Evaluation Matrix Research Articles

Strategies for environmental sustainability: analyzing the green and digital transitions

Purpose Green and digital transitions (twin transition) help in achieving climate neutrality, cutting down the pollution level and reviving biodiversity. Organizations can mitigate their ecological footprint, improve their digital portfolio and fulfil their ethical responsibilities by fostering a sustainable global community and adhering to a well-defined plan. In such backdrop, this study aims to propose strategies by identifying the strengths, weaknesses, opportunities and threats of the twin transition and to categorize and evaluate the internal and external factors for achieving environmental sustainability. Design/methodology/approach Following a comprehensive review of the literature, 27 factors have been identified and analyzed using the strengths, weakness, opportunities and threat matrix (SWOT). Threats, opportunities, weakness, strength matrix analysis approach is used to limit risk, overcome weaknesses and take advantage of opportunities. Effective strategies are designed and implemented using strategic planning action and evaluation matrix (SPACE) and quantitative strategic planning (QSPM) approach. Findings The study found that aggressive strategies considering economic strength and global opportunities must be used to accomplish green growth objectives. Out of three strategies, the focus on green technological innovation by taking advantage of financial mechanisms at the international level is highly recommended to achieve environmental sustainability. Research limitations/implications Through promoting green and digital transformation, the current study provides insightful policy recommendations for economic development. It may help to improve operational efficacy and to obtain a competitive edge by successfully limiting their adverse environmental effects in the global market through the implementation of twin transition. The process of developing a strategy aids stakeholders, including society, workers and other parties, in giving environmental sustainability a top priority while making financial decisions. Social implications Strategy development helps society, employees and all the other stakeholders to prioritize their green and digital transition decisions for environmental sustainability. Originality/value The current study identifies the factors formulating strategies using SWOT, TOWS, SPACE and QSPM matrix analysis for achieving environmental sustainability in the pervasive twin transition.

Comparison of K-Nearest Neighbor and Naïve Bayes algorithms for hoax classification in Indonesian health news

The categorization of health-related hoaxes is paramount in determining if they report facts. This paper analyzes the accuracy of the K-Nearest Neighbor (KNN) and the Naïve Bayes Classifier as two algorithms for health news hoaxes classification. Text mining was employed by feature extraction employing the TF-IDF method from the news headlines to classify the clusters. A prototype model was used to develop the system. Models assessment included confusion matrices and k-fold cross-validation. K=3 KNN model attained an average accuracy of 82.91%, precision of 85.3% and recall of 79.38% with no predictors included. The best performance was recorded for using the Naive Bayes model at fixation of K=3 KNN model at an average accuracy of 86.42%, precision level of 88.10% and recall high of 84.05%. These findings suggest that the KNN surfaces in the last model level rather than in the absence of the Naive Bayes model concerning classifying the hoax position of health news visible through the confusion evaluative matrix. Although related studies have been conducted in the past, this study is dissimilar in terms of its preprocessing methods, size of the data, and outcomes. The dataset consists of 1219 hoaxes labelled and 1227 facts labelled news headlines

Robust competency-based evaluation matrix for directive PMO services in construction projects

The interest in using project management office (PMO) services in organizations to manage their construction projects is growing in light of rising economic, technological, and social developments based on their ability to achieve organizational goals while avoiding risks. Accordingly, organizations use PMO services to manage their technical and financial project issues to periodically evaluate PMO performance and services in a scientific, practical, and measurable way to ensure successful project path via PMO. Therefore, this research aims to develop a performance evaluation system that enables organizations to follow up and evaluate the PMO performance to ensure that PMO manages the organizations’ expectations and goals successfully according to certain quality, scope, and cost. The study builds on significant findings in PMO competence indexes as evaluation matrix, which includes five basic categories with 136 indexes covering the project life cycle. The matrix was developed based on literature analysis and supplemented with experts’ interviews in construction management. The developed robust competency-based index (RCI) for directive PMO supports the organizations to conduct client satisfaction, correction, or partial/total change of the PMO’s competence flow within five construction project life cycle and process, i.e. governance, portfolio, information, execution, and contract issues.

