Search Results (1,203)

The phases of high-entropy alloys (HEAs) are crucial to their material properties. Although meta-learning can recommend a desirable algorithm for materials designers, it does not utilize the optimal solution information of similar historical problems in the HEA field. To address this issue, a transferable meta-learning model (MTL-AMWO) based on an adaptive migration walrus optimizer is proposed to predict the phases of HEAs. Firstly, a transferable meta-learning algorithm frame is proposed, which consists of meta-learning based on adaptive migration walrus optimizer, balanced-relative density peaks clustering, and transfer strategy. Secondly, an adaptive migration walrus optimizer model is proposed, which adaptively migrates walruses according to the changes in the average fitness value of the population over multiple iterations. Thirdly, balanced-relative density peaks clustering is proposed to cluster the samples in the source and target domains into several clusters with similar distributions, respectively. Finally, the transfer strategy adopts the maximum mean discrepancy to find the most matching historical problem and transfer its optimal solution information to the target domain. The effectiveness of MTL-AMWO is validated on 986 samples from six datasets, including 323 quinary HEAs, 366 senary HEAs, and 297 septenary HEAs. The experimental results show that the MTL-AMWO achieves better performance than other algorithms. Full article

The Tibetan Plateau is the “Asia Water Tower” and is pivotal for Asia and the whole world. Groundwater is essential for sustainable development in its alpine regions, yet its chemical quality increasingly limits its usability. The present research examines the hydrochemical characteristics and origins of phreatic groundwater in alpine irrigation areas. The study probes the chemical signatures, quality, and regulatory mechanisms of phreatic groundwater in a representative alpine irrigation area of the Tibetan Plateau. The findings indicate that the phreatic groundwater maintains a slightly alkaline and fresh status, with pH values ranging from 7.07 to 8.06 and Total Dissolved Solids (TDS) between 300.25 and 638.38 mg/L. The hydrochemical composition of phreatic groundwater is mainly HCO₃-Ca type, with a minority of HCO₃-Na·Ca types, closely mirroring the profile of river water. Nitrogen contaminants, including NO₃⁻_, NO₂⁻, and NH₄⁺, exhibit considerable concentration fluctuations within the phreatic aquifer. Approximately 9.09% of the sampled groundwaters exceed the NO₂⁻ threshold of 0.02 mg/L, and 28.57% surpass the NH₄⁺ limit of 0.2 mg/L for potable water standards. All sampled groundwaters are below the permissible limit of NO₃⁻ (50 mg/L). Phreatic groundwater exhibits relatively good potability, as assessed by the entropy-weighted water quality index (EWQI), with 95.24% of groundwaters having an EWQI value below 100. However, the potential health risks associated with elevated NO₃⁻ levels, rather than NO₂⁻ and NH₄⁺, merit attention when such water is consumed by minors at certain sporadic sampling locations. Phreatic groundwater does not present sodium hazards or soil permeability damage, yet salinity hazards require attention. The hydrochemical makeup of phreatic groundwater is primarily dictated by rock–water interactions, such as silicate weathering and cation exchange reactions, with occasional influences from the dissolution of evaporites and carbonates, as well as reverse cation-exchange processes. While agricultural activities have not caused a notable rise in salinity, they are the main contributors to nitrogen pollution in the study area’s phreatic groundwater. Agricultural-derived nitrogen pollutants require vigilant monitoring to avert extensive deterioration of groundwater quality and to ensure the sustainable management of groundwater resources in alpine areas. Full article

