A New Loss Generation Body Force Model for Fan/Compressor Blade Rows: Application to Uniform and Non-Uniform Inflow in Rotor 67

Despite advances in computational power, the cost of time-accurate flows in axial compressor and fan stages with spatially non-uniform inflow is still too high for design-stage use in industry. Body force modeling reduces the computation time to practical levels, mainly by reducing the problem to a steady one. These computations are important to determine efficiency penalties associated with non-uniform inflows. Previous studies of body force methods have, in most cases, relied on computations with the presence of the blades to calibrate loss models. In some recent studies, uncalibrated models have been used, but such models can drop off in accuracy at conditions where separation would occur on the blade surfaces. In this paper, a neural network-based loss model introduced in a recent paper by the authors is implemented for NASA rotor 67 for both uniform and non-uniform inflow conditions. For uniform inflow, the spanwise trend of entropy variation is generally captured with the new body force model. Although there are discrepancies at some span fractions, the present model generally predicts the compressor's isentropic efficiency to within 3% compared to bladed RANS simulations. For non-uniform inflow, we consider a stagnation pressure profile representative of boundary layer ingestion. The results show that the region of maximum entropy generation is captured by the present model and the prediction of isentropic efficiency penalty due to the non-uniform inflow is only 0.2 points less than that determined from bladed computations.

Digital transformation of machining workshop’s manufacturing system

The article proposes the method of intellectualization workshop’s manufacturing system. The digital technology is widely developed today so intellectualization of manufacturing system is relevant today. The use of AMT-supported manufacturing process in manufacturing system contributes to obtaining advantages in material and financial terms and reducing the cost of time resources.The use of automation technology in workshop process assumes cutbacks to its funding and ensures its digitalization. The digitalization is aimed at eliminating production errors and shortcomings already at the design stage of the production process. In consequence of this technological defects of products are reduced and the technological process is well organized. The use of an innovative approach to the development of the technological process of manufacturing products allows reducing time resources and increasing technical efficiency. Actualization of the suggested approach provided with system of computer-aided planning multiproduct technologic process. This system is developed by the authors and aimed at planning the intelligent product campaign. The presented algorithmic and software support for the formation of a rational set of control-and-measuring tools provides the development of a rational technological process for manufacturing a product and automation of the production’s technological preparation.

An Overdispersed Black-Box Variational Bayesian–Kalman Filter with Inaccurate Noise Second-Order Statistics

Aimed at the problems in which the performance of filters derived from a hypothetical model will decline or the cost of time of the filters derived from a posterior model will increase when prior knowledge and second-order statistics of noise are uncertain, a new filter is proposed. In this paper, a Bayesian robust Kalman filter based on posterior noise statistics (KFPNS) is derived, and the recursive equations of this filter are very similar to that of the classical algorithm. Note that the posterior noise distributions are approximated by overdispersed black-box variational inference (O-BBVI). More precisely, we introduce an overdispersed distribution to push more probability density to the tails of variational distribution and incorporated the idea of importance sampling into two strategies of control variates and Rao–Blackwellization in order to reduce the variance of estimators. As a result, the convergence process will speed up. From the simulations, we can observe that the proposed filter has good performance for the model with uncertain noise. Moreover, we verify the proposed algorithm by using a practical multiple-input multiple-output (MIMO) radar system.

Opportunity cost determines free-operant action initiation latency and predicts apathy

Background Apathy, a disabling and poorly understood neuropsychiatric symptom, is characterised by impaired self-initiated behaviour. It has been hypothesised that the opportunity cost of time (OCT) may be a key computational variable linking self-initiated behaviour with motivational status. OCT represents the amount of reward which is foregone per second if no action is taken. Using a novel behavioural task and computational modelling, we investigated the relationship between OCT, self-initiation and apathy. We predicted that higher OCT would engender shorter action latencies, and that individuals with greater sensitivity to OCT would have higher behavioural apathy. Methods We modulated the OCT in a novel task called the ‘Fisherman Game’, Participants freely chose when to self-initiate actions to either collect rewards, or on occasion, to complete non-rewarding actions. We measured the relationship between action latencies, OCT and apathy for each participant across two independent non-clinical studies, one under laboratory conditions (n = 21) and one online (n = 90). ‘Average-reward’ reinforcement learning was used to model our data. We replicated our findings across both studies. Results We show that the latency of self-initiation is driven by changes in the OCT. Furthermore, we demonstrate, for the first time, that participants with higher apathy showed greater sensitivity to changes in OCT in younger adults. Our model shows that apathetic individuals experienced greatest change in subjective OCT during our task as a consequence of being more sensitive to rewards. Conclusions Our results suggest that OCT is an important variable for determining free-operant action initiation and understanding apathy.

