Fuzzy Combinative Distance-based Assessment Research Articles

Overall aerodynamic performance of the airfoils with different amplitudes via a fuzzy decision making based Taguchi methodology

In this study, the overall aerodynamic performance of the airfoils with variable amplitude geometric structure for lift-type vertical axis wind turbine has been analyzed and optimized using Fuzzy Analytic Hierarchy Process (AHP)-Fuzzy COmbinative Distance-based Assessment (CODAS) based on Taguchi Method. In the design of the experiment, leading edge (LE) tubercle airfoil models, Reynolds number, and angle of attack are utilized as the control factors. Drag, lift, and lift to drag ratio performances by comparing the airfoils according to the baseline model are calculated and the combination of these performances is considered as the overall aerodynamic performance response. The experiments are carried out according to the orthogonal matrix L18. Since the importance of each response is different on lift-type vertical axis wind turbines, the importance weights of these responses were obtained by Fuzzy AHP according to the evaluation of the decision maker. Multiple responses are converted into a single response (the overall aerodynamic performance) via Fuzzy CODAS which enables to model uncertainty in order to overcome the physical effects that may arise in experiments. Thereby, a multi objective Taguchi methodology was applied for the overall aerodynamic performance with a novel Fuzzy logic based multi criteria decision analysis approach. As a result, Model-4, having tubercle design parameters of amplitude of 0.025c and 0.0125c and wavelength of 0.5c and 0.125c, was found to have the best performance with the novel Taguchi approach, which saves time and cost. In addition, it has been determined that the airfoils, Reynolds number and angle of attack are significant and what level of contribution to the overall aerodynamic performance. At this point, the most important control factor has emerged as the angle of attack.

Proportional intuitionistic fuzzy CODAS method: Cloud service provider selection application

Intuitionistic fuzzy sets aims at taking the hesitancy of an expert into account in assigning a membership degree or a non-membership degree. The direct assignment of decimal numbers for membership and non-membership degrees of an element in intuitionistic fuzzy sets is not practical. Besides, the assigned degrees are generally composed of one digit or at most two digits after dot. This problem has not been addressed as much as it deserves in the literature. The hypothesis of the paper is that the determination of proportional relationships between membership and non-membership degrees is more appropriate than the direct assignment to obtain the degrees. Proportional intuitionistic fuzzy (PIF) sets require only the proportion relations between an intuitionistic fuzzy set’s parameters. The accuracy of the results obtained with multi-criteria decision-making models definitely depends on how accurately the membership degrees are determined. In this paper, we extend Combinative distance-based assessment (CODAS) method by using single-valued proportional intuitionistic fuzzy sets. We compare the proposed PIF CODAS method with ordinary fuzzy CODAS method. A cloud service provider selection problem is handled to show the validity of the proposed PIF CODAS method. Additionally, a comparative analysis and a sensitivity analysis together with a discussion are presented.

Green logistics park location selection with circular intuitionistic fuzzy CODAS method: The case of Istanbul

In this study, it is aimed to integrate CODAS method with circular intuitionistic fuzzy sets as a new solution method for MCDM problems. Containing a radius notation with degrees of central membership and non-membership degrees is the main advantage of circular intuitionistic fuzzy in decision making. On the other side, Combinative Distance-based Assessment (CODAS) method contains many advantages such as basing on two types of distance calculations (Euclidean and Taxicab distances) comparing with other MCDM methods. When the advantages of circular intuitionistic fuzzy sets and CODAS method are considered, proposed circular intuitionistic fuzzy CODAS method (CIFS-CODAS) presents many superiorities compared to other MCDM techniques. By this way, an application for green logistics park location selection will be handled by using CIFS-CODAS to show the validity of the methodology. After, a comparative analysis with intuitionistic fuzzy CODAS (IFS-CODAS), intuitionistic fuzzy TOPSIS (IFS-TOPSIS) and intuitionistic fuzzy EDAS (IFS-EDAS) methods will be performed for green logistics park location selection problem to confirm the robustness of presented method. Green logistics and Green Deal are also emphasized considering environmental factors as a scope of the article. Finally, the results will be evaluated in the context of the logistics sector and green logistics.

