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The feasibility of bioprocess development relies heavily on the successful application of primary recovery and purification techniques. Aqueous two-phase extraction (ATPE) disrupts the definition of "unit operation" by serving as an integrative and intensive technique that combines different objectives such as the removal of biomass and integrated recovery and purification of the product of interest. The relative simplicity of processing large samples renders this technique an attractive alternative for industrial bioprocessing applications. However, process development is hindered by the lack of easily predictable partition behaviours, the elucidation of which necessitates a large number of experiments to be conducted. Liquid handling devices can assist to address this problem; however, they are configured to operate using low viscosity fluids such as water and water-based solutions as opposed to highly viscous polymeric solutions, which are typically required in ATPE. In this work, an automated high throughput ATPE process development framework is presented by constructing phase diagrams and identifying the binodal curves for PEG6000, PEG3000, and PEG2000. Models were built to determine viscosity- and volume-independent transfer parameters. The framework provided an appropriate strategy to develop a very precise and accurate operation by exploiting the relationship between different liquid transfer parameters and process error. Process accuracy, measured by mean absolute error, and device precision, evaluated by the coefficient of variation, were both shown to be affected by the mechanical properties, particularly viscosity, of the fluids employed. For PEG6000, the mean absolute error improved by six-fold (from 4.82% to 0.75%) and the coefficient of variation improved by three-fold (from 0.027 to 0.008) upon optimisation of the liquid transfer parameters accounting for the viscosity effect on the PEG-salt buffer utilising ATPE operations. As demonstrated here, automated liquid handling devices can serve to streamline process development for APTE enabling wide adoption of this technique in large scale bioprocess applications. • Automation minimised experimental set-up for binodal curve construction. • High throughput screening and automation can expedite ATPS characterisation. • Physicochemical properties of fluids impact the automated transfer characteristics. • Transfer volume and speed are critical parameters to determine optimal transfer. • Correlation of viscosity and transfer parameters minimises manipulation errors. [ABSTRACT FROM AUTHOR]

This article addresses the design of a compact heat exchanger for the cooling of electronic systems. The Quality Function Deployment (QFD) method is used to identify crucial product features to improve device performance and key customer requirements. The QFD simplifies management processes, allowing modifications to device components, such as design parameters (dimensions and materials) and operating conditions (flow type and preferred temperature range). The study was applied to analyse the fundamental features of a compact heat exchanger, assessing their impact on enhancing heat transfer intensity during fluid flow through mini-channels. The thermal efficiency of the compact heat exchanger was tested experimentally. The results allow to verify the results obtained from the numerical simulations due to Simcenter STAR-CCM+. Consequently, the experimental part was reduced in favour of numerical simulations conducted using this commercial CFD software version 2020.2.1 Build 15.04.01. The numerical simulations performed with the aid of CFD showed increases in the heat transfer coefficient of up to 180% compared to the case treated as a reference. The application of the QFD matrix significantly reduces the time required to develop suitable design and material solutions and determine the operating parameters for the cooling of miniature electronic devices. [ABSTRACT FROM AUTHOR]

Chinese traditional cultural symbols possess great aesthetic and cultural value, and are widely utilized in product design. In this study, we explore the relationship between metaphor design based on traditional cultural symbols, customer experience and cultural identity, and further estimate how these three variables stimulate consumers' perceived value to generate consumers' purchase intention. Based on existing traditional cultural literature and Stimulus-organism-response theory (SOR), we proposed a theoretical research model to characterize the relationship among metaphor design based on traditional cultural symbols, customer experience, cultural identity, perceived value and consumers' purchase intention. A research survey was conducted and 262 questionnaires were collected in total with 241 valid. We used Smart PLS graph version 3.0 for data analysis. Results indicate that the cognition of metaphor design based on traditional cultural symbols and customer experience has a direct and significant impact on the emotional value thereby, eliciting consumers' purchase intention, metaphor design based on traditional cultural symbols is directly and indirectly (i.e., through customer experience or perceived value) positively associated with consumers' purchase intention, also customer experience is directly and indirectly (i.e., through perceived value) associated with consumer purchase intention, cultural identity mediates the indirect effect of customer experience and perceived value on purchase intention, the moderating role of cultural identity between customer experience and perceived value is not significant. Our findings help to expand the existing literature on consumer purchase intentions by rationally using traditional cultural symbols in the product metaphor design. [ABSTRACT FROM AUTHOR]

Classifying customers according to their characteristics can effectively meet the genuine needs of different customer groups. It also helps enterprises formulate reasonable marketing strategies and obtain considerable profits. Currently, there are many ways to classify customers. However, the procedures involved are complicated and cannot comprehensively and objectively reflect customer characteristics. Therefore, a customer group classification model is designed based on the deep cross network (DCN). The DCN algorithm can automatically learn simple data features, achieving data clustering. For the defects in this model, the deep weighted k-means clustering network (DWKCN) customer group classification method is constructed, improving the DCN algorithm. From the results, the algorithm has a high accuracy of 99.5%. Therefore, the proposed DWKCN algorithm can realize the customer group's precise division and the marketing plan design, providing the references for different types of customers to formulate personalized needs. [ABSTRACT FROM AUTHOR]

The concept of integrated sustainable product design has recently emerged, aiming to incorporate downstream life cycle performance into the initial product design to enhance sustainability. Various sustainable product design tools based on life cycle assessment or quality function deployment have been established while the impact of reliability on circular practices has received limited attention. Recognizing the critical role of product reliability in post-design performance, this paper develops a product design optimization model that considers the warranty performance and the effect of end-of-use options. The model takes into account the effect of uncertain operating conditions on product reliability. Two optimization goals including the minimization of expected unit life cycle cost and environmental impact are achieved by the model. To demonstrate the benefits of the integrated approach, the model is applied to an electric motor design problem. The results highlight that integrating end-of-use options in the early design phase leads to adjustments in component selection and reliability design. Moreover, the circular utilization of used products enables cost savings throughout the product's life cycle and contributes to environmental impact reduction. Lastly, the study analyzes the effects of operating conditions, warranty policies, and take-back prices for used products on design decisions, providing valuable insights for product designers. [ABSTRACT FROM AUTHOR]

