Pairwise Evaluation Research Articles

Mapping of groundwater potential zones of Khordha District using GIS and AHP approaches

The most dependable source of fresh water is groundwater. Groundwater supplies are severely threatened by a number of factors, including urbanization, industrialization, and population growth. The amount, quality and variables affecting groundwater supplies are significantly impacted by climate variability. The fall in groundwater levels is often exacerbated by poor quality surface water resources and unreliable monsoons. Therefore, in order to supplement the groundwater supply, it is important to locate and define the groundwater potential zone (GPZ). The analysis is conducted for the Khordha district, where groundwater rather is a primary source for agricultural uses. In order to determine the possible groundwater zones, many factors, including geomorphology, geology, elevation, slope, precipitation, soil type, soil texture, drainage density (DD), lineament density (LD), Land use/Land cover (LULC), and lineament density (LD), are constructed as separate layers in the geographical information system (GIS) backdrop. The multi-criteria decision-making technique and the Analytic Hierarchy Process (AHP), which enable pairwise evaluation of criteria impacting the potential zone, were utilized to establish the weights for the different layers and after that, the weighted overlay analysis (WOA) tool in ArcGIS10.8 was used to produce the final groundwater potential map. The output map of specified region was delineated into five new classes-very good, good, moderate, poor, and very poor of which 12% (325.1745 km2) falls under ‘very low’, 22% (603.9765 km2) under ‘low’, 26% (700.7715 km2) under ‘moderate’, 26% (694.2591 km2) under ‘high’, 14% (376.7553 km2) under ‘very high’. Approximately 1395 km2 area concerning 52% of study region, falls under ‘high’ and ‘very high’ categories of GPZ. Validation of the generated GPWZ map was done with data acquired from Central groundwater board. The accuracy assessment was done by kappa coefficient error matrix, and based on overall accuracy, the obtained map was 81.538% accurate to field value. As dependable results were produced with the proposed methodology, future management plans incorporating natural and artificial recharge practices can be created in these locations with effectiveness.

Generating Usage-related Questions for Preference Elicitation in Conversational Recommender Systems

A key distinguishing feature of conversational recommender systems over traditional recommender systems is theirability to elicit user preferences using natural language. Currently, the predominant approach to preference elicitation is to ask questions directly about items or item attributes. Users searching for recommendations may not have deep knowledge of the available options in a given domain. As such, they might not be aware of key attributes or desirable values for them. However, in many settings, talking about the planned use of items does not present any difficulties, even for those that are new to a domain. In this article, we propose a novel approach to preference elicitation by asking implicit questions based on item usage. As one of the main contributions of this work, we develop a multi-stage data annotation protocol using crowdsourcing, to create a high-quality labeled training dataset. Another main contribution is the development of four models for the question generation task: two template-based baseline models and two neural text-to-text models. The template-based models use heuristically extracted common patterns found in the training data, while the neural models use the training data to learn to generate questions automatically. Using common metrics from machine translation for automatic evaluation, we show that our approaches are effective in generating elicitation questions, even with limited training data. We further employ human evaluation for comparing the generated questions using both pointwise and pairwise evaluation designs. We find that the human evaluation results are consistent with the automatic ones, allowing us to draw conclusions about the quality of the generated questions with certainty. Finally, we provide a detailed analysis of cases where the models show their limitations.

Multipartite Entity Resolution: Motivating a K-Tuple Perspective (Student Abstract)

Entity Resolution (ER) is the problem of algorithmically matching records, mentions, or entries that refer to the same underlying real-world entity. Traditionally, the problem assumes (at most) two datasets, between which records need to be matched. There is considerably less research in ER when k > 2 datasets are involved. The evaluation of such multipartite ER (M-ER) is especially complex, since the usual ER metrics assume (whether implicitly or explicitly) k < 3. This paper takes the first step towards motivating a k-tuple approach for evaluating M-ER. Using standard algorithms and k-tuple versions of metrics like precision and recall, our preliminary results suggest a significant difference compared to aggregated pairwise evaluation, which would first decompose the M-ER problem into independent bipartite problems and then aggregate their metrics. Hence, M-ER may be more challenging and warrant more novel approaches than current decomposition-based pairwise approaches would suggest.

