Search Results (719)

With rapid urbanization, the generation of municipal solid waste is growing, placing ever-increasing pressure on cities to close, remediate and repurpose landfill sites in environmentally sustainable and climate-adaptive ways. Traditional landfill final covers such as compacted clay and geosynthetic systems are intended to limit infiltration; yet their conceptual designs often fail in performance longevity due to effects such as desiccation, settlement, root intrusion, freeze–thaw cycling and extreme rainfall. Phytocapping, or evapotranspiration/store-and-release cover technology is the use of vegetated soil profiles to provide storage for percolating rainfall, return water to the atmosphere through evapotranspiration and support biologically mediated oxidation of methane. Phytocapping is a green-inclusive nature-based climate adaptation strategy for urban landfill closure. This study explores hydrological performance, methane mitigation, ecological co-benefits, economic feasibility, climate sensitivity, monitoring requirements and regulatory barriers linked to phytocapping systems. Field evidence is strongest in Australia and the United States, especially through ACAP- and A-ACAP-style programs, while evidence from humid tropical, monsoon, freeze–thaw and low-resource urban contexts is comparatively lacking. As reported in published studies, well-designed phytocaps can result in reduced percolation compared to traditional clay caps. Reported publications also mention considerable construction-cost savings, depending on site conditions and design assumptions. Methane-related outcomes vary by measurement method and site context, with studies reporting surface flux reductions, methane oxidation and landfill gas attenuation as distinct performance indicators. These advantages are counter-balanced by design uncertainties that vary from site to site, limited long-term monitoring data, climate transferability concerns, and regulatory systems still firmly anchored in prescriptive low-permeability barriers. This review proposes a policy-oriented analytical framework that bridges the gap between technical performance evidence, urban co-benefits, staged monitoring and performance-based landfill closure regulation. As such, phytocapping should be considered not as a general-purpose substitute for engineered covers, but as a climate-responsive nature-based solution that can complement urban waste servicing infrastructure, ecological restoration and adaptive governance of landfills when properly designed, monitored and regulated. Full article

Network functions virtualization (NFV) is an emerging technology that enables flexible service deployment for supporting the Beyond 5G/6G network. NFV transforms physical network devices into virtual network functions (VNF) over Edge Computing capabilities, thereby facilitating the agility of network services and reducing management costs. To effectively monitor Internet of Things (IoT) network resources, service function chaining (SFC) is used for its virtualizations to ensure the multi-service requirements are sufficiently in capability, scalability, and flexibility for computation workloads alignments. However, to satisfy the resource availability requirements and efficiency under several conditions, SFC reconfiguration methods face the challenges in meeting significant latency requirement of delay-sensitive applications while reaching the importance of energy saving on orchestration timespan. In this paper, we propose task management-aware SFC and orchestrating schemes, namely GNN-PPO. In this framework, we utilize the Graph Neural Network (GNN), which relies on the message-passing neural network (MPNN), to capture all the abstraction of physical resource nodes and link capabilities over MEC node states. In particularly, GNN is divided construction into two phrases: (1) GNN represents nodes for all the Mobile edge computing (MEC) nodes, which have a global view on resources of computation and communicational capabilities that could serve as carriers; (2) VNFs are transferred into graph networks by using feature-extraction MPNN to manage each VIM that seeks an optimal and reliable analysis of traffic fluctuations. Lastly, Deep Reinforcement Learning (DRL) is used to embrace the network determination in policy strategy, which utilizes a Proximal Policy Gradient (PPO). On the other hand, we propose a novel network architecture based on PPO to perform the design for the optimization of resource utilization and facilitate energy consumption on MEC servers under diverse setting scenarios, which enables continuous policy enforcement for our system. With the experimental results, we compare our proposed solution with reference schemes in terms of rewards with learning rate and batch size, average request acceptance, SFC success, packet delivery, throughput, and resource utilization ratio that confirm the scheme’s scalability and practical suitability for IoT network deployment. Full article

