As society confronts increasingly complex demands and the growing need for carbon-neutral architecture,AI-driven design methodologies are evolving rapidly.However,the lack of a unified integration platform in the desi...As society confronts increasingly complex demands and the growing need for carbon-neutral architecture,AI-driven design methodologies are evolving rapidly.However,the lack of a unified integration platform in the design process continues to hinder AI’s integration into real-world workflows.To address this challenge,we introduce ArchiWeb,a web-based platform specifically built to support AI-driven processes in early-stage architectural design.ArchiWeb transforms architectural representation and problem formulation by utilizing lightweight data protocols and a modular algorithmic network within an interactive web environment.Through its cloud-native,open-architecture framework,ArchiWeb enables deeper integration of AI technologies while accelerating the accumulation,sharing,and reuse of design knowledge across projects and disciplines.Ultimately,ArchiWeb aims to drive architectural design toward greater intelligence,efficiency,and sustainability―supporting the transition to data-informed,computationally enabled,and environmentally responsible design practices.展开更多
Magnesium-ion batteries hold promise as future energy storage solutions,yet current Mg cathodes are challenged by low voltage and specific capacity.Herein,we present an AI-driven workflow for discovering high-performa...Magnesium-ion batteries hold promise as future energy storage solutions,yet current Mg cathodes are challenged by low voltage and specific capacity.Herein,we present an AI-driven workflow for discovering high-performance Mg cathode materials.Utilizing the common characteristics of various ionic intercalation-type electrodes,we design and train a Crystal Graph Convolutional Neural Network model that can accurately predict electrode voltages for various ions with mean absolute errors(MAE)between0.25 and 0.33 V.By deploying the trained model to stable Mg compounds from Materials Project and GNoME AI dataset,we identify 160 high voltage structures out of 15,308 candidates with voltages above3.0 V and volumetric capacity over 800 mA h/cm^(3).We further train a precise NequIP model to facilitate accurate and rapid simulations of Mg ionic conductivity.From the 160 high voltage structures,the machine learning molecular dynamics simulations have selected 23 cathode materials with both high energy density and high ionic conductivity.This Al-driven workflow dramatically boosts the efficiency and precision of material discovery for multivalent ion batteries,paving the way for advanced Mg battery development.展开更多
This Highlight discusses the landmark study by Zhao et al.(Science,2025)that presents a transformative strategy against citrus Huanglongbing(HLB).The work identifies the E3 ubiquitin ligase PUB21 as a central suscepti...This Highlight discusses the landmark study by Zhao et al.(Science,2025)that presents a transformative strategy against citrus Huanglongbing(HLB).The work identifies the E3 ubiquitin ligase PUB21 as a central susceptibility(S)factor,degrading the defense regulator MYC2.Crucially,the study harnesses natural resistance(dominantnegative PUB21DN mutant)and pioneers AI-driven design to develop a 14-amino acid peptide(APP3-14).This peptide dually combats HLB by stabilizing MYC2(inhibiting PUB21)and directly targeting the unculturable pathogen Candidatus Liberibacter asiaticus(CLas),achieving>90%bacterial reduction in field trials.The research also exposes how a CLas effector(SDE5,Sec-delivered effector 5)hijacks the PUB21-MYC2 axis.This work establishes"defense protein stabilization"as a powerful new paradigm for breeding resistant crops and controlling recalcitrant pathogens,exemplified by the innovative integration of AI in peptide therapeutics for plants.展开更多
Recent advances in artificial intelligence(AI)have led to the development of sophisticated algorithms that significantly improve image analysis capabilities.This combination of AI and microscopic imaging is transformi...Recent advances in artificial intelligence(AI)have led to the development of sophisticated algorithms that significantly improve image analysis capabilities.This combination of AI and microscopic imaging is transforming the way we interpret and analyze imaging data,simplifying complex tasks and enabling innovative experimental methods previously thought impossible.In smart manufacturing,these improvements are especially impactful,increasing precision and efficiency in production processes.This review examines the convergence of AI with particle image analysis,an area we refer to as“particle vision analysis(PVA).”We offer a detailed overview of how this technology integrates into and impacts various fields within the physical sciences and materials sectors,where it plays a crucial role in both innovation and operational improvements.We explore four key areas of advancement-namely,particle classification,detection,segmentation,and object tracking-along with a look into the emerging field of augmented microscopy.This paper also underscores the vital role of the existing datasets and implementations that support these applications,which provide essential insights and resources that drive continuous research and development in this fast-evolving field.Our thorough analysis aims to outline the transformative potential of AI-driven PVA in improving precision in future manufacturing at the microscopic scale and thereby preparing the ground for significant technological progress and broad industrial applications in nanomanufacturing,biomanufacturing,and pharmaceutical manufacturing.This exploration not only highlights the advantages of integrating AI into conventional manufacturing processes but also anticipates the rise of next-generation smart manufacturing,which is set to revolutionize industry standards and operational practices.展开更多