Evaluation of the current technologies used for the physical security and safety of selected railway tunnel portals as a case study in the Czech Republic

The security and safety of the railway tunnels require technical measures, information processes, and trained experts. Proper implementation and integration of these elements is crucial to protect lives, property, and the operation of the rail network. The purpose of this study is to identify and describe technological security and safety measures at the portals of selected railway tunnels greater than 1000 m in the conditions of the Czech Republic and their subsequent evaluation. The method of semi-structured interviews with experts with experience in the implementation of security and safety measures was used for the elaboration. SWOT analysis was used to assess the safety level of selected railway tunnels. The evaluation was carried out based on multi-criteria decision making and pairwise evaluation on the principle of Fuller’s triangle. The technical solutions of two currently known technological solutions in the case study for accident elimination based on lidar detection supplemented by cameras are characterized by fundamental differences. The solution in the Ejpovice tunnel focuses on the detection of objects in the immediate vicinity of the portal or already entered it. The solution in the Březenský tunnel focuses on a larger detection zone extending tens of meters in front of the tunnel portal and is divided into two parts with different logic, the pre-alarm and the alarm itself. Integration of individual elements into a proactive and automated system that uses modern AI-based analytical algorithms and respects the process and technology specifics of the railway environment is important for the safety and security of the railway tunnel.

Decision support based on performance data using the analytic hierarchy process without expert judgement

Goal: This article proposes a decision model based on the Analytic Hierarchy Process that allows carrying out the evaluation of alternatives in a multicriteria problem, without expert judgement. Design / Methodology / Approach: The algorithm is based on AHP. The novelty is the transformation of a performance data matrix into pairwise evaluation matrices, instead of using experts’ judgement. Results: The algorithm was applied in a defense procurement problem for the choice of a light 4x4 vehicle for amphibious operations. The results allowed ranking the 17 models based on catalog data. Limitations of the investigation: the algorithm depends on the availability of catalog data, not always available in open sources in the defense industry. Practical implications: Decision support involves several activities in Operations Management and AHP has been frequently applied to solve problems in this sector. The proposed algorithm allows performing deterministic or probabilistic evaluations, depending on the degree of uncertainty and precision involving the systems’ performance data. These assessments are composed of scenarios to facilitate decision making. Originality / Value: AHP typically uses experts for pairwise judgments. However, human judgment is subject to outcomes that involve bias and cognitive distortions. Few studies have modeled the AHP without experts, even so they used human judgment in some part of the process. The approach proposed here does not require human judgment and returns two different results, based on the database precision. This new approach gives decision makers a different perspective and can alter the final choice.

Assessment of the Tumor-Stroma Ratio and Tumor-Infiltrating Lymphocytes in Colorectal Cancer: Inter-Observer Agreement Evaluation.

To implement the new marker in clinical practice, reliability assessment, validation, and standardization of utilization must be applied. This study evaluated the reliability of tumor-infiltrating lymphocytes (TILs) and tumor-stroma ratio (TSR) assessment through conventional microscopy by comparing observers' estimations. Intratumoral and tumor-front stromal TILs, and TSR, were assessed by three pathologists using 86 CRC HE slides. TSR and TILs were categorized using one and four different proposed cutoff systems, respectively, and agreement was assessed using the intraclass coefficient (ICC) and Cohen's kappa statistics. Pairwise evaluation of agreement was performed using the Fleiss kappa statistic and the concordance rate and it was visualized by Bland-Altman plots. To investigate the association between biomarkers and patient data, Pearson's correlation analysis was applied. For the evaluation of intratumoral stromal TILs, ICC of 0.505 (95% CI: 0.35-0.64) was obtained, kappa values were in the range of 0.21 to 0.38, and concordance rates in the range of 0.61 to 0.72. For the evaluation of tumor-front TILs, ICC was 0.52 (95% CI: 0.32-0.67), the overall kappa value ranged from 0.24 to 0.30, and the concordance rate ranged from 0.66 to 0.72. For estimating the TSR, the ICC was 0.48 (95% CI: 0.35-0.60), the kappa value was 0.49 and the concordance rate was 0.76. We observed a significant correlation between tumor grade and the median of TSR (0.29 (95% CI: 0.032-0.51), p-value = 0.03). The agreement between pathologists in estimating these markers corresponds to poor-to-moderate agreement; implementing immune scores in daily practice requires more concentration in inter-observer agreements.

