Search Results (424)

Public transit demand forecasting is a foundational component of sustainable urban mobility, enabling efficient operation, equitable service provision, and planning of public transit systems. Urban imagery, such as aerial images, contains rich information about urban sociodemographic characteristics and the built environment, offering particular value for data-scarce regions where conventional datasets are limited or outdated. However, there is limited research on using these images for public transit demand forecasting. This study introduces a deep learning approach for predicting transit ridership using aerial images. The method employs an encoder–decoder architecture to functionally separate image-derived latent representations into sociodemographic and physical environment vectors, which are subsequently used as inputs to a neural network for ridership prediction. Using data from Seoul, South Korea, the effectiveness of the proposed method is evaluated against three baseline configurations. The results show that the sociodemographic latent vector captures spatially organized residential characteristics, while the physical environment vector encodes distinct urban landscape patterns such as dense housing, traditional street grids, open spaces, and natural environments. The proposed model, which uses only imagery-derived latent features, substantially outperforms the pure image baseline and narrows the performance gap with census-informed models, reducing sMAPE by 25–60% depending on the mode. Combining imagery with census variables yields the highest accuracy, confirming their complementary nature. These findings highlight the potential of imagery-based approaches as a scalable, cost-efficient, and sustainable tool for data-driven transit planning. Full article

The transition toward sustainable transport systems requires decision-support tools that help organizations navigate strategic choices under environmental, economic, and operational constraints. This study introduces the Holistic Multi-Period Fleet Planning Problem (HMPFPP), a nonlinear optimization model designed to support long-term, sustainability-oriented fleet modernization. The model integrates investment costs, operational performance, emission limits, and dynamic demand into a unified analytical framework, enabling organizations to assess the long-term consequences of their decisions. A notable feature of the HMPFPP is the inclusion of outsourcing as a strategic option, which expands the decision space and helps maintain service performance when internal fleet capacity is constrained. An illustrative ten-year scenario demonstrates that the model generates non-uniform but cost-efficient transition pathways, in which legacy vehicles are gradually replaced by cleaner technologies, and temporary fleet downsizing can be optimal during low-demand periods. Outsourcing is activated only when joint emission and budget constraints make fully internal service provision infeasible. Across the tested instance, the HMPFPP is solved within seconds on standard hardware, confirming its computational tractability for exploratory planning. Taken together, these results indicate that data-driven optimization based on the HMPFPP can provide transparent and robust support for sustainable fleet management and transition planning. Full article

Rapid urban expansion has heightened the demand for accurate, scalable, and real-time methods to assess tree health and the provision of ecosystem services. Urban trees are the major contributors to air-quality improvement and climate change mitigation; however, their monitoring is mostly constrained to inherently subjective and inefficient manual inspections. In order to break this barrier, we put forward a lightweight multimodal deep-learning framework that fuses RGB imagery with environmental and biometric sensor data for a combined evaluation of tree-health condition as well as the estimation of the daily oxygen production and CO₂ absorption. The proposed architecture features an EfficientNet-B0 vision encoder upgraded with Mobile Inverted Bottleneck Convolutions (MBConv) and a squeeze-and-excitation attention mechanism, along with a small multilayer perceptron for sensor processing. A common multimodal representation facilitates a three-task learning set-up, thus allowing simultaneous classification and regression within a single model. Our experiments with a carefully curated dataset of segmented tree images accompanied by synchronized sensor measurements show that our method attains a health-classification accuracy of 92.03% while also lowering the regression error for O₂ (MAE = 1.28) and CO₂ (MAE = 1.70) in comparison with unimodal and multimodal baselines. The proposed architecture, with its 5.4 million parameters and an inference latency of 38 ms, can be readily deployed on edge devices and real-time monitoring platforms. Full article

