Variability Of Conditions Research Articles

The use of neural networks in the foresight process

The article considers scenarios of application of neural networks of different architectures to fulfill tasks in the foresight process. The purpose of the article is to determine at what stages of the foresight procedure the application of neural networks is justified and with what architecture. The differences between foresight and the process of forecasting are revealed. In addition, the concept of foresight, its main stages and phases, classification are considered. It is substantiated that the application of neural networks can significantly facilitate the foresight procedure at such stages as collection and processing of primary information, development of scenarios and solutions to problems, communication, and report preparation. It is shown that different types of neural networks are suitable for different foresight tasks. It is revealed that neural networks can process a larger amount of data and automatically detect complex patterns, which makes them more effective under conditions of environmental uncertainty and variability. The article ephasises the importance of further research and development of methods for applying neural networks in foresight processes with consideration to the specifics of particular industries and types of tasks. In the course of the research, the authors used analytical methods of diagnostics, establishing cause-and-effect relationships, etc.

PROBLEMS OF PROVIDING GEOLOGICAL EXPLORATION FOR THE ECONOMIC ASSESSMENT OF MINERAL DEPOSITS

PROBLEMS OF PROVIDING GEOLOGICAL EXPLORATION FOR THE ECONOMIC ASSESSMENT OF MINERAL DEPOSITS

A secondary model for the effect of pH on the variability in growth fitness of Listeria innocua strains

Variability in microbial growth is a keystone of modern Quantitative Microbiological Risk Assessment (QMRA). However, there are still significant knowledge gaps on how to model variability, with the most common assumption being that variability is constant. This is implemented by an error term (with constant variance) added on top of the secondary growth model (for the square root of the growth rate). However, this may go against microbial ecology principles, where differences in growth fitness among bacterial strains would be more prominent in the vicinity of the growth limits than at optimal growth conditions.This study coins the term “secondary models for variability”, evaluating whether they should be considered in QMRA instead of the constant strain variability hypothesis. For this, 21 strains of Listeria innocua were used as case study, estimating their growth rate by the two-fold dilution method at pH between 5 and 10. Estimates of between-strain variability and experimental uncertainty were obtained for each pH using mixed-effects models, showing the lowest variability at optimal growth conditions, increasing towards the growth limits. Nonetheless, the experimental uncertainty also increased towards the extremes, evidencing the need to analyze both sources of variance independently. A secondary model was thus proposed, relating strain variability and pH conditions. Although the modelling approach certainly has some limitations that would need further experimental validation, it is an important step towards improving the description of variability in QMRA, being the first model of this type in the field.

Formation of complex discrete samples for electrocardiogram isoline drift extraction devices

In the automatic analysis of an electrocardiosignal, in order to adequately assess its amplitude parameters, it is necessary to ensure that there is no isoline drift in the signal. The direct removal of the isoline drift using a high-pass filter leads to distortion of the shape and amplitude parameters of the electrocardiosignal elements. When extracting the isoline drift based on interpolation of samples on the PQ segment, the frequency of the extracted isoline drift signal cannot even theoretically exceed half the heart rate, but in reality it is an order of magnitude less. The purpose is to develop a device for the formation of complex discrete samples of the electrocardiosignal, ensuring the isolation without loss of informative components of the electrocardiosignal of the isoline drift with a frequency range comparable to the heart rate, in conditions of variability of the sampling frequency due to heart rate variability. The possibility of expanding the frequency range of the isoline drift signal isolated from the electrocardiosignal based on the real-time transformation of isoline drift samples taken at the TP interval into complex discrete samples has been substantiated. When forming complex discrete samples, the variability of the sampling period due to heart rate variability is taken into account. A device for forming complex discrete samples of the isoline drift signal has been proposed, allowing extraction of isoline drift with an extended frequency range without loss the informative components of the electrocardiosignal. The developed device can be used both directly as part of mobile electrocardiographs designed to monitor a person's ECG in the conditions of his daily activities, and in equipment for processing daily electrocardiograms before analyzing their parameters.