The whole process control algorithm of inflatable dustproof shed construction in substation engineering based on graph neural network

Abstract During the construction of an inflatable dust-proof shed in substation engineering, it is one of the key issues in power system research to use scientific whole-process control algorithms to complete substation engineering decisions. At present, the control algorithm has some problems, such as low control accuracy and poor convergence speed. Therefore, the whole process control algorithm of inflatable dust shed construction in substation engineering based on a graph neural network is proposed. The control indexes of the whole process of inflatable dust-proof shed construction in substation engineering are divided into the highest level, the middle level, and the lowest level. The discriminant matrix is established to obtain the single-factor fuzzy evaluation matrix of the middle-level index. Thus, the control indexes are constructed. According to this index, the basic operation flow of the graph neural network is constructed, the objective function of the graph neural network is set, the control steps of the whole process of inflatable dust-proof shed construction in substation engineering is analyzed, and the algorithm research is realized. The experimental results show that the convergence steps are greatly reduced, the control accuracy is high, and the convergence speed is significantly improved after applying the algorithm proposed in this paper.

Enhancing information fusion and feature selection efficiency via the PROMETHEE method for multi-source dynamic decision data sets

With the surge in big data, the complexity of synthesizing information from multiple sources has become a critical challenge for feature selection methodologies. Feature selection is the process of reducing the number of attributes in data. Traditional single-source centric approaches are inefficient, requiring extensive preprocessing for multi-source data consolidation prior to feature selection. At the same time, an information fusion method is needed to transform the multi-source information system with selected features into a single-source information system. This paper introduces a novel multi-source information fusion and feature selection approach that seamlessly integrates the Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) with a dynamic adaptation mechanism. This method is adept at addressing the complexities introduced by the evolving nature of feature and information source dimensions. The Attribute Evaluation Matrix (AEM) and the Attribute Preference Degree Matrix (APDM) are proposed to systematically assess and rank the significance of attributes within a static decision-making framework. Following this, an information fusion method using the source center is proposed. The dynamic feature selection and information fusion methods are proposed to deal with the condition when number of attributes and samples change. Extensive experimental validation confirms that this method not only reduces the computational overhead associated with multi-source feature selection but also significantly enhances the efficiency as the volume and variety of data sources increase.

A Z-number-based three-way decision method with classification-based state determination for the evaluation of new energy enterprises

New energy enterprises are important promoting factors for sustainable development of modern society. Limited budgets of governments require that new energy enterprises should be efficiently evaluated before they are founded. Existing evaluation methods ignored the hesitation of experts to some alternatives. Although the three-way decision method has been applied widely as a method to evaluate alternatives, the determination of the state set has not been deeply discussed. To solve these challenges, this paper proposes a Z-number-based three-way decision method with classification-based state determination, which can assign alternatives with hesitation to a boundary region for further consideration and compute the conditional probability with a classification-based method. First, since traditional fuzzy sets cannot ensure the reliability of decision information, an evaluation matrix based on Z-numbers is constructed. Second, a fuzzy best-worst method is applied to determine the weights of criteria. Third, the conditional probability is computed based on classification-based state sets that are obtained by a sorting method. An example regarding the evaluation and selection of new energy enterprises demonstrates the validity and stability of the proposed method. The comparison analysis shows that our proposed method can divide alternatives into different regions efficiently and is less affected by the variation of parameters.

Risk Classification of Food Incidents Using a Risk Evaluation Matrix for Use in Artificial Intelligence-Supported Risk Identification.

Foodborne illnesses and mortalities persist as a significant global health issue. The World Health Organization estimates that one out of every ten individuals becomes ill following the consumption of contaminated food. However, in the age of digitalization and technological progress, more and more data and data evaluation technologies are available to counteract this problem. A specific challenge in this context is the efficient and beneficial utilization of the continuously increasing volume of data. In pursuit of optimal data utilization, the objective of the present study was to develop a Multi-Criteria Decision Analysis (MCDA)-based assessment scheme to be prospectively implemented into an overall artificial intelligence (AI)-supported database for the autonomous risk categorization of food incident reports. Such additional evaluations might help to identify certain novel or emerging risks by allocating a level of risk prioritization. Ideally, such indications are obtained earlier than an official notification, and therefore, this method can be considered preventive, as the risk is already identified. Our results showed that this approach enables the efficient and time-saving preliminary risk categorization of incident reports, allowing for the rapid identification of relevant reports related to predefined subject areas or inquiries that require further examination. The manual test runs demonstrated practicality, enabling the implementation of the evaluation scheme in AI-supported databases for the autonomous assessment of incident reports. Moreover, it has become evident that increasing the amount of information and evaluation criteria provided to AI notably enhances the precision of risk assessments for individual incident notifications. This will remain an ongoing challenge for the utilization and processing of food safety data in the future.