Value Propositions of Public Adult Hearing Rehabilitation in Denmark.

Objective: To obtain and evaluate detailed descriptions of potential value propositions as seen by adults undergoing hearing rehabilitation with hearing aids. Design: Semi-structured interviews with patients and audiologists, a literature search, and the inclusion of domain knowledge from experts and scientists were used to derive value propositions. A two-alternative forced-choice paradigm and probabilistic choice models were used to investigate hearing aid users' preferences for the value propositions through an online platform. Study sample: Twelve hearing aid users (mean age 70, range 59-70) and eleven clinicians were interviewed. A total of 173 experienced hearing aid users evaluated the value propositions. Results: Twenty-nine value propositions as described by patients, clinicians, and hearing care experts where identified, from which twenty-one value propositions were evaluated. Results of the pair-wise evaluation method show that the value propositions judged to be the most important for the hearing aid users were: "13. To solve the hearing problem you have", "09. Thorough diagnosis of the hearing", and "16. The hearing aid solution is adapted to individual needs", which are related to finding the correct hearing solution and to be considered in the process. The value propositions judged to be least important were: "04 Next of kin and others involved in the process", "26. To be in the same room as the practitioner", and "29. The practitioner's human characteristics", related to the involvement of others in the process and the proximity and personal manner of the practitioners.

The Effect of Face Illustrations in Package Design on Product Evaluation

In this study, we compared and evaluated whether different facial expressions on the package resulted in different evaluations of the confectionery by presenting participants in the experiment with different facial expressions on a fictitious package and conducting a pairwise evaluation. In the result, it revealed differences in the evaluation of “visual taste” and “familiarity” depending on differences in facial expression, although the effects of the combination and individual differences in preference were also observed. As for the influence of each part of the face, it was found that the mouth part influenced “familiarity,” while the eye part had a significant influence on the “visual taste” evaluation. This suggests that having a face on the confectionery package with an appropriate facial expression for the product itself will help people feel more comfortable with the product and understand information such as the type and strength of the product’s flavor.

Prognostic value of the TyG index for in-hospital mortality in nondiabetic COVID-19 patients with myocardial injury.

SUMMARYOBJECTIVE:The purpose of this study was to explore the efficacy of the triglyceride glucose (TyG) index on in-hospital mortality in nondiabetic coronavirus disease 2019 (COVID-19) patients with myocardial injury.METHODS:This was a retrospective study, which included 218 nondiabetic COVID-19 patients who had myocardial injury. The TyG index was derived using the following equation: log [serum triglycerides (mg/dL) ×fasting blood glucose (mg/dL)/2].RESULTS:Overall, 49 (22.4%) patients died during hospitalization. Patients who did not survive had a higher TyG index than survivors. In multivariate Cox regression analysis, it was found that the TyG index was independently associated with in-hospital death. A TyG index cutoff value greater than 4.97 was predicted in-hospital death in nondiabetic COVID-19 patients with myocardial damage, with 82% sensitivity and 66% specificity. A pairwise evaluation of receiver operating characteristic (ROC) curves demonstrated that the TyG index (AUC: 0.786) had higher discriminatory performance than both triglyceride (AUC: 0.738) and fasting blood glucose (AUC: 0.660) in predicting in-hospital mortality among these patients.CONCLUSIONS:The TyG index might be used to identify high-risk nondiabetic COVID-19 patients with myocardial damage.

