Multifactor Experiment Research Articles

An Empirical Study on the Effect of Training Data Perturbations on Neural Network Robustness.

The vulnerability of modern neural networks to random noise and deliberate attacks has raised concerns about their robustness, particularly as they are increasingly utilized in safety- and security-critical applications. Although recent research efforts were made to enhance robustness through retraining with adversarial examples or employing data augmentation techniques, a comprehensive investigation into the effects of training data perturbations on model robustness remains lacking. This paper presents the first extensive empirical study investigating the influence of data perturbations during model retraining. The experimental analysis focuses on both random and adversarial robustness, following established practices in the field of robustness analysis. Various types of perturbations in different aspects of the dataset are explored, including input, label, and sampling distribution. Single-factor and multi-factor experiments are conducted to assess individual perturbations and their combinations. The findings provide insights into constructing high-quality training datasets for optimizing robustness and recommend the appropriate degree of training set perturbations that balance robustness and correctness, and contribute to understanding model robustness in deep learning and offer practical guidance for enhancing model performance through perturbed retraining, promoting the development of more reliable and trustworthy deep learning systems for safety-critical applications.

Optimization of mechanical properties and water resistance of sawdust-magnesium oxychloride cement composites based on orthogonal tests

Sawdust-magnesium oxychloride cement composites (SMOCC) are new low-carbon and environmentally friendly building materials. Its development not only reduces carbon dioxide emissions, but the use of sawdust can improve wood utilization rates and play important roles in protecting the environment. Nonetheless, SMOCC's performance is affected by various factors, and its overall sustainability remains to be comprehensively evaluated, which limits widespread application. For this reason, the effects of different factors (including MgO/MgCl2 molar ratio, MgCl2 solution concentration, and content of NaH2PO4) on the performance of SMOCC were initially obtained through single-factor experimental analysis. Next, multi-factor experiments were performed using the Taguchi orthogonal experimental method. The effects of different multifactor on the mechanical properties and water resistance of SMOCC were systematically investigated using response surface methodology, and relevant polynomial regression models were established and verified. Subsequently, the composition of the hydration products and the SMOCC's micromorphology were investigated using X-ray diffraction and scanning electron microscopy. The results indicated that the strength and water resistance of the SMOCC were affected by single factors as well as the interactions of multi-factor. The optimum contents of MgO/MgCl2 molar ratio, MgCl2 solution concentration, and content of NaH2PO4 were 11, 25 %, and 0.5 %, respectively, under which the SMOCC exhibited excellent strengths and water resistance. In addition, the proposed quadratic polynomial models accurately predicted the properties of SMOCC and reflected the effects of multi-factor interaction on the mechanical strength and water resistance of SMOCC.

Analysis and Testing of Pre-Cut Sugarcane Seed Stalk Sawing Performance Parameters

Sugarcane is an important economic crop in tropical and subtropical regions. Presawing planting is an important method for achieving automated and precise planting with sugarcane planting machines. The sawing process is a key stage in planting management, affecting not only the germination and survival rates of sugarcane, but also reflecting the mechanical performance of sawing. To reduce the peak sawing force and enhance the sawing surface quality of sugarcane seedlings, this study utilized a central composite experimental design method. Single-factor and multi-factor experiments were conducted with a specially designed sugarcane stalk sawing experimental rig to investigate the impact of factors such as the stalk diameter feeding speed, and sawing speed on the peak sawing force and sawing surface quality. Upon being developed and validated, multivariate mathematical regression models elucidated the relationships among these factors. The experimental results showed that the order of influence of each factor on the peak sawing force was the stalk diameter, feed speed, and sawing speed, while for the sawing surface quality, the sequence was the sawing speed, stalk diameter, and feed speed. Correspondingly, the determination coefficients for the peak sawing force and sawing surface quality prediction models were 0.9708 and 0.9675. With a maximum error of 7.6% for the peak sawing force and an average relative error of 7.1%, and a maximum error of 3.5% for the sawing surface quality and an average relative error of 2.83%, the calculated results from the regression models were in good agreement with the experimental findings. This indicates that the models are capable of quickly and accurately predicting the peak sawing force and sawing surface quality of sugarcane stalks under different conditions. The research findings provide valuable insights for the development and optimization of sugarcane stalk presawing equipment and related experimental studies.

