A Study of Buyer Motivations at the Bottom of Pyramid in Urban India

The Indian bottom of pyramid (BoP) segment contributes around 85% of the total national household market. This study attempts to ascertain the purchase behaviour of customers at the Indian urban BoP. It endeavours to appreciate the viewpoint of the urban BoP consumers in the purchase process with reference to their purchase basket comprising of products mainly across categories such as grocery, perishables and basic consumer durables. The study starts with qualitative grounded theory followed by quantitative survey-based approach. It presents and validates emergent themes to give insights about purchase behaviour of consumers at urban BoP. The empirical findings of the study discovered five consumer motivations through factor analysis. The subsequent result of the cluster analysis showed that the urban BOP market is heterogeneous. Since the size of cluster is substantial, companies must make marketing efforts to target them on a priority basis. The study proposes a conceptual model of consumer motivation supported by the self-determination Theory for the urban BoP market in India.

SKIMMR: Facilitating knowledge discovery in life sciences by machine-aided skim reading

Background: Unlike full reading, 'skim-reading' involves the process of looking quickly over information in an attempt to cover more material whilst still being able to retain a superficial view of the underlying content. Within this work, we specifically emulate this natural human activity by providing a dynamic graph-based view of entities automatically extracted from text. For the extraction, we use shallow parsing, co-occurrence analysis and semantic similarity computation techniques. Our main motivation is to assist biomedical researchers and clinicians in coping with increasingly large amounts of potentially relevant articles that are being published ongoingly in life sciences. Methods: To construct the high-level network overview of articles, we extract weighted binary statements from the text. We consider two types of these statements, co-occurrence and similarity, both organised in the same distributional representation (i.e., in a vector-space model). For the co-occurrence weights, we use point-wise mutual information that indicates the degree of non-random association between two co-occurring entities. For computing the similarity statement weights, we use cosine distance based on the relevant co-occurrence vectors. These statements are used to build fuzzy indices of terms, statements and provenance article identifiers, which support fuzzy querying and subsequent result ranking. These indexing and querying processes are then used to construct a graph-based interface for searching and browsing entity networks extracted from articles, as well as articles relevant to the networks being browsed. Last but not least, we describe a methodology for automated experimental evaluation of the presented approach. The method uses formal comparison of the graphs generated by our tool to relevant gold standards based on manually curated PubMed, TREC challenge and MeSH data. Results: We provide a web-based prototype (called `SKIMMR') that generates a network of inter-related entities from a set of documents which a user may explore through our interface. When a particular area of the entity network looks interesting to a user, the tool displays the documents that are the most relevant to those entities of interest currently shown in the network. We present this as a methodology for browsing a collection of research articles. To illustrate the practical applicability of SKIMMR, we present examples of its use in the domains of Spinal Muscular Atrophy and Parkinson's Disease. Finally, we report on the results of experimental evaluation using the two domains and one additional dataset based on the TREC challenge. The results show that the presented method for machine-aided skim reading outperforms tools like PubMed regarding focused browsing and informativeness of the browsing context.

SKIMMR: Facilitating knowledge discovery in life sciences by machine-aided skim reading

