Free energy of conformational change in a single chain of polyvinylidene fluoride using molecular simulations

Polyvinylidene fluoride (PVDF) has several crystal forms of which the α-form is nonpolar, while the β-form is polar and has the highest piezoelectric constant. α PVDF, when stretched, transforms into the β form, which has wide applications in sensors and actuators. Steered molecular dynamics simulations are used to study the transformation of a single chain of PVDF from a trans–gauche conformation to an all trans one. The Helmholtz free energy change (∆F) is estimated using Jarzynski's equality. The transformation starts at the chain ends followed by the transformation of the remaining chain. The free energy change for the transformation is found to be always positive, indicating that the TGTG' form has higher thermodynamic stability than the all trans form throughout the studied temperature range. With increasing temperature, free energy change for the transformation increases monotonically.     

MRMR Based Feature Vector Design for Efficient Citrus Disease Detection

In recent times, the images and videos have emerged as one of the most important information source depicting the real time scenarios. Digital images nowadays serve as input for many applications and replacing the manual methods due to their capabilities of 3D scene representation in 2D plane. The capabilities of digital images along with utilization of machine learning methodologies are showing promising accuracies in many applications of prediction and pattern recognition. One of the application fields pertains to detection of diseases occurring in the plants, which are destroying the widespread fields. Traditionally the disease detection process was done by a domain expert using manual examination and laboratory tests. This is a tedious and time consuming process and does not suffice the accuracy levels. This creates a room for the research in developing automation based methods where the images captured through sensors and cameras will be used for detection of disease and control its spreading. The digital images captured from the field's forms the dataset which trains the machine learning models to predict the nature of the disease. The accuracy of these models is greatly affected by the amount of noise and ailments present in the input images, appropriate segmentation methodology, feature vector development and the choice of machine learning algorithm. To ensure the high rated performance of the designed system the research is moving in a direction to fine tune each and every stage separately considering their dependencies on subsequent stages. Therefore the most optimum solution can be obtained by considering the image processing methodologies for improving the quality of image and then applying statistical methods for feature extraction and selection. The training vector thus developed is capable of presenting the relationship between the feature values and the target class. In this article, a highly accurate system model for detecting the diseases occurring in citrus fruits using a hybrid feature development approach is proposed. The overall improvement in terms of accuracy is measured and depicted.     

Investigation of structural,luminescence, and anti-bacterial properties of novel Zn1−xEuxAl2−yO4Sry phosphor

Journal of Materials Science: Materials in Electronics - In this research work, we synthesized novel zinc aluminate (Zn1?xEuxAl2?yO4Sry) sky bluish phosphors at 500 °C via...     

Artefacts in statistical analyses of network motifs: general framework and application to metabolic networks

Few-node subgraphs are the smallest collective units in a network that can be investigated. They are beyond the scale of individual nodes but more local than, for example, communities. When statistically over- or under-represented, they are called network motifs. Network motifs have been interpreted as building blocks that shape the dynamic behaviour of networks. It is this promise of potentially explaining emergent properties of complex systems with relatively simple structures that led to an interest in network motifs in an ever-growing number of studies and across disciplines. Here, we discuss artefacts in the analysis of network motifs arising from discrepancies between the network under investigation and the pool of random graphs serving as a null model. Our aim was to provide a clear and accessible catalogue of such incongruities and their effect on the motif signature. As a case study, we explore the metabolic network of Escherichia coli and show that only by excluding ever more artefacts from the motif signature a strong and plausible correlation with the essentiality profile of metabolic reactions emerges.     

Seismic response of precast reinforced concrete wall subjected to cyclic in-plane and constant out-of-plane loading

This paper provides insight into the seismic behavior of a full-scale precast reinforced concrete wall under in-plane cyclic loading combined with out-of-plane loading replicated by sand backfill to simulate the actual condition of basement walls. The tested wall exhibited flexural cracks, owing to the high aspect ratio and considerable out-of-plane movement due to lateral pressure from the backfill. The wall performed satisfactorily by exhibiting competent seismic parameters and deformation characteristics governed by its ductile response in the nonlinear phase during the test with smaller residual drift. Numerical analysis was conducted to validate experimental findings, which complied with each other. The numerical model was used to conduct parametric studies to study the effect of backfill density and aspect ratio on seismic response of the proposed precast wall system. The in-plane capacity of walls reduced, while deformation characteristics were unaffected by the increase in backfill density. An increase in aspect ratio leads to a reduction in in-plane capacity and an increase in drift. Curves between the ratio of in-plane yield capacity and design shear load of walls are proposed for the backfill density, which may be adopted to determine the in-plane yield capacity of the basement walls based on their design shear.     

