Variability Of Conditions Research Articles

Relationship Between Sea-level Variability and Meteorological Conditions in Abu-Qir Bay (Alexandria – Egypt)

Relationship Between Sea-level Variability and Meteorological Conditions in Abu-Qir Bay (Alexandria – Egypt)

Probabilistic solar nowcasting based on all-sky imagers

The interest in shortest-term solar irradiance forecasts (nowcasts) increases steadily with the increase share of distributed solar power generation. Such solar irradiance nowcasts are beneficial for different stakeholders, from generation to transmission and demand, in order to tackle challenges caused by the variability of solar irradiance. In space and time highly resolved nowcasts can be obtained by all sky imager (ASI) systems, which analyze the sky conditions from sky images. Deterministic nowcasts from ASI systems are subject to significant uncertainties. Reliable uncertainty information are very helpful for any application, in order to derive practical benefit from nowcasts. Therefore, such nowcasts should be probabilistic in nature, which provide probability distributions. Meaningful indicators for the uncertainties at hand are provided by prediction intervals for distinct confidence levels derived from the probability distributions. Thus, a real time capable nonparametric probabilistic quantile nowcasting method based on deterministic ASI nowcast is developed. The method takes irradiance variabilities as main predictor of nowcast uncertainties into account. A benchmark against three distinct baseline models is conducted over an extensive data set, using a variety of recently recommended scores. Overall average continuous ranked probability skill scores (Clear-Sky Dependent Climatology as baseline) for nowcasts up to 20 min ahead of 0.72 ± 0.08 (direct normal irradiance) and 0.62 ± 0.09 (global horizontal irradiance) are reached. For a better evaluation of the actual performance of the probabilistic nowcasts, a discretization of the validation data set into eight irradiance variability conditions is performed. All scores are determined for each of these distinct conditions.

Canopy Composition and Spatial Configuration Influences Beta Diversity in Temperate Regrowth Forests of Southeastern Australia

Structural features of the overstorey in managed and unmanaged forests can significantly influence plant community composition. Native Acacia species are common in temperate eucalypt forests in southeastern Australia. In these forests, intense disturbances, such as logging and wildfire, lead to high densities of regenerating trees, shrubs, and herbs. The tree layer is dominated by Acacia and Eucalyptus, that compete intensely for resources in the first decades after stand establishment. The relative abundance and size of Acacia and Eucalyptus varies widely due to stochastic factors such as dispersal, microsite variability, and weather and climatic conditions. This variability may influence the structure and composition of the herbaceous and shrub species. In the temperate forests of southeastern Australia, understorey plant diversity is assumed to be influenced by Acacia species density, rather than Eucalyptus density. To quantify the influence of Acacia and Eucalyptus density on plant community composition, we used remote sensing and machine learning methods to map canopy composition and then compare it to understorey composition. We combined unoccupied aerial vehicle (UAV or drone) imagery, supervised image classifications, and ground survey data of plant composition from post-logging regrowth forests in the Central Highlands of southeastern Australia. We found that aggregation and patch metrics of Eucalyptus and Acacia were strongly associated with understorey plant beta diversity. Increasing aggregation of Acacia and the number of Acacia patches had a significant negative effect on plant beta diversity, while the number of Eucalyptus patches had a positive influence. Our research demonstrates how accessible UAV remote sensing can be used to quantify variability in plant biodiversity in regrowth forests. This can help forest managers map patterns of plant diversity at the stand-scale and beyond to guide management activities across forested landscapes.

