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This paper investigates the multi-level warehouse layout problem with indeterminate factors, in which the monthly demands and horizontal transportation distances are described by uncertain variables. We first consider the distribution function of the total cost for transportation. Second, two uncertain models, namely, the chance-constrained programming model and the chance-maximum programming model, are developed to lay out the multi-level warehouse under uncertainty. Some properties of the models are discussed to solve the models. The properties point out that the optimal solution to the chance-constrained programming model is equivalent to a corresponding deterministic model. Additionally, we also discuss the relation between the chance-constrained programming model and the chance-maximum programming model, which leads to an effective approach to search for the optimal solution to the chance-maximum programming model. Finally, a numerical experiment is illustrated to show the ideas of the proposed models. [ABSTRACT FROM AUTHOR]

This case report describes entropy based on statistics of the novel coronavirus infection in the Russian Federation. Entropy (in bits) determined on the basis of the ratio of patients with clinical manifestations of the disease and asymptomatic carrier state, increased from 0.7219 by April 1, 2020, when the ratio of such patients was 4/5 to 1/5, to 0.8813 by April 14, 2020, when the ratio of the two groups of patients was 7/10 to 3/10. On May 3, 2020, the ratio of the two groups leveled off, reaching % to %, so the entropy was 1.0000. An analysis of the data from April 19, 2020 to June 1, 2020 allowed us to clarify the average proportion between patients with and without symptoms, which is 14/25 to 11/25 with a certain bias towards patients with clinical manifestations of the novel coronavirus infection, and the entropy for this ratio is 0.9896. We found a transformation of entropy of the novel coronavirus infection which is explained by different proportions of patients with and without symptoms of the disease. [ABSTRACT FROM AUTHOR]

Abstract: One of the problems encountered in the design of manufacturing systems is how to arrange the machines on the surface of the workshop, which is commonly referred to as a layout problem. Such a problem has been widely investigated in the literature. Most approaches use optimization technique to determine the position of each facility, assuming that the required data is available. Unfortunately, this assumption is often unrealistic, since the study design of a workshop is obviously conducted much before it is operating, so that data related to customer demands, for example, is generally not known with enough precision. Indeed, if good forecasts about what is to be produced in the next weeks can be available, they will obviously become more and more unreliable as the considered period of time will increase, so that layout found using classical approaches can turn out not to be relevant on the medium or long term. We propose an approach to design a robust layout in a context where the certainty of the information available decreases over time, which is usually the case for real applications. We propose a resolution approach based on a fuzzy evolutionary algorithm, which includes uncertain customer demands for each product. We show how this problem can be stated as a fuzzy dynamic layout problem with growing uncertainty over time. We suggest an evolutionary algorithm with adapted operators. Their performances are first tested using 2crisp layout problems already published. Then the impact of increasing uncertainty is studied using a suggested benchmark. The results of our experiments show the importance of considering the degradation of the information for designing robust layouts. [Copyright &y& Elsevier]

Results of modeling of the Raman spectrograms of polyester fibers are presented when drawing nanoparticles of gold on them. As a result of the carried-out modeling were allocated: initial ranges, background components and the Raman ranges without background components. For simplification of procedures of mathematical modeling it was chosen 7 informative resonant peaks of the received spectrograms. Further processing was carried out only on coordinates of resonant peaks. For detection of reliability of control of nanoparticles of gold on making components of the Raman ranges when using conditions of information uncertainty in work the method of an assessment of multidimensional reliability on joint probability of crossing of dispersions of normal distributions of intensivnost of the Raman spectrograms without nano-particles and with nanoparticles on fibers depending on wave numbers on all range of their changes is offered. Values of reliability of control on making components of the Raman spectrograms were as a result received and the most exact method for monitoring procedure of nanoparticles of gold on a surface of fibers is revealed. [ABSTRACT FROM AUTHOR]

