Search Results (4,019)

Accurate prediction of a robot’s dynamic parameters, including mass and moment of inertia, is essential for adequate motion planning and control in autonomous systems. Traditional methods often depend on manual computation or physics-based modelling, which can be time-consuming and approximate for intricate, real-world environments. Recent advances in machine learning, primarily through artificial neural networks (ANNs), offer profitable alternatives. However, the potential of quantum-inspired models in this context remains largely uncharted. The current research assesses the predictive performance of a classical artificial neural network (CANN) and a quantum-inspired artificial neural network (QANN) in estimating a car-like mobile robot’s mass and moment of inertia. The predictive accurateness of the models was considered by minimizing a cost function, which was characterized as the RMSE between the predicted and actual values. The outcomes indicate that while both models demonstrated commendable performance, QANN consistently surpassed CANN. On average, QANN achieved a 9.7% reduction in training RMSE, decreasing from 0.0031 to 0.0028, and an 84.4% reduction in validation RMSE, dropping from 0.125 to 0.0195 compared to CANN. These enhancements highlight QANN’s singular predictive accuracy and greater capacity for generalization to unseen data. In contrast, CANN displayed overfitting tendencies, especially during the training phase. These findings emphasize the significance of quantum-inspired neural networks in enhancing prediction precision for involved regression tasks. The QANN framework has the potential for wider applications in robotics, including autonomous vehicles, uncrewed aerial vehicles, and intelligent automation systems, where accurate dynamic modelling is necessary. Full article

Artificial intelligence encompasses the capacity of machines to emulate human faculties, including reasoning, learning, planning, and creativity. Presently, chronic liver disease, liver cirrhosis, primary liver tumors, and liver metastasis represent significant and escalating causes of mortality worldwide. Consequently, there is a growing demand for more efficient and minimally invasive tools to enhance the diagnostic and therapeutic approaches for these ailments. Over recent years, endeavors have been directed towards employing artificial intelligence within hepatology to advance the diagnosis and treatment of liver diseases. This paper aims to outline the latest developments in the application of artificial intelligence within the realm of liver disease. Full article

This study presents a high-fidelity digital twin (DT) framework designed to evaluate and improve vertiport operations for Advanced Air Mobility (AAM). By integrating Unreal Engine, AirSim, and Cesium, the framework enables real-time simulation of Unmanned Aerial Vehicles (UAVs), including unmanned electric vertical take-off and landing (eVTOL) operations under nominal and disrupted conditions, such as adverse weather and engine failures. The DT supports interactive visualisation and risk-free analysis of decision-making protocols, vertiport layouts, and UAV handling strategies across multi-scenarios. To validate system realism, mixed-reality experiments involving physical UAVs, acting as surrogates for eVTOL platforms, demonstrate consistency between simulations and real-world flight behaviours. These UAV-based tests confirm the applicability of the DT environment to AAM. Intelligent algorithms detect Final Approach and Take-Off (FATO) areas and adjust flight paths for seamless take-off and landing. Live environmental data are incorporated for dynamic risk assessment and operational adjustment. A structured capacity evaluation method is proposed, modelling constraints including turnaround time, infrastructure limits, charging requirements, and emergency delays. Mitigation strategies, such as ultra-fast charging and reconfiguring the layout, are introduced to restore throughput. This DT provides a scalable, drone-integrated, and data-driven foundation for vertiport optimisation and regulatory planning, supporting safe and resilient integration into the AAM ecosystem. Full article

Background. Food resilience is the ability of the food system to adapt to external and internal disturbances and maintain the outcome of food security. This paper focuses on shaping the concept of urban food resilience regarding the operation of urban food infrastructure and its capacity to provide food security. Methods. To achieve this, a methodology based on the pillars defined by the Food and Agriculture Organization (FAO) for food security, i.e., availability, accessibility, and stability, is used, operationalized from a spatial approach, and evaluated in terms of urban food resilience. Three simple indexes are built, i.e., diversity, redundancy, and short-term stability, and combined into a composite index: the Urban Food Resilience Index (UFRI). Results. The results are analysed from a spatial and quantitative perspective, linking scores with urban surface area, population, and density. The study examines the reality of Chilean intermediate cities distributed throughout the country, using the La Serena–Coquimbo Conurbation as a case study. Conclusions. The ultimate goal is to provide a straightforward methodology for assessing urban food resilience in countries with limited data access, thereby providing a foundation for informed urban planning decision-making. Full article

