Air traffic flow management has been a major means for balancing air traffic demandand airport or airspace capacity to reduce congestion and flight delays.However,unpredictable fac-tors,such as weather and equipment m...Air traffic flow management has been a major means for balancing air traffic demandand airport or airspace capacity to reduce congestion and flight delays.However,unpredictable fac-tors,such as weather and equipment malfunctions,can cause dynamic changes in airport and sectorcapacity,resulting in significant alterations to optimized flight schedules and the calculated pre-departure slots.Therefore,taking into account capacity uncertainties is essential to create a moreresilient flight schedule.This paper addresses the flight pre-departure sequencing issue and intro-duces a capacity uncertainty model for optimizing flight schedule at the airport network level.The goal of the model is to reduce the total cost of flight delays while increasing the robustnessof the optimized schedule.A chance-constrained model is developed to address the capacity uncer-tainty of airports and sectors,and the significance of airports and sectors in the airport network isconsidered when setting the violation probability.The performance of the model is evaluated usingreal flight data by comparing them with the results of the deterministic model.The development ofthe model based on the characteristics of this special optimization mechanism can significantlyenhance its performance in addressing the pre-departure flight scheduling problem at the airportnetwork level.展开更多
Navigation system integrity monitoring is crucial for mission(e.g.safety)critical applications.Receiver autonomous integrity monitoring(RAIM)based on consistency checking of redundant measurements is widely used for m...Navigation system integrity monitoring is crucial for mission(e.g.safety)critical applications.Receiver autonomous integrity monitoring(RAIM)based on consistency checking of redundant measurements is widely used for many applications.However,there are many challenges to the use of RAIM associated with multiple constellations and applications with very stringent requirements.This paper discusses two positioning techniques and corresponding integrity monitoring methods.The first is the use of single frequency pseudorange-based dual constellations.It employs a new cross constellation single difference scheme to benefit from the similarities while addressing the differences between the constellations.The second technique uses dual frequency carrier phase measurements from GLONASS and the global positioning system for precise point positioning.The results show significant improvements both in positioning accuracy and integrity monitoring as a result of the use of two constellations.The dual constellation positioning and integrity monitoring algorithms have the potential to be extended to multiple constellations.展开更多
Mechanical, physical and manufacturing properties of east iron make it attractive for many fields of application, such as cranks and cylinder holds. As in design of all metals, fatigue life prediction is an intrinsic ...Mechanical, physical and manufacturing properties of east iron make it attractive for many fields of application, such as cranks and cylinder holds. As in design of all metals, fatigue life prediction is an intrinsic part of the design process of structural sections that are made of cast iron. A methodology to predict high-cycle fatigue life of cast iron is proposed. Stress amplitude-strain amplitude, strain amplitude-number of loading cycles relationships of cast iron are investigated. Also, fatigue life prediction in terms of Smith, Watson and Topper parameter is carried out using the proposed method. Results indicate that the analytical outcomes of the proposed methodology are in good accordance with the experimental data for the two studied types of cast iron： EN-GJS-400 and EN-GJS-600.展开更多
In safety-critical systems such as transportation aircraft, redundancy of actuators is introduced to improve fault tolerance. How to make the best use of remaining actuators to allow the system to continue achieving a...In safety-critical systems such as transportation aircraft, redundancy of actuators is introduced to improve fault tolerance. How to make the best use of remaining actuators to allow the system to continue achieving a desired operation in the presence of some actuators failures is the main subject of this paper. Considering that many dynamical systems, including flight dynamics of a transportation aircraft, can be expressed as an input affine nonlinear system, a new state repre- sentation is adopted here where the output dynamics are related with virtual inputs associated with the intended operation. This representation, as well as the distribution matrix associated with the effectiveness of the remaining operational actuators, allows us to define different levels of fault tol- erant governability with respect to actuators＇ failures. Then, a two-stage control approach is devel- oped, leading frst to the inversion of the output dynamics to get nominal values for the virtual inputs and then to the solution of a linear quadratic （LQ） problem to compute the solicitation of each operational actuator. The proposed approach is applied to the control of a transportation air- craft which performs a stabilized roll maneuver while a partial failure appears. Two fault scenarios are considered and the resulting performance of the proposed approach is displayed and discussed.展开更多
