Search Results (450)

User requirements are the core driving force behind the iterative development of complex products. Their comprehensive collection, accurate interpretation, and effective integration directly affect design outcomes. However, current practices often depend heavily on single-source data and designer intuition, resulting in incomplete, biased, and fragile design decisions. Moreover, multi-source heterogeneous user requirements often exhibit inherent asymmetry and imbalance in both structure and contribution. To address these issues, this study proposes a symmetric and balanced optimization method for multi-source heterogeneous user requirements in complex product design. Multiple acquisition and analysis approaches are integrated to mitigate the limitations of single-source data by fusing complementary information and enabling balanced decision-making. Firstly, unstructured text data from online reviews are used to extract initial user requirements, and a topic analysis method is applied for modeling and clustering. Secondly, user interviews are analyzed using a fuzzy satisfaction analysis, while eye-tracking experiments capture physiological behavior to support correlation analysis between internal preferences and external behavior. Finally, a cooperative game-based model is introduced to optimize conflicts among data sources, ensuring fairness in decision-making. The method was validated using a case study of oxygen concentrators. The findings demonstrate improvements in both decision robustness and requirement representation. Full article

With the increase in the global aging population, the demand for elderly-friendly game products is growing rapidly. To address existing limitations, particularly in user demand extraction and design parameter setting, this study proposed a design framework integrating the BTM–AHP–AD–TOPSIS methods. The goal was to accurately identify the core needs of elderly users and translate them into effective design solutions. User reviews of elderly-friendly game products were collected from e-commerce platforms using Python 3.8-based web scraping. The Biterm Topic Model (BTM) was employed to extract user needs from review texts. These needs were prioritized using the Analytic Hierarchy Process (AHP) and translated into specific design parameters through Axiomatic Design (AD). Finally, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was applied to comprehensively evaluate multiple design schemes and select the optimal solution. The results demonstrate that the proposed design path offers a holistic method for progressing from need extraction to design evaluation. It effectively overcomes previous limitations, including inefficient need extraction, limited scope, unclear need weighting, and unreasonable design parameters. This method enhances user acceptance and satisfaction while establishing rigorous design processes and scientific evaluation standards, making it well suited for developing elderly-friendly products. Full article

Online reviews play a critical role in shaping consumer decisions and providing valuable insights to enhance the products and services for businesses. As visual content becomes increasingly prevalent in reviews, it is essential to understand how image complexity influences review helpfulness. Despite the growing importance of images, the impact of color diversity and texture homogeneity on review helpfulness remains underexplored. Grounded in Information Diagnosticity Theory and Dual Coding Theory, this study investigates the relationship between image complexity and review helpfulness, as well as the moderating role of review text readability. Using a large-scale dataset from the hotel and travel sectors, the findings reveal that color diversity has a positive effect on review helpfulness, while texture homogeneity follows an inverted U-shaped relationship with helpfulness. Furthermore, text readability strengthens the positive impact of texture homogeneity, making moderately homogeneous images more effective when paired with clear and well-structured text. Heterogeneity analysis demonstrates that these effects vary across product categories. The results advance the understanding of multimodal information processing in online reviews, providing actionable guidance for platforms and businesses to refine the review systems. Full article

Automated quality assurance is gaining popularity across application areas; however, automatization for monitoring and control of product quality in waste processing is still in its infancy. At the same time, research on this topic is scattered, limiting efficient implementation of already developed strategies and technologies across research and application areas. To this end, the current work describes a scoping review conducted to systematically map available sensor-based quality assurance technologies and research based on the PRISMA-ScR framework. Additionally, the current state of research and potential automatization strategies are described in the context of construction and demolition waste processing. The results show 31 different sensor types extracted from a collection of 364 works, which have varied popularity depending on the application. However, visual imaging and spectroscopy sensors in particular seem to be popular overall. Only five works describing quality control system implementation were found, of which three describe varying manufacturing applications. Most works found describe proof-of-concept quality prediction systems on a laboratory scale. Compared to other application areas, works regarding construction and demolition waste processing indicate that the area seems to be especially behind in terms of implementing visual imaging at higher technology readiness levels. Moreover, given the importance of reliable and detailed data on material quality to transform the construction sector into a sustainable one, future research on quality monitoring and control systems could therefore focus on the implementation on higher technology readiness levels and the inclusion of detailed descriptions on how these systems have been verified. Full article

