Geometrical Analysis For Interactive Aid Research Articles

Assessment of contamination and potential ecological risks of heavy metals in riverine sediments from gold mining and pristine areas in Ghana

This study explore the characteristics of heavy metal pollution, contamination levels, and potential ecological risks in riverine sediments found in both gold mining and pristine areas. Such investigations are vital for the ongoing monitoring and preservation of water bodies and overall ecosystem health. In total, 44 composite sediment samples were collected from seven pristine environments and four mining sites. These samples underwent preparation, digestion, and analysis for heavy metal content, utilizing the inductively coupled plasma emission mass spectrometer (ICP-MS). Various tools and models, including the geo-accumulation index, enrichment factor, and degree of contamination, were employed to assess the impact of pollution on the environment. Modified ecological risk index were also used to evaluate potential ecological risks. The average concentrations of heavy metals in pristine sites spanned from 0.01±0.01 (Cd) to 73,753.64 ± 388.15 mgkg−1 (Fe), while in mining sites, they ranged from 0.04±0.02 (Cd) to 56,394.25±400.66 mgkg−1 (Fe). Comparing these concentrations against the USEPA Ecological Screening Values, Mean Shales levels, and the Canadian ISQG, it became evident that Pb, Cd, Zn, Ni, Co, and Mn concentrations generally remained below the recommended guideline values in both pristine and mining areas. However, concentrations of Cr, As, Cu, and Hg exceeded the prescribed threshold limits, particularly in the mining regions. Through Principal Component Analysis (PCA) and Geometrical Analysis for Interactive Aid (GAIA) modeling, two primary sources of heavy metals were identified: anthropogenic-related and geogenic-related. GAIA and PCA together explained 78.53 % and 79.40 % of the total variability in heavy metal concentrations, respectively. Overall, the pollution and ecological risk assessment indicated low to moderate contamination levels, with a notable exception of high arsenic contamination in the Nyam river. The findings of this study hold significance for assessing sediment conditions and river quality in mining communities within Ghana and globally. They also provide empirical data to recommend measures for mitigating water contamination in such communities.

A novel fuzzy SECA model based on fuzzy standard deviation and correlation coefficients for resilient-sustainable supplier selection

Selecting the best out of a pool of potential suppliers is one of the most significant challenges in supply chain management. Although numerous works have studied supplier selection (SS) as a function of individual criterion (e.g., sustainability or resiliency), few studies have explored it as an all-in-one. To address this gap, this study proposes a novel fuzzy multi-criteria decision-making (MCDM) framework for a real-world color and glaze case study. First, resilient-sustainable criteria are identified for evaluating the performance of the suppliers. Expert opinions and literature review are then utilized to establish a set of criteria in two different categories: sustainability and resiliency. This two-pronged approach ensured that important criteria are considered in the evaluation process. Next, a new fuzzy simultaneous evaluation of criteria and alternatives (FSECA) model is developed to highlight the most crucial criteria and the best supplier. The proposed model utilizes a predetermined set of criteria reference weights as benchmarks to calculate the final weights that closely align with these reference points. Moreover, the consideration of fuzzy standard deviation and correlation coefficients of fuzzy numbers are incorporated to take into account the inherent variability and uncertainty in the data, resulting in a more accurate and robust analysis of the data. To validate the proposed method, the findings are compared to the state-of-the-art fuzzy approaches for SECA method, and other MCDM techniques such as fuzzy weighted aggregated sum product assessment (FWASPAS), fuzzy technique for order performance by similarity to ideal solution (FTOPSIS), fuzzy Multi-Objective Optimization on the basis of Ratio Analysis (FMOORA), fuzzy simple additive weighting (FSAW), and fuzzy complex proportional assessment (FCOPRAS). Moreover, by applying the geometrical analysis for interactive aid (GAIA), a visualization approach, the results demonstrate that the performance of the proposed framework is satisfactory and highlight the most crucial criteria, such as the supplier responsiveness in resiliency and delivery in economic impact of sustainability categories. Since choosing supplier is a strategic decision, the proposed work can help managers to choose the right suppliers.

