Geographic information system-based multi-criteria decision-making analysis for investment assessment of wind-photovoltaic-shared energy storage power stations: a case study of Shanxi Province.

As the center of the development of power industry, wind-photovoltaic (PV)-shared energy storage project is the key tool for achieving energy transformation. This research seeks to construct a feasible model for investment appraisal of wind-PV-shared energy storage power stations by combining geographic information system (GIS) and multi-criteria decision-making (MCDM) method. Firstly, a comprehensive criteria system is established from the perspectives of orography, economy, resources, climate, and society, and the evaluation data is described using probabilistic linguistic term sets (PLTSs). Then, to avoid the weight deviation produced by the single weighting approach, a comprehensive weighting model including the best-worst method (BWM) and entropy weight method is provided to calculate the weights of criteria. Next, expert weights are calculated based on trust analysis. Finally, alternatives are ranked by the improved gained and lost dominance score (GLDS) method. To verify the validity of the model, an empirical investigation is carried out in Shanxi Province. The results show that the economy is the primary factor influencing the investment decision. Among all the projects approved by the government, alternative F4 located in Yanzhuang Town, Yuanping City is the best investment object. Furthermore, to illustrate the stability of the result, triple sensitivity analysis and comparative analysis are conducted in Shanxi Province. This study expands the application scope of GIS and MCDM method by first providing support for government and investors to identify optimal investment targets.

Two-stage optimal site selection for waste-to-energy plant using single-valued neutrosophic sets and geographic information system based multi-criteria decision-making approach: A case study of Beijing, China.

Waste-to-energy (WtE) plant has been attached great importance by more and more countries because of the harmless, energy reducing and recycling characteristics, and has become an important part of the city's normal operation and energy structure. Whereas, the reasonable site selection of the WtE plant is the key to ensure its sustainable development. This paper is designed to provide a two-stage model for the site selection of WtE plants. Firstly, a comprehensive assessment criteria system including environmental, economic, technical and social factors is established by selecting the reasonable influence criterias from the current relevant literature. Considering the fuzziness of the criteria and the bounded knowledge of decision makers, the single-valued neutrosophic sets (SVNSs) are introduced to describe the fuzzy information, andcreatively combined with decision-making and trial evaluation laboratory-analytical network process (DANP) method to determine the influence grades of criterias and calculate the weights. After that, the relevant geographic information layers are collected and the preliminary alternatives are obtained by Geographic information system (GIS) from an overall perspective, then the extend evaluation based on distance from average solution (EDAS) method is utilized to rank the alternatives under the SVNSs environment. Finally, the model is tested to select the optimal alternative for siting the WtE plants in Beijing as an example. It is verified that the model is of great stability and feasibility by sensitivity analysis and comparative analysis. The result shows that this paper can provide a new theoretical basis for the planning of WtE plants.

Multi-state Markov model in outcome of mild cognitive impairments among community elderly residents in Mainland China.

BACKGROUND: Although knowledge of established risk factors for Alzheimer's disease (AD) can logically contribute to the search for predictors of the progression of cognitive impairment, it has not yet been firmly established where in the cognitive impairment process these risk factors exert their effects and how to predict quantitatively for the progression of mild cognitive impairments (MCI) to AD. This study aimed to determine whether known risk factors increased the risk of progression from MCI to AD and to make prediction based on transition probabilities. METHODS: Based on ten examinations of 600 community-dwelling MCI residents and cognitive assessments to classify individuals into MCI, global impairment, and AD, a multi-state Markov Cox's regression model was used and the hazard ratios with their confidence intervals and transition probabilities were estimated. RESULTS: Multivariate analysis showed that gender, age, and hypertension were statistically significant predictors of transition from MCI to global impairment; age, education, and reading statistically influenced transition from global impairment to MCI; gender, age, hypertension, diabetes, and apolipoprotein E geneε4 status were statistically associated with transition from global impairment to AD. Subjects at MCI were more likely (67%) to remain in that cognitive state at the next cognitive assessment than to transition to cognitive deterioration. For global impairment, probability of remaining in the same state was only 18% and that of forward transition was three times more likely than that of backward transition. CONCLUSIONS: Known risk factors influenced differently for different transitions. Transition from global impairment was more likely to worsen to severe cognitive deterioration than transition from MCI.

[Multi-state model in the evaluation of outcome on mild cognitive impairment to Alzheimer's disease].

OBJECTIVE: The aim of this study was to introduce the multi-state Markov model for the prediction of mild cognitive impairment (MCI) to Alzheimer's disease (AD) and to find out the related factors for AD prevention and early intervention among the elderly. METHODS: MCI, moderate to severe cognitive impairment, and AD were defined as state 1, 2 and 3, respectively. A three-state homogeneous model with discrete states and discrete times from data on six follow-up visits was constructed to explore factors for various progressive stages from MCI to AD. Transition probability and survival curve were made after the model fit assessment. RESULTS: At the level of 0.05, data from the multivariate analysis showed that gender (HR=1.23, 95%CI: 1.12-1.38), age (HR=1.37, 95%CI: 1.07-1.72), hypertension (HR=1.54, 95%CI: 1.31-2.19) were statistically significant for the transition from state 1 to state 2, while age (HR=0.78, 95%CI: 0.69-0.98), education level (HR=1.35, 95%CI: 1.09-1.86) and reading (HR=1.20, 95%CI: 1.01-1.41) were statistically significant for transition from state 2 to state 1, and gender (HR=1.59, 95%CI: 1.33-1.89), age (HR=1.33, 95%CI: 1.02-1.64), hypertension (HR=1.22, 95%CI: 1.11-1.43), diabetes (HR=1.52, 95%CI: 1.12-2.00), ApoEe4 (HR=1.44, 95%CI: 1.09-1.68) were statistically significant for transition from state 2 to state 3. Based on the fitted model, the three-year transition probabilities during each state at average covariate level were estimated. CONCLUSION: To delay the disease progression of MCI, phase by phase prevention measures could be adopted based on the main factors of each stage. Multi-state Markov model could imitate the natural history of disease and showed great advantage in dynamically evaluating the development of chronic diseases with multi-states and multi-factors.