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Traditional dynamic and flutter analysis demands a detailed finite element model of the aircraft in terms of its mass and stiffness distribution. However, in absence of these details, modal parameters obtained from experimental tests can be used to predict the flutter characteristics of an aircraft. The purpose of this paper is to develop an improved and reliable method to predict the flutter characteristics of an aircraft structure of unknown configuration under an anticipated aerodynamic loading using software such as MSC Nastran and experimental modal parameters (such as mode shapes, natural frequencies and damping) from ground vibration tests.

A finite element model with nodes representing the test points on the aircraft is created with appropriate boundary constraints. A direct matrix abstraction program has been written for NASTRAN software that carries out a normal modes analysis and replaces the mass normalized eigenvalues and vectors with the experimentally obtained modal parameters. The flutter analysis proceeds with the solution of the flutter equation in the flutter module of NASTRAN.

The method has been evaluated for a light composite aircraft and its results have been compared with flight flutter tests and the flutter speeds obtained from the finite element model with actual stiffness and mass distributions of the aircraft.

The methodology developed helps in the realistic prediction of flutter characteristics of a structure with known geometric configuration and does not need material properties, mass or stiffness distributions. However, experimental modal parameters of each configuration of the aircraft are required for flutter speed estimation.

The proposed methodology requires experimental modal parameters of each configuration of the aircraft for flutter speed estimation.

The paper shows that an effective method to predict flutter characteristics using modal parameters from ground vibration tests has been developed.

The purpose of this paper is to present a quick inspection method based on the post-flight data to examine static aeroelastic behavior for transport aircraft subjected to instantaneous high g-loads.

In the present study, the numerical approach of static aeroelasticity and two verified cases will be presented. The non-linear unsteady aerodynamic models are established through flight data mining and the fuzzy-logic modeling of artificial intelligence techniques based on post-flight data. The first and second derivatives of flight dynamic and static aeroelastic behaviors, respectively, are then estimated by using these aerodynamic models.

The flight dynamic and static aeroelastic behaviors with instantaneous high g-load for the two transports will be analyzed and make a comparison study. The circumstance of turbulence encounter of the new twin-jet is much serious than that of four-jet transport aircraft, but the characteristic of stability and controllability for the new twin-jet is better than those of the four-jet transport aircraft; the new twin-jet transport is also shown to have very small aeroelastic effects. The static aeroelastic behaviors for the two different types can be assessed by using this method.

As the present study uses the flight data stored in a quick access recorder, an intrusive structural inspection of the post-flight can be avoided. A tentative conclusion is to prove that this method can be adapted to examine the static aeroelastic effects for transport aircraft of different weights, different sizes and different service years in tracking static aeroelastic behavior of existing different types of aircraft. In future research, one can consider to have more issues of other types of aircraft with high composite structure weight.

This method can be used to assist airlines to monitor the variations of flight dynamic and static aeroelastic behaviors as a complementary tool for management to improve aviation safety, operation and operational efficiency.

Ground vibration testing (GVT) results can be used as system parameters for predicting flutter, which is essential for aeroelastic clearance. This paper aims to compute GVT-based flutter in time domain, using unsteady air loads by matrix polynomial approximations.

The experimental parameters, namely, frequencies and mode shapes are interpolated to build an equivalent finite element model. The unsteady aerodynamic forces extracted from MSC NASTRAN are approximated using matrix polynomial approximations. The system matrices are condensed to the required shaker location points to build an aeroelastic reduced order state space model in SIMULINK.

The computed aerodynamic forces are successfully reduced to few input locations (optimal) for flutter simulation on unknown structural system (where stiffness and mass are not known) through a case study. It is demonstrated that GVT data and the computed unsteady aerodynamic forces of a system are adequate to represent its aeroelastic behaviour.

Airforce of every nation continuously upgrades its fleet with advanced weapon systems (stores), which demands aeroelastic flutter clearance. As the original equipment manufacturers does not provide the design data (stiffness and mass) to its customers, a new methodology to build an aeroelastic system of unknown aircraft is devised.

A hybrid approach is proposed, involving GVT data to build an aeroelastic state space system, using rationally approximated air loads (matrix polynomial approximations) computed on a virtual FE model for ground flutter simulation.

