible additional computational cost.

PLC Based Automatic Control of Overhead Trolley Crane for Industrial Application

This paper portrays the PLC Based Automatic Control of Overhead Trolley Cranes for Industrial Application. In a conventional mechanical crane control framework, all control gadgets are wired legitimately to one another. The activity of overhead voyaging crane is totally constrained by utilizing Programmable Logic Controller (PLC) utilized for mechanization of certifiable procedures. For example, control of apparatus on production line sequential construction systems. The framework arrangement of activity is planned by stepping stool outline and PLC programming. The automatic operation provides less manpower and the overall production can be increased. At last, for productive activity, and acceptance engine with DC in modern applications. This paper Programmable Logic Controller (PLC) based automation system is used to control the crane movements. This basically presents the programming and operation of an overhead traveling crane. It picks up the container and moves in the upward direction and then in the horizontal direction and vice versa. The program is designed by using ladder programming. The operation is designed by a ladder diagram and PLC programming software named WPL for DELTA PLC. The magnetic sensors are used. These sensors sense the overhead traveling crane motion.