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AbstractIn this study, we consider the problems of testing for a mean vector and testing the equality of two mean vectors with monotone missing data. We propose new test statistics similar to the simplified Hotelling’s T2-type test statistic for one-sample and two-sample problems under general-step monotone missing data. Approximate upper percentiles of this new statistics are provided by asymptotic expansion along with transformed test statistics based on Bartlett adjustment. Approximate simultaneous confidence intervals for pairwise comparisons among mean vectors are also presented. Furthermore, we investigated the asymptotic behavior of the proposed statistics using a Monte Carlo simulation, and the approximate accuracy of the proposed approximations are provided and discussed. Finally, the numerical power for several statistics is given. [ABSTRACT FROM AUTHOR]

This research aims to apply Strategic Environmental Assessment (SEA) at the strategic action. Plan and program of the mass transit system in Uttaradit province, Thailand. Some steps of SEA process were applied to such strategic action by focusing on the development and assessment of the appropriate alternatives together with the selected appropriate indicators. The alternatives for mass transit development plan were proposed as following; Alternative 1: No Development Plan (or No Action Plan), Alternative 2: Conventional Mass Transit System Development Plan (Conventional Plan), Alternative 3: Smart and Environmentally Friendly Mass Transit Development Plan (Smart plan). With the Pairwise Comparison and the Impact Matrix Analysis, the Smart Plan was assessed as the appropriate alternative when compared with the Conventional Plan and No Action Plan as for 49.36%, 32.17%, and 18.47 % respectively. The mass transit system under the Smart Plan were proposed as Road, Railway, and Road with Railway systems. On the same assessment procedure, the impact score of the Road with Railway, Railway and Road system were 36.64 %, 33.90% and 29.46% respectively. The appropriate alternative mass transit system was road and railway system. SEA was practical tool applied to the strategic level of the mass transit development. [ABSTRACT FROM AUTHOR]

Nonparametric combinations of permutation tests for pairwise comparison of scale parameters, based on deviances, are examined. Permutation tests for comparing two or more groups based on the ratio of deviances have been investigated, and a procedure based on Higgins' RMD statistic was found to perform well, but two other tests were sometimes more powerful. Thus, combinations of these tests are investigated. A simulation study shows a combined test can be more powerful than any single test. [ABSTRACT FROM AUTHOR]

Nonparametric combinations of permutation tests for pairwise comparison of scale parameters, based on deviances, are examined. Permutation tests for comparing two or more groups based on the ratio of deviances have been investigated, and a procedure based on Higgins’ RMD statistic was found to perform well, but two other tests were sometimes more powerful. Thus, combinations of these tests are investigated. A simulation study shows a combined test can be more powerful than any single test.

Consider the choice of outcome for overall treatment benefit in a clinical trial which measures the first time to each of several clinical events. We describe several new variants of the win ratio that incorporate the time spent in each clinical state over the common follow‐up, where clinical state means the worst clinical event that has occurred by that time. One version allows restriction so that death during follow‐up is most important, while time spent in other clinical states is still accounted for. Three other variants are described; one is based on the average pairwise win time, one creates a continuous outcome for each participant based on expected win times against a reference distribution and another that uses the estimated distributions of clinical state to compare the treatment arms. Finally, a combination testing approach is described to give robust power for detecting treatment benefit across a broad range of alternatives. These new methods are designed to be closer to the overall treatment benefit/harm from a patient's perspective, compared to the ordinary win ratio. The new methods are compared to the composite event approach and the ordinary win ratio. Simulations show that when overall treatment benefit on death is substantial, the variants based on either the participants' expected win times (EWTs) against a reference distribution or estimated clinical state distributions have substantially higher power than either the pairwise comparison or composite event methods. The methods are illustrated by re‐analysis of the trial heart failure: a controlled trial investigating outcomes of exercise training. [ABSTRACT FROM AUTHOR]

