Second Law Analysis and Optimization of Elliptical Pin Fin Heat Sinks Using Firefly Algorithm

One of the most significant considerations in the design of a heat sink is thermal management due to increasing thermal flux and miniature in size. These heat sinks utilize plate or pin fins depending upon the required heat dissipation rate. They are designed to optimize overall performance. Elliptical pin fin heat sinks enhance heat transfer rates and reduce the pumping power. In this study, the Firefly Algorithm is implemented to optimize heat sinks with elliptical pin-fins. The pin-fins are arranged in an inline fashion. The natureinspired metaheuristic algorithm performs powerfully and efficiently in solving numerical global optimization problems. Based on mass, energy, and entropy balance, three models are developed for thermal resistance, hydraulic resistance, and entropy generation rate in the heat sink. The major axis is used as the characteristic length, and the maximum velocity is used as the reference velocity. The entropy generation rate comprises the combined effect of thermal resistance and pressure drop. The total EGR is minimized by utilizing the firefly algorithm. The optimization model utilizes analytical/empirical correlations for the heat transfer coefficients and friction factors. It is shown that both thermal resistance and pressure drop can be simultaneously optimized using this algorithm. It is demonstrated that the performance of FFA is much better than PPA.     

Optimised synthesis of ZnO‐nano‐fertiliser through green chemistry: boosted growth dynamics of economically important L. esculentum

Mounting‐up economic losses to annual crops yield due to micronutrient deficiency, fertiliser inefficiency and increasing microbial invasions (e.g. Xanthomonas cempestri attack on tomatoes) are needed to be solved via nano‐biotechnology. So keeping this in view, the authors’ current study presents the new horizon in the field of nano‐fertiliser with highly nutritive and preservative effect of green fabricated zinc oxide‐nanostructures (ZnO‐NSs) during Lycopersicum esculentum (tomato) growth dynamics. ZnO‐NS prepared via green chemistry possesses highly homogenous crystalline structures well‐characterised through ultraviolet and visible spectroscopy, Fourier transform infrared spectroscopy, X‐ray diffraction and scanning electron microscope. The ZnO‐NS average size was found as small as 18 nm having a crystallite size of 5 nm. L. esculentum were grown in different concentrations of ZnO‐NS to examine the different morphological parameters includes time of seed germination, germination percentage, the number of plant leaves, the height of the plant, average number of branches, days count for flowering and fruiting time period along with fruit quantity. Promising results clearly predict that bio‐fabricated ZnO‐NS at optimum concentration resulted as growth booster and dramatically triggered the plant yield.Inspec keywords: zinc compounds, II‐VI semiconductors, wide band gap semiconductors, nanostructured materials, nanofabrication, ultraviolet spectra, visible spectra, Fourier transform infrared spectra, X‐ray diffraction, scanning electron microscopy, crystallites, biomedical materials, nanomedicineOther keywords: ZnO‐nanofertiliser, green chemistry, boosted growth dynamics, L. esculentum, mounting‐up economic losses, micronutrient deficiency, fertiliser inefficiency, microbial invasions, Xanthomonas cempestri, nanobiotechnology, zinc oxide‐nanostructures, Lycopersicum esculentum, high‐homogenous crystalline structures, ultraviolet spectroscopy, visible spectroscopy, Fourier transform infrared spectroscopy, X‐ray diffraction, scanning electron microscopy, crystallite size, morphological parameters, seed germination, germination percentage, plant leaves, ZnO     

Multiply distorted image quality assessment based on feature level fusion and optimal feature selection

