Number-based Approach Research Articles

Task Location to Improve Human–Robot Cooperation: A Condition Number-Based Approach

This paper proposes and implements an approach to evaluate human–robot cooperation aimed at achieving high performance. Both the human arm and the manipulator are modeled as a closed kinematic chain. The proposed task performance criterion is based on the condition number of this closed kinematic chain. The robot end-effector is guided by the human operator via an admittance controller to complete a straight-line segment motion, which is the desired task. The best location of the selected task is determined by maximizing the minimum of the condition number along the path. The performance of the proposed approach is evaluated using a criterion related to ergonomics. The experiments are executed with several subjects using a KUKA LWR robot to repeat the specified motion to evaluate the introduced approach. A comparison is presented between the current proposed approach and our previously implemented approach where the task performance criterion was based on the manipulability index of the closed kinematic chain. The results reveal that the condition number-based approach improves the human–robot cooperation in terms of the achieved accuracy, stability, and human comfort, but at the expense of task speed and completion time. On the other hand, the manipulability-index-based approach improves the human–robot cooperation in terms of task speed and human comfort, but at the cost of the achieved accuracy.

A novel two-phase group decision-making model for circular supplier selection under picture fuzzy environment.

Circular supply chain management (CSCM), which incorporates circular thinking into supply chain management, promotes supply chain sustainability by offering a novel and compelling viewpoint. In the CSCM, supplier selection is crucial in establishing a competitive edge among businesses by decreasing environmental degradation and related supply chain expenses. This paper aims to propose a novel two-phase group decision-making approach, which combines the picture fuzzy Analytical Hierarchical Process (PF-AHP) and grey Measurement of Alternatives and Ranking According to Compromise Solution (MARCOS-G) model to select railway material suppliers within the scope of circular economy perspective. In group decision-making, a picture fuzzy number-based approach aggregates individual decision makers'(DMs) opinions and provides a lower level of computing complexity and a higher level of performance. The PF-AHP has been used to produce the weights of the criteria, and the MARCOS-G technique has been used to rank the suppliers and choose the most appropriate one. The results and verification of the novel method are carried out throughout a comprehensive sensitivity analysis. For this purpose, 50 scenarios with changes in the weight values of criteria were developed. In the validation analysis, a comparison with other grey COPRAS, TOPSIS, ARAS and WASPAS methods were performed. Due to the ranking results obtained from Spearman's correlation analyses, the MARCOS-G is almost consistent and it can be seen that the A4 remains the top supplier in all 50 scenarios.

Forecasting CO2 emissions of China's cement industry using a hybrid Verhulst-GM(1,N) model and emissions' technical conversion

The cement industry is a significant contributor to anthropogenic CO2. For China, the cement industry is crucial for development, considering the surging urbanization. CO2 emissions from the industry are detrimental to the planet, its ecosystem, and inhabitants. Forecasting of the emissions is a critical step in the emissions' mitigation strategies, and to achieve sustainable development. However, the level of uncertainty accompanying CO2 estimates leads to discrepancies in predictions. The current work aims to study the estimation of cement industry CO2 emissions from an uncertainty-driven technical perspective, and present a forecast for the Chinese cement industry emissions using a novel grey prediction model. By modeling the framework of China's cement industry and the CO2 emissions estimation techniques as grey systems with partially known information, this study develops an interval grey number-based approach to calculate the relative uncertainty. A grey sequence is generated from the whitenization of the interval grey numbers to represent annual emissions from different sources. The proposed approach is more flexible than the conventional midpoint estimate-based approach recommended by JCGM. The proposed model, V-GM(1,N), is found to give the highest accuracy of 97.29% in simulating the actual cement industry CO2 emissions data from 2005 to 2018. Comparative analysis of the proposed model with other forecasting models revealed the superiority of the model. The proposed framework, involving the forecasting model and uncertainty analysis approach, is likely to facilitate the decision-makers in making realistic and reliable forecasts at reasonable computational costs.

