Search Results (3,374)

The stability of shield tunnel segmental linings is highly sensitive to the lateral pressure coefficient, especially under weak, heterogeneous, and variable geological conditions. However, the mechanical behavior of steel plate-reinforced linings under such conditions remains insufficiently characterized. This study aims to investigate the effects of varying lateral pressures on the structural performance of reinforced tunnel linings. To achieve this, a custom-designed full-circumference loading and unloading self-balancing apparatus was developed for scaled-model testing of shield tunnels. The experimental methodology allowed for precise control of loading paths, enabling the simulation of realistic ground stress states and the assessment of internal force distribution, joint response, and load transfer mechanisms during the elastic stage of the structure. Results reveal that increased lateral pressure enhances the stiffness and bearing capacity of the reinforced lining. The presence and orientation of segment joints, as well as the bonding performance between epoxy resin and expansion bolts at the reinforcement interface, significantly influence stress redistribution in steel plate-reinforced zones. These findings not only deepen the understanding of tunnel behavior in complex geological environments but also offer practical guidance for optimizing reinforcement design and improving the durability and safety of shield tunnels. Full article

Plant growth-promoting rhizobacteria (PGPRs) are well known for their capacity to enhance the growth and survival of in vitro-grown plants. However, their effect on Nardostachys jatamansi (D. Don) DC., a critically endangered medicinal plant in the Indian Himalayan Region, is still unknown. In this study, a simple, reproducible protocol for in vitro propagation of N. jatamansi was established using shoot tip explants, cultured on Murashige and Skoog (MS) medium supplemented with different plant growth regulators, including N6-benzylaminopurine, thidiazuron (TDZ), and naphthalene acetic acid (NAA). MS media supplemented with 2.0 μM TDZ and 0.5 µM NAA created a significant shoot induction with an average of 6.2 shoots per explant. These aseptically excised individual shoots produced roots on MS medium supplemented with Indole Butyric Acid or NAA within 14 days of the transfer. The PGPR, viz., Bacillus subtilis and Pseudomonas corrugata, inoculation resulted in improved growth, higher chlorophyll content, and survival of in vitro-rooted plants (94.6%) after transfer to the soil. Moreover, the PGPRs depicted a two-fold higher total phenolics (45.87 mg GAE/g DW) in plants. These results clearly demonstrate the beneficial effects of P. corrugata and B. subtilis on the growth, survival, and phytochemical content of N. jatamansi. Full article

The diffusion coefficient (DC) of CO₂ in brine is a key parameter in geological carbon sequestration and CO₂-Enhanced Oil Recovery (EOR), as it governs mass transfer efficiency and storage capacity. This study employs three machine learning (ML) models—Random Forest (RF), Gradient Boost Regressor (GBR), and Extreme Gradient Boosting (XGBoost)—to predict DC based on pressure, temperature, and salinity. The dataset, comprising 176 data points, spans pressures from 0.10 to 30.00 MPa, temperatures from 286.15 to 398.00 K, salinities from 0.00 to 6.76 mol/L, and DC values from 0.13 to 4.50 × 10⁻⁹ m²/s. The data was split into 80% for training and 20% for testing to ensure reliable model evaluation. Model performance was assessed using R², RMSE, and MAE. The RF model demonstrated the best performance, with an R² of 0.95, an RMSE of 0.03, and an MAE of 0.11 on the test set, indicating high predictive accuracy and generalization capability. In comparison, GBR achieved an R² of 0.925, and XGBoost achieved an R² of 0.91 on the test set. Feature importance analysis consistently identified temperature as the most influential factor, followed by salinity and pressure. This study highlights the potential of ML models for predicting CO₂ diffusion in brine, providing a robust, data-driven framework for optimizing CO₂-EOR processes and carbon storage strategies. The findings underscore the critical role of temperature in diffusion behavior, offering valuable insights for future modeling and operational applications. Full article