The growing trends in the usage of hand held devices necessitate the need to design them with low power consumption and less area design. Besides, information security is gaining enormous importance in information transmission and data storage technology. In addition, today’s technology world is connected, communicated and controlled via the Internet of Things (IoT). In many applications, the most standard and widely used cryptography algorithm for providing security is Advanced Encryption Standard (AES). This paper aims to design an efficient model of AES cryptography for low power and less area.

First, the main issues related to less area and low power consumption in the AES encryption core are addressed. To implement optimized AES core, the authors proposed optimized multiplicative inverse, affine transforms and Xtime multipliers functions, which are the core function of AES’s core. In addition, to achieve the high throughput, it uses the multistage pipeline and resource reuse architectures for SBox and Mixcolumn of AES.

The results of optimized AES architecture have revealed that the multistage pipe line and resource sharing are optimal design model in Field Programmable Gate Array (FPGA) implementation. It could provide high security with low power and area for IoT and wireless sensors networks.

This proposed optimized modified architecture has been implemented in FPGA to calculate the power, area and delay parameters. This multistage pipeline and resource sharing have promised to minimize the area and power.

In the cloud-computing environment, privacy preservation and enabling security to the cloud data is a crucial and demanding task. In both the commercial and academic world, the privacy of important and sensitive data needs to be safeguarded from unauthorized users to improve its security. Therefore, several key generations, encryption and decryption algorithms are developed for data privacy preservation in the cloud environment. Still, the outsourced data remains with the problems like minimum data security, time consumption and increased computational complexity. The purpose of this research study is to develop an effective cryptosystem algorithm to secure the outsourced data with minimum computational complexity.

A new cryptosystem algorithm is proposed in this paper to address the above-mentioned concerns. The introduced cryptosystem algorithm has combined the ElGamal algorithm and hyperchaotic sequence, which effectively encrypts the outsourced data and diminishes the computational complexity of the system.

In the resulting section, the proposed improved ElGamal cryptosystem (IEC) algorithm performance is validated using the performance metrics like encryption time, execution time, decryption time and key generation comparison time. The IEC algorithm approximately reduced 0.08–1.786 ms of encryption and decryption time compared to the existing model: secure data deletion and verification.

The IEC algorithm significantly enhances the data security in cloud environments by increasing the power of key pairs. In this manuscript, the conventional ElGamal algorithm is integrated with the pseudorandom sequences for a pseudorandom key generation for improving the outsourced cloud data security.

Currently, in the health-care sector, information security and privacy are increasingly important issues. The improvement in information security is highlighted in adopting digital patient records based on regulation, providers’ consolidation, and the growing need to exchange information among patients, providers, and payers.

Big data on health care are likely to improve patient outcomes, predict epidemic outbreaks, gain valuable insights, prevent diseases, reduce health-care costs and improve analysis of the quality of life.

In this paper, the big data analytics-based cybersecurity framework has been proposed for security and privacy across health-care applications. It is vital to identify the limitations of existing solutions for future research to ensure a trustworthy big data environment. Furthermore, electronic health records (EHR) could potentially be shared by various users to increase the quality of health-care services. This leads to significant issues of privacy that need to be addressed to implement the EHR.

This framework combines several technical mechanisms and environmental controls and is shown to be enough to adequately pay attention to common threats to network security.

Quality is considered one of the essential competitive priorities for managing the Small, Medium Electronics Enterprises (SMEE) market in China; here, the most critical competing priorities and precondition for sector survival and growth are analysed. Instead, small and medium-sized enterprises are significantly disadvantaged and limited to validation capital.

Quality results are controlled by continuous measurement, irrespective of the product form, as a fundamental issue among firms. Hence, it enables the electronics markets to deal with various aspects of quality programs with effective deployment of IoT-assisted identify management (IIDM) model.

It enables the electronics markets to deal with various aspects of quality programs with effective deployment of IIDM model available to them without wasting in unimportant and non-productive areas. IIDM makes it essential for SMEEs to analyze total quality management (TQM) towards better understanding the relationship between internal structures and external ones to achieve better quality results.

Analyzes TQM towards better understanding shows the relationship between internal and external structures to achieve better quality results.

