Search Results (7,280)

In this study, an electrochemical valorization strategy on liquid byproducts from hazelnut shell gasification was developed to couple waste remediation with energy-efficient hydrogen production. The aqueous phase, rich in organic compounds, is processed in an anion exchange membrane (AEM) cell, where pure hydrogen evolved at the cathode while organic pollutants are oxidized at the anode. First, the feedstock is thoroughly characterized using gas chromatography–mass spectrometry (GC-MS), identifying a complex matrix of water-soluble aromatic compounds such as phenols, catechols, and other aromatics compounds, with concentrations reaching up to 2.9 g/kg for catechols. Then, the electro-reforming process is optimized using Nickel oxide–hydroxide (Ni(O)OH) electrodes with a loading of 0.75 mg/cm². This methodology relies on the favorable thermodynamics of organic oxidation, which requires a lower onset potential (0.4 V) compared to the oxygen evolution reaction (OER) observed in the alkaline control (0.52 V), and the low overpotential of the Nickel oxide–hydroxide electrode towards the oxidized species. Consequently, the organic load undergoes progressive oxidation into hydrophilic and less bioaccumulating species and carbon dioxide, allowing for the simultaneous generation of pure hydrogen at the cathode at a reduced cell voltage. Elevated stability was observed, with a substantial abatement—78% of the initial organic load—of organic compounds achieved over 80 h at a fixed cell voltage of 0.5 V, and a specific energy consumption for hydrogen production of $38.5 \mathrm{M}\mathrm{J}\cdot {\mathrm{k}\mathrm{g}}_{{\mathrm{H}}_2}^{-1}$. This represents a step forward in the development of technologies that reduce the energy intensity of hydrogen generation while valorizing biomass gasification residues. Full article

DNMT1 variants are linked to complex neurodegenerative syndromes, yet their variant-specific functional and epigenomic consequences remain poorly defined. DNMT1 variants were identified in eight patients using gene-panel or whole-exome sequencing. Functional effects were assessed by site-directed mutagenesis and transient expression in HEK293T cells. Genome-wide methylation profiling of peripheral blood leukocyte DNA was performed using Nanopore sequencing, enabling direct quantification of 5-methylcytosine (5mC). CpG island-level differential methylation and gene set enrichment analysis (GSEA) were conducted. Variants in the replication foci targeting sequence (RFTS) domain (p.Y511H, p.Y540C, p.H569R) exhibited reduced DNMT1 protein expression, decreased enzymatic activity, and cytosolic aggregation. Variants in the C-terminal catalytic domain (p.A1334V and p.P1546S) showed reduced protein expression with relatively mild enzymatic impairment. Patients carrying the p.Y511H variant demonstrated a significant reduction in global 5mC levels compared with controls. Principal component analysis revealed distinct methylomic profiles separating most patients from controls, with marked intra- and inter-familial heterogeneity. CpG island-level analysis identified a single significantly hypomethylated region in p.Y511H carriers, and GSEA revealed differential enrichment of multiple Gene Ontology biological pathways. This study defines domain-dependent functional effects of DNMT1 variants and provides the first nanopore-based methylome analysis, revealing variant-specific and heterogeneous epigenomic alterations. Full article

