Sampling Frequency Research Articles

Impact of Key DMD Parameters on Modal Analysis of High-Reynolds-Number Flow Around an Idealized Ground Vehicle

This study provides a detailed analysis of the convergence criteria for dynamic mode decomposition (DMD) parameters, with a focus on sampling frequency and period in high-Reynolds-number flows. The analysis is based on flow over an idealized road vehicle, the Ahmed body (Re=7.7×105), using computational fluid dynamics (CFD) data from improved delayed detached eddy simulation (IDDES). The pressure and velocity spectrum analysis validated IDDES’s ability to capture system dynamics, consistent with existing studies. For a comprehensive understanding of the contributions of different components of the circle, the Ahmed body was divided into three regions: (a) front; (b) side, lower, and upper surfaces; and (c) rear fascia. Both pressure and skin-friction drag were analyzed in terms of frequency spectra and cumulative energy. Key findings show that a 90% contribution to the pressure drag comes from modes with a frequency of less than 26 Hz (St = 0.187), while the friction drag requires 84 Hz (St = 0.604) for similar energy capture. This study highlights the significance of accounting for intermittency and non-stationary behavior in turbulent flows for DMD convergence. A minimum of 3000 snapshots is necessary for the convergence of DMD eigenvalues, and sampling frequency ratios between 5 and 10 are needed to achieve a reconstruction error of less than 1%. The sampling period’s convergence showed that T*=250 (equivalent to 20 cycles of the slowest coherent structures) stabilizes coherent mode shapes and energy levels. Beyond this, DMD may become unstable. Additionally, mean subtraction was found to improve DMD stability. These results offer critical insights into the effective application of DMD in analyzing complex vehicle flow fields.

Estimation of Solar Irradiance using the measured parameters of the photovoltaic system by Artificial Neural Network

This article aims to estimate irradiance using measured parameters from a real photovoltaic system. For this study, output current and voltage values were used from a set of solar panels with a capacity of 10 kW and a 10 kVA power inverter, which provide the necessary data for irradiance estimation. The data was collected at a sampling frequency of 15 seconds. To achieve this goal, an Artificial Neural Network (ANN) was applied to the reference data, which includes time series of solar irradiance, current, and voltage produced by the photovoltaic panels on different days of the year under varying weather conditions. Once the ANN is trained, its performance will be validated by comparing the estimation generated by the network with data from a reference cell on the days selected for the study. Additionally, the model will be evaluated using data from other days, not used in the training, to verify its ability to generalize under different meteorological conditions.

Chronic Carriage of Leptospira interrogans Genotype Associated With the Australis Serogroup by Naturally-Infected Hedgehogs (Erinaceus europaeus) at a Wildlife Health Centre in Northwestern France.

Leptospirosis is a widespread zoonosis caused by bacteria in the genus Leptospira. Basic epidemiological information is crucial to mitigating disease risk but is lacking for leptospirosis; notably, the hosts responsible for maintaining Leptospira remain largely unknown. Frequently observed near human habitations, hedgehogs (Erinaceus europaeus) are taken to wildlife rescue centres when found sick or injured. Thus, they may pose a risk to human and animal health if they carry pathogenic Leptospira. This study aimed to describe Leptospira carriage in a hedgehog population and the potential clinical impacts of the infection. We investigated Leptospira carriage frequency and diversity in urine samples from 69 hedgehogs at a wildlife rescue centre, between April and June 2022. We used quantitative PCR, typing of the 16S rRNA and lfb1 genes, variable number tandem repeat and multispacer sequence typing to characterise Leptospira DNA. An analysis of urinary biochemical parameters was conducted to assess renal function. We detected Leptospira DNA in 25 (35%) of the urine samples, of which 21 were successfully typed. The latter analysis revealed a limited degree of genetic diversity. L. interrogans (n = 19) predominated, and the only genotype detected was related to the Australis serogroup (n = 17). We also noted the presence of L. borgpetersenii (n = 1) and L. kirschneri (n = 1). There was no relationship between infection status and urinalysis parameters. These results suggest hedgehogs may act as long-term shedders of Leptospira in natural ecosystems.

