Search Results (31)

Architectural design stands as a highly knowledge-intensive field, with designers serving as the linchpin for its premium development. China’s construction industry is now navigating a transitional phase of slower growth, where a misalignment in designer capabilities significantly obstructs the nation’s shift from being a mere “construction giant” to becoming a true “construction powerhouse”. Based on the spatial mismatch model and Geodetector, this study empirically analyzes the mismatch relationship among designers and its influencing factors using panel data from 31 provinces in China from 2013 to 2023, and proposes strategies for cultivating architectural design talents. Findings reveal that China’s architectural designers exhibit spatial supply imbalance, and complex trends in designer allocation-simultaneous growth and decline coexist. China exhibits diverse types of architect mismatch: 22.58% of regions are in a state of Positive Mismatch, and 12.90% experience Negative Mismatch. In over one-third of regions, the architectural design talent market can no longer self-correct architect mismatch through market mechanisms, urgently requiring collaborative intervention policies from governments, design associations, and enterprises to address architect supply–demand governance. For a smooth transition during the transformation and upgrading of the construction and design industries, the architectural design talent market should accommodate frictional designer mismatch. The contribution of designer mismatch varies significantly, with factors such as innovation, industrial structure, and fiscal self-sufficiency exerting more direct influence, while other factors play indirect roles through dual-factor enhancement effects and nonlinear enhancement effects. The insights from the analysis results and conclusions for future designer cultivation include fostering an interdisciplinary teaching model for designers through university–enterprise collaboration, enhancing education in AI and intelligent construction literacy, and establishing an intelligent service platform for designer supply–demand matching to promptly build a new differentiated and precise designer supply system. Full article

Under the dual backdrop of the Healthy China strategy and the concept of sustainable development, optimizing the spatial layout of primary healthcare facilities is important for fairly distributing healthcare resources and strengthening the resilience of the public health system in a sustainable way. This study introduces an innovative 3D spatial resilience evaluation framework, covering transmission (service accessibility), diversity (facility type matching), and stability (supply demand balance). Unlike traditional accessibility studies, the concept of “resilience” here highlights a system’s ability to adapt to sudden public health events through spatial reorganization, contrasting sharply with vulnerable systems that lack resilience. Method-wise, the study uses an improved Gaussian two-step floating catchment area method (Ga2SFCA) to measure spatial accessibility, applies a geographically weighted regression model (GWR) to analyze spatial heterogeneity factors, combines network analysis tools to assess service coverage efficiency, and uses spatial overlay analysis to identify areas with supply demand imbalances. Harbin is located in northeastern China and is the capital of Heilongjiang Province. Since Harbin is a typical central city in the northeast region, with a large population and clear regional differences, it was chosen as the case study. The case study in Harbin’s main urban area shows clear spatial differences in medical accessibility. Daoli, Nangang, and Xiangfang form a highly accessible cluster, while Songbei and Daowai show clear service gaps. The GWR model reveals that population density and facility density are key factors driving differences in service accessibility. LISA cluster analysis identifies two typical hot spots with supply demand imbalances: northern Xiangfang and southern Songbei. Finally, based on these findings, recommendations are made to increase appropriate-level medical facilities, offering useful insights for fine-tuning the spatial layout of basic healthcare facilities in similar large cities. Full article