To address the failure risks associated with long-term service of subsea Christmas tree-wellhead systems under the complex marine environment of the South China Sea, a multi-factor coupled mechanical analysis method is proposed to evaluate the system’s mechanical characteristics and ensure the safety of deepwater oil and gas production. A dynamic model of lateral vibration under seismic loading is established, considering the combined effects of earthquakes, ocean currents, and seabed soil resistance. Based on the actual operating parameters of a well in the Lingshui area of the South China Sea, a three-dimensional finite element model of the subsea Christmas tree-wellhead assembly was developed in ABAQUS 2023. The combined effects of ocean currents, seismic loading, and corrosion over long-term service were simulated to compute and analyze the distributions of stress, bending moment, and associated failure risk. The results indicate that, under a once-in-a-century current combined with seismic waves of intensity V–VI, the system risk remains controllable. However, when the seismic intensity exceeds level VII, the maximum stress and bending moment reach 324.9 MPa and 6.02 MN·m, respectively, surpassing the allowable limits for an X56-grade surface conductor. Considering corrosion effects over a 25-year service life, the extreme stress values increase by 1–5% while the bending moment increases slightly; corrosion significantly amplifies the system’s failure risk. An analysis of the mudline burial height of the subsea wellhead during long-term service shows that, within a range of 1–7 m, variations in system loading are minimal. Based on the mechanical characteristics analysis, it is recommended that the design of subsea Christmas trees and wellheads incorporate regional seismic history, specify X56-grade surface conductors to mitigate corrosion effects, and install leakage-monitoring devices at critical locations to ensure the long-term service safety of the subsea Christmas tree-wellhead system. Full article

Accurate lithologic maps derived from satellite imagery underpin structural interpretation, mineral exploration, and geohazard assessment. However, automated mapping in complex terranes remains challenging because spectrally similar units, narrow anisotropic bodies, and ambiguous contacts can degrade boundary fidelity. In this study, we propose SegNeXt-HFCA, a hierarchical multiscale fusion network with coordinate attention for lithologic segmentation from a Sentinel-2/DEM feature stack. The model builds on SegNeXt and introduces a hierarchical multiscale encoder with coordinate attention to jointly capture fine textures and scene-level structure. It further adopts a class-frequency-aware hybrid loss that combines boundary-weighted online hard-example mining cross-entropy with Lovász-Softmax to better handle long-tailed classes and ambiguous contacts. In addition, we employ a robust training and inference scheme, including entropy-guided patch sampling, exponential moving average of parameters, test-time augmentation, and a DenseCRF-based post-refinement. Two study areas in the Beishan orogen, northwestern China (Huitongshan and Xingxingxia), are used to evaluate the method with a unified 10-channel Sentinel-2/DEM feature stack. Compared with U-NetFormer, PSPNet, DeepLabV3+, DANet, LGMSFNet, SegFormer, BiSeNetV2, and the SegNeXt backbone, SegNeXt-HFCA improves mean intersection-over-union (mIoU) by about 3.8% in Huitongshan and 2.6% in Xingxingxia, respectively, and increases mean pixel accuracy by approximately 3–4%. Qualitative analyses show that the proposed framework better preserves thin-unit continuity, clarifies lithologic contacts, and reduces salt-and-pepper noise, yielding geologically more plausible maps. These results demonstrate that hierarchical multiscale fusion with coordinate attention, together with class- and boundary-aware optimization, provides a practical route to robust lithologic mapping in structurally complex regions. Full article