The article frames the co-design activities developed in Phase 3 of the DeinSite project, aimed at promoting product innovation in traditional craft districts through dialogue between young designers, artisans, museums, and SMEs in Southern Italy. It introduces the DeinSite Co-Design Framework, a replicable methodological model integrating three complementary types of co-design–Open, Participatory, and Prototype-Led Co-Design–that guide the entire design process, from field research to co-creation to digital experimentation. Within this framework, the article delves into the Manus Maris workshop, focused on cameo and coral, a key sector of Campania’s productive activity. The workshop interprets the marine environment as a creative, cultural, and productive ecosystem, inspiring new narratives and formal languages that combine artisanal memory and technological innovation. Through collaborative activities, physical–digital prototyping, and an intergenerational exchange between designers and craftsmen, Manus Maris has generated experimental jewellery collections reinterpreting the local tangible and intangible heritage. The findings highlight the framework’s potential as a tool for activating systemic innovation in Southern Italy’s artisanal districts, enhancing museums as cultural and production hubs and promoting new synergies between tradition and contemporary design. The proposed model is a transferable methodology, useful for regenerating “Handmade in Italy” supply chains and constructing territorial ecosystems oriented towards innovation, sustainability, and international competitiveness. Full article

The efficiency of scientific and technological achievement transformation is constrained by supply–demand matching challenges. Concurrently, Artificial Intelligence (AI) offers novel pathways for digital-intelligence service platforms to mitigate this challenge. To resolve AI investment decision problems of such platforms, this study constructs a bilateral matching model involving high-quality/low-quality technology providers and high-capability/low-capability technology seekers. Based on expected value theory and Stackelberg games, it derives optimal AI investment strategies for the Commercial Platform (platform’s expected revenue maximisation objective) and the Public Welfare Platform (social expected revenue maximisation objective). Findings indicate that higher AI investment contributes to a rise in the matching probability between high-quality providers and high-capability demanders. Owing to incomplete benefit internalization, platforms of different types show divergent willingness for AI investment. The AI investment level of the Commercial Platform is lower than that of the Public Welfare Platform, which results in losses of expected matching value. Furthermore, declines in AI technology costs and reduced external selection value of suppliers will drive platforms to raise their AI investment intensity. This research provides theoretical foundations for optimising AI strategies in online technology transfer service platforms and informing targeted government interventions. Full article

This study investigates digital leadership as a networked social process by analyzing how influential actors operating across professional and institutional domains construct leadership discourse and draw on transformational leadership (TFL) principles within Twitter (X) networks, with particular attention to the skill-transfer gaps that persist between formal academic preparation and workforce demands. Social Network Analysis (SNA) using the NodeXL program was used to examine the relational structure of that discourse across a dataset of 1186 Twitter accounts and 1362 relational ties. The analysis identified 27 prominent actors operating within a distinct community cluster whose discourse spanned politics, health, technology, media, and education, with thematically diverse but uneven engagement with leadership topics. Combining semantic cluster analyses, inductive thematic mapping, and a supplementary exploratory factor analysis (EFA), the study finds that the four TFL principles (individualized consideration, intellectual stimulation, inspirational motivation, and idealized influence) are unevenly represented in this discourse. The EFA condensed the co-occurrence structure into three platform-shaped factors, with the strongest support for individualized consideration and no coherent factor for idealized influence, indicating partial rather than comprehensive alignment with the four-dimensional TFL model. The findings position digital leadership as a relational and iterative social process, sustained through repeated interactions, endorsements, and positional recognition within platform-based publics that extend across academic, industry, and socio-political boundaries. The study highlights social media as a networked yet uneven environment for leadership development and the broader social negotiation of skill-transfer challenges across digital professional contexts. Full article

The construction industry is a major contributor to global greenhouse gas (GHG) emissions. Different strategies have been implemented to reduce the environmental impact of construction sites and create better city environments for construction workers and citizens. Electrification of construction machinery is one such measure and is rapidly evolving. However, existing literature has largely concentrated on either electrification of road vehicles or emission reduction via the electrification of a building’s operational energy use. This paper presents a systematic literature review on available publications focusing on the electrification of construction sites, identifying and analyzing the key drivers and barriers influencing this. In addition, it provides recommendations for better and effective electrification of construction sites. A total of 55 publications were analyzed to extract insights and organize findings into eight key themes: requirements, technology and market, economic, process and operations, infrastructure, knowledge and experience, environmental, and attitude. The findings indicate strong interconnections between the barriers and drivers to electrification of construction sites. Clear policy frameworks, strategic public procurement, knowledge sharing initiatives, and robust data systems emerged as critical enablers for scaling emission-free construction sites. The lessons learnt are largely drawn from Norwegian experiences but are highly transferable to other cities and regions and offer practical insights into policy design, procurement strategies, and collaborative models for actors interested in reducing GHG emissions and transition into electrification of construction sites. Full article

