Search Results (6,011)

To address the issue of insufficient accuracy in traditional settlement prediction methods for shield tunneling undercrossing in composite strata in Hangzhou, this paper proposes a particle swarm optimization (PSO)-based Bidirectional Long Short-Term Memory neural network (Bi-LSTM) prediction model for high-precision dynamic prediction of ground settlement under small-sample conditions. Shield tunneling is a key method for urban tunnel construction. This paper presents the measurement and prediction of ground settlement caused by shield tunneling undercrossing existing tunnels in composite strata in Hangzhou. The longitudinal ground settlement curve resulting from shield tunnel excavation was analyzed using measured data, and the measured lateral ground settlement was compared with the Peck empirical formula. Using PSO, the performance of three machine learning models in predicting the maximum ground settlement at monitoring points was compared: Long Short-Term Memory neural network (LSTM), Gated Recurrent Unit neural network (GRU), and Bi-LSTM. The linear relationships between different input parameters and between input parameters and the output parameter were analyzed using the Pearson correlation coefficient. Based on this analysis, the model was optimized, and its prediction performance before and after optimization was compared. The results show that the Bi-LSTM model optimized with the PSO algorithm demonstrates superior performance, achieving both accuracy and stability. Full article

This paper aims to assess the impact of the New Energy Vehicle (NEV) mandate policy on the passenger vehicle market in China, with a focus on its effectiveness in promoting NEV adoption. In response to global climate goals and energy security concerns, China has implemented various NEV policies, including the phase-out of direct subsidies and the introduction of the NEV mandate policy (dual-credits policy). This policy, which combines NEV credits and Corporate Average Fuel Consumption (CAFC) credits, aims not only to promote NEV adoption but also to support industrial policy objectives by helping the auto industry leapfrog traditional internal combustion engines and become globally competitive. In this study, a System Dynamics (SD) model was developed using Vensim software (10.2.2) to simulate interactions between automakers, government policies, and consumer behaviors. The results show that the NEV mandate policy significantly boosts NEV sales, with projections indicating that NEV sales will reach 15 million units by 2030, accounting for 55% of the passenger vehicle market. Additionally, the study finds that tightening NEV credits standards and increasing the NEV credit proportion requirements can further enhance market growth, with stricter measures post-2023 being crucial to achieving a 50% market share. In contrast, under a scenario where the dual-credits policy ends in 2024, the NEV market share would still grow but would fall short of the 50% target by 2030. The findings suggest that stronger policy measures will be essential to maintain long-term market momentum. Full article

This study examines community resilience in post-conflict tourism destinations of the Global South, where externally initiated and controlled tourism development often prevails. Using a conceptual research approach grounded in a comprehensive literature review, the paper identifies critical conditions for resilience-building in these fragile contexts. It demonstrates that post-conflict tourism development typically unfolds in three stages: an initial phase of rapid growth driven by external stakeholders, followed by community awakening to tourism’s impacts, and culminating in community-led efforts to regain control. The study argues that even when initial tourism development exceeds local adaptive capacities, it can initiate a gradual process of resilience-building through proactive community action and supportive policies. The transformative potential of amenity migrants is emphasized, as they can shift from being stressors to becoming agents of change, fostering resilience, provided they are successfully integrated into local communities. The paper also advocates for longitudinal research to better understand the dynamics of amenity migrants’ assimilation and their role in resilience-building, particularly in the Global South, where empirical evidence remains limited. The findings provide valuable insights for designing strategies to achieve sustainable and inclusive tourism development in post-conflict and other vulnerable destinations, offering a pathway to empower local communities and foster long-term resilience. Full article

With the rapid global proliferation of electric vehicles (EVs), their integration as a significant load component within power systems increasingly influences the stable operation and planning of electrical grids. However, the high uncertainty and randomness inherent in EV users’ charging behaviors render accurate load forecasting a challenging task. In this context, the present study proposes a Particle Swarm Optimization (PSO)-enhanced Long Short-Term Memory (LSTM) network forecasting model. By combining the global search capability of the PSO algorithm with the advantages of LSTM networks in time-series modeling, a PSO-LSTM hybrid framework optimized for seasonal variations is developed. The results confirm that the PSO-LSTM model effectively captures seasonal load variations, providing a high-precision, adaptive solution for dynamic grid scheduling and charging infrastructure planning. This model supports the optimization of power resource allocation and the enhancement of energy storage efficiency. Specifically, during winter, the Mean Absolute Error (MAE) is 3.896, a reduction of 6.57% compared to the LSTM model and 10.13% compared to the Gated Recurrent Unit (GRU) model. During the winter–spring transition, the MAE is 3.806, which is 6.03% lower than that of the LSTM model and 12.81% lower than that of the GRU model. In the spring, the MAE is 3.910, showing a 2.71% improvement over the LSTM model and a 7.32% reduction compared to the GRU model. Full article

