Search Results (2,471)

This study aims to systematically reveal, from the perspective of organizational scale, the differences between large and small architectural design organizations in China in terms of characteristics of production activities, technological capabilities and innovation levels, resource integration capabilities, and client groups, and to quantify the priority order of clients’ attention to architectural design products, thereby providing a reference for industry structure optimization and strategic decision making. This research combines case analysis and comparative study to construct a four-dimensional comparative framework. The results show that large design organizations, leveraging their advantages in technological research and development as well as resource integration, focus on large-scale complex projects, technology-driven projects, cultural landmark projects, and multi-stakeholder collaborative projects, primarily serving government agencies and large enterprises. In contrast, small design organizations excel in flexibility, concentrating on small-scale simple projects, specialized niche projects, localized projects, and short-cycle, low-budget projects, serving individual owners and small businesses. Furthermore, this study adopts the Analytic Hierarchy Process (AHP) to establish an evaluation model. Twenty experts from architectural design organizations, construction organizations, and research institutions were invited to score the survey questionnaires, and quantitative weight analysis was performed. The research findings provide support for the optimization of the industry. Full article

As technologies become increasingly diverse and complex, Intelligent Massage Systems (IMS) are evolving from traditional mechanically executed modes toward personalized and predictive health interventions. However, the field still lacks a unified grading standard for intelligence, making it difficult to quantitatively assess a system’s overall intelligence level. To address this gap, this paper proposes a task-driven six-level (L0–L5) classification framework and constructs a Massage-Driven Task (MDT) model that decomposes the massage process into six subtasks (S1–S6). Building on this, we design a three-dimensional evaluation scheme comprising a Functional Delegation Structure (FDS), an Anomaly Perception Mechanism (APM), and a Human–Machine Interaction Boundary (HMIB), and we select eight key performance indicators to quantify IMS intelligence across the perception–decision–actuation–feedback closed loop. We then determine indicator weights via the Delphi method and the Analytic Hierarchy Process (AHP), and obtain dimension-level scores and a composite intelligence score S0 using normalization and weighted aggregation. Threshold intervals for L0–L5 are defined through equal-interval partitioning combined with expert calibration, and sensitivity is verified on representative samples using ±10% data perturbations. Results show that, within typical error ranges, the proposed grading framework yields stable classification decisions and exhibits strong robustness. The framework not only provides the first reusable quantitative basis for grading IMS intelligence but also supports product design optimization, regulatory certification, and user selection. Full article

This study addresses this gap by proposing a multilevel fuzzy evaluation model combined with an analytic hierarchy process (AHP) to quantify the greenness of furniture products across their entire lifecycle. Focusing on an office desk as a case study, we developed an indicator system encompassing environmental attributes, resource efficiency, energy consumption, economic costs, and quality performance. Weighting results revealed that environmental attributes (27.2%) and resource efficiency (27.2%) dominated the greenness evaluation, with material recycling rate (33.5%) and solid waste pollution (24.3%) as critical sub-indicators. The prototype achieved a moderate greenness score of 70.38/100, highlighting optimization potential in renewable material adoption (10% current rate) and modular design for disassembly. Mechanically recycled materials could reduce lifecycle emissions by 18–25% in key categories. The model demonstrates scalability for diverse furniture types and informs policy-making by prioritizing high-impact areas such as toxic material reduction and energy-efficient manufacturing, thus amplifying its global and interdisciplinary multiplier effects. Full article

