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  • Differences and Similarities between Coronavirus and other Viruses

    Abdul-Al, Mohamed; Abd-Alhameed, Raed A.; Youseffi, Mansour; Qahwaji, Rami S.R.; Shepherd, Simon J. (2020-09-03)
    Coronavirus is the most dangerous virus in the world wide and it can easy spread between people, animals and plants because it is existing on one strand of RNA (Ribonucleic Acid) and it can duplicate faster than any virus. The source of coronavirus is still unknown, but some sources said that it came from seafood market and other sources said that it came from bat and snakes. It starts in Wuhan; China and every day the fatality increases. The symptoms are like a SARS-CoV (acute respiratory syndrome coronavirus)) and MERS-CoV (Middle East Respiratory Syndrome Coronavirus). By using nucleotide sequence of coronavirus from NCBI (National Center for Biotechnology Information) and some programs that ran on Matlab, the results show that there are some differences and similarities between coronavirus and other viruses such as Ebola, Flu-b, Hepatitis B, HIV and Zika especially for DEBs (distinct excluded blocks) program that shows at 5bp (base pair) there is a common with slightly difference between coronavirus “cgggg” and Ebola virus “cgtgg”. The aim from this study is to find a way to help doctors and scientists to stop spreading the coronavirus or to destroy it.
  • Maintenance Scheduling With Delay-time Modelling - An Overview

    Du, Jing-Yu; Li, Jian-Ping; Hu, Yim Fun; Guan, X.; Si, M.; Liu, B. (2021-09-01)
    Effective maintenance is a key for infrastructures’ high operational reliability. The integration of corrective repairs and schedule-based failure preventions has been a mainstream of modern maintenance, and an associated policy-making technique, delay-time modelling, is overviewed in this paper for optimising the maintenance cost-efficiency in different practical scenarios, including imperfect, opportunistic and nested maintenance. A few typical examples of its applications in minimising maintenance operating expenses are discussed in this paper and their results are explained to better demonstrate the benefits of the technique. This work aims to prepare for the future applications of the delay-time modelling in railway maintenance policy making.
  • Leg Length Discrepancy: A Study on In-Shoe Plantar Pressure Distribution

    Abu-Faraj, Z.O.; Abdul-Al, Mohamed; Al-Deeb, R.A. (2015-10-16)
    Leg length discrepancy (LLD) refers to the medical a condition where legs are of different lengths. This condition might affect gait and posture, and may lead to various orthopedic disorders that can have serious repercussions on the individual, be it physiological, psychological, social, economic, or ergonomic. In order to ameliorate the rehabilitation of individuals with LLD, it is imperative to understand the biomechanics of LLD in these individuals. Hence, the current study’s objective is to characterize the plantar pressures of individuals with Leg Length Discrepancy in comparison to those of asymptomatic individuals. This pilot study employs a pedar-x in-shoe pedobarograph system to gather the history of dynamic plantar pressures in one adult male individual with LLD and a representative adult normal volunteer with no diagnosed neurological or musculoskeletal disorders. The obtained results reveal quantifiable differences in the study metrics between the two individuals during walking. These results provide a proof-of-concept for this study, and may serve as diagnostic tools to better rehabilitate individuals with LLD and, thus, provide them with a better quality of life. Future work is to incorporate an extended study of 10 normal individuals versus 10 individuals with LLD, and includes both males and females, as well as both adults and adolescents.
  • Developing multifunctional/smart civil engineering materials to fight viruses

