Search Results (371)

The scientific evaluation of ecological total factor productivity of cultivated land use (ETFPCLU) is fundamental for advancing sustainable utilization of cultivated land resources and safeguarding national food security and ecological stability. Using the epsilon-based measure and the global Malmquist–Luenberger (EBM–GML) index, this study quantifies and decomposes ETFPCLU across China. Spatial–temporal variations and convergence patterns are systematically investigated via an analytical toolkit comprising the spatial mismatch index, Dagum’s Gini coefficient decomposition, and convergence models. The results indicate that Chinese ETFPCLU increased by an average of 2.1% per year from 2001 to 2022, primarily attributed to technical change (TC), with limited contributions from efficiency change (EC). The spatial mismatch between ETFPCLU and TC, as well as EC, is predominantly characterized by low to medium mismatch types, exhibiting a high degree of spatial distribution similarity; inter-regional differences are the main contributors to regional disparities. Furthermore, except for the central region, significant σ-convergence exists in ETFPCLU across the country and in other regions, alongside absolute β-convergence and conditional β-convergence in the four major regions. The analysis concludes that to enhance ETFPCLU, it is essential to strengthen technological innovation, synergistically improve technological efficiency, formulate ecological protection policies tailored to local conditions, and foster collaboration among regions for cultivated land protection. Full article

ECF σ factors, which constitute the most abundant and diverse group of the σ⁷⁰-family, are important signal response regulatory proteins in bacterial adaptative responses to harsh environmental changes and for bacterial survival. Their activity is commonly controlled by specific and reversible interactions with their cognate anti-σ factors (soluble or transmembrane proteins), which directly or indirectly sense the environmental signals and transmit them to their partner σ factor. The genome of pathogenic L. interrogans is predicted to encode 11 ECF σ^E-type factors and more than 30 regulators predicted as anti-σ factors, anti-anti-σ factors, and regulators of anti-anti-σ factors. We have recently demonstrated that one of the L. interrogans ECF σ factors, i.e., LIC_12757, indeed functions as a transcriptional factor and is autoregulated at the transcriptional level. This study is a next step towards determining key aspects of LIC_12757 functioning in Leptospira. By using genetic and proteomic approaches, we provide strong evidence that the LIC_12757 activity is controlled via interactions with its putative FecR-like regulator, LIC_12756. We also demonstrate that LIC_12756 exhibits not only an anti-σ activity but also acts as a positive regulator of LIC_12757 in the presence of specific environmental cues. Interestingly, we found that the nutrient-limiting conditions, including iron deficiency, may act as specific signals for the LIC_12757 activation. In conclusion, we identified the L. interrogans regulatory system consisting of an ECF σ factor, LIC_12757, and a FecR-like regulator, LIC_12756, which is most likely involved in the response of pathogenic Leptospira to iron and nutrient limitation, and thus also likely involved in their response to host-induced stress. Full article

Light detection and ranging (LiDAR) sensor technology for people detection offers a significant advantage in data protection. However, to design these systems cost- and energy-efficiently, the relationship between the measurement data and final object detection output with deep neural networks (DNNs) has to be elaborated. Therefore, this paper presents augmentation methods to analyze the influence of the distance, resolution, noise, and shading parameters of a LiDAR sensor in real point clouds for people detection. Furthermore, their influence on object detection using DNNs was investigated. A significant reduction in the quality requirements for the point clouds was possible for the measurement setup with only minor degradation on the object list level. The DNNs PointVoxel-Region-based Convolutional Neural Network (PV-RCNN) and Sparsely Embedded Convolutional Detection (SECOND) both only show a reduction in object detection of less than 5% with a reduced resolution of up to 32 factors, for an increase in distance of 4 factors, and with a Gaussian noise up to $\mu =0$ and $\sigma =0.07$. In addition, both networks require an unshaded height of approx. 0.5 m from a detected person’s head downwards to ensure good people detection performance without special training for these cases. The results obtained, such as shadowing information, are transferred to a software program to determine the minimum number of sensors and their orientation based on the mounting height of the sensor, the sensor parameters, and the ground area under consideration, both for detection at the point cloud level and object detection level. Full article

