Stability Classes Research Articles

What anesthesiologists need to know about Antidepressants and other Psychotropic Drugs

Psychotropic medications are widely prescribed and are a mainstay treatment for various mental health disorders. A significant portion of patients undergoing surgery or various procedures take these medications. With the rising prevalence of mental health conditions, anesthesiologists increasingly find themselves encountering patients who depend on these medications, making the understanding of potential interactions with anesthetic agents crucial during the perioperative period. Appreciating the adverse-effect profiles and familiarity with the clinically relevant drug interactions that may occur in the perioperative setting are imperative to ensure the best possible outcome in delivering patient care. This review focuses on various classes of psychotropic agents, including antidepressants, antipsychotics, mood stabilizers, and anxiolytics. It covers the pharmacodynamics and pharmacokinetics of these medication classes and their interactions with agents commonly used in anesthesia.

On the Exploration of Quantum Polar Stabilizer Codes and Quantum Stabilizer Codes with High Coding Rate.

Inspired by classical polar codes, whose coding rate can asymptotically achieve the Shannon capacity, researchers are trying to find their analogs in the quantum information field, which are called quantum polar codes. However, no one has designed a quantum polar coding scheme that applies to quantum computing yet. There are two intuitions in previous research. The first is that directly converting classical polar coding circuits to quantum ones will produce the polarization phenomenon of a pure quantum channel, which has been proved in our previous work. The second is that based on this quantum polarization phenomenon, one can design a quantum polar coding scheme that applies to quantum computing. There are several previous work following the second intuition, none of which has been verified by experiments. In this paper, we follow the second intuition and propose a more reasonable quantum polar stabilizer code construction algorithm than any previous ones by using the theory of stabilizer codes. Unfortunately, simulation experiments show that even the stabilizer codes obtained from this more reasonable construction algorithm do not work, which implies that the second intuition leads to a dead end. Based on the analysis of why the second intuition does not work, we provide a possible future direction for designing quantum stabilizer codes with a high coding rate by borrowing the idea of classical polar codes. Following this direction, we find a class of quantum stabilizer codes with a coding rate of 0.5, which can correct two of the Pauli errors.

Distinct H3K9me3 heterochromatin maintenance dynamics govern different gene programs and repeats in pluripotent cells.

H3K9me3-heterochromatin, established by lysine methyltransferases (KMTs) and compacted by HP1 isoforms, represses alternative lineage genes and DNA repeats. Our understanding of H3K9me3-heterochromatin stability is presently limited to individual domains and DNA repeats. We engineered Suv39h2 KO mouse embryonic stem cells to degrade remaining two H3K9me3-KMTs within one hour and found that both passive dilution and active removal contribute to H3K9me3 decay within 12-24 hours. We discovered four different H3K9me3 decay rates across the genome and chromatin features and transcription factor binding patterns that predict the stability classes. A "binary switch" governs heterochromatin compaction, with HP1 rapidly dissociating from heterochromatin upon KMTs' depletion and a particular threshold level of HP1 limiting pioneer factor binding, chromatin opening, and exit from pluripotency within 12 hr. Unexpectedly, receding H3K9me3 domains unearth residual HP1β peaks enriched with heterochromatin-inducing proteins. Our findings reveal distinct H3K9me3-heterochromatin maintenance dynamics governing gene networks and repeats that together safeguard pluripotency.

Organoselenium Compounds as an Emerging Class of Stabilizers of applied Nanomaterials for Applications in the Catalysis of Organic Reactions

Organoselenium Compounds as an Emerging Class of Stabilizers of applied Nanomaterials for Applications in the Catalysis of Organic Reactions

Investigation of bio-corrosion behavior, structural and thermal properties of Ni20Ti50Sn30 high-temperature shape memory alloy

