Preference Ranking Research Articles

The development and validation of a macronutrient and taste preference ranking task in multiethnic Asian population

Understanding how changes in individual food preferences in a dynamic environment translate into dietary choices helps to unravel the aetiology of suboptimal nutrition in the population. To facilitate the measurements of food preferences in a multiethnic Asian population, we aimed to develop and validate the Macronutrient and Taste Preference Ranking Task (MTPRT) in an Asian population. We shortlisted 1770 local food items based on the predominant macronutrient groups (≥50 % macronutrient content is protein or fat or carbohydrates), classified their taste clusters, standard-photographed a final list of 36 food items, and developed an electronic survey through cognitive interviews with 30 participants. We then validated the MTPRT amongst 66 participants (mean (SD) age = 47.4 (11.7) years, 57.6 % male, with Chinese: Malay: Indian ethnicity ratio 3:1:1), through random crossover administration of sweet and savoury standard meals with equivalent macronutrients across 2 study visits over a week. The average duration taken to complete MTPRT was 14.1 (5.3) minutes. The sweet and savoury taste preference scores were inversely correlated (r = -0.9, p < 0.0001). The liking and preference for savoury taste were reduced after consuming savoury meals (p = 0.0009). The intra-person correlations for MTPRT scores were all r > 0.6, p < 0.001. The macronutrient preference scores were all positively associated with the self-reported macronutrient intakes derived from the Food Frequency Questionnaire, independent of sex, age, ethnicity, and BMI. Our study demonstrates the validity and reproducibility of MTPRT in multiethnic Asian populations for future evaluation of food preferences in multiethnic Asian population.

The roles of social information, asocial information, and initial bias in nest-building decisions.

Animals can use asocial (e.g., environmental cues) or social (e.g., conspecific behavior) information when making decisions. We investigated decisions made by zebra finches when asocial and social sources conveyed agreeing or conflicting information, and assessed the influence of initial bias on decision making. Finches completed an initial preference test ranking preference for three colors of nest-building material. Birds in the agree group (n = 14) then observed demonstrators build nests using nonpreferred color material (social information) that matched the environment color (asocial information). Birds in the conflict group (n = 15) observed demonstrators build nests with nonpreferred color material that did not match the cage environment (another nonpreferred color). A final preference test assessed any changes in color preference. The agree group reduced average preference for their initially preferred color, but did not significantly increase average preference for the asocial/social colors. The conflict group also reduced average preference for the initially preferred color and also increased preference for the socially demonstrated color. Observers with stronger initial bias were less likely to choose the socially demonstrated color than observers with weaker initial bias. This shows that social information informs nest-building decisions, even when in conflict with asocial information. However, bias influences social information use and adds nuance to how different individuals use information when making decisions. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Comparative Ranking Preferences Decision Analysis through a Novel Fuzzy TOPSIS Technique for Vehicle Selection

Acquiring a vehicle or financing its purchase is often considered a luxury, particularly for middle-class households. In addition to their primary concern, consumers prioritize distinguishing elements such as vehicle type, size, capacity, engine power, fuel efficiency, safety features, and life-cycle costs. Furthermore, the choice of vehicle has multiple factors and is contingent upon the financial prosperity of the household, allowing for precise expression. The decision-making process is complex and entails selecting the most appropriate alternatives. This study proposes a novel fuzzy technique for order of preference by similarity to the ideal solution (FTOPSIS) method to solve vehicle selection multi-criteria decision-making (MCDM) problems. Decision-makers express their opinions on each alternative and criterion in linguistic terms, in terms of generalized interval type-2 trapezoidal fuzzy numbers (GIT2TrFNs). The FTOPSIS process involved utilizing the defuzzification of GIT2TrFNs to acquire the normalization matrix. Finally, the proposed approach prioritizes the options and chooses the best vehicle when faced with conflicting criteria, as illustrated through a numerical illustration.

