Development Of Computational Models Research Articles

Multi-Modal in Vitro Experiments Mimicking the Flow Through a Mitral Heart Valve Phantom.

Fluid-structure interaction (FSI) models are more commonly applied in medical research as computational power is increasing. However, understanding the accuracy of FSI models is crucial, especially in the context of heart valve disease in patient-specific models. Therefore, this study aimed to create a multi-modal benchmarking data set for cardiac-inspired FSI models, based on clinically important parameters, such as the pressure, velocity, and valve opening, with an in vitro phantom setup. An in vitro setup was developed with a 3D-printed phantom mimicking the left heart, including a deforming mitral valve. A range of pulsatile flows were created with a computer-controlled motor-and-pump setup. Catheter pressure measurements, magnetic resonance imaging (MRI), and echocardiography (Echo) imaging were used to measure pressure and velocity in the domain. Furthermore, the valve opening was quantified based on cine MRI and Echo images. The experimental setup, with 0.5% cycle-to-cycle variation, was successfully built and six different flow cases were investigated. Higher velocity through the mitral valve was observed for increased cardiac output. The pressure difference across the valve also followed this trend. The flow in the phantom was qualitatively assessed by the velocity profile in the ventricle and by streamlines obtained from 4D phase-contrast MRI. A multi-modal set of data for validation of FSI models has been created, based on parameters relevant for diagnosis of heart valve disease. All data is publicly available for future development of computational heart valve models.

A quasi-realistic computational model development and flow field study of the human upper and central airways.

The impact of drug delivery and particulate matter exposure on the human respiratory tract is influenced by various anatomical and physiological factors, particularly the structure of the respiratory tract and its fluid dynamics. This study employs computational fluid dynamics (CFD) to investigate airflow in two 3D models of the human air conducting zone. The first model uses a combination of CT-scan images and geometrical data from human cadaver to extract the upper and central airways down to the ninth generation, while the second model develops the lung airways from the first Carina to the end of the ninth generation using Kitaoka's deterministic algorithm. The study examines the differences in geometrical characteristics, airflow rates, velocity, Reynolds number, and pressure drops of both models in the inhalation and exhalation phases for different lobes and generations of the airways. From trachea to the ninth generation, the average air flowrates and Reynolds numbers exponentially decay in both models during inhalation and exhalation. The steady drop is the case for the average air velocity in Kitaoka's model, while that experiences a maximum in the 3rd or 4th generation in the quasi-realistic model. Besides, it is shown that the flow field remains laminar in the upper and central airways up to the total flow rate of 15l/min. The results of this work can contribute to the understanding of flow behavior in upper respiratory tract.

Computer Simulation of a Capillary “Jump” of a Mercury Drop or an Air Bubble Under Conditions of Short-Term Weightlessness

The capillary “jump” of drop of mercury or an air bubble that occurs during the transition of gravity acceleration from earth to microgravity is called a drop of mercury sharp “jump” starting from the bottom of a container with water, where it is located, to its top or a sharp “jump” of the surface of the air shell, which has turned into a spherical air bubble, starting from the top of a container with water down to the bottom of the container. Article presents both a computer model of a capillary “jump” of a mercury drop in a container with water and computer model of a capillary “jump” of an air bubble in a container with water, previously developed by author during the transition of gravitational acceleration from earthly to microgravity. The developed computer models, implemented in the Turbo C and Turbo Basic computer languages, permitted to simulate on the computer display a capillary “jump” of mercury drop and an air bubble in container with water; quantitatively to calculate the energy of motion of a capillary “jump” of a mercury drop and an air bubble in container with water during the transition of the acceleration of gravity from earth to microgravity. The results of calculations of the energy of motion of a capillary “jump” of a mercury drop showed that the surface tension of a mercury drop plays a significant role in the driving of a mercury drop compared to its density. Flight tests have shown that the developed computer models can be successfully used in main systems of spacecraft under conditions of microgravity to predict the occurrence of a capillary “jump” of wide range real mercury drops and air bubbles in these systems.