Solar thermal technology is an important component of low-carbon energy systems, but its application potential is constrained by two key factors: the inherent limits of energy flux density and the temporal mismatch between supply and demand. This study examined efficiency losses in building heating systems in Northwest China caused by the mismatch between supply and demand in intermittent solar thermal storage systems. Three typical building heating models (Day–Night Intermittent Mode, Day–Night + Monthly Intermittent Mode, and Composite Intermittent Mode (Day–Night + Weekly + Monthly)) were constructed through SketchUp, integrating the Transient System Simulation Tool (TRNSYS) with improved calculation methods in an innovative way. The study first examined regional energy consumption patterns and the temporal characteristics of building occupancy and then proposed a collaborative optimization framework for thermal collection and storage, focused on improving the dynamic matching algorithm of the thermal collection area ratio and the tank volume ratio and establishing a tank capacity calculation model that considers the time-varying characteristics of heat demand and fluctuations in thermal collection efficiency during the intermittent heating cycle. The results show that compared with continuous operation, the intermittent strategy reduces the annual cumulative heat load by 13–33%, among which the Day–Night Intermittent Mode shows the daily peak load reaches 1.8 times the normal value during restart, while the daily fluctuation amplitude of the Day–Night + Monthly Intermittent Mode decreases by 42%. The corresponding solar energy guarantee rate reaches 86–88%, and the heat storage loss is reduced by 19–27%. The time-varying coupling design method established in this study provides an optimization path that takes into account both system efficiency and economy for intermittent heating scenarios. The proposed dynamic capacity configuration criterion has universal guiding value for the design of solar district heating systems. Full article

The innovation demand and supply matching requires an accurate and time-consuming analysis of patents and the identification of their technological domains; since these tasks can be particularly challenging, this is why recent studies have evaluated the possibility of adopting Artificial Intelligence based on NLP techniques. Here, we present an automated workflow for patent analysis and classification devoted to the Italian patent scenario. High-quality data from the online platform KnowledgeShare (KS) were investigated: KS is the first patent management platform on the Italian innovation scene. A not secondary aspect consisted in determining which words mostly influenced patent classification, thus characterizing the corresponding research areas. Several models were compared to ensure the workflow’s robustness; Logistic Regression (LR) resulted in the best-performing model, and its performance compared well with the State of the Art. For each technological domain in the KS database, we evaluated and discussed its characteristic words; furthermore, a further analysis was focused on explaining why some domains, such as “Packaging” and “Environment,” were particularly confounding. This last aspect is of paramount importance to identify cross-contamination effects among research areas. Full article

Here we take the example of Italy to demonstrate a country-level approach to the design of a sustainable system of Earth Observation (EO)-based products to match the demand/supply for monitoring coastal zones and to guide the development of new products based on national/local users’ needs complementary to Copernicus Core Services products and its future development. With support from the Coastal Thematic Consultation Board of the Italian Copernicus User Forum, we applied a standardized methodology involving elicitation, selection, analysis, validation, and requirement management. Our findings reveal a strong national need in EO-based products for coastal monitoring and services provision. The survey results offer insights into how existing products and services meet user needs on the national scale, for monitoring several parameters pertaining to four classes, biological, geomorphological, physical, and chemical, highlighting additional demands and integration opportunities with the evolving European Copernicus Coastal Hub. The innovation of this work lies in the design of a foundation for a holistic approach to complement European and national EO systems, both in terms of data to be acquired with synergistic satellite missions and in situ infrastructures and in terms of the development of sustainable products, models, and algorithms for downstream value-added services. Full article

With the continuous advancement of the Internet and information technologies, the capacity for development and integration of vehicle and cargo resources has been significantly enhanced, driving the rapid emergence of vehicle–cargo matching platforms. Serving as critical intermediaries between vehicle owners and cargo owners, vehicle–cargo matching platforms effectively address key challenges in traditional logistics, such as low matching efficiency and information asymmetry. As a result, they significantly improve the intelligence and precision of logistics resource allocation. However, at the current stage, vehicle–cargo matching platforms rarely promote freight pooling services, leading to resource underutilization. Due to the freight pooling matching problem involving the combination and allocation of multiple vehicle owners and cargo owners, which is highly complex, few scholars have conducted research on such issues. The lack of coordinated optimization in matching models may result in inefficiencies, and the limited consideration of individual user preferences can lead to low user satisfaction. Therefore, this paper focuses on the freight pooling matching problem in vehicle–cargo matching platforms. To improve matching efficiency and fully consider user preferences, the theory of stable matching is introduced into the freight pooling matching problem. It defines the concepts of combination preferences and group stability based on combination preferences, establishes a group stable matching model for the freight pooling business of vehicle–cargo matching platforms, and verifies the stability of the model through theoretical proof. Since this model is a mixed-integer linear programming model with relatively few decision variables but a large number of constraints, this paper introduces the cutting-plane algorithm. Based on the characteristics of the problem, the algorithm is improved by removing ineffective constraints and only using key constraints, significantly reducing computational complexity, optimizing the solving process, and greatly improving the model’s solution efficiency. This approach aligns well with the characteristics of the vehicle–cargo freight-pooling matching model. The research results indicate that the group stable matching model significantly improves platform revenue, vehicle owners’ profits, and cargo owners’ satisfaction across various supply and demand scenarios. Additionally, the cutting-plane algorithm reduces computation time by 97% and decreases the number of constraints during the solving process by 99%. The stable matching theory and solution algorithm proposed in this paper can provide users with precise matching schemes, significantly improving matching efficiency, user satisfaction, platform revenue and platform competitiveness. It demonstrates high innovation and practical application value. Full article