In Europe, computation of displacement demand for seismic assessment of existing buildings is essentially based on a simplified formulation of the N2 method as prescribed by Eurocode 8(EC8). However, a lack of accurac...In Europe, computation of displacement demand for seismic assessment of existing buildings is essentially based on a simplified formulation of the N2 method as prescribed by Eurocode 8(EC8). However, a lack of accuracy of the N2 method in certain conditions has been pointed out by several studies. This paper addresses the assessment of effectiveness of the N2 method in seismic displacement demand determination in non-linear domain. The objective of this work is to investigate the accuracy of the N2 method through comparison with displacement demands computed using non-linear timehistory analysis(NLTHA). Results show that the original N2 method may lead to overestimation or underestimation of displacement demand predictions. This may affect results of mechanical model-based assessment of seismic vulnerability at an urban scale. Hence, the second part of this paper addresses an improvement of the N2 method formula by empirical evaluation of NLTHA results based on EC8 ground-classes. This task is formulated as a mathematical programming problem in which coefficients are obtained by minimizing the overall discrepancy between NLTHA and modified formula results. Various settings of the mathematical programming problem have been solved using a global optimization metaheuristic. An extensive comparison between the original N2 method formulation and optimized formulae highlights benefits of the strategy.展开更多
Piezoelectric resonant de-icing systems are attracting great interest.This paper aims to assess the implementation of these systems at the aircraft level.The article begins with the model to compute the power requirem...Piezoelectric resonant de-icing systems are attracting great interest.This paper aims to assess the implementation of these systems at the aircraft level.The article begins with the model to compute the power requirement of a piezoelectric resonant de-icing system sized from the prototype detailed in Part 1/2 of this article.Then the mass,drag,and fuel consumption of this system and the subcomponents needed for its implementation are assessed.The features of a piezoelectric resonant de-icing system are finally computed for aircraft similar to Airbus A320 aircraft and aircraft of different categories(Boeing 787,ATR 72 and TBM 900)and compared with the existing thermal and mechanical ice protection systems.A sensitivity analysis of the main key sizing parameters of the piezoelectric de-icing system is also performed to identify the main axes of improvement for this technology.The study shows the potential of such ice protection systems.In particular,for the realistic input parameters chosen in this work,the electro-mechanical solution can provide a 54% reduction in terms of mass and a 92% reduction in terms of power consumption for an A320 aircraft architecture,leading to a 74% decrease in the associated fuel consumption compared to the actual air bleed system.展开更多
In a context of growing efforts to develop sustainability strategies, energy-related issues occupy central stage in the built environment. Thus, the energy performance of housings has improved radically over the past ...In a context of growing efforts to develop sustainability strategies, energy-related issues occupy central stage in the built environment. Thus, the energy performance of housings has improved radically over the past decades. Yet other types of buildings, in particular commercial centers, haven’t received the same level of interest. As a result, there is a need for effective and practical measures to decrease their energy consumption, both for heating and electricity. The objective of the paper is to demonstrate that it is possible, through coherent strategies, to integrate energy issues and bioclimatic principles into the design process of commercial centers. It analyzes the exemplary case study of Marin Commercial Center (Switzerland). The interdisciplinary approach, based on integrated design strategies, aimed at increasing the energy efficiency while keeping the cost comparable to the market cost. The main design principles include natural ventilation, nighttime cooling with energy recovery and natural lighting, as well as optimization of mechanical systems. The results of the simulations show that Marin Center attains the best energy performance observed so far among Swiss commercial centers. It also meets the Swiss Minergie standard. The paper thus questions traditional design processes and outlines the need for interdisciplinary evaluation and monitoring approaches tailored for commercial centers. Even though most crucial decisions are taken during the early stages, all phases of the process require systematic optimization strategies, especially operating stages. Recommendations include legal measures, in particular in the fields of ventilation and air-conditioning, education, professional development and technology transfer, and financial incentives for the replacement of energy intensive installations.展开更多
Soil organic carbon (SOC) losses due to poor soil management in dryland are now well documented. However, the influence of soil properties on organic carbon change is not well known. The groundnut plant (Arachis hypog...Soil organic carbon (SOC) losses due to poor soil management in dryland are now well documented. However, the influence of soil properties on organic carbon change is not well known. The groundnut plant (Arachis hypogaea L.), and the dominant crop system in the Senegal’s Soudanian zone, have been compared with semi-natural savanna. Leaves, stems and roots biomass were measured, and soil characteristics were analysed. The total leaves and stems biomass was 1.7 and 2.7 Mg ha-1 dry matter in groundnut fields and savanna respectively. Total SOC stocks were low (8 to 20 Mg C·ha-1 within upper 0.2 m depth, 20 to 64 Mg C·ha-1 within upper 1 m depth) and were significantly lower (P δ13C values show that SOC quality is transformed from the savanna plants (C4/C3 mixed-pools) to C3-pools in groundnut cultivated zone, with the organic matter signature more preserved in the clayey soils. This study confirms that converting woodland to groundnut fields provokes texture transformation and SOC loss. The results call for the extreme necessity to regenerate the wooded zone or encourage practices that favour SOC restitution.展开更多