There is no lack of studies dealing with the consistency of evaluations performed by pairwise comparison in the decision-making literature. Mostly, these studies offer algorithms for reducing the inconsistency of evaluations and indices to measure the evaluation's consistency degree. The focus on these two research fronts does not cover all the gaps associated with the inconsistent evaluation problem. The existing algorithms are difficult to implement and do not preserve the original evaluations since the original evaluation matrix is replaced with a new matrix. Furthermore, the inconsistency of pairwise comparison has been associated with the specialist's bounded rationality only at the theoretical-conceptual level. This research investigates the relationship between the lack of specialist knowledge and the inconsistency of evaluations, as well as introduces an approach that ensures the evaluation's consistency by reducing the specialist's cognitive stress when comparing a high number of alternatives. The results reveal that the specialist's limited knowledge about the topic does not impact the degree of consistency of the evaluations as expected. The evaluation's consistency degree is 59% lower when the specialist does have no knowledge about the decision topic but has theoretical knowledge and experience in evaluating alternatives by pairwise comparison. This is a remarkable contribution with a high degree of universality and applicability because instructing decision-makers on the inconsistency problem is a cheaper, easier way to increase the evaluation's consistency degree without altering the original information. Furthermore, the introduced approach reduces the number of evaluations and evaluation time by 8.0 and 7.8 times, respectively. [ABSTRACT FROM AUTHOR]

The development of vehicle drive systems targets different goals, which are partly contradictory. While the focus is often on increasing efficiency and—depending on the type of drive system—performance, the aim is to simultaneously reduce costs, weight, and volume as much as possible. This goal generally presents a conflict of objectives; for example, a gain in efficiency usually correlates with higher costs, or an increase in performance reduces the maximum achievable efficiency. Therefore, each drive system represents a compromise among these goals, and depending on the main focus, the development can be influenced. The methods presented in this work serve as a methodological framework for the evaluation of vehicle drive systems. The procedure involves evaluating different drive concepts based on defined criteria and comparing these evaluations with one another. These criteria can be selected freely and weighted differently, depending on the individual focus. In the sense of a holistic assessment, a system evaluation factor ultimately serves as an indicator, which is composed of the rating values of the individual criteria, taking into account their specific weightings. With the help of the novel method presented in this paper, the complexity of comparing differently designed powertrains is reduced, and a holistic assessment covering relevant viewpoints is possible. Such an all-encompassing view is helpful in the early development phase and is required as an evaluation basis for further, groundbreaking decisions in concept development. [ABSTRACT FROM AUTHOR]

The development of vehicle drive systems targets different goals, which are partly contradictory. While the focus is often on increasing efficiency and—depending on the type of drive system—performance, the aim is to simultaneously reduce costs, weight, and volume as much as possible. This goal generally presents a conflict of objectives; for example, a gain in efficiency usually correlates with higher costs, or an increase in performance reduces the maximum achievable efficiency. Therefore, each drive system represents a compromise among these goals, and depending on the main focus, the development can be influenced. The methods presented in this work serve as a methodological framework for the evaluation of vehicle drive systems. The procedure involves evaluating different drive concepts based on defined criteria and comparing these evaluations with one another. These criteria can be selected freely and weighted differently, depending on the individual focus. In the sense of a holistic assessment, a system evaluation factor ultimately serves as an indicator, which is composed of the rating values of the individual criteria, taking into account their specific weightings. With the help of the novel method presented in this paper, the complexity of comparing differently designed powertrains is reduced, and a holistic assessment covering relevant viewpoints is possible. Such an all-encompassing view is helpful in the early development phase and is required as an evaluation basis for further, groundbreaking decisions in concept development.

Agricultural biogas plants are a renewable source of energy and at the same time enable the disposal of biodegradable waste generated in agriculture and the food industry. In Poland, a program aimed at constructing agricultural biogas plants has been in operation since 2010 with the goal of producing 1.7 billion Nm3 of biogas, which has not been achieved. Factors that could influence the development of this energy source were identified based on a register of agricultural biogas producers from the National Agriculture Support Center and data from the Energy Regulatory Office. These factors are technology, substrates, state energy policy, profitability, population density in the commune and the spatial arrangement of the commune resulting from spatial development plans. A pairwise comparison analysis using the DEMATEL method was conducted for these factors. It allowed us to conclude that they are population density and the lack of local spatial development plans in most rural areas. The situation may be improved by the obligation to develop such plans for the entire area of each commune and by locating biogas plants near livestock farms and agri-food processing plants. The selected DEMATEL method is mature and comprehensively verified. It enables research to be carried out in other contexts, taking into account the correlations between factors. It is a universal method, and after collecting expert opinions, research can be expanded. The obtained results of the analysis will allow for further research by collecting the opinions of experts such as biogas plant users, local communities, local government officials and other stakeholders. In addition, further analysis of key factors will be carried out using the DEMATEL method for several scenarios. The PESTEL method will be used to identify key factors. [ABSTRACT FROM AUTHOR]

