Search Results (669)

Infectious disease transmission is a complex dynamic process governed by intrinsic causal mechanisms rather than simple statistical correlations. Although deep learning paradigms have demonstrated powerful nonlinear representation capabilities, their “black-box” and purely data-driven nature often lead to a severe lack of causal consistency and logical transparency. To bridge this gap, this paper proposes CCSANet (Causally Constrained SEIR-Aware Network), an interpretable forecasting framework that seamlessly embeds epidemiological priors directly into the neural architecture. The model integrates SEIR dynamics into a temporal causal discovery framework, utilizing a mechanism-aware prior loss to guide a CausalFormer in learning a global temporal causal graph from multi-source heterogeneous data. This ensures that the identified relationships strictly adhere to the fundamental evolutionary logic of contagion. Subsequently, the extracted causal subgraphs are encoded as structural priors within a Causal-SCI-Block via a specialized masking mechanism, effectively forcing information to propagate exclusively along epidemiologically legitimate pathways. To ensure deep alignment between neural representations and physical reality, a causal strength alignment loss is introduced to synchronize the network’s attention weights with actual transmission intensities. Experimental evaluations on real-world multi-city datasets demonstrate that this integrated approach significantly outperforms baselines such as LSTM, Informer, and its predecessor, ESASNet. Under a 7-day sliding window configuration, the model maintains a Coefficient of Determination $R^2$ stably above 0.97, achieving an accuracy improvement of 5.5% to 6.2% and an 8% to 10% reduction in SMAPE, thereby demonstrating that coupling causal discovery with SEIR constraints substantially enhances both predictive precision and physical interpretability. Full article

Rooftop photovoltaic systems are a key component of sustainable urban energy strategies; however, their performance is strongly influenced by environmental variability across spatial scales. This study develops and validates a mathematical model to quantify the influence of Global Horizontal Irradiation (GHI), air temperature, wind speed, and dust on rooftop solar energy efficiency at country, regional, and city levels. The model is applied to environmental and energy data from 96 countries and 17 regions and further validated using four large-scale rooftop PV projects in Bahrain. The results show strong agreement between predicted and actual solar energy production, with coefficients of determination of R² = 0.77 at the country level, R² = 0.84 at the regional level, and R² = 0.998 at the city level, while mean absolute percentage errors generally remain below 10%. Regression and sensitivity analyses showed that at least one environmental factor exerts a statistically significant influence on rooftop solar energy yield, supporting the alternative research hypothesis. GHI is identified as the most influential driver at the national scale, whereas temperature and dust effects become more pronounced at finer spatial resolutions. Deployment gap analysis further reveals substantial untapped rooftop solar potential, highlighting the importance of non-environmental constraints in shaping real-world solar adoption. Full article

Municipal leadership has become increasingly central to addressing global challenges such as war-related displacement, migration governance, and climate change, reflecting a broader shift toward polycentric and networked forms of multilateralism. This study examines how cities have expanded their international roles over the past decade, responding to governance gaps with pragmatic, people-centred action. Using a qualitative, theory-informed comparative case study design, it draws on three original case studies grounded in direct practitioner experience: European municipal cooperation supporting Ukraine during war; city engagement in shaping the Global Compact for Migration; and municipal leadership in advancing climate action and the emerging climate mobility agenda. Across these cases, the analysis identifies consistent patterns of multi-scalar municipal agency, including decentralized humanitarian action, norm-setting in international negotiations, and innovations in multilevel climate governance. Cities leverage transnational networks—such as the Mayors Migration Council and the C40 Cities Climate Leadership Group—to amplify political influence, exchange solutions, and secure resources, even as fiscal pressures and political polarization increasingly constrain local capacity. It concludes that cities are becoming important actors in shaping global governance, yet their effectiveness depends on institutionalized representation, enhanced fiscal autonomy, and stronger protections for local leaders. Embedding municipalities more fully within evolving multilateral architectures can better align global commitments with local implementation and improve the resilience and legitimacy of international policy coordination. Full article

