Search Results (1,849)

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

This study compares the Stackelberg game model and the entropy weight method for allocating intercity water rights in Heilongjiang Province (2014–2021). The entropy method objectively determines indicator weights, while the Stackelberg framework simulates leader–follower interactions between the water authority and users to balance efficiency and satisfaction. Under the same total water rights cap, the Stackelberg scheme achieves a comprehensive benefit of CNY 14,966 billion, 4% higher than the entropy method (CNY 14,436 billion). The results and comprehensive benefits of the two schemes are close to each other in the cities of Qiqihaer, Daqing, Hegang, etc., but the allocation method of the game theory is more in line with the practical needs and can meet the water demand of each region, and the entropy right method is more useful for the cities of Jiamusi, Jixi, and Heihe, while for other cities the water rights allocation appeared to be unreasonable. While the entropy approach is transparent and data-driven, it lacks dynamic feedback and may under- or over-allocate in rapidly changing contexts. The Stackelberg model adapts to varying demands, better aligning allocations with actual needs. We discuss parameter justification, sensitivity, governance assumptions, and potential extensions, including hybrid modeling, climate change integration, stakeholder participation, and real-time monitoring. The findings provide methodological insights for adaptive and equitable water allocation in regions with strong regulatory capacity. Full article

This work investigates the intricate dynamics of the Carreau hybrid nanofluid’s inclined magnetohydrodynamic (MHD) flow, exploring both active and passive control modes. The study incorporates critical factors, including Arrhenius activation energy across a stretched sheet, chemical interactions, and nonlinear thermal radiation. The formulation of the boundary conditions and governing equations is inherently influenced by symmetric considerations in the physical geometry and flow assumptions. Such symmetry-inspired modeling facilitates dimensional reduction and numerical tractability. The analysis employs realistic boundary conditions, including convective heat transfer and control of nanoparticle concentration, which are solved numerically using MATLAB’s bvp5c solver. Findings indicate that an increase in activation energy results in a steeper concentration boundary layer under active control, while it flattens in passive scenarios. An increase in the Biot number ($Bi$) and relaxation parameter ($\mathsf{\Gamma }$) enhances heat transfer and thermal response, leading to a rise in temperature distribution in both cases. Additionally, the 3D surface plot illustrates elevation variations from the surface at low inclination angles, narrowing as the angle increases. The Nusselt number demonstrates a contrasting trend, with thermal boundary layer thickness increasing with higher radiation parameters. A graphical illustration of the average values of skin friction, Nusselt number, and Sherwood number for both active and passive scenarios highlights the impact of each case. Under active control, the Brownian motion’s effect diminishes, whereas it intensifies in passive control. Passive techniques, such as zero-flux conditions, offer effective and low-maintenance solutions for systems without external regulation, while active controls, like wall heating and setting a nanoparticle concentration, maximize heat and mass transfer in shear-thinning Carreau fluids. Full article

Powerful jetted radio sources for which the luminosity in directed kinetic energy has been empirically determined, independent of assumptions, are considered. The total outflow lifetime of each source determined in the context of detailed cosmological studies was found to depend only upon the luminosity in directed kinetic energy (L). The distributions of L, total outflow lifetime, and total outflow energy each have a broad range of values, as do the supermassive black hole masses. The total outflow energy relative to the black hole mass is a small number with a small dispersion. Three explanations of these remarkable results are considered. This could indicate (1) the efficiencies with which black hole irreducible mass is increased and spin mass energy is extracted during the outflow event, (2) that the merger of two supermassive black holes occurs over a timescale commensurate with the independently determined outflow lifetime and that these mergers lead to the production of the low-frequency gravitational wave background, or (3) that feedback shuts off black hole accretion due to energy injected into the ambient medium. Full article

