Search Results (785)

Online monitoring technology for transformers is a crucial safeguard for power supply, and diagnosing dissolved gases in 10 kV distribution transformer oil is considered an effective criterion for transformer fault detection. Using density functional theory, this paper simulated the adsorption process of five dissolved gases in a 10 kV distribution transformer on Os-modified C₃N nanosheets, and by calculating the band structure, differential charge density, density of states, and work function, the related sensing and adsorption mechanisms were revealed. The results indicate that Os modification significantly enhances the gas-sensing response of C₃N nanosheets, particularly for capturing C₂H₂ and CO, which is primarily attributed to the d-orbital electrons of the doped metal. The adsorption capability of Os-modified C₃N nanosheets of dissolved gases follows the order C₂H₂ > CO > H₂ > CO₂ > CH₄, with the adsorption type being physico-chemical adsorption, and these findings provide a theoretical foundation for developing high-sensitivity gas sensors for detecting dissolved gases in a 10 kV distribution transformer. Full article

Collaborative clustering is an ensemble technique that enhances clustering performance by simultaneously and synergistically processing multiple data dimensions or tasks. This is an active research area in artificial intelligence, machine learning, and data mining. A common approach to co-clustering is based on non-negative matrix factorization (NMF). While widely used, NMF-based co-clustering is limited by its bilinear nature and fails to capture the multilinear structure of data. With the objective of enhancing the effectiveness of non-negative Tucker decomposition (NTD) in image clustering tasks, in this paper, we propose a dual-graph constrained sparse non-negative Tucker decomposition NTD (GDSNTD) model for co-clustering. It integrates graph regularization, the Frobenius norm, and an $l_1$ norm constraint to simultaneously optimize the objective function. The GDSNTD mode, featuring graph regularization on both factor matrices, more effectively discovers meaningful latent structures in high-order data. The addition of the $l_1$ regularization constraint on the factor matrices may help identify the most critical original features, and the use of the Frobenius norm may produce a more highly stable and accurate solution to the optimization problem. Then, the convergence of the proposed method is proven, and the detailed derivation is provided. Finally, experimental results on public datasets demonstrate that the proposed model outperforms state-of-the-art methods in image clustering, achieving superior scores in accuracy and Normalized Mutual Information. Full article

Efficient coalbed methane (CBM) recovery combined with carbon dioxide (CO₂) sequestration is a promising strategy for sustainable energy production and greenhouse gas mitigation. However, the molecular mechanisms controlling pressure-dependent CH₄ displacement by CO₂ in coal nanopores remain insufficiently understood. In this study, molecular dynamics simulations were conducted to investigate CO₂-driven CH₄ recovery in a slit-pore coal model under driving pressures of 15, 20, and 25 Mpa. The simulations quantitatively captured the competitive adsorption, diffusion, and migration behaviors of CH₄, CO₂, and water, providing insights into how pressure influences enhanced coalbed methane (ECBM) recovery at the nanoscale. The results show that as the pressure increases from 15 to 25 Mpa, the mean residence time of CH₄ on the coal surface decreases from 0.0104 ns to 0.0087 ns (a 16% reduction), reflecting accelerated molecular mobility. The CH₄–CO₂ radial distribution function peak height rises from 2.20 to 3.67, indicating strengthened competitive adsorption and interaction between the two gases. Correspondingly, the number of CO₂ molecules entering the CH₄ region grows from 214 to 268, demonstrating higher invasion efficiency at elevated pressures. These quantitative findings illustrate a clear shift from capillary-controlled desorption at low pressure to pressure-driven convection at higher pressures. The results provide molecular-level evidence for optimizing CO₂ injection pressure to improve CBM recovery efficiency and CO₂ storage capacity. Full article

