Handle Research Articles

Optimization of the shape of a hydrokinetic turbine’s draft tube and hub assembly using Design-by-Morphing with Bayesian optimization

Finding the optimal design of a hydrodynamic or aerodynamic surface is often impossible due to the expense of evaluating the cost functions (say, with computational fluid dynamics) needed to determine the performances of the flows that the surface controls. In addition, inherent limitations of the design space itself due to imposed geometric constraints, conventional parameterization methods, and user bias can restrict all of the designs within a chosen design space regardless of whether traditional optimization methods or newer, data-driven design algorithms with machine learning are used to search the design space. We present a 2-pronged attack to address these difficulties: we propose (1) a methodology to create the design space using morphing that we call Design-by-Morphing (DbM); and (2) an optimization algorithm to search that space that uses a novel Bayesian Optimization (BO) strategy that we call Mixed variable, Multi-Objective Bayesian Optimization (MixMOBO). We apply this shape optimization strategy to maximize the power output of a hydrokinetic turbine. Applying these two strategies in tandem, we demonstrate that we can create a novel, geometrically-unconstrained, design space of a draft tube and hub shape and then optimize them simultaneously with a minimum number of cost function calls. Our framework is versatile and can be applied to the shape optimization of a variety of fluid problems.

COOPS: A Code Obfuscation Method Based on Obscuring Program Semantics

As reverse engineering technology develops rapidly, the financial loss caused by software security issues is urgent. Therefore, how to effectively protect software is a critical problem to solve. The software protection method based on code obfuscation is an effective way, and constructing an effective obfuscation algorithm can increase the cost of reverse software. It is conspicuous that current development of code obfuscation focuses on increasing the complexity of the code structure without paying much attention to the protection of program semantic information, which may help experienced attackers improve their analysis efficiency. This paper proposes COOPS for protecting software based on program semantic information, in which functions are regarded as basic semantic units. The switch relationship between the intrafunction control flow and the interfunction calling is established. The interfunction calling can be hidden in the intrafunction control flow, and in reverse, the intrafunction control flow can also be converted to interfunction calling. In this way, considering intraprogram function semantic unit level discrete, this method reconstructs the intraprogram semantic relationship. To determine the relative effectiveness, we have evaluated COOPS on OpenSSL and SpecInt-2000 test sets. For both of them, the function calling graphs before and after obfuscation differ more than 90%, which means COOPS significantly changes the control flow of the program. The evaluation shows that compared with O-LLVM, COOPS manifests strong resistance to Asm2vec and other program similarity analysis techniques and significantly improves the level of software protection rather than necessitating time-consuming and heavyweight problems.

An Approach to Compute the Multi-Objective Programming Problem Under Triangular Fuzzy Numbers

The most important one advantages of the operation research (OR) theory is displayed of this paper for tackling multi-objective function programming problems (MOFPP). In this study, we present an approach for multi-objective fractional programming problems (MOFrPP) using fuzzy numbers (FN) coefficients in the objective functions (OF). The objective functions' (OF) parameters are all considered to be fuzzy triangular fuzzy numbers (FTrFN). This problem's multi-objective function (MOF) case was resolved by transforming the MOF to a linear programming problem (LnPP) that could be resolved using the simplex method (SM). The problem is being solved using algorithms. Then the value is compared with a result, which obtained by simplex method (SM). These techniques are illustrated with numerical examples (NE). This study shows the reliability of our methods and their utility to this specific set of multi-objective functions (MOF).

