Search Results (4,090)

Heavy oil and extra-heavy oil represent mobility-limited petroleum resources because supramolecular associations of asphaltenes and resins, together with strong interfacial resistance, generate extremely high apparent viscosity. In recent years, nanotechnology has emerged as a promising approach for viscosity management and enhanced oil recovery (EOR). This review critically examines recent advances in nano-assisted viscosity reduction from a reservoir-operational perspective and organizes the literature into two field-relevant categories: metal-based and non-metal nano-systems. Metal-based nanoparticles (NPs) mainly promote catalytic aquathermolysis and related bond-cleavage and hydrogen-transfer reactions under hydrothermal conditions, enabling partial upgrading and persistent viscosity reduction during thermal recovery. In contrast, non-metal nano-systems—particularly silica- and graphene-oxide-derived materials—primarily operate through interfacial and structural regulation mechanisms at low or moderate temperatures. These effects include wettability alteration, interfacial-film stabilization, modification of asphaltene aggregation behavior, and the formation of dispersed-flow regimes such as Pickering-type emulsions that reduce apparent flow resistance in multiphase systems. Beyond summarizing nanomaterial types, this review emphasizes reservoir-scale considerations governing field applicability, including brine stability, NPs transport and retention in porous media, and formulation compatibility. Comparative analysis highlights the distinct operational windows of thermal catalytic nano-systems and cold-production nano-systems, providing a reservoir-oriented framework for designing nano-assisted viscosity-reduction technologies. Full article

Forest harvesting transportation planning must balance operational efficiency with environmental sustainability, because timber transportation can cause both soil disturbance and carbon emissions. However, most vehicle routing studies primarily focus on economic objectives such as distance or cost minimization, whereas environmental impacts are often considered separately. The integrated optimization of ecological disturbance and carbon emissions remains limited in forest transportation planning. To address this gap, this study formulates a multi-vehicle routing optimization model for timber transportation that simultaneously minimizes transportation distance, makespan, soil disturbance, and CO₂ emissions within a hierarchical forest road network. An enhanced evolutionary algorithm, Eco-Constrained Lévy-flight Local Search NSGA-II (ECLS-NSGA-II), is proposed to improve convergence and maintain environmentally favorable routing solutions. Simulation experiments comparing ECLS-NSGA-II with NSGA-II, MOPSO, MOEA/D, and WS-GA demonstrate that the proposed method achieves superior performance across all objectives, producing shorter routes, lower completion times, and reduced CO₂ emissions while maintaining minimal ecological disturbance. Additional experiments on randomly generated networks further confirm the robustness of the proposed approach. These results indicate that the proposed framework provides an effective methodological tool for environmentally sustainable timber transportation planning in forest operations. Full article

Milk fat synthesis in dairy cows is a complex process affected by ruminal fermentation, systemic metabolism, and mammary gland activity. To explore the metabolic interplay across these systems, a multi-tissue metabolomics approach (rumen fluid, plasma, and milk) using ultra-high-performance liquid chromatography–mass spectrometry was used to identify metabolic differences between Chinese Holstein cows with high (H-MF, 5.82 ± 0.41%) and low (L-MF, 3.60 ± 0.12%) milk fat content under the same diet. The bovine mammary epithelial cells (BMECs) were also cultured to evaluate the impact of a key metabolite, malic acid (MA), on lipid metabolism. Our findings reveal distinct metabolic profiles across rumen fluid, plasma, and milk, with 96, 109, and 79 differential metabolites, respectively, between the L-MF and H-MF groups. In rumen fluid, H-MF cows showed higher levels of lauric acid and succinic acid, linked to fatty acid biosynthesis, while the L-MF cows had elevated citraconic and orotic acids, associated with amino acid metabolism and liver stress. Plasma from the H-MF cows contained higher β-hydroxybutyric acid, methionine sulfoxide, and phosphatidylcholine, supporting lipogenesis, whereas L-MF plasma showed increased 3-hydroxy-L-proline, indicating tissue catabolism. In milk, the L-MF cows had higher MA, while the H-MF cows exhibited elevated L-carnitine, linked to fatty acid β-oxidation. Metabolite trend analysis during rumen fluid–plasma–milk showed that 211 metabolites were classified into 8 profiles. Profile 1 had the largest number of metabolites whose levels were down-regulated from rumen to plasma and enriched in lipid metabolism. Profile 3 (mainly related to amino acid metabolism) and profile 4 (mainly related to energy metabolism) exhibited opposite trends from plasma to milk. In vitro, 200 μM of MA reduced the triglyceride content in BMECs and down-regulated lipogenic genes and their protein expression levels (fatty acid synthase, stearoyl-CoA desaturase and sterol regulatory element binding protein 1). These results highlight how rumen fluid, plasma, and milk metabolites collectively influence milk fat synthesis, with MA acting as a key regulator of lipid metabolism in mammary epithelial cells. Full article