Prioritizing solutions to mitigate the risks due to adoption of intermodal railroad freight transportation for achieving sustainable development goals

Multiple cleaner strategies are being devised by governments worldwide to incorporate approaches aligning with sustainable development goals (SDGs), placing particular emphasis on the modal shift from unimodal road to intermodal railroad (IRR) freight transportation. This shift is gaining traction due to its documented positive impact on environmental, economic, and social sustainability. However, adopting IRR freight transportation in emerging economies faces hurdles, primarily attributable to inherent modal shift risks (MSRs). Addressing these MSRs requires the implementation of modal shift solutions (MSSs). This study aims to prioritize the MSSs to mitigate the impact of MSRs in IRR adoption, focusing on achieving SDGs with a case study conducted in the Indian logistics sector. The study is structured into four phases. The first phase identifies 37 sub-MSRs, categorized into seven main MSRs, accompanied by the 39 MSSs for IRR freight transportation adoption. The second phase employs the spherical fuzzy Bayesian best-worst method (SF-BBWM) to assess the relative weights of main and sub-MSRs. The third phase uses the spherical fuzzy C-means clustering (SF-CMC) algorithm to identify crucial MSSs. Finally, the fourth phase utilizes the spherical fuzzy combinative distance-based assessment (SF-CODAS) to prioritize these crucial MSSs. From the identified MSSs, pivotal solutions include developing an integrated physical IRR network across the freight transport chain and fostering relationships between stakeholders through long-term contracts and strategic partnerships. The available results are discussed to facilitate logistics stakeholders’ understanding of the implementation of MSSs aligning with SDGs to mitigate the impact of MSRs.

Fermatean Fuzzy Group Decision-Making Based CODAS Approach for Taxation of Public Transit Investments

The provision of public goods is always a controversial issue in many aspects. Even some additional financial resources can provide public transit systems, the tax revenues constitute a significant part of the investments in practice. Yet, governments should make an important decision regarding tax schemes. This article focuses on how to identify the best tax scheme to finance public transit investments. A three-phase Fermatean fuzzy group decision-making approach is developed to solve the highlighted problem. Qualifications and experience of experts are considered to distinct their reputations. A Fermatean fuzzy direct rating method is introduced to evaluate criteria importance. A Fermatean fuzzy combinative distance-based assessment method is formulated to assess tax schemes. The study provides valuable policy implications and decision-making guidelines using a real-life case. The results show that beneficiaries of public services should pay more, and tax collectors are encouraged to use progressive tax schemes instead of the equal distribution of the cost to the taxpayers. The sensitivity analysis to changes in the threshold parameter is given. The comparative analysis with all state-of-the-art Fermatean fuzzy set based multicriteria decision-making methods is provided. The Fermatean fuzzy group decision-making approach is highly robust and reliable. The approach can solve other public transportation problems.

A Novel Combined Fuzzy MCDM Approach Based on IMF-SWARA and F-CODAS for Consulting Firm Selection

In today's challenging industry conditions, where being good is not enough to be successful, companies trying to be the best, need consultancy in different fields. Consulting firms provides consultancy services to businesses, and they need to determine the most appropriate one for them. Fuzzy MCDM (Multi Criteria Decision Making) methods are appropriate to solve consulting firm selection problem. In this study, consulting firm selection problem of a textile company operating in Istanbul, Turkey is handled by using a novel combined fuzzy MCDM method based on IMF-SWARA (Improved Fuzzy Stepwise Weight Assessment Ratio Analysis) and F-CODAS (Fuzzy COmbinative Distance-based Assessment) methods. The importance weights of the criteria are calculated with IMF-SWARA method. Findings indicate that the top three important criteria are respectively, experience, references, and reliability. Then, F-CODAS method is used to rank the consulting firms and the best one is presented to the Human Resources department of the textile company. This study contributes to the existing literature in various aspects. It suggests a novel combined fuzzy MCDM method to solve consulting firm selection and a new Fuzzy CODAS based on TFNs is proposed. Moreover, HR managers can use the findings of this study to evaluate consulting firms.