Background/Objectives: The study aimed to investigate how cognitive variables (performance IQ, verbal short-term memory, working memory, and ADHD symptomatology) impact lexical representations in children with high-functioning autism spectrum disorder (HF-ASD). Methods: Participants were two groups (n1 = n2 = 20) of monolingual Greek-speaking children, aged 7 to 12 years, with and without HF-ASD matched in age, gender, and cognitive skills. Results: Overall, the HF-ASD group had more immature lexical representations than the control group, even though the two groups were similar in naming. In both groups, naming was correlated moderately with verbal short-term memory but only age predicted significantly semantic knowledge. In the ASD group, a bilateral predictive relationship was revealed between output motor programming skills and stored phonological knowledge, supporting theoretical assumptions of the psycholinguistic model of speech. Finally, a different pattern of interrelations was observed between cognitive and lexical variables in the two groups. Conclusions: The findings of the current study indicate that ASD children may map and process new vocabulary differently compared to typically developing peers. Full article

This paper analyzes the volatility relationship between the Romanian BET index and the U.S. SFT-500 index during the period 2019–2024, with a particular focus on the impact of political and geopolitical shocks. The study investigates whether financial markets in emerging economies react symmetrically or asymmetrically to external shocks originating from mature markets, especially during periods of political uncertainty. The research period includes four major systemic events: the COVID-19 pandemic, the military conflict in Ukraine, the 2024 U.S. presidential elections, and the 2024 Romanian elections, all of which generated significant volatility in global markets. The methodological approach combines time series econometrics with the Impulse Indicator Saturation (IIS) technique to identify structural breaks and outliers, without imposing exogenous assumptions about the timing of events. The econometric model includes autoregressive and lagged exogenous variables to estimate the influence of the SFT-500 index on the BET index, while IIS variables capture unanticipated political and economic shocks. Additionally, a Fractionally Integrated GARCH (FIGARCH) specification is applied to model the persistence of volatility over time, capturing the long-memory behavior often observed in emerging markets like Romania. The results confirm a statistically significant but partial synchronization between the two markets, with lagged and contemporaneous effects from the SFT-500 index on the BET index. Volatility in Romania is markedly higher and longer-lasting during domestic political episodes, confirming that local factors are a primary source of market instability. For investors, this underscores the need to embed political risk metrics into emerging market portfolios. For policymakers, it highlights how stronger institutions and transparent governance can dampen election- and crisis-related turbulence. Full article

Snow crabs (Chionoecetes opilio), a commercially valuable species in Korean waters, have been managed under the Total Allowable Catch (TAC) system since 2002. However, stock assessment has been limited due to difficulties in estimating key ecological traits such as growth, maturity, and mortality. In this study, the Bayesian Schaefer Model (BSM), implemented within CMSY++ framework, was applied to assess the stock status of snow crabs in Korean waters. BSM requires catch and abundance index data, such as catch per unit effort (CPUE) or biomass, as well as prior information on species resilience and relative biomass (B/k). To improve the reliability of B/k priors, we developed a method to calculate them quantitatively using fishery data, sales amounts, and biological information, unlike the qualitative assumptions on stock and fishing conditions proposed in previous research. Two standardized CPUE indices with differing temporal trends in recent years were used as abundance indices. To address the structural uncertainty associated with these divergent trends, we applied a grid-based approach by treating each CPUE index as an independent model scenario and integrating the posterior distributions. A total of 12,000 posterior estimates (6000 per index) were generated through the BSM and used to construct a Kobe plot. Results indicate that the current biomass is slightly above the level supporting maximum sustainable yield, and fishing mortality slightly below the optimal level, suggesting that the stock is healthy and sustainably exploited. Future research should aim to establish a systematic framework for developing quantitative B/k priors to enhance stock assessment accuracy. Full article

