Impact of Inspected Buffers on Production Parameters of Construction Processes

This paper examines the production implications of quality control inspections conducted on the buffer between processes in a construction project by modeling the linkage between these processes. Inspection of partially completed work at the end of one activity but before the beginning of work by the next activity is fairly common. Work that is deemed to be of sufficient quality is then made available for the next activity. Work that is deemed insufficient requires rework, typically by the trade appropriate to the activity that fed into the buffer, to bring the work into compliance. This has implications for workload management for that trade, of course, as well as for the reliability of work flow to the successor or downstream processes. While such situations are common in all construction sectors, an example from the residential construction sector was examined via a simulation model augmented by field data collected from residential construction projects. The impacts of the work flow into the predecessor process, the inspection pass rate, and resource availability were examined. The inspection pass rate was found to dramatically affect the reliability of work flow, unless resources are unlimited. Furthermore, the inspection pass rate was found to be functionally related to the production parameters of the process.     

Fabrication and characterizations of PCDTBT: PC71BM bulk heterojunction solar cell using air brush coating method

Organic solar cells have been significantly attracted due to the need to develop an inexpensive clean and sustainable renewable energy source. We report on the fabrication of poly [N-9″-hepta-decanyl-2,7-carbazole-alt-5,5-(4′,7′-di-2-thienyl-2′,1′,3′-benzothiadiazole)]/[6, 6]-phenyl-C₇₁-butyric acid methyl ester blend active layer using airbrush spray-coating method in mixed solvents. Optical absorption of the active layers was analyzed using UV–visible spectral studies in the wavelength range from 300 to 800 nm. The surface morphology of the active layers deposited with different parameters (spraying time and substrate-nozzle distance) was examined using atomic force microscopy. The current density–voltage (J–V) characteristics of photovoltaic cells were measured under the illumination of simulated solar light with 100 mW cm^?2 (AM 1.5G) by an Oriel 1000 W solar simulator. The power conversion efficiency of the solar cell is more than 5 %.     

Fertilizer requirements for specified yield targets. II. Field verification of mathematical models for the estimation of soil and fertilizer nutrient efficiencies

Sugarcane response data from field experiments conducted between May 1979 and August 1981 on a sandy clay loam soil (Udic Haplustalf) of Coimbatore district, Tamil Nadu State, India were used in the present investigation. Soil () and fertilizer () nutrient efficiencies for the amount of fertilizer required for specified cane yield targets were computed from this data by three procedures, viz., conventional deduction procedure, Tamil Nadu Agricultural University Model I [TNAU Model I] and Model II [TNAU Model II].In the case of nitrogen, both TNAU Model I and TNAU Model II gave more realistic estimates of and than those determined by the conventional deduction procedure. The differences in the predicted amounts of fertilizer nitrogen required between these two models were well within the permissible limits of variation indicating that both these approaches can be followed for the amount of nitrogen required for specified yield targets.The Olsen's procedure for available phosphorus estimation was inadequate to explain the relationship between soil available phosphorus and sugarcane response as indicated by results obtained using the TNAU Model II. The incorporation of the term in this model caters for the actual situation in the field in respect of the relationship between soil and fertilizer phosphorus availabilities and phosphorus uptake by sugarcane proving usefulness of this model for assessing the amount of phosphorus required for specified cane yield targets.The results indicated that a considerable amount of potassium from the soil reserve was released into the soil available pool due to a priming effect. This fraction was preferentially absorbed by sugarcane compared to the fractions extracted by 0.1 N HNO₃ as indicated by results obtained using the TNAU Model II. In this case too, the actual situation regarding the relationship between soil and fertilizer potassium availabilities and potassium uptake by sugarcane is catered for by this model proving its superiority over the other two procedures for assessing the amount of potassium required for specified yield targets.     

Inverted emulsion polymerization route to polyaniline‐3D nanofiber network using sulfonated‐p‐cresol as novel dopant

Sulfonated‐p‐cresol (SPC) was used as novel dopant for the first time in the synthesis of polyaniline in 3D nanofiber networks (PANI‐3D). Polyaniline in 3D nanofiber network was prepared using organic solvent soluble benzoyl peroxide as oxidizing agent in presence of SPC and sodium lauryl sulfate (SLS) surfactant via inverted emulsion polymerization pathway. The influence of synthesis conditions such as the concentration of the reactants, stirring/static condition, and temperature etc., on the properties and formation of polyaniline nanofiber network were investigated. Polyaniline in 3D nanofiber network with 40–160 nm (diameter), high yield (134 wt % with respect to aniline used), and reasonably good conductivity (0.1 S/cm) was obtained in 24 h time. © 2010 Wiley Periodicals, Inc. J Appl Polym Sci, 2011     

Template-free mechanochemical route to prepare crystalline and electroactive polydiphenylamine nanostructures

A facile and sustainable mechanochemical route for the synthesis of undoped polydiphenylamine (PDPA) and inorganic acid doped nanostructures are reported. Field emission scanning electron microscopic (FESEM) images highlighted the formation of distinctly different nanostructures for each of the inorganic acid doped PDPA. Elemental analysis carried out for the polymers revealed the presence of more repeating units in their backbone. The X-ray diffraction (XRD) results of the as-prepared PDPA nanostructures indicated the high degree of crystallinity ever reported for PDPA. Spectroscopic profile of the polymers showed that the prepared PDPA is in a doped conducting form. Electrochemical studies performed for the polymeric particles ascertained the redox behaviour and the good electrochemical activity of obtained PDPA samples. The probable mechanistic aspect behind the formation of PDPA nanostructures through this simple and efficient route is discussed.     