As global energy production progressively turns toward a green environment and economy, one of the safety challenges to the maritime industry that has arisen lies within offshore wind farms (OWFs). As renewable sources of energy whose numbers are rapidly expanding, their impact to the safety of navigation of the ships that navigate in their vicinity ought to be examined further. An ever-growing number of OWFs has led to safety concerns that have never been taken into consideration before. This article gives a structured quantitative analysis and an in-depth review of the literature connected to the safety of navigation, collision probability, and risk assessment that OWFs pose to all maritime industry agents. In this article, the main concerns of the impact of OWFs to the safety of navigation are analyzed using a combination of both the PRISMA and PICOC methodologies. Various types of scientific papers such as journal articles, conference proceedings, MSc theses, PhD theses, and online works of research are collated into a detailed bibliometric analysis and categorized by the most relevant parameters providing valuable perspectives on the current state of art in the field. The findings of this research emphasize the need for a further and more thorough analysis on the theoretical installment of OWFs and their inevitable impact on increasing maritime traffic complexity. The results of this article can form a strong basis for further scientific development in the field and can give useful insights to all maritime industry stakeholders dealing with OWFs. Full article

The continuous release of global precipitation products offers a stable data source for flood forecasting in areas without rainfall gauges. However, due to constraints of forecast timeliness, only no/short-lag precipitation products can be utilised for flood forecasting, but these products are prone to significant errors. Therefore, the keys of flood forecasting in areas lacking rainfall gauges are selecting appropriate precipitation products, improving the accuracy of precipitation products, and reducing the errors of precipitation products by combination with hydrology models. This paper first presents the current no/short-lag precipitation products that are continuously updated online and for which the download of long series historical data is supported. Based on this, this paper reviews the utilisation methods of multi-source precipitation products for flood forecasting in areas with insufficient rainfall gauges from three perspectives: methods for precipitation product performance evaluation, multi-source precipitation fusion methods, and methods for coupling precipitation products with hydrological models. Finally, future research priorities are summarized: (i) to construct a quantitative evaluation system that can take into account both the accuracy and complementarity of precipitation products; (ii) to focus on the improvement of the areal precipitation fields interpolated by gauge-based precipitation in multi-source precipitation fusion; (iii) to couple real-time correction of flood forecasts and multi-source precipitation; and (iv) to enhance global sharing and utilization of rain gauge–radar data for improving the accuracy of satellite-based precipitation products. Full article

This article investigates how digital competences contribute to the production of social capital and professional recognition through a systematic review of international literature. Drawing on 62 peer-reviewed articles indexed in Web of Science, Scopus, and ERIC, the review identifies the most frequently mobilized theoretical frameworks, the predominant types and sources of recognition, and the associated dimensions of social capital. The findings reveal a growing emphasis on communicative and network-based digital competences—particularly digital communication, information management, and virtual collaboration—as key assets in professional contexts. Recognition is shown to take predominantly non-material, extrinsic, and visibility-oriented forms, with social media platforms emerging as central sites for the performance and circulation of digital competences. The results indicate that social media proficiency has become a central determinant of social recognition, favoring individuals who possess not only digital fluency but also the ability to strategically develop and mobilize their networks. This dynamic reframes signal theory in light of today’s platformed ecosystems: recognition no longer depends increasingly on one’s capacity to render competences legible, visible, and endorsed within algorithmically mediated environments. Those who master the codes of visibility and reputation-building online are best positioned to convert recognition into social capital and professional opportunity. Full article