As protected areas (PAs) expand globally at an accelerating rate, reconciling biodiversity conservation with socioeconomic development in adjacent communities has become a critical challenge for landscape sustainability. This systematic review synthesizes literature (1990–2025) to trace three interconnected transitions: (1) the conceptual evolution from exclusionary to inclusive PA–community paradigms, grounded in shifting perceptions of cultural landscapes; (2) the governance transformation from tokenistic participation to power-sharing co-management frameworks; and (3) the spatial planning progression from fragmented “island” models to integrated protected area networks (PANs) leveraging ecological corridors. Our analysis reveals that disconnected PA–community relationships exacerbate conservation–development conflicts, particularly where cultural landscapes are undervalued. A key finding is that cultural–natural synergies act as pivotal mediators for conservation efficacy, necessitating context-adaptive governance approaches. This study advances landscape planning theory by proposing a rural landscape network framework that integrates settlement patches, biocultural corridors, and PA matrices to optimize ecological connectivity while empowering communities. Empirical insights from China highlight pathways to harmonize stringent protection with rural revitalization, underscoring the capacity of PANs to bridge spatial and socio-institutional divides. This synthesis provides a transformative lens for policymakers to scale locally grounded solutions across global conservation landscapes. Full article

Urbanization is a driving force of landscape transformation. One of the ecosystems most vulnerable to urban expansion processes is montane forests located in high altitude mountainous regions. Despite their significance for biodiversity, regulation of the hydrological cycle, stability, prevention of soil erosion, and potential for organic carbon storage, these forest ecosystems show high vulnerability and risk due to the global urbanization process. We analyzed the potential variations produced by land cover change in some attributes related to soil organic matter in transitional forest fragments due to the expansion of a predominantly urban matrix landscape. We identified and characterized a fragment of a high montane evergreen forest in the Western Cordillera of the Northern Andes located in the urban limits of Quito. Then, we comparatively analyzed the variations in the attributes associated with soil organic carbon: soil organic matter, density, texture, nitrogen, phosphorus, and pH. We also considered the following soil coverages: forest, eucalyptus plantations, and grassland. We viewed the latter two as hinge coverages between forests and urban expansion. Finally, we estimated variations in soil organic carbon stock in the three analyzed coverages. For the montane forest fragment, we identified 253 individuals distributed among 18 species, corresponding to 10 families and 14 genera. We found significant variations in soil attributes associated with organic matter and an estimated 66% reduction in the carbon storage capacity of montane soils when they lose their natural cover and are replaced by Eucalyptus globulus plantations. Urban planning strategies should consider the conservation and restoration of natural and degraded peri-urban areas, ensuring sustainability and utilizing nature-based solutions for global climate change adaptation and mitigation. Peri-urban agroforestry systems represent an opportunity to replace and restore conventional forestry or crop plantation systems in peri-urban areas that affect the structure and function of ecosystems and, therefore, the goods and services derived from them. Full article

The air transportation industry is challenged to address airport curbside delay problems that affect landside service quality and can potentially impact check-in operations. Methodological advances guided by industry requirements are needed to support curbside improvement studies. Existing methods require verification of assumptions prior to application or need expensive surveys to acquire data for use in microsimulations. A probability-based macrosimulation method is advanced for the evaluation of the level of service and capacity of the curbside processor. A key component of the method is the simulation of the stochastic balance of demand and available curb space for unloading/loading tasks using the Monte Carlo simulation model. The method meets the planning and operation requirements with the ability to analyze conditions commonly experienced at the curb area. Example applications illustrate the flexibility of the method in evaluating existing as well as planned facilities of diverse designs and sizes. The developed method can contribute to curbside processor delay reduction and due to the macroscopic nature of the method, the data requirements can be met by an airport authority without costly surveys. Full article

