Search Results (39)

Lightweight high-entropy alloy (HEA) coatings are highly desirable for advanced surface protection. This study presents a novel fabrication method for Al-Mg-Ti-Cu-Ni-Cr lightweight HEA coatings via laser cladding combined with in situ alloying, using a specially designed cable-type composite wire consisting of an Al-Mg core sheathed with Cu, Ti, Ni, and Cr-Ni wires. The fabricated coatings exhibit homogeneous composition, high microhardness, and excellent corrosion resistance. Notably, the Al_43.5Mg₂Ni₂₈Cu₁₅Ti_11.5 coating achieves a microhardness of 627 HV_0.1 and a corrosion current density of 5.5 × 10⁻⁶ A/cm², while the Al_43.6Mg_2.1Cr_2.5Ni_25.2Cu_15.2Ti_11.4 coating shows 523 HV_0.1 and a lower current density of 2.8 × 10⁻⁶ A/cm². Mechanical analysis reveals that the enhanced hardness stems from synergistic strengthening effects—severe lattice distortion, B2 phase coherent precipitation, and grain refinement. The superior corrosion resistance is primarily attributed to a compact Cr₂O₃ passive film. This work provides a new strategy for designing and additively manufacturing lightweight HEA coatings. Full article

Cable wires provide a viable technical pathway for the laser additive manufacturing of high-entropy alloys (HEAs). However, the complex interplay of structural and material parameters of cable wires leads to significant variations in molten pool dynamics, which poses challenges to the fabrication of high-quality HEA coatings. To clarify the effects of these key factors on molten pool behavior, a multi-physics numerical model for the laser cladding of Al₅₀Si₆Ti₈Cr₁₂Cu₁₂Ni₁₂ cable wires was established in this study. A dedicated physical model for cable wires was developed, and the Level Set Method was employed to track fluid interfaces throughout the cladding process. Based on the proposed model, the temperature distribution, stress fields, and elemental homogeneity within the molten pool were systematically investigated. The results reveal that chromium (Cr) addition induces a viscosity reduction, and a torsional pitch of ≤4 mm is critical for achieving defect-free, compositionally uniform HEA coatings, which provides novel insights for process optimization and alloy design of cable-wire laser cladding. Full article

Cellular receptor recognition exerts fundamental roles during coronavirus infection. Clarifying the regulatory mechanism of virus receptor helps to better understand viral infection, transmission and pathogenesis; predict potential host and how viral escape from immune system; prevent coronavirus infection or develop treatment therapy. Herein, we summarize current understanding of host receptor recognition mechanisms in the different genera of coronavirus family. And we also review diverse methodologies of identification and clarification of virus receptors. The integration of structural biology, multi-omics, computational predictions, synthetic biology and artificially engineered viral receptors, provide a powerful framework for elucidating coronavirus–receptor interactions. This also supports the development of broad-spectrum antiviral agents, smart biosensors, and intervention strategies against emerging coronaviruses. Full article

The Xingkai Lake topmouth culter (Culter alburnus) is an endemic, economically valuable fish in Heilongjiang that is highly sensitive to ammonia. However, the systemic effects of acute ammonia stress on its liver have not been determined. The objective of this study was to elucidate the changes in and relationships among stress biomarkers, antioxidant defense mechanisms, apoptosis indicators, and histopathological alterations in the liver of C. alburnus, a fish species native to Xingkai Lake, China, under acute ammonia exposure. Guided by the findings of a 96 h-LC₅₀ assay, the researchers subjected the fish to 48 h of acute exposure at specified total ammonia nitrogen (TAN) concentrations of 30 mg/L, 36 mg/L, and 40 mg/L. A comprehensive assessment of physiological and biochemical markers, including cortisol (COR), blood ammonia (Amm), blood glucose (Glu), aspartate aminotransaminase (AST), alanine aminotransaminase (ALT), catalase (CAT), superoxide dismutase (SOD), glutathione peroxidase (GSH-Px), and malondialdehyde (MDA), revealed pronounced physiological stress and oxidative damage, particularly in the high-concentration groups. The physiological effects of ammonia exposure on C. alburnus showed a clear concentration and time dependence. Notably, elevated ammonia levels significantly upregulated apoptosis-associated genes such as P53, Bax, and Caspase-3. These findings were further substantiated by terminal deoxynucleotidyl transferase-mediated dUTP nick end labeling (TUNEL) assays and histopathological examinations. Overall, the study demonstrated that acute ammonia exposure exerted substantial impacts on the physiological, biochemical, and genetic expression profiles of C. alburnus in Xingkai Lake, leading to sustained stress and oxidative damage, especially at elevated concentrations (30–40 mg/L). Full article