Permanent magnetic couplings provide critical advantages for deep-sea systems through static-sealed, contactless power transmission. However, conventional metallic isolation sleeves incur significant eddy current losses, limiting efficiency and high-speed operation. Limited torque capacities fail to meet the operational demands of harsh marine environments. This study presents a novel permanent magnet coupling featuring a ceramic isolation sleeve engineered for deep-sea cryogenic ammonia submersible pumps. The ceramic sleeve eliminates eddy current losses and provides exceptional corrosion resistance in acidic/alkaline environments. To withstand 3.5 MPa hydrostatic pressure, a 6-mm-thick sleeve necessitates a 10 mm operational air gap, challenging magnetic circuit efficiency. To address this limitation, an improved 3D magnetic equivalent circuit (MEC) model was developed that explicitly accounts for flux leakage and axial end-effects, enabling the accurate characterization of large air gap fields. Leveraging this model, a Taguchi method-based optimization framework was implemented by balancing key parameters to maximize the torque density. This co-design strategy achieved a 21% increase in torque density, enabling higher torque transfer per unit volume. Experimental validation demonstrated a maximum torque of 920 Nm, with stable performance under simulated deep-sea conditions. This design establishes a new paradigm for high-power leak-free transmission in corrosive, high-pressure marine environments, advancing applications from deep-sea propulsion to offshore energy systems. Full article

Understanding groundwater quality in karst environments is essential, particularly in semi-arid regions where water resources are highly vulnerable to both climatic variability and anthropogenic pressures. The Ghar Boumaaza karst aquifer, located in the semi-arid Tlemcen Mountains of Algeria, represents a critical yet understudied water resource increasingly threatened by climate change and human activity. This study integrates hydrochemical analysis, multivariate statistical techniques, and predictive modeling to assess groundwater quality and characterize the relationship between total dissolved solids (TDSs) and discharge (Q). An analysis of 66 water samples revealed that 96.97% belonged to a Ca²⁺–HCO₃⁻ facies, reflecting carbonate rock dissolution, while 3% exhibited a Cl⁻–HCO₃⁻ facies associated with agricultural contamination. A principal component analysis identified carbonate weathering (40.35%) and agricultural leaching (18.67%) as the dominant drivers of mineralization. A third-degree polynomial regression model (R² = 0.953) effectively captured the nonlinear relationship between TDSs and flow, demonstrating strong predictive capacity. Independent validation (R² = 0.954) confirmed the model’s robustness and reliability. This study provides the first integrated hydrogeochemical assessment of the Ghar Boumaaza system in decades and offers a transferable methodological framework for managing vulnerable karst aquifers under similar climatic and anthropogenic conditions. Full article

Planning conservation for multiple species while accounting for habitat availability and connectivity under uncertain land-use changes presents a major challenge. This study proposes a protocol to identify strategic conservation areas by assessing the functional connectivity of key bird species under future land-use scenarios in the Chaco Serrano of Córdoba, Argentina. We modeled three land-use scenarios for 2050: business as usual, sustainability, and intensification. Using the Equivalent Connected Area index, we evaluated functional connectivity for Chlorostilbon lucidus, Polioptila dumicola, Dryocopus schulzii, Milvago chimango, and Saltator aurantiirostris for 1989, 2019, and 2050, incorporating information about habitat specialization and dispersal capacity to reflect differences in ecological responses. All species showed declining connectivity from 1989 to 2019, with further losses expected under future scenarios. Connectivity declines varied by species and were not always proportional to habitat loss, highlighting the complex relationship between land-use change and functional connectivity. Surprisingly, the sustainability scenario led to the greatest losses in connectivity, emphasizing that habitat preservation alone does not ensure connectivity. Using the Integral Connectivity Index, we identified habitat patches critical for maintaining connectivity, particularly those vulnerable under the business as usual scenario. With a spatial prioritization analysis we identified priority conservation areas to support future landscape connectivity. These findings underscore the importance of multispecies, connectivity-based planning and offer a transferable framework applicable to other regions. Full article

Although Brassica rapa (B. rapa) is vital in agricultural production and vulnerable to the pathogen Plasmodiophora, the intracellular water–nutrient metabolism and immunoregulation of Plasmodiophora infection in B. rapa leaves remain unclear. This study aimed to analyze the responsive mechanisms of Plasmodiophora-infected B. rapa using rapid detection technology. Six soil groups planted with Yangtze No. 5 B. rapa were inoculated with varying Plasmodiophora concentrations (from 0 to 10 × 10⁹ spores/mL). The results showed that at the highest infection concentration (PWB5, 10 × 10⁹ spores/mL) of B. rapa leaves, the plant electrophysiological parameters showed the intracellular water-holding capacity (IWHC), the intracellular water use efficiency (IWUE), and the intracellular water translocation rate (IWTR) declined by 41.99–68.86%. The unit for translocation of nutrients (UNF) increased by 52.83%, whereas the nutrient translocation rate (NTR), the nutrient translocation capacity (NTC), the nutrient active translocation (NAT) value, and the nutrient active translocation capacity (NAC) decreased by 52.40–77.68%. The cellular energy metabolism decreased with worsening Plasmodiophora infection, in which the units for cellular energy metabolism (∆G_E) and cellular energy metabolism (∆G) of the leaves decreased by 44.21% and 78.14% in PWB5, respectively. Typically, based on distribution of B-type dielectric substance transfer percentage (BPn), we found PWB4 (8 × 10⁹ spores/mL) was the maximal immune response concentration, as evidenced by a maximal BPn_R (B-type dielectric substance transfer percentage based on resistance), with increasing lignin and cork deposition to enhance immunity, and a minimum BPn_Xc (B-type dielectric substance transfer percentage based on capacitive reactance), with a decreasing quantity of surface proteins in the B. rapa leaves. This study suggests plant electrophysiological parameters could characterize intracellular water–nutrient metabolism and immunoregulation of B. rapa leaves under various Plasmodiophora infection concentrations, offering a dynamic detection method for agricultural disease management. Full article

