Effects of DEM Source,Spatial Resolution and Drainage Area Threshold Values on Hydrological Modeling

Water Resources Management - Automated computation of hydrological and morphometric parameters of any watershed are not only depended on Digital Elevation Model (DEM) resolution but also affected...     

Fuzzy Multiobjective and Linear Programming Based Management Models for Optimal Land-Water-Crop System Planning

In this article, linear programming and fuzzy optimization models are developed for planning and management of available land-water-crop system of Mahanadi-Kathajodi delta in eastern India. The models are used to optimize the economic return, production and labour utilization, and to search the related cropping patterns and intensities with specified land, water, fertilizer and labour availability, and water use pattern constraints. Due to extreme backwardness of the study area, it has been decided to keep all the three objectives of the linear programming models at the same priority level to obtain the compromised solution in a fuzzy environment that incorporates the imprecision in fuzzy goals and fuzzy constraints. These non-structural models facilitate the conjunctive use of available surface water and groundwater resources. A comparative evaluation along with the benefit-cost ratios of the existing and proposed farming systems is also presented.     

Electroless Plating of Copper on Fluorinated Polyimide Films Modified by Plasma Graft Copolymerization and UV‐induced Graft Copolymerization with 4‐Vinylpyridine

Surface modification of two types of fluorinated polyimide (FPI) films, either by plasma polymerization and deposition of 4‐vinylpyridine (4VP) or by UV‐induced graft copolymerization with 4VP under atmospheric conditions, was carried out for adhesion enhancement with the electrolessly deposited copper. X‐ray photoelectron spectroscopy (XPS) and Fourier transform infrared (FTIR) results revealed that the pyridine groups in the plasma polymerized 4VP (pp‐4VP) layer could be preserved to a large extent under proper glow discharge conditions. The grafted 4VP layer with well‐preserved pyridine groups was used not only as the chemisorption sites for the palladium complexes (without the need for prior sensitization by SnCl₂) during the electroless plating of copper, but also as an adhesion promotion layer for the electrolessly deposited copper. The T‐peel adhesion strength of the electrolessly deposited copper with both the 4VP plasma‐polymerized FPI (pp‐4VP‐FPI) film and the 4VP graft‐copolymerized FPI (4VP‐g‐FPI) film was much higher than that of the electrolessly deposited copper with the pristine or the Ar plasma‐treated FPI films. The high adhesion strength between the electrolessly deposited copper and the surface‐modified FPI film was attributed to the fact that the plasma‐polymerized and the UV graft‐copolymerized 4VP chains were covalently tethered on the FPI surfaces, as well as the fact that these grafted 4VP polymer chains were spatially and reactively distributed into the copper matrix.

     

Effect of AlN addition on the electrical resistivity of pressureless sintered SiC ceramics with B4C and C

The effect of 0–12 wt% AlN addition on the electrical resistivity of SiC ceramics pressureless sintered with 0.7 wt% B₄C and 2.5 wt% C additives was investigated. The elemental analysis of SiC grains revealed a codoping of Al and N in the SiC lattice with a higher N concentration with 1 wt% AlN addition and a higher Al concentration with 12 wt% AlN addition. The electrical resistivity decreased by four orders of magnitude (1.7 × 10⁵ → 8.3 × 10¹ Ω cm) with 1 wt% AlN addition due to the increased carrier density (1.7 × 10¹⁰ → 2.3 × 10¹⁵ cm⁻³) caused by excess N-derived donors. However, subsequent AlN addition (4 → 12 wt%) led to an increase (2.9 × 10³ → 1.2 × 10⁴ Ω‧cm) in electrical resistivity due to (1) increased Al dopants which act as deep acceptors for trapping N-derived carriers causing a decrease in carrier density (2.3 × 10¹⁵ → 5.9 × 10¹³ cm⁻³), (2) the formation of electrically insulating SiC-AlN solid solution, and (3) the presence of electrically insulating AlN grains at the grain boundaries.     

Using dynamic time warping distances as features for improved time series classification

Dynamic time warping (DTW) has proven itself to be an exceptionally strong distance measure for time series. DTW in combination with one-nearest neighbor, one of the simplest machine learning methods, has been difficult to convincingly outperform on the time series classification task. In this paper, we present a simple technique for time series classification that exploits DTW’s strength on this task. But instead of directly using DTW as a distance measure to find nearest neighbors, the technique uses DTW to create new features which are then given to a standard machine learning method. We experimentally show that our technique improves over one-nearest neighbor DTW on 31 out of 47 UCR time series benchmark datasets. In addition, this method can be easily extended to be used in combination with other methods. In particular, we show that when combined with the symbolic aggregate approximation (SAX) method, it improves over it on 37 out of 47 UCR datasets. Thus the proposed method also provides a mechanism to combine distance-based methods like DTW with feature-based methods like SAX. We also show that combining the proposed classifiers through ensembles further improves the performance on time series classification.     

