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Intelligence scholars are drawing on behavioural decision theory to improve decision-making under risk and uncertainty in intelligence and counterintelligence. Such an undertaking is essentially lacking without the Austrian school's concepts of knowledge, discovery, (entrepreneurial) judgement, ignorance, rational calculation and, more generally, its analysis of human action in the face of true uncertainty. Decision theory, both orthodox and behavioural, depicts decision rather narrowly as a prioritisation task undertaken within a delineated problem space where the probabilities "sum to one". From such a perspective, certain perennial challenges in intelligence and counterintelligence appear resolvable when in fact they are not, at least not when approached from the usual direction. We explain how Austrian concepts can complement efforts to improve intelligence decision-making. We conclude that the future strategic value of intelligence analysis is located beyond information acquisition, however fast and however vast. Intelligence agencies have no price signals to help them determine how much intelligence to produce. And governments have no price signals to moderate their appetites for the intelligence product. Ultimately, those agencies that recognise the implications of intelligence agencies as non-price institutions and adapt their decision-making processes may find that they have the upper hand over their rivals. [ABSTRACT FROM AUTHOR]

With the evolution of complexity in decision-making problems, information is increasingly characterized by dynamic updates. Therefore, we introduce the concept of dynamic probabilistic linguistic term set (DPLTS) to solve the multi-criteria group decision-making (MCGDM) problems with dynamic probabilistic linguistic information characterized by multiple consecutive time points. DPLTS can help experts provide theoretical basis for dynamic decision-making problems. By combining real-time data with analytical models, it can provide decision-makers with effective strategies. Then we present some basic operations and aggregation operators for DPLTSs. The belief interval (BI) interpretation based on Dempster-Shafer evidence theory and BI measure of DPLTSs are also presented to reduce information uncertainty. After that, we construct two MCGDM methods, including the aggregation-based method and the BI method. We use the concept of time degree to reflect the cognition of the importance of multiple consecutive time points for decision-makers and obtain the time weight vector with the help of a nonlinear programming model. Finally, the proposed methods are applied to a case of pharmaceutical innovation capacity evaluation. By the sensitivity analysis of time degree, the characteristics of the methods are summarized and compared, then the effectiveness of the methods is proved. [ABSTRACT FROM AUTHOR]

This paper presents a rich class of information theoretical measures designed to enhance the accuracy of portfolio risk assessments. The Mean-Variance model, pioneered by Harry Markowitz, revolutionized the financial sector as the first formal mathematical method to risk-averse investing in portfolio optimization theory. We analyze the effectiveness of this with the models that replace expected portfolio variance with measures of information (uncertainty of the portfolio allocations to the different assets) and five major practical issues. The empirical analysis is carried out on the historical data of Indian financial stock indices by application of portfolio optimization problem with information measures as the objective function and constraints derived from the return and the risk. Our findings indicate that the information measures with parameters can be used as an adequate supplement to traditional portfolio optimization models such as the mean-variance model. [ABSTRACT FROM AUTHOR]

Intuitionistic fuzzy sets have been widely studied and applied as an important means of dealing with information uncertainty. However, the existing intuitionistic fuzzy sets and their extension methods are limited and single in their fuzzy spatial representation of information. Under this environment, this paper proposes a new generalized fuzzy set, called qth Rung Root Orthopair Fuzzy Sets (q-RROFS). Since the q-RROFS can adjust the range of fuzzy space expression by the parameter q, it is superior to intuitionistic fuzzy sets, SR-fuzzy sets, and CR-fuzzy sets. We give some definitions and properties of q-RROFS and give their proofs. Under the q-RROFS, we give its operations and properties and introduce four new weighted aggregation operators, namely, qth Rung Root Orthopair Fuzzy-weighted average operator (q-RROFWA), qth Rung Root Orthopair Fuzzy-weighted geometric operator (q-RROFWG), qth Rung Root Orthopair Fuzzy-weighted power average operator (q-RROFWPA), and qth Rung Root Orthopair Fuzzy-weighted power geometric operator (q-RROFWPG). We discuss the properties of these operators in detail and follow the proof procedure. Then, we give a Multi-criteria decision-making approach under q-RROFS. Finally, we illustrate the effectiveness and applicability of the proposed methodology through practical application examples and comparisons with other methods. [ABSTRACT FROM AUTHOR]

