Search Results (861)

Channeling in cementing causes interlayer interference, severely restricting oilfield recovery. Existing channeling plugging agents, such as cement and gels, often lead to reservoir damage or insufficient strength. Oil-soluble resin (OSR) particles show great potential in selective plugging of channeling fractures due to their excellent oil solubility, temperature/salt resistance, and high strength. However, their application is limited by the efficient filling and retention in deep fractures. This study innovatively combines the OSR particle plugging system with the mature rapid filtration loss plugging mechanism in drilling, systematically exploring the influence of particle size and sorting on their filtration, packing behavior, and plugging performance in channeling fractures. Through API filtration tests, visual fracture models, and high-temperature/high-pressure (100 °C, salinity 3.0 × 10⁵ mg/L) core flow experiments, it was found that well-sorted large particles preferentially bridge in fractures to form a high-porosity filter cake, enabling rapid water filtration from the resin plugging agent. This promotes efficient accumulation of OSR particles to form a long filter cake slug with a water content <20% while minimizing the invasion of fine particles into matrix pores. The slug thermally coalesces and solidifies into an integral body at reservoir temperature, achieving a plugging strength of 5–6 MPa for fractures. In contrast, poorly sorted particles or undersized particles form filter cakes with low porosity, resulting in slow water filtration, high water content (>50%) in the filter cake, insufficient fracture filling, and significantly reduced plugging strength (<1 MPa). Finally, a double-slug strategy is adopted: small-sized OSR for temporary plugging of the oil layer injection face combined with well-sorted large-sized OSR for main plugging of channeling fractures. This strategy achieves fluid diversion under low injection pressure (0.9 MPa), effectively protects reservoir permeability (recovery rate > 95% after backflow), and establishes high-strength selective plugging. This study clarifies the core role of particle size and sorting in regulating the OSR plugging effect based on rapid filtration loss, providing key insights for developing low-damage, high-performance channeling plugging agents and scientific gradation of particle-based plugging agents. Full article

Accurate and timely forecasting of cross-regional tourist flows is essential for sustainable destination management, yet existing models struggle with sparse data, complex spatiotemporal interactions, and limited interpretability. This paper presents SE-TFF, a multi-scale tourism-flow forecasting framework that couples a Squeeze-and-Excitation (SE) network with reinforcement-driven optimization to adaptively re-weight environmental, economic, and social features. A benchmark dataset of 17.8 million records from 64 countries and 743 cities (2016–2024) is compiled from the Open Travel Data repository in github (OPTD) for training and validation. SE-TFF introduces (i) a multi-channel SE module for fine-grained feature selection under heterogeneous conditions, (ii) a Top-K attention filter to preserve salient context in highly sparse matrices, and (iii) a Double-DQN layer that dynamically balances prediction objectives. Experimental results show SE-TFF attains 56.5% MAE and 65.6% RMSE reductions over the best baseline (ARIMAX) at 20% sparsity, with 0.92 × 10³ average MAE across multi-task outputs. SHAP analysis ranks climate anomalies, tourism revenue, and employment as dominant predictors. These gains demonstrate SE-TFF’s ability to deliver real-time, interpretable forecasts for data-limited destinations. Future work will incorporate real-time social media signals and larger multimodal datasets to enhance generalizability. Full article

Background: Liquid chromatography-mass spectrometry (LC-MS), widely used in metabolomics, is often limited by low ionization efficiency and ion suppression, which reduce overall metabolite detectability and quantification accuracy. To address these challenges, chemical isotope labeling (CIL) LC-MS has emerged as a powerful approach, offering high sensitivity, accurate quantification, and broad metabolome coverage. This method enables comprehensive profiling by targeting multiple submetabolomes. Specifically, amine-/phenol- and hydroxyl-containing metabolites are labeled using dansyl chloride under distinct reaction conditions. While this strategy provides extensive coverage, the sequential analysis of each submetabolome reduces throughput. To overcome this limitation, we propose a two-channel mixing strategy to improve analytical efficiency. Methods: In this approach, samples labeled separately for the amine/phenol and hydroxyl submetabolomes are combined prior to LC-MS analysis, leveraging the common use of dansyl chloride as the labeling reagent. This integration effectively doubles throughput by reducing LC-MS runtime and associated costs. The method was evaluated using human urine and serum samples, focusing on peak pair detectability and metabolite identification. A proof-of-concept study was also conducted to assess the approach’s applicability in putative biomarker discovery. Results: Results demonstrate that the two-channel mixing strategy enhances throughput while maintaining analytical robustness. Conclusions: This method is particularly suitable for large-scale studies that require rapid sample processing, where high efficiency is essential. Full article