Suppliers nowadays can be directly approached in business-to-business (B2B) E-commerce websites. This makes the product and service information of certain suppliers accessible in online reviews. Therefore, online reviews have become important for B2B supplier evaluation and selection. Recently, the sustainability of food packaging has attracted increasing attention from companies and consumers. This study developed a novel multi-criteria decision making (MCDM) method called Percentage Assessment with Synergistic Comparisons And Aggregated Ranks (PASCAAR) to support the selection of sustainable food package suppliers based on online review information in B2B E-commerce websites. Such a method used three different percentage comparisons between alternatives and the minimal options, and then aggregates the comparisons with their ranks. This study confirmed the effectiveness of PASCAAR by applying it to a case study to select the supplier of sustainable food packages (i.e., biodegradable food containers) from six candidates in the B2B E-commerce website by considering multi-dimensional online review information and their own product properties. Using PASCAAR, this study obtained the outcome that the third candidate is the most suitable one, as quantitative results indicate this supplier has the highest PASCAAR score. Based on the results, this study further conducted thorough sensitivity tests to validate the results. It can be found that, compared with the classical MCDM methods in measuring the performance of alternatives and aggregating evaluation scores, the PASCAAR method can have more robust and informative results. This study also developed a PASCAAR Solver to enable easy implementation of this method. This study contributes to the existing literature by providing new ranking and aggregation ideas in MCDM and can offer practitioners a more informative and highly actionable method for supplier selection and decision support system development by utilizing online review information. Full article

The development of artificial intelligence has enabled the automation of an increasing number of processes and actions in the online environment, from creating unique and engaging content to simulating user behaviors (likes, comments, reviews). This automation has brought several positives to the online environment, including reduced working time and better results, among others. However, at the other end of the spectrum, consumer trust is starting to decline. Before the advent of artificial intelligence, reviews were often the opinions of other customers who had tried the product or service in question. With the evolution of these reviews, providers can now automatically post them to create a favorable image. Given the increasing concern among young people about environmental issues, this study investigates how AI-generated content affects their trust in sustainability-related online reviews and how this trust influences their pro-environmental purchasing decisions. Quantitative research was conducted in the article, based on which a conceptual model of the degree of trust users have in online reviews and reactions in the context of artificial intelligence was developed. The research methodology involved conducting quantitative research and constructing variables based on the data collected. The results revealed significant links between the evolution of artificial intelligence and the degree of trust users place in general feedback found in online environments. Full article

Eggshell quality inspection plays a pivotal role in enhancing the commercial value of poultry eggs and ensuring their safety. It effectively enables the screening of high-quality eggs to meet consumer demand for premium egg products. This paper analyzes the surface characteristics, ultrastructure, and mechanical properties of poultry eggshells. It systematically reviews current advances in eggshell quality inspection technologies and compares the suitability and performance of techniques for key indicators, including shell strength, thickness, spots, color, and cracks. Furthermore, the paper discusses challenges in non-destructive testing, including individual egg variations, species differences, hardware precision limitations, and inherent methodological constraints. It summarizes commercially available portable and online non-destructive testing equipment, analyzing core challenges: the cost–accessibility paradox, speed–accuracy trade-off, algorithm interference impacts, and the technology–practice gap. Additionally, the paper explores the potential application of several emerging technologies—such as tactile sensing, X-ray imaging, laser-induced breakdown spectroscopy, and fluorescence spectroscopy—in eggshell quality inspection. Finally, it provides a comprehensive outlook on future research directions, offering constructive guidance for subsequent studies and practical applications in production. Full article

Real-time mining (RTM) has become increasingly significant in response to the growing need for sustainable mineral resource extraction, driven by global population growth and technological progress. This innovative approach addresses critical challenges, such as declining ore grades, deeper and less accessible deposits, and rising energy costs, by integrating advanced online grade monitoring, data analysis, and process optimization. By employing real-time grade control, dynamic mine planning, and production optimization, it enhances the efficiency of resource extraction while minimizing environmental and social impacts. Originally proposed about a decade ago, RTM has gained attention for its potential to revolutionize the industry. This review examines recent advancements in closed-loop concepts, emphasizing the integration of advanced sensors and data analytics to enable continuous monitoring and adaptive decision making across the mining value chain. It highlights the role of online sensor technologies in providing high-resolution data for process optimization and evaluates various mining optimization techniques. The paper also explores data assimilation methods, such as Kalman filters and artificial intelligence (AI), showcasing their ability to continuously update models and reduce operational uncertainties. Ultimately, it proposes a comprehensive framework for adaptive, data-driven mining operations that promote sustainable development, enhance profitability, and improve decision-making capabilities. Full article

