Search Results (2,220)

Lactococcus garvieae is a major bacterial pathogen responsible for lactococcosis outbreaks in aquaculture, resulting in substantial economic losses worldwide. Accurate identification of L. garvieae remains challenging because of its genetic similarity to other Lactococcus species and the limited field applicability of many existing molecular diagnostic methods. Therefore, there is an urgent need for a rapid, highly specific, and field-deployable analytical method that enables accurate identification of L. garvieae outside conventional laboratory settings. In this study, a one-pot analytical strategy integrating enzymatic recombinase amplification (ERA) with CRISPR/Cas12a detection was developed, enabling fluorescence or lateral flow dipstick (LFD) readouts within a single closed reaction tube. The one-pot ERA-CRISPR/Cas12a assays achieved a detection limit of 10 copies/reaction. When combined with a rapid DNA release protocol, qualitative detection could be completed within 50 min without the need for sophisticated instrumentation. In parallel, a TaqMan quantitative PCR assay was established as an analytical benchmark, exhibiting a detection limit of 20 copies/reaction with high linearity and good reproducibility. Clinical evaluation using 136 diseased fish samples demonstrated full concordance between the one-pot ERA-CRISPR/Cas12a and qPCR assays, with both methods achieving a positive detection rate of 23.5% (32/136). In addition, the ERA-CRISPR/Cas12a platform was successfully validated under simulated field conditions using a portable reaction device. This study presents a rapid and field-deployable CRISPR-based platform for the early detection and epidemiological surveillance of lactococcosis. Full article

To investigate the effects of distribution characteristics of microscopic voids (including the connectivity degree, pore-throat morphology, and size) on the permeability performance of open-graded friction course (OGFC) asphalt mixtures with steel slag as the anti-skid wearing course, two-dimensional computed tomography (CT) images of OGFC steel slag asphalt mixture specimens were first obtained via X-ray technology. The MATLAB R2022b-based image subtraction algorithm was then adopted to identify the interconnected voids inside the specimens to quantitatively characterize the morphological differences in interconnected voids in OGFC steel slag asphalt mixtures with different gradations. Furthermore, Finite Element simulation by ANSYS 2021 R1 was conducted to explore the influences of the diversion angle of interconnected voids on the water flow characteristics of OGFC steel slag asphalt mixtures, involving the variation laws of water flow velocity, water pressure and flow path in the diversion structure, thereby analyzing the resultant effects on the permeability performance of the mixtures. The results show that the combination of X-ray CT scanning and image processing technology enables more convenient, accurate and intuitive characterization of the internal void distribution characteristics of the mixtures. It was found that the pore-throat properties, including size, length, quantity and equivalent diameter, are the dominant factors restricting the permeability capacity of OGFC steel slag asphalt mixtures. As the diversion angle increases from 20° to 60°, the pressure gradient increases by up to 103.92%. After passing through the diversion section, the flow velocity increases by approximately four times. The streamline density at the channel axis is 4.2–4.5 times that near the channel wall. This study realizes the rapid extraction of void characteristics and the identification of key influencing factors on the permeability performance of OGFC steel slag asphalt mixtures, an achievement that cannot be attained by the previous macroscopic research on the permeability performance of such mixtures. Full article

Distributed acoustic sensing (DAS) has been widely applied to injection–production profile monitoring in horizontal wells because it provides continuous full-wellbore coverage, real-time acquisition, and straightforward long-term deployment. In practical downhole operations, however, DAS measurements are frequently compromised by optical-signal attenuation, loss of fiber–casing/formation coupling, and environmental noise. Meanwhile, the mechanisms governing flow-induced acoustic responses remain insufficiently understood, which continues to impede quantitative diagnosis and interpretation of injection–production profiles based on DAS data. To address these challenges, this study performed controlled laboratory-scale physical simulation experiments of single-phase flow in a horizontal wellbore, systematically investigating DAS acoustic responses under two wellbore diameters (25 mm and 50 mm) and a range of flow velocities. Power spectral density (PSD) was derived using the fast Fourier transform to identify flow-sensitive characteristic frequency bands, and frequency-band energy (FBE) was further used to establish an optimal quantitative relationship with flow velocity. The results show that: (1) DAS energy is dominated by low-frequency components (<100 Hz), with the total energy increasing nonlinearly as flow velocity rises, accompanied by a progressive broadening of the characteristic bands; (2) the feature bands identified using an adaptive method based on energy difference statistics applied to PSD frequency-domain features exhibit a higher signal-to-noise ratio and greater physical clarity than traditional wide frequency bands; furthermore, by employing a feature band merging strategy, the distribution characteristics of flow energy can be captured more comprehensively; and (3) FBE exhibits a strong nonlinear dependence on flow velocity, with a power-law model delivering the best theoretical fit, whereas a cubic model (FBE ∝ V³) achieves high accuracy and robustness for practical applications. The proposed workflow—“PSD peak identification–characteristic band delineation–FBE regression”—establishes a methodological foundation for quantitative DAS-based monitoring of horizontal-well injection–production profiles in both laboratory and field settings, and it provides a basis for subsequent intelligent monitoring and interpretation under multiphase-flow conditions. Full article