The excellent moisture transport properties of yarns play a crucial role in improving the liquid moisture transfer behavior within textiles and maintaining their thermal-wet comfort. However, the current research on the moisture management performance of fabrics made from yarns with excellent liquid transport properties primarily compares the wicking results, without considering the varying requirements of testing conditions due to differences in human sweating rates during daily activities. Moreover, the understanding of moisture transport mechanisms in yarns within fabrics under different testing conditions remains insufficient. In this study, two types of twisted combination yarns, composed of hydrophobic profiled polyester filaments and hydrophilic spun yarns to form a hydrophobic-hydrophilic gradient along the axial direction of the yarn, were developed and compared with profiled polyester filaments to understand the liquid migration behaviors in the knitted fabrics formed by these yarns. Results showed that hydrophobic profiled polyester filament yarn demonstrated superior liquid transport performance with infinite saturated liquid supply (vertical wicking test). In contrast, the twisted combination yarns exhibited better moisture diffusion properties under limited liquid droplet supply conditions (droplet diffusion test and moisture management test). These contradictory findings indicated that the amount of liquid moisture supply in testing conditions significantly affected the moisture transport performance of yarns within fabrics. It was revealed that the liquid moisture in the twisted combination yarns migrated through capillary wicking for moisture transfer. Under an infinite saturated liquid supply condition, the higher the content of hydrophilic fibers in the spun yarns, the greater the amount of moisture transferred, demonstrating an excellent liquid transport performance. Under the limited liquid droplet supply conditions, both the volume of liquid water and the moisture absorption capacity of the yarn jointly influence internal moisture migration within the yarn. It provided a theoretical reference for testing the internal moisture wicking performance of fabrics under different states of human sweating. Full article

Energy storage technology is crucial for addressing the intermittency of renewable energy sources and plays a key role in power systems and electronic devices. In the field of energy storage systems, multivalent vanadium-based oxides have attracted widespread attention. Among these, vanadium dioxide (VO₂) is distinguished by its key advantages, including high theoretical capacity, low cost, and strong structural designability. The diverse crystalline structures and plentiful natural reserves of VO₂ offer a favorable foundation for facilitating charge transfer and regulating storage behavior during energy storage processes. This mini review provides an overview of the latest progress in VO₂-based materials for energy storage applications, specifically highlighting their roles in lithium-ion batteries, zinc-ion batteries, photoassisted batteries, and supercapacitors. Particular attention is given to their electrochemical properties, structural integrity, and prospects for development. Additionally, it explores future development directions to offer theoretical insights and strategic guidance for ongoing research and industrial application of VO₂. Full article

With the rapid development of smart devices and the Internet of Things (IoT), the explosive growth of data has placed increasingly higher demands on real-time processing and intelligent decision making. Cloud-edge collaborative computing has emerged as a mainstream architecture to address these challenges. However, in sky-ground integrated systems, the limited computing capacity of edge devices and the inconsistency between cloud-side fusion results and edge-side detection outputs significantly undermine the reliability of edge inference. To overcome these issues, this paper proposes a cloud-edge collaborative model adaptation framework that integrates deep reinforcement learning via Deep Q-Networks (DQN) with local feature transfer. The framework enables category-level dynamic decision making, allowing for selective migration of classification head parameters to achieve on-demand adaptive optimization of the edge model and enhance consistency between cloud and edge results. Extensive experiments conducted on a large-scale multi-view remote sensing aircraft detection dataset demonstrate that the proposed method significantly improves cloud-edge consistency. The detection consistency rate reaches 90%, with some scenarios approaching 100%. Ablation studies further validate the necessity of the DQN-based decision strategy, which clearly outperforms static heuristics. In the model adaptation comparison, the proposed method improves the detection precision of the A321 category from 70.30% to 71.00% and the average precision (AP) from 53.66% to 53.71%. For the A330 category, the precision increases from 32.26% to 39.62%, indicating strong adaptability across different target types. This study offers a novel and effective solution for cloud-edge model adaptation under resource-constrained conditions, enhancing both the consistency of cloud-edge fusion and the robustness of edge-side intelligent inference. Full article