Off-chain payments in the Internet of Things (IoT) enhance the efficiency and scalability of blockchain transactions. However, existing privacy mechanisms face challenges, such as the disclosure of payment channels and transaction traceability. Additionally, the rise of quantum computing threatens traditional public key cryptography, making the development of post-quantum secure methods for privacy protection essential. This paper proposes a post-quantum ring signature scheme based on hash functions that can be applied to off-chain payments, enhancing both anonymity and linkability. The scheme is designed to resist quantum attacks through the use of hash-based signatures and to prevent double spending via its linkable properties. Furthermore, the paper introduces an improved Hash Time-Locked Contract (HTLC) that incorporates a Signature of Knowledge (SOK) to conceal the payment path and strengthen privacy protection. Security analysis and experimental evaluations demonstrate that the system strikes a favorable balance between privacy, computational efficiency, and security. Notably, the efficiency benefits of basic signature verification are particularly evident, offering new insights into privacy protection for post-quantum secure blockchain. Full article

Plant diseases significantly undermine agricultural productivity. This study introduces an improved YOLOv10n model named WD-YOLO (Weighted and Double-scale YOLO), an advanced architecture for efficient plant disease detection. The PlantDoc dataset was initially enhanced using data augmentation techniques. Subsequently, we developed the DSConv module—a novel convolutional structure employing double-scale weighted convolutions that dynamically adjust to different scale perceptions and optimize attention allocation. This module replaces the conventional Conv module in YOLOv10. Furthermore, the WTConcat module was introduced, dynamically merging weighted concatenation with a channel attention mechanism to replace the Concat module in YOLOv10. The training of WD-YOLO incorporated knowledge distillation techniques using YOLOv10l as a teacher model to refine and compress the architectural learning. Empirical results reveal that WD-YOLO achieved an mAP50 of 65.4%, outperforming YOLOv10n by 9.1% without data augmentation and YOLOv10l by 2.3%, despite having significantly fewer parameters (9.3 times less than YOLOv10l), demonstrating substantial gains in detection efficiency and model compactness. Full article

Deep learning models have obvious advantages in detecting oil spills, but the training of deep learning models heavily depends on a large number of samples of high quality. However, due to the accidental nature, unpredictability, and urgency of oil spill incidents, it is difficult to obtain a large number of labeled samples in real oil spill monitoring scenarios. Surprisingly, few-shot learning can achieve excellent classification performance with only a small number of labeled samples. In this context, a new cross-domain few-shot SAR oil spill detection network is proposed in this paper. Significantly, the network is embedded with a hybrid attention feature extraction block, which consists of a coordinate attention module to perceive the channel information and spatial location information, as well as a global self-attention transformer module capturing the global dependencies and a multi-scale self-attention module depicting the local detailed features, thereby achieving deep mining and accurate characterization of image features. In addition, to address the problem that it is difficult to distinguish between the suspected oil film in seawater and real oil film using few-shot due to the small difference in features, this paper proposes a double loss function category determination block, which consists of two parts: a well-designed category-perception loss function and a traditional cross-entropy loss function. The category-perception loss function optimizes the spatial distribution of sample features by shortening the distance between similar samples while expanding the distance between different samples. By combining the category-perception loss function with the cross-entropy loss function, the network’s performance in discriminating between real and suspected oil films is thus maximized. The experimental results effectively demonstrate that this study provides an effective solution for high-precision oil spill detection under few-shot conditions, which is conducive to the rapid identification of oil spill accidents. Full article

In-band full-duplex (IBFD) technology has attracted significant attention for its potential to double the spectral efficiency by enabling a simultaneous transmission and reception over the same frequency channel. However, achieving high isolation between closely spaced transmit and receive paths remains a critical challenge. In this paper, a novel compact co-polarized monopole antenna with self-decoupling capability is proposed based on common-mode/differential-mode (CM/DM) theory. By innovatively folding the ends of the monopole elements, the antenna exploits the distinct behaviors under CM and DM excitations at a close spacing to achieve simultaneous impedance matching in both modes. This effectively enhances the isolation between antenna elements. The design enables self-interference suppression without requiring any additional decoupling structures, even under compact antenna and port spacing. Measurement results confirm that the proposed antenna achieves over 20 dB isolation within the 3.4–3.6 GHz operating band, with a compact spacing of 0.008 λ₀ (λ₀ corresponds to the wavelength at the center frequency). Full article