The transition to Industry 4.0 and 5.0 underscores the need for integrating humans into manufacturing processes, shifting the focus towards customization and personalization rather than traditional mass production. However, human performance during task execution may vary. To ensure high human–robot teaming (HRT) performance, it is crucial to predict performance without negatively affecting task execution. Therefore, to predict performance indirectly, significant factors affecting human performance, such as engagement and task load (i.e., amount of cognitive, physical, and/or sensory resources required to perform a particular task), must be considered. Hence, we propose a framework to predict and maximize the HRT performance. For the prediction of task performance during the development phase, our methodology employs features extracted from physiological data as inputs. The labels for these predictions—categorized as accurate performance or inaccurate performance due to high/low task load—are meticulously crafted using a combination of the NASA TLX questionnaire, records of human performance in quality control tasks, and the application of Q-Learning to derive task-specific weights for the task load indices. This structured approach enables the deployment of our model to exclusively rely on physiological data for predicting performance, thereby achieving an accuracy rate of 95.45% in forecasting HRT performance. To maintain optimized HRT performance, this study further introduces a method of dynamically adjusting the robot's speed in the case of low performance. This strategic adjustment is designed to effectively balance the task load, thereby enhancing the efficiency of human–robot collaboration. [ABSTRACT FROM AUTHOR]

Managers and experts today realize that maintaining customer satisfaction is impossible without addressing customers' needs and turning them into necessities. In this type of research, three methods are employed, including quality function deployment, hierarchical analysis, and TOPSIS technique for order preference by similarity to the ideal solution with the fuzzy logic approach at each stage. In this paper, a fuzzy-multi-criterion decision making approach is conceived to develop an identifying and prioritizing process. In the proposed methodology, distance-based optimization methodology that is, TOPSIS integrated with fuzzy system, identifies the features of general selection criteria. Since criteria are mostly based on the subjective judgment of 'technical requirement' and 'customer requirement', AHP and QFD (quality function deployment) methodology have been integrated into AHP–QFD methodology. Mehr housing mass building companies in Tehran Province are the population of this study, which belongs to the applied research category and has been conducted by the descriptive-survey method. Based on the results, the most crucial demands from the Mehr housing company are reasonable prices, constructor commitment to on-time rendering, and earthquake safety, respectively. Hence, mass construction companies should pay particular attention to these aspects. In this regard, 15 technical necessities have been investigated. Results indicate that the old established contractor company, the proximity of the city to the new settlement, and the consideration of the construction engineering organization are the most crucial. Because necessities cannot be ranked exclusively based on the results of the house quality, two other criteria of cost and applicability were also taken into account, and necessities were once again ranked. Therefore, the most crucial necessities are the cooperative nature of the company, the old established contractor company, and the cultural stratification of the complex. [ABSTRACT FROM AUTHOR]

Modern consumption patterns lead to massive waste, which poses challenges in storage and highlights the urgent need for more sustainable product development. Customer feedback on products plays a crucial role in product design, yet previous studies overlooked these invaluable insights. In response, this study introduces a novel systematic methodology that integrates the strengths of text mining, Quality Function Deployment (QFD), and the Theory of Inventive Problem Solving (TRIZ). Text mining techniques are utilized to extract customer requirements from online platforms, while QFD is used to translate these requirements into technical specifications. By integrating the contradiction matrix from TRIZ theory with the triptych, technical conflicts are resolved. The design process for next-generation smart glasses is employed as an illustrative case to validate the proposed integrated innovation design approach. Analytical outcomes suggest that the introduced methodology can effectively address sustainable product design challenges and sets the stage for future advancements in smart glasses. [ABSTRACT FROM AUTHOR]

Quality Function Deployment (QFD) is a powerful approach for improving product quality that can transform customer requirements (CRs) into engineering characteristics (ECs) during product manufacturing. The limitations of traditional QFD methods lead to imprecise quantification of CRs and difficulty in accurately mapping customer needs. To address these issues, this paper introduces an innovative QFD approach that integrates extended hesitant fuzzy linguistic term sets (EHFLTSs), CRITIC, and cumulative prospect theory. The method expresses the subjectivity and hesitancy of decision makers when evaluating the relationship between ECs and CRs using EHFLTSs, considering the conflicts among CRs. The CRITIC is used to comprehensively evaluate the comparison strength and conflict between indicators, and the cumulative prospect theory is utilized to derive the prioritization of ECs. A case study is presented to demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

The objective of this research is to enhance the precision and efficiency of design concept assessments during the initial stages of new product creation. Design concept evaluation, which occurs at the end of the conceptual design phase, is a critical step in product development. The outcome of this evaluation significantly impacts the product's eventual success, as flawed design concepts are difficult to remedy in later stages. However, the evaluation of new product concepts is a procedure that encompasses elements of subjectivity and ambiguity. In order to deal with the problem, a novel decision-making method for choosing more logical new product concepts is introduced. Basically, the evaluation process is outlined in three main phases: the construction of evaluation index system for design concept alternatives, the calculation of weights for evaluation criteria and decision-makers, the selection of the best design concept alternatives. These stages are composed of a hybrid method based on kano model, multiplicative analytic hierarchy process (AHP) method, the entropy of IVPFS and improved grey relational projection (GRP) under interval-valued picture fuzzy set (IVPFS). The novel approach integrates the strength of interval-valued picture fuzzy number in handling vagueness, the advantage of multiplicative AHP and the merit of improved GRP method in modelling multi-criteria decision-making. In final, the effectiveness of the proposed model is validated through comparisons with other models. The potential applications of this study include but are not limited to product development, industrial design, and innovation management, providing decision-makers with a more accurate and comprehensive design concept evaluation tool. [ABSTRACT FROM AUTHOR]

