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In structural, earthquake and aeronautical engineering and mechanical vibration, the solution of dynamic equations for a structure subjected to dynamic loading leads to a high order system of differential equations. The numerical methods are usually used for integration when either there is dealing with discrete data or there is no analytical solution for the equations. Since the numerical methods with more accuracy and stability give more accurate results in structural responses, there is a need to improve the existing methods or develop new ones. The paper aims to discuss these issues.

In this paper, a new time integration method is proposed mathematically and numerically, which is accordingly applied to single-degree-of-freedom (SDOF) and multi-degree-of-freedom (MDOF) systems. Finally, the results are compared to the existing methods such as Newmark's method and closed form solution.

It is concluded that, in the proposed method, the data variance of each set of structural responses such as displacement, velocity, or acceleration in different time steps is less than those in Newmark's method, and the proposed method is more accurate and stable than Newmark's method and is capable of analyzing the structure at fewer numbers of iteration or computation cycles, hence less time-consuming.

A new mathematical and numerical time integration method is proposed for the computation of structural responses with higher accuracy and stability, lower data variance, and fewer numbers of iterations for computational cycles.

The paper's aim is to present a method for integrating high‐performance circuit components onto flexible substrates using self‐assembly. The basic process of self‐assembly at the micrometer‐scale is reviewed and recent work in building functional parts such as silicon transistors and compound semiconductor light emitting diodes, as well as their integration onto flexible plastic templates, is reported.

A micron‐scale self‐assembly method was used for building flexible circuits. In micron‐scale self‐assembly, functional micro‐components are independently microfabricated and subsequently allowed to self‐assemble on a template with electrical interconnects and corresponding binding sites in a fluid.

The self‐assembly process can achieve heterogeneous integration with a potentially very high yield. Successful assembly of functional micro‐components such as LEDs and transistors on plastic has been demonstrated.

The paper demonstrates fabrication techniques for free‐standing micro‐components with novel designs, low‐temperature fabrication on thin plastic sheets, and using capillary‐gravity‐based self‐assembly for the integration of crystalline inorganic semiconductor components onto unconventional substrates such as flexible polymers.

Two-stage production systems including a processing shop and an assembly stage are widely used in various manufacturing industries. These two stages are usually studied independently which may not lead to ideal results. This paper aims to deal with a two-stage production system including a job shop and an assembly stage.

Some exact methods are proposed based on branch and bound (B&B) approach to minimize the total completion time of products. As B&B approaches are usually time-consuming, three efficient lower bounds are developed for the problem and variable neighborhood search is used to provide proper upper bound of the solution in each branch. In addition, to create branches and search new nodes, two strategies are applied including the best-first search and the depth-first search (DFS). Another feature of the proposed algorithms is that the search space is reduced by releasing the precedence constraint. In this case, the problem becomes equivalent to a parallel machine scheduling problem, and the redundant branches that do not consider the precedence constraint are removed. Therefore, the number of nodes and computational time are significantly reduced without eliminating the optimal solution.

Some numerical examples are used to evaluate the performance of the proposed methods. Comparison result to mathematical model (mixed-integer linear programming) validates the performance accuracy and efficiency of the proposed methods. In addition, computational results indicate the superiority of the DFS strategy with regard to CPU time.

Studies about the scheduling problems for two-stage production systems including job shop followed by an assembly stage traditionally present approximate method and metaheuristic algorithms to solve the problem. This is the first study that introduces exact methods based on (B&B) approach.

The purpose of this study is to propose a new mathematical model that integrates strategic decision-making with tactical-operational decision-making in order to optimize production and scheduling decisions.

This study presents a multi-objective optimization framework to make production planning, scheduling and maintenance decisions. An epsilon-constraint method is used to solve small instances of the model, while new hybrid optimization algorithms, including multi-objective particle swarm optimization (MOPSO), non-dominated sorting genetic algorithm, multi-objective harmony search and improved multi-objective harmony search (IMOHS) are developed to address the high complexity of large-scale problems.