Innovative demand-responsive transport services are spreading in most urban areas, allowing dynamic matching between demand and supply and enabling travellers to request shared rides in real-time via mobile applications. They are used both as an alternative to public transport and as an access/egress leg to mass transit stations, i.e., acting as a feeder service. In low-demand areas and small-sized cities, it is often difficult to provide effective and cost-efficient public transport, thus resulting in an extensive use of private vehicles. Using an agent-based modelling approach, this study compares the performance of fixed-route transit (FRT) and demand-responsive transit (DRT), where optional stops can be activated on demand. The aim is to identify the conditions allowing DRT to become more advantageous than FRT in small-sized cities, both for travellers and the transport operator. A real-time matching algorithm identifies optimal trip chains (i.e., public transport lines; pick-up, drop-off and transfer stops; and time windows) for travel requests, dynamically updating vehicles’ routes and schedules. The model is applied to the city of Caltanissetta, Italy, where a transit service with six FRT urban lines is currently operating. Travel patterns were reconstructed from thousands of travel requests collected by a Mobility-as-a-Service platform within one-year. The main findings demonstrate the benefits of DRT in providing a higher quality of service, reducing riding times for passengers, and enhancing service efficiency without burdening operating costs. The DRT reduced the vehicle-kilometres travelled by up to 5% compared to FRT while decreasing passenger ride times by approximately 10%. An economic analysis showed reductions in operator unit costs of up to 3.4% for low-demand rates, confirming the advantages of flexible operations in small-sized cities. Full article

Current technological innovation in mega projects faces the problem of mismatch between supply and demand, where technology demand-side entities struggle to translate engineering problems into precise scientific research language, while technology supply-side entities fail to capture authentic scenario parameters from engineering sites. This study employs an evolutionary game model to thoroughly investigate behavioral interaction processes among governance entities, demand-side entities, and intermediary collaborative innovation platforms during technological innovation supply–demand matching. By constructing and deriving a tripartite evolutionary game model, this research analyzes the impacts of initial states, the matching effort coefficient, the innovation risk coefficient, and other factors on the evolution of scientific technological innovation supply–demand matching. Additionally, this study simulates the dynamic evolutionary processes of strategic selection. The findings reveal that the initial states of the three parties do not influence behavioral evolution. Furthermore, the subsidy coefficient, additional benefits, and risk coefficient emerge as the most significant parameters affecting tripartite strategy selection. The research outcomes and managerial implications provide crucial reference value for enhancing the alignment between scientific research supply and demand in mega projects, thereby promoting the transformation of scientific and technological achievements in major railway engineering projects. Full article

