Spatial accessibility to healthcare services is commonly determined by three core components: demand, supply, and traffic speed. Although understanding which factors contribute to accessibility changes can help prioritize interventions to enhance accessibility in underserved areas, limited research has examined the extent of their individual contributions. To better capture the local dynamics that shape healthcare accessibility, this study decomposes spatial accessibility to primary healthcare services using the chain substitution method (CSM), which quantifies the impact of each component by substituting them one by one. By examining how the order of factor substitution affects the relative impact of each factor on spatial accessibility, we analyzed the importance of substitution order in the CSM. This study found that the order of factor substitution plays a significant role in measuring the relative contribution of each factor. To mitigate the effects of substitution order, we proposed an averaging CSM that uses the average value across all possible substitution combinations. Based on the averaging CSM, our findings offer insight for healthcare policymakers and urban planners by clarifying how demand, supply, and traffic speed interact in determining accessibility, ultimately supporting targeted interventions in underserved areas.

A new determination has been conducted on the structural details of intermolecular hydrogen bonds in liquid methanol. High precision time-of-flight (TOF) neutron diffraction measurements have been carried out on liquid methanol samples with H/D isotopically substituted for the hydroxyl hydrogen atom in order to obtain further geometrical information on the hydrogen-bonded methanol molecules. The intermolecular distances and coordination numbers concerning the neighboring methanol molecules have been determined from the least-squares fitting analyses of the observed intermolecular H0-H0, X-H0, and X-X partial structure factors (H0: hydroxyl hydrogen, X: other atoms except for H0) aH0H0inter(Q)-1, aXH0inter(Q)-1, and aXXinter(Q)-1. The nearest neighbor hydrogen-bonded H0···H0 and H0···O distances have been determined to be 2.34 ± 0.04 and 1.91 ± 0.02 Å, respectively. The bond angle determined from aXH0inter(Q)-1, ∠H0···O-C = 114 ± 6°, agrees well with the value obtained from aXXinter(Q)-1, ∠O···O-C = 109 ± 8°, indicating the formation of a linear hydrogen bond between the neighboring methanol molecules. X-ray diffraction measurements have additionally been carried out for liquid methanol with natural abundance to reveal the structural parameters concerning orientational correlation of the neighboring methanol molecules in the liquid state. The determined bond angle, ∠O···O-C = 103.0 ± 0.5°, agrees well with that derived from the present neutron data. It has been confirmed that a strong orientational correlation with a nearly antiparallel configuration of molecules is present between the nearest neighbor hydrogen-bonded methanol molecules.

Censored geochemical data, particularly below detection limits, challenge mineral exploration by biasing anomaly delineation and spatial patterns. This study presents a multi-stage framework combining Bayesian Gaussian Random Field (BGRF) modeling with Random Forest (RF) learning, enhanced by fractal-based background separation, to accurately predict censored Au concentrations. 14 samples with gold concentrations below 5 ppb were hypothesized as censored data to enable a more accurate evaluation of the model's performance based on their real Au concentrations. Unlike constant substitution methods, the framework preserves censored information and reconstructs spatial variability through probabilistic inference and nonlinear learning. The BGRF model incorporates spatial coordinates and Cu as the principal covariate to capture spatial autocorrelation and inter-element associations, producing probabilistic estimates for hypothesized censored data (HCD) that are then used to train the RF under a 5-fold out-of-fold scheme. The HCD estimated by spatial BGRF covariate model were performed as inputs for RF prediction model. A targeted calibration and scaling procedure reduces detection-limit bias and improves low-range predictions. Comparative analyses show that the calibrated and scaled RF-BGRF model substantially enhances accuracy and preserves realistic geochemical structures, outperforming half the detection limit (LD-half) or the detection limit divided by the square root of two (LD-rad2) approaches. This framework offers a promising tool for refining left-censored geochemical data in complex geological environments.

