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Development of decision support system for analysis and solutions of prolonged
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Development of decision support system for analysis and solutions of prolonged st...
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TITLE: Development of decision support system for analysis and solutions of prolo...
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Introduction24
In industrial workplaces, a lot of jobs require workers to perform...
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forms of productivity, workers’ compensation and health treatment costs [4]. For4...
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To counter the above limitations, this study was aimed at developing a decision72...
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were also obtained through this stage. This is carried out by obtaining informati...
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activities of the workers, discomfort and fatigue experienced by the workers whil...
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domain, and the Rapid Entire Body Assessment [7] was applied to analyze the worki...
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and DSSfPS model are well fitted under Manufacturing Model of MOSES architecture1...
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Development of ergonomic workstation model194
The information model of Ergonomic ...
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All information on workplace, worker’s profile, and data about risk factors will ...
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Working memory of the DSSfPS model239
The DSSfPS Model is equipped with eight wor...
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In the second stage, the rule set for PSSI Rule was developed. The PSSI Rule has2...
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2. search rule sets in knowledge base to be matched with data from the working287...
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Integrating the ergonomic workstation model and DSSfPS model312
Both Ergonomic Wo...
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directly from the companies. Data on risk factors including working posture, musc...
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Discussion362
Decision support system has been recognized as one of efficient dec...
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In the knowledge base, the current study developed the rule sets utilizing if-the...
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In this study, forward chaining method was used to develop the inference engine.4...
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support system are comparable to conventional method. The advantage of using the4...
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conducting this study. Special thank goes to Miyazu (M) Sdn. Bhd. and Kulitkraf S...
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8. Department of Safety and Health, Ministry of Human Resources, Malaysia.484
Gui...
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15. Harding, J. A., Omar, A. R., & Popplewell, K. Applications of QFD within a508...
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Retrieved February 25,2013, from:533
http://www.sciencedirect.com/science/article...
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Table 1: Working memory of DSSfPS Model and functions
Working Memory Function
Wor...
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Table 2: Summary of knowledge base of DSSfPS model
Knowledge Knowledge descriptio...
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Table 3: Validation of knowledge for working posture
Data and information Results...
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Table 4: Validation of knowledge for muscle activity
Data and information Results...
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Table 5: Validation of knowledge for standing duration
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Table 6: Validation of knowledge for holding time
Data and information Results (D...
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Table 7: Validation of knowledge for whole-body vibration
Data and information Re...
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Table 8: Validation of knowledge for indoor air quality
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Table 9: Validation for PSSI
Risk factors Risk level Rating criterion Multiplier ...
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Figure 1: An integrated environment between Ergonomic Workstation model and
DSSfP...
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Figure 2: Information structure in the Ergonomic Workstation model to represent h...
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Figure 3: DSSfPS model and its mechanisms
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Figure 4: Structure of knowledge base and working memory in the DSSfPS model
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  1. 1. Accepted Manuscript Development of decision support system for analysis and solutions of prolonged standing at workplace Isa Halim, PhD Hambali Arep, PhD Seri Rahayu kamat, PhD Rohana Abdullah, MSc Abdul Rahman Omar, PhD Ahmad Rasdan Ismail, PhD PII: S2093-7911(14)00026-2 DOI: 10.1016/j.shaw.2014.04.002 Reference: SHAW 41 To appear in: Safety and Health at Work Received Date: 30 December 2013 Accepted Date: 15 April 2014 Please cite this article as: Halim I, Arep H, kamat SR, Abdullah R, Rahman Omar A, Ismail AR, Development of decision support system for analysis and solutions of prolonged standing at workplace, Safety and Health at Work (2014), doi: 10.1016/j.shaw.2014.04.002. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
  2. 2. M ANUSCRIPT ACCEPTED ACCEPTED MANUSCRIPT Development of decision support system for analysis and solutions of prolonged standing1 at workplace2 3 Isa HALIM, PhD1 , Hambali AREP, PhD1 , Seri Rahayu KAMAT, PhD1 , Rohana4 ABDULLAH, MSc2 , Abdul Rahman OMAR, PhD3 , Ahmad Rasdan ISMAIL, PhD4 5 1 Faculty of Manufacturing Engineering, Universiti Teknikal Malaysia Melaka, 76100 Durian6 Tunggal, Hang Tuah Jaya, Melaka, Malaysia.7 2 Faculty of Engineering Technology, Universiti Teknikal Malaysia Melaka, 76100 Durian8 Tunggal, Hang Tuah Jaya, Melaka, Malaysia.9 3 Faculty of Mechanical Engineering, Universiti Teknologi MARA, 40450 Shah Alam,10 Selangor, Malaysia.11 4 Faculty of Mechanical Engineering, Universiti Malaysia Pahang, 26600 Pekan, Pahang,12 Malaysia.13 14 15 Correspondence to:16 Isa Halim17 Faculty of Manufacturing Engineering18 Universiti Teknikal Malaysia Melaka19 76100 Durian Tunggal, Hang Tuah Jaya, Melaka, Malaysia.20 Tel.: +606-331650121 Fax: +606-331641122 e-mail: isa@utem.edu.my23 24 Running tittle: Decision support system for prolonged standing25
