A comprehensive understanding of the relationships among carbon emissions, the industrial structure and economic growth holds great significance for China's transition to a low-carbon economy, industrial structure optimization, and achievement of energy conservation and emission reduction targets. We selected six major industrial sectors (agriculture, industry, construction, transportation, retail and accommodation and other industries) as research objects, introduced the extended STIRPAT decomposition model, Tapio decoupling model and the grey relation analysis to discuss the relationship among the three. Results showed that (i) since 2000, the proportions of value added of agriculture, manufacturing, and transportation are negatively correlated with carbon emissions, while those of construction, retail and accommodation, and other industries are positively correlated with carbon emissions. (ii) The overall economic growth and carbon emissions of these six major industries have experienced the process of decoupling-coupling-decoupling-coupling-decoupling. (iii) The relevance of these six industries to GDP is ranked as follows: transportation > manufacturing > retail andaccommodation > agriculture > construction > other industries. Additionally, accelerating the achievement of a clean energy structure, strengthening the strength and speed of industrial structure adjustment and reducing the dependence on fossil energy are the key steps for China to reach carbon emissions peak goal.
Building Information Modeling (BIM), also called n-D Modeling or Virtual Prototyping Technology, is a revolutionary development that is quickly reshaping the Architecture-Engineering-Construction (AEC) industry. BIM is both a technology and a process. The technology component of BIM helps project stakeholders to visualize what is to be built in a simulated environment to identify any potential design, construction or operational issues. The process component enables close collaboration and encourages integration of the roles of all stakeholders on a project. The paper presents an overview of BIM with focus on its core concepts, applications in the project life cycle and benefits for project stakeholders with the help of case studies. The paper also elaborates risks and barriers to BIM implementation and future trends.
Abstract Whilst a plethora of literature has viewed technological progress as one of the critical influencing factors in relation to energy consumption and CO2 emissions, little effort has been made to identify the sectoral differences in the relationship between technological progress and CO2 emissions. Thereby, this paper remedies the inadequacies in the extant literature by empirically investigating the impact of technological progress on CO2 emissions, specifically taking into account its heterogeneous effect in various economic sectors. This paper first decomposes the effect of technological progress as taking place in heavy industry, in light industry, in construction industry, and in service industry. A panel quantile regression approach and a balanced city panel data model are then applied, covering the period 2001–2013 in China. The empirical results show that technological progress in heavy industry and light industry surprisingly facilitates a higher level of CO2 emissions, despite its prominent contribution to energy efficiency improvement. Meanwhile, as distinct from the effect it shows in the heavy and light industries, technological progress in construction and service industries exerts a negative effect on CO2 emissions. Additionally, this paper further discovers that the strength of the multiple impacts of technological progress also varies with the level of emitters. By studying the heterogeneous effect brought about by technological progress in different economic sectors and emitters, this paper provides a comprehensive understanding of the relationship between CO2 emissions and technological progress.
Concrete cracking is a common issue that compromises structural integrity and reduces service life. With increasing emphasis on sustainability and carbon neutrality in the construction industry, effective crack repair is essential—not only to restore structural performance but also to reduce the need for reconstruction, thereby lowering cement consumption and associated carbon emissions. However, traditional chemical-based repair methods may pose risks to human health and the environment. This study investigates the application of soybean urease-induced calcium carbonate precipitation (SICP), a cost-effective biomineralization technique, for concrete crack repair. The effects of different crack widths (0.1–2.0 mm) and repair strategies (immersion, injection, and sand-filling methods) on crack healing performance were experimentally evaluated. Ultrasonic transmission time and unconfined compressive strength (UCS) tests were conducted to assess the recovery of mechanical properties. Additionally, X-ray diffraction (XRD) and scanning electron microscopy (SEM) analyses were used to characterize the calcium carbonate crystals formed within the cracks. Results showed that SICP treatment reduced ultrasonic transit time and partially restored mechanical strength. Repair effectiveness decreased with increasing crack width across all methods. The sand-filling method yielded superior results for wider cracks (1.0–2.0 mm), while the injection method was more effective for narrower ones (0.1–0.5 mm). Microstructural analyses confirmed the formation of calcium carbonate, with distinct differences in crystal morphology and distribution depending on the repair technique. This study provides valuable insights into the selection of appropriate repair methods for cracks of different widths, contributing to the development of efficient crack repair strategies.
