Devising an electronically supported heritage conservation method for the Valley of Flowers in the Indian Himalayas

The Valley of Flowers is a national park in the Himalayan state of Uttarakhand in India that was classified as a world natural heritage site in 1988. Around 1982, its maximum carrying capacity was fixed at 60 persons per day, which has been called excessive by experts and observers, given the extremely fragile and immensely valuable nature of the Valley's heritage. This, in monetary terms, can be put at millions of dollars, and is considerably more viewed in terms of knowledge of breeding medicinal plants in cold climates that are being affected by climate change, which its microclimate and ecology present. Given the state's poor resources and the fact that tourism is one of the most important industries for development and conservation, this research assesses the heritage value of the Valley and develops a programme for conservation, including a computerised program for permits, whose value can easily be raised from the current paltry Rs 150 per person. Accompanied by fallow periods and marketing through the Internet to aim for educated tourists, the program ensures that the maximum carrying capacity of the Valley is never exceeded, thus spreading out the number of tourists over its 3.5-month season, while allowing flexibility in booking for chance groups and small families that can pay more.     

Biogeography based optimization for mining rules to assess credit risk

Financial institutions, by and large, rely on the use of machine learning techniques to improve the classic credit risk assessment model for reduction of costs, delivery of faster decisions, guaranteed credit collections, and risk mitigations. As such, several data mining and machine learning approaches have been developed for computation of credit scores over the last few decades. Moreover, the existing rule-based classification algorithms tend to generate a number of rules with a large number of conditions in the antecedent part. However, these algorithms fail to demonstrate high predictive accuracy while balancing coverage and simplicity. Thus, it becomes quite a challenging task for the researchers to generate an optimal rule set with high predictive accuracy. In this paper, we present an effective rule based classification technique for the prediction of credit risk using a novel Biogeography Based Optimization (BBO) method. The novel BBO in the context of rule mining is named as locally and globally tuned biogeography based rule-miner (LGBBO-RuleMiner). This is applied for discovering optimal rule set with high predictive accuracy from the dataset containing both the categorical and continuous attributes. The performance of the proposed algorithm is compared against a variety of rule-miners such as OneR (1R), PART, JRip, Decision Table, Conjunctive Rule, J48, and Random Tree, along with some meta-heuristic based rule mining techniques by considering two credit risk datasets obtained from University of California, Irvine (UCI) repository. It is found from the comparative study that the proposed rule miner in ten independent runs of ten-fold cross validation outperforms all of the aforesaid algorithms in terms of predictive accuracy, coverage, and simplicity.     

Effectiveness of cause‐related marketing for differential positioning of market entrant in developing market: An exploratory study in Indian context

Cause‐related marketing is being increasingly used for forging strong relation between the brand and the customer. It is primarily being used by the well‐established brands to further strengthen their position in the market as it leads to a positive image of the brand, and customers are more receptive towards such brands because they tend to provide them with tangible as well as intangible benefits. This study tends to identify the effectiveness of cause‐related marketing as a marketing tool for newly launched products in developing market of India, which has so far remained a less explored area. A sample of 150 consumers was taken, and case studies were undertaken for validating these findings. The findings suggest that cause‐related marketing campaigns, even at early stages of brand development, can lead to customer trial and differential positioning thus providing these new entrants with an opportunity to interact with the customers. The proposed framework can provide useful insights to brand managers to design cause‐related marketing campaigns for newly launched brands. The results and findings have been deduced from real‐time market research, and it can help in furthering research in the field of cause‐related marketing in India and other developing economies with similar market conditions.     

The role of productive failure in 3D printing in a middle school setting

International Journal of Technology and Design Education - In this research we explore the pedagogical affordances associated with the use of a 3D printer in a middle school classroom...     

Travel and Aspects of Societal Structure: A Comparison of India and the United States

Most theoretical studies in tourism depend on the methods of illustrative comparison and case study. The limitations of the case study approach have lately been discussed. Like the method of illustrative comparison, it does not allow deductive development of theory. To rectify this situation, and to overcome the lacunae in methodology that Dann et al. (1988) and Nash (1996) complain about, the method of formal or systematic comparison can be brought to bear on tourism in various societies. In such a study, this paper compares two large, complex societies - India and the USA - and looks at the ramifications of travel. It analyses the institutionalisation of travel in a modern (US) and a modernising (Indian) society, including aspects of societal structure that are reflected in the language, and debates whether MacCannell's (1989) argument that tourism is a 'modern ritual' can be borne out. The paper concludes by looking at some of the theoretical implications and discussing the practical implications of the study for development of tourism in India.     

Cost drivers for breast,lung, and colorectal cancer care in a commercially insured population over a 6-month episode: an economic analysis from a health plan perspective

Aims: In the absence of clinical data, accurate identification of cost drivers is needed for economic comparison in an alternate payment model. From a health plan perspective using claims data in a commercial population, the objective was to identify and quantify the effects of cost drivers in economic models of breast, lung, and colorectal cancer costs over a 6-month episode following initial chemotherapy.

Research design and methods: This study analyzed claims data from 9,748 Cigna beneficiaries with diagnosis of breast, lung, and colorectal cancer following initial chemotherapy from January 1, 2014 to December 31, 2015. We used multivariable regression models to quantify the impact of key factors on cost during the initial 6-month cancer care episode.

Results: Metastasis, facility provider affiliation, episode risk group (ERG) risk score, and radiation were cost drivers for all three types of cancer (breast, lung, and colorectal). In addition, younger age (p?p?p?p?p?Conclusions: Value-based reimbursement models in oncology should appropriately account for key cost drivers. Although claims-based methodologies may be further augmented with clinical data, this study recommends adjusting for the factors identified in these models to predict costs in breast, lung, and colorectal cancers.