Background/Objectives: Pseudomonas aeruginosa is one of the leading causes of ventilator-associated pneumonia (VAP) and ventilator-associated tracheobronchitis (VAT), with a worldwide spread of difficult-to-treat high-risk clones. This study aimed to investigate the virulence potential and to characterize phenotypic and genotypic antimicrobial resistance (AMR) in P. aeruginosa causing VAP/VAT in the Intensive Care Unit (ICU), University Hospital of Split, Croatia. Methods: The study included P. aeruginosa isolates obtained from ICU patients who met the criteria for VAP or VAT, between January 2023 and January 2024. Isolates were identified using MALDI-TOF MS and tested for antimicrobial susceptibility (AST). A subset of phenotypically multidrug-resistant (MDR) isolates was further analyzed using whole-genome sequencing (WGS) and multilocus sequence typing. Results: A high rate of resistance was detected to ceftazidime (23.4%), imipenem (39.6%), and meropenem (43.8%). WGS confirmed the presence of multiple AMR genes, including the bla_VIM-2 gene, whose genetic environment highlights a complex MDR locus integrating multiple AMR determinants and mobile genetic elements. All tested isolates possessed genes for class C (bla_PDC34, bla_PDC374 or bla_PDC16) and class D (bla_OXA-2, bla_OXA-10 or bla_OXA-50) β-lactamases, fosA, aph(3′)-IIb and crpP genes. Additionally, WGS analysis revealed the presence of numerous virulence genes including those for adherence (Type IV pili and Fap protein production), motility (such as flgF), biofilm formation (e.g., algE and mucE), quorum sensing (lasI, lasR, rhlI and rhlR), exotoxin (toxA and plcH) and exoenzyme activity (exoU, exoT, exoS, exoY, pcrV, hcp1 and lasA). The isolates belonged to four different sequence types: ST235, ST446, the high-risk ST253 and the widely distributed ST395. Phylogenomic comparison demonstrated that the isolates from this study do not originate from a single clonal source, but instead represent multiple globally distributed high-risk P. aeruginosa lineages introduced into the clinical setting. Conclusions: Due to the emergence of high-risk clones with broad AMR and strong virulence potential, ineffectiveness of standard empirical therapy may be anticipated, highlighting the urgent need for new therapeutic approaches (including those targeting major virulence factors). Full article

Accurate segmentation of fruit tree leaf diseases is critical for yield protection and precision crop management, yet it is challenging due to complex field conditions, irregular leaf morphology, and diverse lesion patterns. To address these issues, Disease-Seg, a lightweight real-time segmentation framework, is proposed. It integrates CNN and Transformer with a parallel fusion architecture to capture local texture and global semantic context. The Extended Feature Module (EFM) enlarges the receptive field while retaining fine details. A Deep Multi-scale Attention mechanism (DM-Attention) allocates channel weights across scales to reduce redundancy, and a Feature-weighted Fusion Module (FWFM) optimizes integration of heterogeneous feature maps, enhancing multi-scale representation. Experiments show that Disease-Seg achieves 90.32% mIoU and 99.52% accuracy, outperforming representative CNN, Transformer, and hybrid-based methods. Compared with HRNetV2, it improves mIoU by 6.87% and FPS by 31, while using only 4.78 M parameters. It maintains 69 FPS on 512 × 512 crops and requires approximately 49 ms per image on edge devices, demonstrating strong deployment feasibility. On two grape leaf diseases from the PlantVillage dataset, it achieves 91.19% mIoU, confirming robust generalization. These results indicate that Disease-Seg provides an accurate, efficient, and practical solution for fruit leaf disease segmentation, enabling real-time monitoring and smart agriculture applications. Full article

Reliable measurement of multiphase flow is fundamental to production evaluation in complex oil and gas wells. However, conventional sensors often suffer from low integration, limited measurement capability, and potential environmental impact. To address these challenges, a photoelectric composite three-phase flow sensor is developed, integrating multiple electrode rings for water holdup and liquid-phase velocity measurement, with dual optical-fiber probes for gas holdup and gas-phase velocity detection. A slip model is further applied to quantify the dependence of slip velocity on liquid holdup based on measured phase rates. Experimental results demonstrate high sensitivity to bubble-flow structures, accurate extraction of gas holdup and phase velocities, and stable full-range water holdup calibration from 0% to 100% at 5 V and 15 V with effective temperature and salinity compensation. And compared with existing technologies, the sensor designed in this paper has the advantages of high integration, a simple structure, multiple measurement parameters, and higher water-holding capacity resolution in low-saturation areas, providing more advanced technical means for conventional profile three-phase flow logging. Full article