87 Awardee Talk: Frequency of Energy Supplementation Modulates Growth, Reproduction, Immune Function, and Fetal Programming in Beef Cattle

Abstract Fluctuations in nutrient intake and metabolism can be detrimental to beef cattle performance. Multiple studies evaluated the impacts of reducing the frequency of energy supplementation from daily (7X) to once (1X) or 3 times (3X) weekly during heifer development, calf preconditioning, and late gestation of beef cows on growth, reproduction, immune function, and progeny performance. Reducing the supplementation frequency from 7X to 3X weekly: (1) did not impact average daily gain (ADG; P = 0.52) but hastened (P ≤ 0.05) puberty and pregnancy attainment of beef heifers; and (2) reduced ADG (P = 0.02), increased (P ≤ 0.03) plasma indicators of physiological stress (cortisol and haptoblogin), and decreased (P ≤ 0.05) humoral immune response against pathogens associated with bovine respiratory disease (BRD) in preconditioning beef steers. Reducing the supplementation frequency from 7X to either 3X or 1X weekly during late gestation did not (P ≥ 0.21) impact cow prepartum body condition score and reproduction, but linearly decreased (P = 0.05) calf preweaning growth. The negative effects of infrequent energy supplementation may be attributed to greater fluctuations in plasma concentrations of insulin growth factor-1, glucose, insulin, and urea nitrogen compared to frequent energy supplementation. Later attempts to offset such negative effects of infrequent supplementation observed that neither increasing the supplementation amount (P ≥ 0.21) nor altering the timing of vaccination relative to weaning (P ≥ 0.12) prevented performance reductions in beef cattle supplemented infrequently. Only a gradual reduction in supplementation frequency for preconditioning calves prevented a reduction on growth and improved (P ≤ 0.05) the immune response against BRD pathogens. Combined, these results revealed that frequent energy supplementation during preconditioning of beef steers, development of replacement beef heifers, and late gestation of beef cows was required to optimize their growth, immunity, and reproduction, and the growth performance of their progeny.

Learning from what we know: How to perform vulnerability prediction using noisy historical data

Vulnerability prediction refers to the problem of identifying system components that are most likely to be vulnerable. Typically, this problem is tackled by training binary classifiers on historical data. Unfortunately, recent research has shown that such approaches underperform due to the following two reasons: a) the imbalanced nature of the problem, and b) the inherently noisy historical data, i.e., most vulnerabilities are discovered much later than they are introduced. This misleads classifiers as they learn to recognize actual vulnerable components as non-vulnerable. To tackle these issues, we propose TROVON, a technique that learns from known vulnerable components rather than from vulnerable and non-vulnerable components, as typically performed. We perform this by contrasting the known vulnerable, and their respective fixed components. This way, TROVON manages to learn from the things we know, i.e., vulnerabilities, hence reducing the effects of noisy and unbalanced data. We evaluate TROVON by comparing it with existing techniques on three security-critical open source systems, i.e., Linux Kernel, OpenSSL, and Wireshark, with historical vulnerabilities that have been reported in the National Vulnerability Database (NVD). Our evaluation demonstrates that the prediction capability of TROVON significantly outperforms existing vulnerability prediction techniques such as Software Metrics, Imports, Function Calls, Text Mining, Devign, LSTM, and LSTM-RF with an improvement of 40.84% in Matthews Correlation Coefficient (MCC) score under Clean Training Data Settings, and an improvement of 35.52% under Realistic Training Data Settings.

Developmental oxygen preadapts ventricular function of juvenile American alligators, Alligator mississippiensis.

Developmental oxygen is a powerful stressor that can induce morphological and functional changes in the cardiovascular systems of embryonic and juvenile vertebrates. This plasticity has been ascribed, at least in part, to the unique status of the developing cardiovascular system, which undergoes organogenesis while meeting the tissue oxygen demands of the embryo. We have previously reported an array of functional and morphological changes in embryonic American alligators that persist into juvenile life. Most notably, cardiac enlargement as well as functional parameters of anesthetized juvenile alligators remains after embryonic hypoxic exposure. Because the effects of developmental oxygen in crocodilians have only been investigated in anesthetized animals, we explored the pressure dynamics of both ventricles as well as systemic pressure in response to stressors of acute hypoxia and swimming. Our current findings demonstrate that developmental programming of cardiac function (intraventricular pressure and heart rate) does persist into juvenile life, but it is chamber-specific and depends on the experimental manipulation. Acute hypoxic exposure revealed that juvenile alligators that had experienced 10% O2 as embryos maintain right ventricle function and increase left ventricle function during exposure. Finally, the data indicate blood flow in the left aorta must originate from the left ventricle during acute hypoxia and swimming.