With the growing adoption of Artifical Intelligence(AI),AI-driven autonomous techniques and automation systems have seen widespread applications,become pivotal in enhancing operational efficiency and task automation a...With the growing adoption of Artifical Intelligence(AI),AI-driven autonomous techniques and automation systems have seen widespread applications,become pivotal in enhancing operational efficiency and task automation across various aspects of human living.Over the past decade,AI-driven automation has advanced from simple rule-based systems to sophisticated multi-agent hybrid architectures.These technologies not only increase productivity but also enable more scalable and adaptable solutions,proving particularly beneficial in industries such as healthcare,finance,and customer service.However,the absence of a unified review for categorization,benchmarking,and ethical risk assessment hinders the AI-driven automation progress.To bridge this gap,in this survey,we present a comprehensive taxonomy of AI-driven automation methods and analyze recent advancements.We present a comparative analysis of performance metrics between production environments and industrial applications,along with an examination of cutting-edge developments.Specifically,we present a comparative analysis of the performance across various aspects in different industries,offering valuable insights for researchers to select the most suitable approaches for specific applications.Additionally,we also review multiple existing mainstream AI-driven automation applications in detail,highlighting their strengths and limitations.Finally,we outline open research challenges and suggest future directions to address the challenges of AI adoption while maximizing its potential in real-world AI-driven automation applications.展开更多
Theburgeoning e-commerce industry hasmade online customer reviews a crucial source of feedback for businesses.Sentiment analysis,a technique used to extract subjective information from text,has become essential for un...Theburgeoning e-commerce industry hasmade online customer reviews a crucial source of feedback for businesses.Sentiment analysis,a technique used to extract subjective information from text,has become essential for understanding consumer sentiment and preferences.However,traditional sentiment analysis methods often struggle with the nuances and context of natural language.To address these issues,this study proposes a comparison of deep learningmodels that figure out the optimalmethod to accurately analyze consumer reviews onwomen’s clothing.CNNs excel at capturing local features and semantic information,while LSTMs are adept at handling long-range dependencies and contextual understanding.By integrating these two deep learning techniques,our model aims to achieve better performance in sentiment classification.The models were trained and evaluated on a dataset of women’s clothing reviews sourced from Kaggle.The dataset was pre-processed to clean and tokenize the text data,and word embeddings were used to represent words as numerical vectors.The CNN component of the model extracts local features from the text,while the LSTM component captures long-range dependencies and contextual information.The outputs of the CNN and LSTM layers are then concatenated and fed into a fully connected layer for final sentiment classification.Experimental results demonstrate that the hybrid model outperforms traditional machine learning techniques and other deep learning models in terms of accuracy,precision,recall,and F1-score.By accurately classifying sentiment,identifying key themes,and predicting future trends,our model can provide valuable insights to businesses in the apparel industry.These insights can be used to improve product design,marketing strategies,and customer service,ultimately leading to increased customer satisfaction and business success.展开更多
This study explores the innovative application of intelligent technology in the task-based Chinese teaching method,focusing on the effectiveness of real-time guidance of speech recognition and intelligent analysis tec...This study explores the innovative application of intelligent technology in the task-based Chinese teaching method,focusing on the effectiveness of real-time guidance of speech recognition and intelligent analysis technology on learners'pronunciation,grammar,and vocabulary.In the experiment,100 Chinese second language learners were divided into intelligent assistant groups and traditional teaching groups for comparative observation.According to the data,the task completion efficiency of the intelligent group increased by 20%,and the language proficiency evaluation index increased by an average of 30%.More than 80%of learners reported that the instant feedback mechanism effectively improved their confidence and participation in learning.The research proves that intelligent technology can build dynamic learning paths and optimize language acquisition efficiency through personalized training modules.Although the system has technical bottlenecks in the dimension of understanding cultural context,the experimental results provide empirical support for the deep integration of intelligent technology and language teaching,and lay the technical foundation for further research and development of a culturally sensitive intelligent teaching system.展开更多
