Search Results (32)

In response to the global goal of carbon neutrality, the tourism industry faces mounting pressure to reduce emissions. Characteristic towns that rely on traditional, high-emission models urgently require low-carbon tourism transformation strategies to meet environmental targets while preserving cultural heritage and economic vitality. This study investigates the low-carbon transition pathways of tourism in characteristic towns, using the three-dimensional impact mechanism of tourists, residents, and enterprises as a conceptual entry point. Drawing on empirical research conducted in Zhouzhuang and Tongli—two ancient towns in Suzhou—the study identifies key drivers and barriers to the development of low-carbon tourism. Results indicate that the overall low-carbon transformation score for Suzhou’s characteristic towns is 63.3, suggesting a moderate level of progress. Specifically, Zhouzhuang scored 66.9, while Tongli lagged behind at 57.6, highlighting notable disparities in transition efforts. The study applies multi-agent game theory and system dynamics to analyze the interactive mechanisms among tourists, residents, and enterprises in the low-carbon transition. Our findings reveal that tourists’ low-carbon consumption behaviors, residents’ environmental awareness, and enterprises’ green investments significantly influence the transition process. Further analysis using a chain mediation model shows that policy support positively affects low-carbon outcomes by promoting enterprise investment and influencing resident behavior. The study’s innovation lies in its development of an integrated analytical framework that captures the dynamic interplay among multiple stakeholders, offering a comprehensive perspective on low-carbon tourism transformation in characteristic towns. This study contributes to the sustainable tourism literature and provides valuable insights for policymakers and practitioners working toward carbon neutrality in tourism destinations. Full article

Bearings are essential rotational components that enable mechanical equipment to operate effectively. In real-world industrial environments, bearings are subjected to high temperatures and loads, making failure prediction and health management critical for ensuring stable equipment operations and safeguarding both personnel and property. To address long-tail defect identification, we propose a coupled time–frequency attention model that accounts for the long-tail distribution and pervasive noise present in production environments. The model efficiently learns amplitude and phase information by first converting the time-domain signal into the frequency domain with the Fast Fourier Transform (FFT) and then processing the data using a real–imaginary attention mechanism. To capture dependencies in long sequences, a multi-head self-attention mechanism is then implemented in the time domain. Furthermore, the model’s ability to fully learn features is enhanced through the linear coupling of time–frequency domain attention, which effectively mitigates noise interference and corrects imbalances in data distribution. The performance of the proposed model is compared with that of advanced models under the conditions of imbalanced label distribution, cross-load, and noise interference, proving its superiority. The model is evaluated using the Case Western Reserve University (CWRU) and laboratory bearing datasets. Full article

This article takes alluvial silty sand in the alluvial plain area as the research object. Through a combination of theoretical analysis, finite element simulation, and on-site testing, the engineering characteristics of alluvial silty sand under traffic loads, as well as the feasibility of using alluvial silty sand as roadbed filling material in practical engineering, are systematically expounded on for the first time. The research results indicate that the influence of vehicle speed on the distribution and depth of dynamic stress is relatively small, while the moisture content (optimal 7.8%) and compaction degree (>94%) are the key factors determining the performance of the roadbed. Specifically, the displacement at the top of the roadbed varies with changes in moisture content. An increase in compaction degree is beneficial for reducing settlement and enhancing the stability of the roadbed. Through comparative analysis of finite element simulation and on-site testing, it was found that although the initial settlement of alluvial silt filling is large, the settlement rate is fast and can stabilize in a short period of time. Its long-term performance can still meet engineering requirements. Research has shown that alluvial silt can be used as an economical and reasonable roadbed filling material, but in practical applications, strict control of moisture content and compaction degree is required to optimize roadbed performance. Full article

Accurate photovoltaic (PV) power forecasting is crucial for effective smart grid management, given the intermittent nature of PV generation. To address these challenges, this paper proposes the Temporal Bottleneck-enhanced Bidirectional Temporal Convolutional Network with Multi-Head Attention and Autoregressive (TB-BTCGA) model. It introduces a temporal bottleneck structure and Deep Residual Shrinkage Network (DRSN) into the Temporal Convolutional Network (TCN), improving feature extraction and reducing redundancy. Additionally, the model transforms the traditional TCN into a bidirectional TCN (BiTCN), allowing it to capture both past and future dependencies while expanding the receptive field with fewer layers. The integration of an autoregressive (AR) model optimizes the linear extraction of features, while the inclusion of multi-head attention and the Bidirectional Gated Recurrent Unit (BiGRU) further strengthens the model’s ability to capture both short-term and long-term dependencies in the data. Experiments on complex datasets, including weather forecast data, station meteorological data, and power data, demonstrate that the proposed TB-BTCGA model outperforms several state-of-the-art deep learning models in prediction accuracy. Specifically, in single-step forecasting using data from three PV stations in Hebei, China, the model reduces Mean Absolute Error (MAE) by 38.53% and Root Mean Square Error (RMSE) by 33.12% and increases the coefficient of determination ($R^2$) by 7.01% compared to the baseline TCN model. Additionally, in multi-step forecasting, the model achieves a reduction of 54.26% in the best MAE and 52.64% in the best RMSE across various time horizons. These results underscore the TB-BTCGA model’s effectiveness and its strong potential for real-time photovoltaic power forecasting in smart grids. Full article

