Search Results (1,468)

Precambrian tropical glaciation is an enigma of Earth’s climate. Overlooking fundamental difference of land/sea icelines, it was equated with a global frozen ocean, which is at odds with the sedimentary evidence of an active hydrological cycle, and its genesis via the runaway ice–albedo feedback conflicts with the mostly ice-free Proterozoic when its trigger threshold was well exceeded by the dimmer sun. In view of these shortfalls, I put forth two key hypotheses of the tropical glaciation: first, if seeded by mountain glaciers, the land ice would advance on sea level to be halted by above-freezing summer temperature, which thus abuts an open cozonal ocean; second, a tropical supercontinent would block the brighter tropical sun to cause the required cooling. To test these hypotheses, I formulate a minimal tropical/polar box model to examine the temperature response to a varying tropical land area and show that tropical glaciation is indeed plausible when the landmass is concentrated in the tropics despite uncertain model parameters. In addition, given the chronology of paleogeography, the model may explain the observed deep time climate to provide a unified account of the faint young Sun paradox, Precambrian tropical glaciations, and Phanerozoic glacio-epochs, reinforcing, therefore, the uniformitarian principle. Full article

High-entropy alloys (HEAs) have shown promise as materials with improved mechanical properties compared to traditional materials. Achieving the desired mechanical properties depends on the alloy composition, both in terms of percentage and elements. Laser directed energy deposition technology allows for the production of products with various complex geometries and dimensions (L-DED). It has been found that HEA FeCoNiCrCu alloys can be divided into regions with high concentrations of Fe + Co + Cr and Cu elements. Dendrite growth directions of HEA FeCoNiCrCu are (111) and (200), and the average microhardness is around 240 HV. All samples have cracks vertically along the height. Fine-grained and dendrite structures were observed. Cu-element is mainly found in cracks. The HEA FeCoNiCrCu alloy was compared with another HEA FeCoCrMnNi, successfully obtained by the same L-DED technology. Comparing the two HEAs FeCoNiCrCu and FeCoCrMnNi obtained with the same deposition parameters can help determine the impact of one element on the phase composition, microstructure and mechanical properties of the high-entropy alloy. Replacing the element Mn with Cu in HEA FeCoNiCrMn led to a shift in the dendrite growth from one to two predominant directions and a decrease in the average microhardness by 20%. Full article

During pump operation, the pump-turbine system experiences unstable fluctuations in outlet pressure, which induces turbulence and additional energy losses. Understanding the impact of outlet pressure variations on the internal flow field is crucial for the further development of turbine units. This study employs numerical methods to systematically analyze the effects of outlet pressure changes on flow characteristics and energy loss. The results show that a decrease in outlet pressure to P0.9BEP significantly increases entropy production in the double-row stay guide vane region, primarily due to flow separation and vortex formation. In the flow passage, sealing gap, and tailpipe regions, entropy production is mainly driven by wall effects, while secondary flows influence the spiral case. The vortex distribution in the double-row stay guide vane is complex, with different variation trends observed in the active and fixed guide vane regions. Outlet pressure changes affect the interaction between the flow passage blades and the fluid, leading to localized flow separation and directly impacting energy loss in downstream components. Additionally, the rate of change in outlet pressure significantly influences vortex generation and dissipation. This research provides new theoretical insights and research directions for performance optimization and energy loss control in pump-turbine systems. Full article

Well-production forecasting plays a crucial role in oil and gas development. Traditional methods, such as numerical simulations, require substantial computational effort, while empirical models tend to exhibit poor accuracy. To address these issues, machine learning, a widely adopted artificial intelligence approach, is employed to develop production forecasting models in order to enhance the accuracy of oil and gas well-production predictions. This research focuses on the geological, engineering, and production data of 435 fracturing wells in the North China Oilfield. First, outliers were detected, and missing values were handled using the mean imputation and nearest neighbor methods. Subsequently, Pearson correlation coefficients were utilized to eliminate linearly irrelevant features and optimize the dataset. By calculating the gray correlation degrees, maximum mutual information, feature importance, and Shapley additive explanation (SHAP) values, an in-depth analysis of various dominant factors was conducted. To further assess the importance of these factors, the entropy weight method was employed. Ultimately, 19 features that were highly correlated with the target variable were successfully screened as inputs for subsequent models. Based on the AutoGluon framework, model training was conducted using 5-fold cross-validation combined with bagging and stacking techniques. The training results show that the model achieved an R² of 0.79 on the training set, indicating good fitting ability. This study offers a promising approach for the development of oil and gas production forecasting models. Full article