Materials of engineering and construction. Mechanics of materials
The mining industry generates vast quantities of gold mine tailings (GMTs), which present serious environmental management challenges while simultaneously offering untapped potential as alternative construction materials. This review provides a comprehensive and critical evaluation of GMT valorization pathways in the context of sustainable concrete production and supplementary cementitious materials (SCMs). Synthesizing data from over 50 peer-reviewed studies, the paper systematically analyzes the physical, chemical, mineralogical, and microstructural characteristics of GMTs in relation to their impact on various construction materials’ performance. Four principal utilization strategies are examined: as fine aggregate replacements in concrete, in SCMs, and reactive components in both geopolymer and ultra-high-performance concrete (UHPC). The review explores the effectiveness of mechanical, thermal, chemical, and combined activation methods in enhancing the reactivity of GMTs, particularly in systems where native pozzolanic activity is limited. Particular attention is given to the environmental behavior of GMTs, especially their leaching potential, with compiled findings confirming that heavy metal mobility can be effectively mitigated through incorporation into stable cementitious matrices. Notably, most GMTs exhibit high silica content and favorable alumino-silicate ratios, supporting their integration into alkali-activated and blended binder systems. A complementary bibliometric analysis reveals a critical research gap regarding GMT-specific studies, establishing the novelty and timeliness of this work. The findings demonstrate that, when properly processed, GMTs can contribute to enhanced performance, low-carbon construction materials, aligning with circular economy goals and sustainability targets. This review offers a unified framework for GMT valorization and provides clear research directions to enable industrial-scale implementation and regulatory acceptance.
Materials of engineering and construction. Mechanics of materials
Abstract 3D printing, or additive manufacturing, is a technology which adopts layer-by-layer additive deposition process to build three-dimensional objects. Over the past decade, 3D printing has been attracting more and more attention in the building and construction industry. Compared with conventional concrete casting techniques, 3D printing contributes to higher efficiency with freeform construction, greatly reduced labor and much less construction waste. However, 3D printable cementitious materials are different from conventional concrete in terms of rheology, printability, and mechanical performances. This paper aims to systematically bridge the gap between the requirement and research and development of 3D printable cementitious materials to date. Guided by 3D printing process and multi-level design of cementitious materials, the requirements for 3D printable cementitious material at different material development levels are discussed. This paper provides insights for the future development of 3D printable cementitious materials for building and construction by controlling the basic inputs of materials to obtain desired structural performance.
Sander De Coninck, Emilio Gamba, Bart Van Doninck
et al.
The adoption of AI-powered computer vision in industry is often constrained by the need to balance operational utility with worker privacy. Building on our previously proposed privacy-preserving framework, this paper presents its first comprehensive validation on real-world data collected directly by industrial partners in active production environments. We evaluate the framework across three representative use cases: woodworking production monitoring, human-aware AGV navigation, and multi-camera ergonomic risk assessment. The approach employs learned visual transformations that obscure sensitive or task-irrelevant information while retaining features essential for task performance. Through both quantitative evaluation of the privacy-utility trade-off and qualitative feedback from industrial partners, we assess the framework's effectiveness, deployment feasibility, and trust implications. Results demonstrate that task-specific obfuscation enables effective monitoring with reduced privacy risks, establishing the framework's readiness for real-world adoption and providing cross-domain recommendations for responsible, human-centric AI deployment in industry.
Louise McCormack, Diletta Huyskes, Dave Lewis
et al.