Community-scale biomass power plants (CSBPPs) offer a decentralized approach for electricity generation by utilizing locally available biomass while delivering socioeconomic benefits. Site selection plays a critical role in the success of CSBPPs and requires the consideration of diverse spatial and non-spatial factors. This study presents a spatial decision-support tool for identifying suitable CSBPP sites in Thailand’s Eastern Economic Corridor (EEC), which comprises the Chachoengsao, Chonburi, and Rayong provinces. A geoprocessing workflow integrating Geographic Information Systems (GISs), Multi-Criteria Decision-Making (MCDM), and the Analytic Hierarchy Process (AHP) was developed using ModelBuilder tools in ArcGIS Pro (version 3.0.2). Thirteen sub-criteria related to geographical, infrastructural, and socioeconomic–cultural dimensions, along with exclusion zones, were evaluated by 15 experts from diverse stakeholder groups. Biomass availability from five major economic crops was combined with other spatial data layers, incorporating expert-assigned weights and suitability scores. The findings indicated a remaining biomass energy potential was 34,156 TJ, with sugarcane residues contributing over 80%. Approximately 20% of the EEC area (about 0.262 million hectares) was classified as highly suitable for CSBPP development, revealing several viable site options. The proposed model offers a flexible and replicable framework for regional biomass planning and can be adapted to other locations by adjusting the criteria and integrating optimization techniques. Full article

To improve the user perception of design decisions for living spaces in rural tourism-based elderly housing scientifically, a design approach is proposed from the perspective of user needs. This approach establishes an innovative model for the design of living spaces in rural tourism-based elderly housing by integrating the Kano model, the Analytic Hierarchy Process (AHP), and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Firstly, the KJ method is used to extract the raw user needs, and after data cleaning, the Kano model is applied to categorize the attributes of these initial needs. Subsequently, the AHP method is used to construct a hierarchical model of user needs, enabling the calculation of the weight values of needs across different levels. User needs with higher weight values are integrated to create the design, and three design schemes are proposed for comparative analysis. Finally, the TOPSIS method is used to comprehensively evaluate the three design schemes derived from the user needs items identified by the Kano model and AHP method, thereby validating the feasibility of each design scheme. Experimental results show that the user needs-driven living space, constructed using the Kano model, AHP method, and TOPSIS method, transforms subjective concepts into specific design parameters through both qualitative and quantitative methods. This approach not only effectively addresses user needs but also provides solid theoretical support for the design of living spaces in rural tourism-based elderly housing. Full article

This study applies a multicriteria decision analysis to explore how experts from different backgrounds assess traditional and emerging criteria for urban cycling. A hierarchical model with 7 main criteria and 31 subcriteria was evaluated by 30 specialists from academic, technical, and user-focused groups. Using pairwise comparisons and aggregated judgments, this study reveals points of agreement and divergence among expert priorities. Safety and infrastructure were rated as the most important factors. In contrast, contextual and technological aspects, such as Multimodality, Environmental Quality, Shared Systems, and Digital Solutions, received moderate to lower weights, with differences linked to expert profiles. These results highlight how different disciplinary perspectives influence the understanding of bikeability-related factors. Conceptually, the findings support a broader view of cycling conditions that incorporates both established and emerging criteria. Methodologically, this study demonstrates the value of the Analytic Hierarchy Process (AHP) as a participatory and transparent tool to integrate diverse stakeholder opinions into a structured evaluation model. This approach can support cycling mobility planning and policymaking. Future applications may include case studies in specific cities, combining expert-based priorities with local spatial data, as well as longitudinal research to track changes in cycling conditions over time. Full article

Recent advancements in emerging technologies such as IoT and AI have driven digital innovation, while also accelerating the sophistication of cyberattacks and expanding the attack surface. In particular, inter-state cyber warfare, sophisticated ransomware threats, and insider-led personal data breaches have emerged as significant new security risks. In response, this study proposes a Privacy-Aware Integrated Log System model developed to mitigate diverse security threats. By analyzing logs generated from personal information processing systems and security systems, integrated scenarios were derived. These scenarios are designed to defend against various threats, including insider attempts to leak personal data and the evasion of security systems, enabling scenario-based contextual analysis that goes beyond simple event-driven detection. Furthermore, the Analytic Hierarchy Process (AHP) was applied to quantitatively assess the relative importance of each scenario, demonstrating the model’s practical applicability. This approach supports early identification and effective response to personal data breaches, particularly when time and resources are limited by focusing on the top-ranked scenarios based on relative importance. Therefore, this study is significant in that it goes beyond fragmented log analysis to establish a privacy-oriented integrated log system from a holistic perspective, and it further validates its operational efficiency in field applications by conducting an AHP-based relative importance evaluation. Full article

