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Review Recurring Topics

Cognitive Neuroscience of Attention
From brain mechanisms to individual differences in efficiency

  • Aspects of activation, selection and control have been related to attention from early to more recent theoretical models. In this review paper, we present information about different levels of analysis of all three aspects involved in this central function of cognition. Studies in the field of Cognitive Psychology have provided information about the cognitive operations associated with each function as well as experimental tasks to measure them. Using these methods, neuroimaging studies have revealed the circuitry and chronometry of brain reactions while individuals perform marker tasks, aside from neuromodulators involved in each network. Information on the anatomy and circuitry of attention is key to research approaching the neural mechanisms involved in individual differences in efficiency, and how they relate to maturational and genetic/environmental influences. Also, understanding the neural mechanisms related to attention networks provides a way to examine the impact of interventions designed to improve attention skills. In the last section of the paper, we emphasize the importance of the neuroscience approach in order to connect cognition and behavior to underpinning biological and molecular mechanisms providing a framework that is informative to many central aspects of cognition, such as development, psychopathology and intervention.

    Citation: M. Rosario Rueda, Joan P. Pozuelos, Lina M. Cómbita, Lina M. Cómbita. Cognitive Neuroscience of Attention From brain mechanisms to individual differences in efficiency[J]. AIMS Neuroscience, 2015, 2(4): 183-202. doi: 10.3934/Neuroscience.2015.4.183

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  • Aspects of activation, selection and control have been related to attention from early to more recent theoretical models. In this review paper, we present information about different levels of analysis of all three aspects involved in this central function of cognition. Studies in the field of Cognitive Psychology have provided information about the cognitive operations associated with each function as well as experimental tasks to measure them. Using these methods, neuroimaging studies have revealed the circuitry and chronometry of brain reactions while individuals perform marker tasks, aside from neuromodulators involved in each network. Information on the anatomy and circuitry of attention is key to research approaching the neural mechanisms involved in individual differences in efficiency, and how they relate to maturational and genetic/environmental influences. Also, understanding the neural mechanisms related to attention networks provides a way to examine the impact of interventions designed to improve attention skills. In the last section of the paper, we emphasize the importance of the neuroscience approach in order to connect cognition and behavior to underpinning biological and molecular mechanisms providing a framework that is informative to many central aspects of cognition, such as development, psychopathology and intervention.



    With the development of structural optimization theory and computer technology, many finite-element software tools (e.g., HyperWorks, Nastran, Ansys and TOSCA) have integrated structural optimization design modules to reduce the structural weight, improve the structural performance and increase safety [1,2]. Altair HyperWorks is a comprehensive open-architecture simulation platform that provides best-in-class technology for designing and optimizing high-performance, efficient products. Users have full access to the full suite of design, engineering, visualization and data management solutions offered by Altair and its technology partners. However, HyperWorks software, like other optimization software tools, only integrates the function of Eulerian buckling analysis and does not perform the analytical calibration of the buckling strength of stiffened plate grids. After the manual buckling check, there may be stiffened plates that do not meet the requirements; thus, the thickness of the plate or the cross-sectional dimensions of the reinforcing bars must be readjusted, and after the adjustment, the results must be reviewed, which is time-consuming and labor-intensive. Therefore, it is important to study the problem of dimensional optimization, and dimensional optimization design software tools must consider the buckling restraint of reinforced plates to ensure structural strength and stability. In this regard, Altair HyperMesh provides the best solution.

    The stiffened plate structure is a beam-slab coupled structure; thus, the stiffened plate structure is susceptible to local instability when it is subjected to compression, bending and shear. The external forces often reach their peak, and in severe cases, the thin-walled structure can get damaged [3,4]. Due to the geometric diversity of stiffened plates, considering the buckling strength of stiffened plates is challenging. Mansour [5], Troitsky and Hoppmann [6] and Brush and Almroth [7] analytically calculated the buckling strength of stiffened plates by treating them as orthogonal plates. Steen [8] used the energy method and a discrete stiffened plate model to analyze the buckling pattern and behavior of a one-way equal-span uniformly stiffened plate after the occurrence of buckling instability. Li and Bettess [9] used the energy method to analyze the critical stress of one-way equal-span uniformly stiffened plates. Przemieniecki [10] used the relationship between strain-displacement and large deflection to establish the stiffness matrix of slat elements, and he studied the local stability of stiffened plates. Wang and Wu [11] developed an optimization preprocessing interface in Excel based on the CSR code requirements and used the Isight platform multiparameter-driven Mars2000 ship strength calibration software to optimize the dimensions of the mid-section structure of a tanker. Han et al. [12] proposed a fast optimization method for the hull plate frame under buckling constraints. By utilizing the property that the panel buckling utilization factor varies monotonically with the panel thickness and is localized (less correlated) with the surrounding panel thickness, a two-stage optimization method based on dimensionality reduction was proposed to optimize the dimensional design of a double-deck bottom of an oil tanker by using an agent model.

    Altair HyperMesh provides a good custom software development environment, and users can write Tcl/Tk functions to achieve specific functions. In addition, HyperMesh provides a wealth of integrated internal functions, which can be referenced in Tcl functions in a specified format to achieve certain modular functions. The combined use of Tcl/Tk [13] provides many benefits to application developers and users, especially for rapid development. Full-fledged applications can be written entirely in Tcl scripts, thus allowing users to develop at a higher level than C/C++ and Java. Tk hides many details that C or Java programmers must address. There is less to learn and less code to write in Tcl and Tk than the toolset of the foundation.

    In this paper, the HyperMesh [14] optimization software based on Tcl/Tk and the Operation and Maintenance Link (OML) language with stiffened-plate buckling strength specification equilibrium [15] was used for the finite-element meshing of the stiffened plate; obtaining the mean stress, stress gradient and other parameters for calibrating the stiffened plate; setting the DRESP3 cards; and linking external OML function files. We developed the program "buckling constraints of stiffened plate", including the program "buckling constraint of panel" (for 2D plates and shell units) and the program "buckling constraint of minor components" (for 1D beam cells). The application of the dimensional optimization design of the platform structure demonstrates that the program developed in this study is easy to use and provides a good optimization effect.

    According to Lloyd's specifications [15], the design stress of the plate subjected to compression or shear (the plate design stress is taken as the average stress of the shell cells inside the panel) should meet the buckling strength requirements presented in Table 1.