The industrial landscape has evolved from mass production to demand-driven systems in a competitive market. Companies face constant change, prompting a need for efficiency and flexibility in operations. The Toyota production system, developed in the 1950s, emphasizes waste reduction and pull-based production. This article explores how the Toyota system is applied in a metallurgical company to reduce waste and improve processes through Kaizen. The general objective of this article is to analyze the pulled production system and its contribution to reducing inventories in the production process in a metallurgical company. This study was operationalized through a qualitative analysis, with the aid of the case study research method within a metallurgical production context. The study participants are a specific sector of the company and data collection involves documentary analysis and secondary data. Data analysis includes content analysis, focusing on reducing lead time and inventories in the production process. Through the implementation of the pull system and the value flow, the production process was optimized, reducing stocks, facilitating quality control, and allowing the rapid identification of anomalies. The development of a future state flow map showed how Toyota Production System tools, such as 5S, visual management and Kanban, can be applied to promote continuous improvement in the manufacturing process. The implementation of the pull system in the sector brought improvements such as production on customer demand, reduced and qualified stocks, ensuring timely deliveries in line with the philosophy of the Toyota Production System, which prioritizes production according to the customer's needs. [ABSTRACT FROM AUTHOR]

Kansei Engineering (KE) is a product design method that aims to develop products to meet users' emotional preferences. However, traditional KE faces the problem that the acquisition of Kansei factors does not represent the real consumers demands based on manual and reports, and using traditional methods to calculate relationship between Kansei factors and specific design elements, which can lead to the omission of key information. To address these problems, this study adopts text mining and backward propagation neural networks (BPNN) to propose a product form design method from a multi-objective optimization perspective. Firstly, Term Frequency-Inverse Document Frequency (TF-IDF) and WordNet are used to extract key user Kansei requirements from online review texts to obtain more accurate Kansei knowledge. Secondly, the BPNN is used to establish the non-linear relationship between product Kansei factors and specific design elements, and a preference mapping prediction model is constructed. Finally, BPNN is transformed into an iterative prediction value of non-dominated sorting genetic algorithm-II (NSGA-II), and the model is solved through multi-objective evolutionary algorithm (MOEA) to obtain the Pareto optimal solution set that satisfies the user's multiple emotional needs, and the fuzzy Delphi method is used to obtain the best product form design scheme that meets the user's multiple emotional images. Using the example of electric bicycle form design could show that this proposed method can effectively complete multi-objective product solutions innovation design. [ABSTRACT FROM AUTHOR]

To tackle the challenges of weak sensing capacity for multi-scale objects, high missed detection rates for occluded targets, and difficulties for model deployment in detection tasks of intelligent roadside perception systems, the PDT-YOLO algorithm based on YOLOv7-tiny is proposed. Firstly, we introduce the intra-scale feature interaction module (AIFI) and reconstruct the feature pyramid structure to enhance the detection accuracy of multi-scale targets. Secondly, a lightweight convolution module (GSConv) is introduced to construct a multi-scale efficient layer aggregation network module (ETG), enhancing the network feature extraction ability while maintaining weight. Thirdly, multi-attention mechanisms are integrated to optimize the feature expression ability of occluded targets in complex scenarios, Finally, Wise-IoU with a dynamic non-monotonic focusing mechanism improves the accuracy and generalization ability of model sensing. Compared with YOLOv7-tiny, PDT-YOLO on the DAIR-V2X-C dataset improves mAP50 and mAP50:95 by 4.6% and 12.8%, with a parameter count of 6.1 million; on the IVODC dataset by 15.7% and 11.1%. We deployed the PDT-YOLO in an actual traffic environment based on a robot operating system (ROS), with a detection frame rate of 90 FPS, which can meet the needs of roadside object detection and edge deployment in complex traffic scenes. [ABSTRACT FROM AUTHOR]

This paper presents a Fuzzy-QFD model to support the selection of startups to participate in acceleration programs. It proposes to model the selection process by Startup Selection relating the technology transfer and the innovative business modelling. The study provides a quantitative analysis to identify the best startups for participation in accelerated programs. To achieve such a goal, the model uses a multi-criteria decision-making based on QFD (Quality Function Deployment). This study is a pioneer in presenting a model that prioritises the selection of startups by using technology transfer criteria and business modelling components. The model was applied in an acceleration program in Brazilian, and the results indicate that the model is able to rank the companies by presenting which startups stand out. Therefore, the method can be used in prioritisation problems or in the selection of companies to participate in acceleration programs that hold technology transfer in their activities. [ABSTRACT FROM AUTHOR]

The freemium model, a business model that provides basic functions for free and makes a profit by providing advanced functions at a premium, has been widely adopted in various industries. To acquire enough customers and generate revenues from the freemium model, the proper allocation of free and premium functions is essential, but little research has been done in this direction. This study proposes a two-stage approach that combines the Kano model and the technology acceptance model (TAM) as a conceptual design method of freemium services. First, Kano analysis is performed to divide the functions of services into three types, must-be, one-dimensional, and attractive functions. The must-be functions are regarded as free functions, and low and high-level one-dimensional functions are classified as free and premium functions, respectively. Then, we perform path analysis based on TAM to identify additional premium functions that affect willingness to pay. We allocate free and premium functions of personal cloud service using the proposed approach and compare the results with the current functions of Dropbox, One Drive, and Google Drive. The proposed two-stage approach is expected to be practically used for designing service concepts based on the freemium business model in various industries. [ABSTRACT FROM AUTHOR]

