Diffusion Based Channel Gains Estimation in WSN Using Fractional Order Strategies

In this study, it is proposed that the diffusion least mean square (LMS) algorithm can be improved by applying the fractional order signal processing methodologies. Application of Caputo’s fractional derivatives are considered in the optimization of cost function. It is suggested to derive a fractional order variant of the diffusion LMS algorithm. The applicability is tested for the estimation of channel parameters in a distributed environment consisting of randomly distributed sensors communicating through wireless medium. The topology of the network is selected such that a smaller number of nodes are informed. In the network, a random sleep strategy is followed to conserve the transmission power at the nodes. The proposed fractional order modified diffusion LMS algorithms are applied in the two configurations of combine-then-adapt and adapt-then-combine. The average squared error performance of the proposed algorithms along with its traditional counterparts are evaluated for the estimation of the Rayleigh channel parameters. A mathematical proof of convergence is provided showing that the addition of the nonlinear term resulting from fractional derivatives helps adjusts the autocorrelation matrix in such a way that the spread of its eigenvalues decreases. This increases the convergence as well as the steady state response even for the larger step sizes. Experimental results are shown for different number of nodes and fractional orders. The simulation results establish that the accuracy of the proposed scheme is far better than its classical counterparts, therefore, helps better solves the channel gains estimation problem in a distributed wireless environment. The algorithm has the potential to be applied in other applications related to learning and adaptation.     

Reinforced concrete deep beam shear strength capacity modelling using an integrative bio-inspired algorithm with an artificial intelligence model

The design and sustainability of reinforced concrete deep beam are still the main issues in the sector of structural engineering despite the existence of modern advancements in this area. Proper understanding of shear stress characteristics can assist in providing safer design and prevent failure in deep beams which consequently lead to saving lives and properties. In this investigation, a new intelligent model depending on the hybridization of support vector regression with bio-inspired optimization approach called genetic algorithm (SVR-GA) is employed to predict the shear strength of reinforced concrete (RC) deep beams based on dimensional, mechanical and material parameters properties. The adopted SVR-GA modelling approach is validated against three different well established artificial intelligent (AI) models, including classical SVR, artificial neural network (ANN) and gradient boosted decision trees (GBDTs). The comparison assessments provide a clear impression of the superior capability of the proposed SVR-GA model in the prediction of shear strength capability of simply supported deep beams. The simulated results gained by SVR-GA model are very close to the experimental ones. In quantitative results, the coefficient of determination (R²) during the testing phase (R² = 0.95), whereas the other comparable models generated relatively lower values of R² ranging from 0.884 to 0.941. All in all, the proposed SVR-GA model showed an applicable and robust computer aid technology for modelling RC deep beam shear strength that contributes to the base knowledge of material and structural engineering perspective.

     

Correction to: Automatic COVID-19 detection mechanisms and approaches from medical images: a systematic review

Multimedia Tools and Applications -     

Indoor mould growth prediction using coupled computational fluid dynamics and mould growth model

This study investigates, using in-situ and numerical simulation experiments, airflow and hygrothermal distribution in a mechanically ventilated academic research facility with known cases of microbial proliferations. Microclimate parameters were obtained from in-situ experiments and used as boundary conditions and validation of the numerical experiments with a commercial computational fluid dynamics (CFD) analysis tool using the standard k–ε model. Good agreements were obtained with less than 10% deviations between the measured and simulated results. Subsequent upon successful validation, the model was used to investigate hygrothermal and airflow profile within the shelves holding stored components in the facility. The predicted in-shelf hygrothermal profile was superimposed on mould growth limiting curve earlier documented in the literature. Results revealed the growth of xerophilic species in most parts of the shelves. The mould growth prediction was found in correlation with the microbial investigation in the case-studied room reported by the authors elsewhere. Satisfactory prediction of mould growth in the room successfully proved that the CFD simulation can be used to investigate the conditions that lead to microbial growth in the indoor environment.     

