Search Results (7,169)

It has always been a research topic for some meteorologists to design a new and reasonable calculation scheme of the intensity of the Tibetan Plateau (TP) summer monsoon (TPSM). Existing indices are defined based on dynamic factors. However, the intensity of the TPSM can also be influenced by thermal factors. We therefore propose defining a TPMI in terms of horizontal temperature advection within the main body of the TP. This provides a new index that directly quantifies the extent to which the thermal forcing in the TP region regulates the monsoon system. The new index emphasizes the importance of the atmospheric asymmetry structure in measuring TPSM strength, represents the variability of the TPSM circulation system, effectively reflects the meteorological elements, and accurately represents the climate variation. Tropospheric temperature (TT) and TPSM are linked by the new index. These significant centers of correlation are characterized by alternating positive and negative phases along the Eastern European Plain, across the Turan Plain, and into southwestern and northeastern China. The correlation coefficients are found to be significantly out of phase between high and low altitudes in the vertical direction. This research broadens our minds and helps us to develop a new approach to measuring TPSM strength. It can also predict extreme weather events in advance based on TPMI changes, providing a scientific basis for disaster warnings and the management of agriculture and water resources. Full article

Predicting short-term buy and sell signals in financial markets remains a significant challenge for algorithmic trading. This difficulty stems from the data’s inherent volatility and noise, which often leads to spurious signals and poor trading performance. This paper presents a novel algorithmic trading model for silver that combines fine-tuned Convolutional Neural Networks (CNNs) with a decision filter based on the Relative Strength Index (RSI). The technique allows for the prediction of buy and sell points by turning time series data into chart images. Daily silver price per ounce data were turned into chart images using technical analysis indicators. Four pre-trained CNNs, namely AlexNet, VGG16, GoogLeNet, and ResNet-50, were fine-tuned using the generated image dataset to find the best architecture based on classification and financial performance. The models were evaluated using walk-forward validation with an expanding window. This validation method made the tests more realistic and the performance evaluation more robust under different market conditions. Fine-tuned VGG16 with the RSI filter had the best cost-adjusted profitability, with a cumulative return of 115.03% over five years. This was nearly double the 61.62% return of a buy-and-hold strategy. This outperformance is especially impressive because the evaluation period was mostly upward, which makes it harder to beat passive benchmarks. Adding the RSI filter also helped models make more disciplined decisions. This reduced transactions with low confidence. In general, the results show that pre-trained CNNs fine-tuned on visual representations, when supplemented with domain-specific heuristics, can provide strong and cost-effective solutions for algorithmic trading, even when realistic cost assumptions are used. Full article

The growing use of composites in automotive and aerospace fields highlights the need for effective joining of dissimilar materials. Adhesive bonding offers significant advantages over traditional methods. Therefore, comprehensively exploring the relationship between multiple design variables and joint strength, and subsequently achieving accurate prediction of joint strength based on this understanding, is essential for enhancing the effectiveness and efficiency of adhesive joint structural optimization. However, the joint—the critical yet weakest part—has strength governed by complex structural variables that are not fully understood, limiting optimization potential. Based on the effectiveness of finite element simulation in tensile fracture mechanics, this study developed a deep neural network (DNN). Combining the DNN model with a genetic algorithm (GA), both single-objective and multi-objective optimization were conducted. The single-objective optimization focused solely on maximizing joint strength, while the multi-objective GA further quantified the Pareto optimal trade-offs between joint strength and bond area, identifying compromise solutions. The effectiveness of the optimized parameters was validated, demonstrating higher efficiency and accuracy compared to traditional optimization methods such as response surface methodology (RSM). This integrated approach provides a robust framework for predicting joint strength and achieving effective optimization of bonded structures. Full article

