Facility location, particularly in the context of international investments by global enterprises, stands out as a paramount concern within the purview of top management\'s strategic decision-making process. The selection of a suitable location plays a pivotal role in determining the ultimate achievement of organizational objectives. The process of selecting an appropriate location requires the comprehensive analysis of a substantial volume of data, encompassing diverse tangible and intangible evaluation criteria that may exhibit inherent conflicts. This paper addresses the challenge of determining the best location for a manufacturing facility by employing alternative performance measures within the framework of the data envelopment analysis (DEA) model. In a performance evaluation process, not only positive but also negative aspects should be determined. This paper, therefore, proposes a double-frontier DEA-AR model, which is an integrated approach that incorporates the efficient frontier, anti-efficient frontier, and assurance region weight restrictions, with the aim of increasing the discrimination ability of the DEA method. An efficient frontier evaluates the information of each location from a positive viewpoint, while the worst side is evaluated by an anti-efficient frontier. The technique of weight restrictions, which allows incorporating expert opinion into the assessment, is also applied with both frontiers to restrict the regions of weights to some specific area. The prescribed approach is illustrated by a numerical example of selecting the best location among ten different countries under consideration of 22 selection criteria obtained from PEST analysis. The results show that the proposed alternative performance measures significantly improve discrimination capability, enabling the ranking of candidates based on their suitability for the optimal location.

BACKGROUND: To improve the site selection process for clinical trials, we expanded a site survey to include standardized assessments of site commitment time, team experience, feasibility of tight timelines, and local medical community equipoise as factors that might better predict performance. We also collected contact information about institutional research services ahead of site onboarding.
OBJECTIVE: As a first step, we wanted to confirm that an expanded survey could be feasible and generalizable-that asking site teams for more details upfront was acceptable and that the survey could be completed in a reasonable amount of time, despite the assessment length.
METHODS: A standardized, two-part Site Assessment Survey Instrument (SASI), examining qualitative components and with multiple contact list sections, was developed using a publicly accessible dashboard and later transferred to a REDCap platform. After multiple rounds of internal testing, the SASI was deployed 11 times for multicenter trials. Follow-up questionnaires were sent to site teams to confirm that an expanded survey instrument is acceptable to the research community and could be completed during a brief work shift.
RESULTS: Respondents thought the SASI collected useful and relevant information about their sites (100%). Sites were \"comfortable\" (90%) supplying detailed information early in the site selection process and 57% completed the SASI in one to two hours.
CONCLUSIONS: Coordinating centers and sites found the SASI tool to be acceptable and helpful when collecting data in consideration of multicenter trial site selection.

The eddy covariance technique, commonly applied using flux towers, enables the investigation of greenhouse gas (e.g., carbon dioxide, methane, nitrous oxide) and energy (latent and sensible heat) fluxes between the biosphere and the atmosphere. Through measuring carbon fluxes in particular, eddy covariance flux towers can give insight into how ecosystem scale photosynthesis (i.e., gross primary productivity) changes over time in response to climate and management. This chapter is designed to be a beginner\'s guide to understanding the eddy covariance method and how it can be applied in photosynthesis research. It introduces key concepts and assumptions that apply to the method, what materials are required to set up a flux tower, as well as practical advice for site installation, maintenance, data management, and postprocessing considerations. This chapter also includes examples of what can go wrong, with advice on how to correct these errors if they arise. This chapter has been crafted to help new users design, install, and manage the best towers to suit their research needs and includes additional resources throughout to further guide successful eddy covariance research activities.

