A modified super-efficiency measure based on simultaneous input–output projection in data envelopment analysis

Super-efficiency data envelopment analysis (SE-DEA) models have been developed and applied in many situations. However, under the condition of variable returns to scale (VRS), infeasibility of the SE-DEA model may occur and restrict its application. A modified SE-DEA measure based on simultaneous input–output projection is proposed as a way to systematically characterize the super-efficiency in both inputs and outputs. The modified measure overcomes the infeasibility problem while providing ease of computation and interpretation. The practicability of the proposed measure in real applications and its comparison to other super-efficiency measures are illustrated empirically using an example.     

Measuring energy efficiency performance of China’s transport sector: A data envelopment analysis approach

This study presents an application of data envelopment analysis (DEA) approach with considering undesirable outputs, and not only examines the energy efficiency performance of China’s transport sector from 2003 to 2009, but also maximizes energy-saving potential of transport sector of China’s 30 administrative regions. Environmental DEA technology is adopted, which treats undesirable measures by distinguishing between weak and strong disposability. The output directional distance function is used, which explicitly expands desirable outputs and simultaneously reduces undesirable outputs. From the empirical results, the lowest number of efficient regions was recorded in 2007 or 2008, which eventually slightly recovered. Similarly, the overall average energy efficiency reached the lowest record in 2007 or 2008. These results are consistent with the policy guidance in the 11th Five-Year Plan (2006–2010) period that a series of new policies in the transport sector are implemented to reverse the trend of energy intensity increase. The Eastern area generally performed better than the Central area and Western area. The Central area was better than the Western area, except in some critical years such as in 2008, the financial crisis significantly affected port turnover. The total potential energy saving from 2003 to 2009 was 35.72 Mtce, the total potential energy saving was approximately 2.75% of the total transport energy use.     

A decision system using fuzzy cognitive map and multi-group data envelopment analysis to estimate hospitals’ outputs level

Due to its vital role in healthcare system, performance evaluation of hospitals is indispensable. In addition, hospitals try to achieve desired and efficient conditions by careful planning based on their present facilities. Several studies have been conducted on hospital evaluation, but nearly none of them has taken into consideration the difference in the nature of performance in respect of hospitals’ managerial construction, funding, and type of services provided by them. Furthermore, hospitals’ outputs have not been estimated in respect of cause and effect relationships between inputs and outputs for achieving efficient conditions. In the present study, first, a new approach for hospital evaluation is presented according to the differences in the nature of their performances while categorized in groups. Then, optimal outputs for each hospital in its own group are dealt with using results obtained from multi-group data envelopment analysis and the method of fuzzy cognitive map. The activation Hebbian learning (AHL) algorithm is adapted to the concept of efficiency and is conducted to estimate the outputs of inefficient hospitals. In the present study, 27 hospitals located in the provincial capitals in northwest of Iran are categorized in four groups including general governmental hospitals, specialty governmental hospitals, private hospitals, and social security hospitals. Afterward, optimal outputs are estimated for the inefficient hospitals by using the proposed modified AHL algorithm. The results indicate that when the hospitals have been evaluated in groups, efficiency scores of hospitals have changed. Also, given the cause and effect relationships between inputs and outputs in each group can help to decision and policy makers to estimate the optimal outputs that have caused inefficient hospitals become efficient.

     

The adaptive fuzzy time series model with an application to Taiwan’s tourism demand

In this study, an adaptive fuzzy time series model for forecasting Taiwan’s tourism demand is proposed to further enhance the predicted accuracy. We first transfer fuzzy time series data to the fuzzy logic group, assign weights to each period, and then use the proposed adaptive fuzzy time series model for forecasting in which an enrollment forecasting values is applied to obtain the smallest forecasting error. Finally, an illustrated example for forecasting Taiwan’s tourism demand is used to verify the effectiveness of proposed model and confirmed the potential benefits of the proposed approach with a very small forecasting error MAPE and RMSE.     

Atanassov’s intuitionistic fuzzy submodules with respect to a t-norm

In this paper, t-norm based intuitionistic fuzzy submodules are defined, and their various properties are investigated. We also show that if an intuitionistic fuzzy set of a module is an intuitionistic fuzzy submodule with respect to a t-norm, then it is also an intuitionistic fuzzy submodule with respect to the annihilation t-norm. We investigate the nature of intuitionistic fuzzy submodules of a module with the help of their respective cut sets. We obtain some results based on (α, β)-level sets and t-norm based α-cut sets of an intuitionistic fuzzy submodule with respect to a t-norm T.     