The EU Artificial Intelligence (AI) Act directs businesses to assess their AI systems to ensure they are developed in a way that is human-centered and trustworthy. The rapid adoption of AI in the industry has outpaced ethical evaluation frameworks, leading to significant challenges in accountability, governance, data quality, human oversight, technological robustness, and environmental and societal impacts. Through structured interviews with fifteen industry professionals, paired with a literature review conducted on each of the key interview findings, this paper investigates practical approaches and challenges in the development and assessment of Trustworthy AI (TAI). The findings from participants in our study, and the subsequent literature reviews, reveal complications in risk management, compliance and accountability, which are exacerbated by a lack of transparency, unclear regulatory requirements and a rushed implementation of AI. Participants reported concerns that technological robustness and safety could be compromised by model inaccuracies, security vulnerabilities, and an overreliance on AI without proper safeguards in place. Additionally, the negative environmental and societal impacts of AI, including high energy consumption, political radicalisation, loss of culture and reinforcement of social inequalities, are areas of concern. There is a pressing need not just for risk mitigation and TAI evaluation within AI systems but for a wider approach to developing an AI landscape that aligns with the social and cultural values of the countries adopting those technologies.
Rinieri Lorenzo, Gori Giacomo, Melis Andrea
et al.
The OPC UA protocol is becoming the de facto standard for Industry 4.0 machine-to-machine communication. It stands out as one of the few industrial protocols that provide robust security features designed to prevent attackers from manipulating and damaging critical infrastructures. However, prior works showed that significant challenges still exists to set up secure OPC UA deployments in practice, mainly caused by the complexity of certificate management in industrial scenarios and the inconsistent implementation of security features across industrial OPC UA devices. In this paper, we present Pk-IOTA, an automated solution designed to secure OPC UA communications by integrating programmable data plane switches for in-network certificate validation and leveraging the IOTA Tangle for decen- tralized certificate distribution. Our evaluation is performed on a physical testbed representing a real-world industrial scenario and shows that Pk-IOTA introduces a minimal overhead while providing a scalable and tamper-proof mechanism for OPC UA certificate management.
In this paper, a data-driven system identification method is presented based on the data fusion of a dynamic model and flight test data. The dynamic model is built by a combination of nonlinear auto-regressive networks (NARX) and the steady-state model. In such a combination, NARX can calibrate the dynamic characteristics of high-pressure and low-pressure rotor speed based on automatic control system steady-state models. As such, the calibrated engine model’s output speed is able to meet the requirements of simulation test tolerance accuracy. To enhance the robustness of the dynamic model against measurement noise, the Kalman filter is used to fuse the model prediction and the measurement data with noise. As such, the fused model can efficiently remove the influence of measurement noise and improve prediction accuracy. The proposed method supports the construction of reliable and environment-adaptive platforms for simulation application verification and provides high-fidelity simulation incentives for the realization of simulation test scenarios in the aviation industry.
Aquatic germplasm resources refer to genetic materials of aquatic animals and plants that possess actual or potential value, including species, subspecies, varieties, strains, <i>etc</i>. These resources hold significant economic, ecological, and scientific importance in the fields of fisheries and agriculture, serving as the foundation for aquaculture, genetic improvement, and biodiversity conservation. Aquatic germplasm resources encompass aquatic organisms such as fish, shrimp, shellfish, algae, and echinoderms, along with their genetic material. They are not only a core element supporting the sustainable development and international competitiveness of the aquaculture industry but also a strategic resource for safeguarding national food security, ecological security, and biodiversity. Building strengthened protection of aquatic germplasm resources, how to enhance the scientific utilization and innovative development of superior aquatic germplasm is both an urgent national need for biological and genetic diversity security and a pressing demand for high-quality, healthy aquatic products driven by growing public expectations. This review conducts a safety assessment from three perspectives: fundamental research on the utilization of aquatic germplasm resources, relevant technologies and platforms, and artificially cultivated and genetically improved germplasm. Based on a review of research progress in these areas, it analyzes existing problems within these three aspects and proposes recommendations for improving the utilization of aquatic germplasm resources. Future recommendations for enhancing the safety of aquatic germplasm resource utilization in China mainly include: strengthening the construction of a shared genomic resource platform for aquatic species based on China's independent intellectual property; increasing efforts to decipher the genetic mechanisms underlying economically important traits; advancing the development of low-cost, high-throughput, and intelligent technologies for precise phenotyping and genotyping, along with platforms for precise functional gene manipulation; and enhancing science popularization regarding transgenic and gene editing technologies while strengthening the protection of intellectual property rights for new varieties.