The intersection of artificial intelligence(AI)and software engineering marks a transformative phase in the technology industry.This paper delves into AI-driven software engineering,exploring its methodologies,implica...The intersection of artificial intelligence(AI)and software engineering marks a transformative phase in the technology industry.This paper delves into AI-driven software engineering,exploring its methodologies,implications,challenges,and benefits.Drawing from data sources such as GitHub and Bitbucket and insights from industry experts,the study offers a comprehensive view of the current landscape.While the results indicate a promising uptrend in the integration of AI techniques in software development,challenges like model interpretability,ethical concerns,and integration complexities emerge as significant.Nevertheless,the transformative potential of AI within software engineering is profound,ushering in new paradigms of efficiency,innovation,and user experience.The study concludes by emphasizing the need for further research,better tooling,ethical guidelines,and education to fully harness the potential of AI-driven software engineering.展开更多
The nature-culture divide,a longstanding conceptual separation between human beings and the natural environment,is increasingly challenged by the pressing need to address climate change.This urgency calls for design a...The nature-culture divide,a longstanding conceptual separation between human beings and the natural environment,is increasingly challenged by the pressing need to address climate change.This urgency calls for design approaches that can synthesise social and sustainable aspects,creating environmental-user-centric solutions.Our study aimed to bridge this divide by exploring the integration of digital and human crafts,with a focus on wood upcycling furniture as a case study.It investigates the flow of design information,creating an interactive feedback loop between physical and digital domains.To ensure the workflow aligns with stakeholder needs,the study engages professionals interdisciplinarily,including designers,informaticists,and engineers,to collectively test and reflect on the process.The proposed pipeline was then compared with the collaborative pipeline that emerged,incorporating stakeholder perspective to refine the system design.The resulting workflow embraced 3D scanning,AIdriven design generation,VR user scenario simulation,and AR-assisted physical fabrication.The digital and physical furniture prototypes suggest new avenues for design informatics by synthesising objective mathematical decisions with subjective semiotic inputs.By exploring the integration of human and machine crafts in the co-creation process,the reflections contribute to sustainable urban and community construction(SDG 11),revealing potentials for scalability in architectural production.展开更多
Artificial intelligence(AI)is transforming the tourism industry and affecting on natural ecology,making it more environmentally friendly,efficient and personalized.In 2025,AI technologies are being actively implemente...Artificial intelligence(AI)is transforming the tourism industry and affecting on natural ecology,making it more environmentally friendly,efficient and personalized.In 2025,AI technologies are being actively implemented to reduce the carbon footprint,optimize resources,and improve the travel experience.Here are the key applications of AI in environmentally sustainable smart tourism:AI in smart tourism is not just a technological trend,but a necessity for the sustainable development of the industry.Paper analyses personalized and green travel experience and smart tourism.AI-based applications(Google ARCore)allow tourists to get information about attractions without paper booklets.Virtual tours reduce the need for physical travel by reducing the carbon footprint.Platforms offer routes with minimal impact on nature(for example,hiking trails instead of car tours).Tourists can offset their carbon footprint through AI tools by financing tree planting.The introduction of AI solutions allows combining economic benefits with environmental responsibility,creating a future where travel becomes safer for the planet.Paper confirms idea about sustainable tourism development in developing countries and focus on premium ecotourism.Instead of mass tourism,AI helps promote unique destinations(safaris,diving,ethnographic tours),which increases income with less environmental damage.Smart cities with AI-driven transport and energy-saving solutions make tourism more sustainable.展开更多
The integration of Artificial Intelligence (AI) and Internet of Things (IoT), known as AIoT, presents a transformative framework for modernizing campus IT operation and maintenance. This paper details the design of a ...The integration of Artificial Intelligence (AI) and Internet of Things (IoT), known as AIoT, presents a transformative framework for modernizing campus IT operation and maintenance. This paper details the design of a hierarchical AIoT architecture that leverages edge computing for real-time decision-making and cloud analytics for long-term optimization, achieving a higher system availability while reducing data transmission costs. The proposed system addresses critical challenges in traditional campus management such as energy inefficiency, reactive maintenance, and resource underutilization through intelligent applications like predictive resource allocation and environmental control. Furthermore, the design incorporates a robust, AI-driven cybersecurity framework and intelligent data processing paradigms, such as federated learning, which enhance maintenance efficiency and reduce false alarms. The transition to an AIoT-enabled campus is not merely a technological upgrade but a strategic shift towards a predictive, efficient, and sustainable operational model, fundamentally enhancing the management of university infrastructures.展开更多
基金funded by the National Natural Science Foundation of China(No.52378008)Postgraduate Research Innovation Program of Jiangsu Province(No.KYCX22_0189).
