Trusted artificial intelligence for environmental assessments: An explainable high-precision model with multi-source big data

Environmental assessments are critical for ensuring the sustainable development of human civilization.The integration of artificial intelligence(AI)in these assessments has shown great promise,yet the"black box"nature of AI models often undermines trust due to the lack of transparency in their decision-making processes,even when these models demonstrate high accuracy.To address this challenge,we evaluated the performance of a transformer model against other AI approaches,utilizing extensive multivariate and spatiotemporal environmental datasets encompassing both natural and anthropogenic indicators.We further explored the application of saliency maps as a novel explainability tool in multi-source AI-driven environmental assessments,enabling the identification of individual indicators'contributions to the model's predictions.We find that the transformer model outperforms others,achieving an accuracy of about 98%and an area under the receiver operating characteristic curve(AUC)of 0.891.Regionally,the environmental assessment values are predominantly classified as level II or III in the central and southwestern study areas,level IV in the northern region,and level V in the western region.Through explainability analysis,we identify that water hardness,total dissolved solids,and arsenic concentrations are the most influential indicators in the model.Our AI-driven environmental assessment model is accurate and explainable,offering actionable insights for targeted environmental management.Furthermore,this study advances the application of AI in environmental science by presenting a robust,explainable model that bridges the gap between machine learning and environmental governance,enhancing both understanding and trust in AI-assisted environmental assessments.

Research on Virtual Agent Perception Model

An intelligent virtual environment is described for training users in the operation of complex engineering systems. After analyzing the original model of virtual environment, a virtual agent perception model was put forward. The information layer was inserted into original virtual environment. The model classifies all kinds of information and offers a way for knowledge description of virtual environment, and contributes to set up feeling model for the Virtual Agent within virtual environment.

A Quarter Century of Work to Revolutionize Architecture, Engineering and Construction Enterprise Information Systems

More than two decades ago, object-oriented representation of AEC （architecture engineering and construction） projects started to offer the promise of seamless communication of semantic data models between computer-based systems used from the design stage to the operation of the facilities. BIM （building information modelling） emerged and appeared as a means to store all relevant data generated during the life-cycle of the facilities. But this upstream view of the built environment, arising from the design and construction stages, extended to the downstream operations where building and industrial facilities appeared more and more as huge dynamic data producers and concentrators while being operated. This created new challenges leading to what is referred to as ISCs （intelligent and smart constructions）. The current state of the art is that final constructions still contain various and increasingly versatile control and service systems, which are hardly standardised, and not interconnected among themselves. Monitoring, maintenance and services are done by specialised companies, each responsible of different systems, which are relying on customised software and techniques to meet specific user needs and are based on monolithic applications that require manual configuration for specific uses, maintenance and support. We demonstrate in this paper that the early promises of integration across the actors and along the life-time of facilities have gone a long way but will only be delivered through enhanced standardisation of computerized models, representations, services and operations still not yet fully accomplished 25 years after work started.

Green Charm from Green Design

The way of making people like to use the product by emotional and aesthetic design and making product with charm is illustrated. Examples show that to solve the green charm problem and protect environment only by people＇s responsibility and duty is far from enough. The most important is to attract people loving the green product from the bottom of their heart and to realize the harmonious union of the nature and human beings.

Intelligent Development Environment and Software Knowledge Graph

Software intelligent development has become one of the most important research trends in software engineering. In this paper, we put forward two key concepts -- intelligent development environment （IntelliDE） and software knowledge graph -- for the first time. IntelliDE is an ecosystem in which software big data are aggregated, mined and analyzed to provide intelligent assistance in the life cycle of software development. We present its architecture and discuss its key research issues and challenges. Software knowledge graph is a software knowledge representation and management framework, which plays an important role in IntelliDE. We study its concept and introduce some concrete details and examples to show how it could be constructed and leveraged.

A Promising Technology for 6G Wireless Networks:Intelligent Refl ecting Surface

The intelligent information society,which is highly digitized,intelligence inspired,and globally data driven,will be deployed in the next decade.The next 6G wireless communication networks are the key to achieve this grand blueprint,which is expected to connect everything,provide full dimensional wireless coverage and integrate all functions to support full-vertical applications.Recent research reveals that intelligent reflecting surface(IRS)with wireless environment control capability is a promising technology for 6G networks.Specifically,IRS can intelligently control the wavefront,e.g.,the phase,amplitude,frequency,and even polarization by massive tunable elements,thus achieving fine-grained 3-D passive beamforming.In this paper,we first give a blueprint of the next 6G networks including the vision,typical scenarios,and key performance indicators(KPIs).Then,we provide an overview of IRS including the new signal model,hardware architecture,and competitive advantages in 6G networks.Besides,we discuss the potential application of IRS in the connectivity of 6G networks in detail,including intelligent and controllable wireless environment,ubiquitous connectivity,deep connectivity,and holographic connectivity.At last,we summarize the challenges of IRS application and deployment in 6G networks.As a timely review of IRS,our summary will be of interest to both researchers and practitioners engaging in IRS for 6G networks.