A systematic review of maternal exposures and the risk of autism spectrum disorders in their infants

Background Maternal exposures associated with autism spectrum disorder remain poorly understood. Objective The purpose of this systematic review was to assess the current understanding of maternal exposures and their associations with increased or decreased risk of infant autism. Study Design We searched Ovid MEDLINE databases and reference lists in 2021 for studies looking at maternal factors analyzed for an association with ASD. The primary outcome was autism or autism spectrum disorder diagnosis. Exposures were grouped as biomarkers, environmental, occupational, medication, genetic variability, and maternal illnesses and conditions. PRISMA guidelines were followed. Descriptive summary statistics were evaluated. Results Of 433 reports identified, 47 studies published between 2006 and 2021 fulfilled the inclusion criteria. Exposures consistently associated with a significant increased risk of autism spectrum disorder outcomes included: benzoate, 10-hydroxydecanoate, methyl jasmonate, 1-methylhistidine, nonanoic acid, organohalogens, ornithine, polyunsaturated fatty acids, agricultural pesticides, heavy metals (arsenic, cadmium, chromium, lead, low levels of manganese, tin), cigarette smoking, hypotaurine, indoor household pesticides, methanol, newborn complications at delivery, occupational exposures, solvents, strontium, urate, antibiotics, placental differently methylated regions, diabetes, maternal fever, maternal depression, androgen-associated conditions and psychiatric disorders. Factors consistently associated with a significant decreased risk included: quinoline, air conditioning, cesium, copper, 3-hydroxybutanic acid, maternal fruit consumption, mercury, and phenylalanine. Conclusions This review provides evidence that there are several potential pregnancy exposures and biomarkers associated with an autism spectrum disorder diagnosis in children. Genetic mutations may play role in impact of exposures’ risks. Continued work on identifying potential exposures and biomarkers is needed to better counsel pregnant individuals.

Revisiting the implications of Liouville's theorem to the anisotropy of cosmic rays

We present a solution to Liouville's equation for an ensemble of charged particles propagating in magnetic fields. The solution is presented using an expansion in spherical harmonics of the phase space density, allowing a direct interpretation of the distribution of arrival directions of cosmic rays. The results are found for chosen conditions of variability and source distributions. We show there are two conditions for an initially isotropic flux of particles to remain isotropic while traveling through a magnetic field: isotropy and homogeneity of the sources. In case isotropically-distributed sources inject particles continuously in time, a transient magnetic induced dipole will appear. This dipole will vanish if the system reaches a steady-state. The formalism is used to analyze the data measured by the Pierre Auger Observatory, contributing to the understanding of the dependence of the dipole amplitude with energy and predicting the energy in which the quadrupole signal should be measured.

Ретроспективный анализ эволюции системы продовольственного обеспечения

A retrospective study of the evolutionary transformation of the concept of food security in the context of Russian reality has been conducted. Currently, food security, as an integral part of national security, has become the cornerstone for maintaining economic stability and political autonomy in the country. With increasing geopolitical volatility and private food crises, this topic is becoming increasingly relevant, and maintaining national food security has taken a leading position among the priorities for each State. At the same time, the analysis of scientific research on this topical issue proves that the formation of national food security depends on its regional food security, therefore it is also especially important to study and improve regional food supply systems. It is determined that food security is an active integration process of interaction between internal and external factors, such as agricultural production, grain stocks, import and export of grain, international food aid, etc. It is noted that the modern food supply system must be considered as a complex system of macroeconomic organization that ensures a balance between food and the demand of the population. The analysis of the elements and characteristics of this system makes it possible to identify the most important problems of its functioning and development, the solution of which contributes to improving the efficiency of work and, as a result, to guarantee the necessary and sufficient level of national food security in conditions of high variability of the surrounding environment. At the same time, the national food security system is closely interrelated with the level of regional food management systems, and the modern concept of food security implies the need to ensure constant access to physical and economic nutrition for every citizen.