Evaluation of nature-based tourism potential in protected and sensitive areas by CRITIC and PROMETHEE-GAIA methods

This study aims to quantitatively evaluate the nature-based tourism (NBT) potential in three Karagöl (“blacklake” in Turkish), Borçka, Şavşat and Murgul, which are located in Artvin Province. Among the mentioned lakes, Şavşat Karagöl has been taken under protection as a national park, and Borçka Karagöl has been brought under protection as a nature protection area. Murgul Karagöl, on the other hand, does not have any protection status. The Criteria Importance Through Inter-criteria Correlation (CRITIC) and Preference Ranking Organisation Method for Enrichment Evaluation (PROMETHEE)-Geometrical Analysis for Interactive Aid (GAIA) techniques were used to evaluate the nature-based tourism potentials of the lakes. In this context, twenty-six criteria obtained from the literature and weighted by the CRITIC technique were evaluated by comparing them with the three available alternatives. In the next stage, the evaluation of the alternatives according to the weighted criteria was provided by the PROMETHEE-GAIA technique. According to the results, the lake with the highest NBT potential in lakes in Artvin Province is Borçka Karagöl Nature Park. The lake with the lowest potential is Murgul Karagöl, which has not yet been discovered, and its infrastructure has not been completed. It is thought that the results may be significant in providing various practical implications for local stakeholders on NBT that can be developed in the region.

Application of a Decision-Making Tool for Ranking Wellness Tourism Destinations

One of the tourism industry’s segments with the strongest growth rates today is health tourism. Health tourism includes two subgroups: medical tourism (traveling outside one’s country of residence for the purpose of receiving medical care, such as surgery and health services) and wellness tourism (travel to specific locations for health promotion in a preventive way). The economic strength and sustainable growth of nations can both benefit from health tourism. This study applies a methodology to quantify the potential of Portuguese wellness tourism (thermal spas in Northern Portugal) using a multi-criteria decision making (MCDM) tool, namely the preference ranking organization method for enrichment evaluations (PROMETHEE) and geometrical analysis for interactive aid (GAIA), to achieve a robust evaluation and ranking the alternatives. Therefore, in this study, the aim is to rank ten thermal spas in Northern Portugal in terms of fifteen indicators, mostly related to digital services, containing the tourism data obtained between 2020 and 2022. The suggested approach offers trustworthy and reliable outcomes for any qualitative or quantitative criteria to assess thermal spas, which is crucial for consumers, businesses, and even governments. The results showed that PROMETHEE and GAIA can be implemented as an effective method in wellness tourism destinations evaluation.

Penerapan Metode PROMETHEE-GAIA Dalam Pemeringkatan Perguruan Tinggi di Indonesia

The Ministry of Research, Technology and Higher Education has been conducting university rankings since 2015. The goal is to map or build Indonesian universities. This ranking is known as university clustering, and it is separated into two categories: non-vocational/academic institutions and vocational universities. The goal of this research is to create higher education clusters based on the four key indicators that have been identified. The Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) II decision model in Multi-Criteria Decision Making (MCDM) was employed, together with Geometrical Analysis for Interactive Aid (GAIA) as geometric analysis in the form of decision-making visualization. This investigation yields alternative 2 with a phi of 0.5125 as a compelling value, followed by alternatives 3 and 1. According to the data, the second university out of the five evaluated had the best performance based on the four weights of the Indonesian higher education clustering indicator.

The fate of nitrogen and sulphur during co-liquefaction of algae and bagasse: Experimental and multi-criterion decision analysis

The removal of nitrogen (N) and sulphur (S) from biocrude oil produced using hydrothermal liquefaction (HTL), is important for the production of high quality renewable fuels. Here the effect of co-liquefaction of bagasse and algae was analysed. Algae (Chlorella vulgaris and Cyanobacteria) were mixed with bagasse (1:1) subjected to HTL at 250–350 °C for 10–60 min. Higher HTL temperatures had a positive effect in increasing the biocrude yield and slightly reduced N content; S did not show a consistent trend. Most of the nitrogen (~66%) and sulphur (~80%) were recovered in the aqueous phase rather than in the biocrude phase, opening the opportunity to recycle these nutrients for algae cultivation. Co-liquefying bagasse with algae improved the biocrude yield (54 wt%) compared to pure Cyanobacteria (47.5 wt%). It also reduced N content from 7 wt% (Cyanobacteria biocrude) to 4.2 wt% (Cyanobacteria: Bagasse) and S from 0.7 wt% to 0.4 wt%. Principal Component Analysis (PCA) analysis identified that biocrude yield is positively correlated with the initial lipid content and anti-correlated with the carbohydrates fraction. Biocrude N content is closely related to the initial amount of proteins in the algae. The Preference Ranking Organization METHod for Enrichment of Evaluations and its descriptive complement Geometrical Analysis for Interactive Aid (PROMETHEE and GAIA) analysis ranked the co-liquefaction of Chlorella vulgaris and bagasse (1:1) at 350 °C and 60 min as one of the best overall combination in terms of biocrude yield, N and S content.