Theoretical founda-tions of the marketing concept of management and formation of a strategy for the development of the enterprise under conditions of sustainabi-lity

The article examined the essence of the marketing concept of enterprise development management, studied the theoretical and methodological approaches to the formation of the marketing concept of enterprise development management, investigated the types of marketing concepts of enterprise development, and also analyzed the marketing tools used in the improvement of enterprise development management. It was established that the marketing activity of the enterprise should contribute to the acquisition of competitive advantages in conditions of instability and variability of the external environment. Therefore, undoubtedly, the effectiveness and efficiency of the development of the enterprise depends on the results of the marketing activity, which becomes one of the elements of the enterprise management system. During the research, the author characterized the concentration of agricultural crop market participants and presented the characteristics of their marketing concept. The role and place of the marketing concept in the management system is considered, and the principles of its application at enterprises are established. The peculiarities of the formation of the marketing concept of enterprise development management in modern conditions are considered. The interrelated components of the marketing concept of the enterprise development management process are defined. And based on the improvement of the management system, marketing concepts of the development of enterprises were formed, which are determined by many reasons; the goals of managing the marketing activities of the enterprise and its principles, structure and other elements of the marketing concept are reflected. The stages of the efficiency of functioning and development of the enterprise when using the marketing concept of management are defined. The elements of the marketing concept of business development management and the factors influencing marketing strategies of business development management are also presented. The purpose of the article is a theoretical study of the essence, meaning and features of the marketing concept of enterprise development management and a generalization of scientific and practical proposals for improving the efficiency of the enterprise. The solution to the scientific problem arises in the study and generalization of the theoretical foundations and methodological approaches of researching the marketing concept of enterprise development management. Keywords: marketing concept, management, development, marketing potential, marketing strategy.

Effectiveness of variative development of educational programs in pedagogical processes

Diversity of education is one of the main principles and directions of modern education. The content of educational programs and the variability in their implementation mean that each educational institution, even each pedagogue, can create their own educational programs and use different methods of their implementation. The main condition of variability is that it should be developed in accordance with the main principles and requirements of the state curriculum, as well as the requirements of the state.

ARTIFICIAL NEURAL NETWORK APPLICATION FOR HEAVY EQUIPMENT GAS EMISSION CONTROL ON ROCK BREAKING ACTIVITY

Applications of artificial intelligence (AI) software in mining activities, both for equipment automation, data analysis and processing, identification of patterns and features data, upto determining solutions have been carried out by several mining companies. This is mainly due to mining activities naturally are always facing uncertainty and natural variability conditions. One of the AI applications is to control fuel consumption aimed at increasing the efficiency of fuel use, while in the same time reducing exhaust gas emissions from internal combustion engines, which are one of the causes of rising greenhouse gases (GHG).Utilization of AI in aimed to control fuel consumption in Rock Breaking activities in limestone quarry in the Sukabumi area, resulting a deviation rate of 0.17 for fuel consumption prediction, which is fall in “ the good category”. Increasing the volume and variety of data for “machine learning” would improve AI performance.Keywords : Artificial intelligence application, fuel consumption, ICE gas emission control.

The effects of bilingualism on children’s cross-situational word learning under different variability conditions

In the current study, we examined the separate and combined effects of exemplar and speaker variability on monolingual and bilingual children’s cross-situational word learning performance. Results revealed that children’s word learning performance did not differ when the input varied in a single dimension (i.e., exemplars or speakers) compared with a condition with no variability independent of their linguistic background. However, when performance in conditions that varied in a single dimension (i.e., exemplars or speakers) was compared with a condition that varied in multiple dimensions (i.e., exemplars and speakers), bilingual word learning advantages were observed; bilinguals were more likely to learn word–referent associations than monolinguals. Together, results suggest that children can learn and generalize word–referent associations from input that varies in exemplars and speakers and that bilingualism may bolster learning under conditions of increased input variability.

Multi-Model In-Plant Logistics Using Milkruns for Flexible Assembly Systems under Disturbances: An Industry Study Case

Mass customisation demand requires increasingly flexible assembly operations. For the in-plant logistics of such systems, milkrun trains could present advantages under high variability conditions. This article uses an industrial study case from a global white-goods manufacturing company. A discrete events simulation model was developed to explore the performance of multi-model assembly lines using a set of operational and logistics Key Performance Indicators. Four simulation scenarios analyse the separate effects of an increased number of product models and three different sources of variability. The results show that milkruns can protect the assembly lines from upstream process disturbances.