This study investigates the influence of substitution methods for left-censored values on exploratory data analysis (EDA) of the incineration bottom ash (IBA). IBA, a by-product of municipal solid waste incineration, contains a wide range of economically valuable elements, many of which are frequently reported below detection limits due to analytical constraints. The study aims to evaluate the impact of different substitution methods on descriptive statistics, correlation analysis, and regression modeling outcomes. Four widely used substitution approaches were compared: (i) replacement with half of the detection limit, (ii) random values from a uniform distribution, (iii) robust regression on order statistics (ROS), and (iv) tobit regression (applied in both small and large variants). Five trace elements with different proportions of censored values (13–67%) were analyzed using a dataset of 52 weekly samples collected throughout 2021 at the Krakow Thermal Waste Treatment Plant. The impact of each method was assessed using descriptive statistics, Pearson correlation matrices, and multiple linear regression models. Additional analyses incorporated 11 auxiliary elements to enhance correlation and regression model robustness.The results show that substitution methods significantly affect data distributions, particularly for elements with high censoring rates. ROS and tobit regression produced more stable statistical outputs and narrower histograms compared to simpler methods. Furthermore, regression model performance improved with substitution compared to raw data, with tobit methods demonstrating the highest accuracy for elements with strong inter-element correlations. The findings provide methodological guidance for reliable data handling in IBA analysis and recovery assessments.

The primary focus of accurate and cost-effective computation in machines endowed with advanced cognitive abilities is to enhance the accuracy and speed of learning in the bio-inspired spiking machine vision networks. This paper introduces a novel reinforcement burst time dependent plasticity (RBTDP) learning algorithm, implemented as a digital circuit within a spiking network that utilizes low-cost neuron circuits. This paper introduces an efficient hardware solution that employs linear substitution technique, motivated by the need for precise and fast calculations that minimize costly resource consumption in machine vision platforms, particularly those utilizing neural networks. The suggested digital designs, emphasizing the linear substitution method within digital learning and neuron blocks, are meticulously detailed to ensure maximum speed enhancement, minimal resource utilization, and high accuracy. The suggested digital learning mechanism and neuron modules were employed to build a bio-inspired spiking vision network consisting of three layers and Actor and Critic neural population, which supports unsupervised and reinforcement training, utilizing excitatory AMPA and inhibitory GABA neural interactions. Consequently, the suggested bio-inspired spiking network, utilizing the proposed RBTDP learning method, demonstrated exceptional performance in spiking vision networks. Upon training on the MNIST, CIFAR10, and CIFAR100 datasets for 6, 6, and 30 training iterations respectively, the model achieved remarkable accuracies of 98.2%, 94%, and 75.6%. These results reflect both enhanced accuracy and faster convergence compared to earlier studies.

This study applies the Flash Pressurized Flotation (FPF) process, which generates stable and uniform microbubbles under low-pressure conditions without a high-pressure saturator, and comparatively evaluates its treatment efficiency and energy-saving performance against the conventional Dissolved Air Flotation (DAF) process from a Best Available Technology (BAT) perspective. The bubble generation characteristics of the FPF process were quantitatively analyzed by evaluating the effects of major operational parameters, including the number of orifices, number of plates, nozzle configuration, flow rate, and pressure. Experiments were conducted using synthetic lake water, where bubble size was measured by the Particle Counter Method (PCM) and bubble volume was determined by the Water Substitution Method. Increasing the number of orifices from 5 to 15 reduced the average bubble size from 4.3 μm to 4.1 μm and increased the bubble volume from 1.1 mL/L to 1.5 mL/L. Under a flow rate of 2.04 L/min and a pressure of 2 atm, the FPF process showed relatively superior bubble generation characteristics with an average bubble size of 26 μm and a bubble volume of 60 mL/L, while the FPF-type dual-plate nozzle exhibited the best performance, producing an average bubble size of 31.5 μm and a bubble volume of 51 mL/L. Compared with the DAF process at the same pressure, the FPF process achieved higher removal efficiencies for phosphorus (94.1%) and turbidity (89.9%), whereas the DAF process showed removal efficiencies of 30.1% and 33.1%, respectively. The specific energy consumption of the FPF process was 0.0024 kWh/m³, representing an approximately 98% reduction compared to that of the DAF process, demonstrating the potential of FPF as a low-energy flotation process suitable for small- and medium-sized water treatment plants and as a BAT.