Optimisation of some technological factors of pretreatment by pullulanase enzymatic hydrolysis combined with hydrothermal process to produce resistant starch (RS3) from rice flour ingredients

The purpose of the study was to conduct a multi-factor experiment and optimize some technological factors of pretreatment by pullulanase enzymatic hydrolysis combined with hydrothermal process to produce resistant starch (RS3) from rice flour of IR50404 paddy variety. The multi-factor experimental plan used the Box-Behnken planning model with 3 independent variables corresponding to the variation range: Pullulanase enzyme concentration (1.5-2.5%); Hydrolysis temperature (52-58oC); Hydrolysis time (8-12 hours). The objective functions include: RS3 resistant starch content (%), reducing sugar content (%), and solubility (%). The experiments were conducted under the same conditions with the parameters determined from single-factor experiments: Prepare enzymatic hydrolysis solution with rice starch/water concentration of 15% at pH 5.5; Hydrothermal mode includes 3 heat treatment/cooling cycles: Autoclaving (130oC, 60minutes) →Cooling and storage time(4oC, 18 hours). Experimental data processing and optimization used Design -Expert 7.1 software with expected function algorithm with the desire that the RS3 resistant starch content, reducing sugar content and solubility reach their maximum values, corresponding to the important coefficients of 5/5, 4/5, and 4/5. The results were the determination of the optimal enzymatic hydrolysis pretreatment regime with pullulanase enzyme concentration of 2.2%, hydrolysis temperature of 54.4oC for 10.4 hours, corresponding to RS3 resistant starch content reached 47.58±0.22%, reducing sugar content reached 14.12±0.08%, and solubility reached 68.52 ± 0.18% after hydrothermal process.

ПОВЫШЕНИЕ ТОЧНОСТИ РЕГРЕССИОННЫХ МОДЕЛЕЙ МЕТОДАМИ ПОЛИНОМИАЛЬНОЙ АППРОКСИМАЦИИ

The study objective is to assess the influence of the approximation method on the accuracy of the regression model.
 The task is to increase the accuracy of regression models using polynomial approximation methods.
 Research methods: the least square method, the polynomial approximation method.
 The novelty of the work: dependencies for the regression analysis of a multifactor experiment using polynomial approximation is developed, the influence of the approximation method on the accuracy of the regression model is analyzed.
 The study results: as a result of applying the developed dependencies, the accuracy of single-factor regression equations increased by 2.4 times, two-factor equations - by 1.7 times.
 Conclusions: the use of polynomial approximation makes it possible to increase the accuracy of single-factor and multifactor regression models, which increases their adequacy, the significance of coefficients and expands the possibilities of their application.

In situ extraction and enrichment of multiple amino acids by integrated mechanochemical extraction and aqueous two-phase system

A highly efficient and environment-friendly extraction technology was established to extract and enrich multiple amino acids (AAs) in situ by deep eutectic solvent (DES)-assisted mechanochemical extraction (MCE) and aqueous two-phase system (ATPS). Firstly, the DES was prepared from betaine and ethylene glycol in a 1:2 M ratio. Then, a series of conditions were evaluated by single-factor and multifactor experiments to select the optimum experimental parameters. The highest extraction yields of AAs were obtained by DES-assisted MCE and ATPS enrichment, including that the amount of DES was 0.97 mL, the milling time was 4.88 min, the amount of salt was 8 g, and the solid-to-liquid ratio was 0.055 g mL−1. After extracting, the diluted AA extracts were analyzed by a high-performance liquid chromatography-fluorescence detector after pre-column derivatization with 6-aminoquinolyl-N-hydroxysccinimidyl carbamate. The methodological validation demonstrated the proposed method exhibited satisfactory limits of detection ranged from 0.11 to 0.82 ng mL−1, reliable interday precision (%RSD) of 0.014–0.067 % for retention time and 1.29–3.34 % for the peak area, acceptable reproducibility with %RSD below 4.1 %, and good %recovery between 90.3 % and 122.5 % at three spiked concentrations. The surface morphology of raw, only ground, and DES-ground sample powders was tested by scanning electron microscopy to further explore the changes in the physicochemical properties of samples. By comparing with other AAs extraction methods, the developed method is not only eco-friendly, but greatly improves the yield of free AAs extracted from chrysanthemum.

