Search Results (3,096)

Existing CO₂ leakage risk assessment frameworks for CO₂ capture, geological storage and utilization (CCUS) projects face limitations due to subjective biases and poor adaptability to long-term scale sequestration. This study proposed a dynamic risk assessment method for CO₂ leakage based on a timeliness analysis of different leakage paths and accurate time-dependent numerical simulations, and it was applied to the first CO₂ enhanced gas recovery (CCUS-EGR) pilot project of China in the Maokou carbonate gas reservoir in the Wolonghe gas field. A 3D geological model of the Maokou gas reservoir was first developed and validated. The CO₂ leakage risk under different scenarios including wellbore failure, caprock fracturing, and new fracture activation were evaluated. The dynamic CO₂ leakage risk of the CCUS-EGR project was then quantified using the developed method and numerical simulations. The results revealed that the CO₂ leakage risk was observed to be the most pronounced when the caprock integrity was damaged by faults or geologic activities. This was followed by leakage caused by wellbore failures. However, fracture activation in the reservoir plays a neglected role in CO₂ leakage. The CO₂ leakage risk and critical risk factors dynamically change with time. In the short term (at 5 years), the project has a low risk of CO₂ leakage, and well stability and existing faults are the major risk factors. In the long term (at 30 years), special attention should be paid to the high permeable area due to its high CO₂ leakage risk. Factors affecting the spatial distribution of CO₂, such as the reservoir permeability and porosity, alternately dominate the leakage risk. This study established a method bridging gaps in the ability to accurately predict long-term CO₂ leakage risks and provides a valuable reference for the security implementation of other similar CCUS-EGR projects. Full article

This paper introduces a methodology for forecasting spring hydrographs based on projections from regional climate models. The primary study objective was to evaluate how climate change may affect spring discharge. A statistical–analytical modeling approach was developed and applied to the Jósva spring catchment in the Aggtelek Karst region of Hungary. Historical data served to establish a regression relationship between rainfall and peak discharge. This approach is particularly useful for predicting discharge in cases where only historical rainfall data are available for calibration. Baseflow recession was analyzed using a two-component exponential model, with hydrograph decompositionand parameter optimization performed on the master recession curve. Future discharge time series were generated using rainfall data from two selected regional climate model scenarios. Both scenarios suggest a decline in baseflow discharge during different periods of the 21st century. The findings indicate that climate change is likely to intensify hydrological extremes in the coming decades, irrespective of whether moderate or high CO₂ emission scenarios unfold. Full article

The aim of this study is to compare the performance of the multi-layer and the single-layer CO₂ injection methods used in offshore carbon capture and storage (CCS) through TOUGH-FLAC numerical simulations. Four key indicators, namely CO₂ saturation, pore pressure, vertical displacement, and Coulomb Failure Stress (CFS), are employed as indices to assess the storage capacity of reservoirs and the mechanical stability of caprocks. Numerical simulation results show that the multi-layer injection method increases the CO₂ migration distance and reduces CFS values compared with the single-layer injection method. After 1 year of injection, the combined CO₂ migration distance across two aquifers in Case 3 is 610 m, which is greater than that obtained using single-layer injection in Cases 1 and 2 (350 m and 380 m, respectively). Additionally, deep saline aquifers demonstrate superior CO₂ storage capacity due to higher overburden pressure, which also reduces the risk of caprock failures. After 30 years of injection, in Cases 1 and 2, the maximum CFS values are 0.591 and 0.567, respectively, and the CO₂ migration distances are 2400 m and 2650 m, respectively. Overall, the findings of this study indicate that the multi-layer injection method, particularly in deep saline aquifers, provides a safer and more efficient CO₂ injection approach for offshore CCS projects. Full article