The resolution and accuracy of airborne/spaceborne SAR are continuously improving, making it an effective means for observing ocean dynamic processes and detecting marine targets. In contrast, utilizing its unique orbital angular momentum (OAM) mode, vortex radar does not require temporal accumulation to achieve azimuthal resolution, making it particularly suitable for observing moving sea surfaces. This capability enables stable and continuous monitoring of dynamic ocean scenes. This paper proposes a vortex radar imaging method based on three-dimensional sea surface scattering characteristics: first, a three-dimensional wind-driven sea surface geometric model is established based on the Elfouhaily sea spectrum, and its scattering characteristics under different incident angles, wind speeds, and wind directions are analyzed using the semi-deterministic facet-based two-scale method; then, two-dimensional range-azimuth imaging is achieved through coordinate transformation, echo modeling, pulse compression, and fast Fourier transform (FFT) in OAM mode domain, with the correctness of the imaging algorithm verified through multiple point target imaging results. Finally, simulation results of two-dimensional sea surface vortex imaging under different incident angles are presented, and the influence of wind speed and direction on sea surface vortex imaging is analyzed. The study shows that the vortex imaging system can effectively reflect wave fluctuations and wind direction characteristics, demonstrating the feasibility and potential of vortex radar imaging in oceanographic applications. Full article

Pacific white shrimp (Litopenaeus vannamei) is a major aquaculture product worldwide. For consumers, discriminating domestic from imported sources of shrimp meat, and individual domestic sources, can be highly desirable because of the different meat quality and environmental contamination from geographically different origins of shrimp. This study evaluated the potential of stable isotope analysis (δ¹³C, δ¹⁵N, δ²H, δ¹⁸O) with chemometric models to authenticate the origins of Pacific white shrimp sold in China. Shrimp samples from domestic (Guangxi, Fujian, Shandong, Inner Mongolia) and foreign (Ecuador) sources were analyzed, using statistical analyses. The four-isotope model achieved 89.3% cross-validation accuracy in distinguishing domestic and foreign shrimp, with an overall prediction Area Under the Curve (AUC) of 0.901 (95% CI: 0.819–0.983)—significantly outperforming single-isotope models. Differences in δ¹³C and δ¹⁵N reflected feed source variations, while δ²H and δ¹⁸O (Variable Importance in the Projection (VIP) > 1, key discriminatory indicators) mirrored geographic environmental difference. Although δ¹⁵N did not differ significantly among groups, the combination of all four isotopes reduced limitations of individual δ²H/δ¹⁸O use. This approach enhanced the precision, reliability, and applicability of stable isotope analysis for origin authentication by leveraging complementary isotopic data and robust statistical frameworks. These findings demonstrate the proposed model’s potential as a cost-effective, copyright-compliant framework for shrimp origin authentication, with implications for isotopic traceability across food science fields. Full article

Accurate stress measurement is essential to evaluating structural integrity and plays a pivotal role in the health monitoring and predicting the service life of steel infrastructures. This study proposes a deep learning approach for stress prediction based on longitudinal critically refracted (LCR) ultrasonic waves. The model integrates gated recurrent units (GRU), attention mechanisms, and one-dimensional convolutional neural networks (1D-CNN), enabling direct stress prediction from raw ultrasonic signals without the need for manual feature extraction or explicit physical modeling. To validate the approach, LCR signals were acquired using a custom-built piezoelectric ultrasonic system from 20# steel specimens subjected to uniaxial stresses ranging from 0 to 200 MPa. A dataset comprising 4200 samples was augmented to enhance training efficiency. The proposed model achieved a mean absolute error of 1.94 MPa. Generalization tests demonstrated high accuracy across diverse stress levels, with average errors below 3 MPa, highlighting the model’s robustness. This research presents an accurate, intelligent, and calibration-free ultrasonic method for stress evaluation, providing practical support for stress evaluation in steel structures under actual operating conditions. Full article