This paper studies the effects of reducing information uncertainty on matching outcomes in a college choice setting. Turkey reformed its college admissions in 1999, changing the preference submission process for 4-year programs from pre-exam to post-exam submission, both under the constrained Deferred Acceptance mechanism. A conceptual framework illustrates that the reform changed matching outcomes in two ways: (i) fewer programs with unfilled seats, (ii) more assortative matching between students and programs, i.e., higher cutoff ranks for more highly ranked programs. Results from a difference-in-differences strategy confirm such predictions and find that the reform led to less under-capacity among lower-ranked programs and stronger assortative matching between high-achieving students and highly ranked programs. Moreover, we find some suggestive evidence that the post-exam submission may have led to fewer enrolled students overall but more female students in 4-year programs. [ABSTRACT FROM AUTHOR]

The neural network optimal control strategy based on a fuzzy differential game is proposed for the tripartite UAV confrontation systems consisting of the attackers, defenders, and targets. Firstly, the tripartite UAV mutual confrontation model is constructed and a nonlinear differential control system is established. Secondly, combining the fuzzy evaluation method and differential game theory, the tripartite UAV are divided into two parts of the confrontation game: attackers-defenders and attackers-targets. The optimal control strategies for the attackers, defenders and targets parties are derived separately. Then, the tripartite UAV game model is considered to be difficult to solve directly. The evaluation neural network is introduced to approximate the optimal value function using an adaptive dynamic programming method. The convergence of the evaluation neural network weights and the stability of the nonlinear differential control system are proved by using Lyapunov stability theory. Finally, the effectiveness of the tripartite UAV confrontation game control strategy designed in this paper is verified by simulation. [ABSTRACT FROM AUTHOR]

Non-financial information disclosed by firms is an important channel for investors and analysts to understand the operating conditions of firms and a major source of information other than financial data. This study examines the impact of disclosure of forward-looking non-financial information content by listed companies on the information efficiency of the capital market. Using data from A-share listed companies in China's Shanghai and Shenzhen markets from 2007 to 2022, it is found that forward-looking non-financial information content has a positive impact on improving capital market information efficiency, while analysts' attention plays a moderating role. The mechanism study finds that non-financial information disclosure with forward-looking content affects the accuracy of analysts' earnings forecasts, the type of audit opinion, and the degree of financialisation of firms, which in turn have different impacts on the information efficiency of the capital market. The heterogeneity test finds that disclosure of non-financial information with forward-looking content will more significantly improve the information efficiency of the capital market if the listed company receives a high level of media attention, as well as listed companies with higher quality of internal control. This study explores the forward-looking characteristics of non-financial information in depth, enriches the study of information efficiency in the capital market, and provides theoretical suggestions for the content and direction of corporate non-financial information disclosure. [ABSTRACT FROM AUTHOR]

The robustness of components/products/systems is a concern for both the company that sells them and the consumers who use them. Failure Modes and Effect Analysis (FMEA) which has evolved over 60 years, is a powerful and effective risk identification tool for both design and manufacturing, often combined with Multiple Criteria Decision-Making methods to increase its utility. However, most previous FMEA studies have neglected the interdependence of the failure modes. To address this issue, this study develops a novel method, the TEchnique for Defining the Influential Relationship of Failure Modes, to determine the risk prioritization of the failure modes. The interaction among failure modes is effectively identified and incorporated into the model. The primary analysis procedure can be divided into three parts: (i) the assessment of the initial risk scores of the failure modes; (ii) the definition of the degree of interdependence among the failure modes; and (iii) the integration of the final risk scores based on dependency. The model is used to conduct a case study for a multinational manufacturer of switch mode power supplies as a demonstration case. The practicality of the proposed model was validated through model comparisons and expert feedback. The results show that "an unstable output voltage", "no power-good signal", "recession in efficiency", "burning out of components", and "the cooling fan does not operate" are the top five risky failure modes. The research findings can be used as the basis for improvement strategies in R&D design and by quality assurance departments to enhance product reliability. [ABSTRACT FROM AUTHOR]