The widespread adoption of electric buses represents a major step forward in sustainable transportation, but also brings new operational challenges, particularly in terms of improving their efficiency and controlling costs. Therefore, battery energy consumption management is a key approach for addressing these issues. Accurate prediction of energy consumption and interpretation of the influencing factors are essential for improving operational efficiency, optimizing energy use, and reducing operating costs. Although existing studies have made progress in battery energy consumption prediction, challenges remain in achieving high-precision modeling and conducting a comprehensive analysis of the influencing features. To address these gaps, this study proposes a two-layer stacking framework for estimating the energy consumption of electric buses. The first layer integrates the strengths of three nonlinear regression models—RF (Random Forest), GBDT (Gradient Boosted Decision Trees), and CatBoost (Categorical Boosting)—to enhance the modeling capacity for complex feature relationships. The second layer employs a Linear Regression model as a meta-learner to aggregate the predictions from the base models and improve the overall predictive performance. The framework is trained on 2023 operational data from two electric bus routes (NO. 355 and NO. W188) in Changsha, China, incorporating battery system parameters, driving characteristics, and environmental variables as independent variables for model training and analysis. Comparative experiments with various ensemble models demonstrate that the proposed stacking framework exhibits superior performance in data fitting. Furthermore, XGBoost (Extreme Gradient Boosting, version 2.1.4) is introduced as a surrogate model to approximate the decision logic of the stacking framework, enabling SHAP (SHapley Additive exPlanations) analysis to quantify the contribution and marginal effects of influencing features. The proposed stacked and surrogate models achieved superior battery energy consumption prediction accuracy (lowest MSE, RMSE, and MAE), significantly outperforming benchmark models on real-world datasets. SHAP analysis quantified the overall contributions of feature categories (battery operation parameters: 56.5%; driving characteristics: 42.3%; environmental data: 1.2%), further revealing the specific contributions and nonlinear influence mechanisms of individual features. These quantitative findings offer specific guidance for optimizing battery system control and driving behavior. Full article

Background: Hand, foot, and mouth disease (HFMD) typically exhibits spatiotemporal clustering. This study aimed to analyze the spatiotemporal heterogeneity of HFMD in Fujian Province, China, and to identify the associations of air pollutants and socioeconomic factors with the incidence. Methods: Daily reported HFMD case data, daily air pollutant data, and socioeconomic data in Fujian Province from 2014 to 2023 were collected for analysis. A descriptive analysis was used to describe the epidemiological trends of HFMD. Spatial autocorrelation analysis was applied to explore the spatiotemporal clustering characteristics. The associations between risk factors and HFMD incidence were evaluated using the generalized additive model (GAM). Results: HFMD incidence in Fujian has decreased since 2019, and the peak in each year occurred between May and June. Distinct high–high and low–low clustering areas were identified. The cumulative exposure–response curves for SO₂, NO₂, and CO showed a monotonically increasing trend, with relative risks (RRs) < 1 at concentrations lower than the median levels (SO₂ ≈ 4 μg/m³, NO₂ ≈ 16 μg/m³, CO ≈ 1 mg/m³). In contrast, the curves for O₃ and PM_2.5 showed a decreasing trend, with RR < 1 at concentrations above the median levels (O₃ ≈ 55 μg/m³, PM_2.5 ≈ 20 μg/m³). Among socioeconomic factors, only the proportion of the population under 15 years old was found to be associated with HFMD incidence. Conclusions: HFMD incidence in Fujian exhibited distinct spatiotemporal clustering. The incidence was associated with the concentrations of air pollutants. Targeted interventions should be implemented in high-risk areas to mitigate HFMD transmission, with particular attention given to the environmental and demographic factors. Full article