CAR-T cell therapy has shown substantial efficacy in haematological malignancies, but its application to solid tumours remains limited by poor effector-cell infiltration, functional exhaustion, antigenic heterogeneity, and an immunosuppressive microenvironment. In this study, we develop a new spatiotemporal mathematical model of CAR-T therapy for solid tumours that integrates these resistance mechanisms within a single reaction–diffusion framework. The model is formulated as a system of partial differential equations describing functional and exhausted CAR-T cells, antigen-positive and antigen-low tumour subpopulations, and chemokine, immunosuppressive, and hypoxic fields. Steady-state analysis and finite-difference simulations showed that therapeutic outcome is governed by the interplay between CAR-T cell infiltration, exhaustion, and antigen escape. The model reproduces partial tumour regression followed by residual tumour persistence, therapy-driven enrichment of antigen-low cells, and reduced efficacy under stronger immunosuppressive and hypoxic conditions. In the combination therapy scenario considered here, repeated simulated CAR-T cell administration together with attenuation of the suppressive microenvironment improves tumour control. The proposed model provides a mechanistic basis for analysing resistance and for future optimisation studies of CAR-T therapy in solid tumours. Full article

Background/Objectives: Since 2015, pulmonary arterial hypertension (PAH)-specific medications have been fully reimbursed in Lithuania. To describe the current situation of PAH treatment in the country and to determine survival during different PAH treatment regimens. Methods: The data from the Institute of Hygiene and the State Data Agency of Lithuania cases with administrative codes I27.0 and I27.8 have been evaluated. Results: In 2025, 225 confirmed cases of PAH were treated with PAH-specific medications in two PH centers. At least one PAH-specific medication was prescribed to 163 (72.4%) female and 62 (27.6%) male patients. Among these, 96 (42.7%) received sildenafil monotherapy, 82 (36.4%) received a combination of sildenafil and an ERA, 36 (16.0%) were on triple PAH-specific therapy (including selexipag or treprostinil), and 11 (4.9%) received other regimens due to specific medical considerations. The age of adults treated with sildenafil monotherapy vs. other therapies was 63.9 ± 14.8 (n = 117) and 51.5 ± 17.3 (n = 116) years, respectively (p < 0.05). A total of 191 PAH patients who received targeted therapy died during the observational period 2017–2025. Of these, 105 received monotherapy, 57 sildenafil and endothelin receptor antagonist and 29 triple therapies (treprostinil [n = 19], selexipag [n = 6], or inhaled iloprost [n = 4] were prescribed as the third drug). Patients who died and received triple therapy were younger than those on mono- and dual therapy (age at diagnosis 45.0 ± 21.6, 67.2 ± 14.7 and 61.6 ± 16.3 years, respectively, p < 0.01). Survival was longer in patients on dual therapy compared with monotherapy (43.1 ± 28.1 vs. 31.7 ± 25.0 months, p = 0.04), and the longest was in those receiving triple therapy (59.9 ± 29.4 months; p < 0.05). Conclusions: The availability of reimbursed medications dramatically increased the number of treated PAH cases in Lithuania. In 2025, most of the PAH patients received sildenafil monotherapy. Patients treated with sildenafil only were significantly older than the rest of cohort. In the survival analysis, combination PAH therapies were more often prescribed to younger patients and were associated with longer duration of life than monotherapy. Full article

Viral infections remain a global public health challenge. Stimulating the innate immune system is a potent therapeutic strategy that promotes pathogen clearance, directly impacting disease severity and clinical outcomes. Interferons and interferon-stimulated genes (ISGs) are critical components of this antiviral defense system. Neomycin, an aminoglycoside antibiotic, can induce ISG expression and help establish an antiviral state. In this study, we demonstrated that neomycin induces the production of pro-inflammatory cytokines (IL1β, TNFα, IL6, GM-CSF, and IFN-γ) in peripheral mononuclear blood cells (PBMCs) and activates key antiviral ISGs, including MxA, OAS1, and IRF7. The protein expression profiles elicited by neomycin were comparable to those induced by poly(I:C). Intranasal delivery of neomycin to CBA and BALB/c mice induced various ISGs in both the respiratory tract and splenic tissues. Prophylactic administration of neomycin significantly inhibited influenza B virus replication in the lung and nasal turbinates of CBA mice in a sublethal infection model. Overall, our data suggest that neomycin, when used prophylactically alone or combined with other antiviral strategies, shows considerable potential for the attenuation of influenza B virus infections. Full article