Consistency indices quantify the degree of transitivity and proportionality violations in a pairwise comparison matrix (PCM), forming a cornerstone of the Analytic Hierarchy Process (AHP) and Analytic Network Process (ANP). Several methods have been proposed to compute consistency, including those based on the maximum eigenvalue, dot product, Jaccard index, and the Bose index. However, these methods often overlook two critical aspects: (i) vector projection or directional alignment, and (ii) the weight or importance of individual elements within a pointwise evaluative structure. The first limitation is particularly impactful. Adjustments made during the consistency improvement process affect the final priority vector disproportionately when heavily weighted elements are involved. Although consistency may improve numerically through such adjustments, the resulting priority vector can deviate significantly, especially when the true vector is known. This indicates that approaches neglecting projection and weighting considerations may yield internally consistent yet externally incompatible vectors, thereby compromising the validity of the analysis. This study builds on the idea that consistency and compatibility are intrinsically related; they are two sides of the same coin and should be considered complementary. To address these limitations, it introduces a novel metric, the Consistency Index G (CI-G) based on the compatibility index G. This measure evaluates how well the columns of a PCM align with its principal eigenvector, using CI-G as a diagnostic component. The proposed approach not only refines consistency measurement but also enhances the accuracy and reliability of derived priorities. Full article

Amid China’s push for new urbanization and refined urban governance, urban villages function as key transitional spaces in the process of rural–urban spatial restructuring. Their internal differentiation and typological governance approaches warrant systematic exploration. This study examines 140 urban villages located in the core and peripheral areas of Kunming as empirical cases. By innovatively integrating polycentric urban theory with spatial accessibility theory, we construct a dual-dimensional classification framework. Employing the Analytic Hierarchy Process (AHP), we develop a comprehensive evaluation system encompassing ecological, spatial, social, and economic dimensions. Our findings reveal the following: (1) Urban villages with different levels of accessibility within the same region tend to exhibit broadly similar characteristics across most evaluation dimensions. However, outlier cases demonstrate distinct development trajectories that transcend spatial constraints, driven by unique mechanisms underlying their atypical evolution. (2) Cross-regional comparisons highlight systematic disparities across several dimensions, most notably in ecological quality, spatial efficiency, and economic vitality. Based on spatial differentiation, we propose five governance models tailored to varied urban village types. The proposed typological governance framework provides a replicable methodology for addressing urban-rural transition challenges in diverse contexts. By emphasizing the spatial heterogeneity of informal settlements and advocating for place-specific strategies based on geographic endowments, this model enables policymakers to move beyond one-size-fits-all approaches. For Chinese cities, it offers a systematic toolkit to classify urban villages according to their regional roles and developmental potentials, informing tailored regeneration plans. Globally, the framework’s emphasis on context-sensitive typology and multidimensional evaluation can guide the upgrading of informal settlements in rapidly urbanizing regions, particularly where rural-urban interfaces face similar fragmentation pressures. Full article