    Table 1.  Buckling strength requirements of the panel under different stress states.
    Stress state Buckling strength requirements
    Short side under pressure $ {\lambda }_{x}\le {\lambda }_{cr} $
    Long side under pressure $ {\lambda }_{y}\le {\lambda }_{cr} $
    Two-way compression When $ 1\le {A}_{R}\le \sqrt{2}, {\lambda }_{x}+{\lambda }_{y}\le {\lambda }_{cr} $
    When $ \sqrt{2}\le {A}_{R}\le 8, {\lambda }_{x}^{2}+{\lambda }_{y}^{2}\le {\lambda }_{cr} $
    Short side compression and shear $ {\lambda }_{x}^{2}+{\lambda }_{t}^{2}\le {\lambda }_{cr} $
    Long side compression and shear $ {\lambda }_{y}^{2}+{\lambda }_{t}^{2}\le {\lambda }_{cr} $
    Two-way compression and shear $ {\lambda }_{x}^{2}+{\lambda }_{y}^{2}+{\lambda }_{t}^{2}\le {\lambda }_{cr} $
    Note: $ {\lambda }_{x}={\sigma }_{x}/{\sigma }_{xcr} $, $ {\lambda }_{y}={\sigma }_{y}/{\sigma }_{ycr} $, $ {\lambda }_{t}={\tau }_{xy}/{\tau }_{cr} $; $ {\lambda }_{cr} $ is the allowable buckling utilization factor; $ {A}_{R} $ is the plate aspect ratio; $ {\sigma }_{x}, {\sigma }_{y}, {\tau }_{xy} $ is the short side, long side compression stress and shear design stress; if $ {\sigma }_{x}, {\sigma }_{y} $ is the tensile stress, the stress component is taken as zero, and the calculation is generally taken as the average stress value of the plate edge in the face; $ {\sigma }_{xcr}, {\sigma }_{ycr} $ and $ {\tau }_{cr} $ denote the plate in the short side, long side compression and shear stress under the action of the critical buckling stress.

     | Show Table
    DownLoad: CSV

    For short-side compression, the critical buckling stress can be expressed as

    $ {\sigma }_{xcr} = {\sigma }_{xE}\ \ \ \ {\sigma }_{xE}\le \frac{{R}_{eH}}{2} , $ (1)
    $ {\sigma }_{xcr} = {R}_{eH}\left(1-\frac{{R}_{eH}}{4{\sigma }_{xE}}\right)\ \ \ \ {\sigma }_{xE} > \frac{{R}_{eH}}{2} , $ (2)

    where $ {R}_{eH} $ is the yield strength of the material (N/mm2; for carbon steel, $ {R}_{eH} $ = 235 N/mm2), and $ {\sigma }_{xE} $ is the elastic buckling stress.

    According to Przemieniecki [10], the design stresses of the secondary members (obtained from the stresses in each beam cell within the panel weighted using the cell length average) must be less than 0.8 times the critical buckling stress in the compression bar buckling mode.

    For the column buckling mode without rotation in the cross section (perpendicular to the plane of the plate), the ideal elastic stress $ {\sigma }_{E} $ of the longitudinal bone can be calculated as follows:

    $ {\sigma }_{E} = 10{C}_{f}E\frac{{I}_{a}}{{A}_{te}{l}^{2}} , $ (3)

    where Cf is the end constraint coefficient (in this paper, Cf = 1), E is the elastic modulus of the material (N/mm2; 2.06 × 105 MPa for carbon steel), Ia is the moment of inertia of the support material (mm4), Ate is the cross-sectional area of the support material (mm2) and l is the support material span distance (mm). The strip plate should be included in the calculations, and the width of the strip plate is taken as the support material spacing.

    The critical buckling stress calculation of the longitudinal bone $ {\sigma }_{c} $ is similar to the critical stress calculation of the panel $ {\sigma }_{xE} $ and is not discussed in detail here; please see the specification requirements.

    On the HyperWorks/HyperMesh platform, during dimensional optimization, the strength and displacement constraints of the structure can be obtained by creating a type-Ⅰ response by using the OptiStruct module and setting the DRESP1 card; however, the stability of the structure cannot be obtained by setting the type-Ⅰ response. Thus, we developed an alternative procedure of "considering panel buckling constraints" in HyperMesh by using the panel buckling theory to consider the strength, displacement and stability of the structure during dimensional optimization. By setting the DRESP3 (type-Ⅲ external response) card, the OptiStruct solver takes as input the positive stresses at the nodes x and y around the panel (the approximate calculation coefficients φ and buckling coefficient k), the average x, y and xy stresses of each panel and the material- and model-related parameters into the OML function for the buckling factor calculation, and it outputs the calculated buckling factor. The final calculated buckling factors are derived and returned to the OptiStruct solver for constraint judgment and optimization iteration.

    The custom software development program for considering panel buckling constraints includes 30 subroutines, and the main programs are the automatic creation of the panel buckling subroutine and the creation of the panel buckling constraint subroutine. The automatic panel buckling creation subroutine automatically divides the panel according to the user-selected panel area, whereas the panel buckling constraint creation subroutine yields the buckling constraint according to the divided panel.

    The main advantage of the custom software development program is that it considers the panel buckling constraints (Figure 1). First, the software interface button is setup. Next, according to the software prompts, the cells where buckling constraints must be applied are selected. The parameters related to the change domain are inputted, and the relevant parameters are optimized. Then, according to the selected cells, the automatic panel partition algorithm is used to achieve automatic panel partition, and the design domain of the plate is set. Next, according to the divided plate, the average stress of the four corner cells is used as the average stress of the panel to obtain the stress response of each cell in the x, y and xy directions. Finally, the average stress response of each plate is obtained, the DRESP3 card is set, the external OML function file is linked and the buckling constraints of each panel are obtained.

    Figure 1.  Steps involved in the custom software development of the program for the consideration of the buckling constraints of the panel.