Management of Alternative Organic Fertilizer Sources for Wheat Production: Evidence from Decomposition Dynamics and Agronomic, Economic, and Farmers’ Preferences

The increasing cost of mineral fertilizer has posed a barrier for smallholder farmers, resulting in lower application rates, declined soil fertility status, and crop yield. Therefore, it is vital to look for cheap, impactful, and locally accessible organic fertilizer sources. Thus, two-year on-farm studies were conducted to evaluate potential organic fertilizer sources based on their decomposition dynamics and agronomic, economic, and farmer preference responses. The organic fertilizer sources used were dried tree leaves of Croton macrostachyus (Cm) and Erythrina bruci (Eb) and farm yard manure (FYM) and their mixture at ratios of 1 : 1:0, 2 : 1:0, and 1 : 1:1 Cm : Eb : FYM, respectively. The organic materials were buried using litter bags at 5 tons ha−1 rate in the crop field and retrieved at the weeks of two (14 days), four (28 days), six (42 days), and eight (56 days) after placement. Data were collected that included dry mass (DM) remaining, daily decomposition rate (k), and half-life (t1/2). Furthermore, to validate the findings of litter bag trials, the mixture of three organic fertilizer sources [1 : 1 : 0, 2 : 1 : 0, and 1 : 1 : 1 of Cm : Eb : FYM] and four application times (two, four, six, and eight weeks before sowing) were evaluated on bread wheat (Triticum aestivum) as a test crop in factorial randomized complete block design in a factorial arrangement with three replications. Data on agronomic, economic, and farmers’ preferences were recorded. The result revealed an increased weight loss over 56 days in the order of 2 : 1 : 0 &gt; 1 : 1 : 0 &gt; 1 : 1 : 1. The k value significantly (P&lt;0.05) varied among organic fertilizer sources. The k value (% day−1) in 2013 varied between 3.42% and 0.023%, whereas in 2014, it was 3.10% and 0.23% for Cm and FYM, respectively. The k value was in the order of Cm &gt; 2 : 1 : 0 &gt; 1 : 1 : 0 &gt; Eb &gt; 1 : 1 : 1 &gt; FYM. Applying organic fertilizers two weeks before sowing significantly improved wheat performance compared to applications made eight weeks prior. For example, the mean grain yield was 4.1 tons ha−1 and 2.6 tons·ha−1 at two and eight weeks before sowing, respectively. Although there were no statistically significant differences among the mixed sources, the 2 : 1 and 1 : 1 over 1 : 1 : 1 proportion have resulted in a higher grain yield. Farmers showed the highest preference rank for 2 Cm : 1 Eb and 1 Cm : 1 Eb, incorporated two weeks before sowing. The partial budget analysis demonstrated the highest net benefit from applying two weeks before sowing and 2 Cm : 1 Eb organic material mixture. Considering the findings generated from the decomposition test, agronomic and economic evaluations, and farmers’ preferences, we could conclude that the wheat growers should use 2 Cm : 1 Eb and incorporate it into the soil two weeks before sowing.

INTEGRATION OF TAGUCHI AND PROMETHEE FOR CNC MILLING MACHINING PARAMETER OPTIMIZATION ON AA6061

In the manufacturing industry, machining has developed quite rapidly from the use of conventional machines to unconventional machines. Unconventional machines that are often used today are optimize computer numerically controlled (CNC), the use of CNC in the manufacturing industry provides many benefits in product quality and productivity. One of them is CNC milling, this type is one of the main machines on the production floor. Machining optimization becomes the main goal to achieve the ideal response in order to produce products with good and consistent quality and productivity. Surface quality leads to surface roughness, while productivity leads to material removal rate. This study aims to optimize CNC milling machining parameters on AA6061 with Taguchi experimental design and preference ranking organization method for enrichment evaluation (PROMETHEE) method. Machining was controlled using wet machining conditions to maintain temperature during machining. Experiments were conducted nine times with three factors and levels. These factors included spindle speed, feed rate, and depth of cut. The result of this research is the ideal value of the combination of surface roughness and material burning rate which is 0.565 (experiment 3). This best experiment is influenced by spindle speed 2600 rpm, feed rate 65 mm/min, and depth of cut 2.5 mm. Feed rate has the largest contribution in influencing the response which is 43.23%, followed by depth of cut 25.24%, and spindle speed 15.91%.