Measuring complexity in mega construction projects: fuzzy comprehensive evaluation and grey relational analysis

PurposeMega construction projects (MCPs), characterized by their vast scale, numerous stakeholders and complex management, often face significant uncertainties and challenges. While existing research has explored the complexity of MCPs, it predominantly focuses on qualitative analysis and lacks systematic quantitative measurement methods. Therefore, this study aims to construct a complexity measurement model for MCPs using fuzzy comprehensive evaluation and grey relational analysis.Design/methodology/approachThis study first constructs a complexity measurement framework through a systematic literature review, covering six dimensions of technical complexity, organizational complexity, goal complexity, environmental complexity, cultural complexity and information complexity and comprising 30 influencing factors. Secondly, a fuzzy evaluation matrix for complexity is constructed using a generalized bell-shaped membership function to effectively handle the fuzziness and uncertainty in the assessment. Subsequently, grey relational analysis is used to calculate the relational degree of each complexity factor, identifying their weights in the overall complexity. Finally, the weighted comprehensive evaluation results of project complexity are derived by combining the fuzzy evaluation results with the grey relational degrees.FindingsTo validate the model’s effectiveness, the 2020 Xi’an Silk Road International Conference Center construction project is used as a case study. The results indicate that the overall complexity level of the project is moderate, with goal complexity being the highest, followed by organizational complexity, environmental complexity, technical complexity, cultural complexity and informational complexity. The empirical analysis demonstrates that the model can accurately reflect the variations across different dimensions of MCP complexity and can be effectively applied in real-world projects.Originality/valueThis study systematically integrates research on MCPs complexity, establishing a multidimensional complexity measurement framework that addresses the limitations of previous studies focusing on partial dimensions. Moreover, the proposed quantitative measurement model combines fuzzy comprehensive evaluation and grey relational analysis, enhancing the accuracy and objectivity of complexity measurement while minimizing subjective bias. Lastly, the model has broad applicability and can be used in MCPs across different countries and regions, providing a scientific and effective basis for identifying and managing MCP complexity.

An Analysis of Alternative Strategy Model for Agroindustry Business Development of Crystal Guava in Malang Raya

PT. Kreasi Tani Bumiaji is agroindustry company located in Banaran, Bumiaji Village, Bumiaji District, Batu City, East Java. This company focuses on processing agricultural products in the form of crystal guava and becomes a superior local product. This study aimed at analyzing alternative strategy of agroindustry business development of crystal guava as a product in PT. Kreasi Tani Bumiaji. Descriptive-quantitative method was used to collect the primary data from interviews and questionnaire items. Eight participants from the company were selected by using purposive sampling technique. The secondary data were obtained from academic articles. The data were analyzed by using SWOT analysis, Internal Factor Evaluation (IFE) Matrix, External Factor Evaluation (EFE) Matrix, Internal-External (IE) Matrix, and QSPM. The results of analysis showed that main internal factor is diversification of processed products with the score 0.277. Meanwhile, external factor is internship collaboration with the score 0.418. The position of company in IE matrix lies on the fifth cell which showed that stable growth with coordinate scores 2.930; 2.594. According to 15 alternative strategies identified using SWOT analysis, prioritized strategy based on QSPM analysis refers to business partnership with investors for the development of production facilities, business concept, and marketing with the highest score 7.23.

The Strategy of Agroindustry Development of Teng-Teng Apel (A Case Study on Hilwa Nusantara Shop in Batu City)

This study is conducted by purpose of identifying company environment and analyzing the strategy for agroindustry development of Teng-Teng Apel (cake made from apple fruit) in Hilwa Nusantara Shop in Batu City. The methods of data analysis used in this study are internal and external company analysis, Internal Factor Evaluation (IFE) Matrix, External Factor Evaluation (EFE), SWOT analysis, and Quantitative Strategic Planning Matrix (QSPM) analysis. The results of the study have showed that SWOT analysis indicates IFAS score 2.84 and EFAS value 2.94. Moreover, agroindustry position of Teng-Teng Apel of Hilwa Nusantara Shop is at Quadrant One (I). Based on company position, there are three alternative strategies, 1. To increase production capacity and quality using technology, 2. To extend market in developing sales volume, and 3. To maintain relationship and trust with raw material suppliers. First becomes first priority for Hilwa Nusantara with TAS value 5.49.