Economic Efficiency of Solar and Rainwater Systems—A Case Study

The study deals with the analysis of data from a selected tourism facility and the implementation of a solar system and a rainwater system, which are an alternative to commonly used energy sources. The objective is to evaluate the potential savings from the use of the solar system for water heating and the rainwater system for purposes other than potable use with respect to local conditions of sunlight and rainwater variability. The facility holds 257 beds allocated in 124 rooms on 5 floors. The result of the contribution is an economic evaluation of the efficiency of investments in the proposed systems and the resulting recommendations in the field of energy flows in the selected facility. Appropriate formulas including the consumption of hot water, the amount of energy needed to heat water, the cost of the consumed energy necessary for heating and the average annual rainfall in the location, water consumption for clearly identified purposes of alternation with rainwater, the initial investment costs and operating costs of the solar and rainwater systems, were used for an economic evaluation of the investment into the solar and rainwater systems. Based on the economic evaluation, the economic efficiency of the proposed systems and the return on investment was calculated. The payback periods for the solar system used for hot water heating and the rainwater system used for non-potable purposes, accepting the local conditions, are 7 and 15 years, respectively.

Analiza konwergencji gospodarczej i społecznej krajów Unii Europejskiej w czasach kryzysu

Purpose – Determine the level of social and economic convergence of the various countries of the European Union and its changes over time by creating rankings of countries. Research method – A synthetic measure of development was created using various normalization methods. In addition, the quality of the classification carried out was checked, using Kendall’s rank concordance coefficient, which made it possible to examine whether there is a process of gamma convergence between the countries of the European Union. The study was carried out for selected 8 years from the period 2000–2022, which made it possible to assess changes over time. Results – The occurrence of statistically significant gamma convergence in the group of countries studied was confirmed throughout the period under review. In 2009, the convergence process was weakened due to the economic collapse caused by the global economic crisis, and in 2016 it was weakened by the occurrence of the banking crisis in Europe and the onset of reflation after a period of falling prices in the economy. The outbreak of armed conflict in Ukraine in 2022, also indicates a negative impact on the gamma convergence process. It should be noted that Brexit did not have a significant impact on the convergence process in the group of countries studied. Originality / value / implications / recommendations – Constructing a synthetic measure of social and economic development makes it possible to rank countries according to accepted measures that meet the conditions of universality, variability, validity and stability.

The use of machine learning tools for modeling the countermeasure environment of robotic means and mobile objects of monitoring

In this article, an analysis of research related to the problems and areas of application of machine learning was carried out and it becomes clear that reinforcement learning is an important tool for the development of highly effective and adaptive control systems. This approach opens perspectives for the development of intelligent systems that can effectively adapt to changes in the environment and provide a reliable and optimal level of control under conditions of uncertainty and variability. In the course of the research, a complex environment modelling technique was developed for the task of searching and chasing moving objects by robotic means as part of reinforcement learning. The methodology includes defining goals and objectives, modelling the environment, defining states, actions, and rewards, formulating the state transition function, defining the reward function, modelling completion criteria, using convolutional neural networks to analyse the environment, and integrating the model into reinforcement learning algorithms. A feature of the developed methodology is its ability to adapt to various environmental conditions and dynamic changes occurring in it. The use of graphical representation of the environment allows you to reproduce in detail the real conditions in which the robotic means operate, including obstacles, areas of visibility and different types of surfaces. The use of convolutional neural networks for environmental analysis is an innovative approach that allows you to automate the process of determining the impact of the environment on the parameters of robotic vehicles and moving objects. CNN training on the collected data about the environment provides accurate prediction of influence coefficients in different areas, which allows optimizing strategies of robot behaviour depending on changes in conditions. In general, the developed environment modelling technique allows not only to accurately reproduce the real conditions in which robotic means operate, but also provides flexibility and adaptability when environmental conditions change. This opens up vast opportunities for training and improving reinforcement learning algorithms, increasing the effectiveness of robots in performing their tasks in various real-world scenarios.

