Search Results (682)

Grassland degradation influences arthropod community structure and abundance, which, in turn, modulate element cycling in grassland ecosystems through predation and soil structure modification. In order to explore the influence of degradation on arthropods in Bayinbuluk alpine grassland, we selected four degraded transects (i.e., non-degraded: ND, lightly degraded: LD, moderately degraded: MD, and heavily degraded: HD) to collect soil samples and determine their composition, spatial distribution, and diversity patterns, in addition to the factors driving community change. Following identification and analysis, the following results were obtained: (1) A total of 342 soil arthropods were captured in this study, belonging to 4 classes, 11 orders, and 24 families. (2) With the intensification of degradation, the dominant groups exhibited significant alteration: the initial dominant groups were Pygmephoridae and Microdispidae; however, as the level of degradation became more severe, the dominant groups gradually shifted to Campodeidae and Formicidae, as these groups are more adaptable to environmental changes. (3) Common groups included six families, including Parasitoididae and Onychiuridae, and rare groups included 16 families, such as Macrochelidae. (4) As degradation intensified, both the species diversity and population size of the arthropod community increased. Our Redundancy Analysis (RDA) results demonstrated that the key driving factors affecting the arthropod community were soil organic carbon (SOC), electrical conductivity (EC), soil total nitrogen (TN), and available nitrogen (AN). The above results indicate that grassland degradation, by altering soil properties, increases arthropod diversity, induces alterations in the dominant species, and reduces mite abundance, with these changes being closely related to soil carbon and nitrogen contents. The results of this study provide basic data for understanding the changes in soil arthropod communities during the degradation of alpine grasslands and also offer support for the sustainable development of soil organisms in grassland ecosystems. Full article

In order to solve the problem of mainlobe interference in small hydroacoustic array signal processing, this paper proposes a beamforming method based on the high-resolution direction of arrival (DOA) estimation and interference coherence matrix (ICM) reconstruction. The DOA estimation is first performed using an improved sparse iterative covariance-based (SPICE) method, unaffected by the coherent signal, and it can provide highly accurate DOA estimation for multiple targets. The fitted signal energy distribution obtained from the SPICE is then utilized for the reconstruction of the signal coherence matrix. The reconstructed ICM matrix is used to construct a blocking masking matrix and an eigen-projection matrix to suppress the mainlobe interference signal. Compared with existing methods, the method in this paper possesses better mainlobe interference suppression ability. Within the mainlobe interference interval angle of 3° to 13.5° from the signal of interest (SOI) based on eight-element uniform linear arrays, the method in this paper can enhance the signal-to-interference ratio (SIR) by about 15.59 dB on average compared with the interference-free suppression of conventional beamforming (CBF) and outperforms the other interference suppression methods simultaneously. Simulations and experiments demonstrate the effectiveness of this method in mainlobe interference scenarios. Full article

Against the backdrop of the profound restructuring in global climate governance, China’s energy management system is undergoing a comprehensive transition from dual energy consumption control to dual carbon emissions control. This policy shift fundamentally alters the underlying logic of energy-focused regulation and inevitably impacts the economy–energy–environment (3E) system. This study innovatively constructs a “Triangular Trinity” theoretical framework integrating internal, intermediate, and external triangular couplings, as well as providing a granular analysis of their transmission relationships and feedback mechanisms. Using Guangdong Province as a case study, this study takes the dual control emissions policy within the external triangle as an entry point to research the restructuring logic of dual carbon emissions control for the coupling coordination mechanisms of the 3E system. The key findings are as follows: (1) Policy efficacy evolution: During 2005–2016, dual energy consumption control significantly improved energy conservation and emissions reduction, elevating Guangdong’s 3E coupling coordination. Post 2017, however, its singular focus on total energy consumption revealed limitations, causing a decline in 3E coordination. Dual carbon emissions control demonstrably enhances 3E systemic synergy. (2) Decoupling dynamics: Dual carbon emissions control accelerates economic–carbon emission decoupling, while slowing economic–energy consumption decoupling. This created an elasticity space of 5.092 million tons of standard coal equivalent (sce) and reduced carbon emissions by 26.43 million tons, enabling high-quality economic development. (3) Mechanism reconstruction: By leveraging external triangular elements (energy-saving technologies and market mechanisms) to act on the energy subsystem, dual carbon emissions control leads to optimal solutions to the “Energy Trilemma”. This drives the systematic restructuring of the sustainability triangle, achieving high-order 3E coupling coordination. The Triangular Trinity framework constructed by us in the paper is an innovative attempt in relation to the theory of energy transition, providing a referenceable methodology for resolving the contradictions of the 3E system. The research results can provide theoretical support and practical reference for the low-carbon energy transition of provinces and cities with similar energy structures. Full article