Accelerated urbanization and continuous infrastructure renewal have led to a rapid increase in construction and demolition waste (CDW), which accounts for approximately 20–50% of municipal solid waste in many developed countries. Consequently, effective management and resource utilization of CDW have become critical challenges for sustainable urban development. To address these challenges, this study develops an integrated analytical framework for CDW recycling systems. Specifically, it constructs a “cloud-edge-terminal” collaborative recycling system and clarifies the interactions among material, information, and value flows. A three-dimensional coupling framework is further established to reconceptualize CDW management as a multivariate decision-making problem, alongside a multidimensional evaluation structure to support practical implementation and system optimization. Methodologically, the study adopts an integrative review approach supported by knowledge mapping analysis. A structured literature search and screening process was conducted using the Web of Science Core Collection (2015–2026) to ensure transparency and reproducibility in the literature identification and sample construction. The results propose a multidimensional coupling framework integrating resource coordination, information communication, and market trading into a unified decision system. The framework contributes an engineering-oriented analytical paradigm that promotes hierarchical decision coordination, dynamic multi-objective regulation, and integrated management of CDW recycling systems. Full article

Quantitative microstructure evaluation of sedimentary rock thin sections is essential for revealing reservoir flow mechanisms and assessing reservoir quality. However, traditional manual identification is inefficient and prone to subjectivity. Although current deep learning approaches have improved efficiency, most remain confined to single tasks and lack a pathway to translate image recognition into quantifiable geological parameters. Moreover, these methods struggle with cross-scale feature extraction and accurate grain boundary localization in complex textures. To overcome these limitations, this study proposes a three-stage automated analysis framework integrating intelligent lithology identification, sandstone grain segmentation, and quantitative analysis of fabric parameters. To address scale discrepancies in lithology discrimination, Rock-PLionNet integrates a Partial-to-Whole Context Fusion (PWC-Fusion) module and the Lion optimizer, which mitigates cross-scale feature inconsistencies and enables accurate screening of target sandstone samples. Subsequently, to correct boundary deviations caused by low contrast and grain adhesion, the PetroSAM-CRF strategy integrates polarization-aware enhancement with dense conditional random field (DenseCRF)-based probabilistic refinement to extract precise grain contours. Based on these outputs, the framework automatically calculates key fabric parameters, including grain size and roundness. Experiments on 3290 original multi-source thin-section images show that Rock-PLionNet achieves a classification accuracy of 96.57% on the test set. Furthermore, PetroSAM-CRF reduces segmentation bias observed in general-purpose models under complex texture conditions, enabling accurate parameter estimation with a roundness error of 2.83%. Overall, this study presents an intelligent workflow linking microscopic image recognition with quantitative analysis of geological fabric parameters, providing a practical pathway for digital petrographic evaluation in hydrocarbon exploration. Full article

