Quality factor control of mechanical resonators using variable phononic bandgap on periodic microstructures

The quality factor (Q-factor) is an important parameter for mechanical resonant sensors, and the optimal values depend on its application. Therefore, Q-factor control is essential for microelectromechanical systems (MEMS). Conventional methods have some restrictions, such as additional and complicated equipment or nanoscale dimensions; thus, structural methods are one of the reasonable solutions for simplifying the system. In this study, we demonstrate Q-factor control using a variable phononic bandgap by changing the length of the periodic microstructure. For this, silicon microstructure is used because it has both periodicity and a spring structure. The bandgap change is experimentally confirmed by measuring the Q-factors of mechanical resonators with different resonant frequencies. The bandgap range varies depending on the extended structure length, followed by a change in the Q-factor value. In addition, the effects of the periodic structure on the Q-factor enhancement and the influence of stress on the structural length were evaluated. Although microstructures can improve the Q-factors irrespective of periodicity; the result of the periodic microstructure is found to be efficient. The proposed method is feasible as the novel Q-factor control technique has good compatibility with conventional MEMS.

Multi-Layered Blockchain Governance Game

The research designs a new integrated system for the security enhancement of a decentralized network by preventing damages from attackers, particularly for the 51 percent attack. The concept of multiple layered design based on Blockchain Governance Games frameworks could handle multiple number of networks analytically. The Multi-Layered Blockchain Governance Game is an innovative analytical model to find the best strategies for executing a safety operation to protect whole multiple layered network systems from attackers. This research fully analyzes a complex network with the compact mathematical forms and theoretically tractable results for predicting the moment of a safety operation execution are fully obtained. Additionally, simulation results are demonstrated to obtain the optimal values of configuring parameters of a blockchain-based security network. The Matlab codes for the simulations are publicly available to help those whom are constructing an enhanced decentralized security network architecture through this proposed integrated theoretical framework.

Investigation of the Efficiency of Manufacturing Polymer Molds by FDM-Printing

The results of experimental studies of the efficiency of manufacturing polymer foundry models by the FDM method are presented. Rational consumables for 3D-printing are revealed. The optimal values of the shrinkage value of the 3D-model during printing and the parameters of the 3D-printer operation mode are revealed. It has been established that polymer casting molds are rationally manufactured by FDM printing, using ABS-plastic as a consumable, while making a correction to linear dimensions at the stage of creating a 3D-model to eliminate plastic shrinkage.

Initial state radiation in the Herwig 7 angular-ordered parton shower

We study the simulation of initial-state radiation in angular-ordered parton showers in order to investigate how different interpretations of the ordering variable affect the logarithmic accuracy of such showers. This also enables us to implement a recoil scheme which is consistent between final-state and initial-state radiation. We present optimal values of the strong coupling and intrinsic transverse momentum to be used in each version of the parton shower, tuned using Z0-boson production at the LHC at 7 TeV. With these tuned showers, we perform a phenomenological study of the Drell-Yan process at several centre-of-mass energies.

SUPPORT VECTOR MACHINE FOR DETERMINING EULER ANGLES IN AN INERTIAL NAVI-GATION SYSTEM

The paper discusses the improvement of the accuracy of an inertial navigation system created on the basis of MEMS sensors using machine learning (ML) methods. As input data for the classifier, we used information obtained from a developed laboratory setup with MEMS sensors on a sealed platform with the ability to adjust its tilt angles. To assess the effectiveness of the models, test curves were constructed with different values of the parameters of these models for each core in the case of a linear, polynomial radial basis function. The inverse regularization parameter was used as a parameter. The proposed algo-rithm based on MO has demonstrated its ability to correctly classify in the presence of noise typical for MEMS sensors, where good classification results were obtained when choosing the optimal values of hy-perparameters.

A New Variable-Censoring Control Chart Using Lifetime Performance Index under Exponential and Weibull Distributions

In reliability theory or life testing, exponential distribution and Weibull distribution are frequently considered to model the lifetime of the components or systems. In this paper, we design a control chart based on the lifetime performance index using Type II censoring for exponential and Weibull distributions. Average run length helps to measure the performance of the proposed control chart. The optimal values of the number of failure items and decision criteria used to decide whether the process is in-control or out-of-control based on the sample results are determined such that the in-control average run length is as close as to the specified average run length values. We simulate the data to illustrate the performance of the proposed control chart.