In this study, Ni20Ti50Sn30 (atomic %) sample was prepared using the arc melting method. Some thermodynamic parameters (phase transformation temperatures, PTT; enthalpy, ΔH; and phase transformation hysteresis, H) obtained from the phase transformations of the alloy were determined using differential scanning calorimetry (DSC). The sample exhibited high-temperature shape memory alloy (HTSMA) properties. X-ray (XRD) analysis was performed to determine the crystal structure properties at the alloy. The presence of B2, B19′, Ti2Ni, β-Ti, Ni3Sn4, TiNiSn and Ti3Sn phases was identified through X-ray analysis of the alloy. Scanning electron microscopy (SEM) was used to examine the pre- and post-corrosion surface morphologies of the sample, with EDX analysis being performed to reveal chemical structure determinations (matrix phases, precipitates and weathering zones). The presence of dendritic arms was observed, while martensite plates were absent in all SEM images. Particularly in the SEM image taken after corrosion, it was noted that the TiO2 layer placed on the surface of the matrix rich in Ti elements altered the surface morphology of the alloy. The presence of Ti and O elements in the post-corrosion EDX analysis confirmed the existence of this layer. Biocompatibility studies of the alloy were conducted using potentiodynamic corrosion tests and electrochemical impedance spectroscopy (EIS) methods. It was determined that the NiTiSn sample was in the excellent stability class according to corrosion standards.

Radiation dose assessments for generic nuclear power plants part I: Routine operation

The main task of the site evaluation report for nuclear installation concerning nuclear safety is the protection of the public and environment from the radiological consequences of radioactive releases in both cases of normal operation and accident conditions. The atmosphere is the most important pathway to be considered in the assessment of the environmental impact of radioactive materials released from nuclear facilities. The present study is a comprehensive investigation of environmental assessments for the dose calculation model resulting from routine operation NPPs of 1000 Mw (e). The procedure consists of different parts, beginning with the manipulation of collected meteorological data such as wind speed and direction, temperature, incoming solar radiation and utilizing a set of empirical formulae for evaluating night solar radiation emitted from the ground, and then evaluating hourly stability classes and joint frequency distribution of winds by developing code. The dilution factor was evaluated using computer code (XOQDOQ code). Finally, the radiation dose assessments resulting from the routine operation of NPPs were calculated. The processes are adapted with IAEA recommendations documents, safety guides, and ICRP recommendations. The results reveal that there is no detectable value that affects the people surrounding the site nor the environmental area concerning low population zone or exclusion area.

Exponential stability of non-instantaneous impulsive second-order fractional neutral stochastic differential equations with state-dependent delay

The aim of this manuscript mainly concerned with a new class of exponential stability for non-instantaneous impulsive second-order fractional neutral stochastic differential equations (NIIFNSDEs) with state-dependent delay driven by Poisson jump in a separable Hilbert spaces. Firstly, a more appropriate concept of mild solution is introduced. Secondly, sufficient conditions are derived for the existence of mild solution by means stochastic analysis, fractional calculus approach and Mönch fixed point theorem with appropriate hypotheses on non-linear continuous functions combined with solution operator. Finally, stability results are derived based on the pth moment exponential stable with the help of new impulsive integral inequality techniques. In addition, an example is provided to validate the theoretical results. This manuscript is the unique combination of the new theoretical investigation which is simulated numerically. Our work extends the works of Arthi et al. (2014), Das et al. (2016), Huang et al. (2018), Pandey et al. (2014), Wang et al. (2018), Yan and Jia (2016).

Adversarial training for signal modulation classification based on Ulam stability theory

The modulation classification of radar, communication, and other radiation source signals under complex electromagnetic conditions is an important task in the field of military electronic countermeasures. Deep learning is an important tool for automatic modulation classification (AMC) tasks. However, AMC models based on deep neural networks are easily attacked by adversarial methods such as gradient attacks, and even under small perturbations, the model may exhibit significant differences in prediction. To address adversarial attacks, we proposed a Ulam stability adversarial training method, which could improve the robust accuracy and the natural accuracy of the adversarial training model. We introduced a class of Jensen type Ulam stability theorem, which transforms the Jensen condition into a special class of stability preserving regularization terms for adversarial training models, enhancing the model's adversarial robustness. More importantly, our method provided a framework for improving the stability of adversarial training, which can be combined with existing adversarial training methods to effectively enhance their adversarial defense capabilities. In the experimental section, we compared four types of adversarial training methods on three types of modulation recognition datasets, and the results showed that Ulam adversarial training improved robust accuracy by an average of 4.8% and natural accuracy by an average of 2.2% on three datasets. The difference between the best and final accuracies of the models decreased by an average of 4.5%.