Observer-invariant histopathology using genetics-based machine learning

Prostate cancer accounts for one-third of noncutaneous cancers diagnosed in US men and is a leading cause of cancer-related death. Advances in Fourier transform infrared spectroscopic imaging now provide very large data sets describing both the structural and local chemical properties of cells within prostate tissue. Uniting spectroscopic imaging data and computer-aided diagnoses (CADx), our long term goal is to provide a new approach to pathology by automating the recognition of cancer in complex tissue. The first step toward the creation of such CADx tools requires mechanisms for automatically learning to classify tissue types—a key step on the diagnosis process. Here we demonstrate that genetics-based machine learning (GBML) can be used to approach such a problem. However, to efficiently analyze this problem there is a need to develop efficient and scalable GBML implementations that are able to process very large data sets. In this paper, we propose and validate an efficient GBML technique——based on an incremental genetics-based rule learner. exploits massive parallelisms via the message passing interface (MPI) and efficient rule-matching using hardware-implemented operations. Results demonstrate that is capable of performing prostate tissue classification efficiently, making a compelling case for using GBML implementations as efficient and powerful tools for biomedical image processing.     

Biocompatible antimicrobial cotton fibres for healthcare industries: a biogenic approach for synthesis of bio‐organic‐coated silver nanoparticles

Cotton fibres coated with biogenically fabricated silver nanoparticles (SNPs) are most sought material because of their enhanced activity and biocompatibility. After successful synthesis of SNPs on cotton fibres using leaf extract of Vitex negundo Linn, the fibres were studied using diffuse reflectance spectroscopy, scanning electron microscopy, nanoparticle tracking analysis, energy dispersive X‐ray, and inductively coupled plasma atomic emission spectrometry. The characterisation revealed uniformly distributed spherical agglomerates of SNPs having individual particle size around 50 nm with the deposition load of 423 μg of silver per gram of cotton. Antimicrobial assay of cotton–SNPs fibres showed effective performance against pathogenic bacteria and fungi. The method is biogenic, environmentally benign, rapid, and cost‐effective, producing highly biocompatible antimicrobial coating required for the healthcare industry.Inspec keywords: cotton, health care, nanoparticles, coatings, silver, fibres, nanofabrication, scanning electron microscopy, X‐ray chemical analysis, atomic emission spectroscopy, plasma applications, microorganisms, biotechnologyOther keywords: biocompatible antimicrobial cotton fibre coating, healthcare industry, bioorganic‐coated silver nanoparticle synthesis, biogenically fabricated silver nanoparticle, SNP, leaf extraction, Vitex negundo Linn, diffuse reflectance spectroscopy, scanning electron microscopy, nanoparticle tracking analysis, energy dispersive X‐ray spectrometry, inductively coupled plasma atomic emission spectrometry, uniformly distributed spherical agglomerate, antimicrobial assay, pathogenic bacteria, fungi, Ag     

Randomized constraint solvers: a comparative study

The complexity of constraints is a major obstacle for constraint-based software verification. Automatic constraint solvers are fundamentally incomplete: input constraints often build on some undecidable theory or some theory the solver does not support. This paper proposes and evaluates several randomized solvers to address this issue. We compared the effectiveness of a symbolic solver (CVC3), a random solver, two heuristic search solvers, and seven hybrid solvers (i.e. mix of random, symbolic, and heuristic solvers). We evaluated the solvers on a benchmark generated with a concolic execution of 9 subjects. The performance of each solver was measured by its precision, which is the fraction of constraints that the solver can find solution out of the total number of constraints that some solver can find solution. As expected, symbolic solving subsumes the other approaches for the 4 subjects that only generate decidable constraints. For the remaining 5 subjects, which contain undecidable constraints, the hybrid solvers achieved the highest precision (fraction of constraints that a solver can find a solution out of the total number of satisfiable constraints). We also observed that the solvers were complementary, which suggests that one should alternate their use in iterations of a concolic execution driver.     

Formal Analysis of Multiparty Contract Signing

We analyze the multiparty contract-signing protocols of Garay and MacKenzie (GM) and of Baum and Waidner (BW). We use a finite-state tool, Mocha, which allows specification of protocol properties in a branching-time temporal logic with game semantics. While our analysis does not reveal any errors in the BW protocol, in the GM protocol we discover serious problems with fairness for four signers and an oversight regarding abuse-freeness for three signers. We propose a complete revision of the GM subprotocols in order to restore fairness.     

Fast and efficient extraction algorithm for high-speed interconnects with arbitrary boundaries

In this paper, we present an accurate and general interconnect model for planar transmission line interconnects with arbitrary boundary conditions. Based on the unified approach, we develop a SPICE-compatible parameter extraction algorithm that can be used in high-performance computer-aided-design applications. A range of multilayered interconnect geometries with arbitrary boundaries are analyzed. Different typical configurations of ground placement are considered to verify the applicability of this method. For all such cases, results are compared for admittance, line parameters, and delay giving physical insight on the effect of boundary conditions on them. Compared with existing industry standard numerical field-solvers, like HFSS, the proposed model demonstrates more than 10× speedup within 2% accuracy.