In vitro comparative study of sorption and solubility of three modified glass ionomer cements

Aim: To evaluate and compare the sorption and solubility of three modified glass ionomer cements –Glass Hybrid Restorative System (Equiaforte), Zirconomer Improved and resin modified glass ionomer cement (RMGIC) in artificial saliva. Materials And Methods: A total of 45cylindrical specimens (4mm diameter and 6mm height) were prepared for each material following the manufacturer’s instructions. After subjecting the specimens to thermocycling of 500 cycles between 5 and 55 degree Celsius with the dwell time 15secs,45 specimens evaluated for sorption and solubility, first weighing them by a precision weighing scale (W1), then immersing them in artificial saliva for 28days and weighing them(W2), and finally dehydratingin an oven for 24 hours and weighing them(W3). Since the obtained values show normal distribution differences between the groups data were analyzed using the one way analysis of variance (ANOVA). For pairwise evaluation between the groups post hoc dunnet t3 test was used. Results: The sorption and solubility values in artificial saliva were minimum for zirconomer improved glass ionomer cement with the mean value of sorption 52.91 ± 1.73 µg/mm3 and solubility 10.47 ± 1.52 µg/mm3 when compared with other groups.The difference between groups was found to be statistically significant (p<0.05) in terms of sorption and solubility.

A multi-attribute approach to ranking departments based on performance: a balanced scorecard pilot study

Departments are assessed using performance evaluation systems (PES) based on their important performance criteria. The sum of the weighted KPIs’ actualization scores creates the departments' performance ratings, which are used to rank departments from highest to lowest performing. However, this strategy does not account for departmental variances. We used multi-attribute decision-making (MADM) techniques in this study to ascertain the performance of several divisions inside a business. Our study's structure is divided into two distinct parts. In the first stage, pair-wise evaluation matrices are built and attribute weights are established using the Analytic Hierarchical Process (AHP) to determine attribute precedence. The second step employs the Elimination and Choice Expressing the Reality (ELECTRE) methodology to rank the departments using the attribute weights generated in the first stage using the AHP method. At the conclusion of the paper, a case study is presented to demonstrate how departments are organized in accordance with performance management principles within a corporation that use the balance-scorecard program for performance evaluation.

A Scanning Electron Microscope Evaluation of the Efficacy of Different Fluoride-releasing Dental Restorative Materials to Prevent Enamel Demineralization: An In Vitro Study

Aim of the present research was to investigate the effectiveness of various fluoride-releasing dental restorative agents in preventing demineralization of enamel. Eighty human mandibular permanent molar teeth constituted the study group. All samples were subjected to storage in thymol, after which they were taken out to prepare alike proximal box in each. Inductions of artificial enamel surface lesions were done by placing the teeth in demineralizing solution for 96 hours. Subsequently, all 80 molars were randomly assigned to any of the four groups (i.e., 20 in every individual group) according to the restoration as group A: giomer (composite resin containing surface pre-reacted glass-ionomer fillers), group B: compomer (polyacid-modified composite resin), group C: resin-modified glass-ionomer cement (RMGIC), group D: fluoride-releasing composite. After this, the pH cycling was performed, and the samples were subjected to examination beneath scanning electron microscope (SEM). Higher mean areas of remineralization were noted when RMGIC (96.34 ± 0.06) was used followed by the compomer (109.52 ± 0.17), giomer (118.39 ± 0.82), and the fluoride-releasing composite group (129.27 ± 0.31) in that order. A statistically significant difference was seen amid the investigational groups that utilized different restorative agents (p <0.001). A pairwise evaluation that was performed revealed that except for the giomer group and the compomer group, a statistically significant difference (p <0.001) was found among the experimental groups. This research infers that the RMGIC-treated samples exhibited significantly superior performance in preventing enamel demineralization in comparison to compomer, giomer as well as fluoride-releasing composites. One among the highly frequently employed anticariogenic materials is fluorides. Owing to this characteristic, they are integrated into numerous restorative substances. Nevertheless, the quantity and speed of fluoride release differ in different agents, which translates to the efficacy of the restorative agent in avoiding demineralization about the restoration.

