• QEAM: An Approximate Algorithm Using P Systems with Active Membranes

      Zhang, G.; Chen, J.; Gheorghe, Marian; Ipate, F.; Wang, X. (2015)
      This paper proposes an approximate optimization approach, called QEAM, which combines a P system with active membranes and a quantum-inspired evolutionary algorithm. QEAM uses the hierarchical arrangement of the compartments and developmental rules of a P system with active membranes, and the objects consisting of quantum-inspired bit individuals, a probabilistic observation and the evolutionary rules designed with quantum-inspired gates to specify the membrane algorithms. A large number of experiments carried out on benchmark instances of satisfiability problem show that QEAM outperforms QEPS (quantum-inspired evolutionary algorithm based on P systems) and its counterpart quantum-inspired evolutionary algorithm.
    • Qualitative and quantitative analysis of systems and synthetic biology constructs using P systems

      Konur, Savas; Gheorghe, Marian; Dragomir, C.; Mierla, L.M.; Ipate, F.; Krasnogor, N. (2015-01-16)
      Computational models are perceived as an attractive alternative to mathematical models (e.g., ordinary differential equations). These models incorporate a set of methods for specifying, modeling, testing, and simulating biological systems. In addition, they can be analyzed using algorithmic techniques (e.g., formal verification). This paper shows how formal verification is utilized in systems and synthetic biology through qualitative vs quantitative analysis. Here, we choose two well-known case studies: quorum sensing in P. aeruginosas and pulse generator. The paper reports verification analysis of two systems carried out using some model checking tools, integrated to the Infobiotics Workbench platform, where system models are based on stochastic P systems.
    • A quantitative method for evaluating the photoreactivation of ultraviolet damaged microorganisms.

      Beggs, Clive B. (2002)
      The lethal effect of ultraviolet (UV) light on microorganisms is well known and many studies have been undertaken into the effects of UV induced damage. Most of this work has been experimental; by comparison relatively little theoretical work has been undertaken to analyse the kinetics of the UV inactivation process, or to develop quantitative methodologies to support the experimental work. This paper presents a new and simple model for quantifying the photolysis rate. A theoretical study is also presented in this paper which quantifies photolysis rates for E. coli O26 and E. coli O157:H7. This study uses experimental data collected by Tosa and Hirata, and reveals the photolysis rate for E. coil O26 during the UV irradiation process to be 4.69 x 10(-3) m2 J(-1). By comparison, E. coli O157:H7 is much more susceptible to UV induced damage than E. coli O26, having a photolysis constant of only 2.09 x 10(-3) m2 J(-1).
    • Quantum mechanics on profinite groups and partial order

      Vourdas, Apostolos (2013)
      Inverse limits and profinite groups are used in a quantum mechanical context. Two cases are considered: a quantum system with positions in the profinite group Z(p) and momenta in the group Q(p)/Z(p), and a quantum system with positions in the profinite group (Z) over cap and momenta in the group Q/Z. The corresponding Schwatz-Bruhat spaces of wavefunctions and the Heisenberg-Weyl groups are discussed. The sets of subsystems of these systems are studied from the point of view of partial order theory. It is shown that they are directed-complete partial orders. It is also shown that they are topological spaces with T-0-topologies, and this is used to define continuity of various physical quantities. The physical meaning of profinite groups, non-Archimedean metrics, partial orders and T-0-topologies, in a quantum mechanical context, is discussed.
    • Quantum probabilities as Dempster-Shafer probabilities in the lattice of subspaces.

      Vourdas, Apostolos (2014-08)
      The orthocomplemented modular lattice of subspaces L[H(d)] , of a quantum system with d-dimensional Hilbert space H(d), is considered. A generalized additivity relation which holds for Kolmogorov probabilities is violated by quantum probabilities in the full lattice L[H(d)] (it is only valid within the Boolean subalgebras of L[H(d)] ). This suggests the use of more general (than Kolmogorov) probability theories, and here the Dempster-Shafer probability theory is adopted. An operator D(H1,H2) , which quantifies deviations from Kolmogorov probability theory is introduced, and it is shown to be intimately related to the commutator of the projectors P(H1),P(H2) , to the subspaces H 1, H 2. As an application, it is shown that the proof of the inequalities of Clauser, Horne, Shimony, and Holt for a system of two spin 1/2 particles is valid for Kolmogorov probabilities, but it is not valid for Dempster-Shafer probabilities. The violation of these inequalities in experiments supports the interpretation of quantum probabilities as Dempster-Shafer probabilities.
    • Quantum ReLU activation for Convolutional Neural Networks to improve diagnosis of Parkinson’s disease and COVID-19

