Search Results (122)

Achieving efficient demulsification of water-in-heavy oil (W/HO) emulsions remains a critical issue that urgently needs to be addressed in the heavy oil industry. Despite being a new generation of green demulsification materials, magnetic carbon nanoparticles still suffer from low demulsification efficiency when applied to water-in-heavy oil emulsions. Herein, polyethyleneimine-modified magnetic carbon nanoparticles (P-MCNs) were successfully prepared via a surface functionalization strategy. The demulsification performance of P-MCN in water-in-heavy oil (W/HO) emulsions was evaluated via the standard bottle test. The results demonstrated that P-MCN (500 ppm) achieved effective water removal within 60 min at 50 °C. Microscopic visualization characterization revealed that the efficient water removal from W/HO emulsions by P-MCN is attributed to its high interfacial activity. Specifically, P-MCN can rapidly migrate to the heavy oil–water interface and effectively disrupt the interfacial film through electrostatic interactions and hydrogen bonding, thereby achieving efficient demulsification of W/HO emulsions. This study provides a solid theoretical foundation for the further development of magnetic carbon nanoparticles with higher demulsification efficiency for applications in the petroleum industry. Full article

Transient receptor potential vanilloid 5 (TRPV5) is a calcium- and pH-sensitive ion channel. It plays a role in tumor biology and cellular calcium homeostasis. Due to the inverse pH gradient in solid tumors (extracellular acidosis and increased intracellular pH), TRPV5 is interesting as a signaling molecule in tumors, as the altered pH in the tumor microenvironment (TME) impacts tumor growth and metastasis. This is the first study to analyze the expression of TRPV5 in common skin cancers, i.e., basal cell carcinomas (BCC), squamous cell carcinomas (SCC), malignant melanomas (MM) and melanocytic nevi (MCN). The results showed a significantly lower expression of TRPV5 in BCC than in all other tumor entities analyzed. While less than half of the BCC were positive for TRPV5, SCC, MM, and MCN exhibited a high level of positive staining results. These results suggest that TRPV5 may especially help as a novel marker in the differentiation of SCC from BCC. The low expression of TRPV5 in BCC, a rarely metastatic tumor, may also point to a role of TRPV5 in the progression of epithelial skin tumors. Further functional studies, however, are needed to clarify the exact role of TRPV5 in skin tumors. Full article

Trends in Micro- and Nano-Lithography required for future development of large area applications ranging from high-packing-density electronics to solar cells are surveyed and outlined. Strategies to use direct laser writing to define etch masks over large areas by: (i) fixed beam moving stage and (ii) moving beam moving stage approaches are presented. The extension of planar 2D and stacked 2D (or 2.5D) fabrication methods into 3D micro- and nano-fabrication is discussed. One of the essential future characteristics of 3D nanolithography is real-time feedback capability. This can be realised via inherent 3D-capable holography, which bridges lithographic exposure control, wavefront sensing, and adaptive feedback, providing a pathway to stitch-free, large-area 3D patterning. The future of micro-fabrication is expected to evolve via highly specialised 3D architecture design and reduction in post-processing steps. Full article

In response to the shortcomings of current mobile communication network (MCN) fault diagnosis methods, such as the insufficient robustness of time-series-spectrum features and the limited ability to capture long-distance dependencies, an improved convolutional neural network is proposed, along with a hybrid diagnosis method based on time-frequency perception and a lightweight deep network (TL-FDN). The TL-FDN introduces a time-series-spectrum feature enhancement module (TFN-E) at the input end, and enhances the robustness of features through a learnable Gabor filter bank. The main architecture employs a hybrid module that integrates a lightweight convolution (LiConv-Block) and a broadcast self-attention (BSA) mechanism (Former-Block), effectively balancing the efficiency of local feature extraction with the capture of global time-series dependencies. Additionally, the model uses a multi-task loss function to achieve joint diagnosis of fault type and fault location. The experimental results show that the average accuracy of the proposed TL-FDN method is 98.6%, which is 3.5% higher than that of the standard convolutional + standard attention baseline method. To strictly evaluate the performance improvement, this paper conducted a non-parametric Wilcoxon signed-rank test in 10 independent experiments. The p-values of the core model indicators were all strictly less than 0.05. These results statistically confirm the superiority of TL-FDN in the fault type identification and location tasks, while maintaining a lightweight parameter quantity suitable for edge-end deployment. Full article

