Search Results (830)

RNA origami enables genetically encoded, single-stranded RNA nanostructures that can self-assemble through co-transcriptional folding and are increasingly deployed as scaffolds for biosensing, synthetic biology, and nanomedicine. A recurring practical bottleneck is scalable, solution-phase readout of whether a designed scaffold has reached its intended base-paired architecture, whether it undergoes slow maturation or kinetic trapping, and how its stability is distributed across motifs. Here, we adapt microfluidic modulation spectroscopy (MMS) as a label-free structural biosensor for RNA folding by exploiting the rich 1760–1600 cm⁻¹ vibrational fingerprints of RNA bases and base pairs. MMS alternates between sample and composition-matched buffer measurements in a microfluidic transmission cell to automatically subtract the solvent background, enabling high-quality spectral measurement from microliter volumes under native solution conditions. Using a six-helix-bundle-with-clasp (6HBC) RNA origami as a model, we established an analysis workflow (baselined second derivative and constrained deconvolution) to quantify paired versus unpaired populations. Thermal ramping resolves multiple unfolding events and yields an unfolding barcode that differs between young and mature ensembles. Importantly, MMS tracks post-transcriptional maturation from a kinetically trapped young conformer toward a more compact, base-paired mature state, consistent with prior cryo-EM/SAXS observations for 6HBC RNA origami. Together, these results position MMS as a rapid, automated, and scalable complement to high-resolution structure determination for engineering dynamic RNA origami biosensors. Full article

Deep learning-based crop mapping from hyperspectral satellite data offers immense potential for capturing subtle phenological differences, yet leveraging sparse time series remains a major methodological challenge. This study evaluates the ability of the EnMAP sensor to identify nine major crop types in the intensive agricultural landscape of Southeastern Hungary. We utilized a limited time series (November, March, August) to benchmark two modeling strategies: a single-date dual-stream spatial–spectral 2D-CNN (DSS-2D) and a multi-temporal 3D-SE-ResNet. Model performance was assessed using parcel-level spatial cross-validation to ensure realistic accuracy estimates and reduce spatial autocorrelation bias. The results demonstrate that the DSS-2D model achieved superior single-date accuracy (OA > 97%), significantly outperforming pixel-based baselines. Furthermore, the multi-temporal 3D-SE-ResNet achieved a robust seasonal accuracy of 92.9%, effectively compensating for temporal sparsity by exploiting the deep spectral information of the SWIR domain. This study confirms that treating hyperspectral data as a 3D volume enables the extraction of phenological traits even from limited observations. These findings provide a strong proof-of-concept for the operational feasibility of future missions such as Copernicus CHIME for continental-scale food security monitoring. Full article

Investigations into the production of gas hydrates from marine sediments have demonstrated that commercial viability necessitates a daily gas production rate of 130,000 to 200,000 m³. However, the second-round trial production in the South China Sea yielded only 28,700 m³/day, falling short of the rule-of-thumb for economic feasibility. Given the coexistence of natural gas hydrates (NGHs) and shallow gas in the subsurface reservoirs of the South China Sea, a co-production strategy (simultaneously exploiting NGHs and shallow gas) was proposed to reduce costs and enhance production efficiency. In this study, a large-scale, three-dimensional, multi-phase, and multi-component model was established based on the NGHs–shallow gas symbiotic system in the Qiongdongnan Basin. A dual horizontal well configuration was designed to extract NGHs from the hydrate-bearing layer and natural gas from the underlying shallow gas layer. Co-production via dual horizontal wells expanded the hydrate dissociation zone from the near-wellbore region to deeper strata, particularly enhanced the dissociation of NGHs in the region between the two horizontal wells. By the 10th year of simulation, the peak and cumulative volume rate of CH₄ released from hydrate dissociation increased to 3.52 and 1.45 times under the co-production scenario, resulting in a 2.4-fold improvement in NGH recovery efficiency. Sensitivity analyses of bottom hole pressure and length of the horizontal intervals revealed that reducing bottom hole pressure significantly improved the daily and accumulative gas production from hydrate-bearing reservoirs. The length of horizontal intervals emerged as a critical factor influencing the dissociation of NGHs, whereas it had negligible impact on gas production from shallow gas reservoir with satisfied permeability. This study provides insights into optimizing the development of marine hydrate resources via integrated exploitation strategies. Full article