The mechanical exfoliability of two-dimensional (2D) nanomaterials is fundamentally governed by the exfoliation energy (Eex). Conventional strategies for determining Eex mainly depend on either indirect experimental approaches or computationally demanding first-principles calculation, which impose significant demands on time and computational resources. Data-driven methods provide an efficient alternative, yet the limited interpretability of machine learning predictions often restricts their applicability. Herein, we construct a physically interpretable descriptor P43, by coupling a CatBoost model (RMSE = 57.83 meV/atom) with the Sure Independence Screening and Sparsifying Operator (SISSO) for predicting and designing mechanically exfoliable 2D nanomaterials. Remarkably, P43 achieves a recall rate of 99.0% in identifying mechanically exfoliable 2D nanomaterials using four concise parameters. Further analysis of its electronic (φ) and geometric (θ) contributions reveals that φ and θ play dominant roles in predicting low-Eex (<150 meV/atom) and high-Eex (>350 meV/atom) 2D nanomaterials, respectively. Leveraging P43 within the element substitution method (ESM) framework, we screened A- and B-site combinations and discovered 8946 novel AB-type 2D structures, including 111 candidates confirmed as exfoliable in the 2DMatPedia database. Overall, this study provides new insights into the Eex, highlighting the significant role of P43 in accelerating ESM for the development of novel 2D nanomaterials.

This paper proposes a novel approach to designing intelligent knowledge assessment systems for students in the discipline of «Integral Calculus» within a distance learning framework. Unlike the majority of existing knowledge control systems, which rely on static databases of predefined tasks and answers, the developed system eliminates such fixed repositories to enhance flexibility, personalization, and objectivity in evaluation. Instead, task conditions are dynamically generated based on predefined rules incorporating random numbers, ensuring each assignment is unique and tailored to individual learner needs. The generation process begins with selecting from four main categories of integrals: basic integration formulas, substitution methods, integration by parts, and integrals involving quadratic trinomials. Each category includes subclasses or templates where parameters—such as coefficients, exponents, and integration limits—are randomly assigned within specified constraints. The generated function undergoes correctness checks before presentation to the user via a web-based interface. A key distinguishing feature is the response format: users provide the analytical antiderivative rather than a numerical result. The system evaluates answers by computing the definite integral using numerical (Simpson's method) and analytical (Newton-Leibniz theorem) approaches. Simpson's rule approximates the integral using step size and function values. The Newton-Leibniz method uses the antiderivative to compute the difference at limits, ignoring the constant. The student's input is parsed, and its value is compared to the etalon. Implemented as a scalable web application, the system offers real-time feedback and analytics. It addresses limitations of traditional tools like Moodle or Khan Academy by providing unlimited variability and objective grading.

In the context of the world transitioning to the Industry 4.0 stage and the intensive implementation of digitalization, human capital is acquiring a key role as a factor of highly efficient and innovative economic growth. The development of digital literacy is becoming an essential component of the basic skills required in modern society.The relevance of this study lies in the need to assess the level of human potential development in the EAEU countries, which is one of the key indicators of digital economy development. The aim of the article is to identify the factors influencing the dynamics of the Human Development Index (HDI) and to forecast its development both for Kazakhstan and across the EAEU. The article examines the main HDI indicators: life expectancy, education level, and quality of life of the population. To achieve this aim, an economic-statistical analysis and a factor-determined analysis using the chain substitution method were applied; the results of the study are presented in a comparative analysis. The information base consisted of statistical data from the Bureau of National Statistics of the Agency for Strategic Planning and Reforms of the Republic of Kazakhstan, EEC reports, and UN Development Programme materials for the period 2018–2024. The analysis of HDI dynamics in the EAEU countries revealed that Kazakhstan shows significant progress (average HDI – 0.817), second only to Russia in terms of growth rate. The most significant factor driving Kazakhstan’s progress is the increase in gross national income (GNI) per capita in PPP terms (up to USD 36,600 in 2024). Despite these achievements, Kazakhstan ranks 174th out of 189 countries worldwide in healthcare spending (3.3% of GDP), which restrains the growth of the life expectancy index (LEI). To improve its position, Kazakhstan requires a comprehensive approach, including investments in education, healthcare, and the development of non-resource sectors of the economy.