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AbstractMultiple comparisons in hypothesis testing often encounter structural constraints in various applications. For instance, in structural Magnetic Resonance Imaging for Alzheimer’s Disease, the focus extends beyond examining atrophic brain regions to include comparisons of anatomically adjacent regions. These constraints can be modeled as linear transformations of parameters, where the sign patterns play a crucial role in estimating directional effects. This class of problems, encompassing total variations, wavelet transforms, fused LASSO, trend filtering, and more, presents an open challenge in effectively controlling the directional false discovery rate. In this article, we propose an extended Split Knockoff method specifically designed to address the control of directional false discovery rate under linear transformations. Our proposed approach relaxes the stringent linear manifold constraint to its neighborhood, employing a variable splitting technique commonly used in optimization. This methodology yields an orthogonal design that benefits both power and directional false discovery rate control. By incorporating a sample splitting scheme, we achieve effective control of the directional false discovery rate, with a notable reduction to zero as the relaxed neighborhood expands. To demonstrate the efficacy of our method, we conduct simulation experiments and apply it to two real-world scenarios: Alzheimer’s Disease analysis and human age comparisons. Supplementary materials for this article are available online. [ABSTRACT FROM AUTHOR]

The Analytic Hierarchy Process (AHP) is a widely used used multi-criteria decision-making method (MCDM). This method is based on pairwise comparison, which forms the so-called Pairwise Comparison Matrix (PCM). PCMs usually contain some errors, which can have an influence on the eventual results. In order to avoid incorrect values of priorities, the inconsistency index (ICI) has been introduced in the AHP by Saaty. However, the user of the AHP can encounter many definitions of ICIs, of which values are usually different. Nevertheless, a lot of these indices are based on a similar idea. The values of some pairs of these indices are characterized by high values of a correlation coefficient. In my work, I present some results of Monte Carlo simulation, which allow us to observe the dependencies in AHP. I select some pairs of ICIs and I evaluate values of the Pearson correlation coefficient for them. The results are compared with some scatter plots that show the type of dependencies between selected ICIs. The presented research shows some pairs of indices are closely correlated so that they can be used interchangeably. [ABSTRACT FROM AUTHOR]

The purpose of the paper was to introduce the basic concepts， calculation methods， two medical examples and SAS implementation related to the multivariate analysis of variance （MANOVA） for the quantitative data with single factor two-level and multi-level design. Basic concepts included experimental factors and attribute factors， analysis of variance and covariance， independent variables and covariates， matrix and determinant. Calculation methods involved the general statistics， four MANOVA test statistics and pairwise comparison of multiple mean vectors. The two medical examples were measurement results of quantitative indicators related to patients before taking offline under two types of offline modes and investigation data on growth and development of 8-year-old boys in three different types of areas. With the help of SAS software， the multivariate analysis of variance and covariance were carried out on the quantitative data in the two cases， and how to reasonably select covariates was explained and discussed. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to point out the questionable results of a published paper with respect to the Analytical Hierarchy Process. They created contradictory matrices such that there are pairwise comparison matrices that pass the consistency test but are contradictory. The main purpose of this paper is to point out that their six cases for contradiction can be simplified as two cases. Moreover, we show that their contradictory phenomenon is a local property that will not influence the result of the consistent test. [ABSTRACT FROM AUTHOR]

Minamata convention on mercury requests mercury wastes landfilling in environmentally safe manners. Owing to great difficulties to form public acceptance of landfill site construction, this study focused on emotional appraisal to mercury. The author quantitatively evaluated cognitive aversions toward mercury and other harmful/hazard-like objects like dioxin using two types of pairwise comparison methods. The results of both methods showed good agreement. Mercury received the second strongest aversion, following radioactive wastes. Gender and age gave no significant impact on mercury aversion at 5% significance level. When perceived knowledge of mercury was neutral or positive, higher awareness resulted in stronger mercury aversion. In contrast, mercury aversion was almost constant regardless of negative level of the awareness. When the attitude toward construction of mercury waste landfill site was mitigated from rejection to neutral, mercury aversion decreased. On the other hand, mercury aversion increased when the attitude was shifted from neutral to acceptance. Rejection attitude might be affected by mercury aversion. In contrast, acceptance attitude was formed by rational consideration of mercury landfill necessity, not weak aversion. To design a supporting process for public acceptance formation, stronger mercury aversion perceived by both acceptance and rejection attitude persons should be concerned. [ABSTRACT FROM AUTHOR]