The ELECTRE multicriteria analysis approach based on Atanassov’s intuitionistic fuzzy sets

In recent decades, intuitionistic fuzzy sets have been applied to many different fields; however, few current studies have used the ELECTRE method to solve multi-criteria decision-making problems with intuitionistic fuzzy information. The purpose of this paper is to develop a new method, the intuitionistic fuzzy ELECTRE method, for solving multi-criteria decision-making problems. Atanassov’s intuitionistic fuzzy set (A-IFS) characteristics are simultaneously concerned with the degree of membership, degree of non-membership, and intuitionistic index, and people can use A-IFS to describe uncertain situations in decision-making problems. We use the proposed method to rank all alternatives and determine the best alternative. The proposed method can also use imperfect or insufficient knowledge of data to deal with decision-making problems. Finally, two practical examples are given that illustrate the procedure of the proposed method.     

Using syntactic text analysis to estimate educational tasks’ difficulty and complexity

We suggest a routine for automatic estimation of complexity and difficulty of educational tasks. This routine is based on syntactic text analysis, phrases’ predicative structures identification, and semantic network construction. Then we develop a mathematical model which employs a notion on semantic distance between notions–words to calculate the amount of knowledge in a semantic network. We show that the amount of knowledge in a semantic network is a measure in the set of all semantic networks, and the semantic distance makes this set the metric one.     

Learning opponent’s beliefs via fuzzy constraint-directed approach to make effective agent negotiation

This work presents a general framework of agent negotiation with opponent learning via fuzzy constraint-directed approach. The fuzzy constraint-directed approach involves the fuzzy probability constraint and the fuzzy instance reasoning. The proposed approach via fuzzy probability constraint can not only cluster the opponent’s information in negotiation process as proximate regularities to improve the convergence of behavior patterns, but also eliminate the noisy hypotheses or beliefs to enhance the effectiveness on beliefs learning. By using fuzzy instance method, our approach can reuse the prior opponent knowledge to speed up the problem-solving, and reason the proximate regularities to acquire desirable results on predicting opponent behavior. In addition, the proposed interaction method enables the agent to make a concession dynamically based on expected objectives. Moreover, experimental results suggest that the proposed framework allows an agent to achieve a higher reward, a fairer deal, or a smaller cost of negotiation.     

Contributing to appliances’ energy efficiency with Internet of Things,smart data and user engagement

The project OpenFridge has designed and developed the Internet of Things data system with semantic and data analytics enablers for building new services on a top of typical home appliance data—in particular, refrigerators. In order to identify the real-life potential of the system i.e. the innovative set-up from the technical as well as user acceptance sides, pilots with real users have been run, and eventually the data has been collected in a collaborative crowdsourcing manner. The most active users of the system have been surveyed. The paper describes the OpenFridge platform and approach, its evaluation results with the real life users, and discusses the lessons learned and open issues. Though still early for the massive market deployment, the system demonstrates the feasibility of the approach of the users interacting with the semantic energy data and eventually opening it up for the data economy. Also, preferences towards more pervasive ways to interact with such systems were identified.     

Rough sets to help medical diagnosis – Evidence from a Taiwan’s clinic

Medical data usually contain high degree of uncertainty that leads to problems such as diagnosis disparity. With the continually rising expenditures of health care, controlling the level of the expenditures by improving the diagnosis process is an emergent task and poses a greater challenge. Rough sets have been shown to be a useful technique to deal with data with uncertainty. This paper applies rough sets to identify the set of significant symptoms causing diseases and to induce decision rules using the data of a Taiwan’s otolaryngology clinic. The data are limited to rhinology and throat logy and contain 657 records with each record including 12 condition attributes and one decision attribute. The clinic’s physician agrees with the set of significant symptoms identified and favors the reduct containing one critical symptom. To generate decision rules, we use LEM2 and Explore algorithms and discuss these rules in terms of numbers of rules and implications of certain rules. Our results show that LEM2 generates fewer numbers of certain rules and LEM2’s proportions of approximate rules are higher. We also observe that LEM2’s certain rules form a more sensible pattern that Explore lacks. Discovery of the pattern is considered to be potentially helpful in improving the medical diagnosis.     

The Ockham’s razor applied to COVID-19 model fitting French data

This paper presents a data-based simple model for fitting the available data of the Covid-19 pandemic evolution in France. The time series concerning the 13 regions of mainland France have been considered for fitting and validating the model. An extremely simple, two-dimensional model with only two parameters demonstrated to be able to reproduce the time series concerning the number of daily demises caused by Covid-19, the hospitalizations, intensive care and emergency accesses, the daily number of positive tests and other indicators, for the different French regions. These results might contribute to stimulate a debate on the suitability of much more complex models for reproducing and forecasting the pandemic evolution since, although relevant from a mechanistic point of view, they could lead to nonidentifiability issues.     

