Capacity building in economics : education and research in transition economies

The development of the institutional capacity to create and evaluate economic policies remains a critical need-and constraint-in most transition economies if they are to complete the successful passage to fully functioning market economies. To take an active role in the transition process, economic policymakers, business leaders, government officials, and others need a thorough grounding in market-based economics. This requires strengthening economics education and providing support for qualified economists to teach economics at all levels and to carry out high-quality research and policy analysis. Although the education systems in a handful of countries have already risen to the challenge, in many other transition countries, the structure of educational and research institutes remains grounded in the Communist model. This paper presents findings from a comprehensive study assessing the state of economics education and research in 24 countries in East-Central Europe and the former Soviet Union. While 20 countries were initially included because preliminary assessments showed that they lacked the capability to offer high-quality economics education, four additional countries-the Czech Republic, Hungary, Russia, and Ukraine-were included to highlight five centers of excellence that they already host. Based on the experience of these successful centers, the study's findings, and information gathered from a series of donor meetings in Berlin, New York, and Washington, D.C., this paper presents an approach to building new indigenous capacity for teaching and research on market-based economics in regions where the need is particularly critical-the Caucasus, Central Asia, and Southeast Europe.Curriculum&Instruction,Decentralization,Agricultural Knowledge&Information Systems,Teaching and Learning,Public Health Promotion,Tertiary Education,Health Monitoring&Evaluation,Agricultural Knowledge&Information Systems,Teaching and Learning,Curriculum&Instruction

TOWARDS GLOBAL E-AGRICULTURE: THE CHALLENGE OF WEB-BASED DECISION SUPPORT SYSTEMS FOR GROWERS

Globalization is influencing several agriculture aspects: market globalization has increased export from producing to consuming countries where different food safety or pesticide residue regulations apply, and has raised awareness of global problems linked to agriculture production (i.e., chemical pesticide pollution). Pests, diseases and weeds may cause significant damages to growers and the cost of pesticide increases. Environmental pollution and risk of unwanted residues on food forced researchers to find ways to optimize pesticide applications. However, extension services and research in pest management is often fragmented and efforts to develop support tools for pest management are often duplicated. Furthermore, sometimes the knowledge does not spread from research centers to growers due to difficulties in knowledge transfer. Decision support systems (DSS) are widely used for assisting with integrated pest management (IPM), crop nutrition, and other aspects of information transfer. Developing highly portable and especially web-based DSSs that can be easily adapted to new environments is therefore desirable in view of agriculture globalization. Web-based models and DSSs have the major advantage of reducing software development, maintenance, and distribution costs, while making the relevant knowledge easily accessible to growers world-wide. This paper presents two examples of web-based agricultural DSSs and demonstrates the potential use of these systems in a wide application range in order to adapt to the needs of globalization. Allowing the choice of different values for the parameters renders these DSSs very flexible. Their development process integrated agricultural expertise from two distinct research centers with information systems know-how from a third center, over two countries, demonstrating the need for a global software development that crosses country borders. The results show that it is possible to satisfy the prerequisites: reducing software development cost by enlarging the number of users and reaching growers among whom specific knowledge on diseases is not yet established

The Role of Community Based Information Centres in Development: Lessons for Rural Zimbabwe

Community based information proposals from the library profession in Zimbabwe should in theory fit well with government strategic goals for a “knowledge based society”. In reality, information technology has opened floodgates for international and national development by bringing in a plethora of community based information systems and services. This paper highlights developmental issues initiated by different community based information centres. It defines community, information, rural development and enunciates on community centers throughout their evolution to the modern community based information centres. The premise of the paper is that establishing community based information centres in Zimbabwean rural areas will strengthen and empower rural people to be among global players. The major scale of this paper is to provide platforms for erecting these centres in rural Zimbabwe for the purpose of providing everyone with useful, practical information for daily chores. Rural Zimbabwe has a bigger share in national and international development which can be achieved by harnessing community based information systems and services. Despite low level penetration of community based centres in Zimbabwe, the Matabeleland South initiatives have capacitated Zimbabweans to follow the trend. The centres should be spread throughout the country and provide information for development. Community based information centres can play a significant role in meeting social economic targets for rural people by connecting, empowering rural populace to developmental floodgates. The paper finally shows how community information based centres complement support for the government and agencies in resource sharing and enhances the services available through such centres. Keywords: information, information technology, rural development, and community based information centres: Zimbabwe

CREAMINKA: An Intelligent Ecosystem for Supporting Management and Information Discovery in Research and Innovation Fields in Universities