PT Tinkerbels Permata Indah is one of the companies engaged in the coffee processing industry. The high market demand for coffee products made from robusta coffee requires PT Tinkerbels Permata Indah to continuously produce more coffee effectively and efficiently. Therefore, companies need to measure productivity to determine the performance of using several inputs to produce output. This research aimed to analyze the productivity level of robusta coffee bean production and to make a proposed improvement plan for PT Tinkerbels Permata Indah. The method used to measure productivity was the objective matrix (OMAX) method. The weight of each criterion was obtained with the help of the pairwise comparison method by filling out a questionnaire by the three experts involved. The results show that the company's highest productivity achievement was 5.893 in April 2022, and the lowest was in September 2022, with a current value of 0.880. Proposed improvements include companies needing to pay attention to their warehouses by maintaining temperature and humidity so that materials are not damaged by warehouse pests, implementing flexible daily hours to reduce wastage of employee work time, implementing an energy-saving culture, conducting energy audits, and implementing machinery maintenance management.

This survey study aimed to determine the benefits of extracurricular activities for teacher candidates by performing a scaling study with pairwise comparison. Participants were 365 teacher candidates studying at a higher education institution. The data collection tool was designed by taking the opinions of three field experts and two measurement and evaluation experts about the benefits that emerged from the literature. Each student was asked to rank the benefits of extracurricular activities by making a pairwise comparison. In the analysis of the data, as the chi-square value for error calculated with the Case V model was higher than expected at the 0,05 significance level, the Case III model was used for scaling. In this way, the benefits of extracurricular activities for the development of teacher candidates were scaled through pairwise comparison. The results showed that the order of benefits from the biggest to the smallest were: supporting personal development, supporting social development, supporting teaching skills, instilling positive affective qualities, supporting academic development and supporting physical development. Recommendations for the future include using more extracurricular activities to develop the above-mentioned skills and conducting studies to explore the reasons for the less preferred benefits.

Performing pairwise comparisons (PCs) that reflect preferences between pairs of decision-making elements is an approach widely used in decision modelling. For complex problems, the number of elements to be pairwise compared may be very large, something that may lead to a final calculation of not well-supported priorities and, as a consequence, to the drawing of wrong practical conclusions. Decision-making must usually be performed from the available incomplete body of information, and this article explores the possibility of producing reliable results by using just a sample of comparisons. We analyse and solve two specific cases of considerable interest: the sample consists of: (i) a balanced and unbiased set of PCs; and (ii) the PCs obtained by comparing all the elements against a reduced number of pivotal elements. This latter sample includes two practical cases: one in which the eliciting actor is more familiar with the pivotal specific elements; and another in which the Best-Worst method has been previously used to identify the two extreme elements under comparison. The approach employed, developed within linearisation theory, is supported with suitable proofs and examples. [ABSTRACT FROM AUTHOR]

A crucial, both from theoretical and practical points of view, problem in preference modelling is the number of questions to ask from the decision maker. We focus on incomplete pairwise comparison matrices based on graphs whose average degree is approximately 3 (or a bit more), i.e., each item is compared to three others in average. In the range of matrix sizes we considered, n = 5 , 6 , 7 , 8 , 9 , 10 , this requires from 1.4n to 1.8n edges, resulting in completion ratios between 33% ( n = 10 ) and 80% ( n = 5 ). We analyze several types of union of two spanning trees (three of them building on additional ordinal information on the ranking), 2-edge-connected random graphs and 3-(quasi-)regular graphs with minimal diameter (the length of the maximal shortest path between any two vertices). The weight vectors are calculated from the natural extensions, to the incomplete case, of the two most popular weighting methods, the eigenvector method and the logarithmic least squares. These weight vectors are compared to the ones calculated from the complete matrix, and their distances (Euclidean, Chebyshev and Manhattan), rank correlations (Kendall and Spearman) and similarity (Garuti, cosine and dice indices) are computed in order to have cardinal, ordinal and proximity views during the comparisons. Surprisingly enough, only the union of two star graphs centered at the best and the second best items perform well among the graphs using additional ordinal information on the ranking. The union of two edge-disjoint spanning trees is almost always the best among the analyzed graphs. [ABSTRACT FROM AUTHOR]