Interval fuzzy number-based approach for modeling an uncertain fuzzy yield learning process

An interval fuzzy number-based approach was proposed in this study to model an uncertain yield learning process. The study aimed to overcome the limitations of present methods, wherein the lower and upper bounds of the yield are generally determined by few extreme cases, thus resulting in an unacceptable widening of the yield range. In the proposed interval fuzzy number-based approach, the range of yield was divided into two sections, namely inner and outer sections, which corresponded with the lower and upper membership functions of a fuzzy yield forecast based on interval fuzzy numbers, respectively. To fulfill different managerial objectives, in this approach, all actual values are included in the outer section, whereas most of these values fall within the inner section. To derive the values of parameters in a fuzzy yield learning model based on interval fuzzy numbers, a mixed binary nonlinear programming model was proposed and optimized. The interval fuzzy number-based approach was applied to two real-time cases for evaluating its effectiveness. According to experimental results, the performance of the proposed method was superior to that of several existing methods, particularly in terms of forecasting precision for the average range. Forecasting accuracy obtained using the interval fuzzy number-based approach was satisfactory.

Evaluation of in-line particle measurement with an SFT-probe as monitoring tool for process automation using a new time-based buffer approach

The Process Analytical Technology (PAT) initiative published by the Food and Drug administration (FDA) describes PAT systems as important tools to optimize pharmaceutical processes in order to ensure the final product quality. Real-time particle size measurements provide a promising approach for process automation of fluid bed granulation processes. This work focuses on the introduction and evaluation of a new time-based buffer approach for in-line spatial filtering technique (SFT) in order to provide a minimum time delay of the real-time measurement. Comparing the new buffer system to the conventionally used number-based approach, a reduced influence of the particle rate and a constant response time was found. A performance qualification of the Parsum probe IPP 80 using this buffer approach according to the ICH guideline Q2 was performed to investigate whether common variations of process parameters influence the real-time particle size measurement. Focussing on the main requirements for process control, hereby a good precision and specificity as well as an adequate robustness was found. Summarized, this study identified the time-based buffer system as more suitable buffer system for process automation.

Performance and calibration of two subgrid extinction models for turbulent diffusion combustion in an under-ventilated enclosure fire

The fast-chemistry eddy dissipation combustion model fails to predict abrupt flame self-extinguishment when the oxygen content falls below the critical threshold. To overcome this limitation without recourse to the highly resolved simulations with the detailed chemical mechanisms, the bespoke subgrid extinction model is required. In this work, two subgrid extinction models are implemented in ANSYS Fluent and applied. The first extinction model utilizes conventional critical flame temperature concept, and the second one is the Damköhler number-based approach. The model formulation is used as derived earlier from the perfectly stirred reactor model, with the finite-rate reaction kinetics and radiative heat losses from the unresolved reaction zone.To evaluate model performance and to select optimum model parameters, the burner fire with the constant fuel supply rate (corresponding to the nominal heat release rate 400 kW in complete combustion) is considered in 4x6x4.5 m compartment with the 0.3 m diameter lower air inlet connected to the ambient atmosphere. Conjugate heat transfer at the compartment walls and transient thermal conductivity in the compartment structures are accounted for.Provided that the model parameters are optimized, both extinction models enable prediction of flame self-extinguishing, and the predicted times for flame to extinguish are similar to those recorded in the experiments. For the CFT model, best agreement with the experimental time to flame extinguishment is obtained if the critical flame temperature is set to 1900 K. For the Damköhler number-based approach, the optimum value of 0.25 is found for the model constant in the proportionality relationship between the resolved strain rate and the residence time in the reaction zone, which is not resolved in the simulations.In spite of the similar performance of the two extinction models in the experimental scenario with weakly strained flame, the Damköhler number-based model is expected to be applicable in a wider range of scenarios including highly strained flames such as those affected by strong jets, sprays, and cross-winds.

Defining an ‘older’ patient in the context of therapeutic decision making: perspectives of Australian pharmacists and nurses

Our aim was to explore Australian nurses and pharmacist’s perspectives on defining an ‘older’ patient in the context of decision making around pharmacotherapy. A qualitative study was conducted using semi-structured interviews, facilitated by a purpose-designed interview guide. Practitioners were recruited from the primary care (i.e. registered community pharmacists, registered community nurses, general practice nurses) and tertiary care settings (i.e. referral hospitals, specialist clinics). Non-prescribing health professionals directly involved in medication management (i.e. nurses, pharmacists) with experience in caring for older patients. This exploratory study identified three key themes: (1) defining ‘older’ patients is difficult, given the heterogeneity of the population; (2) age is more than a number and, therefore, cannot be used alone for tailoring and managing a patient’s treatment; and (3) a contemporary definition of an ‘older’ patient needs to be integrated into guidelines for treating aged patients. Overall, Australian nurses and pharmacists shared similar perspectives about defining an ‘older’ patient, favouring holistic assessments of individual patients. Non-prescribing health practitioners, such as nurses and pharmacists, advocate an individualised approach, rather than a number-based approach, to decision making in older patients.