The worldwide need for energy as well as environmental challenges have promoted the creation of sustainable power solutions. The combination of different working fluids is used for an organic Rankine cycle-powered vapor compression cycle (ORC-VCC) to deliver cooling applications. The selection of an appropriate working fluid significantly impacts system performance, efficiency, and environmental impact. The research evaluates possible working fluids to optimize the ORC-VCC system. Firstly, Artificial Neural Network (ANN)-derived models are used for exergy destruction ( E d t o t ) and heat exchanger total heat transfer capacity ( U A t o t ). Later on, multi-objective optimization was carried out using the acquired models for E d t o t and U A t o t using the Genetic Algorithm (GA) followed by the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The optimization results showcase Decane ORC-R600a VCC as the best candidate for the ORC-VCC system; the values of E d t o t and U A t o t were found to be 24.50 kW and 6.71 kW/K, respectively. The research data show how viable it is to implement biogas-driven ORC-VCC systems when providing air conditioning capabilities. Full article

Disability Certification is an instrument that can contribute to the identification of persons with disabilities who are potential beneficiaries of social protection programmes. In Colombia, the disability certification process was changed in 2020 to include a human rights perspective. However, little information exists on how far the process aligns with the recommendations made by the Convention on the Rights of Persons with Disabilities or to what extent it provides useful information for the operation of the country’s social protection system. This study aims to analyse how the changes implemented in 2020 have contributed to the identification and determination of persons with disabilities in social protection programmes in Colombia. This research uses an analytical-descriptive design, in which legal documents related to the social protection programme and disability certification were analysed. In addition, we conducted semi-structured interviews in Colombia with people with disabilities, professionals conducting the certification, and with key stakeholders, and we analysed the information using thematic analysis. Colombia changed its certification process in 2020, moving from a capacity-to-work assessment to a process of assessing functioning capacity and participation limitations following the International Classification of Functional Disability and Health. However, the new certification process has not provided an instrument to contribute to determining persons with disabilities who should be prioritised or receive social benefits in Colombia. The certification does not establish the support needs of people with disabilities, does not recommend reasonable adjustments to support their participation in society, and has not been used systematically in social protection programmes. Finally, individuals with disabilities commented that the disability certification is a process that does not open up opportunities to receive social benefits and social transfers, and in most cases, it is an expensive and lengthy process. Disability certification in Colombia is not an instrument that determines the needs of people with disabilities, nor the reasonable accommodations that they require in order to participate in society. Full article

Nanoimprint lithography (NIL) has emerged as a promising sub-10 nm patterning at low cost; yet, robust process control remains difficult because of time-consuming physics-based simulators and labeled SEM data scarcity. We propose a data-efficient, two-stage deep-learning framework here that directly reconstructs post-imprint SEM images from binary design layouts and delivers calibrated pixel-by-pixel uncertainty simultaneously. First, a shallow U-Net is trained on conformalized quantile regression (CQR) to output 90% prediction intervals with statistically guaranteed coverage. Moreover, per-level errors on a small calibration dataset are designed to drive an outlier-weighted and encoder-frozen transfer fine-tuning phase that refines only the decoder, with its capacity explicitly focused on regions of spatial uncertainty. On independent test layouts, our proposed fine-tuned model significantly reduces the mean absolute error (MAE) from 0.0365 to 0.0255 and raises the coverage from 0.904 to 0.926, while cutting the labeled data and GPU time by 80% and 72%, respectively. The resultant uncertainty maps highlight spatial regions associated with error hotspots and support defect-aware optical proximity correction (OPC) with fewer guard-band iterations. Extending the current perspective beyond OPC, the innovatively model-agnostic and modular design of the pipeline here allows flexible integration into other critical stages of the semiconductor manufacturing workflow, such as imprinting, etching, and inspection. In these stages, such predictions are critical for achieving higher precision, efficiency, and overall process robustness in semiconductor manufacturing, which is the ultimate motivation of this study. Full article

