General Uncertainty Research Articles

Совершенствование профилактики усиления социального недовольства в субъектах Российской Федерации

The article presents the results of the development of a system of proposals for limiting the activity of social discontent networks in the regions of the Russian Federation. Under the conditions of increasing global and domestic problems, as well as increasing general uncertainty in the social, economic and political spheres, the study of the activity of social discontent networks acquires strategic importance in the context of improving the basic practices of regional governance. Taking into account the results of a comprehensive study aimed at determining the scale, technologies and features of the management of discontent networks using modern methods and tools of socio-media predictive analytics, the authors propose a set of practical measures aimed at mitigating the negative effects of their activity.

Uncertainty relation based on metric-adjusted skew information with quantum memory

The uncertainty principle is one of the most essential features of quantum mechanics. Recently, uncertainty relations of skew information have been widely studied. In this paper, general and conditional uncertainty relations based on metric-adjusted skew information are put forward to study the case of the uncertainty relation with the existence of a quantum memory for the bipartite quantum system. These uncertainty relations include the product form and the sum form. The results show that the lower bounds contain two parts: one is characterizing the degree of compatibility of two measurements, and the other one is the quantum correlation between the measured system and the quantum memory. These results may play an important role in witnessing entanglement and quantum key distribution.

Covid-19 And Movie Theaters

The Entertainment Industry underwent a drastic change during the Covid-19 pandemic. The movie theater industry was especially hard hit, as it temporarily lost most of its customer base to general panic and uncertainty as a result of the pandemic. This study aims to determine what long-term effect the Covid-19 pandemic has had on the Movie Theater Industry. To assess any change in the public's view of Movie Theaters, an online survey was distributed among 11th and 12th graders in a West Texas High School, which asked participants to respond to a set of questions inquiring about past and present entertainment habits and expectations. Analysis of the responses demonstrated that the opinion and future likelihood of visiting movie theaters for entertainment had decreased notably, over the course of the pandemic, potentially giving rise to more convenient entertainment alternatives, and implying that the movie theater industry will likely need to adapt and alter the experience they provide in order to retain their audience. Further research is needed to look closer into the economic and social impacts of the Covid-19 pandemic.

PoBO: A Polynomial Bounding Method for Chance-Constrained Yield-Aware Optimization of Photonic ICs

Conventional yield optimization algorithms try to maximize the success rate of a circuit under process variations. These methods often obtain a high yield but reach a design performance that is far from the optimal value. This article investigates an alternative yield-aware optimization for photonic ICs: we will optimize the circuit design performance while ensuring a high yield requirement. This problem was recently formulated as a chance-constrained optimization, and the chance constraint was converted to a stronger constraint with statistical moments. Such a conversion reduces the feasible set and sometimes leads to an over-conservative design. To address this fundamental challenge, this article proposes a carefully designed polynomial function, called optimal polynomial kinship function, to bound the chance constraint more accurately. We modify existing kinship functions via relaxing the independence and convexity requirements, which fits our more general uncertainty modeling and tightens the bounding functions. The proposed method enables a global optimum search for the design variables via polynomial optimization. We validate this method with a synthetic function and two photonic IC design benchmarks, showing that our method can obtain better design performance while meeting a prespecified yield requirement. Many other advanced problems of yield-aware optimization and more general safety-critical design/control can be solved based on this work in the future.

Measurement uncertainty estimation technology: justification, conditions and experience of implementation in medical practice

In accordance with GOST R ISO 15189–2015 ‘Medical laboratories. Particular requirements for quality and competence’ for the report on the values of the measured value in the patient’s samples, the laboratory must determine and use the measurement uncertainty in the analytical phase. The article discusses in detail the differences between the general analytical error and measurement uncertainty. The authors have tested a typical algorithm for estimating measurement uncertainty. Mandatory conditions and an algorithm for calculating measurement uncertainty (at the analytical stage) in medical laboratories using quantitative immunochemiluminescence analysis are presented.

How do climate risk and clean energy spillovers, and uncertainty affect U.S. stock markets?