Selection of Indigenous Algal Species for Potential Biodiesel Production

Currently, India utilizes an enormous amount of fossil fuels and a major quantity of fossil fuels are imported from other countries. It’s a giant load on the Indian Economy. The burning of fossil fuels causes global warming. Carbon neutral, renewable fuels are essential for environmental protection and it’s economically sustainable for India. Biofuels attention day by day due to a rise in energy demands and environmental concerns. Biodiesel produced from algal oil a possible renewable and carbon-neutral substitute to fossil fuels. The feasibility of the algal-based biodiesel industry depends on the selection of adequate species regarding commercial oil yields and oil quality. Present research work to bioprospecting and screening of 19 algal and blue-green algal species, the oil percentage and the fatty acid profiles, used for analyzing the biodiesel fuel properties. Oil from Tolypothrix phyllophila algal strain and compared it with another eighteen algal and blue-green algal strains from different literature. Tolypothrix phyllophila algal strain contains approximately 12.6% lipid on a dry weight basis. We also compared the FAME profile of 19 algal and blue-green algal strains and calculated and compared the fuel properties such as cetane number, Iodine Value, etc. of the biodiesel derived from these algal and blue-green algal oils based on chain length and saturation. We also investigated the 19 algal and blue-green algal fatty acid profiles and its suitability for biodiesel production and strains selection through PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluations) and GAIA (geometrical analysis for interactive aid) analysis.

Optimized simultaneous pressurized fluid extraction and in-cell clean-up, and analysis of polycyclic aromatic hydrocarbons (PAHs), and nitro-, carbonyl-, hydroxy -PAHs in solid particles

The development, modification and optimization of analytical methods capable of simultaneous extraction and in-cell clean-up of extracts for subsequent determination of parent PAHs and their associated transformed nitro-PAHs (NPAH), carbonyl-PAHs (CPAH) and hydroxy-PAHs (HO-PAH) products (TPPs) is essential for reducing the time and cost of analysis. The aim of this study was to modify and optimize the pressurized fluid extraction (PFE) technique capable of simultaneous extraction and in-cell clean-up of PAHs and TPPs in urban dust standard reference material and road dust for GC-MS analyses. In this study, multivariate data analysis such as factor analysis (FA), and preference ranking organisation method for enrichment evaluation (PROMETHEE) and geometrical analysis for interactive aid (GAIA) were used to assess the performance of the methods. As the key outcome of the study, an optimized selective reaction monitoring (SRM) Triple Quadrupole (TQ) electron ionization (EI)-GC/MS for measuring PAHs and TPPs without derivatization of polar HO-PAHs was developed. The limits of detection (LOD) for parent PAHs, CPAHs, NPAHs and HO-PAHs using Shimadzu TQ were 1.0–5.0 pg, 1.0–5.0 pg, 1.0–50.0 pg, and 1.0–25.0 pg, respectively. The PROMETHEE-GAIA analysis of the results showed that a combination of 3% deactivated silica gel and activated alumina (2:1) as in-cell clean-up material, and sequential PFE extraction (200 °C ASE temperature, 9 min preheat time and 3 times extraction cycle) using 100% hexane followed by hexane/DCM (1:1) is the best condition for analytes extraction from road dust. An optimized, fast and reliable GC/MS method operated solely in electron ionization (EI) mode was developed for measuring all analytes. The outcomes of this study will contribute significantly to future research on PAHs and TPPs, thereby promoting a safe and sustainable environment.