Genomic sequencing of clinical samples to identify emerging variants of SARS-CoV-2 has been a key public health tool for curbing the spread of the virus. As a result, an unprecedented number of SARS-CoV-2 genomes were sequenced during the COVID-19 pandemic, which allowed for rapid identification of genetic variants, enabling the timely design and testing of therapies and deployment of new vaccine formulations to combat the new variants. However, despite the technological advances of deep sequencing, the analysis of the raw sequence data generated globally is neither standardized nor consistent, leading to vastly disparate sequences that may impact identification of variants. Here, we show that for both Illumina and Oxford Nanopore sequencing platforms, downstream bioinformatic protocols used by industry, government, and academic groups resulted in different virus sequences from same sample. These bioinformatic workflows produced consensus genomes with differences in single nucleotide polymorphisms, inclusion and exclusion of insertions, and/or deletions, despite using the same raw sequence as input datasets. Here, we compared and characterized such discrepancies and propose a specific suite of parameters and protocols that should be adopted across the field. Consistent results from bioinformatic workflows are fundamental to SARS-CoV-2 and future pathogen surveillance efforts, including pandemic preparation, to allow for a data-driven and timely public health response. [ABSTRACT FROM AUTHOR]

Ultra-narrow pulses serve as critical components in numerous applications. These pulses have ultra-fast leading edges that typically function as precision trigger signals to synchronize various instruments. Ultra-narrow pulses inherently exhibit an ultra-wide bandwidth, gaining significant attention in diverse electronic systems encompassing communications, radar imaging, electronic warfare, and others. Although several techniques have been explored for generating ultra-narrow pulses, field programmable gate arrays (FPGAs) offer a promising alternative in terms of flexibility and integration. This study introduces a scalable delay pulse synchronizer method with a resolution of 23 ps. A programmable, successive, narrow pulse sequence operating at a 1-GHz repetition frequency is implemented within a monolithic FPGA. The performance of the proposed method is evaluated using an existing board with a general commercial FPGA in the laboratory. This new method presents a convenient and efficient approach of achieving ultra-narrow pulse synchronization, being applicable across various fields. [ABSTRACT FROM AUTHOR]

The process capability index is a tool for quality measurement and analysis widely used in the industry. It is also a good tool for the sales department to communicate with customers. Although the value of the process capability index can be affected by the accuracy and precision of the process, the index itself cannot be differentiated. Therefore, the process incapability index is directly divided into two items, accuracy and precision, based on the expected value of the Taguchi process loss function. In fact, accuracy and precision are two important reference items for improving the manufacturing process. Thus, the process incapability index is good for evaluating process quality. The process incapability index contains two unknown parameters, so it needs to be estimated with sample data. Since point estimates are subject to misjudgment incurred by the inaccuracy of sampling, and since modern businesses are in the era of rapid response, the size of sampling usually tends to be small. A number of studies have suggested that a fuzzy testing method built on the confidence interval be adopted at this time because it integrates experts and the experience accumulated in the past. In addition to a decrease in the possibility of misjudgment resulting from sampling error, this method can improve the test accuracy. Therefore, based on the confidence interval of the process incapability index, we proposed the fuzzy testing method to assess whether the process capability can attain a necessary level of quality. If the quality level fails to meet the requirement, then an improvement must be made. If the quality level exceeds the requirement, then it is equivalent to excess quality, and a resource transfer must be considered to reduce costs. [ABSTRACT FROM AUTHOR]

Objective: The study aims to develop a mHealth application for seizure management based on the human system integration (HSI) approach. Background: Unmet healthcare needs among people with epilepsy continue to exist despite the advancement in healthcare technology. Current seizure management methods are found to be ineffective. Therefore, a more efficient strategy such as mHealth technology is necessary to aid seizure management. Method: The needs identification phase involved identifying the user requirements by interviewing 10 stakeholders and conducting thematic analysis and needs interpretation technique. In the solution identification phase, the system requirements were derived using various human-centered design and systems engineering approaches and were evaluated through quality function deployment to determine design targets. For the design and evaluation phase, the design targets were reflected in the app through the iterative prototyping process, and the interface and functional design were evaluated by seven human factors and ergonomics experts and four stakeholders, respectively. Results: Three primary needs and ten user requirements were derived from the needs identification phase. Ten out of fifteen system requirements were selected as design targets to be included in the final prototype. Results of the evaluation showed that the interface design of the proposed app showed superior usability compared to a competitor app and that the app functions were beneficial for the stakeholders. Conclusion: The mHealth app designed through the HSI framework showed good potential in addressing the main issues in seizure management. Application: The mHealth app design methodology based on the HSI approach can be applied to the design of small-scale systems in various domains. [ABSTRACT FROM AUTHOR]

The Biomedical Engineering Maintenance Services (BEMS) is a comprehensive maintenance program that ensures the safety and reliability of medical devices. Significant and crucial devices are identified and prioritized for best practice prior to the equipment life cycle to mitigate functional problems, alarmed by the Fourth Industrial Revolution (4IR) underlying the modernization agenda. A model of multi‐criteria decision‐making (MCDM) to prioritize medical devices according to their criticality is presented in this paper, with the utilization of quality function deployment (QFD) and fuzzy logic in the development of the model through a quantitative survey of experts from all regions in Malaysia. As a result, a customized version of the Asset Criticality Assessment (ACA) is developed and is recommended for use in more than 144 Ministry of Health (MOH) hospitals. Subsequently, real data of four selected devices are pulled from the Asset and Services Information System (ASIS) to demonstrate a relevant and comparable end‐result using the QFD and fuzzy logic. In essence, the key contribution of the customized ACA model is that it assesses a promising evaluation with a broader range on both the performance of medical devices and the appropriate asset replacement choices. This leads to an effective maintenance strategy for each device and the modernization of reliability computation metrics. [ABSTRACT FROM AUTHOR]