Finding the Nation with the Highest Rate of Deaths due to Cancer Using PROMETHEE

Cancer is a growing problem in today’s world. Cancer is considered to be the leading cause of death in many countries. The incidence rates for various cancer forms vary by nation and can depend on many factors such as the lifestyle of the people, obesity rates, exposure to certain chemicals, family history, and others. As preventing cancer is a significant public health challenge, it is important to identify which country is leading in death rates due to the disease. This study uses a Multi-criteria decision-making (MCDM) method named Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) to perform this test and rank the countries on this basis. The outcome of the decision-making process is to create awareness among people to reduce the effects caused by cancer in that particular country

Evaluation of personal protective equipment to protect health and safety in pesticide use

IntroductionAgriculture emerges as one of the most dangerous industries in the world, considering injury and illness rates. After the service sector in Turkey, the next large-scale sector is the agricultural sector, which constitutes 20% of the general employment. The exposure of farmers to pesticides, used to increase the quality and productivity of agricultural products, causes health risks via the mouth, respiration, skin, and eyes. Pesticide use in Turkey is increasing; the annual average increase is estimated at 1.2%. Exposure to pesticides can be reduced by wearing personal protective equipment to protect against health and safety hazards.ObjectiveThis study aimed to determine the importance of personal protective equipment using the multi-criteria decision-making method to prevent the risk of injury and disease resulting from pesticide use.Materials and methodsThe Analytical Hierarchy Process (AHP) method was used to find the weights of the criteria determined by expert opinion and a literature review. The Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) was used to rank personal protective equipment.ResultsPersonal protective equipment includes masks, gloves, overalls, safety shoes, glasses, and hats. The use of multi-criteria decision-making methods in health and safety in the agricultural sector will contribute to the literature.ConclusionEmphasizing the use of personal protective equipment, especially when using pesticides, will increase the rate of use of protective measures.

Pemeringkatan Kinerja Dosen pada Perguruan Tinggi Swasta Menggunakan Algoritma Simple Additive Weighting

The Tri Dharma of Higher Education is an obligation for lecturers while carrying out their duties as lecturers at higher education institutions, the implementation of which is regulated in Law Number 20 of 2003 concerning the National Education System. The Tri Dharma of Higher Education is a lecturer's obligation, including Education and Teaching, Research and Development, and Community Service. Lecturers need Support and motivation to implement quality Tri Dharma, especially at "X" Private Universities. Providing rewards or awards can motivate lecturers to give their best performance to Tri Dharma. Student feedback is also needed as evaluation material for lecturers to measure their teaching abilities. Rewarding lecturers can be done by ranking lecturer performance, especially at private universities. The SAW (Simple Additive Weighting) algorithm ranks lecturer performance through the criteria of education and teaching, research, community service, and student feedback. An assessment of several subcriteria presents each criterion. The normalized scoring matrix is ​​the ranking preference. From the results of data processing on lecturer performance and feedback from students, with a sample of 25 lecturers, a ranking score was obtained on a scale of 0 to 1, where a score of 1 is the highest ranking. The lecturer performance ranking process involves lecturers, students, and the Study Program Management Unit. A lecturer performance rating information system is needed to facilitate all actors' involvement in the lecturer performance rating process and provide valid and timely rating results to stakeholders.

Analyzing university students’ mode choice preferences by using a hybrid AHP group-PROMETHEE model: evidence from Budapest city

Over the last decades, the analysis of mode choice preferences has become a vital aspect of enhancing the quality of public transport services. Most papers aim to derive conclusions from large-scale surveys. However, for specific homogenous patterns, such as university students, a smaller group of evaluators might also be sufficient. Such a survey can rather be considered as an expert survey, in which few representatives might express the preference of a larger community, thus, a different methodology can be more effective than the traditional statistical techniques. This paper aims to introduce a new approach that combines two multi-criteria decision-making methods, the analytic hierarchy process and the preference ranking organization method for enrichment evaluation, for a hybrid consensual model by aggregating the individual priorities defined by each decision-maker. The introduced model presents advantages in terms of reducing time, cost, and effort compared to statistical methods and requires solely necessary information from the users via objective and subjective evaluations. The model’s effectiveness is tested with real-world data from the city of Budapest; highlighting the significant impact of the underground mode on users’ behavior toward public transport. In addition, the outcomes are compared to other existing results of student preference surveys.