As tunnel engineering continues to advance toward deeper, longer, and more complex projects, the risks encountered during the construction phase have evolved into a combination of various disaster types and the accumulation of multiple contributing factors. Traditional empirical and semi-empirical risk management methods are increasingly revealing shortcomings in terms of timeliness, accuracy, and the ability to process multi-source data. In recent years, driven by advancements in computing power and sensor technology, artificial intelligence algorithms (AI algorithms) such as machine learning and deep learning have been rapidly adopted in tunnel construction risk management. This paper retrieved relevant literature from the Web of Science database covering the period from 2010 to 2025. After rigorous screening, 96 highly relevant papers were selected for bibliometric analysis. This paper systematically reviews research progress from two perspectives: algorithmic models and engineering applications. The review indicates that, in terms of algorithmic models, traditional machine learning, convolutional neural network, recurrent neural network, generative adversarial network, Transformer, and graph neural network constitute a multi-level technical framework encompassing feature representation, risk perception, and intelligent decision-making. In terms of applications, AI algorithms have been widely integrated into typical scenarios such as geological hazard identification and prediction, surrounding rock stability and deformation prediction, rock burst assessment and early warning, lining defect detection and structural safety assessment, construction-induced ground settlement prediction, and tunnel gas and fire hazard prediction, significantly enhancing risk identification and early warning capabilities. However, several challenges remain, including the scarcity of high-quality datasets, the prevalence of noisy, incomplete, and heterogeneous monitoring data, insufficient coupling between model interpretability and engineering mechanisms, limited cross-project transferability, and the lack of integrated management systems for multi-hazard lifecycle control. Based on this, this paper proposes future research directions in areas such as data infrastructure development, integration of mechanism constraints, and multi-hazard collaborative modeling, aiming to provide guidance for the further development of intelligent risk management in tunnel construction. Full article

Using a sample of 297 prefecture-level cities in China from 2010 to 2022 and drawing on green patent transfer data, this study constructs a directed weighted network and applies social network analysis, a modified gravity model, and quadratic assignment procedure (QAP) regression to examine the spatial structural evolution, node topology characteristics, and driving factors of China’s green technology transfer (GTT) network. The results show that: (1) From 2010 to 2022, the number of nodes grew from 249 to 292, network coverage increased from 83.8% to 98.3%, and the number of edges expanded by a factor of 14.47. Network density and average degree also rose markedly. The spatial structure evolved from an initially sparse and fragmented configuration into a polycentric complex network centered on the Beijing–Tianjin–Hebei region, the Yangtze River Delta, and the Chengdu–Chongqing economic circle. (2) In terms of node topology, the intermediary and control capacities of cities exhibit dynamic changes, with central and western cities gaining growing influence within the network. (3) Cohesive subgroup analysis identifies four functional blocks, revealing a multi-level technology spillover path of “core—secondary—regional—peripheral.” (4) QAP regression further identifies the digital economy, geographic location, high-speed rail mileage, industrial structure, and government environmental concern as key drivers of network formation and evolution. This study offers a new perspective on understanding cross-regional green technology transfer and provides theoretical grounding and policy references for promoting regional collaborative innovation and green low-carbon development. Full article

In view of the frequent occurrence of trapped annular pressure and the increasingly prominent risk of wellbore integrity under the periodic high-intensity injection and production conditions of gas storage wells, a trapped annular pressure prediction model suitable for deep gas storage wells is established based on the comprehensive heat transfer characteristics of the tubing string-cement sheath-formation. The calculation results of the model are in good agreement with field-measured pressure data, with a coincidence degree of about 95%. Based on the established model, the influence laws of four major factors, including tubing specification and dimension, thermophysical properties of annular fluid, casing material characteristics and daily gas production rate, on trapped annular pressure are systematically analyzed. Meanwhile, the pressure control effects of three measures, namely Annulus A pressure relief, application of insulated tubing and nitrogen injection into Annulus B, are quantitatively compared for the case well. The research results show that adopting tubing with larger outer diameter and thinner wall thickness, injecting fluid with lower thermal expansion coefficient or higher isothermal compressibility coefficient into the annulus and appropriately reducing daily gas production can effectively decrease trapped annular pressure. Among them, the influence of fluid properties on trapped annular pressure is far greater than that of pipe material parameters. Among the three pressure control measures, nitrogen injection into Annulus B presents the optimal pressure control effect; when the nitrogen volume accounts for approximately 3% of the total annular fluid volume, the trapped annular pressure is reduced by about 82%. The research findings provide a theoretical basis and technical guidance for the prediction and control of trapped annular pressure in gas storage wells. It is recommended to prioritize the nitrogen injection technology for Annulus B in the well construction stage, and realize pressure management for producing wells by combining Annulus A pressure relief and production regulation. Full article