Unmanned aerial vehicle (UAV) clusters are increasingly deployed in power system applications, such as transmission line inspection, fault diagnosis, and post-disaster emergency communication restoration. Nonetheless, limitations of range and battery capacity have rendered the assurance of uninterrupted task operation a critical concern. Efficient cooperation and energy replenishment solutions are crucial for effective UAV cluster scheduling to resolve this issue. This study proposes an innovative scheduling method that integrates UAV path planning with laser-based remote charging technology. Initially, a scheduling model incorporating both energy consumption and task completion time is established. Subsequently, an integrated laser-powered UAV model is proposed, unifying charging operations with mission execution processes. Furthermore, a distributed robust optimization (DRO) framework is proposed to handle spatiotemporal uncertainties, particularly those caused by weather conditions. Finally, the proposed scheduling method is applied to a disaster recovery scenario of a power system. Simulation results demonstrate that the proposed strategy significantly outperforms traditional scheduling methods without remote charging by achieving higher task completion rates and improved energy efficiency. These findings substantiate the effectiveness and engineering feasibility of the proposed method in enhancing UAV cluster operational capabilities under stringent energy constraints. Full article

To enhance the overall operational efficiency of heavy haul railway port stations, which serve as critical hubs in rail–water intermodal transportation systems, this study develops a novel scheduling optimization method that integrates operation plans and resource allocation. By analyzing the operational processes of heavy haul trains and shunting operation modes within a hybrid unloading system, we establish an integrated scheduling optimization model. To solve the model efficiently, a dual-agent advantage actor–critic with Pareto reward shaping (DAA2C-PRS) algorithm framework is proposed, which captures the matching relationship between operations and resources through joint actions taken by the train agent and the shunting agent to depict the scheduling decision process. Convolutional neural networks (CNNs) are employed to extract features from a multi-channel matrix containing real-time scheduling data. Considering the objective function and resource allocation with capacity, we design knowledge-based composite dispatching rules. Regarding the communication among agents, a shared experience replay buffer and Pareto reward shaping mechanism are implemented to enhance the level of strategic collaboration and learning efficiency. Based on this algorithm framework, we conduct experimental verification at H port station, and the results demonstrate that the proposed algorithm exhibits a superior solution quality and convergence performance compared with other methods for all tested instances. Full article

This study addresses the critical need for accurate tourism demand (TD) forecasting in Bulgaria using economic indicators, developing robust predictive models to navigate post-pandemic market volatility. The COVID-19 pandemic exposed tourism’s vulnerability to systemic shocks, highlighting deficiencies in traditional forecasting approaches. Bulgaria’s tourism industry, characterized by strong seasonal variations and economic sensitivity, requires enhanced methodologies for strategic planning in uncertain environments. The research employs comprehensive comparative analysis of machine learning (ML) and deep machine learning (DML) methodologies. Monthly overnight stay data from Bulgaria’s National Statistical Institute (2005–2024) were integrated with COVID-19 case data, Consumer Price Index (CPI) and Bulgarian Gross Domestic Product (GDP) variables for the same period. Multiple approaches were implemented including Prophet with external regressors, Ridge regression, LightGBM, and gradient boosting models using inverse MAE weighting optimization, alongside deep learning architectures such as Bidirectional LSTM with attention mechanisms and XGBoost configurations, as each model statistical significance was estimated. Contrary to prevailing assumptions about deep learning superiority, traditional machine learning ensemble approaches demonstrated superior performance. The ensemble model combining Prophet, LightGBM, and Ridge regression achieved optimal results with MAE of 156,847 and MAPE of 14.23%, outperforming individual models by 10.2%. Deep learning alternatives, particularly Bi-LSTM architectures, exhibited significant deficiencies with negative R² scores, indicating fundamental limitations in capturing seasonal tourism patterns, probable data dependence and overfitting. The findings, provide tourism stakeholders and policymakers with empirically validated forecasting tools for enhanced decision-making. The ensemble approach combined with statistical significance testing offers improved accuracy for investment planning, marketing budget allocation, and operational capacity management during economic volatility. Economic indicator integration enables proactive responses to market disruptions, supporting resilient tourism planning strategies and crisis management protocols. Full article