In 2024, significant increases in surface temperature were recorded in Batam City and Bintan Regency, marking the highest levels observed in regional climate monitoring. The rapid conversion of vegetated land into residential and industrial areas has been identified as a major contributor to the acceleration of local climate warming. Climatological analysis also revealed extreme temperature fluctuations, underscoring the urgent need to understand spatial patterns of temperature distribution in response to climate change and weather variability. This research uses a Cellular Automata–Artificial Neural Network (CA−ANN) approach to model spatial and temporal changes in land surface temperature across the Riau Islands. To overcome the limitations of single-model predictions in a geographically diverse and unevenly developed region, Landsat satellite imagery from 2014, 2019, and 2024 was analyzed. Surface temperature data were extracted using the Brightness Temperature Transformation method. The CA−ANN model, implemented via the MOLUSCE platform in QGIS, incorporated additional environmental variables, such as rainfall distribution, vegetation density, and drought indices, to simulate future climate scenarios. Model validation yielded a Kappa accuracy coefficient of 0.72 for the 2029 projection, demonstrating reliable performance in capturing complex climate–environment interactions. The projection results indicate a continued upward trend in surface temperatures, emphasizing the urgent need for effective mitigation strategies. The findings highlight the essential role of remote sensing and spatial modeling in climate monitoring and policy formulation, especially for small island regions susceptible to microclimatic changes. Despite the strengths of the CA−ANN modeling framework, several inherent limitations constrain its application, particularly in the complex and heterogeneous context of tropical island environments. Notably, the accuracy of model predictions can be limited by the spatial resolution of satellite imagery and the quality of auxiliary environmental data, which may not fully capture fine-scale microclimatic variations. Full article

As a core component of industrial robots, the transmission accuracy life (TAL) of rotary vector (RV) reducers constitutes a primary factor determining the high-precision operation of robotic systems. However, current life evaluation methods for RV reducers predominantly rely on conventional bearing strength life calculations, while neglecting its transmission accuracy degradation during operation. To address this limitation, a static analysis model of RV reducers is established, through which a calculation method for transmission accuracy and TAL is presented. Simultaneously, tooth surface and bearing wear models are developed based on Archard’s wear theory. Through coupled analysis of the aforementioned models, the transmission accuracy degradation law of RV reducers is revealed. The results show that during the operation of the RV reducer, the transmission error (TE) maintains relative stability over time, whereas the lost motion (LM) exhibits a continuous increase. Based on this observation, LM is defined as the evaluation metric for TAL, and a novel TAL estimation model is proposed. The feasibility of the developed TAL estimation model is ultimately validated through accelerated transmission accuracy degradation tests on RV reducers. The error between the predicted and experimental results is 11.06%. The proposed TAL estimation model refines the life evaluation methodology for RV reducers, establishing a solid foundation for real-time transmission accuracy compensation in reducer operation. Full article

Financial time series display inherent nonlinearity and high volatility, creating substantial challenges for accurate forecasting. Advancements in artificial intelligence have positioned deep learning as a critical tool for financial time series forecasting. However, conventional deep learning models often fail to accurately predict future trends in complex financial data due to inherent limitations. To address these challenges, this study introduces a WOA-BiLSTM-ARIMA hybrid forecasting model leveraging parameter optimization. Specifically, the whale optimization algorithm (WOA) optimizes hyperparameters for the Bidirectional Long Short-Term Memory (BiLSTM) network, overcoming parameter tuning challenges in conventional approaches. Due to its strong capacity for nonlinear feature extraction, BiLSTM excels at modeling nonlinear patterns in financial time series. To mitigate the shortcomings of BiLSTM in capturing linear patterns, the Autoregressive Integrated Moving Average (ARIMA) methodology is integrated. By exploiting ARIMA’s strengths in modeling linear features, the model refines BiLSTM’s prediction residuals, achieving more accurate and comprehensive financial time series forecasting. To validate the model’s effectiveness, this paper applies it to the prediction experiment of future spread data. Compared to classical models, WOA-BiLSTM-ARIMA achieves significant improvements across multiple evaluation metrics. The mean squared error (MSE) is reduced by an average of 30.5%, the mean absolute error (MAE) by 20.8%, and the mean absolute percentage error (MAPE) by 29.7%. Full article