GRAPH — an readout ASIC for large MCP based detectors

We present a programmable 16 channel, mixed signal, low power readout ASIC, having the project historically named Gigasample Recorder of Analog waveforms from a PHotodetector (GRAPH). It is designed to read large aperture single photon imaging detectors using micro channel plates for charge multiplication, and measuring the detector's response on crossed strips anodes to extrapolate the incoming photon position. Each channel consists of a fast, low power and low noise charge sensitive amplifier, which provides a myriad of coarse and fine programmable options for gain and shaping settings. Further, the amplified signal is recorded using, to our knowledge novel, the Hybrid Universal sampLing Architecture (HULA) ADC. A kind of mixed signal double buffer memory, that enables concurrent waveform recording, and selected event digitized data extraction. The sampling frequency is freely adjustable between few kHz up to 125 MHz, while the chip's internal digital memory holds a history 2048 samples for each channel, with a digital headroom of 12 bits. An optimized region of interest sample-read algorithm allows to extract the information just around the event pulse peak, while selecting the next event, thus substantially reducing the operational dead time. The chip is designed in 130 nm TSMC CMOS technology, and its power consumption is around 47 mW per channel.

Hierarchical-based self-triggered control for linear systems with external disturbances

This paper introduces a hierarchical-based robust self-triggered control mechanism for linear systems with disturbances, with the goal of achieving anti-interference and computational resource saving. To address the potential external interference in the system, the H∞ performance index is introduced, and the self-triggered controller is developed based on this index to guarantee the system’s desired anti-interference capabilities. Furthermore, a hierarchical framework is employed to facilitate collaborative optimization of triggering mechanisms and control strategies. Within this framework, the triggering interval values and control input values are computed sequentially using a cyclic iteration approach. By upholding the H∞ performance standard and incorporating a hierarchical optimization framework, the system’s sampling frequency is notably diminished, anti-interference performance is safeguarded, and the convergence speed of the state trajectory is accelerated. The efficacy of the proposed method is validated through two illustrative examples.

Advanced Digital Signal Processing, Interpolation and Extrapolation Based Symmetrical Fault Assessment in Transmission Line

This article describes a novel method for locating and detecting triple line to ground (LLL-G) faults in 110 km of 132 kV transmission lines. To find the transmission line fault, the three phase system currents were first recorded at the appropriate sampling frequency. Then, the total harmonic distortion (THD) and direct current (DC) components were computed using the Fast Fourier transform (FFT). When compared to normal conditions, high values of THD and DC components have been seen in fault situations; fault has been identified as a result. The fault distance in the transmission line has been measured using spline extrapolation or linear interpolation. With this method, very encouraging outcomes have been obtained for fault assessment in transmission line.

Energy-Aware Feature and Classifier for Behaviour Recognition of Laying Hens in an Aviary System

Long-term monitoring of animal behaviours requires energy-aware features and classifiers to support onboard classification. However, limited studies have been conducted on the behaviour recognition of laying hens, especially in aviary systems. The objective of this study was to configure key parameters for developing onboard behaviour monitoring techniques of aviary laying hens, including proper sliding window length, energy-aware feature, and lightweight classifier. A total of 19 Jingfen No.6 laying hens were reared in an aviary system from day 30 to day 70. Six light-weight accelerometers were attached to the back of birds for behaviour monitoring with a sampling frequency of 20 Hz. Laying hen behaviours were categorized into four groups, including static behaviour (resting and standing), ingestive behaviour (feeding and drinking), walking, and jumping. Two different window lengths (0.5 s and 1 s) were tested. The SD of each axial acceleration was considered the only classification feature. The results indicated that performing denoise procedure before feature extraction can improve the classification accuracy by 10-20%. The 1-s window length yielded better accuracy than the 0.5-s window, especially for ingestive and walking behaviours. Classification models based on X-axis accelerations were better than those of Y- and Z-axis with the recognition accuracies of static, ingestive, walking, and jumping behaviours being 97.4%, 89.6%, 95.7%, and 98.5%, respectively. The study might provide insights into developing onboard behaviour recognition algorithms for laying hens.

Predicting the exposure of mycophenolic acid in children with autoimmune diseases using a limited sampling strategy: A retrospective study.