The construction industry, a cornerstone of global economic growth, faces frequent safety accidents due to its complex environments and multi-party collaboration, impeding sustainable development. These incidents arise from interlinked causal factors, including human error, management shortcomings, technical failures, and environmental conditions. This study systematically reviews construction accident causation research by integrating scientometric analysis and qualitative methods, using VOSviewer to analyze literature from Scopus and Web of Science databases, with 110 peer-reviewed articles selected through a validated Boolean search strategy. VOSviewer was used for bibliometric visualization to map research trends, co-authorship networks, and keyword co-occurrences. In addition, a qualitative synthesis was conducted to review common data sources and examine key issues, including risk factor identification, accident type classification, causality analysis, and the optimization of research strategies. The study aims to systematically review the current state of construction accident causation research, highlighting key trends in data-driven and AI-based safety interventions. Findings reveal a shift toward data-driven, intelligent approaches, with artificial intelligence techniques—such as large models (capable of understanding complex patterns from massive datasets), graph neural networks (suitable for modeling relationships between contributing factors), and natural language processing (for extracting insights from textual accident reports)—enhancing accident prevention and risk prediction. Challenges persist, however, in data quality, causal exploration depth, and interdisciplinary integration. These findings underscore the need for further advancements in data accuracy and model scalability, which could inform more effective safety management practices and policy frameworks. Key contributions include filling the bibliometric gap in this field, offering a novel framework combining quantitative and qualitative insights, and highlighting advanced technology applications, thus providing theoretical and practical guidance for future safety management. Future research is recommended to leverage AI, foster interdisciplinary collaboration, and develop precise prevention systems to address these gaps.
Engineering (General). Civil engineering (General), City planning
Rice aroma is influenced by many factors, including selenium (Se) fertilizer. In this study, we investigated the effects of different Se species on the volatile organic compounds (VOCs) in three indica rice varieties over 2022 and 2023 by forliar spray. The VOCs were analyzed using HS-SPME-GC-MS. The results showed that both Se nanoparticles (SeNPs) and sodium selenite (Na<sub>2</sub>SeO<sub>3</sub>) significantly increased the contents of most VOCs in all three varieties, with SeNPs exhibiting a more pronounced effect. PCA and OPLS-DA revealed distinct clustering of the VOCs based on Se treatments and rice varieties. By variable importance in projection (VIP) analysis with FDR correction, Na<sub>2</sub>SeO<sub>3</sub> yielded 7 markers, whereas SeNP treatment identified 18. Every marker detected under Na<sub>2</sub>SeO<sub>3</sub> was fully encompassed within the SeNPs set. Three-factor ANOVA indicated that there are significant interaction effects among Se species, rice variety, and planting year. Additionally, the effect sizes were evaluated in the key VOCs to quantify the effect of Se species, rice variety, and planting year. The findings highlight Se fertilizers to enhance rice aroma and suggest selecting appropriate Se species and rice varieties for aroma improvement.
Zhipeng Ma, Bo Nørregaard Jørgensen, Michelle Levesque
et al.
Digitalization is challenging in heavy industrial sectors, and many pi-lot projects facing difficulties to be replicated and scaled. Case studies are strong pedagogical vehicles for learning and sharing experience & knowledge, but rarely available in the literature. Therefore, this paper conducts a survey to gather a diverse set of nine industry cases, which are subsequently subjected to analysis using the business model canvas (BMC). The cases are summarized and compared based on nine BMC components, and a Value of Business Model (VBM) evaluation index is proposed to assess the business potential of industrial digital solutions. The results show that the main partners are industry stakeholders, IT companies and academic institutes. Their key activities for digital solutions include big-data analysis, machine learning algorithms, digital twins, and internet of things developments. The value propositions of most cases are improving energy efficiency and enabling energy flexibility. Moreover, the technology readiness levels of six industrial digital solutions are under level 7, indicating that they need further validation in real-world environments. Building upon these insights, this paper proposes six recommendations for future industrial digital solution development: fostering cross-sector collaboration, prioritizing comprehensive testing and validation, extending value propositions, enhancing product adaptability, providing user-friendly platforms, and adopting transparent recommendations.