The genus of Nitraria L. are Tertiary-relict desert sand-fixing plants, which are an important forage and agricultural product, as well as an important source of medicinal and woody vegetable oil. In order to provide a theoretical basis for better protection and utilization of species in the Nitraria L., this study collected global distribution information within the Nitraria L., along with data on 29 environmental and climatic factors. The Maximum Entropy (MaxEnt) model was used to simulate the globally suitable distribution areas for Nitraria L. The results showed that the mean AUC value was 0.897, the TSS average value was 0.913, and the model prediction results were excellent. UV-B seasonality (UVB-2), UV-B of the lowest month (UVB-4), precipitation of the warmest quarter (bio18), the DEM (Digital Elevation Model), and annual precipitation (bio12) were the key variables affecting the distribution area of Nitraria L, with contributions of 54.4%, 11.1%, 8.3%, 7.4%, and 4.1%, respectively. The Nitraria L. plants are currently found mainly in Central Asia, North Africa, the neighboring Middle East, and parts of southern Australia and Siberia. In future scenarios, except for a small expansion of the 2030s scenario model Nitraria L., the potential suitable distribution areas showed a decreasing trend. The contraction area is mainly concentrated in South Asia, such as Afghanistan and Pakistan, North Africa, Libya, as well as in areas of low suitability in northern Australia, where there was also significant shrinkage. The areas of expansion are mainly concentrated in the Qinghai–Tibet Plateau to the Iranian plateau, and the Sahara Desert is also partly expanded. With rising Greenhouse gas concentrations, habitat fragmentation is becoming more severe. Center-of-mass migration results also suggest that the potential suitable area of Nitraria L. will shift northwestward in the future. This study can provide a theoretical basis for determining the scope of Nitraria L. habitat protection, population restoration, resource management and industrial development in local areas. Full article

These days, AI and machine learning (ML) have become pervasive in numerous fields. However, the maritime industry has faced challenges due to the dynamic and unstructured nature of environmental inputs. Hydrodynamic models, vital for predicting ship responses and estimating sea states, rely on diverse data sources of varying fidelities. The effectiveness of ML models in real-world applications hinges on the diversity, range, and quality of the data. Linear simulation techniques, chosen for their simplicity and cost-effectiveness, produce unrealistic and overly optimistic results. Conversely, high-fidelity experiments are prohibitively expensive. To address this, the study introduces an innovative feature engineering that incorporates uncertainty into features of linear models derived from higher fidelity modeling. This enhances productive data entropy, positively enhancing feature classification and improving the accuracy and feasibility of ML models in hydrodynamic responses of floating vessels. Tested with data from a known geometrical shape exposed to regular and irregular waves, the technique employs Ansys Aqwa for linear models. The results demonstrate the efficiency of the proposed technique, expanding the applicability of ML models in realistic scenarios. The application of the proposed approach extends beyond and can be further applied to any stochastic process, which expands the ML application for realistic use cases. Full article

The entropy production in the polarization phenomena occurring in the underlimiting regime, when an electric current circulates through a single cation-exchange membrane system, has been investigated in the 3–40 °C temperature range. From the analysis of the current–voltage curves and considering the electro-membrane system as a unidimensional heterogeneous system, the total entropy generation in the system has been estimated from the contribution of each part of the system. Classical polarization theory and the irreversible thermodynamics approach have been used to determine the total electric potential drop and the entropy generation, respectively, associated with the different transport mechanisms in each part of the system. The results show that part of the electric power input is dissipated as heat due to both electric migration and diffusion ion transports, while another part is converted into chemical energy stored in the saline concentration gradient. Considering the electro-membrane process as an energy conversion process, an efficiency has been defined as the ratio between stored power and electric power input. This efficiency increases as both applied electric current and temperature increase. Full article