The Analytical Hierarchy Process (AHP) is a reliable, rigorous, and robust method for eliciting and quantifying subjective judgments in multi-criteria decision-making (MCDM). Despite the many benefits, the complications of the pairwise comparison process and the limitations of consistency in AHP are challenges that have been the subject of extensive research. AHP revolutionized how we resolve complex decision problems and has evolved substantially over three decades. We recap this evolution by introducing five new hybrid methods that combine AHP with popular weighting methods in MCDM. The proposed methods are described and evaluated systematically by implementing a widely used example in the AHP literature. We show that (i) the hybrid methods proposed in this study require fewer expert judgments than AHP but deliver the same ranking, (ii) a higher degree of involvement in the hybrid voting AHP methods leads to higher acceptability of the results when experts are also the decision-makers, and (iii) experts are more motivated and attentive in methods requiring fewer pairwise comparisons and less interaction, resulting in a more efficient process and higher acceptability. [ABSTRACT FROM AUTHOR]

The Consistency Index and the Consistency Ratio of the analytic hierarchy process (AHP) were designed to measure the ratio of inconsistent judgments among pairwise comparisons (PCs), which have been the principal indices for the past four decades. Definitions of inconsistency measures for PCs have yet to be established, however, because of the difficulty in quantifying subjectivity in judgments. Therefore, an empirical review that can take such subjective factors into account is essential. In this paper, the Consistency Ratio is thus reviewed using subjective data, and then a new inconsistency index for PCs is proposed based on the review. The review is based on subjective data obtained from two opinion surveys, which focuses on the relationship between the Consistency Ratio and two indicators: (1) the conformity of the results of the AHP and that of the ranking method, and (2) the goodness-of-fit of weight elicited by the AHP to human perception. A new inconsistency index is then proposed based on the mathematical property of a pairwise comparison matrix and further validated based on the conformity and the goodness-of-fit of weight. The results show that the proposed index detects inconsistency among real-world PCs more sensitively than could the Consistency Ratio; the index might suggest the reliability of the output of a pairwise comparison matrix. [ABSTRACT FROM AUTHOR]

The aim of this paper is to present a new method for generating random pairwise comparison matrices with a given inconsistency ratio (CR) interval using inconsistency reduction algorithms. Pairwise comparison (PC) is a popular technique for multi-criteria decision-making, its purpose is to assign weights to the compared entities, thus ranking them from best to worst. The presented method combines the traditional random generation of comparison matrices supported by inconsistency reduction algorithms: the "Xu and Wei" algorithm and the "Szybowski" algorithm. This paper presents research that shows an increase in performance when generating such matrices relative to the standard random comparison matrix generation procedure using the "Szybowski" algorithm. The other algorithms also improve the process, but to a lesser extent, making the “Szybowski" supporting algorithm the preferred solution for the new process. As a result of the research, a free online tool "PC MATRICES GENERATOR" has also been made available to efficiently generate a large number of comparison matrices with a given CR factor range, any matrix size, and any number of matrices, enabling much more efficient and less time-consuming research in many fields that use comparison matrices, as the analytic hierarchy/network process (AHP/ANP), ELECTREE, PAPRIKA, PROMETHE, VIKOR or the Best-Worst method (BWM).

The ability of natural systems to provide services is critical to the sustainability of the environment. The increasing environmental pressures necessitate the creation of conditions conducive to the preservation and sustainability of natural ecosystems. A review of similar studies reveals that when evaluating ecosystem services, more consideration should be given to evaluating ecosystem stability. The Ukrainian Biosphere Reserve was chosen as the study polygon due to its importance as a conservation area and the diversity of natural ecosystems. The Analytical Hierarchy Method, in this case, provides a very straightforward way to analyze such complicated systems. The first step is to identify and value the services offered by the Biosphere Reserve’s wetland, forest, and aquatic ecosystems. Then, four criteria (C1–C4) and impacts (P1–Pn) that directly affect the ecological sustainability of a certain ecosystem within each criterion were identified. The overall weight of each impact on specific ecosystem sustainability was computed and estimated to reach the main goal (G). The integrated ecological stability evaluation index (CEI) was then calculated using the overall weight values. Wetland and forest ecosystem indices were calculated to be 0.63 and 0.64, indicating that these systems are moderately stable. The stability index for aquatic ecosystems is 0.75, indicating that the system is ecologically stable. As a result, it is critical to focus on reducing negative factors affecting wetlands and forest ecosystems, such as sylvatization and/or recreational activities, to strengthen ecosystem stability and the ability to provide services.