Sensor data are increasingly used in monitoring spatiotemporal phenomena for diverse applications such as flood management, urban traffic, air quality control, forest fire management, etc. Real-time modelling and representation of such evolving phenomena is fundamental for efficient and near-real-time decision-making processes. In addition to simple and local alerts about occurring changes over time at a given location, as is the case in Sensor Event Service (SES), the decision-making process may require more global spatial information, such as knowing if the monitored phenomenon is expanding or contracting around a given spot or if it is moving from one spot to another, especially for non-punctual spatial features. For such cases, spatiotemporal information should be computed over the whole set of distributed data from which the geometry of monitored phenomena can be assessed. This paper proposes an event-driven fuzzy rule-based decentralized spatial reasoning approach to compute spatiotemporal changes occurring in vague shape phenomena from distributed sensor data streams. Inferring local and partial spatial changes from individual nodes over the sensor network is prior to the computation of developing changes that the monitored phenomenon undergoes over the whole area covered by the sensor network. In this approach, we suggest a Fuzzy-Extended Spatiotemporal Change Pattern (FESTCP) to compute spatiotemporal changes about fuzzy regions. To evaluate our method, simulated case studies of ambient air pollution in Quebec City are carried out. The results reveal that the proposed method could provide satisfactory information about spatiotemporal changes in real-world phenomena monitored by a sensor network for a real-time decision-making process. Full article

Demand response is shifting towards continuous coordination of flexible demand, storage, and distributed generation across buildings and prosumer communities. Multi-agent reinforcement learning has gained attention because it can support decentralized execution under partial observability while still learning coordinated behavior through centralized training. This systematic review follows PRISMA 2020 guidance and synthesizes $n=70$ peer-reviewed studies published in the 2021 to 2025 window, covering building clusters, grid-aware district coordination, program-level aggregation, industrial demand response, and transactive energy mechanisms. The results show that the dominant evaluation context is grid-responsive building clusters, with growing reliance on benchmark environments that standardize interfaces and encourage reproducible multi-KPI reporting. Across the methods, centralized training with decentralized execution is the prevailing pattern, often combined with attention-based critics or value factorization to handle heterogeneity and global rewards. Reward design and constraint handling emerge as primary determinants of stability, since objectives mix cost, peak, ramp, comfort, and emissions, while rebound and synchronized behavior are recurring risks. A descriptive and cross-variable quantitative synthesis is also provided, showing that publication activity increased from three studies (4.3%) in 2021 to 28 studies (40.0%) in 2025, with the strongest concentration in 2024–2025. Quantitatively, grid-responsive building clusters accounted for 26 of 70 studies (37.1%), actor–critic methods for 24 studies (34.3%), CityLearn for 16 studies (22.9%), and cost-based evaluation was reported in 64 studies (91.4%), whereas robustness testing appeared in only 16 studies (22.9%). Across the reviewed studies, peak reduction was reported in 55 (78.6%) studies, whereas robustness testing appeared in only 16 studies (22.9%) and transferability or deployment realism in 11 (15.7%), indicating that evaluation remains much stronger for operational performance than for real-world generalization. Full article