This study presents the development of a reliable predictive model for evaluating key physical and mechanical properties of cement-based composites modified with granite powder, a waste byproduct from granite rock cutting. The research addresses the need for more sustainable materials in the concrete industry by exploring the potential of granite powder as a supplementary cementitious material (SCM) to partially replace cement and reduce CO₂ emissions. The experimental program included standardized testing of samples containing up to 30% granite powder, focusing on compressive strength at 7, 28, and 90 days, bonding strength at 28 days, and packing density of the fresh mixture. A multilayer perceptron (MLP) artificial neural network was employed to predict these properties using four input variables: granite powder content, cement content, sand content, and water content. The network architecture, consisting of two hidden layers with 10 and 15 neurons, respectively, was selected as the most suitable for this purpose. The model achieved high predictive performance, with coefficients of determination (R) exceeding 0.9 and mean absolute percentage errors (MAPE) below 6% for all output variables, demonstrating its robustness and accuracy. The findings confirm that granite powder not only contributes positively to concrete performance over time, but also supports environmental sustainability goals by reducing the carbon footprint associated with cement production. However, the model’s applicability is currently limited to mixtures using granite powder at up to 30% cement replacement. This research highlights the effectiveness of machine learning, specifically neural networks, for solving multi-output problems in concrete technology. The successful implementation of the MLP network in this context may encourage broader adoption of data-driven approaches in the design and optimization of sustainable cementitious composites. Full article

Steel–ultra-high-performance concrete (UHPC) composite beams with small-rib-height perfobond strip connectors (SRHPBLs) exhibited advantages of light weight and high bearing capacity, demonstrating the potential for applications of UHPC in bridge engineering. During service stages, the composite beams were usually under combined tension–shear loads, rather than pure shear loads. Nevertheless, there were research gaps in the static behavior of SRHPBLs embedded in UHPC under combined tension–shear loads, which limited their applications in practice. To address this issue, systematic experimental and theoretical analyses were conducted in the present study, considering the test variables of tension–shear ratio, row number, and strip number. It was demonstrated that the tension–shear ratio had less effect on ultimate shear strength, initial shear stiffness, and ultimate slip of SRHPBLs. When the tension–shear ratio was increased from 0 to 0.42, the shear capacity, initial shear stiffness, and slip at peak load of SRHPBLs decreased by 24.31%,19.02%, and 22.00%, respectively. However, increasing the row number and strip number significantly improved the shear performance of SRHPBLs. Compared to the single-row specimens, the shear capacity and initial shear stiffness of the three-row specimens increased by an average of 92.82% and 48.77%, respectively. The shear capacity and initial shear stiffness of the twin-strip specimens increased by an average of 103.84% and 87.80%, respectively, compared to the single-strip specimens. Finally, more accurate models were proposed to predict the shear–tension relationship and ultimate shear capacity of SRHPBLs embedded in UHPC under combined tension–shear loads. Full article

Tensile strength and elastic modulus are key mechanical properties for continuous basalt fibers, which are inherently sustainable materials derived from naturally occurring volcanic rock. This study employs five ensemble learning models, including Extra Tree Regression, Random Forest, Extreme Gradient Boosting, Categorical Gradient Boosting, and Light Gradient Boosting Machine, to predict the tensile strength and elastic modulus of basalt fibers based on chemical composition. Model performance was evaluated using the coefficient of determination (R²), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). Following hyperparameter optimization, the Extreme Gradient Boosting model demonstrated superior performance for tensile strength prediction (R² = 0.9152, MSE = 0.2867, RMSE = 0.5354, and MAE = 0.6091), while CatBoost excelled in elastic modulus prediction (R² = 0.9803, MSE = 0.1209, RMSE = 0.3478, and MAE = 0.2692). SHapley Additive exPlanations (SHAP) analysis identified CaO and SiO₂ as the most significant features, with dependency analysis further revealing optimal ranges of critical variables that enhance mechanical performance. This approach enables rapid data-driven basalt selection, reduces energy-intensive trials, lowers costs, and aligns with sustainability by minimizing resource use and emissions. Integrating machine learning with material science advances eco-friendly fiber production, supporting the circular economy in construction and composites. Full article