In the condition of waste heat recovery from mine return air with a temperature of 20~30 °C and velocity about 4 to 8 m/s, the structure of gravity-type heat pipe with fin increases the heat exchange areas and meanwhile increases the resistance of air flow, which consumes a large amount of main fan power driven by a motor. Furthermore, the resistance of air flow increases greatly with the velocity of the air flow. In this paper, the gravity-type heat pipe with elliptical smooth surface is studied to decrease the resistance and loss of energy of the air flow. In order to obtain the influence of ellipticity on heat transfer efficiency and energy loss under the condition of a certain heat transfer area of the heat pipe, the heat transfer efficiency of a single pipe and a pipe bundle with different ellipticities is studied by using numerical simulation based on the equal section perimeter. The results show that the reasonable change of ellipticity can increase specific enthalpy and decrease entropy production. When the pipe is single, the ellipticity is 0.56 and the specific enthalpy is the largest, increasing by 12.08%. The ellipticity of the pipe bundle is 0.61, and the specific enthalpy is the largest, increasing by 19.28%. The entropy production slightly increased by 10.4%. Moreover, the empirical formula of single pipe heat transfer with an error less than 5% and the empirical formula of pipe bundle heat transfer with an error less than 2.2% are obtained. The empirical formula of pipe bundle heat transfer at different temperatures is modified, and the error is less than 5%, which provides the fundamental data for deep research, development, and engineering design of gravity-type heat pipe heat energy exchange system of underground return airflow in coal mines. Full article

This study addresses the accessibility challenges faced by individuals with visual impairments due to limited access to graphic information, which significantly impacts their educational and social integration. Traditional methods for producing tactile graphics are labor-intensive and require specialized expertise, limiting their availability. Recent advancements in generative models, such as GANs, diffusion models, and VAEs, offer potential solutions to automate the creation of tactile images. In this work, we propose a novel generative model conditioned on text prompts, integrating a Bidirectional and Auto-Regressive Transformer (BART) and Vector Quantized Variational Auto-Encoder (VQ-VAE). This model transforms textual descriptions into tactile graphics, addressing key requirements for legibility and accessibility. The model’s performance was evaluated using cross-entropy, perplexity, mean square error, and CLIP Score metrics, demonstrating its ability to generate high-quality, customizable tactile images. Testing with educational and rehabilitation institutions confirmed the practicality and efficiency of the system, which significantly reduces production time and requires minimal operator expertise. The proposed approach enhances the production of inclusive educational materials, enabling improved access to quality education and fostering greater independence for individuals with visual impairments. Future research will focus on expanding the training dataset and refining the model for complex scenarios. Full article

Self-organization in complex systems is a process associated with reduced internal entropy and the emergence of structures that may enable the system to function more effectively and robustly in its environment and in a more competitive way with other states of the system or with other systems. This phenomenon typically occurs in the presence of energy gradients, facilitating energy transfer and entropy production. As a dynamic process, self-organization is best studied using dynamic measures and principles. The principles of minimizing unit action, entropy, and information while maximizing their total values are proposed as some of the dynamic variational principles guiding self-organization. The least action principle (LAP) is the proposed driver for self-organization; however, it cannot operate in isolation; it requires the mechanism of feedback loops with the rest of the system’s characteristics to drive the process. Average action efficiency (AAE) is introduced as a potential quantitative measure of self-organization, reflecting the system’s efficiency as the ratio of events to total action per unit of time. Positive feedback loops link AAE to other system characteristics, potentially explaining power–law relationships, quantity–AAE transitions, and exponential growth patterns observed in complex systems. To explore this framework, we apply it to agent-based simulations of ants navigating between two locations on a 2D grid. The principles align with observed self-organization dynamics, and the results and comparisons with real-world data appear to support the model. By analyzing AAE, this study seeks to address fundamental questions about the nature of self-organization and system organization, such as “Why and how do complex systems self-organize? What is organization and how organized is a system?”. We present AAE for the discussed simulation and whenever no external forces act on the system. Given so many specific cases in nature, the method will need to be adapted to reflect their specific interactions. These findings suggest that the proposed models offer a useful perspective for understanding and potentially improving the design of complex systems. Full article

In this study, we aim to construct an evaluation system to accurately measure the development status and trends of China’s new quality productivity, which is pivotal for the sustainable development of the Chinese economy. In light of the current lack of a standardized evaluation index system and precise measurement methods, we have established an evaluation index system comprising three dimensions—scientific and technological innovation, industrial upgrading, and factor transformation—in alignment with the essence and traits of new quality productivity. By the combination of the entropy method and multi-period weights, we assess the development level of new quality productivity across China’s 31 provinces from 2013 to 2022. The findings reveal the following: (1) Substantial regional disproportions exist among provinces in the advancement of new quality productivity, with Shanghai and Beijing demonstrating a notable first-mover advantage. (2) While the levels of new quality productivity in most provinces are generally modest, an overall positive development trajectory is observed. Drawing upon these outcomes, a set of targeted development strategies are put forward, such as leading scientific and technological innovation, promoting industrial upgrading, and realizing the transformation of elements. This article can enhance our understanding of the spatiotemporal development pattern of China’s new quality productivity, offering a novel theoretical framework and practical approach for fostering new quality productivity tailored to unique circumstances. Consequently, it may facilitate the promotion of economic sustainability. Full article