  3. 3. M ANUSCRIPT ACCEPTED ACCEPTED MANUSCRIPT TITLE: Development of decision support system for analysis and solutions of prolonged1 standing at workplace2 ABSTRACT3 Background: Prolonged standing has been hypothesized as one of vital contributors to4 discomfort and muscle fatigue to workers at workplace.5 Objectives: The objective of this study is to develop a decision support system that can6 provide systematic analysis and solutions in minimizing discomfort and muscle fatigue7 associated with prolonged standing.8 Methods: Integration of object oriented programming (OOP) and Model Oriented9 Simultaneous Engineering System (MOSES) were deployed to design the architecture of10 the decision support system.11 Results: Validation of the decision support system was carried out in two manufacturing12 companies. The validation process demonstrated that the decision support system has13 generated reliable results.14 Conclusion: Therefore, this study concluded that the decision support system is a reliable15 advisory tool for analysis and solutions related to discomfort and muscle fatigue16 associated with prolonged standing. The decision support system is suggested to undergo17 further testing for commercialization purpose.18 19 Keywords: Decision support system, Discomfort, Ergonomics, Fatigue, Prolonged20 standing21 22 23
  4. 4. M ANUSCRIPT ACCEPTED ACCEPTED MANUSCRIPT Introduction24 In industrial workplaces, a lot of jobs require workers to perform in standing25 position. One of the advantages of standing is that it can provide a large degree of26 freedom to workers for manipulating materials and tools at the workstation. In addition,27 standing position is practical and effective when the workers are operating large28 machines and huge workpieces, reaching of materials and tools, and pushing and pulling29 any excessive loads. For instance, a worker who works at a conventional surface grinding30 machine requires large degree of freedom to push and pull the machine table when31 performing grinding process on the workpiece’s surface. This kind of job nature is nearly32 impossible to be performed in sitting position, so standing is the best option. Standing33 working position also promotes the worker to be more productive and consequently34 contributes to higher productivity for the organization. However, when workers spent a35 long period of time in standing position throughout their working hours, they may36 experience discomfort and muscle fatigue at the end of workday. In the long run, they37 will potentially experience occupational injuries such as work-related musculoskeletal38 disorders, chronic venous insufficiency, and carotid atherosclerosis [1]. A worker is39 considered to be exposed to prolonged standing if he or she spent over fifty percent of the40 total working hours during a full work shift in standing position [2]. Working in standing41 position for an extended period of time has been recognized as a vital contributor to42 decrease workers’ performance in industry. It includes occupational injuries, productivity43 decrement, increased of treatment and medical costs, and lead to subjective discomfort to44 the workers. When workers perform jobs in prolonged standing, static contraction45 occurred particularly in their back and legs, thus resulted in discomfort and muscle46 fatigue [3]. Besides, the employers will also be affected due to lost of revenue in the47
  5. 5. M ANUSCRIPT ACCEPTED ACCEPTED MANUSCRIPT forms of productivity, workers’ compensation and health treatment costs [4]. For48 example, pain in the lower back due to prolonged standing can affect the ability of a49 worker to perform job that involving flexion or extension postures. This may in turn50 affect the productivity of the worker. In addition, workers who suffered from51 occupational injuries must be referred to clinical experts for health treatment, which52 definitely involve substantial amount of consultancy and medication costs.53 Many ergonomics simulation systems have been introduced to improve54 productivity, comfort and safety of workers in manufacturing workplaces [5], [6]. In55 industrial ergonomics, numerous assessment tools have been suggested for assessing and56 analyzing risk factors associated with prolonged standing. For examples, the Rapid Entire57 Body Assessment (REBA) and guidelines on standing at work can be applied to analyze58 and minimize postural stress due to standing jobs [7], [8]. However, almost all existing59 methods are available in the form of pen and paper-based and do not provide a60 comprehensive assessment on prolonged standing. The methods are time consuming,61 difficult to retrieve electronically, manual data processing, and causes human error when62 performing calculations. In addition, the existing assessment methods are seen as63 individual and isolated tools, hence it is difficult to perform multi assessments64 concurrently [9]. Up till now, innovative best practices and the underlying measurement65 science for assessing and analyzing the risk factors associated with prolonged standing,66 and documenting the results of the computations do not exist. As a consequence, the67 authorized institutions and industry practitioners who are concerned with occupational68 safety and health do not have a reliable measurement technique to assess and analyze the69 risk factors associated with prolonged standing jobs in the workplace. This includes70 proposing solutions to minimize the risk level.71