    The interface of the "considering panel buckling constraints" program is shown in Figure 2. The "Parameter Setting" module is used to set the material correlation coefficient and dimensional optimization correlation coefficient of the stiffened panel area. "Upper and lower thickness percentage" refers to the upper- and lower-limit percentage of thickness variation of the 2D plate cells during dimensional optimization, "E" refers to the modulus of elasticity of the material in the selected partition, "ReH" refers to the yield strength of the material in the selected partition, "Tr" refers to the thickness reduction when considering the buckling restraint of the stiffened panel and "BK Factor" is the buckling factor. The "Panel Information" module displays the information regarding the divided panel buckling. The "Define Panel" module is used to define the partition of panel buckling, including the panel name, automatic creation of the design variation field, manual creation of the design variation field, deletion of the divided panel, setting of the same variation area and the function of buckling constraint creation for stiffened plates. The "Define Panel" module includes the following functions:

    Figure 2.  Program interface for the consideration of the panel buckling constraints.

    To better reflect the specific functions of each button of the program, we selected a simple stiffened plate structure to test each function (Figure 6(a)).

    The "Auto Create" button performs the function of automatically dividing the panel and setting the corresponding buckling constraints. The user must enter the name of the panel (e.g., "auto_test") to be divided in the "Panel Name" textbox (Figure 3).

    Figure 3.  Prerequisites for using the "Auto Create" button.

    After clicking on the "Auto Create" button, the first guidance interface pop-up opens for subsequent operations, beginning with the "Row, Column" module (Figure 4). For instance, for dividing the panel into three rows and two columns, the "3, 2" string must be entered and "proceed" must be clicked.

    Figure 4.  "Auto Create" button: "Row, Column" module.

    Then, the second instruction screen pop-up opens for the subsequent operation: the "Select Elements" module (Figure 5). Here, the cells in the model that need to be divided into panels must be selected (Figure 6(a)).

    Figure 5.  "Auto Create" button: "Select Elements" module.
    Figure 6.  Display comparison before and after dividing the panel.

    Subsequently, the panel gets divided into six standard stiffened plates according to the self-programmed automatic panel partition algorithm (Figure 6(b)).

    The algorithm for automatically dividing the panel first judges the plane for the selected cell (judging it as the xoy, xoz or zoy plane) and then identifies the surrounding corner points of the selected cell with the control nodes of the panel (Figure 6(b)) so that the coordinates of all nodes can be obtained. The nodes are then numbered from the bottom-left to the top-right for each row and column, and each region is individually numbered according to the coordinates of the numbered nodes and cells. Based on the coordinates of the numbered nodes and the coordinates of the center of mass, we can determine in which area the unit exists. Finally, according to the existence of the area in the region, we can redistribute the component.

    The "Manual Create" button is used for dividing the panel and setting the corresponding buckling constraints, mainly for irregular and unrecognized panels. Because the "Manual Create" button has a similar interface to the "Auto Create" button, it is not described in detail.

    The "Set Same Designvars" button is used for setting the same variation area. The divided plate grid is a single piece by piece. If the plate grids have different thicknesses, that means that the manufacturing requirements are not satisfied; thus, the "Set Same Designvars" function must be used to redefine the variation relationship between the plate grid pieces. We selected "auto_test4", "auto_test5" and "auto_test6" as the same optimized variation area (Figure 7).

    Figure 7.  "Set Same Designvars" button.

    The "Create Responses" button is used for considering panel buckling constraints. The buckling response is obtained by selecting the external card and setting the DRESP3 (design response) (type-Ⅲ response: external response) card to input the results obtained using the OptiStruct solver into the OML program. Next, it is judged whether the selected plate meets the buckling requirements. Taking the uto_test3 response as an example, the DRESP3 card settings are shown in Figure 8.

    Figure 8.  DRESP3 card settings.

    "GROUP" is the group identifier referenced by the DRESP3 batch data entry, "HLIB" (high-level international baccalaureate) was selected as the identifier in this program; "FUNC" is the name of the external function written, and "SHBK" was selected as the external function.

    "DESVAR" (design variable) refers to the thickness of the optimized change area, which changes with the optimization process. In this program, DESVAR = 1 and this refers to the thickness of the panel.

    "DTABLE" (design table) refers to the fixed-parameter table and it does not change with the optimization process. In this program, DTABLE = 6, indicating, from left to right, the long-edge length, short-edge length, long- and short-edge coefficients, thickness discount value, material yield strength and modulus of elasticity of the material corresponding to the ID number.

    "DRESP1/2" refers to the first type and second type of response called, which change with the optimization process. In this program, DRESP1/2 = 11, indicating, from top to bottom, the x-directional positive stress of the four corner cells of the panel, the y-directional positive stress of the four corner cells of the panel, the average x-directional positive stress of the panel, the average y-directional positive stress of the panel and the average xy-directional stress of the panel (shear). The positive stresses of the nodes x and y around the panel are used to approximate the coefficients corresponding to them, and then k is obtained. The buckling factor of the panel is calculated by using the relevant function in OML. When setting the DRESP1/2 cards, the DRESP1 card should be placed before the DRESP2 card; otherwise, the OptiStruct solver will calculate incorrectly.

    Conventional finite-element analysis of stiffened plates involves a 1D beam cell and a 2D plate and shell cell. The stability of the 1D beam unit is considered in the optimization by performing the plate elementization of the 1D unit; however, the time cost of the corresponding model free-edge check and the computational time cost is high. For this reason, based on the secondary member buckling theory, we developed the alternative procedure of "taking into account the secondary member buckling constraint" in HyperWorks/HyperMesh to consider the strength, displacement and stability of the 1D line and 2D plate and shell units simultaneously for dimensional optimization.

    Because the HyperMesh platform does not yield the axial stress response of the beam unit, the average value of the stresses at each corner point must be used to obtain the axial stress response of the beam unit by setting the DRESP2 card. By setting the DRESP3 (type-Ⅲ external response) card, the web height, net web thickness, panel width, net panel thickness, strip thickness, reinforcement spacing (these six parameters are used to calculate the moment of inertia of the new reinforcement formed by the 1D and 2D units, cross-sectional area and height of the strip from the center of the form), reinforcement span and reinforcement axial stress are inputted into the OptiStruct solver, and the final calculated buckling factor is outputted by the OptiStruct solver for constraint judgment and optimization iteration.