Growing municipal solid wastes, environmental deterioration, and the world’s increasing energy demand highlight the urgent need for effective, sustainable energy recovery solutions. Uncontrolled municipal solid wastes contribute explicitly to the global crises of climate change, pollution, and biodiversity loss. Food and organic waste are converted into value-added products using biochemical and thermochemical techniques. Anaerobic digestion (AD) is a versatile, multi-phase waste-to-energy technology that transforms organic waste into renewable energy in an oxygen-free environment. AD uses microorganisms to break down waste, yielding biogas (mostly methane and carbon dioxide) and digestate, a nutrient-fortified by-product. Compared with traditional Single-Stage Anaerobic Digesters (SSAD), Two-Stage Anaerobic Digesters (TSAD) offer notable benefits by separating hydrolysis–acidogenesis from acetogenesis–methanogenesis. These include increased methane yield, improved process control, increased microbial stability, and resistance to inhibitory substances. According to the literature, TSAD systems have been shown to increase methane yield by about 10–30% compared to SSAD. This article covers the dynamics of the microbial population at various stages, the impact of operational factors (HRT, OLR, pH, and temperature), and novel reactor designs with modular and multi-state functions. In line with Oman’s Vision 2040, this study discusses the continuous operation of a two-phase AD co-digestion process and the in-depth techno-economic feasibility of decentralized waste management through optimized biogas production. Optimizing the carbon-to-nitrogen (C/N) ratio within the range of 20–30 in co-digestion systems significantly enhances microbial activity and methane production. The potential of recent developments, such as microbial immobilization, biogas generation techniques, and hybrid integration with photobioreactors or electrochemical systems, to enhance the scalability and efficiency of bioconversion is addressed in a TSAD system. In addition to encouraging circular economy principles through efficient organic waste valorization, this review identifies TSAD as a promising approach to achieving the SDGs related to sustainable cities, clean energy, and responsible consumption. Full article

Global gridded population datasets are widely used in urban analysis, risk assessment, and sustainability monitoring, including the calculation of indicators for the Sustainable Development Goals (SDGs). Despite their broad use, their behaviour at local scales in shrinking cities remains insufficiently understood. This study evaluates three global population datasets—WorldPop, GHS-POP, and GPWv4—in seven Croatian city cores using official census data as reference. Croatia represents a relevant case due to long-term population decline combined with relatively stable built-up extents. Population estimates were compared at the city-core level for the period 2001–2021, and spatial differences between datasets were examined using pixel-level residuals, built-up intensity metrics, and land-cover stratification. The results show that WorldPop and GHS-POP achieve similar accuracy in city-total estimates, with relative errors generally ranging between about 2% and 10%, but differ systematically in their spatial allocation of population. GHS-POP concentrates population within built-up areas, while WorldPop redistributes a substantial share into non-built-up land-cover classes, exceeding GHS-POP by approximately 290,000 inhabitants outside built-up areas, whereas GHS-POP concentrates over one million additional inhabitants within built-up zones. GPWv4 often shows the smallest city-level errors but produces spatially uniform population surfaces that limit its suitability for intra-urban analysis. The findings highlight that model choice can strongly influence spatial indicators used in SDG-related and sustainability assessments, highlighting the need for context-specific evaluation of global population datasets in shrinking urban environments. Full article

Background and Objectives: Stroke remains a major contributor to global morbidity and mortality, with substantial geographic variation in incidence and outcomes. Although declining trends in stroke incidence and mortality have been documented in several Western European populations, countries in Eastern Europe have historically experienced a disproportionately high cardiovascular disease burden. Comprehensive long-term evaluations assessing simultaneous trends in stroke attack rates, mortality, and case-fatality in Lithuania are limited. This study aimed to investigate 24-year trends (2000–2023) in stroke epidemiology among working-age residents of Kaunas city. Materials and Methods: Data were derived from the Kaunas population-based stroke registry and included individuals aged 25–64 years. Age-standardized attack rates, mortality rates, and case-fatality rates per 100,000 population were calculated using the World Health Organization standard population. Temporal trends were assessed using Joinpoint regression analysis to estimate annual percentage changes (APCs) with corresponding 95% confidence intervals (CIs). Analyses were stratified by sex, age group (25–54 and 55–64 years), and stroke subtype (ischemic and hemorrhagic). Results: During 2000–2023, overall stroke attack rates declined significantly in both sexes, with a more pronounced reduction observed among females. Stroke mortality decreased significantly among females over the entire study period, whereas no significant overall change was observed among males, largely due to increases during 2010–2021 that attenuated earlier and subsequent improvements. Case-fatality rates demonstrated no significant overall long-term trend in either sex but exhibited marked temporal variability, including significant increases during 2010–2021 followed by substantial declines after 2021. Age-stratified analyses confirmed significant reductions in attack rates across both age groups. Ischemic stroke incidence declined significantly in both sexes, while hemorrhagic stroke mortality decreased significantly among males and females. The period 2021–2023 was characterized by pronounced reductions in mortality and case-fatality across multiple subgroups. Conclusions: Over the past two decades, the stroke burden among working-age residents of Kaunas has declined substantially, particularly among females. Despite period-specific deteriorations, recent improvements underscore the impact of advances in stroke prevention and acute care. Sustained risk factor control and continued healthcare system development remain essential to maintain favourable trends. Full article

