Search Results (1,105)

This paper proposes and experimentally demonstrates a fiber ring laser (FRL) salinity sensing system based on a Sagnac loop (SL) formed by a tapered polarization-maintaining fiber (TPMF). The operating principle is that salinity modulates the birefringence of the polarization-maintaining fiber (PMF), causing a shift in the interference wavelength of the SL transmission spectrum, while the FRL narrows the optical spectrum and enhances the signal-to-noise ratio (SNR). In the experiment, the SL consists of a 20-cm-long PMF with a tapered waist diameter of 10.86 μm. Over the salinity range of 0‰ to 30‰, the sensitivity of the laser-based sensing system is 97 pm/‰, which agrees well with the 93 pm/‰ sensitivity obtained using a broadband light source (BBS), and the salinity exhibits a good linear relationship with the wavelength shift, with a coefficient of determination (R²) of 0.997. Meanwhile, the ring laser cavity improves the SNR of the sensing system from 22 dB to approximately 54 dB, and compresses the 3-dB bandwidth from 1.75 nm to 0.06 nm. Further adopting the support vector regression (SVR) algorithm for linear regression modeling of the spectral data, the results show that the mean absolute error (MAE) decreases from 0.50‰ to 0.04‰, the root mean square error (RMSE) decreases from 0.54‰ to 0.11‰, and R² reaches as high as 0.99988. To the best of our knowledge, this is the first work that combines salinity laser sensing with an artificial intelligence algorithm. The proposed sensor leverages the narrow linewidth and high SNR advantages of the FRL together with the high-precision linear fitting capability of the SVR algorithm, achieving significantly improved accuracy for salinity measurement compared to conventional spectral demodulation. Full article

Viral myocarditis (VMC), predominantly driven by Coxsackievirus B3 (CVB3) infection and the resultant excessive immune response, lacks effective treatments and specific antiviral drugs in clinical practice. Chlorogenic acid (CGA) has been proven to have significant antiviral and anti-inflammatory properties. This study evaluated the potential and mechanism of action of CGA against CVB3-induced viral myocarditis. Our research results showed that CGA significantly alleviated myocardial tissue damage in vivo. This protective effect was accompanied by effective inhibition of myocardial inflammatory responses and viral replication. Further in vitro experiments confirmed that CGA significantly inhibited the replication of CVB3 in a dose-dependent manner, and its inhibitory effect mainly targeted the replication stage of the viral life cycle. Mechanistically, CGA treatment correlates with reduced ZBP1 expression and accelerated ZBP1 degradation involving the ubiquitin–proteasome pathway, accompanied by suppressed activation of PANoptosis markers. These findings suggest that CGA alleviates CVB3-induced myocarditis through concerted antiviral and anti-inflammatory effects, with ZBP1-mediated PANoptosis as a potential contributing mechanism. Full article

In embodied-intelligence Industrial Internet of Things (IIoT), multi-AGV intelligent warehousing requires continuous processing of latency-sensitive tasks, such as environmental perception, inventory monitoring, and anomaly detection. Due to limited onboard computing capability and energy capacity, purely local execution can hardly satisfy real-time requirements, whereas fully cloud-based processing may incur excessive transmission delay and backhaul overhead. To address this issue, this paper investigates the joint optimization of AGV service-point migration and task offloading under a cloud-edge-end collaborative architecture. Considering the impact of service-point selection on wireless access, MEC resources, movement delay, and energy consumption, as well as the effect of offloading decisions on transmission, computation, and AGV-side energy cost, a dual-time-scale optimization model is formulated to minimize the long-term accumulated system delay while satisfying task latency and AGV energy constraints. To solve the resulting mixed discrete problem, a DPSO-MAPPO algorithm is proposed, where DPSO searches service-point plans satisfying movement and conflict constraints at the slow time scale, and MAPPO learns coordinated multi-AGV offloading policies at the fast time scale. The delay and energy feedback further enables coordination between the two types of decisions. Simulation results show that the proposed algorithm converges stably, reduces system delay by 13.55% compared with benchmark algorithms, and improves total energy consumption and energy-violation control. Full article