In recent years, deep learning has attracted considerable attention owing to its ability to address complex problems in various fields. One notable problem is metric space learning, which is aimed at learning feature embeddings through the calculation of the metric space similarity of feature vectors in the embedding space by training embedding models. However, research on metric space similarity has been limited, and the existing methods, such as those based on the cosine similarity or concatenate layer, exhibit drawbacks in terms of flexibility and performance. For example, to apply the cosine similarity, the shapes of the two vectors must be identical, and thus, an advanced comparator cannot be used. Moreover, the concatenate layer cannot reflect the positions of the elements in the two vectors, leading to deteriorated performance and learning success rates. To address these limitations, this paper proposes a specialized artificial neural network layer named SimilarNet, designed to compare two feature vectors while considering the positions of their elements to produce an output in a vector format. By leveraging the advantages of the cosine similarity and concatenation layer, SimilarNet can effectively compare two vectors, enabling the construction of trained comparison models using multidimensional activation functions. In addition, SimilarNet can realize 1:1 comparisons of data with different shapes, unlike cosine similarity. The results of experiments conducted on various datasets indicate that models employing SimilarNet outperform those with the concatenate layer in terms of the comparison accuracy by 4.3% to 26.5% and learning success rate by 5% to 75%.

Standard setting of extended performances is fraught with difficulties due to the subjective nature of the scoring decision. This article introduces and reports results of a novel systematic method for setting standards for assessments that require extended performances: the Scaled Exemplar Standard Setting method (SESS). This two-stage method brings together (1) pairwise comparison of samples to scale performances and (2) standard setting involving multiple standards with tasks presented to judges in a validated order. By utilizing paired comparison methodology in the first stage, the judgment is made more manageable by chunking it into a series of pairs. This method produces scaled performances that can be checked for internal consistency of the judgments. The validated, ordered performances are used in the standard setting stage, making the task for judges as transparent and straightforward as possible. Recommendations and implications for standard setting are discussed in light of the results.

This study aims to assess the relationship between the surface roughness of cotton plain-woven fabric before and after each pretreatment processing stage. According to the findings of this study, the surface roughness values of desized fabric increased in the warp and weft directions. On the other hand, the roughness of plain-woven fabric reduced in both the warp and weft directions during the scouring, bleaching, and mercerization processes. The weft roughness values of greige, scoured, bleached, and mercerized fabrics are higher than the warp roughness values. Mercerized fabrics provide the smoothest fabric surface compared to other treatment stages. The pretreatment stages have statistically significant impacts on the surface roughness in both the warp and weft directions at a 95% confidence interval. The pairwise analysis reveals that pretreatment of the fabrics in the warp and weft directions is statistically significant for grey with desizing, grey with scouring, grey with bleaching, grey with mercerizing, desizing with scouring, desizing with bleaching, desizing with mercerization, scouring with mercerization, and bleaching with mercerization, but not for scouring with bleaching. Mercerized fabric is highly recommended for producing textile products with unique requirements in terms of tactilely sensitive surface properties. [ABSTRACT FROM AUTHOR]

The number of foreign tourists visiting Polewali Mandar Regency is relatively small and decreases year after year. This shows that tourism development is still not optimal. In fact, Polewali Mandar is a district in West Sulawesi Province that has the potential to develop a tourism village. One alternative to increase the number of tourists is to develop a tourism village as a new tourist attraction. The problem is that there is no model and government policy related to tourism villages. So, the aims of the research are to identify the potential of a tourism village, to analyze and determine the location of a tourism village, and to recommend a pattern for the development and management of a tourism village in Polewali Mandar Regency. The method used is descriptive statistical analysis, pairwise comparison, and the Exponential Comparison Method (ECM). The results of the study show that the tourism potential of Polewali Mandar Regency consists of nature, culture, agriculture, and typical products spread across sub-districts and villages. The analysis of determining the location of a tourist village shows that Panyampa Village in Campalagian District has a high potential for development as a tourist village. The tourist village can be developed by providing tourism activities based on local natural potential, such as sea and rivers, pond farming and coconut plantations, and Parrawana art culture. The management of the Panyampa Tourism Village is carried out by the Panyampa BUMDes in collaboration with organizations within Panyampa Village. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to study the effects of different weather conditions on typical concrete work tasks' productivity. Weather is one important factor that has a negative impact on construction productivity. Knowledge about how weather affects construction works is therefore important for the construction industry, e.g. during planning and execution of construction projects. Design/methodology/approach: A questionnaire survey method is used involving means to perform pairwise comparisons of different weather factors according to the analytical hierarchical process (AHP). The survey also contains means to enable assessment of the loss in productivity for typical work tasks exposed to different weather types. The survey targets practitioners involved in Swedish concrete construction projects, and the results are compared with previous research findings. Findings: The survey covers responses from 232 practitioners with long experience of concrete construction. The pairwise comparisons reveal that practitioners rank precipitation as the most important followed by wind and temperature. The loss in productivity varies significantly (from 0 to 100%) depending on the type of work and the type of weather factor considered. The results partly confirm findings reported in previous research but also reveal a more complex relationship between weather and productivity indicating several underlying influencing factors such as type of work, type of weather (e.g. rain or snow) and the intensity of each weather factor. Originality/value: This paper presents new data about how 232 practitioners assess the effects of weather on construction productivity involving novel means to perform objective rankings such as the AHP methodology. [ABSTRACT FROM AUTHOR]