  6. 6. M ANUSCRIPT ACCEPTED ACCEPTED MANUSCRIPT To counter the above limitations, this study was aimed at developing a decision72 support system to assess, analyze, and propose solutions to minimize discomfort and73 muscle fatigue associated with prolonged standing at industrial workplaces. Six risk74 factors related to prolonged standing jobs such as working posture, muscle activity,75 standing duration, holding time, whole-body vibration, and indoor air quality [10] were76 considered as the basis to develop the knowledge of the decision support system. These77 risk factors are related to human, machine, and environment at the workplace. All risk78 factors were analyzed individually to obtain their risk levels. Then, the risk levels of each79 risk factor were assigned with multipliers to represent their severity for discomfort and80 fatigue. A strain index arises through multiplicative interactions between the assigned81 multipliers. Then, the developed decision support system proposes alternative solutions to82 minimize the risk levels corresponding to the strain index. In the developed decision83 support system, Ergonomic Workstation model and Decision Support System for84 Prolonged Standing (DSSfPS) model were designed to function as data capturing and85 analysis respectively.86 Materials and methods87 There are three major stages involved to develop the decision support system.88 Stage 1: Knowledge acquisition89 The first stage in developing the decision support system is knowledge acquisition.90 In a decision support system, knowledge can be considered as the ‘brain’ to process input91 data and information that are supplied to the system. The knowledge can be obtained by92 extracting, structuring, and organizing knowledge from one or more sources [11]. In this93 study, the knowledge acquisition was performed to determine the risk factors that94 contribute significantly to discomfort, and muscle fatigue associated with prolonged95 standing jobs. In addition, the ergonomics evaluation tools to analyze the risk factors96
  7. 7. M ANUSCRIPT ACCEPTED ACCEPTED MANUSCRIPT were also obtained through this stage. This is carried out by obtaining information from97 reliable sources such as published literatures, on-site observations in industries,98 interviews with management staff and production workers, guidelines and standards from99 the authorized organizations, and experts’ opinions.100 Reviews on articles were performed through hard-bound publications such as101 magazines, journals, and guidelines, as well as on-line databases. The main purposes of102 literature review are to attain the risk factors that contribute significantly to discomfort103 and muscle fatigue associated with prolonged standing jobs. Besides, literature review is104 also useful to identify ergonomics evaluation tools and control measures that can be105 applied to assess, analyze, and minimize the discomfort, and muscle fatigue. The106 identified risk factors, methods, and control measures were compiled to be considered in107 the decision support system.108 Series of on-site observations were conducted with three metal stamping companies109 in Malaysia. The main purpose of the on-site observations was to identify risk factors that110 are present in the standing workstations. A video camcorder was used to record the risk111 factors in the workstations. The video camcorder was set close to the workstation to112 record postures, movements and job cycles while workers are performing their jobs. The113 process of video recording took about 30 minutes to ensure all risk factors, working114 practices, and job processes in the workstation are completely recorded. The advantage of115 video recording is that recorded information is easy to replay, stop or pause so that116 postures, movements and job cycles in the workstation can be monitored. In addition, the117 risk factors captured by video recording can be considered as knowledge for the decision118 support system.119 In addition to the on-site observations, interview sessions with management staff120 and production workers have been performed to acquire the personal background and job121
  8. 8. M ANUSCRIPT ACCEPTED ACCEPTED MANUSCRIPT activities of the workers, discomfort and fatigue experienced by the workers while122 performing jobs in prolonged standing, history of pain and treatment taken, and123 suggestions to improve standing workstations. The Prolonged Standing Questionnaire124 [12] was applied during the interview sessions. Through the interview sessions, the125 authors were able to acquire useful information that could not be obtained during the on-126 site observations such as the solutions applied by the management staff and production127 workers to minimize discomfort and muscle fatigue associated with prolonged standing.128 Authorized organizations such as International Organization for Standardization129 (ISO) and Department of Safety and Health of Malaysia (DOSH) are good sources to130 acquire information on prolonged standing at workplace. These institutions have131 published regulations and guidelines on standing at workplace. Guidelines on standing at132 workplace [8], code of practice for indoor air quality [13], and standards of comfort133 levels due to acceleration values of whole-body vibration [14] were referred to establish134 the knowledge of decision support system.135 Experts include ergonomic practitioners, medical doctors, physiotherapists, safety136 and health engineers, and academicians are the significant contributors to develop the137 knowledge of decision support system. Their opinions and advices were gathered through138 discussions in seminars and conferences. One of the outcomes from the discussion was139 the selection of ergonomics evaluation tools to be applied in the decision support system.140 All risk factors related to discomfort and muscle fatigue associated with prolonged141 standing which are compiled from the abovementioned sources have been categorized142 into three domains namely human, machine and environment. Each risk factor from these143 domains was equipped with ergonomics evaluation tools to analyze and quantify the risk144 levels. For example, risk factor associated with awkward working posture has been145 identified as a vital contributor to discomfort and fatigue. It is categorized under human146
  9. 9. M ANUSCRIPT ACCEPTED ACCEPTED MANUSCRIPT domain, and the Rapid Entire Body Assessment [7] was applied to analyze the working147 posture when a worker performs jobs in prolonged standing. The process to integrate the148 risk factors and ergonomics evaluation tools will be performed in second stage.149 Stage 2: Knowledge integration150 The second stage continues with integrating all the knowledge obtained through the151 abovementioned sources. All risk factors that contribute significantly to discomfort and152 muscle fatigue associated with prolonged standing are suited with ergonomics evaluation153 tools to quantify the risk levels. Based on the risk factors, Prolonged Standing Strain154 Index (PSSI) was developed. Details of the PSSI development can be found in the recent155 publication [10].156 Stage 3: Ergonomic workstation model and DSSfPS model157 This study deployed the Model Oriented Simultaneous Engineering System158 (MOSES) architecture [9], [15] to develop the decision support system. The decision159 support system has two main models namely Ergonomic Workstation model and160 Decision Support System for Prolonged Standing (DSSfPS) model. The Ergonomic161 Workstation model captures data and information on workplace, worker, working162 posture, muscles activity, standing duration, holding time, whole-body vibration, and163 indoor air quality in the workplaces.164 DSSfPS model on the other hand comprises knowledge and established ergonomics165 evaluation tools to analyze data about risk factors that are captured by Ergonomic166 Workstation model. The DSSfPS model is functioned to quantify risk levels of each risk167 factor, and compute Prolonged Standing Strain Index (PSSI) value. The DSSfPS model168 will suggest alternative solutions with respect to PSSI value to minimize discomfort and169 fatigue while performing jobs in standing workstation. A working memory in DSSfPS170 model is used to save all results from the analysis. Both Ergonomic Workstation model171