Degrowth scholars emphasize the importance of the foundational economy (FE) for ‘living well within planetary boundaries’. The foundational economy describes the provision and regulation of everyday goods and services needed for the satisfaction of basic needs, such as housing, care, education, energy, food and mobility. However, there is a lack of conceptual models linking FE production and consumption to biodiversity conservation and restoration. This paper develops an ecological–economic model of ecosystem services, biodiversity conservation, and the foundational economy. It embeds FE sectors in the whole economy and provides economic arguments both on the supply side (e.g., economies of scale, scope and density; transaction costs) as well as on the demand side (e.g., trust in institutions; universal basic services; willingness to accept changes) in favor of resource efficiency. Compared to extractive and financialized business models, the FE production has major environmental advantages, especially if connected to public and not-for-profit economic activities. Though FE production is certainly a necessary condition for biodiversity conservation, it is not per se a sufficient strategy. The foundational economy is also embedded in natural processes; thus, respective institutional, legal and economic frameworks are needed to limit the environmental impacts of FE. Full article

Integrating perennial grain crops into agricultural systems can become a key milestone for increasing the provision of ecosystem services of food production systems. Intermediate wheatgrass is a novel perennial grain and forage crop that is undergoing domestication. Potential trade-offs between resource allocation and reproductive and vegetative plant structures can challenge the response to selection for both grain and forage production under dual-purpose use. Our goal was to understand the genetic relationship between grain and forage yield components, quantify potential trade-offs between vegetative and reproductive allocation, and optimize the response to selection under dual-purpose management. Phenological, grain, and forage traits were evaluated in 30 half-sib families across two field experiments conducted over three years. No trade-offs were detected between grain and forage yield traits, indicating that the simultaneous improvement of both traits is feasible. Grain yield per spike and spikes per plant are promising secondary traits for indirect selection, given their moderate-to-high heritability (h² = 0.58 and 0.41) and strong Pearson correlation coefficients with grain yield per plant (0.68 and 0.82). These traits could be assessed in the first year, increasing genetic gain per unit time. Intermediate wheatgrass germplasm could therefore be efficiently developed by shortening the time to first evaluation, using secondary traits, and performing selection under dual-purpose management. Full article

This paper presents a traffic-driven scaling framework for a digital twin proxy pool (DTPP) in vehicular edge computing (VEC), designed to eliminate the latency and synchronization issues inherent in conventional digital twin (DT) migration approaches. The core innovation lies in replacing the migration of vehicle DTs between edge servers (ESs) with instantaneous switching within a pre-allocated pool of DT proxies, thereby achieving zero migration latency and continuous synchronization. The proposed architecture differentiates between short-term DTs (SDTs) hosted in edge-side in-memory databases for real-time, low-latency services, and long-term DTs (LDTs) in the cloud for historical data aggregation. A queuing-theoretic model formulates the DTPP as an M/M/c system, deriving a closed-form lower bound for the minimum number of proxies required to satisfy a predefined queuing-delay constraint, thus transforming quality-of-service targets into analytically computable resource allocations. The scaling mechanism operates on a cloud–edge collaborative principle: a cloud-based predictor, employing a TCN-Transformer fusion model, forecasts hourly traffic arrival rates to set a baseline proxy count, while edge-side managers perform monotonic, 5 min scale-ups based on real-time monitoring to absorb sudden traffic bursts without causing service jitter. Extensive evaluations were conducted using the PeMS dataset. The TCN-Transformer predictor significantly outperforms single-model baselines, achieving a mean absolute percentage error (MAPE) of 17.83%. More importantly, dynamic scaling at the ES reduces delay violation rates substantially—for instance, from 13.57% under static provisioning to just 1.35% when the minimum proxy count is 2—confirming the system’s ability to maintain service quality under highly dynamic conditions. These findings shows that the DTPP framework provides a robust solution for resource-efficient and latency-guaranteed DT services in VEC. Full article