In the last decade, as cities increasingly seek sustainable development pathways within the cultural and creative economy, cultural-creative events have gained prominence as strategic instruments for urban transformation. Among them, city design weeks have emerged as complex service systems that connect creative industries, urban governance, and tourism development. This research aims to understand how cultural-creative events (represented by design weeks) facilitate sustainable tourism development from a service design perspective. Adopting a qualitative comparative research design, the study examines 30 design weeks selected through a cross-validated process with the World Design Weeks global network and UNESCO City of Design network. Data from 2020 to 2025 is collected primarily through expert interviews, official reports, and media materials in relation to the United Nations Sustainable Development Goals (SDGs). Grounded in the service design perspective, four Service Design Levels are summarized into 17 assessment dimensions, and experts applied Likert scale to evaluate the relative service intensity of each case. Through cross-case analysis, the findings reveal four distinct models of design weeks, reflecting different configurations of service intensity and strategic orientation. The study contributes theoretically by extending service design theory to cultural-creative tourism research, and practically by providing guidance for the organizers of cultural-creative events seeking to support sustainable city tourism development. Future research may incorporate quantitative impact assessments to further refine these models. Full article

Background: Hormone receptor-positive (HR+), Human Epidermal growth factor Receptor 2 (HER2-negative) metastatic breast cancer (MBC) represents a substantial proportion of breast cancer cases in Saudi Arabia. Despite the established efficacy of cyclin-dependent kinase 4 and 6 (CDK4/6) inhibitors, particularly Palbociclib, in randomized control trials, real-world data from local institutions in Saudi Arabia remain limited. Objectives: This study aimed to evaluate progression-free survival (PFS), overall survival (OS), and toxicity profile among HR+, HER2-negative MBC female patients treated with Palbociclib at King Fahad Medical City (KFMC). Methods: A retrospective study was conducted on female patients with HR+/HER2-negative MBC treated with oral palbociclib combined with endocrine therapy (ET) at KFMC between January 2021 and September 2024. Data were collected from electronic health records. Descriptive statistics were conducted using mean for continuous variables and frequency for categorical variables. Survival analyses were conducted using Cox regression, log-rank tests and Kaplan–Meier analysis. Results: A total of 169 female patients with HR+/HER2− MBC were included. In the first-line setting, the median PFS was 20.14 months (95% CI: 14.65–30.49), compared with 11.3 months (95% CI: 7.98–not estimable) in the second-line setting. For OS, the median OS values were 53.1 months (95% CI: 41.2–not estimable) in the first-line group and 23.7 months (95% CI: 18.5–not estimable) in the second-line group. Significant predictors of shorter PFS included age, Body Mass Index (BMI), type of ET, cancer type, line of therapy, family history of cancer, and history of VTE. Visceral metastasis (HR = 3.087; p = 0.0229) and ECOG performance status of 4 (HR = 13.86; p = 0.0156) were associated with significantly shorter OS. The most common hematological adverse events (AEs) were neutropenia (45.6%), followed by anemia (5.9%), leukopenia (5.3%), and back pain (5.3%). Most toxicities were managed with dose reduction, holding treatment, or supportive care. Conclusions: Palbociclib demonstrated favorable survival outcomes and a manageable safety profile, with neutropenia being the most common AE. This study provides region-specific real-world evidence supporting the use of Palbociclib in HR+/HER2− MBC. These findings align with global trial data and highlight the importance of individualized treatment in clinical practice. Full article

