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Article
Publication date: 1 January 2006

Wenchao Tian, Jianyuan Jia, Guiming Chen and Guangyan Chen

The “Snap back” problem of the micro‐cantilever remains one of the dominant failure mechanisms in the Micro Electro‐mechanical System (MEMS). By analyzing the Hamaker micro…

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Abstract

The “Snap back” problem of the micro‐cantilever remains one of the dominant failure mechanisms in the Micro Electro‐mechanical System (MEMS). By analyzing the Hamaker micro continuum medium and solid physics principle, the consistency model of Wigner‐Seitz (W‐S) continuum medium is presented. The gap revision coefficients of the body with the face‐centered cubic structure are derived, which include the attractive force and the repulsive one. The adhesion model of the 500 µ m X 1 µ m silicon micro‐cantilever coated by Au is established. The micro‐cantilever static relationship between the elastic force and the adhesion force is discussed. The reason of the microcantilever “snap back” problem, an instable balanced point, is discovered. Increasing the rigidity of the micro‐cantilever, a method to avoid the micro‐cantilever “snap back” to happen, is put forward, which improves MEMS structure design and enhances MEMS reliability.

Details

Multidiscipline Modeling in Materials and Structures, vol. 2 no. 1
Type: Research Article
ISSN: 1573-6105

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Article
Publication date: 5 January 2010

Ping He

The purpose of this paper is to make objective descriptions on various money‐laundering techniques and to put forward countermeasures in order to combat money laundering more…

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Abstract

Purpose

The purpose of this paper is to make objective descriptions on various money‐laundering techniques and to put forward countermeasures in order to combat money laundering more effectively and efficiently.

Design/methodology/approach

This paper based on 20 simplified money‐laundering cases, describes various money‐laundering techniques, analyses the reasons why these methods prevail, and points out the future efforts to be made in the fight against money laundering.

Findings

As usual, the ways of money laundering include cash smuggling, making use of banks or insurance company, or making use of shell‐company or front‐company. Nowadays, criminals also turn to real estate, lottery, international trade, offshore company to launder money. Sometimes lawyers, accountants are exploited by money launderers. With the wide use of electronic money and internet, criminals prefer to launder money through non‐face to face transactions. The fight against money laundering is the fight between justice and evil. It is of great importance to pierce the secret veil of money laundering so that we can combat money laundering more effectively and efficiently.

Originality/value

This paper prevents a comprehensive description of, and comments on, various money‐laundering techniques and future efforts to be made in the fight against money laundering, which would be beneficial to policy makers, enforcement authorities, and judicial professionals.

Details

Journal of Money Laundering Control, vol. 13 no. 1
Type: Research Article
ISSN: 1368-5201

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Article
Publication date: 22 June 2022

Suvarna Abhijit Patil and Prasad Kishor Gokhale

With the advent of AI-federated technologies, it is feasible to perform complex tasks in industrial Internet of Things (IIoT) environment by enhancing throughput of the network…

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Abstract

Purpose

With the advent of AI-federated technologies, it is feasible to perform complex tasks in industrial Internet of Things (IIoT) environment by enhancing throughput of the network and by reducing the latency of transmitted data. The communications in IIoT and Industry 4.0 requires handshaking of multiple technologies for supporting heterogeneous networks and diverse protocols. IIoT applications may gather and analyse sensor data, allowing operators to monitor and manage production systems, resulting in considerable performance gains in automated processes. All IIoT applications are responsible for generating a vast set of data based on diverse characteristics. To obtain an optimum throughput in an IIoT environment requires efficiently processing of IIoT applications over communication channels. Because computing resources in the IIoT are limited, equitable resource allocation with the least amount of delay is the need of the IIoT applications. Although some existing scheduling strategies address delay concerns, faster transmission of data and optimal throughput should also be addressed along with the handling of transmission delay. Hence, this study aims to focus on a fair mechanism to handle throughput, transmission delay and faster transmission of data. The proposed work provides a link-scheduling algorithm termed as delay-aware resource allocation that allocates computing resources to computational-sensitive tasks by reducing overall latency and by increasing the overall throughput of the network. First of all, a multi-hop delay model is developed with multistep delay prediction using AI-federated neural network long–short-term memory (LSTM), which serves as a foundation for future design. Then, link-scheduling algorithm is designed for data routing in an efficient manner. The extensive experimental results reveal that the average end-to-end delay by considering processing, propagation, queueing and transmission delays is minimized with the proposed strategy. Experiments show that advances in machine learning have led to developing a smart, collaborative link scheduling algorithm for fairness-driven resource allocation with minimal delay and optimal throughput. The prediction performance of AI-federated LSTM is compared with the existing approaches and it outperforms over other techniques by achieving 98.2% accuracy.

Design/methodology/approach

With an increase of IoT devices, the demand for more IoT gateways has increased, which increases the cost of network infrastructure. As a result, the proposed system uses low-cost intermediate gateways in this study. Each gateway may use a different communication technology for data transmission within an IoT network. As a result, gateways are heterogeneous, with hardware support limited to the technologies associated with the wireless sensor networks. Data communication fairness at each gateway is achieved in an IoT network by considering dynamic IoT traffic and link-scheduling problems to achieve effective resource allocation in an IoT network. The two-phased solution is provided to solve these problems for improved data communication in heterogeneous networks achieving fairness. In the first phase, traffic is predicted using the LSTM network model to predict the dynamic traffic. In the second phase, efficient link selection per technology and link scheduling are achieved based on predicted load, the distance between gateways, link capacity and time required as per different technologies supported such as Bluetooth, Wi-Fi and Zigbee. It enhances data transmission fairness for all gateways, resulting in more data transmission achieving maximum throughput. Our proposed approach outperforms by achieving maximum network throughput, and less packet delay is demonstrated using simulation.

Findings

Our proposed approach outperforms by achieving maximum network throughput, and less packet delay is demonstrated using simulation. It also shows that AI- and IoT-federated devices can communicate seamlessly over IoT networks in Industry 4.0.

Originality/value

The concept is a part of the original research work and can be adopted by Industry 4.0 for easy and seamless connectivity of AI and IoT-federated devices.

Details

International Journal of Pervasive Computing and Communications, vol. 20 no. 4
Type: Research Article
ISSN: 1742-7371

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