Rapid urbanization,alongside escalating resource depletion and ecological degradation,underscores the critical need for innovative urban development solutions.In response,sustainable smart cities are increasingly turn...Rapid urbanization,alongside escalating resource depletion and ecological degradation,underscores the critical need for innovative urban development solutions.In response,sustainable smart cities are increasingly turning to cutting-edge technologiesdsuch as Generative Artificial Intelligence(GenAI),Foundation Models(FMs),and Urban Digital Twin(UDT)frameworksdto transform urban planning and design practices.These transformative tools provide advanced capabilities to analyze complex urban systems,optimize resource management,and enable evidence-based decision-making.Despite recent progress,research on integrating GenAI and FMs into UDT frameworks remains scant,leaving gaps in our ability to capture complex urban flows and multimodal dynamics essential to achieving environmental sustainability goals.Moreover,the lack of a robust theoretical foundation and real-world operationali-zation of these tools hampers comprehensive modeling and practical adoption.This study introduces a pioneering Large Flow Model(LFM),grounded in a robust foundational framework and designed with GenAI capabilities.It is specifically tailored for integration into UDT systems to enhance predictive an-alytics,adaptive learning,and complex data management functionalities.To validate its applicability and relevance,the Blue City Project in Lausanne City is examined as a case study,showcasing the ability of the LFM to effectively model and analyze urban flowsdnamely mobility,goods,energy,waste,materials,and biodiversitydcritical to advancing environmental sustainability.This study highlights how the LFM addresses the spatial challenges inherent in current UDT frameworks.The LFM demonstrates its novelty in comprehensive urban modeling and analysis by completing impartial city data,estimating flow data in new locations,predicting the evolution of flow data,and offering a holistic understanding of urban dynamics and their interconnections.The model enhances decision-making processes,supports evidence-based planning and design,fosters integrated development strategies,and enables the development of more efficient,resilient,and sustainable urban environments.This research advances both the theoretical and practical dimensions of AI-driven,environmentally sustainable urban devel-opment by operationalizing GenAI and FMs within UDT frameworks.It provides sophisticated tools and valuable insights for urban planners,designers,policymakers,and researchers to address the com-plexities of modern cities and accelerate the transition towards sustainable urban futures.展开更多
Rapid urbanization,alongside escalating resource depletion and ecological degradation,underscores the urgent need for innovative paradigms in urban development.In response,sustainable smart cities are increasingly lev...Rapid urbanization,alongside escalating resource depletion and ecological degradation,underscores the urgent need for innovative paradigms in urban development.In response,sustainable smart cities are increasingly leveraging advanced technological frameworks—most notably the convergence of Artificial Intelligence of Things(AIoT)and Cyber-Physical Systems(CPS)—as critical enablers for transforming their management and planning processes.Within this dynamic landscape,Urban Brain(UB)and Urban Digital Twin(UDT)have emerged as prominent AIo T-powered city platforms.Defined by their complex functionalities and multi-layered architectures,these systems exemplify Cyber-Physical Systems of Systems(CPSoS),offering a cohesive foundation for integrating real-time operational responsiveness with strategic predictive foresight.Despite notable technological progress,a critical gap persists in effectively integrating the distinct yet complementary capabilities of UB and UDT within a structured and scalable framework.To the best of our knowledge,research on the explicit fusion of UB's real-time analytics—enabled through stream processing—with UDT's predictive analytics—driven by simulation modeling—is scant,if not absent.Most existing studies continue to treat UB and UDT as siloed systems,failing to recognize the critical need to synchronize their respective operational and strategic functions.This fragmentation limits the ability of urban systems to respond both adaptively and proactively to the complex,interrelated challenges of environmental sustainability.To address this critical gap,this study introduces a novel foundational framework—Artificial Intelligence of Things for Sustainable Smart City Brain and Digital Twin Systems—designed to synergistically integrate UB and UDT as AIo T-enabled platforms within a unified CPSo S architecture.This framework addresses the critical disconnect between real-time operational management and strategic predictive planning,delivering an integrated pathway for advancing environmentally sustainable smart city development goals.Harnessing the complementary strengths of UB and UDT,it empowers cities to respond dynamically to immediate urban demands while ensuring consistent alignment with long-term sustainability goals.UB's real-time analytics enhance the efficiency of daily urban operations,whereas UDT's predictive modeling anticipates and simulates future scenarios.Together,they establish a synergistic feedback loop:UB's real-time insights continuously inform UDT's strategic simulations,while UDT's long-range forecasts iteratively refine UB's operational decision-making.The framework thus equips researchers,practitioners,and policymakers with a robust methodology for designing and implementing adaptive,efficient,and resilient urban ecosystems.It facilitates the development of intelligent urban environments that can advance environmental sustainability by integrating solid theoretical foundations with actionable strategies.展开更多