      Parisi, Luca; Neagu, Daniel; Ma, R.; Campean, I. Felician (Elsevier, 2022-01)
      This study introduces a quantum-inspired computational paradigm to address the unresolved problem of Convolutional Neural Networks (CNNs) using the Rectified Linear Unit (ReLU) activation function (AF), i.e., the ‘dying ReLU’. This problem impacts the accuracy and the reliability in image classification tasks for critical applications, such as in healthcare. The proposed approach builds on the classical ReLU and Leaky ReLU, applying the quantum principles of entanglement and superposition at a computational level to derive two novel AFs, respectively the ‘Quantum ReLU’ (QReLU) and the ‘modified-QReLU’ (m-QReLU). The proposed AFs were validated when coupled with a CNN using seven image datasets on classification tasks involving the detection of COVID-19 and Parkinson’s Disease (PD). The out-of-sample/test classification accuracy and reliability (precision, recall and F1-score) of the CNN were compared against those of the same classifier when using nine classical AFs, including ReLU-based variations. Findings indicate higher accuracy and reliability for the CNN when using either QReLU or m-QReLU on five of the seven datasets evaluated. Whilst retaining the best classification accuracy and reliability for handwritten digits recognition on the MNIST dataset (ACC = 99%, F1-score = 99%), avoiding the ‘dying ReLU’ problem via the proposed quantum AFs improved recognition of PD-related patterns from spiral drawings with the QReLU especially, which achieved the highest classification accuracy and reliability (ACC = 92%, F1-score = 93%). Therefore, with these increased accuracy and reliability, QReLU and m-QReLU can aid critical image classification tasks, such as diagnoses of COVID-19 and PD.
    • Quince seed mucilage-based scaffold as a smart biological substrate to mimic mechanobiological behavior of skin and promote fibroblasts proliferation and h-ASCs differentiation into keratinocytes

      Izadyari Aghmiuni, A.; Heidari Keshel, S.; Sefat, Farshid; Akbarzadeh Khiyavi, A. (2020-01)
      The use of biological macromolecules like quince seed mucilage (QSM), as the common curative practice has a long history in traditional folk medicine to cure wounds and burns. However, this gel cannot be applied on exudative wounds because of the high water content and non-absorption of infection of open wounds. It also limits cell-to-cell interactions and leads to the slow wound healing process. In this study to overcome these problems, a novel QSM-based hybrid scaffold modified by PCL/PEG copolymer was designed and characterized. The properties of this scaffold (PCL/QSM/PEG) were also compared with four scaffolds of PCL/PEG, PCL/Chitosan/PEG, chitosan, and QSM, to assess the role of QSM and the combined effect of polymers in improving the function of skin tissue-engineered scaffolds. It was found, the physicochemical properties play a crucial role in regulating cell behaviors so that, PCL/QSM/PEG as a smart/stimuli-responsive bio-matrix promotes not only human-adipose stem cells (h-ASCs) adhesion but also supports fibroblasts growth, via providing a porous-network. PCL/QSM/PEG could also induce keratinocytes at a desirable level for wound healing, by increasing the mechanobiological signals. Immunocytochemistry analysis confirmed keratinocytes differentiation pattern and their normal phenotype on PCL/QSM/PEG. Our study demonstrates, QSM as a differentiation/growth-promoting biological factor can be a proper candidate for design of wound dressings and skin tissue-engineered substrates containing cell.
    • Raman spectroscopic analysis of cyanobacterial colonization of hydromagnesite, a putative martian extremophile

      Edwards, Howell G.M.; Jorge Villar, Susana E.; Moody, Caroline D.; Newton, Emma M.; Russell, M.J. (2005)
      Raman spectra of an extremophile cyanobacterial colony in hydromagnesite from Lake Salda in Turkey have revealed a biogeological modification which is manifest as aragonite in the stratum associated with the colony. The presence of key spectral biomarkers of organic protectant molecules such as (8-carotene and scytonemin indicate that the survival strategy of the cyanobacteria is significantly one of UV-radiation protection. The terrestrial location of this extremophile is worthy of consideration further because of its possible putative link with the White Rock formations in Sabaea Terra and Juventae Chasma on Mars.
    • Raman spectroscopic and potentiometric studies of acidity level and dissociation of citric acid in aqueous solution