Mycophenolic acid (MPA) is commonly used to treat autoimmune diseases in children, and therapeutic drug monitoring is recommended to ensure adequate drug exposure. However, multiple blood sampling is required to calculate the area under the plasma concentration-time curve (AUC), causing patient discomfort and waste of human and financial resources. This study aims to use machine learning and deep learning algorithms to develop a prediction model of MPA exposure for pediatric autoimmune diseases with optimizing sampling frequency. Pediatric autoimmune patients' data were collected at Nanfang Hospital between June 2018 and June 2023. Univariate analysis was applied for feature selection. Ten algorithms, including Random Forest, XGBoost, LightGBM, Gradient Boosting Decision Tree, CatBoost, Artificial Neural Network, Grandient Boosting Machine, Transformer, Wide&Deep, and TabNet, were employed for modeling based on two, three, or four concentrations of MPA. A total of 614 MPA AUC0-12h samples from 209 patients were enrolled. Among the 10 models evaluated, the Wide&Deep model exhibited the best predictive performance. The predictive performance of the Wide&Deep model using four and three blood concentration points was similar (R 2 ≈ 1 for four points; R 2 = 0.95 for three points). No significant difference in accuracy within ±30% was observed between models utilizing three and four blood concentration points (p = 0.06). This study demonstrates that in the Wide&Deep model, MPA exposure can be accurately estimated with three sampling points in children with autoimmune diseases. This model could help reduce discomfort in pediatric patients without reducing the accuracy of MPA exposure estimates in clinical practice.

Processing techniques for metrological improvement of low-cost smart meter hardware solution for IEC 61000-4-7 Class I harmonics measurements

The work investigates the feasibility of harmonic analysis implementation on smart meter microcontroller devices, according to IEC 61000-4-30 Class A and IEC 61000-4-7 Class I requirements. The final aim was to evaluate to what extent Class I harmonic analysis can be integrated into existing low-cost hardware platforms for smart metering, which normally have limited hardware features, especially concerning the ADC and the possibility of varying the sampling frequency with high resolution, according to the power system signal frequency. An extended experimental characterization is carried out on a case study device, aimed at analyzing its performances in terms of both measurement accuracy and computational burden. To increase metrological ADC behavior and decrease computational costs, sampling strategies and optimized interpolation algorithm have been implemented and tested verifying the feasibility of harmonic analysis implementation on smart meter microcontroller devices, according to IEC 61000-4-30 Class A and IEC 61000-4-7 Class I requirements.

Job Satisfaction of University Teachers: A Comparative Study of Gender, Age and Marital Status

This study's major aim was to compare university teachers' job satisfaction regarding gender, age, and marital status. The current study was descriptive in nature, and data was gathered by survey methods. 164 University of Kotli teachers made up the study population. The researcher chose 113 teachers to serve as the study's sample. The study's research tool was a standardized instrument. The researcher gathered the data through in-person visits to university instructors. The data was analyzed using SPSS, a statistical software for social science. In order to analyze the data, the researcher used the independent sample t-test, frequency, percentage, mean, and analysis of variance (ANOVA). It was found that teachers receive recognition from their immediate supervisors, and they give assistance when needed. Moreover, the immediate supervisor appreciates good teaching and assists in improving instruction. On the other hand, the immediate supervisor does not provide backup to teachers, and they are not willing to listen to suggestions from teachers. It is also recommended that immediate supervisors avoid giving meaningless instructions to the teachers so that they do not feel bored/ill.

The Shift to English Medium Instruction: Investigating Teacher Challenges at Elementary School Level

The aim of the study was to investigate teachers' challenges at the elementary school level in District Sialkot after shifting to English medium instruction. This research study used a mixed-method design. The study population consisted of all the teachers teaching Social Studies (S.St.), Mathematics (Maths), and Science (Sc) to grades 7 and 8 in District Sialkot. A random stratified sampling technique was applied to select 1,500 elementary school teachers. The number of respondents was divided into two groups, males and females, comprising 722 males and 778 females, while 1,208 were from rural areas and 292 from urban areas. All the selected teachers taught (S.St), (Maths), and (Sc) in English in grades 7 and 8. The results were obtained by using a questionnaire designed on a five-point Likert scale with five closed-ended items related to perceptions about English as a medium of instruction and two open-ended items regarding possible remedies to the problems identified. Data sets were analyzed by frequency, percentage, and independent sample t-test. The findings of the research study showed irrespective of gender; educators found it hard to instruct (S.St), (Maths), and (Sc) after the change of medium of instruction from Urdu to English; instructor problems in rural areas were as prevalent and often more prevalent than those found in urban areas. The study concluded that the introduction of the medium of instruction (MI) of teaching from Urdu to English alone resulted in a deteriorating performance among teachers who were not involved in professional development before this change.

