Intelligent Educational Evaluation of Research Performance between Digital Library and Open Government Data

This study evaluates institutional research performance in benchmark technological universities in Taiwan through intelligent research databases (SciVal) in digital libraries with Ministry of Education open data to explore the performance of research indicators and the research trend of topic clusters to ascertain accountability for decision makers. The research performance of eight benchmark technological universities in Taiwan is compared in this study. In addition, the trends in research topics in the top 10% of journals are explored. Descriptive statistics, correlation, ANOVA, and the Boston Consulting Group matrix were used in this study. Research personnel, publications, productivity, total citations, number of international collaborations, and academic research income in 2018 significantly positively correlated with each other. From 719 records of research topics, topic clusters and school types are the significant factors in research outputs. Biosensors, electrodes, and voltammetry are the leading topic clusters in the research trend. The topic cluster of decision-making, fuzzy sets, and models has the best growth rate in the SciVal results. This analysis provides useful insights to policymakers to improve institutional administration and research resource allocation.

A Modified Extrema Pattern with Multilinear Matrix Decomposition based RLC scheme for Efficient Serial Remote Sensing Images Mining

The growth of serial remote sensing images (SRSI) offers abundant information for determining sequential spatial patterns in several fields like vegetation cover, urban development, and agricultural monitoring. Or else, traditional sequential pattern-mining algorithms cannot be applied efficiently or directly to remote sensing images. Here a new technique is proposed for enhancing the mining efficacy of spatial sequential patterns from raster serial remote sensing images (SRSI) based on pixel grouping approach. The modified extrema pattern is employed to offering grey-scale invariant transform of intensity values unlike previously employed local ternary pattern. The pattern features are computed by transformation process from which the multilinear matrix decomposition of the image is made by computing the covariance estimation on recognizing their orthogonal component. The matrix decomposition is then attained based on run length encoding process (RLC). The two rows of RLC vectors are intersected to attain pixel group matrix. Finally, the compressed image is attained in an efficient manner with effective mining time. The performance outcome reveals that the technique offered in this paper is capable of extracting spatial sequential patterns from SRSI effectively. The proposed system ensures that the entire patterns are extracted at a lower time consumption.

Energy Portfolio of the Eastern Poland Macroregion in the European Union

The European Union’s New Green Deal generates changes in the socio-economic development of regions. An element of this change is the transformation of the regional profiles of the energy portfolio towards more pro-ecological ones. The aim of this article is to identify the conditions and reasons for the transformation of the energy portfolio of the Eastern Poland macroregion, and to define the directions of the energy portfolio evolution caused by the need to implement the goals of the European Green Deal by 2030, in order to consequently develop the forecast energy portfolio for the Eastern Poland macroregion. It has been assumed that the energy transformation of the Eastern Poland macroregion will be implemented through gradual transformation of the energy portfolio towards dispersed sources of local and civic energy. The research is based on both qualitative and quantitative approaches. The qualitative research was carried out on the basis of the diagnostic survey method with the use of the proprietary questionnaire as well as with the analysis of the content of strategic documents of the voivodships included in the Eastern Poland macroregion. The basis for the preparation of research questions was the extended PEST method which stands for: political, economic, socio-cultural, technological. Research efforts were focused on developing an energy portfolio for the region using the assumptions of the Boston consulting group matrix (a strategic planning tool). The originality of the conducted research consists in the adaptation of the BCG matrix to the development of the forecast energy portfolio and the preparation of a proposal for the aggregated energy portfolio of the Eastern Poland macroregion. The obtained results indicate that the macroregion’s energy portfolio is diversified, though with the key significance of conventional sources. It is predicted that by 2030, the following sources will change their positions within the portfolio for the Eastern Poland macroregion: solar energy, wind energy and bioenergy. However, it will retain the character of a “youth wallet”.

A Modified Extrema Pattern with Multilinear Matrix Decomposition based RLC scheme for Efficient Serial Remote Sensing Images Mining

The growth of serial remote sensing images (SRSI) offers abundant information for determining sequential spatial patterns in several fields like vegetation cover, urban development, and agricultural monitoring. Or else, traditional sequential pattern-mining algorithms cannot be applied efficiently or directly to remote sensing images. Here a new technique is proposed for enhancing the mining efficacy of spatial sequential patterns from raster serial remote sensing images (SRSI) based on pixel grouping approach. The modified extrema pattern is employed to offering grey-scale invariant transform of intensity values unlike previously employed local ternary pattern. The pattern features are computed by transformation process from which the multilinear matrix decomposition of the image is made by computing the covariance estimation on recognizing their orthogonal component. The matrix decomposition is then attained based on run length encoding process (RLC). The two rows of RLC vectors are intersected to attain pixel group matrix. Finally, the compressed image is attained in an efficient manner with effective mining time. The performance outcome reveals that the technique offered in this paper is capable of extracting spatial sequential patterns from SRSI effectively. The proposed system ensures that the entire patterns are extracted at a lower time consumption.