文摘As society confronts increasingly complex demands and the growing need for carbon-neutral architecture,AI-driven design methodologies are evolving rapidly.However,the lack of a unified integration platform in the design process continues to hinder AI’s integration into real-world workflows.To address this challenge,we introduce ArchiWeb,a web-based platform specifically built to support AI-driven processes in early-stage architectural design.ArchiWeb transforms architectural representation and problem formulation by utilizing lightweight data protocols and a modular algorithmic network within an interactive web environment.Through its cloud-native,open-architecture framework,ArchiWeb enables deeper integration of AI technologies while accelerating the accumulation,sharing,and reuse of design knowledge across projects and disciplines.Ultimately,ArchiWeb aims to drive architectural design toward greater intelligence,efficiency,and sustainability―supporting the transition to data-informed,computationally enabled,and environmentally responsible design practices.
基金supported by the National Key R&D Program of China(2022YFA1203400)the National Natural Science Foundation of China(W2441009)。
文摘Magnesium-ion batteries hold promise as future energy storage solutions,yet current Mg cathodes are challenged by low voltage and specific capacity.Herein,we present an AI-driven workflow for discovering high-performance Mg cathode materials.Utilizing the common characteristics of various ionic intercalation-type electrodes,we design and train a Crystal Graph Convolutional Neural Network model that can accurately predict electrode voltages for various ions with mean absolute errors(MAE)between0.25 and 0.33 V.By deploying the trained model to stable Mg compounds from Materials Project and GNoME AI dataset,we identify 160 high voltage structures out of 15,308 candidates with voltages above3.0 V and volumetric capacity over 800 mA h/cm^(3).We further train a precise NequIP model to facilitate accurate and rapid simulations of Mg ionic conductivity.From the 160 high voltage structures,the machine learning molecular dynamics simulations have selected 23 cathode materials with both high energy density and high ionic conductivity.This Al-driven workflow dramatically boosts the efficiency and precision of material discovery for multivalent ion batteries,paving the way for advanced Mg battery development.
基金National Key R&D Program of China(2023YFC2604805-4)National Key Research and Development Program of Xizang Autonomous Region(XZ202401ZY0034).
文摘This Highlight discusses the landmark study by Zhao et al.(Science,2025)that presents a transformative strategy against citrus Huanglongbing(HLB).The work identifies the E3 ubiquitin ligase PUB21 as a central susceptibility(S)factor,degrading the defense regulator MYC2.Crucially,the study harnesses natural resistance(dominantnegative PUB21DN mutant)and pioneers AI-driven design to develop a 14-amino acid peptide(APP3-14).This peptide dually combats HLB by stabilizing MYC2(inhibiting PUB21)and directly targeting the unculturable pathogen Candidatus Liberibacter asiaticus(CLas),achieving>90%bacterial reduction in field trials.The research also exposes how a CLas effector(SDE5,Sec-delivered effector 5)hijacks the PUB21-MYC2 axis.This work establishes"defense protein stabilization"as a powerful new paradigm for breeding resistant crops and controlling recalcitrant pathogens,exemplified by the innovative integration of AI in peptide therapeutics for plants.
基金funding support from the US National Science Foundation(2229092)supported by the Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship,a program of Schmidt Sciences,LLC.
