Risk Prioritization and Case Study in the Pharmaceutical Sector with Fine-Kinney, Fuzzy Fine Kinney and Fuzzy Cognitive Mapping Approach

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Risk Prioritization and Case Study in the Pharmaceutical Sector with Fine-Kinney, Fuzzy Fine Kinney and Fuzzy Cognitive Mapping Approach

https://doi.org/10.21203/rs.3.rs-3978667/v1

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The globalizing and shrinking world, technology, and mechanization rates have caused a rapid increase. With increasing mechanization, studies on occupational health and safety and risk analysis are increasing day by day. This process is being closely followed by companies in the pharmaceutical industry, and studies on the subject are increasing. Risk analysis and prevention studies make a significant contribution to reducing occupational accidents and errors. In the studies carried out, it is necessary to know and correctly analyze the sector, the company, the culture, and the employees. After the correct analysis, it is extremely important to determine the most appropriate method for the sector and the company.

In this study, the risk situation was analyzed for a pharmaceutical company in the pharmaceutical industry. After Fine-Kinney analysis, a transition to fuzzy Fine-Kinney analysis was made in order to avoid the uncertainty in the method. The analysis performed was to determine the risk, the current situation, and the risk score. After the analysis, the risks identified by the Fuzzy Fine-Kinney method were prioritized using the Fuzzy Cognitive Mapping method. The aim was to show the difference between the two methods by blurring the values obtained in the Fine Kinney analysis and switching to the Fuzzy Fine Kinney method. A recommendation is given for the risks that may occur during the risk prioritization of an application. The aim of the study is to reveal hidden risks in the pharmaceutical industry and to guide future studies.

Risk Analysis

Pharmaceutical Industry

Fine-Kinney

Fuzzy Fine-Kinney

Fuzzy Cognitive Mapping

Risk assessment studies are examined under three main headings: qualitative, quantitative, and hybrid. An examination of these studies shows that there are more than 150 risk assessment methods. Not every method is actively used, and not every method is suitable for every sector. Choosing the right risk assessment method is of great importance in terms of the effectiveness, traceability, and contribution of the study to the literature (Erten, 2017). It is of great importance to adopt appropriate approaches to assess and reduce the risk arising from potential losses. In this context, it is necessary to benefit from accurate and reliable techniques (Leoni, 2023).

Manufacturing plants are critical components of the global economy and supply chain. Planning an interconnected production network of materials is analytically difficult. This inventory planning application allows decision-makers to monitor the range and quantity of numerous production parameters while achieving the goal of reducing the spread of risk through the system (Bourgeois, 2023). At the same time, sustainable development and resilience play an important role in today's world. Zarei et al. (2023) proposed a decision-making model for many activities by taking sustainability and resilience information into account.

In this study, a risk analysis was carried out on a pharmaceutical company operating in the pharmaceutical industry. The study began by identifying the company's current situation and risks. The identified risks were first analyzed using the Fine-Kinney method. After the first analysis, the risk values were blurred and switched to the Fuzzy Fine-Kinney method. After the analysis, the risks were prioritized using the Fuzzy Cognitive Mapping method. The aim of the study is to uncover the hidden issues in risk analysis and assessment in the pharmaceutical industry and to provide decision-makers with ideas for risk prioritization by creating a hybrid method for real-world problems.

The study consists of five chapters. In this introduction, general information about the study is given, and hybrid methods used in the literature are mentioned. The second part, Materials and Methods, discusses the Fine-Kinney and Fuzzy Fine-Kinney methods used in the study. In the third chapter of the case study, the research model was established, general information about the company was given, the application was explained, and the findings were included. In the fourth chapter, the prioritization of existing risks, the concept of risk is explained, and the fuzzy cognitive mapping method used in the prioritization of risks is introduced. This section also includes the results of the application. In the last chapter, the conclusion, the analyses made in accordance with the application results, and the contribution of the study to the literature are explained in this section.

If we look at the studies done with Fine-Kinney and Fuzzy Fine-Kinney in the literature, we see that these methods are used as hybrids with many other methods. AHP, ANP, Topsis, Vikor, and FMEA are some of these methods. A detailed table of the hybrid methods used in the literature is shown in Table 1. However, it has been observed that the Fuzzy Cognitive Mapping method is not used as a hybrid in the literature. The Fuzzy Cognitive Mapping method was used in this study to look at the methods from a different perspective and to develop a new analysis.