Deep Learning Assisted Automatic Methodology for Implanted MIMO Antenna Designs on Large Ground Plane

This paper provides a novel methodology for designing implanted multiple-input and multiple-output (MIMO) antennas in the automatic fashion. The proposed optimization consists of two sequential phases for firstly configuring the geometry of an implanted MIMO antenna and then sizing the design parameters through the hierarchy top-down optimization (TDO) and regression deep neural network (DNN), respectively. It tackles the difficulty in constructing the structure of antennas and also provides optimal values for the determined variables, sufficiently. This methodology results in valid electromagnetic (EM)-verified post-layout generation that is ready-to-fabricate. The effectiveness of the proposed optimization-oriented method is verified by designing and optimizing the implanted MIMO antenna in the frequency band of 4.34–4.61 GHz and 5.86–6.64 GHz suitable for medical applications at the emerging wireless band. For our design, we employ the actual biological tissues as bone, liquid (%1 sodium chloride, %40 sugar in distilled water), and plexiglass surroundings with a bio-compatible substrate, as aluminium oxide on a large ground plane, that is suitable to be used in a particular biomedical applications involving smart implants.

Optimize the Fluctuations on Iraqi Economic Growth based on the oil prices using linear programming approach

This study discus the Fluctuations on Iraqi Economic Growth using linear programming approach. This article proposes an operational approach for research to maximize customer satisfaction by optimizing the quality levels of numerous quality attributes of a physical product within the limits of the manufacturer's requirements. The linear model of programming has been shown using the data acquired for an example bicycle product. The optimal values for the quality of selected bicycle quality features have been calculated using optimization software. A cost-benefit analysis for some quality features of the crude oil was conducted to validate the proposed model. The model discussed in this article considers not only pleasure of customers but also satisfaction of the producer with the costs associated with the product

Stock Influence on Growth, Morphological and Biochemical Leaf Parameters Prunus domestica L.

Vegetation growth peculiarities and morphological and physical-biochemical features of Prunus domestica L. Utro and Yaichnaya Sinyaya varieties leaves grafted on different growing strength rootstocks were studied. Low-growing clonal rootstocks 140–1 and Novinka decreased the trees growing strength on 15–20% in comparison with strong-growing stocks; medium-growing rootstocks OPA-15-2 and OP-23-23 reduced it on 10%. The longest growing activity and the largest sprouts length was stated on these rootstocks as well, i.e. 1.3–1.4 times more than on other ones. Stable sprouts average length decrease was registered on grafted stocks 140–1 and Novinka. Leaf surface index value on the trees grafted on clonal rootstocks OPA-15-2 and OP-23-23 was on 40% higher than on control, i.e. 4.3 leaves m2/crown projection area m2. Optimal values of total increment, sprouts average length, leaves area and the largest part of physiological-biochemical parameters were stated at medium-growing clonal rootstocks OPA-15-2 and OP-23-23 use. Plum leaves blades were hypostomatic; numerous stomata were located on the abaxial (bottom) side of leaves. Stomata were located in interveinal space irregularly. Stomata length size varied from 14.6 μm (Utro/seedlings) to 22.1 μm (Yaichnaya Sinyaya/OP-23-23). The rootstock has influence on the process of photosynthesis, antioxidant activity, accumulation of minerals and metabolic answerin the leaves.

Updated Absolute Gravity Rate of Change Associated With Glacial Isostatic Adjustment in Southeast Alaska and Its Utilization for Rheological Parameter Estimation

In Southeast Alaska (SE-AK), rapid ground uplift of up to 3 cm/yr has been observed associated with post-Little Ice Age glacial isostatic adjustment (GIA). Geodetic techniques such as global navigation satellite system (GNSS) and absolute gravimetry have been applied to monitor GIA since the last 1990s. Rheological parameters for SE-AK were determined from dense GNSS array data in earlier studies. However, the absolute gravity rate of change observed in SE-AK was inconsistent with the ground uplift rate, mainly because few gravity measurements from 2006 to 2008 resulted in imprecise gravity variation rates. Therefore, we collected absolute gravity data at six gravity points in SE-AK every June in 2012, 2013, and 2015, and updated the gravity variation rate by reprocessing the absolute gravity data collected from 2006 to 2015. We found that the updated gravity variation rate at the six gravity points ranged from −2.05 to −4.40 μGal/yr, and its standard deviation was smaller than that reported in the earlier study by up to 88 %. We also estimated the rheological parameters to explain the updated gravity variation rate, and their optimal values were determined to be 55 km and 1.2 × 10^19 Pa s for lithospheric thickness and upper mantle viscosity, respectively. These optimal values are consistent with those independently obtained from GNSS observations, and this fact indicates that absolute gravimetry can be one of the most effective methods in determining sub-surface structural parameters associated with GIA accurately. Moreover, we utilized the gravity variation rates for estimating the ratio of gravity variation to vertical ground deformation at the six gravity points in SE-AK. The viscous ratio values were obtained as −0.168 and −0.171 μGal/mm from the observed data and the calculated result, respectively. These ratios are greater (in absolute) than those for other GIA regions (−0.15 to −0.16 μGal/mm in Antarctica and Fennoscandia) because glaciers in SE-AK have melted more recently than in other regions.