The coupling coordination of new urbanization (NU) and rural revitalization (RR) is the key research that focuses on promoting integrated development between urban and rural areas. The entropy weight method, coupling coordination model, and obstacle model were used to explore the development level, coupling coordination degree, and obstacle factors of NU and RR in the basin area of Sichuan Province from 2013 to 2021 in this study. The results show the following: The levels of NU and RR in the basin area of Sichuan Province show an uptrend. Central cities exhibit higher levels of RR and NU. Under the influence of a central city, regional central cities have obvious growth in RR and NU. There are fluctuations in the RR and NU indicators in node cities. The coupling coordination degree of NU and RR in the basin area of Sichuan Province continues to rise, and the coordination levels are mainly basic coordination and moderate coordination. The coupling coordination degree is higher in central and regional central cities, while the coupling coordination degree of node cities is relatively lower. The levels of agricultural modernization, public infrastructure and medical resources, and rural governance are the main factors influencing the coupling coordination of NU and RR in the basin area of Sichuan Province. Affected by terrain, economy, and other factors, the level of urban–rural integration in the basin area of Sichuan Province is obviously different. With the continuous improvement of policies, the coupling coordination level of NU and RR is gradually developing towards a positive trend. Full article

Mulching could effectively improve the soil hydrothermal environment, improve changes in the soil structure, increase entropy, and conserve soil moisture to solve the problem of grain reduction caused by perennial drought in Northwest China. Thus, a two-growing-season field experiment (2020–2021) with five treatments (PM1, biodegradable plastic film mulching; PM2, plastic film mulching; SM1, straw strip mulching; SM2, crushed corn straw full mulching; and CK, no mulching as the control) was conducted to investigate the effects of different mulching materials on the soil hydrothermal environment, soil aggregate distribution, stability, and tuber yield of rainfed potato farmland in Northwest China. Over two growing seasons, mulching planting, on average, increased (p < 0.05) the soil moisture at the 0–200 cm depth by 9.0% relative to CK (SM2 (11.6%) > SM1 (10.3%) > PM2 (8.6%) > PM1 (7.0%)). The mulching treatments significantly regulated the soil temperature during the whole growth period, in which plastic mulching significantly increased the soil temperature of the 0–25 cm soil depth during the whole growth period by 2.1 °C (PM2 (2.1 °C) > PM1 (2.0 °C)); meanwhile, straw mulching significantly reduced the soil temperature by 1.4 °C (SM2 (0.9 °C) > SM1 (0.6 °C)). All mulching treatments improved the soil macroaggregate content and soil aggregate stability in all soil depths from 0 to 40 cm, with increases of 31.4% and 27.1% in the mean weight diameter (MWD) and 22.6% and 21.2% in the geometric mean diameter (GWD) compared with CK, respectively. Straw and plastic mulching significantly increased the fresh tuber yield by 12.5% and 12.6% compared with CK, respectively. The increases were greatest in SM2 and PM2. Crushed corn straw full mulching is difficult to sow and harvest; therefore, straw strip mulching could improve the soil hydrothermal environment, increase production, and provide an environmentally friendly technology for dryland potato production. Full article

The relative energetic stability, mechanical properties, and thermodynamic behavior of B2-AlRE (RE = Sc, Y, La-Lu) second phases in Al alloys have been investigated through the integration of first-principles calculations with the quasi-harmonic approximation (QHA) model. The results demonstrate a linear increase in the calculated equilibrium lattice constant a₀ with the ascending atomic number of RE, while the enthalpy of formation ΔH_f exhibits more fluctuating variations. The lattice mismatch δ between B2-AlRE and Al matrix is closely correlated with the transferred electron et occurring between Al and RE atoms. Furthermore, the mechanical properties of the B2-AlRE phases are determined. It is observed that the calculated elastic constants C_ij, bulk modulus B_H, shear modulus G_H, and Young’s modulus E_H initially decrease with increasing atomic number from Sc to Ce and then increase up to Lu. The calculated Cauchy pressure C₁₂-C₄₄, Pugh’s ratio B/G, and Poisson’s ratio ν for all AlRE particles exhibit a pronounced directional covalent characteristic as well as uniform deformation and ductility. With the rise in temperature, the calculated vibrational entropy (S_vib) and heat capacity (C_V) of AlRE compounds exhibit a consistent increasing trend, while the Gibbs free energy (F) shows a linear decrease across all temperature ranges. The expansion coefficient (α_T) sharply increases within the temperature range of 0~300 K, followed by a slight change, except for Al, AlHo, AlCe, and AlLu, which show a linear increase after 300 K. As the atomic number increases, both S_vib and C_V increase from Sc to La before stabilizing; however, F initially decreases from Sc to Y before increasing up to La with subsequent stability. All thermodynamic parameters demonstrate similar trends at lower and higher temperatures. This study provides valuable insights for evaluating high-performance aluminum alloys. Full article

