Search Results (116)

Grey systems theory has provided a change in paradigm related to how numbers and their mathematics are perceived. By including various levels of knowledge associated with the variables, the theory has succeeded in modelling systems characterised by incomplete or partially known information. Among the methods offered by the grey systems theory, the grey clustering approach offers a distinct perspective on clustering methodology by allowing researchers to define degrees of importance for the variables included in the analysis. Despite its expanding use across disciplines, a comprehensive synthesis of grey clustering research is lacking. In this context, this study aims to provide a comprehensive and structured overview of the research field associated with grey clustering and its applications, rather than the more rhetorical formulation previously included. By using a PRISMA approach, a dataset containing papers related to grey clustering is extracted from the Clarivate Web of Science database and analysed through bibliometric tools and further enhanced by providing thematic maps and topics discovery through the use of Latent Dirichlet Allocation (LDA) and BERTopic analyses. The final dataset includes 318 articles, and their examination allows for a detailed assessment of publication trends, thematic structures, and methodological directions. The annual scientific production showcased an increase of 10.78%, while the thematic analysis revealed key themes related to performance management, risk assessment, evaluation models for enhancing organisational performance, urban and regional planning, civil engineering, industrial engineering and automation, and risk evaluation for health-related issues. Additionally, a detailed review of the most-cited papers has been performed to highlight the role of grey clustering in various research fields. Full article

With worsening energy and environmental issues, new energy vehicles (NEVs) have emerged as the future of the automotive industry, as they aim to address the high energy consumption and carbon emissions of traditional fuel vehicles. However, due to the industry’s short development history, limited available data, and incomplete supporting systems, most existing NEV research focuses on theoretical analysis, which hinders the achievement of accurate sales predictions. Today, online reviews influence consumer decisions and thus provide a new perspective for sales forecasting. Based on consumer behavior theory and neural network principles, our research selects factors influencing NEV sales (covering economics, technological, policy, and consumer dimensions, including preprocessed crawled online reviews), constructs an index system screened via grey relational analysis, and establishes five models (SARIMA, GRU, Seq2Seq, Attention-GRU, Attention-Seq2Seq) for training and testing. The study supports the use of online reviews in NEV sales prediction and proves that the model based on cutting-edge technology of Attention-Seq2Seq can outperform the other four methods presented above. Through this, the current contributions advance marketing innovation by helping NEV stakeholders understand relevant information using a predictive model from online reviews, which leads to precise product improvement and optimal distribution of resources as well as precise adoption of marketing strategies. Full article

Background: Innovative methods to test healthcare interventions have recently emerged to help provide more targeted, effective, and scalable interventions. Given the importance of the early years for children’s development, improved interventions for vulnerable children and families have become public health imperatives. Traditional randomized control trials (RCTs), considered the gold standards, have serious limitations due to high costs, time demands, and issues with the generalizability of the results. Indeed, new accelerated methods are being considered to improve the efficiency of RCTs. Thus, we compared innovative methods with RCTs in their ability to test and implement interventions. We also provided recommendations for best practices in the child-health research. Methods: A realist review was undertaken to identify and make recommendations on what works for whom and under what circumstances. This realist meta-review was conducted as an umbrella review of reviews, supplemented by a synthesis of the targeted grey-literature, to report both peer-reviewed and practice-based evidence on evaluation methods for community child-health interventions. We searched electronic databases, including MEDLINE, PubMed, EMBASE, PsycINFO, CINAHL, and the grey literature, and provided references. We identified, selected, and appraised sources if they were (1) written in English, (2) answered our research question, (3) described/criticized a method for intervention evaluation, and (4) focused on community-based health interventions. Results: For our final analysis, out of 5167 identified documents, we selected those that criticized or reviewed RCTs (n = 13) and innovative methods (n = 31). Following Pawson’s recommendations, we developed an extraction tool to promote a consistent approach and assessed to what degree each method enabled evaluation, was theory driven, offered clear guidelines, provided clear methods or tools, fostered innovation, was fast and generalizable, worked for who and under what circumstances, and focused on children and child-related research. Conclusions: Innovative and accelerated methods offer promising alternatives to the traditional RCTs for evaluating community-based child health interventions. Among these, the Innovate, Develop, Evaluate, Adapt, and Scale (IDEAS) method emerged as the most integrative and context-sensitive approach to evaluate early interventions in a variety of settings. Other innovative methods were not well-developed, compromising the internal validity of studies focused on promoting children’s health in community settings. Graphical abstract synthesizes the phases of RCTs and contrasts them with IDEAS. Full article