Background: Spleen-preserving (SP) distal pancreatectomy (DP) with splenic vessels resection (SVR) (Warsaw procedure, WP) is an option for the treatment of tumors with low malignant potential. The reverse blood flow through the short gastric arteries (SGA) explains the preservation of the spleen after SVR, but leaves the source of the blood supply to the SGAs hidden. The types of blood supply to the spleen after WP and their incidence have not been previously described, nor has the significance of these types for locally advanced pancreatic head cancer (LAPHC) surgery been determined. Aim: To determine the main types of spleen blood supply after WP, and to assess the feasibility and safety of splenic artery (SA) rotation for the organ-preserving surgery of LAPHC. Methods: Retrospective analyses of demographic and perioperative data, including CT scans, overall (OS) and progression-free (PFS) survival after 71 SP DP SVR and 41 SP SVR pancreaticoduodenectomies (PD) and total pancreatectomies (TP) for LAPHC (2007–2025). Results: In 134 SP procedures, SA was resected in 115 cases (71DP, 9 TP, 3 central, and 32 PD). Indications for surgery were MCN (41), IPMN (14), CSA (3), NEN (25), SPPN (8), PHDAC (40), sarcoma (1), autoimmune (1), and calculous chronic pancreatitis (1). There were no deaths or ischemia-related splenectomies. Morbidity—31% (n23); Dindo–Clavien (D-C) > 3b-2.8%; POPF-grade B-n7 (10.6%); splenic infarctions on CT after SVR-n18 (23%), one symptomatic. CT revealed three types of arterial blood supply to the spleen after SPDP SVR: left gastric artery (LGA) type (n50, 70, 5%), gastro-epyploic arcade (GEA) type (n9, 12, 5%), and an intermediate type (n12, 17%). Spleen- and pancreas tail-preserving SVR pancreatectomies for LAPHC (n41) were accompanied by rotation of the SA to substitute resected SMA (n19) and CHA (n15) for 26 Whipples and 8TPs. There were no ischemic complications. D-C > 3–19.5%. Median OS and PFS for PDAC were 35 and 21 months for 29.5 months median follow-up. Conclusions: Despite the preservation of blood flow through all potential sources of splenic blood supply following resection of the splenic artery, the main collaterals supplying the spleen after WP are LGA branches (~90%). This knowledge, with strict adherence to the developed criteria, allows for the safe preservation of the spleen, pancreatic tail, and stomach during pancreatectomies with SA resection, including its rotation for the substitution of the SMA and CHA in LAPHC. Full article

Full-waveform hyperspectral LiDAR offers a new approach for precise forest ecological monitoring by simultaneously acquiring the three-dimensional structure and continuous spectral information of targets. However, uncertainty in the backscattering cross-section and the inseparability of the reflectance coefficient lead to systematic underestimation of multi-echo reflectance retrieved using traditional methods. This limitation significantly hinders quantitative applications. The existing multi-echo reflectance correction using neighborhood single-echo reflectance (MCNS) method provides an effective solution by establishing proportional models between similar targets, laying an important foundation for the extraction of multi-echo reflectance. However, its applicability in complex forest scenes is limited due to its dependence on specific vegetation single-echo samples. To address this, an iterative correction method based on ground reflectance baseline, namely Bare-Ground-Echo-Based Iterative Correction of Multi-Echo Reflectance for Hyperspectral LiDAR (BGE-ICMER), is proposed. Using ground single-echo reflectance as a stable baseline, a multi-target energy distribution model is constructed based on energy conservation, and backscattering cross-section proportions for each echo are iteratively solved to recover true reflectance. Validation using a high-fidelity dataset generated by the Large-Scale remote sensing data and image Simulation framework (LESS) confirmed the effectiveness of the proposed method. This dataset encompasses three typical tree species with vegetation layers ranging from two to four, incorporates micro-topographic ground surfaces and ten spectral channels from 500 to 1000 nm, thereby capturing the structural and spectral complexity of real forests. The results showed that coefficients of determination (R²) between the corrected and true reflectance exceeded 0.9560, with an RMSE below 0.0418 and MAE below 0.0360. The average relative error was reduced from 26.66% to 10.07%, representing a 62.22% improvement in accuracy. Even in the most challenging scenarios with four-layer vegetation occlusion within this dataset, no significant error accumulation occurred. These results demonstrate the robustness and effectiveness of the proposed method for multi-echo reflectance extraction. This study lays a foundation for more accurate forest biochemical attribute assessment and enables the vertical characterization of multiple targets using high-resolution spectral reflectance. Full article