To address urban flooding challenges exacerbated by climate change and urbanization, this study develops an integrated assessment framework combining the analytic hierarchy process (AHP), entropy weight method, and cloud model to quantify urban flood resilience. Resilience is deconstructed into resistance, adaptability, and recovery and evaluated through 24 indicators spanning water resources, socio-economic systems, and ecological systems. Subjective (AHP) and objective (entropy) weights are optimized via minimum information entropy, with the cloud model enabling qualitative–quantitative resilience mapping. Analyzing 2014–2024 data from 27 Chinese sponge city pilots, the results show resilience improved from “poor to average” to “good to average”, with a 2.89% annual growth rate. Megacities like Beijing and Shanghai excel in resistance and recovery due to infrastructure and economic strengths, while cities like Sanya enhance resilience via ecological restoration. Key drivers include water allocation (27.38%), economic system (18.41%), and social system (17.94%), with critical indicators being population density, secondary industry GDP ratio, and sewage treatment rate. Recommendations emphasize upgrading rainwater storage, intelligent monitoring networks, and resilience-oriented planning. The model offers a scientific foundation for urban disaster risk management, supporting sustainable development. This approach enables systematic improvements in adaptive capacity and recovery potential, providing actionable insights for global flood-resilient urban planning. Full article

Rapid urban growth leads to an extension of artificial surfaces and inefficient energy management, an increase in urban heat islands, and local climate change. This has increased the need for green infrastructure and urban trees are playing an important role. It is important to ensure that tree groups can withstand climate warming and disturbances. This study investigated the physiological parameters of Tilia tomentosa ‘Seleste’ trees situated in a medium-sized Hungarian city, examining their relationship with microclimatic differences observed on opposing sides of a street. Instruments placed on 10 trees recorded air temperature and humidity, revealing a significant difference in total insolation, which resulted in higher maximum daily temperatures on the sunny side. These microclimatic variations were found to significantly affect physiological attributes, particularly pigment content. Trees on the sunny side exhibited a higher relative water content and a higher ratio of chlorophyll a/b, indicative of light acclimatisation. Trees on the sunny side exhibited a higher relative water content and a higher ratio of chlorophyll a/b, indicating an acclimatisation to light. Furthermore, a positive correlation was observed between pigment content, total insolation, and growing degree days. The findings demonstrate how fine-scale microclimate differences influence tree physiology, providing crucial physiological indicators that inform the capacity of urban trees to provide vital ecosystem services, such as local climate regulation. This emphasises the importance of climate-conscious urban planning, as even small-scale climate change can have a broader impact. Full article

This narrative review consolidates existing evidence about the interaction between Epstein-Barr virus (EBV) and SARS-CoV-2 in cancer development. EBV is a recognized oncogenic driver, whereas COVID-19 may heighten cancer risk by immunological dysregulation, persistent inflammation, and reactivation of latent viruses. We underscore molecular similarities (e.g., NF-κB activation, T-cell exhaustion) and clinical ramifications for high-risk individuals, stressing the necessity for interdisciplinary research to alleviate dual viral risks. EBV, a well-known oncogenic virus, has been linked to numerous malignancies, including lymphomas, nasopharyngeal carcinoma, and gastric cancer. Through the production of viral proteins that interfere with immune evasion, cellular signaling, and genomic integrity, it encourages malignant transformation and ultimately results in unchecked cell proliferation. Because of its capacity to induce tissue damage, immunological dysregulation, and chronic inflammation, COVID-19, which is brought on by the SARS-CoV-2 virus, has become a possible carcinogen. The virus’s influence on cellular pathways and its long-term effects on the immune system may raise the chance of malignancy, particularly in people with pre-existing vulnerabilities, even if direct correlations to cancer are still being investigated. When two viruses co-infect a host, the review highlights the possibility of synergistic effects that could hasten the development of cancer. It describes how overlapping mechanisms like inflammation, immune suppression, and viral reactivation may be used by a combined EBV and COVID-19 infection to exacerbate carcinogenic processes. Gaining an understanding of these relationships is essential for creating tailored treatment plans and enhancing cancer prevention in high-risk groups. Full article