Buildings are among the largest contributors to global energy consumption and carbon emissions,making their transformation essential for advancing environmental sustainability goals.Innovative technologies such as art...Buildings are among the largest contributors to global energy consumption and carbon emissions,making their transformation essential for advancing environmental sustainability goals.Innovative technologies such as artificial intelligence(AI)and digital twins(DTs)offer powerful tools for optimizing performance in smart,green,and zero-energy buildings.However,existing research remains fragmented—AI and AI-driven DT applications are often confined to isolated functions or specific building types—resulting in a limited,non-cohesive understanding of their collective potential in the built environment.This fragmentation,in turn,has hindered the development of integrated strategies that link building-level efficiencies with the broader environmental objectives of smart cities.To address these interrelated gaps,this study conducts a comprehensive systematic review of leading-edge AI and AI-powered DT solutions applied across smart,green,and zero-energy buildings.It aims to provide a holistic understanding of how these solutions enhance environmental performance through the analysis of key building-related indicators.By synthesizing,comparing,and evaluating recent research,it examines how AI and AI-powered DT technologies facilitate integrated,system-level strategies that promote environmentally sustainable smart practices across the built environment.The study reveals that AI enhances smart buildings by enabling dynamic energy optimization,occupant-centered environmental control,improved thermal comfort,renewable energy integration,and predictive system management.In green buildings,AI contributes to greater resource efficiency,minimizes construction and operational waste,promotes the use of sustainable materials,strengthens cost estimation and risk assessment processes,and supports adaptive design strategies.For zero-energy buildings,AI facilitates multi-objective optimization,advances explainable and transparent AI-driven control systems,supports performance benchmarking against net and nearly zero-energy standards,and enables renewable energy integration tailored to diverse climatic and regulatory contexts.Furthermore,AI-powered DTs enable real-time environmental monitoring,predictive analytics,anomaly detection,and adaptive operational strategies,thereby enhancing building performance,energy optimization,and resilience.At broader spatial scales,these technologies foster interconnected urban ecosystems,advancing environmental sustainability,sustainable development,and smart city initiatives.Building on these insights,this study introduces a novel integrated framework that positions AI and AI-driven DTs as systemic enablers of environmentally sustainable smart built and urban environments,emphasizing their cross-scale convergence in promoting carbon neutrality,circular economy principles,climate resilience,and regenerative urban strategies.The findings offer actionable pathways for advancing research agendas,inform practical strategies for building and urban system design,and provide evidence-based recommendations for policymakers committed to fostering more intelligent,sustainable,and resilient urban futures.This work establishes AI and AI-driven DTs as transformative catalysts for realizing the next generation of resource-efficient,carbon-neutral,and ecologically integrated urban ecosystems.展开更多
The recent advancements made in the realms of Artificial Intelligence(AI)and Artificial Intelligence of Things(AIoT)have unveiled transformative prospects and opportunities to enhance and optimize the environmental pe...The recent advancements made in the realms of Artificial Intelligence(AI)and Artificial Intelligence of Things(AIoT)have unveiled transformative prospects and opportunities to enhance and optimize the environmental performance and efficiency of smart cities.These strides have,in turn,impacted smart eco-cities,catalyzing ongoing improvements and driving solutions to address complex environmental challenges.This aligns with the visionary concept of smarter eco-cities,an emerging paradigm of urbanism characterized by the seamless integration of advanced technologies and environmental strategies.However,there remains a significant gap in thoroughly understanding this new paradigm and the intricate spectrum of its multifaceted underlying dimensions.To bridge this gap,this study provides a comprehensive systematic review of the burgeoning landscape of smarter eco-cities and their leadingedge AI and AIoT solutions for environmental sustainability.To ensure thoroughness,the study employs a unified evidence synthesis framework integrating aggregative,configurative,and narrative synthesis approaches.At the core of this study lie these subsequent research inquiries:What are the foundational underpinnings of emerging smarter eco-cities,and how do they intricately interrelate,particularly urbanism paradigms,environmental solutions,and data-driven technologies?What are the key drivers and enablers propelling the materialization of smarter eco-cities?What are the primary AI and AIoT solutions that can be harnessed in the development of smarter eco-cities?In what