Managerial decision-making: exploration strategies in dynamic environments

PurposeWe argue that a fundamental issue regarding how to search and how to switch between different cognitive modes lies in the decision rules that influence the dynamics of learning and exploration. We examine the search logics underlying these decision rules and propose conceptual prompts that can be applied mentally or computationally to aid managers’ decision-making.Design/methodology/approachBy applying Multi-Armed Bandit (MAB) modeling to simulate agents’ interaction with dynamic environments, we compared the patterns and performance of selected MAB algorithms under different configurations of environmental conditions.FindingsWe develop three conceptual prompts. First, the simple heuristic-based exploration strategy works well in conditions of low environmental variability and few alternatives. Second, an exploration strategy that combines simple and de-biasing heuristics is suitable for most dynamic and complex decision environments. Third, the uncertainty-based exploration strategy is more applicable in the condition of high environmental unpredictability as it can more effectively recognize deviated patterns.Research limitations/implicationsThis study contributes to emerging research on using algorithms to develop novel concepts and combining heuristics and algorithmic intelligence in strategic decision-making.Practical implicationsThis study offers insights that there are different possibilities for exploration strategies for managers to apply conceptually and that the adaptability of cognitive-distant search may be underestimated in turbulent environments.Originality/valueDrawing on insights from machine learning and cognitive psychology research, we demonstrate the fitness of different exploration strategies in different dynamic environmental configurations by comparing the different search logics that underlie the three MAB algorithms.

Changes in photovoltaic power output variability due to climate change in China: A multi-model ensemble mean analysis

Solar photovoltaic (PV) power plays a crucial role in mitigating climate change. However, climate change may amplify weather variability and extreme conditions. The extreme conditions can increase the very low PV output and thereby increase the need for grid stabilization services. This study examined how weather variability affects PV power output in the near- (2025–2054) and far-future (2071–2100). The ensemble mean calculated using seven global climate models participating in the coupled model intercomparison project phase 6 for three different shared socioeconomic pathways (SSPs) (SSP126, SSP245, SSP585) was used for the assessment. The standard deviation of the monthly PV power output and the share of very low monthly PV power output were used to assess the variability of PV power output. The findings indicate that the summer PV power output was projected to decrease by 6%–8% in central and northern Tibet under a high emissions scenario (SSP585). The summer months with low PV power output were projected to increase in western regions of China, known for its abundant solar resources. The findings of this study provide valuable insight for energy planners to make up for the influence of future weather variability.

Modeling the influence of climate on groundwater flow and heat regime in Brandenburg (Germany)

This study investigates the decades-long evolution of groundwater dynamics and thermal field in the North German Basin beneath Brandenburg (NE Germany) by coupling a distributed hydrologic model with a 3D groundwater model. We found that hydraulic gradients, acting as the main driver of the groundwater flow in the studied basin, are not exclusively influenced by present-day topographic gradients. Instead, structural dip and stratification of rock units and the presence of permeability contrasts and anisotropy are important co-players affecting the flow in deep seated saline aquifers at depths >500 m. In contrast, recharge variability and anthropogenic activities contribute to groundwater dynamics in the shallow (<500 m) freshwater Quaternary aquifers. Recharge fluxes, as derived from the hydrologic model and assigned to the parametrized regional groundwater model, reproduce magnitudes of recorded seasonal groundwater level changes. Nonetheless, observed instances of inter-annual fluctuations and a gradual decline of groundwater levels highlight the need to consider damping of the recharge signal and additional sinks, like pumping, in the model, in order to reconcile long-term groundwater level trends. Seasonal changes in near-surface groundwater temperature and the continuous warming due to conductive heat exchange with the atmosphere are locally enhanced by forced advection, especially in areas of high hydraulic gradients. The main factors controlling the depth of temperature disturbance include the magnitude of surface temperature variations, the subsurface permeability field, and the rate of recharge. Our results demonstrate the maximum depth extent and the response times of the groundwater system subjected to non-linear interactions between local geological variability and climate conditions.