Optimisation of processing parameters for wear properties of polylactic acid biopolymer parts in FDM process

ABSTRACT Currently, additive manufacturing (AM) technology has been expanding in advanced engineering and biomedical applications. The fused deposition modelling (FDMTM) method, also known as fused filament fabrication (FFF), is one of the most extensively used AM methods owing to its simple operation, reliability and potential to manufacture intricate components. The mechanical and wear properties of these components depend mainly on the proper selection of processing parameters. This study aims to determine the optimum parameters for low wear rate, less frictional force and good surface roughness. To achieve this aim, a hybrid approach based on user preference ranking is adopted to evaluate the effect of the four processing parameters including nozzle temperature, fill density, layer thickness and printing speed, on the wear behaviour, frictional force and surface roughness. The study procedure includes Taguchi L27 experiment design, finding the weight ratios, calculating utility index (UI) values and optimising UI values using Taguchi methodology for maximum utilisation. The results found that at 220°C of nozzle temperature, 100% fill density, 0.24 mm layer thickness and a printing speed of 60 mm/s are optimal parameters for the maximum utility index value. From the results, the percentage of contribution of fill density, nozzle temperature, layer thickness and printing speed were observed as 69.071%, 15.974%, 5.431% and 2.306%, respectively.

Industry 4.0 and supply chain sustainability: benchmarking enablers to build reliable supply chain

PurposeThe existing literature reflects that the connection between enablers of Industry 4.0 (I4.0), Supply Chain (SC) sustainability and reliability is understudied. To cover this gap, the purpose of this study is to identify and benchmark the enablers of I4.0 for SC sustainability to build a Reliable Supply Chain (RSC).Design/methodology/approachThis study benchmarks the I4.0 enablers for SC sustainability for building a RSC and analyses them with a multi-method approach. The identified potential enablers are validated empirically. A multi-method approach of Analytical Hierarchy Process (AHP), Decision Making Trial and Evaluation Laboratory (DEMATEL) and Preference Ranking for Organization Method for Enrichment Evaluation (PROMETHEE-II) was used to investigate the influence of the identified benchmarking enablers and develop an interrelationship diagram among the identified enablers.FindingsThis study benchmarks the potential enablers of I4.0 to achieve high ecological-economic-social gains in SCs considering the Indian scenario. Digitalization of the supply chain, decentralization, smart factory technologies and data security and handling are the most prominent enablers of I4.0 for SC sustainability to build a RSC.Originality/valueThe findings from the study may benefit managers, practitioners, specialists, researchers and policymakers interested in I4.0 sustainability applications.

Analysis of earthquake resilience factors for buildings: the case of Istanbul high-rise buildings

PurposeThe objective of this study is to investigate the concept of “earthquake resistance” in high-rise buildings and assess the current level of structural strength in the areas where these structures are situated. The study aims to identify and implement necessary measures to enhance resilience in these areas. A comprehensive literature review was conducted to develop a conceptual framework focusing on earthquake resistance's meanings, stages and physical elements to achieve these goals.Design/methodology/approachThis study focuses on Istanbul, a city known for its high earthquake risk, specifically targeting the Atasehir district. The research utilizes the DEMATEL (Decision-Making Trial and Evaluation Laboratory) method to evaluate urban resilience parameters. Additionally, the Fuzzy TOPSIS (Preference Ranking Technique by Similarity) method is employed to analyze the location of five buildings in Atasehir, using criterion weights derived from this methodology.FindingsThe findings indicate that resilience varies depending on the distance of the buffers. Moreover, the amount and quality of urban equipment in the study areas have a significant impact on the earthquake resistance level of the surrounding areas where high-rise buildings are situated. Building upon this analysis, the study suggests the implementation of measures aimed at augmenting the quantity and quality of urban facilities in the study areas, consequently enhancing urban resilience.Originality/valueThe originality and value of this study lie in its examination of seismic resilience within the context of high-rise buildings and the identification of necessary measures to increase resilience in areas where these structures are prevalent. By focusing on Istanbul, a city with a high earthquake risk, and specifically selecting the Atasehir district as the study area, this research provides a comprehensive conceptual framework for understanding urban resilience and its physical components. Moreover, the study offers a fresh perspective on urban resilience by highlighting the influence of tall buildings on the surrounding areas. Ultimately, it provides practical recommendations for architects, urban planners and other stakeholders to improve regional earthquake resilience.