Background/Objectives: Central sensitization (CS) is identified as a cause of pain in various musculoskeletal diseases, and breathing pattern disorders (BPDs) are reported to be correlated with chronic pain. This study aimed to analyze the relationship between CS and BPDs through regression analysis. Methods: A cross-sectional study was designed according to the strengthening the reporting of observational studies in epidemiology (STROBE) guidelines. Forty participants with moderate to extreme CS (central sensitization inventory for Koreans; CSI-K ≥ 40) were enrolled, and their respiratory motion (manual assessment of respiratory motion; MARM), respiratory function (self-evaluation of breathing questionnaire; SEBQ), respiratory muscle strength (maximal inspiratory pressure; MIP, maximal expiratory pressure; MEP), pain intensity (numeric pain rating scale; NPRS), pain cognition (Korean version of pain catastrophizing scale; K-PCS), muscle tone and stiffness were measured. Results: Among participants with moderate to extreme CS, 82.5% showed BPDs and 42.5% reported severe pain intensity. Regression analysis revealed significant relationships between respiratory and pain variables. K-PCS demonstrated significant negative relationships with MARM area (β = −0.437, R² = 0.191) and positive relationships with SEBQ (β = 0.528, R² = 0.279). In the subgroup with BPDs, strong regression relationships were found between MARM area and NPRS usual pain (β = −0.486, R² = 0.237) and K-PCS (β = −0.605, R² = 0.366). Multiple regression analysis showed that MARM area and SEBQ together explained 41.2% of variance in pain catastrophizing. The comprehensive muscle stiffness prediction model using CSI-K, K-PCS, and muscle tone showed remarkably high explanatory power (R² = 0.978). Conclusions: In individuals with moderate to extreme CS, respiratory dysfunction was prevalent and significantly predictable through regression models with pain intensity and pain cognition. These quantitative regression relationships between breathing mechanics, pain measures, and muscle properties provide clinical prediction tools and suggest the importance of assessing breathing patterns in CS management. Full article

To investigate how sand erosion impacts the anti-icing performance of wind turbine blade surfaces, this study experimentally examines the individual and interactive effects of four key factors—the freezing temperature, separation temperature, surface roughness of eroded blade coatings, and loading rate on ice adhesion properties.The results from single-factor analyses reveal that the ice adhesion strength increases linearly with decreasing separation temperature. A more nuanced relationship emerges when considering the freezing temperature relative to the separation temperature: when the freezing temperature exceeds the separation temperature, the adhesion strength rises linearly as the separation temperature drops; conversely, when the freezing temperature is lower than the separation temperature, the adhesion strength decreases linearly with falling separation temperature. Higher loading rates correlate with reduced ice adhesion, while increased surface roughness induced by sand erosion leads to greater adhesion strength. Orthogonal array testing demonstrates the hierarchy of these factors’ influence on post-erosion ice adhesion, as follows: separation temperature > loading rate > freezing temperature > surface roughness of sand-eroded coatings. Notably, the separation temperature and loading rate exert the most significant effects. Furthermore, a regression equation for ice adhesion strength is established based on orthogonal test results, which can effectively predict ice adhesion strength under untested parameter combinations. These findings provide critical foundational data and a reliable theoretical tool to inform the development and optimization of practical de-icing systems in engineering applications. Full article

The shear strength parameters of landslide zones are the necessary data basis for landslide stability evaluation and landslide surge disaster chain research. It is important to determine the physical and mechanical parameters of landslide zones scientifically and reasonably. In this study, four small residual landslide deposits near the Hei Duo Village road in Diebu County, Gansu Province, were investigated. The research involved detailed field investigations, the construction of landslide engineering geological models, and the use of the transfer coefficient method for simultaneous/inverse inversion and sensitivity analysis of the strength parameters of the four landslides. Based on the inversion results, an analysis of the landslide formation mechanism was conducted. The inversion results yielded the shear strength parameters of the sliding surface soil as c = 30.12 kPa and φ = 21.08°. It was found that the excavation at the base of the slope is the direct triggering factor for the landslides, with the 3# landslide being the most affected by the base excavation. In terms of the type of movement, all four landslides belong to the retrogressive landslide, with the maximum shear strain increment mainly concentrated at the slope angle after excavation. The slope body experiences shear failure, which is in good agreement with the field conditions. The study provides reference for stability prediction and disaster prevention and control of reservoir bank slope. Full article