Reliable perception of small floating objects is critical for the management of aquatic environments and supports key applications, including the autonomous navigation of Unmanned Surface Vehicles (USVs), waterborne debris monitoring, and Search and Rescue (SAR) operations. However, accurate detection in dynamic water-surface environments remains a significant challenge, as targets are frequently obscured by high-frequency wave clutter, and feature distributions are destabilized by covariate shifts caused by illumination. To address these limitations, this study proposes YOLO11-MCN, a real-time detection framework that integrates two architectural components specifically designed for water-surface monitoring. The Multi-Scale Contextual Attention (MSCA) module distinguishes target signatures from background noise by aggregating contextual information across heterogeneous receptive fields, thereby suppressing false positives generated by waves. The Channel Normalization Attention Mechanism (CNAM) addresses illumination instability through feature statistic calibration based on Group Normalization, effectively mitigating covariate shifts induced by extreme lighting variations. Furthermore, these components are complemented by a high-resolution P2 detection head, which recovers the geometric details of small-scale targets typically lost during downsampling. Extensive experiments conducted on a dataset of 5812 images demonstrate that YOLO11-MCN achieves an mAP@0.5 of 92.7%, outperforming the YOLO11n baseline by 5.9 percentage points. Robustness evaluations confirm that MSCA and CNAM significantly reduce missed detections under severe wave clutter and backlighting conditions. With a recall of 90.5%, an inference speed of 94 FPS on desktop hardware, and a compact footprint of 3.89M parameters and 14.8 GFLOPs, the proposed framework offers a robust and efficient solution for intelligent water-surface surveillance systems within the single-class detection paradigm evaluated in this study, with strong potential for edge-device deployment following platform-specific optimization. Full article

To improve the visible-light-driven photocatalytic degradation efficiency of g-C₃N₄-based photocatalysts toward organic pollutants, a MoS₂/g-C₃N₄ composite precursor was employed in this work, and the phase composition and defect environment of Mo species were regulated by post-annealing under air and N₂ atmospheres, respectively, thereby constructing Mo-based/g-C₃N₄ (MCN) composites with distinct structural evolution characteristics. The results showed that the photocatalytic activity of the as-sonicated MCN composite toward methylene blue (MB) was only moderately improved, among which the 15% loading sample exhibited the best performance with a degradation efficiency of about 42.0% within 60 min. In contrast, annealing at 400 °C under N₂ resulted in only a slight activity change, whereas the sample treated at 400 °C in air (Air-15% MCN) achieved an MB degradation efficiency of 99.9% within 60 min, together with a much higher pseudo-first-order reaction rate constant than that of the air-treated sample at a lower temperature. XRD, FT-IR and XPS analyses revealed that air annealing induced the conversion of MoS₂ into highly crystalline MoO₃ (or MoO_3−x), leading to the formation of a reconstructed MoO_3−x/g-C₃N₄ composite interface. Meanwhile, the increased high-binding-energy component in the O 1s spectrum and the EPR signal around g ≈ 2.00 further suggested the presence of more abundant defect-related centers in the air-treated sample. Although Air-15% MCN possessed a lower specific surface area than the untreated and N₂-treated samples, it displayed enhanced visible-light absorption, higher transient photocurrent response, lower interfacial charge-transfer resistance, and accelerated carrier dynamics, indicating that the activity enhancement mainly originated from atmosphere-induced phase transformation, interfacial reconstruction, defect-related active centers, and improved charge separation/transfer, rather than from the surface area effect. Based on the above results, a possible interfacial charge-transfer pathway is tentatively proposed for the g-C₃N₄/MoO_3−x interface formed after air treatment, which contributes to the efficient utilization of photogenerated carriers and the rapid degradation of MB. This work demonstrates that atmosphere-induced phase transformation is a simple and effective strategy for regulating the structure and photocatalytic performance of Mo-based/g-C₃N₄ composites, and provides useful guidance for the design of efficient visible-light photocatalysts. Full article