In this study, shale samples with diverse lithofacies from the Lower Silurian Longmaxi Formation in the Fuling Field were investigated to evaluate the variations in pore characteristics and methane adsorption capacity (MAC) of different shale lithofacies. A set of experiments were performed, such as total organic carbon (TOC) content, X-ray diffraction (XRD), field emission–scanning electron microscopy (FE-SEM), low-pressure gas (CO₂/N₂) adsorption, and high-pressure methane adsorption. Combined with TOC content and mineral composition, three types of shale lithofacies were identified, including organic-rich (OR) argillaceous-rich siliceous (S-3) shale lithofacies, organic-moderate (OM) argillaceous/siliceous mixed (M-2) shale lithofacies, and organic-lean (OL) siliceous-rich argillaceous (CM-1) shale lithofacies. Through detailed comparative analyses, we found that OR S-3 shales possess the maximum TOC content, the most developed heterogeneous organic micro-mesopores, the largest pore volume (PV), and the highest pore surface area (PSA); consequently, they display the strongest MAC. Conversely, OL CM-1 shales have the lowest TOC content and the highest clay content, and thus the smallest PSA and the poorest methane adsorption performance. In conclusion, considering the excellent gas storage potential, sustained shale gas production, and brittle response to hydraulic fracturing, OR S-3 shales are superior to shale gas exploration and exploitation compared with OM M-2 and OL CM-1 shales. Full article

Heat creep is a critical failure mechanism in fused filament fabrication (FFF) extrusion systems, arising from insufficient thermal isolation between the hot end and cold end. It causes premature polymer softening, extrusion instability, and nozzle clogging, especially when active cooling is reduced or lost. This study experimentally evaluates passive cooling strategies for mitigating heat creep in consumer-class printers by exploiting ambient thermal stratification within the build volume. Vertical air-temperature gradients above heated build plates were measured for enclosed, semi-enclosed, and open-frame architectures, revealing pronounced stratification. Cold-end temperatures were then quantified for a stock extruder under forced and natural convection while printing polylactic acid (PLA) and acrylonitrile butadiene styrene (ABS). Finally, a modified cold-end using a heat pipe to relocate heat rejection to an elevated heat sink was tested under identical conditions, assuming fan failure. Elevated heat-rejection locations experienced lower ambient temperatures and improved natural-convection heat transfer. Relative to the stock configuration, the augmented design reduced cold-end temperatures and improved thermal stability during representative printing cycles without continuous active cooling—the improvement percent is ~8%. The results demonstrate that coupling heat-pipe conduction with environmental thermal gradients can mitigate heat creep and improve extruder reliability with lower energy demand. Full article

In the late high water cut stage of medium- and high-permeability sandstone reservoirs, remaining oil becomes highly dispersed, and rational optimization of the water injection rate is crucial for further recovery improvement. However, a quantitative relationship between pore-scale remaining oil mobilization and macroscopic displacement efficiency is still lacking, limiting mechanistic guidance for field-scale rate optimization and water cut control. To address this issue, variable-rate waterflooding experiments were conducted on medium- and high-permeability sandstone cores to establish the relationships among displacement rate, injected pore volume multiples, and oil displacement efficiency and to identify the optimal rate. In addition, an X-CT-based heterogeneous pore-scale geometric model and a series of idealized pore-throat models were used for oil–water two-phase flow simulations. Combined with an orthogonal experimental design, the effects of displacement rate, wettability, oil viscosity, interfacial tension, and pore structure parameters on remaining oil mobilization and displacement efficiency were systematically evaluated. The results indicate an optimal displacement rate of 2–3 mL/min. At low rates, remaining oil cannot be effectively mobilized, whereas excessively high rates intensify fingering and channeling along high-capacity pathways, reducing the final displacement efficiency. Simulations further show that a moderate rate increase markedly lowers remaining oil saturation, while overly high rates induce severe channeling. Orthogonal analysis reveals that displacement rate and wettability are the dominant factors, with normalized weights of 46.3% and 30.5%, respectively. Larger pore diameters, smaller pore-throat ratios, and higher coordination numbers reduce mobilization pressure and remaining oil saturation. This work elucidates pore-scale remaining oil mobilization kinetics under variable-rate waterflooding and provides a quantitative microscopic basis for rate optimization and fine-scale remaining oil exploitation in mature waterfloods. Full article