Firms with internal control weakness (ICW) problems are less likely to provide managers with timely and precise information useful for internal resource management. The real options theory implies that managers in ICW firms, faced with information uncertainty, are more likely to postpone downward adjustments of slack resources by exercising an option to wait until more information about future business prospects becomes available. Based upon this theory, we hypothesize and find that selling, general, and administrative (SG&A) costs are stickier for ICW firms than for non-ICW firms. We also find that the effect of ICW on SG&A cost stickiness is primarily attributable to internal information control problems, and becomes weakened significantly after firms remediate previously reported ICW. This impact of ICW on SG&A cost stickiness is robust to controlling for the possible influence of omitted variables, accounting for potential endogeneity in the presence of ICW, and using a firm-specific measure of cost stickiness. Our results are consistent with the prediction of the real options theory in that poor information quality associated with ICW incents managers to postpone downward adjustments of SG&A resources until the information uncertainty is resolved. [ABSTRACT FROM AUTHOR]

Purpose: Inter-organisational governance is an important enabler for information processing, particularly in relationships undergoing digital transformation (DT) where partners depend on each other for information in decision-making. Based on information processing theory (IPT), the authors theoretically and empirically investigate how governance mechanisms address information asymmetry (uncertainty and equivocality) arising in capturing, sharing and interpreting information generated by digital technologies. Design/methodology/approach: IPT is applied to four cases of public–private relationships in the Dutch infrastructure sector that aim to enhance the quantity and quality of information-based decision-making by implementing digital technologies. The investigated relationships are characterised by differing degrees and types of information uncertainty and equivocality. The authors build on rich data sets including archival data, observations, contract documents and interviews. Findings: Addressing information uncertainty requires invoking contractual control and coordination. Contract clauses should be precise and incentive schemes functional in terms of information requirements. Information equivocality is best addressed by using relational governance. Identifying information requirements and reducing information uncertainty are a prerequisite for the transformation activities that organisations perform to reduce information equivocality. Practical implications: The study offers insights into the roles of both governance mechanisms in managing information asymmetry in public–private relationships. The study uncovers key activities for gathering, sharing and transforming information when using digital technologies. Originality/value: This study draws on IPT to study public–private relationships undergoing DT. The study links contractual control and coordination as well as relational governance mechanisms to information-processing activities that organisations deploy to reduce information uncertainty and equivocality. [ABSTRACT FROM AUTHOR]

Uncertainty is a central concept to library and information science. Its physical manifestation is defined as the amount of entropy or noise in information—an idea whose origins lie in the information theory movement of the post–World War II era. Borrowing from the tenets of information theory, uncertainty is also defined as a cognitive state of uneasiness that is often described in negative terms by information behaviorists. Hence, the reduction of uncertainty is a major goal of information seeking. However, little attention is paid to positive uncertainty in information behavior research, and it may be argued that there are mechanisms or traits that can motivate information seekers to embrace uncertainty and find joy in it. In the age of a global pandemic that has been defined by uncertainty, the call for a positive take on uncertainty is more important than ever. This essay explores the mechanisms of joy in information uncertainty. [ABSTRACT FROM AUTHOR]

■■■: We study an optimal investment problem under default risk where related information such as loss or recovery at default is considered as an exogenous random mark added at default time. Two types of agents who have different levels of information are considered. We first make precise the insider's information flow by using the theory of enlargement of filtrations and then obtain explicit logarithmic utility maximization results to compare optimal wealth for the insider and the ordinary agent. [ABSTRACT FROM AUTHOR]