Rolling bearings play a crucial role in industrial equipment, and their failure is highly likely to cause a series of serious consequences. Traditional deep learning-based bearing fault diagnosis algorithms rely on large amounts of training data; training and inference processes consume significant computational resources. Thus, developing a lightweight and suitable fault diagnosis algorithm for small samples is particularly crucial. In this paper, we propose a bearing fault diagnosis algorithm based on probabilistic decoupling knowledge distillation and meta-learning (MIX-MPDKD). This algorithm is lightweight and deployable, performing well in small sample scenarios and effectively solving the deployment problem of large networks in resource-constrained environments. Firstly, our model utilizes the Model-Agnostic Meta-Learning algorithm to initialize the parameters of the teacher model and conduct efficient training. Subsequently, by employing the proposed probability-based decoupled knowledge distillation approach, the outstanding performance of the teacher model was imparted to the student model, enabling the student model to converge rapidly in the context of a small sample size. Finally, the Paderborn University dataset was used for meta-training, while the bearing dataset from Case Western Reserve University, along with our laboratory dataset, was used to validate the results. The experimental results demonstrate that the algorithm achieved satisfactory accuracy performance. Full article

Mountain regions play a crucial role in maintaining global biodiversity, with altitude exerting a significant influence on soil microbial diversity by altering plant diversity, soil nutrients, and microclimate. However, differences in microbial community composition between topsoil (0–10 cm deep) and subsoil (10–20 cm deep) remain poorly understood. Here, we aimed to assess soil microbial diversity, microbial network complexity, and microbial community assembly in the topsoil and subsoil layers of the dry–hot Jinsha River valley in southwestern China. Using high-throughput sequencing in soil samples collected along an altitudinal gradient, we found that bacterial diversity in topsoil decreased with increasing altitude, while bacterial diversity in subsoil showed no altitude-dependent changes. Fungal diversity in topsoil also varied with altitude, while subsoil fungal diversity showed no change. These findings suggest that microbial diversity in topsoil was more sensitive to changes in altitude than subsoil. Bacterial community assembly tended to be governed by stochastic processes, while fungal assembly was deterministic. Soil bacterial and fungal network complexity was enhanced with increasing altitude but reduced as diversity increased. Interestingly, the presence of woody plant species negatively affected bacterial and fungal community composition in both soil layers. Soil pH and water content also negatively affected microbial community composition, while organic carbon and total nitrogen positively influenced the microbial community composition. Simultaneously, herb and woody plant diversity mainly affected soil bacterial diversity in the topsoil and subsoil, respectively, while woody plant diversity mainly affected soil fungal diversity in subsoil and soil nutrients had more effect on soil fungal diversity. These findings suggest that altitude directly and indirectly affects microbial diversity in topsoil, subsequently influencing microbial diversity in subsoil through nutrient availability. Full article

Changes in the jet stream not only affect the persistence of climate change and the frequency of extreme weather but are also closely related to climate change phenomena such as global warming. The manual way of drawing the jet stream axes in meteorological operations suffers from low efficiency and subjectivity issues. Automatic identification algorithms based on wind field analysis have some shortcomings, such as poor generalization ability, and it is difficult to handle merging and splitting. A semi-supervised learning jet stream axis identification method is proposed combining consistency learning and self-training. First, a segmentation model is trained via semi-supervised learning. In semi-supervised learning, two neural networks with the same structure are initialized with different methods, based on which pseudo-labels are obtained. The high-confidence pseudo-labels are selected by adding perturbation into the feature layer, and the selected pseudo-labels are incorporated into the training set for further self-training. Then, the jet stream narrow regions are segmented via the trained segmentation model. Finally, the jet stream axes are obtained with the skeleton extraction method. This paper uses the semi-supervised jet stream axis identification method to learn features from unlabeled data to achieve a small amount of labeled data to effectively train the model and improve the method’s generalization ability in a small number of labeled cases. Experiments on the jet stream axis dataset show that the identification precision of the presented method on the test set exceeds about 78% for SOTA baselines, and the improved method exhibits better performance compared to the correlation network model and the semi-supervised method. Full article