Purpose The purpose of this study is to develop an innovative analytical framework that examines the impact of environmental auditing on carbon information disclosure from a three-dimensional perspective encompassing resource flow, value flow and information flow. Using a data sample of Chinese listed chemical companies, the study investigates whether resource and environmental auditing contributes to improving the carbon disclosure quality of enterprises within this specific industry in China. Design/methodology/approach The study examines the carbon disclosure data of Chinese A-share chemical enterprises from 2013 to 2022 by using a series of empirical strategies, including parallel trend testing, multiperiod difference-in-differences, placebo testing and variable substitution methods. Building on these analyses, the research develops a three-dimensional analytical framework to assess the impact of resource and environmental auditing on enterprises’ carbon disclosure performance. The empirical evidence is then used to evaluate whether such auditing enhances the quality of carbon information disclosure in the chemical industry. Findings The study reveals that resource and environmental audits significantly enhance the quality of corporate carbon disclosure, with a more pronounced effect on enterprises that adopt such audits. Specifically: (1) Resource and environmental audits directly improve the quality of carbon information disclosure by fostering greater transparency and accountability. (2) These audits encourage local governments to strengthen the enforcement of environmental policies, thereby indirectly improving disclosure quality. On one hand, this is reflected in stricter environmental penalties and higher environmental tax burdens, which reinforce effective regulatory mechanisms. On the other hand, it is manifested in increased environmental subsidies, which ensure the efficient implementation of supportive policies. (3) Strengthening corporate carbon disclosure systems through resource and environmental audits not only improves firms’ environmental performance but also enhances their economic and social outcomes, underscoring the synergistic relationship between environmental sustainability and economic development. (4) Effective management of resource and environmental audit results can help management improve the green innovation capability of enterprise production processes, reduce waste emissions, lower production costs, enhance resource recycling efficiency, provide audit experience evidence for the green transformation of enterprises and promote the improvement of performance of enterprise environmental management. Originality/value This study offers a novel perspective on the influence of resource and environmental auditing on the quality of corporate carbon information disclosure. By constructing an analytical framework that integrates the three dimensions of resource flow, value flow and information flow, it advances understanding of how environmental auditing shapes disclosure practices. The findings provide theoretical support and a practical model for promoting high-quality green economic development and strengthening environmental governance in the chemical industry, while also contributing to the standardized and coordinated advancement of environmental, economic and social benefits in chemical enterprises.

ABSTRACTThe CSR framework reflects the ecological strategies of competition (C), stress tolerance (S), and ruderal (R) that plants employ to adapt to disturbances. This framework provides a robust foundation for exploring community assembly and successional dynamics within degraded ecosystems such as karst. However, the influence of functional traits and species diversity on ecological strategic trade‐offs during vegetation succession remains unclear. The space‐for‐time substitution method was employed to examine the influence of functional traits and species diversity on ecological strategic trade‐offs during vegetation succession. Five successional stages were identified in the karst community: herb, herb‐shrub transition, shrubbery, shrub‐tree transition, and Tree forest. For each stage, five 100 m × 100 m plots were established, with each plot containing four 10 m × 10 m subplots, resulting in a total of 25 plots and 100 subplots. Plant leaf traits from 2503 individuals of 160 woody species were measured and quantified for CSR components at individual and community levels. The redundancy analysis (RDA), Mantel tests, and a random forest model were applied to explore associations in CSR strategies, diversity, and functional traits. Our results revealed a strategic trade‐off, with a shift from the dominance of the S strategy to increased contributions from C and R strategies at both individual and community levels as vegetation succession progressed. This transition was closely associated with woody species diversity and leaf macronutrient traits of carbon, nitrogen, and phosphorus. RDA and Mantel test results further indicated that Hill numbers (q = 0, q = 1), leaf water content, and leaf carbon content were key determinants of ecological strategy shifts. Additionally, the random forest model identified soil pH as the strongest predictor of the C strategy, whereas Hill number (q = 0) was the primary determinant of S and R strategies. In conclusion, the dominance of the S strategy in the early successional stages gradually shifted toward C and R strategies during succession progress. There were strategic trade‐offs of CSR associated with species diversity and leaf functional traits, embodied in S relative to C and R strategies.

ABSTRACT In this work, we report the first crosslinking of vanillin with various 1,3‐dithiol‐2‐yl derivatives bearing a wide range of side groups. This original class of vanillinated dithiole molecules was synthesized using a simple one‐pot substitution method in a phosphite medium, involving the desulfurization of 1,3‐dithiol‐2‐thione and a hydrogen transfer from the C5‐position of 4‐hydroxy‐3‐methoxybenzaldehyde to the C2’‐position of the dithiole ring. The new molecules were identified by spectroscopic techniques including FT‐IR, UV–vis, 1 H NMR, 13 C NMR, and 2D NMR ( 1 H‐ 13 C HSQC and HMBC), as well as high‐resolution mass spectrometry (HMRS). Electrochemical behavior was also studied using cyclic voltammetry. Furthermore, the structures were confirmed by x‐ray crystallography. Total electron density, allowing identification of electrophilic and nucleophilic regions. These newly synthesized vanillin derivatives exhibited neuroprotective activity relevant to the management of Alzheimer's disease and demonstrated antidiabetic potential.