Evaluation of the Impacts of Oil Pollution and a Sustainability Assessment: A Focus Group Case Study of Ogoniland, Nigeria

The growing impact of climate change has piqued public interest in determining the effects of people's actions on future generations. However, while environmentalists continue to advocate for accountability, most people in the places where the majority of these pollutions occur, particularly in oil-producing communities, have no say in their future. The study proposes a qualitative approach and the creation of a focus group environmental matrix as a unique technique for oil spill management that considers people's perceptions. This study takes a qualitative approach, relying on a focus group discussion of 12 open-ended questions about oil spills effects on the ecosystem. The questions were all about the spills' impact on residents. The qualitative study was performed between March and May 2018 in Gio, Ogale, Kwakwa, and KeDere Local Governments in Ogoniland. Gender-based strategies were studied with pre-determined questions and a total of 48 participants. Participants were indigenous who were conversant with the chosen topic and were asked leading questions. Finally, matrix analysis was used, which matched the people's perspectives. As a result, the total environmental components were affected. The following are the effects of oil pollution recognised by the focus group matrix: unsustainable income, devalued economy, devalued culture, weathering mangrove, respiratory disorders, inaccuracy in reaction, communication barrier, and economic deterioration. It ascertained that the focus group technique to promote environmental restoration and sustainability is vital. It allows for a more direct connection with the most vulnerable and provides policymakers with potential solutions.

A Novel Extension of the Technique for Order Preference by Similarity to Ideal Solution Method with Objective Criteria Weights for Group Decision Making with Interval Numbers

This paper presents an extension of the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method with objective criteria weights for Group Decision Making (GDM) with Interval Numbers (INs). The proposed method is an alternative to popular and often used methods that aggregate the decision matrices provided by the decision makers (DMs) into a single group matrix, which is the basis for determining objective criteria weights and ranking the alternatives. It does not use an aggregation operator, but a transformation of the decision matrices into criteria matrices, in the case of determining objective criteria weights, and into alternative matrices, in the case of the ranking of alternatives. This ensures that all the decision makers’ evaluations are taken into account instead of their certain average. The numerical example shows the ease of use of the proposed method, which can be implemented into common data analysis software such as Excel.

Spatial Pattern and Driving Mechanism of Urban–Rural Income Gap in Gansu Province of China

The urban–rural income gap is a principal indicator for evaluating the sustainable development of a region, and even the comprehensive strength of a country. The study of the urban–rural income gap and its changing spatial patterns and influence factors is an important basis for the formulation of integrated urban–rural development planning. In this paper, we conduct an empirical study on 84 county-level cities in Gansu Province by using various analysis tools, such as GIS, GeoDetector and Boston Consulting Group Matrix. The findings show that: (1) The urban–rural income gap in Gansu province is at a high level in spatial correlation and agglomeration, leading to the formation of a stepped and solidified spatial pattern. (2) Different factors vary greatly in influence, for example, per capita Gross Domestic Product, alleviating poverty policy and urbanization rate are the most prominent, followed by those such as floating population, added value of secondary industry and number of Internet users. (3) The driving mechanism becomes increasingly complex, with the factor interaction effect of residents’ income dominated by bifactor enhancement, and that of the urban–rural income gap dominated by non-linear enhancement. (4) The 84 county-level cities in Gansu Province are classified into four types of early warning zones, and differentiated policy suggestions are made in this paper.

Change Characteristics and Multilevel Influencing Factors of Real Estate Inventory—Case Studies from 35 Key Cities in China

High inventory is a common issue in urban real estate markets in many countries, posing a threat to the sustainable development of macroeconomics and society. This study built an analytical framework for the evolution of real estate inventory and its driving mechanisms and conducted an empirical study on 35 key cities in China. The findings show that, first, China has a huge real estate inventory with significant spatial heterogeneity. Second, the real estate inventory in China first rises and then falls, presenting an inverted U-shaped change trend; however, the spatial heterogeneity first falls and then rises, characterized by a U-shaped evolutionary change. Third, the present characteristics and evolutionary paths vary among different types of real estate inventory, mainly showing growth, stability, and inverted U-shaped changes. Fourth, the influencing factors of real estate inventory are increasingly diversified, and different factor pairs show bifactor-enhanced and nonlinearly-enhanced interaction effects, with a more intricate and complex driving mechanism. Fifth, four types of policy areas were divided according to the Boston Consulting Group Matrix, and it is recommended that the design of de-stocking policies should be dominated by “key factors” for cities in the stars and cows policy areas, while “important factors” and “auxiliary factors” should be equally emphasized for cities in the question policy area; the cities in the dogs policy area should keep the status quo as much as possible with avoidance of undesirable or excessive interventions.

LES 3

Set similarity search is a problem of central interest to a wide variety of applications such as data cleaning and web search. Past approaches on set similarity search utilize either heavy indexing structures, incurring large search costs or indexes that produce large candidate sets. In this paper, we design a learning-based exact set similarity search approach, LES 3 . Our approach first partitions sets into groups, and then utilizes a light-weight bitmap-like indexing structure, called token-group matrix (TGM), to organize groups and prune out candidates given a query set. In order to optimize pruning using the TGM, we analytically investigate the optimal partitioning strategy under certain distributional assumptions. Using these results, we then design a learning-based partitioning approach called L2P and an associated data representation encoding, PTR, to identify the partitions. We conduct extensive experiments on real and synthetic datasets to fully study LES 3 , establishing the effectiveness and superiority over other applicable approaches.