Magnetic nanocomposite sorbents are increasingly explored for the remediation of metal-contaminated waters; however, high abiotic removal efficiency may not always translate into biological safety. The present study evaluated the single and combined effects of dissolved cobalt (II) ions and magnetite–chitosan nanocomposites (MCN) in zebrafish (Danio rerio) embryos and larvae. MCN (30 wt.% Fe₃O₄) were synthesized via co-precipitation and crosslinking and physiochemically characterized. Adsorption experiments conducted in fish incubation medium demonstrated the efficacy of divalent Co removal and were well described by the Langmuir isotherm model, with a maximum experimental capacity of 20.08 mg g⁻¹. The biological endpoints encompassed survival, hatching, heart rate, locomotor behavior, and oxidative stress biomarkers in early-stage zebrafish. The presence of cobalt (II) was found to result in a reduced hatching success rate, the induction of persistent bradycardia, and the occurrence of oxidative stress, as evidenced by a decline in SOD activity and an increase in H₂O₂ and MDA levels. The study found that MCN alone did not lead to mortality or increase peroxide levels or lipid peroxidation, although a modest decrease in SOD activity was observed. In contrast, combined exposure to cobalt and MCN resulted in significant delayed mortality (>85% at 96 h) and early neuromotor impairment. These findings indicate that high abiotic sorption efficiency alone does not guarantee reduced biological toxicity when nanomaterial–metal interactions occur. Consequently, safety assessments of remediation nanomaterials should explicitly consider nanomaterial–metal interactions and developmental stage-specific biological responses. Full article

Biopolymers such as chitosan are considered important candidates for water purification due to their non-toxicity, biodegradability, natural origin, biocompatibility, and potential for modification to provide additional capabilities, such as incorporating nanomaterials for magnetism to enable rapid separation or adding functional groups to enhance selectivity towards target adsorbates. This study investigated adsorption of Co (II), traced by Co-60 radionuclide, systematically evaluated in natural aquatic matrices selected according to water body type: seawater (Baltic Sea) and freshwater systems further distinguished as lentic (Lake Balsys) and lotic (Neris River) environments, using synthesized magnetite–chitosan nanocomposites (MCNs) with varying loadings of Fe₃O₄ (10–30 wt. %) nanoparticles providing different levels of magnetization. Comprehensive characterization (TEM, FTIR, AFM, XRD, and Mössbauer spectroscopy) confirmed successful integration of magnetite nanoparticles within the chitosan matrix and reproducible structural properties. An optimal magnetization of 11 emu/g was achieved at 20 wt. % Fe₃O₄, enabling rapid magnetic separation within <1 min without compromising sorption capacity. Adsorption isotherm models were applied to investigate the adsorption parameters, and sorption kinetics were studied, yielding a maximum adsorption capacity of 14.93 mg/g for MCN-10 in seawater and 11.95 mg/g for MCN-20 in freshwater with observed equilibrium within 120 min. These promising results indicate that the MCN is a suitable nanocomposite for the removal of Co (II) ions and the Co-60 radionuclide from aquatic media. Full article

The effect of the confinement of fluorophores (rhodamine 6G) in nano-cavities of porous 3D sculptured coatings made by glancing-angle deposition (GLAD) was investigated by fluorescence-lifetime imaging microscopy (FLIM). Shortening of fluorescence/ photoluminescence lifetime by ∼10% was observed from the dye-permeated (in liquid) structure; however, there was no rotational hindrance of dye molecules. When dried, a strong rotational hindrance $89\%$ was observed for the orientation along the ordinary optical axis (slow-axis), and the hindrance was smaller than $57\%$ for the extraordinary direction (fast axis). Light-intensity distribution inside the nano-structure with a form birefringence was numerically modeled using plane-wave illumination and a dipole source. Nanoscale localization of light intensity due to dipole nature $I\sim 1/radiu{s}^6$ and boundary conditions for E-field allows efficient energy deposition inside the region of lower refractive index (nanogaps). Full article

Pancreatic cystic lesions (PCLs) are increasingly detected due to widespread use of cross-sectional imaging. They encompass a heterogeneous group of lesions, ranging from benign pseudocysts and serous cystic neoplasms (SCNs) to premalignant mucinous cystic neoplasms (MCNs) and intraductal papillary mucinous neoplasms (IPMNs), as well as rare malignant entities such as solid pseudopapillary epithelial neoplasm (SPENs) and cystic pancreatic neuroendocrine tumors (cystic PanNETs). Management of PCLs depends on their malignant potential; therefore, an accurate classification is essential for optimizing treatment. This narrative review summarizes current knowledge on the epidemiology, imaging characteristics, diagnosis, and management of PCLs, highlighting the role of CT, MRI, MRCP, and endoscopic ultrasound. Recent advances in radiomics for lesion characterization and risk stratification, particularly in IPMNs, are discussed. Full article