    The custom software development program considering secondary member buckling constraints includes seven subroutines, and the subroutine for creating secondary member buckling constraints is the main subroutine which is used to create secondary member buckling constraints according to the properties of the beam unit selected by the user. Custom software development is performed as follows (Figure 9). First, the upper-limit and lower-limit percentages are set when optimizing the beam unit parameter size, and according to the software prompt, the beam unit properties required for imposing secondary member buckling restraints are selected. Next, the strip plate with the beam unit is selected, the support material spacing and support material span are inputted according to the software prompt and then the selected beam unit type is judged as PBEAML/PBARL. According to the selected beam unit properties and upper- and lower-limit percentages, the beam unit change domain and change relationship are set; the beam unit axial stress response is set using the DRESP2 card as one of the base responses input to the external OML function. Finally, each secondary member buckling restraint is established, the DRESP3 class card is set and the external OML function file is linked.

    Figure 9.  Idea of the custom software development of the program for the consideration of buckling constraints of the secondary component.

    Consider the panel buckling constraint program interface shown in Figure 10. The "percentage of upper and lower limit" module is used to set the coefficients related to the dimensional design variation domain of 1D beam cells. "Length" refers to the percentage of the upper and lower limits of web height and panel width variations in 1D beam cells during dimensional optimization. "Thickness" refers to the percentage of upper and lower limits of web thickness and panel thickness variations in 1D beam cells during dimensional optimization. In the "Parameter Setting" module, "E" and "ReH" are defined in the same way as in the panel buckling constraint calculation procedure, "Spacing" refers to the width of the selected property (i.e., the spacing of the reinforcing material) and "Length" refers to the span of the selected property.

    Figure 10.  Program interface of consideration of secondary component buckling constraints.

    The "Create JG Buckling Responses" button is used to set the corresponding buckling constraints based on the selected beam element properties. First, the percentage of upper and lower limits of the beam cell design domain must be entered in the "Percentage of upper and lower limit" textbox; then, the "Create JG Buckling Responses" button must be clicked. The first guidance interface pop-up opens––the "Properties" module, where the user selects the area where the angle buckling constraints must be set and clicks on "proceed". Then, the second guidance interface pop-up opens for subsequent operations––the "Select Designvar" module, wherein the algorithm determines whether the beam cell with the plate is a change domain. If the beam cell with the plate is a change area, the user must select the change area of the plate. If the beam cell with the plate is not a change area, the user must click on "proceed". Then, the third guidance interface pop-up opens for subsequent operations––the "Select Component" module, wherein the algorithm determines whether the strip plate is a change domain and requires the user to select the component where the strip plate is located and click on "proceed". Then, according to the relevant judgment algorithm in the program, the selected beam unit property buckling constraints can be created.

    The design flow of dimensional optimization with buckling constraints (Figure 11) is as follows. The finite-element model is established according to the design, and the working loads and boundary conditions specified by the code are determined. In the detailed design stage of structural dimensional optimization, the finite-element model employs 2D plate and shell units and 1D beam units. Static analysis is performed for each working condition to provide guidance for subsequent dimensional optimization by dividing the design domain and setting the strength, deformation and buckling constraints. When dividing the design area, in addition to the conventional selection of the design area, each of the areas with large compressive stresses must be used as a design area, and the upper limit of the design area must be set according to the actual conditions. When setting the constraints, three aspects should be considered: strength, deformation and stability (yield strength). Strength and deformation constraints can be set directly according to the relevant requirements; however, the buckling constraints of the structure cannot be set directly in the software and must be set by following the custom software development procedures of "considering buckling constraints of panel" and "considering buckling constraints for secondary members". When considering the buckling restraint of the structure, the location where the compressive stress of the plate shell and beam is larger must be selected to save time and cost. During dimensional optimization, the lightest mass and the smallest volume of the structure are usually employed as the objective function. The finite-element model may have multiple material densities; thus, when optimizing the size, it is more reasonable to set the overall mass of the structure as the objective function. If certain constraints are not satisfied, the design domain must be redefined and the upper and lower limits of the optimization parameters must be increased or decreased.

    Figure 11.  Dimensional optimization design process with buckling constraints.

    We aimed to achieve a lightweight platform structure. The finite-element model is shown in Figure 12 (light blue is the boundary condition). The plates in the model were simulated by quadrilateral and a small number of triangular plates and shell units, and the bones were simulated by beam units. The material was low-alloy high-strength structural steel Q355, with a yield strength of 355 MPa, modulus of elasticity of 2.06 × 105 MPa, Poisson's ratio of 0.3 and density of 7.85 t/m3. A uniform load of 33.85 kN/m2 was applied at the top plate of the platform structure (Figure 13).

    Figure 12.  Platform finite-element model.
    Figure 13.  Schematic of the platform load layout.

    The design domain should be divided according to the design drawings and stress distribution. In addition to the locations where the compressive stress is large and local instability may occur, the design area should be divided separately to facilitate the subsequent placement of buckling constraints. Due to space constraints, only the top plate and the main beam of the top plate are used as examples for the design area partition in this paper. As can be seen from the stress cloud diagram (Figures 14 and 15), the compressive stress was mainly concentrated on both sides of the two platforms and the upper and lower middle sides of the right platform; thus, the design area of the top plate buckling partition was divided into 14 blocks (Figure 16), and the design area of the beam buckling partition was divided into two blocks (Figure 17). The design area was divided into the preliminary design area (containing only the constraints of the original software) and the buckling constraint design area (containing the buckling constraints of the custom software development software). The preliminary design area was divided into 12 plate and shell unit areas (1–12) and four beam unit areas (13–16), and the buckling constraint design area consisted of 24 plate and shell unit areas (1–24) and two beam unit areas (25 and 26).

    Figure 14.  Top-plate shear-stress contour.
    Figure 15.  Beam-element axial-stress contour.
    Figure 16.  Top-plate design area (buckling).
    Figure 17.  Beam design area (buckling).

    The design area corresponding to the final set of optimization constraints is shown in Table 2.

    Table 2.  Design area corresponding to the constraints.
    Type Design area Numerical value
    von Mises stress (MPa)— $ {\sigma }_{e} $ Preliminary design area: 1–12 Buckling constraint area:1–24 284
    x-way positive stress (MPa)— $ {\sigma }_{x} $ Preliminary design area: 1–12 Buckling constraint area:1–24 284
    y-way positive stress (MPa)— $ {\sigma }_{y} $ Preliminary design area: 1–12 Buckling constraint area:1–24 284
    xy direction shear stress (MPa)— $ \tau $ Preliminary design area: 1–12 Buckling constraint area:1–24 163
    Axial stress (MPa)— $ {\sigma }_{l} $ Preliminary design area: 13–16 Buckling constraint area:25, 26 284
    Deformation (mm) Maximum deformation of top plate 74.69
    Allowable buckling utilization factor — $ {\lambda }_{cr} $ Buckling constraint area: 1–24 0.950

     | Show Table
    DownLoad: CSV

    The optimization results are presented in Table 3.