This study presents a novel hybrid Bat-Artificial Bee Colony (BA-ABC) algorithm for energy-efficient optimization in Wireless Sensor Networks (WSNs), addressing the critical challenge of limited node energy and network lifetime degradation. The proposed framework integrates the rapid local convergence of the Bat Algorithm with the robust global exploration of the Artificial Bee Colony to achieve unified optimization of clustering and routing processes. An adaptive multi-objective fitness function is developed to balance energy consumption, network lifetime, and communication efficiency, enabling dynamic, efficient resource utilization across varying network conditions. Comprehensive simulations conducted in MATLAB R2024a demonstrate that the proposed BA-ABC algorithm significantly outperforms conventional and recent optimization approaches. The results show a reduction in total energy consumption of approximately 22–30%, an improvement in network lifetime of 18–25%, and a latency reduction of nearly 24% compared to baseline methods such as Ant Colony Optimization (ACO). Statistical validation, including confidence intervals and hypothesis testing, confirms the robustness, stability, and consistency of the proposed framework across multiple simulation runs. Unlike existing hybrid and machine-learning-based approaches, the BA-ABC algorithm achieves high optimization performance without introducing excessive computational overhead or complex training requirements, making it suitable for resource-constrained WSN environments. Furthermore, the proposed method demonstrates strong scalability and adaptability, positioning it as a practical solution for real-world applications, including smart cities, environmental monitoring, and healthcare systems. This work contributes to the advancement of intelligent WSN optimization by providing a scalable, adaptive, and computationally efficient hybrid framework aligned with emerging trends in next-generation IoT-enabled networks. Full article

Globally, the combined pollution of fine particulate matter (PM_2.5) and ground-level ozone (O₃) poses severe challenges to public health and sustainable urban development. Recent data indicate that the annual average PM_2.5 concentration in the vast majority of cities worldwide fails to meet World Health Organization safety standards, with air pollution causing millions of premature deaths annually. As a nature-based solution, the purification efficacy of vegetation remains poorly quantified due to unclear coupling mechanisms with local meteorological conditions. This study systematically reviewed and synthesized 229 empirical studies published between 2000 and 2025 from Web of Science and China National Knowledge Infrastructure (CNKI), aiming to clarify the quantitative relationships and regulatory mechanisms of plant–meteorological synergistic purification of PM_2.5–O₃. Following double-blind independent screening (κ = 0.85) and data extraction, a quantitative minimal feasible synthesis approach was adopted due to high data heterogeneity. The results indicated the following. (1) The median canopy purification efficiency of urban vegetation for PM_2.5 was 18.2% (IQR: 12.5–30.1%, n = 17), with a median dry deposition velocity (Vd–PM) of 0.05 cm s⁻¹ (0.02–30 cm s⁻¹, n = 15). The median dry deposition velocity (Vd–O₃) for O₃ was 0.55 cm s⁻¹ (0.12–1.82 cm s⁻¹, n = 8), with non-stomatal deposition contributing approximately 35%. (2) Meteorological factors exhibit nonlinear regulation: relative humidity (RH) > 70% significantly enhances PM_2.5 adsorption, wind speeds of 1.5–3.0 m s⁻¹ are optimal for PM_2.5 deposition, and temperatures > 30 °C generally inhibit plant uptake of both pollutants (n = 7). (3) Functional traits strongly correlate with purification efficacy: species with high leaf roughness (R² = 0.8), high stomatal conductance, and low BVOC emissions (e.g., Ginkgo biloba, Platycladus orientalis) exhibit optimal synergistic purification potential. Species with high BVOC emissions (Populus przewalskii, Eucalyptus robusta) can increase daily net O₃ pollution equivalents by up to 86 g and must be strictly avoided. Based on quantitative evidence, a green space planning decision matrix indexed by climate zone and pollution type was developed, specifying vegetation configuration patterns, functional group selection, and key design parameters (canopy closure, green belt width, etc.) for different scenarios. This study provides an actionable scientific basis for precision planning and climate-adaptive management of urban green infrastructure. Full article