    Ding, S.; Wang, J.; Dong, S.; Ashour, Ashraf F.; Liu, Y.; Qiu, L.; Han, B.; Ou, J. (2022-01)
    The on-going COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS- CoV-2) has posed an extraordinary threat to global public health, wealth and well-being. As the carrier of human life and production, infrastructures need to be upgraded to mitigate and prevent the spread of viral diseases. Developing multifunctional/smart civil engineering materials to fight viruses is a promising approach to achieving this goal. In this perspective, the basic introduction on virus and its structure is provided. Then, the current design principles of antiviral materials and structures are examined. Subsequently, the possibility of developing active/passive antiviral civil engineering materials (including cementitious composites, ceramics, polymers and coatings) is proposed and envisaged. Finally, the future research needs and potential challenges to develop antiviral civil engineering materials are put forward. The proposed strategies to develop multifunctional/smart antiviral civil engineering materials will aid in the construction of smart infrastructures to prevent the spread viruses, thus improving human life and health as well as sustainability of human society.
  • Biometrics in the World of Electronic Borders

    Kumi Kyeremeh, George; Abdul-Al, Mohamed; Abduljabbar, Nabeel; Qahwaji, Rami S.R.; Abd-Alhameed, Raed A. (2021-12-06)
    Recently, the demand for border crossing has increased massively, with the aim to increase the processing and clearance speed at border crossing points (BCP). The attempt to improve travel convenience, Border Cross Point (BCP) output, and national security result in automated border control (ABC) with biometric technology having a major effect on the efficiency, and safety of the control processes. The border processing of BCP can be increased by automating biometric recognition and facilitated by clearance procedures. This paper discussed the two structures of an e-gate (ABC) and a prospective benefit of biometrics to the EU border in terms of accuracy, integrity, robustness, and efficiency. Challenges posed by biometrics in border control systems were identified and recommendations such as multimodal systems and smart systems with AI and machine learning were suggested to assist travelers to cross border points faster.
  • Stem Cell Niche Microenvironment: Review

    Abdul-Al, Mohamed; Kyeremeh, George K.; Saeinasab, M.; Heidari Keshel, S.; Sefat, Farshid (2021-07-28)
    The cornea comprises a pool of self‐regenerating epithelial cells that are crucial to preserving clarity and visibility. Limbal epithelial stem cells (LESCs), which live in a specialized stem cell niche (SCN), are crucial for the survival of the human corneal epithelium. They live at the bottom of the limbal crypts, in a physically enclosed microenvironment with a number of neighboring niche cells. Scientists also simplified features of these diverse microenvironments for more analysis in situ by designing and recreating features of different SCNs. Recent methods for regenerating the corneal epithelium after serious trauma, including burns and allergic assaults, focus mainly on regenerating the LESCs. Mesenchymal stem cells, which can transform into self‐renewing and skeletal tissues, hold immense interest in tissue engineering and innovative medicinal exploration. This review summarizes all types of LESCs, identity and location of the human epithelial stem cells (HESCs), reconstruction of LSCN, and artificial stem cells for self‐renewal.
  • Biomaterials for breast reconstruction: Promises, advances, and challenges

    Abdul-Al, Mohamed; Zaernia, Amir; Sefat, Farshid (2020-11)
    Breast reconstruction is the opportunity that provides the chance of having breast after undergoing surgical removal of the breast tissue due to cancer-related surgery. However, this varies on the stage of the cancer diagnosis and the procedure undertaken. There are many regenerative medicine methods that provide several initiatives and direct solutions to problems such as the development of “bioactive tissue,” which can regenerate adipose tissues with similar normal functions and structures. There have been several studies which have previously explored for the improvement of breast reconstruction including different variations of biomaterials, different fabrication and processing techniques, cells as well as growth factors which enable bioengineers and tissue engineers to reconstruct a suitable breast for patients with breast cancer. Many factors such as shape, proper volume, mechanical properties have been studies but very scattered with not adequate solutions for existing patients worldwide. This review article aims to cover recent advances in biomaterials, which can be used for reconstruction of breasts as well as looking at the various factors that might lead to individuals needing reconstruction and the materials that are available. The focus would be to look at the various biomaterials that are available to use for reconstruction, their properties, and their structural integrity.
  • Performance of Multimodal Biometric Systems Using Face and Fingerprint (Short Survey)