    Table 3.  Size optimization results for each constraint.
    Constraint ORI T1 T2
    Numerical Parameters (mm) Numerical Parameters (mm) Numerical Parameters (mm)
    Quality (t) 20.71 / 17.20 / 17.72 /
    $ {\sigma }_{e} $ (MPa) 269.7 / 282.8 / 280.5 /
    $ {\sigma }_{x} $ (MPa) 267 / 283.2 / 280.5 /
    $ {\sigma }_{y} $ (MPa) 266.4 / 282.9 / 280.3 /
    $ \tau $ (MPa) 136.4 / 131.1 / 125.4 /
    $ {\sigma }_{l} $ (MPa) 151.7 / 283.7 / 272.7 /
    Deformation (mm) 59.31 / 66.58 / 66.06 /
    BKshell_1 0.047 10 0.110 7 0.097 7
    BKshell_2 0.232 10 0.629 7 0.579 7
    BKshell_3 0.275 10 0.849 7 0.750 7
    BKshell_4 0.229 10 0.602 7 0.554 7
    BKshell_5 0.046 10 0.103 7 0.089 7
    BKshell_6 0.045 10 0.109 7 0.120 7
    BKshell_7 0.186 10 0.540 7 0.276 9
    BKshell_8 0.222 10 0.722 7 0.239 11
    BKshell_9 0.187 10 0.549 7 0.220 10
    BKshell_10 0.045 10 0.113 7 0.126 7
    BKshell_11 0.179 6 0.413 4.5 0.398 5
    BKshell_12 0.179 6 0.417 4.5 0.398 5
    BKshell_13 0.176 6 0.399 4.5 0.383 5
    BKshell_14 0.178 6 0.412 4.5 0.392 5
    BKshell_15 0.135 6 1.259 4.5 0.422 6
    BKshell_16 0.025 6 0.365 4.5 0.101 6
    BKshell_17 0.265 8 1.937 6 0.787 7
    BKshell_18 0.088 8 0.621 6 0.416 6
    BKshell_19 0.444 6 2.122 4.5 0.663 6
    BKshell_20 0.276 6 1.216 4.5 0.389 6
    BKshell_21 0.227 6 0.405 4.5 0.404 5
    BKshell_22 0.221 6 0.400 4.5 0.399 5
    BKshell_23 0.180 6 0.285 4.5 0.172 6
    BKshell_24 0.192 6 0.323 4.5 0.192 6
    BKbeam_25(max) 0.462 L140 × 27
    *6 × 11
    0.813 L105 × 20
    *4.5 × 8
    0.599 L105 × 25
    *4.5 × 10
    BKbeam_26(max) 0.419 L140 × 27
    *6 × 11
    0.712 L110 × 20
    *4.5 × 8
    0.510 L170 × 25
    *6 × 11

     | Show Table
    DownLoad: CSV

    By adjusting the initial design size, setting the EXTERNAL option of the RESPRINT card in the CONTRAL card to output all buckling factors to the output file and setting DESMAX (maximum iteration step) in the OPTI control card as 0 to achieve the buckling calibration of T1, we obtained the buckling factors of the BKshell_1–24 and BKbeam _25–26 buckling factors of the design area.

    The initial state ORI is the design solution that was repeatedly debugged by the designer, and all constraints were satisfied. T1 is the optimization result obtained by using the general optimization software; its structural mass was decreased, and the structural stress was increased slightly to meet the strength requirements, but part of the design area did not meet the stability buckling strength requirements. T2 is the optimization result obtained by using the software developed through the custom software development performed in this study and considering the stiffened-plate buckling custom software development. A comparison between T1 and ORI demonstrated the practicality of the dimensional optimization design, and a comparison between T2 and T1 demonstrated the rationality and necessity of the dimensional optimization method considering buckling constraints.

    A comparison of the optimization results is shown in Figure 18.

    Figure 18.  Changes in the buckling factor with size optimization.

    In this study, the size optimization program was developed based on the HyperWorks/Optistruct commercial software platform by introducing the buckling strength requirement of the stiffened plate as a constraint.

    The imposed constraints ensure good strength, displacement and stability of the platform structure and enable realizing the lightweight design of the platform structure, thus demonstrating the rationality and necessity of the dimensional optimization method considering buckling constraints. Through the dimensional optimization of the actual engineering structure, we demonstrated that the custom software development program could help the designer to realize the optimal structural design solution to ensure good strength and stability of the structure. The program has a preprocessing module with process automation and good operation performance, which saves time and improves the efficiency of the optimized design.

    The Marine Design & Research Institute of China is acknowledged for providing the initial design scheme.

    The authors declare that there is no conflict of interest.