Table 1. Hybrid Fine-Kinney and Fuzzy Fine-Kinney Methods Used in the Literature

2.1 Fine-Kinney Method

The Fine-Kinney method was developed by William T. Fine in 1971 for use at the California Naval Weapons Center under the title "Mathematical Evaluation for Hazard Control" (Fine, 1971). In 1976, the method was transformed from mathematical to graphical and published under the NWC-TP-5865 standard as "Practical Risk Analysis in Security Management" (Kinney, Wiruth, 1976).

The Fine-Kinney method is a method used in risk assessment to determine which tasks should be prioritized according to the rating results and where resources should be allocated first. The fact that it is easy to understand and apply is one of the reasons why it was chosen.

In the Fine-Kinney risk analysis, which is a quantitative risk analysis method, there are three parameters: probability (0), frequency (F), and severity (S), and the risk score of the hazards is obtained by multiplying these three parameters. The risk score obtained as a result of the multiplication is grouped into 5 classes: acceptable risk, possible risk, significant risk, high risk, and very high risk. The priority of the measures to be taken is determined according to the high risk value.

The Fine-Kinney risk score evaluation table is shown in Table 2.

Table 2

Risk clusters of Fine-Kinney (Kinney, Wiruth, 1976)
Risk Situation	Risk Score
Very high risk; consider discontinuing operation	R < 20
High risk; immediate correction required	20 < R < 70
Substantial risk; correction needed	70 < R < 200
Possible risk; attention indicated	200 < R < 400
Risk; perhaps acceptable	R > 400

2.2 Fuzzy Fine-Kinney Method

The Fine-Kinney method has become one of the preferred methods in terms of ease of understanding and applicability. The Fuzzy Fine-Kinney method was developed to avoid the uncertainty that can arise when evaluating the parameters of the method. New decision rules have been created by blurring the probability, frequency, and intensity parameters with the Fuzzy Fine-Kinney method.

As shown in the figure above, probability, frequency, and intensity parameters are used as inputs in the Fuzzy Fine-Kinney method; the risk score is defined as the output. The rules created for inputs and outputs with triangular membership functions were coded with the help of the Matlab Fuzzy Logic Designer program using the "Mamdani min max" method. (Oturakçı, Dağsuyu, 2017).

A lower and an upper value in the tables used in the Fine-Kinney method were used in the probability, frequency, and intensity parameters generated using membership functions. For example, the "rare but likely" parameter in the probability parameter is blurred, while the "strong likelihood" parameter value of "6" and the "extremely low likelihood" parameter value of "1" were included in the membership function.

The table for the evaluation of the blurred risk score is explained in Table 3.

Table 3

Risk clusters of Fuzzy Fine-Kinney
Risk Situation	Risk Score
Very high risk; consider discontinuing operation	(0,20,20)
High risk; immediate correction required	(20,70,200)
Substantial risk; correction needed	(70,20,300)
Possible risk; attention indicated	(200,300,400)
Risk; perhaps acceptable	(300,400,400)

3.1 Research Model

Risk prevention is an extremely important issue in order to become an important part of the healthcare system, to develop the country's pharmaceutical industry, and to protect the global health of people and companies. In order to carry out activities in the sector successfully, risks must be correctly identified and analyzed. Any negative situation in business can disrupt both the supply of medicines and the effectiveness of the healthcare and pharmaceutical industries. The rapid growth of the healthcare and pharmaceutical industries, especially after the COVID-19 pandemic, has increased healthcare waste emissions and potential risks worldwide (Dixit and Dutta, 2024).

Risks and inappropriate decisions in risk prioritization can cause major accidents. It is important to present the right information at the right time to improve the quality of decisions and process the right decision (Zhu, 2021). Failure to identify risks correctly and in a timely manner can lead to problems in the long run. This situation affects environmental and economic performance in the long run. Zhang et al. (2021) found through empirical analysis that the relationship between environmental performance and economic performance has weakened.

This study aims to prioritize and rank risks for companies in the pharmaceutical industry.

The application steps are shown in Fig. 2.