Ecological engineering (EE) has a profound impact on land-use dynamics, leading to alterations in ecosystem services (ESs). However, an appropriate EE implementation intensity that can balance the tradeoffs associated with altered ESs well has always been a concern for researchers and policymakers. In this study, we set the transition probability of farmland, bare land, and desertification land to forest and natural shrub, with 2010–2020 as the natural implementation scenario, as 10% for the low-intensity implementation scenario (LIS), 30% for the medium-intensity scenario, and 50% for the high-intensity scenario. The patch-generating land-use simulation (PLUS) model was used to project land-use patterns and the Integrated Valuation of Ecosystem Service and Tradeoffs (InVEST) model was used to simulate changes in the quality of ESs under four EE implementation intensities in 2030. We then performed a quantitative tradeoff analysis on the dominant ESs under four scenarios and used the production possibility frontier (PPF) curve to identify the optimal EE implementation intensity scenario. Our results indicated that an increase in EE implementation intensity would lead to an increase in soil retention, water purification, habitat quality, and carbon storage, but also to a decrease in water yield, aggravating the tradeoffs between water yield and other ESs. In all EE implementation intensity scenarios, the LIS had the lowest tradeoff intensity index and balanced ESs well, and thus was the optimal EE implementation scenario in Qinghai province. Our results provide knowledge to help decision makers select the appropriate EE intensity to maintain sustainable development. The integrated methodology can also be applied in other conservation regions to carry out practical land management. Full article

Agroforestry systems are considered effective methods of carbon sequestration. In these systems, most of the carbon is stored in the soil, and the pattern of tree planting can influence the spatial distribution of organic matter input into the soil. However, limited information is available about the extent of this influence. In this study, the horizontal and vertical distributions of soil organic carbon (SOC) and labile fractions were investigated in four planting systems: a pure poplar (Populus deltoides cv. “35”) planting system, a wide-row (14 m spacing) poplar and wheat (Triticum aestivum L.) agroforestry system, a narrow-row (7 m spacing) poplar and wheat agroforestry system, and a pure wheat field. The results showed that although the poplar system had the highest vegetation biomass (147.50 t ha⁻¹), the agroforestry systems overall had higher SOC contents than the pure poplar system and wheat fields. Especially in the wide-row agroforestry system, the SOC, readily oxidizable carbon, and dissolved organic carbon contents were, respectively, 25.3%, 42.4%, and 99.3% higher than those of the pure poplar system and 60.3%, 148.7%, and 6.3% higher than those of the wheat field in a 1 m soil profile, and it also had the highest fine root biomass. However, the microbial biomass carbon content was highest in the pure poplar system. The SOC of the three poplar planting systems was spatially heterogeneous, with the highest values occurring at 1.5 m in the narrow-row systems and within the tree rows in the wide-row system, similar to the distribution of fine root biomass. Additionally, we found that the larger the diameter at the breast height of the trees, the greater their positive effect on SOC at greater distances. Full article

Ginkgo biloba is one of the most widely cultivated dioecious timber trees in China. Understanding sex-related differences and how they affect growth traits and wood properties is crucial for informed management and optimal utilization of ginkgoes. In the present study, we collected 42 ginkgo samples and conducted DNA molecular identification to determine their sex. The result was a 1:1 ratio of male to female specimens. In addition, we measured 16 growth-trait and wood-property indices for these samples using advanced equipment, such as X-ray diffraction (XRD) and the Hitman ST300 standing tree tool. For growth traits, significant differences were observed between male and female ginkgoes in terms of the diameter at breast height (DBH), clear bole height (CBH), height, and volume. Significant differences were identified in wood properties between male and female ginkgoes in terms of the degree of cellulose crystallinity (DCC), cell length, cell wall thickness, and wall-to-lumen ratio. Tracheids from female trees were found to be wider, with thicker cell walls, than those from male trees. Principal component analysis (PCA) showed that there was a slight separation between the sexes in terms of all growth traits, whereas there was no separation in wood properties. The membership function value (MFV) also showed that male ginkgo exhibited a more robust phenotype than female ginkgo. The selection of male ginkgo for breeding and utilization offers distinct advantages for practical production. Full article

In this paper, the research on the fatigue damage mechanism of tire rubber materials is the core, from designing fatigue experimental methods and building a visual fatigue analysis and testing platform with variable temperature to fatigue experimental research and theoretical modeling. Finally, the fatigue life of tire rubber materials is accurately predicted by using numerical simulation technology, forming a relatively complete set of rubber fatigue evaluation means. The main research is as follows: (1) Mullins effect experiment and tensile speed experiment are carried out to explore the standard of the static tensile test, and the tensile speed of 50 mm/min is determined as the speed standard of plane tensile, and the appearance of 1 mm visible crack is regarded as the standard of fatigue failure. (2) The crack propagation experiments were carried out on rubber specimens, and the crack propagation equations under different conditions were constructed, and the relationship between temperature and tearing energy was found out from the perspective of functional relations and images, and the analytical relationship between fatigue life and temperature and tearing energy was established. Thomas model and thermo-mechanical coupling model were used to predict the life of plane tensile specimens at 50 °C, and the predicted results were 8.315 × 10⁵ and 6.588 × 10⁵, respectively, and the experimental results were 6.42 × 10⁵, with errors of 29.5% and 2.6%, thus verifying the accuracy of thermo-mechanical coupling model. Full article