Contamination identification, source apportionment and health risk assessment of trace elements at different fractions of atmospheric particles at iron and steelmaking areas in China.

China has the largest share of global iron and steel production, which is considered to play a significant contribution to air pollution. This study aims to investigate trace element contamination at different fractions of particulate matter (PM) at industrial areas in China. Three PM fractions, PM2.1-9.0, PM1.1-2.1 and PM1.1, were collected from areas surrounding iron and steelmaking plants at Kunming, Wuhan, Nanjing and Ningbo in China. Multiple trace elements and their bioavailability, as well as Pb isotopic compositions, were analysed for identification of contaminants, health risk assessment and source apportionment. Results showed that PM particles in the sites near industrial areas were associated with a range of toxic trace elements, specifically As, Cr(VI), Cd and Mn, and posed significant health risks to humans. The isotopic Pb compositions identified that coal and high temperature metallurgical processes in the steelmaking process were the dominant contributors to local air pollution in these sites. In addition to iron and steelmaking activities, traffic emissions and remote pollution also played a contributing role in PM contamination, confirmed by the differences of Pb isotopic compositions at each PM fraction and statistical results from Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) and Geometrical Analysis for Interactive Aid (GAIA). The results presented in this study provide a comprehensive understanding of PM emissions at iron and steelmaking areas, which helps to guide subsequent updates of air pollution control guidelines to efficiently minimise environmental footprint and ensure long term sustainability of the industries.

A hybrid multi-criteria decision-making model for optimal coal blending

Purpose The purpose of this paper is thus to develop a hybrid decision-making model for optimal coal blending strategy. Coal is one of the major resources contributing to generation of electricity and anthropogenic carbon-dioxide emission. Being formed from dead plant matter, it undergoes a series of morphological changes from peat to lignite, and finally to anthracite. Because of non-uniform distribution of coal over the whole earth and continuous variation in its compositions, coals mined from different parts of the world have widely varying properties. Hence, it requires an ideal blending strategy such that the coking coal having the optimal combination of all of its properties can be used for maximum benefit to the steel making process. Design/methodology/approach In this paper, a multi-criteria decision-making approach is proposed while integrating preference ranking organization method for enrichment of evaluations (PROMETHEE II and V) and geometrical analysis for interactive aid (GAIA) method to aid in formulating an optimal coal blending strategy. The optimal decision is arrived at while taking into account some practical implications associated with blending of coal, such as coal price from different reserves. Findings Different grades of coal are ranked from the best to the worst to find out the composition of constituent coals in the final blending process. Coals from the mines of two different geographical regions are considered here so as to prove the applicability of the proposed model. Adoption of this hybrid decision-making model would subsequently improve the performance of coal after blending and help in addressing some sustainability issues, like less pollution. Originality/value As this model takes into account the purchase price of coals from different reserves, it is always expected to provide more realistic solutions. Thus, it would be beneficial to deploy this decision-making model to different blending optimization problems in other spheres of a manufacturing industry. This model can further accommodate some more realistic criteria, such as availability of coal in different reserves as a topic of future research work.

Multi-criteria Assessment of Heavy Metals contaminations in waters and ranking the sites by using PROMETHEE/GAIA method.

In this study, Multi-criteria decision making (MCDM) approach was applied to assess and evaluate the heavy metals (Cr, Mn, Co, Ni, Cu, Zn, As, Cd, Pb and Hg) pollution in Dilovası region (one of the largest industrial area in Turkey). The heavy metal content of 10 different locations have been evaluated and these locations are ranked according to their metal contents by using PROMETHEEGAIA method, which is one of the pairwise comparisons MCDM methods. PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluation) method was used to rank the locations according to their heavy metal content and GAIA (Geometrical Analysis for Interactive Aid) method was used to analyse and show the relations between alternatives (locations) and criteria (heavy metal). Analysis indicated that location d4 (small harbor of Hereke) is the most polluted especially by Pb, Cd, Cr and As. The location d8 is the least polluted area which is the farthest places from the harbors and industrialized zones. The ranking results clearly showed that the most contaminated locations are wastewater discharge points and small ports. The study also showed that PROMETHEE/GAIA method is very helpful to analyse environmental problems.