Intermodal Terminal Subsystem Technology Selection Using Integrated Fuzzy MCDM Model

Intermodal transportation is the use of multiple modes of transportation, which can lead to greater sustainability by reducing environmental impact and traffic congestion and increasing the efficiency of supply chains. One of the preconditions for efficient intermodal transport is the efficient intermodal terminal (IT). ITs allow for the smooth and efficient handling of cargo, thus reducing the time, cost, and environmental impact of transportation. Adequate selection of subsystem technologies can significantly improve the efficiency and productivity of an IT, ultimately leading to cost savings for businesses and a more efficient and sustainable transportation system. Accordingly, this paper aims to establish a framework for the evaluation and selection of appropriate technologies for IT subsystems. To solve the defined problem, an innovative hybrid multi-criteria decision making (MCDM) model, which combines the fuzzy factor relationship (FFARE) and the fuzzy combinative distance-based assessment (FCODAS) methods, is developed in this paper. The FFARE method is used for obtaining criteria weights, while the FCODAS method is used for evaluation and a final ranking of the alternatives. The established framework and the model are tested on a real-life case study, evaluating and selecting the handling technology for a planned IT. The study defines 12 potential variants of handling equipment based on their techno-operational characteristics and evaluates them using 16 criteria. The results indicate that the best handling technology variant is the one that uses a rail-mounted gantry crane for trans-shipment and a reach stacker for horizontal transport and storage. The results also point to the conclusion that instead of choosing equipment for each process separately, it is important to think about the combination of different handling technologies that can work together to complete a series of handling cycle processes. The main contributions of this paper are the development of a new hybrid model and the establishment of a framework for the selection of appropriate IT subsystem technologies along with a set of unique criteria for their evaluation and selection.

A novel risk assessment model for work-related musculoskeletal disorders in tea harvesting workers

Work-related musculoskeletal disorders (WMSDs) are the most common occupational health problems in agriculture workers due to repetitive and excessive force movement activities involved in their job processes. The Fine-Kinney method has been commonly used as a quantitative evaluation method in risk assessment studies. Classically, the risk value via Fine–Kinney is calculated by the mathematical multiplication irrespective of the degree of importance of each risk parameter (probability (P), exposure (E), and consequence (C)). Hence, a novel risk management model was proposed based on integrating Fine-Kinney and spherical fuzzy AHP-TOPSIS. First, each risk parameter is weighted using the spherical fuzzy AHP (SF-AHP). Second, the spherical fuzzy TOPSIS (SF-TOPSIS) method is used for hazard ranking. The proposed model is applied to evaluate risks in tea harvesting workers for work-related musculoskeletal disorders. Subsequently, a sensitivity analysis is carried out to test the proposed model. Finally, we compare the proposed model’s applicability and effectiveness with the spherical fuzzy COmbinative Distance-based ASsessment (SF-CODAS) method based on Fine-Kinney. The ranking similarity between the proposed Fine-Kinney-based SF-TOPSIS and SF-CODAS methods is checked by applying Spearman’s rank correlation coefficient, in which 92% of rankings are matched.

Fuzzy decision support system for sustainable operational performance optimization for boiler unit in milk process industry

This work proposes a new sustainable operational performance optimization-based decision support system for assessing the failure behaviour of the Boiler Unit (BU) in the milk process industry. A novel fuzzy Jaya Lambda–Tau Optimization (JLTO) approach-based modelling was established for tabulating various reliability indices of BU. Fuzzy Set (FS) theory-based concepts were applied to consider the vagueness/uncertainty involved in the collected data. JLTO approach-based results show decrease in the boiler unit’s availability with the increase in uncertainty level. Further, to improve the boiler unit’s availability using qualitative data obtained from expert’s opinion, Failure Mode and Effect Analysis (FMEA) was performed. To address the shortcomings of conventional FMEA approach-based results and to pinpoint the riskier failure causes contributing to decrease in system’s availability, Fuzzy-Complex Proportional Assessment (FCOPRAS) decision-making approach was integrated with FMEA approach. Fuzzy Combinative Distance-based Assessment (FCODAS) approach was also applied to compare the ranking results for accurate decision-making. The proposed integrated framework predicts the failure behaviour with high accuracy by considering the uncertainty and finding the riskier failure causes of the considered system which is one of the main drawbacks of existing FS theory-based integrated models. The proposed model-based results will help in developing an optimal digitalized maintenance schedule, which will be beneficial for improving the overall performance of the considered unit. The digitalized maintenance schedule will also be useful for BU of various other industries across the globe. Sensitivity analysis was also done for assessing the robustness of the proposed sustainable operational performance optimization-based decision support system.