Selection of Optimal Segmentation Algorithm for Satellite Images by Intuitionistic Fuzzy PROMETHEE Method

The combination of MCDM and fuzzy sets offers new potential ways to solve the challenges posed by complex image contents, such as selecting the optimal segmentation algorithm or evaluating the segmentation quality based on various parameters. Since no single segmentation algorithm can achieve the best results on satellite image datasets, it is essential to determine the most appropriate segmentation algorithm for each satellite image, the content of which can be characterized by relevant visual features. In this research, we proposed a set of visual criteria representing the fundamental aspects of satellite image segmentation. The segmentation algorithms chosen for testing were evaluated for their performance against each criterion. We introduced a new method to create a decision matrix for each image using fuzzy fusion, which combines the image content vector and the evaluation matrix of the studied segmentation algorithms. An extension of the Preference Ranking Organization Method Enrichment Evaluation (PROMETHEE) using intuitive fuzzy sets (IFSs) was applied to solve this problem. The results acquired by the proposed methodology were validated by comparing them with those obtained in expert ratings and by performing a sensitivity analysis.

Graph model for conflict resolution based on the combination of probabilistic uncertain linguistic and EDAS method

The ranking of decision makers (DMs)’ preferences for feasible states in the graph model for conflict resolution (GMCR) is crucial for accurately determining stability results. This paper addresses the issue of subjective ranking methods lacking theoretical foundation and causing ambiguity when the number of feasible states is high by proposing the implementation of the multi-attribute decision-making (MADM) method in the GMCR. The paper utilizes the average level to choose evaluation based on distance from average solution (EDAS) method for determining the DM’s preference ranking, which can effectively reduce the impact of anomalous evaluations. Further, the PUL-EDAS method based on probabilistic uncertainty linguistics (PUL) is developed, which overcomes the shortcomings of the traditional EDAS method, which only applies to the simple evaluation of information. The PUL aligns with DMs’ daily evaluation practice by providing an interval for the quality of qualitative linguistic evaluations. Furthermore, it utilizes an objective aggregation method to calculate comprehensive evaluation information from all DMs. In addition, the four fundamental stability definitions, applicable solely under crisp preferences, are extended to the PUL context, providing related extended definitions. Finally, to ensure the scientific validity and practicality of the proposed theory, this paper selects digital rural governance as the research context for conflict calculus analysis, comparing it with other MADM methods in the preference ranking section.

Optimizing CI Engine Ethanol Fuel Induction Techniques Using the AHP-PROMETHEE II Hybrid Decision Model

Ethanol along with nanoparticles stands out as a promising alternative in the pursuit of environmentally sustainable fuel options, offering a potential solution to the dual challenge of curbing NOx and PM/soot emissions while optimizing engine performance in compliance with stringent pollution regulations for compression ignition (CI) engines. The research study aims to optimize ethanol fuel induction techniques for CI engines. It utilizes a hybrid decision-making approach that integrates the analytic hierarchy process- AHP- for problem structuring and the derivation of preference weights. Subsequently, the preference ranking organization method for enrichment evaluations-PROMETHEE II is applied to assess and rank the existing alternatives. The study entails a methodical assessment of diverse ethanol induction methods across varying engine load ranges, considering multiple criteria including engine performance, emissions, combustion behavior, and exhaust after-treatment efficiency. Hybrid AHP-PROMETHEE II model provides criteria weights and ranks ethanol induction techniques and fuel blends across low, medium, and high engine loads for decision-making. It ensures that the method chosen aligns with goals, such as reducing NOx and soot emissions, optimizing engine performance, enhancing combustion, and minimizing exhaust after-treatment costs for CI engines. According to the research findings, the hybrid AHP-PROMETHEE II model identifies the CI engine operating at medium load with ethanol blending (DE10) and without the use of nanoparticles as the preferred choice. Additionally, AHP-PROMETHEE II (AHP derived criteria weights) and PROMETHEE II (direct rating derived criteria weights) models, suggested DE10 with nanoparticle (DE10_NP) using blending technique at low load and combined blending-fumigation technique with nanoparticles at high load. However, at medium load, PROMETHEE II recommends DE10_NP, while AHP-PROMETHEE II recommends DE10 blending technique. To assess the performance and reliability of this model, the consistency ratio and Spearman's rank correlation coefficient indices were computed, yielding values of 0.05 and 0.59, respectively. Both indices fall below the predetermined threshold limits, indicating a high level of consistency of the model.