  10. 10. M ANUSCRIPT ACCEPTED ACCEPTED MANUSCRIPT and DSSfPS model are well fitted under Manufacturing Model of MOSES architecture172 (Figure 1).173 Insert FIGURE 1 here174 The Ergonomic Workstation model is represented by a workstation, the place of a175 worker to perform jobs. The data and information captured by the Ergonomic176 Workstation model are then supplied to DSSfPS model for further analysis. The users177 (e.g. ergonomics experts) can apply the outcomes of analysis to perform modification on178 the design of workstation to minimize discomfort and muscle fatigue associated with179 prolonged standing jobs. The communication between both models in the decision180 support system is achieved when information and data captured by the Ergonomics181 Workstation model are supplied to DSSfPS model through an integrated computer182 environment.183 The programming architecture of Ergonomic Workstation model and DSSfPS184 model was developed using Object Oriented Programming (OOP). In addition, Java185 programming language and open source database db4o (Oracle Corporation, USA) was186 used as programming language and database engine respectively. The OOP is a187 programming paradigm uses interactions of class and attribute (sometimes called object)188 to design the computer programs. The advantage of using OOP is that, any modifications189 to the information architecture are required, can be done easily. For example, if a system190 developer wishes to modify the information architecture for certain reasons, he or she can191 manipulate respective class without interfering other classes.192 193
  11. 11. M ANUSCRIPT ACCEPTED ACCEPTED MANUSCRIPT Development of ergonomic workstation model194 The information model of Ergonomic Workstation was designed by considering195 workers in their actual workplaces. Usually, a workplace is represented by the hierarchy196 of company, department, and workstation. In the workstation, it consists of at least a197 human (worker), machine, and working environment. When a worker is performing jobs198 in standing position, his or her workstation is considered as a critical point where199 ergonomics principles should be taken into account to minimize discomfort and muscle200 fatigue.201 In the Ergonomic Workstation information model, the relationship of each human-202 machine-environment constituent in a workstation built their own classes represented by203 the clouds as shown in the information structure (Figure 2). In the information structure,204 the primary class of the Ergonomic Workstation is COMPANY. Then, DEPARTMENT205 is assigned as a sub-class of COMPANY. Consecutively, WORKSTATION class is206 created as sub-class of DEPARTMENT. To represent machine and environment207 constituents, whole-body vibration (WBV) and indoor air quality (IAQ) classes are208 created respectively. The WORKER class consists of five sub-classes to represent job209 activity, working posture, muscle activity, standing duration, and holding time. Except210 for job activity, all classes represent factors need to be assessed and analyzed when a211 worker is performing jobs in prolonged standing. Even though there is no ergonomics212 evaluation carried out for the job activity; however, its information is useful to decide213 further analysis. For example, if a worker is exposed to activity of jobs associated with214 ‘standing with forward bending’, ergonomics evaluation associated with working posture215 assessment will be carried out.216
  12. 12. M ANUSCRIPT ACCEPTED ACCEPTED MANUSCRIPT All information on workplace, worker’s profile, and data about risk factors will be217 saved in Ergonomic Workstation model. The data and information will be manipulated218 by the DSSfPS model for further analysis.219 Insert FIGURE 2 here220 Development of DSSfPS model221 222 The DSSfPS model is used to analyze the risk factors, quantify standing risk level,223 and obtain alternative solutions to improve the workstation design improvement. The224 DSSfPS model plays an important role to provide alternative solutions to design an225 ergonomic standing workstation; therefore its knowledge development should be given a226 high priority to ensure the system can achieve desired performances.227 As depicted in Figure 3, the DSSfPS model has three main mechanisms: a working228 memory, a knowledge base, and an inference engine. In addition, Graphical User229 Interfaces (GUIs) are provided in the system to facilitate communication between the230 decision support system and the users. The relationship between working memory and231 knowledge base in the DSSfPS model is clearly shown in Figure 4.232 Insert FIGURE 3 here233 Insert FIGURE 4 here234 235 236 237 238
  13. 13. M ANUSCRIPT ACCEPTED ACCEPTED MANUSCRIPT Working memory of the DSSfPS model239 The DSSfPS Model is equipped with eight working memories. Generally, the240 working memories functioned as the media to temporarily store the data and results of241 analysis. Table 1 summarizes the functions of each working memory.242 Insert TABLE 1 here243 244 Knowledge base of the DSSfPS model245 The knowledge base of DSSfPS model serves as a shell to occupy rule sets for the246 ergonomic evaluation tools such as Posture Rule, Muscle Rule, Holding Rule, Standing247 Rule, WBV Rule, and IAQ Rule. In addition, PSSI Rule is also embedded in the248 knowledge base. The rule sets consist of a list of if statements and a list of then conditions249 to process any input data and provide set of alternative solutions to minimize discomfort250 and muscle fatigue associated with prolonged standing. The development of rule sets251 involved two stages. The first stage is assigning each ergonomics evaluation tool into a252 class. Then, the formulation of rules to reach final results is performed in the second253 stage.254 In the first stage, the ergonomics knowledge was assigned to several classes to255 accommodate the following rule sets:256 1. Posture Rule – containing rules for working posture analysis,257 2. Muscle Rule – containing rules for muscles activity analysis,258 3. Holding Rule – containing rules for holding duration analysis,259 4. Standing Rule – containing rules for standing time analysis,260 5. WBV Rule – containing rules for whole-body vibration exposure analysis, and261 6. IAQ Rule – containing rules for indoor air quality analysis.262