This study aims to develop, implement, and evaluate an Early Warning System (EWS) to alert communities and government agencies in KwaZulu-Natal, South Africa, about extreme weather events (EWEs) and related disease outbreaks. The project focuses on eThekwini and Ugu municipalities, using a participatory, co-creation approach with communities and health providers. A systematic review will be undertaken to understand the impact of climate change on disease outbreaks and design an EWS that integrates data from rural and urban healthcare and environmental contexts. It will assess disease burden at primary healthcare clinics, examine health needs and community experiences during EWEs, and evaluate health system resilience. The project will also evaluate the design, development, and performance of the EWS intervention, including its implementation costs. Ethical approval will be sought, and informed consent obtained from participants. Based on the findings, recommendations will be made to the Department of Health to enhance early warning systems and health system resilience in response to EWEs and disease outbreaks. Full article

To reduce CO₂ emissions, CO₂ geological storage is recognized as an effective approach to decrease atmospheric carbon concentration. Sequestration in deep saline aquifers has become a research focus. However, the physicochemical property changes in saline formations induced by CO₂ injection remain unclear, making it difficult to assess their CO₂ storage potential. This study focuses on saline aquifers within the Jurassic Badaowan formation (J₁b), Sangonghe formation (J₁s), and Cretaceous Tugulu Group (K₁tg) of the Baijiahai Uplift in the Junggar Basin. An integrated methodology combining laboratory experiments—including CO₂ static immersion tests, dynamic displacement tests, X-ray diffraction (XRD), mercury injection capillary pressure (MICP), nuclear magnetic resonance (NMR) measurements, and mechanical testing—with CMG-based numerical modeling was employed to analyze CO₂ storage mechanisms and evaluate storage potential. The results show that after CO₂ immersion, extensive dissolution of calcite in J₁s, clay swelling/cementation in J₁b, and extensive dissolution of calcite in K₁tg all lead to increased porosity and permeability, with the J₁b formation exhibiting superior CO₂ storage capacity, the highest MICP-derived porosity, and the greatest NMR-measured porosity among the three formations. Numerical simulations further confirmed J₁b’s leading sequestration volume. Based on integrated experimental and simulation results, the J₁b formation is identified as the optimal reservoir for CO₂ storage. However, to manage potential mechanical instability during real-world injection scenarios, injection pressures and rates should be carefully controlled and continuously monitored to avoid formation fracturing and ensure long-term storage security. This study provides a reference for implementing saline aquifer CCUS projects. Full article

Concrete is fundamental to modern construction, comprising 70% of all building materials and supporting an industry projected to reach $15 trillion by 2030. Predicting compressive strength—a key factor for structural safety and resource efficiency—remains a challenge, as conventional models often fail to capture the dynamic, time-dependent nature of strength development across mix compositions and curing intervals. This study proposes an integrated modeling framework using Markov Chain analysis and regression, validated on 135 samples from 27 mixtures with varying proportions of Portland Cement (PC), Fly Ash (FA), and Blast Furnace Slag (BFS) over curing periods from 3 to 180 days. The Markov Chain framework, integrated with regression analysis, models strength transitions across 10 states (9–42 MPa), with high accuracy (R² = 0.977, standard error = 3.27 MPa). Curing time (β = 0.079), PC proportion (β = 0.063), and BFS proportion (β = 0.051) are identified as key drivers, while higher FA content (β = 0.019) enhances long-term durability. Model validation using Coefficient of Variation (CoV = 15.57%) and mean absolute error confirms robust and consistent performance across mix designs. The framework supports tailored mix strategies—PC for early strength, BFS for durability, FA for sustainability—empowering engineers to optimize mix selection and curing strategies for efficient and durable concrete applications. Full article

Knowledge Graph Reasoning (KGR) aims to deduce missing or novel knowledge by learning structured information and semantic relationships within Knowledge Graphs (KGs). Despite significant advances achieved by deep neural networks in recent years, existing models typically extract non-linear representations from explicit features in a relatively simplistic manner and fail to fully exploit semantic heterogeneity of relation types and entity co-occurrence frequencies. Consequently, these models struggle to capture critical predictive cues embedded in various entities and relations. To address these limitations, this paper proposes a relation aware spectral decoupling attention network for KGR (RASD). First, a spectral decoupling attention network module projects joint embeddings of entities and relations into the frequency domain, extracting features across different frequency bands and adaptively allocating attention at the global level to model frequency specific information. Next, a relation-aware learning module employs relation aware filters and an augmentation mechanism to preserve distinct relational properties and suppress redundant features, thereby enhancing representation of heterogeneous relations. Experimental results demonstrate that RASD achieves significant and consistent improvements over multiple leading baseline models on link prediction tasks across five public benchmark datasets. Full article