As organizations are increasingly challenged to find new sources of profit improvement, cost reduction becomes a top priority on the business agenda. Expectations for cost reductions are ongoing and influence both new and existing products and services. The costs for new product and service introductions are managed differently than ongoing cost reductions. Purchasing plays a central role, with different goals, in cost control for new products and services versus ongoing cost savings. This research uses a case study methodology to understand the conflict purchasing faces in managing both new product costs and ongoing cost reductions. Due to goal incongruence between new product development and ongoing savings initiatives, purchasing may act in its own best interest, rather than in the best interest of the organization or team. This is both a contracting and an information uncertainty problem, creating an opening for passive opportunism by purchasing. Thus, agency theory and information processing theory (IPT) are combined to examine how information uncertainty can be reduced and contractual goal alignment improved in these situations. The outcome of this research is to expose potential goal misalignment between new product development cost processes and ongoing cost savings, and suggest theoretically grounded methods for reducing the potential conflict. [ABSTRACT FROM AUTHOR]

Subjective time dilation is an effect discovered using the oddball paradigm, where expanding visual stimuli, but not shrinking ones, induce a prolongation of subjective temporal perception compared to static stimuli. This disparity is often seen as another demonstration of humans' evolved reaction to approaching threats, since visual expansion resembles approaching potential threats and warrants extra attention. In this study, we show that by manipulating the relative sizes of stimuli, both expanding and shrinking stimuli can induce prolongation of subjective time in an oddball paradigm. We propose an alternative explanation based on information theory, linking subjective temporal perception to entropy, an objective property of stimulus that is information uncertainty. Temporal function as a logistic function thus serves as the framework inside which content functions like information processing are able to operate coherently. [ABSTRACT FROM AUTHOR]

Probabilistic prediction models exist to reduce surprise about future events. This paper explores the evaluation of such forecasts when the event of interest is rare. We review how the family of Brier-type scores may be ill-suited to evaluate predictions of rare events, and we offer an alternative to information-theoretical scores such as Ignorance. The reduction in surprise provided by a set of forecasts is represented as information gain, a frequent loss function in machine learning training, meaning the reduction in ignorance over a baseline having received a new forecast. We evaluate predictions of a synthetic dataset of rare events and demonstrate the differences in interpretation of the same datasets depending on whether the Brier or Ignorance score is used. While the two types of scores are broadly similar, there are substantial differences in interpretation at extreme probabilities. Information gain is measured in units of bits, an irreducible unit of information, that allows forecasts of different variables to be comparatively evaluated fairly. Further insight from information-based scores is gained via a similar reliability–discrimination decomposition as found in Brier-type scores. We conclude by crystallising multiple concepts to better equip forecast-system developers and decision-makers with tools to navigate complex trade-offs and uncertainties that characterise meteorological forecasting. To this end, we also provide computer code to reproduce data and figures herein. [ABSTRACT FROM AUTHOR]

Although information theory resolves the inconsistencies (known in the form of famous enigmas) of the traditional approach of thermostatistics, its place in the corresponding literature is not what it deserves. This article supports the idea that this is mainly due to epistemological rather than scientific reasons: the subjectivity introduced into physics is perceived as a problem. Here is an attempt to expose and clarify where exactly this subjectivity lies: in the representation of reality and in probabilistic inference, two aspects that have been integrated into the practice of science for a long time and which should no longer frighten anyone but have become explicit with information theory. [ABSTRACT FROM AUTHOR]

Despite the adage "hope for the best, plan for the worst," site-level proactive plans produced by construction planning meetings show planning for the best. Planning meetings comprise both proactive and reactive discussions. Although not all the information provided during meetings is useful, limited efforts have been made to measure the amount of useful information embedded in reactive versus proactive discussions. This research aims to determine the appropriate trade-offs between the number of reactive and proactive discussions at each site-planning level. By utilizing information theory and the Chow–Liu tree, the authors demonstrate to what extent and in what order meeting participants should address site-planning level reactivity and proactivity to obtain more useful information effectively through a case study. The results indicate that proactive planning discussions improve percent plan complete (PPC) 20% more than reactive ones in general. However, missing information due to not undertaking reactive planning could put PPC at greater risk (2% more) than proactive planning, implying high uncertainty at the lowest level of site planning. This research contributes to the body of knowledge by innovatively developing an information theory-based approach to quantify the uncertainty in proactive and reactive discussions for construction planning. Using the information theory approach, this research enables project managers to quantify the uncertainty in reactive and proactive planning discussions. By adjusting the framework and parameters, the approach can have general applications in planning meetings. [ABSTRACT FROM AUTHOR]