Study of Heat Transfer and Leakage Characteristics of Brush Seals Based on Local Temperature Non-Equilibrium Model

In this study, to improve the accuracy of the brush seal heat transfer model, based on the finite volume method (FVM) coupled with the three-dimensional Reynold-averaged Naviers-Stokes equations (RANS) equations of the Local temperature non-equilibrium (LTNE) model, and a mathematical model of the heat transfer and leakage characteristics of the brush seal was established. The distribution of the pressure, flow and temperature fields of the brush seal are analyzed. User-defined function (UDF) programming was performed for the LTNE model. And the LTNE model is then compared with the local temperature equilibrium (LTE) model in terms of the factors influencing the heat transfer and leakage characteristics. The results show that the maximum brush filament temperature increases with an increases in the pressure ratio, interference, and speed for both models; the fluid flow rate increases with an increases in the pressure ratio, interference, and speed; and the leakage rate increases with an increases in the pressure ratio and decreases with an increases in interference and speed. The maximum temperature of the brush filament under the LTNE model was found to be higher than that under the LTE model, but the maximum temperature difference does not exceed 3.1%. Additionally, the fluid flow rate under the LTNE model was higher than that under the LTE model, and the flow rate difference does not exceed 3.4%. And the leakage rate under the LTNE model was lower than that under the LTE model, and the leakage rates differ by no more than 9.0%. Ultimately, numerical analysis of the brush seal under the LTNE model was found to be more effective and consistent with actual working conditions than alternative models.

IE-AK: A novel adaptive sampling strategy based on information entropy for Kriging in metamodel-based reliability analysis

This article focuses on the adaptive Kriging metamodel-based reliability analysis for reducing a sequential number of calls of the complex original functions. To avoid the repetitive and tedious deterministic response analysis with stochastic simulation method (including Monte Carlo Simulation and its various improvement, such as importance sampling, subset simulation) in reliability analysis, herein a novel sequential sampling strategy related to Kriging metamodel is proposed, which is implemented based on information entropy theory. In addition, the generalized F-discrepancy method is simultaneously quoted to further optimize the candidate pool to improve the effectiveness of the training metamodel. Finally, a new structural reliability analysis method is proposed, which continuously reduces the number of deterministic analysis of structures without sacrificing accuracy. To highlight the applicability of the method and verify its accuracy and effectiveness, a series of typical examples are tested and compared, including highly nonlinear limit state functions, high-dimension performance function with analytic expressions and dynamic reliability analysis of nonlinear engineering structures subject to seismic excitation with implicit performance function. Numerical results show that significant computational savings and desired accuracy can be achieved when dealing with different reliability analysis cases.

Systematic design of Cauchy symmetric structures through Bayesian optimization

Using a new Bayesian Optimization algorithm to guide the design of mechanical metamaterials, we design nonhomogeneous 3D structures possessing the Cauchy symmetry, which dictates the relationship between continuum and atomic deformations. Recent efforts to merge optimization techniques with the design of mechanical metamaterials has resulted in a concentrated effort to tailor their elastic and post elastic properties. Even though these properties of either individual unit cells or homogenized continua can be simulated using multi-physics solvers and well established optimization schemes, they are often computationally expensive and require many design iterations, rendering the validation stage a significant obstacle in the design of new metamaterial designs. This study aims to provide a framework on how to utilize miniscule computational cost to control the elastic properties of metamaterials such that specific symmetries can be accomplished. Using the Cauchy symmetry as a design objective, we engineer structures through the strategic arrangement of 5 different unit cells in a 5×5×5 cubic symmetric microlattice structure. This lattice design, despite constituting a design space with 510 3D lattice configurations, can converge to an effective solution in only 69 function calls as a result of the efficiency of the new Bayesian optimization scheme. To validate the mechanical behavior of the design, the lattice structures were fabricated using multiphoton lithography and mechanically tested, revealing a close correlation between experiments and simulated results in the elastic regime. Ultimately, a similar methodology can be utilized to design metamaterials with other material properties, aspiring to control properties at different length scales, an endeavor that requires inordinate computation cost.