Background/Objectives: Ovarian cancer is a heterogeneous malignancy with molecular subtypes that strongly influence prognosis and therapy. High-dimensional mRNA data can capture this biological diversity, but its complexity and noise limit robust subtype characterization. Furthermore, current classification approaches often fail to reflect subtype-specific transcriptional programs, underscoring the need for computational strategies that reduce dimensionality and identify discriminative molecular features. Methods: We designed a multi-stage feature selection and network analysis framework tailored for high-dimensional transcriptomic data. Starting with ~65,000 mRNA features, we applied unsupervised variance-based filtering and correlation pruning to eliminate low-information genes and reduce redundancy. The applied supervised Select-K Best filtering further refined the feature space. To enhance robustness, we implemented a hybrid selection strategy combining recursive feature elimination (RFE) with random forests and LASSO regression to identify discriminative mRNA features. Finally, these features were then used to construct a gene co-expression similarity network. Results: This pipeline reduced approximately 65,000 gene features to a subset of 83 discriminative transcripts, which were then used for network construction to reveal subtype-specific biology. The analysis identified four distinct groups. One group exhibited classical high-grade serous features defined by TP53 mutations and homologous recombination deficiency, while another was enriched for PI3K/AKT and ARID1A-associated signaling consistent with clear cell and endometrioid-like biology. A third group displayed drug resistance-associated transcriptional programs with receptor tyrosine kinase activation, and the fourth demonstrated a hybrid profile bridging serous and endometrioid expression modules. Conclusions: This pilot study shows that combining unsupervised and supervised feature selection with network modeling enables robust stratification of ovarian cancer subtypes. Full article

Blue hydrogen technology, generated from natural gas through carbon capture and storage (CCS) technology, is a promising solution to mitigate greenhouse gas emissions and meet the growing demand for clean energy. To improve the sustainability of blue hydrogen, it is crucial to explore alternative feedstocks, production methods, and improve the efficiency and economics of carbon capture, storage, and utilization strategies. Two established technologies for hydrogen synthesis are Steam Methane Reforming (SMR) and Autothermal Reforming (ATR). The choice between SMR and ATR depends on project specifics, including the infrastructure, energy availability, environmental goals, and economic considerations. ATR-based facilities typically generate hydrogen at a lower cost than SMR-based facilities, except in cases where electricity prices are elevated or the facility has reduced capacity. Both SMR and ATR are methods used for hydrogen production from methane, but ATR offers an advantage in minimizing CO₂ emissions per unit of hydrogen generated due to its enhanced energy efficiency and unique process characteristics. ATR provides enhanced utility and flexibility regarding energy sources due to its autothermal characteristics, potentially facilitating integration with renewable energy sources. However, SMR is easier to run but may lack flexibility compared to ATR, necessitating meticulous management. Capital expenditures for SMR and ATR hydrogen reactors are similar at the lower end of the capacity spectrum, but when plant capacity exceeds this threshold, the capital costs of SMR-based hydrogen production surpass those of ATR-based facilities. The less profitably scaled-up SMR relative to the ATR reactor contributes to the cost disparity. Additionally, individual train capacity constraints for SMR, CO₂ removal units, and PSA units increase the expenses of the SMR-based hydrogen facility significantly. Full article

Monitoring indoor air quality is vital for health, as CO₂ is a major pollutant. An automated system that accurately forecasts CO₂ levels can optimize HVAC management, preventing sudden increases and reducing energy waste while maintaining occupant comfort. Traditionally, such systems require extensive datasets collected over months to train algorithms, making them computational expensive and inefficient. To address this limitation, an adaptive IoT-based platform has been developed, leveraging a limited set of recent data to forecast CO₂ trends. Tested in a real-world setting, the system analyzed parameters such as physical activity, temperature, humidity, and CO₂ to ensure accurate predictions. Data acquisition was performed using the Smartex WWS T-shirt for physical activity data and the UPSense UPAI3-CPVTHA environmental sensor for other measurements. The chosen sensor devices are wireless and minimally invasive, while data processing was carried out on a low-power embedded PC. The proposed forecasting model adopts an innovative approach. After a 5-day training period, a Generative Adversarial Network enhances the dataset by simulating a 10-day training period. The model utilizes a Generative Adversarial Network with a Long Short-Term Memory network as the generator to predict future CO₂ values based on historical data, while the discriminator, also a Long Short-Term Memory network, distinguishes between actual and generated CO₂ values. This approach, based on Conditional Generative Adversarial Networks, effectively captures data distributions, enabling more accurate multi-step probabilistic forecasts. In this way, the framework maintains a Root Mean Square Error of approximately 8 ppm, matching the performance of our previous approach, while reducing the need for real training data from 10 to just 5 days. Furthermore, it achieves accuracy comparable to other state-of-the-art methods that typically requires weeks or even months of training. This advancement significantly enhances computational efficiency and reduces data requirements for model training, improving the system’s practicality for real-world applications. Full article

