A micromechanics model for the prediction of static strength of off-axis unidirectional laminates

In this paper a micromechanics model using the concentric cylinder assemblage model and the Mori-Tanaka average stress scheme is used to predict the static strength of unidirectional angle ply laminates. The predicted strengths agree with experimental results for Glass/Epoxy and Graphite/Epoxy systems.     

Electrochemical cell current requirements for toxic organic waste destruction in Ce(IV)-mediated electrochemical oxidation process

The electrochemical cell for cerium oxidation and reactor for organic destruction are the most important operation units for the successful working mediated electrochemical oxidation (MEO) process. In this study, electrochemical cells with DSA electrodes of two types, single stack and double stack connected in series, were used. The performances towards the electrochemical generation of Ce(IV) in nitric acid media at 80 °C were studied. The current-voltage curves and cerium electrolysis kinetics showed the dependence on number of cell stacks needed to be connected in series for the destruction of a given quantity of organic pollutant. The presence of an optimum region for Ce(III) oxidation with a contribution of oxygen evolution, especially at low Ce(III) concentration (high conversion ratios), was found. The cells were applied for the Ce(IV) regeneration during the organic destruction. The cell and reactor processes were fitted in a simple model proposed and used to calculate the current needed in terms of Ce(III) oxidation rate and the number of cell stacks required for maintaining Ce(IV)/Ce(III) ratio at the same level during the organic destruction. This consideration was based on the kinetic model previously developed by us for the organic destruction in the MEO process.     

Synthesis and metal uptake studies on poly(8‐hydroxy‐5‐azoquinolinephenylacrylate‐formaldehyde) resin and its metal complexes

8‐hydroxy‐5‐azoquinolinephenylacrylate‐formaldehyde (8H5AQPA‐F) macromonomer was prepared from acryloylchloride, with condensation products of 8‐hydroxy‐5‐azoquinolinephenol‐formaldehyde, and polymerized in DMF at 70°C using benzoyl peroxide as free radical initiator. Poly(8H5AQPA‐F) was characterized by infrared and nuclear magnetic resonance spectroscopic techniques. Polychelates were obtained in alkaline solution of polymeric ligand, with the aqueous solution of Cu(II) and Ni(II). Elemental analysis of polychelates suggests that the metal to ligand ratio is about 1:2. The polymer metal complexes were also characterized by IR, XRD, magnetic moments, and thermal analysis. The effects of pH and electrolyte on the metal uptake behavior of the resin were also studied. © 2007 Wiley Periodicals, Inc. J Appl Polym Sci 104: 797–802, 2007     

Influence of modified forms of urea and nitrogen levels on weed growth and grain yield of lowland rice

The growth of weeds and their subsequent reduction of rice yield as affected by N source neem cake coated urea (NCU), dicyandiamide coated urea (DCU), rock phosphate coated urea (RPCU), urea supergranules (USG) and prilled urea (PU) was studied on a clay loam soil at Coimbatore, India. Experiments were conducted in northeast monsoon (NEM) 1981, summer 1982, and southwest monsoon (SWM) 1982 seasons.The crop was associated with eleven weed species, and the dominant weeds wereEchinochloa crus-galli, Cyperus difformis andMarsilea quadrifolia. The weed flora varied between seasons. Deep placement of USG reduced the dry weight of weeds in NEM and summer seasons at 60, 90 and 120 Kg N ha^–1 whereas it increased the dry weight at 60 and 90 but not 120 Kg N ha^–1 in SWM season. The dry weight of weeds decreased with increased N rates for all N sources during NEM and summer seasons. In SWM season, dry weight of weeds increased with increased N rates for all N sources except USG. The grain yield of rice was drastically reduced with the deep placement of USG at 60 but not 120 Kg N ha^–1 in SWM season. The differential effect of the N sources between seasons was due to the change of the weed flora. Dominance ofE. crus-galli during SWM season had greater influence on weed dry weight and grain yield of rice.Nitrogen uptake by weeds was frequently greater in unfertilized plots, particularly in NEM and summer seasons. In SWM season, the apparent fertilizer N recovery by weeds was high for USG. It decreased from 53% for 60 Kg USG-N ha^–1 to 4% for 120 Kg USG-N ha^–1.Contribution from the part of Ph.D. work of the first author at Department of Agronomy, Tamil Nadu Agricultural University, Coimbatore-641 003, Tamil Nadu, India.     