A Parameter-Oriented FFT Signal Processing App

In power systems, some components such as harmonics / interharmonics and noise arise in electrical signals due to varying sources and loads, resulting in changes in the purity of the electrical signal. Therefore, continuous monitoring and accurate analysis of electrical signals become mandatory. One of the common methods in this field is the Fast Fourier Transform (FFT), which subjects electrical signals to continuous analysis through sliding windows at the intervals formed by the standard of IEC-61000-4-7. From this analysis, various parameters are derived and compared with the threshold values specified in other standard, the IEEE-1159. However, due to variational field conditions, as well as factors such as frequencies of sampling, main, and additional components, measurement window, and Signal-to-Noise Ratio (SNR), various measurement errors, occur. These difficulties further complicate the already difficult task of accurate measurement in variable conditions and can lead to errors in preventive measures and control procedures. It is therefore of great importance to gain an understanding of the effects of these parameters and to implement improvements to the methods accordingly. This study emphasizes the significance of parameter selection for FFT and presents an investigation of FFT responses in terms of different parameters. Furthermore, it presents measurement errors according to changes in the signal and presents a basic interface design showing these errors. It was found that even small changes, such as a 1/2000 change in the sampling frequency, a 0.5 Hz shift in the fundamental frequency, or a 1/1000 difference in the measurement window, resulted in significant errors. These findings highlight the necessity of judicious parameter selection for accurate computation and signal monitoring and demonstrate that improvements in the FFT method are needed to adapt to changing conditions.

A Hybrid Structure Noise Shaping SAR ADC

Hybrid structure noise shaping (NS) SAR ADC combines both SAR ADC and sigma–delta ADC technologies with the advantages of high precision and low power consumption. In NS SAR ADC, mismatch error shaping (MES) method is used to solve the mismatch of low-level capacitors, and Data Weighted Averaging (DWA) method is used to solve the mismatch of high-level capacitors. The single-input comparator is improved into a dual-input comparator. The buffer and integrator circuits are realized by the static op-amp. The noise of op-amp is reduced by chopper technology and the accuracy of ADC is improved. A high-precision NS-SAR ADC with a sampling frequency of 1MSps and bandwidth of 25[Formula: see text]kHz was designed using the GSMC 90[Formula: see text]nm process. When the input signal is at full scale, the ENOB of the NS SAR ADC is higher than 14.73 bits, the SFDR is higher than 98[Formula: see text]dB, the average power consumption is 567.93[Formula: see text][Formula: see text]W and the quality factor is 169.2[Formula: see text]dB.

Allele ages provide limited information about the strength of negative selection.

For many problems in population genetics, it is useful to characterize the distribution of fitness effects (DFE) of de novo mutations among a certain class of sites. A DFE is typically estimated by fitting an observed site frequency spectrum (SFS) to an expected SFS given a hypothesized distribution of selection coefficients and demographic history. The development of tools to infer gene trees from haplotype alignments, along with ancient DNA resources, provides us with additional information about the frequency trajectories of segregating mutations. Here, we ask how useful this additional information is for learning about the DFE, using the joint distribution on allele frequency and age to summarize information about the trajectory. To this end, we introduce an accurate and efficient numerical method for computing the density on the age of a segregating variant found at a given sample frequency, given the strength of selection and an arbitrarily complex population size history. We then use this framework to show that the unconditional age distribution of negatively selected alleles is very closely approximated by re-weighting the neutral age distribution in terms of the negatively selected SFS, suggesting that allele ages provide little information about the DFE beyond that already contained in the present day frequency. To confirm this prediction, we extended the standard Poisson Random Field (PRF) method to incorporate the joint distribution of frequency and age in estimating selection coefficients, and test its performance using simulations. We find that when the full SFS is observed and the true allele ages are known, including ages in the estimation provides only small increases in the accuracy of estimated selection coefficients. However, if only sites with frequencies above a certain threshold are observed, then the true ages can provide substantial information about the selection coefficients, especially when the selection coefficient is large. When ages are estimated from haplotype data using state-of-the-art tools, uncertainty about the age abrogates most of the additional information in the fully observed SFS case, while the neutral prior assumed in these tools when estimating ages induces a downward bias in the case of the thresholded SFS.