Background/Objectives: Food banks and pantries play a critical role in improving food security through allocating essential resources to households that lack consistent access to sufficient and nutritious food. However, these organizations encounter significant operational challenges, including variability in food donations, volunteer shortages, and difficulties in matching supply with demand. Artificial intelligence (AI) has become increasingly prevalent in various sectors of the food industry and related services, highlighting its potential applicability in addressing these operational complexities. Methods: This study systematically reviewed empirical evidence on AI applications in food banks and pantry services published before 15 April 2025. The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A comprehensive keyword and reference search was conducted in 11 electronic bibliographic databases: PubMed, Web of Science, Scopus, MEDLINE, APA PsycArticles, APA PsycInfo, CINAHL Plus, EconLit with Full Text, Applied Science & Technology Full Text (H.W. Wilson), Family & Society Studies Worldwide, and SocINDEX. Results: We identified five peer-reviewed papers published from 2015 to 2024, four of which utilized structured data machine learning algorithms, including neural networks, K-means clustering, random forests, and Bayesian additive regression trees. The remaining study employed text-based topic modeling to analyze food bank and pantry services. Of the five papers, three focused on the food donation process, and two examined food collection and distribution. Discussion: Collectively, these studies show the emerging potential for AI applications to enhance food bank and pantry operations. However, notable limitations were identified, including the scarcity of studies on this topic, restricted geographic scopes, and methodological challenges such as the insufficient discussion of data representativeness and statistical power. None of the studies addressed AI ethics, including model bias and fairness, or discussed intervention and policy implications in depth. Further studies should investigate innovative AI-driven solutions within food banks and pantries to help alleviate food insecurity. Full article

In the context of the accelerating new technological revolution and industrial transformation, the issue of talent supply and demand matching has become increasingly urgent. Precise matching talent supply and demand is a critical factor in expediting the implementation of technological innovations. However, traditional methods relying on interpersonal networks for talent ability collection, demand transmission, and matching suffer from inefficiency and are often influenced by the subjective intentions of intermediaries, posing significant limitations. To address this challenge, we propose a novel approach named TSDM for talent supply and demand matching. TSDM leverages prompt learning with pre-trained large language models to extract detailed expressions of talent ability and demand from unstructured documents while utilizing the powerful text comprehension capabilities of pre-trained models for feature embedding. Furthermore, TSDM employs talent-specific and demand-specific encoding networks to perform deep learning on talent and demand features, capturing their comprehensive representations. In a series of comparative experiments, we validated the effectiveness of the proposed model. The results demonstrate that TSDM significantly enhances the accuracy of talent supply and demand matching, offering a promising approach to optimize human resource allocation. Full article

Urban forests, as vital components of green infrastructure, provide essential ecosystem services (ESs) that support urban sustainability. However, rapid urban expansion and increased density threaten these forests, creating significant imbalances between the supply and demand for these services. Understanding the characteristics of ecosystem services and reasonably dividing ecological management zones are crucial for promoting sustainable urban development. This study introduces an innovative ecological management zoning framework based on the matching degree and synergies relationships of ESs. Focusing on Fuzhou’s fourth ring road area in China, data from 1038 urban forest sample plots were collected using mobile LIDAR. By integrating the i-Tree Eco model and Kriging interpolation, we assessed the spatial distribution of four key ESs—carbon sequestration, avoided runoff, air purification, and heat mitigation—and analyzed their supply–demand relationships and synergies. Based on these ecological characteristics, we employed unsupervised machine learning classification to identify eight distinct ecological management zones, each accompanied by targeted recommendations. Key findings include the following: (1) ecosystem services of urban forests in Fuzhou exhibit pronounced spatial heterogeneity, with clearly identifiable high-value and low-value areas of significant statistical relevance; (2) heat mitigation, avoided runoff, and air purification services all exhibit synergistic effects, while carbon sequestration shows trade-offs with the other three services in high-value areas, necessitating targeted optimization; (3) eight ecological management zones were identified, each with unique ecological characteristics. This study offers precise spatial insights into Fuzhou’s urban forests, providing a foundation for sustainable ecological management strategies. Full article