The monoculture planting in terraced tea plantations has led to severe soil degradation, which poses a significant threat to the growth of tea plants. However, the mechanisms by which intercropping systems improve soil health through the regulation of soil microbial communities at the micro-topographical scale of terraced tea plantations (i.e., terrace surface, inter-row, and terrace wall) remain unclear. This study investigates the effects of intercropping Ophiopogon japonicus in a five-year tea plantation on the soil physicochemical properties, enzyme activities, and microbial community structure and functions across different micro-topographical features of terraced tea plantations in Wuyi Mountain. The results indicate that intercropping significantly improved the soil organic matter, available nutrients, and redox enzyme activities in the inter-row, terrace surface, and terrace wall, with the effects gradually decreasing with increasing distance from the tea plant rhizosphere. In the intercropping group, tea leaf yield increased by 13.17% (fresh weight) and 19.29% (dry weight) compared to monoculture, and the disease indices of new and old leaves decreased by 40.63% and 38.7%, respectively. Intercropping strengthened the modularity of bacterial networks and the role of stochasticity in shaping bacterial communities in different micro-topographic environments, in contrast to the patterns observed in fungal communities. The importance of microbial phyla such as Proteobacteria and Ascomycota in different micro-topographical features was significantly regulated by intercropping. In different micro-topographical zones of the terraced tea plantation, beneficial bacterial genera such as Sinomonas, Arthrobacter, and Ferruginibacter were significantly enriched, whereas potential fungal pathogens like Nigrospora, Microdochium, and Periconia were markedly suppressed. Functional annotations revealed that nitrogen cycling functions were particularly enhanced in inter-row soils, while carbon cycling functions were more prominent on the terrace surface and wall. This study sheds light on the synergistic regulatory mechanisms between micro-topographical heterogeneity and intercropping systems, offering theoretical support for mitigating soil degradation and optimizing management strategies in terraced tea agroecosystems. Full article

This paper presents a robustness benchmark evaluation and optimization for vehicle detection. Real-time vehicle detection has become an essential means of data perception in the transportation field, covering various aspects such as intelligent transportation systems, video surveillance, and autonomous driving. However, evaluating and optimizing the robustness of vehicle detection in real traffic scenarios remains challenging. When data distributions change, such as the impact of adverse weather or sensor damages, model reliability cannot be guaranteed. We first conducted a large-scale robustness benchmark evaluation for vehicle detection. Analysis revealed that adverse weather, motion, and occlusion are the most detrimental factors to vehicle detection performance. The impact of color changes and noise, while present, is relatively less pronounced. Moreover, the robustness of vehicle detection is closely linked to its baseline performance and model size. And as the severity of corruption intensifies, the performance of models experiences a sharp drop. When the data distribution of images changes, the features of the vehicles that the model focuses on are weakened, making the activation level of the targets significantly reduced. By evaluation, we provided guidance and direction for optimizing detection robustness. Based on these findings, we propose TDIRM, a traffic-degraded image restoration model based on stable diffusion, designed to efficiently restore degraded images in real traffic scenarios and thereby enhance the robustness of vehicle detection. The model introduces an image semantics encoder (ISE) module to extract features that align with the latent description of the real background while excluding degradation-related information. Additionally, a triple control embedding attention (TCE) module is proposed to fully integrate all condition controls. Through a triple condition control mechanism, TDIRM achieves restoration results with high fidelity and consistency. Experimental results demonstrate that TDIRM improves vehicle detection mAP by 6.92% on real dense fog data, especially for small distant vehicles that were severely obscured by fog. By enabling semantic-structural-content collaborative optimization within the diffusion framework, TDIRM establishes a novel paradigm for traffic scene image restoration. Full article

The unclear time-distance decay law between supply and demand, which makes it difficult to coordinate their fairness, is a key factor in the disordered spatiotemporal development of supply and demand. In order to advance the standardized renewal of living circles with a people-oriented approach, this paper clarifies the time-distance decay law between supply and demand embedded in residents’ walking time-distance distributions and explores the spatiotemporal organizational order of supply and demand in living circle units aligned with this law. First, 24 sets of walking time-distance distribution data, each showing significant distribution differences, were standardized using a mean-based transformation relative to their own time-distance means, resulting in an observable law of relative time-distance distribution. Subsequently, an explanatory mechanism for the relative time-distance distribution was constructed based on the generation and decay effects of the relative time-distance between standard supply and demand. A functional model was fitted and verified, indicating that the decay law of the relative time-distance follows a negative exponential function with a parameter value of 2.08, and the time-distance threshold has a stable twofold relationship with the time-distance mean. Finally, by drawing an analogy between living circle units and the relative time-distance distribution and taking their standards as the mean time-distance, the study explored the layout relationships among living circle units. The spatiotemporal organizational order for same-level units was characterized by “one form where the time-distance mean and threshold serve as the service radius and influence radius, and another where supply and demand are placed at 0.5 times the time-distance mean inside and outside, respectively”. For different-level units, the order featured “one form where the center of the upper-level spatiotemporal unit is located at the four corners of the lower-level units, with a service radius twice that of the lower-level unit. Another form ensures coordination of service radius differences between upper and lower units through a twofold speed relationship in travel modes, guaranteeing relative fairness in travel time consumption for the same type of supply across different levels”. Additionally, an ideal layout pattern for living circle units was proposed, featuring a “snowflake-shaped” facility layout and a “twice-speed” transfer method. The findings suggest that the 15, 10, and 5 min living circle planning is not merely about the availability of services within their time-distance standards, but about ensuring multifaceted accessibility fairness for residents within these standards. Full article