A state-wise performance appraisal of the Indian roads using PROMETHEE-GIS approach

PurposeA road network provides arterial arrangement to facilitate business, transport, social integration and economic progress of any nation. During the last seven decades after independence, road transport infrastructure in India has expanded manifold, both in terms of spread (total length and density of road) and capacity (number of on-road registered vehicles, and volume of passenger and freight traffic handled). But, with the enrichment of road transport network in India, the number of traffic accidents and total cost for maintaining the road infrastructure also keeps on increasing. It becomes necessary to evaluate state-wise performance of the Indian roads using some mathematical tools. The paper aims to discuss this issue.Design/methodology/approachIn this paper, using preference ranking organization method for enrichment of evaluations (PROMETHEE) and geometrical analysis for interactive aid (GAIA) approaches, an attempt is made to appraise the state-wise performance of Indian roads based on 12 critically important criteria. A geographic information system method and a hue-saturation-value color coding scheme are also employed to identify the influence of individual criterion on the overall rank of 29 Indian states.FindingsIt is observed that amongst all the considered states, the road conditions in the states of Mizoram and Arunachal Pradesh are really satisfactory, whereas Bihar and Uttar Pradesh are the lagging states requiring governmental intervention and support to enhance their road network infrastructure.Practical implicationsThis analysis would help the decision makers to identify the strengths and deficiencies of each Indian state with respect to its road conditions so that proper promotional and growth actions can be implemented.Originality/valueFrom the review of the existing literature, it is quite evident that till date, no research work has been conducted in order to evaluate the performance of roads, and their conditions and characteristic features in the Indian context. In this paper, the state-wise performance of the Indian roads is appraised based on several identified parameters using a combined PROMETHEE-GAIA approach.

Spatial distribution, accumulation and human health risk assessment of heavy metals in soil and groundwater of the Tano Basin, Ghana

Soil serves as a vast matrix for heavy metal accumulation and subsequent redistribution to critical aspects of the environment such as groundwater. Soil pollution study is essential for sustainable human health and ecosystem protection. This study provides vital insight into the fate, accumulation, interactions, and health risk posed by heavy metals in soil and groundwater by employing geochemical accumulation index (Igeo), risk assessment models and multivariate data analysis techniques such as principal component analysis (PCA), preference ranking organisation method for enrichment evaluation (PROMETHEE) and geometrical analysis for interactive aid (GAIA). The median Igeo estimates show moderate to strong Pb accumulation levels whilst all the other metals indicate uncontaminated to moderate levels. The PCA output point to anthropogenic origin of Pb and Cd in the Tano Basin and surrounding communities. PROMETHEE-GAIA results indicate that Pb, Cd, Zn and Fe accumulated in the soil matrix may potentially leach into the groundwater resources. The carcinogenic lifetime risks posed by Pb, Cd, and Ni metals to adults are within the tolerable acceptable risk and thus do not present an immediate danger in the study area. Due to the significant toxicity, bioaccumulation and biomagnification properties of Pb and Cd in the environment, areas associated with significant anthropogenic activities require regular monitoring and evaluation in order to ensure that these metals are consistently below the regulatory limits. This study has further elucidated the subject of heavy metal pollution and is therefore expected to enhance sustainable protection of the environment and human health.

A multi-criteria decision support model for optimal cotton fibre blending

As cotton is a natural fibre, its different physical properties are likely to immensely vary depending on the variations in its seed type, area where it is grown and the climatic conditions. This may affect the characteristics of the final products. Blending is a process of combining different cotton fibres together to synergize their physical properties. For successful blending, it is always required to determine the types of the constituent fibres in the final mix and their appropriate proportions. In this paper, a multi-criteria optimization model in the form of a decision support framework is developed while integrating preference ranking organization method for enrichment of evaluations (PROMETHEE II and V) and geometrical analysis for interactive aid (GAIA) approach. This model is observed to have great potentiality in ranking the considered cotton fibre alternatives from the best to the worst, identifying the top ranked cotton fibres and finding out the constituent fibres in the final blend along with their proportions. Two cotton fibre varieties, such as the Egyptian and Ethiopian types are considered here to demonstrate the applicability of the developed model.