Efficient heart disease prediction‐based on optimal feature selection using DFCSS and classification by improved Elman‐SFO

Prediction of cardiovascular disease (CVD) is a critical challenge in the area of clinical data analysis. In this study, an efficient heart disease prediction is developed based on optimal feature selection. Initially, the data pre‐processing process is performed using data cleaning, data transformation, missing values imputation, and data normalisation. Then the decision function‐based chaotic salp swarm (DFCSS) algorithm is used to select the optimal features in the feature selection process. Then the chosen attributes are given to the improved Elman neural network (IENN) for data classification. Here, the sailfish optimisation (SFO) algorithm is used to compute the optimal weight value of IENN. The combination of DFCSS–IENN‐based SFO (IESFO) algorithm effectively predicts heart disease. The proposed (DFCSS–IESFO) approach is implemented in the Python environment using two different datasets such as the University of California Irvine (UCI) Cleveland heart disease dataset and CVD dataset. The simulation results proved that the proposed scheme achieved a high‐classification accuracy of 98.7% for the CVD dataset and 98% for the UCI dataset compared to other classifiers, such as support vector machine, K‐nearest neighbour, Elman neural network, Gaussian Naive Bayes, logistic regression, random forest, and decision tree.Inspec keywords: cardiovascular system, medical diagnostic computing, feature extraction, regression analysis, data mining, learning (artificial intelligence), Bayes methods, neural nets, support vector machines, diseases, pattern classification, data handling, decision trees, cardiology, data analysis, feature selectionOther keywords: efficient heart disease prediction‐based, optimal feature selection, improved Elman‐SFO, cardiovascular disease, clinical data analysis, data pre‐processing process, data cleaning, data transformation, values imputation, data normalisation, decision function‐based chaotic salp swarm algorithm, optimal features, feature selection process, improved Elman neural network, data classification, sailfish optimisation algorithm, optimal weight value, DFCSS–IENN‐based SFO algorithm, DFCSS–IESFO, California Irvine Cleveland heart disease dataset, CVD dataset, high‐classification accuracy     

‘Bricks and mortar’ nanoparticle self‐assembly using polymers

Developments in self‐assembly methods allow access to hierarchical materials featuring a wide range of functionality and applications. Polymer‐based self‐assembly of nanoparticles opens up new avenues for the fabrication of highly structured nanocomposites that can serve as bridges between ‘bottom‐up’ and ‘top‐down’ methods. Of various interactions leading to self‐assembly of nanocomposites, hydrogen bonding and electrostatic interactions are commonly utilized. In this review, we illustrate the design and subsequent property tuning of various self‐assembled nanocomposite materials that were developed based on these interactions. Copyright © 2007 Society of Chemical Industry     

Didecyl ester of 4‐sulfophthalic acid as a plast dopant and emulsifier in the chemical polymerization of aniline into polyaniline salt

Polyaniline salt was synthesized through the chemical oxidation of aniline with sodium persulfate as the oxidant and didecyl ester of 4‐sulfophthalic acid via three different polymerization pathways (aqueous, emulsion, and interfacial). In these polymerization processes, the ester acted as a novel plast dopant and as an emulsifier. The yield, conductivity, and number of ester units present in the polyaniline salts were determined. A polyaniline salt prepared by emulsion polymerization was soluble in chloroform and showed excellent solution‐processing properties. Polyaniline samples prepared by aqueous or interfacial polymerization were not soluble in chloroform. A soluble polyaniline salt was successfully synthesized through the washing of an organic layer containing the polyaniline salt with water in emulsion polymerization. X‐ray diffraction spectra of polyaniline salts prepared by the three different methods showed an ordered, layer‐type supramolecular structure. © 2010 Wiley Periodicals, Inc. J Appl Polym Sci, 2010     

General slow viscous flows in a two-fluid system

Summary A general solution of the creeping flow equations suitable for a flow that is bounded by a nondeforming planar interface is presented. New compact representations for the velocity and pressure fields are given in terms of two scalar functions which describe arbitrary Stokes flow. A general reflection theorem is derived for a fluid-fluid interface problem containing Lorentz reflection formula as a particular case. The theorem allows a better interpretation of the image system for various singularities in the presence of a planar interface. The general solution is further used to describe the first-order approximation of the deformed interface by performing normal stress balance. It is found that the normal stress imbalance and the interface displacement are independent of the viscosity ratio of two fluids (!) and only depend on the location of initial singularity.     

Analyzing the performance of fuzzy cognitive maps with non-linear hebbian learning algorithm in predicting autistic disorder

The soft computing technique of fuzzy cognitive maps (FCM) for modeling and predicting autistic spectrum disorder has been proposed. The FCM models the behavior of a complex system and is used to develop new knowledge based system applications. FCM combines the robust properties of fuzzy logic and neural networks. To overwhelm the limitations and to improve the efficiency of FCM, a good learning method of unsupervised training could be applied. A decision system based on human knowledge and experience with a FCM trained using unsupervised non-linear hebbian learning algorithm is proposed here. Through this work the hebbian algorithm on non-linear units is used for training FCMs for the autistic disorder prediction problem. The investigated approach serves as a guide in determining the prognosis and in planning the appropriate therapies to special children.