We establish a theory whose structure is based on a fixed variable and an algebraic inequality and which improves the well-known least squares theory. The mentioned fixed variable plays a basic role in creating such a theory. In this direction, some new concepts, such as p-covariances with respect to a fixed variable, p-correlation coefficients with respect to a fixed variable, and p-uncorrelatedness with respect to a fixed variable, are defined in order to establish least p-variance approximations. We then obtain a specific system, called the p-covariances linear system, and apply the p-uncorrelatedness condition on its elements to find a general representation for p-uncorrelated variables. Afterwards, we apply the concept of p-uncorrelatedness for continuous functions, particularly for polynomial sequences, and we find some new sequences, such as a generic two-parameter hypergeometric polynomial of the ${}_{4}F_3$ type that satisfies a p-uncorrelatedness property. In the sequel, we obtain an upper bound for 1-covariances, an improvement to the approximate solutions of over-determined systems and an improvement to the Bessel inequality and Parseval identity. Finally, we generalize the concept of least p-variance approximations based on several fixed orthogonal variables. Full article

(1) Background: Elemental imaging methods such as XRF, SEM/TEM-EDS, LIBS and LA-ICP-MS are widely used in clinical diagnostics. Based on the results obtained, it is possible to assess the safety of both standard and innovative therapies, diagnose diseases, detect pathogens or determine intracellular processes. In addition to bioimaging, these techniques are used for semi-quantitative and quantitative analyses. Some of them also enable highly valuable speciation of analytes. However, the quality of information about elemental tissue composition depends on a number of different factors. Although the crucial parameters of quantitative analysis are the same for each technique, their impact varies depending on the bioimaging method. Due to the fact that imaging results are often crucial in clinical decision-making, it is important to clearly indicate and describe the parameters affecting the quality of results in each technique. Therefore, the aim of this review is to describe the influence of these crucial parameters on bioimaging results based on the methodology and results of studies published in the last ten years. (2) Methods: In order to collect relevant publications, the Scopus database was searched using the keywords “element AND imaging AND human tissue”. Next, studies were selected in which methodological aspects allowed relevant conclusions to be made regarding the quality of the results obtained. (3) Results: One of the most important parameters for all techniques is measurement selectivity resulting from the complexity of human tissue. Quantitative analyses using bioimaging techniques are difficult due to the lack of suitable calibration materials. For the same reason, it is challenging to assess the accuracy of the results obtained. Particular attention should be paid to the results obtained for trace elements. (4) Conclusions: The discussed bioimaging techniques are a powerful tool in the elemental analysis of human tissues. Nevertheless, in order to obtain reliable results, a number of factors influencing the measurements must be taken into account. Full article

Heavy metal pollution of lake sediments is one of the prominent ecological and environmental problems worldwide, and it is of great significance to conduct research on heavy metal pollution in lake sediments to protect the ecological environment, safeguard human health, and promote sustainable development. As an integral part of Dongting Lake, Datong Lake holds a crucial ecological position. More than 10 years ago, due to a series of factors, including excessive fertilizer application and fishing, the water quality of Datong Lake declined, resulting in varying degrees of contamination by Cd, Mn, and other heavy metals in the sediments. After 2017, Datong Lake began to establish a mechanism for protecting and managing the lake, and its ecological and environmental problems have been significantly improved. To clarify the current situation of heavy metal contamination in the sediments of Datong Lake, 15 sediment samples were collected from the lake, and the contents of soil heavy metals Cd, As, Pb, Cr, Cu, Mn, Ni, and Zn were determined. A Monte Carlo simulation was introduced to carry out the ecological and human health risk evaluation of the sediments in the study area to overcome the problem of low reliability of the results of ecological and human health risk evaluation due to the randomness and incompleteness of the environmental data as well as the differences in the human body parameters. The results and conclusions show that (1) the average values of Cd, Pb, Cr, Cu, Mn, Ni, and Zn contents in the sediments of Datong Lake are higher than the background values of soil elements in the sediments of Dongting Lake, and the average values of As contents of heavy metals are lower than the background values of the soil, and the heavy metal contamination in the sediments in the study area is dominated by slight contamination, and the possibility of point-source contamination is slight. (2) The results of both the Geo-accumulation index and Enrichment factor evaluation showed that the degree of heavy metal contamination of sediments was Ni > Cu > Cr > Mn > Cd > Pb > Zn > As. (3) The average value of the single ecological risk index of heavy metal elements, in descending order, was as follows: Cd > As > Pb > Cu > Ni > Cr > Zn > Mn; all the heavy metal elements were at the level of light pollution, and the average value of the comprehensive ecological risk index was 32.83, which is a slight ecological risk level. (4) Both non-carcinogenic and carcinogenic risks for all populations in the study area remain low following heavy metal exposure via ingestion and dermal pathways. Ecological and health risk assessments identified As and Cd as exhibiting significantly higher sensitivity than other heavy metals. Consequently, continuous monitoring and source-tracking of these elements are recommended to safeguard long-term ecological integrity and public health in the region. Full article