文摘Recent advances in artificial intelligence(AI)have led to the development of sophisticated algorithms that significantly improve image analysis capabilities.This combination of AI and microscopic imaging is transforming the way we interpret and analyze imaging data,simplifying complex tasks and enabling innovative experimental methods previously thought impossible.In smart manufacturing,these improvements are especially impactful,increasing precision and efficiency in production processes.This review examines the convergence of AI with particle image analysis,an area we refer to as“particle vision analysis(PVA).”We offer a detailed overview of how this technology integrates into and impacts various fields within the physical sciences and materials sectors,where it plays a crucial role in both innovation and operational improvements.We explore four key areas of advancement-namely,particle classification,detection,segmentation,and object tracking-along with a look into the emerging field of augmented microscopy.This paper also underscores the vital role of the existing datasets and implementations that support these applications,which provide essential insights and resources that drive continuous research and development in this fast-evolving field.Our thorough analysis aims to outline the transformative potential of AI-driven PVA in improving precision in future manufacturing at the microscopic scale and thereby preparing the ground for significant technological progress and broad industrial applications in nanomanufacturing,biomanufacturing,and pharmaceutical manufacturing.This exploration not only highlights the advantages of integrating AI into conventional manufacturing processes but also anticipates the rise of next-generation smart manufacturing,which is set to revolutionize industry standards and operational practices.
文摘With the growing adoption of Artifical Intelligence(AI),AI-driven autonomous techniques and automation systems have seen widespread applications,become pivotal in enhancing operational efficiency and task automation across various aspects of human living.Over the past decade,AI-driven automation has advanced from simple rule-based systems to sophisticated multi-agent hybrid architectures.These technologies not only increase productivity but also enable more scalable and adaptable solutions,proving particularly beneficial in industries such as healthcare,finance,and customer service.However,the absence of a unified review for categorization,benchmarking,and ethical risk assessment hinders the AI-driven automation progress.To bridge this gap,in this survey,we present a comprehensive taxonomy of AI-driven automation methods and analyze recent advancements.We present a comparative analysis of performance metrics between production environments and industrial applications,along with an examination of cutting-edge developments.Specifically,we present a comparative analysis of the performance across various aspects in different industries,offering valuable insights for researchers to select the most suitable approaches for specific applications.Additionally,we also review multiple existing mainstream AI-driven automation applications in detail,highlighting their strengths and limitations.Finally,we outline open research challenges and suggest future directions to address the challenges of AI adoption while maximizing its potential in real-world AI-driven automation applications.
文摘Theburgeoning e-commerce industry hasmade online customer reviews a crucial source of feedback for businesses.Sentiment analysis,a technique used to extract subjective information from text,has become essential for understanding consumer sentiment and preferences.However,traditional sentiment analysis methods often struggle with the nuances and context of natural language.To address these issues,this study proposes a comparison of deep learningmodels that figure out the optimalmethod to accurately analyze consumer reviews onwomen’s clothing.CNNs excel at capturing local features and semantic information,while LSTMs are adept at handling long-range dependencies and contextual understanding.By integrating these two deep learning techniques,our model aims to achieve better performance in sentiment classification.The models were trained and evaluated on a dataset of women’s clothing reviews sourced from Kaggle.The dataset was pre-processed to clean and tokenize the text data,and word embeddings were used to represent words as numerical vectors.The CNN component of the model extracts local features from the text,while the LSTM component captures long-range dependencies and contextual information.The outputs of the CNN and LSTM layers are then concatenated and fed into a fully connected layer for final sentiment classification.Experimental results demonstrate that the hybrid model outperforms traditional machine learning techniques and other deep learning models in terms of accuracy,precision,recall,and F1-score.By accurately classifying sentiment,identifying key themes,and predicting future trends,our model can provide valuable insights to businesses in the apparel industry.These insights can be used to improve product design,marketing strategies,and customer service,ultimately leading to increased customer satisfaction and business success.
文摘This study explores the innovative application of intelligent technology in the task-based Chinese teaching method,focusing on the effectiveness of real-time guidance of speech recognition and intelligent analysis technology on learners'pronunciation,grammar,and vocabulary.In the experiment,100 Chinese second language learners were divided into intelligent assistant groups and traditional teaching groups for comparative observation.According to the data,the task completion efficiency of the intelligent group increased by 20%,and the language proficiency evaluation index increased by an average of 30%.More than 80%of learners reported that the instant feedback mechanism effectively improved their confidence and participation in learning.The research proves that intelligent technology can build dynamic learning paths and optimize language acquisition efficiency through personalized training modules.Although the system has technical bottlenecks in the dimension of understanding cultural context,the experimental results provide empirical support for the deep integration of intelligent technology and language teaching,and lay the technical foundation for further research and development of a culturally sensitive intelligent teaching system.