The computational results demonstrate that the metaheuristic algorithms are effective in obtaining economic solutions within a reasonable computational time. In particular, the results show that the IMOHS algorithm is able to provide optimal Pareto solutions for the proposed model compared to the other three algorithms.

This study presents a new mathematical model that simultaneously determines green production planning and scheduling decisions by minimizing the sum of the total cost, makespan, lateness and energy consumption criteria. Integrating production and scheduling of a shop floor is critical for achieving optimal operational performance in production planning. To the best of the authors' knowledge, the integration of production planning and maintenance has not been adequately addressed.

The purpose of this paper is to propose a new design strategy to enhance the bandwidth and efficiency of the power amplifier.

To realize the introduced design strategy, a power amplifier was designed using TSMC CMOS 0.18um technology for operating in the Ka-band, i.e. the frequency range of 26.5-40 GHz. To design the power amplifier, first, a power divider (PD) with a very wide bandwidth, i.e. 1-40 GHz, was designed to cover the whole Ka-band. The designed Doherty power amplifier consisted of two different amplification paths called main and auxiliary. To amplify the signal in each of the two pathways, a cascade distributed power amplifier was used. The main reason for combining the distributed structure and cascade structure was to increase the gain and linearity of the power amplifier.

Measurements results for designed power dividers are in good agreement with simulations results. The simulation results for the introduced structure of the power amplifier indicated that the gain of the proposed power amplifier at the frequency of 26-35 GHz was more than 30 dB. The diagram of return loss at the input and output of the power amplifier in the whole Ka-band was less than −8dB. The maximum power-added efficiency (PAE) of the designed power amplifier was 80%. The output P1dB of the introduced structure was 36 dB and the output power of the power amplifier was 36 dBm. Finally, the IP3 value of the power amplifier was about 17 dB.

The strategy presented in this paper is based on the usage of Doherty and distributed structures and a new wideband power divider to benefit from their advantages simultaneously.

This study aims to report the development and experimental evaluation of three innovative corrosion-resistant modified epoxy coatings, namely, nanocomposite/toughened, self-healing and hybrid epoxy coatings, for application on steel substrates.

The corrosion resistance of these coatings was evaluated in a highly corrosive environment of salt fog spray for 2,500 h of exposure. Electrochemical impedance spectroscopy (EIS) measurements in sustained exposure to NaCl in a saturated Ca(OH)2 solution, rust creepage measurements at the location of scribe formed in the coatings and adhesion strength test were used to assess the performance of the innovative coatings. Commercially available marine-grade protective epoxy coatings were used as the reference coatings.

The test results showed that the modified epoxy coatings exhibited excellent corrosion resistance when exposed to an aggressive environment for extended periods. The EIS measurements, rust creepage measurements, pull-off strength and visual appearance of the aged modified–epoxy–coated specimens confirmed the enhanced corrosion resistance of the modified epoxy coatings.

Among the three types of modified coatings, the hybrid epoxy coating stands out to be the best performer.

The purpose of this paper is to present an integrated strategy for inventory control and preventive maintenance planning for a single-machine production system with increasing failure rates.

There are three scenarios for solving presented model. The strategy is such that the production component is placed under maintenance as soon as it reaches the m level or in the event of a malfunction earlier than m. Maintenance completion time is not predictable. As a result of periodic maintenance, a buffer stock h is held and the production component starts to produce from period A with the maximum throughput to satisfy demand and handle the shortage. A numerical algorithm to find the optimal policy is developed. The algorithm is implemented using MATLAB software.

The authors discovered that joint optimization mainly reduces production system costs. Cs is holding cost of a product unit during a unit of time. The authors consider two values for Cs, consist of, Cs = 1 and Cs = 2. By comparing the two cases, it is concluded that by reducing the cost from Cs = 2 to Cs = 1, the optimal scenario does not differ. The amount of decision variables decreases.

This paper is the provision of a model in which the shortage of back order type is considered, which greatly increases the complexity of the problem compared to similar issues. The methods for solving such problems are provided by the numerical algorithm, and the use of buffers as a way to compensate for the shortage in the event of a complete shutdown of the production line which is a very effective and efficient way to deal with customer loss.