Response of tree growth to drought variability in arid areas: Local hydroclimate and large-scale precipitation

The impact of drought on terrestrial ecosystems is increasing, and the spatiotemporal heterogeneity of drought changes exacerbates the difficulty of determining ecosystem responses, especially in arid regions far from oceans. Tree rings have been widely used to understand how forest ecosystems respond to drought. However, the link between local hydroclimate variations related to tree rings and large-scale climate changes is not clear in the Qilian Mountains. Here, we used the tree ring width index to analyze the trend of Picea crassifolia growth and its relationship with climate in the middle Qilian Mountains. The results showed that the radial growth trend of Picea crassifolia is synchronized in the middle Qilian Mountains by calculating the Gleichläufigkeit index (GLK). Our analyses indicated that tree radial growth is positively correlated with drought during the growing season. Tree growth responds stably to drought (scPDSI and SPEI) and precipitation but unstably to temperature during 1950-2019. We further traced the meteorological factors that cause regional drought changes associated with radial growth. An increased total precipitation and decreased evaporation contribute to drought alleviation, favoring an increased tree radial growth. The increased total precipitation is mainly due to increased large-scale precipitation, which is related to water vapor transport changes. This study attempts to explore the influence of large-scale meteorology on regional drought change and its related tree radial growth response, which helps us to better understand the changes in forest ecosystems under climate change.

Long‐term phytoplankton dynamics in two High Arctic lakes (north‐east Greenland)

Abstract Primary producers form the base of lake ecosystems and, due to their often short lifecycles, respond rapidly to changing conditions. As the Arctic is warming nearly four times faster than the global average, we see major shifts in environmental conditions, which impacts lake ecosystem functioning. Previous studies have found a general increase in primary productivity due to climate warming. However, few long‐term studies have included changes in phytoplankton community composition and biomass in relation to warming in Arctic lakes and it therefore remains unclear how different algal taxa and thus the community respond. We investigated how climate warming affects phytoplankton community composition, taxon richness and biomass in High Arctic lakes, using a unique 23‐year data series on phytoplankton in two shallow lakes at Zackenberg, north‐east Greenland, one with Arctic charr (Salvelinus alpinus) and one without fish. We further elucidated the role of physico‐chemical variables and zooplankton grazers in the changes observed. Few major changes were observed in phytoplankton community composition over time, but the year‐to‐year variation was large. Taxon richness did, however, increase throughout the monitoring period, and in both lakes there was a significant increase in diatom biomass coinciding with increasing conductivity. Additionally, phytoplankton biomass was greater during warmer years with earlier ice melt. We further found that nutrient levels were positively associated with the total phytoplankton biomass in both lakes, indicating that expected increased nutrient levels, due to climate change, may lead to a greater phytoplankton biomass in High Arctic lakes in the future. The large year‐to‐year variability, in both climate and environmental conditions, makes it difficult to predict weather patterns and their consequences for lake ecosystems in the Arctic region. This underlines the importance of long‐term monitoring programmes across the circumpolar Arctic and collaboration across regions and institutes within large scale studies.

An experimental study on the identification of the root bolts' state of wind turbine blades using blade sensors

AbstractBolt looseness may occur on wind turbine (WT) blades exposed to operational and environmental variability conditions, which sometimes can cause catastrophic consequences. Therefore, it is necessary to monitor the loosening state of WT blade root bolts. In order to solve this problem, this paper proposes a method to monitor the looseness of blade root bolts using the sensors installed on the WT blade. An experimental platform was first built by installing acceleration and strain sensors for monitoring bolt looseness. Through the physical experiment of blade root bolts' looseness, the response data of blade sensors is then obtained under different bolt looseness numbers and degrees. Afterwards, the sensor signal of the blade root bolts is analyzed in time domain, frequency domain, and time‐frequency domain, and the sensitivity features of various signals are extracted. So the eigenvalue category as the input of the state discrimination model was determined. The LightGBM (light gradient boosting machine) classification algorithm was applied to identify different bolt looseness states for the multi‐domain features. The impact of different combinations of sensor categories and quantities as the data source on the identification results is discussed, and a reference for the selection of sensors is provided. The proposed method can discriminate four bolt states at an accuracy of around 99.8% using 5‐fold cross‐validation.