      Elbagerma, Mohamed A.; Alajtal, Adel I.; Edwards, Howell G.M.; Azimi, G.H.; Verma, K.D.; Scowen, Ian J. (2015)
      The dissociation constant is one of the most important characteristics of a pharmaceutical chemical moiety which has to be estimated with accuracy. The development of in-situ speciation methods in solutions with parallel measurements using Raman spectroscopy (molecular) and pH (macroscopic) for the identification, characterization, and quantitative determination of citric acid species in aqueous solution by numerical data treatment using a multiwavelength curve fitting program over a range of pH values is described. As a result, the first, second and third stepwise dissociation constants of citric acid have been evaluated as 3.02±0.06, 4.78±0.06 and 6.02±0.04, respectively. From these data over the pH range 2.38-6.16 an excellent agreement with literature values was achieved.
    • Raman spectroscopic studies of the cure of dicyclopentadiene (DCPD)

      Brown, Elaine C.; Barnes, S.E.; Coates, Philip D.; Corrigan, N.; Edwards, Howell G.M.; Harkin-Jones, E. (2009-06-30)
      The cure of polydicyclopentadiene conducted by ring-opening metathesis polymerisation in the presence of a Grubbs catalyst was studied using non-invasive Raman spectroscopy. The spectra of the monomer precursor and polymerised product were fully characterised and all stages of polymerisation monitored. Because of the monomer's high reactivity, the cure process is adaptable to reaction injection moulding and reactive rotational moulding. The viscosity of the dicyclopentadiene undergoes a rapid change at the beginning of the polymerisation process and it is critical that the induction time of the viscosity increase is determined and controlled for successful manufacturing. The results from this work show non-invasive Raman spectroscopic monitoring to be an effective method for monitoring the degree of cure, paving the way for possible implementation of the technique as a method of real-time analysis for control and optimisation during reactive processing. Agreement is shown between Raman measurements and ultrasonic time of flight data acquired during the initial induction period of the curing process.
    • Random matrix theory based spectrum sensing for cognitive radio networks

      Ahmed, A.; Hu, Yim Fun; Noras, James M.; Pillai, Prashant; Abd-Alhameed, Raed A.; Smith, A. (2015-11-05)
      Dynamic Spectrum Access (DSA) for secondary usage of underutilized radio spectrum is currently of great interest for radio regulatory authorities and for cellular network operators. However, the co-existence of multiple devices operating in the same bands, such as wireless microphones which also operate in TV bands, poses a challenge to DSA. Efficient and proactive spectrum sensing could prevent harmful interference between collocated devices, but existing blind spectrum sensing schemes such as energy detection and schemes based on Random Matrix Theory (RMT) have performance limitations. We propose a new blind spectrum sensing scheme for cognitive radio. The proposed scheme uses a new formula for the estimation of noise variance. The scheme has been evaluated through extensive simulations on wireless microphone signals and shows higher performance as compared to energy detection and RMT-based sensing schemes such as MME and EME. It also shows higher performance in terms of probability of detection (Pd).
    • Random projectors with continuous resolutions of the identity in a finite-dimensional Hilbert space

      Vourdas, Apostolos (2019-10)
      Random sets are used to get a continuous partition of the cardinality of the union of many overlapping sets. The formalism uses Möbius transforms and adapts Shapley's methodology in cooperative game theory, into the context of set theory. These ideas are subsequently generalized into the context of finite-dimensional Hilbert spaces. Using random projectors into the subspaces spanned by states from a total set, we construct an infinite number of continuous resolutions of the identity, that involve Hermitian positive semi-definite operators. The simplest one is the diagonal continuous resolution of the identity, and it is used to expand an arbitrary vector in terms of a continuum of components. It is also used to define the function on the 'probabilistic quadrant' , which is analogous to the Wigner function for the harmonic oscillator, on the phase-space plane. Systems with finite-dimensional Hilbert space (which are naturally described with discrete variables) are described here with continuous probabilistic variables.
    • Ranking strategies to support toxicity prediction: a case study on potential lxr binders

