Non-target Objects Research Articles

The Impact of Cues on Joint Attention in Children with Autism Spectrum Disorder: An Eye-Tracking Study in Virtual Games.

Joint attention (JA), a core deficit in children with autism spectrum disorder (ASD), is crucial for social interaction, emotional understanding, and cognitive development. This study aims to compare and analyze the eye-tracking data of ASD and typically developing children (TDC) during virtual games, exploring how different cue types affect JA performance in ASD children. A total of 31 TDC and 40 ASD children participated in the study. Using eye-tracking devices, we recorded the children's eye movements as they played virtual games, selecting the correct target based on cues provided by virtual characters. Our findings revealed that different cue types significantly impacted the game scores of ASD children but had no significant effect on TDC, highlighting a notable disparity between the two groups. ASD children showed a lower fixation frequency, irregular fixation paths, and increased attention to non-target objects compared to TDC. Interestingly, among the three cue types, ASD children exhibited a preference for the third type, leading to longer fixation on the region of interest and higher game scores. These results underscore the importance of cue selection in enhancing JA in ASD children. This study provides novel insights into the JA deficits in ASD children and offers a scientific basis for the development of targeted and individualized intervention programs.

No difference in prior representations of what to attend and what to ignore

ABSTRACT Adaptive behaviour requires the selection of relevant, and the ignoring of irrelevant visual information. Previous work has shown that prior information about target (i.e., relevant) and non-target (i.e., irrelevant) objects facilitates such selection, presumably by enabling observers to create a working memory template for or against such objects. Using a cued visual search task, here we aimed to investigate whether, and how, target and non-target templates differ in terms of neural representation, as measured through fMRI activity patterns. Two hypotheses were tested: (1) while target representations are activated, non-target representations are suppressed prior to search, and thus these representations differ, and (2) target and non-target templates share similar initial representations but involve different control signals in anticipation of, or upon encountering the search display. Behaviourally, both target and non-target cues facilitated search compared to baseline. Using multivariate pattern and representational similarity analyses, the findings revealed little support for the suppression hypothesis. Posterior brain regions primarily coded for the category content of the template and did so similarly for target and non-target information. Frontal cortical areas, particularly the lateral prefrontal cortex and frontal eye fields, instead showed sensitivity to the status of the template, regardless of the object category it belonged to. These results are most consistent with the hypothesis that prior to selection, target and non-target information are represented similarly in terms of content, but differ in terms of the associated control signals.

Robust one-class classification using deep kernel spectral regression

The existing one-class classification (OCC) methods typically presume the existence of a pure target training set and generally face difficulties when the training set is contaminated with non-target objects. This work addresses this aspect of the OCC problem and formulates an effective method that leverages the advantages of kernel-based methods to achieve robustness against training label noise while enabling direct deep learning of features from the data to optimise a Fisher-based loss function in the Hilbert space. As such, the proposed OCC approach can be trained in an end-to-end fashion while, by virtue of a Tikhonov regularisation in the Hilbert space, it provides high robustness against the training set contamination.Extensive experiments conducted on multiple datasets in different application scenarios demonstrate that the proposed methodology is robust and performs better than the state-of-the-art algorithms for OCC when the training set is corrupted by contamination.

Saliency as Pseudo-Pixel Supervision for Weakly and Semi-Supervised Semantic Segmentation.

Existing studies on semantic segmentation using image-level weak supervision have several limitations, including sparse object coverage, inaccurate object boundaries, and co-occurring pixels from non-target objects. To overcome these challenges, we propose a novel framework, an improved version of Explicit Pseudo-pixel Supervision (EPS++), which learns from pixel-level feedback by combining two types of weak supervision. Specifically, the image-level label provides the object identity via the localization map, and the saliency map from an off-the-shelf saliency detection model offers rich object boundaries. We devise a joint training strategy to fully utilize the complementary relationship between disparate information. Notably, we suggest an Inconsistent Region Drop (IRD) strategy, which effectively handles errors in saliency maps using fewer hyper-parameters than EPS. Our method can obtain accurate object boundaries and discard co-occurring pixels, significantly improving the quality of pseudo-masks. Experimental results show that EPS++ effectively resolves the key challenges of semantic segmentation using weak supervision, resulting in new state-of-the-art performances on three benchmark datasets in a weakly supervised semantic segmentation setting. Furthermore, we show that the proposed method can be extended to solve the semi-supervised semantic segmentation problem using image-level weak supervision. Surprisingly, the proposed model also achieves new state-of-the-art performances on two popular benchmark datasets.