Potentially toxic elements such as cadmium (Cd) in agricultural soils represent a global concern due to their toxicity and potential accumulation in the food chain. However, our understanding of cadmium’s complex sources and the mechanisms controlling its spatial distribution across diverse edaphic and geological contexts remains limited, particularly in underexplored agricultural regions. Our study aimed to assess the total accumulated Cd content in soils under avocado cultivation and its association with edaphic, geochemical, and geomorphological variables. To this end, we considered the total concentrations of other metals and explored their associations to gain a better understanding of Cd’s spatial distribution. We analyzed 26 physicochemical properties, the total concentrations of 22 elements (including heavy and trace metals such as As, Ba, Cr, Cu, Hg, Ni, Pb, Sb, Se, Sr, Tl, V, and Zn and major elements such as Al, Ca, Fe, K, Mg, and Na), and six geospatial variables in 410 soil samples collected from various avocado-growing regions in Peru in order to identity potential associations that could help explain the spatial patterns of Cd. For data analysis, we applied (1) univariate statistics (skewness, kurtosis); (2) multivariate methods such as Spearman correlations and principal component analysis (PCA); (3) spatial modeling using the Geodetector tool; and (4) non-parametric testing (Kruskal–Wallis test with Dunn’s post hoc test). Our results indicated (1) the presence of hotspots with Cd concentrations exceeding 3 mg·kg⁻¹, displaying a leptokurtic distribution (skewness = 7.3); (2) dominant accumulation mechanisms involving co-adsorption and cation competition (Na⁺, Ca²⁺), as well as geogenic co-accumulation with Zn and Pb; and (3) significantly higher Cd concentrations in Leptosols derived from Cretaceous intermediate igneous rocks (diorites/tonalites), averaging 1.33 mg kg⁻¹ compared to 0.20 mg·kg⁻¹ in alluvial soils (p < 0.0001). The factors with the greatest explanatory power (q > 15%, Geodetector) were the Zn content, parent material, geological age, and soil taxonomic classification. These findings provide edaphogenetic insights that can inform soil cadmium (Cd) management strategies, including recommendations to avoid establishing new plantations in areas with a high risk of Cd accumulation. Such approaches can enhance the efficiency of mitigation programs and reduce the risks to export markets. Full article

This study investigates the weathering crust composite of serpentine, pyroxenite and granite in the Niangniangmiao area, the weathering crusts inside and outside the mining area were compared respectively, systematically revealing the distribution patterns, migration pathways, and ecological element activity characteristics of high-background elements (e.g., chromium (Cr) and nickel (Ni)) through precise sampling, the Tessier five-step sequential extraction method, and a migration coefficient model. Key findings include: (1) Element distribution and controlling mechanisms: The average Cr and Ni contents in the serpentinite profile are significantly higher than those in pyroxenite. However, the semi-weathered pyroxenite layer exhibits an inverted Cr enrichment ratio in relation to serpentinite, 1.8× and 1.2×, respectively, indicating that mineral metasomatic sequences driven by hydrothermal alteration dominate element differentiation; the phenomenon of inverted enrichment of high-background elements occurs in the weathering crust profiles of the two basic rocks. (2) Dual impacts of mining activities on heavy metal enrichment: Direct mining increases topsoil Cr content in serpentinite by 40% by disrupting parent material homology, while indirect activities introduce exogenous Zn and Cd (Spearman correlation coefficients with Cr/Ni are from ρ = 0.58 to ρ = 0.72). Consequently, the bioavailable fraction ratio value of Ni outside the mining area (21.14%) is significantly higher than that within the area (14.30%). (3) Element speciation and ecological element activity: Over 98% of Cr in serpentine exists in residual fractions, whereas the Fe-Mn oxide-bound fraction (F3) of Cr in extra-mining pyroxenite increases to 5.15%. The element activity in ecological systems ranking of Ni in soil active fractions (F1 + F2 = 15%) follows the order: granite > pyroxenite > serpentine. Based on these insights, a scientific foundation for targeted remediation in high-background areas (e.g., prioritizing the treatment of semi-weathered pyroxenite layers) can be provided. Full article