A novel pythagorean fuzzy multi-criteria decision-making methodology for e-scooter charging station location-selection

This study focuses on how to determine the location of electric scooter (e-scooter) charging stations. In the station location determination problem, the sustainability perspective, which is one of the indispensable elements of smart city practices, is focused. To solve the highlighted problem, new three-stage pythagorean fuzzy group decision-making methodology is developed. First, experts are evaluated, considering that their experience and knowledge levels may differ. Afterwards, criteria are determined from both opinions from experts and literature review, and the weights of these criteria are calculated using the Pythagorean Fuzzy Stepwise Weight Assessment Ratio Analysis (PF-SWARA) method. Finally, Pythagorean Fuzzy COmbinative Distance-based Assessment (PF-CODAS) is proposed to evaluate alternative locations. A real case study is conducted in Istanbul and both policy and managerial implications are provided to decision-makers. The results show that the economic factors are important in determining the stations’ location.

An interval-valued intuitionistic fuzzy-based CODAS for sustainable supplier selection

With the advent of information age, supplier selection has attracted researchers’ attention in the field of supply chain management. Optimal supplier selection meets the competitiveness of industries and satisfaction of customers in the supply chain management. However, considering sustainable factors including environmental and social parameters, alongside customers’ expectations are the main challenge of supplier selection. Moreover, incomplete and hesitation among experts’ judgment result in uncertainty that leads the more complex assessment of criteria. In this paper, an innovative multi-criteria decision-making method was developed to address supplier selection process and supplier order allocation based on customers’ expectations with respect to the sustainable primers. The proposed method was based on fuzzy combinative distance-based assessment (CODAS) extended with interval-valued intuitionistic fuzzy (IVIF) for multi-criteria sustainable supplier selection. The proposed model was combined with hesitation linguistic set to provide two goals including a: dealing with experts’ hesitation as well as imprecise and vague of decision makers’ knowledge in order to express assessment values. b: coping with missing or nonexistence of attribute assessment values. The proposed method was performed on a steel mill as a case study to illustrate the effectiveness of decision process on four suppliers in detail. The results showed that the presented model could consider conflicting parameters and enhance high-order imprecision and uncertainty.

A novel interval-valued spherical fuzzy CODAS: Reopening readiness evaluation of academic units in the era of COVID-19

Combinative distance-based assessment (CODAS) is a multi-criteria decision-making (MCDM) method that is based on the Euclidean and Hamming distances of alternatives from the average scores of attributes. Spherical fuzzy sets, as the recent extensions of ordinary fuzzy sets, were developed based on Pythagorean and neutrosophic sets and enable decision-makers to express their membership, non-membership, and hesitancy degrees independently and in a larger domain than most other fuzzy extensions. This paper proposes a new interval-valued spherical fuzzy CODAS method and provides extra space for catching the vagueness in the nature of the problem. The feasibility and practicality of the proposed model are illustrated with an application for evaluating the reopening readiness of academic units for campus education in the era of COVID-19. Three decision-makers from a higher education institution evaluate four academic units with respect to five strategic criteria and prioritize them according to their readiness levels for the campus type of education. Sensitivity and comparative analyses, theoretical and practical contributions, limitations, and future research avenues are also presented in the study.

Sustainability prioritization of technologies for cleaning up soils polluted with oil and petroleum products: A decision support system under complex spherical fuzzy environment

The transition towards a sustainable land management (SLM) needs for utilizing appropriate technologies for controlling soil pollution, and ensures the development of environmental, economic, technical, and social dimensions in region. Using these technologies for cleaning up soils polluted with oil and petroleum products in Behbahan city (in the southwest of Iran) is considered. The soil pollution control technologies (SPCTs) can assist local authorities, governments, investors, and developers to reduce climate change, mitigate soil, water, and air pollution and construct the sustainable communities. With the aim of balance between the issues in the context of sustainability policy, criteria (principles) including environmental, economic, technical, and social aspects are considered. The research takes into consideration the applicability of a novel multi-criteria decision-making approach namely complex spherical fuzzy set-based CODAS (Combinative distance-based assessment). The approach can be used to choose the suitable technology in a sustainable mode, considering the principles related to sustainability pillars. It comprises various technologies for soil pollution control and prioritizes technologies from the most to least as: Biorestoration technology, Excavation technology, Soil venting technology, Soil flushing, and Hydraulic barriers technology.