Design and Experiment of Oil-Electric Hybrid Air-Suction Sorghum Plot Seeder

To tackle the issues of low seeding accuracy and seed injury caused by the seeders utilized at a small scale and in the plot seeding of sorghum in mountainous or hilly regions, this study presents the design of an oil–electric hybrid air suction sorghum plot seeder. The main working parts of the seeder are described, and the performance of the seed-mixing device is simulated using EDEM software. An oil–electric hybrid drive mode is used to provide power for operation and to the seed-metering device and fan. Additionally, a sowing control and monitoring system is designed using a single-chip microcomputer controller to ensure uniform plant spacing at different forward speeds. A multi-factor experiment is conducted using the central synthesis method to determine the optimal operating parameters of the seed-metering device through bench tests. The results show that a profile hole diameter of 2.5 mm on the seed tray, a negative-pressure chamber vacuum of 8.0 kPa, and a seed-metering device speed of 28 r/min result in a 95.95% pass rate, 0.5% missing rate, and 3.55% reseeding rate. The deviation between the experimental and analytical results that validate the optimum parameters is kept within acceptable limits. Field tests are conducted at different forward speeds using the optimum parameter combinations, and a comparison is made with the widely used duckbill planter. The results show pass, missing, and reseeding rates of 94.41%, 2.3%, and 3.29%, respectively. The missing monitoring error is less than 7.19%. All of the indices of the oil–electric hybrid air suction sorghum plot seeder are superior to those of the duckbill planter; thus, it fulfills the agronomic requirements for seeding a sorghum plot.

Photosynthetic responses of Larix kaempferi and Pinus densiflora seedlings are affected by summer extreme heat rather than by extreme precipitation

The frequency and intensity of summer extreme climate events are increasing over time, and have a substantial negative effect on plants, which may be evident in their impact on photosynthesis. Here, we examined the photosynthetic responses of Larix kaempferi and Pinus densiflora seedlings to extreme heat (+ 3 °C and + 6 °C), drought, and heavy rainfall by conducting an open-field multifactor experiment. Leaf gas exchange in L. kaempferi showed a decreasing trend under increasing temperature, showing a reduction in the stomatal conductance, transpiration rate, and net photosynthetic rate by 135.2%, 102.3%, and 24.8%, respectively, in the + 6 °C treatment compared to those in the control. In contrast, P. densiflora exhibited a peak function in the stomatal conductance and transpiration rate under + 3 °C treatment. Furthermore, both species exhibited increased total chlorophyll contents under extreme heat conditions. However, extreme precipitation had no marked effect on photosynthetic activities, given the overall favorable water availability for plants. These results indicate that while extreme heat generally reduces photosynthesis by triggering stomatal closure under high vapor pressure deficit, plants employ diverse stomatal strategies in response to increasing temperature, which vary among species. Our findings contribute to the understanding of mechanisms underlying the photosynthetic responses of conifer seedlings to summer extreme climate events.

Final assessment of multifactor experiments of heat transfer in the underwater wet welding

The multifactorial nature and interconnection of phenomena occurring during welding, especially under water, are important for deepening knowledge in this area. An analysis of the research carried out and the solutions obtained in the field of underwater wet welding of low-carbon steels shows that in this area, adequate assessments of the effectiveness of various types of welding with comprehensive consideration of the interaction of various physical phenomena, significantly different in nature from each other, have not been obtained. Integrated approaches to the assessment and analysis of heat transfer coefficients from the side of the arc combustion surface and from the opposite side are proposed. For the first time, the potential values were calculated as a change in the values of heat transfer indicators for various welding conditions based on verified experimental data. The ultimate goal of the work is to calculate the cooling rates of the metal of the welded joint, which will subsequently make it possible to predict the mechanical properties of the weld metal and the likelihood of the formation of hardening structures.

Optimization of Fermentation Process for New Anti-Inflammatory Glycosylceramide Metabolite from Aspergillus sp.

A novel ceramide compound, named Aspercerebroside A (AcA), was successfully isolated from the ethyl acetate layer of the marine symbiotic fungus Aspergillus sp. AcA exhibited notable anti-inflammatory activity by effectively inhibiting the production of nitric oxide (NO) in RAW 264.7 cells at concentrations of 30 μg/mL and 40 μg/mL, offering a promising avenue for the treatment of inflammatory diseases. To optimize the yield of glycosylceramide (AcA), a series of techniques, including single-factor experiments, orthogonal experiments, and response surface optimization, were systematically employed to fine-tune the composition of the fermentation medium. Initially, the optimal carbon source (sucrose), nitrogen source (yeast extract powder), and the most suitable medium salinity (14 ppt) were identified through single-factor experiments. Subsequently, orthogonal experiments, employing an orthogonal table for planning and analyzing multifactor experiments, were conducted. Finally, a mathematical model, established using a Box-Behnken design, comprehensively analyzed the interactions between the various factors to determine the optimal composition of the fermentation medium. According to the model's prediction, when the sucrose concentration was set at 37.47 g/L, yeast extract powder concentration at 19.66 g/L, and medium salinity at 13.31 ppt, the predicted concentration of glycosylceramide was 171.084 μg/mL. The experimental results confirmed the model's accuracy, with the actual average concentration of glycosylceramide under these conditions measured at 171.670 μg/mL, aligning closely with the predicted value.