No reference image quality assessment (NR-IQA) has received considerable importance in the last decade due to a rise in the use of multimedia content in our daily lives. Due to limitations in technology, multiple distortions may be introduced in the images that need to be assessed. Recently feature selection has shown promising results for single distorted NR-IQA and their effectiveness on multiple distorted images still need to be addressed. In this paper, impact of feature level fusion and feature selection on multiple distorted image quality assessment is presented. To this end features are extracted from multiple distorted images using six NR-IQA techniques (BLIINDS-II, BRISQUE, CurveletQA, DIIVINE, GM-LOG, SSEQ) that extract features in different (discrete cosine transform, spatial, curvelet transform, wavelet transform, spatial and gradient, spatial and spectral) domains. The extracted features from different domains are fused to generate a single feature vector. All combinations of feature-level fusion from six different techniques have been evaluated. Three different feature selection algorithms (genetic search, linear forward search, particle swarm optimization) are then applied to select optimum features for NR-IQA. The selected features are then used by the support vector regression model to predict the quality score. The performance of the proposed methodology is evaluated for two multiple distorted IQA databases (LIVE multiple distorted image dataset (LIVEMD), multiply distorted image database (MDID2017)), two singly synthetically distorted IQA databases (Tampere image database (TID2013), Computational and subjective image quality database (CSIQ)), and one screen content IQA database (Screen content image quality database (SIQAD)). Experimental results show that the fusion of features from different domains gives better performance in comparison to existing multiple-distorted NR-IQA techniques with SROCC scores of 0.9555, 0.9587, 0.6892, 0.9452, and 0.7682 on the LIVEMD, MDID, TID2013, CSIQ, and SIQAD databases respectively. Moreover, the performance is further improved when the genetic search feature selection algorithm is applied to fused features to remove the redundant and irrelevant features. The SROCC scores are improved to 0.9691, 0.9723, and 0.6897 for LIVEMD, MDID, and TID2013 databases respectively.

     

Use of the Long-Term Pavement Performance Database in the Pavement Engineering Curriculum

In this paper the writers demonstrate the inclusion of the Long-Term Pavement Performance (LTPP-DATAPAVE 3.0) data within the pavement engineering curriculum at Michigan State University (MSU). The paper presents two examples, one from an undergraduate course on pavement rehabilitation and one from a graduate course on pavement analysis and design. These examples illustrate the use of LTPP data in computing responses, predicting traffic, developing rehabilitation strategies, and predicting performance of both rigid and flexible pavements.     

Analysis of the Biomarkers for Neurodegenerative Diseases in Aged Progranulin Deficient Mice

Neurodegenerative diseases are debilitating impairments that affect millions of people worldwide and are characterized by progressive degeneration of structure and function of the central or peripheral nervous system. Effective biomarkers for neurodegenerative diseases can be used to improve the diagnostic workup in the clinic as well as facilitate the development of effective disease-modifying therapies. Progranulin (PGRN) has been reported to be involved in various neurodegenerative disorders. Hence, in the current study we systematically compared the inflammation and accumulation of typical neurodegenerative disease markers in the brain tissue between PGRN knockout (PGRN KO) and wildtype (WT) mice. We found that PGRN deficiency led to significant neuron loss as well as activation of microglia and astrocytes in aged mice. Several characteristic neurodegenerative markers, including α-synuclein, TAR DNA-binding protein 43 (TDP-43), Tau, and β-amyloid, were all accumulated in the brain of PGRN-deficient mice as compared to WT mice. Moreover, higher aggregation of lipofuscin was observed in the brain tissue of PGRN-deficient mice compared with WT mice. In addition, the autophagy was also defective in the brain of PGRN-deficient mice, indicated by the abnormal expression level of autophagy marker LC3-II. Collectively, comprehensive assays support the idea that PGRN plays an important role during the development of neurodegenerative disease, indicating that PGRN might be a useful biomarker for neurodegenerative diseases in clinical settings.     

Influence of end-group modification on interaction of amphiphilic poly(oxyethylene)-<Emphasis Type="Italic">b</Emphasis>-poly(oxybutylene) block copolymers with ionic surfactants