Water pollution is a global concern, especially due to iron and manganese, which, at high concentrations, affect water quality by altering taste, odor, and color. This work explores the sustainable synthesis of hydrochar from orange peel and bagasse using hydrothermal carbonization (HTC) and a 2³ factorial design to optimize Fe²⁺ and Mn²⁺ removal for water treatment polishing. HTC was performed by varying (1) temperature (100–200 °C), (2) residence time (8–14 h), and (3) activation agent (H₃PO₄ or NaOH), with a central point at 150 °C for 11 h without activation. Characterization was performed using FTIR, TGA, SEM, nitrogen adsorption (BET) for surface area determination, elemental analysis, Brønsted acidity measurements, and zeta potential analysis. The hydrochar synthesized at 100 °C for 14 h with NaOH (HC6) showed the best Fe²⁺ and Mn²⁺ removal performance. The equilibrium time was 400 min, with pseudo-first-order kinetics best fitting the Fe²⁺ adsorption data, while pseudo-second-order kinetics provided the best fit for Mn²⁺ adsorption. The adsorption process was best described by the Freundlich and Langmuir isotherms, with maximum adsorption capacities (q_max) of 21.44 and 33.67 mg g⁻¹ for Fe²⁺ and Mn²⁺, respectively. It can be concluded that HTC-derived hydrochars offer a sustainable and efficient solution for Fe²⁺ and Mn²⁺ removal. This strategy presents a potentially valuable approach for sustainable water treatment, offering advantages for industrial application by operating at lower temperatures and eliminating the need for biomass drying, thereby reducing energy consumption and environmental impact. Full article

Perforated materials are widely used in various fields, including in medicine, for example, in trays for placing and storing cutting tools and for sterilizing disposable materials. Currently, the effective elastic modulus of orthopedic plates is higher than the effective elastic modulus of human bone tissue (the effective elastic modulus of bone ranges between 10 and 30 GPa, depending on the type of bone). This difference in effective elastic modulus leads to the phenomenon known as the stress shielding effect, where the bone experiences insufficient mechanical loading. One potential approach to influence the effective elastic modulus of orthopedic plates is through perforations in their design. Stainless steel 316L has garnered significant interest among medical engineering specialists due to its lower weight, higher strength, and superior biocompatibility. The elastic properties of perforated constructions are influenced by their internal quality, dimensions, shapes, and the overall perforation area, making their study important. An experiment was conducted on perforated plates of 316L stainless steel with perforation areas ranging from 3% to 20%. Increasing the perforation area in perforated 316L stainless steel plates (perforated plates had dimensions of 50 mm in height, 20 mm in width, and 1 mm in thickness; hole diameter of 1 mm; and pitch between the holes of 2, 3, 4, and 5 mm) from 3% to 20% resulted in a decrease in Young’s modulus of the perforated plates from 199 GPa to 147.8 GPa, determined using a non-destructive method for determining resonant frequencies using a laser vibrometer. A three-point bending test on the perforated plates confirmed these findings, demonstrating a consistent trend of decreasing Young’s modulus with increasing perforation area, from 194.4 GPa at 3.14% to 142.6 GPa at 19.63%. The three-point bending method was also employed in this study to determine the Young’s modulus of the perforated plates in order to reinforce the obtained results on the elastic properties by determining the resonance frequencies with a laser vibrometer. It was discovered that the Young’s modulus of a perforated plate cannot be determined solely by the perforation area, as it depends on both the perforation diameter and the pitch between the perforations. In addition, finite element method (FEM) simulations were conducted, revealing that increasing perforation diameter and decreasing pitch significantly reduce the Young’s modulus—with values dropping from 201.5 GPa to 72.6 GPa across various configurations. Full article