The Yunyao Aerospace Constellation Program is the core project being developed by Yunyao Aerospace Technology Co., Ltd., Tianjin, China. It aims to provide scientific data for weather forecasting, as well as research on the ionosphere and neutral atmosphere. It is expected to launch 90 high time resolution weather satellites. Currently, the Yunyao space constellation provides nearly 16,000 BDS, GPS, GLONASS, and Galileo multi-system occultation profile products on a daily basis. This study initially calculates the precise orbits of Yunyao LEO satellites independently using each GNSS constellation, allowing the derivation of the neutral atmospheric refractive index profile. The precision of the orbit product was evaluated by comparing carrier-phase residuals (ranging from 1.48 cm to 1.68 cm) and overlapping orbits. Specifically, for GPS-based POD, the average 3D overlap accuracy was 4.93 cm, while for BDS-based POD, the average 3D overlap accuracy was 5.18 cm. Simultaneously, the global distribution, the local time distribution, and penetration depth of the constellation were statistically analyzed. BDS demonstrates superior performance with 21,093 daily occultation profiles, significantly exceeding GPS and GLONASS by 15.9% and 121%, respectively. Its detection capability is evidenced by 79.75% of profiles penetrating below a 2 km altitude, outperforming both GPS (78.79%) and GLONASS (71.75%) during the 7-day analysis period (DOY 169–175, 2023). The refractive index profile product was also compared with the ECWMF ERA5 product. At 35 km, the standard deviation of atmospheric refractivity for BDS remains below 1%, while for GPS and GLONASS it is found at around 1.5%. BDS also outperforms GPS and GLONASS in terms of the standard deviation in the atmospheric refractive index. These results indicate that Yunyao satellites can provide high-quality occultation product services, like for weather forecasting. With the successful establishment of the global BDS-3 network, the space signal accuracy has been significantly enhanced, with BDS-3 achieving a Signal-in-Space Ranging Error (SISRE) of 0.4 m, outperforming GPS (0.6 m) and GLONASS (1.7 m). This enables superior full-link occultation products for BDS. Full article

Despite a global trend toward socially engaged higher education, architectural pedagogy continues to grapple for a coherent approach that systematically and genuinely integrates socio-cultural dimensions into design studio teaching practices. Defined as the interwoven social, cultural, and political factors that shape the built environment, the socius is treated peripherally within architectural pedagogy, limiting students’ capacity to develop civic agency, spatial justice awareness, and critical reflexivity in navigating complex societal conditions. This article argues for a socius-centric reorientation of architectural pedagogy, postulating that socially engaged studio models, which include Community Design, Design–Build, and Live Project, must be conceptually integrated to fully harness their pedagogical merits. The article adopts two lines of inquiry: first, mapping the theoretical underpinnings of the socius across award-winning pedagogical innovations and Google Scholar citation patterns; and second, defining the core attributes of socially engaged pedagogical models through a bibliometric analysis of 87 seminal publications. Synthesising the outcomes of these inquiries, the study offers an advanced articulation of studio learning as a process of social construction, where architectural knowledge is co-produced through role exchange, iterative feedback, interdisciplinary dialogue, and emergent agency. Conclusions are drawn to offer pragmatic and theoretically grounded pathways to reshape studio learning as a site of civic transformation. Full article