ANALYSIS OF MULTIFACTOR IMPRECISE DATA USING NEUTROSOPHIC STATISTICS

In multifactor experiments, data collected may correspond to the categorical variables, which place individuals/items into one of several groups (categories). The values of a categorical variable are levels for the categories and distribution of a categorical variable lists the count or percent of individuals/items falling into each category. For example, a two-way table describes two categorical variables by arranging counts according to a row variable and a column variable and each combination of values for two variables is called a cell. However, sometimes a few or every cell counts may be imprecise numbers because of missing or incomplete information on the sample individuals, or investigators negligence, or any other reasons. In such situations, data need to be analyzed using neutrosophic logic and neutrosophic statistics. Generally, the objective of contingency table analysis is to study goodness of model fit for discrete or continuous distributions, testing for homogeneity, and testing for independence. In this paper, we consider neutrosophic analysis of categorical data, which is arranged in contingency tables. We discuss results obtained from the neutrosophic contingency table analysis using data observed in various applications.

Parameters of a combined plow rotor for the climatic conditions of the Bukhara region

In this article, pretreatment of the soil in Bukhara includes simultaneous softening, compaction and crushing of plant residues. As a result, the movement of agricultural machinery in the field is reduced, and the compaction of the subsurface layer is reduced by 5-7 times. For this purpose, the installation and grounding of an additional rotary mill assembly to the fork body is described. Pre-sowing processing consists in the justification of constructive and technological parameters and modes of operation of the crop-milling aggregate, which improve the quality and reduce the tensile resistance. Theoretical studies were carried out using the laws of mathematical analysis and theoretical mechanics. Multi-factor experiment planning methods have been used to conduct experimental research. The results of the study were processed using the methods of Mathematical Statistics.

High softening performance indicators of plug-softener

Currently, ripper plows are widely used in the world, with working bodies "paraglider" type. In the conditions of Uzbekistan, their quality work does not meet the agro technical requirements. The aim study is to study the influence the parameters upper ripper on the performance working body with an inclined rack. The proposed working body consists of an inclined rack, an upper and lower ripper, as well as a chisel. Theoretical studies technological process the rippers are carried out using the basic provisions classical agricultural mechanics and mathematical analysis. Experimental studies were carried out using a specially designed experimental setup in accordance with the developed private and standard methods using the theory of planning single-factor and multifactor experiments. Analytical dependences for determining the width upper ripper are obtained. Based on experiments, empirical formulas were obtained that allow analyzing the performance working body depending on the width grip and the depth processing upper ripper. It is established that to ensure the required quality tillage, the width upper ripper should be within 15.3-22.6 cm and the depth tillage within 7-10 sm.

European Green Deal: An Experimental Study of the Biomass Filtration Combustion in a Downdraft Gasifier

This study presents the experimental results obtained from hybrid filtration combustion using biomass pellets. The experiments were carried out using a porous media gasifier filled with pellets and inert material. The gasifying agent used was an air–steam mixture, with 40% being steam. The dependence of the temperature in the gasifier’s reaction zone from the volume percentage of inert porous material in the gasifier, the specific heat capacity of this material, as well as the air–steam blowing rate, was investigated. The multifactor experiment method was used. A maximum temperature of 1245 °C was achieved using 28 vol% of porous material with a heat capacity of 1000 J/(kg·°C) and at a blowing rate of 42 m3/h. The maximum hydrogen content in the syngas was 28 vol%. This was achieved at an air–steam blowing rate of 42 m3/h and 40 vol% porous material, with a heat capacity of 1000 J/(kg·°C). The calorific value of the syngas was 12.6 MJ/m3. The highest CO content in the gas was 28 vol% and was obtained at 20 vol% porous material with a heat capacity of 1000 J/(kg·°C) and a blowing rate of 42 m3/h. The obtained information is applicable in the design, management, and control of gas production by way of a hybrid filtration combustion process in a downdraft gasifier.