This article aims to solve Riemann problem for the generalized Chaplygin gas Euler equations of compressible fluid flow with zeroth-order terms modeling acoustic generation and analyze limiting behaviors of the solutions as the pressure approaches to zero. Thanks to variable substitution method, the Riemann solutions with five kinds of different structures involving delta-shocks are constructed, even though the nonhomogeneous generalized Chaplygin gas Euler system is strictly hyperbolic with two characteristic fields being genuinely nonlinear. The generalized Rankine–Hugoniot relation and delta-entropy condition of the delta-shock are clarified. Unlike the homogeneous case and nonhomogeneous case involving a single friction term, all waves containing rarefaction waves, shocks, and delta-shock are bent into curves and propagate at an exponential rate. Additionally, it is proved that delta-shock and vacuum of the zero-pressure Euler equations including the same zeroth-order terms can be formed as the limits of shocks and rarefaction waves of the nonhomogeneous generalized Chaplygin gas Euler equations.

Objectives: We developed and evaluated statistical and machine-learning approaches to convert categorical smoking variables into continuous values, addressing temporal discontinuity caused by questionnaire format changes in the National Health Insurance Service–National Sample Cohort (NHISNSC). Using repeated measurements from the same individuals, we compared strategies for transforming objective multiple-choice responses into subjective numerical values. Methods: We analyzed 44,755 smokers who completed health examinations during 2007-2010 and answered both objective (2007-2008) and subjective (2009-2010) smoking questionnaires. After temporally correcting smoking-duration variables, we compared simple substitution rules (median, mean, mode, midpoint), regression models, and machine-learning algorithms (random forest, gradient boosting, XGBoost, K-Nearest neighbors, support vector regression). Performance was assessed using mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and the coefficient of determination (R²). Results: For smoking duration, random forest performed best (MSE=35.18, R²=0.70), followed by XGBoost (MSE=35.30, R²=0.70) and gradient boosting (MSE=36.63, R²=0.68). For daily cigarette consumption, random forest (MSE=32.02, R²=0.38) and XGBoost (MSE=32.07, R²=0.38) outperformed alternatives. Machine-learning models consistently exceeded simple substitution methods; notably, the midpoint approach performed poorly for daily consumption with negative explained variance (R²=-0.10). Predicted values generally respected category boundaries, with minor discrepancies in extreme categories. Conclusions: Machine-learning approaches—particularly random forest and XGBoost—substantially outperformed traditional statistical conversions when mapping categorical smoking variables to continuous values. The proposed framework preserves temporal continuity in longitudinal health surveys affected by questionnaire changes and is portable to other public-health databases undergoing similar methodological transitions.

This review article considers various approaches to solving problems for processing character strings included in the materials of the Unified State Examination (USE) in informatics. Among them are the brute force method, finite automata, one-pass algorithms, substitution method, dynamic programming, regular expressions. Some of these tools are insufficiently covered in school textbooks, although mastering them can significantly improve the efficiency of solving problems on processing character strings in the conditions of USE. For each of the considered methods we present characteristic problems that can be effectively solved by this method. All solutions are given in Python. The article can be useful for informatics teachers, as well as for pupils with an advanced level of programming skills.