In order to elucidate the factors regulating nutrient dynamics in plant–soil interactions across various latitudes within the karst climax community, this study focused on the karst forest climax community in Guizhou Province, Southwest China. We analyzed and compared the differences in carbon, nitrogen, and phosphorus content, as well as stoichiometry, in plant leaves and soils under various growing conditions. Additionally, redundancy analyses were conducted to investigate the stoichiometric correlations between plants and soil. The research findings indicate the following: (1) Leaf carbon content (LCC) and the carbon-to-nitrogen ratio (LCN) exhibit significant differences across various latitudes, with the lowest values observed in high-latitude regions. (2) Soil organic carbon (SOC) and the carbon-to-nitrogen ratio (SCN) also show significant variations across latitudes, with the lowest concentrations found in high-latitude regions and the highest in low-latitude regions. (3) The variability in leaf nutrient element ratios among karst region climax communities is greatest in low-latitude areas. This study found that the carbon content (LCC), nitrogen content (LNC), and carbon-to-nitrogen ratio (LCN) of leaves in karst climax community plants decrease as latitude increases. This suggests that plants regulate the nutrient utilization efficiency of carbon content (LCC), nitrogen content (LNC), and phosphorus content (LPC) in their leaves to maintain the nutrients necessary for their growth and development along the latitudinal gradient. The sensitivity of soil organic carbon (SOC), carbon-to-nitrogen (SCN), and carbon-to-phosphorus (SCP) ratios to latitudinal changes were particularly pronounced in the karst climax community. Additionally, plant leaf stoichiometry was significantly influenced by soil phosphorus content (SPC) in mid- and high-latitude regions, while factors other than soil nitrogen content (SNC) had a more substantial impact on plant leaf stoichiometry in low-latitude areas. The findings of this study are highly significant for guiding nutrient management in karst forest ecosystems and for the restoration of degraded karst forest vegetation. Full article

The Crayfish Optimization Algorithm (COA) has limitations that affect its optimization performance seriously. The competition stage of the COA uses a simplified mathematical model that concentrates on relations of distance between crayfish only. It is deprived of a stochastic variable and is not able to generate an applicable balance between exploration and exploitation. Such a case causes the COA to have early convergence, to perform poorly in high-dimensional problems, and to be trapped by local minima. Moreover, the low activation probability of the summer resort stage decreases the exploration ability more and slows down the speed of convergence. In order to compensate these shortcomings, this study proposes an Improved Crayfish Optimization Algorithm (ICOA) that designs the competition stage with three modifications: (1) adaptive step length mechanism inversely proportional to the number of iterations, which enables exploration in early iterations and exploitation in later stages, (2) vector mapping that increases stochastic behavior and improves efficiency in high-dimensional spaces, (3) removing the X_shade parameter in order to abstain from early convergence. The proposed ICOA is compared to 12 recent meta-heuristic algorithms by using the CEC-2014 benchmark set (30 functions, 10 and 30 dimensions), five engineering design problems, and a real-world ROAS optimization case. Wilcoxon Signed-Rank Test, t-test, and Friedman rank indicate the high performance of the ICOA as it solves 24 of the 30 benchmark functions successfully. In engineering applications, the ICOA achieved an optimal weight (1.339965 kg) in cantilever beam design, a maximum load capacity (85,547.81 N) in rolling element bearing design, and the highest performance (144.601) in ROAS optimization. The superior performance of the ICOA compared to the COA is proven by the following quantitative data: 0.0007% weight reduction in cantilevers design (from 1.339974 kg to 1.339965 kg), 0.09% load capacity increase in bearing design (COA: 84,196.96 N, ICOA: 85,498.38 N average), 0.27% performance improvement in ROAS problem (COA: 144.072, ICOA: 144.601), and most importantly, there seems to be an overall performance improvement as the COA has a 4.13 average rank while the ICOA has 1.70 on CEC-2014 benchmark tests. Results indicate that the improved COA enhances exploration and successfully solves challenging problems, demonstrating its effectiveness in various optimization scenarios. Full article