Achieving professional qualifications using micro-credentials: a case of small packages and big challenges in higher education

PurposeThis study examined the factors impeding the implementation of micro-credentials and accepting it as a credible source of earning professional qualifications and certifications necessary for pursuing higher education or other career goals.Design/methodology/approachThe factors were identified by reflecting on the recent literature and Internet resources coupled with in-depth brainstorming with experts in the field of micro-credentials including educators, learners and employers. Two ranking methods, namely Preference Ranking for Organization Method for Enrichment Evaluation (PROMETHEE) and multi-objective optimization based on ratio analysis (MOORA), are used together to rank the major challenges.FindingsThe results of this study present that lack of clear definitions, ambiguous course descriptions, lack of accreditation and quality assurance, unclear remuneration policies, lack of coordination between learning hours and learning outcomes, the inadequate volume of learning, and lack of acceptance by individuals and organizations are the top-ranked and the most significant barriers in the implementation of micro-credentials.Research limitations/implicationsThe findings can be used by educational institutions, organizations and policymakers to better understand the issues and develop strategies to address them, making micro-credentials a more recognized form of education and qualifications.Originality/valueThe novelty of this study is to identify the primary factors influencing the implementation of micro-credentials from the educators', students' and employers' perspectives and to prioritize those using ranking methods such as PROMETHEE and MOORA.

Ethnobotanical study of traditional medicinal plants used by the local people in Habru District, North Wollo Zone, Ethiopia

BackgroundEthiopia is a country located in the Horn of Africa, which combines richness in plant resources and cultures of human plant use. The people of Habru District of North Wollo Zone (Amhara Region, Ethiopia) have a long history of use of plant resources for various purposes including in traditional herbal remedy preparation and use. However, the district has not been adequately studied for its ethnobotanical resources and the associated knowledge. This study focused on human medicinal plants and their traditional uses in Habru District. The objective of the study was to document and analyze the plant species used by the local communities to treat human ailments along with the associated traditional knowledge and practices.MethodologyThe study was carried out in Habru District from June 2021 to December 2022. Ethnobotanical data were collected using semi-structured interviews, guided field walks, 13 focus group discussions (one at the district level and 12 at the kebele/subdistrict level) and market surveys. A total of 388 informants (250 males and 138 females) were selected from all 13 kebeles within Habru District using systematic random sampling, and 42 key informants were purposively selected. Descriptive statistics, preference ranking, direct matrix ranking, informant consensus factor and fidelity level were applied for data analysis.ResultsThe results provide insights into the medicinal plant diversity within Habru District, where 134 plant species in 110 genera and 54 families were documented, including 2 endemics, highlighting the district’s significance in biodiversity conservation and healthcare delivery. Disease prevalence analysis showed that gastrointestinal and parasitic ailments (ICF = 0.85), febrile diseases (ICF = 0.84), and culture-related conditions exhibit high informant consensus factors. Remedy preparation involves various plant parts, predominantly leaves (47.3%), followed by roots (22.1%), fruits (7.0%), and seeds (5.8%). Freshly harvested plant parts were frequently used (58.2%), while 24.7% involved both dried and fresh parts. Oral application (47.3%) and topical use (31.8%) are the major routes of remedy administration. The marketability of medicinal plants was evident, with 16.4% of the species reported as marketable, including Terminalia brownii Fresen. Myrtus communis L., Ruta chalepensis L., Olea europaea L. subsp. cuspidata (Wall. & G.Don) Cif., Allium sativum L. and Capsicum annuum L. Multipurpose plants such as Solanum somalense Franchet. (91.3% FL), Ocimum lamiifolium Hochst. ex. Benth. (88.9% FL), and Verbascum sinaiticum Benth. (85.7% FL) exhibited notable healing potentials.ConclusionThe current study underscores the intricate relationship between the local community and medicinal plants, emphasizing the importance of biodiversity conservation and health care and acknowledging the dynamic interplay between cultural heritage and ecosystem health. The results contribute to the development of sustainable conservation strategies, healthcare practices and the preservation of traditional knowledge, and highlight the interdependence of human societies and their natural environments. Community-based conservation initiatives with active participation of local communities are desirable for the conservation and sustainable use of medicinal plant species and their habitats. Raising public awareness about the sustainable harvesting and utilization of marketed medicinal plants (e.g., Terminalia brownii Fresen.) that are under threat is also important to ensure their availability for future generations and contribution to socioeconomic development.