Aiming at a critical gap in evaluating human-centered spatial quality during urban stock renewal, this study aimed to develop a quantitative methodology to evaluate emotional vitality in urban commercial complexes. Focusing on five representative Beijing complexes selected according to Beijing urban renewal best practices, the research establishes an integrated evaluation framework combining emotional attachment theory with multi-criteria decision-making method. The methodology employs scales to measure emotional attachment, and implements an improved TOPSIS model integrating Entropy Weight and Grey Relational Analysis of Multi-Criteria Decision-Making method for comprehensive assessment. As a result, key findings demonstrate that small-scale built environment features significantly enhance spatial vitality through emotional attachment mechanisms. Material characteristics, including historical material reinterpretation and innovative structural elements, prove fundamental in fostering attachment, while non-material features exhibit dynamic influences that evolve with temporal and contextual factors. The study reveals that successful emotional attachment requires balanced integration of physical and social features, with material characteristics serving as the foundation for sustained emotional vitality. The research contributes an evidence-based evaluation system that bridges theoretical constructs from environmental psychology with practical urban design applications. By objectively quantifying emotional attachment and identifying specific spatial features that enhance these emotional experience, this methodology provides valuable tools for urban designers, planners, and governors seeking to create more engaging and humanized commercial environments. The framework further offers scientific support for decision-making in urban renewal projects and establishes a replicable approach for vibrant urban space evaluation. Full article

As tourism develops, more study focuses on tourism sustainable development assessment. To solve ambiguous indicators and subjective weight distributions in such evaluations, this paper proposes a hybrid model combining Fuzzy AHP (FAHP) and Grey Relational TOPSIS (GR-TOPSIS). A 13-secondary-indicator evaluation system is established across four dimensions (economy, society, environment, culture), distinguishing positive/negative indicators based on tourism’s local impacts. FAHP builds a triangular fuzzy judgment matrix, with confidence ranking to determine index weights and consistency tests to ensure weight rationality. Grey relational theory improves TOPSIS, which integrates Euclidean distance and grey relational degree to form a hybrid closeness index, overcoming traditional TOPSIS’s poor fuzzy data handling. Verified with seven tourist regions in our cases, the method yields indicator weights and final superiority–inferiority rankings. Among the seven evaluated regions, Lijiang Qinghsui (P4) achieves the highest sustainable development level (hybrid closeness: 0.693), while P6 performs the poorest. Among the 13 indicators, Tourism Revenue Contribution is the most important (weight: 0.189) and Tourists’ Cultural Respect Degree (F13) is the least important (weight: 0.015). Compared with traditional TOPSIS, this innovative model quantifies sustainable tourism development levels, offering a scientific basis for regional tourism decision-making. Full article

Assessing the accessibility of urban metro stations is essential for optimizing metro system planning and improving travel efficiency for residents. This study proposes an innovative evaluation framework—the CWM-GRA-TOPSIS model—for comprehensive metro station accessibility assessment. First, a multi-dimensional indicator system is established, encompassing three key dimensions, to-metro accessibility, by-metro accessibility, and land use accessibility, which are further refined into 14 secondary indicators for detailed analysis. A Combination Weighting Method (CWM) is then introduced, integrating the Analytic Hierarchy Process (AHP) for subjective weighting and the Criteria Importance Through Intercriteria Correlation (CRITIC) method for objective weighting, with their integration optimized through Game Theory. Subsequently, the overall accessibility of metro stations is evaluated and ranked using a hybrid Grey Relational Analysis (GRA) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) approach. The proposed method is applied to Wuhan, China, to demonstrate its effectiveness and applicability. Results show that the CWM-GRA-TOPSIS model, by balancing objectivity and practical relevance, provides a more reliable and systematic approach for identifying accessibility disparities and formulating targeted improvement strategies for urban metro systems. Full article