Improving customer satisfaction is the key factor in enhancing the core competitiveness of hotels, as higher customer satisfaction can lead to long-term benefits such as a positive reputation, customer loyalty, and sustained profitability. Multi-source user-generated content (UGC) can provide high-quality and sufficient information for improving customer satisfaction; however, related research is limited. Therefore, a method considering multi-source UGC to improve customer satisfaction is proposed in this paper. First, the service attributes of the hotels that customers care about are obtained from multi-source UGC. Then, evaluation information is obtained by processing multi-source UGC based on the heuristic-systematic model, and the credibility of the evaluation information is measured. Furthermore, evaluation information is combined based on evidence theory to estimate the importance and performance of hotel service attributes. Finally, impact asymmetry-gap analysis (IAGA) is proposed to generate customer satisfaction improvement strategies for different attributes. The application of the method is illustrated using data from actual hotels. [ABSTRACT FROM AUTHOR]

Unmanned Aircraft Systems (UASs) offer many benefits in wildfire monitoring when compared to traditional wildfire monitoring technologies. Wildfire spread is a highly heterogeneous process with non-uniform spreading speed and fireline intensity in both space and time. When planning the path of a UAS for monitoring a dynamically growing wildfire, it is important to consider the uneven spread of the fire because different parts of the fire boundary would need different levels of monitoring attention. This paper presents a real-time path planning algorithm for a UAS to autonomously monitor the perimeter of a spreading wildfire with uneven importance. The proposed path planning algorithm allows the UAS to focus on the most active regions of a wildfire while still covering the entire fire perimeter. The design and implementation of this algorithm are described, and an in-depth analysis of the performance of the algorithm is provided. Experiment results based on simulated wildfires demonstrate the effectiveness and robustness of the proposed algorithm for monitoring dynamic wildfires. [ABSTRACT FROM AUTHOR]

We draw on (Merton, The Journal of Finance 42:483-510, 1987) to develop predictions for the benefits of voluntary disclosures by firms pursuing an initial public offering (IPO) prior to when they begin providing regulated financial information via their IPO prospectus. We find that voluntarily issuing press releases and attending investor and industry conferences are common disclosure activities prior to filing the IPO prospectus. Consistent with these disclosures enhancing investor awareness, we find positive associations with subsequent information acquisition by prospective investors and the financial press during the IPO filing period. These relations remain significant in tests exploiting the passage of the 2005 Securities Offering Reform as a source of variation in issuers' ability to provide disclosures designed to attract attention from prospective investors. Consistent with pre-prospectus disclosures enhancing the visibility of the firm, we find that, while direct associations between pre-prospectus voluntary disclosures and IPO pricing are limited, there are significant indirect effects operating through filing-period information acquisition by prospective investors and media coverage. We find no evidence that issuers' disclosure activities serve as hype, as the IPO price impact does not reverse post-offering. Overall our evidence is consistent with pre-prospectus voluntary disclosures benefiting issuers by enhancing awareness, which leads to improvements in firm valuations. [ABSTRACT FROM AUTHOR]