Evaluating the performance of suppliers based on using the R'AMATEL-MAIRCA method for green supply chain implementation in electronics industry

Abstract Green supply chain management (GSCM) practitioners striving to create a healthier environment should first identify the key criteria pertinent to the process of implementing the appropriate sustainable policies, particularly in the most rapidly growing electronics sector. Since the decision to adopt GSCM in electronics industry is associated with the use of a multi-dimensional approach involving a number of qualitative criteria, the paper examines GSCM based on fifteen criteria expressed in five dimensions and proposes a multi-criteria evaluation framework for selecting suitable green suppliers. In real life, the assessment of this decision is based on vague information or imprecise data of the expert's subjective judgements, including the feedback from the criteria and their interdependence. Thus to treat this uncertainty in multi-criteria decision making (MCDM) process, rough number (RN) is applied here using only the internal knowledge in the operative data available to the decision-makers. In this way objective imprecisions and uncertainties are used and there is no need to rely on models of assumptions. Instead of different external parameters in the application of RN, the structure of the given data is used. Therefore, the identified components are incorporated into a rough DEMATEL-ANP (R'AMATEL) method, combining the Decision Making Trial and Evaluation Laboratory Model (DEMATEL) and the Analytical Network Process (ANP) in a rough context. In group decision making, a rough number-based approach aggregates individual judgements and handles imprecision. The structure of the relationships between the criteria expressed in different dimensions is determined by using the rough DEMATEL (R'DAMETEL) method and building an influential network relation mapping, based on which the rough ANP (R'ANP) method is implemented to obtain the respective criteria weights. Then, the rough multi-attribute Ideal-Real Comparative Analysis (R'MAIRCA) is used to evaluate the environmental performance of suppliers for each evaluation criterion. Sensitivity analysis is performed to determine the impact of the weights of criteria and the influence of the decision maker's preferences on the final evaluation results. Applying the Spearman's rank correlation coefficient and other ranking methods, the stability of the alternative rankings based on the variation in the criteria weights is checked. The results obtained in the study show that the proposed method significantly increases the objectivity of supplier assessment in a subjective environment.

Selection of an optimal treatment method for acute pulpitis disease

The present paper is devoted to selection of an optimal treatment method for acute pulpitis disease by using Z number-based approach. We consider application of different treatment methods depending on development stages and symptoms of the disease. The effectiveness of application of different treatment methods in each stage of the disease is linguistically evaluated by a dentist. The stages of the disease are also linguistically described by a dentist. Dentist’s linguistic evaluations are represented by Z information. The total effectiveness of the each considered treatment method is calculated by using Z number. The most effective treatment method is determined by using ranking of discrete Z-numbers.

Number-Based Approach to Insulin Taxonomy.

This article describes a number-based system for the classification of insulin regimes. It utilizes a patient-centered variable (number of injections per day) and pharmacokinetic/dynamic characteristics to craft a taxonomic system that is able to incorporate all available insulin preparations and coformulations. This framework of systematics is robust enough to include various molecules that have been recently developed. It serves to enhance understanding of the subject, and facilitates the practical or clinical usage of theoretical knowledge. We propose that number-based insulin taxonomic models should be used in clinical guidelines and recommendations rather than restricting ourselves to pharmaceutical-based classifications. PubMed articles including both review articles and clinical trials published since the year 1990 were searched, to gather evidence and information on the various types of insulins available, and how they can be used, based on the number or frequency of injections prescribed per day.

Seasonality, diversity and aesthetic valuation of landscape plots: An integrative approach to assess landscape quality on different scales