ways do AI and AIoT technologies contribute to fostering environmental sustainability practices,and what potential benefits and opportunities do they offer for smarter eco-cities?What challenges and barriers arise in the implementation of AI and AIoT solutions for the development of smarter eco-cities?The findings significantly deepen and broaden our understanding of both the significant potential of AI and AIoT technologies to enhance sustainable urban development practices,as well as the formidable nature of the challenges they pose.Beyond theoretical enrichment,these findings offer invaluable insights and new perspectives poised to empower policymakers,practitioners,and researchers to advance the integration of eco-urbanism and AI-and AIoT-driven urbanism.Through an insightful exploration of the contemporary urban landscape and the identification of successfully applied AI and AIoT solutions,stakeholders gain the necessary groundwork for making well-informed decisions,implementing effective strategies,and designing policies that prioritize environmental well-being.展开更多
基金supported by the National Natural Science Foundation of China(Nos.U2033203,U1833126,61773203,61304190)。
文摘Air traffic flow management has been a major means for balancing air traffic demandand airport or airspace capacity to reduce congestion and flight delays.However,unpredictable fac-tors,such as weather and equipment malfunctions,can cause dynamic changes in airport and sectorcapacity,resulting in significant alterations to optimized flight schedules and the calculated pre-departure slots.Therefore,taking into account capacity uncertainties is essential to create a moreresilient flight schedule.This paper addresses the flight pre-departure sequencing issue and intro-duces a capacity uncertainty model for optimizing flight schedule at the airport network level.The goal of the model is to reduce the total cost of flight delays while increasing the robustnessof the optimized schedule.A chance-constrained model is developed to address the capacity uncer-tainty of airports and sectors,and the significance of airports and sectors in the airport network isconsidered when setting the violation probability.The performance of the model is evaluated usingreal flight data by comparing them with the results of the deterministic model.The development ofthe model based on the characteristics of this special optimization mechanism can significantlyenhance its performance in addressing the pre-departure flight scheduling problem at the airportnetwork level.
文摘Navigation system integrity monitoring is crucial for mission(e.g.safety)critical applications.Receiver autonomous integrity monitoring(RAIM)based on consistency checking of redundant measurements is widely used for many applications.However,there are many challenges to the use of RAIM associated with multiple constellations and applications with very stringent requirements.This paper discusses two positioning techniques and corresponding integrity monitoring methods.The first is the use of single frequency pseudorange-based dual constellations.It employs a new cross constellation single difference scheme to benefit from the similarities while addressing the differences between the constellations.The second technique uses dual frequency carrier phase measurements from GLONASS and the global positioning system for precise point positioning.The results show significant improvements both in positioning accuracy and integrity monitoring as a result of the use of two constellations.The dual constellation positioning and integrity monitoring algorithms have the potential to be extended to multiple constellations.
文摘Mechanical, physical and manufacturing properties of east iron make it attractive for many fields of application, such as cranks and cylinder holds. As in design of all metals, fatigue life prediction is an intrinsic part of the design process of structural sections that are made of cast iron. A methodology to predict high-cycle fatigue life of cast iron is proposed. Stress amplitude-strain amplitude, strain amplitude-number of loading cycles relationships of cast iron are investigated. Also, fatigue life prediction in terms of Smith, Watson and Topper parameter is carried out using the proposed method. Results indicate that the analytical outcomes of the proposed methodology are in good accordance with the experimental data for the two studied types of cast iron： EN-GJS-400 and EN-GJS-600.
文摘In safety-critical systems such as transportation aircraft, redundancy of actuators is introduced to improve fault tolerance. How to make the best use of remaining actuators to allow the system to continue achieving a desired operation in the presence of some actuators failures is the main subject of this paper. Considering that many dynamical systems, including flight dynamics of a transportation aircraft, can be expressed as an input affine nonlinear system, a new state repre- sentation is adopted here where the output dynamics are related with virtual inputs associated with the intended operation. This representation, as well as the distribution matrix associated with the effectiveness of the remaining operational actuators, allows us to define different levels of fault tol- erant governability with respect to actuators＇ failures. Then, a two-stage control approach is devel- oped, leading frst to the inversion of the output dynamics to get nominal values for the virtual inputs and then to the solution of a linear quadratic （LQ） problem to compute the solicitation of each operational actuator. The proposed approach is applied to the control of a transportation air- craft which performs a stabilized roll maneuver while a partial failure appears. Two fault scenarios are considered and the resulting performance of the proposed approach is displayed and discussed.