To address the critical challenge of excessive junction temperature caused by ultra-high heat flux densities (>100 W/cm²) in deep-sea LED Fish-Attracting Lamp (FAL) arrays, this study proposes a hybrid thermal management scheme integrating interfacial micro-texturing, chimney-effect convection, and heat pipe phase-change heat transfer, achieving the unification of passive high-efficiency heat dissipation and pressure-resistant sealing. The FAL housing structure is reconfigured using topology optimization to construct chimney-effect enhanced flow channels integrated with heat pipe bundle arrays, thereby establishing efficient heat conduction pathways from the Phenolic Resin Substrate (PRS) to the structural periphery. Micro-Element Texture (MET) arrays are fabricated at the PRS thermal interface to enhance interfacial thermal conductance. Based on multi-physics coupled numerical simulation, a parametric mapping model correlating geometric topology with thermal performance is established through response interface methodology, enabling the parametric optimization of micro-texture configurations. A thermal interface performance testing platform is constructed to validate the accuracy and reliability of the numerical model. Experimental results demonstrate that the integrated heat pipe technology effectively suppresses LED junction temperature rise; moreover, groove-type MET arrays oriented perpendicular to the gravity direction not only significantly increase the effective heat dissipation area but also optimize the dynamic characteristics of natural convection. This proposed solution reduces the maximum operating temperature of deep-sea FALs by 6.70% compared with conventional structures, providing an effective engineering solution for thermal structural design of high-power illumination systems. Full article

The Architecture, Engineering, and Construction (AEC) industry has significant potential to improve productivity, quality, and sustainability of its projects through emerging digital technologies. Advances in technology and the complexity of what new graduates need to learn have resulted in persistent training gaps and have highlighted new needs to be addressed in education. One of the new needs is the level of learners’ awareness of new technologies and their adoption practices. This research examines how current education and training practices in the selected sample of the Australian AEC sector support or hinder the development of digital capabilities. The set of technologies considered in this study focuses on Artificial Intelligence (AI), Building Information Modelling (BIM), Digital Twins (DTs), Virtual and Augmented Reality (VR/AR), and the Internet of Things (IoT). A mixed-method design integrates a structured survey of industry professionals and students, along with semi-structured interviews of industry and academic stakeholders, to evaluate exposure, self-rated capability, training participation, organisational support, and perceptions of graduate preparedness. Findings show comparatively higher maturity in BIM, but limited capability in other technologies, inconsistent formal training, and barriers linked to time, cost, organisational priorities, and rapid technological change. Qualitative findings and interpretation of preparedness-related survey responses indicate that stakeholders place greater value on transferable, interdisciplinary digital competencies than on narrow tool-specific proficiency. The research delivers statistically robust findings and actionable recommendations that address the identified barriers and promote the development of a skilled workforce in the AEC industry. Full article

Traditional cryogenic air separation units are unsuitable for distributed, small-scale liquid oxygen production. Cryocooler-based liquefaction technology offers an alternative solution, featuring a large cooling capacity, high efficiency, a compact structure, and rapid start–stop capability. In this paper, an oxygen liquefaction system based on a high-capacity Stirling cryocooler was developed. Because the heat transfer performance of cryocoolers varies significantly across different temperature ranges, heat exchanger designs must be tailored to specific operating conditions. However, research on cold-end heat exchangers for large-capacity cryocoolers used in liquefaction systems remains limited. In the liquid oxygen temperature range, factors such as liquid film formation and incomplete condensation severely affect heat transfer performance and must be considered. In this paper, numerical simulations were performed to analyze the condensation behavior of oxygen, with particular attention paid to the matching between the heat exchange structure and the cooling capacity. Subsequently, a small-scale experimental system was constructed and tested. The successful operation of the experimental system validated the feasibility of the proposed heat exchanger design. Under the conditions of 300 K and an oxygen inlet gauge pressure of 0.45 MPa, the system achieved a liquefaction capacity of 7.4 L/h, corresponding to a cooling capacity of 787 W. The specific power consumption was 0.89 kW·h/kg, with a coefficient of performance (COP) of 0.116. This performance is competitive among small-scale cryocooler-based oxygen liquefaction systems. This study provides both theoretical and experimental support for further performance optimization and engineering application of such cryocoolers in liquid oxygen production. Full article