Cotton fibre selection and grading – a PROMETHEE-GAIA-based approach

PurposeIn spinning industries, selection of the most appropriate fibre for yarn manufacturing plays an important role for achieving an optimal mix of several yarn characteristics, like maximum tenacity, elasticity and spinning ability; and minimum unevenness and hairiness. Identification of the best suited cotton fibre from a set of available alternatives in presence of different conflicting physical properties is often treated as a multi-criteria decision-making (MCDM) problem. The paper aims to discuss this issue.Design/methodology/approachIn this paper, the preference ranking organisation method for enrichment of evaluations (PROMETHEE) and geometrical analysis for interactive aid (GAIA) methods are integrated to solve a cotton fibre selection problem. The PROMETHEE II method ranks the alternative cotton fibres based on their net outranking flows, whereas GAIA acts as a visual aid to strongly support the derived selection decision. The weight stability intervals for all the considered fibre properties (criteria) over which the position of the top-ranked cotton fibre remains unchanged are also determined.FindingsThe clusters of cotton fibres formed in the developed GAIA plane act as a yard stick for their appropriate grading to aid the blending process. The ranking of 17 cotton fibres as achieved applying the combined PROMETHEE-GAIA approach highly corroborates with the observations of the past researchers which proves its immense potentiality and applicability in solving fibre selection problems.Originality/valueTwo MCDM methods in the form of PROMETHEE II and GAIA are integrated to provide a holistic approach for cotton fibre grading and selection while taking into consideration all the available cotton fibre properties.

Ranking of branded products using aspect-oriented sentiment analysis and ensembled multiple criteria decision-making

In this paper, we proposed a novel aspect-oriented sentiment analysis and combined it with an ensemble of well-known multiple criteria decision-making (MCDM) methods such as TOPSIS, VIKOR, PROMETHEE II, ELECTRE III and FMADM for comparing a given set of products and services. To demonstrate the effectiveness of the proposed approach, we performed comparative study of a set of mobile phones and digital cameras using customer feedback available on twitter and e-commerce customer reviews. Eventually, the final ranks of the products were obtained by ensembling five MCDM methods through simple majority voting. During the whole process, we contributed to the state-of-the-art into four folds: first, we developed a novel aspect-oriented opinion determination approach. Second, proposal of a hybrid aspect-level sentiment score computation approach. Third, we developed two domain ontologies in order to represent prominent aspects and their synonyms for mobile phone and digital camera. Finally, we combined aspect-oriented sentiment analysis with ensembled MCDM to perform aspect-level comparative study of a set of alternatives. Further, VIKOR sensitivity analysis is also performed. Eventually, a detailed visual analytics is presented on a geometrical analysis for interactive aid (GAIA) plane.

Unsupervised classification and multi-criteria decision analysis as chemometric tools for the assessment of sediment quality: A case study of the Danube and Sava River

The aim of this study was to evaluate the quality of freshwater sediments by means of three chemometric techniques for multi-criteria analysis and decision: self-organizing network (SON), self-organizing map (SOM) and PROMETHEE&GAIA (Preference Ranking Organization Method for Enrichment Evaluation with Geometrical Analysis for Interactive Aid). Selected chemometric techniques were applied to the results of Pb, Cd, Zn, Cu, Ni, Cr, Hg and As content in thirty Danube and fourteen Sava river sediment samples from Serbia. The potential toxicity of sediments was estimated using Probable Effect Concentrations quotients (mean PEC-Q).According to the SON analysis the Danube sediment samples were divided into three classes, Class I (mean PEC-Q range 0.27–0.51), Class II (mean PEC-Q range 0.50–0.70), and Class III (mean PEC-Q range 0.77–0.97), while the Sava samples were divided into two classes, Class II (two samples, mean PEC-Q values 0.65 and 0.69) and Class III (mean PEC-Q range 0.69–1.00). Using the SOM cluster analysis, both Danube and Sava sediment samples were classified into five subclusters, on the basis of the metal concentration level and further ranked into three levels (for remediation, moderately polluted and not polluted) by the use of multi-criteria ranking PROMETHEE method. Graphical presentation of the results obtained by PROMETHEE method using GAIA descriptive tool has provided an insight into the distribution of examined elements in sediments and has shown a significant correlation between some elements.On the basis of the results obtained, it has been concluded that the proposed chemometric approach could provide useful information in the sediment quality assessment.