Vaccinium mandarinorum Diels, a wild blueberry species distributed in the south of the Yangtze River in China, holds significant ecological and commercial value. Understanding its potential distribution and response to climate change is crucial for effective resource utilization and scientific introduction. By using the Maximum Entropy (MaxEnt) model, we evaluated V. mandarinorum’s potential distribution under current (1970–2000) and future climate change scenarios (2041–2060, 2061–2080, and 2081–2100) based on 216 modern distribution records and seven bioclimatic variables. The results showed that the MaxEnt model could effectively simulate the historical distribution and suitability degree of V. mandarinorum. The top two major environmental variables were precipitation of the driest quarter and annual precipitation, considering their contribution rates of 61.3% and 23.4%, respectively. Currently, the high suitability areas were mainly concentrated in central and northern Jiangxi province, central and southern Zhejiang province, southern Anhui province, central and northern Fujian province, and the border areas of Hunan and Guangxi provinces, covering 21.5% of the total suitable area. Future projections indicate that habitat will shift to higher latitudes and altitudes and that habitat quality will decline. Strategies are required to protect current V. mandarinorum populations and their habitats. The study results could provide an important theoretical reference for the optimization of planting distribution and ensure the sustainable production of the blueberry industry. Full article

Entropies for alkane isomers longer than C₁₀ are computed using our recently developed linear regression model for thermochemical properties which is based on second-order group contributions. The computed entropies show excellent agreement with experimental data and data from Scott’s tables which are obtained from a statistical mechanics-based correlation. Entropy production and heat input are calculated for the hydroisomerization of C₇ isomers in various zeolites (FAU-, ITQ-29-, BEA-, MEL-, MFI-, MTW-, and MRE-types) at 500 K at chemical equilibrium. Small variations in these properties are observed because of the differences in reaction equilibrium distributions for these zeolites. The effect of chain length on heat input and entropy production is also studied for the hydroisomerization of C₇, C₈, C₁₀, and C₁₄ isomers in MTW-type zeolite at 500 K. For longer chains, both heat input and entropy production increase. Enthalpies and absolute entropies of C₇ hydroisomerization reaction products in MTW-type zeolite increase with higher temperatures. These findings highlight the accuracy of our linear regression model in computing entropies for alkanes and provide insight for designing and optimizing zeolite-catalyzed hydroisomerization processes. Full article

Bimetallic NiCr nanoparticles decorated on carbon nanofibers (NiCr@CNFs) were synthesized through electrospinning and investigated as catalysts for hydrogen generation from the dehydrogenation of sodium borohydride (SBH). Four distinct compositions were prepared, with chromium content in the catalysts ranging from 5 to 25 weight percentage (wt%). Comprehensive characterization confirmed the successful formation of bimetallic NiCr@CNFs. Notably, among the compositions, the catalyst containing 20 wt% Cr exhibited the highest efficiency in SBH dehydrogenation. Kinetic studies revealed that hydrogen production followed a first-order reaction with respect to the catalyst quantity. Additionally, the reaction time decreased with increasing temperature. The activation energy (Ea), entropy change (ΔS), and enthalpy change (ΔH) were calculated as 34.27 kJ mol⁻¹, 93.28 J mol·K⁻¹, and 31.71 kJ mol⁻¹, respectively. The improved catalytic performance is attributed to the synergistic interaction between Ni and Cr. This study proposes a promising strategy for the advancement of Ni-based catalysts. Full article

Soybean is a critical agricultural commodity, serving as a vital source of protein and vegetable oil, and contributing significantly to the economies of producing nations. However, soybean yields are frequently compromised by disease and pest infestations, which, if not identified early, can lead to substantial production losses. To address this challenge, we propose AgriNAS, a method that integrates a Neural Architecture Search (NAS) framework with an adaptive convolutional architecture specifically designed for plant pathology. AgriNAS employs a novel data augmentation strategy and a Spatial–Time Augmentation (STA) method, and it utilizes a multi-stage convolutional network that dynamically adapts to the complexity of the input data. The proposed AgriNAS leverages powerful GPU resources to handle the intensive computational tasks involved in NAS and model training. The framework incorporates a bi-level optimization strategy and entropy-based regularization to enhance model robustness and prevent overfitting. AgriNAS achieves classification accuracies superior to VGG-19 and a transfer learning method using convolutional neural networks. Full article