Influence of soil density on gas permeability and water retention in soils amended with in-house produced biochar

Biochar has been used as an environment-friendly enhancer to improve the hydraulic properties(e.g.suction and water retention) of soil.However,variations in densities alter the properties of the soil-biochar mix.Such density variations are observed in agriculture(loosely compacted) and engineering(densely compacted) applications.The influence of biochar amendment on gas permeability of soil has been barely investigated,especially for soil with diffe rent densities.The maj or obj ective of this study is to investigate the water retention capacity,and gas permeability of biochar-amended soil(BAS) with different biochar contents under varying degree of compaction(DOC) conditions.In-house produced novel biochar was mixed with the soil at different amendment rates(i.e.biochar contents of 0%,5% and 10%).All BAS samples were compacted at three DOCs(65%,80% and 95%) in polyvinyl chloride(PVC)tubes.Each soil column was subjected to drying-wetting cycles,during which soil suction,water content,and gas permeability were measured.A simplified theoretical framework for estimating the void ratio of BAS was proposed.The experimental results reveal that the addition of biochar significantly decreased gas permeability k_g as compared with that of bare soil(BS).However,the addition of 5%biochar is found to be optimum in decreasing kg with an increase of DOC(i.e.k_(g,65%) k_(g,80%) k_(g,95%)) at a relatively low suction range(200 kPa) because both biochar and compaction treatment reduce the connected pores.     

Impact of different layers on performance of OLED

Microsystem Technologies - This paper explores how different layers in an organic light emitting diode (OLED) impacts its performance. Here, different layers of OLED similar to hole/electron...     

Cattaneo–Christov double-diffusion model with Stefan blowing effect on copper–water nanofluid flow over a stretching surface

We often encounter many processes where the cooling rate is a key factor in deciding the features of a desired product. Due to increasing demands of controlled cooling systems, an effort is made to theoretically study the effect of volume fraction on mixed convective Cu–water nanofluid flow over a stretching surface with activation energy and thermal radiation. The nonlinear dynamical system is simplified using apt similarity variables and the obtained ordinary differential equations are dealt numerically using Runge–Kutta–Fehlberg method and shooting scheme. The thermal and solutal equations are modeled considering Cattaneo–Christov double-diffusion model. The flow problem is studied considering velocity slip and zero mass flux state at the surface. As a novelty, the present case considers the blowing effect at the surface to study massive species transport during nanofluid flow with Cattaneo–Christov double-diffusion model. The results show that an increase in strength of thermal radiation increases temperature and buoyancy ratio parameter, thereby escalating the skin friction coefficient. When thermal relaxation parameter changes from 0.001 to 0.005, heat transfer coefficient improves by 24.36%. Furthermore, with the change in value of the blowing parameter from 0.1 to 0.1015, the maximum value concentration of nanoparticles that is attained during the flow is increased by 7.15%.     

Medium chain triglycerides (MCT): State-of-the-art on chemistry,synthesis, health benefits and applications in food industry

Medium chain triglycerides (MCT) are esters of fatty acids with 6 to 12 carbon atom chains. Naturally, they occur in various sources; their composition and bioactivity are source and extraction process-linked. The molecular size of MCT oil permits unique metabolic pathways and energy production rates, making MCT oil a high-value functional food. This review details the common sources of MCT oil, presenting critical information on the various approaches for MCT oil extraction or synthesis. Apart from conventional techniques, non-thermal processing methods that show promising prospects are analyzed. The biological effects of MCT oil are summarized, and the range of need-driven modification approaches are elaborated. A section is devoted to highlighting the recent trends in the application of MCT oil for food, nutraceuticals, and allied applications. While much is debated about the role of MCT oil in human health and wellness, there is limited information on daily requirements, impact on specific population groups, and effects of long-term consumption. Nonetheless, several studies have been conducted and continue to identify the most effective methods for MCT oil extraction, processing, handling, and storage. A knowledge gap exists and future research must focus on technology packages for scalability and sustainability.