Explore the vast range of titles available.

In this study, we proposed an optimal agricultural site selection method based on the third law of geography and geographic similarity, and took the vegetable moss growing area in Hongshan District, Wuhan, China, as an example. The geography of the source region (Hongshan District) and the target region (Hubei Province) was examined and the temperature, precipitation, topographical factors and soil were selected as the environmental covariates. The specific design of the experiment included the steps of creating sample points and points to be inferred, classifying the environmental factors, calculating and ranking geographic environmental similarity, and modifying in missing values. The results showed that the areas with high similarity between the source and target regions were mainly concentrated in the southeastern part of Hubei, i.e., the area suitable for growing Hongshan vegetable moss, while the western part of Hubei was mostly a low-similarity area. The methodology of this study provides an important theoretical reference for crop site selection and is of practical application.

Pakistan has sufficient wind energy potential across various locations of the country. However, so far, wind energy development has not attained sufficient momentum matching its potential. Amongst various other challenges, the site selection for wind power development has always been a primary concern of the decision-makers. Principally, wind project site selection decisions are driven by various multifaceted criteria. As such, in this study, a robust research framework comprising of factor analysis (FA) of techno-economic and socio-political factors, and a hybrid analytical hierarchy process (AHP) and fuzzy technique for order of preference by similarity to ideal solution (FTOPSIS) have been used for the prioritization of sites in the southeastern region of Pakistan. The results of this study reveal economic and land acquisition as the most significant criteria and sub-criteria, respectively. From the eight different sites considered, Jamshoro has been prioritized as the most suitable location for wind project development followed by Hyderabad, Nooriabad, Gharo, Keti Bandar, Shahbandar, Sajawal, and Talhar. This study provides a comprehensive decision support framework comprising of FA and a hybrid AHP and Fuzzy TOPSIS for the systematic analysis to prioritize suitable sites for the wind project development in Pakistan.

Landing site selection is of fundamental importance for lunar landing mission and it is closely related to the scientific goals of the mission. According to the widely concerned lunar science goals and the landing site selection of the ongoing lunar missions; China has carried out the selection of landing site for a series of Chang’ E (CE) missions. Under this background, this paper firstly introduced the principles, process, method and result of landing site selection of China’s Lunar Exploration Program (CLEP), and then analyzed the support of the selected landing sites to the corresponding lunar research. This study also pointed out the outcomes that could possibly contribute to the key lunar questions on the basis of the selected landing sites of CE-4 and CE-5 such as deep material in South Pole-Aitken (SPA) basin, lunar chronology, volcanic thermodynamics and geological structure evolution history of the Moon. Finally, this approach analyzed the development trend of China’s follow-up lunar landing missions, and suggested that the South Pole Region of the Moon could be the landing site of high priority for the future CE missions.

Multi-criteria decision-making (MCDM) methods are susceptible to the subjectivity of experts when especially they use linguistic terms for assessment. This subjectivity and vagueness in the evaluation process have been handled by the recent extensions of ordinary fuzzy sets such as type-2 fuzzy sets, hesitant fuzzy sets, intuitionistic fuzzy sets, Pythagorean fuzzy sets and neutrosophic sets. Pythagorean fuzzy sets are superior to the other extensions with a more flexible definition of membership function. A novel Pythagorean fuzzy AHP method has been developed for MCDM. The developed method has been applied to a landfill site selection problem for the city of Istanbul in Turkey. The proposed method has successfully evaluated the landfill location alternatives with respect to the considered criteria. The results are compared with ordinary fuzzy AHP, and it is revealed that the proposed method produces consistent and informative outcomes better representing the uncertainty of decision-making environment. Robustness of the decision given by the proposed method is ensured by conducting one-at-a-time sensitivity analysis.

Identification of hospital sites and their ranking is important for the planning and development of any country's health infrastructure. The site selection problem is a typical multi-criteria decision making problem involving multiple stakeholders and their interests. Multi-Criteria Decision Analysis (MCDA) is a promising approach to solve a location-based problem due to the constitution of various criteria involved in decision making. In this research, eleven criterion are chosen which are classified under three main criteria; socio-economic, geographical and environmental. This research aims to identify the appropriate MCDA method for the selection of a new hospital sites. Here, two MCDA methods named Analytical Hierarchy Process (AHP) and Fuzzy AHP (FAHP) are used. Further, Geographical Information System (GIS) based MCDA methodology is proposed in this paper. The results obtained with both AHP and FAHP methods are compared. This comparison is based on criterion rankings, proposed hospital locations and sensitivity analysis. The main difference in results is shown in the result of sensitivity analysis in which constant variation in site ranking is obtained when weight change analysis is performed using AHP. The FAHP result shows only one variation in site ranking after a change in weight from +10 to +20%. The result suggests that FAHP may be a better approach to the hospital site selection problem.