The flavivirus NS1 protein is a component of the viral replication complex and plays diverse, yet poorly understood, roles in the viral life cycle. To enable real-time visualization of the developing replication organelle and biochemical analysis of tagged NS1 and its interacting partners, we engineered a replication-competent yellow fever virus (YFV) replicon encoding a C-terminal fusion of NS1 with green fluorescent protein (NS1–GFP). The initial variant was non-viable in the absence of trans-complementation with wild-type NS1; however, viability was partially restored through the introduction of co-adaptive mutations in GFP (Q204R/A206V) and NS4A (M108L). Subsequent cell culture adaptation generated a 17-nucleotide frameshift within the NS1–GFP linker, resulting in a more flexible and less hydrophobic linker sequence. The optimized genome, in the form of a replicon, replicates in packaging cells that produce YFV structural proteins, as well as in naive BHK-21 cells. In the packaging cells, the adapted NS1–GFP replicon produces titers of infectious particles of approximately 10^6 FFU/mL and is genetically stable over five passages. The expressed NS1–GFP fusion protein localizes to the endoplasmic reticulum and co-fractionates with detergent-resistant heavy membranes, a hallmark of flavivirus replication organelles. This NS1–GFP replicon provides a novel platform for studying NS1 functions and can be further adapted for proximity-labeling strategies aimed at identifying the still-unknown protease responsible for NS1–NS2A cleavage. Full article

The therapeutic efficacy of antibiotics has been significant in extending human life expectancy by combating virulent bacterial infections. Nevertheless, multidrug-resistant (MDR) microorganisms remain a global crisis as these bacteria have developed resistance to conventional antibacterial agents. An unexplored antibiotic target found exclusively in bacteria is the Na⁺-translocating NADH:ubiquinone oxidoreductase (NQR), which is an indispensable membrane-bound bacterial enzyme complex that enables cellular functionality and is present in many infectious bacterial species, including Vibrio cholerae and H. influenzae. NQR serves as an essential complex in the bacterial electron transport chain (ETC) and operates as a highly conserved primary Na⁺ pump that drives many bioenergetic functions. This six-subunit protein shuttles electrons from NADH to ubiquinone, which drives the translocation of Na⁺ ions and creates a gradient that provides the driving force for various cellular processes. We have synthesized and evaluated a series of 1,4-naphthoquinones that exhibit high potency against NQR with minimal cytotoxicity and potential to serve as new, NQR-targeting antibacterial agents for use against V. cholerae. Full article

The rapid development of Convolutional Neural Networks (CNNs) has markedly boosted the performance of object detection in remote sensing. Nevertheless, tiny objects typically account for an extremely small fraction of the total area in remote sensing images, rendering existing IoU-based or area-based evaluation metrics highly sensitive to minor pixel deviations. Meanwhile, classic detection models face inherent bottlenecks in efficiently mining discriminative features for tiny objects, leaving the task of tiny object detection in remote sensing images as an ongoing challenge in this field. To alleviate these issues, this paper proposes a tiny object detection method based on Normalized Gaussian Label Assignment and Multi-scale Hybrid Attention. Firstly, 2D Gaussian modeling is performed on the feature receptive field and the actual bounding box, using Normalized Bhattacharyya Distance for precise similarity measurement. Furthermore, a candidate sample quality ranking mechanism is constructed to select high-quality positive samples. Finally, a Multi-scale Hybrid Attention module is designed to enhance the discriminative feature extraction of tiny objects. The proposed method achieves 25.7% and 27.9% AP on the AI-TOD-v2 and VisDrone2019 datasets, respectively, significantly improving the detection capability of tiny objects in complex remote sensing scenarios. Full article

In mineral processing, visual-based online particle size analysis systems depend on high-precision image segmentation to accurately quantify ore particle size distribution, thereby optimizing crushing and sorting operations. However, due to multi-scale variations, severe adhesion, and occlusion within ore particle clusters, existing segmentation models often exhibit undersegmentation and misclassification, leading to blurred boundaries and limited generalization. To address these challenges, this paper proposes a novel semantic segmentation model named VTC-Net. The model employs VGG16 as the backbone encoder, integrates Transformer modules in deeper layers to capture global contextual dependencies, and incorporates a Convolutional Block Attention Module (CBAM) at the fourth stage to enhance focus on critical regions such as adhesion edges. BatchNorm layers are used to stabilize training. Experiments on ore image datasets show that VTC-Net outperforms mainstream models such as UNet and DeepLabV3 in key metrics, including MIoU (89.90%) and pixel accuracy (96.80%). Ablation studies confirm the effectiveness and complementary role of each module. Visual analysis further demonstrates that the model identifies ore contours and adhesion areas more accurately, significantly improving segmentation robustness and precision under complex operational conditions. Full article