In this study, we attempt to revisit how dependent the US stock market returns are on climate change related risks (CCRR). In this regard, we use a spillover and connectedness network analysis to assess the strength of the causal effect and transmission pathway of CCRR proxies (green index, carbon price, general and climate uncertainty) on US Small Minus Big (SMB) and High Minus Low (HML) factors. Statically, our findings reveal that the indexes of the clean energy and new energy innovation industry (green companies) and climate policy uncertainty are drivers of the transmission spillover network, especially in extreme market scenarios. Dynamically, CCRR proxies act as net contributors and/or net receipts of shock spillovers over time and across the quantiles of the joint distribution. We further find that the spillover connectedness network is very sensitive to market states and that the strength of the effects of CCRR proxies are more pronounced under bust and boom markets. Our findings are beneficial for environmental investors and policymakers responsible for portfolio diversification strategies.

Bubbles all the way down? Detecting and date-stamping bubble behaviours in NFT and DeFi markets

ABSTRACT Amid surging market values and widespread regulatory discussion, NFT and DeFi markets are widely perceived as being simply speculative in nature. This paper detects the existence and dates of price bubbles in the NFT and DeFi markets by applying SADF and GSADF tests. We document that NFT and DeFi markets both exhibit speculative bubbles, with NFT bubbles being more recurrent and having higher average explosive magnitudes than DeFi bubbles. The price bubbles in the NFT and DeFi markets are highly correlated with market hype and with more general cryptocurrency market uncertainty. We do find periods where bubbles are not detected, suggesting that these markets do have some intrinsic value and should not be dismissed as simply bubbles.

Interaction between Hygroscopic Seeding and Mixed-Phase Microphysics in Convective Clouds

Abstract Intentional release of hygroscopic particles, or seeding, in convective clouds is one of the postulated methods to artificially enhance rainfall. Motivated by the general uncertainty in the underlying physics, this work employs a large-eddy simulation code together with a detailed aerosol–cloud microphysics model to investigate the conditions and processes conducive to seeding in the United Arab Emirates. Mixed-phase processes are identified as the main source for rainfall in convective clouds in this area owing to the continental aerosol characteristics and a high cloud-base altitude relatively close to the freezing level. Subsequently, our model experiments highlight the importance of mixed-phase processes in mediating the effects of hygroscopic seeding on rainfall as well. The seeding particles acted to accelerate riming by increasing the number of large droplets taken above the freezing level by the convective updrafts. The rime fraction was increased by up to 15%, which promotes the growth of the frozen hydrometeors, eventually leading to enhanced rainfall via melting. The peak enhancement in surface rainfall was up to 20%–30%, although this is almost certainly an overestimation relative to real-world operations because of the simplified description of the seeding in the model. The strongest rain enhancement was obtained with a high background aerosol concentration of approximately 4500 cm−3, whereas reduced aerosol resulted in weaker enhancement. The latter case showed an overall higher rime fraction indicating an already efficient precipitation formation process, which suppressed the seeding-induced enhancement. The conclusions of our work encourage more careful consideration of the mixed-phase processes in quantifying the hygroscopic seeding effects in continental convective clouds.

On general uncertainty data and general uncertainty variable for reliability growth analysis of major aerospace equipment

PurposeThe purpose of this paper is to explore new ways and lay a solid foundation to solve the problem of reliability growth analysis of major aerospace equipment with various uncertainty data through propose new concepts of general uncertainty data (GUD) and general uncertainty variable (GUV) and build the operation system of GUVs.Design/methodology/approachThe characteristics of reliability growth data of major aerospace equipment and the limitations of current reliability growth models have been analyzed at first. The most commonly used uncertainty system analysis methods of probability statistics, fuzzy mathematics, grey system theory and rough set theory have been introduced. The concepts of GUD and GUV for reliability growth data analysis of major aerospace equipment are proposed. The simplified form of GUV based on the “kernel” and the degree of uncertainty of GUV is defined. Then an operation system of GUVs is built.Findings(1) The concept of GUD; (2) the concept of GUV; (3) The novel operation rules of GUVs with simplified form.Practical implicationsThe method exposed in this paper can be used to integrate complex reliability growth data of major aerospace equipment. The reliability growth models based on GUV can be built for reliability growth evaluation and forecasting of major aerospace equipment in practice. The reliability evaluation example of a solid rocket motor shows that the concept and idea proposed in this paper are feasible. The research of this paper opens up a new way for the analysis of complex uncertainty data of reliability growth of major aerospace equipment. Moreover, the operation of GUVs could be extended to the case of algebraic equation, differential equation and matrix which including GUVs.Originality/valueThe new concepts of GUD and GUV are given for the first time. The novel operation rules of GUVs with simplified form were constructed.