This work presents a novel, compact (six pieces), low-cost (<$500 USD), and easy-to-manufacture metallography mounting device. The device is designed to produce high-quality polymer encapsulated samples that rival those obtained from commercial equipment ($5000–$10,000 USD). Utilizing the House of Quality (HoQ) framework within Quality Function Deployment (QFD), the device prioritizes critical customer requirements, including safety (validated via finite element method, FEM), affordability, and compatibility with standard hydraulic presses. FEM analysis under 29 MPa pressure revealed a maximum Von Mises stress of 80 MPa, well below the AISI 304 stainless steel yield strength of 170 MPa, yielding a static safety factor of 2.1. Fatigue analysis under cyclic loading (mean stress ${\sigma }_m$ = 40 MPa, amplitude stress ${\sigma }_a$ = 40 MPa) using the Modified Goodman Criterion demonstrated a fatigue safety factor of 3.75, ensuring infinite cycle durability. The device was validated at 140 °C (413.15 K) with a 5-min dwell time, encapsulating samples in a cylindrical configuration (31.75 mm diameter) using a 200 W heating band. Benchmarking confirmed performance parity with commercial systems in edge retention and surface uniformity, while reducing manufacturing complexity (vs. conventional 100-piece systems). This solution democratizes access to metallography, particularly in resource-constrained settings, fostering education and industrial innovation. Full article

Based on the panel data of 30 provinces in China from 2013 to 2022, this paper constructs a measurement index system for the symbiotic level of digital innovation ecosystems from three dimensions: the symbiosis of digital innovation subjects, the digital innovation environment, and digital innovation interaction. This paper applies the entropy weight TOPSIS method, Dagum Gini coefficient decomposition, and spatial convergence analysis to empirically examine the symbiotic levels, regional disparities, and spatial convergence of China’s digital innovation ecosystem. The results are as follows: (i) At the national level, the symbiotic level of China’s digital innovation ecosystem has generally increased, creating a spatial distribution pattern that is “high in the east, flat in the middle, and low in the west”. (ii) From a regional perspective, the major disparities between regions are the primary factors contributing to the overall difference in the symbiotic level of China’s digital innovation ecosystem. (iii) From the perspective of σ convergence, regional disparities in the symbiotic level of the digital innovation ecosystem are constantly expanding, and uneven regional development is intensifying. (iv) From the perspective of absolute β convergence, regions with lower levels of symbiosis in the digital innovation ecosystem have a faster growth rate of symbiosis than regions with higher levels of symbiosis, and there is a certain spatial spillover effect. (v) From the perspective of conditional β convergence, economic structure and innovation application can accelerate the spatial convergence of China’s digital innovation ecosystem symbiosis to a certain extent. Full article

A polydimethylsilosane/multiwalled carbon nanotube (PDMS/MWCNT) nanocomposite, as a tensile-strain-sensing material, was manufactured using a simple solution casting method. The percolation threshold, the relationship between the temperature and resistance, the tensile sensitivity, and the mechanism of the tensile sensitivity of the PDMS/MWCNT nanocomposite were studied, along with its application in concrete crack monitoring. The results show that the PDMS/MWCNT nanocomposite demonstrated a significant percolation phenomenon. The resistance change ratio of the PDMS/MWCNT nanocomposite changed linearly with the environmental temperature, gradually decreasing with an increasing environmental temperature. The PDMS/MWCNT nanocomposite had a higher tensile sensitivity, and the sensing factor was 6.65 when the volume fraction of carbon nanotubes was 1.26 v/v% near the percolation threshold, and the sensing factor of the PDMS/MWCNT nanocomposite decreased with an increase in the volume fraction of carbon nanotubes. The relationship between the relative electrical conductivity of the PDMS/MWCNT nanocomposite and the tensile strain can be expressed as ln(σ/σ₀) = Aε. In addition, the quantitative relationship between the electrical conductivity of the PDMS/MWCNT nanocomposite and the volume fraction of carbon nanotubes was obtained based on the tunneling effect theory and the effective medium model. PDMS/MWCNT nanocomposites can be used as a sensing material to monitor the propagation of concrete cracks under the impact of a free-falling ball. Full article

