Conditions Of High Variability Research Articles

Dynamic adaptation in power transmission: integrating robust optimization with online learning for renewable uncertainties

IntroductionThe increasing integration of renewable energy sources, such as wind and solar, into power grids introduces significant challenges due to their inherent variability and unpredictability. Traditional fossil-fuel-based power systems are ill-equipped to maintain stability and cost-effectiveness in this evolving energy landscape.MethodsThis study presents a novel framework that integrates robust optimization with online learning to dynamically manage uncertainties in renewable energy generation. Robust optimization ensures system resilience under worst-case scenarios, while the online learning component continuously updates operational strategies based on real-time data. The framework was tested using an IEEE 30-bus test system under varying levels of renewable energy integration.ResultsSimulation results show that the proposed framework reduces operational costs by up to 12% and enhances system reliability by 1.4% as renewable energy integration increases from 10% to 50%. Additionally, the need for reserve power is significantly reduced, particularly under conditions of high variability in renewable energy outputs.DiscussionThe integration of robust optimization with online learning provides a dynamic and adaptive solution for the sustainable management of power transmission systems. This approach not only improves economic and environmental outcomes but also enhances grid stability, making it a promising strategy for addressing the challenges posed by the increasing reliance on renewable energy.

91EMF Interventions to Reduce Emergency Department Admissions for High-Variation Conditions Under an Alternative Payment Model

91EMF Interventions to Reduce Emergency Department Admissions for High-Variation Conditions Under an Alternative Payment Model

ANALYSIS OF ROUTING PROTOCOLS CHARACTERISTICS IN AD-HOC NETWORK

Background. Wireless ad-hoc networks are becoming increasingly prevalence in remote areas, in extreme environments, even in military operations, and in scenarios where setting up infrastructure networks is not possible. Research of ad-hoc routing protocols problems allows improving the efficiency of their operation in conditions of high variability in packet loss or instability of network operation when the speed of users changes. Objective. The purpose of the paper is analysis of packet loss dependency from a network operation time, study of a user speed influence on a network efficiency, and research of network operation efficiency with different routing protocols. Methods. The method of routing protocols efficiency evaluation is the simulation of their operation in an ad-hoc network on a test data set and research of a network indicators dependency in time under different loads and changing mobility of users. Results. The conducted research demonstrated that user’s mobility at different speeds significantly affects the network operation as a whole. The instability of users' positions leads to a significant increase in route search time and packet transmission time. Among researched GPSR, DSDV, and AODV protocols, the latter proved to be the best because it has the lowest percentage of data loss and the lowest average time of message send and receive operations. Conclusions. The work is dedicated to the actual problem of developing and setting parameters of ad-hoc network. Received research results indicate the need to choose the optimal routing protocol depending on specific application conditions, such as user movement speed and network stability. The proposed solutions can be the first stage of complex processing of packets in the mobile network and justify the choice of AODV protocol as a basis for further improvement.

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.

Multiple images captured from a single encounter do not promote face learning.

Viewing multiple images of a newly encountered face improves recognition of that identity in new instances. Studies examining face learning have presented high-variability (HV) images that incorporate changes that occur from moment-to-moment (e.g., head orientation and expression) and over time (e.g., lighting, hairstyle, and health). We examined whether low-variability (LV) images (i.e., images that incorporate only moment-to-moment changes) also promote generalisation of learning such that novel instances are recognised. Participants viewed a single image, six LV images, or six HV images of a target identity before being asked to recognise novel images of that identity in a face matching task (training stimuli remained visible) or a memory task (training stimuli were removed). In Experiment 1 (n = 71), participants indicated which image(s) in 8-image arrays belonged to the target identity. In Experiment 2 (n = 73), participants indicated whether sequentially presented images belonged to the target identity. Relative to the single-image condition, sensitivity to identity improved and response biases were less conservative in the HV condition; we found no evidence of generalisation of learning in the LV condition regardless of testing protocol. Our findings suggest that day-to-day variability in appearance plays an essential role in acquiring expertise with a novel face.

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.