The amenity function of landscapes is of growing importance in the Common Agricultural Policy (CAP) of the European Union as well as in the reformed direct payment system of Swiss agricultural policy. This is reflected in a growing amount of direct payments for landscape stewardship, which enhances the need for landscape quality assessments. A good assessment system should be based on sound indicators applicable on different scales.Diversity indices are often used to evaluate landscape quality. However, the aspect of seasonality is often neglected, although it is a typical quality of landscapes in temperate zones. This paper aims at including the aesthetic valuation (measured as rating scores) and seasonality, as a substitute for quality in a diversity index.Overall, 27 landscape plots of 1km2 size with mapped land-use types were used to calculate diversity indices in two different ways: firstly by using the accumulated area of each landscape element (area-based approach) or the number of landscape elements (number-based approach) per plot, both times weighted by rating scores. Secondly with a “simplified element list” where only the landscape elements with a rating score above the mean of all rating scores were considered as single elements, while the remaining elements were aggregated to one element after weighting their area by the rating score. Furthermore for all types of calculation the accumulated absolute seasonal diversity was calculated.Plotting the accumulated absolute seasonal diversity on the x-axis against the mean diversity index values revealed, that only using the “simplified element list” can mitigate the effect of diversity index calculations where an increasing number of elements in a certain area automatically results in increasing diversity values, irrespective of their impact on aesthetic quality. This presentation in a x–y plot offers information to interpret the results, and can be applied in national and international monitoring systems.This novel approach is limited due to its restriction on 27 landscape plots of 1km2 size and to the lack of seasonal rating scores for all landscape elements that can be found in Switzerland. Additional data have thus to be provided. However, this should hardly be extra work in assessment projects if the number of fields and area sizes for each element can be taken from farm databases.

Analysis of Coupled Bending-Torsional Vibration of Beams in the Presence of Uncertainties

Several methods are presented for the modeling and analysis of uncertain beams and other structural elements/systems. By representing each uncertain parameter as an interval number, the vibration problem associated with any uncertain system can be expressed in the form of a system of nonlinear interval equations. The resulting equations can be solved using the exact or a truncation-based interval analysis method. An universal grey number-based approach and an interval-discretization method are proposed to obtain more efficient and/or more accurate solutions. Specifically, the problem of the coupled bending-torsional vibration of a beam involving uncertainties is considered. It is found that the range of the solution (response) increases with increasing levels of uncertainty in all the methods. Numerical examples are presented to illustrate the computational aspects of the methods presented and also to indicate the high accuracy of the interval-discretization approach in finding the solution of practical uncertain systems. The results given by the different interval analysis methods (including the universal grey number-based analysis) are compared with those given by the Monte Carlo method (probabilistic approach) and the results are found to be in good agreement with those given by the interval analysis-based methods for similar data.

Experimental validation of an effective carbon number-based approach for the gas chromatography–mass spectrometry quantification of ‘compounds lacking authentic standards or surrogates’

For the quantitative analysis of ‘compounds lacking authentic standards or surrogates’ (CLASS) in environmental media, we previously introduced an effective carbon number (ECN) approach to develop an empirical equation for the prediction of their response factor (RF). In this research, a series of laboratory experiments were carried out to benchmark the reliability of an ECN approach for sorbent tube/thermal desorption/gas chromatography (GC)/mass spectrometry (MS) applications. First, the ECN values were determined using external calibration data from 25 reference volatile organic compounds (VOCs) using two MS dectectors (quadrupole (Q) and time-of-flight (TOF)). Then, a certified standard mixture of 54 VOCs was analyzed by each system as a simulated unknown sample. The analytical bias, assessed in terms of percentage difference (PD) between the certified and ECN-predicted mass values, averaged 19.2±16.1% (TOF-MS) and 28.2±27.6% (Q-MS). The bias using a more simplified carbon number (CN)-based prediction increased considerably, yielding 53.4±53.3% (TOF-MS) and 61.7±81.3% (Q-MS). However, the bias obtained using the ECN-based prediction decreased significantly to yield average PD values of 9.84±7.28% (TOF-MS) and 16.8±8.35% (Q-MS), if the comparison was limited to 26 (out of 54) VOCs with CN≥4 (i.e., 25 aromatics and hexachlorobutadiene).

Automatic peak selection by a Benjamini-Hochberg-based algorithm.

A common issue in bioinformatics is that computational methods often generate a large number of predictions sorted according to certain confidence scores. A key problem is then determining how many predictions must be selected to include most of the true predictions while maintaining reasonably high precision. In nuclear magnetic resonance (NMR)-based protein structure determination, for instance, computational peak picking methods are becoming more and more common, although expert-knowledge remains the method of choice to determine how many peaks among thousands of candidate peaks should be taken into consideration to capture the true peaks. Here, we propose a Benjamini-Hochberg (B-H)-based approach that automatically selects the number of peaks. We formulate the peak selection problem as a multiple testing problem. Given a candidate peak list sorted by either volumes or intensities, we first convert the peaks into -values and then apply the B-H-based algorithm to automatically select the number of peaks. The proposed approach is tested on the state-of-the-art peak picking methods, including WaVPeak [1] and PICKY [2]. Compared with the traditional fixed number-based approach, our approach returns significantly more true peaks. For instance, by combining WaVPeak or PICKY with the proposed method, the missing peak rates are on average reduced by 20% and 26%, respectively, in a benchmark set of 32 spectra extracted from eight proteins. The consensus of the B-H-selected peaks from both WaVPeak and PICKY achieves 88% recall and 83% precision, which significantly outperforms each individual method and the consensus method without using the B-H algorithm. The proposed method can be used as a standard procedure for any peak picking method and straightforwardly applied to some other prediction selection problems in bioinformatics. The source code, documentation and example data of the proposed method is available at http://sfb.kaust.edu.sa/pages/software.aspx.