Scalable and effective artificial intelligence for multivariate radar environment

Ground based radar is a key system for aerial defence of any country. Current generations of radar use Moving Target Indicator (MTI) filter, Constant False Alarm Rate (CFAR) and Peak Detection (PD) algorithms for detection of target and rejection of non-target (static clutter, dynamic clutter, interference and noise) objects. These conventional Radar Signal Processing (RSP) techniques find their limitation when either targets are not detected or false alarms are generated due to lack of built-in intelligence. In order to eliminate both the issues, a realistic radar environment has been analyzed using Artificial Intelligence (AI). Special emphasis has been made on invalid detections caused by weather clutter, which was never worked upon previously using AI. A novel deep learning based object detection approach has been devised keeping radar environment, high accuracy requirement and real-time constraint of radar in view. The proposed approach has been validated for its efficacy through standalone testing as well as Poof of Concept (PoC) implementation on a real radar. Integration of AI model in radar platform also involves a novel implementation scheme, whereby results of AI model are augmented by conventional RSP. Inference time of AI models is a bottleneck for their use in radar systems, in this context, new strategies have also been analyzed to achieve a real-time performance for radar application. As the objective of this research is to devise scalable and effective solutions for upcoming generations of radar; therefore, the work is of practical value for realistic deployments.

Global Dilated Attention and Target Focusing Network for Robust Tracking

Self Attention has shown the excellent performance in tracking due to its global modeling capability. However, it brings two challenges: First, its global receptive field has less attention on local structure and inter-channel associations, which limits the semantics to distinguish objects and backgrounds; Second, its feature fusion with linear process cannot avoid the interference of non-target semantic objects. To solve the above issues, this paper proposes a robust tracking method named GdaTFT by defining the Global Dilated Attention (GDA) and Target Focusing Network (TFN). The GDA provides a new global semantics modeling approach to enhance the semantic objects while eliminating the background. It is defined via the local focusing module, dilated attention and channel adaption module. Thus, it promotes semantics by focusing local key information, building long-range dependencies and enhancing the semantics of channels. Subsequently, to distinguish the target and non-target objects both with rich semantics, the TFN is proposed to accurately focus the target region. Different from the present feature fusion, it uses the template as the query to build a point-to-point correlation between the template and search region, and finally achieves part-level augmentation of target feature in the search region. Thus, the TFN efficiently augments the target embedding while weakening the non-target objects. Experiments on challenging benchmarks (LaSOT, TrackingNet, GOT-10k, OTB-100) demonstrate that the GdaTFT outperforms many state-of-the-art trackers and achieves leading performance. Code will be available.

Айтрекинг-исследование визуального внимания детей с нарушением слуха в ситуации обучения

<p>Potential mechanisms underlying atypical joint attention that impede effective learning are analyzed using the example of the consequences of hearing impairment. A sample of preschool children with hearing impairment after cochlear implantation (sensorineural hearing loss, ICD-10 class H90) was studied. For the study, an experimental situation was created that would allow tracing the learning difficulties in children with hearing impairments associated with the skills of joint attention. In the course of completing a training task jointly with an adult in children with hearing impairment, eye movements were recorded with a portable tracker in the form of Pupil Headset glasses. In the course of the study, it was possible to identify and visualize markers of oculomotor activity that impede their effective learning: distribution of visual attention over a wide area of the visual field, not in a focused mode; in the trajectory of eye movements and the position of fixations, there is a preference for non-social signals, non-target objects, difficulties in switching attention from one object to another. The main manifestation of joint attention deficit in preschoolers with hearing impairment is a decrease in the volume and time of stable maintenance of synchronicity of perception in the learning process. The parameters of oculomotor activity can serve as an indicator of the ability of a child with a hearing impairment to maintain attention to the sample form and a diagnostic indicator of the possible number of errors in the learning process. Such indicators are the duration and number of fixations. The role of multimodal means of maintaining visual attention to the sample in the process of teaching children with hearing impairment is shown. It was revealed that the attention of a child with hearing impairment to the face of an adult is part of the joint attention to the object, which improves the effectiveness of learning and is associated with a longer visual attention to the object (teaching pattern).</p>

The effect of memory load on object reconstruction: Insights from an online mouse-tracking task.