High variability in learning materials benefits children’s pattern practice

Concrete materials (e.g., pictures, objects) are believed to be helpful with learning, but not in all circumstances. Variability in these materials (i.e., using different materials vs. the same materials) could be an important factor. We compared how variability in concrete images influenced children’s learning about repeating patterns (e.g., ABBABBABB). A total of 87 children aged 4 to 6 years from the United States (75% White; 44% female) completed an experiment via Zoom in which they received brief pattern training. Children were randomly assigned into Low, Medium, and High Variability training conditions, which differed in terms of whether the same materials were used over and over or they varied in their perceptual features. Children in the Low Variability condition performed better at the beginning of training, but this trend ultimately reversed. Children in the High Variability condition performed best by the end of training and on the posttest. Using variable materials may allow children to extract common structures across instances.

Intra-hour PV power forecasting based on sky imagery

This paper introduces an upgraded version of the PV2-state model [Paulescu et al. Renew Energy 195 (2022) 322] for intra-hour photovoltaic (PV) power forecasting. The model incorporates real-time adjustments to the estimated clear-sky PV power or the transmittance of clouds, considering the Sun coverage by clouds. A physics-based approach for processing cloud field information from an all-sky imager is proposed to intra-hour forecast SSN, a binary quantifier stating whether the Sun is shining (SSN = 1) or not (SSN = 0). The model performance with the new procedure is investigated from three perspectives: forecast accuracy, forecast precision and the response to the variability in the state-of-the-sky. The study was conducted with high-quality 1-min data collected from a micro-PV plant, on a sample of days with scattered clouds. PV2-state demonstrates notable performance particularly under challenging conditions, where models typically struggle to perform well. In terms of skill score, the overall model performance ranges between 10 and 20%, and under very high variability conditions even exceeding four times the purely statistical forecast. For the operationally relevant 15-min horizon, PV2-state exhibits a noteworthy precision, with two-thirds of forecasts falling within a 10% tolerance interval. Even under severe conditions roughly one third falls in this 10% interval.

Influences of talker variability, reading ability, and language ability on word learning

Studies show that talker variability is beneficial for learning second language contrasts, though less is known about variability in processing speech in a speaker’s native language. Talker variability benefits have also been found for word learning studies with children, with better performance for children in the high variability condition than those in the low variability condition. Furthermore, it has been shown that individuals with a range of reading and language abilities perform differently on tasks that tap into phonological processes. Therefore, the current study examines how individual differences in reading and language abilities influence word learning and the extent to which talker variability improves performance. One hundred and forty listeners were randomly assigned to three conditions: (1) single talker, (2) multiple talker (randomized), and (3) multiple talker (blocked). Our results showed that individuals with poorer reading or language skills performed worse than individuals with higher reading and language skills. Surprisingly, variability was not beneficial and instead, listeners in the single talker condition performed better than those in the multiple talker conditions. These results suggest that talker variability benefits in word learning may be dependent on other aspects such as the amount of difference between talkers or the task.

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.

Environment Context Variability and Incidental Word Learning: A Virtual Reality Study.

Previous research has shown that changes in the scenarios in which something is learned and recalled, respectively, may result in a subpar performance in memory recollection. The current study aimed to evaluate how changes in the visuo-perceptual environmental learning context impact incidental vocabulary learning. To this end, a highly immersive virtual reality setting was created, and participants were required to read eight distinct stories visually presented to them. A novel word was delivered twice in every paragraph and embedded in each story. Stories could be displayed either in a high variability condition, where each paragraph was shown in a new environment context (four different classrooms) or in a low variability condition, where each paragraph was shown in the same context. The findings obtained across four assessment tasks (free recall, recognition, picture matching, and sentence completion) demonstrated that significant visuo-perceptual variability did not bring about any disadvantages in word learning. Thus, perceptual information from a physically diverse environment could provide a variety of instructional and educational beneficial possibilities in the absence of a learning disadvantage.

Learning Words in Two Languages: Manipulating Exemplar Variability for Within- and Cross-Language Generalization.