This chapter presents a new proposal for supporting the management of research processes in universities and higher education centers. To this aim, the authors have developed a comprehensive ecosystem that implements a knowledge model that addresses three innovative aspects of research: (i) acceleration of knowledge production, (ii) research valorization and (iii) discovery of improbable peers. The ecosystem relies on ontologies and intelligent modules and is able to automatically retrieve information of major scientific databases such as SCOPUS and Science Direct to infer new information. Currently, the system is able to provide guidelines to create improbable research peers as well as automatically generate resilience graphics and reports from more than 17,000 tuples of the ontological database. In this work, the authors describe in detail an important aspect of support systems for research management in higher education: the development and valorization of competences of students collaborating in research process and startUPS of universities. Furthermore, a knowledge model of entrepreneurship (startUPS) as well as an analyzer of general and specific competences based on data mining processes is presented

A rigorous approach to facilitate and guarantee the correctness of the genetic testing management in human genome information systems

<p>Abstract</p> <p>Background</p> <p>Recent medical and biological technology advances have stimulated the development of new testing systems that have been providing huge, varied amounts of molecular and clinical data. Growing data volumes pose significant challenges for information processing systems in research centers. Additionally, the routines of genomics laboratory are typically characterized by high parallelism in testing and constant procedure changes.</p> <p>Results</p> <p>This paper describes a formal approach to address this challenge through the implementation of a genetic testing management system applied to human genome laboratory. We introduced the Human Genome Research Center Information System (CEGH) in Brazil, a system that is able to support constant changes in human genome testing and can provide patients updated results based on the most recent and validated genetic knowledge. Our approach uses a common repository for process planning to ensure reusability, specification, instantiation, monitoring, and execution of processes, which are defined using a relational database and rigorous control flow specifications based on process algebra (ACP). The main difference between our approach and related works is that we were able to join two important aspects: 1) process scalability achieved through relational database implementation, and 2) correctness of processes using process algebra. Furthermore, the software allows end users to define genetic testing without requiring any knowledge about business process notation or process algebra.</p> <p>Conclusions</p> <p>This paper presents the CEGH information system that is a Laboratory Information Management System (LIMS) based on a formal framework to support genetic testing management for Mendelian disorder studies. We have proved the feasibility and showed usability benefits of a rigorous approach that is able to specify, validate, and perform genetic testing using easy end user interfaces.</p

A model to integrate Data Mining and On-line Analytical Processing: with application to Real Time Process Control

Since the widespread use of computers in business and industry, a lot of research has been done on the design of computer systems to support the decision making task. Decision support systems support decision makers in solving unstructured decision problems by providing tools to help understand and analyze decision problems to help make better decisions. Artificial intelligence is concerned with creating computer systems that perform tasks that would require intelligence if performed by humans. Much research has focused on using artificial intelligence to develop decision support systems to provide intelligent decision support. Knowledge discovery from databases, centers around data mining algorithms to discover novel and potentially useful information contained in the large volumes of data that is ubiquitous in contemporary business organizations. Data mining deals with large volumes of data and tries to develop multiple views that the decision maker can use to study this multi-dimensional data. On-line analytical processing (OLAP) provides a mechanism that supports multiple views of multi-dimensional data to facilitate efficient analysis. These two techniques together can provide a powerful mechanism for the analysis of large quantities of data to aid the task of making decisions. This research develops a model for the real time process control of a large manufacturing process using an integrated approach of data mining and on-line analytical processing. Data mining is used to develop models of the process based on the large volumes of the process data. The purpose is to provide prediction and explanatory capability based on the models of the data and to allow for efficient generation of multiple views of the data so as to support analysis on multiple levels. Artificial neural networks provide a mechanism for predicting the behavior of nonlinear systems, while decision trees provide a mechanism for the explanation of states of systems given a set of inputs and outputs. OLAP is used to generate multidimensional views of the data and support analysis based on models developed by data mining. The architecture and implementation of the model for real-time process control based on the integration of data mining and OLAP is presented in detail. The model is validated by comparing results obtained from the integrated system, OLAP-only and expert opinion. The system is validated using actual process data and the results of this verification are presented. A discussion of the results of the validation of the integrated system and some limitations of this research with discussion on possible future research directions is provided