The Chengdu–Chongqing Economic Mega Region (CCEMR), as a strategic economic hub in Western China, is increasingly facing challenges in balancing urban growth, agricultural stability, and ecological conservation within its territorial spatial planning framework. This study addresses the critical need to integrate multidimensional resilience assessment into China’s territorial spatial planning system. A framework for functional resilience assessment was developed through integrated GIS spatial analysis, with three resilience dimensions explicitly aligned to China’s “Three Zones and Three Lines” (referring to urban, agricultural, and ecological space and spatial control lines) territorial planning system: urban resilience was evaluated using KL-TOPSIS ranking, where weights were derived from combined Delphi expert consultation and AHP; agricultural resilience was quantified through the entropy method for weight determination and GIS raster calculation; and ecological resilience was assessed via a Risk–Recovery–Potential (RRP) model integrating Ecosystem Risk, Recovery Capacity (ERC), and Service Value (ESV) metrics, implemented through GIS spatial analysis and raster operations. Significant spatial disparities emerge, with only 1.29% of CCEMR exhibiting high resilience (concentrated in integrated urban–ecological zones like Chengdu). Rural and mountainous areas demonstrate moderate-to-low resilience due to resource constraints, creating misalignments between resilience patterns and current territorial spatial zoning schemes. These findings provide scientific evidence for optimizing the delineation of the Three Major Spatial Patterns: urbanized areas, major agricultural production zones, and ecological functional zones. In this research, a transformative methodology is established for translating resilience diagnostics directly into territorial spatial planning protocols. By bridging functional resilience assessment with statutory zoning systems, this methodology enables the following: (1) data-driven resilience construction for the Three Major Spatial Patterns (urbanized areas, major agricultural production zones, and ecological functional zones); (2) strategic infrastructure prioritization; and (3) enhanced cross-jurisdictional coordination mechanisms. The framework positions spatial planning as a proactive tool for adaptive territorial governance without requiring plan revision. Full article

Wetland ecosystems are critical for biodiversity conservation and carbon sequestration, underpinning climate regulation and sustainable development. Accurate prediction of wetland evolution is therefore essential for informed regional planning, particularly in International Wetland Cities. As one of the first designated International Wetland Cities, Haikou exemplifies the intensifying pressures faced by coastal wetlands in rapidly urbanizing regions, balancing economic development imperatives with ecological conservation. This study addresses this challenge by employing the PLUS model to simulate the spatiotemporal dynamics of wetland evolution in Haikou from 2010 to 2030 under four distinct scenarios: Business-as-Usual (BAU), Ecological Conservation (EC), Economic Development (ED), and Multi-Objective Development (MOD). The integrated approach combines landscape pattern dynamics analysis, land-use transition matrices, and quantitative assessment of driving factor contributions. Key findings reveal significant historical wetland loss between 2010 and 2020 (21.01 km²), characterized by substantial declines in artificial wetlands (paddy fields: −14.43 km²; agricultural ponds: −8.99 km²) alongside resilient growth in natural wetlands (rivers: +2.70 km²; mangroves: +1.25 km²), highlighting fundamental trade-offs between economic and ecological priorities. Scenario projections indicate that unregulated development (ED) would exacerbate wetland loss (−26.33 km²; dynamic change rate: −0.61%), including unprecedented river fragmentation (−16.0%). Conversely, strict conservation (EC) achieves near net-zero wetland loss (−0.05%) but constrains economic development capacity by 24%. Critically, the MOD scenario demonstrates an effective balance, maintaining 86% of EC’s wetland preservation efficacy while satisfying 73% of ED’s development demand. This is achieved through strategic interventions including establishing wetland protection constraints and optimizing bidirectional land conversion rules, yielding synergistic benefits. Spatial analysis identifies key conflict hotspots such as Nandu River shoreline, Dongzhai Port mangroves, necessitating targeted management strategies aligned with the heterogeneity of driving factors. This study advances the framework for sustainable wetland governance by demonstrating how multi-objective spatial planning can transform ecological-economic trade-offs into synergistic co-benefits. It provides a transferable methodological approach for coastal cities in the Global South and other International Wetland City. Full article