Silent Speech Recognition Based on Surface Electromyography Using a Few Electrode Sites Under the Guidance From High-Density Electrode Arrays

Although surface electromyogram recorded from high-density electrode array is believed to carry sufficient spatial information that can benefit the decoding of motor intentions, the complexity of using the array hindered its widespread applications especially in wearable devices. This study is aimed to develop a non-acoustic modality of silent speech recognition that transfers knowledge learned from high-density array to a system using a few channels, with both high portability and performance. A convolutional neural network was established for recognizing a vocabulary of 33 Chinese words during subvocal speech production. The network was trained by the data recorded from face and neck muscles using two arrays with 64 channels in the source domain. Then it was calibrated through a transfer learning approach to grant its adaption to a new target domain with the data recorded by 8 separated electrodes, while its good capability of characterizing subvocal speech word patterns is expected to be maintained. The proposed method significantly outperformed three common classification approaches and the baseline approach without transfer learning (a network trained with data just from the target domain). Under conditions of electrode shift and cross-user variability, it still obtained performance improvements. The method is demonstrated to be viable for transfer learning across domains of electrode settings and it facilitates to improve the performance of silent speech recognition systems using separate electrode sites under the guidance from high-density of arrays.

Підприємницький ризик: сутність та класифікація в залежності від втрат, які несе підприємець

In conditions of high competition and variability of economic conditions, timely insurance of business risks can help the enterprise to preserve its financial resources and ensure stability and security in the market. The article aims to systematize business risks by groups of losses and generalize and characterize the types of insurance policies that can be useful to the enterprise due to their manifestations. It was found that entrepreneurial risk in economic activity is a generalizing term for a group of risks that arise at various stages of the capital cycle as a result of the actions of the enterprise itself, competitors, counterparties, changes in the market situation, technological failures and other internal and external factors. Types of business risks are systematized. Four main groups of enterprise losses due to business risks were identified, with the selection of types of insurance policies that can be useful for enterprises in case of corresponding losses. Different conditions for insurance against cyber threats and liability for environmental damage in Ukraine were disclosed under four main costs depending on the type of damage subject to compensation. The results of the study show that business risk is a complex category that covers various aspects of business activity, protection against which occurs mainly within the scope of property insurance and liability insurance, but at the same time can go beyond these fields of insurance, if it is determined by the needs of business activity Business risk includes financial risks, such as market instability, changes in the economic environment, exchange rate fluctuations, rising costs, insufficient financial resources. It also covers operational risks related to business management, including supply chain issues, staffing issues, technology failures, legal conflicts, and more.

Simulating the conditions of variability for service system- A case of Automobile Service Centre

Simulating the conditions of variability for service system- A case of Automobile Service Centre

Bivariate Discrete Time Series Model for Assessing the effects of Rainfall and Temperature on Road Accidents: The case of Morogoro and Pwani Regions in Tanzania

In this article the contributions of weather on the occurrences of road accidents in Tanzania particularly for Morogoro and Pwani Regions is discussed. The correlation between road accidents and weather variables have indirect relationship taking into consideration that the later affects other factors like humans (drivers and other road users), state of vehicles and infrastructure before contributing to the incidence of road accidents. A bivariate discrete time series BINAR(1) model is formulated, and contribution of weather variability conditions in determining and forecasting road accidents is assessed using real data from Tanzania Meteorological Authority and Tanzania Police Force. The results show that, the contribution of weather variables specifically rainfall and temperature on road accidents vary either positively or negatively depending on the particular situation. Therefore, model properties are used to obtain correlation of these factors to road accidents as well as forecasting road accidents. It is not straight forward that increase (decrease) of rainfall or temperature lead to rise (decline) of road accidents. This indicates that, there might be other factors contributing to road accidents that need to be considered for achieving minimal if not zero road accidents. Therefore, we recommend the Government of Tanzania through the Tanzania Police Force to strictly enforce road safety laws and other necessary bylaws as well as providing education about road safety regulations to all road users. This will result to road accidents to be reduced significantly during rain and dry (high temperature) seasons.