  14. 14. M ANUSCRIPT ACCEPTED ACCEPTED MANUSCRIPT In the second stage, the rule set for PSSI Rule was developed. The PSSI Rule has263 several rules that are used to perform PSSI calculation and obtain recommendation to264 minimize discomfort and muscle fatigue, based on the PSSI value. The development of265 PSSI Rule began with assigning the results of risk factors analysis (in terms of risk266 levels) to multipliers to represent their severity for discomfort and fatigue. A PSSI value267 arises through multiplicative interactions between these multipliers. Then, potential268 solutions to minimize the risk levels were recommended corresponding to the PSSI value.269 Table 2 summarizes the knowledge base of DSSfPS model which include risk factors or270 knowledge, knowledge description, numbers of rules, questions, and alternative answers.271 Insert TABLE 2 here272 273 Inference engine of the DSSfPS model274 In DSSfPS model, an inference engine is used to achieve results (risk levels, PSSI275 value and recommendations) by matching rule sets in the knowledge base and data276 available in the working memory. The method applied to design the inference mechanism277 is forward chaining. Forward chaining works by processing the data first, and keep using278 the rules in knowledge base to draw new conclusions given by those data [16], [17]. This279 study applied forward chaining because it operates via top-down approach which takes280 data available in the working memory then generate results based on satisfied conditions281 of rules in the knowledge base. In DSSfPS model, the inference engine performs these282 functions:283 1. supply background information of worker such as workplace’s profile, personal284 details, job activities, and data about risk factors that have been captured by285 Ergonomic Workstation model to working memory in DSSfPS model,286
  15. 15. M ANUSCRIPT ACCEPTED ACCEPTED MANUSCRIPT 2. search rule sets in knowledge base to be matched with data from the working287 memory to obtain results (risk levels, PSSI value and recommendations), and288 3. retrieve updated working memory database to display outcomes of the analysis.289 The inference engine of DSSfPS model works in three stages: between GUIs and290 working memory, between working memory and knowledge base, and at working291 memory to display the analysis outcomes.292 Graphical user interfaces (GUIs)293 In the decision support system, the Graphical User Interfaces (GUIs) are used as294 communication medium between the user, Ergonomic Workstation model, and DSSfPS295 model. The GUIs were designed using facilities available in the NetBeans IDE 6.8296 (Oracle Corporation, USA). The user provides information from the actual industrial297 workstation such as workplace’s information, worker’s profile, and the data about risk298 factors to the database in Ergonomic Workstation model through the GUIs. In the299 decision support system, ten GUIs have been designed to perform various functions.300 A main menu GUI was designed to enable a user to populate a database in the301 Ergonomic Workstation model. When the program is executed, the main menu GUI will302 appear. In the main menu GUI, there are several menus:303 1. File menu – to exit from the decision support system,304 2. Manage Database menu – to create databases,305 3. Populate Database menu – to provide data and information about the risk factors to306 be analyzed,307 4. Display Information menu – to preview data and results of analysis, and308 5. Help menu – to guide the user about the basic operation of the decision support309 system.310 311
  16. 16. M ANUSCRIPT ACCEPTED ACCEPTED MANUSCRIPT Integrating the ergonomic workstation model and DSSfPS model312 Both Ergonomic Workstation model and DSSfPS model have to be integrated in a313 decision support system to enable prospective users to analyze standing jobs in their314 workplaces. To employ the decision support system, a user should create a database for315 Ergonomic Workstation model and a working memory for DSSfPS model. Then, the user316 should supply information on workplace, worker, and data of risk factors from the actual317 workplaces and store them into the database in Ergonomic Workstation model. The318 inference engine retrieves the data and information from the Ergonomic Workstation319 database and sends to the database in working memory. Then, the inference engine320 examines and matches rules embedded in the knowledge base of DSSfPS model with321 data and information from working memory to generate results. The obtained results will322 be saved in the working memory. The inference engine retrieves the saved results in the323 working memory for display purpose.324 Validation of the decision support system325 A validation process was conducted to ensure the knowledge of developed decision326 support system able to perform its functionality with an acceptable level of accuracy. To327 achieve that, this study has performed the validation process through a technical328 validation using two real case studies [18]. According to technical validation, the results329 generated by decision support system should be consistent with the results of330 conventional methods when assessing and analyzing a similar case study [19].331 The validation process was carried out through two on-site observations at a metal332 stamping company in Shah Alam, Malaysia and a footwear manufacturer in Petaling333 Jaya, Malaysia. To validate the knowledge of the developed decision support system,334 twenty six workers (n=13 from each company) were selected as samples of study. Data335 and information about workplace, workstation, worker, and risk factors were acquired336