Background/Objectives: Occupational well-being and professional quality of life are essential for healthcare sustainability. While clinical staff are presumed to experience higher stress, few studies have compared their experience to that of non-clinical personnel within the same institution. Methods: This observational study involved 63 employees from an Italian research hospital: 36 healthcare workers in critical care and 27 administrative staff. Participants completed the Brief Coping Orientation to Problems Experienced Inventory (Brief COPE), an ad hoc organizational questionnaire, and the ProQoL Version 5 (administered to clinical staff only). Non-parametric tests (Mann–Whitney U and Chi-square) were used to explore group differences. Results: No significant differences emerged between groups in coping styles or strategies. Significant differences were observed only in reports of work-related injuries (42% of healthcare staff vs. 4% of administrative staff; p = 0.002) and perceived disruption caused by vacation requests (64% vs. 26%; p = 0.006). Other organizational indicators such as job dissatisfaction, intention to leave, or perceived managerial support did not differ significantly. ProQoL results showed that 53% of healthcare workers had moderate to high burnout, and 47.2% scored high on compassion fatigue, while only 2.7% showed high levels of secondary traumatic stress. Conclusions: Despite distinct operational contexts, healthcare and administrative staff reported broadly similar experiences in terms of coping and organizational well-being. These findings challenge assumptions of stark differences across professional roles and suggest that workplace well-being strategies should address the needs of both clinical and non-clinical staff. Full article

The spatial extent of the global land monsoon (GLM), known as the global land monsoon area, is a fundamental climate characteristic with significant socio-ecological implications. While the influence of natural external forcing on GLM intensity during the last millennium (950–1850) is becoming increasingly understood, the responses of the GLM area remain less explored. This study investigates the forced interdecadal variability in the GLM area using the Community Earth System Model Ensemble, focusing on two key drivers: global mean surface temperature (GMST) changes and variations in the tropical Pacific temperature gradient (TPTG). Our analysis reveals that these drivers explain approximately 33% of forced GLM area variance. Global cooling (Cool-GMST) and weakened Pacific gradients (Weak-TPTG) induce significant area contractions of −0.37% and −0.74%, respectively. Most notably, the response to compound forcing is highly non-linear. Concurrent episodes of strong cooling and Weak-TPTG induce a substantially amplified GLM area reduction of −1.37%, far exceeding the linear sum of the individual driver effects. This non-linear amplification, driven by synergistic decreases in both APR and SPF, challenges the conventional assumptions used to model and attribute monsoon boundary changes. This discovery of a non-linear threshold-dependent behavior in the monsoon’s spatial extent, which contrasts with the more linear response of monsoon intensity, is a key finding of our study. This distinction is critical for interpreting paleoclimate records, and serves as a strong indication that future climate projections must account for such non-linearities to avoid underestimating the risk of abrupt monsoon boundary shifts under combined natural and anthropogenic stressors. Full article

This study aims to examine the impact of compressor station availability on the techno-economic aspects of natural gas pipeline transportation, using the proposed Trans-Saharan Gas Pipeline (TSGP) project as a case study. A scenario-based technical and economic analysis was conducted to highlight the economic sensitivities of the systems to availability. The economic assessment of the project was performed using a discounted cash flow method, considering lifecycle costs. The techno-economic model was developed using MATLAB R2020b, accounting for variations in ambient temperatures at the compressor station under different flow conditions. Findings indicate an 8.41% increase in project lifecycle cost in one scenario compared to the baseline, assuming a 15% discount rate. However, the baseline case with a 100% compressor station availability assumption appears unrealistic, as shown by its lifecycle cost and net present value estimates. This is because constant operating conditions throughout the project lifecycle are impossible. Additionally, when station availability increases by 7.87% and the cost of standby units rises by 10.24%, avoided income loss due to station unavailability increases by 14.06%. This reveals a trade-off between the extra capital expenditure on standby units and the savings from avoiding income loss. Furthermore, the impact of 2% and 4% escalation rates of fuel and maintenance costs on lifecycle costs results in a rise of 2.70% and 6.15%, respectively, in one scenario compared to the 0% escalation rate. The results demonstrate the significant influence of compressor station availability analysis on pipeline projects, particularly in reducing engine downtime costs and enhancing project revenue. Therefore, the methods presented here help in understanding the importance of compressor station availability in pipeline techno-economics, leading to more effective resource and financial management. Full article

