A bio-inspired design strategy: Organization of tryptophan-appended naphthalenediimide into well-defined architectures induced by molecular interactions

The chemistry of molecular assemblies involves weak yet complex non-covalent interactions, and the molecular organization of the π-conjugated material is crucial in determining the performance of an organic electronic device. Herein we demonstrate a bioinspired design strategy to tune the self-assembly of naphthalenediimides (NDIs) by minute structural variations, π-π stacking, hydrophobic interactions and metal interactions. We address some of the limitations associated with current design strategies, such as restriction to a specific molecular interaction or the difficulty in controlling the assembly due to several complicated intermolecular interactions. Hydrophobic-effect-induced J-type aggregation and sodium-interaction-induced H-type aggregation of tryptophan-appended NDIs have been illustrated. (1)H NMR spectra further reveal sodium cation-π interactions in tryptophan-appended NDIs, while NMR and IR spectroscopic studies confirm the structural variations associated with the molecular assembly. In summary, the molecular organization has been successfully transformed from nanospheres to particles, nanobelts, fibers and fractals. Such drastic changes in the morphology are clear and striking evidence of the importance of non-trivial weak non-covalent forces.     

Evaluation of Phytoremediation for Field-Scale Degradation of Total Petroleum Hydrocarbons

Laboratory studies have shown phytoremediation to be a feasible method for remediating surface soils contaminated with organic compounds. Evaluation of this technology in the field is difficult because of the inherent spatial heterogeneity in the hydraulic and chemical properties of the soil. In this study, total petroleum hydrocarbon (TPH) degradation was monitored in a field site with three vegetative treatment plots, and one control plot undergoing natural attenuation. Within each treatment, TPH concentrations were monitored at 20 locations over time to study the phytoremediation potential of the different vegetative treatments. For comparing the performance of these treatments in a quantitative manner, first-order kinetics were assumed to be applicable at the local scale. The degradation rates and the initial contaminant concentrations were treated as spatially correlated random fields. Field-scale behavior was evaluated based on temporal variations of the means and variances of concentrations. Our results indicate the importance of spatial variability for an accurate assessment of phytoremediation in the field. From the degradation rate constants and mean reduction in TPH, rye grass and St. Augustine grass appear to be superior to sorghum and the unvegetated control in reducing contaminant concentrations in the field.     

Hydroponic-based smart irrigation system using Internet of Things

Agriculture is a science that deals with the cultivating the soil, growing crops, and raising livestock. In India, the agricultural sector contributes GDP of around 16% and 10% of export earnings. Due to the advancement in smart technologies, the different types of irrigation techniques such as hydroponics and aeroponics are utilized for higher crop yields in a small area. In this work, the smart irrigation system using Internet of Things (IoT) is proposed for hydroponic system. Further, the experimental prototype is developed, and real-time physical data such as temperature, humidity, and water flow are monitored, controlled, and logged with the help of ThingSpeak IoT platform. Results demonstrate that the proposed system is capable of monitoring and controlling the hydroponic environment and can be able to yield higher crop production.     

Holistic verification of handwritten phrases

In this paper, we describe a system for rapid verification of unconstrained off-line handwritten phrases using perceptual holistic features of the handwritten phrase image. The system is used to verify handwritten street names automatically extracted from live US mail against recognition results of analytical classifiers. Presented with a binary image of a street name and an ASCII street name, holistic features (reference lines, large gaps and local contour extrema) of the street name hypothesis are “predicted” from the expected features of the constituent characters using heuristic rules. A dynamic programming algorithm is used to match the predicted features with the extracted image features. Classes of holistic features are matched sequentially in increasing order of cost, allowing an ACCEPT/REJECT decision to be arrived at in a time-efficient manner. The system rejects errors with 98 percent accuracy at the 30 percent accept level, while consuming approximately 20/msec per image on the average on a 150 MHz SPARC 10     

The role of holistic paradigms in handwritten word recognition

The holistic paradigm in handwritten word recognition treats the word as a single, indivisible entity and attempts to recognize words from their overall shape, as opposed to their character contents. In this survey, we have attempted to take a fresh look at the potential role of the holistic paradigm in handwritten word recognition. The survey begins with an overview of studies of reading which provide evidence for the existence of a parallel holistic reading process,in both developing and skilled readers. In what we believe is a fresh perspective on handwriting recognition, approaches to recognition are characterized as forming a continuous spectrum based on the visual complexity of the unit of recognition employed and an attempt is made to interpret well-known paradigms of word recognition in this framework. An overview of features, methodologies, representations, and matching techniques employed by holistic approaches is presented     

Devanagari OCR using a recognition driven segmentation framework and stochastic language models

This paper describes a novel recognition driven segmentation methodology for Devanagari Optical Character Recognition. Prior approaches have used sequential rules to segment characters followed by template matching for classification. Our method uses a graph representation to segment characters. This method allows us to segment horizontally or vertically overlapping characters as well as those connected along non-linear boundaries into finer primitive components. The components are then processed by a classifier and the classifier score is used to determine if the components need to be further segmented. Multiple hypotheses are obtained for each composite character by considering all possible combinations of the classifier results for the primitive components. Word recognition is performed by designing a stochastic finite state automaton (SFSA) that takes into account both classifier scores as well as character frequencies. A novel feature of our approach is that we use sub-character primitive components in the classification stage in order to reduce the number of classes whereas we use an n-gram language model based on the linguistic character units for word recognition.     

Enhancement of wear and corrosion resistance of metal-matrix composites by laser coatings

A novel technique based on laser-induced chemical reduction of metal salts has been developed to produce surface coatings on metal-matrix composites (MMCs). The substrate is predeposited with a paste, containing concentrated salts of the elements to be coated along with a thickening agent, and then subjected to high power laser radiation. The rise in surface temperature during laser irradiation led to the decomposition of salts to their native metals. The combination of metal and metalloid elements in the reaction zone forms an amorphous layer due to the specific chemical ratio and rapid cooling rate. The thickness of the coatings obtained were of the order of 50–100 m. The coatings exhibited amorphous and microcrystalline structures, possessed hardness in the range of 300–1700 H_v (substrate hardness 80–90 H_v), had superior sliding wear resistance and excellent corrosion resistance. The advantages of this process include the formation of complex coatings on MMCs by a simple, versatile technique which does not require any vacuum or inert atmosphere.     

GP-based secondary classifiers

Genetic programming (GP) is used to evolve secondary classifiers for disambiguating between pairs of handwritten digit images. The inherent property of feature selection accorded by GP is exploited to make sharper decision between conflicting classes. Classification can be done in several steps with an available feature set and a mixture of strategies. A two-step classification strategy is presented in this paper. After the first step of the classification using the full feature set, the high confidence recognition result will lead to an end of the recognition process. Otherwise a secondary classifier designed using a sub-set of the original feature set and the information available from the earlier classification step will help classify the input further. The feature selection mechanism employed by GP selects important features that provide maximum separability between classes under consideration. In this way, a sharper decision on fewer classes is obtained at the secondary classification stage. The full feature set is still available in both stages of classification to retain complete information. An intuitive motivation and detailed analysis using confusion matrices between digit classes is presented to describe how this strategy leads to improved recognition performance. In comparison with the existing methods, our method is aimed for increasing recognition accuracy and reliability. Results are reported for the BHA test-set and the NIST test-set of handwritten digits.