246 research outputs found
Subnational Determinants of Foreign Direct Investments in the Russian Federation
No full textOur purpose is to examine the determinants of subnational distribution of Foreign Direct Investments (FDI) in the key fifteen regions of Russia over the period of 2005-2011 using panel data. Within the most important economic regions of the country we found market seeking is still the main purpose of foreign inward investments. As a result, the size of the Russian consumer market presents a significant influence on the foreign economic activities alongside trade openness and government economic incentives. Our results from regression analysis indicate that gross regional product per capita, trade openness and the existence of special economic zones have significant positive impact on the regional distribution of FDI in the Russian Federation
Provenance-based trust for grid computing: Position Paper
No full textCurrent evolutions of Internet technology such as Web Services, ebXML, peer-to-peer and Grid computing all point to the development of large-scale open networks of diverse computing systems interacting with one another to perform tasks. Grid systems (and Web Services) are exemplary in this respect and are perhaps some of the first large-scale open computing systems to see widespread use - making them an important testing ground for problems in trust management which are likely to arise. From this perspective, today's grid architectures suffer from limitations, such as lack of a mechanism to trace results and lack of infrastructure to build up trust networks. These are important concerns in open grids, in which "community resources" are owned and managed by multiple stakeholders, and are dynamically organised in virtual organisations. Provenance enables users to trace how a particular result has been arrived at by identifying the individual services and the aggregation of services that produced such a particular output. Against this background, we present a research agenda to design, conceive and implement an industrial-strength open provenance architecture for grid systems. We motivate its use with three complex grid applications, namely aerospace engineering, organ transplant management and bioinformatics. Industrial-strength provenance support includes a scalable and secure architecture, an open proposal for standardising the protocols and data structures, a set of tools for configuring and using the provenance architecture, an open source reference implementation, and a deployment and validation in industrial context. The provision of such facilities will enrich grid capabilities by including new functionalities required for solving complex problems such as provenance data to provide complete audit trails of process execution and third-party analysis and auditing. As a result, we anticipate that a larger uptake of grid technology is likely to occur, since unprecedented possibilities will be offered to users and will give them a competitive edge
ASME 3rd International Conference on Microchannels and Minichannels, Parts A and B
No full textAndres Carrano (with James B. Taylor and Satish G. Kandlikar) is a contributing author, Characterization of the Effect of Surface Roughness and Texture on Fluid Flow: Past, Present, and Future (Keynote), pp. 11-18.
Proceedings of ASME 3rd International Conference on Nanochannels, Microchannels and Minichannels.https://digitalcommons.fairfield.edu/engineering-books/1053/thumbnail.jp
A quantitative data representation framework for structural and functional MR Imaging with application to prostate cancer detection
No full textProstate cancer (CaP) is currently the second leading cause of cancer-related deaths in the United States among men, but there is a paucity of non-invasive image-based information for CaP detection and staging in vivo. Studies have shown the utility of multi-protocol magnetic resonance imaging (MRI) to improve CaP detection accuracy by using both T2-weighted (T2w), dynamic contrast enhanced (DCE), and diffusion weighted (DWI) MRI information. In this thesis, we present methods for quantitative representation of structural and functional imaging data with the objective of building automated classifiers to improve CaP detection accuracy in vivo. In vivo disease presence was quantified via extraction of textural signatures from T2w MRI. Evaluation of these signatures showed that CaP appearance within each of the two dominant prostate regions (central gland, peripheral zone) is significantly different. A classifier trained on zone-specific features also yielded a higher detection accuracy compared to a simpler, monolithic combination of all the texture features. While a number of automated classifiers are available, classifier choice must account for limitations in dataset size and annotation (such as with in vivo prostate MRI). A comprehensive evaluation of different classifier schemes was undertaken for the specific problem of automated CaP detection via T2w MRI on a zonewise basis. It was found that simple classifiers yielded significantly improved CaP detection accuracies compared to complex classifiers. Fundamental differences must be overcome when constructing a unified quantitative representation of structural (T2w) and functional (DCE, DWI) MRI. We present a novel technique, referred to as consensus embedding, which constructs a lower dimensional representation (embedding) from a high dimensional feature space such that information (class-based or otherwise) is optimally preserved. Consensus embedding is shown to result in an improved representation of the data compared to alternative DR-based strategies in a variety of experimental domains. A unified quantitative representation of T2w, DCE, and DWI prostate MRI was constructed via the consensus embedding framework. This yielded an integrated classifier which was more accurate for CaP detection in vivo as compared to using structural and functional information individually, or using a naive combination of such differing types of information.Ph. D.Includes bibliographical referencesIncludes vitaby Satish Easwar Viswanat
Fault Diagnosis of Semiconductor Random Access Memories
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Reason: Restricted to UIUC communityOpen Restriction set for Item 100821 on 2019-11-15T17:33:23Z with date null by [email protected] Services Electronics Program / DAAB-07-72-C-0259OpenCoordinated Science Laboratory was formerly known as Control Systems Laboratory"Author name appears as ""Satish Munkund Thatte"" in front matter
Indian Women in Doctoral Education: Some Encouraging Signs, the Path Ahead, and Lessons for Inclusivity