How do habitat variability and management regime shape the spatial heterogeneity of birds within a large Mediterranean urban park?

Large urban parks can support a diverse bird community. However, the effects of variability among habitats and of park management on bird assemblages are poorly understood. We studied bird communities within the Yarkon Park, Tel Aviv, the largest urban park in Israel. We examined species richness, abundance and community composition across 20 locations that differ in levels of park management to identify habitat variables responsible for variation in bird richness and composition. Of 91 recorded bird species, 13 were aliens (14%), 4 were urban exploiters (4%), 54 were urban adapters (60%) and 20 were migrants (22%). Management had a significant effect on native bird richness and bird community structure varied among areas with different management regimes. Species richness of all the above species’ groups was lowest in intensively managed areas. Areas with intermediate levels of management had higher or equal richness compared to unmanaged areas. The majority of urban exploiters were found at all locations within the park reaching their highest abundances in intensively managed areas. Species richness of alien birds did not vary across levels of management. Bird species richness was negatively associated with lawn cover and with distance from nearest water source and was positively associated with the number of woody plant species. We suggest that urban parks should be designed such that the heterogeneity of native vegetation is preserved, if we aim to maintain native bird species diversity and minimize urban exploiter and alien species. When lightly managed or unmanaged, urban parks can retain large remnants of sub-natural habitats and can serve as important contributors to the conservation of native biodiversity within a large urban metropolis.     

Shell versus beam representation of pipes in the evaluation of tunneling effects on pipelines

The problem of tunneling effects on pipelines is approached in the paper. Previous solutions treated the pipeline as a simple Euler–Bernoulli beam. Strictly speaking, this treatment cannot be rigorous as the pipe itself is actually a three dimensional structure loaded all around. It is therefore possible that the Euler–Bernoulli beam representation is not suitable for all cases. The current paper examines this issue by comparing analysis results of soil-pipe-tunnel interaction based on two different formulations. In one formulation the pile is represented as a Euler–Bernoulli beam, while in the other it is treated as a three dimensional structure composed of shell elements. The soil behavior and the tunneling induced displacements are identical in the two formulations, thus any variation in behavior is solely a function of the employed pipe representation method.It is found that when the relative material stiffness of the pipe and soil is small, the pipeline does not behave as a beam, and the results of the two theories differ. As the relative pipe-soil material stiffness increases the two solutions approach each other and eventually coincide for large relative pipe-soil material stiffness values. It is shown that typical concrete and steel pipes can be well represented as simple beams, while polyethylene pipes may require the shell element representation for more accurate predictions.     

Experimental and modeling asphaltene precipitation in presence of DBSA using PC-SAFT EOS

Asphaltene precipitation and subsequent deposition in production tubing and topside facilities present significant cost penalties to crude oil production. Therefore, it is highly desirable to predict their phase behavior and the efficiency of dispersants in preventing or delaying deposition. Very few studies have been carried out on the molecular interactions between asphaltenes and different dispersants. As a result, the mechanisms by which dispersants stabilize asphaltenes are still open to discussion. The authors introduced a new method to characterize asphaltenes in perturbed chain statistical association fluid theory equation of state (EOS; perturbed-chain statistical association fluid theory EOS [PC-SAFT-EOS]) and correctly model the effect of dodecyl benzene sulfonic acid (DBSA) dispersant on the thermodynamic behavior of asphaltenes. Using the filtration method the effect of the ionic dispersant (DBSA) on asphaltene precipitation for different concentrations of n-heptane was measured experimentally, then modeled through PC-SAFT EOS. In the approach only the hard-chain and the dispersion terms are taken into consideration, and PC-SAFT parameters were calculated based on Gonzales et al. (2007 Gonzalez, D. L., Hirasaki, G. J., Creek, J., and Chapman, W. G. (2007). Modeling of asphaltene precipitation due to changes in composition using the perturbed chain statistical associating fluid theory equation of state. Energy Fuels 21:1231–1242.[Crossref], [Web of Science ®] , [Google Scholar]) based on molecular weight (Mw) and aromaticity factor (γ). Additionally, the model could correctly predict the amount of asphaltene precipitation upon addition of DBSA dispersant.     