In this paper, we work with the umbrella matrix Lie group and its Lie algebra. We show that the Lie subalgebra of the PC matrix is the Lie algebra of the umbrella matrix. Furthermore, we obtain an isomorphism between the Lie subgroup of PC matrices and the umbrella matrix Lie group. [ABSTRACT FROM AUTHOR]

In many real‐world applications, designs can only be evaluated pairwise, relative to each other. Nevertheless, in the simulation literature, almost all the ranking and selection procedures are developed based on the individual performances of each design. This research considers the statistical ranking and selection problem when the design performance can only be simulated pairwise. We formulate this new problem using the optimal computing budget allocation approach and derive the asymptotic optimality condition based on some approximations. The numerical study indicates that our approach can reduce the number of simulations required to confidently identify the best design. [ABSTRACT FROM AUTHOR]

Consistency indexes are used to determine the consistency of pairwise comparison evaluations used in decision-making methods, especially the Analytical Hierarchical Process (AHP). Consistency ratios are used as an important tool for decision makers in deciding the consistency of pairwise comparisons by defining threshold values for consistency indices. Among the 14 consistency calculation methods defined by the literature review, the threshold value is defined as 0,1 only in Saaty's consistency ratio (CR) and for Crawford and Williams's geometric consistency ratio (GCR). In this study, random indices are calculated for Takeda and Golden-Wang consistency methods and consistency ratios similar to CR and GCR methods are defined based on these random indexes. The threshold value is set as 0.1 for these consistency ratios, which are compatible with Saaty's CR values. GWCR and MCCR consistency ratio calculation formulas are developed for Golden -Wang (GW) and Takeda (MC) consistency methods, respectively. Correlation and compatible rates are measured between the GWCR and MCCR consistency rates and the CR consistency values. The validity of the Golden-Wang and Takeda consistency ratio equations, which show high agreement and correlation with CR, is demonstrated. Thus, decision makers who use Golden -Wang and Takeda consistency methods in the consistency values of pairwise comparison matrices (HRC) are able to make a more accurate and faster evaluation at the 0.1 consistency threshold. [ABSTRACT FROM AUTHOR]

Vegetable crop quality loss commences right along the route of harvesting, transportation, storage, and delivery to consumers. Despite the huge postharvest loss of horticultural crops recorded, no reports have been disclosed on alternative preservation technologies in Ethiopia. Herein, an analytical hierarchy process was employed to select the best food preservation technology. Pairwise comparison of the criteria with respect to the goal was performed based on the suggestion's food technology experts. Considering the economic feasibility and personal skill, fermentation (29.0%) took the greatest weight followed by the hot water treatment technique (27.3%). Likewise, those techniques for tomato preservation in different ways like solar dryer, cold house storage, and modified atmosphere packaging account for 20.7%, 15.6%, and 7.3% respectively. This finding would serve as input data for small to large holding farmers, retailers, investors as well as stakeholders in the food processing sector and support the sustainable development intent. [ABSTRACT FROM AUTHOR]