The measurement and improvement of urban land use efficiency (ULUE) are crucial for sustainable development in China’s Canal Cities (CCCs). Drawing on the theories of production factors, spatial externalities, and agglomeration economy, this study proposes a framework that explicitly addresses the trade-offs and synergies of sustainable land use. A comprehensive ULUE evaluation index system was established. The super-SBM (Slack-Based Measure) and Global Malmquist–Luenberger (GML) index models were employed to assess the green efficiency of urban land use from 2002 to 2023, while Kernel Density Estimation (KDE) and the optimal parameters-based geographical detector (OPGD) model were used to investigate the spatiotemporal evolution and influencing factors of ULUE. The results reveal a distinctive V-shaped trend in efficiency, marked by significant spatial disequilibrium and predominantly technology-driven sustainable growth. Furthermore, ULUE exhibits a spatial distribution characterized by bipolar and multipolar differentiation, accompanied by concurrent concentration and dispersion, with high-value clusters dominating the spatial clustering type. Government regulation emerges as the dominant factor influencing ULUE, underscoring the pivotal role of policy intervention in guiding the sustainable development of land use. The interactions among pairs of influencing factors strengthened over time; notably, the interaction between government regulation and other factors is the strongest. Four-quadrant analysis profoundly reveals the underlying mechanism, distinguishing a high-quality, sustainable development model driven by technological innovation and a resource-dependent economic growth model. The findings provide valuable insights for promoting green development and formulating sustainable land use policies in CCCs. Full article

Northern pike (Esox lucius) myofibrillar protein (MP) forms inherently weak gels due to endogenous proteolytic activity and the low thermal stability of fish myosin, limiting its application in surimi products. This study investigated the reinforcing effect and underlying mechanism of rice protein amyloid fibrils (RFs) on pike MP gels. Dynamic rheology revealed that RFs increased both the storage and loss moduli in a concentration-dependent manner, with the 5% group exhibiting an approximately threefold increase in the G′ at 100 rad/s relative to the control. The gel strength, hardness, and chewiness increased progressively with the RF content, whereas the water-holding capacity peaked at 1–3% RFs and declined sharply at 5% RFs. Microstructural imaging showed that moderate RF levels promoted a dense, homogeneous network architecture, while excessive RFs induced phase separation and structural heterogeneity. Hydrophobic interactions and hydrogen bonds were strengthened via RF incorporation, while disulfide bonds decreased monotonically with the increasing fibril concentration. FTIR spectroscopy revealed an α-helix-to-β-sheet transition, with the β-sheet content reaching a maximum of 49.37% at 3% RFs, and SDS-PAGE confirmed that the RF–MP interactions were predominantly non-covalent in nature. These results demonstrate that RFs reinforce pike MP gels through a molecular mechanism involving rigid fibrils acting as structural scaffolds within the protein network and a progressive shift from disulfide-mediated covalent crosslinking toward non-covalent stabilization via hydrophobic interactions and hydrogen bonding. The 1–3% RF range delivers the most balanced gel properties, while excessive fibril loading at 5% induces over-aggregation and impairs water retention. These findings establish amyloid fibrils as effective structural modifiers for freshwater fish gel products and provide a mechanistic basis for their application in surimi processing. Full article