A comprehensive time-series dataset linked to cyanobacterial blooms in Lake Taihu

Lake Taihu has a history of recurrent harmful cyanobacterial blooms. There is a need to better understand the aquatic ecosystem of Lake Taihu in order to improve methods for controlling the cyanobacterial blooms. Based on the field measurement and satellite remote sensing, we produced and collected a time-series dataset, including the water quality, bio-optics, climate, and anthropogenic data of Lake Taihu (THQBCA), which could provide comprehensive information regarding cyanobacterial blooms. The THQBCA dataset contains 26 variables organized into four categories: water quality, bio-optics, climate, and anthropogenic data. The water quality and climate data are field measured data with sampling frequency from daily to quarterly, and bio-optics and anthropogenic data are satellite-derived annual data. The dataset spans more than 15 years (8 of which cover approximately 35 years, 4 of which cover 20 years), and the spatial resolutions of the satellite-derived data range from 30 m to 500 m. This dataset is expected to advance research on evaluating and predicting cyanobacterial blooms, and support science-based management decisions for sustainable ecological development.

Features of Planar Localization of Acoustic Emission Sources via the Inglada’s Triangulation Algorithm

This paper presents a methodology for enhancing the efficiency of acoustic emission (AE) source detection during planar localization using the Inglada’s algorithm. The study analyzes the main factors affecting the accuracy of AE source localization when using a standard planar localization approach. These factors include the threshold-based method of determining the signal registration time by AE sensors, which is based on detecting the moment when the rising wavefront voltage exceeds the discrimination threshold (uth), the signal sampling frequency (fd), and the influence of the medium’s dispersion properties on the attenuation of signal amplitude and wave propagation speed. To reduce the impact of these factors on the localization accuracy of AE sources, a novel methodology is proposed based on the use of correlation dependencies of AE pulse propagation speed on the amplitude of the recorded signals, as well as on accounting for the delay in the registration time of AE pulses during threshold detection. A series of preliminary experiments was conducted to implement the proposed methodology, where AE pulses were generated using an electronic simulator with a maximum amplitude level of um = 45—90 dB. The position of the AE pulse source varied in the range of 150 to 700 mm relative to the receiving sensors of the antenna array. As a result of applying the developed methodology, the probability of AE source detection increased to p = 0,71, compared to p = 0,36 when using the standard approach.

The Unsteady Shock-Boundary Layer Interaction in a Compressor Cascade – Part 1: Measurements With Time-Resolved PIV

Abstract Time-resolved PIV is performed in the transonic cascade to elucidate the shock-boundary layer interaction. Measurements are conducted in the center blade passage of the cascade at a sampling frequency of 20 kHz or 0.18 convective time units (CTU) for the acquisition of multiple time sequences with duration of approx. 2000 CTU. The sampling rate is sufficient to capture fluctuations of the separating region and accompanying movements of the shock system. To align both instantaneous density variations in the shock system and velocity data obtained in spatially separated regions, shadowgraphs are acquired synchronously with TR-PIV recordings at the same sampling rate. An analysis of power spectral densities along near-wall rows in PIV data reveals the chord-wise spatial distribution of specific peaks and bands. The upstream propagation of disturbances beyond the excursion range of the main shock was demonstrated for the dominant buffet frequency as well as for the high-frequency tone and its first harmonic using cross-correlation maps. A spectral proper orthogonal decomposition (SPOD) indicates that upstream of the main shock oblique shock waves occur at higher order harmonics of the fundamental buffet frequency as well for specific high-frequency tones. To locate possible sources of distinct tones at the entrance of the center passage, a SPOD of high-speed schlieren recordings is performed, capturing the shock systems in three neighboring blade passages. Results indicate that with these high-frequency tones opposing vibrations of bow and lip shocks occur for neighboring blades.