Quality function deployment (QFD) is a customer-driven product development technique that converts customer requirements (CRs) into design attributes (DAs) of a product and service. Nevertheless, in real situations, the traditional QFD method has been found that possesses some deficiencies, such as the accuracy assessment of relationships between CRs and DAs, and the inter-relationships among DAs. To fill in the above gaps, this study develops a new QFD approach by a CoCoSo-based ranking method under Pythagorean fuzzy environment. To begin with, an extended Pythagorean fuzzy decision-making trial and evaluation laboratory (DEMATEL) method is proposed to identify the relationships within DAs. Second, the aggregation method of the weighted average method and objective penalty function are propounded to construct the programming models for calculating the importance of DAs under Pythagorean fuzzy setting. Third, a new CoCoSo-based ranking method for Pythagorean triangular fuzzy numbers (PTrFNs) is proposed to obtain the ranking of DAs. Lastly, a case regarding "Ping An Health" mobile medical App is carried out to verify the effectiveness and superiority of the proposed QFD approach. The results show that the top DA is perceptibility. Therefore, perceptibility should be focus on firstly in the "Ping An Health" App design, such as system fluency, interface comfort and network stability. Additionally, the results show that the new QFD can express experts' hesitant assessment information, deal with the interrelations among DAs, and yield more precise rankings of DAs in QFD. [ABSTRACT FROM AUTHOR]

Understanding customer emotions and preferences is paramount for success in the dynamic product design landscape. This paper presents a study to develop a prediction pipeline to detect the aspect and perform sentiment analysis on review data. The pre-trained Bidirectional Encoder Representation from Transformers (BERT) model and the Text-to-Text Transfer Transformer (T5) are deployed to predict customer emotions. These models were trained on synthetically generated and manually labeled datasets to detect the specific features from review data, then sentiment analysis was performed to classify the data into positive, negative, and neutral reviews concerning their aspects. This research focused on eco-friendly products to analyze the customer emotions in this category. The BERT and T5 models were finely tuned for the aspect detection job and achieved 92% and 91% accuracy, respectively. The best-performing model will be selected, calculating the evaluation metrics precision, recall, F1-score, and computational efficiency. In these calculations, the BERT model outperforms T5 and is chosen as a classifier for the prediction pipeline to predict the aspect. By detecting aspects and sentiments of input data using the pre-trained BERT model, our study demonstrates its capability to comprehend and analyze customer reviews effectively. These findings can empower product designers and research developers with data-driven insights to shape exceptional products that resonate with customer expectations. [ABSTRACT FROM AUTHOR]

Despite the great promise of machine learning in the structural seismic analysis, the deployment of advanced neural networks has been limited in practical applications because of the high costs of data acquisition. This paper introduces a new framework that integrates the powerful learning ability of physics-informed neural networks (PINNs) with the effectiveness of pseudo-labelling in data augmentation to improve the accuracies of seismic response predictions of structures. The architecture of PINNs consists of two blocks of gated recurrent unit-fully connected neural networks (GRU-FCNNs) and one block of ordinary differential equation (ODE) that leverages the knowledge of structural dynamics. The first block of GRU-FCNNs serves as a generator of pseudo-labels when drawing upon the input of unlabelled datasets. The second block of GRU-FCNNs in combination with the ODE block is a selector of reliable pseudo-labels. The performance of PINNs trained on limited labelled data can be significantly improved by successively selecting reliable pseudo-labels from the generator and selector to supplement training datasets. The effectiveness of pseudo-labelling in PINNs is validated and compared with PINNs without pseudo-labelling through case studies with simulation datasets and real datasets from experiment. The results show that the proposed framework is effective and robust in improving prediction accuracies of structural seismic responses with limited labelled data. [ABSTRACT FROM AUTHOR]

Product and supply chain systems are a class of complex nonlinear systems due to the uncertain demand fluctuation and unexpected events, and Takagi–Sugeno (T-S) fuzzy systems are suitable for modelling such product and supply chain systems with different production rates. According to the uncertainty customer demands, different subsystems which describe the corresponding production-inventory processes are orchestrated by the T-S fuzzy rule. Change control is designed to handle the supply chain blocking and event breaking caused by uncertain factors. Then, sufficient conditions are given such that the exponential stability with a prescribed bullwhip effect attenuation level can be guaranteed, and the control synthesis problem is transformed into the solvability of linear matrix inequalities for the product and supply chain systems. An example is introduced to verify the validness and efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

The intensive workload associated with the preparation of high-quality DNA libraries remains a key obstacle toward widespread deployment of sequencing technologies in remote and resource-limited areas. We describe the development of single-use microfluidic devices driven by an advanced pneumatic centrifugal microfluidic platform, the PowerBlade, to automate the preparation of Illumina-compatible libraries based on adaptor ligation methodology. The developed on-chip workflow includes enzymatic DNA fragmentation coupled to end-repair, adaptor ligation, first DNA cleanup, PCR amplification, and second DNA cleanup. This complex workflow was successfully integrated into simple thermoplastic microfluidic devices that are amenable to mass production with injection molding. The system was validated by preparing, on chip, libraries from a mixture of genomic DNA extracted from three common foodborne pathogens (Listeria monocytogenes, Escherichia coli and Salmonella enterica serovar Typhimurium) and comparing them with libraries made via a manual procedure. The two types of libraries were found to exhibit similar quality control metrics (including genome coverage, assembly, and relative abundances) and led to nearly uniform coverage independent of GC content. This microfluidic technology offers a time-saving and cost-effective alternative to manual procedures and robotic-based automation, making it suitable for deployment in remote environments where technical expertise and resources might be scarce. Specifically, it facilitates field practices that involve mid- to low-throughput sequencing, such as tasks related to foodborne pathogen detection, characterization, and microbial profiling. [ABSTRACT FROM AUTHOR]