The position of diplomacy in Iranian sports

Sports diplomacy is a pervasive phenomenon that at the diplomatic level is one of the most important components of communication between countries and nations, and therefore it is important to know what is the position of Iran, as a powerful country in the field of diplomacy in this area. The purpose of this study was to investigate the position of diplomacy in Iranian sports. The research method was descriptive-analytical and applied in terms of purpose. The statistical population of the study was all political-sports managers and experts in the field of sports diplomacy who were selected by purposive and accessible sampling. The data collection method included library studies, semi-structured interviews with experts and finally the Delphi method in three stages. In order to identify the strategic position, the Internal Factor Evaluation Matrix (IFE) and the External Factor Evaluation Matrix (EFE) were used. The results showed that the position of Iranian sports diplomacy is in the position of WO (conservative strategy). In this regard, it is concluded that high-level officials and policymakers in the Ministry of Sports, the Ministry of Foreign Affairs and other relevant institutions should focus their plans on addressing weaknesses and taking advantage of existing opportunities.

Unveiling and modelling the impact of safety drivers on the overall success of construction projects in developing countries

The construction industry faces significant risks, leading to high accident rates and injuries, underscoring the urgent need for effective safety implementation. In developing countries, safety implementation is often inadequate, leading to increased incidents and project failures. This study investigates how implementing safety drivers (SDs) can enhance overall project success (OPS) in construction through a five-step process. Initially, systematic searches and scientometric analyses are conducted to review existing literature and establish a foundational understanding. A questionnaire is then distributed to experts in the Egyptian construction sector. The third step involves exploratory factor analysis (EFA) to categorize relevant variables. Following this, partial least squares-structural equation modeling (PLS-SEM) tests the proposed conceptual model and analyzes relationships among the variables. The final step employs Fuzzy Synthetic Evaluation (FSE) to rank SDs categories. Findings from PLS-SEM indicate that SDs have a medium to high impact on OPS, accounting for 58.8% of the total influence. The FSE results reveal that the planning and implementation group ranks as the most significant at 3.48 on evaluation matrix. This research contributes to the limited literature on safety in developing countries like Egypt by proposing a novel model that links SDs to OPS, offering practical insights for the construction industry.

Combat Effectiveness Evaluation of UAV Intelligent Swarm Based on AHP-Fuzzy Comprehensive Evaluation

In order to evaluate the combat effectiveness of UAV intelligent swarm, this paper puts forward the establishment of UAV intelligent swarm combat effectiveness evaluation index system based on availability, credibility and combat ability, analyzes the factors affecting the combat effectiveness of UAV intelligent swarm, establishes the effectiveness evaluation model, determines the weight of each factor by AHP, and evaluates the effectiveness index by fuzzy evaluation matrix. Using this method to evaluate the combat effectiveness of UAV intelligent swarm, the evaluation result is objective and reasonable, which provides theoretical reference for the optimal design and use of UAV intelligent swarm system.

Research on Optimization Path of Higher Education Management Based on Collaborative Filtering Algorithm

In order to better realize the optimization of university education management, this paper puts forward the research on the optimization path of university education management under collaborative filtering algorithm. The optimization of higher education management is divided into several management directions. First, the teaching resource recommendation management, using collaborative filtering algorithm based on emotional tendency, analyzes the comments of users of teaching resources, and recommends the teaching resources to users next time according to the emotional tendency obtained from the analysis. Second, the library book recommendation management, constructing the characteristic model of library book borrowing users, uses collaborative filtering algorithm to generate nearest neighbors, calculating the similarity of users, and recommending books that meet their characteristics for users according to the similarity. Finally, the management of personalized course selection recommendation in colleges and universities, which uses the historical information of students to establish the evaluation matrix of course selection, searches the nearest neighbors according to the evaluation matrix, and produces the personalized course selection recommendation results of the students according to the nearest neighbors. According to the experiment, the recommended users can be satisfied with the recommended results in different directions of education management. After applying this method, the overall quality of college students has been significantly improved, the expenditure of education management in colleges and universities has been significantly reduced, and the optimization effect of education management in colleges and universities has been remarkable.