Background: Unlike full reading, 'skim-reading' involves the process of looking quickly over information in an attempt to cover more material whilst still being able to retain a superficial view of the underlying content. Within this work, we specifically emulate this natural human activity by providing a dynamic graph-based view of entities automatically extracted from text. For the extraction, we use shallow parsing, co-occurrence analysis and semantic similarity computation techniques. Our main motivation is to assist biomedical researchers and clinicians in coping with increasingly large amounts of potentially relevant articles that are being published ongoingly in life sciences. Methods: To construct the high-level network overview of articles, we extract weighted binary statements from the text. We consider two types of these statements, co-occurrence and similarity, both organised in the same distributional representation (i.e., in a vector-space model). For the co-occurrence weights, we use point-wise mutual information that indicates the degree of non-random association between two co-occurring entities. For computing the similarity statement weights, we use cosine distance based on the relevant co-occurrence vectors. These statements are used to build fuzzy indices of terms, statements and provenance article identifiers, which support fuzzy querying and subsequent result ranking. These indexing and querying processes are then used to construct a graph-based interface for searching and browsing entity networks extracted from articles, as well as articles relevant to the networks being browsed. Last but not least, we describe a methodology for automated experimental evaluation of the presented approach. The method uses formal comparison of the graphs generated by our tool to relevant gold standards based on manually curated PubMed, TREC challenge and MeSH data. Results: We provide a web-based prototype (called `SKIMMR') that generates a network of inter-related entities from a set of documents which a user may explore through our interface. When a particular area of the entity network looks interesting to a user, the tool displays the documents that are the most relevant to those entities of interest currently shown in the network. We present this as a methodology for browsing a collection of research articles. To illustrate the practical applicability of SKIMMR, we present examples of its use in the domains of Spinal Muscular Atrophy and Parkinson's Disease. Finally, we report on the results of experimental evaluation using the two domains and one additional dataset based on the TREC challenge. The results show that the presented method for machine-aided skim reading outperforms tools like PubMed regarding focused browsing and informativeness of the browsing context.

SKIMMR: Facilitating knowledge discovery in life sciences by machine-aided skim reading

Background: Unlike full reading, 'skim-reading' involves the process of looking quickly over information in an attempt to cover more material whilst still being able to retain a superficial view of the underlying content. Within this work, we specifically emulate this natural human activity by providing a dynamic graph-based view of entities automatically extracted from text. For the extraction, we use shallow parsing, co-occurrence analysis and semantic similarity computation techniques. Our main motivation is to assist biomedical researchers and clinicians in coping with increasingly large amounts of potentially relevant articles that are being published ongoingly in life sciences. Methods: To construct the high-level network overview of articles, we extract weighted binary statements from the text. We consider two types of these statements, co-occurrence and similarity, both organised in the same distributional representation (i.e., in a vector-space model). For the co-occurrence weights, we use point-wise mutual information that indicates the degree of non-random association between two co-occurring entities. For computing the similarity statement weights, we use cosine distance based on the relevant co-occurrence vectors. These statements are used to build fuzzy indices of terms, statements and provenance article identifiers, which support fuzzy querying and subsequent result ranking. These indexing and querying processes are then used top construct a graph-based interface for searching and browsing entity networks extracted from articles, as well as articles relevant to the networks being browsed. Results: We provide a prototype (called SKIMMR) that generates a network of inter-related entities from a set of documents which users may explore through our interface. When a particular area of the entity network looks interesting to a user, the tool displays the documents that are most relevant entities currently shown in the network. We present this as a methodology for browsing a collection of research articles. To illustrate the practical applicability of SKIMMR, we present examples of its use in the domains of Spinal Muscular Atrophy and Parkinson's Disease. Finally, we describe a methodology for automated experimental evaluation of SKIMMR instances. The method uses formal comparison of the graphs generated by our tool to relevant gold standards based on manually curated PubMed, TREC challenge and MeSH data. The results of experiments performed on three different instances of SKIMMR show that the presented method for machine-aided skim reading outperforms state of the art tools like PubMed regarding focused browsing and informativeness of the browsing context. Conclusions: In preliminary trials, sample users find new, interesting and non-trivial facts with the tool. Our evaluation showed a high potential of the presented work for facilitating knowledge discovery in life sciences.

SKIMMR: Facilitating knowledge discovery in life sciences by machine-aided skim reading