    Abdul-Al, Mohamed; Kyeremeh, George K.; Parchin, Naser O.; Abd-Alhameed, Raed A.; Qahwaji, Rami S.R.; Rodriguez, J. (2021-10-27)
    Biometric authentication is the science and engineering of assessing and evaluating bioinformatics from the human body in order to increase system security by providing reliable and accurate behaviors and classifiers for personal identification and authentication. Its solutions are widely used in industries, governments, and the military. This paper reviews the multimodal biometric systems that integrated both faces and fingerprints as well as shows which one has the best accuracy and hardware complexity with the methods and databases. Several methods have been used in multimodal biometric systems such as KNN (K-Nearest Neighbor), CNN (Convolutional Neural Network), PCA (Principal Component Analysis), and so on. A multimodal biometric system for face and fingerprints that uses an FoM (Figure of Merit) to compare and show between the articles the best accuracy that have used multimodal biometric system face and fingerprints methods. The best performance has been found is 99.43% by using the cascade multimodal method.
  • Comparative Techno-Economic Analysis of Carbon Capture Processes: Pre-Combustion, Post-Combustion, and Oxy-Fuel Combustion Operations

    Kheirinik, M.; Ahmed, Shaab; Rahmanian, Nejat (MDPI, 2021-12-08)
    Evaluation of economic aspects is one of the main milestones that affect taking rapid actions in dealing with GHGs mitigation; in particular, avoiding CO2 emissions from large source points, such as power plants. In the present study, three kinds of capturing solutions for coal power plants as the most common source of electricity generation have been studied from technical and economic standpoints. Aspen HYSYS (ver.11) has been used to simulate the overall processes, calculate the battery limit, and assess required equipment. The Taylor scoring method has been utilized to calculate the costliness indexes, assessing the capital and investment costs of a 230 MW power plant using anthracite coal with and without post-combustion, pre-combustion, and oxy-fuel combustion CO2 capture technologies. Comparing the costs and the levelized cost of electricity, it was found that pre-combustion is more costly, to the extent that the total investment for it is approximately 1.6 times higher than the oxy-fuel process. Finally, post-combustion, in terms of maturity and cost-effectiveness, seems to be more attractive, since the capital cost and indirect costs are less. Most importantly, this can be applied to the existing plants without major disruption to the current operation of the plants.
  • Functional Modelling of Systems with Multiple Operation Modes: Case Study on an Active Spoiler System

    Yildirim, Unal; Campean, I. Felician (SAE International, 2021-11)
    This article presents the application of the Enhanced Sequence Diagram (ESD) for the analysis of the functionality of a system with shape-changing aspects in the context of its multiple operational modes, considering an active rear spoiler as a case study. The article provides new insights on the ESD support for model-based capture and articulation of functional requirements across multiple operation modes of the same system, with appropriate detail on attributes and metrics, and the alignment of these attributes and metrics in line with the concept of time through scope lines. The article also provides a comprehensive argument and discussion, exemplified based on the case study, for the support that the ESD provides for early systems functional and architecture analysis, within the context of a broader model-based Failure Mode Analysis methodology.
  • A reliability inspired strategy for intelligent performance management with predictive driver behaviour: A case study for a diesel particulate filter

    Doikin, Aleksandr; Campean, I. Felician; Priest, Martin; Lin, C.; Angiolini, E. (2021-08)
    The increase availability of operational data from the fleets of cars in the field offers opportunities to deploy machine learning to identify patterns of driver behaviour. This provides contextual intelligence insight that can be used to design strategies for online optimisation of the vehicle performance, including compliance with stringent legislation. This paper illustrates this approach with a case study for a Diesel Particulate Filter, where machine learning deployed to real world automotive data is used in conjunction with a reliability inspired performance modelling paradigm to design a strategy to enhance operational performance based on predictive driver behaviour. The model-in-the-loop simulation of the proposed strategy on a fleet of vehicles showed significant improvement compared to the base strategy, demonstrating the value of the approach.
  • A Framework to Handle Uncertainties of Machine Learning Models in Compliance with ISO 26262