3.2 Application

In this study, a risk analysis was conducted for a pharmaceutical company in the pharmaceutical industry. This company, based in Turkey, with its 1500 employees, aims to improve the quality of life by offering first-class, accessible, and effective products with an innovative perspective without compromising sustainable quality standards. Ophthalmology, neurology, endocrinology, cardiology, rheumatology, gastroenterology, urology, pulmonology, etc. It has a broad portfolio of more than 450 pharmacological products in medical fields.

This study examined the general situation of the company and grouped the current risks under 86 headings. During the risk identification phase, a team was formed consisting of the company's OHS expert, the head of each department, and an employee working in the department. Identified risks; the section was examined under the headings of source of danger, hazardous situation, risk, and current situation. The risks were first analyzed using the Fine-Kinney method, and then the fuzzy Fine-Kinney method was used to eliminate the uncertainties that can arise with the Fine-Kinney method.

In the second stage of the application, MATLAB software, which is designed for technical calculations and solving mathematical problems, was used for the Fuzzy Fine-Kinney method. For this part, the Fuzzy Inference System (FIS) was used in the Fuzzy Logic section, where a model is created using fuzzy logic in MATLAB. For fuzzy risk analysis, the input parameters, such as probability, frequency, and severity, are first made fuzzy. The fuzzy values were inferred using the rule base. The parameters that were fuzzy using linguistic variables are explained in Table 4. Finally, the obtained fuzzy data was clarified, and a risk score was obtained.

Table 4

Transforming Values for Probability, Exposure Ratings, Consequence and Risk Score into Linguistic Variables
Possibility	Unexpected, Low Chance, Extremely Unlikely, Rare But Possible, High Likelihood, Very Likely
Exposure Ratings	Quite Rarely, Infrequently, Rarely, Occasionally, Often, Constantly
Consequence	Considerable, Important, Serious, Very Serious, Disaster, Disaster
Risk Score	Acceptable Risk, Possible Risk, Significant Risk, High Risk, Very High Risk

216 rules have been written for input parameters to be used in MATLAB. The written rules are entered in the Rule Window (Rule Editor) section in MATLAB. The rules are written using the "AND" conjunction between the "IF-THEN" conditions.

As a result of the written rules, the membership function graphs obtained by using the membership function writing window (Membership Function Editor) in MATLAB are shown in the Appendix. The risk score was obtained as a result of the "Mandami Fuzzy Inference Process" with the written rules. The obtained risk scores were flushed with the centroid method. For the centroid method used in the flushing process, the "Fuzzy Inference System Window (FIS editor)" in MATLAB was used. Following this process, a risk score was obtained for each risk.

As a result of the analyses, the two methods were compared, and the aim was to provide guidance for future studies.

3.3 Results

The risks identified were analyzed by a team that included the company's OHS expert, the head of each department, and an employee working in the department. After analysis, risk scores were determined using the Fine-Kinney risk scale. To avoid the uncertainty caused by the Fine-Kinney method, the Fuzzy Fine-Kinney method was used, and the risk scores were determined according to the Fuzzy Fine-Kinney Risk Scale.

The detailed distribution table of the analyzed risks is explained in Table 5.

Table 5

Distribution of Identified Risks by Fine-Kinney and Fuzzy Fine-Kinney Risk Scale
Risk Situation	The Fine-Kinney	Fuzzy Fine-Kinney Method
Very high risk; consider discontinuing operation	4	0
High risk; immediate correction required	26	4
Substantial risk; correction needed	47	72
Possible risk; attention indicated	9	10
Risk; perhaps acceptable	0	0

As a result of the study, the comparison of the risk percentages according to the Fine Kinney and Fuzzy Fine-Kinney methods is given in Fig. 3.

The distribution of risks analyzed according to the Fine-Kinney and Fuzzy Fine-Kinney methods in the company according to departments is explained in Table 6.