Background: Unlike full reading, 'skim-reading' involves the process of looking quickly over information in an attempt to cover more material whilst still being able to retain a superficial view of the underlying content. Within this work, we specifically emulate this natural human activity by providing a dynamic graph-based view of entities automatically extracted from text. For the extraction, we use shallow parsing, co-occurrence analysis and semantic similarity computation techniques. Our main motivation is to assist biomedical researchers and clinicians in coping with increasingly large amounts of potentially relevant articles in life sciences. Methods: To construct the high-level network overview of articles, we extract weighted binary statements from the text. We consider two types of these statements, co-occurrence and similarity, both organised in the same distributional representation (i.e., in a vector-space model). For the co-occurrence weights, we use point-wise mutual information that indicates the degree of non-random association between two co-occurring entities. For computing the similarity statement weights, we use cosine distance based on the relevant co-occurrence vectors. These statements are used to build fuzzy indices of terms, statements and provenance article identifiers, which support fuzzy querying and subsequent result ranking. These indexing and querying processes are then used top construct a graph-based interface for searching and browsing entity networks extracted from articles, as well as articles relevant to the networks being browsed. Results: We provide a web-based prototype (called `SKIMMR') that generates a network of inter-related entities from a set of documents which a user may explore through our interface. When a particular area of the entity network looks interesting to a user, the tool displays the documents that are most relevant to those entities of interest currently shown in the network. We present this as a methodology for browsing a collection of research articles. To illustrate the practical applicability of SKIMMR, we present examples of its use in the domains of Spinal Muscular Atrophy and Parkinson's Disease. Last but not least, we describe a methodology for automated experimental evaluation of SKIMMR instances. The method uses formal comparison of the graphs generated by our tool to relevant gold standards based on manually curated PubMed, TREC challenge and MeSH data. The results of experiments performed on three different instances of SKIMMR show that the presented method for machine-aided skim reading outperforms state of the art tools like PubMed regarding focused browsing and informativeness of the browsing context. Conclusions: In preliminary trials, users find new, interesting and non-trivial facts with SKIMMR. Our evaluation showed a high potential of the presented work for facilitating knowledge discovery in life sciences.

A Heuristic Approach to Train Dispatch Planning

Decision support tools that employ the use of computational intelligence are natural candidates to tackle the problem of dispatching trains in a railroad, allowing for the construction of flexible algorithms that explore the solution space optimizing the result for a defined objective, besides supporting the insertion of knowledge to the solution search process. We present a solution based on discrete-event simulation and heuristics to find feasible routes for all the trains. Decisions about passings and overtakings follow a set of heuristics that adhere to the railway circulation rules. Empirical results show that this algorithm runs in polynomial time. Some possibilities for improving the results provided by the tool are discussed. The first is to collect information from one planning run to feed the next run and improve the subsequent result, this approach is called adaptive. We use a rules executing environment that process a set of facts collected from previous executions of the planning tool and alter the results based on the set of rules and collected facts. The second approach is based on the simultaneous execution of different algorithms, an architecture to support it is being developed and the goal is to be able to choose the best result provided by a set of planning algorithms and not just one.

Fast hash functions and convolution product

We propose a new simple and efficient family of hash functions based on matrix-vector multiplications with a competitive software implementation. The hash design combines a hard mathematical problem based on solving a system of linear equations with special-random requirements and the fast computation of the convolution product algorithm. Such a mixing was often unrealizable. For security, the one-way and collision resistant criteria are based on the fact that inverting the compression function for random values is infeasible in reasonable time. In a subsequent result, we conjecture a general framework for producing secure matrix multiplication hash functions.

An Analytical Study on the Spherical Spindle of an Ultra Precision Machine for Use in Nano Machining Technology

Air spindles in ultra precision machines produce rotational movements for the cutting tool or work piece. The common combination of a simple cylindrical rotor and stator is the design for most spindles. If the length of such a spindle is longer than usual, it will deviate from its stable situation and start vibrating during operation, especially in high rotational speeds. In order to overcome the vibration problem, one of possible solutions is the application of a spherical rotor and stator. The manufacturing and assembly limitations do not allow obeying the spherical shape exactly. Thus, the design has been committed according to a quasi-sphere. This form of the rotor will be more stable. The subsequent result of stability improvement will be less air pressure and power consumption. There are some specific characterizations of the spindle which must be calculated for the spherical case. For this purpose a computer model of the object was made. Then, the model was put under finite element study to find the best air pressure and air flow velocity condition.

The Significance of the Retrograde Patency Test in Microarterial Anastomosis

The patency of a microarterial anastomosis was tested both in the antegrade and retrograde directions. Experimental and clinical observations showed that if patency was achieved in both directions, the subsequent result was much better than in those where patency was demonstrated only in the antegrade, but not in the retrograde direction.