Amphiphilic poly(oxyethylene)-b-poly(oxybutylene) block copolymers having hydrophilic block end-capped with neutral dimethylamino (DE₇₉B₃₄) and cationic trimethyl ammonium (TE₇₉B₃₄) groups, respectively were investigated for their interactions with ionic surfactants, sodium dodecyl sulfate (SDS) and cetyltrimethylammonium bromide (CTAB) using tensiometry, conductometry and dynamic light scattering. The self-assembly of DE₇₉B₃₄ and TE₇₉B₃₄ occur at 0.7 g/dm³ and 0.8 g/dm³, respectively. TE₇₉B₃₄ binds favorably with oppositely charged SDS, causing a substantial change in surface tension compared to other surfactant-polymer systems. In mixed polymer-surfactant systems, the micellization was promoted in the presence of SDS, but it was suppressed with CTAB. Such behavior is expected for cationic block copolymers, since they can engage into ion-pair formation with anionic SDS. For neutral polymers, the behavior is reflective of surfactants’ head group hydrophobicity. The head groups of CTAB are more hydrophobic and their existence in the proximity of hydrophobic segments of polymers induces shape transition to non-spherical morphologies. Gibb’s free energy of adsorption at air – water interface is negative for SDS, CTAB and surfactant-block copolymer systems, indicating that the process is highly spontaneous. The increase in entropy of TE₇₉B₃₄ during micelle formation with temperature is due to disturbance of hydrophobic structure of water molecules, thus hydrophobic parts are removed from bulk solution to the interface and also in the interior of micelle the freedom of hydrophobic part is increased. The dynamic laser light scattering results revealed that due to presence of block copolymers pre-micellar aggregates were favored.     

Design and development of ceramic-based composites with tailored properties for cutting tool inserts

A successful approach to the development of tailored cutting tool materials requires the development of innovative concepts at each step of manufacturing, from the material design, synthesis of composite powders, to their processing and sintering. In this paper, a computational design approach is applied in the development of reinforced ceramic-based cutting tool inserts with tailored structural and thermal properties. Several potential filler materials are considered at the material design stage for the improvement of structural and thermal properties of a selected matrix material. Properties, such as an improved thermal conductivity and reduced coefficient of thermal expansion are essential for an effective cutting tool insert to absorb thermal shock at varying temperatures. In addition, structural properties such as elastic modulus have to be maintained within a moderate range. A mean-field homogenization theory and effective medium approximation using an in-house code are applied for predicting potential optimum structural and thermal properties for the required application. This is done by considering the effect of inclusions as a function of volume fraction and particle size in the ceramic base matrix. Single inclusion composites such as alumina-silicon (Al₂O₃-SiC) and alumina-cubic boron nitride (Al₂O₃-cBN) as well as hybrid composite such as alumina-silicon-cubic boron nitride (Al₂O₃-SiC-cBN) are developed using the Spark Plasma Sintering (SPS) process in line with the designed range of filler size and volume fraction to validate the computational results. It is found that the computational material design approach is precise enough in predicting the target properties of a designed hybrid composite material for cutting tool inserts.     

LC-MS/MS-based determination of chloramphenicol,thiamphenicol, florfenicol and florfenicol amine in poultry meat from the Punjab-Pakistan

A simple, reliable and sensitive liquid chromatography-tandem mass spectrometry-based confirmatory method was redeveloped and validated for the simultaneous determination of chloramphenicol, thiamphenicol, florfenicol and florfenicol amine in chicken muscles. The analytes were extracted from minced chicken muscle with acetonitrile and ammoniated water mixture. A second extraction with ethyl acetate was followed by evaporation and dissolution of the residue in ammoniated methanol before defatting with n-hexane. Finally, the extract was further cleaned up by dispersive solid phase extraction using C-18 end-capped dispersive material. The validation protocol was adapted from the European Commission Decision 2002/657/EC and all the performance characteristics were successfully satisfied. The recoveries of all the analytes were found to be in the range of 86.4–108.1% and the precision values, within day and between days, ranged from 2.7% to 11% and 4.4% to 16.3%, respectively. The method was tested in various incurred samples and applied to analyse a wide range of random poultry market samples (n = 120) collected from three cities of the Punjab, Pakistan. Chloramphenicol and florfenicol residues were detected at low levels in less than 11% of the samples. Chloramphenicol was detected only in 4 samples with the concentration range of 0.17–0.477 µg kg^–1, whereas the levels of florfenicol/florfenicol amine residues detected in 9 samples ranged from 8.7 to 32.8 µg kg^–1. Moreover, most of the florfenicol residues were identified as tissue bound, extractable only after strong acid hydrolysis.