Promoting the coupled and coordinated development of new-type urbanization and rural revitalization is important for achieving high-quality and sustainable growth in China. This study follows a people-centered and coordinated development approach and is aligned with the Sustainable Development Goals (SDGs). It builds a comprehensive evaluation framework for the two systems and measures and interprets their coupling and coordination. On this basis, and under the background of China’s territorial spatial planning, the study draws implications for land and spatial governance. The core of the study is to answer the following questions: What are the spatiotemporal patterns of the coupling coordination level between new-type urbanization and rural revitalization in China from 2014 to 2023? How has the coordination of their development speed evolved? What are the main sources of regional differences? Which factors are the key drivers that promote coordinated development between the two systems? The main findings are as follows. (1) The national coupling coordination degree increases steadily. Spatially, there is a pattern of “eastern region leading, central and northeastern regions catching up, and western region showing internal divergence”. This pattern is consistent with differences in development intensity and accessibility across regions. (2) From 2019 to 2023, the coordination of development speed improved in most provinces. A few developed or special provinces show short-term mismatch, which may reflect timing gaps between land-use controls and the provision of public services. (3) Gaps between regions are the main source of overall differences, and there is a trend toward convergence. This is in line with interregional equalization and the narrowing of efficiency gaps. (4) Well-being of residents, social development, and digital innovation are the core driving forces. Digital inclusive finance and the intensity of parcel delivery services also provide important support. There are clear interaction effects among the driving factors, and these effects are stronger in areas where planning improves accessibility and reduces transaction costs. Full article

Health financing is a core determinant of the resilience and equity of health systems. Using WHO’s three-pillar framework as an orienting reference—rather than a prescriptive template—this article analyzes the evolution, structural shortcomings, and policy dilemmas of the Greek health financing model, within a comparative European context. While many EU countries have strengthened public financing to ensure universal access, Greece maintains a hybrid, fragmented model in which out-of-pocket payments play a disproportionately large role. Despite recurrent reform attempts, Greece has not developed a cohesive public system with a clear commitment to social solidarity. Instead, the system has silently shifted into a de facto semi-privatized two-tier model that exacerbates social inequities, limits access and undermines efficiency. Drawing on international experience and documented policy lessons, the article proposes a strategic redesign of the health financing architecture. The proposal is conceptual and does not enter implementation specifics. Its central axis is the establishment of two national single purchasers of health services by level of care, with a clear allocation of responsibilities and authority, the Ministry of Health for hospital care, and the National Organization for Healthcare Services Provision (EOPYY) for primary, outpatient, and post-acute/rehabilitation care, to strengthen prevention, equitable access, and chronic care management while easing pressure on hospitals. The proposed model includes targeted investments in human resources and infrastructure, the enhancement of prospective payment mechanisms, the strengthening of primary care networks, and the leveraging of innovation. At the same time, it provides for reforms in governance, digital transformation of the system, and reallocation of resources based on principles of equity and efficiency. The proposed overall restructuring aims to strengthen financial protection, reduce inequities in access, and improve health outcomes through a publicly oriented, socially responsive, and strategically governed system. Full article

(1) Background: Public service platforms (PSPs) have become increasingly popular for delivering public services. Typically, their fixed pricing and subsidy strategies overlook the participation of various stakeholders, resulting in inefficient supply of public services. This study aims to use China’s eldercare services as an example to examine its dynamic pricing and subsidy strategies. (2) Methods: Game theory was utilized to develop a two-level Stackelberg game framework considering the decision-making sequences among stakeholders with competing objectives. On this basis, the two-level Stackelberg game was solved based on the maximization of social welfare, platform profit, and utility, pursued by the government, private sector, and service providers, respectively. (3) Results: The service supply duration is determined by the service price. Specifically, when the service price falls within an appropriate range, the optimal supply duration increases with the service price. The results further show that the optimal service price increases with the platform’s commission rate but declines with government subsidies. Furthermore, the optimal government subsidy increases with the platform’s commission rate in a balanced market. By contrast, in an unbalanced market, where demand is either below the minimum supply or above the maximum supply, the government will withdraw subsidies entirely once the commission rate exceeds a certain threshold, thereby curbing excessive commission charges. (4) Conclusions: This study contributes to the body of knowledge of platform development by examining PSPs’ dynamic pricing and government subsidy strategies. Practically, this two-level Stackelberg game framework for PSPs will help improve the efficiency of public services provision for consumers and maximize social welfare and platforms’ profit for the government and private sector, respectively. Full article