Zeta-functions play a fundamental role in many fields where there is a norm or a means to measure distance. They are usually given in the forms of Dirichlet series (additive), and they sometimes possess the Euler product (multiplicative) when the domain in question has a unique factorization property. In applied disciplines, those zeta-functions which satisfy the functional equation but do not have Euler products often appear as a lattice zeta-function or an Epstein zeta-function. In this paper, we shall manifest the underlying principle that automorphy (which is a modular relation, an equivalent to the functional equation) is intrinsic to lattice (or Epstein) zeta-functions by considering some generalizations of the Eisenstein series of level $2\varkappa$ to the complex variable level s. Naturally, generalized Eisenstein series and Barnes multiple zeta-functions arise, which have affinity to dissections, as they are (semi-) lattice functions. The method of Lewittes (and Chapman) and Kurokawa leads to some limit formulas without absolute value due to Tsukada and others. On the other hand, Komori, Matsumoto and Tsumura make use of the Barnes multiple zeta-functions, proving their modular relation, and they give rise to generalizations of Ramanujan’s formula as the generating zeta-function of ${\sigma }_s\left(n\right)$, the sum-of-divisors function. Lewittes proves similar results for the 2-dimensional case, which holds for all values of s. This in turn implies the eta-transformation formula as the extreme case, and most of the results of Chapman. We shall unify most of these as a tapestry of ideas arising from the merging of additive entity (Dirichlet series) and multiplicative entity (Euler product), especially in the case of limit formulas. Full article

Noisy inputs pose significant challenges for reinforcement learning (RL) agents navigating real-world environments. While animals demonstrate robust spatial learning under dynamic conditions, the mechanisms underlying this resilience remain understudied in RL frameworks. This paper introduces a novel comparative analysis of predecessor feature (PF) and successor feature (SF) algorithms under controlled noise conditions, revealing several insights. Our key innovation lies in demonstrating that SF algorithms achieve superior noise resilience compared to traditional approaches, with cumulative rewards of $2216.88\pm 3.83$ (mean ± SEM), even under high noise conditions ($\sigma =0.5$) in one-dimensional environments, while Q learning achieves only $19.22\pm 0.57$. In two-dimensional environments, we discover an unprecedented nonlinear relationship between noise level and algorithm performance, with SF showing optimal performance at moderate noise levels ($\sigma =0.25$), achieving cumulative rewards of $2886.03\pm 1.63$ compared to $2798.16\pm 3.54$ for Q learning. The $\lambda$ parameter in PF learning is a significant factor, with $\lambda =0.7$ consistently achieving higher $\lambda$ values under most noise conditions. These findings bridge computational neuroscience and RL, offering practical insights for developing noise-resistant learning systems. Our results have direct applications in robotics, autonomous navigation, and sensor-based AI systems, particularly in environments with inherent observational uncertainty. Full article

Gaussian quadrature rules are commonly used to approximate integrals with respect to a non-negative measure $d\widehat{\sigma }$. It is important to be able to estimate the quadrature error in the Gaussian rule used. A common approach to estimating this error is to evaluate another quadrature rule that has more nodes and higher algebraic degree of precision than the Gaussian rule, and use the difference between this rule and the Gaussian rule as an estimate for the error in the latter. This paper considers the situation when $d\widehat{\sigma }$ is a Chebyshev measure that is modified by a linear factor and a linear divisor, and investigates whether the rules in a recently proposed new class of quadrature rules for estimating the error in Gaussian rules are internal, i.e., if all nodes of the new quadrature rules are in the interval $(-1,1)$. These new rules are defined by two measures, one of which is a modified Chebyshev measure $d\widehat{\sigma }$. The other measure is auxiliary. Full article