This study aims to assess and analyze the urban food resilience of the Zhengzhou metropolitan area, proposing innovative assessment frameworks and methodologies. Utilizing a dual-level analysis approach that combines long-term planning impact analysis (2000–2020) with short-term resilience assessment (2018–2022), the study integrates public government data and Geographic Information System (GIS) data, employing spatial analysis, Analytic Hierarchy Process (AHP), and fuzzy comprehensive evaluation techniques. Findings from 2000 to 2020 indicate that urban planning within the metropolitan area has significantly impacted the food system. Urbanization has led to reduced agricultural land, but improvements in infrastructure have enhanced the efficiency of the food supply chain. Woodland and grassland areas have remained relatively stable, providing an ecological buffer for the food system. Building on this, the short-term assessment from 2018 to 2022 reveals significant dynamic changes and a continuous improvement trend in food resilience, though there is still room for enhancement. Food supply chain management and emergency preparedness and management contributed the most to overall resilience. Notably, extreme events such as the COVID-19 pandemic and the “7.20 Flood Disaster” prompted the adoption of innovative measures to enhance food resilience. The study develops a multidimensional theoretical framework and assessment system for urban food resilience, offering new perspectives and methods for understanding and enhancing urban food resilience. The results highlight the critical role of urban planning in enhancing food resilience, recommending the integration of the food system into comprehensive urban planning, strengthening regional collaboration, and enhancing public engagement. These findings provide an important basis for policymaking and practice aimed at improving the long-term adaptability and short-term recovery capabilities of urban food systems. Full article

To unravel the general relationship between bus travel and land use around bus stops and along bus routes and to promote their coordinated development, this paper explores a method to estimate passenger flow volumes from and to bus stops based on land use types, intensities, and spatial distributions around bus stops and along bus routes. Firstly, following the principle of the gravity model, which considers traffic volumes analogous to gravity based on trip generation and distance impedance between traffic analysis zones (TAZs), a gravitational logic estimation method for passenger flow volumes from and to bus stops was constructed with land use elements between bus stop TAZs and the upstream and downstream collections of bus stop TAZs. Building upon this, the passenger flow volumes from and to 38 bus stops in the Xueyuan Square area of Dalian during weekday morning peak hours were taken as the experimental objects. The basic estimation models of two gravity sets corresponding to passenger flow volumes from and to bus stops were constructed using the bus travel generation based on the aggregation of area-based origin unit method and the bus travel distance impedance based on the probability density method. Finally, the reliability of the estimation method of passenger flow volumes from and to bus stops was verified by regression fitting between the surveyed values of passenger flow volume and the estimated values of the basic models. The results indicate that the fuzzy estimation and transformation of bus travel based on land use elements, which serves as a crucial lever for facilitating strategic alignment in transit-oriented development (TOD), can be effectively achieved by using the area-based origin unit method to aggregate bus travel generation and the probability density method to evaluate the bus travel distance impedance. Full article

Intercropping, a well-established agroecological technique designed to bolster ecological stability, has been shown to have a significant impact on soil health. However, the specific effects of tea/Trachelospermum jasminoides intercropping on the physicochemical properties and functional microbial community structure in practical cultivation have not been thoroughly investigated. In this study, we utilized high-throughput sequencing technology on the 16S/ITS rDNA genes to assess the impact of tea intercropping with T. jasminoides on the composition, diversity, and potential functions of the soil microbial community in tea gardens. The results indicated that the tea/T. jasminoides intercropping system significantly increased pH levels, soil organic matter, available nitrogen, phosphorus, potassium, and enzyme activity, ultimately augmenting soil nutrient levels. Furthermore, an in-depth analysis of the bacterial co-occurrence network and topological structure portrayed a more intricate and interconnected soil bacterial community in tea gardens. Remarkably, the abundance of beneficial genera, including Burkholderia, Mesorhizobium, Penicillium, and Trichoderma, underwent a substantial increase, whereas the relative abundance of pathogenic fungi such as Aspergillus, Fusarium, and Curvularia experienced a marked decline. Functional predictions also indicated a notable enhancement in the abundance of microorganisms associated with nitrogen and carbon cycling processes. In summary, the intercropping of tea and T. jasminoides holds the potential to enrich soil nutrient content, reshape the microbial community structure, bolster the abundance of functional microorganisms, and mitigate the prevalence of pathogenic fungi. Consequently, this intercropping system offers a promising solution for sustainable tea garden management, overcoming the limitations of traditional cultivation methods and providing valuable insights for sustainable agriculture practices. Full article