The increase in the use of Renewable Energy Sources (RES) provides many advantages such as reducing the environmental problems and sustainability. In this study, a long-term optimum RES settlement strategic plan is conducted for 81 provinces in Turkey by considering real data. Biomass energy, solar energy, hydroelectric energy, geothermal energy, and wind energy are considered RES sources. Energy consumption until the 2050 year is estimated with the regARIMA method, and then a weighted goal programming model was developed in which the outputs of the regARIMA method and risk analysis are integrated. The results of the regARIMA method are tested, and the test results indicate that an R2 value close to 1 indicates that the model is suitable, a low and negative MPE value is neutral, and a MAPE value below 4% indicates high accuracy of the model. Using GAMS 23.5 optimization software program, the developed weighted goal programming model is solved optimally. In this integrated model developed, the objectives of minimizing the installation time, minimizing the investment cost, minimizing the annual cost, maximizing the carbon emission reduction, maximizing the usage time, and minimizing the risk are considered. When the results obtained regarding the number of installations according to the model are examined, the decisions are made for 53% wind energy, 23% biomass energy, 13% hydroelectric energy, 9% solar energy, and 2% geothermal energy. Computational results show that the effective solutions are obtained by minimizing the sum of goals values, covering all provinces in Turkey, and considering real data.

Disease surveillance aims to collect data at different times or locations, to assist public health authorities to respond appropriately. Surveillance of the simian malaria parasite, Plasmodium knowlesi, is sparse in some endemic areas and the spatial extent of transmission is uncertain. Zoonotic transmission of Plasmodium knowlesi has been demonstrated throughout Southeast Asia and represents a major hurdle to regional malaria elimination efforts. Given an arbitrary spatial prediction of relative disease risk, we develop a flexible framework for surveillance site selection, drawing on principles from multi-criteria decision-making. To demonstrate the utility of our framework, we apply it to the case study of Plasmodium knowlesi malaria surveillance site selection in western Indonesia. We demonstrate how statistical predictions of relative disease risk can be quantitatively incorporated into public health decision-making, with specific application to active human surveillance of zoonotic malaria. This approach can be used in other contexts to extend the utility of modelling outputs.

With the rapidly increasing world population and the need for industrialization, energy supply has become an important global problem. A significant part of the world\'s energy needs is provided by fossil fuels. About half of all global coal deposits are low-quality coals, including lignite. Karapınar/Konya, also the study area, has Turkiye\'s second richest lignite reserve. The region\'s lignite reserve can be used in thermal power plants for electricity generation in terms of its nature and the amount is an important opportunity to meet the energy demand for both region and country. The region contains many sinkholes, and the potential for the formation of new sinkholes makes the site selection for thermal power plants in the region an even more strategic decision. This study aims to propose the most suitable thermal power plant site for the region by using Multi-Criteria Decision Making methods and Geographic Information Systems in an integrated way. Within the scope of the study, a total of twelve sub-criteria were taken into consideration under the main criteria of Geological, Economic and Environmental. The Best-Worst Method was applied to determine the criteria weights, and by using the weights, a suitability map for the thermal power plant installation site was produced and candidate regions were determined. TOPSIS was applied to determine the most suitable location among the candidate regions. The Candidate Region in the easternmost part of Karapinar district was chosen as the most suitable site for the thermal power plant installation.

China\'s tiered strategy to enhance county-level waste incineration for energy aligns with the sustainable development goals (SDGs), emphasizing the need for comprehensive assessments of waste-to-energy (WtE) plant suitability. Traditional assessment methodologies face challenges, particularly in suggesting innovative site alternatives, adapting to new data sets, and their dependence on strict assumptions. This study introduced enhancements in three pivotal dimensions. Methodologically, it leverages data-driven machine learning (ML) approaches to capture the complex relationships essential for site selection, reducing dependency on strict assumptions. In terms of predictive performance, the integration of oversampling with stacked ensemble models enhances the diversity and generalizability of ML models. The area under curve (AUC) scores from four ML models, enhanced by the oversampled dataset, demonstrated significant improvements compared to the original dataset. The stacking model excelled, achieving a score of 92%. It also led in overall Precision and Recall, reaching 85.2% and 85.08% respectively. Nevertheless, a noticeable discrepancy existed in Precision and Recall for positive classes. The stacking model topped Precision scores at 83.1%, followed by eXtreme Gradient Boosting (XGBoost) (82.61%). In terms of Recall, XGBoost recorded the lowest at 85.07%, while the other three classifiers all marked 88.06%. From an industry applicability standpoint, the stacking model provides innovative location alternatives and demonstrates adaptability in Hunan province, offering a reusable tool for WtE location. In conclusion, this study not only enhances the methodological aspects of WtE site selection but also provides practical and adaptable solutions, contributing positively to sustainable waste management practices.