A structured framework for performance optimization using JBLTO, FCOPRAS and FCODAS methodologies

PurposeThe proposed hybridized framework provides a new performance optimization-based paradigm for analysing the failure behaviour of paneer unit (PU) in the dairy industry.Design/methodology/approachA novel fuzzy Jaya-based Lambda–Tau Optimization (JBLTO) approach-based mathematical modelling was developed for calculating various reliability indices of the considered unit. Failure mode and effect analysis (FMEA) was carried using qualitative information gathered from system's expert opinions. Fuzzy-complex proportional assessment (FCOPRAS) approach was integrated within FMEA to recognize the most critical failure causes associated with various subsystem/components.FindingsThe availability of the unit falls by 0.053% as the uncertainty level increases from ±15 to ±25% and further decreases to 0.323% as the uncertainty level increases from ±25 to ±60%. Failure causes, namely wearing in gears of gearbox (MST4), an impeller's cavitation and/or corrosion (CFP4), winding failure of electric motor (WS9), were recognized as the most critical failure causes with FCOPRAS final performance scores of 100, 100 and 100 and fuzzy combinative distance-based assessment (FCODAS) resultant assessment score of 0.5997, 1.1898 and 1.6135.Originality/valueJBLTO approach-based reliability results were compared with traditional particle swarm optimization-based Lambda–Tau (PSOBLT) and traditional fuzzy Lambda–Tau (FLT) approaches for confirming the downward trend in the system's availability. The ranking results of qualitative analysis are compared with the implementation of FCODAS technique. Sensitivity analysis was executed to evaluate the robustness of the proposed hybridized framework.

Investigating the sustainable development goals derived due to adoption of circular economy practices

Recently, the novel concept of the circular supply chain (CSC) has gained substantial attention amongst researchers across the globe. It is due to the issues of unsustainability in supply chain operation of manufacturing industry. CSC amalgamates the circular economy (CE) into the supply chain of manufacturing organizations. Business organizations can achieve several sustainable development goals by adopting CSC as an innovative strategy. However, limited attention has been given to its implementation in emerging economies. Thus, this research aim is to identify and analyze the essential CE practices that help to accomplish the several sustainable development goals (SDGs) of CSC management. This research presents an integrated framework of Pythagorean fuzzy analytic hierarchy process (PF-AHP) and Pythagorean fuzzy combinative distance-based assessment (PF-CODAS) techniques. PF-AHP is employed to determine the relative importance of CE practices, whereas PF-CODAS method ranks the SDGs derived due to the adoption of CE practices. The effectiveness of the proposed framework is validated with the help of an Indian manufacturing organization. The finding of this research reveals that practices based on 'government', 'management', and 'economy' initiatives play a significant role and contribute 50 % of its influence on the effective CSC adoption, whereas, 'mitigate waste and enhance environmental sustainability', is identified as the most critical SDG realized due to adoption of CE practices. Sensitivity analysis is conducted to check the robustness of proposed methods. This research provides the systematic, accurate, and valuable decision support tools to practitioners to execute the CE practices efficiently for achieving the various sustainability goals.

Selecting a green supplier utilizing the new fuzzy voting model and the fuzzy combinative distance-based assessment method

• In this paper, the selection of a Green Supplier is performed using a new hybrid Multi Attribute Decision Making-Data Envelopment Analysis method. • The fuzzy version of the Preferential Voting model is designed in this paper. • A new secondary goal model using the Combinative Distance-based Assessment method is proposed in the Cross Efficiency ranking method. • The proposed new method for Selecting a Green Supplier in an Automotive Group has been implemented. Significant increase in environmental concerns has led organizations to consider the necessary measures to apply green supply chain management to improve their environmental and economic performance. An important way to implement green supply chain management is to revise the methods of purchasing and selecting green suppliers. In this paper, a new fuzzy Data Envelopment Analysis (DEA) model for green supplier selection by collecting expert votes is presented. Then a way to improve the cross-efficiency method to achieve a complete ranking is suggested. In the improved method, a new secondary goal model based on the concepts of the Fuzzy COmbinative Distance-based ASsessment (CODAS) method will be presented. The proposed models were then used to select a green supplier in an automotive group. In the present case study, a complete ranking for green suppliers is obtained.

