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.     

Automatic ultrastructure segmentation of reconstructed cryoEM maps of icosahedral viruses.

We present an automatic algorithm to segment all the local and global asymmetric units of a three-dimensional density map of icosahedral viruses. This approach is readily applicable to the structural analysis of a broad range of virus structures that are reconstructed using cryo-electron microscopy (cryo-EM) technique. Our algorithm includes three major steps operating on the three dimensional density map: the detection of critical points of the volumetric density function, the detection of global and local symmetry axes, and, finally, the boundary segmentation of all the asymmetric units. We demonstrate the efficacy of our algorithm and report our results on several experimental volumetric datasets, consisting of both reconstructed cryo-EM molecular density maps taken from the European Bioinformatics Institute archive, as well our own synthetically generated (blurred) maps calculated from X-ray resolution molecular structural data taken from the Protein Data Bank.     

Carbohydrate content of potato (Solanum tuberosum L.) tubers treated with Isopropyl-N (3-chlorophenyl) carbamate under different storage conditions

Post harvest application of Isopropyl-N (3-chlorophenyl) carbamate considerably reduced the degradation of starch when potato tubers were stored in an evaporative cooling chamber. The starch degraded at a faster rate when tubers were kept under refrigerated and room storage conditions.     

Modeling of in situ monitored laser reflectance during MOCVD growth of HgCdTe

An effective way to in situ monitor the metalorganic chemical vapor deposition (MOCVD) of HgCdTe/CdTe/ZnTe on GaAs or GaAs/Si substrates is presented. Specular He-Ne laser reflectance was used to in situ monitor the growth rates, layer thickness, and morphology for each layer in the grown multilayer structure. In situ monitoring has enabled precise measurements of ZnTe nucleation and CdTe buffer layer thicknesses. Monitoring the constancy of reflectance during the thicker CdTe buffer growth where absorption in the CdTe reduces reflectance to just the surface component has led to optimum buffer growth ensuring good quality of subsequently grown HgCdTe. During the interdiffused multilayer process (IMP) HgCdTe growth, because multiple interfaces are present within the absorption length, a periodic reflectance signal is maintained throughout this growth cycle. A theoretical model was developed to extract IMP layer thicknesses from in situ recorded experimental data. For structures that required the growth of a larger band gap HgCdTe cap layer on top of a smaller band gap active layer, in situ monitored reflectance data allowed determination of alloy composition in the cap layer as well. Continuous monitoring of IMP parameters established the stability of growth conditions, translating into depth uniformity of the grown material, and allowed diagnosis of growth rate instabilities in terms of changes in the HgTe and CdTe parts of the IMP cycle. A unique advantage of in situ laser monitoring is the opportunity to perform “interactive” crystal growth, a development that is a key to real time MOCVD HgCdTe feedback growth control.     

Effect of coupling agents on thermal and electrical properties of mica/epoxy composites

The influence of 3-aminopropyltriethoxysilane, 3-glycidyloxypropyltrimethoxysilane, and neoalkoxytric(dioctyl pyrophosphato)zirconate on thermal expansion behavior, dielectric strength, and arc resistance of mica/epoxy composites has been investigated. The addition of mica up to 30% resulted in the reduction of thermal expansion with respect to neat resin. However, the coefficient of linear thermal expansion of 30% mica treated with aminosilane was the least among the various coupling agent-coated filler/epoxy composites. Mica (30%)/epoxy composites showed the highest dielectric strength values (26 kV/mm), but the highest arc resistance was obtained in zirconate-treated mica (30%)/epoxy composite. © 1995 John Wiley & Sons, Inc.     

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.