Controlling the time evolution of a probability distribution that describes the dynamics of a given complex system is a challenging problem. Achieving success in this endeavour will benefit multiple practical scenarios, e.g., controlling mesoscopic systems. Here, we propose a control approach blending the model predictive control technique with insights from information geometry theory. Focusing on linear Langevin systems, we use model predictive control online optimisation capabilities to determine the system inputs that minimise deviations from the geodesic of the information length over time, ensuring dynamics with minimum "geometric information variability". We validate our methodology through numerical experimentation on the Ornstein–Uhlenbeck process and Kramers equation, demonstrating its feasibility. Furthermore, in the context of the Ornstein–Uhlenbeck process, we analyse the impact on the entropy production and entropy rate, providing a physical understanding of the effects of minimum information variability control. [ABSTRACT FROM AUTHOR]

The structure of fibrous assemblies is highly complex, being both random and regular at the same time, which leads to the complexity of its mechanical behaviour. Using algorithms such as machine learning to process complex mechanical property data requires consideration and understanding of its information principles. There are many different methods and instruments for measuring flexible material mechanics, and many different mechanics models exist. There is a need for an evaluation method to determine how close the results they obtain are to the real material mechanical behaviours. This paper considers and investigates measurements, data, models and simulations of fabric's low-stress mechanics from an information perspective. The simplification of measurements and models will lead to a loss of information and, ultimately, a loss of authenticity in the results. Kolmogorov complexity is used as a tool to analyse and evaluate the algorithmic information content of multivariate nonlinear relationships of fabric stress and strain. The loss of algorithmic information content resulting from simplified approaches to various material measurements, models and simulations is also evaluated. For example, ignoring the friction hysteresis component in the material mechanical data can cause the model and simulation to lose more than 50% of the algorithm information, whilst the average loss of information using uniaxial measurement data can be as high as 75%. The results of this evaluation can be used to determine the authenticity of measurements and models and to identify the direction for new measurement instrument development and material mechanics modelling. It has been shown that a vast number of models, which use unary relationships to describe fabric behaviour and ignore the presence of frictional hysteresis, are inaccurate because they hold less than 12% of real fabric mechanics data. The paper also explores the possibility of compressing the measurement data of fabric mechanical properties. [ABSTRACT FROM AUTHOR]

Learning from datasets that suffer from differences in absolute frequency of classes is one of the most challenging tasks in the machine learning field. Efforts have been made to tackle the problem of class imbalance by providing solutions at data and algorithmic levels. In these cases, in order to categorize the solutions according to problem class imbalance level and to obtain meaningful and consistent interpretations from the experiments, it is essential to be able to quantify the extent of dataset imbalance. A competent scale to summarize the severity of data inter-class imbalance, requires to meet at least the following three conditions: (1) the ability to calculate the imbalance extent for both binary and multi-class datasets, (2) output within a definite and fixed range of values, (3) being correlated with the performance of different classifiers. Nevertheless, none of the scales introduced so far satisfy all the enumerated requirements. In this study, we propose an informative scale called imbalance factor (IF) based on information theory, which, independent of the number of data classes, quantifies dataset imbalance extent in a single value in the range of [0, 1]. Besides, IF offers various limiting cases with different growth rates according to its α order. This property is critical as it can settle the possibility of having the same extent for distinct distributions. Eventually, empirical experiments indicate that with an average correlation of 0.766 with the classification accuracies over 15 real datasets, IF is remarkably more sensitive to class imbalance changes than other previous scales. [ABSTRACT FROM AUTHOR]