Total organic carbon variability of lower Jurassic successions in the Mandawa Basin, SE Tanzania

Over many decades, an understanding of total organic carbon (TOC) variability in various sedimentary basins has been constrained by the availability of suitable samples for geochemical analysis. Many attempts rely on selected sidewall core or cuttings samples, which do not provide a continuous lithological record of the succession. As a result, TOC variability as a response to facies evolution is often underestimated. An advancement in TOC evaluation from well logging techniques often has permitted a continuous analysis of source rock intervals, while eliminating issues associated with obtaining discrete physical samples. Geochemical and wireline geophysical logs investigation here aimed at constraining TOC variability and depositional conditions prevailing during the early Jurassic in the Tanzanian Mandawa Basin (Mbuo, Nondwa and Mihambia Formations), which to date are poorly documented. TOC was deduced from both well cuttings samples and wire log data. Depositional conditions were defined via the use of spectral Gamma-Ray (Th/U, U) logging and lithofacies data. Sediment composition and TOC from cuttings were compared to illustrate how organic matter distribution is influenced by changes in sediment type. Complex mineral composition of heterolithic facies dominated log-response, complicated log interpretation, and thus limited the effectiveness of well logging techniques to predict TOC. Cuttings data reveal three organic-rich intervals, i) at the transition of Upper Mbuo to basal Nondwa Formation with the highest TOC of 3.5 (wt%), ii) in the Nondwa Formation with a TOC of 2 (wt%), and iii) in the Mihambia Formation with a TOC of 2.8 (wt%). An inverse linear relationship between Th/U and TOC signifies relatively shallow deposition in a well-mixed water column under oxic conditions. Intervals with higher TOC imply organic matter preservation under suboxic conditions upon two marine incursions in the Mihambia and Nondwa Formations, whereas a limnic environment prevailed at the Upper Mbuo to Nondwa transition. Inverse trends in TOC versus clastic components, a positive linear relationship between TOC and carbonates, and higher amount of clastics (>57 wt%) in the Mbuo and Mihambia Formations suggest a dominantly clastic sedimentary system with relatively stable carbonate and organic matter input in proximity to the source area. We, therefore, argue that the organic rich intervals observed in the Upper Mbuo, Nondwa and Mihambia Formations were deposited during periods of higher organic productivity driven by a rise in eustatic sea level, and coincide with a decline in terrigenous input from the hinterland.

Innovative Potential of Regional Economic Systems under Conditions of Variability of Ecological and Economic Processes

In this paper, the authors studied the theoretical basis for the formation of the innovative potential for the development of ecological and economic regional systems, analyzed the indicators characterizing the level of innovative development of regions, developed recommendations for improving the innovative potential of ecological and economic regional systems. The influence of external factors, including the global pandemic, on regional economic systems requires an adequate identification of the dependence of the process of forming innovative potential on the degree of their development. In this regard, the difference in socio-economic conditions for the development of regions, as well as cardinal changes in scientific paradigms and theories, require a reasoned justification of the principles of the concept of forming the innovative potential of economic regional systems in the context of the rapidity of technological patterns and ecological influences. All this reinforces the need to substantiate the theoretical and methodological key concepts of development and formation of the innovative potential of economic systems of regions, determined by an understanding of the economic essence of the category “innovation potential” adapted to modern conditions of economic development, considering the factor dependence of the formation of stable and dynamic development of economic mesosystems.