Mucoadhesive dosage form of lidocaine hydrochloride: I. Mucoadhesive and physicochemical characterization

The aim of this study was to characterize a buccal mucoadhesive film using lidocaine and its hydrochloride salt (LDHCL) as a model drug. Buccal films were developed using carbopol 971P as a mucoadhesive polymer, and glycerol as a plasticizer. Scanning Electron Microscope, Differential Scanning Calorimetry, X-ray powder diffraction, and Fourier Transform Infra Red techniques were used to characterize the mucoadhesive films. Bioadhesive properties were evaluated using the Universal Instron Instrument with chicken pouch as a model tissue.

LDHCL and its base were present in carbopol 971P films in a molecular dispersion state without exerting any effect on the glass transition of these films. The mucoadhesive force between the chicken pouches and the film containing glycerol did not change by time during the tested period (1-20 min), while increased with increasing the amount of glycerol (10-40% w/w of polymer content). Furthermore, a linear increase in the mucoadhesive force was accompanied by the increase in the film thickness, while a linear decrease followed by plateau was obtained when loading the patch with LDHCL at concentration above 1 mg/cm².

Loading carbopol film with lidocaine base, in a concentration up to 6 mg/cm² decreased linearly the mucoadhesive properties, which could be attributed to salt formation between the acidic carboxylic moiety of carbopol and basic lidocaine.     

Data interpretation for spectral sensors with correlated bands

New classes of spectral sensors are emerging that have significant overlap in the band spectral response functions. While conventional sensors such as the Multispectral Thermal Images (MTI) or Landsat may have responses with a few percent overlap between adjacent bands, some of the emerging sensors can have more than 50% correlation among all spectral bands. The traditional geometrical models used to describe spectral data fail when such high levels of correlation exist. In this paper we present a generalized geometrical model that relies on functional analysis. We define a sensor space and a scene space that can be used to characterize the suitability of a sensor for a particular spectral sensing task. We demonstrate that classifiers based on first-order distance and angle metrics fail for sensors with highly correlated bands unless appropriate preprocessing is carried out. We further show that second-order statistical classifiers are largely immune to many of the problems introduced by the correlated band responses.     

In silico design of small RNA switches

The discovery of natural RNA sensors that respond to a change in the environment by a conformational switch can be utilized for various biotechnological and nanobiotechnological advances. One class of RNA sensors is the riboswitch: an RNA genetic control element that is capable of sensing small molecules, responding to a deviation in ligand concentration with a structural change. Riboswitches are modularly built from smaller components. Computational methods can potentially be utilized in assembling these building block components and offering improvements in the biochemical design process. We describe a computational procedure to design RNA switches from building blocks with favorable properties. To achieve maximal throughput for genetic control purposes, future designer RNA switches can be assembled based on a computerized preprocessing buildup of the constituent domains, namely the aptamer and the expression platform in the case of a synthetic riboswitch. Conformational switching is enabled by the RNA versatility to possess two highly stable states that are energetically close to each other but topologically distinct, separated by an energy barrier between them. Initially, computer simulations can produce a list of short sequences that switch between two conformers when trigerred by point mutations or temperature. The short sequences should possess an additional desirable property; when these selected small RNA switch segments are attached to various aptamers, the ligand binding mechanism should replace the aforementioned event triggers, which will no longer be effective for crossing the energy barrier. In the assembled RNA sequence, energy minimization folding predictions should then show no difference between the folded structure of the entire sequence relative to the folded structure of each of its constituents. Moreover, energy minimization methods applied on the entire sequence could aid at this preprocessing stage by exhibiting high mutational robustness to capture the stability of the formed hairpin in the expression platform. The above computer-assisted assembly procedure together with application specific considerations may further be tailored for therapeutic gene regulation. Index Terms-Design of RNA switches, energy minimization methods, RNA folding predictions.