Lithium-ion batteries have become crucial energy carriers in multiple core fields owing to their excellent comprehensive performance. Nevertheless, as battery energy and power densities continue to rise and operating conditions grow increasingly complex, thermal safety issues have become increasingly prominent. Immersion liquid cooling technology has attracted widespread attention in academic and engineering fields for its outstanding heat transfer and temperature uniformity performance. As a core component of this technology, the selection of liquid coolants is of vital importance. Various coolants investigated in existing studies generally suffer from limitations to varying degrees. Against this backdrop, intrinsically safe fluorocarbon C₇H₄F₁₂O (3F-135) serves as an ideal liquid cooling medium for lithium-ion batteries, thanks to its high thermal stability, superior electrical insulation and environmental friendliness (zero ODP, extremely low GWP). However, its decomposition mechanism and reaction pathways under extreme thermal runaway conditions of batteries remain unclear. In this study, a tube furnace was adopted to simulate high-temperature environments induced by thermal runaway, and gas chromatography–mass spectrometry (GC-MS) was employed to analyze decomposition products and decomposition ratios of 3F-135. Subsequently, density functional theory (DFT) calculations were utilized to construct the pyrolysis reaction network of 3F-135. Ultimately, the dominant pyrolysis pathways in different temperature ranges were clarified, providing theoretical support for the application and selection of intrinsically safe liquid coolants. Full article

Multi-terminal voltage source converter-based high voltage direct current (VSC-MTDC) technology has become an efficient solution for grid integration of large-scale and long-distance offshore wind power. When onshore grid power fluctuations elevate the DC voltage of VSC-MTDC system, the surplus power causing the DC overvoltage issue can be effectively transferred through the power shifting method. To enhance the power shifting capability of receiving-end converters (RECs) and mitigate DC overvoltage, this paper proposes a coordinated power shifting strategy for VSC-MTDC based on the extended operation region. Firstly, the topology and control model of the VSC-MTDC system integrating offshore wind farms is established. Secondly, considering constraints containing apparent power, AC bus voltage, AC current, and voltage modulation ratio, the extended operation region model with regard to the overload capacity of REC is constructed. Furthermore, the coordinated active power shifting strategy for multiple converters is proposed to cope with onshore grid power fluctuations. Finally, simulation models of three-terminal and six-terminal VSC-MTDC systems are built using PSCAD V5 software. Simulation results show that the proposed strategy can exploit the system’s operational flexibility and reduce the risk of DC overvoltage, thus enhancing the disturbance immunity of VSC-MTDC system against onshore grid fluctuations. Full article

Recent technological advances and the reinvigoration of NASA’s Artemis program have increased the feasibility of lunar habitats and supporting infrastructure, necessitating the development of specialized foundation systems capable of maintaining stability under transferred structured loads. Site investigation techniques, including in situ testing, sampling, and geophysical mapping, must therefore be adapted for lunar conditions, while construction using regolith requires an improved understanding of lunar soil mechanics. Foundations must also endure extreme thermal fluctuations, reduced gravity, radiation exposure, micrometeoroid impacts, and lunar seismicity to ensure long-term performance. Consequently, enhanced knowledge of the monotonic and cyclic geotechnical behavior of lunar soils is essential. Owing to the limited availability of in situ testing opportunities and returned lunar materials, high-fidelity simulants that replicate regolith behavior are required for experimental studies. This research investigates the static behavior of several contemporary lunar simulants and compares their responses with terrestrial benchmark soils. The results indicate that the overall stress–strain trends of lunar simulants broadly resemble those of terrestrial soils; however, the particle morphology and distinctive mineralogical compositions, including basaltic and anorthositic constituents, yield higher values of certain geomechanical parameters. Comparison with terrestrial datasets further suggests that carefully selected benchmark soils may facilitate the development of a next generation of lunar simulants with improved fidelity to lunar regolith. Full article