Using satellite remote sensing analysis to evaluate a socio-economic and ecological model of deforestation in Rondônia,Brazil

The effectiveness of an integrated socio-economic and ecological simulation model for estimating patterns and rates of deforestation in Rondônia, Brazil is evaluated using Landsat data and landscape pattern metrics. The Percent Cleared, Contagion, and Fractal Dimension of image classifications are compared to those determined from model outputs. Results indicate that rates and spatial patterns of deforestation are similar between model outputs and Landsat image analysis. Differences in clearing patterns between the model and Landsat data are due in part to topography, localized farming obstacles and the patchiness of clearings. The effects of varying spatial resolution on the metrics is also examined.     

Using MODIS time series data to estimate aboveground biomass and its spatio-temporal variation in Inner Mongolia’s grassland between 2001 and 2011

It is critical to understanding grassland biomass and its dynamics to study regional carbon cycles and the sustainable use of grassland resources. In this study, we estimated aboveground biomass (AGB) and its spatio-temporal pattern for Inner Mongolia’s grassland between 2001 and 2011 using field samples, Moderate Resolution Imaging Spectroradiometer normalized difference vegetation index (MODIS-NDVI) time series data, and statistical models based on the relationship between NDVI and AGB. We also explored possible relationships between the spatio-temporal pattern of AGB and climatic factors. The following results were obtained: (1) AGB averaged 19.1 Tg C (1 Tg = 10¹² g) over a total area of 66.01 × 10⁴ km² between 2001 and 2011 and experienced a general fluctuation (coefficient of variation = 9.43%), with no significant trend over time (R² = 0.05, p > 0.05). (2) The mean AGB density was 28.9 g C m^?2 over the whole study area during the 11 year period, and it decreased from the northeastern part of the grassland to the southwestern part, exhibiting large spatial heterogeneity. (3) The AGB variation over the 11 year period was closely coupled with the pattern of precipitation from January to July, but we did not find a significant relationship between AGB and the corresponding temperature changes. Precipitation was also an important factor in the spatial pattern of AGB over the study area (R² = 0.41, p < 0.001), while temperature seemed to be a minor factor (R² = 0.14, p < 0.001). A moisture index that combined the effects of precipitation and temperature explained more variation in AGB than did precipitation alone (R² = 0.45, p < 0.001). Our findings suggest that establishing separate statistical models for different vegetation conditions may reduce the uncertainty of AGB estimation on a large spatial scale. This study provides support for grassland administration for livestock production and the assessment of carbon storage in Inner Mongolia.     

A case study of applying data mining techniques in an outfitter’s customer value analysis

This study applies K-means method, fuzzy c-means clustering method and bagged clustering algorithm to the analysis of customer value for an outfitter in Taipei, Taiwan. These three techniques bear similar philosophy for data classification. Thus, it would be of interest to know which clustering technique performs best in a real world case of evaluating customer value. Using cluster quality assessment, this study concludes that bagged clustering algorithm outperforms the other two methods. To conclude the analyses, this study also suggests marketing strategies for each cluster based on the results generated by bagged clustering technique.     

Human factors data traceability and analysis in the European Community’s Major Accident Reporting System

This paper reports on human factors data traceability and analysis of the European Community’s Major Accident Reporting System (MARS). This is the main EU instrument to major accident data collection, analysis and dissemination for process industry according to the provisions of the Seveso II Directive. To date, the MARS database counts approximately 700 Seveso-type major events (November 2008). The MARS system is investigated in terms of human factors data and case studies exploring the relevance of human factors in accident events causation and identification issues. The human factors model and taxonomy as it is applied in the MARS system is reviewed. Criteria to enhance traceability and analysis about human related causes are then considered in detail. Finally, certain limitations affecting the system are pointed out. Findings are expected to favour future modelling and research efforts toward further MARS system improvements.     

Using technology to encourage a healthier lifestyle in people with Down’s syndrome

Universal Access in the Information Society - This article reports on the development of a mobile app developed to encourage healthier lifestyles, with emphasis on food intake, by people with...     

A solution to measure traveler’s transfer tolerance for walking mode and dockless bike-sharing mode

The Journal of Supercomputing - Dockless bike-sharing is more and more popular all over the world, which enables passengers to choose destinations more flexibly, so that the transfer radius between...