The shipping market is unpredictable and volatile due to some uncontrollable factors such as epidemic, conflicts and natural disasters. There is always an imperfect match between the supply capacity of liner companies and the actual demand of the market, which leads to a waste of slot resources and/or unsatisfied customer demand. Furthermore, the trade off between empty container transportation and laden container transportation is the crucial problem of strategic importance for liner companies. To deal with the above problem, this paper aims to develop a new solution to the collaborative optimization problem of container slot allocation and empty container repositioning by exploring the resource allocation, storage, and repositioning methods collaboratively. An online booking platform is introduced in this paper, and no-shows and customer preferences are considered in the analysis. An innovative integer programming model is established based on an online booking mode and a delivery-postponed strategy. A new branch-and-cut algorithm is then proposed to solve the problem. Finally, numerical experiments are conducted to verify the effectiveness of the proposed model and algorithm. The experimental results show that collaborative optimization can remarkably enhance the revenue of liner companies along with increasing the utilization of slot resources. Full article

Market convergence challenges socially sustainable supply chain management (SSSCM) due to the increasing competition. Identifying market convergence trends allows companies to respond quickly to market changes and improve supply chain resilience (SCR). Conventional approaches are one-sided and biased and cannot predict market convergence trends comprehensively and accurately. To address this issue, we propose a framework based on info2vec that solves the problem of matching multidimensional data by using the technology layer as the focal layer and the supply chain as the supporting layer. The framework enriches the supply chain dimension with the technology dimension. A knowledge graph is constructed to facilitate cross-domain information connectivity by integrating different data sources. The nodes in the knowledge graph were characterized using a representation learning algorithm, which enhanced feature mining during supply chain and market convergence. Changes in market demand were predicted based on link prediction experiments. Market convergence has an impact on firm cooperation and, thus, on SCR. The framework recommends potential technological and innovative cooperation opportunities for firms. In this way, it has been demonstrated to improve SSSCM through network resilience experiments. This method predicts market convergence efficiently based on the supply chain knowledge graph, which provides decision support for enterprise development. Full article

Practice shows that digital platforms could enhance disruptive innovation. Given that digital platforms have always encountered imbalance problems, this study intended to explore which factor configurations could promote disruptive innovation sustainably from the perspective of supply and demand matching. This study constructed a theoretical framework referring to the TOE framework. Based on 25 questionnaires from China, the fuzzy-set qualitative comparative analysis (fsQCA) method was used to explore the configurations of disruptive innovations. This study found the following: (1) None of the five factors in the dimensions of technology, organization, or environment could constitute a necessary condition for enabling disruptive innovation alone. (2) There were four supply and demand matching configurations that could lead to highly disruptive innovation. Based on the homogeneous characteristics of the results, the four paths were divided into “technology-organization driven transition” types and “organization-environment collaborative transition” types. (3) Non-highly disruptive innovation included three specific configurations, all of which lacked the core conditions in technical and organizational dimensions, suggesting the importance of technical and organizational factors for disruptive innovation. This study provides guidance on supply and demand matching for platform enterprises to continuously create disruptive innovation. However, the data from China may limit the results’ applicability to a more expansive setting. Full article

In the platform economy, platform enterprises connect producers and customers, matching diverse supply with diverse demand to achieve profit goals. The big data assets and marketing relationships owned by platform enterprises are complementary assets, and their modular organizational structure is crucial for enhancing enterprise value innovation. The existing literature lacks in-depth exploration of the impact of complementary assets on platform enterprises. Therefore, the research purpose of this article is to investigate the impact of complementary assets and organizational modularity on platform enterprise value innovation. Taking Meituan as the research object, this paper adopts the single case-study method of grounded theory coding to explore the relationship between complementary assets, organizational modularity, and platform enterprise value innovation. Research has shown that organizational modularity includes two dimensions, dependency and structural arrangement, while value innovation includes three dimensions, customer value innovation, product value, and innovation ability. Among them, complementary assets and organizational modularity both have a positive impact on platform enterprise value innovation, and organizational modularity plays a moderating role in the impact of complementary assets on platform enterprise value innovation. The research results have enriched the research on complementary assets, organizational modularity, and value innovation theory, providing a theoretical basis and inspiration for enterprise organizational design and better utilization of complementary assets. Full article