This article aims to describe how exemplar variability can manipulate the word learning environment to maximize within- and cross-language generalization in Spanish-English bilinguals. Furthermore, we examined sources of individual variability that predicted word learning. Nineteen Spanish-English bilingual children participated in a word learning task presenting words in both languages. Children learned words either in a high variability condition (in which multiple exemplars are introduced with the target word) or in a no variability condition (in which the same referent is used with the target word). Word learning was tracked over the course of the training, and retention was examined once the training was discontinued. Children's generalization of referents within and across languages was also examined. The exemplar variability effect was observed in within-language generalization trials, whereas cross-language generalization was less robust. Nevertheless, cross-language associations emerged in examining the role of language proficiency, such that semantic skills in English predicted word retention across languages. Similarly, children's propensity to code-switch during language production was positively correlated with retention of words learned in the high variability condition. The findings show that Spanish-English bilingual children may make use of exemplar variability to support word learning in different ways compared with monolinguals. The exemplar variability effect interacts with children's acquired language skills and word learning abilities at the start of the intervention. This study provides preliminary evidence from which future research can develop word learning interventions that are responsive to the needs of multilinguals. https://doi.org/10.23641/asha.19241856.

Analyzing the accuracy of detecting steganograms formed by adaptive steganographic methods when using artificial neural networks

This paper reports a comparative analysis of accuracy in the detection of steganograms formed according to adaptive steganographic methods, using steganography detectors based on common and specialized types of artificial neural networks. The results of the review of modern convolutional neural networks applied for the tasks of digital image stegoanalysis have established that the accuracy of operating the steganography detectors based on these networks is significantly compromised when processing image packets characterized by a significant variability of statistical parameters. The performance accuracy of steganography detectors based on the modern statistical model of container images maxSRMd2 has been investigated, as well as on the latest convolutional and «hybrid» artificial neural networks, in particular, GB-Ras and ASSAF networks, when detecting steganograms formed according to the adaptive steganographic methods HUGO and MiPOD. It was established that the use of the statistical model maxSRMd2 makes it possible to significantly (up to 30 %) improve the accuracy of steganogram detection in the case of analyzing those images that are characterized by a high level of natural noise. It was found that the use of the ASSAF network makes it possible to significantly (up to 35 %) reduce an error of steganogram detection compared to current steganography detectors based on the GB-Ras network and the maxSRMd2 statistical model. It was determined that the high accuracy of the ASSAF network-based steganography detector is maintained even in the most difficult case of image processing with high noise and poor filling of the container image with stegodata (less than 10 %). The results reported here are of theoretical interest for designing high-precision steganography detectors capable of working under conditions of high variability in image parameters.

An interval two-stage robust stochastic programming approach for steam power systems design and operation optimization under complex uncertainties

In this study, an interval two-stage robust stochastic programming approach is developed for the design and operation optimization of steam power systems under complex uncertainties by integrating interval-parameter programming and robust optimization into two-stage stochastic programming. The approach has the following advantages: (1) it extends the traditional two-stage stochastic programming method by enabling uncertainties presented as both probability distributions and discrete intervals to be effectively incorporated within the optimization framework, and could permit in-depth analyses of various policy scenarios when the promised policy targets are violated; (2) it could effectively reflect system risks generated by stochastic programming process, overcome the deficiency of uncontrollable risks in system and enhance the robustness of optimization, such that it is suitable for risk-aversion planners under high-variability conditions. One case of a refinery engineering project is provided for demonstrating the applicability and availability of the proposed approach. The numerical results provides ample decision-making space for decision makers to evaluate trade-offs between system economy and stability.

The impact of adaptability on the competitiveness of non-State pension funds

Subject. This article deals with the analysis of competitiveness, which is an important component of the strategic management of a non-State pension fund. Objectives. The article aims to study the existing approaches to the analysis of competitiveness, determine the role of the indicator of adaptability of competitiveness of non-State pension funds in conditions of high variability of the external environment, and formulate recommendations for drawing up criteria for the enterprise competitiveness taking into account the specifics of the activities of the funds. Methods. For the study, I used analysis, and the systems, and structural and functional approaches. Results. The article defines and classifies the factors affecting the competitiveness of non-State pension funds in modern market conditions. It substantiates the influence of the indicator of adaptability on the competitiveness of non-State pension funds. The article also proposes an approach to ranking this indicator, which can be applied regardless of the chosen method for assessing the competitiveness of non-State pension funds. Conclusions. The article concludes that the testing of the assessment of the non-State pension fund competitiveness using the author-proposed adaptability indicator helps determine the level of non-State pension fund competitiveness at the current time, track the changes, and identify the existing problems, the causes of their occurrence, and thereby ensure the conditions under which the non-State pension fund has the opportunity to promptly respond and adapt to external changes thus ensuring its stability in the market.