Food preferences and nutrient composition in captive Southern brown howler monkeys, Alouatta guariba clamitans.

Studies of food preferences in captive primates have so far mainly been restricted to frugivorous species. It was therefore the aim of the present study to assess the occurrence of spontaneous food preferences in a mainly folivorous primate, the captive Southern brown howler monkey, and to analyze whether these preferences correlate with nutrient composition. Using a two-alternative choice test, we presented ten male and five female adult Alouatta guariba clamitans with all possible binary combinations of ten types of food that are part of their diet in captivity and recorded their choice behavior. We found the howler monkeys to display the following rank order of preference: banana > mango > watermelon > papaya > beetroot > apple > pear > orange > cucumber > tomato. This preference ranking significantly and positively correlated with the total carbohydrate content and with the sucrose content of the food items. We also found significant positive correlations between the food preference ranking and the content of the minerals copper and magnesium. Male and female howler monkeys did not differ significantly in their food preference rankings. These results suggest this howler monkeys under human care are not opportunistic, but selective feeders with regard to maximizing their net gain of energy as only the content of carbohydrates, but not the contents of total energy, proteins, or lipids significantly correlated with the displayed food preferences. Thus, the food preferences of this primate are similar to those reported in several species of frugivorous primates tested with cultivated fruits and vegetables.

Carbon emission reduction contribution analysis of electricity enterprises in urban green development: A quantum spherical fuzzy sets-based decision framework

As managers of urban power grids (UPGs), electricity enterprises are responsible for ensuring energy security and stability during the urban green transformation process. They are also a key driving force in promoting green power transmission and integration. Therefore, exploring the green attributes of UPGs and accurately enhancing their green support capabilities are of significant importance. This study addresses the perceived contributions of electricity enterprises to carbon emission reduction and proposes a decision framework within a quantum-spherical fuzzy (QSF) environment. First, from the perspectives of economy, technology, policy, and management, a carbon emission reduction contribution evaluation system for electric enterprises was constructed. In the QSF environment, this study identifies the importance of various attributes in the evaluation system. Subsequently, an electric enterprise carbon emission reduction perception model was developed based on the preference ranking organization method for enrichment evaluations and prospect theory (PROMETHEE-PT). An empirical validation of the proposed model was conducted in Hefei, China, demonstrating the robustness and effectiveness of the evaluation framework. The study's results were verified by comparing them with those of other methods.

Land use suitability analysis using an approach combining GIS, regionalization, and PROMETHEE II

ABSTRACT Land-use Suitability Analysis, which aims to identify the suitability of a given area for a particular use by using a certain number of criteria, is a domain that has received a lot of attention from researchers during the last 20 years and most of the works that have been proposed rely on the use of multi-criteria methods which are based on a complete aggregation of alternatives such as Weighted Linear Combination (WLC). The outranking methods, which are multi-criteria methods that rely on a partial aggregation of alternatives, have rarely been used. In the context of Land-use Suitability Analysis, outranking methods quickly reach their computational limits because they require performing comparisons between each pair of pixels. To reduce the initial number of alternatives, we propose to proceed to the regionalization of the study area by grouping neighbouring pixels that have similar properties in homogeneous zones. We then use PROMETHEE II (Preference Ranking Organization Method for Enrichment of Evaluations), an outranking method whose objective is to rank a set of alternatives from the best to the worst. A study for the choice of a landfill site was carried out using our approach and the obtained results were compared with those of WLC.