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Entrepreneurial development in India relies on generational mentoring, family business skill set, and exploration of technological progress. The know-how of skills, learned and refined over generations, leads to sustained development that is then converted into technology. Inheritance is one aspect that plays a major role in skill development. The transformation of skill into a technological component paves the way for the successful entrepreneurship. The spark initiated through inherited skill sets and ignited through the addition of technology results in useful entrepreneurial value. The basic societal needs remain the same while the trends take a changed form with the advent in technology. This requires the conversion of the time-honored skills through adaptation of the technological exponent in the changing societal trends. This paper offers an optimization model with maximization of entrepreneurial value as the objective and business output as the constraint. Skill and technology are taken as the variables. The model uses the primer value theorem as the computational tool, which solves the model in the indeterminate domain to suggest different strategies. The concept is demonstrated through three case examples.

This paper reports one set of contributions from an exploratory study of management education institutions in India to ground the Institution Building (IB) theory. It develops empirically a general processual model of institution building using five concepts: context; capability development, innovative thrust, and penetration (performance); and process mechanisms. The paper outlines the various process mechanisms affecting institutional development. Implications for planned change at macrosystem (social change) and microsystem (organizational change) levels are highlighted.

An underdeveloped aspect of Organizational Development literature is the need for understanding individual organizational consultants and their styles, which influence their interventions. It is through understanding styles that one can begin to appreciate and understand the process of application of behavioral science knowledge to the development of organizations. In this paper an attempt is made to provide insights into consulting styles through a summary of results of research on 21 leading organizational consultants, 11 in the U.S.A. and 10 in India. Three "approach-related" and three "person-related" elements of styles are elaborated upon, using data from in-depth "client-centered" interviews with the consultants. Two distinct styles of organizational consulting emerge from the data and their relation to values, approaches, and consulting experience are discussed, as are their interrelations.

AbstractThis paper highlights the potential physical health problems of people with profound learning disability in a hospital population. Assessments were made under seven probable health care needs of this population. Uncomplicated Body Mass Index (BMI) <20 (underweight) was a common finding, but low Body Mass Index alone was of little significance in this population. The majority of participants had well controlled epilepsy, were vulnerable to pressure sores, and prone to constipation. The number of consultations with a doctor were similar to the general population, but all had regular annual physical examinations. A small but significant number of people required a high level of health care which can only be provided with a high level of nursing care and regular medical support. The implications of the recent shift of focus of the National Health Service and its effect on this population is discussed.

Background As many countries seek to slow the spread of COVID-19 without reimposing national restrictions, it has become important to track the disease at a local level to identify areas in need of targeted intervention. Methods In this prospective, observational study, we did modelling using longitudinal, self-reported data from users of the COVID Symptom Study app in England between March 24, and Sept 29, 2020. Beginning on April 28, in England, the Department of Health and Social Care allocated RT-PCR tests for COVID-19 to app users who logged themselves as healthy at least once in 9 days and then reported any symptom. We calculated incidence of COVID-19 using the invited swab (RT-PCR) tests reported in the app, and we estimated prevalence using a symptom-based method (using logistic regression) and a method based on both symptoms and swab test results. We used incidence rates to estimate the effective reproduction number, R(t), modelling the system as a Poisson process and using Markov Chain Monte-Carlo. We used three datasets to validate our models: the Office for National Statistics (ONS) Community Infection Survey, the Real-time Assessment of Community Transmission (REACT-1) study, and UK Government testing data. We used geographically granular estimates to highlight regions with rapidly increasing case numbers, or hotspots. Findings From March 24 to Sept 29, 2020, a total of 2 873 726 users living in England signed up to use the app, of whom 2 842 732 (98·9%) provided valid age information and daily assessments. These users provided a total of 120 192 306 daily reports of their symptoms, and recorded the results of 169 682 invited swab tests. On a national level, our estimates of incidence and prevalence showed a similar sensitivity to changes to those reported in the ONS and REACT-1 studies. On Sept 28, 2020, we estimated an incidence of 15 841 (95% CI 14 023–17 885) daily cases, a prevalence of 0·53% (0·45–0·60), and R(t) of 1·17 (1·15–1·19) in England. On a geographically granular level, on Sept 28, 2020, we detected 15 (75%) of the 20 regions with highest incidence according to government test data. Interpretation Our method could help to detect rapid case increases in regions where government testing provision is lower. Self-reported data from mobile applications can provide an agile resource to inform policy makers during a quickly moving pandemic, serving as a complementary resource to more traditional instruments for disease surveillance.