    Vasudevan, Vinod; Abdullatif, Amr; Kabir, Sohag; Campean, I. Felician (2022)
    Assuring safety and thereby certifying is a key challenge of many kinds of Machine Learning (ML) Models. ML is one of the most widely used technological solutions to automate complex tasks such as autonomous driving, traffic sign recognition, lane keep assist etc. The application of ML is making a significant contributions in the automotive industry, it introduces concerns related to the safety and security of these systems. ML models should be robust and reliable throughout and prove their trustworthiness in all use cases associated with vehicle operation. Proving confidence in the safety and security of ML-based systems and there by giving assurance to regulators, the certification authorities, and other stakeholders is an important task. This paper proposes a framework to handle uncertainties of ML model to improve the safety level and thereby certify the ML Models in the automotive industry.
  • A Model-Based Reliability Analysis Method Using Bayesian Network

    Kabir, Sohag; Campean, I. Felician (Springer, 2022)
    Bayesian Network (BN)-based methods are increasingly used in system reliability analysis. While BNs enable to perform multiple analyses based on a single model, the construction of robust BN models relies either on the conversion from other intermediate system model structures or direct analyst-led development based on experts input, both requiring significant human effort. This article proposes an architecture model-based approach for the direct generation of a BN model. Given the architectural model of a system, a systematic bottom-up approach is suggested, underpinned by failure behaviour models of components composed based on interaction models to create a system-level failure behaviour model. Interoperability and reusability of models are supported by a library of component failure models. The approach was illustrated with application to a case study of a steam boiler system.
  • Driver Behaviour Clustering Using Discrete PDFs and Modified Markov Algorithm

    Kartashev, K.; Doikin, A.; Campean, I. Felician; Uglanov, A.; Abdullatif, A.; Zhang, Q.; Angiolini, E.; aiR-FORCE project, funded as Proof of Concept by the Institute of Digital Engineering. (Springer, 2022)
    This paper presents a novel approach for probabilistic clustering, motivated by a real-world problem of modelling driving behaviour. The main aim is to establish clusters of drivers with similar journey behaviour, based on a large sample of historic journeys data. The proposed approach is to establish similarity between driving behaviours by using the Kullback-Leibler and Jensen-Shannon divergence metrics based on empirical multi-dimensional probability density functions. A graph-clustering algorithm is proposed based on modifications of the Markov Cluster algorithm. The paper provides a complete mathematical formulation, details of the algorithms and their implementation in Python, and case study validation based on real-world data.
  • Driver Behaviour Modelling: Travel Prediction Using Probability Density Function

    Uglanov, A.; Kartashev, K.; Campean, I. Felician; Doikin, A.; Abdullatif, A.; Angiolini, E.; Lin, C.; Zhang, Q.; aiR-FORCE project, funded as Proof of Concept by the Institute of Digital Engineering (Springer, 2022)
    This paper outlines the current challenges of driver behaviour modelling for real-world applications and presents the novel method to identify the pattern of usage to predict upcoming journeys in probability sense. The primary aim is to establish similarity between observed behaviour of drivers resulting in the ability to cluster them and deploy control strategies based on contextual intelligence and datadriven approach. The proposed approach uses the probability density function (PDF) driven by kernel density estimation (KDE) as a probabilistic approach to predict the type of the upcoming journey, expressed as duration and distance. Using the proposed method, the mathematical formulation and programming algorithm procedure have been indicated in detail, while the case study examples with the data visualisation are given for algorithm validation in simulation.
  • Mastering continuous improvement (CI): the roles and competences of mid-level management and their impact on the organisation’s CI capability