Assessment of radiological consequences to a hypothetical accident of the 3-MW TRIGA Mark-II nuclear research reactor of Bangladesh

The radiological consequences to a hypothetical accident are assessed for the 3-MW TRIGA Mark-II Nuclear Research Reactor of Bangladesh for both wet and dry seasons. Accident simulations are crucial for assessing the potentialrisks in the event of a nuclear accident. ORIGEN code was used to carry out the facility's radionuclide inventory. In the event of a hypothetical accident, the Total Effective Dose Equivalent (TEDE) is assessed using HotSpots. The findings indicate that for stability class A during the rainy season, the selected radionuclides arrived at a downwind distance of 0.03 km with an arrival time of less than one minute, and the highest TEDE, respiratory time-integrated air concentration, and ground deposition activity, respectively, were marked at 2.8E-12 Sv, 9.4E-01 Bq-sec/m3, and 2.4E-05 kBq/m2. In the case of I-131 at 0.03 km from the source, the thyroid gland is the most sensitive human organ, followed by the lung, esophagus, thymus, surface bone, skin, and breast. The estimated Committed Effective Dose Equivalent (CEDE) from I-131 is 9.1E-11 Sv. In contrast, the adrenal was the most sensitive organ with a maximum CEDE of 1.3E-13 Sv in the case of Cs-137 at a downwind distance of 0.03 km. The highest TEDE values for the general public and the vicinity around the TRIGA Mark II reactor facility are lower than the annual dose limits.

Green buffers near industrial plants, examples in Jefferson County TX

Green buffers between industrial plants and residential areas have multiple purposes in mitigating air pollution and protecting the environment. This study focuses on providing information on examples of green space development by industries near residential areas in Jefferson County, Texas. One represents a full buffer where residences were totally removed, another is a partial buffer where some residences were removed in a neighborhood and the third represents vegetation of a formerly industrial facility. Additional estimates on benefits of buffer spaces with respect to a pollutant plume are also considered. The study used geospatial and property data from 1966 to 2020. The transition from a residential area to a full or partial buffers highlights possible mitigation of air pollution impacts while serving additional functions, such as boosting stormwater infiltration and improving the aesthetic attractiveness of the area. The transformation from oil tanks to forested space may display the potential for environmental remediation, but not all spaces are suitable for fully forested buffers, as safety and other limitations should be considered. With the analysis of the classic Plume Equation insights can be provided regarding the possible impact of green buffers on ground-level pollutant concentrations. The calculations for atmospheric stability classes A-F and for various stack heights provide an understanding of how different conditions may affect the pollutant concentrations. The presence of a green buffer can play a crucial role in controlling ground-level pollution concentrations and reduce a community’s perception of risk.

An approach of radiological impact analysis due to a simulated accident of the TRIGA research reactor of Bangladesh

The present study focused on the radiological impact analysis by assessing radiological doses due to a postulated TRIGA Mark-II research reactor accident at AERE, Savar, Dhaka, Bangladesh. Core inventory and source term of radionuclides from different groups such as noble gas, halogen, tellurium, alkali metal, lanthanide, alkaline earth metal, cerium and transition metal were analyzed for 365 days of operation at full power (3 MWth) due to a postulated nuclear accident. The HotSpot code was used for the dose calculation, and the dose values of TED, and Thyroid were calculated for a 40 km radius from the reactor site. Local meteorological data from Bangladesh Meteorological Department were used for the dose assessment and doses were calculated in the three dominant directions such as South, Southeast, and West. The TED and Thyroid dose values are assessed and compared to the PAZs dose criteria of the U.S. Environmental Protection Agency. It is found that doses are higher in stability class F, and evacuation, sheltering, and iodine prophylaxis are needed at longer distances. Since outside of the AERE campus is densely populated, these calculated data may help to ensure radiological safety by taking required protective actions like evacuation, sheltering, and iodine tablet consumption for the same type of accidental situation.

The Lagrangian Atmospheric Radionuclide Transport Model (ARTM) – sensitivity studies and evaluation using airborne measurements of power plant emissions