Laser Caliper Reliability in Upper-Stem Diameter Measurements by Multiple Users

Considering the uncertainty of upper-stem diameter measurements and the fact that there are few studies on the accuracy of diameters using the Mantax Black caliper with Gator Eyes (Haglöf, Långsele, Sweden), the aim of this research is to check laser caliper reliability in upper-stem diameter measurements. The study was conducted in Parc Aventura Braşov (Romania), where a target tree was marked with visible signs at 1 m, 3 m, 5 m, 7 m, 9 m, and 13 m above the ground, and the diameters of the six sections were measured using a conventional caliper and climbing equipment. Later on, 14 forest mensurationists used a laser caliper to measure the diameters of the marked sections 13 m away from the tree, maintaining the direction of measurement. Each user performed repeated independent measurements of the upper-stem diameters, resulting in 14 data sets with 10 values for every section and a total number of 840 observations. Applying ANOVA for all the sections, we found that there are significant differences between the data sets collected by many users, and the pairwise t-test and the Benjamini-Hochberg method showed significant differences. Taking into account the analysis of the individual errors in measuring the upper-stem diameters using a laser caliper, we were able to identify the data sets affected by abnormal errors. By measuring the diameters along the stem up to 13 m above the ground using a laser caliper, one out of 2.4 measurements up to one out of approximately 1.5 was determined with an error below 2 cm. At heights above 5 m, a maximum of one out of five measurements was affected by errors above 4 cm. In addition, it was noted that there is generally a tendency to underestimate the upper-stem diameter and volume estimate when the laser caliper is used for the measurements. The absolute mean error varied between 1.46 cm and 2.52 cm along the stem and the root mean squared error varied between 1.84 cm and 3.04 cm. Nevertheless, general uncertainty about this subject remains, because if we measure upper-stem diameters without contact with the trunk, we will never know whether a single reading shows a negligible error to be used for calibrating taper equations or for increasing volume estimation accuracy. Consequently, we recommend that when used for this purpose, diameters should be measured several times, by experienced users who have proven their skill in measurements that yield smaller errors.

Comparative response of global energy firm stocks to uncertainties from the crude oil market, stock market, and economic policy

In this study, a comparative analysis of the impacts of uncertainties induced by government economic policy and the crude oil and stock markets on the stock returns of 62 energy firms is carried out across different market states. We find that economic policy uncertainty (EPU) has a stronger influence than the two market-based uncertainties. However, the oil market uncertainty (OVX) outperforms the general stock market uncertainty (VIX) for the causality-in-mean, but the reverse holds for the causality-in-variance. Stronger causal impacts are also observed during the normal and bearish market states, in that order. Finally, more significant connection is attributed to the causality-in-mean than the causality-in-variance. Thus, uncertainties, especially the one induced by the economic policy, should not be jettisoned in modeling and forecasting stock returns. The regime-switching regression model also supports the role of uncertainties in predicting the stock returns of the energy firms, with particular emphasis on adverse impact in most regimes. The fact that causality is stronger at the middle and lower quantiles also suggests the poor risk-hedging power of the stocks at the normal and bearish market states.

Robust convex optimization: A new perspective that unifies and extends

Robust convex constraints are difficult to handle, since finding the worst-case scenario is equivalent to maximizing a convex function. In this paper, we propose a new approach to deal with such constraints that unifies most approaches known in the literature and extends them in a significant way. The extension is either obtaining better solutions than the ones proposed in the literature, or obtaining solutions for classes of problems unaddressed by previous approaches. Our solution is based on an extension of the Reformulation-Linearization-Technique, and can be applied to general convex inequalities and general convex uncertainty sets. It generates a sequence of conservative approximations which can be used to obtain both upper- and lower- bounds for the optimal objective value. We illustrate the numerical benefit of our approach on a robust control and robust geometric optimization example.