Blending of a novel all sky imager model with persistence and a satellite based model for high-resolution irradiance nowcasting

High shares of variable energy sources such as photovoltaics (PV) make balancing network load and generation increasingly challenging. Electricity grids with high PV-penetration benefit from the consideration of intra-minute and intra-hour variabilities via nowcasts (shortest-term forecasts).In this study we present a novel all-sky imager (ASI) nowcasting system which is benchmarked against an established ASI method, a satellite nowcasting system and persistence. Nowcasts of our novel ASI method, satellite method and persistence are subsequently combined to a hybrid model with improved accuracy.ASI systems exploit sky images from fisheye cameras to analyze sky conditions, predict upcoming cloud situations and derive irradiance nowcasts from these. Our ASI method uses a novel machine-learning (ML) based approach that makes direct use of pixel values and other image features. Measurements from a spatially distributed irradiance measuring network of eight stations within a radius of 10km around the camera position are used to train our ASI and our hybrid model and validate our nowcasting results. In the evaluation we put a special focus on irradiance variability conditions. The high share of stable cloud situations within our dataset (∼68 %) results in a relatively good performance of persistence nowcasts. In higher variability situations persistence is quickly outperformed by all other nowcasting methods.Our ASI method clearly outperforms the reference ASI method at all lead times (LTs). It moreover exhibits a significantly lower root mean square error (RMSE) than the satellite-based method up to LT≤11min ahead and a lower mean absolute error (MAE) throughout the entire interval. The hybrid model brings about an RMSE improvement score of 5-13% over the respective optimal individual method.

Robust speed estimation of an induction motor under the conditions of rotor time constant variability due to the rotor deep-bar effect

Robust speed estimation of an induction motor under the conditions of rotor time constant variability due to the rotor deep-bar effect

Novel Slumped SRAM Configuration using QCA Leveraging Differential Voltage Sensing for Enhanced Stability and Efficiency

This paper presents a novel Slumped Static Random-Access Memory (SRAM) configuration utilizing Quantum-dot Cellular Automata (QCA) technology, aimed at achieving enhanced stability and efficiency. Traditional CMOS-based SRAM designs face significant challenges related to power consumption and scalability as technology nodes shrink. QCA, with its potential for ultra-low power dissipation and high-density integration, emerges as a promising alternative. Our proposed SRAM configuration leverages a unique differential voltage sensing mechanism to bolster the stability of the memory cells, particularly under conditions of variability and noise. Through detailed simulations and comparative analysis, we demonstrate that the Slumped SRAM configuration not only improves static noise margin (SNM) but also reduces power consumption and enhances overall readwrite speed. The results indicate a substantial improvement in stability and operational efficiency, positioning this design as a viable solution for future high-performance, low-power memory applications. Through detailed simulations and comparative analysis, we demonstrate that the Slumped SRAM configuration achieves a static noise margin (SNM) improvement of 35% over conventional CMOS-based SRAM designs. Additionally, the proposed design reduces power consumption by 40% and enhances readwrite speed by 25%. These results indicate a substantial improvement in stability and operational efficiency, positioning this design as a viable solution for future high-performance, low-power memory applications.

A rapid method for assessing seed drought resistance using integrated ID-BOA-SVM.