Many studies have dealt with the habitat requirements of cavity‐nesting birds, but there is no meta‐analysis on the subject and individual study results remain vague or contradictory. We conducted a meta‐analysis to increase the available evidence for nest‐site selection of cavity‐nesting birds. Literature was searched in Web of Science and Google Scholar and included studies that provide data on the habitat requirements of cavity‐nesting birds in temperate and boreal forests of varying naturalness. To compare nest and non‐nest‐tree characteristics, the following data were collected from the literature: diameter at breast height (DBH) and its standard deviation (SD), sample size of trees with and without active nest, amount of nest and available trees described as dead or with a broken crown, and amount of nest and available trees that were lacking these characteristics. Further collected data included bird species nesting in the cavities and nest‐building type (nonexcavator/excavator), forest type (coniferous/deciduous/mixed), biome (temperate/boreal), and naturalness (managed/natural). From these data, three effect sizes were calculated that describe potential nest trees in terms of DBH, vital status (dead/alive), and crown status (broken/intact). These tree characteristics can be easily recognized by foresters. The results show that on average large‐diameter trees, dead trees, and trees with broken crowns were selected for nesting. The magnitude of this effect varied depending primarily on bird species and the explanatory variables forest type and naturalness. Biome had lowest influence (indicated by ΔAIC). We conclude that diameter at breast height, vitality, and crown status can be used as tree characteristics for the selection of trees that should be retained in selectively harvested forests. We conducted a meta‐analysis to gain insights into which tree characteristics are important during nest‐site selection by cavity‐nesting birds. Our results show that diameter at breast height (DBH), tree vitality, and crown status all significantly influence the suitability as nest trees. These findings also have important implications for the selection of trees to be retained in retention forestry.

This study utilizes an integrated Geographic Information System (GIS)-based Multi-Criteria Decision-Making (MCDM) approach to perform Solar Power Plant Site Selection (SPPSS) in Kermanshah Province, Iran. It introduces a novel group weighting method, the Dempster-based Best-Worst Method (DBWM), which combines weights vectors derived from experts’ opinions. The study also conducts a comprehensive sensitivity analysis comparing four GIS-based models for SPPSS. Findings indicate that the Inverse Distance Weighted (IDW) method is the most precise for interpolation, which was subsequently applied in the analysis. Results demonstrate that the GIS-based DBWM-Technique for Order Preference by Similarity to Ideal Solution (GIS-based DBWM-TOPSIS) model is the most stable, identifying slope as the primary criterion for SPPSS. Based on this model, 3% of the area is classified as very low suitability, 9% as low, 24% as moderate, 33% as high, and 31% as very high suitability. The study highlights the substantial impact of selecting appropriate spatial analysis techniques and uses normalization to standardize input criteria with varied units and ranges, enhancing comparability within the MCDM process. Eslamabad-e Gharb, Kangavar, and Gilan-e Gharb counties emerged as the most suitable locations for solar power plant (SPP) development.

Fil: Cockle, Kristina Louise. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Biología Subtropical. Instituto de Biología Subtropical - Nodo Puerto Iguazú | Universidad Nacional de Misiones. Instituto de Biología Subtropical. Instituto de Biología Subtropical - Nodo Puerto Iguazú; Argentina. University of British Columbia; Canadá

Landfill site selection is a multi-attribute decision problem, through which factors like available land area, soil conditions, climatological conditions, and economic considerations are investigated in detail. Frequently, it is a challenge to come up with “the one” solution while tackling such complex systems. Therefore, use of tools such as fuzzy analytic hierarchy process (fuzzy AHP) and fuzzy technique for order preference by similarity to ideal solution (fuzzy TOPSIS) should be preferred in order to emphasize pros and cons for each of the studied options. In this study, three possible landfill sites for the city of Istanbul are evaluated through expert opinion and by facilitating fuzzy AHP and fuzzy TOPSIS. Initially, the landfill site selection problem is presented in the framework of a model and then the model is mathematically solved by calculating the individual criterion weights. In conclusion, considering the rapid rate of urbanization for the city of Istanbul, the possible landfill sites convey similar overall results, but differ in specific criteria.

Rare species reintroductions are an increasingly common conservation strategy, but often result in poor survival of reintroduced individuals. Reintroduction sites are chosen primarily based on historical occupancy and/or abiotic properties of the site, with much less consideration given to properties of the larger biotic community. However, ecological niche theory suggests that the ability to coexist with other species is determined in part by the degree of functional similarity between species. The degree to which functional similarity affects the survival of reintroduced plants is poorly understood, but has important implications for the allocation of limited conservation resources. We collected a suite of abiotic, biotic, and functional trait variables centered on outplanted individuals from four reintroduced rare plant species and used logistic regression and model selection to assess their influence on individual survival. We show that higher functional similarity between reintroduced individuals and the local community, measured by differences between their multivariate functional traits and the community-weighted mean traits of their immediate neighbors, increases survival and is a stronger predictor of survival than local variation in abiotic factors, suggesting that the functional composition of the biotic community should be incorporated into site selection to improve reintroduction success.