A Novel Reliability Analysis Approach under Multiple Failure Modes Using an Adaptive MGRP Model

In this paper, a novel MRGP-SS method is proposed to deal with the reliability analysis problems under multiple failure modes. First, a random moving quadrilateral grid sampling (RMQGS) method is proposed to improve the randomness and uniformity of initial samples. Second, an adaptive procedure, which combines the multiple response Gaussian process (MRGP) model and the novel active learning functions, is proposed to efficiently and accurately produce surrogate models for failure surfaces. In this regard, two novel learning functions are introduced to adapt to different iterative cycles, one is employed to correct the quality of samples, and the other is used to search for the samples closest to the limit state surface. Third, the subset simulation (SS) is integrated into the adaptive MRGP model to estimate the failure probability under multiple failure modes with fewer function calls and time consumption. Numerical and engineering case studies are finally provided to demonstrate the effectiveness of the proposed method.

Aeneas: Rust verification by functional translation

We present Aeneas, a new verification toolchain for Rust programs based on a lightweight functional translation. We leverage Rust’s rich region-based type system to eliminate memory reasoning for a large class of Rust programs, as long as they do not rely on interior mutability or unsafe code. Doing so, we relieve the proof engineer of the burden of memory-based reasoning, allowing them to instead focus on functional properties of their code. The first contribution of Aeneas is a new approach to borrows and controlled aliasing. We propose a pure, functional semantics for LLBC, a Low-Level Borrow Calculus that captures a large subset of Rust programs. Our semantics is value-based, meaning there is no notion of memory, addresses or pointer arithmetic. Our semantics is also ownership-centric, meaning that we enforce soundness of borrows via a semantic criterion based on loans rather than through a syntactic type-based lifetime discipline. We claim that our semantics captures the essence of the borrow mechanism rather than its current implementation in the Rust compiler. The second contribution of Aeneas is a translation from LLBC to a pure lambda-calculus. This allows the user to reason about the original Rust program through the theorem prover of their choice, and fulfills our promise of enabling lightweight verification of Rust programs. To deal with the well-known technical difficulty of terminating a borrow, we rely on a novel approach, in which we approximate the borrow graph in the presence of function calls. This in turn allows us to perform the translation using a new technical device called backward functions. We implement our toolchain in a mixture of Rust and OCaml; our chief case study is a low-level, resizing hash table, for which we prove functional correctness, the first such result in Rust. Our evaluation shows significant gains of verification productivity for the programmer. This paper therefore establishes a new point in the design space of Rust verification toolchains, one that aims to verify Rust programs simply, and at scale. Rust goes to great lengths to enforce static control of aliasing; the proof engineer should not waste any time on memory reasoning when so much already comes “for free”!

Multi-scale thermal radiation effects correction via a fast surface fitting with Chebyshev polynomials.

In an uncooled infrared imaging system, thermal radiation effects are caused by the heat source from the target or the detection window, which affects the ability of target detection, tracking, and recognition seriously. To address this problem, a multi-scale correction method via a fast surface fitting with Chebyshev polynomials is proposed. A high-precision Chebyshev polynomial surface fitting is introduced into thermal radiation bias field estimation for the first time, to the best of our knowledge. The surface fitting in the gradient domain is added to the thermal radiation effects correction model as a regularization term, which overcomes the ill-posed matrix problem of high-order bivariate polynomials surface fitting, and achieves higher accuracy under the same order. Additionally, a multi-scale iterative strategy and vector representation are adopted to speed up the iterative optimization and surface fitting, respectively. Vector representation greatly reduces the number of basis function calls and achieves fast surface fitting. In addition, split Bregman optimization is used to solve the minimization problem of the correction model, which decomposes the multivariable optimization problem into multiple univariate optimization sub-problems. The experimental results of simulated and real degraded images demonstrate that our proposed method performs favorably against the state of the art in thermal radiation effects correction.