As AI-enabled embedded systems such as smart TVs and edge devices demand efficient video processing, Versatile Video Coding (VVC/H.266) becomes essential for bandwidth-constrained Multimedia Internet of Things (M-IoT) applications. However, its block-based coding often introduces compression artifacts. While CNN-based methods effectively reduce these artifacts, maintaining robust performance across varying quantization parameters (QPs) remains challenging. Recent QP-adaptive designs like QA-Filter show promise but are still limited. This paper proposes DRIFT, a QP-adaptive in-loop filtering network for VVC. DRIFT combines a lightweight frequency fusion CNN (LFFCNN) for local enhancement and a Swin Transformer-based global skip connection for capturing long-range dependencies. LFFCNN leverages octave convolution and introduces a novel residual block (FFRB) that integrates multiscale extraction, QP adaptivity, frequency fusion, and spatial-channel attention. A QP estimator (QPE) is further introduced to mitigate double enhancement in inter-coded frames. Experimental results demonstrate that DRIFT achieves BD rate reductions of 6.56% (intra) and 4.83% (inter), with an up to 10.90% gain on the BasketballDrill sequence. Additionally, LFFCNN reduces the model size by 32% while slightly improving the coding performance over QA-Filter. Full article

Reconfigurable Intelligent Surfaces (RISs) are among the key technologies envisaged for sixth-generation (6G) multiple-input multiple-output (MIMO)–orthogonal frequency division multiplexing (OFDM) wireless systems. However, their passive nature and the frequent absence of a line-of-sight (LoS) path in dense urban environments make uplink channel estimation and localization challenging tasks. Therefore, to achieve channel estimation and localization, this study models the RIS-mobile station (MS) channel as a double-sparse angular structure and proposes a hybrid channel parameter estimation framework for RIS-assisted MIMO-OFDM systems. In the hybrid framework, Simultaneous Orthogonal Matching Pursuit (SOMP) first estimates coarse angular supports. The coarse estimates are refined by a novel refinement stage employing a Variational Bayesian Expectation Maximization (VBEM)-based Off-Grid Sparse Bayesian Learning (OG-SBL) algorithm, which jointly updates azimuth and elevation offsets via Newton-style iterations. An Angle of Arrival (AoA)–Angle of Departure (AoD) matching algorithm is introduced to associate angular components, followed by a 3D localization procedure based on non-LoS (NLoS) multipath geometry. Simulation results show that the proposed framework achieves high angular resolution; high localization accuracy, with 97% of the results within 0.01 m; and a channel estimation error of 0.0046% at 40 dB signal-to-noise ratio (SNR). Full article

A collaborative inter-laboratory study was conducted to characterise the performance of the novel 250 W External Discharge Plasma Thruster (XPT) with a channel-less Hall effect-type thruster designed to address lifetime limitations and lower-power efficiency challenges in conventional Hall effect thrusters. This study aimed to validate performance measurements across different facilities and thrust stands, investigating potential facility effects on thrust characterisation. Performance testing was conducted both at the University of Surrey using a torsional thrust balance and at the University of Southampton with a double inverted pendulum thrust stand, providing independent verification of the thrust and efficiency metrics. The comparison highlighted the importance of cross-facility testing with differing background pressures, calibration methods, and thrust balance types. These differences provide valuable insights, ensuring more robust and reliable low-power thruster characterisation. The XPT thruster demonstrated consistent performance across both the University of Surrey and University of Southampton facilities, with thrust levels ranging from 1.60 mN to 11.8 mN, specific impulses from 327 s to 1067 s, and anode efficiencies up to 11%. Higher anode voltages and mass fluxes at Southampton enabled extended operational envelopes, revealing performance plateaus at elevated powers, particularly for flow rates above 8 sccm. Cross-facility testing highlighted facility-dependent influences, with Southampton achieving a higher thrust and specific impulse at lower flow rates (5–6 sccm) due to increased anode currents, while discrepancies between test sites of up to 25% were observed at higher flow rates (8–10 sccm) and powers above 200 W. Characterisation identified an optimal operating range at 200 W of anode power with a mass flux below 8 sccm. This work underscores the importance of inter-laboratory validation in electric propulsion testing and provides insights into the best practices for assessing next-generation Hall effect-type thrusters. Full article

Soft-growing robots based on the eversion principle are renowned for their ability to rapidly extend along their longitudinal axis, allowing them to access remote, confined, or otherwise inaccessible spaces. Their inherently compliant structure enables safe interaction with delicate environments, while their simple actuation mechanisms support lightweight and low-cost designs. Despite these benefits, implementing effective navigation mechanisms remains a significant challenge. Previous research has explored the use of pneumatic artificial muscles mounted externally on the robot’s body, which, when contracting, induce directional bending. However, this method only offers limited bending performance. To enhance maneuverability, pneumatic artificial muscles embedded in between the walls of double-walled eversion robots have also been considered and shown to offer superior bending performance and force output as compared to externally attached muscle. However, their adoption has been hindered by the complexity of the current manufacturing techniques, which require individually sealing the artificial muscles. To overcome this multi-stage fabrication approach in which muscles are embedded one by one, we propose a novel single-step method. The key to our approach is the use of non-heat-sealable inserts to form air channels during the sealing process. This significantly simplifies the process, reducing production time and effort and improving scalability for manufacturing, potentially enabling mass production. We evaluate the fabrication speed and bending performance of robots produced in this manner and benchmark them against those described in the literature. The results demonstrate that our technique offers high bending performance and significantly improves the manufacturing efficiency. Full article