Abstract. The Atmospheric Radionuclide Transport Model (ARTM) operates at the meso-γ scale and simulates the dispersion of radionuclides originating from nuclear facilities under routine operation within the planetary boundary layer. This study presents the extension and validation of this Lagrangian particle dispersion model and consists of three parts: (i) a sensitivity study that aims to assess the impact of key input parameters on the simulation results, (ii) the evaluation of the mixing properties of five different turbulence models using the well-mixed criterion, and (iii) a comparison of model results to airborne observations of carbon dioxide (CO2) emissions from a power plant and the evaluation of related uncertainties. In the sensitivity study, we analyse the effects of the stability class, roughness length, zero-plane displacement factor, and source height on the three-dimensional plume extent as well as the distance between the source and maximum concentration at the ground. The results show that the stability class is the most sensitive input parameter as expected. The five turbulence models are the default turbulence models of ARTM 2.8.0 and ARTM 3.0.0, one alternative built-in turbulence model of ARTM, and two further turbulence models implemented for this study. The well-mixed condition tests showed that all five turbulence models are able to preserve an initially well-mixed atmospheric boundary layer reasonably well. The models deviate only 6 % from the expected uniform concentration below 80 % of the mixing layer height, except for the default turbulence model of ARTM 3.0.0 with deviations of up to 18 %. CO2 observations along a flight path in the vicinity of the lignite power plant Bełchatów, Poland, measured by the Deutsches Zentrum für Luft- und Raumfahrt (DLR) Cessna aircraft during the Carbon Dioxide and Methane Mission (CoMet) campaign in 2018 allowed for evaluation of model performance for the different turbulence models under unstable boundary layer conditions. All simulated mixing ratios are of the same order of magnitude as the airborne in situ data. An extensive uncertainty analysis using probability distribution functions, statistical tests, and direct spatio-temporal comparisons of measurements and model results help to quantify the model uncertainties. With the default turbulence setups of ARTM versions 2.8.0 and 3.0.0, the plume widths are underestimated by up to 50 %, resulting in a strong overestimation of the maximum plume CO2 mixing ratios. The comparison of the three alternative turbulence models shows good agreement of the peak plume CO2 concentrations, the CO2 distribution within the plumes, and the plume width, with a 30 % deviation in the peak CO2 concentration and a less than 25 % deviation in the measured CO2 plume width. Uncertainties in the simulations may arise from the different spatial and temporal resolutions of simulations and measurements in addition to the turbulence parametrisation and boundary conditions. The results of this work may help to improve the accurate representation of real plumes in very unstable atmospheric conditions through the selection of distinct turbulence models. Further comparisons at different stability regimes are required for a final assessment of model uncertainties.

Predictive modeling of oocyte maternal mRNA features for five mammalian species reveals potential shared and species-restricted regulators during maturation.

Oocyte maturation is accompanied by changes in abundances of thousands of mRNAs, many degraded and many preferentially stabilized. mRNA stability can be regulated by diverse features including GC content, codon bias, and motifs within the 3'-untranslated region (UTR) interacting with RNA binding proteins (RBPs) and miRNAs. Many studies have identified factors participating in mRNA splicing, bulk mRNA storage, and translational recruitment in mammalian oocytes, but the roles of potentially hundreds of expressed factors, how they regulate cohorts of thousands of mRNAs, and to what extent their functions are conserved across species has not been determined. We performed an extensive in silico cross-species analysis of features associated with mRNAs of different stability classes during oocyte maturation (stable, moderately degraded, and highly degraded) for five mammalian species. Using publicly available RNA sequencing data for germinal vesicle (GV) and MII oocyte transcriptomes, we determined that 3'-UTR length and synonymous codon usage are positively associated with stability, while greater GC content is negatively associated with stability. By applying machine learning and feature selection strategies, we identified RBPs and miRNAs that are predictive of mRNA stability, including some across multiple species and others more species-restricted. The results provide new insight into the mechanisms regulating maternal mRNA stabilization or degradation.NEW & NOTEWORTHY Conservation across species of mRNA features regulating maternal mRNA stability during mammalian oocyte maturation was analyzed. 3'-Untranslated region length and synonymous codon usage are positively associated with stability, while GC content is negatively associated. Just three RNA binding protein motifs were predicted to regulate mRNA stability across all five species examined, but associated pathways and functions are shared, indicating oocytes of different species arrive at comparable physiological destinations via different routes.

A Model for Low-Frequency, Anisotropic Wind Fluctuations and Coherences in the Marine Atmosphere