Blockchain Socket Factories with RMI-Enabled Framework for Fine-Grained Healthcare Applications.

The usage of digital and intelligent healthcare applications on mobile devices has grown progressively. These applications are generally distributed and access remote healthcare services on the user’s applications from different hospital sources. These applications are designed based on client–server architecture and different paradigms such as socket, remote procedure call, and remote method invocation (RMI). However, these existing paradigms do not offer a security mechanism for healthcare applications in distributed mobile-fog-cloud networks. This paper devises a blockchain-socket-RMI-based framework for fine-grained healthcare applications in the mobile-fog-cloud network. This study introduces a new open healthcare framework for applied research purposes and has blockchain-socket-RMI abstraction level classes for healthcare applications. The goal is to meet the security and deadline requirements of fine-grained healthcare tasks and minimize execution and data validation costs during processing applications in the system. This study introduces a partial proof of validation (PPoV) scheme that converts the workload into the hash and validates it among mobile, fog, and cloud nodes during offloading, execution, and storing data in the secure form. Simulation discussions illustrate that the proposed blockchain-socket-RMI minimizes the processing and blockchain costs and meets the security and deadline requirements of fine-grained healthcare tasks of applications as compared to existing frameworks in work.

PE Parser: A Python package for Portable Executable files processing

<h2>Abstract</h2> <i>PE Parser</i> is a Python package to parse and work with the hexadecimal representation of executables' binary content and its assembly language source code. <i>PE Parser</i> has been designed to provide a class-based and user-friendly interface for the extraction of well-known features commonly used for the task of malware detection and classification such as byte and opcode N-Grams, API function calls, the frequency of use of the registers, characteristics of the Portable Executable file sections, among others. In addition, <i>PE Parser</i> has various command line tools to visualize the executables as grayscale images or as a stream of entropy values.

ATTRIBUTE EVALUATION METHODS IN A TOP-DOWN TABLE-DRIVEN PARSER

The article outlines methods for efficient computation of attributes in a table-driven top-down parsing process. This methods are based on the introduction of a stack of attributes into the parser. Semantic actions using this stack are defined by one of three methods. The first method is based on the fact that two parsers: table-driven and recursive descent parsers have the same use of depth first traversal. Therefore, the mechanisms for computing attributes in recursive descent are also applicable in a table-driven parser. Then the attribute stack will act like a frame stack of function calls. The second method assumes that the attribute stack is the memory of an abstract machine. For the source language of the translator, the structural-operational semantics is specified, which puts the translation result in line with the language constructions. The semantic actions of the translation scheme are implemented as analyzing and applying rules of an abstract machine built on the basis of structural-operational semantics. The third method assumes that an abstract syntax tree in postfix form will be formed in the attribute stack. Semantic actions for the attribute stack are built in such a way that the expressions formed on the stack are evaluated as far as possible. The article shows that the application of the three proposed methods to the scheme of translation of arithmetic expressions gives the same result. The proposed methods can significantly reduce the amount of memory used and make the design of the translator more understandable.

Adaptive Sampling for Interpolation of Reduced-Order Aeroelastic Systems

A new strategy for the interpolation of parametric reduced-order models of dynamic aeroelastic systems is introduced. Its aim is to accelerate the numerical exploration of geometrically nonlinear aeroelastic systems over large design spaces or multiple flight conditions. The parametric reduced-order models are obtained from high-dimensional models by Krylov-subspace projection. They are subsequently interpolated to acquire realizations inexpensively anywhere in the parameter space, where having the state space as opposed to interpolating an output metric permits the use of linear analysis tools. The interpolation scheme is heavily conditioned by the available training points; thus, a novel methodology based on adaptive sampling and a combinatorial use of the available true-systems knowledge is presented, whereby we reuse all known data of the true models as different combinations of training and testing data to build statistical surrogates of the interpolation error across the parameter space and refine the sampling in those regions that need it. This minimizes the number of costly to evaluate function calls and ensures that parameter space regions are sampled according to the underlying system dynamics. The initial implementation of this adaptive sampling strategy is demonstrated on a very flexible wing with a complex stability envelope.