Assuming the underlying statistical distribution of data is critical in information theory, as it impacts the accuracy and efficiency of communication and the definition of entropy. The real-world data are widely assumed to follow the normal distribution. To better comprehend the skewness of the data, many models more flexible than the normal distribution have been proposed, such as the generalized alpha skew-t (GAST) distribution. This paper studies some properties of the GAST distribution, including the calculation of the moments, and the relationship between the number of peaks and the GAST parameters with some proofs. For complex probability distributions, representative points (RPs) are useful due to the convenience of manipulation, computation and analysis. The relative entropy of two probability distributions could have been a good criterion for the purpose of generating RPs of a specific distribution but is not popularly used due to computational complexity. Hence, this paper only provides three ways to obtain RPs of the GAST distribution, Monte Carlo (MC), quasi-Monte Carlo (QMC), and mean square error (MSE). The three types of RPs are utilized in estimating moments and densities of the GAST distribution with known and unknown parameters. The MSE representative points perform the best among all case studies. For unknown parameter cases, a revised maximum likelihood estimation (MLE) method of parameter estimation is compared with the plain MLE method. It indicates that the revised MLE method is suitable for the GAST distribution having a unimodal or unobvious bimodal pattern. This paper includes two real-data applications in which the GAST model appears adaptable to various types of data. Full article

Soft matter electrolytes could solve the safety problem of widely used liquid electrolytes in Li-ion batteries which are burnable upon heating. Simultaneously, they could solve the problem of poor contact between electrodes and solid electrolytes. However, the ionic conductivity of soft matter electrolytes is relatively low when mechanical properties are relatively good. In the present review, mechanisms of ionic conduction in soft matter electrolytes are discussed in order to achieve higher ionic conductivity with sufficient mechanical properties where soft matter electrolytes are defined as polymer electrolytes and polymeric or inorganic gel electrolytes. They could also be defined by Young’s modulus from about ${10}^5$ Pa to ${10}^9$ Pa. Many soft matter electrolytes exhibit VFT (Vogel–Fulcher–Tammann) type temperature dependence of ionic conductivity. VFT behavior is explained by the free volume model or the configurational entropy model, which is discussed in detail. Mostly, the amorphous phase of polymer is a better ionic conductor compared to the crystalline phase. There are, however, some experimental and theoretical reports that the crystalline phase is a better ionic conductor. Some methods to increase the ionic conductivity of polymer electrolytes are discussed, such as cavitation under tensile deformation and the microporous structure of polymer electrolytes, which could be explained by the conduction mechanism of soft matter electrolytes. Full article

At present, there is a growing emphasis on safeguarding image data, yet conventional encryption methods are full of numerous limitations. In order to tackle the limitations of conventional color image encryption methodologies, such as inefficiency and insufficient security, this paper designs an expedited encryption method for color images that uses DNA coding in conjunction with multiple chaotic systems. The encryption algorithm proposed in this paper is based on three-dimensional permutation, global scrambling, one-dimensional diffusion and DNA coding. First of all, the encryption algorithm uses three-dimensional permutation algorithms to scramble the image, which disrupts the high correlation among the image pixels. Second, the RSA algorithm and the SHA-256 hashing algorithm are utilized to derive the starting value necessary for the chaotic system to produce the key. Third, the image is encrypted by using global scrambling and one-dimensional diffusion. Finally, DNA coding rules are used to perform DNA computing. The experimental results indicate that the encryption scheme exhibits a relatively weak inter-pixel correlation, uniform histogram distribution, and an information entropy value approaching eight. This shows that the proposed algorithm is able to protect the image safely and efficiently. Full article