Rapid detection and identification of airborne bacteria are critical for safeguarding human health, yet current technologies remain inadequate. To address this gap, we developed a multifunctional biochip that synergistically integrated a heptagonal micropillar array with a silver nanostructure–polydopamine–co–chitosan (AgNS@PDA–co–CS) composite gel to achieve highly efficient sampling, capture, enrichment, and in situ SERS detection of airborne bacteria. The integrated micropillar array increased the capture efficiency of S. aureus in aerosols from 11.4% (with a flat chip) to 86.3%, owing to its high specific surface area and its ability to generate chaotic vortices that promote bacterial impaction. Subsequent functionalization with the AgNS@PDA–co–CS gel improved the capture efficiency further to >99.9%, due to the synergistic effect of the gel’s adhesive properties and the abundant capture sites provided by the nanostructure, which collectively ensure robust bacterial retention. The incorporated AgNS also served as SERS-active sites, enabling direct identification of captured S. aureus at concentrations as low as 10⁵ CFU m⁻³ after 20 min of sampling. Furthermore, the platform successfully distinguished among three common bacterial species—S. aureus, E. coli, and Bacillus cereus—based on their SERS spectral profiles combined with principal component analysis (PCA). This work presents a synergistic strategy for simultaneous bacterial sampling, capture, enrichment, and detection, offering a promising platform for rapid airborne pathogen monitoring. Full article

CO₂ mixing is one of the implementation techniques of carbon capture utilization and storage (CCUS) in concrete to tailor the performance of cementitious materials and reduce the carbon footprint. Therefore, increasing the total amount of carbon capture capacity of cement-based materials has become the key point of recent research. This study investigates the influence of Cal-Al layered double oxide (LDO) and mixing parameters on key properties of cement pastes under CO₂ mixing, including mechanical performance, microstructure, phase assemblages, and carbon capture capacity. A particular emphasis was placed on evaluating a novel bubble mixing technique, which was developed to enhance the conventional atmospheric mixing process. The results indicate that, compared to the traditional method, bubble mixing reduced the mixing intensity by 10% but increased the effective carbon sequestration capacity by 0.68%. The observed strength reduction after bubble mixing was consistent with higher water adsorption, indicating the formation of a more porous structure. A higher carbon capture efficiency was achieved with bubble mixing compared to atmospheric mixing, as revealed by further investigation. Crucially, the introduction of LDO significantly enhanced the carbon capture capacity, with improvements of up to 34% compared to the groups without LDO. This highlights the substantial potential of LDO in reducing the carbon footprint of cementitious materials and offers a novel insight for enhancing CO₂ mixing in cement. Full article

With the goal of achieving carbon neutrality, promoting the clean and low-carbon transformation of energy assets, as exemplified by existing thermal power units, has emerged as a pivotal challenge in addressing climate change and achieving sustainable development. Arrangements and technologies such as the electricity–carbon–certificate multi-market, microgrids with direct green power connections, and carbon capture and storage (CCS) retrofitting provide favorable conditions for facing the aforementioned challenge. Based on an analysis of how liquid-storage CCS retrofitting affects the flexibility of thermal power units, this manuscript proposes a bi-level optimization model and solution method for capacity allocation for grid-connected microgrids, while considering CCS retrofits under multi-markets. This approach overcomes two key deficiencies in the existing research: first, neglecting the relationship between electricity–carbon coupling characteristics and unit flexibility and its potential impacts, and second, the significant deviation of scenarios constructed from real policy and market environments, which limits its ability to provide timely and relevant references. A case study in southern China demonstrates that first, multi-market implementation significantly boosts microgrids’ investment in and absolute consumption of renewable energy. However, its effect on reducing carbon emissions is limited, and renewable power curtailment may surge, potentially deviating from the original intent of carbon neutrality policies. In this case study, renewable energy installed capacity and consumption rose by 17.09% and 22.64%, respectively, while net carbon emissions decreased by only 3.32%, and curtailed power nearly doubled. Second, introducing liquid-storage CCS, which decouples the CO₂ absorption and desorption processes, into the capacity allocation significantly enhances microgrid flexibility, markedly reduces the risk of overcapacity in renewable energy units, and enhances investment efficiency. In this case study, following CCS retrofits, renewable energy unit installed capacity decreased by 24%, while consumption dropped by only 7.28%, utilization hours increased by 22%, and the curtailment declined by 78.05%. Third, although CCS retrofitting can significantly reduce microgrid carbon emissions, factors such as current carbon prices, technological efficiency, and economic characteristics hinder large-scale adoption. In this case study, under multi-markets, CCS retrofitting reduced net carbon emissions by 86.16%, but the annualized total cost rose by 3.68%. Finally, based on the aforementioned findings, this manuscript discusses implications for microgrid development decision making, CCS industrialization, and market mechanisms from the perspectives of research directions, policy formulation, and practical work. Full article