Myelin is essential for efficient neural signaling and can be quantitatively evaluated using the T1-weighted/T2-weighted (T1w/T2w) ratio as a proxy for regional myelin content. Myelin covariance networks (MCNs) reflect correlated myelin patterns across brain regions, enabling the investigation of topological organization. However, a vertex-level map of myelin covariance gradients and their cognitive associations remains underexplored. The objective of this study was to construct and characterize vertex-level MCNs, identify their principal gradients, map their higher-order topological landscape, and determine their associations with cognitive functions and other multimodal cortical features. We conducted a cross-sectional, secondary analysis of publicly available data from the Human Connectome Project (HCP). The dataset included T1w/T2w MRI data from 1096 healthy adult participants (age 22–37). All original data collection and sharing procedures were approved by the Washington University institutional review board. Our procedures involved (1) constructing a vertex-wise MCN from T1w/T2w ratio data; (2) applying gradient analysis to identify principal organizational axes; (3) calculating network connectivity strength; (4) performing cognitive meta-analysis using Neurosynth; and (5) using graphlet analysis to assess higher-order topology. Our results show that the primary myelin gradient (Gradient 1) spans from sensory-motor to association cortices, strongly associates with connectivity strength (r = 0.66), and shows a functional dissociation between affective processing and sensorimotor domains. Furthermore, Gradient 2, as well as the positive and full connectivity strength, showed robust correlations with fractional anisotropy (FA), a DTI metric reflecting white matter microstructure. Our higher-order analysis also revealed that negative and positive myelin covariance connections exhibited distinct topologies. Negative connections were dominated by star-like graphlet structures, while positive connections were dominated by path-like and triangular structures. This systematic vertex-level investigation offers novel insights into the organizational principles of cortical myelin, linking gray matter myelin patterns to white matter integrity, and providing a valuable reference for neuropsychological research and the potential identification of biomarkers for neurological disorders. Full article

This study addresses challenges in recycling electronic waste (e-waste) by developing a self-degrading electrical wire coating material using graphitic carbon nitride (g-C₃N₄). Two types, melamine-derived carbon nitride (MCN) and urea-derived carbon nitride (UCN), were synthesized and evaluated for their photocatalytic activity by measuring the decolorization rate of rhodamine-B (RhB). UCN demonstrated superior photocatalytic performance compared to the widely used TiO₂. When incorporated into PVC film, UCN achieved a maximum weight loss of 68% in photodegradation tests after 40 days of irradiation, contributing to reduced environmental impact. A UCN-mixed coating for a vinyl-insulated cable prototype showed that photodecomposition in water facilitated copper wire separation. The study also indicated that water is vital for the decomposition process, while UCN enhanced stiffness and tensile strength of the material without compromising elongation and electrical insulation properties. Full article

This study investigates the adoption of blockchain technology (BCT) and financing decisions for capital-constrained manufacturers in live streaming supply chains, where product quality information is asymmetric. Although BCT can improve information transparency and consumer trust, its high cost hinders widespread adoption. Based on supply chain financing theory, this research uses a game-theoretic model with linear demand to analyze manufacturers’ BCT adoption and financing strategies under different capital conditions, comparing four scenarios: non-adoption and non-financing (NN), adoption and non-financing (NB), adoption with loan financing from Multi-Channel Networks (MCNs) (LB), and adoption with investment cost-sharing financing from MCNs (CB). Results show that BCT adoption increases market demand and manufacturer profits. The LB strategy is optimal when the manufacturer has sufficient capital and the MCN has a low-investment cost-sharing ratio. In contrast, CB is preferred when the MCN bears a higher share of investment costs, regardless of the manufacturer’s capital. The manufacturer’s financing choice also influences MCN cooperation: MCNs favor CB under high commission rates and low cost-sharing ratios but prefer NB if investment costs are high. These results suggest that manufacturers should select financing based on their capital and cost-sharing terms, while MCNs can adjust cooperation strategies according to commission rates and cost-sharing levels. Full article