Computational investigations of Ka-226T helicopter coaxial rotor aerodynamics in the vortex ring state

The vortex ring states are observed when the rotor is flowed at positive angles of attack. For the main rotor, these conditions are realized during a steep descent of the helicopter at low speeds. The rotor vortex ring states are affected by significant phenomena related to the behavior of its aerodynamics, including negative phenomena. The latter is, firstly, referred to decrease in rotor thrust, increase in the required power, pulsations of thrust and torque, unsteady flapping blade motion, etc. In terms of helicopter piloting, it means a sharp loss of altitude, increase in the control force, a high level of vibration, defocusing attenuation of rotor spinning cone, as well as controllability deterioration. All these factors determine the relevance of research on these modes and the importance of solving a problem of defining their boundaries. Recently, due to the rapid development of computer technologies and computational models, it has become practical to perform numerical research of the rotor aerodynamics in vortex ring states. The paper presents the study results of Ka-226T helicopter coaxial rotor aerodynamics in steep descent modes in the field of vortex ring states. The angles of rotor attack αВ = 90...30° and the range of vertical descent velocities Vу = 0...26 m/s are considered. The original nonlinear bladed vortex model of the rotor developed at the Moscow Aviation Institute (MAI) was used. The total and distributed aerodynamic rotor characteristics were calculated. The shapes of the vortex wake and the rotor flow patterns were analyzed. The boundaries of the vortex ring states in velocity coordinates “Vx − Vу” were constructed according to various criteria reflecting the known features of these states. The results obtained significantly complement the existing experience of experimental and numerical research in this field.

AMPred-CNN: Ames mutagenicity prediction model based on convolutional neural networks

Mutagenicity assessment plays a pivotal role in the safety evaluation of chemicals, pharmaceuticals, and environmental compounds. In recent years, the development of robust computational models for predicting chemical mutagenicity has gained significant attention, driven by the need for efficient and cost-effective toxicity assessments. In this paper, we proposed AMPred-CNN, an innovative Ames mutagenicity prediction model based on Convolutional Neural Networks (CNNs), uniquely employing molecular structures as images to leverage CNNs’ powerful feature extraction capabilities. The study employs the widely used benchmark mutagenicity dataset from Hansen et al. for model development and evaluation. Comparative analyses with traditional ML models on different molecular features reveal substantial performance enhancements. AMPred-CNN outshines these models, demonstrating superior accuracy, AUC, F1 score, MCC, sensitivity, and specificity on the test set. Notably, AMPred-CNN is further benchmarked against seven recent ML and DL models, consistently showcasing superior performance with an impressive AUC of 0.954. Our study highlights the effectiveness of CNNs in advancing mutagenicity prediction, paving the way for broader applications in toxicology and drug development.

Spectral characterization of human leg EMG signals from an open access dataset for the development of computational models.

Large-scale neuromusculoskeletal models have been used for predicting mechanisms underlying neuromuscular functions in humans. Simulations of such models provide several types of signals of practical interest, such as surface electromyographic signals (EMG), which are compared with experimental data for interpretations of neurophysiological phenomena under study. Specifically, realistic characterization of spectral properties of simulated EMG signals is important for achieving powerful inferences, whereas considerations should be taken for myoelectric signals of different muscles. In this study, we characterized spectral properties of surface interference pattern EMG signals and motor unit action potentials (MUAP) acquired from three plantar flexor muscles: Soleus (SO), Medial Gastrocnemius (MG), and Lateral Gastrocnemius (LG); and one dorsiflexor muscle: Tibialis Anterior (TA). Surface EMG signals were acquired from 20 participants using the same convention for electrode placement. Specifically, interference pattern EMG signals were obtained during isometric constant force contractions at 5%, 10% and 20% of maximum voluntary contraction (MVC), whereas surface MUAPs were decomposed from surface EMG signals obtained at low contraction forces. We compared the spectrum median frequency (MDF) estimated from interference pattern EMG signals across muscles and contraction intensities. Additionally, we compared MDF and durations of MUAPs between muscles. Our results showed that MDF of interference pattern EMG signals acquired from TA were higher compared to SO, MG, and LG for all contraction intensities i.e., 5%, 10%, and 20% MVC. Consistently, MUAPs acquired from TA also had higher MDF values and shorter durations compared to the other leg muscles. We provide herein a dataset with the surface MUAPs waveforms and interference pattern EMG signals obtained for this study, which should be useful for implementing and validating the simulation of myoelectrical signals of leg muscles. Importantly, these results indicate that spectral properties of myoelectrical signals should be considered for improving EMG modeling in large-scale neuromusculoskeletal models.