Grain refinement through borides is known to be suppressed when TiAl is welded with a laser beam. As β grains do not primarily nucleate on boride at a high cooling rate, a mixture of nitrogen and argon is applied as a protecting gas for the formation of TiN during solidification. The phase transformation is changed correspondingly from Liquid → Liquid + β → β → α + β → α + γ+ β → α₂ + γ + B2 to Liquid → TiN + Liquid → β+ TiN → α + γ + TiN → α₂ + γ+ TiN. It is found that β grains prefer to nucleate heterogeneously on the suspending TiN in the melt with orientation relationship ${\text{{}111\text{}}}_{\mathrm{TiN}}//{\left\{110\right\}}_{\mathsf{\beta }}$, leading to refined β grains. α₂ colonies that were thus modified into fine non-dendritic grains. The effects of nitrogen as a shielding atmosphere on the microstructure evolution of TiAl are elaborately studied. Full article

In order for mathematical models to make credible contributions, it is essential for them to be verified and validated. Currently, verification and validation (V&V) of these models does not meet the expectations of the system biology and systems pharmacology communities. Partially as a result of this shortfall, systemic V&V of existing models currently requires a lot of time and effort. In order to facilitate systemic V&V of chosen hypothalamic-pituitary-adrenal (HPA) axis models, we have developed a computational framework named VeVaPy—taking care to follow the recommended best practices regarding the development of mathematical models. VeVaPy includes four functional modules coded in Python, and the source code is publicly available. We demonstrate that VeVaPy can help us efficiently verify and validate the five HPA axis models we have chosen. Supplied with new and independent data, VeVaPy outputs objective V&V benchmarks for each model. We believe that VeVaPy will help future researchers with basic modeling and programming experience to efficiently verify and validate mathematical models from the fields of systems biology and systems pharmacology. Full article

For correct chromosome segregation in mitosis, eukaryotic cells must establish chromosome biorientation where sister kinetochores attach to microtubules extending from opposite spindle poles. To establish biorientation, any aberrant kinetochore–microtubule interactions must be resolved in the process called error correction. For resolution of the aberrant interactions in error correction, kinetochore–microtubule interactions must be exchanged until biorientation is formed (the SWAP process). At initiation of biorientation, the state of weak kinetochore–microtubule interactions should be converted to the state of stable interactions (the SWITCH process)—the conundrum of this conversion is called the initiation problem of biorientation. Once biorientation is established, tension is applied on kinetochore–microtubule interactions, which stabilizes the interactions (the STABILIZE process). Aurora B kinase plays central roles in promoting error correction, and Mps1 kinase and Stu2 microtubule polymerase also play important roles. In this article, we review mechanisms of error correction by considering the SWAP, SWITCH, and STABILIZE processes. We mainly focus on mechanisms found in budding yeast, where only one microtubule attaches to a single kinetochore at biorientation, making the error correction mechanisms relatively simpler. Full article

Roots play a major role in reinforcing and stabilizing soil. The pullout mechanical characteristics of soil reinforcement and slope protection of the root systems of dominant shrub species (Pyracantha and Geranium) were estimated by in-situ pullout tests in a karst area, in which roots were pulled out from soil to reliably test the pulling force. The goals of this study were to discover the pullout mechanical properties of roots in karst areas and to try to analyse the impact of the root system on landslide control. The F–s curves were multipeak curves with a noticeable main peak and main double peaks. The curves showed a linear increasing trend at the initial stage of drawing and decreased rapidly after reaching the peak. The F–s curves of root systems inserted into rock cracks showed secondary fluctuations in the later stage of drawing, and rock cracks stimulated the tensile efficiency of the root system more effectively. Field in situ pullout results indicate that tree roots fail progressively rather than simultaneously. The maximum pulling force had a linear relationship with the increase in soil thickness and a disproportionate increasing trend with the increasing number of broken roots. The displacement of the maximum peak was different between the two tree species and was concentrated at 5–15 cm and 5–25 cm for Pyracantha and Geranium, respectively. The maximum pulling force of Geranium was 1.29 times that of Pyracantha, and the root system of Geranium had strong pullout resistance. We concluded that the peak distribution of the F–s curves was affected by broken roots and rock cracks, while soil thickness and the number of broken roots had positive effects on the maximum pulling force, all of which is helpful in understanding the effect of root pullout mechanical properties on landslides in karst areas. Full article