This research aims to determine the appropriate level of effort required for each step of the Observe–Plan–Do–Check–React (OPDCA) cycle to improve the workflow reliability of a construction project. Empirical data on detailed weekly meeting minutes over 18 weeks and the planned and actual starting and finish times of 475 activities were collected from a bridge construction case project. The information theory approach was utilized to measure the information gained from discussions pertaining to the OPDCA cycle during weekly planning meetings. Cooperative game theory and the Shapley notation of fairness were used to compute the contribution of each OPDCA step to workflow reliability. Results showed that "Observe", "Plan", "Do", "Check", and "reAct", contributed 18%, 23%, 23%, 24%, and 12% to observed variations in workflow reliability measured by the percent plan complete (PPC). Also, findings revealed that synergy exists between the "Check" step and other steps in the OPDCA cycle. The methodology developed in this paper has potential implications for engineering managers. The method can be generalized to help project managers find the balance between planning and control efforts to improve workflow. It also provides proven techniques for continuous improvement during project execution to facilitate project success. Furthermore, at the organizational level, the developed method can help higher-level managers make informed investment decisions for employees' training and development to improve performance in future projects. [ABSTRACT FROM AUTHOR]

SUMMARY Trust model plays an important role in ensuring the security of interactions in peer-to-peer (P2P) systems where a peer's trust evaluation depends on the interaction experience of its own and recommendation information from other peers. However, current trust models have limitations in solving not only the issues of time efficiency of direct interaction information but also the reliability and inconsistency of recommendation information. In this paper, we propose a Dempster-Shafer evidence theory based trust model (ETTM) for P2P systems. The primary goal of ETTM is to be able to address information uncertainty and conflicting recommendation problems in a reputation-based P2P environment. To make D-S theory fits into P2P applications, we creatively revise the combination rules and achieve greatly improved results. To further improve the accuracy and performance, ETTM filters out noisy referrals if they are not compatible with most other evidence. In addition, a feedback-based probabilistic searching algorithm is proposed to find the referrals with improved searching success rate and lowered network traffic. Experimental results show ETTM has a clear advantage in aggregating recommendation information. Moreover, ETTM is more robust and can generate a higher successful transaction rate than some other existing frameworks. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Background: The Michaelis-Menten constants (K(m) and V(max)) operated by the Information Theory were employed for detection of breast cancer., Methods: The rate of enzymatic hydrolysis of fluorescein diacetate (FDA) in live peripheral blood mononuclear cells (PBMC), derived from healthy subjects and breast cancer (BC) patients, was assessed by measuring the fluorescence intensity (FI) in individual cells under incubation with either the mitogen phytohemagglutinin (PHA) or with tumor tissue, as compared to control. The data were processed by the Information Theory to determine the parameters and test conditions, which can best discriminate between the different groups. The normalized mutual information (uncertainty coefficients) was used as the measure of correlation/discrimination., Results: An estimated general correlation was established between the K(m)/V(max) parameters and the examined patterns in the different bioassays. The information-theoretical analysis revealed the relative diagnostic value of each parameter., Conclusion: It was found that K(m) and V(max) as individual parameters show relatively low correlations with the presence or absence of disease, yet in combination often provide a good diagnostic measure. Based on the relative diagnostic values of each parameter, a diagnostic decision making rule was constructed. The diagnostic rule provided correct diagnosis for 37 out of 40 subjects.

This paper examines whether credit ratings convey information about the firm's future earnings to the capital markets. Using the future earnings response coefficient methodology, we find that the current stock returns of rated firms reflect more future earnings than do the stock returns of non-rated firms. We also find that the market reflect more future earnings in current returns for higher-rated firms. In addition, we present evidence that returns impound future earnings to a greater extent after a ratings initiation or upgrade. We empirically eliminate the possibility that our findings are driven by earnings smoothing, market liquidity or omitted default risk factors associated with ratings. Our results are robust to controlling for potential omitted variables, endogeneity bias, loss versus profit firms, and serial correlation of error terms. Overall, the evidence suggests that credit ratings help disseminate private information to reduce information uncertainty about the firm's future profitability among market participants. [ABSTRACT FROM AUTHOR]