While Artificial Intelligence (AI) promises significant enhancements for Clinical Decision Support Systems (CDSSs), the opacity of many AI models remains a major barrier to clinical adoption, primarily due to interpretability and trust challenges. Explainable AI (XAI) seeks to bridge this gap by making model reasoning understandable to clinicians, but technical XAI solutions have too often failed to address real-world clinician needs, workflow integration, and usability concerns. This study synthesizes persistent challenges in applying XAI to CDSS—including mismatched explanation methods, suboptimal interface designs, and insufficient evaluation practices—and proposes a structured, user-centered framework to guide more effective and trustworthy XAI-CDSS development. Drawing on a comprehensive literature review, we detail a three-phase framework encompassing user-centered XAI method selection, interface co-design, and iterative evaluation and refinement. We demonstrate its application through a retrospective case study analysis of a published XAI-CDSS for sepsis care. Our synthesis highlights the importance of aligning XAI with clinical workflows, supporting calibrated trust, and deploying robust evaluation methodologies that capture real-world clinician–AI interaction patterns, such as negotiation. The case analysis shows how the framework can systematically identify and address user-centric gaps, leading to better workflow integration, tailored explanations, and more usable interfaces. We conclude that achieving trustworthy and clinically useful XAI-CDSS requires a fundamentally user-centered approach; our framework offers actionable guidance for creating explainable, usable, and trusted AI systems in healthcare. Full article

Declining fertility and population aging intensify labor shortages, making women’s reemployment after caregiving a policy priority. Using Taiwan as a case study, this study develops a real-time public opinion analysis system to complement delayed surveys and capture emerging barriers in labor-market reintegration. Drawing on 2022–2024 social media posts, the system applies sentiment co.mputing, clustering, and algorithmic attention to map four phases: withdrawal, intention, search, and reintegration. Findings show that younger women stress flexibility and childcare, while older returnees prioritize skill renewal and confidence rebuilding; sectoral variation supports life-cycle and clockspeed theories. Policy recommendations emphasize subsidies, training, quotas, and street-level implementation. Beyond technical contributions, the study embeds digital transformation (DT) into labor governance, showing a shift from as-is retrospective surveys to to-be-real-time monitoring. This transformation enhances policy agility, inclusiveness, and alignment with citizens’ lived experiences. The system thus functions as both a tool for rapid intervention and a DT-driven theoretical lens extending reemployment scholarship, offering transferable insights for aging societies. Full article

Global warming has become a major challenge facing human society, with carbon dioxide (CO₂) emissions being its primary driver. Carbon capture, utilization, and storage (CCUS) represents a promising technology for mitigating CO₂ emissions from industrial and energy sectors. However, challenges such as high energy consumption, lengthy construction cycles, significant costs, and inadequate policy and market mechanisms hinder the widespread adoption of CCUS technology. This paper reviews the potential, applications, and related policies of CCUS technology, highlighting current research progress and obstacles. First, it provides a comprehensive overview of the CCUS technology framework, detailing developments and engineering applications in capture, transport, enhanced oil recovery, and storage technologies. Through global case studies and analysis, the review also examines advancements in CCUS infrastructure and technology strategies, along with operational experiences from major global projects. Second, it delves into the mechanisms, applications, and challenges of CCUS-related technologies, which are crucial for advancing their industrial deployment. It also outlines policy measures adopted by different countries to support CCUS technology development and large-scale deployment. Finally, it projects future directions for CCUS technology and policy development. Full article