Fish allergy, mainly caused by Parvalbumin (PV), is a worldwide health issue with few effective mitigation options. This study investigated Maillard conjugation using chitosan (CS) and various saccharides to modify the structural, functional, and allergenic properties of turbot (Scophthalmus maximus) PV. Structural analyses, including SDS-PAGE, Western blotting, FTIR spectroscopy, and Circular dichroism, confirmed successful conjugation and significant changes in secondary structure, including decreases in α-helical content and increases in β-sheet and random-coil fractions. Glycation significantly boosted antioxidant activity, with total phenolic content (TPC) increasing up to 10.3 times and DPPH radical scavenging reaching 74.5% in the CS–xylose–PV conjugate (CXTPV). Indirect ELISA revealed notable (p < 0.05), sugar-dependent reductions in IgE-binding capacity, with reductions of up to approximately 72% for CXTPV. RBL-2H3 cell assays showed decreased β-hexosaminidase release (about 75% reduction), lowered IL-6 secretion, and strong inhibition of IL-4 production, indicating reduced allergenic potential and immune regulation. CXTPV demonstrated the best overall performance. These findings suggest that CS–saccharide Maillard conjugation is an effective approach for creating hypoallergenic marine ingredients with improved bioactive properties. Full article

The implementation of stringent regulatory policies for foodborne pathogens necessitates ultra-sensitive analytical methods. Digital detection, characterized by absolute quantification and tolerance to complex matrices, serves as a robust approach for food safety monitoring. This review summarizes recent advances in digital detection for foodborne pathogens, including nucleic acid amplification-based platforms such as droplet digital PCR and digital isothermal amplification, as well as emerging preamplification-free approaches based on enzyme-mediated signal conversion, functional nanomaterials, and microfluidic devices. We also profile the applications of digital detection technologies for achieving highly specific and accurate detection of foodborne pathogens and discuss their capabilities in viable bacteria quantification, antimicrobial resistance analysis, and multiplex detection. We finally discuss emerging trends, including partition-free digital detection and artificial intelligence-assisted analysis. These advances are expected to promote the development of intelligent and data-driven food safety surveillance strategies. Full article

Background: Liberia modernized vaccination data systems in 2023–2025 by piloting a District Health Information System (DHIS2)-based Digital Vaccination Registry (Electronic Immunization Registry, EIR) to address the limitations of paper-based workflows and of a proprietary COVID-19 electronic platform (offline gaps, lack of unique identifiers, performance issues and cost). Objective: To assess a pilot platform by evaluating training, registry use and device management, utility for routine immunization, vaccine logistics and Adverse Events Following Immunization (AEFI) data, and routine immunization data quality in the DHIS2 mobile application compared with paper registers. Methods: Using the Public Health Informatics Institute’s Collaborative Requirements Development Methodology, stakeholders defined requirements, trained users and implemented a pilot. Mixed methods were used; a mini data audit was performed, and qualitative data were collected across 19 facilities in Montserrado, Gbarpolu and Grand Bassa. Seventy-eight health workers were trained to use the DHIS2 mobile application. Results: The future state design replaces paper aggregation steps with real-time mobile entry to a national registry and dashboard. Dual entry persisted during high-volume periods. The mini data audit found discrepancies between facility paper registers and DHIS2-EIR entries for child enrollment data and, Bacillus Calmette Guérin and Diphtheria–Pertussis–Tetanus dose administration records Participants attributed these discrepancies to internet and device problems and challenges navigating the system. Participants requested a training manual, improved connectivity at point of service, integration with supportive supervision, additional staff and system features (field to record hospital number, automated next visit date, and vaccination status prompts). Conclusions: Lessons from the pilot will inform country-wide implementation, including planned linkage with electronic birth and death registration to enable a unique child identifier and reduce manual errors and delays. Full article