The purpose of this paper is to present a mathematical model and a new hybrid algorithm for flexible job shop scheduling problem with assembly operations. In this problem, each product is produced by assembling a set of several different parts. At first, the parts are processed in a flexible job shop system, and then at the second stage, the parts are assembled and products are produced.

As the problem is non-deterministic polynomial-time-hard, a new hybrid particle swarm optimization and parallel variable neighborhood search (HPSOPVNS) algorithm is proposed. In this hybrid algorithm, particle swarm optimization (PSO) algorithm is used for global exploration of search space and parallel variable neighborhood search (PVNS) algorithm for local search at vicinity of solutions obtained in each iteration. For parameter tuning of the metaheuristic algorithms, Taguchi approach is used. Also, a statistical test is proposed to compare the ability of metaheuristics at finding the best solution in the medium and large sizes.

Numerical experiments are used to evaluate and validate the performance and effectiveness of HPSOPVNS algorithm with hybrid particle swarm optimization with a variable neighborhood search (HPSOVNS) algorithm, PSO algorithm and hybrid genetic algorithm and Tabu search (HGATS). The computational results show that the HPSOPVNS algorithm achieves better performance than competing algorithms.

Scheduling of manufacturing parts and planning of assembly operations are two steps in production systems that have been studied independently. However, with regard to many manufacturing industries having assembly lines after manufacturing stage, it is necessary to deal with a combination of these problems that is considered in this paper.

This paper proposed a mathematical model and a new hybrid algorithm for flexible job shop scheduling problem with assembly operations.

Factors influencing management of construction and demolition (C&D) waste within the Iranian context have yet to be investigated. The purpose of this paper is to define and address this knowledge gap, through development of a model to map the associations among the primary factors affecting C&D waste at project, industry and national levels.

A conceptual model is developed based on synthesising the findings of available studies on factors affecting C&D waste with a focus on developing countries. For collecting data, the study drew upon a questionnaire survey of 103 Iranian construction practitioners. The strength and significance of associations among these factors to modify and validate the model were assessed using the structural equation modelling-partial least squares approach.

Major factors affecting C&D waste management and their level of importance were identified at project, industry and national levels. Results clearly showed that the government should review regulations pertaining to C&D waste management and make sure they are implemented properly. The “polluter pays principle” is a useful guide in devising effective policies and regulations for the Iranian context.

This study contributes to the field through presenting the first major study on C&D waste management in Iran. The study provides a picture of C&D waste management status quo in Iran and encapsulates the factors affecting C&D waste management in the Iranian context at different levels within an integrated model. The findings have practical implications for policy makers and construction practitioners in Iran, similar developing economies and foreign firms planning to operate in Iran.

The purpose of this research paper is to provide a model for reporting quality of financial information based on behavior of listed companies in Tehran Stock Exchange which is based on structural equation modeling approaches.

This study uses applied research and postsemi experimental method of data collection in the field of proofing accounting research with deductive–inductive approach. The statistical population of this study includes the sample of 128 listed companies in the Tehran Stock Exchange between 2007 and 2017. The behavioral characteristics of managers (hidden variables) are measured by observable variables of myopia, opportunistic behavior and overconfidence of managers. Reporting quality of financial information is also investigated based on the scores accrued to each company and the announcement published by the Tehran Stock Exchange based on the companies' rating in terms of the quality of reporting and proper notification.

After insuring the acceptable fitness of the measurement pattern and the structure of research in both approaches, structural equations modeling and regression, the results indicate that there is a significant negative relationship between the behavioral characteristics of managers and the reporting quality of financial information.

Accountants have a critical and difficult responsibility of dealing with transactions and presenting them in the form of financial reports that can be used by interest groups to assess the performance of companies. This critical responsibility becomes meaningful when professional and ethical behaviors are the basis for disclosure of financial reporting. Based on the behavioral characteristics of disclosing financial reporting in emerging capital markets such as Iran, this study can be successful in developing new and theoretical literature in this field.