All-in-one weather-degraded image restoration aims to restore clean images from diverse weather-degraded observations (such as rain, haze, and snow) using a unified model. However, this topic remains challenging due to its ill-posed nature and the scarcity of large-scale paired training data. This article develops a novel semi-supervised learning framework, termed Semi-Supervised Degradation-Aware Learning (S²DAL), to adjust the feature space to align with the unified parameter space for all-in-one adverse weather removal. Specifically, the proposed S²DAL consists of two backbone networks: a Degradation-guided Histogram Transformer (DHformer) for weather-degraded image restoration and a Degradation-guided Convolutional Neural Network (DCNN) for degradation generation. A key component, the Degradation-guided Histogram Transformer (DHT) block, is designed to effectively capture intrinsic image features while suppressing diverse degradation interference through channel shuffling modulation, dynamic-range histogram self-attention, and dual-scale gated feed forward. Furthermore, a Monte Carlo-based Expectation-Maximization (EM) algorithm is introduced to jointly optimize latent variables and network parameters under both labeled and unlabeled data. Extensive quantitative and qualitative results on synthetic and real-world datasets consistently demonstrate that the proposed S²DAL achieves superior restoration performance compared to multiple state-of-the-art fully supervised and semi-supervised approaches. Full article

A computational fluid dynamics (CFD) analysis is conducted to systematically investigate heat transfer enhancement in tubes fitted with grooved twisted tapes and to identify the groove geometry that provides the best thermo-hydraulic performance. Three grooved twisted tape configurations—circular-grooved twisted tapes (CGTT), rectangular-grooved twisted tapes (RGTT), and triangular-grooved twisted tapes (TGTT)—are evaluated and compared with a smooth tube and a conventional twisted tape over a Reynolds number range of 5000–20,000 under isothermal wall conditions. The grooved twisted tapes enhance heat transfer through the combined effects of swirl-induced secondary flows and groove-generated flow disturbances, which intensify turbulent mixing and reduce the thickness of the thermal boundary layer. Compared with the plain tube, the grooved configurations increase the Nusselt number by 1.472–1.98 times while increasing the friction factor by 3.21–3.58 times. Relative to the conventional twisted tape, the grooved designs provide an additional 8.0–12.1% enhancement in heat transfer with only a marginal increase of 0.2–1.5% in friction factor. The thermodynamic analysis indicates that the CGTT configuration exhibits the lowest entropy generation rate and exergy loss throughout the investigated Reynolds number range. In particular, the CGTT achieves a Bejan number of 0.999841 at Re = 5000, demonstrating an excellent balance between heat transfer enhancement and frictional losses. Furthermore, the CGTT attains the highest thermal performance factor (TPF) of 1.294 at Re = 5000 and maintains TPF > 1.0 over the entire Reynolds number range. The overall performance ranking is consistently established as CGTT > TGTT > RGTT based on comprehensive analyses of velocity fields, streamline patterns, turbulent kinetic energy distributions, temperature contours, and thermodynamic characteristics. Although the present study identifies the circular-groove configuration as the optimal design for a twist ratio (y/W) of 3.0, further parametric investigations involving variations in twist ratio, groove dimensions, and groove pitch are required to develop generalized design guidelines. Full article

Although Industry 4.0 has successfully advanced lean manufacturing through digitalization and automation, its primary focus on operational efficiency leaves emerging strategic priorities—human-centricity, sustainability, and resilience—outside its original scope. The Industry 5.0 agenda explicitly elevates these three pillars, creating new potential to drive lean transformation. However, how Industry 5.0 can systematically drive lean manufacturing transformation remains unclear. To address this knowledge gap, this study develops a strategic roadmap. First, a content-centric literature review identifies 12 key enablers for Industry 5.0-driven lean manufacturing. Second, Fuzzy Interpretive Structural Modeling (FISM) and expert opinions determine hierarchical relationships among the enablers and construct a multi-level structural model. Third, Matrices d’Impacts Croisés Multiplication Appliquée à un Classement (MICMAC) analysis evaluates the driving power and dependence of each enabler. Finally, a strategic roadmap is developed based on expert synthesis. The findings reveal that “government regulation and incentives” and “employee skill training” are the most critical enablers, while “value chain design and improvement” and “resource input and return” are the most complex and difficult to develop. The roadmap highlights the mediating role of “stakeholder participation and collaboration.” Importantly, the roadmap addresses potential tensions in lean implementation—such as the carbon footprint trade-off of frequent small-batch transport—by embedding sustainability assessment into value chain design and technology governance. This study offers a practical guide for manufacturers to prioritize investments and sequence actions toward lean transformation in the Industry 5.0 era. The main contribution of this study is a strategic roadmap that explains how Industry 5.0 can enable lean manufacturing transformation through prioritized actions and hierarchical enablers, while reconciling efficiency with sustainability and resilience goals. This roadmap offers a practical guide for manufacturers and policymakers to sequence investments and actions toward lean transformation in the Industry 5.0 era. Full article