Towards a Reconciliation Between Simulational, Ludic, and Historiographical Practices in Research

Background Simulations, ludic or otherwise, have so far struggled to gain a foothold in mainstream historiography. Some authors suggest there may be fundamental incompatibilities between history and the language of simulations and scholarly games. Others believe that designing, employing, and validating historical simulations may be simply too costly and/or labor-intensive to justify their widespread adoption. Intervention This paper intends to identify points of friction between historiography and simulation-based research and suggest practical solutions to these issues. Methods My discussion is based on the description and analysis of a case study, the ThomondSim/ The Triumphs of Turlough research project. The initiative consisted of the development and application of an agent-based computational model (ABM) and a scholarly board game to investigate the possible associations between economic, environmental, and military hazards in 13th and 14th centuries Ireland. Results and Discussion Ensuring the simulations matched historical evidence to a standard deemed acceptable by the historiographical community limited their phase space, compromising their capacity to explore emergent phenomena. The intricacy of the underlying conceptual model suited the ABM better than the board game, which struggled to reconcile complexity with good game design practices. The board game, however, proved to be an effective validating tool for the ABM. Limitations and Suggestions for Further Research The project espoused an overt simulation- and game-centric approach, paying little attention to unguided play. Recent literature suggests that fostering, rather than hindering, playful exploration could address some of the pitfalls identified by this project. Conclusion Play could be a means of reconciliation between simulational, ludic, and historiographical practices. However, to ensure that projects adhere to epistemic standards, it is recommended that a methodology is developed to integrate it into research in ways that can be tested and evaluated.

A machine-based corpus optimization method for extracting domain-oriented technical words: an example of COVID-19 corpus data

Although natural language processing (NLP) refers to a process involving the development of algorithms or computational models that empower machines to understand, interpret, and generate human language, machines are still unable to fully grasp the meanings behind words. Specifically, they cannot assist humans in categorizing words with general or technical purposes without predefined standards or baselines. Empirically, prior researches have relied on inefficient manual tasks to exclude these words when extracting technical words (i.e., terminology or terms used within a specific field or domain of expertise) for obtaining domain information from the target corpus. Therefore, to enhance the efficiency of extracting domain-oriented technical words in corpus analysis, this paper proposes a machine-based corpus optimization method that compiles an advanced general-purpose word list (AGWL) to serve as the exclusion baseline for the machine to extract domain-oriented technical words. To validate the proposed method, this paper utilizes 52 COVID-19 research articles as the target corpus and an empirical example. After compared to traditional methods, the proposed method offers significant contributions: (1) it can automatically eliminate the most common function words in corpus data; (2) through a machine-driven process, it removes general-purpose words with high frequency and dispersion rates –57% of word types belonging to general-purpose words, constituting 90% of the total words in the target corpus. This results in 43% of word types representing domain-oriented technical words that makes up 10% of the total words in the target corpus are able to be extracted. This allows future researchers to focus exclusively on the remaining 43% of word types in the optimized word list (OWL), enhancing the efficiency of corpus analysis for extracting domain knowledge. (3) The proposed method establishes a set of standard operation procedure (SOP) that can be duplicated and generally applied to optimize any corpus data.

Regional differences in stomach stretch during organ filling and their implications on the mechanical stress response

As part of the digestive system, the stomach plays a crucial role in the health and well-being of an organism. It produces acids and performs contractions that initiate the digestive process and begin the break-up of ingested food. Therefore, its mechanical properties are of interest. This study includes a detailed investigation of strains in the porcine stomach wall during passive organ filling. In addition, the observed strains were applied to tissue samples subjected to biaxial tensile tests. The results show inhomogeneous strains during filling, which tend to be higher in the circumferential direction (antrum: 13.2%, corpus: 22.0%, fundus: 67.8%), compared to the longitudinal direction (antrum: 4.8%, corpus: 24.7%, fundus: 50.0%) at a maximum filling of 3500 ml. Consequently, the fundus region experienced the greatest strain. In the biaxial tensile experiments, the corpus region appeared to be the stiffest, reaching nominal stress values above 400 kPa in the circumferential direction, whereas the other regions only reached stress levels of below 50 kPa in both directions for the investigated stretch range. Our findings gain new insight into stomach mechanics and provide valuable data for the development and validation of computational stomach models.