文摘In Europe, computation of displacement demand for seismic assessment of existing buildings is essentially based on a simplified formulation of the N2 method as prescribed by Eurocode 8(EC8). However, a lack of accuracy of the N2 method in certain conditions has been pointed out by several studies. This paper addresses the assessment of effectiveness of the N2 method in seismic displacement demand determination in non-linear domain. The objective of this work is to investigate the accuracy of the N2 method through comparison with displacement demands computed using non-linear timehistory analysis(NLTHA). Results show that the original N2 method may lead to overestimation or underestimation of displacement demand predictions. This may affect results of mechanical model-based assessment of seismic vulnerability at an urban scale. Hence, the second part of this paper addresses an improvement of the N2 method formula by empirical evaluation of NLTHA results based on EC8 ground-classes. This task is formulated as a mathematical programming problem in which coefficients are obtained by minimizing the overall discrepancy between NLTHA and modified formula results. Various settings of the mathematical programming problem have been solved using a global optimization metaheuristic. An extensive comparison between the original N2 method formulation and optimized formulae highlights benefits of the strategy.
文摘Piezoelectric resonant de-icing systems are attracting great interest.This paper aims to assess the implementation of these systems at the aircraft level.The article begins with the model to compute the power requirement of a piezoelectric resonant de-icing system sized from the prototype detailed in Part 1/2 of this article.Then the mass,drag,and fuel consumption of this system and the subcomponents needed for its implementation are assessed.The features of a piezoelectric resonant de-icing system are finally computed for aircraft similar to Airbus A320 aircraft and aircraft of different categories(Boeing 787,ATR 72 and TBM 900)and compared with the existing thermal and mechanical ice protection systems.A sensitivity analysis of the main key sizing parameters of the piezoelectric de-icing system is also performed to identify the main axes of improvement for this technology.The study shows the potential of such ice protection systems.In particular,for the realistic input parameters chosen in this work,the electro-mechanical solution can provide a 54% reduction in terms of mass and a 92% reduction in terms of power consumption for an A320 aircraft architecture,leading to a 74% decrease in the associated fuel consumption compared to the actual air bleed system.
文摘In a context of growing efforts to develop sustainability strategies, energy-related issues occupy central stage in the built environment. Thus, the energy performance of housings has improved radically over the past decades. Yet other types of buildings, in particular commercial centers, haven’t received the same level of interest. As a result, there is a need for effective and practical measures to decrease their energy consumption, both for heating and electricity. The objective of the paper is to demonstrate that it is possible, through coherent strategies, to integrate energy issues and bioclimatic principles into the design process of commercial centers. It analyzes the exemplary case study of Marin Commercial Center (Switzerland). The interdisciplinary approach, based on integrated design strategies, aimed at increasing the energy efficiency while keeping the cost comparable to the market cost. The main design principles include natural ventilation, nighttime cooling with energy recovery and natural lighting, as well as optimization of mechanical systems. The results of the simulations show that Marin Center attains the best energy performance observed so far among Swiss commercial centers. It also meets the Swiss Minergie standard. The paper thus questions traditional design processes and outlines the need for interdisciplinary evaluation and monitoring approaches tailored for commercial centers. Even though most crucial decisions are taken during the early stages, all phases of the process require systematic optimization strategies, especially operating stages. Recommendations include legal measures, in particular in the fields of ventilation and air-conditioning, education, professional development and technology transfer, and financial incentives for the replacement of energy intensive installations.
文摘Soil organic carbon (SOC) losses due to poor soil management in dryland are now well documented. However, the influence of soil properties on organic carbon change is not well known. The groundnut plant (Arachis hypogaea L.), and the dominant crop system in the Senegal’s Soudanian zone, have been compared with semi-natural savanna. Leaves, stems and roots biomass were measured, and soil characteristics were analysed. The total leaves and stems biomass was 1.7 and 2.7 Mg ha-1 dry matter in groundnut fields and savanna respectively. Total SOC stocks were low (8 to 20 Mg C·ha-1 within upper 0.2 m depth, 20 to 64 Mg C·ha-1 within upper 1 m depth) and were significantly lower (P δ13C values show that SOC quality is transformed from the savanna plants (C4/C3 mixed-pools) to C3-pools in groundnut cultivated zone, with the organic matter signature more preserved in the clayey soils. This study confirms that converting woodland to groundnut fields provokes texture transformation and SOC loss. The results call for the extreme necessity to regenerate the wooded zone or encourage practices that favour SOC restitution.
基金support provided by Innosuisse for the Blue City Flagship Project(Flagship ID#PFFS-21-03).