Accurately predicting the vegetation index (VI) of the Yangtze River Basin and analyzing its spatiotemporal trends are essential for assessing vegetation dynamics and providing recommendations for environmental resource management in the region. This study selected the key climate factors most strongly correlated with three vegetation indexes (VI): the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and kernel Normalized Difference Vegetation Index (kNDVI). Historical VI and climate data (2001–2020) were used to train, validate, and test a CNN-BiLSTM-AM deep learning model, which integrates the strengths of Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and Attention Mechanism (AM). The performance of this model was compared with CNN-BiLSTM, LSTM, and BiLSTM-AM models to validate its superiority in predicting the VI. Finally, climate simulation data under three Shared Socioeconomic Pathway (SSP) scenarios (SSP1-1.9, SSP2-4.5, and SSP5-8.5) were used as inputs to the CNN-BiLSTM-AM model to predict the VI for the next 20 years (2021–2040), aiming to analyze spatiotemporal trends. The results showed the following: (1) Temperature, precipitation, and evapotranspiration had the highest correlation with VI data and were used as inputs to the time series VI model. (2) The CNN-BiLSTM-AM model combined with the EVI achieved the best performance (R² = 0.981, RMSE = 0.022, MAE = 0.019). (3) Under all three scenarios, the EVI over the next 20 years showed an upward trend compared to the previous 20 years, with the most significant growth observed under SSP5-8.5. Vegetation in the source region and the western part of the upper reaches increased slowly, while significant increases were observed in the eastern part of the upper reaches, middle reaches, lower reaches, and estuary. The analysis of the predicted EVI time series indicates that the vegetation growth conditions in the Yangtze River Basin will continue to improve over the next 20 years. Full article

The Catholic missions in early twentieth-century Xiamen represent a complex intersection between Western religious ambitions and the cultural intricacies of Southern Fujian, China. Here, the author examines the missionary work of Manuel Prat Pujoldevall, a Spanish Dominican active in Xiamen, including the Kulangsu International Settlement. Drawing on primary archival records and historical sources, this study assesses Prat’s strategies for governance, cultural adaptation, and resource allocation. The findings reveal that Prat’s pragmatic methods significantly influenced local community dynamics while highlighting the challenges he faced in reconciling religious objectives with shifting political and social conditions. Overall, this paper underscores that the long-term success of cross-cultural missionary work depends on a delicate balance between steadfast religious commitment and culturally adapted management, thereby contributing to broader discussions on the interplay between faith and culture in complicated historical contexts. Full article

In complex marine environments, accurate prediction of maneuvering motion is crucial for the precise control of intelligent ships. This study aims to enhance the predictive capabilities of maneuvering motion for intelligent ships in such environments. We propose a novel maneuvering motion prediction method based on Long Short-Term Memory (LSTM) and Multi-Head Attention Mechanisms (MHAM). To construct a foundational dataset, we integrate Computational Fluid Dynamics (CFD) numerical simulation technology to develop a mathematical model of actual ship maneuvering motions influenced by wind, waves, and currents. We simulate typical operating conditions to acquire relevant data. To emulate real marine environmental noise and data loss phenomena, we introduce Ornstein–Uhlenbeck (OU) noise and random occlusion noise into the data and apply the MaxAbsScaler method for dataset normalization. Subsequently, we develop a black-box model for intelligent ship maneuvering motion prediction based on LSTM networks and Multi-Head Attention Mechanisms. We conduct a comprehensive analysis and discussion of the model structure and hyperparameters, iteratively optimize the model, and compare the optimized model with standalone LSTM and MHAM approaches. Finally, we perform generalization testing on the optimized motion prediction model using test sets for zigzag and turning conditions. The results demonstrate that our proposed model significantly improves the accuracy of ship maneuvering predictions compared to standalone LSTM and MHAM algorithms and exhibits superior generalization performance. Full article

With the continuous deterioration of air quality and the increasingly serious environmental problem of air pollution, accurate air quality prediction is of great significance for environmental governance. Air quality index (AQI) prediction based on deep learning is currently a hot research topic. The neural network model method currently used for prediction has difficulty effectively coping with the high volatility of AQI data and capturing the complex nonlinear relationships and long-term dependencies in the data. To address these issues, this paper proposes multivariate air quality forecasting with a residual nested LSTM neural network based on the discrete stationary wavelet transform (DSWT) model. Firstly, the DSWT data-decomposition technique decomposes each AQI data point into multiple sub-signals. Then, each sub-signal is sent to the NLSTM layer for processing to capture the temporal relationships between different pollutants. The processed results are then combined, using residual connections to mitigate issues of gradient vanishing and explosion during the model training process. The inverse mean squared error method is combined with the simple weighted average method, to serve as the weight-update approach. Back propagation is then applied, to dynamically adjust the weights based on the prediction accuracy of each sample, further enhancing the model’s prediction accuracy. The experiment was conducted on the air quality index dataset of 12 observation stations in and around Beijing. The results show that the proposed model outperforms several existing models and data-processing methods in multi-task AQI prediction. There were significant improvements in mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and R square (R²). Full article