An enhanced deep learning approach for vascular wall fracture analysis

AbstractThis work outlines an efficient deep learning approach for analyzing vascular wall fractures using experimental data with openly accessible source codes (https://doi.org/10.25835/weuhha72) for reproduction. Vascular disease remains the primary cause of death globally to this day. Tissue damage in these vascular disorders is closely tied to how the diseases develop, which requires careful study. Therefore, the scientific community has dedicated significant efforts to capture the properties of vessel wall fractures. The symmetry-constrained compact tension (symconCT) test combined with digital image correlation (DIC) enabled the study of tissue fracture in various aorta specimens under different conditions. Main purpose of the experiments was to investigate the displacement and strain field ahead of the crack tip. These experimental data were to support the development and verification of computational models. The FEM model used the DIC information for the material parameters identification. Traditionally, the analysis of fracture processes in biological tissues involves extensive computational and experimental efforts due to the complex nature of tissue behavior under stress. These high costs have posed significant challenges, demanding efficient solutions to accelerate research progress and reduce embedded costs. Deep learning techniques have shown promise in overcoming these challenges by learning to indicate patterns and relationships between the input and label data. In this study, we integrate deep learning methodologies with the attention residual U-Net architecture to predict fracture responses in porcine aorta specimens, enhanced with a Monte Carlo dropout technique. By training the network on a sufficient amount of data, the model learns to capture the features influencing fracture progression. These parameterized datasets consist of pictures describing the evolution of tissue fracture path along with the DIC measurements. The integration of deep learning should not only enhance the predictive accuracy, but also significantly reduce the computational and experimental burden, thereby enabling a more efficient analysis of fracture response.

The Virtual Child.

We are building the world's first Virtual Child-a computer model of normal and cancerous human development at the level of each individual cell. The Virtual Child will "develop cancer" that we will subject to unlimited virtual clinical trials that pinpoint, predict, and prioritize potential new treatments, bringing forward the day when no child dies of cancer, giving each one the opportunity to lead a full and healthy life.

Axon morphology and intrinsic cellular properties determine repetitive transcranial magnetic stimulation threshold for plasticity.

Repetitive transcranial magnetic stimulation (rTMS) is a widely used therapeutic tool in neurology and psychiatry, but its cellular and molecular mechanisms are not fully understood. Standardizing stimulus parameters, specifically electric field strength, is crucial in experimental and clinical settings. It enables meaningful comparisons across studies and facilitates the translation of findings into clinical practice. However, the impact of biophysical properties inherent to the stimulated neurons and networks on the outcome of rTMS protocols remains not well understood. Consequently, achieving standardization of biological effects across different brain regions and subjects poses a significant challenge. This study compared the effects of 10 Hz repetitive magnetic stimulation (rMS) in entorhino-hippocampal tissue cultures from mice and rats, providing insights into the impact of the same stimulation protocol on similar neuronal networks under standardized conditions. We observed the previously described plastic changes in excitatory and inhibitory synaptic strength of CA1 pyramidal neurons in both mouse and rat tissue cultures, but a higher stimulation intensity was required for the induction of rMS-induced synaptic plasticity in rat tissue cultures. Through systematic comparison of neuronal structural and functional properties and computational modeling, we found that morphological parameters of CA1 pyramidal neurons alone are insufficient to explain the observed differences between the groups. Although morphologies of mouse and rat CA1 neurons showed no significant differences, simulations confirmed that axon morphologies significantly influence individual cell activation thresholds. Notably, differences in intrinsic cellular properties were sufficient to account for the 10% higher intensity required for the induction of synaptic plasticity in the rat tissue cultures. These findings demonstrate the critical importance of axon morphology and intrinsic cellular properties in predicting the plasticity effects of rTMS, carrying valuable implications for the development of computer models aimed at predicting and standardizing the biological effects of rTMS.