The rapid proliferation of the Internet of Things (IoT) systems has resulted in large volumes of heterogeneous data that are often difficult to access and exploit due to limited interoperability and complex application programming interfaces. Data spaces address these challenges by providing governed environments for secure and semantically interoperable data sharing, commonly relying on standardized interfaces such as the ETSI NGSI-LD API. While powerful, these interfaces are primarily designed for machine-to-machine interaction and remain difficult to use directly by human operators. In this paper, we propose an architecture that enables natural-language access to IoT data stored in a data space by integrating Large Language Models (LLMs) with the Model Context Protocol (MCP). Experimental results using fastMCP and OpenAI API to access a FIWARE-based data space demonstrate that our solution offers accuracy even for prompts that require advanced reasoning. Full article

The integration of unmanned aerial vehicles (UAVs), autonomous vehicles, and advanced satellite systems in sixth-generation (6G) networks is poised to redefine next-generation communications as well as next-generation intelligent transportation systems. This paper examines the convergence of UAVs, CubeSats, and terrestrial infrastructures that comprise the framework of Space–Aerial–Ground Integrated Networks (SAGINs) as vital enablers of the International Mobile Telecommunications (IMT)-2030 standards. This paper examines the role of UAVs in providing flexible and quickly deployable airborne connectivity. It also discusses how CubeSats enhance global coverage through low-latency relaying and resilient backhaul links from low Earth orbit (LEO). Additionally, the paper highlights how terrestrial systems contribute high-capacity, densely concentrated communication layers that support various end-user applications. By examining their interoperability and coordinated resource allocation, the paper underscores that the seamless interaction of SAGIN nodes is essential for achieving the ultra-reliable, intelligent, and pervasive communication capabilities envisioned by IMT-2030. As 6G aims for ultra-low latency, high reliability, and massive connectivity, UAVs and CubeSats emerge as key enablers for extending coverage and capacity, particularly in remote and dense urban regions. Furthermore, the role of large language models (LLMs) is explored for intelligent network management and real-time data optimization, while quantum communication is analyzed for ensuring security and minimizing latency. The integration of LLMs into quantum-enhanced edge intelligence for SAGINs represents an emerging research frontier for adaptive, high-throughput, and context-aware decision-making. By exploiting quantum-assisted parallelism and entanglement-based optimization, LLMs enhance the processing efficiency of multimodal data across space, aerial, and terrestrial nodes. This paper further investigates distributed quantum inference and multimodal sensor data fusion to enable resilient, self-optimizing communication systems comprising a high volume of data traffic, which is a critical bottleneck in the global connectivity transition. LLMs are envisioned as cognitive control centers capable of generating semantic representations for mission-critical communications that enhance energy efficiency, reliability, and adaptive learning at the edge. The findings of the survey reveal that quantum-enhanced LLMs overcome challenges pertaining to bandwidth allocation, dynamic routing, and interoperability in existing classical communication systems. Overall, quantum-empowered LLMs significantly assist intelligent, autonomous, and immersive communications in SAGIN, while enabling secure, privacy-preserving communication. Full article

With the increasing global energy demand, natural gas hydrates have become a focus of research and development. The South China Sea deepwater area has abundant natural gas hydrate resources, but its low permeability limits the commercialization process. This paper explores how to enhance gas production from natural gas hydrate reservoirs through a combination of fracturing technology and depressurization using numerical simulations. Numerical experiments were conducted under various well types and fracture configurations to evaluate their effects on cumulative gas production. The fracturing layer was optimized for different well types. We employed the embedded discrete fracture model (EDFM) to characterize the fracture structures in the reservoir and coupled it with a conventional hydrate numerical simulator to simulate different fracture morphologies. The results show that fractures in the three-phase layer provide the most significant production enhancement among all tested layers. Fractures within the three-phase layer deliver the largest production gain among all layers tested. By comparing the development effects of different well types, it is found that the combination of horizontal wells and hydraulic fracturing can effectively improve the recovery of hydrates compared with single well types and traditional exploitation methods. In particular, horizontal wells with stimulated reservoir volume (SRV) yield a big rise in gas production compared with the single-fracture model under identical conditions. Fractures in the three-phase layer shows the most significant improvement in production. Horizontal wells under the three-phase layer achieve about an 88.26% increase in production compared with the single-fracture simulation under the same conditions. Full article