    [1] James W (1890) The principles of psychology (H. Holt, New York, NY).
    [2] Posner MI, Petersen SE (1990) The attention system of the human brain. Annu Rev Neurosci 13: 25-42. doi: 10.1146/annurev.ne.13.030190.000325
    [3] Norman DA, Shallice T (1986) Attention to action: willed and automatic control of behavior. Consciousness and Self-Regulation, eds Davison RJ, Schwartz GE, Shapiro D (Plenum Press, New York, NY), pp 1-18.
    [4] Corbetta M, Shulman GL (2002) Control of goal-directed and stimulus-driven attention in the brain. Nat Rev Neurosci 3: 201-215.
    [5] Posner MI, DiGirolamo GJ (1998) Executive attention: Conflict, target detection, and cognitive control. The Attentive Brain, ed Parasuraman R (MIT Press, Cambridge, MA), pp 401-423.
    [6] D’Angelo MC, Milliken B, Jiménez L, et al. (2013) Implementing flexibility in automaticity: Evidence from context-specific implicit sequence learning. Conscious Cogn 22: 64-81. doi: 10.1016/j.concog.2012.11.002
    [7] Rueda MR, Posner MI, Rothbart MK (2005) The development of executive attention: contributions to the emergence of self-regulation. Dev Neuropsychol 28: 573-94. doi: 10.1207/s15326942dn2802_2
    [8] Luu P, Tucker DM, Derryberry D, et al. (2003) Electrophysiological responses to errors and feedback in the process of action regulation. Psychol Sci 14: 47-53. doi: 10.1111/1467-9280.01417
    [9] Dagenbach D, Carr TH (1994) Inhibitory processes in attention, memory, and language (Academic Press, San Diego, CA).
    [10] Petersen SE, Posner MI (2012) The Attention System of the Human Brain: 20 Years After. Annu Rev Neurosci 35: 73-89. doi: 10.1146/annurev-neuro-062111-150525
    [11] Posner MI, Rueda MR, Kanske P (2007) Probing the Mechanisms of Attention. Handb Psychophysiol: 410-432.
    [12] Fan J, McCandliss BD, Sommer T, et al. (2002) Testing the efficiency and independence of attentional networks. J Cogn Neurosci 14: 340-347. doi: 10.1162/089892902317361886
    [13] Callejas A, Lupiàñez J, Funes MJ, et al. (2005) Modulations among the alerting, orienting and executive control networks. Exp brain Res 167: 27-37. doi: 10.1007/s00221-005-2365-z
    [14] Fan J, Gu X, Guise KG, et al. (2009) Testing the behavioral interaction and integration of attentional networks. Brain Cogn 70: 209-220. doi: 10.1016/j.bandc.2009.02.002
    [15] Hackley SA, Valle-Inclán F (2003) Which stages of processing are speeded by a warning signal? Biol Psychol 64: 27-45. doi: 10.1016/S0301-0511(03)00101-7
    [16] Weinbach N, Henik A (2012) The relationship between alertness and executive control. J Exp Psychol Hum Percept Perform 38: 1530-1540. doi: 10.1037/a0027875
    [17] Pozuelos JP, Paz-Alonso PM, Castillo A, et al. (2014) Development of Attention Networks and Their Interactions in Childhood. Dev Psychol 50: 2405-2415. doi: 10.1037/a0037469
    [18] Fox MD, Corbetta M, Snyder AZ, et al. (2006) Spontaneous neuronal activity distinguishes human dorsal and ventral attention systems. Proc Natl Acad Sci U S A 103: 10046-10051. doi: 10.1073/pnas.0604187103
    [19] Dosenbach NUF, Fair DA, Miezin FM, et al. (2007) Distinct brain networks for adaptive and stable task control in humans. Proc Natl Acad Sci U S A 104: 11073-11078. doi: 10.1073/pnas.0704320104
    [20] Posner MI, Rothbart MK (2007) Research on attention networks as a model for the integration of psychological science. Annu Rev Psychol 58: 1-23. doi: 10.1146/annurev.psych.58.110405.085516
    [21] Coull JT, Nobre AC, Frith CD (2001) The noradrenergic a2 agonist clonidine modulates behavioural and neuroanatomical correlates of human attentional orienting and alerting. Cereb Cortex 11: 73-84. doi: 10.1093/cercor/11.1.73
    [22] Aston-Jones G, Cohen JD (2005) An integrative theory of locus coeruleus-norepinephrine function: adaptive gain and optimal performance. Annu Rev Neurosci 28: 403-450. doi: 10.1146/annurev.neuro.28.061604.135709
    [23] Coull JT, Frith CD, Frackowiak RSJ, Grasby PM (1996) A fronto-parietal network for rapid visual information processing: A PET study of sustained attention and working memory. Neuropsychologia 34: 1085-1095. doi: 10.1016/0028-3932(96)00029-2
    [24] Cui RQ, Egkher A, Huter D, et al. (2000) High resolution spatiotemporal analysis of the contingent negative variation in simple or complex motor tasks and a non-motor task. Clin Neurophysiol 111: 1847-1859. doi: 10.1016/S1388-2457(00)00388-6
    [25] Coull JT (2004) fMRI studies of temporal attention: Allocating attention within, or towards, time. Cogn Brain Res 21: 216-226. doi: 10.1016/j.cogbrainres.2004.02.011
    [26] Hillyard SA (1985) Electrophysiology of human selective attention. Trends Neurosci 8: 400-405. doi: 10.1016/0166-2236(85)90142-0
    [27] Mangun GR, Hillyard SA (1987) The spatial allocation of visual attention as indexed by event-related brain potentials. Hum Factors 29: 195-211.
    [28] Desimone R, Duncan J (1995) Neural mechanisms of selective visual attention. Annu Rev Neurosci 18: 193-222. doi: 10.1146/annurev.ne.18.030195.001205
    [29] Corbetta M, Patel G, Shulman GL (2008) The Reorienting System of the Human Brain: From Environment to Theory of Mind. Neuron 58 : 306-324.
    [30] Greicius MD, Krasnow B, Reiss AL, et al. (2003) Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proc Natl Acad Sci U S A 100: 253-258. doi: 10.1073/pnas.0135058100
    [31] Mantini D, Perrucci MG, Del Gratta C, et al. (2007) Electrophysiological signatures of resting state networks in the human brain. Proc Natl Acad Sci U S A 104: 13170-13175. doi: 10.1073/pnas.0700668104
    [32] Visintin E, De Panfilis C, Antonucci C, et al. (2015) Parsing the intrinsic networks underlying attention: A resting state study. Behav Brain Res 278: 315-322. doi: 10.1016/j.bbr.2014.10.002