Particle separation mechanisms in suspension-feeding fishes: key questions and future directions

Key unresolved questions about particle separation mechanisms in suspension-feeding fishes are identified and discussed, focusing on areas with the potential for substantial future discovery. The published hypotheses that are explored have broad applicability to biological filtration and bioinspired improvements in commercial and industrial crossflow microfiltration processes and microfluidics. As the first synthesis of the primary literature on the particle separation mechanisms of marine, estuarine, and freshwater suspension-feeding fishes, the goals are to enable comparisons with invertebrate suspension-feeding processes, stimulate future theoretical and empirical studies, and further the development of biomimetic physical and computational fluid dynamics models. Of the eight particle separation mechanisms in suspension-feeding fishes, six have been proposed within the past twenty years (inertial lift and shear-induced migration, reduction of effective gap size by vortices, cross-step filtration, vortical flow along outer faces of gill raker plates, ricochet filtration, and lateral displacement). The pace of discovery is anticipated to continue accelerating. Multidisciplinary collaboration and integration among biologists and engineers (including chemical, mechanical, biomedical, and filtration engineering) will result in new perspectives to identify patterns and potential unifying mechanisms across the breadth of suspension-feeding fish taxa, morphology, and function.

What's behind their various activities? Discerning the importance of surface terminations in Ag3PO4 semiconductor from DFT calculations

The surface energy and band gap of inorganic semiconductors are essential properties for a wide variety of applications in materials science, and studying morphologies holds significant importance. However, it is challenging to find a correlation between them at the atomic scale when these materials are multifunctional. This motivates the development of predictive computational models that can optimize these materials based on these quantities. Present findings shed light on the structure and electronic properties of (100), (110), and (111) surfaces of Ag3PO4 by using density functional theory calculations. By introducing two concepts, namely, polyhedron energy and polyhedron band gap, we provide compelling evidence for the effects of surface termination of Ag3PO4 photocatalyst. We compare our predictions to experimental crystal habits, and our results not only allow us to explain their evolution for obtaining a given morphology but also offer a coherent explanation, based on atomic-scale insights, for the discrepancies of morphology-photocatalytic activity relationship of Ag3PO4 in the recent literature. Overall, this work opens a novel avenue for first-principles calculations of morphology and provides a roadmap for the design of more effective inorganic semiconductors with desirable physicochemical properties for potential applications.

Towards multi-omics synthetic data integration.

Across many scientific disciplines, the development of computational models and algorithms for generating artificial or synthetic data is gaining momentum. In biology, there is a great opportunity to explore this further as more and more big data at multi-omics level are generated recently. In this opinion, we discuss the latest trends in biological applications based on process-driven and data-driven aspects. Moving ahead, we believe these methodologies can help shape novel multi-omics-scale cellular inferences.

Recommendation system using bio-inspired algorithms for urban orchards

According to the Food and Agriculture Organization of the United Nations (FAO), climate change is exponentially affecting agricultural production worldwide, with food prices expected to increase by up to 90 percent by 2030 and hunger and malnutrition rates to rise by 2050. This paper presents the development of a platform based on the Internet of Things (IoT) for monitoring urban gardens as a strategy to mitigate hunger, promote food sovereignty and circular economy in areas of food shortage. To this end, an Internet of Things (IoT) architecture is proposed and implemented that involves a social design layer that allows an effective transfer of knowledge to communities and a recommendation system based on evolutionary computation to optimize and maximize the productivity of urban orchards, and thus contribute to the 2030 agenda of the Sustainable Development Goals (SDGs). Finally, three experiments in urban gardens are shown to validate evolutionary computation and artificial intelligence models, such as multiple linear regression, genetic algorithms, ant colony algorithms and spatial estimation and inference algorithms such as the Kriging algorithm. The productivity of urban lettuce orchards is increased between 25 and 45%.

Highly logical and non-emotional decisions in both risky and social contexts: understanding decision making in autism spectrum disorder through computational modeling.

In risky contexts, autism spectrum disorder (ASD) individuals exhibit more logical consistency and non-emotional decisions than do typical adults (TAs). This way of deciding could be also prevailing in social contexts, leading to maladaptive decisions. This evidence is scarce and inconsistent, and further research is needed. Recent developments in computational modeling allow analysis of decisional subcomponents that could provide valuable information to understand the decision-making and help address inconsistencies. Twenty-seven individuals with ASD and 25 TAs were submitted to a framing-task and the ultimatum game (UG). The Rescorla-Wagner computational model was used to analyze UG decisions. Results showed that in the UG, the ASD group exhibited a higher utilitarianism, characterized by lower aversion to unfairness and higher acceptance of offers. Moreover, this way of deciding was predicted by the higher economic rationality found in the framing task, where people with ASD did not manifest emotional biases such as framing effect. These results could suggest an atypical decision making, highly logical and non-emotional, as a robust feature of ASD.