To assess dynamic loads, large offshore wind turbines need detailed and reliable statistical information on the inflow turbulence. We present a model that includes low frequencies down to ∼1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sim 1$$\\end{document} hr-1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{-1}$$\\end{document} using the observed S(f)∝f-5/3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$S(f) \\propto f^{-5/3}$$\\end{document} in that range. The presented model contains a parameter representing the anisotropy of the two-dimensional, incompressible turbulence, and it assumes the low-frequency fluctuations to be homogeneous in the vertical direction. Combined with a three-dimensional model for the smaller scales, the model can predict correlations between different points. We have validated the model against two offshore wind data sets: a nacelle-mounted, forward-looking Doppler lidar with four beams at the Hywind Scotland offshore wind farm and sonic anemometer measurements at the FINO1 research platform in the North Sea. One-point auto spectra and two-point cross spectra were calculated after splitting the data into different atmospheric stability classes. The relative strength of the 2D low-frequency fluctuations to the 3D fluctuations was higher under stable conditions. The combined 2D+3D model was able to fit the measured spectra with good accuracy and could then predict the two-point cross spectra, co-coherences, and phase angles between wind fluctuations at different lateral and vertical separations. Good agreement was found between the measured and predicted values, albeit with exceptions. The model can generate stochastic wind fields for investigating wake meandering in wind farms or dynamic loads on floating wind turbines.

МОДЕЛИРОВАНИЕ РАССЕИВАНИЯ ВЫБРОСА ЗАГРЯЗНЯЮЩИХ ВЕЩЕСТВ

The article considers the modeling of the dispersion of a pollutant during toxicant emissions, depending on the characteristics of atmospheric transport, wind speed and the stability class of the atmosphere. To describe the dependence of the implementation of atmospheric stability clas-ses on wind speed, data corresponding to a certain type of Earth's surface (flat, hilly, etc.) were also analyzed

The effect of organic matter amendments on soil surface stability in conventionally cultivated arable fields

AbstractIn this study, new and traditional organic wastes (green waste compost, farmyard manure (FYM), anaerobic digestate or straw) were ploughed into an arable field experiment at a range of rates (1–3 t C ha−1) and under spring and winter cropping rotations for 5 years. The stability of the soil surface structure (<5 cm) was assessed in Years 3, 4 and 5 to guide the use of organic wastes in arable field management. Aggregate stability was determined by immersing each soil sample in water (fast‐wetting test, relevance to summer storms) and measuring the size distribution of the aggregates (mean weight diameter, MWD). The MWD value was compared with the different classes of aggregate stability (very stable, stable, medium, unstable or very unstable) to classify each result. All the arable field samples were classified as unstable, and there were no significant differences (p > .05) in MWD after any treatment. Potentially, this approach is too imprecise to detect differences, so pore‐scale analyses using CT scanning were used. There were no significant differences in porosity or pore shape (features associated with microbial activity) between the control and organic waste treatments. The results contradicted published data from the Broadbalk experiment (established in 1843), which was the basis for this study, so its soils were studied to understand this discrepancy. Broadbalk soils were classified as unstable, including soils treated with annual FYM applications. Instead, we found a significant relationship between MWD and clay content and previous interpretations had not taken this variable (23%–39% clay content) texture into consideration. In conclusion, to achieve a stable soil surface structure, a 150% improvement in aggregate stability would be needed here and ploughing in organic wastes was not a successful management approach on these arable field experiments.

A new method to calculate a standard set of finite cloud dose correction factors for the level 3 probabilistic safety assessment of nuclear power plants

Level 3 probabilistic safety assessment (PSA) is performed to calculate radionuclide concentrations and exposure dose resulting from nuclear power plant accidents. To calculate the external exposure dose from the released radioactive materials, the radionuclide concentrations are multiplied by two factors of dose coefficient and a finite cloud dose correction factor (FCDCF), and the obtained values are summed. This indicates that a standard set of FCDCFs is required for external exposure dose calculations.To calculate a standard set of FCDCFs, the effective distance from the release point to the receptor along the wind direction should be predetermined. The TID-24190 document published in 1968 provides equations to calculate FCDCFs and the resultant standard set of FCDCFs. However, it does not provide any explanation on the effective distance required to calculate the standard set of FCDCFs. In 2021, Sandia National Laboratories (SNLs) proposed a method to predetermine finite effective distances depending on the atmospheric stability classes A to F, which results in six standard sets of FCDCFs. Meanwhile, independently of the SNLs, the authors of this paper discovered that an infinite effective distance assumption is a very reasonable approach to calculate one standard set of FCDCFs, and they implemented it into the multi-unit radiological consequence calculator (MURCC) code, which is a post-processor of the level 3 PSA codes.This paper calculates and compares short- and long-range FCDCFs calculated using the TID-24190, SNLs method, and MURCC method, and explains the strength of the MURCC method over the SNLs method. Although six standard sets of FCDCFs are required by the SNLs method, one standard sets of FCDCFs are sufficient by the MURCC method. Additionally, the use of the MURCC method and its resultant FCDCFs for level 3 PSA was strongly recommended.