Ambient fine particulate matter (PM2.5) is associated with increased mortality at concentrations below current regulatory standards. Studies of low-level exposure often rely on large administrative cohorts whose geographic and demographic composition may influence observed associations. In a prior analysis, we observed an association between long-term PM2.5 and all-cause mortality among Native American Medicare beneficiaries living in zip codes within the lowest decile of PM2.5 exposure. The present study, a case–control analysis of 1,713,399 low-PM2.5-exposed beneficiaries enrolled in traditional Medicare during 2015–2016, evaluated whether this association could be explained by geographic context, socioeconomic position (SEP), or baseline health status. We used principal components analysis to summarize area-level SEP indicators and beneficiary-level chronic disease diagnoses. In fully adjusted pooled models, PM2.5 was more strongly associated with mortality among Native American beneficiaries (odds ratio, OR = 1.12 per ug/m³; 95% CI 1.06–1.18) than among non-Native American beneficiaries (OR = 1.01 per ug/m³; 95% CI 1.001–1.02). Sequential adjustment among Native Americans showed that state-level geographic clustering accounted for most attenuation of the PM2.5 coefficient, with additional modest attenuation after adjustment for SEP and chronic disease patterns. These findings suggest that PM2.5–mortality associations observed in low-exposure populations may partly reflect geographic composition and underlying health differences within these large cohorts. Full article

Background and Objectives: Chronic kidney disease (CKD) is an increasingly important global health challenge and is frequently accompanied by psychiatric symptoms, including anxiety. A multidimensional assessment of anxiety in hemodialysis (HD) using the Endler Multidimensional Anxiety Scales (EMAS) has not, to our knowledge, been previously reported. We aim to evaluate the reliability, convergent validity, and exploratory domain-level structure of EMAS in HD patients treated at a dialysis center in Craiova, Romania. Materials and Methods: A total of 103 HD patients underwent clinical and sociodemographic/socioeconomic profiling, cognitive screening using the Mini-Mental State Examination (MMSE), and EMAS administration at two time points (4-week interval) for test–retest evaluation. The anxiety subscale of the Depression, Anxiety, and Stress Scale-21R (DASS-21R) was administered to assess convergent validity. Internal consistency (Cronbach’s α), temporal stability (test–retest correlations and intraclass correlation coefficients), and convergent validity (Pearson correlations) were computed. Exploratory factor analyses were conducted on EMAS domain scores (state, trait, and perceived anxiety domains) as an exploratory structural check. Results: EMAS state and trait anxiety scores were higher in women than in men, while perceived anxiety showed a more heterogeneous pattern across dimensions. Total state anxiety increased with age, particularly after 50 years. Domain-level internal consistency was good for state and acceptable for trait components (standardized α ≈ 0.84 and 0.78 across administrations), whereas perceived anxiety domains showed low cross-domain coherence, consistent with context-specific appraisal. The DASS-21R anxiety subscale showed good internal consistency (α = 0.863). Convergent validity analyses indicated small, domain-specific associations between EMAS scores and DASS-21R anxiety. Domain-level EFA supported a theoretically coherent pattern in which state and trait domains clustered distinctly, while perceived anxiety domains formed a partially separable factor; this pattern was broadly consistent across both administrations. Conclusions: In this HD cohort, EMAS demonstrated good reliability and limited but domain-specific evidence of convergent validity, and exploratory domain-level analyses supported its multidimensional organization. Further studies with larger samples are warranted for item-level structural testing and to inform feasibility-oriented shortening for potential clinical use. Full article

The widespread adoption of smart contracts, self-executing agreements on the blockchain, is hindered by the complexity of translating real-world contracts, often written in multiple languages, into their digital counterparts. This paper addresses this challenge by introducing an innovative approach based on Contract Text Markup Language (CTML), an extensible markup language specifically designed to facilitate the automatic generation of smart contracts from multilingual contracts. CTML overcomes traditional method limitations by employing a two-stage transformation process: (1) Contract Abstraction and Markup: CTML redefines grammar rules and incorporates encoding extensions to transform multilingual contracts into structured, marked-up contracts. This process effectively abstracts the essential details of the original contract, enabling language-agnostic interpretation. (2) Domain-Specific Language (DSL) Translation and Smart Contract Code Generation: The marked-up contract is then seamlessly translated into a DSL program, capturing the legal concepts in a machine-readable format. Finally, the DSL program is automatically compiled into executable smart contract code, ready for deployment on the blockchain. The effectiveness of the proposed approach is demonstrated using a legal contract in both English and Chinese. Therefore, the CTML-based approach can automatically generate smart contracts from multilingual contracts, enabling a more inclusive and accessible smart contract ecosystem. Full article