The longevity of railway vehicles is an important factor in their mechanical and structural design. Fatigue is a major issue that affects the durability of railway components, and therefore, knowledge of the fatigue resistance characteristics of critical components, such as the leaf springs, must be extensively investigated. This research covers the fatigue resistance of chromium–vanadium alloy steel, usually designated as 51CrV4, from the high-cycle regime (HCF) (${10}^3$–${10}^4$) up to very high-cycle fatigue (VHCF) (${10}^9$) under the bending loading conditions typical of leaf springs and under uniaxial tension/compression loading, respectively, for a stress ratio, $R_{\sigma }$, of −1. Different test frequencies were considered (23, 150, and 20,000 Hz) despite the material not exhibiting a relatively significant frequency effect. In order to create a new fatigue prediction model, two prediction models, the Basquin SN linear regression model and the Castillo–Fernandez–Cantelli (CFC) model, were evaluated. According to the analysis carried out, the CFC model provided a better prediction of the fatigue failures than the SN model, especially when outlier failure data were considered. The investigation also examined the failure modes, observing multiple cracks for higher loads and single cracks that initiated on the surface or from internal inclusions at lower loading. The present investigation aims to provide a fatigue resistance prediction model encompassing the HCF and VHCF regions for the fatigue design of railway wagon leaf springs, or even for other components made of 51CrV4 with a tempered martensitic microstructure. Full article

To propel the development of a robust methylmercury immobilisation technology, CH₃Hg⁺ adsorption on montmorillonite surfaces was simulated herein using density functional theory. This study involved a thorough molecular-level analysis, including factors such as electron potential energy, molecular orbital configurations, stable adsorption configurations, adsorption energies, charge distributions, and density of states. The principal findings are summarised as follows: (1) CH₃Hg⁺ adsorption on the (001) surface was characterised by an adsorption energy ranging from −27 to −51.7 kJ/mol. In this case, Hg was attracted to the involved silicon–oxygen ring cavities. Meanwhile, on the (010) surface, CH₃Hg⁺ exhibited an adsorption energy ranging between −119.4 and −154.3 kJ/mol. In this case, Hg was attracted to hydroxyl groups such as ≡Al(OH)(OH₂) and ≡Si(OH), forming a covalent bond with the oxygen atom of these groups. (2) Comparative analysis revealed that the adsorption energy of CH₃Hg⁺ on the (010) surface surpassed that on the (001) surface. On the (001) surface, electrostatic interactions were the predominant factor influencing adsorption, while on the (010) surface, electrostatic and covalent bonding interactions were important. Notably, the strength of electrostatic interactions was greater on the (001) surface than on the (010) surface. (3) The formation of covalent bonds between CH₃Hg⁺ and the (010) surface was primarily attributed to the overlap of electron cloud between Hg and surface O atoms. In particular, the interaction between the s orbital of Hg and the p orbital of O facilitated the formation of a σ bond. Overall, these findings provide a theoretical framework for the advancement of efficient in situ immobilisation technologies for methylmercury. Full article

In the present study, a simplified and useful design formula is proposed to predict the ultimate strength of a plate under longitudinal compression. The shape of the elastic buckling strength (${\sigma }_{xE}$) equation is utilised and adjusted to predict the ultimate compressive strength of the plate. In total, 600 cases of reasonable plate scenarios are selected to update the design formula by broadly considering the plate geometry (i.e., plate length, breadth, and thickness), material property (i.e., elastic modulus and yield strength), and initial deflection. The proposed formula, including the factor or coefficient for correction ($C_f$) may help ocean and shore (including onshore, offshore and nearshore) structural engineers improve safety and design the unstiffened plate element used in shipbuilding and oil and gas. Full article