Division-of-focal-plane (DoFP) polarization imaging systems have demonstrated considerable promise in target detection and tracking in complex backgrounds. However, existing methods face challenges, including dependence on complex image preprocessing procedures and limited real-time performance. To address these issues, this study presents a novel histogram of polarization gradient (HPG) feature descriptor that enables efficient feature representation of polarization mosaic images. First, a polarization distance calculation model based on normalized cross-correlation (NCC) and local variance is constructed, which enhances the robustness of gradient feature extraction through dynamic weight adjustment. Second, a sparse Laplacian filter is introduced to achieve refined gradient feature representation. Subsequently, adaptive polarization channel correlation weights and the second-order gradient are utilized to reconstruct the degree of linear polarization (DoLP). Finally, the gradient and DoLP sign information are ingeniously integrated to enhance the capability of directional expression, thus providing a new theoretical perspective for polarization mosaic image structure analysis. The experimental results obtained using a self-developed long-wave infrared DoFP polarization thermal imaging system demonstrate that, within the same FBACF tracking framework, the proposed HPG feature descriptor significantly outperforms traditional grayscale {8.22%, 2.93%}, histogram of oriented gradient (HOG) {5.86%, 2.41%}, and mosaic gradient histogram (MGH) {27.19%, 18.11%} feature descriptors in terms of precision and success rate. The processing speed of approximately 20 fps meets the requirements for real-time tracking applications, providing a novel technical solution for polarization imaging applications. Full article

Understanding the impact of human activities on regional water resources is essential for sustainable basin management. This study examines long-term terrestrial water storage anomalies (TWSA) in the Three Gorges Reservoir Area (TGRA) over two decades, from 2003 to 2023. The analysis utilizes data from the Gravity Recovery and Climate Experiment (GRACE) and its successor mission (GRACE-FO), complemented by Global Land Data Assimilation System (GLDAS) models and ECMWF Reanalysis v5 (ERA5) datasets. The research methodically explores the comparative contributions of natural factors and human activities to the region’s hydrological dynamics. By integrating the GRACE Drought Severity Index (GRACE-DSI), this study uncovers the dynamics of droughts during extreme climate events. It also reveals the pivotal role of the Three Gorges Dam (TGD) in mitigating these events and managing regional water resources. Our findings indicate a notable upward trend in TWSA within the TGRA, with an annual increase of 0.93 cm/year. This trend is largely due to the effective regulatory operations of TGD. The dam effectively balances the seasonal distribution of water storage between summer and winter and substantially reduces the adverse effects of extreme droughts on regional water resources. Further, the GRACE-DSI analysis underscores the swift recovery of TWSA following the 2022 drought, highlighting TGD’s critical role in responding to extreme climatic conditions. Through correlation analysis, it was found that compared with natural factors (correlation 0.62), human activities (correlation 0.91) exhibit a higher relative contribution to TWSA variability. The human-induced contributions were derived from the difference between GRACE and GLDAS datasets, capturing the combined effects of all human activities, including the operations of the TGD, agricultural irrigation, and urbanization. However, the TGD serves as a key regulatory facility that significantly influences regional water resource dynamics, particularly in mitigating extreme climatic events. This study provides a scientific basis for water resource management in the TGRA and similar large reservoir regions, emphasizing the necessity of integrating the interactions between human activities and natural factors in basin management strategies. Full article

The untapped potential of wave energy offers another alternative to diversifying renewable energy sources and addressing climate change by reducing CO₂ emissions. However, development costs to mature the technology remain significant hurdles to adoption at scale and the technology often must compete against other marine energy renewables such as offshore wind. Here, we conduct a real option valuation that includes the uncertain market price of wholesale electricity and managerial flexibility expressed in determining future optimal decisions. We demonstrate the probability that the project’s embedded compound real option value can turn a negative net present value wave energy project to a positive expected value. This change in investment decision uses decision tree analysis, where real options are developed as decision nodes, and models the uncertainty as a risk-neutral stochastic process using chance nodes. We also show how our results are analogous to a financial out-of-the-money call option. Our results highlight the distribution of outcomes and the benefit of a staged long-term investment in wave energy systems to better understand and manage project risk, recognizing that these probabilistic results are subject to the ongoing evolution of wholesale electricity prices and the stochastic process models used here to capture their future dynamics. Lastly, we show that the near-term optimal decision is to continue to fund ongoing development of a reference architecture to a higher technology readiness level to maintain the long-term option to deploy such a renewable energy system through private investment or private–public partnerships. Full article