      Palczewska, Anna Maria; Kovarich, S.; Ciacci, A.; Fioravanzo, E.; Bassan, A.; Neagu, Daniel (2019-05)
      The current paradigm of toxicity testing is set within a framework of Mode-of-Action (MoA)/Adverse Outcome Pathway (AOP) investigations, where novel methodologies alternative to animal testing play a crucial role, and allow to consider causal links between molecular initiating events (MIEs), further key events and an adverse outcome. In silico (computational) models are developed to support toxicity assessment within the MoA/AOP framework. This paper focuses on the evaluation of potential binding to the Liver X Receptor (LXR), as this has been identified among the MIEs leading to liver steatosis within an AOP framework addressing repeated dose and target-organ toxicity. The objective of this study was the development of a priority setting strategy, by means of in silico approaches and chemometric tools, to allow for the screening and ranking of chemicals according to their toxicity potential. As a case study, the present paper outlines the methodologies and procedures that have been developed in the context of the COSMOS/cosmetics safety assessment project [4], which developed computational methods in view of supporting cosmetics safety assessment, to rank chemicals based on their potential binding to LXR. Chemicals are ranked based on molecular and QSAR modelling outcomes. The contribution in this paper is threefold: the QSAR model for LXR dataset, an application of molecular modeling approaches, which have been developed and optimized for drug discovery, in the context of toxicology, and finally ranking chemicals based on diverse modelling outcomes. The novelty in this paper consists of the employment of linear (logistic regression) and non-linear (Random Forest) models in the context of ranking chemicals. The results show that these methods can be successfully applied for prioritization of compounds of major concern for potential liver toxicity, and that they perform better than the ranking methods reported in the literature to date (such as total ordering or data fusion).
    • Rapid creation of skin substitutes from human skin cells and biomimetic nanofibers for acute full-thickness wound repair

      Mahjour, S.B.; Fu, X.; Yang, X.; Fong, J.; Sefat, Farshid; Wang, H. (2015)
      Creation of functional skin substitutes within a clinically acceptable time window is essential for timely repair and management of large wounds such as extensive burns. The aim of this study was to investigate the possibility of fabricating skin substitutes via a bottom-up nanofiber-enabled cell assembly approach and using such substitutes for full-thickness wound repair in nude mice. Following a layer-by-layer (L-b-L) manner, human primary skin cells (fibroblasts and keratinocytes) were rapidly assembled together with electrospun polycaprolactone (PCL)/collagen (3:1 w/w, 8% w/v) nanofibers into 3D constructs, in which fibroblasts and keratinocytes were located in the bottom and upper portion respectively. Following culture, the constructs developed into a skin-like structure with expression of basal keratinocyte markers and deposition of new matrix while exhibited good mechanical strength (as high as 4.0 MPa by 14 days). Treatment of the full-thickness wounds created on the back of nude mice with various grafts (acellular nanofiber meshes, dermal substitutes, skin substitutes and autografts) revealed that 14-day-cultured skin substitutes facilitated a rapid wound closure with complete epithelialization comparable to autografts. Taken together, skin-like substitutes can be formed by L-b-L assembling human skin cells and biomimetic nanofibers and they are effective to heal acute full-thickness wounds in nude mice.
    • RBFNN-based Minimum Entropy Filtering for a Class of Stochastic Nonlinear Systems

      Yin, X.; Zhang, Qichun; Wang, H.; Ding, Z. (2020-01)
      This paper presents a novel minimum entropy filter design for a class of stochastic nonlinear systems which are subjected to non-Gaussian noises. Motivated by stochastic distribution control, an output entropy model is developed using RBF neural network while the parameters of the model can be identified by the collected data. Based upon the presented model, the filtering problem has been investigated while the system dynamics have been represented. As the model output is the entropy of the estimation error, the optimal nonlinear filter is obtained based on the Lyapunov design which makes the model output minimum. Moreover, the entropy assignment problem has been discussed as an extension of the presented approach. To verify the presented design procedure, a numerical example is given which illustrates the effectiveness of the presented algorithm. The contributions of this paper can be included as 1) an output entropy model is presented using neural network; 2) a nonlinear filter design algorithm is developed as the main result and 3) a solution of entropy assignment problem is obtained which is an extension of the presented framework.
    • Reactive extrusion of polyamide 6 using a novel chain extender