Oil and gas pipelines represent critical infrastructure for energy transportation and are essential for ensurin g energy security. The seismic disaster risk assessment of these pipelines is of paramount importance for safeguarding energy supplies. Traditional assessment methodologies primarily focus on the structural integrity of the pipeline body, often neglecting the impact of auxiliary structures and site-specific disaster effects. This study proposes an enhanced risk assessment methodology to address these gaps. This research systematically compiles seismic damage case studies of pipelines from major seismic zones in China. By considering the interactions between auxiliary structure types, site conditions, and forms of disasters, 15 typical operating conditions are identified, and a seismic damage case database is constructed. We develop a failure probability model that integrates geotechnical parameters, structural responses, and ground motion characteristics to assess the impact of liquefaction, site amplification, fault activity, and collapse/landslide phenomena. Utilizing Particle Swarm Optimization (PSO) and Fuzzy Analytic Hierarchy Process (Fuzzy AHP) algorithms, this model quantifies the influence weights and coefficients of these disasters on pipeline auxiliary structures, forming a vulnerability matrix centered around Peak Ground Acceleration (PGA). Additionally, a dual-vulnerability assessment framework is established, and a failure probability formula accounting for the superposition effects of multiple disasters is proposed. This study marks a significant advancement, transitioning from traditional single-pipeline evaluations to “structure-disaster-site” coupling analysis, and provides a scientific basis for pipeline seismic design, operation, and maintenance under specific environmental conditions. This work contributes to the development of quantitative and refined seismic risk assessments for oil and gas pipelines. Full article

This study proposes an integrated computational framework that combines deep learning-based visual perception analysis with multi-criteria decision making to optimize indoor architectural layouts in terms of both visual coherence and sustainability. The framework initially employs a deep learning method leveraging edge pixel feature recombination to extract critical spatial layout features and determine key visual focal points. A fusion model is then constructed to preprocess visual representations of interior layouts. Subsequently, an evolutionary deep learning algorithm is adopted to optimize parameter convergence and enhance feature extraction accuracy. To support comprehensive evaluation and decision making, an improved Analytic Hierarchy Process (AHP) is integrated with the entropy weight method, enabling the fusion of objective, data-driven weights with subjective expert judgments. This dual-focus framework addresses two pressing challenges in architectural optimization: sensitivity to building-specific spatial features and the traditional disconnect between perceptual analysis and sustainability metrics. Experimental results on a dataset of 25,400 building images demonstrate that the proposed method achieves a feature detection accuracy of 92.3%, surpassing CNN (73.6%), RNN (68.2%), and LSTM (75.1%) baselines, while reducing the processing time to under 0.95 s and lowering the carbon footprint to 17.8% of conventional methods. These findings underscore the effectiveness and practicality of the proposed model in facilitating intelligent, sustainable architectural design. Full article

This study assesses and maps the physical flood vulnerability within the Xerias, Krafsidonas, and Anavros ungauged catchments in Volos, Thessaly, Greece, using a Geographical Information Systems (GIS)-based Multi-Criteria Decision Analysis (MCDA) integrated with the Analytic Hierarchy Process (AHP). Six factors influencing flood dynamics were selected including slope, flow accumulation, geology, land use/cover, flood history and burned areas. The factors were weighted using the AHP based on their relative influence in flood occurrence. Physical flood vulnerability was assessed utilizing the Weighted Linear Combination (WLC) method and visualized through thematic flood-vulnerability maps. The analysis indicates that the southwestern and central-southern parts of the study area, which are highly urbanized and industrialized, exhibit the highest physical flood-vulnerability. Specifically, 32.76% of the Xerias catchment, 41.16% of the Krafsidonas catchment, and 34.71% of the Anavros catchment exhibit high to very high flood vulnerability. On the other hand, mountainous areas with steep slopes, permeable lithology, and dense forests exhibit low to very low physical flood vulnerability. The method’s accuracy was verified through sensitivity analysis and comparison with national flood-risk data for the study area. The results emphasize the physical vulnerability of Volos to flooding and the necessity for targeted flood mitigation measures, demonstrating the value of GIS in flood risk management. Full article