Marketing Strategy for Mitraguna Online Financing Products in Increasing the Number of Customers

This study aims to determine potential marketing strategies to increase the number of customers in the online mitraguna financing product of Bank Syariah Indonesia, Kisaran sub-branch office. This research uses qualitative research methodology. In qualitative research, the findings are analyzed and then reported by the researcher. In this study, two different types of data were used, namely primary and secondary. Interviews were used to obtain primary data, while publications from relevant financial institutions as well as relevant scientific literature were used to collect secondary data. The results of the data analysis resulted in the largest threat weight of 0.092538, the largest opportunity weight of 0.571429, the highest weakness weight of 0.210526, and the highest strength weight of 0.421053. The average overall score of the Internal Factor Evaluation matrix is 2.736842, but the average score of the External Factor Evaluation matrix is 2.952381. The results of this study indicate that the marketing strategy that needs to be implemented lies in quadrant V, namely the strategy of maintaining and maintaining by obtaining 5 alternative strategies that are expected to contribute to the Bank Syariah Indonesia Kisaran sub-branch office to increase the number of online mitraguna financing customers.

Metacognitive Knowledge: The Effect of Strategy Evaluation Matrix on University Students’ Reading Comprehension Performance

Understanding written texts is problematic for many university students studying English as a Foreign Language. It is not just about having an extensive vocabulary; students also need tools and strategies to comprehend expository texts. One such tool is the Strategy Evaluation Matrix (SEM), which integrates cognitive strategies and metacognitive knowledge. Research has shown that SEM significantly enhances students' reading comprehension performance. By utilizing SEM, students can improve their understanding of written texts. This study aims to investigate the impact of SEM on students’ reading comprehension performance. To achieve this, a post-test-only nonequivalent control group design was used. Two intact groups from the first semester at the Faculty of Languages, Letters, and Arts, Kenitra, Morocco (FLLAK) were selected. The sample consisted of 66 students in both the control and the experimental groups, using a convenient sampling technique. Data for this study were collected using a TOEFL iBT reading comprehension practice test (i.e. the post-test). Results were analyzed using SPSS version 29 and indicated a significant difference between the experimental and the control groups. Students who were instructed to use SEM while reading and answering comprehension questions scored higher compared to those who did not use it. Therefore, employing SEM in reading comprehension indicates that our educational system should consider integrating psychological tools and theories in the teaching and learning of English as a Foreign Language, specifically in the domain of reading comprehension.

A Relative Projection-Based Multiattribute Group Decision-Making Model With Noncooperative Behavior Management and Application to NEV Supplier Selection.

This article reports a novel consensus model where a group of internal and external experts evaluate alternatives under multiple attributes and provide mutual evaluations. First, different from previous studies, the cognitive and interest conflicts of internal and external experts are considered simultaneously. But interest conflict is emphasized for internal experts, and cognitive conflict is mainly considered for external experts. Second, we explore the categorization and management methods of noncooperative behaviors (NCBs) of experts. The relative projection-based indexes are proposed for the first time to measure the degrees of cognitive and interest conflicts by using multiattribute preference matrices (MAPMs) and the weight vectors of attributes. A group of experts are divided into three categories and the corresponding management strategies are developed. Third, we investigate the consensus mechanism among experts with cognitive and interest conflicts. For reaching an acceptable consensus level, an adjustment process is proposed to revise some local entries in MAPMs and mutual evaluation matrix (MEM). A penalty mechanism is further established to dynamically update the weights of experts. An algorithm is designed to capture the consensus reaching process in multiattribute group decision making, where internal and external experts are distinguished by proposing a parameter. Finally, the high-performance battery supplier selection of new energy vehicle is studied to illustrate the proposed model. The results reveal that the efficiency of reaching consensus can be enhanced by using the developed model with effective management of NCBs.

A comprehensive analysis and comparison of SPI and SPEI for spatiotemporal drought evaluation.