The determinants of bank selection criteria of SMEs: a fuzzy analytic hierarchy approach

PurposeSmall and medium enterprises (SMEs) have been reported as a credit-constrained sector in the earlier literature. Amidst the available external financing options, SMEs are dependent upon banks for their financial needs, hence they offer an important profitable segment for banks. Commercial banks need to develop effective targeting strategies for this segment and ranking the priorities of SMEs in selecting commercial banks will be of great help to them. The purpose of this paper is to implement a fuzzy analytic hierarchy process (FAHP) multi-criteria decision model for commercial bank’s selection by SMEs.Design/methodology/approachThe research process was carried out in two phases. In Phase I, a self-structured scale was developed to measure bank selection criteria of SMEs after an extensive review of the literature of relevant studies on the topic. A sample of 600 SMEs was selected through non-proportionate quota sampling and only 313 valid responses were received. Phase II was conducted to prioritize the extracted factors through FAHP, a multi-criteria decision-making technique. For this purpose, another questionnaire was designed in the form of pair-wise evaluation and the response was taken on the same from those 313 SMEs again.FindingsThe results showed that SMEs bank selection criteria can be categorized under six heads, namely, bank attributes, accommodation of credit needs (AC), bank personnel, financial factors (FF), service quality (SQ) and business knowledge. The research study produced a reliable and valid instrument for studying the bank selection criteria of SMEs. The results further revealed that AC is the most important factor considered by SMEs followed by FF and SQ. Going further, global weights were also calculated through the FAHP which revealed that the most important consideration (variable) viewed upon by SMEs is willingness to accommodate credit needs followed by flexible collateral requirements and absence of hidden charges.Research limitations/implicationsThe results of the present study offer significant insights as to the factors SMEs consider while making a bank selection decision. It is of utmost importance for banks to identify true determinant factors used by SMEs while making bank choice decisions as they offer ample profit and revenue opportunities to banks. The results of the study provide a practical approach to banks that would help them in framing strategies for SMEs customers.Originality/valueThis is the first study of its kind which has not only focused on the hierarchy of factors measuring bank selection criteria of SMEs rather on the hierarchy of single variables also through the calculation of global weights. As banks cannot focus on all the dimensions of the criteria, they can focus on the spirit of that particular criteria.

SVNMPR: A new single‐valued neutrosophic multiplicative preference relation and their application to decision‐making process

The aim of the paper is to present the concept of a multiplicative preference relation (MPR) with the features of the single-valued neutrosophic (SVN) set and named as SVN multiplicative preference relation (SVNMPR). The SVN set (SVNS) handles uncertainties more broadly than the intuitionistic fuzzy set by considering the three independent degree systems with a scale rating of 0–1 and is symmetrical about 0.5. However, for asymmetrical distribution, Saaty discusses the 1–9 scale to display the information. Driven by the advantages of the scale of 1–9 and the independence nature of degrees in SVNS, in this paper, we introduce the concept of SVNMPR with ratings values as SVN numbers with range 1 ∕ q − q with q > 1. The main advantages of the proposed relation are the asymmetric and nonuniform distributions of about 1. To complete address this, we divide the stated work into three parts. First, an idea of SVNMPR is added wherein pairwise evaluation values are represented the usage of SVN numbers over a scale of 1 ∕ q − q with q > 1. Also, we outline the idea of the SVN multiplicative preference set (SVNMPS). Second, to rank the proposed SVNMPS, we define a new ranking method with improved score functions by adding the expert's attitude to the analysis. The relationship between the proposed and the existing functions is also derived. Third, we are defining some new operation laws for SVNMPS to preserve the closeness property. On the basis of these laws, we define a number of operators to aggregate the different pairs of the SVNMPSs and to derive a number of relations and inequalities. Finally, a group decision-making approach is established and the practicality of it is demonstrated with a numerical example and compared with a number of existing approaches.

PROMETHEE-SAPEVO-M1 a Hybrid Approach Based on Ordinal and Cardinal Inputs: Multi-Criteria Evaluation of Helicopters to Support Brazilian Navy Operations