Long-term runoff projection and uncertainty estimates can provide both the changing trends and confidence intervals of water resources, provide basic information for decision makers, and reduce risks for water resource management. In this paper, a grid-based runoff projection and uncertainty framework was proposed through input selection and long short-term memory (LSTM) modelling coupled with uncertainty analysis. We simultaneously considered dynamic variables and static variables in the candidate input combinations. Different input combinations were compared. We employed LSTM to develop a relationship between monthly runoff and the selected variables and demonstrated the improvement in forecast accuracy through comparison with the MLR, RBFNN, and RNN models. The LSTM model achieved the highest mean Kling–Gupta Efficiency (KGE) score of 0.80, representing respective improvements of 45.45%, 33.33%, and 2.56% over the other three models. The uncertainty sources originating from the parameters of the LSTM models were considered, and the Monte Carlo approach was used to provide uncertainty estimates. The framework was applied to the Yellow River Source Area (YRSR) at the 0.25° grid scale to better show the temporal and spatial features. The results showed that extra information about static variables can improve the accuracy of runoff projections. Annual runoff tended to increase, with projection ranges of 148.44–296.16 mm under the 95% confidence level, under various climate scenarios. Full article

The treatment of plants with non-thermal atmospheric-pressure plasma impacts several aspects of plant life. However, the effects of long-term plasma irradiation on crop cultivation are not enough investigated. The purpose of the current study is to address this subject. The growth of tomato plants, the preservation status of harvested tomato fruits, and the microbial community on the surface of harvested tomato fruits were compared between 12 long-term plasma-irradiated plants and 12 air-irradiated plants with statistical analyses. The growth parameters (plant height, number of leaves and fruit bunches, SPAD value, and plant dry weight) of the plants that were periodically irradiated with plasma from the three-leaf stage to the green-enlarged-fruit stage, were the same as those of the air-irradiated controls. However, the preservation status of the tomato fruits harvested from the plasma-irradiated plants was improved in comparison with that of the fruits from the air-irradiated controls. Analysis of the microbiome on the surface of the fruit indicated that long-term plasma irradiation during cultivation promoted an increased bacterial diversity on the fruit surface. Thus, the effect of plasma irradiation on the diversification of microbial population dynamics on tomato fruit may be associated with an improved preservation status of harvested tomato fruits. Full article

The abnormal proliferation of Ulva in the Yellow Sea has instigated the notorious green tide phenomenon. Mitigating this ecological challenge necessitates a holistic comprehension of Ulva’s nitrogen and phosphorus uptake behaviors. Investigating the mechanisms governing nutrient absorption, encompassing factors like concentration, form, and input dynamics, has unveiled their profound influence on nutrient assimilation rates. The nutrient absorption characteristics of Ulva prolifera, including its preference for abundant nutrients, a high nitrogen-to-phosphorus (N/P) ratio, and its ability to efficiently absorb nutrients during pulse nutrient input events, determine its dominant role in the green tide events in the Yellow Sea. Although source control and preemptive salvaging are effective methods for managing green tides, addressing the root causes of these coastal ecological disasters requires the implementation of long-term pollution control strategies that align with sustainable development goals, with a priority on reducing marine eutrophication. This is crucial for the effective management and restoration of the coastal ecosystem in the Yellow Sea. Full article

Based on the potential of DPSCs as the most promising candidates for bone tissue engineering, we comprehensively investigated the time-dependent cellular and molecular changes that occur during their osteodifferentiation. To analyze this area in-depth, we used both cellular and molecular approaches. Morphological changes were monitored using bright-field microscopy, while the production of mineral deposits was quantified spectrophotometrically. The expression of a key mesenchymal stem cell marker, CD90, was assessed via flow cytometry. Finally, protein-level changes in whole cells were examined by fluorescence microscopy. Our results show successful long-term osteodifferentiation of the patient’s DPSCs within 25 days. In differentiated cells, mineralized extracellular matrix production gradually increased; in contrast, the expression of the specific stem cell marker CD90 significantly decreased. We observed dynamic changes in intracellular and extracellular proteins when collagen1 A1 and osteopontin appeared as earlier markers of osteogenesis, while apolipoprotein A2, bone morphogenetic protein 9, dentin sialophosphoprotein, and matrix metalloproteinase 8 were produced mainly in the late stages of this process. A decrease in actin microfilament expression indicated a reduction in cell proliferation, which could be used as another marker of osteogenic initiation. Our results suggest a coordinated process in vitro in which cells synthesize the necessary proteins and matrix components to regulate the growth of hydroxyapatite crystals and form the bone matrix. Full article