Unmanned aerial vehicles (UAVs) have emerged as a pivotal technology for enhancing the agility and resilience of future wireless networks. However, conventional optimization approaches remain predominantly reactive, relying solely on current network conditions for decision making. This proves to be inadequate for handling sudden traffic surges in dynamic environments, resulting in suboptimal service quality. To address this limitation, this paper proposes a novel joint optimization framework integrating spatiotemporal traffic prediction. This equips UAVs with predictive capabilities, thereby facilitating a paradigm shift from passive response to proactive service provision. The main contributions of this work are fourfold: First, a novel closed-loop optimization framework is introduced, deeply integrating an advanced traffic-forecasting module with a communication resource optimization module to provide a systematic, forward-looking decision-making solution for UAV-assisted communications. Second, a cellular traffic predictor based on Gaussian mixture model meta-learning (GMM-ML) is designed. This model effectively captures the periodicity and heterogeneity of traffic data, enabling the precise prediction of future hotspot areas and resolving the challenge of accurate forecasting under small-sample conditions. Third, a long-term discounted mixed-integer nonlinear programming (MINLP) problem model is formulated. This innovatively incorporates a “service readiness reward” for predicted hotspots within the objective function to achieve long-term performance optimization. Fourth, an efficient and convergent predictive iterative association and location optimization (P-IALO) algorithm is developed. Utilizing block coordinate descent and continuous convex approximation techniques, this algorithm decomposes the original complex problem to alternately optimized subproblems of user association and trajectory planning, guaranteeing algorithmic convergence. To validate the effectiveness of the proposed framework, large-scale simulation experiments were conducted using real-world traffic data. The results demonstrate that compared to traditional reactive algorithms, the proposed scheme significantly enhances the overall system throughput by 12%, improves user QoS satisfaction by 9.4%, and reduces service interruptions by 34.2%. Concurrently, the algorithm exhibits favorable convergence speed and robustness, maintaining performance advantages even under predictive errors. Extensive experimentation thoroughly demonstrates the efficacy of this research in enhancing the performance of drone-assisted networks. Full article

The global cruise industry has demonstrated remarkable growth, with modern ships functioning as large-scale floating resorts. Effective food provisioning is a critical operational function that directly impacts both cost efficiency and passenger satisfaction. This task is characterized by massive consumption scales and high demand uncertainty. To address these challenges, this paper develops a two-stage stochastic optimization model for cruise ship food provisioning. The first-stage decisions involve procurement quantities made before the voyage under demand uncertainty, subject to the volumetric constraints of different storage types. The second-stage decisions determine the optimal substitution plan after the actual demand is realized, mitigating shortages by utilizing alternative available items. Solving stochastic programs with continuous distributions is computationally challenging. Therefore, we employ the sample average approximation (SAA) method to obtain tractable solutions, complemented by a full statistical evaluation of solution quality. Numerical experiments using real-world data confirm that a scenario size of 80 achieves an optimal balance with an optimality gap of 0.78%. Sensitivity analysis demonstrates the model’s robust performance and provides valuable managerial insights: higher shortage penalty coefficients significantly reduce stockouts; two-way substitution structures enhance system flexibility; appropriate salvage value accounting reduces total costs; and implementing a service level constraint of ${\lambda }_i=0.80$ optimally balances operational resilience with economic efficiency. These findings support the development of more resilient and cost-effective provisioning strategies, offering cruise operators a practical decision-support tool for managing food provisioning under uncertainty. Full article

Wavelength Division Multiplexing-based Passive Optical Networks (WDM-PONs) are among the most advanced optical networks without active elements, using a wide range of wavelengths to increase network reliability, scalability, and capacity. This ensures the provision of high quality, fast, and available services for end users. In this aim, traffic protection considerations have markedly enhanced their role. Traffic protection schemes can be divided into Point-To-MultiPoint (P2MP) and ring architectures. Traffic protection scenarios of access WDM-PONs in the P2MP architecture include Type B, dual-parented Type B, and Type C, while the ring architecture includes protected access and metropolitan-access WDM-PONs. Any potential traffic protection scheme can be represented by a corresponding reliability block diagram for the purpose of cost–benefit analysis. An important aspect of the WDM-PON design is presented by the Capital (CAPEXs) and Operational (OPEXs) Expenditures, which play a key role in network optimization. Managing them efficiently allows us to achieve an economically sustainable and efficient infrastructure of future passive optical networks involving traffic protection schemes. In this work, we focused on simulation model development for calculating the CAPEX and OPEX costs and the subsequent cost–benefit analysis of possible WDM-PON traffic protection schemes. Full article