2-tuple linguistic $ q $-rung orthopair fuzzy CODAS approach and its application in arc welding robot selection

<abstract><p>Industrial robots enable manufacturers to produce high-quality products at low cost, so they are a key component of advanced production technology. Welding, assembly, disassembly, painting of printed circuit boards, pick-and-place mass production of consumer products, laboratory research, surgery, product inspection and testing are just some of the applications of industrial robots. All functions are done with a high level of endurance, speed and accuracy. Many competing attributes must be evaluated simultaneously in a comprehensive selection method to determine the performance of industrial robots. In this research article, we introduce the 2TL$ q $-ROFS as a new advancement in fuzzy set theory to communicate complexities in data and presents a decision algorithm for selecting an arc welding robot utilizing the 2-tuple linguistic $ q $-rung orthopair fuzzy (2TL$ q $-ROF) set, which can dynamically delineate the space of ambiguous information. We propose the $ q $-ROF Hamy mean ($ q $-ROFHM) and the $ q $-ROF weighted Hamy mean ($ q $-ROFWHM) operators by combining the $ q $-ROFS with Hamy mean operator. We investigate the properties of some of the proposed operators. Then based on the proposed maximization bias, a new optimization model is built, which is able to exploit the DM preference to find the best objective weights among attributes. Next, we extend the COmbinative Distance-Based ASsessment (CODAS) method to 2TL$ q $-ROF-CODAS version which not only covers the uncertainty of human cognition but also gives DMs a larger space to represent their decisions. To validate our strategy, we present a case study of arc welding robot selection. Finally, the effectiveness and accuracy of the method are proved by parameter analysis and comparative analysis results. The results show that our method effectively addresses the evaluation and selection of arc welding robots and captures the relationship between an arbitrary number of attributes.</p></abstract>

A Proposed Framework for Developing FMEA Method Using Pythagorean Fuzzy CODAS

The purpose of this research article is to develop a hybridization between the Failure Mode and Effect Analysis (FMEA) method and the Combinative Distance-Based Assessment (CODAS) method under Pythagorean Fuzzy environment. The traditional FMEA procedure is based on the multiplication between the parameters of severity, occurrence, and detectability where everyone has equal relative importance; therefore, different combinations of these parameters can generate the same result creating uncertainty in the analysis. In this mode, the hybridization proposed in this research deal with relative importance of each parameter; in the fact to have a more suitable combination which consider the level of knowledge of the experts in the assessment. Finally, a numerical case was carried out concerning the public transportation service to validate our proposal; the results show that 31 failure modes and potential risks can be evaluated using user perceptions, a dominant with high level of knowledge about the public transportation service and an apprentice or common user, as team of experts and exploiting the subjectivity of the information in a mathematical model. Also, we compare the results with a variation of the proposed model with the multi-criteria method multi-objective optimization method by relationship analysis (MOORA); it was observed that the convergence of the failure modes depends on the nature of the mathematical model even under the same conditions at the start.

Evaluation and selection strategy for green supply chain using interval-valued q-rung orthopair fuzzy combinative distance-based assessment

Evaluation and selection strategy for green supply chain using interval-valued q-rung orthopair fuzzy combinative distance-based assessment

Sustainability assessment of different nanoparticle for heat exchanger applications: an intuitionistic fuzzy combinative distance-based assessment method

The rate at which the conventional energy sources are depleting is a matter of concern, and there have been major attention on this to make the thermal systems environment friendly, efficient, economic, sustainable, technically reliable. Sustainability of five different types of nanoparticles (Ceramic, carbon based, metal based, polymeric, and lipid based) from the perspective of four aspects involving cost, efficiency, technicality and environmental effect, in heat exchangers has been assessed. The analysis is carried out using the intuitionistic fuzzy combative distance based assessment (IFCODAS) method. In order to measure the sustainability of nanoparticles, a set of eleven evaluating criteria have been accredited on the basis of expert opinions and focus group meetings. By amalgamating the intuitionistic fuzzy set (IFS) theory as well as the use of distance-based assessment (CODAS) method, the IFCODAS method has permitted the decision-makers to rate the alternative five nanoparticles pertaining to each criterion. On the basis of the results obtained from IFCODAS method, it is observed that the carbon based nanoparticles have an immense potential to provide significantly reliable and sustainable thermal system than other nanoparticles.