Confidence guides priority between forthcoming tasks

Humans can estimate confidence in their decisions, and there is increasing interest on how this feeling of confidence regulates future behavior. Here, we investigate whether confidence in a perceptual task affects prioritizing future trials of that task, independently of task performance. To do so, we experimentally dissociated confidence from performance. Participants judged whether an array of differently colored circles was closer to blue or red, and we manipulated the mean and variability of the circles’ colors across the array. We first familiarized participants with a low mean low variability condition and a high mean high variability condition, which were matched in performance despite participants being more confident in the former. Then we made participants decide in which order to complete forthcoming trials for both conditions. Crucially, prioritizing one condition was associated with being more confident in that condition compared to the other. This relationship was observed both across participants, by correlating inter-individual heterogeneity in prioritization and in confidence, and within participants, by assessing how changes in confidence with accuracy, condition and response times could predict prioritization choices. Our results suggest that confidence, above and beyond performance, guides prioritization between forthcoming tasks, strengthening the evidence for its role in regulating behavior.

ОСОБЛИВОСТІ ІНТЕРНЕТ-ПРОДАЖІВ В B2B-СЕКТОРІ

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N1 Repetition-Attenuation for Acoustically Variable Speech and Spectrally Rotated Speech.

The amplitude of the event-related N1 wave decreases with repeated stimulation. This repetition-attenuation has not previously been investigated in response to variable auditory stimuli, nor has the relative impact of acoustic vs. perceptual category repetition been studied. In the present study, N1 repetition-attenuation was investigated for speech and spectrally rotated speech with varying degrees of acoustic and perceptual category variation. In the speech condition, participants (n = 19) listened to stimulus trains consisting of either the same vowel exemplar (no variability condition), different exemplars of the same vowel (low variability condition), or different exemplars of two different vowels (high variability condition). In the rotated speech condition, the spectrally rotated counterparts of the vowels were presented. Findings show N1 repetition-attenuation in the face of acoustic and perceptual category variability, but no impact of the degree of variability on the degree of N1 attenuation. Speech stimuli resulted in less attenuation than the acoustically matched non-speech stimuli, which is in line with previous findings. It remains unclear if the attenuation of the N1 wave is reduced as a result of stimuli being perceived as belonging to perceptual categories or as a result of some other characteristic of speech.

How does a partner's motor variability affect joint action?

Motor learning studies demonstrate that an individual's natural motor variability predicts the rate at which she learns a motor task. Individuals exhibiting higher variability learn motor tasks faster, presumably because variability fosters exploration of a wider space of motor parameters. However, it is unclear how individuals regulate variability while learning a motor task together with a partner who perturbs their movements. In the current study, we investigated whether and how variability affects performance and learning in such joint actions. Participants learned to jointly perform a sequence of movements with a confederate who was either highly variable or less variable in her movements. A haptic coupling between the actors led to translation of partner's movement variability into a force perturbation. We tested how the variability and predictability of force perturbations coming from a partner foster or hamper individual and joint performance. In experiment 1, the confederate produced more or less variable range of force perturbations that occurred in an unpredictable order. In experiment 2, the confederate produced more or less variable force perturbations in a predictable order. In experiment 3, the confederate produced more or less variable force perturbations in which the magnitude of force delivered was predictable whereas the direction of the force was unpredictable. We analysed individual performance, measured as movement accuracy and joint performance, measured as interpersonal asynchrony. Results indicated that in all three experiments, participants successfully regulated the variability of their own movements. However, individual performance was worse when partner produced highly variable force perturbations in an unpredictable order. Interestingly, predictability of force perturbations offset the detrimental effects of variability on individual performance. Furthermore, participants in the high variability condition achieved higher flexibility and resilience for a wide range of force perturbations, when the partner produced predictable movements. Participants improved their joint performance with a highly variable partner only when the partner produced partially predictable movements. Our results indicate that individuals involved in a joint action selectively rely on either their own or their partner's variability (or both) for benefitting individual and joint action performance, depending on the predictability of the partner' movements.