Solving a running crab spider puzzle: delimiting Cleocnemis Simon, 1886 with implications on the phylogeny and terminology of genital structures of Philodromidae

BackgroundAmong the 16 Neotropical genera of Philodromidae, Cleocnemis has the most troublesome taxonomic situation. Remarkable morphological differences among several genera historically said to be related to Cleocnemis denote controversial notions and general uncertainty about the genus identity. Thus, to clarify the genus limits and contribute to the understanding of Neotropical Philodromidae, we conducted a morphological analysis, along with Bayesian Inference and Maximum Likelihood molecular phylogenetic analyses focusing on Cleocnemis and related genera of Thanatinae. All of the 14 species previously placed in Cleocnemis were studied, and eight of them included in the molecular analyses based on fragments of 28S rDNA, histone H3, 16S rDNA, and cytochrome oxidase I (COI).ResultsCleocnemis was recovered as polyphyletic. Most of its species are distributed into six lineages allocated into five morphologically recognizable groups: Group I [Cleocnemis heteropoda], representing Cleocnemis sensu stricto and two new junior synonyms, Berlandiella and Metacleocnemis; Group II [Tibelloides bryantaecomb. nov., Tibelloides punctulatuscomb. nov., Tibelloides reimoserinom. nov., and Tibelloides taquaraecomb. nov.], representing Tibelloidesgen. rev., which was not recovered as monophyletic; Group III [Fageia moschatacomb. nov., Fageia roseacomb. nov.], representing the genus Fageia; Group IV [“Cleocnemis” lanceolata]; and Group V [“Cleocnemis” mutilata, “Cleocnemis” serrana, and “Cleocnemis” xenotypa]. Species of the latter two groups are considered incertae sedis. Cleocnemis spinosa is maintained in Cleocnemis, but considered a nomen dubium. Cleocnemis nigra is considered both nomen dubium and incertae sedis. We provide a redelimitation of Cleocnemis, redescription, neotype designation, and synonymy of type-species C. heteropoda. Taxonomic notes on composition, diagnosis, and distribution for each cited genus are also provided. Phylogenetic results support the division of Philodromidae into Thanatinae new stat. and Philodrominae new stat. and suggest expansion of their current compositions. Terminology of genital structures of Philodromidae is discussed.ConclusionsOur results bring light to Cleocnemis taxonomy and enhance the understanding of the relationships within Philodromidae, especially through the assessment of neglected Neotropical taxa.

Reliability-Based Collapse Assessment of Wind-Excited Steel Structures within Performance-Based Wind Engineering

As inelastic design for wind is embraced by the engineering community, there is an increasing demand for computational tools that enable the investigation of the nonlinear behavior of wind-excited structures and subsequent development of performance criteria. To address this need, a probabilistic collapse assessment framework for steel structures is proposed in this paper. The framework is based on the integration of a high-fidelity fiber-based nonlinear structural modeling environment with a wind-tunnel-informed stochastic wind load model to perform nonlinear time history analysis. General uncertainty is propagated using a stratified sampling scheme enabling the efficient estimation of reliabilities associated with rare events. The adopted models for simulating high-fidelity nonlinear structural behavior were found, in general, to be adequate for capturing phenomena, including progressive yielding, buckling, and low-cycle fatigue, that are essential for wind induced collapse analysis. In particular, the adopted fatigue model was found to be capable of predicting damage and potential fiber/section fracture associated with non–fully reversing stress-strain cycles that are characteristic of wind loading. Through illustration on a 45-story archetype steel building, critical discussions on the types of observed collapse mechanisms, the difference between along-wind and across-wind nonlinear behavior, reliabilities associated with first yield, and collapse are presented. A probabilistic description of the residual and peak story drifts is also provided through development of fragility functions.

Summaries of Papers In This Issue

Summaries of Papers In This Issue

An adaptive unknown input approach to brain wave EEG estimation

Objective:Real time, high fidelity estimation and reconstruction of brain wave dynamics is complicated by the nonstationary and nonlinear effects of electroencephalography (EEG) measures. This work aims to introduce a novel state space architecture, which continuously updates a linear brain wave model and estimates the exogenous input to the brain wave system. This continuous update is adaptive, because the estimator continuously monitors its own performance and can modify its own parameters by a closed loop action. This highly nonlinear estimator should reconstruct the EEG measure in real time for analysis and diagnostic information. Methods:The development of this adaptive unknown input estimator focuses on treating the known phenomena of brain waves. The adaptive law accounts for nonlinearities in the EEG measure while the input estimator simultaneously accounts for the exogenous input to the system. The estimator is further robust to general process uncertainty in the plant dynamics. Results:The stability of the adaptive unknown input estimator for EEG measures is shown. This estimator is shown to outperform standard linear models, such as Kalman filtering techniques especially at points where the EEG measure changes sharply. Conclusion:This adaptive unknown input estimator reconstructs unmodeled EEG data in real time by accounting for the nonlinear brain wave dynamics. Because the adaptive law updates a linear model over time, much of the interpretability and intuition of linear state space modeling is preserved.