In this paper, we find that price and earnings momentum are pervasive features of international equity markets even when controlling for data-snooping biases. For Europe, we show price momentum to be subsumed by earnings momentum on an aggregate level. However, this rationale can hardly be sustained on a country level. Also, the above explanation is confined to certain time periods in the USA. Since we cannot establish a decent relation between momentum and macroeconomic risks, we suspect a behavior-based explanation to be at work. In fact, we find momentum profits to be more pronounced for portfolios characterized by higher information uncertainty. Hence, the momentum anomaly may well be rationalized in a model of investors underreacting to fundamental news. Finally, we find that momentum works better when limited to stocks with high idiosyncratic risk or higher illiquidity, suggesting that limits to arbitrage deter rational investors from exploiting the anomaly. [ABSTRACT FROM AUTHOR]

Abstract Task parameters still affect reaction times even when all necessary information for executing an action is presented prior to a Go signal to execute the action. Hypotheses in terms of short-term memory capacity, residual activation, and a separate motor-programming stage have been suggested to explain what can and cannot be prepared prior to a delayed Go signal. To test these hypotheses, we used a delayed response task, in which participants were to initiate a movement at onset of an imperative Go signal following the target stimulus. Across Experiments 1–3 we varied task properties including stimulus type, information uncertainty and response complexity, respectively, while controlling other factors. We also varied the time available to process the response by randomly varying the interval between onset of the target and the Go signal (i.e., the stimulus onset asynchrony, or SOA). If the preparation process is completed before initiation, the examined factor should display a strong interaction with SOA, with its effect disappearing at long SOAs. Our results showed strong, weaker, and no interaction patterns for the three factors, respectively, favoring the separate stage hypothesis, according to which response preparation is separated into steps to arrange kinematic specifications into muscle-controllable terms. Highlights • 3 experiments required a signaled response to be withheld until a go signal onset. • An effect of stimulus type on RT at short delays disappeared at long delays. • An effect of set size on RT decreased but was still significant at long delays. • An effect of response complexity on RT did not interact with delay length. • Response selection but not response initiation can be preplanned. [ABSTRACT FROM AUTHOR]

Simple Summary: According to CDC, autism spectrum disorder (ASD) was diagnosed in one of every 36 children in the United States in 2020. Over 2000 genes have been identified as altered in ASD, yet a coherent model of ASD etiology is lacking. Here, we present a novel hypothesis of ASD origins based on common gene network features involving an interplay of glial and neuronal cells in the developing brain, starting during fetal development. We report statistical findings with implications for understanding the causes of ASD, early detection, and the development of new treatments. Fetal neuroinflammation and prenatal stress (PS) may contribute to lifelong neurological disabilities. Astrocytes and microglia, among the brain's non-neuronal "glia" cell populations, play a pivotal role in neurodevelopment and predisposition to and initiation of disease throughout lifespan. One of the most common neurodevelopmental disorders manifesting between 1–4 years of age is the autism spectrum disorder (ASD). A pathological glial–neuronal interplay is thought to increase the risk for clinical manifestation of ASD in at-risk children, but the mechanisms remain poorly understood, and integrative, multi-scale models are needed. We propose a model that integrates the data across the scales of physiological organization, from genome to phenotype, and provides a foundation to explain the disparate findings on the genomic level. We hypothesize that via gene–environment interactions, fetal neuroinflammation and PS may reprogram glial immunometabolic phenotypes that impact neurodevelopment and neurobehavior. Drawing on genomic data from the recently published series of ovine and rodent glial transcriptome analyses with fetuses exposed to neuroinflammation or PS, we conducted an analysis on the Simons Foundation Autism Research Initiative (SFARI) Gene database. We confirmed 21 gene hits. Using unsupervised statistical network analysis, we then identified six clusters of probable protein–protein interactions mapping onto the immunometabolic and stress response networks and epigenetic memory. These findings support our hypothesis. We discuss the implications for ASD etiology, early detection, and novel therapeutic approaches. We conclude with delineation of the next steps to verify our model on the individual gene level in an assumption-free manner. The proposed model is of interest for the multidisciplinary community of stakeholders engaged in ASD research, the development of novel pharmacological and non-pharmacological treatments, early prevention, and detection as well as for policy makers. [ABSTRACT FROM AUTHOR]