In this study, we present a comprehensive numerical investigation of alternating-current electrothermal (ACET) pumping strategies tailored for energy-efficient microfluidic applications. Using coupled electrokinetic and thermal multiphysics simulations in narrow microchannels, we systematically explore the effects of channel geometry, electrode asymmetry and external heating on flow performance and thermal management. A rigorous mesh convergence study confirms velocity deviations below ±0.006 µm/s across the entire operating envelope, ensuring reliable prediction of ACET-driven flows. We demonstrate that increasing channel height from 100 µm to 500 µm reduces peak temperatures by up to 79 K at a constant 2 W heat input, highlighting the critical role of channel dimensions in convective heat dissipation. Introducing a localized external heat source beneath asymmetric electrode pairs enhances convective circulations, while doubling the fluid’s electrical conductivity yields a ~29% increase in net flow rate. From these results, we derive practical design guidelines—combining asymmetric electrode layouts, tailored channel heights, and external heat bias—to realize self-regulating, low-power microfluidic pumps. Such devices hold significant promises for on-chip semiconductor cooling, lab-on-a-chip assays and real-time thermal control in high-performance microelectronic and analytical systems. Full article

Microfluidic devices are used in numerous scientific fields and research areas, but device fabrication is still a time- and resource-intensive process largely confined to the cleanroom or a similarly well-equipped laboratory. This paper presents a method to create microfluidic devices in under three hours using the silicone polymer polydimethylsiloxane (PDMS) and a laser cut positive master using PDMS double casting without a cleanroom or other large capital equipment. This method can be utilized by an undergraduate student with minimal training in a laboratory with a modest budget. This paper presents “Same Day Microfluidics” as a fabrication method accessible to research groups not currently fabricating their own microfluidic devices and as an option for established research groups to more quickly create prototype devices. The method is described in detail with timing, materials, and technical considerations for each step and demonstrated in the context of a Y-channel coflow device. Full article

Heart failure is associated with dysregulation in cellular Ca²⁺ that could involve sarcolemmal L-type Ca²⁺ currents (LTCCs). Building on previous observations showing that recombinant Ca_V1.2 channels are upregulated by phosphorylated calmodulin (CaM) variants, the cellular mechanism(s) underlying this posttranslational modification was investigated in cultured cardiomyocytes. Whole-cell LTCCs decreased by ≈75% after silencing the gene coding for casein kinase 2 (CK2), a constitutively active kinase in cardiomyocytes, or after its pharmacological inhibition. The overexpression of the dominant negative phosphoresistant single, double T79A/S81A, or triple T79A/S81A/S101A CaM variants resulted in a similar inhibition. In contrast, the overexpression of CaM WT or its double T79D/S81D and triple T79D/S81D/S101D phosphomimetic variants curtailed the downregulation of LTCCs caused by CK2 partial knockdown, suggesting that CK2 is responsible for the posttranslational modification of these CaM target residues. Catecholamines, triggering the protein kinase A (PKA) cascade, partially rescued LTCCs treated with siRNA without or after the overexpression of either CaM WT or stimulating CaM phosphomimetic variants. More importantly, they thwarted the negative impact of the phosphoresistant CaM variants, altogether arguing that CK2 and PKA are acting in synergy to regulate the activity of LTCCs. We conclude that CK2-mediated phosphorylation processes exacerbate the Ca²⁺ load associated with heart failure. Full article

Arterial hypertension (HTN) is a growing global health concern, with limited tools available for early detection. Previous studies identified the overexpression of the epithelial sodium channel (ENaC) as a potential biomarker for HTN. In this work, we optimized and clinically validated a lateral flow immunoassay (LFIA) using gold nanoparticles (AuNPs) functionalized with anti-ENaC antibodies. The test strips were prepared with 10 µL of each component and performed in a 9-point herringbone format. For validation, a double-blind study was conducted using platelet lysates from 200 individuals, classified based on real-time blood pressure measurements. ENaC expression was assessed via both LFIA and Western blotting, which served as the reference method. Receiver operating characteristic (ROC) analysis yielded an AUC of 0.7314 for LFIA and 0.6491 for the Western blot, with LFIA demonstrating higher sensitivity (76.24%) and comparable specificity (61.54%) compared to the Western blot (68.31% and 60.34%, respectively). These results support LFIA as a practical, rapid, and moderately accurate tool for screening ENaC levels and identifying individuals at risk of hypertension. Full article