Designing AI-Based Systems for Qualitative Data Collection and Analysis

With the continuously increasing impact of information systems (IS) on private and professional life, it has become crucial to integrate users in the IS development process. One of the critical reasons for failed IS projects is the inability to accurately meet user requirements, resulting from an incomplete or inaccurate collection of requirements during the requirements elicitation (RE) phase. While interviews are the most effective RE technique, they face several challenges that make them a questionable fit for the numerous, heterogeneous, and geographically distributed users of contemporary IS. Three significant challenges limit the involvement of a large number of users in IS development processes today. Firstly, there is a lack of tool support to conduct interviews with a wide audience. While initial studies show promising results in utilizing text-based conversational agents (chatbots) as interviewer substitutes, we lack design knowledge for designing AI-based chatbots that leverage established interviewing techniques in the context of RE. By successfully applying chatbot-based interviewing, vast amounts of qualitative data can be collected. Secondly, there is a need to provide tool support enabling the analysis of large amounts of qualitative interview data. Once again, while modern technologies, such as machine learning (ML), promise remedy, concrete implementations of automated analysis for unstructured qualitative data lag behind the promise. There is a need to design interactive ML (IML) systems for supporting the coding process of qualitative data, which centers around simple interaction formats to teach the ML system, and transparent and understandable suggestions to support data analysis. Thirdly, while organizations rely on online feedback to inform requirements without explicitly conducting RE interviews (e.g., from app stores), we know little about the demographics of who is giving feedback and what motivates them to do so. Using online feedback as requirement source risks including solely the concerns and desires of vocal user groups. With this thesis, I tackle these three challenges in two parts. In part I, I address the first and the second challenge by presenting and evaluating two innovative AI-based systems, a chatbot for requirements elicitation and an IML system to semi-automate qualitative coding. In part II, I address the third challenge by presenting results from a large-scale study on IS feedback engagement. With both parts, I contribute with prescriptive knowledge for designing AI-based qualitative data collection and analysis systems and help to establish a deeper understanding of the coverage of existing data collected from online sources. Besides providing concrete artifacts, architectures, and evaluations, I demonstrate the application of a chatbot interviewer to understand user values in smartphones and provide guidance for extending feedback coverage from underrepresented IS user groups

Development of a Multicriteria Spatial Decision Support System with Application to the Economic Optimization of Aircraft Based Weather Data Collection

This research is motivated by the economic optimization of the Troposherical Airborne Meteorological Data Reporting (TAMDAR), an aircraft-based meteorological data collection system. In the envisioned TAMDAR system, meteorological data collected by the onboard sensors of selected aircraft are transmitted to the ground as aircraft fly their missions. The data is processed by a national center, which disseminates the data to diverse users such as weather forecasters and aviation control centers. Substantial government funding is required for the implementation and operation of this new data acquisition system and data transmission expenditures constitute the largest portion of the costs. To achieve economic optimization of the data gathering activities, the TAMDAR system requires a multicriteria spatial decision support system (MC-SDSS) that facilitates the efficient selection of the most desirable data points to collect based on a limited budget. To optimize the data collection each data point must be assigned a value by the TAMDAR DSS and a specialized data valuation technique is developed for this purpose. This work presents a design methodology for practical integrated application of multi-attribute utility, simulation and spatial decision analysis techniques in the optimization of aircraft-based weather data collection systems. The TAMDAR DSS demonstrates tools to address a number of challenging decision support design problems such as inherent uncertainty, required subject-matter knowledge, geo-spatial data dimension, resolution of conflicting goals, reduction of complexity and, qualitative judgment. The developed model has wide application to other weather information and data gathering problems

Geomatics tools to record 3D shapes for intervention planning

The paper offers a state of art of Geomatics tools that it is possible to use after a natural and/or human disaster on urban centers or natural landscapes to record the 3D shape. This knowledge is important both for first aid initiatives devoted to safeguard human lives and for support decision on first technical interventions. The same data, if correctly recorded, are the basic step to plan recovering actions and reconstruction strategies. The high automation level of the metric survey techniques open unsolved questions about the correct use of automatic tools both to acquire primary data and the appropriate management of them to give affordable and accurate metric information to the specialists. Image based technologies (e.g. 3D photogrammetry, SFM) and range based instruments (e.g. terrestrial and aerial laser scanning systems) are analyzed in terms of best rules to acquire the necessary primary data by highlighting the most common mistakes that automation approach could generate; the same analysis is developed for the software used to manage those primary data where automation processing are in many cases not well understood. A more skilled use of primary data acquisition instruments and management software will allow a better quality of the resulting 3D models also considering the real needs in the different phases of the emergency after disasters