  17. 17. M ANUSCRIPT ACCEPTED ACCEPTED MANUSCRIPT directly from the companies. Data on risk factors including working posture, muscles337 activity, standing duration, and holding time were observed at three conditions: high,338 medium, and low risk levels. Data on risk factors associated with whole-body vibration339 (WBV) were measured using a vibration accelerometer (QUEST Technologies, USA) at340 the standing workstation. Additionally, the quantities of carbon dioxide and carbon341 monoxide surrounding the workstation were measured using Indoor Air Quality Probe342 IQ-410 (Gray Wolf, USA) [20]. In the decision support system, all data and information343 were supplied to Ergonomic Workstation Model, and DSSfPS Model analyzed them to344 generate results. The data and information were analyzed based on individual basis. In345 other words, all workers have their own data and information about workplace,346 workstation, worker, and risk factors. Similarly, the data about risk factors were analyzed347 through conventional method, for examples, data about working posture and muscles348 activity were analyzed using Rapid Entire Body Assessment worksheet [7] and Rodgers349 Muscle Fatigue Analysis worksheet [21] respectively.350 Then, this study performed a comparison between the results generated by decision351 support system and conventional method to determine the validity of knowledge in the352 developed decision support system. Table 3 to Table 8 present the comparison results of353 knowledge for the six risk factors (working posture, muscle activity, duration, holding354 time, whole-body vibration, and indoor air quality). The result of validation for the PSSI355 is shown in Table 9. Based on the comparison, it is clearly shown that the developed356 decision support system able to generate results comparable to conventional methods. In357 other words, results from both decision support system and conventional method showed358 a good agreement for all analyzed risk factors.359 Insert TABLE 3 to TABLE 9 here360 361
  18. 18. M ANUSCRIPT ACCEPTED ACCEPTED MANUSCRIPT Discussion362 Decision support system has been recognized as one of efficient decision making363 tools in many applications. For example, in medical care, a decision support system that364 provides explicit point of decision making to clinicians has achieved clinician compliance365 rates of 90-95% [22]. This study has developed a decision support system that consists of366 Ergonomic Workstation model and DSSfPS model. Both models were designed using367 OOP. The authors found that OOP is versatile and flexible system especially when368 designing the programming architecture because any modifications and improvement on369 the models which are related to their classes and attributes can be done easily. In other370 words, if the programming architecture has to be modified for certain reasons, the authors371 can manipulate respective class without having to interfere with other classes. This372 advantage enabled the authors to reorganize the programming architecture easily and373 time saving. In addition, designing relationship of a work system and its components374 using OOP is particularly useful for making the designed system easy to be understood375 [23].376 In terms of knowledge base development, this study established the information377 about risk factors by surveying numerous published research works and performing378 onsite-observations at industrial workplaces. The combination of literature review and379 on-site observations has shown an effective way to establish a comprehensive and total380 knowledge base. As a result, information about risk factors related to human-machine-381 environment has been established. Corresponding to each risk factor, this study applied382 reliable sources such as established ergonomics evaluation tools, guidelines, and standard383 for data analysis and decision making. As compared to other decision support system for384 ergonomics application, the acquisition of knowledge is solely referred to literature385 review [24].386
  19. 19. M ANUSCRIPT ACCEPTED ACCEPTED MANUSCRIPT In the knowledge base, the current study developed the rule sets utilizing if-then387 production rule to execute the knowledge. The if-then production rule is preferred388 because the data and information to be analyzed by the developed decision support389 system requires straight forward manipulations. In many rule sets development, fuzzy390 rule is widely used [25], [26], [27], [28], [29] however, the fuzzy rule is not suitable in391 the current study because its decision-making is not always a matter of black and white; it392 often involves grey areas [30].393 Working memory is a vital component in a decision support system as it is used to394 store data and information supplied by the users. In developed decision support system,395 data and information about workplaces, workers, and risk factors captured by the396 Ergonomic Workstation model and results of analysis performed by DSSfPS model are397 saved in a working memory. When the decision support system is used to perform many398 assessments, the data in working memory become larger and there is a possibility of data399 confliction. This may lead to data manipulation errors. To avoid this, the working400 memory has to be cleaned. To clear the working memory, it should be initialized. This401 study created a function to initialize working memory of DSSfPS model by deleting the402 old data. Interestingly, the deletion process in the working memory of DSSfPS model is403 not affecting the data and information about workplaces, workers, and risk factors that404 are saved in the Ergonomic Workstation model. The program code deletes the results of405 previous assessment in the working memory of DSSfPS model, however, the data and406 information about workplaces, workers, and risk factors are still available in the407 Ergonomic Workstation model. The advantage of this system is that data and information408 stored in the Ergonomic Workstation model could be retrieved when further assessment409 is required.410
  20. 20. M ANUSCRIPT ACCEPTED ACCEPTED MANUSCRIPT In this study, forward chaining method was used to develop the inference engine.411 The study prefers this method because it works through top-down approach which takes412 data available in the working memory then generates results based on satisfied conditions413 of rules in the knowledge base. For instance, consider rule sets in the class Posture Rule:414 Rule 1: if reba_score = 1 then posture_risk_level = “Negligible”415 Rule 2: if posture_risk_level = “Negligible” then PostureMultiplier = 1416 Rule 3: if PostureMultiplier = 1 then417 PostureRecommendation = “"The posture should be maintained"418 Based on forward chaining method, start with Rule 1 and go on downward (Rule 3) till a419 rule that fires is found. In other words, the forward chaining method is data-driven420 because the initial data are processed first (reba_score, posture_risk_level,421 PostureMultiplier) and keep using the rules to achieve goal (PostureRecommendation).422 Ten GUIs have been equipped in the decision support system for convenient data423 input, analysis, and display. The GUIs are predominantly used to key in data and424 information about workplaces, workers, risk factors including working posture, muscles425 activity, standing duration, holding time, whole-body vibration, and indoor air quality.426 Each data and information is equipped with individual GUI. The GUIs were text-based427 design with several facilities such as informative message, pull-down menus, check428 boxes, radio button, and OK and Cancel buttons. There is no image provided in the GUIs429 because the constraint of GUIs area and difficult to obtain suitable image to represent430 certain data and information. For example, it is difficult to attach vibration image at431 certain frequency and acceleration in the whole-body vibration GUI because the432 quantities are nearly impossible to be captured.433 At the end, the developed decision support system has undergone validation process.434 Through the validation process, this study found that results generated by the decision435