Why can't we remember everything that we experience? Previous work in the domain of object memory has suggested that our ability to resolve interference between relevant and irrelevant object features may limit how much we can remember at any given moment. Here, we developed an online mouse-tracking task to study how memory load influences object reconstruction, testing participants synchronously over virtual conference calls. We first tested up to 18 participants concurrently, replicating memory findings from a condition where participants were tested individually. Next, we examined how memory load influenced mouse trajectories as participants reconstructed target objects. We found interference between the contents of working memory and what was perceived during object reconstruction, an effect that interacted with visual similarity and memory load. Furthermore, we found interference from previously studied but currently irrelevant objects, providing evidence of object-to-location binding errors. At the greatest memory load, participants were nearly three times more likely to move their mouse cursor over previously studied nontarget objects, an effect observed primarily during object reconstruction rather than in the period before the final response. As evidence of the dynamic interplay between working memory and perception, these results show that object reconstruction behavior may be altered by (i) interference between what is represented in mind and what is currently being viewed, and (ii) interference from previously studied but currently irrelevant information. Finally, we discuss how mouse tracking can provide a rich characterization of participant behavior at millisecond temporal resolution, enormously increasing power in cognitive psychology experiments.

Grading of HCC Biopsy Images using Nucleus and Texture Features.

Hepatocellular carcinoma (HCC) is one of the most critical health problems in the world. For proper treatment, it is important to identify the grade of cancer morbidity from HCC biopsy image. The diagnostic work is not only time-consuming but also subjective. The same biopsy image may be diagnosed as of different grades by different doctors, due to lack of experience or difference in opinion. In this work, we proposed an automatic grading system with classification accuracy matching to an experienced doctor, to help augment the diagnosis process. First, we proposed a segmentation method to isolate all nucleus-like objects present in a biopsy image. Non-target objects (here the target is a single HCC nucleus) present in the biopsy image are isolated too in the segmentation process. To eliminate such non-target objects, we proposed clustering of segmented images and a novel method to filter out target objects. Next, we proposed a two track neural network, where input consists of 2 different images. It combines a single segmented nucleus and a random cropped texture patch of the biopsy image to which the nucleus belongs. At this classifier output, we grade the single nucleus. Finally, a majority voting method is used to identify the grade of the whole biopsy image. We achieved an accuracy of 99.03% for nucleus image grading and 99.67% accuracy for grading biopsy images.

Complex active sonar targets recognition using variable length deep convolutional neural network evolved by biogeography-based optimizer

Due to heterogeneous sound propagation conditions and fluctuating ambient noises, conventional handcrafted feature extraction methods represent poor results and high complexity in underwater sonar wave recognition tasks. In order to address these shortcomings, this paper proposes a hybrid metaheuristic deep learning-based approach. However, model depth may vary under different underwater ocean conditions. The deeper the model, the greater the number of hyperparameters, challenging the search space. It is crucial to have an efficient algorithm that can obtain an accurate model in a reasonable time. Therefore, this paper proposes the Variable-Length Habitat Biogeography-Based Optimizer (VLHBBO) to tune the hyperparameters of a deep conventional neural network. Given that there is no appropriate dataset for training the proposed model, experimental underwater scattering measurement is conducted on several target and non-target objects of the same size in the east of the Persian Gulf and the west of the Oman Sea. Furthermore, this study uses the benchmark datasets obtained from the New Array Technology III program as test datasets. The performance of the proposed model is compared to other underwater target classifiers in terms of eight metrics. The classification results indicate that the proposed VLBBO-DCNN classifier can effectively classify underwater sonar waves into relevant categories.

샴 네트워크 기반의 적은 데이터셋을 이용한 유사한 형태의 특정 객체 인식 연구

Target object recognition by a mobile robot is based on deep learning. To train a deep learning model to differentiate between target and non-target objects, a sufficiently large dataset that can extract features for classifying objects is required. In a general working environment, securing a sufficiently large dataset is difficult because the number of people designated as workers is constantly changing. Classifying the shapes of objects that are similar requires larger datasets than classifying the shapes of objects that are not similar. Therefore, a method for recognizing target objects with similar shapes using a small dataset is required. This paper proposes a Siamese-network–based target object recognition method for recognizing objects with similar shapes based on a small dataset. A trained Siamese network is used to recognize whether the input object is the target object based on its similarity to the target object. The results of target object recognition using the proposed method were experimentally analyzed. Further, ResNet-50 was used to evaluate the performance of the proposed method. Our findings show that the proposed method recognized objects with a difference of approximately 6% using a small dataset, indicating its higher efficiency than the classification-based object recognition method.