The purpose of this paper is to use a neoclassical factor pricing approach to carbon emissions, and consider whether the productivity of carbon emissions differs in Sub-Saharan Africa relative to the rest of the world.

Allowing for possible cross-country dependency and correlation in the effects of the factors of production on the level of gross domestic product per capita, the authors estimate the parameters of a cross-country net production function with carbon emissions as an input.

While there is a “Sub-Saharan Africa effect” whereby carbon emissions are less productive as an input relative to the rest of the world; practically it is equally productive relative to all other countries suggesting a unfavorable distributional impact if Sub-Saharan Africa were to implement carbon emissions reductions consistent with the Kyoto Protocol.

If global warming is not anthropogenic or caused by carbon emissions, the parameter estimates do not inform an optimal and equitable carbon emissions policy based upon Sub-Saharan Africans reducing their short-run living standards.

Fair and equitable global carbon emissions policies should aim to treat Sub-Saharan African countries in proportion to their carbon emissions, and not unfairly impose emissions constraints on them equal to that of countries in the industrialized west.

As Sub-Saharan Africa has a disproportionate number of individuals in the world living on less than one dollar a day, the results suggest “Black Africa” may not be able to afford being a “Green Africa.”

The results are the first to quantify the effects of carbon emissions restrictions on output and their distributional implications for Sub-Saharan Africa.

The main goal of this paper is to study the cloud service discovery mechanisms. In this paper, the discovery mechanisms are ranked in three major classes: centralized, decentralized, and hybrid. Moreover, in this classification, the peer-to-peer (P2P) and agent-based mechanisms are considered the parts of the decentralized mechanism. This paper investigates the main improvements in these three main categories and outlines new challenges. Moreover, the other goals are analyzing the current challenges in a range of problem areas related to cloud discovery mechanisms and summarizing the discussed service discovery techniques.

Systematic literature review (SLR) is utilized to detect, evaluate and combine findings from related investigations. The SLR consists of two key stages in this paper: question formalization and article selection processes. The latter includes three steps: automated search, article selection and analysis of publication. These investigations solved one or more service discovery research issues and performed a general study of an experimental examination on cloud service discovery challenges.

In this paper, a parametric comparison of the discovery methods is suggested. It also demonstrates future directions and research opportunities for cloud service discovery. This survey will help researchers understand the advances made in cloud service discovery directly. Furthermore, the performed evaluations have shown that some criteria such as security, robustness and reliability attained low attention in the previous studies. The results also showed that the number of cloud service discovery–related articles rose significantly in 2020.

This research aimed to be comprehensive, but there were some constraints. The limitations that the authors have faced in this article are divided into three parts. Articles in which service discovery was not the primary purpose and their title did not include the related terms to cloud service discovery were also removed. Also, non-English articles and conference papers have not been reviewed. Besides, the local articles have not been considered.

One of the most critical cloud computing topics is finding appropriate services depending on consumer demand in real-world scenarios. Effective discovery, finding and selection of relevant services are necessary to gain the best efficiency. Practitioners can thus readily understand various perspectives relevant to cloud service discovery mechanisms. This paper's findings will also benefit academicians and provide insights into future study areas in this field. Besides, the drawbacks and benefits of the analyzed mechanisms have been analyzed, which causes the development of more efficient and practical mechanisms for service discovery in cloud environments in the future.

This survey will assist academics and practical professionals directly in their understanding of developments in service discovery mechanisms. It is a unique paper investigating the current and important cloud discovery methods based on a logical categorization to the best of the authors’ knowledge.

This study examines how customer-linking capabilities, the flexibility of back-end management systems, strategic agility and the perceived absorptive capacity of e-commerce influence the organizational performance of online sellers.

A performance model based on the adaptive structuration theory was employed to test the hypotheses using structural equation modeling (SEM) on a dataset of 325 samples with acceptable responses.

The results reveal that the customer-linking capability and flexibility of back-end management systems, strategic agility and assimilation of e-commerce activity significantly influence organizational performance in a virtual environment.

The agility of online sellers is becoming increasingly important, and from the perspective of adaptive structuration theory, back-end management systems have been built to manage buyer orders for online sellers.