High-precision non-contact online voltage monitoring has attracted considerable attention due to its improved safety. Based upon existing research works and validation of non-contact voltage measurement techniques, an enhanced approach for online voltage monitoring is proposed in this paper. By analyzing the influence of the relationship between coupling capacitance and input capacitance on monitoring results, an RC-type signal input circuit with enhanced adaptability has been designed for practical engineering scenarios that may involve large input capacitance. Furthermore, a mixed-signal measurement method based on phase dithering is proposed to eliminate detection errors caused by relative phase drift during synchronous sampling in existing signal injection approaches. This improvement enhances measurement accuracy and offers a more robust theoretical basis for selecting injection signal frequencies. The hardware circuit architecture and data processing scheme presented in this work are straightforward and have been validated using an experimental prototype tested at 50 Hz/500 V and 2000 Hz/300 V. Long-term energized testing demonstrates that the system operates stably at room temperature with a relative measurement error below 0.5%. This study provides a high-precision, easily implementable non-contact measurement solution for online monitoring of low-frequency, low-voltage signals in complex electromagnetic environments such as industrial control signals, low-voltage power signals, and rail transit signals. Full article

Atomic Layer Deposition (ALD) is a key thin-film fabrication technique that enables the growth of ultra-thin, conformal, and compositionally controlled layers for applications in nanoelectronics, optoelectronics, and energy devices. However, the high cost and operational complexity of commercial ALD systems limit their accessibility in academic and emerging research environments. In this work, a low-cost, automated thermal ALD system is designed, assembled, and experimentally validated for the deposition of zinc oxide (ZnO) thin films. The developed system enables precise control of precursor dosing, purge sequences, and substrate temperature via an integrated LabVIEW–Arduino control architecture, allowing reproducible and stable thin-film growth. The design allows the use of various precursors through high-precision three-way diaphragm valves. In addition, the system allows continuous purge gas flow in the reaction chamber, which enhances the drag velocity of the precursor gas, reducing dosage requirement, accelerating chamber saturation time and lowering the total consumption of precursors per deposition cycle. ZnO thin films were successfully grown on silicon and glass substrates at 200 °C using diethylzinc (DEZ) as the metal precursor and hydrogen peroxide (H₂O₂) as the oxidant. The process exhibited self-limiting growth characteristics typical of ALD, yielding a growth per cycle of approximately 0.8 Å. The deposited ZnO films exhibited optical transparency of 70–80% in the visible region, a refractive index of approximately 1.9, and an optical bandgap close to 3.4 eV, which are consistent with values reported for high-quality ZnO films grown in commercial ALD systems. These results demonstrate that the proposed low-cost platform is capable of producing functional ZnO thin films with properties comparable to those obtained with conventional commercial reactors. Overall, this work presents an accessible and scalable thermal ALD system that significantly reduces equipment costs while maintaining reliable process control and film quality, offering a practical framework for expanding thin-film research capabilities across microelectronics, optoelectronics, and nanotechnology laboratories. Full article