The energy transformation of transport infrastructure represents a significant challenge, being implemented along the TEN-T network under the introduced AFIR regulation (Regulation for the Deployment of Alternative Fuels Infrastructure). The goal of this transformation is the development of alternative fuels infrastructure deployed along the Trans-European Transport Network (TEN-T), dedicated to light-duty electric vehicles (eLDVs) and heavy-duty electric vehicles (eHDVs). The measures undertaken must be preceded by an analytical process assessing the assumptions outlined in the AFIR regulation, defining targeted actions for achieving the regulation’s objectives, and evaluating the baseline status as well as projected conditions for the years 2025, 2027, 2030, and 2035. This assessment is essential during the planning and management stages of the energy transformation process of transport infrastructure being undertaken by individual EU Member States. Meeting the targets set by AFIR for transport infrastructure necessitates the development of appropriate research tools. The approach proposed in this article offers an innovative framework for addressing the challenges of energy transformation. The initial step involves designing a model for the energy transformation of transport infrastructure, followed by the definition of indicators to assess the implementation of AFIR objectives. This paper presents a model for the energy transformation of road transport infrastructure, defines the individual elements of the model, specifies indicators for evaluating the transformation process, and conducts a research study incorporating these components. This article aims to elucidate the core aspects of the energy transformation of transport infrastructure, identify actions aligned with achieving the objectives of the AFIR regulation, and perform an evaluation of its implementation. Additionally, the research addresses the question of how the energy transformation of road transport infrastructure is unfolding in Poland. The study is based on the structure of electric vehicles (EVs) and transport infrastructure along the TEN-T network in the territory of Poland. The current level of AFIR compliance for eLDVs for the years 2025, 2027, 2030, and 2035 is approximately 175%, 96%, 37%, and 13%, respectively. In contrast, for eHDVs, the compliance level is around 20%, 0%, and 0% for the TEN-T core network, and approximately 10%, 3%, and 0% for the TEN-T comprehensive network. Full article

This paper explores statistical inferences for the maximum ranked set sampling with unequal samples (MaxRSSU) from the Burr Type-III distribution. Under the assumption that the differences between different multiple MaxRSSU cycles are non-ignorable, classical likelihood and Bayesian approaches are employed for estimation of the model parameters and reliability indices. By taking into account the multiple-cycle effect, the maximum likelihood estimators for the parameters of the Burr Type-III distribution are established, along with an analysis of their existence and uniqueness. Furthermore, approximate confidence intervals are constructed based on asymptotic theory. In addition, a hierarchical Bayesian framework is conducted for analysis, and a Monte Carlo sampling method is proposed for complex posterior computation. Extensive simulation studies are carried out for evaluating the performance of the proposed methods, and a further two real-data examples are also provided for illustration. The numerical results indicate that the proposed methods work satisfactorily and the hierarchical Bayesian approach appears more appealing when the uncertainty cycle effect is involved. Full article

Water quality assessment plays a vital role in environmental monitoring and resource management. This study aims to enhance the predictive modeling of the Water Quality Index (WQI) using a combination of statistical diagnostics and machine learning techniques. Data collected from six river locations in Malaysia are analyzed. The methodology involves collecting water quality data from six river locations in Malaysia, followed by a series of statistical analyses including assumption testing (shapiro–wilk and breusch–pagan tests), diagnostic evaluations, feature importance analysis, and principal component analysis (PCA). Decision tree regression (DTR) and autoregressive integrated moving average (ARIMA) are employed for regression, while random forest is used for classification. Learning curve analysis is conducted to evaluate model performance and generalization. The results indicate that dissolved oxygen (DO) and ammoniacal nitrogen (AN) are the most influential parameters, with normalized importance scores of 1.000 and 0.565, respectively. The breusch–pagan test identifies significant heteroscedasticity (p-value = $\left(3.138e^{-115}\right)$), while the Shapiro–Wilk test confirms non-normality (p-value = 0.0). PCA effectively reduces dimensionality while preserving 95% of dataset variance, optimizing computational efficiency. Among the regression models, ARIMA demonstrates better predictive accuracy than DTR. Meanwhile, random forest achieves high classification performance and shows strong generalization capability with increasing training data. Learning curve analysis reveals overfitting in the regression model, suggesting the need for hyperparameter tuning, while the classification model demonstrates improved generalization with additional training data. Strong correlations among key parameters indicate potential multicollinearity, emphasizing the need for careful feature selection. These findings highlight the synergy between statistical pre-processing and machine learning, offering a more accurate and efficient approach to water quality prediction for informed environmental policy and real-time monitoring systems. Full article