To determine whether sarcoplasmic proteins affected water migration in myofibrils during air-drying, with protein denaturation as an indicator of sarcoplasmic protein changes, the extent of sarcoplasmic protein changes in lamb during air-drying was first studied. The results showed that sarcoplasmic protein’s thermal stability decreased and secondary structure changed, indicating sarcoplasmic protein denatured in lamb during air-drying (35 °C, 60% RH, 3 m/s wind speed). Subsequently, the effect of sarcoplasmic protein solutions, dried at different times and rates, on myofibril protein–water interaction was studied in vitro. Two sets of sarcoplasmic protein solutions were dried for 0, 3, 6, and 9 h in a drying oven, resulting in different degrees of change. These two sets with higher or lower drying rates were achieved by controlling the contact area between sarcoplasmic protein solution and air. These dried sarcoplasmic protein solutions were then mixed with extracted myofibril and incubated for 2 h. The results showed a significant increase in T₂₁ relaxation time of the incubation system when sarcoplasmic protein solution was dried at 35 °C for 3 h. This indicated that myofibrillar protein–water interaction was weakened, facilitating water migration from the inside to the outside of myofibrils. The denaturation degree of sarcoplasmic proteins was slowed by a higher drying rate, thereby alleviating the increase in the amount of immobile water within myofibrils when dried for 6 h. In conclusion, the properties of sarcoplasmic proteins were influenced by both drying rate and time, thereby influencing the water migration within myofibrils during air-drying. Full article

The measurement of hemoglobin is a vital index for diagnosing and monitoring diseases in clinical practice. At present, solutions need to be found for the soreness, high risk of infection, and inconvenient operation associated with invasive detection methods. This paper proposes a method for non-invasively detecting hemoglobin levels based on multi-wavelength photoplethysmography (PPG) signals. AFE4490 and TMUX1109 were used to implement the low-cost collection of an eight-LED transmissive PPG signal. We used seven regular LEDs and one broadband LED (Osram SFH4737) as light sources. Additionally, a finger clip integrating multiple sensors was designed and manufactured via 3D printing to simultaneously monitor the LED–sensor distance and the pressure from the tester’s finger during PPG signal acquisition. We used a method to extract features from PPG signals using a sliding-window’s variance and an evaluation metric for PPG signals based on the AdaCost classification. Data were gathered from 56 participants from the Nephrology department, including 16 anemic patients. Pearson correlation analysis was conducted on the collected data to remove any data with a weak correlation. The advantage of using a broadband LED as a light source was also demonstrated. Several non-invasive hemoglobin regression models were created by applying AdaBoost, BPNN, and Random Forest models. The study’s results indicate that the AdaBoost model produced the best performance, with a mean absolute error (MAE) of 2.67 g/L and a correlation coefficient (R²) of 0.91 The study results show that the device we designed and manufactured can achieve effective non-invasive hemoglobin detection and represents a new methodological approach to obtaining measurements that can be applied in a clinical setting. Full article

In eutrophic lakes, even if external loading is controlled, internal nutrient loading delays the recovery of lake eutrophication. When the input of external pollutants is reduced, the dissolved oxygen environment at the sediment interface improves in a season without algal blooms. As an important part of lake ecosystems, macroinvertebrates are sensitive to hypoxia caused by eutrophication; however, how this change affects macroinvertebrates is still unknown. In this study, we analysed the monitoring data of northern Lake Taihu from 2007 to 2019. After 2007, the external loading of Lake Taihu was relatively stable, but eutrophication began to intensify after 2013, and the nutrients in the sediments also began to decline, which was related to the efficient use of nutrients by algal blooms. The community structure and population density of macroinvertebrates showed different responses in different stages. In particular, the density of oligochaetes and the Shannon–Wiener index showed significant differences in their response to different stages, and their sensitivity to eutrophication was significantly reduced. Under eutrophication conditions dominated by internal loading, frequent hypoxia occurs at the sediment interface only when an algal bloom erupts. When there is no bloom, the probability of sediment hypoxia is significantly reduced under the disturbance of wind. Our results indicate that the current method for evaluating lake eutrophication based on oligochaetes and the Shannon–Wiener diversity index may lose its sensitivity. Full article