文摘The intersection of artificial intelligence(AI)and software engineering marks a transformative phase in the technology industry.This paper delves into AI-driven software engineering,exploring its methodologies,implications,challenges,and benefits.Drawing from data sources such as GitHub and Bitbucket and insights from industry experts,the study offers a comprehensive view of the current landscape.While the results indicate a promising uptrend in the integration of AI techniques in software development,challenges like model interpretability,ethical concerns,and integration complexities emerge as significant.Nevertheless,the transformative potential of AI within software engineering is profound,ushering in new paradigms of efficiency,innovation,and user experience.The study concludes by emphasizing the need for further research,better tooling,ethical guidelines,and education to fully harness the potential of AI-driven software engineering.
文摘The nature-culture divide,a longstanding conceptual separation between human beings and the natural environment,is increasingly challenged by the pressing need to address climate change.This urgency calls for design approaches that can synthesise social and sustainable aspects,creating environmental-user-centric solutions.Our study aimed to bridge this divide by exploring the integration of digital and human crafts,with a focus on wood upcycling furniture as a case study.It investigates the flow of design information,creating an interactive feedback loop between physical and digital domains.To ensure the workflow aligns with stakeholder needs,the study engages professionals interdisciplinarily,including designers,informaticists,and engineers,to collectively test and reflect on the process.The proposed pipeline was then compared with the collaborative pipeline that emerged,incorporating stakeholder perspective to refine the system design.The resulting workflow embraced 3D scanning,AIdriven design generation,VR user scenario simulation,and AR-assisted physical fabrication.The digital and physical furniture prototypes suggest new avenues for design informatics by synthesising objective mathematical decisions with subjective semiotic inputs.By exploring the integration of human and machine crafts in the co-creation process,the reflections contribute to sustainable urban and community construction(SDG 11),revealing potentials for scalability in architectural production.
基金financed as part of the project“Development of a methodology for instrumental base formation for analysis and modeling of the spatial socio-economic development of systems based on internal reserves in the context of digitalization”(FSEG-2023-0008).
文摘Artificial intelligence(AI)is transforming the tourism industry and affecting on natural ecology,making it more environmentally friendly,efficient and personalized.In 2025,AI technologies are being actively implemented to reduce the carbon footprint,optimize resources,and improve the travel experience.Here are the key applications of AI in environmentally sustainable smart tourism:AI in smart tourism is not just a technological trend,but a necessity for the sustainable development of the industry.Paper analyses personalized and green travel experience and smart tourism.AI-based applications(Google ARCore)allow tourists to get information about attractions without paper booklets.Virtual tours reduce the need for physical travel by reducing the carbon footprint.Platforms offer routes with minimal impact on nature(for example,hiking trails instead of car tours).Tourists can offset their carbon footprint through AI tools by financing tree planting.The introduction of AI solutions allows combining economic benefits with environmental responsibility,creating a future where travel becomes safer for the planet.Paper confirms idea about sustainable tourism development in developing countries and focus on premium ecotourism.Instead of mass tourism,AI helps promote unique destinations(safaris,diving,ethnographic tours),which increases income with less environmental damage.Smart cities with AI-driven transport and energy-saving solutions make tourism more sustainable.
文摘The integration of Artificial Intelligence (AI) and Internet of Things (IoT), known as AIoT, presents a transformative framework for modernizing campus IT operation and maintenance. This paper details the design of a hierarchical AIoT architecture that leverages edge computing for real-time decision-making and cloud analytics for long-term optimization, achieving a higher system availability while reducing data transmission costs. The proposed system addresses critical challenges in traditional campus management such as energy inefficiency, reactive maintenance, and resource underutilization through intelligent applications like predictive resource allocation and environmental control. Furthermore, the design incorporates a robust, AI-driven cybersecurity framework and intelligent data processing paradigms, such as federated learning, which enhance maintenance efficiency and reduce false alarms. The transition to an AIoT-enabled campus is not merely a technological upgrade but a strategic shift towards a predictive, efficient, and sustainable operational model, fundamentally enhancing the management of university infrastructures.