This paper presents a new approach based on Multi-Criteria Decision Analysis (MCDA), named PROMETHEE-SAPEVO-M1, through its implementation and feasibility related to the decision-making process regarding the evaluation of helicopters of attack of the Brazilian Navy. The proposed methodology aims to present an integration of ordinal evaluation into the cardinal procedure from the PROMETHEE method, enabling to perform qualitative and quantitative data and generate the criteria weights by pairwise evaluation, transparently. The modeling provides three models of preference analysis, as partial, complete, and outranking by intervals, along with an intra-criterion analysis by veto threshold, enabling the analysis of the performance of an alternative in a specific criterion. As a demonstration of the application, is carried out a case study by the PROMETHEE-SAPEVO-M1 web platform, addressing a strategic analysis of attack helicopters to be acquired by the Brazilian Navy, from the need to be evaluating multiple specifications with different levels of importance within the context problem. The modeling implementation in the case study is made in detail, first performing the alternatives in each criterion and then presenting the results by three different models of preference analysis, along with the intra-criterion analysis and a rank reversal procedure. Moreover, is realized a comparison analysis to the PROMETHEE method, exploring the main features of the PROMETHEE-SAPEVO-M1. Moreover, a section of discussion is presented, exposing some features and main points of the proposal. Therefore, this paper provides a valuable contribution to academia and society since it represents the application of an MCDA method in the state of the art, contributing to the decision-making resolution of the most diverse real problems.

Innovatively Fused Deep Learning with Limited Noisy Data for Evaluating Translations from Poor into Rich Morphology

Evaluation of machine translation (MT) into morphologically rich languages has not been well studied despite its importance. This paper proposes a classifier, that is, a deep learning (DL) schema for MT evaluation, based on different categories of information (linguistic features, natural language processing (NLP) metrics and embeddings), by using a model for machine learning based on noisy and small datasets. The linguistic features are string based for the language pairs English (EN)–Greek (EL) and EN–Italian (IT). The paper also explores the linguistic differences that affect evaluation accuracy between different kinds of corpora. A comparative study between using a simple embedding layer (mathematically calculated) and pre-trained embeddings is conducted. Moreover, an analysis of the impact of feature selection and dimensionality reduction on classification accuracy has been conducted. Results show that using a neural network (NN) model with different input representations produces results that clearly outperform the state-of-the-art for MT evaluation for EN–EL and EN–IT, by an increase of almost 0.40 points in correlation with human judgments on pairwise MT evaluation. It is observed that the proposed algorithm achieved better results on noisy and small datasets. In addition, for a more integrated analysis of the accuracy results, a qualitative linguistic analysis has been carried out in order to address complex linguistic phenomena.

Flood vulnerability assessment using GIS at Fetam watershed, upper Abbay basin, Ethiopia

Flood is a danger to human beings and their properties because less consideration is given for risk analysis and assessment of risk areas. The main aim of this study was to assess the flood vulnerability areas of Fetam watershed. For analyzing this issue, flood generating factors: slope, elevation, land use/land cover, drainage density, rainfall, and soil types were rated and collected to mark out flood vulnerability zones using a multi-criteria evaluation technique within the Geographic Information System (GIS). Maps were constructed using past data on river banks and discharge of earlier floods along with topographic data to illustrate areas susceptible to flood for various discharges. The influences of all overflow distribution factors were calculated using pair-wise evaluation techniques for decisive weighted-overlay investigation of each factor in flood vulnerability assessment. The flood vulnerability valuation map showed that 67.54, 634.11, 280.89, 121.28, and 2.81 km2 areas correspond to very high, high, moderate, low, and very low flood vulnerabilities respectively. Residential areas and farming fields are located along with the flood hazard areas, which are susceptible to flooding. Hence, these areas are vulnerable to social and economic development due to loss of life and damage to properties of resident people. The main result of this study showed that the upstream and center part of Fetam watershed is highly sighted than the downstream part.

The Influence of Criteria Selection Method on Consistency of Pairwise Comparison

The more criteria a human decision involves, the more inconsistent the decision. This study experimentally examines the effect on the degree of pairwise comparison inconsistency by using the (im)possibility of selecting the criteria for the evaluation and the size of the decision-making problem. A total of 358 participants completed objective and subjective tasks. While the former was associated with one possible correct solution, there was no single correct solution for the latter. The design of the experiment enabled the acquisition of eight groups in which the degree of inconsistency was quantified using three inconsistency indices (the Consistency Index, the Consistency Ratio and the Euclidean distance) and these were analysed by the repeated measures ANOVA. The results show a significant dependence of the degree of inconsistency on the method of determining the criteria for pairwise evaluation. If participants are randomly given the criteria, then with more criteria, the overall inconsistency of the comparison decreases. If the participants can themselves choose the criteria for the comparison, then with more criteria, the overall inconsistency of the comparison increases. This statistical dependence exists only for males. For females, the dependence is the opposite, but it is not statistically significant.