Rainfall-induced landslides represent one of the most recurrent geohazards affecting the transportation network of southwestern Calabria (Italy). This study provides an integrated assessment of landslide occurrence and road damage along the Costa Viola by combining detailed geomorphological mapping, multi-temporal analyses, historical documentation (1950–2025), and GIS-based spatial data processing. A total of 261 landslides were mapped, affecting approximately 19% of the study area. Slides constitute the dominant movement type (66.7%), followed by complex landslides, flows, and falls. Landslide distribution is strongly controlled by geological and morphometric factors: more than 80% of mapped phenomena occur in highly fractured granitic and gneissic rocks, over 70% lie within 500 m of faults, and more than 90% are located within 300 m of streams. Slope gradient (25–55°) and local relief (350–550 m) further contribute to slope instability patterns. The historical dataset documents 237 landslide-induced road damage events over 75 years, with a marked increase in occurrence since the early 2000s. Most damage events affected the SS18 road and frequently corresponded to reactivations of pre-existing landslides, highlighting the long-term persistence of slope instability and the seasonal influence of intense autumn–winter precipitation. Overall, the results demonstrate that landslide hazard in the Costa Viola is governed by the interplay between structural, lithological, geomorphic, and climatic factors, compounded by anthropogenic modifications along road corridors. The combined landslide inventory and historical database provide a robust basis for risk mitigation, identification of critical road sectors, and future susceptibility and predictive modelling to support effective territorial planning. Full article

Solid-state welding processes, particularly friction-stir welding (FSW), offer significant advantages for joining different aluminum alloys due to their good mechanical performance, energy efficiency, and cost-effectiveness. The FSW of the AA5052-H32 and AA6261-T6 alloys has not been previously reported. In this study, the effects of the main FSW process parameters on the mechanical behavior of different AA5052/AA6261 alloy joints were systematically investigated. A full factorial experimental design was applied, considering the tool rotation speed (900–1800 rpm) and the welding speed (56–252 mm/min) as control factors, along with their ratio (Rs/Ws). The results of the tensile tests reveal that the joint strength is strongly affected by the interaction between the rotation and welding speeds, with the Rs/Ws ratio is identified as a key parameter governing material flow, plastic deformation, and defect formation. The maximum tensile strength, approximately 198 MPa, corresponding to a mechanical efficiency of 84.4%, was achieved at 1800 rpm and 7 rev/mm, a condition that favored effective material mixing and a defect-free interfacial bond (≈162–186 MPa). The microhardness profiles showed a minimum of approximately 40–50 HV within the TMAZ, on the advancing side. In general, clear quantitative relationships were established between the process parameters and the mechanical properties, which allowed for the identification of optimal operating conditions to produce high-quality FSW joints between the dissimilar materials AA5052/AA6261. Full article

Preterm birth interrupts critical phases of lung development and is associated with long-term alterations in respiratory structure and function. While bronchopulmonary dysplasia (BPD) has traditionally been considered the principal determinant of adverse outcomes, accumulating evidence indicates that prematurity per se contributes substantially to persistent pulmonary impairment. Lung function trajectories in preterm-born children frequently track along lower percentiles from infancy into adolescence and early adulthood, with limited catch-up growth and increased vulnerability to chronic airflow limitation. Assessment of lung function requires a developmentally tailored approach, as feasibility and interpretability vary across age groups. In infancy, non-volitional techniques such as tidal breathing flow-volume loop analysis and raised-volume rapid thoracoabdominal compression allow early evaluation of respiratory mechanics. During toddlerhood, methodological limitations persist, although emerging technologies may expand feasibility. In preschool children, impulse oscillometry enables detection of small airway dysfunction, often preceding spirometric abnormalities. From school age onward, spirometry, body plethysmography, diffusing capacity, and multiple breath washout provide complementary information on obstructive, restrictive, and gas-exchange impairments. Longitudinal studies demonstrate that reduced lung function is not confined to children with BPD and may predispose to early-onset chronic obstructive pulmonary disease-like phenotypes. Early identification of abnormal trajectories and modifiable risk factors supports structured long-term follow-up and preventive strategies. Standardization of age-specific assessment protocols and harmonization of reference values are essential to improve risk stratification and optimize long-term respiratory outcomes in this vulnerable population. Full article

Brush seals represent the most effective sealing technology, offering 5 to 10 times lower leakage flow rates, resulting in an 80% to 90% increase in sealing efficiency. However, key challenges remain in optimizing brush seal performance, including managing high frictional heat, maintaining consistent leakage flow, and preventing mechanical deformation failures within the bristle pack. This study uses a fluid–mechanical coupling method to establish and refine numerical investigation procedures. Using porous media and local thermal non-equilibrium (LTNE) approaches, the effects of the pressure ratio on seal performance are analyzed. The results reveal that the difference between the maximum directional and total deformations is 0.9108 mm, with the total deformation being approximately 79,666% larger than the directional deformation. These findings highlight that the bristle pack must be designed with primary consideration of total deformation to enhance performance and efficiency. The proposed methodologies enable more robust comparative evaluations of alternative brush seal configurations, including two-stage bristle packs and inline structural models. This facilitates the identification of optimized structures that minimize leakage, enhance energy dissipation, and improve the overall seal performance, thereby advancing the porous media model from a general approximation to a design-optimized tool. Full article