The water–energy–food (WEF) system acts as a critical nexus of social–ecological systems. However, rapid urbanization has intensified the regional imbalance in the supply and demand of ecosystem services (ESs). Clarifying the spatiotemporal matching of ecosystem services supply and demand (ESSD) within the WEF framework and revealing the driving mechanisms behind such imbalances are essential to formulating reasonable zoning schemes and targeted optimization strategies for the coordinated development of the regional WEF system. Taking Zhejiang Province as a case study, this research uses water yield (WY), carbon sequestration (CS), and grain production (GP) to characterize the WEF nexus system. It uses the InVEST model to assess WY and CS, applies spatial allocation methods to characterize GP, and integrates socioeconomic data to quantify the demand for the above three ESs. All indicators were standardized and integrated with equal weights to further clarify the comprehensive levels of ESSD. By integrating the Geodetector and K-Means clustering methods, the study analyzes the supply–demand matching of ecosystem services and its driving mechanisms in Zhejiang Province during this period, thereby exploring ecological management zoning and optimization strategies within the WEF system. The study findings indicate that: (1) From the supply perspective, Zhejiang Province’s WY services demonstrate a trend of elevated activity in the southwest and diminished presence in the northeast; high values for CS services are predominantly found in the vegetation-rich areas of the northwest, while high values for GP services are clustered in the northern Zhejiang Plain; from the demand perspective, high values for all three ESs in Zhejiang Province are primarily located in economically active, densely populated urban areas. (2) The correlation between ESSD within Zhejiang Province’s WEF system exhibits significant spatial heterogeneity and is driven by the combined effects of natural and socioeconomic factors, with the interaction between these two factors often producing a synergistic effect. Specifically, annual average precipitation and population density are the dominant factors influencing WY services, NDVI and human footprint are the dominant factors influencing CS services, and population density and GDP are the dominant factors influencing GP services. (3) From 2000 to 2020, the supply–demand ratio for comprehensive ESs in Zhejiang Province generally followed a pattern of being lower in the east and higher in the west. The supply–demand imbalance of ESs intensified in the core areas of eastern cities, whereas the western regions maintained a relatively sound supply–demand balance. (4) The study classifies the counties in Zhejiang Province into four ecological management zones—ecological stable zones, ecological conservation zones, ecological control zones, and ecological restoration zones—and explores differentiated approaches to optimizing these zones and implementing control strategies. Full article

The role of financial institutions in climate governance is increasingly being recognized, particularly in relation to Scope 3 emissions. While existing research has focused primarily on lending and investment activities, the potential influence of insurance operations on lifecycle emissions remains underexplored. This study examines electric vehicle (EV) insurance underwriting as a form of indirect climate governance, with particular attention being paid to claim-related decision processes that affect repair-, replacement-, and battery-related outcomes. A decision-analytical, scenario-based portfolio model is developed to analyze how underwriting and claims parameters may influence lifecycle emissions exposure. The model incorporates literature-informed and scenario-based parameter ranges derived from the lifecycle assessment literature and industry-relevant assumptions, while explicitly accounting for regulatory, technical, and behavioral constraints that limit insurer decision making. An exposure-based attribution framework is applied to link insurance-mediated outcomes to emissions associated with vehicle and battery manufacturing. The results suggest that claim-related parameters—particularly total-loss probability—are associated with variations in modeled emissions exposure within the analytical framework. Scenario analysis indicates that, under plausible parameter configurations, differences in claims decision structures may contribute to variation in lifecycle emissions at the portfolio level. Sensitivity analysis further indicates that these relationships appear stable across a range of parameter assumptions. The findings should be interpreted as scenario-based insights rather than empirical estimates, highlighting potential pathways through which insurance operations may influence emissions outcomes within existing constraints. The study contributes to the literature by extending Scope 3 governance analysis to insurance and by proposing an operational framework for interpreting insurance-associated emissions in lifecycle terms. Full article