Do face-to-face interactions support 6-month-olds' understanding of the communicative function of speech?

Infants by 6months recognize that speech communicates information between third parties. We investigated whether 6-month-olds always expect speech to communicate or whether they also consider social features of communication, like how interlocutors engage with one another. A small sample of infants watched an actor (the Speaker) choose one of two objects to play with (the target). When the Speaker could no longer reach her target object, she turned to a new actor (the Listener) and said a nonsense word. During speech, the actors were either face-to-face, the Speaker was facing away from the Listener, or the reverse. When the actors had been face-to-face, infants looked longer when the Listener selected the non-target object compared to the target. Infants looked equally regardless of what the Listener chose when either actor had been disengaged. Area-of-interest gaze coding suggests that infants were similarly interested in the interaction across conditions, but their pattern of attention to Speaker and Listener differed when the Listener was disengaged during speech. Although these experiments should be replicated with a larger sample, the findings provide initial evidence that 6-month-olds do not expect speech alone to communicate, but also attend to the social context in which speech is produced.

Young children's use of probabilistic reliability and base-rates in decision-making.

Children are skilled reasoners who readily use causal, reliability, and base-rate (i.e., prior probability) information in their decisions. Though these abilities are typically studied in isolation, children often must consider multiple pieces of information to make an informed decision. Four experiments (N = 320) explored the development of children’s ability to use reliability and base-rate information when making decisions about draw outcomes. Experiment 1 examined the age at which children can first compare and choose between probabilistically reliable machines. Three- and 4-year-old children saw machines that were probabilistically reliable at obtaining objects while sampling from uniform distributions (i.e., all target or non-target objects). Although 4-year-old children correctly used reliability in their decisions, 3-year-olds did not. In Experiment 2a, 4- to 6-year-olds were presented with the same probabilistically reliable machines, although they sampled from a mixture of target and non-target items. Here, children tended to choose the machine with the better proportion of targets, regardless of reliability. This was replicated in Experiment 2b. In Experiment 3, children were presented with one perfectly reliable machine and one probabilistically unreliable machine. Here, children continued to mostly choose the machine with the better proportion of targets. These results raise questions about base-rate overuse early in development and highlight the need for additional work on children’s ability to use multiple pieces of information in decision-making.

Geometric 3-D inversion of airborne time-domain electromagnetic data with applications to kimberlite pipes prospecting in a complex medium

We consider an approach to solving 3-D inverse problems of airborne time-domain electromagnetic (TD-EM) survey in complex geoelectrical conditions, when the responses of target objects in the measured signal are relatively small and mixed with responses of rugged topography, 3-D inhomogeneities in overburden layers, and other interfering 3-D objects. For parameterization of the geoelectrical model of the geological medium, global and local structures are used. A block structure is a grid with large cells whose boundaries can move during the inversion process. Block structures are characterized by two types of parameters: geometric and physical. Geometric parameters include the coordinates of the boundaries of blocks in block structures, which are used for recovering local bodies or 3-D inhomogeneities within the layers, and physical parameters are the conductivities inside the blocks. In addition, the geometric parameters include displacements of control points, which are used for describing curved surfaces. The functional employed for inversion includes regularizing terms that allow controlling the self-crossing of geometric boundaries and extension of physical parameters beyond the boundaries of the allowed values range. Regularization coefficients are calculated adaptively at each iteration of the nonlinear 3-D inversion. In order to separate the responses of 3-D inhomogeneities located under the overburdens from those of 3-D inhomogeneities inside the overburdens, we propose to carry out 3-D inversion in two stages. At the first stage, over a shortened time range, we recover the conductivity distribution using global block structures and the shape of the boundaries of the overburden layers using control points. At the second stage, the entire time range is used. In those places where the significant residuals remained at late times, local block structures are employed to recover local objects (including target ones) under the overburden layers. This enables us to define quite accurately the position in plan, top border depth, and type of 3-D inhomogeneities, which are located under the overburdens, at the second stage of geometric inversion. To calculate 3-D time-domain electromagnetic fields, we use the vector finite element method implemented on optimized non-conforming hexahedral meshes with the possibility of adjoining the faces of several finite elements to the face of one finite element. The workability of the proposed approaches is demonstrated on synthetic airborne TD-EM data as well as on experimental airborne data, which are obtained as a result of methodical work over the kimberlite pipe deposit. The proposed geoelectrical model parameterization and the approach to 3-D inversion allow the discovery of relatively small 3-D target objects, when many non-target objects, including 3-D inhomogeneities in the overburdens, give several times greater responses in the measured signals.