This study experimentally investigates the thermal performance of wraparound loop heat pipes (WLHP) using R134a as the working fluid and copper tubing with an outer diameter of 8.5 mm. A dedicated experimental apparatus was developed to evaluate thermal resistance under varying heat loads (200–500 W), inclination angles (15° and 30°), and coolant temperatures (5–15 °C) at a constant coolant flow rate of 3.2 L/min. Key performance metrics, including evaporator wall temperature and overall thermal resistance, were analyzed to identify optimal operating conditions. The results reveal that increasing the heat load significantly reduces thermal resistance, reaching a minimum of 0.056 °C/W at 500 W. An inclination angle of 30° improved heat transfer, lowering the evaporator temperature by approximately 5 °C compared to 15°. Moreover, lower coolant temperatures enhanced the temperature gradient between the evaporator and condenser, further improving heat transfer. Principal component analysis (PCA) was employed for dimensionality reduction and identification of the dominant thermal variables affecting system performance. Based on the experimental dataset, a regression model was developed to predict thermal resistance, achieving a coefficient of determination of R² = 0.96. These findings confirm the WLHP’s potential as an efficient and reliable passive thermal management system for medium- to high-power applications in tropical and industrial environments. Full article

Under China’s “Dual Carbon” goals, the electric vehicle (EV) industry has expanded rapidly, while the imbalance between supply and demand in public charging infrastructure (PCI) has emerged as a critical bottleneck. Accordingly, a structural assessment of PCI demand potential is essential for improving planning effectiveness. Focusing on the seven municipal districts of Qingdao, this study developed a dual-dimensional framework integrating physical space and population activity. Five core factors were incorporated: road network accessibility, road network betweenness, POI functional mixing density, population distribution density, and nighttime light intensity. By integrating Spatial Design Network Analysis (sDNA), Kernel Density Estimation (KDE), and the entropy weighting method, we conducted a structural assessment of PCI demand potential and derived spatial demand tiers and hierarchy. The results indicate that: (1) road network betweenness had the highest weight (0.396), acting as the dominant driver of structural demand potential, followed by POI functional mixing density (0.271), whereas nighttime light intensity (0.151) and population distribution density (0.143) functioned as baseline supportive indicators; (2) spatial demand was classified into five levels (Levels 1–5), with Level 1 hotspots exhibiting a radial spatial structure characterized by “one primary core, four secondary cores, three corridors, and multiple nodes”; and (3) while the existing PCI distribution exhibited overall gradient consistency with the structurally derived demand tiers, quantitative deviation results indicated localized mismatches, including under-allocation in high-demand areas and over-allocation in selected lower-demand pockets. The proposed dual-dimensional framework facilitates the identification of structural demand gradients for PCI by explicitly incorporating traffic-flow potential, functional aggregation, and population concentration. These findings provide planning-oriented diagnostic support for PCI configuration and contribute to the sustainable transformation of urban transportation systems in megacities. Full article

Granites of the Cornubian batholith of SW England and their host rocks are variably cross-cut by quartz–feldspar porphyry (QFP) and microgranite sheet intrusions locally referred to as elvan dykes. These are usually relatively potassic, have a porphyritic texture, and are often spatially and temporally associated with mineralisation. The processes by which they became so K-rich, their interaction with wall rocks, and their role in mineralisation remain poorly understood. Based on studies of a mineralised QFP dyke in the Tregonning Hill area of SW England, we present micro-textural and whole-rock geochemical evidence for potassic and then sericitic metasomatism of the dyke and its slate wall rocks, the latter to a rock strongly resembling granite, which could confound the identification of xenoliths and mapping of granite contacts. The metasomatism was caused by the through-flow of magmatic–hydrothermal fluids via inter-crystal pathways within a fluid–crystal mush, as evidenced by the presence of a network of vermiform micro-quartz veinlets that feed polymetallic quartz veins. QFP dykes acting as fluid–mush conduits, probably tapping larger underlying fluid-enriched mush reservoirs, is consistent with their association with metasomatism and mineralisation. Full article