As a core support for the integration of new energy and smart grids, Vehicle-to-Grid (V2G) networks face a core contradiction between user privacy protection and transaction security traceability—a dilemma that is further exacerbated by issues such as the quantum computing vulnerability of traditional cryptography, cumbersome key management in stateful ring signatures, and conflicts between revocation mechanisms and privacy protection. To address these problems, this paper proposes a post-quantum revocable linkable ring signature scheme based on SPHINCS+, with the following core innovations: First, the scheme seamlessly integrates the pure hash-based architecture of SPHINCS+ with a stateless design, incorporating WOTS+, FORS, and XMSS technologies, which inherently resists quantum attacks and eliminates the need to track signature states, thus completely resolving the state management dilemma of traditional stateful schemes; second, the scheme introduces an innovative “real signature + pseudo-signature polynomially indistinguishable” mechanism, and by calibrating the authentication path structure and hash distribution of pseudo-signatures (satisfying the Kolmogorov–Smirnov test with $D\le 0.05$), it ensures signer anonymity and mitigates the potential risk of distinguishable pseudo-signatures; third, the scheme designs a KEK (Key Encryption Key)-sharded collaborative revocation mechanism, encrypting and storing the $(I,pk,RID)$ mapping table in fragmented form, with KEK split into $KE{K}_1$ (held by the Trusted Authority, TA) and $KE{K}_2$ (held by the regulatory node), with collaborative decryption by both parties required to locate malicious users, thereby resolving the core conflict of privacy leakage in traditional revocation mechanisms; fourth, the scheme generates forward-secure linkable tags based on one-way private key updates and one-time random factors, ensuring that past transactions cannot be traced even if the current private key is compromised; and fifth, the scheme adopts hash commitments instead of complex cryptographic commitments, simplifying computations while efficiently binding transaction amounts to signers—an approach consistent with the pure hash-based design philosophy of SPHINCS+. Security analysis demonstrates that the scheme satisfies the following six core properties: post-quantum security, unforgeability, anonymity, linkability, unframeability, and forward secrecy, thereby providing technical support for secure and anonymous payments in V2G networks in the quantum era. Full article

Jixin fruit (Malus domestica ‘Jixin’) is a high-value specialty fruit of significant economic importance in northeastern and northwestern China. Automatic recognition of fruit maturity is a critical prerequisite for intelligent harvesting. However, challenges inherent to field environments—including heterogeneous ripeness levels among fruits on the same plant, gradual color transitions during maturation that result in ambiguous boundaries, and occlusion by branches and foliage—render traditional image recognition methods inadequate for simultaneously achieving high recognition accuracy and computational efficiency. Although existing deep learning models can improve recognition accuracy, their substantial computational demands and high hardware requirements preclude deployment on resource-constrained embedded devices such as harvesting robots. To achieve the rapid and accurate identification of Jixin fruit maturity, this study proposes Multi-Attention DeepLabV3+ (MA-DeepLabV3+), a streamlined semantic segmentation framework derived from an enhanced DeepLabV3+ model. First, a lightweight backbone network is adopted to replace the original complex structure, substantially reducing computational burden. Second, a Multi-Scale Self-Attention Module (MSAM) is proposed to replace the traditional Atrous Spatial Pyramid Pooling (ASPP) structure, reducing network computational cost while enhancing the model’s perception capability for fruits of different scales. Finally, an Attention and Convolution Fusion Module (ACFM) is introduced in the decoding stage to significantly improve boundary segmentation accuracy and small target recognition ability. Experimental results on a self-constructed Jixin fruit dataset demonstrated that the proposed MA-DeepLabV3+ model achieves an mIoU of 86.13%, mPA of 91.29%, and F1 score of 90.05%, while reducing the number of parameters by 89.8% and computational cost by 55.3% compared to the original model. The inference speed increased from 41 frames per second (FPS) to 81 FPS, representing an approximately two-fold improvement. The model memory footprint is only 21 MB, demonstrating potential for deployment on embedded devices such as harvesting robots. Experimental results demonstrate that the proposed model achieves significant reductions in computational complexity while maintaining high segmentation accuracy, exhibiting robust performance particularly in complex scenarios involving color gradients, ambiguous boundaries, and occlusion. This study provides technical support for the development of intelligent Jixin fruit harvesting equipment and offers a valuable reference for the application of lightweight deep learning models in smart agriculture. Full article