Packaging and modelling method of renewable energy equipment based on unified interface

With the increasing proportion of renewable energy power generation in the power system, the characteristics of renewable energy equipment have a greater impact on the power grid. It is necessary to accurately simulate the renewable energy equipment. Encapsulating the internal logic of the controller directly into a digital model is an important technical route to realize the accurate simulation. However, the current commonly used electromagnetic software includes ADPSS, HYPERSIM, PSCAD, MATLAB and RT-LAB, with different packaging methods, which brings high technical threshold and huge workload. This paper investigates the user-defined modelling methods of these five software, refines the common characteristics, and puts forward the user-defined modelling idea of “unified interface”, which realizes single modelling and multi-platform reuse. This paper proposes a renewable energy packaging and modelling method based on “unified interface”, which includes code architecture, functional architecture, interface function form, programming requirements and conversion methods of different software. Finally, through the case of a photovoltaic controller of a manufacturer, the high consistency of different platforms verifies the correctness of the renewable energy packaging and modelling method based on “unified interface”.

Translating a large subset of stateflow to hybrid CSP with code optimization

Stateflow is a graphical language for modeling hierarchical transition systems, well-known for the complexity of its semantics, which is only informally explained in its user manual. Formal analysis and verification of Stateflow models usually proceed by first translating a subset of Stateflow to a formal language with precise semantics. Most existing work address only “safe” subset of Stateflow and ignore the most complex semantic issues. Moreover, it is difficult to balance simplicity of the translation algorithm with conciseness of the resulting model. In this paper, we describe a two-stage process for translating a large subset of Stateflow to Hybrid CSP, where the first stage is mostly syntax-directed and addresses each feature of Stateflow separately, and the second stage is a code optimization step that simplifies the resulting model using information from static analysis. We further incorporate this method into translation of Simulink models, in particular consider Stateflow charts triggered by various signal sources and function calls. The translation process is thoroughly validated using a hand-designed set of benchmarks, as well as larger case studies from existing work.

The Student Psychological Contract as a Predictor of University Brand Evangelism in Tanzanian Higher Education:

This study sought to extend empirical and theoretical understanding of university brand evangelism as an outcome of the student psychological contract in the higher education sector. The conceptual model was developed and tested using structural equation modeling on 451 students at two colleges of higher education in Tanzania. The findings indicate that the student psychological contract influences university brand love en route to university brand evangelism. Thus, university operations should be well-defined by a social contract that stipulates the obligations of both students and staff. For students to engage in university brand evangelism, higher education institutions must fulfill their expectations as stipulated in the contract. More specifically, their staff should fulfill their obligations emanating from promises made to students. This calls for specific standard procedures to fulfill expectations and thus optimize university brand evangelism.&#x0D; Key words: Higher education sector, higher education institutions, university, branding strategies, student psychological contract, university brand evangelism

Parallel computational tree logic model‐checking on pushdown systems

SummaryModel checking and static analysis have been well studied for program verification. Because of the ability to describe the stack, the pushdown system (PDS) has become a perfect model that is able to accurately model procedure calls and mimic the program's stack. Thus, it is not only a good model for sequential programs but for malware detection as well. However, with the increase of the complexity of programs, the size of models becomes huge as well. Thus, the model‐checking problem is expensive to solve. The computational tree logic (CTL) is a widely used logic and its model checking problem of PDSs can be reduced to the emptiness analysis of an alternating Büchi pushdown system (ABPDS) by determining whether there is an accepting run. When the size of a PDS is huge, the computations can be time‐consuming. To overcome this limitation, we propose a parallel solution. We propose a parallel framework based on the Compute Unified Device Architecture and the corresponding parallel algorithms to solve the emptiness problem of ABPDSs. Moreover, in order to effectively utilize the graphics processing unit, we design a new data structure of variables and an algorithm of management of thread scheduling for the parallel model. We implement our algorithms in a tool and compare our tool to a CTL model checker for PDS as a benchmark. The comparison results indicate an encouraging performance speedup.