    Fannon, S.R.; Munive-Hernandez, J. Eduardo; Campean, I. Felician (2021-11-17)
    Purpose – This paper establishes a comprehensive basis for understanding the roles and competences of midlevel management and their influence on the effectiveness of continuous improvement (CI) capability within an organisation. Design/methodology/approach – This research builds upon the hypothesis that methods alone do not lead to successful CI capability development. It focuses on the role of mid-level management in driving a CI environment that underpins the effectiveness of CI capability. A reference model for the CI environment is synthesised based on critical literature review, integrating CI culture, CI enablers and CI leadership elements.A comprehensive framework is introduced to define CI leadership roles and competence indicators. A quantitative benchmarking study involving structured interviews with 15 UK organisations was undertaken to collect evidence for a causal relationship between CI leadership competences and CI capability. Findings – Analysis of the benchmarking data provides clear evidence of the causal relationship between the CI leadership competences of mid-level management and CI capability of the organisation. Given that the empirical study was structured on the basis of the CI leadership roles and competences framework introduced in this paper, this also provides validation for the proposed framework and the CI environment model. Practical implications – The evidence-based knowledge of the positive relationship between the midmanagement CI leadership competences and the effectiveness of the CI capability informs strategic organisational development interventions towards enhancing CI capability and effectiveness, ultimately underpinning productivity enhancement and sustainability. The framework for mid-level management CI leadership roles, responsibilities and competences introduced in this paper and grounded in underpinning work undertaken within a large automotive Original Equipment Manufacturer (OEM), can be adapted by any organisation. The CI environment reference model should provide a comprehensive support for strategists to communicate the framework for CI capability improvement within an organisation, to enhance acceptability and adherence to improvement actions. Originality/value – This research proves for the first time the significance of the causal relationship between the CI leadership competences and the effectiveness of the CI capability within an organisation, thus filling an important gap between established previous work, focussing on the role of mid-level management on one side and practitioner and team level roles, methodologies and tools. The proposed CI environment model is a theoretical contribution with reference value for both practice and further studies. The comprehensive framework for mid-level management CI leadership roles, responsibilities and competences introduced in this paper provides sound foundation to deliver CI leadership in the workplace.
  • Message from A-MOST 2020 Chairs

    Hierons, Rob; Nunez, Manuel; Pretschner, Alexander; Lefticaru, Raluca (2020-10)
    Welcome to the 16th edition of the Advances in Model-Based Testing Workshop (A-MOST 2020) held on March 23rd, 2020 in Porto as part of ICST 2020, the IEEE International Conference on Software Testing, Verification and Validation.
  • Message from the A-MOST 2021 Workshop Chairs

    Lefticaru, Raluca; Lorber, F.; Turker, U.C. (2021-04)
    We are pleased to welcome you to the 17th edition of the Advances in Model-Based Testing Workshop (A-MOST 2021), collocated with the IEEE International Conference on Software Testing, Verification and Validation (ICST 2021).
  • Spiking Neural P Systems Simulation and Verification

    Lefticaru, Raluca; Gheorghe, Marian; Konur, Savas; Niculescu, I.M.; Adorna, H.N. (IEEE, 2021-03-27)
    Spiking Neural (SN) P systems is a particular class of P systems that abstracts and applies ideas from neurobiology. Various aspects, representations and features have been studied extensively, but the tool support for modelling and analysing such systems is relatively limited. In this paper, we present a methodology that maps some classes of SN P systems to the equivalent kernel P system representations, which allows analysing SN P system dynamics using the kPWORKBENCH tool. We illustrate the applicability of our approach in some case studies, including an example system from synthetic biology.
  • Mutation Testing for RoboChart

    Hierons, R.M.; Gazda, M.; Gomez-Abajo, P.; Lefticaru, Raluca; Merayo, M.G. (Springer International Publishing, 2021-07)
    This chapter describes a test-generation approach that takes as input a model S of the expected behavior of a robotic system and seeds faults into S, leading to a set of mutants of S. Given a mutant M of S, we check whether M is a valid implementation of S, and, if it is not, we find a test case that demonstrates this: a test case that reveals the seeded fault. In order to automate this approach, we used the Wodel tool to seed faults and a combination of two tools, RoboTool and FDR, to generate tests that detect the seeded faults. The result is an overall test-generation technique that can be automated and that derives test cases that are guaranteed to find certain faults.

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