Rapid urbanization and physical development have produced a significant quantity of construction and demolition waste in Bonab, a city in northwest Iran. In order to achieve a high level of accuracy and minimize subjective bias, catastrophe theory (CT) principles were combined in the geographic information system (GIS) environment for identifying suitable locations for safe disposal of the waste. Site selection process for construction waste landfill (CWLF) in Bonab city involved preparation and integration of 12 maps representing elevation, slope, geology, soil type, land use, vegetation, distance to population centers, valuable ecosystems, river, groundwater, and access to the main roadways with the CT to reduce uncertainty associated with subjective evaluations. Evaluation of relative importance of the variables showed that elevation, slope, distance to the city, vegetation cover, and distance to communication networks have greater importance in site selection for construction waste landfill. On the contrary, distance to the river, groundwater level, distance to villages, and valuable ecosystems have the least impact. Suitable areas were identified in parts of the north and west of the city. The identified sites have a gentle slope, low agricultural potential, good access to the communication networks, suitable distance to the city, and no limitations regarding the groundwater level, geological formations, and vegetation cover.

Site selection for pistachio orchards is an important issue for sustainable agricultural policies, crop productivity, agricultural planning, and communities. This study aims to investigate suitable places for pistachio in the Mardin Province (SE Turkey) by considering several variables, such as meteorological data, topographic conditions, economic factors, and soil characteristics, using Geographic Information System (GIS) and Multi-Criteria Decision Analysis. Pistachio farmers, expert opinions, and literature data were used to determine the requirements for pistachio cultivation. Four main assessment criteria (thirteen sub-criteria), sixty value ranges, and fourteen exclusion criteria were determined for the pistachio land suitability assessment. The weighting of the evaluation criteria was calculated using the Analytical Hierarchy Process (AHP). Farmers and experts have stated that meteorological factors are more important than soil, topography, and economic factors. All data were transferred to the GIS environment, and a land suitability map was created using the weighted linear combination method. The results show that Mardin province has very suitable lands for pistachio cultivation. The resulting map determined that the 228,891.59 ha area in Mardin province is very suitable for pistachio. To evaluate the accuracy of the land suitability map generated for pistachio, the Receiver Operating Characteristic (ROC) curve was used. The value of the area under the curve (AUC) was calculated to be 0.806, which indicates that the study is consistent. The created suitability map will be an essential data source for developing sustainable agricultural strategies in the Southeastern Anatolia region.

In the energy transition, the promotion of renewable sources entails the development of storage technologies to manage the mismatch between energy production and demand. In this scenario, the use of CAES (Compressed Air Energy Storage) technology enables the efficient and cost-effective storage of large amounts of energy. However, this technology is developed in salt domes who have an inherent risk associated of underground exploration phase. To address this, we propose to develop an infrastructure (iCAES) in abandoned underground mines, where the exploration phase is completed and well known. For its implementation, this paper defines a structure hierarchization method gathers the technical and socio-economic criteria. It involves a multi-criteria problem, and the correct selection of the location must be based on specific mathematical algorithms. For this case the Analytic Hierarchy Process (AHP) from multi-criteria decision making (MCDM) methods allows quantified by means of a scientific and mathematical scale and the assignment of weights, so that it is possible to evaluate different alternatives. This is possible thanks to the application of the AHP model in absolute terms. The information gathering has been based on the specific case study of coal basins in the north of Spain, in the region of León. Considering the proposed methodology, the most suitable alternative locations to implement iCAES in the region of León were identified.