The Dual-frequency Precipitation Radar (DPR) aboard the Global Precipitation Measurement (GPM) mission provides valuable three-dimensional precipitation structure data on a global scale and has been widely used in hydrometeorological research. However, due to its spatial resolution limitations and inherent algorithmic assumptions, the accuracy of GPM precipitation estimates can exhibit systematic biases, especially under complex terrain conditions or in the presence of variable precipitation structures, such as light stratiform rain or intense convective storms. In this study, we evaluated the near-surface precipitation rate estimates from the GPM-DPR Level 2A product using over 1440 min of disdrometer observations collected across China from 2021 to 2023. Based on three years of stable stratiform precipitation data from the Jinghe station, we developed a least squares linear correction model for radar reflectivity. Independent validation using national disdrometer data from 2023 demonstrated that the corrected reflectivity significantly improved rainfall estimates under light precipitation conditions, although improvements were limited for convective events or in complex terrain. To further enhance retrieval accuracy, we introduced a regionally adaptive R–Z relationship scheme stratified by precipitation type and terrain category. Applying these localized relationships to the corrected reflectivity yielded more consistent rainfall estimates across diverse conditions, highlighting the importance of incorporating regional microphysical characteristics into satellite retrieval algorithms. The results indicate that the accuracy of GPM precipitation retrievals is more significantly influenced by precipitation type than by terrain complexity. Under stratiform precipitation conditions, the GPM-estimated precipitation data demonstrate the highest reliability. The correction framework proposed in this study is grounded on ground-based observations and integrates regional precipitation types with terrain characteristics. It effectively enhances the applicability of GPM-DPR products across diverse environmental conditions in China and offers a methodological reference for correcting satellite precipitation biases in other regions. Full article

This paper presents a combined theoretical, numerical, and experimental analysis to investigate the lateral plastic crushing behavior and energy absorption of circular and non-circular thin-walled rings between two rigid plates. Theoretical solutions incorporating both linear material hardening and power-law material hardening models are solved via numerical shooting methods. The theoretically predicted force-denting displacement relations agree excellently with both FEA and experimental results. The FEA simulation clearly reveals the coexistence of an upper moving plastic region and a fixed bottom plastic region. A robust automatic extraction method of the fully plastic region at the bottom from FEA is proposed. A modified criterion considering the unloading effect based on the resultant moment of cross-section is proposed to allow accurate theoretical estimation of the fully plastic region length. The detailed study implies an abrupt and almost linear drop of the fully plastic region length after the maximum value by the proposed modified criterion, while the conventional fully plastic criterion leads to significant over-estimation of the length. Evolution patterns of the upper and lower plastic regions in FEA are clearly illustrated. Furthermore, the distribution of plastic energy dissipation is compared in the bottom and upper regions through FEA and theoretical results. Purely analytical solutions are formulated for linear hardening material case by elliptical integrals. A simple algebraic function solution is derived without necessity of solving differential equations for general power-law hardening material case by adopting a constant curvature assumption. Parametric analyses indicate the significant effect of ovality and hardening on plastic region evolution and crushing force. This paper should enhance the understanding of the crushing behavior of circular and non-circular rings applicable to the structural engineering and impact of the absorption domain. Full article