This research develops an equation to describe the relationship between stress (σ) and strain (ε) in concrete under different conditions. It includes important parameters from earlier studies to improve predictions of stress–strain behavior, especially for concrete with crumb rubber and stainless-steel fibers at various temperatures. The initial phase assessed three existing stress–strain formulas as a basis for optimization. Using the Genetic Algorithm (GA) and the Whale Optimization Algorithm (WOA), a new equation was created to simulate the stress–strain relationship while considering temperature changes and material additions. Results showed that Formula (1), optimized with the WOA, performed much better than other polynomial and exponential formulas, proving the WOA’s effectiveness over the traditional GA. A comparison of the mechanical properties from experiments and those predicted by the new formula showed a high level of accuracy. Key properties like the maximum stress, strain at maximum stress, modulus of elasticity, and toughness were well captured. The findings highlight how temperature and material composition significantly affect concrete’s mechanical behavior. Overall, this research offers important insights into the factors influencing concrete performance, providing a solid framework for future studies and practical applications in engineering and construction. The proposed formula is a reliable tool for predicting concrete’s mechanical properties under various conditions, which aids in better modeling and optimization in concrete design. Full article

We looked into the ways in which the digital economy helps to speed up the convergence of environmentally responsible economic efficiency across China’s regions by facilitating the flow and optimization of R&D resources. We measured the mobility of R&D capital and personnel across 30 provinces in China from 2001 to 2022 using a gravity model, assessed the efficiency of green economic using the SBM method, and determined the influence of the digital economy by the use of a fixed-effects model. (1) We identified the σ convergence (the absolute gap between per capita income or per capita economic efficiency levels of different economies gradually decreasing over time) and β convergence (the negative correlation between the rate of economic efficiency increase among various economies or regions and their initial level of economic efficiency) characteristics of green economic efficiency, discovering that the digital economy has sped up the process of convergence of environmentally responsible economic efficiency in regional areas. (2) We found a latecomer advantage in the convergence of China’s green economic efficiency, along with the advancement of the digital economy; that is, the green economic efficiency more quickly converged in less developed regions and regions with fewer resources. (3) The digital economy is able to accelerate the convergence of regional green economy efficiency through the use of internal mechanisms such as the efficient flow of research and development factors and the reasonable allocation of those factors. By identifying the impact of the digital economy on the gaps in regional green economic efficiency from the new perspective of the flow and allocation of R&D elements, this study contributes to the existing body of literature. It also provides new information regarding the ways in which the digital economy is driving the development of China’s green economy. We offer policy suggestions based on our findings to assist regions in achieving a balance between the digital economy and industrial development through the utilization of resources that are specific to the location. Full article

Urmia Lake (UL) is the sixth-largest saltwater lake in the world; however, there is a dearth of geotechnical studies on this region. Geotechnical characteristics of a site are considered important from different engineering perspectives. In this research, the results of 255 laboratory tests and the data of 55 in situ tests were used to determine the geotechnical properties of sediment in UL. The changes of parameters in depth are presented in this study. The results indicate that compressibility, initial void ratio, water content, over-consolidated ratio (OCR), and sensitivity have larger values near the lake bed. Moreover, increasing the sediment depth leads to significant reductions in these values. According to the sediment strength analysis through the vane shear and standard penetration tests and the unit weight of sediments, there is an increasing trend caused by the increased depths of layers. Diverse applied correlations are proposed and can be used as preliminary estimates in similar types of sediments in engineering projects as well as scientific studies. Furthermore, undrained shear strength and compression index trends in depth and the Su/σ’_v Curve against OCR are compared with the literature, and the results reveal similar trends in similar sediments. The main minerals identified in these sediments include calcite, dolomite, quartz, calcium chloride, and halite. The salinity of the lake water is caused by the presence of calcium chloride and halite minerals. The Cao factor observed in chemical compounds can have a significant impact on the cohesion of the soil particles. This research provides comprehensive information on the geotechnical characteristics of UL. Moreover, the results of this study show that UL Sediments are soft and sensitive, especially in shallow depths, and they contain a significant amount of organic content; therefore, it is recommended to use suitable improvement methods in future geotechnical and structural designs. This study and similar surveys can help prepare the groundwork for designing safer marine structures. Full article