An integrated rough number-based approach to design concept evaluation under subjective environments

This study aims at improving the quality and effectiveness of concept evaluation in the early phases of new product development. New product concept evaluation has long been recognised as one of the most crucial decisions for the success of product development, because it has significant impact on the downstream development activities. However, evaluating new product concept is a process involving subjectivity and vagueness. In order to manipulate this problem, a novel decision approach for selecting more rational new product concepts is proposed. Basically, two stages of evaluation process are described: the computation of evaluation criteria weights for new product alternatives and the selection of the most appropriate concept. These stages are composed of a hybrid approach based on a rough group analytic hierarchy process (AHP) method and a rough group technique for order of preference by similarity to ideal solution (TOPSIS) method. The novel approach integrates the strength of rough number in handling vagueness, the advantage of AHP in hierarchy evaluation and the merit of TOPSIS in modelling multi-criteria decision-making. Finally, an application in mini-fridge concept evaluation is given to demonstrate the potential of the methodology.

Method for predicting the risk of drug–drug interactions involving inhibition of intestinal CYP3A4 and P-glycoprotein

To develop a method to predict the risk of drug–drug interactions involving the inhibition of intestinal CYP3A4 or P-glycoprotein, data from clinical drug–drug interaction studies of CYP3A4 and/or P-glycoprotein substrates were analysed. The ratio of inhibitor dose (Dosei) to inhibition constant (Ki), termed the drug-interaction number, was used to index intestinal drug–drug interaction.From the analysis, it was found that (1) CYP3A4 inhibitors with a drug-interaction number below 2.8 L have a low risk of interacting with substrates which exhibit intestinal first-pass metabolism and those with a drug-interaction number above 9.4 L have a high risk; (2) P-glycoprotein inhibitors with a drug-interaction number below 10.8 L have a low risk of interacting with P-glycoprotein substrates and those with a drug-interaction number above 27.9 L have a high risk; and (3) the drug-interaction number indexes, 2.8 L and 9.4 L for CYP3A4 and 10.8 L and 27.9 L for P-glycoprotein were validated by data from dual CYP3A4/P-glycoprotein substrates.In conclusion, the drug-interaction number is useful for classifying the risk of drug–drug interactions involving the inhibition of intestinal CYP3A4 and P-glycoprotein. This drug-interaction number-based approach is similar to the method that the US Food and Drug Administration (USFDA) recently proposed in the draft guidance for predicting P-glycoprotein-mediated drug–drug interaction.

Insights into catheter/doppler discrepancies in congenital aortic stenosis

Despite inherent discrepancies between Doppler and catheter gradients in aortic stenosis, the simplified Bernoulli equation is still the accepted noninvasive technique to quantitate severity. The Reynolds number is a dimensionless parameter that characterizes the nature of flow as being viscous, turbulent, or transitional. Recently, in vivo and animal studies have successfully used a Reynolds number-based approach to reconcile Doppler-estimated and catheter-measured discrepancies. At the midrange of Reynolds number, pressure recovery effects are most evident, resulting in “overestimation” of catheter gradients by Doppler. At the lower range of the Reynolds number viscous effects are important, whereas at a higher range, turbulent factors are dominant; both result in a tendency toward agreement. We recorded 18 peak instantaneous gradients from dual left ventricular catheters (15 to 95 mm Hg), while simultaneously recording Doppler velocities before and after intervention in 11 pediatric patients (ages 0.5 to 16 years, mean 4.5). Doppler correlated but overestimated catheter-measured peak instantaneous gradients (y = 0.84x + 18.4, r = 0.8, SEE ± 15.2 mm Hg, mean percent difference 29.9 ± 36) over the range of catheter gradients measured. Accounting for the Reynolds number successfully collapsed data onto a single curve. Our study confirms in a clinical setting the importance of applying fluid dynamic principles such as the Reynolds number to explain apparent discrepancies between catheter and Doppler gradients. These principles provide a foundation for developing clinically appropriate correction factors.