Traditional multi-criteria decision-making (MCDM) methods face problems in sustainable product design evaluation, including weak semantic expression, single weight modeling, and insufficient ranking robustness. To address these issues, this paper proposes an evaluation framework based on Trapezoidal Intuitionistic Fuzzy (TrIF), named TrIF-DEC, which integrates Decision-Making Trial and Evaluation Laboratory (DEMATEL), Entropy, and Combined Compromise Solution (CoCoSo). Firstly, design criteria across four dimensions—social, economic, technological, and environmental—are identified based on sustainability considerations. Then, TrIF is used to capture the fuzziness and hesitation in expert judgments. The DEMATEL and Entropy methods are combined to extract causal relationships between criteria and quantify data variation, enabling the collaborative weighting of subjective and objective factors. Finally, multi-strategy integrated ranking is performed through TrIF-CoCoSo to enhance decision stability. An empirical case study on nursing bed design demonstrates the effectiveness of the proposed framework. Results demonstrate that TrIF-DEC can systematically integrate uncertainty information with multidimensional sustainability goals, providing reliable support for complex product design evaluation. Full article

Land use classification based on multi-modal data fusion has gained significant attention due to its potential to capture the complex characteristics of urban environments. However, effectively extracting and integrating discriminative features derived from heterogeneous geospatial data remain challenging. This study proposes an ensemble learning framework for land use classification by fusing cross-modal deep features from both physical and socioeconomic perspectives. Specifically, the framework utilizes the Masked Autoencoder (MAE) to extract global spatial dependencies from remote sensing imagery and applies long short-term memory (LSTM) networks to model spatial distribution patterns of points of interest (POIs) based on type co-occurrence. Furthermore, we employ inter-modal contrastive learning to enhance the representation of physical and socioeconomic features. To verify the superiority of the ensemble learning framework, we apply it to map the land use distribution of Bejing. By coupling various physical and socioeconomic features, the framework achieves an average accuracy of 84.33 %, surpassing several comparative baseline methods. Furthermore, the framework demonstrates comparable performance when applied to a Shenzhen dataset, confirming its robustness and generalizability. The findings highlight the importance of fully extracting and effectively integrating multi-source deep features in land use classification, providing a robust solution for urban planning and sustainable development. Full article

Monitoring ecosystem dynamics in arid regions requires robust indicators that can capture spatial heterogeneity and diverse ecological drivers. In this study, we introduce and evaluate two novel ecological indices: the Arid-region Remote Sensing Ecological Index (ARSEI), specifically designed for desert environments, and the Composite Remote Sensing Ecological Index (CoRSEI), which integrates both desert and non-desert systems. These indices are compared with the traditional Remote Sensing Ecological Index (RSEI) in the Tarim River Basin from 2000 to 2023. Principal component analysis (PCA) revealed that RSEI maintained the highest structural compactness (average PCA1 = 87.49%). In contrast, ARSEI (average PCA1 = 78.62%) enhanced sensitivity to albedo and vegetation (NDVI) in arid environments. Spearman correlation analysis further demonstrated that ARSEI was more strongly correlated with NDVI (ρ = 0.49) and precipitation (ρ = 0.62) than RSEI, confirming its improved responsiveness under water-limited conditions. CoRSEI exhibited higher internal consistency and spatial adaptability (mean values ranging from 0.45 to 0.56), with slight ecological improvements observed between 2000 and 2023. Ecological drivers varied across habitat types. In desert areas, evapotranspiration, precipitation, and soil moisture were the main determinants of ecological status, showing high coupling and synchrony. In non-desert regions, soil moisture and precipitation remained dominant, but vegetation indices and disturbance factors (e.g., fire density) exerted stronger long-term influences. Partial dependence analyses further confirmed nonlinear, region-specific responses, such as the threshold effects of precipitation on vegetation growth. Overall, our findings highlight the importance of differentiated ecological modeling. ARSEI enhances sensitivity in desert ecosystems, whereas CoRSEI captures landscape-scale variability across desert and non-desert regions. Both indices contribute to more accurate long-term ecological assessments in hyper-arid environments. Full article