This study evaluated the effects of dietary coenzyme Q10 (CoQ10) supplementation on growth performance, slaughter performance, meat quality, antioxidant capacity, serum profiles, and intestinal morphology in Minxinan black rabbits. A total of 250 rabbits were allocated to five dietary treatments containing 0, 30, 60, 120, or 240 mg/kg CoQ10 for 14 weeks after a 1-week adaptation period. Results indicated that supplementation with 60 mg/kg CoQ10 resulted in the highest final body weight (2.83 kg) and average daily gain (29.54 g/day), with a significantly reduced feed-to-gain ratio and mortality rate compared to the control group. Regarding slaughter performance, the 60 mg/kg group significantly reduced the abdominal fat rate. In terms of meat quality, the 60 and 120 mg/kg groups showed significantly reduced drip loss and shear force, while meat lightness (L*) increased in all supplemented groups. Cooking loss was significantly reduced in the 60 mg/kg group. Antioxidant capacity in cardiac muscle and longissimus thoracis et lumborum (LTL) muscle was enhanced, particularly at 60 mg/kg, with significantly elevated activities of superoxide dismutase (SOD), catalase (CAT), glutathione peroxidase (GSH-Px), and total antioxidant capacity (T-AOC), alongside reduced malondialdehyde (MDA) content. Furthermore, the 60 mg/kg group increased LTL muscle polyunsaturated fatty acid (PUFA) content, elevated serum levels of triiodothyronine (T3), growth hormone (GH), and insulin-like growth factor-1 (IGF-1), enhanced immunoglobulin concentrations, and improved intestinal morphology. In conclusion, dietary supplementation with 60 mg/kg CoQ10 improved growth performance, carcass leanness, PUFA content, and antioxidant status in broiler rabbits. Full article

In low-voltage distribution systems, series arc faults caused by poor contact and loose connections are a leading cause of electrical fires. Due to the negative resistance characteristics of arcs, such faults are difficult to detect using conventional overcurrent or leakage protectors. Existing detection methods predominantly rely on wavelet-based feature extraction or threshold-based classifiers. Wavelet transforms require predefined basis functions and lack adaptability to non-stationary current signals from appliances such as induction cookers. Threshold-based classifiers produce excessive false alarms under varying load conditions, as normal non-stationary load waveforms share high-frequency characteristics with arc fault signatures. As a result, existing arc fault protectors exhibit high false alarm rates, limiting practical deployment. To address these limitations, this study proposes a method for diagnosing low-voltage series arc faults based on differential-sliding window higher-order cumulants (HoCs) and stacked autoencoders (SAEs). The method first employs a differential-sliding time window approach to extract HoC features from current signals across seven typical loads, establishing a feature vector database for arc fault patterns. A symmetric stacked autoencoder (SAE) is constructed, trained using layer-wise pretraining to optimize hyperparameters and select the model with the best generalization performance. Experimental results demonstrate that the proposed method achieves a detection accuracy of 96.4% with a false alarm rate of 0% across all tested loads. Full article