The self-consistent field Poisson–Boltzmann framework is applied for analysis of equilibrium partitioning of ampholytic protein-like nanocolloids between buffer solution and weak (pH-sensitive) versus strong polyelectrolyte (polyanionic) brushes with the same net charge per unit area. The position-dependent nanocolloid net charge and the insertion freeenergy profiles are derived as a function of pH and ionic strength in the solution. It is demonstrated that, similar to strong polyelectrolyte brushes, pH-sensitive brushes are capable of the uptake of nanocolloids in the vicinity of the isoelectric point, that is, when the net charge of the colloid in the buffer has either the opposite or the same sign as the ionized monomer units of the brush. At $pI\ge p{K}_{brush}$ and $pH\ge pI$, the particle absorption patterns by similarly (negatively) charged brushes are qualitatively similar in the cases of strong and weak polyelectrolyte brushes, but the freeenergy barrier at the brush periphery is wider for weak than for strong polyelectrolyte brushes, which may cause stronger kinetic hindrance for the nanocolloid uptake by the brush. A decrease in $pH$ below the IEP leads to a monotonic increase in the depth of the insertion freeenergy minimum inside a strong polyelectrolyte brush, whereas for weak polyelectrolyte brushes, a more peculiar trend is predicted: due to competition between the increasing positive charge of the nanocolloid and the decreasing magnitude of the negative charge of the brush, the absorption is weakened at low $pH$. Full article

Damaged material invariably exhibits a lower resonance frequency than undamaged material due to its reduced stiffness. Under fatigue loading, damage accumulates until failure, so changes in resonance frequency can be utilised as a variable to predict fatigue life. Conventional fatigue life prediction methods have a low success rate, prompting the exploration of alternative approaches. We have presented a novel method for predicting the fatigue life of spruce wood based on changes in resonance frequency during fatigue, using a representative specimen (i.e., one out of five specimens tested, with four used for static strength reference). We conducted a low-cycle fatigue test and monitored the resonance frequency alongside the dynamic and static modulus of elasticity. All three types of data were employed to predict fatigue life using between 40% and 100% of the measurement data. Of the two fatigue life prediction methods investigated, the Weibull cycle density distribution using resonance frequency measurements proved most appropriate. The error decreases monotonically with the amount of resonance frequency measurement data used for fatigue life prediction, reaching its lowest value of 1% when the full resonance frequency dataset is used. The proposed fatigue life prediction method should be further validated with a larger sample size, as fatigue is inherently a statistical phenomenon. Full article

This study provides a comprehensive six-year assessment (2018–2023) of heavy metal contamination in the Romanian Black Sea sector, integrating data from seawater, surface sediments, and benthic mollusks. Sampling was conducted across a broad spatial gradient, including transitional, coastal, shelf, and offshore waters beyond 200 m depth. Concentrations of six potentially toxic metals, including cadmium (Cd), lead (Pb), nickel (Ni), chromium (Cr), copper (Cu), and cobalt (Co), were measured to evaluate regional variability, potential sources, and ecological implications. Results indicate some exceedances of regulatory thresholds for Cd and Pb in transitional and coastal waters, associated with Danube River input and coastal pressures. Seabed substrate analysis revealed widespread enrichment in Ni, moderate levels of Cr, and sporadic Cd elevation in Danube-influenced areas, along with localized hotspots of Cu and Pb near port and industrial zones. Biological uptake patterns in mollusks (bivalves Mytilus galloprovincialis and Anadara inequivalvis and gastropod Rapana venosa) highlighted Cd among key metals of concern, with elevated Bioconcentration Factor (BCF) and Biota–Sediment Accumulation Factor (BAF). Offshore waters generally exhibited lower pollution levels. However, isolated exceedances, such as Cr outliers recorded in 2022, suggest that deep-sea inputs from atmospheric or maritime sources may be both episodic in nature and underrecognized due to limited monitoring coverage. The combined use of water, sediment, and biota data emphasize the strength of multi-matrix approaches in marine pollution evaluation, revealing persistent nearshore pressures and less predictable offshore anomalies. These findings contribute to a more complete understanding of heavy metal distribution in the northwestern Black Sea and provide a scientific basis for improving long-term environmental monitoring and risk management strategies in the region. Full article