Topic Review
Networked Control System
An NCS consists of control loops joined through communication networks in which both the control signal and the feedback signal are exchanged between the system and the controller.
  • 1070
  • 01 Apr 2021
Topic Review
Deep Reinforcement Learning Applications
Deep Reinforcement Learning (DRL) combines Reinforcement Learning and Deep Learning. It is more capable of learning from raw sensors or images as input, enabling end-to-end learning, which opens up more applications in robotics, video games, NLP, computer vision, healthcare, and more. A milestone in value-based DRL is employing Deep Q-Networks (DQN) to play Atari games by Google DeepMindin 2013.
  • 979
  • 02 Aug 2021
Topic Review
Geant4-DNA Modeling of Water Radiolysis
In this work, we use the next sub-volume method (NSM) to investigate the possibility of using the compartment-based (“on-lattice”) model to simulate water radiolysis.
  • 208
  • 24 Jun 2021
Topic Review
Green Wireless Sensor Networks
The issue of energy balancing in Wireless Sensor Networks is a pivotal one, crucial in their deployment. This problem can be subdivided in three areas: (i) energy conservation techniques, usually implying minimizing the cost of communication at the nodes since it is known that the radio is the biggest consumer of the available energy; (ii) energy-harvesting techniques, converting energy from not full-time available environmental sources and usually storing it; and (iii) energy transfer techniques, sharing energy resources from one node (either specialized or not) to another one.
  • 199
  • 01 Jul 2021
Topic Review
Data-Driven Production Logistics
A data-driven approach in production logistics is adopted as a response to challenges such as low visibility and system rigidity. within data-driven production logistics, data is the backbone of the system and all the components are bound together with data. Any decision is made based on data rather than intuition or even experience. All production logistics related activities are supported by data, which is constantly collected from data sources such as machines, human resources, sensors, actuators, etc. A data-driven approach facilitates transition towards a smart, autonomous production logistics system.
  • 180
  • 28 Apr 2021
Topic Review
Wireless Sensor Networks
Wireless sensor networks (WSNs) are spatially scattered networks equipped with an extensive number of nodes to check and record different ecological states such as humidity, temperature, pressure, and lightning states. WSN network provides different services to a client such as monitoring, detection, and runtime decision-making against events occurrence.
  • 167
  • 02 Nov 2021
Topic Review
Industry 4.0 and Smart Data
The digital transformation of manufacturing firms, in addition to making operations more efficient, offers important opportunities both to promote the transition to a circular economy and to experiment with new techniques for designing smarter and greener products.
  • 166
  • 18 Sep 2021
Topic Review
Blockchain Acceptance Models
Blockchain is a promising breakthrough technology that is highly applicable in manifold sectors. The adoption of blockchain technology is accompanied by a range of issues and challenges that make its implementation complicated. To facilitate the successful implementation of blockchain technology, several blockchain adoption frameworks have been developed. However, selecting the appropriate framework based on the conformity of its features with the business sector may be challenging for decision-makers. 
  • 163
  • 18 Feb 2022
Topic Review
Cybersickness
Overcoming cybersickness remains elusive for VR developers and practitioners. Symptoms such as discomfort, headache, eye strain, and dizziness during and after VR experiences can be associated with cybersickness. Cybersickness is often compared with motion sickness and simulator sickness. However, cybersickness is categorized as a subset of motion sickness as cybersickness is considered a form of visually induced motion sickness (VIMS) (Weech et al., 2019) and shown to be three times more serious than simulator sickness (Stanney et al., 1997).
  • 147
  • 27 Apr 2021
Topic Review
Machine Learning in T- and B-Cell Epitope Prediction
An antigenic determinant (AD) is a portion of an antigen molecule known as an epitope that is recognized by the human immune system, specifically by antibodies or T and B cells. Recognition of epitopes is considered important in EBPV design to contain pandemics, epidemics, and endemics due to the outbreak of infectious diseases. To design an effective and viable EBPV against different strains of a pathogen, it is important to identify the putative T- and B-cell epitopes. Using the wet-lab experimental approach to identify these epitopes is time-consuming and costly because the experimental screening of a vast number of potential epitope candidates is required. Fortunately, various available machine learning (ML)-based prediction methods have reduced the burden related to the epitope mapping process by decreasing the potential epitope candidate list for experimental trials. Moreover, these methods are also cost-effective, scalable, and fast. 
  • 138
  • 16 Feb 2022
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