Urban park green spaces (UPGSs) play a critical role in enhancing residents’ quality of life, particularly for older adults. However, inequities in accessibility and resource distribution remain persistent challenges in aging urban areas. To address this issue, this study takes Gulou District, Nanjing City, as an example and proposes a comprehensive framework to evaluate the overall quality of UPGSs. Furthermore, an enhanced Gaussian two-step floating catchment area (2SFCA) method is introduced that incorporates (1) a multidimensional park quality score derived from an objective evaluation system encompassing ecological conditions, service quality, age-friendly facilities, and basic infrastructure; and (2) a Gaussian distance decay function calibrated to reflect the walking and public transit mobility patterns of the older adults in the study area. The improved method calculates the accessibility values of UPGSs for older adults living in residential communities under the walking and public transportation scenarios. Finally, factors influencing the social equity of UPGSs are analyzed using Pearson correlation coefficients. The experimental results demonstrate that (1) high-accessibility service areas exhibit clustered distributions, with significant differences in accessibility levels across the transportation modes and clear spatial gradient disparities. Specifically, traditional residential neighborhoods often present accessibility blind spots under the walking scenario, accounting for 50.8%, which leads to insufficient accessibility to public green spaces. (2) Structural imbalance and inequities in public service provision have resulted in barriers to UPGS utilization for older adults in certain communities. On this basis, targeted improvement strategies based on accessibility characteristics under different transportation modes are proposed, including the establishment of multi-tiered networked UPGSs and the upgrading of slow-moving transportation infrastructure. The research findings can enhance service efficiency through evidence-based spatial resource reallocation, offering actionable insights for optimizing the spatial layout of UPGSs and advancing the equitable distribution of public services in urban core areas. Full article

The role of distributed energy resources in distribution networks is evolving to support system operation, facilitated by their participation in local flexibility markets. Future scenarios envision a significant share of low-power resources providing ancillary services to efficiently manage network congestions, offering a competitive alternative to conventional grid reinforcement. Additionally, the interaction between distribution and transmission systems enables the provision of flexibility services at higher voltage levels for various applications. In such cases, the aggregated flexibility of low-power resources is typically represented as a capability envelope at the interface between the distribution and transmission network, constructed by accounting for distribution grid constraints and subsequently communicated to the transmission system operator. This paper revisits this concept and introduces a novel approach for envelope construction. The proposed method is based on a surrogate model composed of a limited set of standard power flow components—loads, generators, and storage units—enhancing the integration of distribution network flexibility into transmission-level optimization frameworks. Notably, this advantage can potentially be achieved without significant modifications to the optimization tools currently available to grid operators. The effectiveness of the approach is demonstrated through a case study in which the adoption of distribution network surrogate models within a coordinated framework between transmission and distribution operators enables the provision of ancillary services for transmission resilience support. This results in improved resilience indicators and lower control action costs compared to conventional shedding schemes. Full article

With the ever-growing reliance on cloud computing, efficient resource allocation is crucial for maximising the effective use of provisioned resources from cloud service providers. Proactive resource management is therefore critical for minimising costs and striving for net zero emission goals. One of the most promising methods involves the use of Artificial Intelligence (AI) techniques to analyse and predict resource demand, such as cloud CPU utilisation. This paper presents MambaNet0, a Mamba-based cloud resource prediction framework. The model is implemented on Google’s Vertex AI workbench and uses the real-world Bitbrains Grid Workload Archive-T-12 dataset, which contains the resource usage metrics of 1750 virtual machines. The Mamba model’s performance is then evaluated against established baseline models, including Autoregressive Integrated Moving Average (ARIMA), Long Short-Term Memory (LSTM), and Amazon Chronos, to demonstrate its potential for accurate prediction of CPU utilisation. The MambaNet0 model achieved a 29% improvement in Symmetric Mean Absolute Percentage Error (SMAPE) compared to the best-performing baseline Amazon Chronos. These findings reinforce the Mamba model’s ability to forecast accurate CPU utilisation, highlighting its potential for optimising cloud resource allocation in contribution to net zero goals. Full article