This study takes 22 megacities and super-large cities in China as research subjects, systematically exploring the coupled system relationship between digital maturity and resilient cities. Using methods such as information entropy theory, coupling coordination degree model, and grey relational coefficient measurement, it conducts comprehensive evaluation analysis, coupling coordination analysis, and factor contribution analysis. The results indicate that digital maturity and resilient cities exhibit a mutually reinforcing relationship. The positive interaction and coupling between digital maturity and resilient cities development have further promoted innovation in China’s urban governance and contributed to the long-term sustainable development of cities. Although the digital maturity and resilience levels of these cities show a fluctuating upward trend, they have not yet reached an ideal state, and significant differences exist among different urban clusters and regions. Megacities and super-large cities demonstrate high coupling characteristics among their internal subsystems, but their coordination level remains relatively low. Key factors such as digital governance policies, digital economy, and disaster-resistant infrastructure play a crucial role in advancing the sustainable development of resilient cities. Therefore, it is urgent to improve the coupling coordination mechanism to enhance sustainable development capacity. Full article

This study employs an improved fractional-order grey multivariable convolution model (FGMC(1,N,2r)) to predict abalone aquaculture output in Fujian, Shandong, and Guangdong. By integrating fractional-order accumulation (r₁, r₂) with a particle-swarm-optimization (PSO) algorithm, the model addresses limitations of handling multivariable interactions and sequence heterogeneity within small-sample regional datasets. Grey relational analysis (GRA) first identified key factors exhibiting the strongest associations with production: abalone production in Fujian and Shandong is predominantly influenced by funding for aquatic-technology extension (GRA degrees of 0.9156 and 0.8357, respectively), while in Guangdong, production was most strongly associated with import volume (GRA degree of 0.9312). Validation confirms that FGMC(1,N,2r) achieves superior predictive accuracy, with mean absolute percentage errors (MAPE) of 0.51% in Fujian, 3.51% in Shandong, and 2.12% in Guangdong, significantly outperforming benchmark models. Prediction of abalone production for 2024–2028 project sustained growth across Fujian, Shandong, and Guangdong. However, risks associated with typhoon disasters (X₆ and import dependency (X₅) require attention. The study demonstrates that the FGMC(1,N,2r) model achieves high predictive accuracy for regional aquaculture output. It identifies the primary drivers of abalone production: technology-extension funding in Fujian and Shandong, and import volume in Guangdong. These findings support the formulation of region-specific strategies, such as enhancing technological investment in Fujian and Shandong, and strengthening seed supply chains while reducing import dependency in Guangdong. Furthermore, by identifying vulnerabilities such as typhoon disasters and import reliance, the study underscores the need for resilient infrastructure and diversified seed sources, thereby providing a robust scientific basis for production optimization and policy guidance towards sustainable and environmentally sound aquaculture development. Full article

The roller press crushing of ore is a complex process involving the interplay of multiple factors. Roller dimensions, gap settings, and rotational speed all influence this process, which in turn affects the comprehensive crushing performance of the high-pressure grinding rolls (HPGR). Therefore, to simultaneously enhance the HPGR’s size reduction effectiveness (SRE) and throughput while controlling its energy consumption, wear, and edge effect, multi-objective parameter optimization of the HPGR is required. This study utilizes the Discrete Element Method (DEM) to simulate ore comminution within an HPGR. By first dividing the release zone into segments, the particle size distribution of the crushed product at different locations within this zone is investigated. Then, the influence of various factors on the SRE at different locations within HPGR is examined through single-factor experiments. Subsequently, the relative influence of roller diameter, roller width, roller speed, and roll gap on the comprehensive crushing performance of the HPGR is determined through signal-to-noise ratio (SNR) analysis and analysis of variance (ANOVA). Finally, multi-objective parameter optimization of the roller press crushing is conducted based on grey relational analysis (GRA), incorporating the weights assigned to different response target. The results indicate that the proportion of unbroken ore particles is relatively significant, primarily due to the edge effect. Further analysis reveals that along the horizontal diameter of the rollers, regions closer to the roller surface exhibit better SRE. Additionally, roller speed is identified as the most influential factor affecting the uniformity of SRE in the HPGR. The application of GRA to the multi-objective optimization of roller press crushing enables effective balancing of the comprehensive crushing performance in HPGR. Full article