A comprehensive assessment of the applicability of RoboColumn as a chromatography scale-down model for use in biopharmaceutical process validation

High-throughput process development has become a standard practice in the biopharmaceutical industry to enable time, cost, and material savings. In downstream biopharmaceutical process development, miniaturized, parallelized chromatography columns, known as RoboColumn, have become the standard for process development, as RoboColumn have shown generally comparable performance to bench and manufacturing scale columns. However, RoboColumn have yet to be widely implemented in process validation and characterization, where many multifactor experiments are typically executed, and there is a strong value proposition for performing high-throughput experiments. The hesitancy to utilize RoboColumn in process validation arises from scale differences that result in exacerbated peak broadening at RoboColumn scale relative to traditional bench or manufacturing scales. Thus, to support reliable application of RoboColumn in process validation, the present study provides a comprehensive investigation to understand how scale differences affect chromatographic performance by comparing RoboColumn, bench, and manufacturing scales using seven different production processes covering three different antibody formats, five different resin types, and three chromatographic modes of operation. RoboColumn chromatographic performance was compared at target and off-target conditions to emulate scale-down model qualification and multifactor studies, respectively. RoboColumn demonstrated good comparability at both target and off-target process conditions. To further demonstrate an understanding of comparability, a study was performed to show a rare case in which product quality offsets may occur as a result RoboColumn scale differences. By showing scale comparability and an understanding of potential offsets, this work demonstrates that RoboColumn can be used in any stage of process development, including process validation and characterization.

Ionic liquid-assisted aqueous two-phase system for the enrichment of multiple monosaccharides from jujube

In this study, a simple, high-efficiency and eco-friendly enrichment method using an ionic liquid (IL)-assisted aqueous two-phase system was proposed for the enrichment of multiple monosaccharides. The parameters affecting the enrichment effect were the type of IL, the mass of the IL, the amount of salt, the vortex time, and the pH value of the solution. All of these parameters were optimized using single-factor tests or multifactor experiments. The optimal conditions were as follows: [C4min]Br was 313.49 mg, the mass of K2HPO4 was 4.6 g, the vortex time was 3.18 min, and the pH value was 9. In addition, the established method was verified to have good linearity (r2 ≥ 0.9991) and high sensitivity, with limits of detection ranging from 6.1 to 34.09 ng/mL and limits of quantification ranging from 20.57 ng/mL to 113.63 ng/mL. Spiked samples had recoveries ranging from 85.68 % to 104.83 %. The present work demonstrated that this established method can be successfully used for the extraction and enrichment of various monosaccharides from jujube.

Using a combination of different conditioners to promote dewatering of digested sludge: Rheological characteristics

The dewatering capacity of sludge is a key factor in sludge disposal and reuse. In this study, the effects of conditioning with three conditioners (polyacrylamide (PAM), poly aluminum sulfate (PAS), and sludge biochar (SAC)) and their combined conditioning effect at different doses on the dewatering performance of digested sludge were systematically investigated. The mechanism of change in dewatering performance was analyzed based on rheological principles. A Box‒Behnken multifactor experiment based on the response surface method (RSM) was also used to establish a quadratic multiple prediction model for the solids content of filter cake to obtain the optimal ratio of coupled treatment. The results showed for individual conditioner use, PAM with a dose of 3‰ had the best effect on sludge dewatering, and the dewatering effect of the combined conditioner sludge treatment was better than that of the sludge treated with individual conditioners, with the solids content of the filter cake exceeding 35%. The Herschel-Bulkley model was used to fit the rheological data, and the results showed that the yield stress decreased with increasing PAM dose and gradually increased with increasing PAS and SAC doses. The thixotropy of sludge after SAC conditioning was evident compared to that after PAM and PAS conditioning. The yield stress of sludge decreased and flowability deteriorated after combined conditioning. There was a linear relationship between the dewatering performance of conditioned sludge and thixotropy and yield stress, which indicated the feasibility of using rheological indices to evaluate changes in sludge dewatering performance.

Examining impacts of policy, technology, and climate extremes on thermoelectric power production and river thermal pollution in the Midwest and Northeast of the United States