Over the years, the integration of Production Management and Maintenance Management has gained significant attention from the scientific community due to its benefits for the company. When searching through the states of the art and practice, it is possible to understand that one of the main challenges for the integration is the lack of systematic, methodological, and scientific approaches and evaluation systems that lead companies into a successful implementation and a clear understanding of the benefits and drawbacks of the process. This paper introduces an original framework that conducts the processes of evaluation, weighting, and aggregation of set of novel indicators proposed by the authors. The main output of the proposal is an integral index that allows us to qualify, in a linguistic domain, the importance of the Production and Maintenance Management integration. At the same time, the proposed framework includes a methodology to evaluate the consensus of the experts, based on the use of linguistic terms with a membership function of the triangular type, which attempts to overcome some deficiencies of previous models identified by the authors in a detailed and complex analysis of the scientific literature. The proposed framework is applied in a plant of the Cuban mechanical industry. The results of this application are clearly presented and discussed, allowing us to verify and validate the proposal while also contributing to its ease of understanding and ultimately to the successful integration of the production and maintenance tasks in the given company. [ABSTRACT FROM AUTHOR]

In order to enhance market share and competitiveness, large banks are increasingly focusing on promoting marketing strategies. However, the traditional bank marketing strategy often leads to the homogenization of customer demand, making it challenging to distinguish among various products. To address this issue, this paper presents a customer demand learning model based on financial datasets and optimizes the distribution model of bank big data channels through induction to rectify the imbalance in bank customer transaction data. By comparing the prediction models of random forest model and support vector machine (SVM), this paper analyzes the ability of the prediction model based on ensemble learning to significantly enhance the market segmentation of e-commerce banks. The empirical results reveal that the accuracy of random forest model reaches 92%, while the accuracy of SVM model reaches 87%. This indicates that the ensemble learning model has higher accuracy and forecasting ability than the single model. It enables the bank marketing system to implement targeted marketing, effectively maintain the relationship between customers and banks, and significantly improve the success probability of product marketing. Meanwhile, the marketing model based on ensemble learning has achieved a sales growth rate of 20% and improved customer satisfaction by 30%. This demonstrates that the implementation of the ensemble learning model has also significantly elevated the overall marketing level of bank e-commerce services. Therefore, this paper offers valuable academic guidance for bank marketing decision-making and holds important academic and practical significance in predicting bank customer demand and optimizing product marketing strategy. [ABSTRACT FROM AUTHOR]

The article presents a heuristic model used to evaluate the level of disorder in the synchronization of airbag operation with a moving phantom head during a frontal collision caused by a change in the longitudinal stiffness. The article describes three areas of research. The first presents the results of the strength tests of longitudinal models: reference and welded joint. Based on these, the relative stiffness decrease coefficient (SDC) of the longitudinal was determined. The second presents an analytical model of the airbag activation algorithm and the impact of SDC on its operation. The third determined the contact time of the phantom head with the airbag, taking into account the SDC. Based on the selected case study, a spatiotemporal analysis was conducted, indicating the level of disorder in the synchronization of the airbag with the moving phantom head due to the change in longitudinal stiffness. [ABSTRACT FROM AUTHOR]

The growing popularity of e-commerce has prompted researchers to take a greater interest in deeper understanding online shopping behavior, consumer interest patterns, and the effectiveness of advertising campaigns. This paper presents a fresh approach for targeting high-value e-shop clients by utilizing clickstream data. We propose the new algorithm to measure customer engagement and recognizing high-value customers. Clickstream data is employed in the algorithm to compute a Customer Merit (CM) index that measures the customer's level of engagement and anticipates their purchase intent. The CM index is evaluated dynamically by the algorithm, examining the customer's activity level, efficiency in selecting items, and time spent in browsing. It combines tracking customers browsing and purchasing behaviors with other relevant factors: time spent on the website and frequency of visits to e-shops. This strategy proves highly beneficial for e-commerce enterprises, enabling them to pinpoint potential buyers and design targeted advertising campaigns exclusively for high-value customers of e-shops. It allows not only boosts e-shop sales but also minimizes advertising expenses effectively. The proposed method was tested on actual clickstream data from two e-commerce websites and showed that the personalized advertising campaign outperformed the non-personalized campaign in terms of click-through and conversion rate. In general, the findings suggest, that personalized advertising scenarios can be a useful tool for boosting e-commerce sales and reduce advertising cost. By utilizing clickstream data and adopting a targeted approach, e-commerce businesses can attract and retain high-value customers, leading to higher revenue and profitability. [ABSTRACT FROM AUTHOR]

Engineering equipment can efficiently assist humans in completing engineering projects and reduce work intensity. As the difficulty and scope of operations increase, higher requirements are placed on function and integration. In product design, excessive user requirements and design constraints lead to discrete results, difficult to converge. Under multi-functionality and high integration constraints, traditional mapping sub-functions to structures one by one results in overabundant mappings, and discrete, redundant structures after mapping, poorly achieving integration and reducing subsequent iteration efficiency. To address these issues, this paper proposes a conceptual design methodology for engineering equipment with function units configuration and schemes iterative solution. First, the Fuzzy Quality Function Deployment (FQFD) method applies fuzzy theory to requirement-to-function conversion, overcoming ambiguity and subjectivity of user requirements. Second, with the goal of integrated structures, an improved FBS method, i.e. Function-Function Units-Structure (FFUS), aggregates sub-functions after decomposition into function units, and then maps to structures, improving engineering equipment integration. Moreover, the Interactive Genetic Algorithm (IGA) method involves users in an interactive process to generate an optimised design scheme, overcoming blind solution generation. Prototype design and simulation of 8VN1 Gas Insulated Switchgear (GIS) handing equipment demonstrate that the methodologies are effective in aiding engineering equipment conceptual design. [ABSTRACT FROM AUTHOR]

With the increasingly fierce market competition, enterprises hope to enhance their competitiveness through cooperation. In view of the uncertainty of coalitions formation in the real environment, the rational formulation of profit imputation principle is the basis for coalitions to maintain stability. This paper proposes a cooperative game with multi-granularity linguistic fuzzy coalitions to study the profit imputation. Firstly, a multi-granularity linguistic label set and a transformation function with preferred linguistic participation level are defined. Moreover, a new imputation of cooperative game with multi-granularity linguistic fuzzy coalitions is given, and its existence, uniqueness and other important properties are discussed. Finally, the effectiveness and practicality of the method is illustrated by applying the Solidarity value of cooperative game with multi-granularity linguistic fuzzy coalitions to collective forest land. [ABSTRACT FROM AUTHOR]