The transition to sustainable energy systems in the agroindustrial sector requires rigorous methodologies that enable a comprehensive and quantitative assessment of the technical and economic viability and environmental impact of photovoltaic integration. This study develops and validates a hybrid multi-criteria methodology structured in three phases: (i) analytical modeling of the load profile and preliminary sizing, (ii) advanced energy simulation using PVsyst for operational optimization and validation against empirical data, and (iii) environmental assessment using life cycle analysis (LCA) under ISO 14040/44 standards. The methodology is applied to a Cuban agroindustrial plant with an annual electricity demand of 290,870 kWh, resulting in the design of a 200 kWp photovoltaic system capable of supplying 291,513 kWh/year, thereby achieving total coverage of the electricity demand. The economic analysis yields an LCOE of 0.064 USD/kWh and an NPV of USD 139,408, while the environmental component allows for a mitigation of 113 t CO₂-eq/year. The robustness of the model is validated by comparison with historical records, yielding an MBE of $-0.65\%$, an RMSE of $2.87\%$, an MAPE of $2.62\%$, and an R² of $0.98$. This comprehensive approach demonstrates its superiority over previous methodologies by effectively integrating the three pillars of sustainability in an agroindustrial context, thus offering a scientifically sound, replicable, and adaptable tool for decision-making in advanced energy projects. The results position this methodology as a benchmark for future research and applications in emerging production scales. Full article

In the context of advancing sustainable energy solutions, this paper provides a detailed modelling study of the process integration of biomass gasification to produce syngas and subsequent methanation for methane production. The process is assumed to take place in a circulating fluidised bed and three adiabatic fixed-bed reactors. To address the low H₂/CO ratio of syngas produced from biomass gasification using air, three pre-methanation scenarios were evaluated: water gas shift reaction (scenario 1), H₂ addition through Power-to-Gas (scenario 2), and splitting syngas into pure H₂ and CO and then recombining them in a 3:1 ratio (scenario 3). The findings reveal that each scenario presents a unique balance of efficiency, decarbonisation potential, and technological integration. Scenario 2 achieves the highest overall efficiency at 62%, highlighting the importance of integrating renewable electricity into the methane industry. Scenario 1, which incorporates WGS and CO₂ capture, offers an environmentally friendly solution with an overall efficiency of 59%. In contrast, Scenario 3, involving H₂/CO separation and recombination, achieves only 44.4% efficiency due to energy losses during separation, despite its operational simplicity. Methane yields were highest in Scenario 1, while Scenario 2 offers the most significant potential for integration with decarbonised power systems. The model was validated using published data and feedstock characteristics from experimental work and industrial projects. The results showed good agreement and supported the accuracy of the simulation in reflecting realistic biomass processing for methane production. Full article

Cross-modal semantic segmentation of individual tree LiDAR point clouds is critical for accurately characterizing tree attributes, quantifying ecological interactions, and estimating carbon storage. However, in forest environments, this task faces key challenges such as high annotation costs and poor cross-domain generalization. To address these issues, this study proposes a cross-modal semantic transfer framework tailored for individual tree point cloud segmentation in forested scenes. Leveraging co-registered UAV-acquired RGB imagery and LiDAR data, we construct a technical pipeline of “2D semantic inference—3D spatial mapping—cross-modal fusion” to enable annotation-free semantic parsing of 3D individual trees. Specifically, we first introduce a novel Multi-Source Feature Fusion Network (MSFFNet) to achieve accurate instance-level segmentation of individual trees in the 2D image domain. Subsequently, we develop a hierarchical two-stage registration strategy to effectively align dense matched point clouds (MPC) generated from UAV imagery with LiDAR point clouds. On this basis, we propose a probabilistic cross-modal semantic transfer model that builds a semantic probability field through multi-view projection and the expectation–maximization algorithm. By integrating geometric features and semantic confidence, the model establishes semantic correspondences between 2D pixels and 3D points, thereby achieving spatially consistent semantic label mapping. This facilitates the transfer of semantic annotations from the 2D image domain to the 3D point cloud domain. The proposed method is evaluated on two forest datasets. The results demonstrate that the proposed individual tree instance segmentation approach achieves the highest performance, with an IoU of 87.60%, compared to state-of-the-art methods such as Mask R-CNN, SOLOV2, and Mask2Former. Furthermore, the cross-modal semantic label transfer framework significantly outperforms existing mainstream methods in individual tree point cloud semantic segmentation across complex forest scenarios. Full article