文摘Rapid urbanization,alongside escalating resource depletion and ecological degradation,underscores the critical need for innovative urban development solutions.In response,sustainable smart cities are increasingly turning to cutting-edge technologiesdsuch as Generative Artificial Intelligence(GenAI),Foundation Models(FMs),and Urban Digital Twin(UDT)frameworksdto transform urban planning and design practices.These transformative tools provide advanced capabilities to analyze complex urban systems,optimize resource management,and enable evidence-based decision-making.Despite recent progress,research on integrating GenAI and FMs into UDT frameworks remains scant,leaving gaps in our ability to capture complex urban flows and multimodal dynamics essential to achieving environmental sustainability goals.Moreover,the lack of a robust theoretical foundation and real-world operationali-zation of these tools hampers comprehensive modeling and practical adoption.This study introduces a pioneering Large Flow Model(LFM),grounded in a robust foundational framework and designed with GenAI capabilities.It is specifically tailored for integration into UDT systems to enhance predictive an-alytics,adaptive learning,and complex data management functionalities.To validate its applicability and relevance,the Blue City Project in Lausanne City is examined as a case study,showcasing the ability of the LFM to effectively model and analyze urban flowsdnamely mobility,goods,energy,waste,materials,and biodiversitydcritical to advancing environmental sustainability.This study highlights how the LFM addresses the spatial challenges inherent in current UDT frameworks.The LFM demonstrates its novelty in comprehensive urban modeling and analysis by completing impartial city data,estimating flow data in new locations,predicting the evolution of flow data,and offering a holistic understanding of urban dynamics and their interconnections.The model enhances decision-making processes,supports evidence-based planning and design,fosters integrated development strategies,and enables the development of more efficient,resilient,and sustainable urban environments.This research advances both the theoretical and practical dimensions of AI-driven,environmentally sustainable urban devel-opment by operationalizing GenAI and FMs within UDT frameworks.It provides sophisticated tools and valuable insights for urban planners,designers,policymakers,and researchers to address the com-plexities of modern cities and accelerate the transition towards sustainable urban futures.
基金financial support provided by Innosuisse for the Blue City Flagship Project(Flagship ID#PFFS21-03)。
文摘Rapid urbanization,alongside escalating resource depletion and ecological degradation,underscores the urgent need for innovative paradigms in urban development.In response,sustainable smart cities are increasingly leveraging advanced technological frameworks—most notably the convergence of Artificial Intelligence of Things(AIoT)and Cyber-Physical Systems(CPS)—as critical enablers for transforming their management and planning processes.Within this dynamic landscape,Urban Brain(UB)and Urban Digital Twin(UDT)have emerged as prominent AIo T-powered city platforms.Defined by their complex functionalities and multi-layered architectures,these systems exemplify Cyber-Physical Systems of Systems(CPSoS),offering a cohesive foundation for integrating real-time operational responsiveness with strategic predictive foresight.Despite notable technological progress,a critical gap persists in effectively integrating the distinct yet complementary capabilities of UB and UDT within a structured and scalable framework.To the best of our knowledge,research on the explicit fusion of UB's real-time analytics—enabled through stream processing—with UDT's predictive analytics—driven by simulation modeling—is scant,if not absent.Most existing studies continue to treat UB and UDT as siloed systems,failing to recognize the critical need to synchronize their respective operational and strategic functions.This fragmentation limits the ability of urban systems to respond both adaptively and proactively to the complex,interrelated challenges of environmental sustainability.To address this critical gap,this study introduces a novel foundational framework—Artificial Intelligence of Things for Sustainable Smart City Brain and Digital Twin Systems—designed to synergistically integrate UB and UDT as AIo T-enabled platforms within a unified CPSo S architecture.This framework addresses the critical disconnect between real-time operational management and strategic predictive planning,delivering an integrated pathway for advancing environmentally sustainable smart city development goals.Harnessing the complementary strengths of UB and UDT,it empowers cities to respond dynamically to immediate urban demands while ensuring consistent alignment with long-term sustainability goals.UB's real-time analytics enhance the efficiency of daily urban operations,whereas UDT's predictive modeling anticipates and simulates future scenarios.Together,they establish a synergistic feedback loop:UB's real-time insights continuously inform UDT's strategic simulations,while UDT's long-range forecasts iteratively refine UB's operational decision-making.The framework thus equips researchers,practitioners,and policymakers with a robust methodology for designing and implementing adaptive,efficient,and resilient urban ecosystems.It facilitates the development of intelligent urban environments that can advance environmental sustainability by integrating solid theoretical foundations with actionable strategies.
基金support provided by Innosuisse for the Blue City Flagship Project(Flagship ID#PFFS-21-03).