A SIMPLIFIED FREQUENCY DOMAIN BEAMFORMING APPROACH FOR ACTIVE AND PASSIVE SURFACE WAVE SURVEYS

Frequency domain beamforming (FDBF) and cylindrical frequency domain beamforming (CFDBF) techniques are among the most robust methods for extracting experimental phase velocity dispersion data from wavefields recorded during either active- or passive-source surface wave testing. Both FDBF and CFDBF transformations rely on calculating the spatiospectral correlation matrix to compute the steered power response spectrum, which is essential for extracting surface wave dispersion data. The dimensions of the spatiospectral correlation matrix are directly influenced by the frequency sampling and the number of sensors employed in the survey setup. When the number of sensors is large, and/or the frequency range is broad, and/or the frequency sampling is dense, the spatiospectral correlation matrix size leads to a greater number of required computations to calculate the steered power response spectrum for both the FDBF and CFDBF techniques. Therefore, we propose a simplified and more computationally efficient frequency domain beamforming algorithm for both active and passive surface wave surveys. This method avoids the calculation of the spatiospectral correlation matrix and its multiplication with the Hermitian transpose of the steering vector, thereby reducing the computational complexity by an order. Furthermore, by leveraging vectorization techniques, the proposed algorithm employs matrix multiplications instead of loops, significantly heightening computational efficiency. We rigorously tested the algorithm using surface wave datasets collected across diverse subsurface conditions with varying sensor arrays and acquisition parameters (e.g., sampling frequencies and window lengths) to evaluate its efficacy. The results demonstrate that our proposed algorithm outperforms the conventional FDBF method in the processing time, highlighting its practical effectiveness. This method shows promise for integration into surface wave data processing software, offering substantial improvements in computational efficiency without compromising accuracy

Comparison of Mean Values and Entropy in Accelerometry Time Series from Two Microtechnology Sensors Recorded at 100 vs. 1000 Hz During Cumulative Tackles in Young Elite Rugby League Players.

Several microtechnology devices quantify the external load of team sports using Global Positioning Systems sampling at 5, 10, or 15 Hz. However, for short, explosive actions, such as collisions, these sample rates may be limiting. It is known that very high-frequency sampling is capable of capturing changes in actions over a short period of time. Therefore, the aim of this study was to compare the mean acceleration and entropy values obtained from 100 Hz and 1000 Hz tri-axial accelerometers in tackling actions performed by rugby players. A total of 11 elite adolescent male rugby league players (mean ± SD; age: 18.5 ± 0.5 years; height: 179.5 ± 5.0 cm; body mass: 88.3 ± 13.0 kg) participate in this study. Participants performed tackles (n = 200), which were recorded using two triaxial accelerometers sampling at 100 Hz and 1000 Hz, respectively. The devices were placed together inside the Lycra vests on the players' backs. The mean acceleration, sample entropy (SampEn), and approximate entropy (ApEn) were analyzed. In mean acceleration, the 1000 Hz accelerometer obtained greater values (p < 0.05). However, SampEn and ApEn were greater with the 100 Hz accelerometer (p < 0.05). A large relationship was observed between the two devices in all the parameters analyzed (R2 > 0.5; p < 0.0001). Sampling frequency can affect the quality of the data collected, and a higher sampling frequency potentially allows for the collection of more accurate motion data. A frequency of 1000 Hz may be suitable for recording short and explosive actions.

Зараженность зерна озимой мягкой пшеницы и тритикале грибами рода Fusarium на территории РСО-Алания

Abstract. This article presents the results of studies of the species composition of fusarium fungi found in the affected ear and grain of winter soft wheat and winter triticale. Molecular genetic methods have revealed new fusarium species for the region, and the dominant species have been identified. More types of micromycetes were found on the susceptible variety. Methods. Confirmation of the species was carried out by PCR with universal primers and further sequencing of nucleotide sequences using the Sanger method. The β-tubulin gene region was amplified for sequencing. Results. 14 species of fungi of the genus Fusarium have been identified. The species F. graminearum dominated in all samples (frequency of occurrence 42.8 %). The species F. avenaceum (23.3 %), F. sporotrichioides and F. equiseti (8.8 % each) were noted with high frequency. Next, in descending order of occurrence: F. oxysporum, F. boothii, F. poae, F. proliferatum, F. incarnatum, F. vorosii, F. tricinctum, F. cerealis, F. sambucinum. Scientific novelty. For the first time, six species of fusarium fungi were found in diseased wheat grain: F. boothii, F. vorosii, F. cerealis, F. sporotrichioides, F. oxysporum, F. incarnatum. The results obtained indicate a community of fusarium fungi that are constantly present in the grain agrocenosis. Four species of fusarium have been found in the diseased triticale grain: F. graminearum. F. oxysporum, F. equiseti and F. boothii. Species differ from each other in terms of moisture and temperature requirements, as well as a set of toxic substances of secondary metabolism that ensure competitive interactions between species and successful colonization of the nutrient substrate. The constant presence of a complex of fusarium fungi in the agrocenosis, occupying various ecological niches, ensures continuous infection of the ear and grain, starting from the flowering phase to harvesting.