Remote work is becoming increasingly common, a trend accelerated by the global COVID‐19 pandemic. Existing remote work research fails to address the challenges and needs of engineers working remotely in Complex Aerospace System Development (CASD), the field responsible for creating and operating aerospace systems. This article presents an exploratory study to understand the challenges, benefits, and strategies when working remotely in CASD. We interviewed 12 CASD engineers working remotely at a major aerospace corporation. We ground our findings in six characteristics of CASD work (complex systems; design paths and feedback loops; relationships with suppliers, customers and regulators; distinct knowledge and skills; one‐off innovation; and high cost of experimentation) and discuss how each of these characteristics challenges remote work. The findings show that CASD requires many teams to work together, and this is encouraged through informal communication, which almost disappears in a remote setting. CASD requires frequent feedback, and we found that feedback was slow when working remotely. Participants found it challenging to demonstrate systems to customers and verify drawings with suppliers, and the interpersonal relationships, which help to bridge disciplinary divides, were harder to maintain remotely. The one‐off nature of the systems designed meant that conceptual work was important, but participants lacked the virtual tools to do this effectively. Lastly, testing hardware components required close virtual communication between technicians and engineers, which was tricky in a detail‐oriented context. This study motivates areas for future work to better understand and address the nuances of remote work by engineers in CASD. [ABSTRACT FROM AUTHOR]

With a rising incidence of product failure issues in the e-commerce sector, particularly for third-party vendors, pinpointing the root cause of these failures is critical. This paper introduces an innovative and secure architecture for product failure detection, adapted to the demands of Industry 4.0. The blockchain architecture leverages a Defensible Byzantine State Machine (DBSM) for the trustworthy authentication of Internet of Things (IoT) devices, contributing to the accurate reporting of product failures. Trust values associated with each device, reflecting its transaction history, are periodically updated. A decrease in these values triggers the extraction of novel failure features from client-generated reports, augmenting the defective product management cloud's capabilities. The architecture exhibits an authentication accuracy of 92%, a figure robust against a varying malicious attack rate (25%, 50%, 75%) in a simulated context. This study ultimately contributes to the development of advanced, secure systems for managing product failure and ensuring customer satisfaction in the e-commerce industry. [ABSTRACT FROM AUTHOR]

Due to Indonesia's tropical climate, it experiences frequent heavy rainfall, which is the primary cause of flooding. Despite various solutions implemented, the issue of flooding persists. Consumers demand an effective emergency flood protection solution to prevent water from entering homes at any time. This product is a flood barrier specifically designed for installation at the front door of houses. The development of this flood barrier product aims to address user needs and ensure flood resistance. To achieve this, the Kano method, and Quality Function Deployment (QFD) are utilized in the product design process, aligning it with customer desires. The Finite Element Analysis (FEA) is employed in testing to assess the flood barrier's ability to contain water effectively. Moreover, the data representing the user requirements is collected from residents in the Baleendah area. Those residents' houses have been affected by flooding. This area faces significant economic losses due to flooding, estimated at IDR 5,490,292,000,000. Bandung Regency is projected to have the highest frequency of flooding incidents in West Java in 2022, contributing to the broader issue of flooding in the province and country. The developed flood barrier product has successfully met most customer needs, leading to customer satisfaction. The product offers a reasonable price, customer customization options, and user-friendly features. The integration of Internet of Things (IoT) capabilities has been well-received by users. The flood barrier product also demonstrates its efficacy in retaining floodwater through its low equivalent elastic strain, equivalent stress, and deformation values against hydrostatic pressure. These attributes enable the product to withstand floodwaters and prevent their entry into households. [ABSTRACT FROM AUTHOR]

High-quality development of socio-economic, ecological environment, tourism industry and natural resources in the Yellow River Basin is a major strategic task for national development. High-quality development in the Yellow River Basin still has a lot of room for improvement, so it is important for the coupled and coordinated development of water resources-socio-economic-ecological environment-tourism industry in the Yellow River Basin region. In this study, we focus on the major strategic issues of high quality development in the Yellow River Basin in the context of the above-mentioned background and the current forms and policies of sustainable development in China, and conduct research on the theory and methods of assessing the sustainable development of regional water resources-socio-economic-ecological environment-tourism industry. However, by analyzing the existing research literature on sustainable development assessment, we find that most of them focus on a single area or two combinations of modal development studies, and focus on exploring micro-coupling mechanisms, so the generality of macro policy support is rather mediocre, which we fill this gap through this study. The study relies on an innovative macroscopic research perspective that draws pioneeringly on the quality function deployment (QFD) theory in the field of product quality management in marketing, which, it is worth stating, allows for a framework of research perspectives from a systematic and holistic perspective. In this framework, we will propose a G1-entropy value method for indicator importance (weight) assessment. It is worth stating that the G1 method used in this paper is different from the traditional G1 method in that we will introduce the identity information weights of experts to improve the reliability of the subjective assignment method. We will also construct an indicator system for studying the regional sustainable development issues in the Yellow River Basin on this basis, so as to complete the ranking and analysis of the nine provinces and the importance of indicators in the Yellow River Basin. This study will provide a comprehensive theoretical basis for Chinese government and related departments to formulate policies for the high-quality development of water resources, socio-economic, ecological environment and tourism industry in the Yellow River Basin, and also provide theoretical and empirical references for the analysis and assessment of similar international regional sustainability cases. [ABSTRACT FROM AUTHOR]