    [33] Umarova RM, Saur D, Schnell S, et al. (2010) Structural connectivity for visuospatial attention: Significance of ventral pathways. Cereb Cortex 20: 121-129. doi: 10.1093/cercor/bhp086
    [34] Buschman TJ, Miller EK (2007) Top-Down Versus Bottom-Up Control of Attention in the Prefrontal and Posterior Parietal Cortices. Sci 315: 1860-1862. doi: 10.1126/science.1138071
    [35] Vossel S, Geng JJ, Fink GR (2013) Dorsal and Ventral Attention Systems: Distinct Neural Circuits but Collaborative Roles. Neurosci 20: 150-159.
    [36] He BJ, AZ Snyder, JL Vincent, et al. (2007) Breakdown of functional connectivity in frontoparietal networks underlies behavioral deficits in spatial neglect. Neuron 53: 905-918. doi: 10.1016/j.neuron.2007.02.013
    [37] Giesbrecht B, Weissman DH, Woldorff MG, et al. (2006) Pre-target activity in visual cortex predicts behavioral performance on spatial and feature attention tasks. Brain Res 1080: 63-72. doi: 10.1016/j.brainres.2005.09.068
    [38] Geng JJ, Mangun GR (2011) Right temporoparietal junction activation by a salient contextual cue facilitates target discrimination. Neuroimage 54: 594-601. doi: 10.1016/j.neuroimage.2010.08.025
    [39] Fan J, Flombaum JI, McCandliss BD, et al. (2003) Cognitive and Brain Consequences of Conflict. Neuroimage 18: 42-57. doi: 10.1006/nimg.2002.1319
    [40] Bush G, Luu P, Posner MI (2000) Cognitive and emotional influences in anterior cingulate cortex. Trends Cogn Sci 4: 215-222. doi: 10.1016/S1364-6613(00)01483-2
    [41] Drevets WC, Raichle ME (1998) Reciprocal suppression of regional cerebral blood flow during emotional versus higher cognitive processes: Implications for interactions between emotion and cognition. Cogn emotioin 12: 353-385. doi: 10.1080/026999398379646
    [42] Botvinick MM, Nystrom L, Fissell K, et al. (1999) Conflict monitoring versus selection-for-action in anterior cingulate cortex. Nature 402: 179-181. doi: 10.1038/46035
    [43] Botvinick MM, Braver TS, Barch DM, et al. (2001) Conflict monitoring and cognitive control. Psychol Rev 108: 624-652. doi: 10.1037/0033-295X.108.3.624
    [44] Kopp B, Tabeling S, Moschner C, et al. (2006) Fractionating the Neural Mechanisms of Cognitive Control. J Cogn Neurosci: 949-965.
    [45] Van Veen V, Carter CS (2002) The timing of action-monitoring processes in the anterior cingulate cortex. J Cogn Neurosci 14: 593-602. doi: 10.1162/08989290260045837
    [46] Posner MI, Sheese BE, Odludaş Y, et al. (2006) Analyzing and shaping human attentional networks. Neural Networks 19: 1422-1429. doi: 10.1016/j.neunet.2006.08.004
    [47] Dosenbach NUF, Fair Da, Cohen AL, et al. (2008) A dual-networks architecture of top-down control. Trends Cogn Sci 12: 99-105. doi: 10.1016/j.tics.2008.01.001
    [48] Rueda MR (2014) Development of Attention. Oxford Handb Cogn Neurosci 1: 296-318.
    [49] Rueda MR, Posner MI, Rothbart MK, et al. (2004) Development of the time course for processing conflict: an event-related potentials study with 4 year olds and adults. BMC Neurosci 5: 39. doi: 10.1186/1471-2202-5-39
    [50] Abundis-Gutiérrez A, Checa P, Castellanos C, et al. (2014) Electrophysiological correlates of attention networks in childhood and early adulthood. Neuropsychologia 57: 78-92. doi: 10.1016/j.neuropsychologia.2014.02.013
    [51] Gießing C, Thiel CM, Alexander-Bloch aF, et al. (2013) Human brain functional network changes associated with enhanced and impaired attentional task performance. J Neurosci 33: 5903-5914. doi: 10.1523/JNEUROSCI.4854-12.2013
    [52] Gao W, Zhub HT, Giovanello KS, et al. (2009) Evidence on the emergence of the brain’s default network from 2-week-old to 2-year-old healthy pediatric subjects. Proc Natl Acad Sci U S A 106: 6790-6795. doi: 10.1073/pnas.0811221106
    [53] Fair DA, Dosenbach NU, Church JA, et al. (2007) Development of distinct control networks through segregation and integration. Proc Natl Acad Sci U S A 104: 13507-13512. doi: 10.1073/pnas.0705843104
    [54] Fair DA, Cohen AL, Power JD, et al. (2009) Functional brain networks develop from a “local to distributed” organization. PLoS Comput Biol 5: e1000381. doi: 10.1371/journal.pcbi.1000381
    [55] Fan J, Wu Y, Fossella JA, et al. (2001) Assessing the heritability of attentional networks. BMC Neurosci 2: 14. doi: 10.1186/1471-2202-2-14
    [56] Marrocco RT, Davidson MC (1998) Neurochemistry of attention. The Attentive Brain, ed Parasuraman R (MIT Press, Cambridge, MA), pp 35-50.
    [57] Congdon E, Lesch KP, Canli T (2008) Analysis of DRD4 and DAT polymorphisms and behavioral inhibition in healthy adults: implications for impulsivity. Am J Med Genet B Neuropsychiatr Genet 147: 27-32.
    [58] Rueda MR, Rothbart MK, McCandliss BD, et al. (2005) Training, maturation, and genetic influences on the development of executive attention. Proc Natl Acad Sci U S A 102: 14931-14936. doi: 10.1073/pnas.0506897102
    [59] Diamond A (2007) Consequences of variations in genes that affect dopamine in prefrontal cortex. Cereb cortex 17: i161-170. doi: 10.1093/cercor/bhm082
    [60] Forbes EE, Brown SM, Kimak M, et al. (2009) Genetic variation in components of dopamine neurotransmission impacts ventral striatal reactivity associated with impulsivity. Mol Psychiatry 14: 60-70. doi: 10.1038/sj.mp.4002086
    [61] Congdon E, Constable RT, Lesch KP, et al. (2009) Influence of SLC6A3 and COMT variation on neural activation during response inhibition. Biol Psychol 81: 144-152. doi: 10.1016/j.biopsycho.2009.03.005
    [62] Mueller EM, Makeig S, Stemmler G, et al. (2011) Dopamine effects on human error processing depend on catechol-O-methyltransferase VAL158MET genotype. J Neurosci 31: 15818-15825. doi: 10.1523/JNEUROSCI.2103-11.2011
    [63] Espeseth T, Sneve MH, Rootwelt H, et al. (2010) Nicotinic receptor gene CHRNA4 interacts with processing load in attention. PLoS One 5: e14407. doi: 10.1371/journal.pone.0014407