This paper defines the term “graffiti” and outlines the origins of this concept. The terminological arrangement allowed for the subject of this research, i.e., illegal graffiti, to be situated in reality, i.e., an urban space. It was assumed that the existence of the tag was associated with a disturbance of spatial order and had an impact on safety in a space. This, in turn, is related to whether the principles of sustainable development in the social dimension are applied. This paper makes reference to theories of security in a space (the “broken windows” theory and the strategy of Crime Prevention Through Environmental Design, CPTED) and shows the problem of illegal graffiti against the background of these theories. A new research aspect of the occurrence of illegal graffiti (scribbles and tags) within urban space is the features that determine its emergence in a spatial dimension. The aim of the analyses in this paper is to obtain information on which geospatial features are generators of illegal graffiti. The research field was limited to the space of one city—Olsztyn—with the assumption that the proposed research methodology would be useful for the spaces of other cities. The research methodology consists of several steps: firstly, we determined a list of features in the surroundings of illegal graffiti using direct interviews, and secondly, we analyzed the frequency of occurrence of these features in the researched locations in space. The next step was to standardize the obtained results using the quotient transformation method with respect to a reference point, where the reference point is the sum of all observations. After that, we assigned ranks for standardized results. The last stage involved an analysis using the GRA type of grey systems to obtain a sequence of strengths of relationships. This sequence allowed us to determine which of the features adopted for analysis have the greatest impact on the creation of illegal graffiti in a space. As indicated by the strength of the relationship, in the analyses conducted, geospatial features such as poor sidewalk condition and neglected greenery have the greatest impact on the occurrence of illegal graffiti. Other features that influence the occurrence of illegal graffiti in a given space include a lack of visibility from neighboring windows and the proximity of a two-way street. It can be assumed that these features are generators of illegal graffiti in the studied area and space. The poor condition of the facade has the least impact on the possibility of illegal graffiti occurring in a given space. Full article

With global energy demand escalating and climate change posing unprecedented challenges, accurate forecasting of regional energy consumption has emerged as a cornerstone for national energy planning and sustainable development strategies. This study develops a novel nonlinear different fractional discrete grey multivariate model (NDFDGM(r_i,N)). This model improves the shortcomings of the conventional GM(1,N) in handling nonlinear relationships and variable differences by introducing different fractional order accumulation and nonlinear logarithmic conditioning terms. In addition, the Firefly Algorithm (FA) was utilized to optimize the model’s hyperparameters, significantly enhancing the prediction accuracy. Through empirical analysis of energy consumption data in China’s eastern, central and western regions and across the country, it has been confirmed that the NDFDGM model outperforms others during both the simulation and forecasting phases, and its predicted MAPE values are, respectively, 1.4585%, 1.4496%, 2.0673% and significantly lower than that of compared models. The findings indicate that this model can effectively capture the complex characteristics of energy consumption, and its prediction results offer a solid scientific foundation for guiding energy strategies and shaping policy decisions. Finally, this paper conducts extrapolation and predictive analysis using the NDFDGM(r_i,N) to explore the development trends of energy consumption in the whole country in the coming three years and puts forward energy policy suggestions for different regions to promote the optimization and sustainable development of the energy structure. Full article