Bayesian emulation and history matching of JUNE.

We analyze JUNE: a detailed model of COVID-19 transmission with high spatial and demographic resolution, developed as part of the RAMP initiative. JUNE requires substantial computational resources to evaluate, making model calibration and general uncertainty analysis extremely challenging. We describe and employ the uncertainty quantification approaches of Bayes linear emulation and history matching to mimic JUNE and to perform a global parameter search, hence identifying regions of parameter space that produce acceptable matches to observed data, and demonstrating the capability of such methods.This article is part of the theme issue ‘Technical challenges of modelling real-life epidemics and examples of overcoming these’.

Generalized uncertainty relations for multiple measurements

The uncertainty relation is regarded as a remarkable feature of quantum mechanics differing from the classical counterpart, and it plays a backbone role in the region of quantum information theory. In principle, the uncertainty relation offers a nontrivial limit to predict the outcome of arbitrarily incompatible observed variables. Therefore, to pursue a more general uncertainty relations ought to be considerably important for obtaining accurate predictions of multi-observable measurement results in genuine multipartite systems. In this article, we derive a generalized entropic uncertainty relation (EUR) for multi-measurement in a multipartite framework. It is proved that the bound we proposed is stronger than the one derived from Renes et al. in [Phys. Rev. Lett. 103,020402(2009) ] for the arbitrary multipartite case. As an illustration, we take several typical scenarios that confirm that our proposed bound outperforms that presented by Renes et al. Hence, we believe our findings provide generalized uncertainty relations with regard to multi-measurement setting, and facilitate the EUR’s applications on quantum precision measurement regarding genuine multipartite systems.

Il lavoro da remoto alla prova dell’emergenza. Implicazioni sociali e organizzative

Remote work entered the lives of many workers since the first months of 2020, when the pandemic spread around the world. The use of remote work then gave rise to what has been defined as "a great social experiment". In the situation of general uncertainty that the pandemic has produced, the usual work regulation and negotiation processes in the workplace have been suspended and replaced by quick and one-sided decisions. The article, on the one hand, explores the modes and times with which organizational transformations have been absorbed by organizations and workers; on the other hand, it investigates the reactions of workers to the messages sent by their companies about the urgent reorganization imposed by the spread of pandemic. While government-level measures were often fragmented and difficult to understand, companies have often been providing a “filter to uncertainty”, through the reorganization of work and the introduction of rules for health safety. In our opinion this is the key to explain the rapidity of the adaptation observed in the transition and its substantial acceptance by the employees, who tended to give up not only conflict, but even “voice” strategies. The metaphor of war and of the presence of a common enemy, combined with the desire to feel useful, have pushed workers to do everything possible to enhance their contribution, including self-training and voluntary working overtime.

The computational relationship between reinforcement learning, social inference, and paranoia.

Theoretical accounts suggest heightened uncertainty about the state of the world underpin aberrant belief updates, which in turn increase the risk of developing a persecutory delusion. However, this raises the question as to how an agent's uncertainty may relate to the precise phenomenology of paranoia, as opposed to other qualitatively different forms of belief. We tested whether the same population (n = 693) responded similarly to non-social and social contingency changes in a probabilistic reversal learning task and a modified repeated reversal Dictator game, and the impact of paranoia on both. We fitted computational models that included closely related parameters that quantified the rigidity across contingency reversals and the uncertainty about the environment/partner. Consistent with prior work we show that paranoia was associated with uncertainty around a partner's behavioural policy and rigidity in harmful intent attributions in the social task. In the non-social task we found that pre-existing paranoia was associated with larger decision temperatures and commitment to suboptimal cards. We show relationships between decision temperature in the non-social task and priors over harmful intent attributions and uncertainty over beliefs about partners in the social task. Our results converge across both classes of model, suggesting paranoia is associated with a general uncertainty over the state of the world (and agents within it) that takes longer to resolve, although we demonstrate that this uncertainty is expressed asymmetrically in social contexts. Our model and data allow the representation of sociocognitive mechanisms that explain persecutory delusions and provide testable, phenomenologically relevant predictions for causal experiments.