Design and simulation of a novel metallic microgripper using vibration to release nano objects actively

In this study, we investigate a novel metallic microgripper which is able to grasp and transport nano particles (nano tubes/wires) and release them on desirable substrate by vibrating the gripper arms. This microgripper consists of a chevron actuator to grip nano object electrothermally and interdigited comb drive systems to generate vibration at the gripper arms electrostatically. Metallic (nickel) properties enable the chevron actuator to close the gap and pick the nano particle at low voltage and temperature. In order to reduce the out of plane bending during operation and also increase the gripping force, thickness of the nickel layer must be increased, hence electroplating process is proposed for deposition of nickel layer. To generate vibration at the end effectors, comb drive systems are stimulated by applying two voltage signals at desired resonant frequency to the stators. Practically, by sweeping the frequency of these signals around the resonant frequency the end effectors start vibrating. The vibration results in overcoming the adhesion forces due to inertial effects.     

Design of sliding mode and model reference adaptive control strategies for multivariable tape transport mechanism: a performance comparison

This paper presents sliding mode control and model reference adaptive control strategies for the tape transport mechanism. A nonlinear multivariable MIMO model of the process, consisting of take-up and supply reel servos for tape tension control and capstan servo for speed control is considered. The sliding mode control is applied for the nonlinear dynamic model of the process, while the model reference adaptive control deals with the linearized one. Moreover, in order to associate with the realistic model of system, design of controllers is accomplished with respect to parametric uncertainties. It is shown that both control strategies can guarantee asymptotic stability of the closed-loop system and tracking of desired outputs with the appropriate pace in the presence of uncertainties. Finally, simulation results demonstrate the effectiveness of the proposed controllers.     

Dynamic properties of calcareous and siliceous sands under isotropic and anisotropic stress conditions

The strain-dependent dynamic properties of sand are generally described by their relative density and mean effective stress, while the contribution of other factors, like soil origin, mineralogy, grain morphology, and initial stress anisotropy, have not been fully recognized. This paper presents the results of an experimental study on the shear modulus and damping ratio of calcareous and siliceous sands of different origins and their identical grain size distribution and stress-density states. Resonant column and cyclic triaxial tests were conducted on reconstituted samples of these two sands obtained from coastal areas. The significance of the initial effective confining pressure and stress anisotropy on the dynamic properties of the sands is evaluated and compared. It is demonstrated that the small-strain shear modulus of the calcareous sand is more affected by an increase in mean effective confining pressure than the siliceous sand. However, the effect of the initial shear on the secant shear modulus of the sands is unique. Based on the test data, a rigorous correction factor is proposed to account for the influence of the initial stress anisotropy on the small-strain shear modulus of the sands. A comparison between the strain-dependent dynamic properties of the calcareous and siliceous sands reveals that the calcareous sand has a higher secant shear modulus, lower damping ratio, and higher linear and volumetric threshold strain. Since the stress-density states and grain size distribution of the two sands were identical in the experiments, the discrepancy in the dynamic properties can be attributed to other factors, including sand origin, grain angularity, mineralogy, and formation processes, which are not commonly taken into account in the current practice.     

A NEW AGENT MATCHING SCHEME USING AN ORDERED FUZZY SIMILARITY MEASURE AND GAME THEORY

In this paper, an agent matching method for bilateral contracts in a multi-agent market is proposed. Each agent has a hierarchical representation of its trading commodity attributes by a tree structure of fuzzy attributes. Using this structure, the similarity between the trees of each pair of buyer and seller is computed using a new ordered fuzzy similarity algorithm. Then, using the concept of Stackelberg equilibrium in a leader–follower game, matchmaking is performed among the sellers and buyers. The fuzzy similarities of each agent with others in its personal viewpoint have been used as its payoffs in a bimatrix game. Through a case study for bilateral contracts of energy, the capabilities of the proposed agent-based system are illustrated.     

Nicotinic Acid Increases Adiponectin Secretion from Differentiated Bovine Preadipocytes through G-Protein Coupled Receptor Signaling

The transition period in dairy cows (3 weeks prepartum until 3 weeks postpartum) is associated with substantial mobilization of energy stores, which is often associated with metabolic diseases. Nicotinic acid (NA) is an antilipolytic and lipid-lowering compound used to treat dyslipidaemia in humans, and it also reduces non-esterified fatty acids in cattle. In mice the G-protein coupled receptor 109A (GPR109A) ligand NA positively affects the secretion of adiponectin, an important modulator of glucose and fat metabolism. In cattle, the corresponding data linking NA to adiponectin are missing. Our objective was to examine the effects of NA on adiponectin and AMPK protein abundance and the expression of mRNAs of related genes such as chemerin, an adipokine that enhances adiponectin secretion in vitro. Differentiated bovine adipocytes were incubated with pertussis toxin (PTX) to verify the involvement of GPR signaling, and treated with 10 or 15 µM NA for 12 or 24 h. NA increased adiponectin concentrations (p ≤ 0.001) and the mRNA abundances of GPR109A (p ≤ 0.05) and chemerin (p ≤ 0.01). Pre-incubation with PTX reduced the adiponectin response to NA (p ≤ 0.001). The NA-stimulated secretion of adiponectin and the mRNA expression of chemerin in the bovine adipocytes were suggestive of GPR signaling-dependent improved insulin sensitivity and/or adipocyte metabolism in dairy cows.     