This paper investigates weak fault feature extraction in AUV thrusters under multi-input signal conditions. Conventional methods often rely on insufficient input signals, leading to a non-monotonic mapping between fault features and fault severity. This, in turn, makes accurate fault severity identification infeasible. To overcome this limitation, this paper increases the number of input signals by utilizing all available measurable signals. To address the problems arising from the expanded signal set, a signal denoising method that combines Feature Mode Decomposition and wavelet denoising is proposed. Furthermore, a signal enhancement technique that integrates energy operators and the Modified Bayes method. Additionally, distinct technical approaches for noise reduction and enhancement are specifically designed for different input signals. Unlike conventional methods that extract features directly from raw input signals, for fault feature extraction and fusion, this study transforms the signals into the time, frequency, and time–frequency domains, extracting diverse fault features across these domains. A sensitivity factor selection method is then employed to identify the sensitive features. These selected features are subsequently fused using Dempster–Shafer evidence theory to construct the final fault feature. Finally, fault severity identification is carried out using the classical grey relational analysis. Pool experiments using the “Beaver II” AUV prototype validate the effectiveness of the proposed method. Full article

This study contributes to scientific theory by analyzing the models proposed within the framework of circular agriculture to determine how the three dimensions of sustainability—environmental, economic, and social—are integrated into their implementation. A systematic review was conducted on articles published between 2016 and 2025, indexed in the Scopus and Web of Science databases, as well as the relevant grey literature. The methodology employed an extensive content analysis designed to minimize bias, applying filters related to specific knowledge areas to delimitate the search scope and enhance the precision of the research. The findings reveal that the research on circular agriculture models is predominantly grounded in the principles of the circular economy and its associated indicators. Moreover, these models tend to focus on environmental metrics, often neglecting a comprehensive exploration of the social and economic dimensions of sustainable development. It can be concluded that a significant gap persists in the literature regarding the circularity of agriculture and its socio-economic impacts and the role of regulatory frameworks, aspects that future research must address in order to achieve sustainability in circular agriculture. Full article

Nature-based solutions (NbS) are increasingly recognized as strategic alternatives and complements to grey infrastructure for addressing water-related challenges in the context of climate change, urbanization, and biodiversity decline. This article presents a critical, theory-informed review of the state of NbS implementation in European water management, drawing on a structured synthesis of empirical evidence from regional case studies and policy frameworks. The analysis found that while NbS are effective in reducing surface runoff, mitigating floods, and improving water quality under low- to moderate-intensity events, their performance remains uncertain under extreme climate scenarios. Key gaps identified include the lack of long-term monitoring data, limited assessment of NbS under future climate conditions, and weak integration into mainstream planning and financing systems. Existing evaluation frameworks are critiqued for treating NbS as static interventions, overlooking their ecological dynamics and temporal variability. In response, a dynamic, climate-resilient assessment model is proposed—grounded in systems thinking, backcasting, and participatory scenario planning—to evaluate NbS adaptively. Emerging innovations, such as hybrid green–grey infrastructure, adaptive governance models, and novel financing mechanisms, are highlighted as key enablers for scaling NbS. The article contributes to the scientific literature by bridging theoretical and empirical insights, offering region-specific findings and recommendations based on a comparative analysis across diverse European contexts. These findings provide conceptual and methodological tools to better design, evaluate, and scale NbS for transformative, equitable, and climate-resilient water governance. Full article

With the acceleration of the oilfield development process during the high water content period, the contradiction between the increase in energy consumption and the decrease in the energy efficiency of the gathering and transportation system has become increasingly obvious. This paper develops a grey relational analysis model using a combination of AHP and EWM. Based on the characteristics of light oil production, a four-level evaluation indicator system is developed. Based on game theory, AHP can provide subjective weights, the EWM can provide objective weights, and subjective and objective combinations are used for a more reasonable assignment. Concurrently, the 0.05 distinguishing coefficient and the ideal reference values are selected as the GRA reference sequence to evaluate the energy consumption of the gathering and transportation system as a whole and each subsystem. The analysis of a light oil block indicates significant room for improvement in the energy efficiency correlation across the system. Taking the central processing station as an example, the grey relational degree of electricity consumption per unit of injected water is measured at 0.12, marking it as the weakest link in the system. This study supports efficiency enhancement by identifying energy consumption bottlenecks within the system. Full article