High-resolution biofabrication requires precise microscale deposition, yet drop-on-demand (DOD) inkjet bioprinting is constrained by a narrow printable viscosity window. Many biocompatible hydrogel precursors display high zero-shear viscosity and strong shear-thinning, so stable droplet ejection typically requires dilution or reformulation that can compromise the biochemical microenvironment. We present a transient shear-enabled jetting method that exploits intrinsic shear-thinning by using a high-frequency electromagnetic microvalve to deliver short, high-pressure pulses. The resulting localized shear dynamically lowers apparent viscosity in the nozzle region and promotes controlled nucleation, ligament formation, necking, and pinch-off. A coupled, rheology-informed modeling framework (axisymmetric transient CFD, valve dynamics, and electromagnetic FEM) links actuation parameters to droplet volume and stability and guides hardware optimization. Experiments with 2.5% (w/v) sodium alginate validate stable droplet generation and tunable droplet size via stroke length and driving conditions. These results define a practical process window for high-resolution droplet printing of high-viscosity shear-thinning hydrogel inks. Full article

Laser Metal Deposition (LMD) is an advanced Additive Manufacturing (AM) technology widely used for metal component fabrication, cladding, and repair. Despite its potential, issues such as geometrical inaccuracies and deposition flaws can significantly affect part quality and process efficiency. Existing optical monitoring approaches mainly focus on geometric features and generally do not provide real-time estimates of deposition efficiency, which is critical for both product performance and resource utilization. Furthermore, evaluating deposition efficiency in industrial settings is often time-consuming and difficult to implement. This preliminary study introduces an innovative in-process methodology for assessing deposition efficiency during multi-track deposition. The approach exploits end-track scan data acquired by a laser line scanning system to estimate the deposited volume and the corresponding deposition efficiency for each track. A validation test on a two-layer sample demonstrates the capability of the method to detect defects induced by partially clogged and non-clogged nozzle conditions. Comparison with metallographic measurements shows an average deviation of 4.3%. By enabling timely identification of powder feeding anomalies and supporting improved powder utilization, the proposed methodology contributes to waste reduction, enhanced process stability, and more sustainable industrial implementation of LMD. Full article

Pore structure and fractal dimension analysis of nephrite deposits are essential for assessing potential quality, conducting investigations, and exploiting jade resources. This research explored nephrite (tremolite) jade from Tonggou in the Luanchuan Group, utilizing techniques such as scanning electron microscopy (SEM), X-ray diffraction (XRD), and low-temperature nitrogen adsorption (LT-N₂GA) to illustrate the pore structure of the jade deposit and to examine its developmental features, complexity, and implications for jade quality assessment. The findings revealed that the Tonggou nephrite jade deposit comprises three varieties of micropores. The nitrogen adsorption curve was similar to type IV, featuring hysteresis loops that were mainly classified as H₂ and H₃, suggesting a predominantly mesoporous nature. The fractal dimensions (D_F1 and D_F2), determined using the FHH model, averaged 2.474 and 2.572, respectively. This implies that the pore surface of the Tonggou jade deposit is irregular, the pore structure is intricate, and displays a pronounced heterogeneity. In the Tonggou deposit, the specific surface area (SSA) and pore volume (PV) show moderate positive and negative correlations with antigorite and calcite, respectively. Tremolite exhibits a strong negative correlation with SSA. The fractal dimension reveals weak, moderate, and strong negative correlations with SSA, PV, and average pore size (APS), respectively. As the content of siliceous minerals increases, the fractal dimension gradually increases. Conversely, a rise in carbonate mineral content results in a gradual decrease in the fractal dimension. Full article