Assessing drought is crucial for effective water resources management and the development of mitigation strategies. Drought indices serve as indispensable tools in this evaluation process, and choosing an appropriate index is vital for accurate drought assessment. The characteristics and classification of drought depend entirely on the chosen index. Based on the existing literature, the Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) are the most commonly and widely used indices, and there is a significant need for a comprehensive comparison between SPI and SPEI to understand their differences and implications for drought assessment. This research aims to compare SPI and SPEI based on drought indices, characteristics, and classifications using the innovative drought classification matrix (IDCM) for spatiotemporal drought evaluation, and the comparison process is done for events and monthly scales. Also, it aims to investigate the comparison between SPEI obtained from in situ meteorological stations and from the SPEI database. The application and the comparison are presented for Istanbul city between 1951 and 2020. The results show similar variations and high correlation (more than 0.65) between SPI and SPEI. For drought characteristics, there is no consistent relationship between SPI and SPEI at the drought event scale. The outcomes revealed that approximately 60% of the months exhibit consistent drought classifications between the two indices. Finally, it shows a significant difference between SPEI based on in situ meteorological stations and satellite data. The comparison between SPI and SPEI based on different aspects is necessary and essential for drought studies and water resources management.

Language Acquisition Games: Its Effects on the Students’ Oral Language Skills

Facing the 21st century English language learners today marks a bit change and a formation of strategized teaching methodology on how facilitators must uphold and seek highest form of learning. K to 12 curriculum aims to acquire highly- developed language skills, with this, teachers must find ways to uplift the oral proficiency level of students as one key component of a well- developed communicative competence. This study investigates using pre-test post- test experimental design to elucidate the level of oral language skills of 60 heterogeneous oral- expressive students prior to and after the administration of the language acquisition games along comprehension, fluency, vocabulary, pronunciation, and grammar. It also identified the significant differences on each aspect. Qualitative descriptive analysis and in- depth observation was used to identify the learning behaviours manifested by the oral- expressive students. Face- to- face ESL pre- and post- oral assessment was undertaken by the respondents and a Stanford FLOSEM (Foreign Language Oral Skills Evaluation Matrix) rubric was utilized to assess oral- expressive students’ level of oral- language skills by an expert validator. Analyses of data revealed an increase of one level from the pre- test to post- test result after the administration of the 30-40 days language acquisition games intervention. Further, there is also a significant difference between pre- and post- test along each aspect of oral language skills and identified seven learning behaviors seen in the viewpoint of the students namely; active engagement through rewards and punishments, everyone made effort to be involved in the game, properly guided through rules given time under pressure, given chance to speak during class discussion, develop inquisitive thinking through answering HOTS questions, and overcome ridicules, boosting confidence. Thus, the researcher devised an output entitled “Language Acquisition Games: A Kit towards Effective Oral Communication Skills for Grade 8 Students” to develop speaking skills of students. Curriculum developers, materials developer and language institutes might integrate these language games into an English language classroom to at least enhance students’ oral language skills.

Construction of on-site DNA pre-treatment device and rapid electrochemical biosensor set for Escherichia coli detection in milk

Restaurants and cafeterias are constantly exposed to bacterial contamination. In such environments, it is highly needed the rapid bacteria detection system with simple pre-treatment device. To solve this problem, this study presented the pre-treatment device and Rapid electrochemical (REC) biosensor set for detecting Escherichia coli (E. coli) gene in milk sample. The pre-treatment device is consisted of tetraethyl orthosilicate (TEOS)-coated bead that operated with sonication method, and it can effectively remove the E. coli residues and extract E. coli DNA well. By analyzing the sequences of various foodborne pathogens, we selected the highly variable region sequences of E. coli DNA to use as the bioprobe for REC biosensor. The selected thiol-modified bioprobe was immobilized onto the fabricated biosensor electrode. For detecting the extracted E. coli gene fragment rapidly, the alternating current electrothermal flow (ACEF) technique was introduced to REC-biosensor. ACEF technique minimize the target-bioprobe binding time to 10 mins. Using the extracted E. coli DNA fragments with the pre-treatment device, the proposed REC-biosensor shows the limit of detection (LOD) in milk samples was 9.235 × 10-5 ng/μL, and the target detection accuracy was 92.5 %, with a maximal error rate up to 6.730 %. Moreover, the biosensor demonstrated high selectivity in electrochemical performance evaluation (Matrix effect = 7.425 %). The developed pre-treatment device provides simple and quick preprocessing time (within 20 min) of E. coli, and REC biosensor provides the rapid detection of E. coli (within 10 min). In conclusion, the proposed pre-treatment device and biosensor set can be easily applied to other types of bacteria, virus or germs in the near future.