In Italy, the management of built assets confiscated from organized crime groups is particularly relevant. Returning these assets to the community is becoming increasingly important for Italian municipalities, thanks to the many social benefits that can be generated (e.g., new spaces to provide community services, a visible and tangible symbol of legality, etc.). The process of redeveloping confiscated buildings, due to procedural complexity and a lack of resources, is currently characterized, on one hand, by a limited number of projects actually implemented compared to the potential of the total number of buildings available and, on the other hand, by the lengthy duration of the redevelopment process (12 years on average), which significantly increases the time it takes for the asset to return social value to the community. The objective of this research was to study, develop, and describe a mechanism that (i) enables an increase in the number of redevelopment actions of confiscated assets, (ii) accelerates the attribution of social value to these assets over time and (iii) extends the social impact of the requalification interventions that provide an opportunity for the professional training of students and the professional qualification of vulnerable people. There are two main tasks of the research shown in this article: (i) to highlight the main critical issues and needs in the management of confiscated assets by Italian municipalities, through a survey conducted among key informants; (ii) to build and test an innovative ‘win–win model’ for the requalification process of confiscated buildings, aimed at overcoming obstacles and anticipating the delivery of social benefits to a large group of stakeholders, including vulnerable people, tested in a pilot project. This “win–win model” combines building requalification and training through the activation of a “construction site school”. All original contributions are derived from the research “Co-WIN”, funded by the “Polisocial Awards 2021”, which developed methods, strategies, and tools capable of reducing social imbalances, with an equity and sustainability perspective. The results illustrated the drivers and challenges for the renovation and reuse of confiscated built assets; the necessary changes in documents and procedures to activate and replicate the “Co-WIN model”; and a training program for the construction-site school based on the social categories involved. Finally, the discussion highlights the network and the mutual benefits for stakeholders, focusing on the social relevance and social impact achievable through applying the Co-WIN model to the requalification process of confiscated buildings. Full article

With the increasingly frequent exploitation and transportation of offshore oil, the threat of oil spill accidents to the marine ecological environment has become increasingly serious. It is urgent to develop efficient and reliable oil film monitoring technology. Based on the marine radar oil spill data, an innovative OAT-NGN hybrid strategy segmentation algorithm was proposed. By integrating the local feature learning ability of a Neural Gas Network (NGN) and the global search strategy of the Oat optimization algorithm (OAT), the proposed method effectively meets the challenges of traditional oil film segmentation methods in complex sea conditions. Firstly, the raw data of marine radar were preprocessed by using co-frequency interference and speckle noise suppression. Then, the OAT algorithm guided the updating of neural weights in the NGN on a global scale for the exploration of a more optimal solution space during the optimization process. Finally, the oil spill segmentation results were projected to the polar coordinate system through post-processing technology. The experimental results showed that this method effectively balanced the problem of false detection and missing detection. Compared with existing methods, OAT-NGN shown stronger adaptability in complex scenarios. In order to improve the segmentation performance, its innovative dynamic weight adjustment mechanism and spatial constraint design provide a new technical path. Full article

Understanding structural regime shifts in crypto asset markets is vital for early detection of systemic risk. This study applies the Generalized Sup Augmented Dickey–Fuller (GSADF) test to daily high-frequency price data of five major crypto assets—BTC, ETH, SOL, AAVE, and BCH—from 2023 to 2025. The results reveal asset-specific structural breaks: BTC and BCH aligned with macroeconomic shocks, while DeFi tokens (e.g., AAVE, SOL) exhibited fragmented, project-driven shifts. The S&P 500 index, in contrast, showed no persistent regime shifts, indicating greater structural stability. To examine inter-asset linkages, we construct co-occurrence matrices based on GSADF breakpoints. These reveal strong co-explosivity between BTC and other assets, and unexpectedly weak synchronization between ETH and AAVE, underscoring the sectoral idiosyncrasies of DeFi tokens. While the GSADF test remains central to our analysis, we also employ a Markov Switching Model (MSM) as a secondary tool to capture short-term volatility clustering. Together, these methods provide a layered view of long- and short-term market dynamics. This study highlights crypto markets’ structural heterogeneity and proposes scalable computational frameworks for real-time monitoring of explosive behavior. Full article