In the context of pursuing high-quality development, the coupling and coordination of the ecosystem and economy has become the fundamental goal and inevitable choice for achieving the sustainable development of urban agglomerations. Based on remote sensing and statistical data for the Pearl River Delta (PRD) region from 2005 to 2020, in this paper, we construct an index system of the ecological and economic levels to assess the ecosystem service value (ESV). We use the equivalent factor method, entropy method, coupling coordination model, and relative development model to systematically grasp the spatial pattern of the levels of the two variables, analyse and evaluate their spatial and temporal coupling and coordination characteristics, and test the factors influencing their coupling and coordination using the geographical and temporal weighted regression (GTWR) model. The results show that ① the ESV in the PRD exhibited a fluctuating decreasing trend, while the level of the economy exhibited a fluctuating increasing trend; ② the coordination degree of the ESV and economy in the PRD exhibited a fluctuating increasing trend, and the region began to enter the basic coordination period in 2007; ③ in terms of the spatial distribution of the coordination degree, there was generally a circular pattern, with the Pearl River Estuary cities as the core and a decrease in the value towards the periphery; ④ the coordinated development model is divided into balanced development, economic guidance, and ESV guidance, among which balanced development is the major type; ⑤ the results of the GTWR reveal that the influencing factors exhibited significant spatial–temporal heterogeneity. Government intervention and openness were the dominant factors affecting the coordination, and the normalised difference vegetation index was the main negative influencing factor. Full article

Acacia mearnsii, among the 100 worst invasive weeds worldwide, negatively impacts native biodiversity, agriculture, and natural ecosystems. Global climate change, characterized by rising temperatures and altered precipitation patterns, enhances the risk of A. mearnsii invasion in Asia, making it crucial to identify high-risk areas for effective management. This study performed species distribution modeling using the maximum entropy (MaxEnt) algorithm to predict the potential introduction and spread of A. mearnsii under various climate scenarios based on shared socio-economic pathways (SSP2-4.5 and SSP5-8.5). Currently, only 4.35% of Asia is invaded, with a high invasion risk identified in six countries, including Bhutan, Lebanon, and Taiwan, where more than 75% of their areas are threatened. Under future climate scenarios, 21 countries face invasion risk, among which 14 countries, such as Georgia, Laos, Republic of Korea, and Turkey, are at moderate to very high risk, potentially encompassing up to 87.89% of their territories. Conversely, Northern Asian countries exhibit minimal changes in invasion risk and are considered relatively safe from invasion. These findings underscore that climate change will exacerbate invasion risks across Asia, emphasizing the urgent need for robust management strategies, including stringent quarantine measures and control efforts, to mitigate the threat of A. mearnsii expansion. Full article

Modulation recognition is an important technology used to correctly identify the modulation modes of wireless signals and is widely used in cooperative and confrontational scenarios. Traditional modulation-recognition algorithms require the assistance of expert experiences, which constrains their applications. With the rapid development of artificial intelligence in recent years, deep learning (DL) is widely advocated for intelligent modulation recognition. Typically, DL-based modulation-recognition algorithms implicitly assume a relatively static scenario in which the signal samples of all the modulation modes can be completely collected in advance. In practical situations, the radio environment is quite dynamic and the signal samples with new modulation modes may appear sequentially, in which the current DL-based modulation-recognition algorithms may require unacceptable time and computing resource consumption to re-train the optimal model from scratch. In this study, we leveraged incremental learning (IL) and designed a novel IL-based modulation-recognition algorithm that consists of an initial stage and multiple incremental stages. The main novelty of the proposed algorithm lies in the new loss function design in each incremental stage, which combines the distillation loss of recognizing old modulation modes and the cross-entropy loss of recognizing new modulation modes. With the proposed algorithm, the knowledge of the signal samples with new modulation modes can be efficiently learned in the current stage without forgetting the knowledge learned in the previous stages. The simulation results demonstrate that the proposed algorithm could achieve a recognition accuracy close to the upper bound with a much lower computing time and it outperformed the existing IL-based benchmarks. Full article