Plain and simple, a corrugated cardboard material provides the right level of protection for goods in transit, which helps keep shipping costs down. As made mostly from wood material, a fairly renewable resource, corrugated cardboards are easily processed and also recycled into paper products and even new boxes. Moreover, thanks to the material's durability, many corrugated packages can be reused multiple times before hitting the recycle bin, which promotes eco-friendly corrugated packaging needs for the environment with no glues or extra binding chemical included inside the packaging at all. The research in this paper proposes a tentative design testbed for a product model of setter tray, wholly made in corrugated paperboard and seated inside packaging with no additional filling material, which, after all, aids realization of a totally eco-friendly corrugated packaging design. A methodical approach, a systematic functional design framework with design thinking methodologies such as function decomposition (FD), quality function deployment (QFD), and morphological matrix, has been introduced to generate a promising concept continuously refined onto a product model. The validation of mechanical properties of the designed product for its functions as desired has been conducted by using not only a computer-aided engineering (CAE) analysis of the computer-aided design (CAD) model, but also an engineering experiment of the product prototypes made of corrugated paperboard, followed by an in-depth discussion on design contributions and future works as well. [ABSTRACT FROM AUTHOR]

The belt conveyor is a key piece of equipment for thermal power plants. Belt mistracking causes higher economic costs, lower production efficiency, and more safety accidents. The existing belt correction devices suffer from poor performance and high costs. Therefore, a design method for coal conveying belt correction devices is proposed in this paper based on fault tree analysis (FTA), quality function deployment (QFD), and inventive problem solving (TRIZ) theory. A user requirement mapping model was constructed to extract five user requirements based on the shortcomings of the existing belt correction devices. Moreover, the FTA model of product characteristics was established to obtain nine key product characteristics by dividing the minimum cut-set. Thus, a QFD model for the belt correction device design was constructed. The relationship matrix R–C relating the user requirements and the product characteristics was deduced based on the binary decision diagram theory to screen the key product characteristics. Furthermore, an innovative design model of the belt correction device was constructed, and the key design elements and contradiction matrix were determined. TRIZ theory was used to resolve the contradictions, and nine novel correction device design schemes were proposed. Finally, the correction performances of the schemes were compared and verified based on the experimental platform, and the final design scheme was determined. The test results showed that the device had good correction performances. The rationality and effectiveness of the innovative design method were verified. [ABSTRACT FROM AUTHOR]

Quality function deployment (QFD) method allows reviewing of customer requirements (CRs) and design requirements (DRs) simultaneously in order to handle the correlations and relationships of CRs and DRs in calculations. It has been often combined with fuzzy numbers, because it provides to gather the judgments of experts in vagueness environment more correctly and easily. This paper suggests employing q-rung orthopair fuzzy number (q-ROFN) for improving the fuzzy QFD approach. The proposed q-ROFN based QFD method uses q-ROFN to adjust the importance degrees of CRs based the relationships between CRs and DRs with the help of the correlations and relationships of CRs and DRs. q-ROFN presents more information than numbers such as intuitionistic fuzzy number and Pythagorean fuzzy number about the correlations and relationships of CRs and DRs. Furthermore, this work proposes integrating Fine-Kinney method and QFD method to cope with limitations of Fine-Kinney method. VIKOR (VIsekriterijumska optimizacija i KOm-promisno Resenje) approach based on q-ROFN is also used to rank DRs. The proposed integrated method provides a novel decision support model for the natural gas pipeline project (NGPP) risk assessment. There is no study about the integration of Fine-Kinney, q-ROFN-based QFD, VIKOR methods. The most significant DR and the second most important DR for NGPP risk assessment are electrical sparks and coating type factor, respectively. The results of this model present a road map to the managers of NGPP for an occupational health and safety policy by ordering the failure modes. [ABSTRACT FROM AUTHOR]

Quality management is a continuous and dynamic approach to business that seeks to increase the quality, competitiveness and productivity of the organization through the process of continuous improvement of the quality of products, services, processes, people and the environment. The production of plastic injection molding tools is characterized by single production and high product prices. Mistakes in the process of development, design and production of tools most often lead to high costs, non–fulfillment of agreed delivery dates and loss of image with the customer. Because of this, the manufacturer should establish an effective quality management system in its processes, and especially the quality control subsystem. In modern production, product quality control is indispensable and is imposed as a mandatory function of the organization. Controlling and testing of the product during the process of its creation and delivery to the customer is carried out according to pre–defined control points and characteristics for control. These elements are defined in the process of design and technological preparation and are an integral part of the appropriate control documentation. Adequate application of control documentation improves product quality, identifying sources of variation in individual processes and establishing controls for their monitoring. This has the effect of increasing customer satisfaction, reducing waste and improving the financial results of the business. The aim of this paper is to analyze the processes of making plastic injection molding tools and to define and implement the control documentation required for their production. [ABSTRACT FROM AUTHOR]

The research on dry bulk terminal improvement is currently limited and lacks focus, despite the increasing global demand for dry bulk cargo which has necessitated terminal expansion to accommodate this demand. The primary aim of this paper is to propose a design for enhancing dry bulk terminal operations and identify a significant factor for improving dry bulk terminal performance through the utilization of the Quality Function Deployment (QFD) method. The fundamental concern in QFD pertains to determining the customer’s desires and formulating an effective strategy to address the identified challenges. These two elements are of utmost importance and are considered essential components within the House of Quality (HOQ) matrices. This study employed systematic procedures that utilized inferential statistics, specifically factor analysis and Spearman correlation, within the context of QFD method. Factor analysis is employed in the process of identifying and selecting the optimal set of unutilized items. The Spearman correlation coefficient was employed to examine and enhance the association between the elements. The findings indicate that there exist significant variables and items that contribute to the enhancement of dry bulk terminal. A study revealed that there exists a moderate and statistically significant correlation between loading time and both labor and machine factors. Additionally, the analysis reveals a substantial correlation between the provision of quality services and the size of the berth. Finally, the analysis reveals a moderate correlation between vehicle control and the operation of lorries. The strategy proposed in this study can be employed by port management to effectively identify solutions in the context of dry bulk terminals. [ABSTRACT FROM AUTHOR]