    [64] Greenwood PM, Parasuraman R, Espeseth T (2012) A cognitive phenotype for a polymorphism in the nicotinic receptor gene CHRNA4. Neurosci Biobeha Rev 36: 1331-1341. doi: 10.1016/j.neubiorev.2012.02.010
    [65] Lundwall Ra, Guo DC, Dannemiller JL (2012) Exogenous visual orienting is associated with specific neurotransmitter genetic markers: A population-based genetic association study. PLoS One 7.
    [66] Zozulinsky P, Greenbaum L, Brande-Eilat N, et al. (2014) Dopamine system genes are associated with orienting bias among healthy individuals. Neuropsychologia 62: 48-54. doi: 10.1016/j.neuropsychologia.2014.07.005
    [67] Sheese BE, Voelker P, Posner MI, et al. (2009) Genetic variation influences on the early development of reactive emotions and their regulation by attention. Cogn Neuropsychiatry 14: 332-355. doi: 10.1080/13546800902844064
    [68] Posner MI, Rothbart MK, Sheese BE (2007) Attention genes. Dev Sci 10: 24-29. doi: 10.1111/j.1467-7687.2007.00559.x
    [69] Bornstein MH, Bradley RH (2003) Socioeconomic status, parenting, and child development (Lawrence Erlbaum Associates Publishers, Mahwah, NJ).
    [70] Bernier A, Carlson SM, Whipple N (2010) From external regulation to self-regulation: early parenting precursors of young children’s executive functioning. Child Dev 81: 326-339. doi: 10.1111/j.1467-8624.2009.01397.x
    [71] Gaertner BM, Spinrad TL, Eisenberg N (2008) Focused attention in toddlers: Measurement, stability, and relations to negative emotion and parenting. Infant Child Dev 17: 339-363. doi: 10.1002/icd.580
    [72] Cipriano EA, Stifter CA (2010) Predicting preschool effortful control from toddler temperament and parenting behavior. J Appl Dev Psychol 31: 221-230. doi: 10.1016/j.appdev.2010.02.004
    [73] Liew J, Chen Q, Hughes JN (2010) Child Effortful Control, Teacher-student Relationships, and Achievement in Academically At-risk Children: Additive and Interactive Effects. Early Child Res Q 25: 51-64. doi: 10.1016/j.ecresq.2009.07.005
    [74] Hackman D, Farah M (2009) Socioeconomic status and the developing brain Daniel. Trends Cogn Sci 13: 65-73. doi: 10.1016/j.tics.2008.11.003
    [75] Wanless SB, McClelland MM, Tominey SL, et al. (2011) The Influence of Demographic Risk Factors on Children’s Behavioral Regulation in Prekindergarten and Kindergarten. Early Educ Dev 22: 461-488. doi: 10.1080/10409289.2011.536132
    [76] Mezzacappa E (2004) Alerting, orienting, and executive attention: developmental properties and sociodemographic correlates in an epidemiological sample of young, urban children. Child Dev 75: 1373-1386. doi: 10.1111/j.1467-8624.2004.00746.x
    [77] Clearfield MW, Niman LC (2012) SES affects infant cognitive flexibility. Infant Behav Dev 35: 29-35. doi: 10.1016/j.infbeh.2011.09.007
    [78] Lawson GM, Duda JT, Avants BB, et al. (2013) Associations between children’s socioeconomic status and prefrontal cortical thickness. Dev Sci 16: 641-652. doi: 10.1111/desc.12096
    [79] Jolles DD, Crone EA (2012) Training the developing brain: a neurocognitive perspective. Front Hum Neurosci 6: 76.
    [80] Tang Y-Y, Posner MI (2009) Attention training and attention state training. Trends Cogn Sci 13: 222-227. doi: 10.1016/j.tics.2009.01.009
    [81] Karbach J, Kray J (2009) How useful is executive control training? Age differences in near and far transfer of task-switching training. Dev Sci 12: 978-990.
    [82] Jaeggi SM, Buschkuehl M, Jonides J, et al. (2011) Short- and long-term benefits of cognitive training. Proc Natl Acad Sci U S A 108: 10081-10086. doi: 10.1073/pnas.1103228108
    [83] Thorell LB, Lindqvist S, Bergman Nutley S, et al. (2008) Training and transfer effects of executive functions in preschool children. Dev Sci 12: 106-113.
    [84] Olesen PJ, Westerberg H, Klingberg T (2004) Increased prefrontal and parietal activity after training of working memory. Nat Neurosci 7: 75-79. doi: 10.1038/nn1165
    [85] Jolles DD, Van Buchem MA, Crone EA, et al. (2013) Functional brain connectivity at rest changes after working memory training. Hum Brain Mapp 34: 396-406. doi: 10.1002/hbm.21444
    [86] McNab F, Andrea V, Lars F, et al. (2009) Changes in cortical dopamine D1 receptor binding associated with cognitive training. Science 323: 800-802. doi: 10.1126/science.1166102
    [87] Tang Y-Y, Posner MI (2014) Training brain networks and states. Trends Cogn Sci 18: 345-350. doi: 10.1016/j.tics.2014.04.002
    [88] Malinowski P (2013) Neural mechanisms of attentional control in mindfulness meditation. Front Neurosci 7: 8.
    [89] Tang Y-Y, Ma YH, Wang JH, et al. (2007) Short-term meditation training improves attention and self-regulation. Proc Natl Acad Sci U S A 104: 17152-17156. doi: 10.1073/pnas.0707678104
    [90] Moore A, Gruber T, Derose J, et al. (2012) Regular, brief mindfulness meditation practice improves electrophysiological markers of attentional control. Front Hum Neurosci 6: 1-15.
    [91] Slagter HA, Lutz A, Greischar LL, et al. (2007) Mental Training Affects Distribution of Limited Brain Resources. Plos Biol 5.
    [92] Hölzel BK, Ott U, Hempel H, et al. (2007) Differential engagement of anterior cingulate and adjacent medial frontal cortex in adept meditators and non-meditators. Neurosci Lett 421: 16-21. doi: 10.1016/j.neulet.2007.04.074
    [93] Tang Y-Y, Lu Q, Fan M, et al. (2012) Mechanisms of white matter changes induced by meditation. Proc Natl Acad Sci: 1-5.
    [94] Posner MI, Tang Y-Y, Lynch G (2014) Mechanisms of white matter change induced by meditation training. Front Psychol 5: 1-4.
    [95] Tang Y-Y, Lua Ql, Gengc XJ, et al. (2010) Short-term meditation induces white matter changes in the anterior cingulate. Proc Natl Acad Sci U S A 107: 15649-15652. doi: 10.1073/pnas.1011043107
    [96] Hebb DO (1949) Organization of behavior (John Wiley & Sons, New York, NY).
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