Accurate modeling of smart composite dampers is crucial for simulation and model-based control. This study focuses on the constitutive modeling of a novel damper that synergistically combines an Entangled Metallic Wire Mesh (EMWM) with a magnetorheological (MR) fluid. Unlike traditional MR dampers, the interaction between the field-responsive MR fluid and the rate-sensitive, deformable EMWM matrix introduces strong coupled current–frequency dependence. To capture this essential characteristic, a control-oriented, bivariate (current–frequency) hysteresis model is formulated, wherein all parameters are explicit, continuous functions of both the control current (I) and excitation frequency (f). A systematic two-step identification method is employed to derive these functions from dynamic tests. A key finding is that the identified damping exponent (α) consistently exceeds unity across the tested operational range. This quantitatively indicates a transition from viscous-dominated to inertial-flow-dominated dissipation within the EMWM matrix, a distinctive mechanism attributed to non-Darcian flow in its porous structure. The fully parameterized model demonstrates high fidelity (R² > 0.99) within the characterized low-frequency, small-amplitude regime and shows reliable predictive capability for interpolated conditions. The presented model serves as a ready-to-use constitutive tool for the simulation and design of low-frequency vibration isolation systems utilizing EMWM-MR composites, and the revealed inertial flow mechanism provides fundamental insight for the development of next-generation adaptive dampers. Full article

Background/Objectives: Urine samples are the most frequently analyzed specimens in clinical microbiology laboratories. Although urine culture remains the gold standard for diagnosing urinary tract infections, it is time-consuming and resource-intensive. Therefore, reliable screening methods capable of predicting urine culture positivity are needed to optimize laboratory workflow. Automated urine analysis based on flow cytometry enables efficient screening and identification of samples with a low probability of bacterial infection, thereby rationalizing microbiological testing. This study evaluated the usefulness of a multivariable approach to support interpretation of flow cytometry results following the implementation of the Sysmex UF-4000 urine flow cytometer. Methods: Routinely collected urine samples from outpatients and hospitalized patients were analyzed using the UF-4000 flow cytometer, with a positivity threshold of ≥100 leukocytes/µL. Urinary parameters were compared between samples with positive and negative cultures. Multivariable logistic regression was applied to identify independent predictors of a positive urine culture. Urinary sediment parameters, including leukocyte, bacterial, fungal, and squamous epithelial cell counts, were assessed as covariates. Results: Urine samples with positive cultures showed significantly higher leukocyte counts (median 355.0, IQR 146.5–1429.4) and bacterial counts (median 9805.2, IQR 1134.3–45,011.5). Fungal and squamous epithelial cell counts differed only slightly between groups, although the differences were statistically significant (p < 0.001). Leukocyte counts were higher in urine samples from which Gram-negative bacteria were isolated compared with samples containing Gram-positive bacterial isolates (p < 0.001). The multivariable model demonstrated the most favorable overall performance, combining high sensitivity with improved specificity and the highest negative predictive value (AUC = 0.927). Optimal cut-off values were 70 leukocytes/µL and 105 bacteria/µL. Conclusions: Leukocyte and bacterial counts were the strongest predictors of positive urine culture results. A multivariable model including only these two parameters demonstrated high diagnostic accuracy and may serve as a practical screening tool to identify urine samples with a low probability of bacterial infection. The implementation of this approach could support more efficient use of urine cultures and help optimize laboratory workflow. Full article

Nanostructured products synthesized using electric arc vapor plasma with various alcohol solutions exhibiting very high enthalpy and low mass flow rates in a direct current discharge in direct contact with a vapor vortex surrounding the arc column were studied. The nanostructured products obtained in our experiments with various alcohol solutions (ethanol, propanol, and benzene) were analyzed using modern nanostructure identification methods. The diameters of the synthesized multi-walled carbon nanotubes (MWNTs) ranged from 9 to 35 nm, single-walled carbon nanotubes (SWNTs) from 2 to 4 nm, and graphene flakes from 1 to 7 sheets, depending on the alcohol solution composition. Fullerene-like structures identified by HRTEM were obtained from a benzene mixture using electric arc vapor plasma synthesis. It is shown that the thermal steam plasma process with various alcohol solutions has great potential for the synthesis of nanotubes and graphene flakes due to the continuous and easy-to-implement method, cheap raw materials and adjustable carbon content due to the combination of different mixture compositions. Full article