  21. 21. M ANUSCRIPT ACCEPTED ACCEPTED MANUSCRIPT support system are comparable to conventional method. The advantage of using the436 decision support system is time saving and systematic data manipulation.437 Conclusion438 A decision support system that is specifically used to analyze risk factors, predict439 risk levels due to standing work, and propose recommendations to minimize discomfort440 and fatigue has been developed in this study. This study identified that working posture,441 muscles activity, standing duration, holding time, whole-body vibration, and indoor air442 quality were the risk factors for discomfort and muscles fatigue while workers are443 performing jobs in standing position. Risk levels of the risk factors can be predicted444 through the Prolonged Standing Stress Index (PSSI). The risk factors and the PSSI have445 been modeled in a decision support system to provide a systematic analysis and solutions446 for discomfort and muscle fatigue associated with prolonged standing. Based on the447 results of validation process, this study concluded that the decision support system is448 reliable for assessing, analyzing, and proposing solutions to minimize discomfort and449 muscle fatigue associated with prolonged standing at industrial workplaces. This study450 suggests further efforts such as extensive testing, attractive and user friendly GUI to451 make the decision support system commercially available.452 Acknowledgements453 The authors would like to acknowledge the Ministry of Higher Education of454 Malaysia, the Faculty of Manufacturing Engineering of Universiti Teknikal Malaysia455 Melaka, the Ministry of Science, Technology and Innovation of Malaysia for funding this456 research under e-Science Research Grant (project no.: 06-01-01-SF0258), the Faculty of457 Mechanical Engineering of Universiti Teknologi MARA and Research Management458 Institute of Universiti Teknologi MARA for providing facilities and assistance in459
  22. 22. M ANUSCRIPT ACCEPTED ACCEPTED MANUSCRIPT conducting this study. Special thank goes to Miyazu (M) Sdn. Bhd. and Kulitkraf Sdn.460 Bhd. for facilitating the workplace surveys.461 462 463 References464 1. Isa H, Omar AR. A review on health effects associated with prolonged standing465 in the industrial workplaces. International Journal of Research and Reviews in466 Applied Sciences. 2011;8(1):14–21. Retrieved January 20, 2013, from:467 www.arpapress.com/Volumes/Vol8Issue1/IJRRAS_8_1_03.pdf468 2. Tomei F, Baccolo TP, Tomao E, Palmi S, Rosati MV. Chronic venous disorders469 and occupation. Am J Ind Med.1999;36(6):653–65.470 3. Krijnen RMA, de Boer EM, Ader HJ, Bruynzeel DP. Diurnal volume changes of471 the lower legs in healthy males with a profession that requires standing. Skin Res472 Technol. 1998;4(1):18–23.473 4. Zander JE, King PM, Ezenwa BN. Influence of flooring conditions on lower leg474 volume following prolonged standing. Int J Ind Ergon. 2004;34(4):279–88475 (dx.doi.org/doi:10.1016/j.ergon.2004.04.014).476 5. Chaffin, D.B. Human simulation for vehicle and workplace design. Hum Factors477 Ergon Manuf. 2007;17:475-84.478 6. Kazmierczak, K., Neumann, W.P., & Winkel, J. A case study of serial-flow car479 disassembly: ergonomics, productivity and potential system performance. Hum480 Factors Ergon Manuf. 2007; 17:331-51.481 7. Hignett S, McAtamney L. Rapid entire body assessment (REBA). Appl Ergon.482 2000;31(2):201–5 (dx.doi.org/doi:10.1016/S0003-6870(99)00039-3).483
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  26. 26. M ANUSCRIPT ACCEPTED ACCEPTED MANUSCRIPT Table 1: Working memory of DSSfPS Model and functions Working Memory Function Worker Memory Compiles data and information on workplace’s profile (company name, worker’s department, worker’s workstation); worker’s profile (name, employee number, age, and job activity). Posture Memory Saves data, information, and result of working posture analysis. Muscle Memory Saves data and information, and result of muscles activity analysis. Standing Memory Saves data, and result of standing duration analysis. Holding Memory Saves data, and result of holding time analysis. WBV Memory Saves data, and result of whole-body vibration analysis. IAQ Memory Saves data, and result of indoor air quality analysis. PSSI Memory Saves result of PSSI and recommendations.
  27. 27. M ANUSCRIPT ACCEPTED ACCEPTED MANUSCRIPT Table 2: Summary of knowledge base of DSSfPS model Knowledge Knowledge description No. of rule No. of question No. of optional answer Working Posture • Trunk movement and posture • Neck movement and posture • Legs posture and condition • Upper arms posture • Lower arms movement • Wrist movement • Load mass and force condition • Coupling condition • Posture activity 299 16 53 Muscles activity • Effort level of neck, shoulders, back, arms/elbow, wrists/ hands/ fingers, legs/ knees, ankles/ feet/ toes • Continuous effort duration of neck, shoulders, back, arms/elbow, wrists/ hands/ fingers, legs/ knees, ankles/ feet/ toes • Effort frequency of neck, shoulders, back, arms/elbow, wrists/ hands/ fingers, legs/ knees, ankles/ feet/ toes 455 21 84 Holding time • Shoulder height • Arm reach distance 10 2 10 Standing duration • Standing period 3 1 3 WBV • Frequency of vibration (foot-to-head direction) • Acceleration of vibration (foot-to-head direction) 166 3 200 IAQ • The quantity of carbon dioxide • The quantity of carbon monoxide • The quantity of formaldehyde • The quantity of respirable particulates • The quantity of TVOC 11 5 - PSSI • To quantify risk level due to prolonged standing jobs in the workstation 25 6 22 Total 969 54 372