Characterization of shale pore architecture forms the scientific basis for effective shale gas exploitation. Deep LMX FM shale from the Luzhou area was analyzed using SANS, LTPA, XRD, and SEM. This study quantitatively characterized the pore structure, focusing on closed-pore development and connectivity, and explored lithological controls. Pore-size distribution shows that micropores and small mesopores dominate the pore volume, with an average median pore diameter of 5.17 nm. Closed pores are abundant, indicated by a high average closed-pore ratio of 28.98%, reflecting generally poor connectivity. Pores smaller than 5 nm contribute 88.12% of the total SSA. Both pore volume and SSA correlate positively with TOC. In organic-rich and moderately organic-rich siliceous shales, these parameters also correlate positively with quartz content. In contrast, for organic-rich mixed shales, they correlate positively with clay mineral content. Among the lithofacies, organic-rich siliceous shale possesses relatively larger pore volume and SSA, along with better pore connectivity, making it the most favorable reservoir facies. Based on pore-structure characteristics and the regional structural setting, we recommend adopting close-spacing hydraulic fracturing with reduced cluster spacing in structurally stable areas to enhance stimulation. In structurally complex areas, engineering designs should prioritize risk mitigation to ensure operational success. Full article

Climate change has intensified drought events, compromising popcorn production, particularly in tropical regions. This study aimed to identify popcorn inbred lines with superior water-use efficiency and responsiveness, and to examine the relationships among morpho-agronomic traits associated with expanded popcorn volume per hectare (VP). Fifty inbred lines were evaluated under well-watered (WW) and water-stressed (WS) conditions across two cropping seasons (2020 and 2021). Water deficit was imposed at pre-anthesis, with the permanent wilting point occurring during early reproductive stages in 2020 and during grain filling in 2021. Principal component analysis and efficiency/responsiveness classification were used to characterize line performance. Significant genotype × water condition × season interactions affected all traits. Water stress reduced VP by 75% in 2020 and 46% in 2021, reflecting the differing timing of stress. Line L477 showed high efficiency and responsiveness, while genotypes such as L213, L221, and L222 were inefficient and non-responsive in both years. Under WW, VP was mainly associated with hundred-grain weight, ear length, and grain number per row, whereas under WS, ear diameter and number of rows per ear were the strongest contributors, indicating that the available genetic variability is more effectively exploited through selective morpho-agronomic criteria tailored to each water scenario. Contrasting crosses between efficient and non-responsive lines (L325 and L481) and inefficient but responsive lines (L513, L625, and L689) are recommended to support the development of hybrids that combine high yield under irrigation with resilience under water-stress conditions. Full article

Brain magnetic resonance imaging (MRI) is highly susceptible to motion artifacts that degrade fine structural details and undermine quantitative analysis. Conventional U-Net-based deep learning approaches for motion artifact reduction typically operate only in the image domain and are often trained on data with simplified motion patterns, thereby limiting physical plausibility and generalization. We propose Sim-DDNet, a simulation-data-based dual-domain network that combines k-space-based motion simulation with a joint image-k-space reconstruction architecture. Motion-corrupted data were generated from T2-weighted Alzheimer’s Disease Neuroimaging Initiative brain MR scans using a k-space replacement scheme with three to five random rotational and translational events per volume, yielding 69,283 paired samples (49,852/6969/12,462 for training/validation/testing). Sim-DDNet integrates a real-valued U-Net-like image branch and a complex-valued k-space branch using cross attention, FiLM-based feature modulation, soft data consistency, and composite loss comprising L1, structural similarity index measure (SSIM), perceptual, and k-space-weighted terms. On the independent test set, Sim-DDNet achieved a peak signal-to-noise ratio of 31.05 dB, SSIM of 0.85, and gradient magnitude similarity deviation of 0.077, consistently outperforming U-Net and U-Net++ across all three metrics while producing less blurring, fewer residual ghost/streak artifacts, and reduced hallucination of non-existent structures. These results indicate that dual-domain, data-consistency-aware learning, which explicitly exploits k-space information, is a promising approach for physically plausible motion artifact correction in brain MRI. Full article