Processing iron-aluminide composites containing carbides or borides

Iron-aluminide composites containing 30–90 vol. % carbides or borides can be processed to near full density (greater than 97% theoretical density). This wide range of ceramic contents enables the tailoring of the composite properties to a variety of applications requiring a combination of the corrosion and oxidation resistance of iron aluminides and the hardness and wear resistance of the ceramic phases. The composites are processed by conventional liquid-phase sintering of mixed powders as well as pressureless melt infiltration. Typical mechanical properties such as hardness, flexure strength, and fracture toughness were evaluated for composites containing different volume fractions of carbide or boride particulates. Furthermore, evaluations of the wear resistance, oxidation resistance, aqueous corrosion resistance, and thermal expansion of the iron-aluminide composites suggest many potential applications for these new materials.     

In-Situ interactive image-based model building for Augmented Reality from a handheld device

Three-dimensional models of objects and their creation process are central for a variety of applications in Augmented Reality. In this article, we present a system that is designed for in-situ modeling using interactive techniques for two generic versions of handheld devices equipped with cameras. The system allows online building of 3D wireframe models through a combination of user interaction and automated methods. In particular, we concentrate in rigorous evaluation of the two devices and interaction methods in the context of 3D feature selection. We present the key components of our system, discuss our findings and results and identify design recommendations.     

Cost Effective Optimal Task Scheduling Model in Hybrid Cloud Environment

In today’s world, Cloud Computing (CC) enables the users to access computing resources and services over cloud without any need to own the infrastructure. Cloud Computing is a concept in which a network of devices, located in remote locations, is integrated to perform operations like data collection, processing, data profiling and data storage. In this context, resource allocation and task scheduling are important processes which must be managed based on the requirements of a user. In order to allocate the resources effectively, hybrid cloud is employed since it is a capable solution to process large-scale consumer applications in a pay-by-use manner. Hence, the model is to be designed as a profit-driven framework to reduce cost and make span. With this motivation, the current research work develops a Cost-Effective Optimal Task Scheduling Model (CEOTS). A novel algorithm called Target-based Cost Derivation (TCD) model is used in the proposed work for hybrid clouds. Moreover, the algorithm works on the basis of multi-intentional task completion process with optimal resource allocation. The model was successfully simulated to validate its effectiveness based on factors such as processing time, make span and efficient utilization of virtual machines. The results infer that the proposed model outperformed the existing works and can be relied in future for real-time applications.     

Machine Learning-Informed Predictive Design and Analysis of Electrohydrodynamic Printing Systems

Electrohydrodynamic (EHD) processes are promising techniques for manufacturing nanoscopic products with different shapes (such as thin films, nanofibers, 2D/3D nanostructures, and nanoparticles) and materials at a low cost using simple equipment. A key challenge in their adoption by nonexperts is the requirement of enormous time and resources in identifying the optimum design/process parameters for the underlying material and EHD system. Machine learning (ML) has made exciting advancements in predictive modeling of different processes, provided it is trained on high-quality datasets at appropriate volumes. This article extends the suitability of such ML-enabled approaches to a new technological domain of EHD spraying and drop-on-demand printing. Different ML models like ridge regression, random forest regression, support vector regression, gradient boosting regression, and multilayer perceptron are trained and their performance using evaluation metrics like RMSE and R2_score is examined. Tree-based algorithms like gradient boosting regression are found to be the most suitable technique for modeling EHD processes. The trained ML models show substantially higher accuracy (average error < 5%) in replicating these nonlinear processes as compared to previously reported scaling laws (average error ≈ 42%) and are well suited for predictive modeling/analysis of the underlying EHD system and process.