Pairwise-based methods such as the free energy perturbation (FEP) method have been widely deployed to compute the binding free energy differences between two similar host-guest complexes. The calculated pairwise free energy difference is either directly adopted or transformed to absolute binding free energy for molecule rank ordering. We investigated, through both analytic derivations and simulations, how the selection of pairs in the experiment could impact the overall prediction precision. Our studies showed that (1) the estimated absolute binding free energy ( ΔG^ ) derived from calculated pairwise differences (ΔΔG) through weighted least squares fitting is more precise in prediction than the pairwise difference values when the number of pairs is more than the number of ligands and (2) prediction precision is influenced by both the total number of pairs and the specifically selected pairs, the latter being critically important when the number of calculated pairs is limited. Furthermore, we applied optimal experimental design in pair selection and found that the optimally selected pairs can outperform randomly selected pairs in prediction precision. In an illustrative example, we showed that, upon weighing ligand structure similarity into design optimization, the weighted optimal designs are more efficient than the literature reported designs. This work provides a new approach to assess retrospective pairwise-based prediction results, and a method to design new prospective pairwise-based experiments for molecular lead optimization. © 2019 Wiley Periodicals, Inc.

District heating is a centralized energetic system, capable of providing heat to different structures. This technology helps achieving energetic efficiency with a high fuel flexibility, enabling the use of various renewable sources, which will lead to a lower pollution potential. Beside the environmental perspective, these systems show a great ability concerning the social context. Benefits such as, tackling fuel poverty, increasing employment and population in areas suffering from desertification, along with forest fire prevention, lead to the social perspective as the primary motivation for the implementation of these systems, creating the concept of Social District Heating (SDH). Therefore, this paper developed a decision support tool to prioritize locations in the most need of an SHD, with the necessary features to include this technology in its area. A methodology intended to assess quantitative and qualitative variables is presented, step by step, evaluating each location relatively to community aspects, background context and council power criteria. The results presented enhance more than one location, however one appears as the most appropriate for the SDH implementation.

The forced-choice (FC) item formats used for noncognitive tests typically develop a set of response options that measure different traits and instruct respondents to make judgments among these options in terms of their preference to control the response biases that are commonly observed in normative tests. Diagnostic classification models (DCMs) can provide information regarding the mastery status of test takers on latent discrete variables and are more commonly used for cognitive tests employed in educational settings than for noncognitive tests. The purpose of this study is to develop a new class of DCM for FC items under the higher-order DCM framework to meet the practical demands of simultaneously controlling for response biases and providing diagnostic classification information. By conducting a series of simulations and calibrating the model parameters with a Bayesian estimation, the study shows that, in general, the model parameters can be recovered satisfactorily with the use of long tests and large samples. More attributes improve the precision of the second-order latent trait estimation in a long test, but decrease the classification accuracy and the estimation quality of the structural parameters. When statements are allowed to load on two distinct attributes in paired comparison items, the specific-attribute condition produces better a parameter estimation than the overlap-attribute condition. Finally, an empirical analysis related to work-motivation measures is presented to demonstrate the applications and implications of the new model. [ABSTRACT FROM AUTHOR]

Development Cooperation initiatives face complex real-world decision-making problems. The present action research investigates the use of Simplified Pairwise Ranking in a case study related to the identification of priority agricultural development interventions in the Collectivity of Katoyi, Nord Kivu, Democratic Republic of Congo. The study identifies roads building, technical training and follow-up, and improved varieties' introduction as the first three priority interventions. The dissertation about the applied methodology against the existing criticism provides information on how simplified pairwise ranking can be a useful and scientifically sound technique for eliciting discussion among a multidisciplinary group and for prioritising alternatives in agricultural development. [ABSTRACT FROM AUTHOR]

Discrimination based on skin color has been documented as a considerable problem in social science research. Most of this research relies on Likert-type ratings of skin color such as the Massey-Martin Scale (MMS). Scholars have raised questions about measurement error in such scales. We hypothesize that the coding of a person's skin color will vary depending on the race of persons previously coded. We find that the MMS is vulnerable to spillover effects: a person's skin is coded as darker, on average, if he is observed following a sequence of White persons than if he is observed following a sequence of Black persons. We also replicate previous work showing that Black and White coders use the scale differently. Finally, having coders cross-reference the palette at the time of coding, rather than recalling the palette from memory, fails to mitigate either race-of-coder or spillover effects. [ABSTRACT FROM AUTHOR]

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Copyright of Journal of Environmental Engineering / Jurnal Teknik Lingkungan is the property of Indonesian Combinatorial Society & Institut Teknologi (ITB) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)