Table 6

Distribution of Identified Risks by Departments
	Fine-Kinney Method				Fuzzy Fine-Kinney Method
Departmant	High	Significant	Possible	Acceptable	High	Significant	Possible
Garden / Recreation Area	1	2	1	1	1	3	1
Building Interior / Entrance	1	3	1		1	4
General	2	23	18	2	3	40	2
Offices		3				3
Production Department		5	2			7
Filling Rooms		3	2	1		5	1
Washing Rooms		3				3
Car park	3	2	1		3	3
Changing Rooms		1				1
Dining hall	2	2	1		2	3
Total	9	47	26	4	10	72	4

4.1 Fuzzy Cognitive Mapping

Cognitive maps are diagrams created to represent conceptual representations with nodes and to reveal the causal relationship between two nodes (Asan, 2011). It is very important to be able to accurately assess the irrationality and risk attitudes of decision-makers in risky and uncertain environments (Kazancoglu et al., 2020).

Cognitive maps were first used in 1948 to reveal and analyze the relationship between the criteria of a system (Tolman, 1948). Fuzzy cognitive maps were first proposed by Bart Kosko in 1986. Studies in this area gained momentum after Bart Kosko stated that the relationship between nodes should be fuzzy (Şahin, 2016). As a result of these studies, the use of fuzzy logic methods in cognitive mapping allowed the relationships between nodes to be defined by fuzzy sets instead of numerical expressions. This allowed experts to convey information in a simpler language than a clear numerical language. This allowed the cognitive mapping method to be easily applied in different fields (Kiraz, 2019). Fuzzy cognitive maps, which are a combination of fuzzy logic and the cognitive mapping method, are graphical system maps created based on the opinions of relevant experts that express the causality between multiple factors (Kosko, 1986).. At the same time, fuzzy cognitive mapping allows us to create a schema of risk perception. The resulting analysis of all these schemas allows the definition of a method that generally allows the identification of all types of causes (Bevilacqua, 2012). Using the knowledge of simulation engineers with many years of experience in simulation projects, the feasibility of the simulation was modeled using BBH. In this process, the nodes represent the positive or negative project influencers in the variables, and BBH is used to determine their interconnectedness (Der Landwehr, 2023).

The calculation of the Ai concept variable value for each Ci concept variable is derived from the activation function used by Kosko for the BBH technique in 1986.

$${\varvec{A}}_{\varvec{i}}^{\left(\varvec{k}+1\right)}=\varvec{f}\left({\varvec{A}}_{\varvec{i}}^{\left(\varvec{k}\right)}+\sum _{\begin{array}{c}j\ne i\\ j=1\end{array}}^{\varvec{N}}\left({\varvec{A}}_{\varvec{j}}^{\left(\varvec{k}\right)}\varvec{*}{\varvec{W}}_{\varvec{i}\varvec{j}}\right)\right)$$

In the FCM method, "fuzzy membership functions" are used to convert the weights between the criteria into numerical variables. Functions such as triangular, trapezoidal, and curvilinear can be used in the membership functions (Bağdatli, 2016). These linguistic variables are blurred using triangular membership functions. The blurred values are translated into linguistic variables as "very weak (VW), weak (W), moderately effective (ME), strong (S), very strong (VG)".

Once the criteria have been established, an expert opinion is used. A matrix is created from the opinions of each expert, and since each expert has equal weight, all matrices are combined into a single matrix.

$$W=f\left(\sum _{k=1}^{N}{b}_{k}{W}_{k}\right)$$

Modeling highly complex systems has always been important. Cognitive mapping: it models the systems, reveals the relationships between the criteria in the system, and allows obtaining preliminary data necessary for the development of the system (Kwahk and Kim, 1999).

4.1.1 Examine Risks

In this study, the general situation of the company was examined, and the current risks were grouped under 86 headings. Identified risks; the section was examined under the headings of source of danger, hazardous situation, risk, and current situation. The identified risks were examined together with the company's OHS expert. As a result of the blurring of the risks, seven important risks that need to be carefully monitored are listed using the Fuzzy Cognitive Mapping method. The risks to be analyzed are listed in Table 7.