Accurately assessing the spatiotemporal dynamics and influencing factors of ecosystem stability in arid desert regions (ADR) is crucial for ecological conservation and the achievement of high-quality regional development. However, existing assessment frameworks generally fail to adapt to the extremely fragile ecological conditions of ADR. Therefore, the Alxa Desert, a typical region, was selected as the research region, and an ecosystem stability assessment framework tailored to regional characteristics (perturbation–resilience–function) was constructed. Perturbation represents external pressure, resilience reflects the capacity for recovery and adaptation, and function serves as the supporting foundation. The three dimensions are dynamically coupled and jointly determine the stability status of the ecosystem in the Alxa Desert. Methodologically, this study innovatively introduces the Cloud Model–Analytic Hierarchy Process (CM-AHP) to calculate indicator weights, which more effectively addressed the widespread fuzziness and uncertainty inherent in ecosystem assessments compared to traditional methods. In addition, spatial autocorrelation methods was applied to reveal the spatial and temporal evolution characteristics of ecosystem stability from 2000 to 2020. Furthermore, the optimal parameters geographical detector model (OPGDM) was applied to analyze the effects of natural and human factors on the spatial differentiation of ecosystem stability in Alxa Desert. In addition, the Markov–FLUS model was employed to simulate the future trends of ecosystem stability over the next two decades. The results indicate that ecosystem stability in Alxa Desert from 2000 to 2020 was primarily characterized by vulnerable and moderate levels, with the area classified as extremely vulnerable decreasing significantly by 10% relative to its extent in 2000. Spatially, higher stability was observed in oasis regions and southeastern mountainous regions, while lower stability was concentrated in the desert hinterlands. Overall, ecosystem stability shifted from vulnerable toward moderate levels, reflecting a trend of gradual improvement. From 2000 to 2020, the Moran’s I varied between 0.78 and 0.81, showing strong spatial clustering. Surfce Soil moisture content (SSMC), Soil organic carbon (SOC), and enhanced vegetation index (EVI) were the primary factors influencing the spatial differentiation of ecosystem stability in Alxa Desert. The interaction between these factors further enhanced their explanatory power. Future forecasting results indicate that ecosystem stability will further improve by 2030 and 2040, particularly in the northern and southern areas of Alxa Left Banner and Alxa Right Banner. The findings can offer a theoretical foundation for future ecological conservation and environmental management in ADR. Full article

The current digital delivery of surface engineering in oil and gas fields faces challenges such as difficulty in integrating multiple heterogeneous data sources, low efficiency in quality reviews, and a lack of unified evaluation standards, which seriously restrict the implementation of intelligent operation and maintenance. Based on this, this study constructs multi-dimensional control rules for data quality covering the entire lifecycle. Based on the characteristics of structured, semi-structured, and unstructured data, five-dimensional review criteria and quantification methods for normative, integrity, consistency, accuracy, and timeliness were developed. At the same time, by integrating the analytic hierarchy process (AHP) and the entropy weight method (EWM), a combined subjective and objective weight evaluation model was established to achieve scientific quantitative calculation of quality indicators. Verification with a project by Southwest Oil and Gas Field shows that the system effectively achieves quantifiable diagnosis and traceability of engineering data quality, revealing the differentiation characteristics of different data types in the quality dimension. The research results provide core methodological support for the establishment of an integrated data governance paradigm of “collection—review—operation and maintenance” in oil and gas fields, facilitating the implementation of intelligent operation and maintenance. Full article

Artificial intelligence (AI) is increasingly transforming the public sector, although the willingness of organizations to adopt such technologies varies widely. Existing models, such as the technology–organization–environment (TOE) model, highlight systemic drivers and barriers but overlook the individual-level factors that are also critical to successful adoption. To address this gap, we propose a decision model that combines the TOE model with the unified theory of acceptance and use of technology (UTAUT) and combines the dimensions of technology, organization, environment, and individual readiness. The model was developed using the Analytic Hierarchy Process (AHP) and supports group decision-making by combining the pairwise comparison matrices of multiple experts into a consolidated priority structure. Specifically, many expert judgments were used to create a group matrix for the four main categories and four additional group matrices for the criteria within each category. This structured approach allows for a systematic assessment of whether a public sector organization is ready for AI adoption. The results show the importance of both systemic factors (such as data, technology, innovation, and readiness for change) and individual factors (such as social influence and voluntariness of use). The final model provides a comprehensive and practical decision-making tool for public sector organizations to assess readiness, identify gaps, and guide the strategic adoption of AI. Full article