Lucas Romao, Marcos Kalinowski, Clarissa Barbosa
et al.
[Context] Software Engineering (SE) education constantly seeks to bridge the gap between academic knowledge and industry demands, with active learning methods like Problem-Based Learning (PBL) gaining prominence. Despite these efforts, recent graduates struggle to align skills with industry needs. Recognizing the relevance of Industry-Academia Collaboration (IAC), Lean R&D has emerged as a successful agile-based research and development approach, emphasizing business and software development synergy. [Goal] This paper aims to extend Lean R&D with PBL principles, evaluating its application in an educational program designed by ExACTa PUC- Rio for Americanas S.A., a large Brazilian retail company. [Method] The educational program engaged 40 part-time students receiving lectures and mentoring while working on real problems, coordinators and mentors, and company stakeholders in industry projects. Empirical evaluation, through a case study approach, utilized structured questionnaires based on the Technology Acceptance Model (TAM). [Results] Stakeholders were satisfied with Lean R&D PBL for problem-solving. Students reported increased knowledge proficiency and perceived working on real problems as contributing the most to their learning. [Conclusion] This research contributes to academia by sharing Lean R&D PBL as an educational IAC approach. For industry, we discuss the implementation of this proposal in an IAC program that promotes workforce skill development and innovative solutions.
In the process of modernization with Chinese characteristics, the dam sector of China will embrace a new development climax. A new layout of the national water network will be formed by the combination of major natural river systems and water infrastructures including water division channels, dams and reservoirs, hydropower stations, river dikes, flood retention areas, etc. The massive investment demand released in the construction of many planned/ongoing projects will drive the reform of the supply side. Many new requirements for modern dam construction and operation will be imposed following the national’s goal of high-quality development, and new challenges will arise from aquatic ecosystem protection and restoration. These strategic needs form the basis for new research and development (R&D) missions of China’s hydraulic-hydropower industry.
To date, the majority of positioning systems have been designed to operate within environments that have long-term stable macro-structure with potential small-scale dynamics. These assumptions allow the existing positioning systems to produce and utilize stable maps. However, in highly dynamic industrial settings these assumptions are no longer valid and the task of tracking people is more challenging due to the rapid large-scale changes in structure. In this paper we propose a novel positioning system for tracking people in highly dynamic industrial environments, such as construction sites. The proposed system leverages the existing CCTV camera infrastructure found in many industrial settings along with radio and inertial sensors within each worker's mobile phone to accurately track multiple people. This multi-target multi-sensor tracking framework also allows our system to use cross-modality training in order to deal with the environment dynamics. In particular, we show how our system uses cross-modality training in order to automatically keep track environmental changes (i.e. new walls) by utilizing occlusion maps. In addition, we show how these maps can be used in conjunction with social forces to accurately predict human motion and increase the tracking accuracy. We have conducted extensive real-world experiments in a construction site showing significant accuracy improvement via cross-modality training and the use of social forces.
Understanding customer lifetime value is key to nurturing long-term customer relationships, however, estimating it is far from straightforward. In the retail banking industry, commonly used approaches rely on simple heuristics and do not take advantage of the high predictive ability of modern machine learning techniques. We present a general framework for modelling customer lifetime value which may be applied to industries with long-lasting contractual and product-centric customer relationships, of which retail banking is an example. This framework is novel in facilitating CLV predictions over arbitrary time horizons and product-based propensity models. We also detail an implementation of this model which is currently in production at a large UK lender. In testing, we estimate an 43% improvement in out-of-time CLV prediction error relative to a popular baseline approach. Propensity models derived from our CLV model have been used to support customer contact marketing campaigns. In testing, we saw that the top 10% of customers ranked by their propensity to take up investment products were 3.2 times more likely to take up an investment product in the next year than a customer chosen at random.