Maximal limited similarity-based rough set model

Non-symmetric similarity relation-based rough set model (NS-RSM) is viewed as mathematical tool to deal with the analysis of imprecise and uncertain information in incomplete information systems with “?” values. NS-RSM relies on the concept of non-symmetric similarity relation to group equivalent objects and generate knowledge granules that are then used to approximate the target set. However, NS-RSM results in unpromising approximation space when addressing inconsistent data sets that have lots of boundary objects. This is because objects in the same similarity classes are not necessarily similar to each other and may belong to different target classes. To enhance NS-RSM capability, we introduce the maximal limited similarity-based rough set model (MLS-RSM) which describes the maximal collection of indistinguishable objects that are limited tolerance to each other in similarity classes. This allows accurate computation to be done for the approximation space. Furthermore, approximation accuracy comparisons have been conducted among NS-RSM and MLS-RSM. The results demonstrate that MLS-RSM model outperforms NS-RSM and can approximate the target set more efficiently.     

Multiscale modeling of elastic properties of cortical bone

We model cortical bone as a composite material with hierarchical structure. At a nanostructural level, bone is composed of cross-linked collagen molecules, containing water and non-collagenous proteins in their gaps, reinforced with hydroxyapatite-like nanocrystals. Such a nanocomposite structure represents a mineralized collagen fibril, which serves as a primary building block of bone. At a sub-microstructural level (few microns), the mineralized collagen fibrils are embedded in an extrafibrillar hydroxyapatite matrix to form a single lamella, which also contains the lacunar cavities. At a microstructural level (hundreds of microns) one can distinguish two lamellar structures in the mature cortical bone: osteons, made of concentric layers of lamellae surrounding long hollow Haversian canals, and interstitial lamellae made of remnants of old osteons. At a mesostructural level (several millimeters), the cortical bone is represented by a random collection of osteons and resorption cavities in the interstitial lamellae. A macrostructural level is the whole bone level containing both the cortical (compact) and trabecular (spongy) bone types. In this paper, we predict analytically the effective elastic constants of cortical bone by modeling its elastic response at these different scales, spanning from the nanostructural to mesostructural levels, using micromechanics methods and composite materials laminate theories. The results obtained at a lower scale serve as inputs for the modeling at a higher scale. The predictions are in good agreement with the experimental data reported in literature.     

Designing and planning a multi-echelon multi-period multi-product closed-loop supply chain utilizing genetic algorithm

Designing and planning a closed-loop supply chain in a comprehensive structure is vital for its applicability. To cope with the design and planning issue of a comprehensive closed-loop supply chain network, this paper develops an extended model, which is multi-echelon, multi-product, and multi-period in a mixed integer linear programming framework. The word “comprehensive,” in our mathematical approach, in designing and planning a closed-loop supply chain problem, can be analyzed from two complementary angles: including all possible entities (facilities) of a real condition and considering minimum limitations on possible flows between entities. In our proposed model, customers can be supplied via manufacturers, warehouses, and distributors, as an example. The proposed model is solved by CPLEX optimization software and by a developed genetic algorithm. During this computational analysis, we compare results of proposed pretuned genetic algorithm with a global optimum of CPLEX solver. Then, a sufficient number of large-size instances are generated and solved by the proposed genetic algorithm. To the best of our knowledge, there has been no similar multi-period multi-product closed-loop supply chain design and planning problem utilizing any kind of meta-heuristics let alone genetic algorithms. Therefore, in this issue, it is an original research, and results prove the acceptable performances of the developed genetic algorithm.     

Hybrid cascode compensation with feedforward stage for high-speed area-efficient three-stage CMOS amplifiers

Hybrid cascode feedforward compensation (HCFC) is proposed for low-power area-efficient three stage amplifiers driving large capacitive loads. With no overhead in power or area, the total compensation capacitor is divided and shared between two internal high-speed loops instead of solely one loop as is common in prior art. Detailed analysis of HCFC shows significant improvement in terms of stability and bandwidth. This is verified for a 1.2-V amplifier driving a 500-pF capacitive load in 90-nm CMOS technology, where HCFC reduces the total capacitor size and improves the gain-bandwidth by at least 30 and 40 %, respectively, compared to the prevailing schemes.     

Design of a neuro-fuzzy–regression expert system to estimate cost in a flexible jobshop automated manufacturing system

We propose a cost estimation model based on a fuzzy rule backpropagation network, configuring the rules to estimate the cost under uncertainty. A multiple linear regression analysis is applied to analyze the rules and identify the effective rules for cost estimation. Then, using a dynamic programming approach, we determine the optimal path of the manufacturing network. Finally, an application of this model is illustrated through a numerical example showing the effectiveness of the proposed model for solving the cost estimation problem under uncertainty.