Analytical Study for Chlorpyrifos Breakdown Derivatives Qualification After Silver Vanadate (Agvo3) Nanoparticles Fragmentation Reaction

The use of pesticides in agriculture has become one of the basic necessities to meet the world's food requirements as a result of the continuous population increase. But sometimes the use of some of these pesticides in large quantities and in high concentrations poses a great danger to the environment and then to humans. Through weather factors, pesticides are transferred to the water and then harm aquatic organisms, which leads to a direct impact and harm to the fish wealth. Monitoring these compounds in the environment requires a large cost of time and money to ensure food safety. Therefore, a new method was used in this study in an attempt to get rid of these compounds in water. The silver vanadate nanoparticles (AgVO3) were synthesized using a novel method in an attempt to eliminate different groups of pesticides that had been detected in Egyptian agricultural crops and water. The effect of 0.1 g of AgVO3 nanoparticles were found on three compounds: chlorpyrifos, dimethoate and malathion. Where a concentration of these compounds, which is 0.5 mg / kg, was used and it was estimated using Exion HPLC coupled with Sciex Qtrap API 6500 + LC-MS/MS System. After 15 minutes of treatment with 0.1 g of AgVO3 nanoparticles, these compounds were undetectable on the device's chromatogram, which is regarded a short period in Affecting these compounds and changing their nature in an attempt to get rid of them. Determination of chlorpyrifos breakdown products by using mass scan of Qtrap API 6500 + LC-MS/MS System and Thermo Scientific Q Exactive Focus Orbitrap LC-MS/MS System was investigated. Supported AgVO3 NPs, according to the study, could be used in long-term environmental remediation because they can be used in aqueous solutions at room temperature without the need for additional stimulus such as UV light.

On the development of mathematical models for the reliability evaluation of aircraft operation in combat conditions

The authors of this study propose a methodological approach to modelling the reliability evaluation of aircraft operation in combat conditions. When developing recommendations on operational and strategic requirements for promising aircraft, a very important aspect is the elicitation of the requirements for their reliability, namely no-failure operation. Reliability as a parameter of any equipment should be set in the technical specifications for development together with other operational requirements in the form of reasonable quantitative indicators – reliability standards. The establishment of specific reliability standards stimulates its growth and creates the basis for rational design, taking into account the requirements of reliable operation. The analysis showed that different models can be used to simulate aircraft reliability. In this case, for example, the final values of mean time to failure (MTTF) would be different. The test results show that the methods and mathematical models used to substantiate the values of time and probability of trouble-free operation of aircraft do not fully correspond to the actual processes of changing their state during the use in the military. This is confirmed by a significant discrepancy in the values of reliability indicators implemented in practice. This was due to the fact that the acquainted mathematical models of aircraft reliability do not take into account the combat conditions in which they are supposed to operate. In addition, the reliability indicators used do not take into account possible changes (decrease) in these indicators during the period of aircraft operation. In general, the shortcomings inherent in the methods and mathematical models currently used to describe the aircraft reliability reduce the accuracy of the results obtained, and also do not quite adequately reflect the features of the corresponding process. When using different models, the cost of time to failure differs significantly. The more factors are taken into account, the greater the operating time to failure will be. This means that when designing aircraft, it is necessary to set the value of this indicator greater than indicated in the form. Taking into account additional factors complicates the model, but at the same time makes it more accurate.