Venous thromboembolism (VTE) is a major cause of morbidity and mortality, underscoring the need for new molecular markers to enable early detection and clarify underlying mechanisms. Iron metabolism is linked to oxidative stress, endothelial injury, and inflammation, all central to thrombosis, yet its transcriptomic contribution to VTE remains unclear. We analyzed gene expression profiles from GSE19151 and GSE48000 using differential expression and weighted gene co-expression network analysis (WGCNA), integrating results with an iron metabolism gene set. Three hub genes were identified, arachidonate 5-lipoxygenase (ALOX5), Rho GTPase activating protein 1 (ARHGAP1), and glucose-6-phosphate dehydrogenase (G6PD), all downregulated in VTE. Gene set enrichment indicated that ALOX5 is involved in endothelial regulation, lipid metabolism, and immune pathways. A three-gene signature showed high diagnostic accuracy (AUC = 0.924 in the discovery cohort; 0.705 in validation). Immune deconvolution revealed broad immune remodeling and associated ALOX5 with multiple immune cell subsets, especially M0 macrophages, and with regulators such as TGFB1 and IL6R. Western blot analysis further showed that ALOX5 protein expression was significantly increased in LPS-activated HUVECs, supporting its involvement in inflammatory endothelial injury. DrugBank screening identified 19 approved drugs targeting ALOX5, supporting its potential for mechanistic and clinical investigation. Full article

This study investigated the impacts of electron beam irradiation (EBI) on the storage quality of passion fruit by regulating membrane lipid composition and reactive oxygen species (ROS)-scavenging capacity. Among the tested doses, 0.2 kGy EBI was the most effective in maintaining fruit quality, as indicated by lower relative electrical conductivity and weight loss, together with higher hue angle and commercially acceptable fruit rate during storage. Mechanistically, EBI reduced superoxide anion production and malondialdehyde accumulation, indicating alleviated oxidative damage and membrane lipid peroxidation. EBI also suppressed the activities of membrane lipid-degrading enzymes, thereby slowing the degradation of phospholipids and unsaturated fatty acids while reducing the accumulation of phosphatidic acid, diacylglycerol, and saturated fatty acids. In addition, EBI enhanced the activities of antioxidant enzymes and maintained higher levels of ascorbic acid, glutathione, and 1,1-diphenyl-2-picrylhydrazyl (DPPH) radical-scavenging ability. These results indicate that EBI improves the storage quality of passion fruit mainly by preserving membrane lipid integrity and strengthening ROS-scavenging capacity. Full article

Cobalt ferrite (CoFe₂O₄) magnetostrictive ultrasonic elliptical vibration cutting (UEVC) tools have recently emerged as a low-cost, low-eddy-loss alternative to piezoelectric and rare-earth-driven cutting heads. The structural design and resonance characterisation of such a dual-bending CoFe₂O₄ UEVC tool was reported in our previous work. The present paper builds directly on that platform and addresses a different objective: to determine how the four primary process variables—feed rate, cutting speed, cutting depth, and inter-channel phase difference—should be set to obtain the best surface quality on a representative ductile metal. Using H62 brass as the workpiece and a single-crystal diamond tool with a 0.2 mm nose radius and 60° included angle, single-factor experiments are run on a custom 5-axis precision lathe, and surface roughness is mapped in both the cutting and the feed direction with a Keyence VK-X1000 confocal microscope (Keyence, Osaka, Japan). The speed ratio K = Vc/(2πfA) is computed for every test point so that each result can be classified as belonging to the continuous-contact or to the intermittent-contact UEVC regime. The results show: (i) feed rate has a non-monotonic effect, with an optimum at 1 μm where ductile-mode separation is achieved without secondary tool-trajectory overlap, reducing the cutting direction roughness by up to 45% with respect to conventional cutting (CC); (ii) the UEVC advantage shrinks at high cutting speeds because the speed ratio approaches unity and the intermittent regime collapses, but is still 12.6%–38% over the 50–375 mm/s range tested; (iii) the relative improvement is largest at low depth and decreases as the depth grows, retaining 11.5%–49% gain over CC across 0.5–10 μm; (iv) the inter-channel phase difference, which controls the geometry of the tool-tip ellipse, is the strongest single lever—at 60°, the trajectory becomes an oblique ellipse whose major axis is tilted with respect to the cutting direction, bringing the cutting direction roughness down to 1.21 μm against 2.82 μm for CC, a 57% reduction. A simple kinematic argument links this optimum to a maximum effective separation duration per cycle and offers a design rule for analogous UEVC tools. Full article