In an era of rapid urbanisation and climate challenges, understanding how urban land patterns contribute to resilience is crucial for sustainable development. This theoretical review introduces a novel framework positing that greater heterogeneity in plot sizes and land uses enhances urban resilience by promoting the long-term preservation of built environments, multifunctional spaces, and socio-cultural adaptability. Drawing on urban morphology, assemblage theory, and resilience science, we argue that fragmented ownership in small-plot fabrics acts as a barrier to large-scale redevelopment, fostering diversity that buffers against shocks. Through comparative case studies of Venice (Italy), Tokyo (Japan), Hong Kong, Mexico City (Mexico), and York (UK), we illustrate how historical small-plot subdivisions have endured centuries, supporting ecological, economic, and social sustainability. The analysis reveals common patterns: ownership fragmentation preserves fine-grained urban forms, enabling adaptive reuse (exaptation) and inclusivity. The five case studies serve an illustrative function, demonstrating how the theoretical linkages between plot heterogeneity, institutional friction, incremental transformation, and long-term resilience outcomes can plausibly operate in real-world historic urban fabrics. This paper addresses a gap in the literature by synthesising plot-level heterogeneity with broader resilience outcomes, offering policy implications for protecting such fabrics amid global urbanisation pressures. The findings align with land system science, emphasising multifunctionality for regenerative urbanism. Full article

This study utilizes digital transformation and patent data from A-share listed companies on the Shanghai and Shenzhen stock exchanges in China between 2011 and 2021 to examine the influence of digital transformation on the quality of cross-regional collaborative innovation. The findings reveal that the cooperative innovation network exhibits pronounced small-world characteristics. In terms of spatio-temporal evolution, China’s urban collaborative innovation network demonstrates a notable quadrilateral spatial structure and has evolved toward a multicenter pattern. Moreover, the advancement of digital transformation positively contributes to both the quality and quantity of cross-regional cooperative innovation. By enhancing the relational embeddedness among cities, digital transformation facilitates improved outcomes in collaborative innovation. Furthermore, when the volume of digital patent applications surpasses a certain threshold, its positive effect on the quality of cross-regional collaborative innovation accelerates. These results provide empirical evidence from a major emerging economy, offering insights that can inform policies and strategies in other regions undergoing digital transition. The mechanisms identified, such as network structure evolution and relational embeddedness, contribute to a broader understanding of how digital transformation shapes innovation dynamics across geographical boundaries in a globalized knowledge economy. Full article

Reconciling economic growth with environmental protection continues to represent a central global challenge. As one of the world’s largest developing economies, China has advanced an ecological civilization strategy that offers a unique opportunity to evaluate how national policy can shape sustainable development trajectories. This study assesses whether China’s ecological civilization construction enhances urban green total factor productivity (GTFP). Using panel data for 283 Chinese cities (2006–2019), this study identifies ecological civilization pilot cities through a standardized and reproducible protocol, measures urban GTFP using the Global Malmquist–Luenberger (GML) index and estimates policy effects with a multi-period difference-in-differences (DID) design that accounts for staggered implementation and overlapping policies. The results indicate that urban GTFP exhibited an overall upward but fluctuating trend during the study period, with regional growth rates ranking East > Central > West and a tendency toward convergence in recent years. The analysis further indicates that national ecological civilization construction policies exert a statistically significant and positive effect on urban GTFP, with the findings remaining robust to parallel trend tests and multiple robustness checks. The promotion effect displays marked regional heterogeneity, being strongest in western cities, followed by eastern and central regions, and remains positive across different urban contexts, including resource-based and non-resource-based cities as well as cities within and outside the Yangtze River Economic Belt. Mechanism analysis further reveals that the policy effect operates primarily through industrial upgrading and green technological innovation, whereas the industrial structure rationalization channel is not statistically significant. Overall, this study provides a transparent and reproducible framework for pilot city identification and causal evaluation, offering policy-relevant insights for differentiated and region-specific ecological governance aimed at balanced regional development, industrial upgrading, and green technological innovation. Full article