In this paper, we explore the connection between a single index model under the real-world probability measure and martingale pricing via minimal relative entropy or Esscher transform, within the context of a one-period market model, possibly incomplete, with multiple risky assets and a single risk-free asset. The minimal relative entropy martingale measure and the Esscher martingale measure coincide in such a market, provided they both exist. From their Radon–Nikodym derivative, we derive a portfolio of risky assets in a natural way, termed portfolio G. Our analysis shows that pricing using the Esscher or minimal relative entropy martingale measure is equivalent to a single index model (SIM) incorporating portfolio G. In the special case of elliptical returns, portfolio G coincides with the classical tangency portfolio. Furthermore, in the case of jointly normal returns, Esscher or minimal relative entropy martingale measure pricing is equivalent to CAPM pricing. Full article

In the context of mines, roof-caving incidents constitute the most common and expensive accidents. To enhance the management and prevention of roof-caving accidents, it is imperative to investigate the factors that contribute to such incidents and comprehend the intricate causal relationships among them. This study aims to classify the causes of these accidents into three categories: basic factors, controllable factors, and sudden factors, based on the mechanism of roof caving. The categorization is primarily determined by two indicators: intervisibility and variability. Furthermore, the study delves into analyzing the mutual influence relationships among these factors and proposes the BCX theory (Basic-Controllable-Sudden causing theory) for roof caving. Subsequently, based on this theory, an index system called BCX is established for roof caving, and the DEMATEL method is employed to analyze the factors within this index system. To attain more accurate results, this study utilizes interval trapezoidal type-2 fuzzy number scale optimization and Tsallis relative entropy to address the limitations of the DEMATEL method. By comparing the outcomes of the traditional and optimal DEMATEL methods, it is observed that the optimal method exhibits superior applicability in the BCX index system of roof caving, with results that align closely with the actual scenario. Therefore, the optimal DEMATEL method’s analysis of centrality, importance, and chain relationships between the factors within the BCX index system will offer valuable guidance for preventing roof-caving accidents in mining operations. Full article

Directed energy deposition (DED), as an additive manufacturing technology, has shown unique advantages in multi-material additive manufacturing and remanufacturing. In this study, two types in-situ alloyed CrCoNi medium entropy alloys that have thin-walled structures with different thicknesses (T1 and T2) were manufactured by the DED process, and the mechanisms of differences in relative density, microstructure, and mechanical properties at different heights were systematically analyzed. In terms of microstructure, the T1 and T2 samples along the building direction exhibit significant differences in crystallographic orientation, grain size, and dislocation density, which are related to the local temperature gradient differences caused by the scanning path and heat accumulation. In terms of mechanical properties at different heights of the two types of thin-walled structures, the yield strength is higher but the elongation is lower at the bottom position of sample, while the yield strength is lower but the elongation is higher at the middle and top positions. The differences of mechanical properties at different heights of the T1 and T2 samples are related to the microstructure and relative density. This finding provides new insights for the design and performance analysis of complex thin-walled structures formed by additive manufacturing. Full article

Comprehensively clarifying the influencing factors of carbon emissions is crucial to realizing carbon emission reduction targets in China. To address this issue, this paper develops a four-level carbon emission influencing factor system from six perspectives: population, economy, energy, water resources, main pollutants, and afforestation. To analyze how these factors affect carbon emissions, we propose an improved partial least squares structural equation model (PLS-SEM) based on a random forest (RF), named RF-PLS-SEM. In addition, the entropy weight method (EWM) is employed to evaluate the low-carbon development level according to the results of the RF-PLS-SEM. This paper takes Shandong Province as an example for empirical analysis. The results demonstrate that the improved model significantly improves accuracy from 0.8141 to 0.9220. Moreover, water resources and afforestation have relatively small impacts on carbon emissions. Primary and tertiary industries are negative influencing factors that inhibit the growth of carbon emissions, whereas total energy consumption, the volume of wastewater discharged and of common industrial solid waste are positive and direct influencing factors, and population density is indirect. In particular, this paper explores the important role of fisheries in reducing carbon emissions and discusses the relationship between population aging and carbon emissions. In terms of the level of low-carbon development, the assessment system of carbon emission is constructed from four dimensions, namely, population, economy, energy, and main pollutants, showing weak, basic, and sustainable stages of low-carbon development during the 1997–2012, 2013–2020, and 2021–2022 periods, respectively. Full article