Developing a new rigorous drilling rate prediction model using a machine learning technique

Drilling rate of penetration (ROP) prediction is an enormously important step to optimize drilling controllable parameters. Therefore, numerous efforts have been done in order to present a more precise estimator model that is still ongoing. The results of the literature review indicated that, between the two approaches followed for ROP modeling, namely physic-based models and data-driven methods, the use of the data-driven methods has grown significantly. The literature review made two points clear: (1) the geomechanical parameters were not considered adequately, and (2) no previous research had applied a combination of least-squares support-vector machines (LSSVM) with cuckoo optimization algorithm (COA), particle swarm optimization (PSO), and genetic algorithms (GA). This led us to use hybrid algorithms for ROP modeling at vertical wells drilled in the southwestern Iran. For this purpose, mud logging parameters (including depth (Depth), mud density (MD), torque (Tor), standpipe pressure (SPP), equivalent circulating density (ECD), weight on bit (WOB), revolutions per minute (RPM), flow rate (FR), and ROP) and geomechanical parameters (including gamma ray (GR), porosity (NPHI), density (RHOB), and uniaxial compressive strength (UCS)) were collected along the studied wells. Histogram analysis and pairwise evaluation of the studied features indicated the presence of outliers among the data points. Accordingly, the Tukey's method was used to omit the outliers. Subsequently, the most relevant features in the ROP prediction were selected using a combination of the non-dominated sorting genetic algorithm II (NSGA-II) with multilayer perceptron (MLP) neural network. The results showed that one could attenuate the modeling error by increasing the number of input parameters into the ROP estimation model. However, the improvement was very subtle when the number of input parameters exceeded six. Therefore, six parameters (UCS, FR, WOB, Depth, MD, and RPM) were used for ROP modeling using LSSVM-COA, LSSVM-PSO, and LSSVM-GA hybrid algorithms. Results of applying these hybrid algorithms indicated that the LSSVM-COA outperformed the other two algorithms in terms of accuracy and the coefficient of determination. In a next step, the LSSVM-COA was trained on both the outlier-omitted and raw datasets, indicating the important role of the outlier omission step in achieving a highly accurate and reliable model. Continuing with the work, in order to evaluate the performance of the LSSVM-COA hybrid model, the model was compared to support-vector regression (SVR)-COA, MLP-COA, Motahhari's, linear multivariate regression (LMR), and nonlinear multivariate regression (NLMR) models. The results confirmed superior accuracy of the LSSVM-COA model over the examined models. The small difference between the obtained levels of error in the training and testing stages with the LSSVM-COA model, as compared to the other models, revealed that the model can be used to predict the ROP at other wells across the field reliably and accurately provided the model be developed with larger sets of data across the field. Predicting ROP with high accuracy at the test Well clearly proved this claim.

Pairwise Versus Multiple Global Network Alignment.

Biological network alignment (NA) aims to identify similar regions between molecular networks of different species. NA can be local or global. Just as the recent trend in the NA field, we also focus on global NA, which can be pairwise (PNA) and multiple (MNA). PNA produces aligned node pairs between two networks. MNA produces aligned node clusters between more than two networks. Recently, the focus has shifted from PNA to MNA, because MNA captures conserved regions between more networks than PNA (and MNA is thus hypothesized to yield higher-quality alignments), though at higher computational complexity. The issue is that, due to the different outputs of PNA and MNA, a PNA method is only compared to other PNA methods, and an MNA method is only compared to other MNA methods. Comparison of PNA against MNA must be done to evaluate whether MNA indeed yields higher-quality alignments, as only this would justify MNA's higher computational complexity. We introduce a framework that allows for this. We evaluate eight prominent PNA and MNA methods, on synthetic and real-world biological networks, using topological and functional alignment quality measures. We compare PNA against MNA in both a pairwise (native to PNA) and multiple (native to MNA) manner. PNA is expected to perform better under the pairwise evaluation framework. Indeed this is what we find. MNA is expected to perform better under the multiple evaluation framework. Shockingly, we find this not always to hold; PNA is often better than MNA in this framework, depending on the choice of evaluation test.