      Tuna, Basak; Benkreira, Hadj (2019-03)
      Polyamide 6 (PA6) is an important engineering thermoplastic, very widely used but prone to thermal degradation during extrusion at temperature not far from its melt temperature (220 oC). Typically, and as measured in this study, PA6 extruded at temperature of 300 oC shows a 40% decrease in tensile modulus compared to non-extruded PA6. To rebuild PA6 molecular weight, the easiest and cheapest method is to use an appropriate chain extender. Many chain extenders have been used in the past but they are essentially suited to nucleophile induced degradation, targeting split PA6 chains carboxyl COOH and amine NH2 end groups. What has been lacking are effective chain extenders for thermally only induced degradation, i.e. for the practical cases where the PA6 is thoroughly dried before extrusion. For such a case, the degradation reaction mechanism dictates that the solution is to develop chain extenders that target the split PA6 chains amide CONH2 groups not the carboxyl COOH and amine NH2 end groups. As amide groups strongly react with anhydride functionalities, we test the effectiveness of a novel chain extender, Joncryl® ADR 3400, a styrene maleic anhydride copolymer with multiple, repeating anhydride functionality. Assessment of chain extension in this study is done as with previous work, using rheology, mechanical and thermal properties of PA6 extruded on its own and with the chain extender. The viscoelastic data conclusively show the efficacy of such chain extender with more than 10 fold changes in the comparative values of the extruded sample storage modulus G' and as much as an 85% increase in the tensile modulus.
    • Real Time Identification of Road Traffic Control Measures

      Almejalli, Khaled A.; Dahal, Keshav P.; Hossain, M. Alamgir (2007)
      The operator of a traffic control centre has to select the most appropriate traffic control action or combination of actions in a short time to manage the traffic network when non-recurrent road traffic congestion happens. This is a complex task, which requires expert knowledge, much experience and fast reaction. There are a large number of factors related to a traffic state as well as a large number of possible control actions that need to be considered during the decision making process. The identification of suitable control actions for a given non-recurrent traffic congestion can be tough even for experienced operators. Therefore, simulation models are used in many cases. However, simulating different traffic actions for a number of control measures in a complicated situation is very time-consuming. This chapter presents an intelligent method for the real-time identification of road traffic actions which assists the human operator of the traffic control centre in managing the current traffic state. The proposed system combines three soft-computing approaches, namely fuzzy logic, neural networks, and genetic algorithms. The system employs a fuzzy-neural network tool with self-organization algorithm for initializing the membership functions, a genetic algorithm (GA) for identifying fuzzy rules, and the back-propagation neural network algorithm for fine tuning the system parameters. The proposed system has been tested for a case-study of a small section of the ring-road around Riyadh city in Saudi Arabia. The results obtained for the case study are promising and demonstrate that the proposed approach can provide an effective support for real-time traffic control.
    • Real-time Design Constraints in Implementing Active Vibration Control Algorithms.

      Hossain, M. Alamgir; Tokhi, M.O. (2006)
      Although computer architectures incorporate fast processing hardware resources, high performance real-time implementation of a complex control algorithm requires an efficient design and software coding of the algorithm so as to exploit special features of the hardware and avoid associated architecture shortcomings. This paper presents an investigation into the analysis and design mechanisms that will lead to reduction in the execution time in implementing real-time control algorithms. The proposed mechanisms are exemplified by means of one algorithm, which demonstrates their applicability to real-time applications. An active vibration control (AVC) algorithm for a flexible beam system simulated using the finite difference (FD) method is considered to demonstrate the effectiveness of the proposed methods. A comparative performance evaluation of the proposed design mechanisms is presented and discussed through a set of experiments.
    • Real-time diagnosis of micro powder injection molding using integrated ultrasonic sensors.

      Cheng, C-C.; Ono, Y.; Whiteside, Benjamin R.; Brown, Elaine C.; Jen, C.K.; Coates, Philip D. (Hanser, 2007)
      Real-time diagnostics of ceramic powder injection molding using a commercial micromolding machine was performed using ultrasound. Miniature ultrasonic sensors were integrated onto the mold insert. Melt front, solidification, temperature variation and part detachment of the feedstock inside the mold cavity were observed. It has been demonstrated that ultrasonic velocity in feedstock inside the mold cavity, the ultrasonic contact duration during which the part and mold are in contact, and holding pressure can be used to assist with optimization of injection and cooling parameters to minimize energy consumption and maximize process efficiency.Real-time diagnostics of ceramic powder injection molding using a commercial micromolding machine was performed using ultrasound. Miniature ultrasonic sensors were integrated onto the mold insert. Melt front, solidification, temperature variation and part detachment of the feedstock inside the mold cavity were observed. It has been demonstrated that ultrasonic velocity in feedstock inside the mold cavity, the ultrasonic contact duration during which the part and mold are in contact, and holding pressure can be used to assist with optimization of injection and cooling parameters to minimize energy consumption and maximize process efficiency.