To clarify the current state of heavy metal contamination in the sediments of lakes in China, the data on six heavy metals derived from the sediment samples of 71 lakes across China from 2003 to 2022 are collected in this study through meta-analysis. Uncertainty analysis is conducted using the Monte Carlo method to evaluate the heavy metals against cumulative characteristics, potential ecological risk, and toxicity indicators. The following conclusions are reached. (1) There is severe pollution in lake sediments in China. The concentrations of Cu, Pb, Zn, Ni, and Cd in lakes exceed their corresponding soil background values. Cr heavy metal contamination exceeded the soil background values in 54.5% of lakes. (2) Cd is the major pollutant in lake sediments across China, followed by Cu, Zn, Pb, Ni, and Cr in descending order. Lakes with higher ecological risk are predominantly concentrated in quadrants 2 and 3, indicating an overall high ecological risk status for Chinese lakes and significant potential ecological hazards. Pb and Cr are identified as the most toxic elements in lake sediments, with the lakes of higher toxicity mainly concentrated in quadrants 3 and 4. (3) Heavy metal pollution shows a significant trend of variation by region. The sources of heavy metals in lake sediments differ between the southern, central, and northern regions of China. In the lakes located in northern China, pollution is largely attributed to mining and industrial emissions, with agriculture as a less significant factor. In the central region, surface runoff and domestic sewage are the main contributors, while industrial and agricultural emissions play a minor role. In the south, industrial emission is the major source of pollution, with agricultural emission and natural factors being less significant. Full article

This scientific research presents the most significant aspects of the structural analysis and verification of the main steel railway bridges in Peru in accordance with the American standard. To this end, linear and finite element analyses (FEMs) were performed using calculation notes in MATHCAD and structural validation software (SAP2000, CSI Bridge, IDEA STATICA and GE05), among others, based on on-site inspections, which allowed results to be obtained to analyze, evaluate and determine the structural performance factors (RF) of the main railway bridges in Peru. For this, data obtained from several railway corridors in Peru were taken into consideration, such as the lines of the Southern Railway Train, Central Andean Railway, Huancayo–Huancavelica Railway Train and the Tacna–Arica Train; in addition to the feasibility studies on the Interoceanic Train project: Iquitos–Yurimaguas; projects administered through Public–Private Partnership PPP as well as by the Regionals Government and MTC-Peru. These data were used in order to be able to warn of certain technical aspects that would influence the recommendations for a locomotive replacement project in which new units had different load distributions between the axles, which would make it necessary to review the tracks and bridges of the same in order to determine if they would be able to withstand the new forces safely, as well as to reinforce structural elements according to the material and the structural condition, and finally, to assess the variation in the increase in train speed in some road corridors to achieve a better FRA (Federal Railway Administration) classification of Class 3, where the presence of structures dating back to the last century has been verified as well (1851–1856–1908). Likewise, the seismic criteria and geotechnical conditions of the most representative areas of the country (acceleration 0.30 g) were included in order to also be able to make technical recommendations that would allow us to ensure the useful life of the structure in service, operation and maintenance conditions. Full article

Accurate predictions of elastic properties under varying doping concentrations and temperatures are critical for designing reliable silicon-based micro-/nano-electro-mechanical systems (MEMS/NEMS). Empirical potentials typically lack accuracy for elastic predictions, whereas density functional theory (DFT) calculations are precise but computationally expensive. In this study, we developed a highly accurate and efficient machine learning-based Deep Potential (DP) model to predict the elastic constants of phosphorus-doped silicon (Si_64−xP_x, x = 0, 1, 2, 3, 4) within a temperature range of 0–500 K. The DP model was rigorously validated against benchmark DFT results. At 0 K, the elastic constants predicted by our DP model exhibited excellent agreement with experimental data, achieving a mean absolute percentage error (MAPE) of only 2.88%. We investigated the effects of doping on elastic constants in single-crystal silicon and determined their second-order temperature coefficients. The calculations demonstrated distinct doping-induced variations, showing pronounced decreases in C₁₁ and C₄₄ and a moderate increase in C₁₂. Finite-element analyses using the fitted temperature coefficients indicated improved thermal stability of silicon resonators through phosphorus doping. Our study explores the integration of machine learning-based atomic-scale simulations with MEMS/NEMS design, providing practical guidance for optimal dopant selection to enhance silicon resonator thermal stability. Full article