Deliberation gated by opportunity cost adapts to context with urgency

The value we place on our time impacts what we decide to do with it. Value it too little, and we obsess over all details. Value it too much, and we rush carelessly to move on. How to strike this often context-specific balance is a challenging decision-making problem. Average-reward, putatively encoded by tonic dopamine, serves in existing reinforcement learning theory as the stationary opportunity cost of time. However, environmental context and the cost of deliberation therein often varies in time and is hard to infer and predict. Here, we define a non-stationary opportunity cost of deliberation arising from performance variation on multiple timescales. Estimated from reward history, this cost readily adapts to reward-relevant changes in context and suggests a generalization of average-reward reinforcement learning (AR-RL) to account for non-stationary contextual factors. We use this deliberation cost in a simple decision-making heuristic called Performance-Gated Deliberation, which approximates AR-RL and is consistent with empirical results in both cognitive and systems decision-making neuroscience. We propose that deliberation cost is implemented directly as urgency, a previously characterized neural signal effectively controlling the speed of the decision-making process. We use behaviour and neural recordings from non-human primates in a non-stationary random walk prediction task to support our results. We make readily testable predictions for both neural activity and behaviour and discuss how this proposal can facilitate future work in cognitive and systems neuroscience of reward-driven behaviour.

Seizing the Opportunity: Lifespan Differences in the Effects of the Opportunity Cost of Time on Cognitive Control

Previous work suggests that lifespan developmental differences in cognitive control reflect maturational and aging-related changes in prefrontal cortex functioning. However, complementary explanations exist: It could be that children and older adults differ from younger adults in how they balance the effort of engaging in control against its potential benefits. Here we test whether the degree of cognitive effort expenditure depends on the opportunity cost of time (average reward rate per unit time): if the average reward rate is high, participants should withhold cognitive effort whereas if it is low, they should invest more. In Experiment 1, we examine this hypothesis in children, adolescents, younger, and older adults, by applying a reward rate manipulation in two cognitive control tasks: a modified Erikson Flanker and a task-switching paradigm. We found that young adults and adolescents reflexively withheld effort when the opportunity cost of time was high, whereas older adults and, to a lesser degree children, invested more resources to accumulate reward as quickly as possible. We tentatively interpret these results in terms of age- and task-specific differences in the processing of the opportunity cost of time. We qualify our findings in a second experiment in younger adults in which we address an alternative explanation of our results and show that the observed age differences in effort expenditure may not result from differences in task difficulty. To conclude, we think that our results present an interesting first step at relating opportunity costs to motivational processes across the lifespan. We frame the implications of further work in this area within a recent developmental model of resource-rationality, which points to developmental sweet spots in cognitive control.

Apparent preferences for cognitive effort fade when multiple forms of effort and delay are interleaved in a foraging environment

Cognitive and physical effort are typically regarded as costly, but demands for effort also seemingly boost the value of prospects under certain conditions. One contextual factor that might influence the perceived value of effort is the mix of different demand types a decision maker encounters in a given environment. Here, we embedded both cognitive and physical effort in a "prey selection" foraging task, which required decision makers not only to evaluate the magnitude and delay of a focal prospective reward but also to estimate the general opportunity cost of time. In two experiments, participants encountered prospective rewards that required equivalent intervals of cognitive effort, physical effort, or unfilled delay. Monetary offers varied per trial, and the two experiments differed in whether the type of effort or delay cost was the same on every trial (between-participant manipulation, n=21 per condition), or varied across trials (within-participant manipulation, n=48). When each participant faced only one type of cost, cognitive effort persistently produced the highest acceptance rate compared to trials with an equivalent period of either physical effort or unfilled delay. We theorized that if cognitive effort were intrinsically rewarding, we would observe the same pattern of preferences when participants foraged for varying cost types in addition to rewards. Contrary to this prediction, in the within-participant experiment, an initially higher acceptance rate for cognitive effort trials disappeared over time amid an overall decline in acceptance rates as participants gained experience with all three conditions. Our results indicate that cognitive demands may reduce the discounting effect of delays, but not because decision makers assign intrinsic value to cognitive effort. Rather, the results suggest that a cognitive effort requirement might influence contextual factors such as subjective delay duration estimates, which can be recalibrated if multiple forms of demand are interleaved.