  28. 28. M ANUSCRIPT ACCEPTED ACCEPTED MANUSCRIPT Table 3: Validation of knowledge for working posture Data and information Results (DSS) Results (CM) Trunk: 0º to 20º flexion Neck: More than 20º flexion Legs: Unstable posture Upper arms: 20º to 45º flexion Lower arms: Less than 60º flexion Wrists: More than 15º flexion Load: Less than 5 kg Coupling: Fair (hand hold is acceptable) Activity: Repeated small range action Medium risk Medium risk Trunk: 0º to 20º flexion Neck: 0º to 20º flexion and twisting Legs: Bilateral weight bearing Upper arms: 45º to 90º flexion Lower arms: 60º to 100º flexion Wrists: More than 15º flexion Load: Less than 5 kg Coupling: Fair (Handhold acceptable) Activity: Repeated small range actions Medium risk Medium risk Trunk: Upright Neck: 0º to 20º flexion Legs: Bilateral weight bearing Upper arms: 20º extension to 20º flexion Lower arms: 60º to 100º flexion Wrists: 0º to 15º flexion Load: Less than 5 kg Coupling: Good (well-fitted handle) Activity: Body parts are static Low risk Low risk
  29. 29. M ANUSCRIPT ACCEPTED ACCEPTED MANUSCRIPT Table 4: Validation of knowledge for muscle activity Data and information Results (DSS) Results (CM) Neck effort level: Moderate-head forward about 20º Neck effort duration: 6 to 20s Neck effort frequency: 1-5/ min Shoulders effort level: Light-arm slightly extended Shoulders effort duration: Less than 6s Shoulders effort frequency: 1-5/ min Back effort level: Moderate-forward bending Back effort duration: 6 to 20s Back effort frequency: 1-5/ min Arms/ elbows effort level: Moderate-Moderate force of lifting Arms/ elbows effort duration: 6 to 20s Arms/ elbows effort frequency: 1-5/ min Wrists/ hands/ fingers effort: Moderate- grip with moderate force Wrists/ hands/ fingers effort duration: 6 to 20s Wrists/ hands/ fingers effort frequency: 1-5/ min Legs/ knees effort level: Moderate-bending forward Legs/ knees effort duration: 6 to 20s Legs/ knees effort frequency: 1-5/ min Ankles/ feet/ toes effort level: Moderate-bending forward Ankles/ feet/ toes effort duration: 6 to 20s Ankles/ feet/ toes frequency: 1-5/ min Medium risk Medium risk Trunk: 0º to 20º flexion Neck: 0º to 20º flexion and twisting Legs: Bilateral weight bearing Upper arms: 45º to 90º flexion Lower arms: 60º to 100º flexion Wrists: More than 15º flexion Load: Less than 5 kg Coupling: Fair (Handhold acceptable) Activity: Repeated small range actions Neck effort : Light-Head turned partly to side and slightly forward Neck effort duration: Less than 6s Neck effort frequency: Less than 1/ min Shoulders effort level: Light-Arm slightly extended Shoulders effort duration: Less than 6s Shoulders effort frequency: Less than 1/ min Back effort level: Moderate-forward bending Back effort duration: Less than 6s Back effort frequency: Less than 1/ min Arms/ elbows effort level: Light-Light lifting Arms/ elbows effort duration: Less than 6s Arms/ elbows effort frequency: Less than 1/ min Wrists/ hands/ fingers effort level: Light-light forces Wrists/ hands/ fingers effort duration: Less than 6s Wrists/ hands/ fingers effort frequency: Less than 1/ min Legs/ knees effort level: Light-standing without bending Legs/ knees effort duration: Less than 6s Legs/ knees effort frequency: Less than 1/ min Ankles/ feet/ toes effort level: Light-standing without bending Ankles/ feet/ toes effort duration: Less than 6s Low risk Low risk
  30. 30. M ANUSCRIPT ACCEPTED ACCEPTED MANUSCRIPT Table 5: Validation of knowledge for standing duration Data and information Results (DSS) Results (CM) More than 1 hour of continuous standing, or more than 4 hrs in total Medium risk Medium risk More than 1 hour of continuous standing, and more than 4 hrs in total High risk High risk
  31. 31. M ANUSCRIPT ACCEPTED ACCEPTED MANUSCRIPT Table 6: Validation of knowledge for holding time Data and information Results (DSS) Results (CM) Shoulder height: 50% Arm reach distance: 25% Low risk Low risk Shoulder height: 50% Arm reach distance: 50% Low risk Low risk Shoulder height: 75% Arm reach distance: 25% Low risk Low risk Shoulder height: 75% Arm reach distance: 50% Low risk Low risk Shoulder height: 100% Arm reach distance: 25% Medium risk Medium risk Shoulder height: 100% Arm reach distance: 50% Low risk Low risk Shoulder height: 50% Arm reach distance: 75% Medium risk Medium risk Shoulder height: 75% Arm reach distance: 75% Medium risk Medium risk Shoulder height:100% Arm reach distance: 75% Medium risk Medium risk
  32. 32. M ANUSCRIPT ACCEPTED ACCEPTED MANUSCRIPT Table 7: Validation of knowledge for whole-body vibration Data and information Results (DSS) Results (CM) Acceleration between 0.100 m/s2 to 0.214 m/s2 Comfort Comfort Acceleration between 0.315 m/s2 to 0.369 m/s2 Little discomfort Little discomfort Acceleration between 0.374 m/s2 to 0.382 m/s2 Fairly discomfort Fairly discomfort
  33. 33. M ANUSCRIPT ACCEPTED ACCEPTED MANUSCRIPT Table 8: Validation of knowledge for indoor air quality Data and information Results (DSS) Results (CM) Carbon dioxide: 4045 ppm Safe Safe Carbon monoxide: 1.67 ppm Safe Safe
  34. 34. M ANUSCRIPT ACCEPTED ACCEPTED MANUSCRIPT Table 9: Validation for PSSI Risk factors Risk level Rating criterion Multiplier Results (Manual) Results (DSS) Working posture Low Safe 2 2x1x244x1x3x1 = 1464Muscle activity Low Little fatigue 1 1464 Standing duration High Unsafe 244 Holding time Low Comfort 1 Whole-body vibration Little discomfort Little discomfort 3 Indoor air quality Safe Safe 1
  35. 35. M ANUSCRIPT ACCEPTED ACCEPTED MANUSCRIPT Figure 1: An integrated environment between Ergonomic Workstation model and DSSfPS model within MOSES architecture INTEGRATED ENVIRONMENT DESIGN FOR FUNCTION MANUFACTURING CODE GENERATION MANUFACTURING MODELPRODUCT MODEL DESIGN FOR MANUFACTURE DSSfPS Ergonomic Workstation
  36. 36. M ANUSCRIPT ACCEPTED ACCEPTED MANUSCRIPT Figure 2: Information structure in the Ergonomic Workstation model to represent human- machine-environment in a workstation COMPANY EXP ERT EXP ERT EXP ERT EXP ERT EXP ERT EXP ERT DEPARTMENT EXP ERT EXP ERT EXP ERT EXP ERT EXP ERT EXP ERT WORKSTATION EXP ERT EXP ERT EXP ERT EXP ERT EXP ERT WORKER IAQ WBV EXP ERT EXP ERT EXP ERT EXP ERT EXP ERT EXP ERT JOB ACTIVITY STANDING DURATION HOLDING TIME MUSCLES ACTIVITY WORKING POSTURE
  37. 37. M ANUSCRIPT ACCEPTED ACCEPTED MANUSCRIPT Figure 3: DSSfPS model and its mechanisms Existing Workstation Design Ergonomics Experts Improved Workstation Design DSSfPS Model Working Memory Knowledge Base Inference Engine Analysis and Design of Standing Workstation GUIs Ergonomics Info database Ergonomics Workstation Model
  38. 38. M ANUSCRIPT ACCEPTED ACCEPTED MANUSCRIPT Figure 4: Structure of knowledge base and working memory in the DSSfPS model DSSfPS Posture Memory Worker Memory Muscle Rule Posture Rule Holding Rule IAQ Rule IAQ Memory KNOWLEDGE BASE WBV Rule PSSI Rule Holding Memory WORKING MEMORY PSSI Memory Muscle Memory Standing Memory WBV Memory Standing Rule

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