Abstract 14494: National Trends in Emergency Department Discharges for Acute Pulmonary Embolism, 2006-2015

Introduction: Outpatient management for selected low-risk patients with acute pulmonary embolism (PE) has emerged as a safe and feasible approach that is currently supported by clinical practice guidelines. However, recent evidence suggests that many patients are still managed with hospital admission. Few studies have assessed recent real-world, national trends in discharge rates for acute PE from the emergency department (ED). Methods: We conducted an observational study leveraging data from the National Hospital Ambulatory Medical Care Survey (NHAMCS) between the years 2006-2020. NHAMCS is a nationally representative probability sample which reflects ED visits within the US. All patients with a primary diagnosis of acute PE were included. Subgroup analysis was performed on patients who were hemodynamically stable (heart rate<110 and systolic blood pressure>100) and those with a PESI risk class I or II. Results: Between 2006-2020, there were an estimated 2,519,606 ED visits for acute PE. Among the whole cohort, the mean age was 58.2 (SE: 1.53) and 59.1% were female. Overall, 22.1% of patients with acute PE were discharged from the ED, which increased from 7.8% between 2006-2008 to 32.1% between 2018-2020. A total of 67.9% of patients were hospitalized, which decreased from 83.7% between 2006-2008 to 61.9% between 2018-2020. When stratified by hemodynamic stability or low PESI risk class, similar trends were observed (Figure). Since 2012, 33.0% of patients with PESI class I or II were discharged and 57.8% were hospitalized. Conclusion: In a representative nationwide sample, rates of discharge from the ED for acute PE have increased over the past fifteen years with the largest increase occurring between 2009-2014. However, since 2012, only one-third of low-risk patients presenting to the ED were discharged for outpatient management and rates appear to have stabilized. We speculate that outpatient management for low-risk acute PE is still underutilized in the US.

Landscape Planning Organisation of the “Severny Les” Park of Voronezh

The relevance of the topic is due to the growth of urban agglomerations, resulting in the problem of preserving natural objects that perform important environmental, aesthetic, recreational functions. One of such objects is the “Severny Les” park, created in 2014, which has been chosen as the subject for the study. The park is located in the Kominternovsky district of Voronezh, its area is 51.57 hectares. The park has the status of a nature park, its territories (together with water areas) include natural complexes and objects of significant ecological and aesthetic value and intended for use in environmental protection, educational and recreational purposes. The main purpose of the study was to determine the recreational value of the park. In the work, recreational and sanitary-hygienic assessments were carried out, the stages of digression and the plantations’ stability classes were determined. The description of tree and shrub vegetation was given together with the identification of its condition. Based on the object’s complex assessment, functional zoning was developed, which made it possible to identify the problems of the territory, reveal its potential, and carry out basic transformations. A range of plants for the park’s landscaping was recommended, taking into account age and seasonal variability, decorativeness, resistance to the park’s conditions and anthropogenic stress.

Artificial neural networks based on segmented model for adaptability and stability evaluation of soybean genotypes

Unlike models based on simple linear regressions, segmented models can better assess the adaptability and stability of genotypes, demonstrating a nonlinear response pattern over environmental variation. However, these methods can be under statistical limitations, such as the Type Error II increase and biased estimates. Therefore, this work aimed to transpose the concepts of adaptability and stability from the statistical analysis of a segmented model to the discriminatory potential of an artificial neural network (ANN) and use it to classify soybean (Glycine max (L.) Merr.) genotypes. An ANN training was carried out with the grain yield of 7,200 soybean genotypes simulated in 15 different environments. The ANN topology chosen was the one that had less than 1% of error in the testing phase with 1,800 simulated genotypes. A total of 9,000 simulated soybean genotypes were previously arranged in 18 different classes, which represented the combination of nine classes of adaptability by the Verma and collaborators (VCM) method and two classes of stability (invariability concept) by the Finlay & Wilkinson (FW) method. Finally, the grain production of ten real soybean genotypes was inputted into the ANN-trained model, and the classification regarding adaptability and stability was obtained. There was 90% agreement between the ANN and VCM analyses regarding the adaptability classification and 20% regarding stability. With the methods presented in this work, it was demonstrated that the potential of using ANNs to assess the adaptability of genotypes is strong. In addition, since stability was introduced in the ANN as a different concept from that used to classify the genotypes by the statistical method, such classification needs to be reviewed and further improved