We present a number of upper and lower bounds for the total variation distances between the most popular probability distributions. In particular, some estimates of the total variation distances in the cases of multivariate Gaussian distributions, Poisson distributions, binomial distributions, between a binomial and a Poisson distribution, and also in the case of negative binomial distributions are given. Next, the estimations of Lévy–Prohorov distance in terms of Wasserstein metrics are discussed, and Fréchet, Wasserstein and Hellinger distances for multivariate Gaussian distributions are evaluated. Some novel context-sensitive distances are introduced and a number of bounds mimicking the classical results from the information theory are proved. [ABSTRACT FROM AUTHOR]

Accessibility to smart devices provides opportunities for side-channel attacks (SCAs) on artificial intelligent (AI) models in the intelligent Internet of Things (IoT). However, the existing literature exposes some shortcomings: 1) incapability of quantifying and analyzing the leaked information through side channels of the intelligent IoT and 2) inability to devise efficient and accurate SCA algorithms. To address these challenges, we propose a side-channel fuzzy analysis-empowered AI model extraction attack in the intelligent IoT. First, the integrated AI model extraction framework is proposed, including power trace-based structure, execution time-based metaparameters, and hierarchical weight extractions. Then, we develop the information theory-based analysis for the AI model extraction via SCA. We derive a mutual information-enabled quantification method, theoretical lower/upper bounds of information leakage, and the minimum number of attack queries to obtain accurate weights. Furthermore, a fuzzy gray correlation-based multiple-microspace parallel SCA algorithm is proposed to extract model weights in the intelligent IoT. Based on the established information-theoretic analysis model, the proposed fuzzy gray correlation-based SCA algorithm obtains high-precision AI weights. Experimental results, consisting of simulation and real-world experiments, verify that the developed analysis method with the information-theoretic perspective is feasible and demonstrate that the designed fuzzy gray correlation-based SCA algorithm is effective for AI model extraction. [ABSTRACT FROM AUTHOR]

The continuous expansion of the construction scale of smart city has reconstructed the urban information pattern. How to maintain the stability of information security while giving full play to the role of information sharing is a practical problem that must be solved for the sustainable development of smart city. Based on the information ecology theory, this paper construct the smart city information security risk evaluation system from six aspects. Then, zGT2FSs is established based on type-2 fuzzy set theory and IAA method, which fully considers the internal and external uncertainty of expert decision-making. According to the calculation results, the key influencing factors of information security risk of smart city are analyzed to provide suggestions and guidance for the formulation of information security control in the process of smart city construction in China. [ABSTRACT FROM AUTHOR]

The uncertainty of information is an important issue that must be faced when dealing with decision-making problems. Randomness and fuzziness are the two most common types of uncertainty. In this paper, we propose a multicriteria group decision-making method based on intuitionistic normal cloud and cloud distance entropy. First, the backward cloud generation algorithm for intuitionistic normal clouds is designed to transform the intuitionistic fuzzy decision information given by all experts into an intuitionistic normal cloud matrix to avoid the loss and distortion of information. Second, the distance measurement of the cloud model is introduced into the information entropy theory, and the concept of cloud distance entropy is proposed. Then, the distance measurement for intuitionistic normal clouds based on numerical features is defined and its properties are discussed, based on which the criterion weight determination method under intuitionistic normal cloud information is proposed. In addition, the VIKOR method, which integrates group utility and individual regret, is extended to the intuitionistic normal cloud environment, and thus the ranking results of the alternatives are obtained. Finally, the effectiveness and practicality of the proposed method are demonstrated by two numerical examples. [ABSTRACT FROM AUTHOR]