No full textFor the total doctorate awarded in India in 2013, the male-to-female ratio was 63:37. This ratio improved to 57:43 in favour of women in 2021. In absolute terms, the number of women awarded doctorates almost doubled in 2021 compared with 2013. In this study, we examine the progress made by Indian women in doctoral education based on annual reports from the All India Survey on Higher Education. The improved ratios and numbers reflect the adoption of an action-oriented approach in dealing with the concepts of equality and inclusion. The reasons for the progress and ways to improve were investigated based on secondary data and interviews with 15 expert senior female research supervisors. The interviews reveal that apart from the mandatory requirement of a PhD qualification for academic progression, other initiatives have been taken by the Government that have encouraged more women to opt for doctoral education. However, more needs to be done to make research easy for women in India, and such areas, as pointed out by the panel of experts, have been discussed. These findings can be used by other nations that want to bring more inclusivity to doctoral education
Job evaluation and incentives - its effects on wages unions and industrial relations, with particular reference to its impact on the socio-economic life of workers in general, 1961
No full textStrategic Partnerships and Ecosystems for Scaling Agri IoT Startups
No full textAgri IoT startups face a paradox: the technology to sense, connect, and orchestrate farm operations exists, but fragmented value chains, low per farmer ARPU, and distribution frictions stall scale. This paper develops an ecosystem lens for scaling Agri IoT ventures through strategic partnerships across telecom operators, agri inputs, machinery OEMs, FPOs/Co ops, off takers, insurers, and public programs. We synthesize platform ecosystem theory and agri innovation literature to derive a partnership archetype matrix (distribution, data, risk sharing, finance, and policy enablement) and a three phase scale playbook (beachhead → network effects → multi sided services). Using a conceptual multiple case synthesis and secondary evidence from emerging markets, we outline governance choices (open vs. curated platforms), data rights and interoperability (LoRaWAN/NB IoT, OGC SensorThings), and unit economics levers (bundling, embedded finance, outcome based pricing). Results indicate that partnering with distribution dense incumbents (input retailers, telcos) reduces CAC by 35–60% compared with direct sales, while risk sharing with insurers/off takers improves adoption of decision support by aligning incentives. We discuss policy rails (digital public infrastructure, e KYC, satellite data) that lower transaction costs. The paper contributes a practical framework—PARTNER—for diagnosing ecosystem gaps and sequencing alliances to cross the scale threshold in smallholder dominated markets
Dynamic Prediction of Delays in Software Projects using Delay Patterns and Bayesian Modeling
No full textModern agile software projects are subject to constant change, making it essential to re-asses overall delay risk throughout the project life cycle. Existing effort estimation models are static and not able to incorporate changes occurring during project execution. In this paper, we propose a dynamic model for continuously predicting overall delay using delay patterns and Bayesian modeling. The model incorporates the context of the project phase and learns from changes in team performance over time. We apply the approach to real-world data from 4,040 epics and 270 teams at ING. An empirical evaluation of our approach and comparison to the state-of-the-art demonstrate significant improvements in predictive accuracy. The dynamic model consistently outperforms static approaches and the state-of-the-art, even during early project phases.Software EngineeringSoftware Technolog
Perceived Usefulness and Adoption Intention of IoT-Enabled Smart Farming Technologies Among Agriculture Graduates in Maharashtra: A Quantitative Study
No full textThe swift adoption of the Internet of Things (IoT) technologies in the agricultural sector has changed the conventional farming process as it allows making decisions based on the data, optimizing resources, and producing sustainably. Although the use of smart farming solutions has become more relevant, the successful implementation of the solutions is mainly determined by the perception and intentions of future agricultural professionals. The proposed quantitative research paper focuses on the perceived usefulness of technologies and adoption intention of IoT-based smart farming technologies among the agrarian economy graduates in Maharashtra, a state with a heterogeneous agrarian economy and an increasing trend in technological involvement in agriculture.
It is based on the Technology Acceptance Model (TAM) and addresses the issue of getting the meaning of perceived usefulness on how it can impact the intention of graduates to adopt the IoT applications, i.e., smart sensors, automated irrigation systems, soil monitoring tools, and data analytics platforms. The primary data were gathered using a designed questionnaire that was given to the graduates in the field of agriculture at the selected universities and colleges in Maharashtra. The statistical methods used to evaluate relationships between perceived usefulness and adoption intention were descriptive analysis, correlation analysis, and regression analysis.
The results show that graduates with the major of agriculture tend to have a positive attitude towards the use of IoT-enabled smart farming technologies, as they see the opportunities to increase productivity and lower the input costs and sustainability of agricultural activities. The perceived usefulness was also found to be a strong predictor of the intention to adopt, which serves as an argument in favor of showing a practical advantage and applicability of smart farming tools in the real world. Nonetheless, the issues of cost, technical complexity, and lack of practical exposure were found to be the factors that can moderate the adoption decisions.
The research adds to the current body of literature by offering empirical data of the technology acceptance of the future agricultural practitioners in a developing agricultural setting. The findings have useful policy implications on policymakers, learning institutions, and technologists to develop specific training modules, curriculum implementation policies, and sensitization efforts that can empower IoT use in agriculture. Finally, increasing the perceived usefulness among the graduates of the agriculture profession may be the key factor in quickening the change in the smart farming technologies in Maharashtra
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