As one of the components comprising food-energy-water systems (FEWS), the energy sector, especially electricity production, is intimately connected to water. Climate extremes-related impacts on water resources will directly affect the interdependence of water, food, and energy. A better understanding of the extent of climate impacts on energy sector and the options to improve water-energy security are needed for planning an overall resilient FEWS. Therefore, we are motivated to examine the climate stress on the thermoelectric power supply using the Water Balance Model coupled with Thermoelectric Power & Thermal Pollution Model (WBM-TP2M), which can simulate water-energy linkages at the power plant, river reach, and regional scales. Using the Midwest (MW) and Northeast (NE) regions as our study area, we design a group of single- and multi-factor experiments both for historical climate period (1980–2019) and a case where we create a series of intensified extremes (2010–2019). The results show that power generation over the two regions features a gradually increasing trend in the past four decades, while, in contrast, thermal pollution has been decreasing steadily since 2005. Heat waves created temporary dips in the generation of electricity and peaks of heavily thermal-polluted stream length. The experiments reveal the significant role of cooling towers in reducing thermal pollution without losing much capacity to generate power, one important measure of resilience against climate extremes. Constraints placed on effluent temperature from the Clean Water Act lead to interrupted operations, which reduces (up to 20%) power generation, increases sensitivity to climatic extremes, but only show a small reduction in thermal pollution. Coal, as a fuel source, is subject to low thermal efficiency and high-water demand, which results in clearly decreased power generation. Overall, our findings suggest that replacing a less thermal-efficient fuel mix and environmentally destructive once-through cooling can move the energy sector towards several beneficial outcomes. Chief among these is a more efficient power production system that uses less water and does so while fostering clean, less carbon-intense technologies (e.g., combined gas cycle turbines, cooling towers, renewable energy), thus linking positive outcomes that simultaneously and positively impact aquatic ecosystems, regional airsheds and human health.

Impact by the operating and structural parameters of a screen on the technological parameters of vibratory basalt sieving

Purpose is to identify a dependence of basalt raw material fine screening upon the operating and structural parameters of sieving equipment. Methods. The research results have been obtained relying upon comparative analysis of basalt raw material fine screening upon the operating and structural parameters of sieving equipment. In this regard, the analyzed grading efficiency characteristics were defined for three densities of basalt rock mass mined in open pits. They are γ = 1.4 g/сm3 for tuff; γ = 2.2 g/сm3 for lava-breccia; and γ = 2.6 g/сm3 for basalt. The rock mass components are extracted selectively and processed separately. Findings. Expediency of complex mining and processing of three basalt rock mass components (being tuff, lava-breccia, and basalt) as well as their selective treatment has been identified. Mathematical modelling has helped generate regression models of fine basalt raw material sieving upon the operating and structural parameters of a screen. The regression models as well as the represented calculation results are indicative of strong correlation between the efficiency of fine basalt raw material screening and the factors involved by the regression models (i.e. rock mass density; inclination angle of a disturbance force of a vibration exciter of a screen; inclination angle of a screen effector; mesh size; specific load on a screen; disturbance frequency of a screen drive; and a screen length). Originality. For the first time, dependencies of fine basalt raw material sieving efficiency upon the operating and structural parameters of a screen have been modelled mathematically. Based upon the multifactor experiment, ideas have been developed concerning the fine grading process; selection of boundaries of each parameter control; and determination of the efficiency as well as a law of changes in technological parameters while controlling them. Practical implications. Use of the findings will help make adequate solutions while selecting instrumental conditions of an operation schedule to prepare basalt raw material for its integrated processing.

Soil nematode assemblages respond to interacting environmental changes.

Multi-factor experiments suggest that interactions among environmental changes commonly influence biodiversity and community composition. However, most field experiments manipulate only single factors. Soil food webs are critical to ecosystem health and may be particularly sensitive to interactions among environmental changes that include soil warming, eutrophication, and altered precipitation. Here, we asked how environmental changes interacted to alter soil nematode communities in a northern Chihuahuan Desert grassland. Factorial manipulations of nitrogen, winter rainfall, and nighttime warming matched predictions for regional environmental change. Warming reduced nematode diversity by 25% and genus-level richness by 32%, but declines dissipated with additional winter rain, suggesting that warming effects occurred via drying. Interactions between precipitation and nitrogen also altered nematode community composition, but only weakly affected total nematode abundance, indicating that most change involved reordering of species abundances. Specifically, under ambient precipitation, nitrogen fertilizer reduced bacterivores by 68% and herbivores by 73%, but did not affect fungivores. In contrast, under winter rain addition, nitrogen fertilization increased bacterivores by 95%, did not affect herbivores, and doubled fungivore abundance. Rain can reduce soil nitrogen availability and increase turnover in the microbial loop, potentially promoting the recovery of nematode populations overwhelmed by nitrogen eutrophication. Nematode communities were not tightly coupled to plant community composition and may instead track microbes, including biocrusts or decomposers. Our results highlight the importance of interactions among environmental change stressors for shaping the composition and function of soil food webs in drylands.