Collision response and obstacle avoidance of the tethered-space net robot system with non-target objects

PurposeThis paper aims to study the encounter issues of the Tethered-Space Net Robot System (TSNRS) with non-target objects on orbit during the maneuver, including the collision issues with small space debris and the obstacle avoidance from large obstacles.Design/methodology/approachFor the collision of TSNRS with small debris, the available collision model of the tethered net and its limitation is discussed, and the collision detection method is improved. Then the dynamic response of TSNRS is studied and a closed-loop controller is designed. For the obstacle avoidance, the variable enveloping circle of the TSNRS has coupled with the artificial potential field (APF) method. In addition, the APF is improved with a local trajectory correction method to avoid the overbending segment of the trajectory.FindingsThe collision model coupled with the improved collision detection method solves the detection failure and speeds up calculation efficiency by 12 times. Collisions of TSNRS with small debris make the local thread stretch and deforms finally making the net a mess. The boundary of the disturbance is obtained by a series of collision tests, and the designed controller not only achieved the tracking control of the TSNRS but also suppressed the disturbance of the net.Practical implicationsThis paper fills the gap in the research on the collision of the tethered net with small debris and makes the collision model more general and efficient by improving the collision detection method. And the coupled obstacle avoidance method makes the process of obstacle avoidance safer and smoother.Originality/valueThe work in this paper provides a reference for the on-orbit application of TSNRS in the active space debris removal mission.

Enhanced the moving object detection and object tracking for traffic surveillance using RBF-FDLNN and CBF algorithm

For traffic surveillance systems (TSS), Moving object detection and tracking is the key technology. On account of moving object-orientation variation, changing weather conditions, moving objects’ appearance, and non-target objects in the background, it's very hard to detection some objects in a traffic sequence of the video frame. Though many methods were introduced to detect and track moving objects in a traffic environment, they could not attain a desirable output. Hence, this work proposes an enhanced system for automatic moving object detection (MOD) and object tracking (OT) using RBF-FDLNN and CFR algorithms. The noises existent in a video frame are initially filtered using a GESFA filter. Next, the conversion of the RGB frame to HSL is done, and the background regions are eradicated from the frame using IGMM. After background removal (BGR), the Motion Estimation (ME) of object and OT are performed using GCS and CFR algorithm, respectively. Subsequently, the required features in the frames are extracted and fed into the RBF-FDLNN classifier for performing object detection (OD). The performance of the proposed method RBF-FDLNN classifier is better than other existing methods in the given video frame. Lastly, the outcomes are proffered to corroborate the proposed method’s effectiveness.

Negative affordance effect: automatic response inhibition triggered by handle orientation of non-target object

Habituated response tendency associated with affordance of an object is automatically inhibited if this affordance cue is extracted from a non-target object. This study presents two go/no-go experiments investigating whether this response control operates in response selection processes and whether it is linked to conflict-monitoring mechanisms. In the first experiment, the participants performed responses with one hand, and in the second experiment, with two hands. In addition, both experiments consisted of two blocks with varying frequency of go conditions (25%-go vs. 75%-go). The non-target-related response inhibition effect was only observed in Experiment 2 when the task required selecting between two hands. Additionally, the results did not reveal patterns typically related to conflict monitoring when go-frequency is manipulated and when a stimulus–response compatibility effect is examined relative to congruency condition of the previous trial. The study shows that the non-target-related response inhibition assists hand selection and is relatively resistant to conflict-monitoring processes.

Updating the dual-mechanism model for cross-sensory attentional spreading: The influence of space-based visual selective attention.