This study investigates the application of near-infrared spectroscopy (NIR) for assessing drought resistance in seeds, aiming to offer a rapid and efficient method suitable for large-scale primary screening. NIR spectroscopy is utilized to analyze four key factors (water, sugars, amino acids content, and genes) associated with maize seed drought responses. Signature NIR bands indicative of drought resistance-related molecules are identified using the Competitive Adaptive Reweighted Sampling (CARS) technique. Furthermore, an Improved Discrete Bayesian Optimization Support Vector Machine (ID-BOA-SVM) classification model is developed to address issues related to sparse features in traditional Bayesian Optimization Support Vector Machines (BOA-SVM). To enhance classification performance, a stacking model integrating Random Forest (RF), ID-BOA-SVM, Logistic Regression (LR), and Gradient Boosted Decision Trees (GBDT) classifiers is constructed, ensuring robustness and minimizing overfitting risks. The model achieves satisfactory recognition accuracy (94.28% accuracy, 94% precision, 94.61% recall, and 94.23% F1-score) even under conditions of substantial interference and dataset variability. This research demonstrates that NIR spectroscopy-derived data can support genetic and physiological studies of drought-resistant seed varieties, facilitating a deeper understanding of drought resistance mechanisms and optimizing breeding strategies.

Альтеральность в контексте процессов субъективации

The article focuses on the socio-philosophical analysis of alterality as an attribute of a social subject in its dynamic aspect. The author considers various types of alterality in the context of subjectification processes. The difficulty of knowing the Other and mutual understanding in the conditions of permanent variability of the social subject is emphasized. Conclusions are formulated about the role of alternality in the space of being a social subject at the group and individual levels. The main directions of implementation of the considered approach in the modern world are indicated.

ПРОФЕССИОНАЛЬНАЯ НАДЕЖНОСТЬ КАК КЛЮЧЕВАЯ ХАРАКТЕРИСТИКА В МОДЕЛИ ЛИЧНОСТИ СОВРЕМЕННОГО СПЕЦИАЛИСТА

The post-industrial and digital development of society is characterized by conditions of high variability, uncertainty and chaos. The entry of network technologies and resources into all spheres of life, the technologization of production and management processes create the prerequisites for the emergence of risks of a different nature in all types of professional activities and social relations. This situation complicates the requirements for a modern specialist, requiring them not only to have subject knowledge and professionally oriented competencies, but also to have developed abilities for the active and transformative development of new techniques and technological solutions, a predictive assessment of possible risks and personal responsibility for the consequences of their choice in non-standard situations. The entry of the digital component is changing the organizational and technological aspects in the field of vocational education, significantly expanding the boundaries of traditional forms of education. The new psychological and pedagogical conditions of the open educational space have an impact on the quality of the educational process and the results of professional training, producing the negative effects of informatization of the education sector and developing the problem of educational risks (vital, psychological, social). The inclusion of the concept of "professional reliability" in the categorical space of pedagogical science is due to its content, which determines the correspondence of the qualities of the subject of professional activity with rapidly changing conditions for the implementation of professional activity. Professional reliability is a key and integrative characteristic in the model of a digital specialist, the content and structural content of which is determined by the specifics and features of a particular professional activity. The structure of professional reliability of specialists in railway transport is represented by a set of motivational-value, activity-technological and reflexive components. Experimental work proved the effectiveness of the developed didactic system for the formation of professional reliability of specialists in railway transport.

Application of nowcasting to reduce the impact of irradiance ramps on PV power plants

Short-term fluctuations in photovoltaic power plants, known as ramps and caused by clouds, challenge grid stability and efficient energy use. These issues are traditionally managed with battery energy storage systems, which, while effective, are expensive. We propose an alternative solution: the use of short-term irradiance forecasts, or nowcasts. Using a photovoltaic power plant in Germany and its simulated model, we demonstrated that nowcasts could cut ramp rate violations by 81%. This led to a reduction in required battery capacity by 71% and the required maximum battery power provided by 48%, at the cost of a 13% curtailment loss, i.e. loss through reduction of power. Our data set of 18 chosen days from 2020 with high variability conditions was scaled up to a year for the economic analysis. From an economic standpoint, nowcasts could lower the Levelized Cost of Electricity by 5.5% from 4.74 to 4.48 EUR cents, and even by 35% to 3.09 EUR cents with ideal forecasting, showing its potential. While nowcasts cannot completely replace batteries, they substantially reduce the need for such storage solutions. This results in cost savings and adherence to grid stability requirements, making nowcasts a complement or partial alternative to battery systems for mitigating power fluctuations in photovoltaic power plants.