Climate adaptive rice planting strategies diverge across environmental gradients in the Indo-Gangetic Plains

Abstract The timing of rice planting has a profound influence on the productivity of the rice-wheat cropping pattern in the Indo-Gangetic Plains (IGP), a system that provides the foundation for food security in South Asia. Nevertheless, strategies for adaptive rice planting in a rapidly changing climate are not well established. In this ex-ante analysis, regional gridded crop model simulations are deployed to investigate the impact of different rice planting strategies on system level productivity, resilience, and environmental benefits. Our results suggest that synchronizing rice planting dates with the monsoon onset substantially outperforms farmer practice (+41%) and static state recommendations in the Eastern IGP. However, planting long-duration rice with the monsoon onset is ineffective in the Northwestern IGP since the later arrival of the monsoon increases the probability of cold damage to rice and terminal heat stress in wheat. Here, fixed planting dates (+12.5%) or planting medium duration varieties at monsoon onset (+18%) performed best. We conclude that resilient and productive rice planting strategies must account for interannual weather variability and divergent climate conditions across sub-regions in the IGP.

Does variability in recognition memory scale with mean memory strength or encoding variability in the UVSD model?

The unequal variance signal detection (UVSD) model of recognition memory assumes that the variance of old item memory strength (σo) is typically greater than that of new items. It has been suggested that this old item variance effect can be explained by the encoding variability hypothesis. However, Spanton and Berry (2020) failed to find evidence for this account, suggesting that σo may simply scale with mean memory strength (d) in the UVSD model. Experiments 1 and 2 examined the effects of encoding variability and strength scaling on old item variance by creating conditions in which mean memory strength and variability in item characteristics was either low or high in 2 × 2 factorial designs. In Experiment 1, overall strength determined estimates of σo, with no effect of item characteristic variability. The same effect of overall strength was found in Experiment 2; there was also a significant effect of item characteristic variability, although this manipulation also had some effect on d and was therefore partially confounded. Experiment 3 similarly found a simultaneous increase in old item variance and memory strength in a design using mixed item characteristic variability conditions in a single-study/test block. We conclude that old item variance increases with mean memory strength in the UVSD model, with uncertainty about the effects of encoding variability, and that future explanations of the old item variance effect should bear this in mind.

Assessing the Information Potential of MIR Spectral Signatures for Prediction of Multiple Soil Properties Based on Data from the AfSIS Phase I Project.

The aim of the study was to assess the predictive potential of mid-infrared (MIR) spectral response in the estimation of 60 soil properties. It is important to know the accuracy limitations in estimating various soil characteristics using various models in conditions of high spatial variability of the environment. To fully assess this potential, three types of algorithms were used in modeling, i.e., partial least squares (PLSR), one-dimensional convolutional neural network (1DCNN), and generalized regression neural network (GRNN). The research used data from 19 sub-Saharan African countries collected as part of the Africa Soil Information Service (AfSIS) Phase I project. The repositories provide 18,250 MIR reflectance recordings and nearly two thousand analytical data records from the determination of many soil properties by reference methods. The modeled subset of these properties included texture (three variables), bulk density, moisture content at soil water characteristic curves (SWCC, 4 variables), total and organic C and total N content (3 variables), total elemental content (32 variables), elemental content in bioavailable forms (12 variables), electrical conductivity, exchangeable acidity, exchangeable bases, pH, and phosphorus sorption index. It is not possible to indicate a universal optimal prediction model for all soil variables. The best prediction results are provided by all regression models for total and organic C, total Fe, total Al and bioavailable Al content, and pH. For bulk density, total N and total K content satisfactory results are provided by specific model type. Many other properties, i.e., texture, SWCC, total Ga, Rb, Na, Ca, Cu, Pb, Hg content, and bioavailable Ca and K content, can be predicted with accuracies sufficient for some less demanding tasks.