© 2015 Macmillan Publishers Limited. Ruminant livestock are important sources of human food and global greenhouse gas emissions. Feed degradation and methane formation by ruminants rely on metabolic interactions between rumen microbes and affect ruminant productivity. Rumen and camelid foregut microbial community composition was determined in 742 samples from 32 animal species and 35 countries, to estimate if this was influenced by diet, host species, or geography. Similar bacteria and archaea dominated in nearly all samples, while protozoal communities were more variable. The dominant bacteria are poorly characterised, but the methanogenic archaea are better known and highly conserved across the world. This universality and limited diversity could make it possible to mitigate methane emissions by developing strategies that target the few dominant methanogens. Differences in microbial community compositions were predominantly attributable to diet, with the host being less influential. There were few strong co-occurrence patterns between microbes, suggesting that major metabolic interactions are non-selective rather than specific. ; We thank Ron Ronimus, Paul Newton, and Christina Moon for reading and commenting on the manuscript. We thank all who provided assistance that allowed Global Rumen Census collaborators to supply samples and metadata (Supplemental Text 1). AgResearch was funded by the New Zealand Government as part of its support for the Global Research Alliance on Agricultural Greenhouse Gases. The following funding sources allowed Global Rumen Census collaborators to supply samples and metadata, listed with the primary contact(s) for each funding source: Agencia Nacional de Investigación e Innovación, Martín Fraga; Alberta Livestock and Meat Agency, Canada, Tim A. McAllister; Area de Ciencia y Técnica, Universidad Juan A Maza (Resolución Proy. N° 508/2012), Diego Javier Grilli; Canada British Columbia Ranching Task Force Funding Initiative, John Church; CNPq, Hilário Cuquetto Mantovani, Luiz Gustavo Ribeiro Pereira; FAPEMIG, Hilário Cuquetto Mantovani; FAPEMIG, PECUS RumenGases, Luiz Gustavo Ribeiro Pereira; Cooperative Research Program for Agriculture Science & Technology Development (project number PJ010906), Rural Development Administration, Republic of Korea, Sang-Suk Lee; Dutch Dairy Board & Product Board Animal Feed, André Bannink, Kasper Dieho, Jan Dijkstra; Ferdowsi University of Mashhad, Vahideh Heidarian Miri; Finnish Ministry of Agriculture and Forestry, Ilma Tapio; Instituto Nacional de Tecnología Agropecuaria, Argentina (Project PNBIO1431044), Silvio Cravero, María Cerón Cucchi; Irish Department of Agriculture, Fisheries and Food, Alexandre B. De Menezes; Meat & Livestock Australia; and Department of Agriculture, Fisheries & Forestry (Australian Government), Chris McSweeney; Ministerio de Agricultura y desarrollo sostenible (Colombia), Olga Lucía Mayorga; Montana Agricultural Experiment Station project (MONB00113), Carl Yeoman; Multistate project W-3177 Enhancing the competitiveness of US beef (MONB00195), Carl Yeoman; NSW Stud Merino Breeders' Association, Alexandre Vieira Chaves; Queensland Enteric Methane Hub, Diane Ouwerkerk; RuminOmics, Jan Kopecny, Ilma Tapio; Rural and Environment Science and Analytical Services Division (RESAS) of the Scottish Government and the Technology Strategy Board, UK, R. John Wallace; Science Foundation Ireland (09/RFP/GEN2447), Sinead Waters; Secretaría de Agricultura, Ganadería, Desarrollo Rural, Pesca y Alimentación, Mario A. Cobos-Peralta; Slovenian Research Agency (project number J1-6732 and P4-0097), Blaz Stres; Strategic Priority Research Program, Climate Change: Carbon Budget and Relevant Issues (Grant No.XDA05020700), ZhiLiang Tan; The European Research Commission Starting Grant Fellowship (336355—MicroDE), Phil B. Pope; The Independent Danish Research Council (project number 4002-00036), Torsten Nygaard Kristensen; and The Independent Danish Research Council (Technology and Production, project number 11-105913), Jan Lassen. These funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. ; Peer Reviewed