文摘Buildings are among the largest contributors to global energy consumption and carbon emissions,making their transformation essential for advancing environmental sustainability goals.Innovative technologies such as artificial intelligence(AI)and digital twins(DTs)offer powerful tools for optimizing performance in smart,green,and zero-energy buildings.However,existing research remains fragmented—AI and AI-driven DT applications are often confined to isolated functions or specific building types—resulting in a limited,non-cohesive understanding of their collective potential in the built environment.This fragmentation,in turn,has hindered the development of integrated strategies that link building-level efficiencies with the broader environmental objectives of smart cities.To address these interrelated gaps,this study conducts a comprehensive systematic review of leading-edge AI and AI-powered DT solutions applied across smart,green,and zero-energy buildings.It aims to provide a holistic understanding of how these solutions enhance environmental performance through the analysis of key building-related indicators.By synthesizing,comparing,and evaluating recent research,it examines how AI and AI-powered DT technologies facilitate integrated,system-level strategies that promote environmentally sustainable smart practices across the built environment.The study reveals that AI enhances smart buildings by enabling dynamic energy optimization,occupant-centered environmental control,improved thermal comfort,renewable energy integration,and predictive system management.In green buildings,AI contributes to greater resource efficiency,minimizes construction and operational waste,promotes the use of sustainable materials,strengthens cost estimation and risk assessment processes,and supports adaptive design strategies.For zero-energy buildings,AI facilitates multi-objective optimization,advances explainable and transparent AI-driven control systems,supports performance benchmarking against net and nearly zero-energy standards,and enables renewable energy integration tailored to diverse climatic and regulatory contexts.Furthermore,AI-powered DTs enable real-time environmental monitoring,predictive analytics,anomaly detection,and adaptive operational strategies,thereby enhancing building performance,energy optimization,and resilience.At broader spatial scales,these technologies foster interconnected urban ecosystems,advancing environmental sustainability,sustainable development,and smart city initiatives.Building on these insights,this study introduces a novel integrated framework that positions AI and AI-driven DTs as systemic enablers of environmentally sustainable smart built and urban environments,emphasizing their cross-scale convergence in promoting carbon neutrality,circular economy principles,climate resilience,and regenerative urban strategies.The findings offer actionable pathways for advancing research agendas,inform practical strategies for building and urban system design,and provide evidence-based recommendations for policymakers committed to fostering more intelligent,sustainable,and resilient urban futures.This work establishes AI and AI-driven DTs as transformative catalysts for realizing the next generation of resource-efficient,carbon-neutral,and ecologically integrated urban ecosystems.
基金funding from the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No.101034260.
文摘The recent advancements made in the realms of Artificial Intelligence(AI)and Artificial Intelligence of Things(AIoT)have unveiled transformative prospects and opportunities to enhance and optimize the environmental performance and efficiency of smart cities.These strides have,in turn,impacted smart eco-cities,catalyzing ongoing improvements and driving solutions to address complex environmental challenges.This aligns with the visionary concept of smarter eco-cities,an emerging paradigm of urbanism characterized by the seamless integration of advanced technologies and environmental strategies.However,there remains a significant gap in thoroughly understanding this new paradigm and the intricate spectrum of its multifaceted underlying dimensions.To bridge this gap,this study provides a comprehensive systematic review of the burgeoning landscape of smarter eco-cities and their leadingedge AI and AIoT solutions for environmental sustainability.To ensure thoroughness,the study employs a unified evidence synthesis framework integrating aggregative,configurative,and narrative synthesis approaches.At the core of this study lie these subsequent research inquiries:What are the foundational underpinnings of emerging smarter eco-cities,and how do they intricately interrelate,particularly urbanism paradigms,environmental solutions,and data-driven technologies?What are the key drivers and enablers propelling the materialization of smarter eco-cities?What are the primary AI and AIoT solutions that can be harnessed in the development of smarter eco-cities?In what ways do AI and AIoT technologies contribute to fostering environmental sustainability practices,and what potential benefits and opportunities do they offer for smarter eco-cities?What challenges and barriers arise in the implementation of AI and AIoT solutions for the development of smarter eco-cities?The findings significantly deepen and broaden our understanding of both the significant potential of AI and AIoT technologies to enhance sustainable urban development practices,as well as the formidable nature of the challenges they pose.Beyond theoretical enrichment,these findings offer invaluable insights and new perspectives poised to empower policymakers,practitioners,and researchers to advance the integration of eco-urbanism and AI-and AIoT-driven urbanism.Through an insightful exploration of the contemporary urban landscape and the identification of successfully applied AI and AIoT solutions,stakeholders gain the necessary groundwork for making well-informed decisions,implementing effective strategies,and designing policies that prioritize environmental well-being.