Research on ecosystem carbon sequestration services and ecological compensation is crucial for advancing carbon neutrality. As a public good, ecosystem carbon sequestration services inherently lead to externalities. Therefore, it is essential to consider externalities in the flow of sequestration services. However, few studies have examined intra- and inter-regional ecosystem carbon sequestration flows, making regional ecosystem carbon sequestration flows less comprehensive. Against this background, the research objectives of this paper are as follows. The flow of carbon sequestration services between Henan Province and out-of-province regions is studied. In addition, this study clarifies the beneficiary and supply areas of carbon sink services in Henan Province and the neighboring regions at the prefecture-level city scale to obtain a more systematic, comprehensive, and actual flow of carbon sequestration services for scientific and effective eco-compensation and to promote regional synergistic emission reductions. The research methodologies used in this paper are as follows. First, this study adopts a meta-coupling framework, designating Henan Province as the focal system, the Central Urban Agglomeration as the adjacent system, and eight surrounding provinces as remote systems. Regional carbon sequestration was assessed using net primary productivity (NEP), while carbon emissions were evaluated based on per capita carbon emissions and population density. A carbon balance analysis integrated carbon sequestration and emissions. Hotspot analysis identified areas of carbon sequestration service supply and associated benefits. Ecological radiation force formulas were used to quantify service flows, and compensation values were estimated considering the government’s payment capacity and willingness. A three-dimensional evaluation system—incorporating technology, talent, and fiscal capacity—was developed to propose a diversified ecological compensation scheme by comparing supply and beneficiary areas. By modeling the ecosystem carbon sequestration service flow, the main results of this paper are as follows: (1) Within Henan Province, Luoyang and Nanyang provided 521,300 tons and 515,600 tons of carbon sinks to eight cities (e.g., Jiaozuo, Zhengzhou, and Kaifeng), warranting an ecological compensation of CNY 262.817 million and CNY 263.259 million, respectively. (2) Henan exported 3.0739 million tons of carbon sinks to external provinces, corresponding to a compensation value of CNY 1756.079 million. Conversely, regions such as Changzhi, Xiangyang, and Jinzhong contributed 657,200 tons of carbon sinks to Henan, requiring a compensation of CNY 189.921 million. (3) Henan thus achieved a net ecological compensation of CNY 1566.158 million through carbon sink flows. (4) In addition to monetary compensation, beneficiary areas may also contribute through technology transfer, financial investment, and talent support. The findings support the following conclusions: (1) it is necessary to consider the externalities of ecosystem services, and (2) the meta-coupling framework enables a comprehensive assessment of carbon sequestration service flows, providing actionable insights for improving ecosystem governance in Henan Province and comparable regions. Full article

Terahertz (THz) communications and simultaneous wireless information and power transfer (SWIPT) hold the potential to energize battery-less Internet-of-Things (IoT) devices while enabling multi-gigabit data transmission. However, severe path loss, blockages, and rectifier nonlinearity significantly hinder both throughput and harvested energy. Additionally, high-power THz beams pose safety concerns by potentially exceeding specific absorption rate (SAR) limits. We propose a sensing-adaptive power-focusing (APF) framework in which a reconfigurable intelligent surface (RIS) embeds low-rate THz sensors. Real-time backscatter measurements construct a spatial map used for the joint optimisation of (i) RIS phase configurations, (ii) multi-tone SWIPT waveforms, and (iii) nonlinear power-splitting ratios. A weighted MMSE inner loop maximizes the data rate, while an outer alternating optimisation applies semidefinite relaxation to enforce passive-element constraints and SAR compliance. Full-stack simulations at 0.3 THz with 20 GHz bandwidth and up to 256 RIS elements show that APF (i) improves the rate–energy Pareto frontier by 30–75% over recent adaptive baselines; (ii) achieves a 150% gain in harvested energy and a 440 Mbps peak per-user rate; (iii) reduces energy-efficiency variance by half while maintaining a Jain fairness index of 0.999;; and (iv) caps SAR at 1.6 W/kg, which is 20% below the IEEE C95.1 safety threshold. The algorithm converges in seven iterations and executes within <3 ms on a Cortex-A78 processor, ensuring compliance with real-time 6G control budgets. The proposed architecture supports sustainable THz-powered networks for smart factories, digital-twin logistics, wire-free extended reality (XR), and low-maintenance structural health monitors, combining high-capacity communication, safe wireless power transfer, and carbon-aware operation for future 6G cyber–physical systems. Full article