Application of PROMETHEE-GAIA Method in the Entry Mode Selection Process in International Market Expansion

Selecting an entry mode for a new organization or expansion is of vital importance to a decision maker, as many potential qualitative and quantitative criteria are to be considered. Entry mode decision is the most critical decision in international expansion; it can affect the performance of the company in the future. This paper solves an entry mode selection problem using PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluation) and GAIA (Geometrical Analysis for Interactive Aid) method which is an effective multi-criteria decision-making (MCDM) tool and is used to deal with complex problems. In order to follow this technique, an investigation has been operated for comparing 5 entry modes such us exporting, franchising, licensing, joint venture and foreign direct investment (FDI) according to 15 criteria. The results, obtained by multi- criteria evaluation, clearly show that this method of entry mode selection for an Ivorian company named FILTISAC is successful and applicable. It is empirically determined that joint venture is the most suitable entry mode for company expansion amongst those selected.

Chemometrics in biomonitoring: Distribution and correlation of trace elements in tree leaves

The concentrations of 15 elements were measured in the leaf samples of Aesculus hippocastanum, Tilia spp., Betula pendula and Acer platanoides collected in May and September of 2014 from four different locations in Belgrade, Serbia. The objective was to assess the chemical characterization of leaf surface and in-wax fractions, as well as the leaf tissue element content, by analyzing untreated, washed with water and washed with chloroform leaf samples, respectively. The combined approach of self-organizing networks (SON) and Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) aided by Geometrical Analysis for Interactive Aid (GAIA) was used in the interpretation of multiple element loads on/in the tree leaves. The morphological characteristics of the leaf surfaces and the elemental composition of particulate matter (PM) deposited on tree leaves were studied by using scanning electron microscopy (SEM) with energy dispersive spectroscopy (EDS) detector. The results showed that the amounts of retained and accumulated element concentrations depend on several parameters, such as chemical properties of the element and morphological properties of the leaves. Among the studied species, Tilia spp. was found to be the most effective in the accumulation of elements in leaf tissue (70% of the total element concentration), while A. hippocastanum had the lowest accumulation (54%). After water and chloroform washing, the highest percentages of removal were observed for Al, V, Cr, Cu, Zn, As, Cd and Sb (>40%). The PROMETHEE/SON ranking/classifying results were in accordance with the results obtained from the GAIA clustering techniques. The combination of the techniques enabled extraction of additional information from datasets. Therefore, the use of both the ranking and clustering methods could be a useful tool to be applied in biomonitoring studies of trace elements.

Performance evaluation of Indian states in tourism using an integrated PROMETHEE-GAIA approach

Tourism development has a unique responsibility in regional policy of almost all countries. It is a well known fact that tourism has a remarkable contribution in sustainable development, economic growth and social benefits for a country, if planned methodically. This is due to its unquestionable advantages and benefits for the local community with respect to economic, social and environmental aspects. Since few decades, it has become a driving area in Indian economic planning strategy to deal with tourism issues for effective utilization of its wide range of destination resources and also optimize the intensity of financial involvement for developing tourist infrastructure in a restraint economic province. This paper applies a simple methodology to quantify the tourism potential of Indian states using an integrated visual decision aid model of preference ranking organization method for enrichment evaluation (PROMETHEE) and geometrical analysis for interactive aid (GAIA). Several social and physical attributes are considered to evaluate and rank the Indian states with respect to their performance in tourism. The adoption of this integrated visual decision aid model identifies Jammu and Kashmir, and Jharkhand as the best and the worst performing states respectively. In GAIA plane, the position of Jammu and Kashmir is the farthest from origin, followed by Madhya Pradesh. A close comparison between these two top performing states reveals that Jammu and Kashmir mainly outperforms Madhya Pradesh with respect to budget allocation, population density, pollution index and cost of living index criteria. The performance of Jharkhand is not at all the best with respect to even a single criterion. In GAIA plane, it is also observed that the considered beneficial and non-beneficial criteria form two different clusters, as expected. This performance evaluation problem is identified to be not at all a hard problem to solve.