Selective attention to visual stimuli can spread cross‐modally to task‐irrelevant auditory stimuli through either the stimulus‐driven binding mechanism or the representation‐driven priming mechanism. The stimulus‐driven attentional spreading occurs whenever a task‐irrelevant sound is delivered simultaneously with a spatially attended visual stimulus, whereas the representation‐driven attentional spreading occurs only when the object representation of the sound is congruent with that of the to‐be‐attended visual object. The current study recorded event‐related potentials in a space‐selective visual object‐recognition task to examine the exact roles of space‐based visual selective attention in both the stimulus‐driven and representation‐driven cross‐modal attentional spreading, which remain controversial in the literature. Our results yielded that the representation‐driven auditory Nd component (200–400 ms after sound onset) did not differ according to whether the peripheral visual representations of audiovisual target objects were spatially attended or not, but was decreased when the auditory representations of target objects were presented alone. In contrast, the stimulus‐driven auditory Nd component (200–300 ms) was decreased but still prominent when the peripheral visual constituents of audiovisual nontarget objects were spatially unattended. These findings demonstrate not only that the representation‐driven attentional spreading is independent of space‐based visual selective attention and benefits in an all‐or‐nothing manner from object‐based visual selection for actually presented visual representations of target objects, but also that although the stimulus‐driven attentional spreading is modulated by space‐based visual selective attention, attending to visual modality per se is more likely to be the endogenous determinant of the stimulus‐driven attentional spreading.

Memory precision for salient distractors decreases with learned suppression

Attention operates as a cognitive gate that selects sensory information for entry into memory and awareness (Driver, 2001, British Journal of Psychology, 92, 53–78). Under many circumstances, the selected information is task-relevant and important to remember, but sometimes perceptually salient nontarget objects will capture attention and enter into awareness despite their irrelevance (Adams & Gaspelin, 2020, Attention, Perception, & Psychophysics, 82[4], 1586–1598). Recent studies have shown that repeated exposures with salient distractor will diminish their ability to capture attention, but the relationship between suppression and later cognitive processes such as memory and awareness remains unclear. If learned attentional suppression (indicated by reduced capture costs) occurs at the sensory level and prevents readout to other cognitive processes, one would expect memory and awareness to dimmish commensurate with improved suppression. Here, we test this hypothesis by measuring memory precision and awareness of salient nontargets over repeated exposures as capture costs decreased. Our results show that stronger learned suppression is accompanied by reductions in memory precision and confidence in having seen a color singleton at all, suggesting that such suppression operates at the sensory level to prevent further processing of the distractor object.

Antagonism of Bacillus thuringiensis to phytopathogenic micromicetes as pathogens of apple trees

Antagonistic microorganisms, producing bactericidal and fungicidal substances, spread in space and occupy various ecological niches. Antagonistic action of microorganisms can be combined with a number of other properties that can be used by humans for their own purposes. Microorganisms — agents of biological products often have a multifunctional effect on the components of the agroecosystem, which under the influence of various environmental factors could have positive and negative consequences. The aim of the study was to study the effect of new highly active entomopathogenic strains of Bacillus thuringiensis on non-target objects — components of the biocenosis of the apple orchard. When developing technologies for biocontrol of the number of apple trees pests using B. thuringiensis revealed an additional positive protective effect — reducing the number of pathogens of fungal diseases on the leaf surface. Treatment of apple leaves with a liquid culture of strain B. thuringiensis 0376 after 72 hours of application reduced the number of scab of Fusicladium dendriticum by 7 times, and the pathogen of monolisis of Monilia fructigena — 9 times. Strain B. thuringiensis 0371 showed the highest level of antagonism, because in vitro completely inhibited the growth and development of micromycetes on the surface of the nutrient medium. On the leaf surface, the application of culture strain 0371 reduced the number of Fusicladium dendriticum by 92%, and Monilia fructigena — by 86% after 72 hours of treatment. In a field experiment when treating apple trees with a culture of this strain, scab development decreased by 7.1%. Strain B. thuringiensis 787 did not show antagonism against phytopathogenicmicromycetes during co-cultivation on nutrient medium, which may be explained by its inability to produce water-soluble exotoxin, in contrast to strains of B. thuringiensis, which are antagonists of scab and apple monolisis. Strain B. thuringiensis 0371 requires further research and development of its formulations, because it is promising for the creation of a complex biological product with insect-fungicidal properties for use in ecological and agriculture friendly technologies.