This study systematically investigates settlement sites that record living patterns of ancient humans, aiming to reveal the interactive mechanisms of human–environment relationships. The core issues of landscape archeology research are the surface spatial structure, human spatial cognition, and social practice activities. This article takes the Han Dynasty settlement site in Sanyangzhuang, Neihuang County, Anyang City, Henan Province, as a typical case. It comprehensively uses ArcGIS 10.8 spatial analysis and remote sensing image interpretation techniques to construct spatial distribution models of elevation, slope, and aspect in the study area, and analyzes the process of the Yellow River’s ancient course changes. A regional historical geographic information system was constructed by integrating multiple data sources, including archeological excavation reports, excavated artifacts, and historical documents. At the same time, the sequences of temperature and dry–wet index changes in the study area during the Qin and Han dynasties were quantitatively reconstructed, and a climate evolution map for this period was created based on ancient climate proxy indicators. Drawing on three dimensions of settlement morphology, architectural spatial organization, and agricultural technology systems, this paper provides a deep analysis of the site’s spatial cognitive logic and the ecological wisdom it embodies. The results show the following: (1) The Sanyangzhuang Han Dynasty settlement site reflects the efficient utilization strategy and environmental adaptation mechanism of ancient settlements for land resources, presenting typical scattered characteristics. Its formation mechanism is closely related to the evolution of social systems in the Western Han Dynasty. (2) In terms of site selection, settlements consider practicality and ceremony, which can not only meet basic living needs, but also divide internal functional zones based on the meaning implied by the orientation of the constellations. (3) The widespread use of iron farming tools has promoted the innovation of cultivation techniques, and the implementation of the substitution method has formed an ecological regulation system to cope with seasonal climate change while ensuring agricultural yield. The above results comprehensively reflect three types of ecological wisdom: “ecological adaptation wisdom of integrating homestead and farmland”, “spatial cognitive wisdom of analogy, heaven, law, and earth”, and “agricultural technology wisdom adapted to the times”. This study not only deepens our understanding of the cultural value of the Han Dynasty settlement site in Sanyangzhuang, but also provides a new theoretical perspective, an important paradigm reference, and a methodological reference for the study of ancient settlement ecological wisdom.

ABSTRACT Using the substitution method and extruded diets, the metabolisable energy (ME) and digestible nutrient contents of soybean meal (SBM), poultry by-product meal with high ash content (PBMha), micronised soybean meal (MSM), and corn gluten meal (CGM) were determined for dogs. A complete and balanced control diet (CD) was formulated. Test diets were prepared by mixing 70% of the CD with 30% of the protein source in evaluation, according to the substitution method requirements. Due to its high fat content, the MSM diet was formulated with 85% of the CD and 15% of MSM to obtain adequate extrusion. Thirty-five Beagle dogs (7 per diet) were used to evaluate the total tract apparent digestibility (TTAD) of each diet, by the total collection of faeces method. Results were applied to calculate the ingredient TTAD and digestible nutrients content. Data were analysed in a completely randomised design and submitted to ANOVA and Tukey test (p < 0.05). The TTAD of DM, OM, CP, and gross energy were higher for CGM (p < 0.05). The TTAD of CP was similar for SBM and MSM, whose values were higher than for PBMha (p < 0.05). Faeces production was higher for dogs fed the SBM diet and PBMha, and faecal DM was lower for dogs fed the SBM diet. The results indicate variable TTAD among protein sources, with vegetable protein sources generally being more digestible than PBMha.

Microbial functional groups activate insoluble rhizosphere phosphorus to mitigate P limitation of Chinese fir plantations.

Algebraic expressions are used in many application areas. In this paper, we propose a novel code optimization technique for algebraic expression. We used two techniques to reduce cost with respect to time. Experimental results are obtained by comparing optimization ratio of existing optimization approach with the proposed approach. Therefore, we used simple substitution method to solve algebraic problem and standard optimization technique and calculate the results that how both techniques reduce the complexity of Algebraic expression. The results show that cost of Algebraic expression using substitution and cost of optimize algebraic expression without substitution results is less than the original optimize Algebraic expression.

Nickel (Ni(II)) complexes, especially those formed with strong ligands such as ethylenediaminetetraacetic acid (EDTA), are difficult to quantify due to their low environmental concentrations and weak ultraviolet (UV) absorbance. These characteristics limit the effectiveness of conventional spectrophotometric methods. Among indirect detection strategies, Fe(III) substitution methods has emerged as a viable approach. However, the associated parameters have not been systematically optimized, resulting in limited sensitivity and practical application. In this study, we systematically refine the Fe(III) substitution approach by simulation-guided experimental design, machine-learning based variables importance analysis, and predictive modeling analysis. Thermodynamic simulations and density functional theory (DFT) calculations guided experimental design. Under optimized conditions, the method achieved a detection limit as low as 1 × 10⁻3mM for Ni-EDTA. Application in surface water, groundwater, and electroplating wastewater showed strong linearity (R2 > 0.96) and good matrix tolerance. In addition, machine learning models were utilized to interpret variable importance and predict recovery performance. Notably, Random Forest Regression (RFR) model demonstrated superior predictive performance (R2 = 0.951) and revealed that both pH and water bath duration are critical factors. This research successfully develops and optimizes a reliable Fe(III) substitution method for environmental monitoring of Ni complexes. The combined approach represents a significant advancement in water quality analysis and provides a promising strategy for addressing the challenges posed by Ni(II) complexes in complex aqueous environments.