(Norraw) modal propositional logic (MCP) tries to be a strict model for quantum and information theory, but it has serious difficulties in syntax, semantics, and metaphysics. According to the guiding definition of minimal hidden variables, this study presents four equivalences between MCP and classical propositional logic (CP). It concludes that MCP is CP containing minimal syntactic hidden variables, modal axiom represents the classification of CP formulas, and possible world is CP formula as assignment background, which establishes a simpler and unified basis for quantum information theory. The black modal of classical propositional logic (BCP), as an assisted discovery method, reveals that modal nature is actually to express the interdependence between things and their environment in a mathematical way, which can express the holism, dialectics, and uncertainty of metaphysics, and use mark hiding (e.g., f (x) is a function of x, f () is the functional expression, functional-e for short, and "□" is, in fact, a cluster of functional-es in which superscript and subscript are hidden) that does not affect the effectiveness of reasoning to express a more simplified, efficient, and consistent artificial intelligence. [ABSTRACT FROM AUTHOR]

Purpose: Drawing on transaction cost economics (TCE) theory and organizational information processing theory (OIPT), this study investigates how the alignments between the characteristics of service (i.e. task complexity and measurement ambiguity) and governance mechanisms (i.e. contract specificity and monitoring) can affect service performance. Design/methodology/approach: The paper uses a rigorously designed survey to collect data from professionals who manage service outsourcing contracts in various industries. The respondent pool consists of randomly selected members of the Institute of Supply Management (ISM). The authors' research question is analyzed using 261 completed and useable responses. Structural equation modeling is adopted to examine the data and test the proposed hypotheses. Findings: The authors find that both contract specificity and monitoring have a positive impact on supplier performance. Further, for high task complexity services, contract specificity is more effective than monitoring, and for high measurement ambiguity services, the opposite is true. Moreover, the effect of contract specificity is mediated by monitoring. Practical implications: Service outsourcers should use both contract specificity and monitoring in governing outsourced services and know that the former depends on the latter during execution. Facing resource constraints, they can prioritize crafting detailed contract provisions over implementing monitoring for highly complex services but consider monitoring as the primary governance tool in services whose outcomes are difficult to measure. Originality/value: This study is the first to couple TCE with OPIT and consider the nature of outsourced services in the choice of governance mechanisms and empirically test the simultaneous effects of contract specificity and monitoring in the context of service outsourcing. [ABSTRACT FROM AUTHOR]

Ecological and evolutionary processes show various population dynamics depending on internal interactions and environmental changes. While crucial in predicting biological processes, discovering general relations for such nonlinear dynamics has remained a challenge. Here, we derive a universal information-theoretical constraint on a broad class of nonlinear dynamical systems represented as population dynamics. The constraint is interpreted as a generalization of Fisher's fundamental theorem of natural selection. Furthermore, the constraint indicates nontrivial bounds for the speed of critical relaxation around bifurcation points, which we argue are universally determined only by the type of bifurcation. Our theory is verified for an evolutionary model and an epidemiological model, which exhibit the transcritical bifurcation, as well as for an ecological model, which undergoes limit-cycle oscillation. This work paves a way to predict biological dynamics in light of information theory, by providing fundamental relations in nonequilibrium statistical mechanics of nonlinear systems. In evolutionary theory, Fisher's fundamental theorem of natural selection establishes a simple relation between the variance of the growth rate and the temporal increase in the average growth rate. Here, the authors extend the theorem based on statistical physics and information theory and show that the speed in dynamical systems describing nonlinear population dynamics is bounded by Fisher information with universal limit exponents only depending on the kind of bifurcation and not on the specific systems. [ABSTRACT FROM AUTHOR]