Table 7

Risks to be analyzed and descriptions of risks
Risk	Explanation
Risk1	Slips and falls, hazardous behavior during manual transfer of raw materials to the production tanks on the platforms, and negativity when transfer tanks are pushed into other rooms.
Risk2	Exposure to hot water (85°C) during sampling
Risk3	Failure to find emergency exits during evacuation, inadequate signage, etc., resulting in evacuation disruptions, chaos, etc.
Risk4	Unfavorable situations when pushing or pulling heavy materials from the scale ramp
Risk5	Exposure of employees in areas where chemicals are used
Risk6	Head-shoulder or hand-arm impacts during cleaning and routine work
Risk7	Contact when using hot water

This study created an 8x8 matrix to assess risks in the pharmaceutical industry. The matrix included seven important risks identified through the Fuzzy Fine-Kinney method and the main criterion 'OHS', among others. Three experts in the pharmaceutical industry were asked to rate these eight criteria using linguistic variables, which were then converted into numerical expressions. Following this stage, the Fuzzy Cognitive Map was created using a weighted average process, with equal weight given to all three experts. The experts were asked to determine the relationship between the criteria using linguistic variables such as Very Weak (WZ), Weak (W), Medium Effective (ME), Strong (S), and Very Strong (VS). It is important to note that criteria that are not related to each other can be left blank. The use of these linguistic variables is solely dependent on the decision-maker's preference. In this study, experts used linguistic expressions to express relationships, as it can be difficult to express numerical variables. The final matrix, which consists of the answers given by the experts, is presented in Table 8.

Table 8

Comparison of Experts' Criteria with the Help of Linguistic Variables
	İSG	Risk1	Risk3	Risk4	Risk5	Risk6	Risk7
Risk1	S		ME			VW
Risk2	VW				VW		W
Risk3	ME			W		VW
Risk4	W	ME	W
Risk5	ME						S
Risk6	VW	W
Risk7	ME				ME

Figure 4 shows the model diagram created after entering the data into the software. The blue lines represent positive relationships between the criteria, with the thickness of the arrows indicating the degree of the relationship. No negative relationships were found in this study. If there were any, they would be shown with a red arrow.

The OHS criterion is the receiving criterion and is affected by other criteria, but it does not affect any criteria. Therefore, all arrows correspond to the OHS criterion. The donor criterion, Risk2, affects the criteria but is not affected by any criteria. Therefore, all arrows point to Risk 2.

4.1.2 Analysis of Examined Risks

This study utilized the sigmoid function, which belongs to the [0,1] interval. The function is executed until FCM reaches a stable state, meaning that the values between the criteria become constant. The simulation process concludes after the iteration, in which all criterion weights converge to a fixed point. The simulation process was terminated after 27 iterations, during which all factor weights remained constant and converged to a fixed point.

The model consists of eight criteria and 18 connections between them. The density index is calculated using the formula C/(N*(N-1)), resulting in a value of 0.32, which is relatively low. A density value of 1 indicates more possible causal relationships. To obtain the number of imaginary connections per component, divide the total number of connections by the total number of components. The model's connection value per component was calculated as 2.25 from 18/8. Donor components are factors that affect other criteria in the model while being unaffected by them. In other words, they are criteria with an indegree value greater than 0 and an outdegree value of 0. This model has one receiver component, which is OSH. Receptive components are only affected by other criteria in the model and do not affect other criteria. In other words, they have an indegree value of 0 and an outdegree value greater than 0. The model also has one transmitter component, which is Risk2. Ordinary components are both affected by a criterion and affect other criteria. The model consists of six ordinary components, namely Risk1, Risk3, Risk4, Risk5, and Risk6. The complexity score is calculated by dividing the receiver components by the transmitter components. In this model, the complexity score is 1, with a maximum value of 8 and a minimum value of 1/8. A complexity score of 1 indicates that there are few relationships between the criteria in this model.

Following this step, the model's criteria are categorized into three groups: driver, receiver, and ordinary. For each criterion, we calculate its indegree, outdegree, and centrality values.

Table 9

Indegree, Outdegree, Centrality Values Calculated for Determined Risks
	Indegree	Outdegree	Centrality
OHS	3.40	0.00	3.40	Receiver
Risk1	1.00	1.60	2.60	Ordinary
Risk2	0.00	0.80	0.80	Driver
Risk3	1.00	1.20	2.20	Ordinary
Risk4	0.40	1.40	1.80	Ordinary
Risk5	0.80	1.40	2.20	Ordinary
Risk6	0.10	0.60	0.70	Ordinary
Risk7	1.20	1.20	2.40	Ordinary

Based on the calculated indegree, outdegree, and centrality values, it is recommended that Risk 1 be addressed first, as it has the highest centrality value after the OHS value. Following Risk 1, the next risks, in order of importance, are Risk 7, Risk 5, Risk 3, Risk 4, Risk 2, and Risk 6.