An IoT enabled computational model and application development for monitoring cardiovascular risks

Technological advancement in the rise of the Internet of Things (IoT) and computational modelling and application development has revolutionized medical care. Monitoring cardiovascular risks is faster, easier, and more accurate than ever. Using IoT-enabled computational modelling and application development, medical professionals can detect, monitor, and predict certain conditions more efficiently. That paper explores the Internet of Things (IoT) enabled computational model and application development that can be used to monitor cardiovascular risks effectively. Cardiovascular diseases are the leading cause of death globally, and currently, there is a lack of comprehensive and reliable monitoring systems to assess the risk of such diseases. IoT enables the integration of different data sources, such as physical activity, diet, BMI and the environmental context, to form a comprehensive tracking tool that can provide accurate cardiovascular risk assessment. The developed application can offer personalized health coaching, leveraging machine learning algorithms to identify patterns and adapt a user's healthcare journey. Ultimately, this paper assesses the potential of IoT technology for monitoring cardiovascular risks and integrating it into current healthcare systems.

Importance of Inter-residue Contacts for Understanding Protein Folding and Unfolding Rates, Remote Homology, and Drug Design.

Inter-residue interactions in protein structures provide valuable insights into protein folding and stability. Understanding these interactions can be helpful in many crucial applications, including rational design of therapeutic small molecules and biologics, locating functional protein sites, and predicting protein-protein and protein-ligand interactions. The process of developing machine learning models incorporating inter-residue interactions has been improved recently. This review highlights the theoretical models incorporating inter-residue interactions in predicting folding and unfolding rates of proteins. Utilizing contact maps to depict inter-residue interactions aids researchers in developing computer models for detecting remote homologs and interface residues within protein-protein complexes which, in turn, enhances our knowledge of the relationship between sequence and structure of proteins. Further, the application of contact maps derived from inter-residue interactions is highlighted in the field of drug discovery. Overall, this review presents an extensive assessment of the significant models that use inter-residue interactions to investigate folding rates, unfolding rates, remote homology, and drug development, providing potential future advancements in constructing efficient computational models in structural biology.

Forecasting the phytosanitary state of agrocenoses: innovation developments

Goal. Development and implementation of new theories, methods, technologies and models that would allow more reliable forecasting of the development of pests and pathogens of agricultural crops.
 Methods. Analysis of innovative development of the Institute of Plant Protection of the National Academy of Agrarian Sciences of Ukraine and other institutions of the Scientific and Methodological Center «Protection of Plants» for 2001—2020. Isolation of those relating to the problem forecasting the development of pests and pathogens of plant diseases in agrocenoses of Ukraine.
 Results. The concept of formation of the phytosanitary state of crops and plantations under the conditions of climate change has been developed. The theory of the dynamics of the number of locusts and the prediction of emergency situations in the agricultural sphere of Ukraine has been developed. The widespread use of pheromone traps for diagnosing the phytosanitary state of agrocenoses of Ukraine is recommended. The cyclicity of outbreaks of mass reproduction of the Anisoplia austriaca beetle and the European corn borer was established, depending on the amount of solar activity. The possibility of applying GIS-technologies for the analysis of long-term data was studied. Predictive computer programs (models) of harvest shortages of rapeseed, sunflower, corn, sugar beet, and winter wheat from a complex of pests have been developed. An algorithm for the application of information technology programs for the analysis of multi-year dynamics of the phytosanitary state of agrocenoses has been developed. The role of the developed computer model of the development of leaf spots of grain crops in determining the moment for the most effective application of fungicides has been established. Methods of forecasting the development of pathogens of grape diseases in the South of Ukraine have been developed. A system of forecasting the phytosanitary state of agrocenoses of Ukraine in the conditions of the year and planning the implementation of protective measures has been developed.
 Conclusions. Innovative developments of the Scientific and Methodological Center «Plant Protection» make it possible to solve the following issues at a modern level: forecasting changes in the agrosphere based on the analysis of a long-term database of hydrothermal conditions and indicators of the phytosanitary state of agrocenoses; prevention of emergency situations in the agricultural sector on the basis of a modern monitoring system using GPS-navigation and the development of regulations for carrying out protective measures; forecasting the development of dangerous pests and diseases of agricultural crops; forecasting crop failures and determining the economic feasibility of chemical plant protection; forecasting the appearance and spread of quarantine pests.