Interaction with mobile platforms changes users’ emotional and cognitive engagements through various stimuli cues that respond to behavioural intentions. Emerging technologies such as artificial intelligence (AI) and augmented reality (AR) foster more engagements and transform a new user–platform interaction paradigm in the e-commerce industry. This study signifies the effects of artificial intelligence and augmented reality in assessing user experience for mobile platforms. In this paper, we develop an interaction–engagement–intention model that considers users’ continuance intention based on perceived user experience. The proposed model uniquely explains a nuanced understanding of how the user–platform interactions evolve interactivity, product fit, artificial intelligence-driven recommendation, and online reviews in perceiving spatial presence and subjective norm. This paper explores the importance of attitude and trust as emotional states that influence the user’s behavioural responses. We validate the consequences of user–platform interactions toward continuance intention by conducting an online questionnaire survey and assessing user experience in augmented reality environments. The results contribute to adopting the co-created values of user–platform interactions through cognitive and emotional engagements that affect users’ continuance intention. The platform industry can apply the research outcomes by considering user experience and its implications to enhance the platforms’ capability with a broader aspect. Full article

The work aims to apply cheap and widely accessible tools based on artificial intelligence to analyze, group, and categorize a large amount of available research literature (from a massive bibliographic search) on the use of Lemna minor for animal feed, not only comprehensively and objectively, but also in a more effective and less time-consuming way. In addition, a comprehensive and critical summary was conducted to highlight recent applications of L. minor in animal feed. The Scopus database was used for the original bibliographic search. Then, a newly developed online and freely available tool called “Jupyter Notebook on Google Colab” was applied to cluster the large volume of bibliographic data (1432 papers) obtained in the basic search, which allowed their reduction until only 148 papers. These papers were reviewed in a traditional way obtaining relevant information about L. minor production, nutritional value, composition, and its application as animal feed. In this sense, the most successful applications were for fish and poultry feeding, reaching levels of inclusion of 15–20% in fish and 5–15% in poultry. It is of great interest because of the expected increase in prices of conventional sources of protein for animal feed. Full article

Images contain more visual semantic information. Consumers first view multimodal online reviews with images. Research on the helpfulness of reviews on e-commerce platforms mainly focuses on text, lacking insights into the product attributes reflected by review images and the relationship between images and text. Studying the relationship between images and text in online reviews can better explain consumer behavior and help consumers make purchasing decisions. Taking multimodal online review data from shopping platforms as the research object, this study proposes a research framework based on the Cognitive Theory of Multimedia Learning (CTML). It utilizes multiple pre-trained models, such as BLIP2 and machine learning methods, to construct metrics. A fuzzy-set qualitative comparative analysis (fsQCA) is conducted to explore the configurational effects of antecedent variables of multimodal online reviews on review helpfulness. The study identifies five configurational paths that lead to high review helpfulness. Specific review cases are used to examine the contribution paths of these configurations to perceived helpfulness, providing a new perspective for future research on multimodal online reviews. Targeted recommendations are made for operators and merchants based on the research findings, offering theoretical support for platforms to fully leverage the potential value of user-generated content. Full article

Large-scale commercial wine fermentation requires the monitoring and control of multiple variables to achieve optimal results. Challenges in measurement arise from turbidity, stratification in large unmixed volumes, the presence of grape skins and solids during red wine fermentations, the small changes in variables that necessitate precise sensors, and the unique composition of each juice, which makes every fermentation distinct. These complications contribute to nonlinear and time-variant characteristics for most control variables. This paper reviews sensors, particularly online ones, utilized in commercial winemaking. It examines the measurement of solution properties (density, weight, volume, osmotic pressure, dielectric constant, and refractive index), sugar consumption, ethanol and glycerol production, redox potential, cell mass, and cell viability during wine fermentation and their relevance as variables that could enhance the estimation of parameters in diagnostic and predictive wine fermentation models. Various methods are compared based on sensitivity, availability of sensor systems, and their appropriateness for measuring properties in large commercial wine fermentations. Additionally, factors influencing the adoption of control strategies are discussed. Finally, potential opportunities for control strategies and challenges for future sensor developments are outlined. Full article