The pharmaceutical industry has been greatly affected by rapidly developing and changing conditions in recent years. Therefore, interest in occupational health and safety and risk management studies is increasing. As a crucial part of this industry, it is extremely important to avoid risks in order to protect the health of people and businesses. Correct identification and analysis of risks are required for success in this regard. In addition to accurate analysis, the participation of senior management and employees, as well as the knowledge and equipment possessed by businesses, also have a significant impact.

A risk analysis was conducted for a pharmaceutical company in the pharmaceutical industry in this study. A team was formed for the evaluation, including the company's OHS expert, the responsible chief for each department, and an employee working in the department. The current risks were grouped under 86 headings. According to the Fine Kinney method, 4 of these risks were analyzed as 'acceptable risk', 26 as 'possible risk', 47 as'significant 'risk', and 9 as 'high isk'. The analysis conducted using the Fine Kinney method revealed no'very 'high risk'. After evaluating its suitability to the sector and ease of application, the Fine-Kinney method was chosen for the identified risks. The study also included the Fuzzy Fine-Kinney method, despite its limitations in analyzing parameter interactions, giving equal weight to probability, frequency, and severity evaluation parameters, and using technical language that may be difficult for non-experts to understand. The Fine-Kinney method uses linguistic variables to describe the probability and frequency parameters. The linguistic variables used for the probability parameter are: 'Unexpected', 'Low Likelihood', 'Very Low Likelihood', 'Rare But Possible', 'High Likelihood', and 'Very High Likelihood'. The linguistic variables used for the frequency parameter are: 'Fairly Rarely', 'Rarely', 'Occasionally', 'Frequently', and 'Continuously'. Linguistic variables such as 'To Be Considered', 'Important', 'Serious', 'Very Serious', and 'Disaster' are used to indicate severity. Similarly, linguistic variables such as 'Acceptable Risk', 'Possible Risk', 'Significant Risk', 'High Risk', and 'Very High Risk' are used to indicate the level of risk based on the Risk Score parameter. The risks were analyzed using the Fuzzy Fine-Kinney method, resulting in 4 being categorized as 'possible risk', 72 as'significant 'risk', and 10 as 'high risk'. The analysis revealed no 'acceptable risk' or 'very high risk'.

The present situation was determined by analyzing it with the Fine-Kinney and Fuzzy Fine-Kinney methods. The Fuzzy Cognitive Mapping method identified seven significant risks that arise in the production department due to risk blurring, which need to be carefully monitored. The purpose is to provide ideas for prioritizing and managing risks. The objective of this study is to identify the concealed aspects of occupational health, safety, and risk analysis in the pharmaceutical industry. Additionally, it aims to provide decision-makers with new recommendations to address potential real-life issues. In addition to receiving suggestions, companies are requested to provide ideas on how to prioritize and rank risks. Furthermore, this study aims to offer guidance for future research by highlighting the differences between the Fine-Kinney and Fuzzy Fine-Kinney methods. Simultaneously, it aims to prioritize the risks and determine which ones will be given priority.

Various methods can be used to identify and prioritize risks in future studies. One approach is to increase the number of experts involved in risk prioritization. Additionally, the study can be applied to different sectors to broaden its scope.

Author Contribution

D.T and A.F.G wrote the main manufacturing textS.E. and İ.T prepared figuresAll authors reviewed the manuscript

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Risk Prioritization and Case Study in the Pharmaceutical Sector with Fine-Kinney, Fuzzy Fine Kinney and Fuzzy Cognitive Mapping Approach

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Abstract

Figures

1 Introduction

2 Materials and Methods

2.1 Fine-Kinney Method

2.2 Fuzzy Fine-Kinney Method

3 Case Study

3.1 Research Model

3.2 Application

3.3 Results

4 Prioritization of Current Risks

4.1 Fuzzy Cognitive Mapping

4.1.1 Examine Risks

4.1.2 Analysis of Examined Risks

5 Conclusion and Recommendations

Declarations

Author Contribution

References

Appendix

Additional Declarations

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