ESG performance has become vital for business in recent years. Consumers, stakeholders and investors alike seek companies that can show that they are environmentally and socially responsible, especially in periods of uncertainty. Using a new measure of uncertainty, the Climate Policy Uncertainty Index developed by Gavriilidis (Measuring climate policy uncertainty, 2021), this study investigates environmental, social and governance (ESG) performance, firm performance and carbon dioxide emission performance in the context of major developments related to climate policy and events. Further, the study provides insights into how ESG performance moderates the impact of climate policy uncertainty on firm performance and carbon dioxide emission performance. Finally, this paper examines how investor sentiment influences ESG performance during periods of high uncertainty. Based on a large sample of 2640 firms (6462 firm-year observations) from the US Fortune 1000 list covering the years 2008–2018, the empirical results demonstrate that climate policy uncertainty positively affects ESG performance and negatively affects firm performance and carbon dioxide emission performance. In addition, we find that firms enhance their ESG performance when investors are pessimistic, especially during periods of high uncertainty. Finally, we show that ESG performance does not moderate the effects of climate policy uncertainty on firm performance and carbon dioxide emission performance. These findings may help managers and policymakers to boost ESG performance to reap the benefits of ESG activities during periods of uncertainty related to changes in climate policy. Further, the results identify a promising channel via investor power that the government and ESG reporting advocates can utilise to encourage firms to act responsibly during periods of uncertainty. [ABSTRACT FROM AUTHOR]

Risk assessment and categorization of terrorist attacks can assist enhance awareness of terrorism and give crucial information support for anti-terrorism efforts. This study utilizes quantitative approaches for the risk assessment and categorization of terrorist attacks. A total of 210,454 terrorist attacks that occurred worldwide from 1970 through 2020 were collected in the Global Terrorism Database, and 22 indicators related to the risk of terrorist attacks were selected. Then, the moment estimation theory and four comprehensive evaluation models were utilized to identify the top 10 riskiest terrorist attacks in the world. Furthermore, the five clustering analysis methods and three evaluation criteria were performed for the risk categorization of terrorist attacks, and the visual analysis was carried out using the kernel density estimation method. The research results have identified the top 10 riskiest global terrorist attacks, which were led by the September 11 terrorist attack event, along with their downward counterfactual events. The spatial distribution of global terrorist attack risk is primarily composed of four "turbulent cores" in the region of Central Asia, Middle East & North Africa, South Asia, and Central America & Caribbean. This study also provided insights and recommendations for anti-terrorism efforts. It has realized the risk assessment and categorization of terrorist attacks, aiding in the swift identification of its risk levels, and holds immense significance for safeguarding global national security and societal stability under new circumstances. [ABSTRACT FROM AUTHOR]

The reliability of medical equipment is paramount for surgical success. Manufacturers are continuously exploring risk assessment and improvement strategies to prevent equipment malfunctions during surgery. Failure mode and effects analysis (FMEA) can primarily be divided into two types: DFMEA (Design FMEA) and PFMEA (Process FMEA). Traditional FMEA serves as a practical tool for identifying and prioritizing key failure modes (FMs). However, past FMEA research has overlooked the interdependencies among FMs, treating them as independent entities, which does not align with real-world operations. This paper introduces a groundbreaking risk dependency assessment model aimed at addressing the influential relationships among FMs. The modified technique for defining the influential relationship of failure modes (modified TEDIR-FMs) is developed to compute the relative risk scores of different FMs accurately. At its core, this paper integrates concepts from multiple criteria decision-making (MCDM) techniques into FMEA, with a particular focus on the interdependencies of failure modes in the manufacturing and design manufacturing processes of medical equipment. The model's application is demonstrated through a case study on a medical device for hernia surgery. The introduction of modified TEDIR-FMs not only enhances the accuracy of risk assessments but also offers a fresh perspective on risk management within the design and manufacturing processes. The research results indicate that the three failure modes with the highest risk are "adhesive does not deploy or only partially deploys from the dispenser," "the mesh does not adhere to the adhesive," and "the adhesive drips from the tip." In the manufacturing and design processes of this device, greater attention should be given to improving the adhesive components. [ABSTRACT FROM AUTHOR]

In recent years, researchers have paid increasing attention to Peer to Peer lending market. In this lending method, the lenders don't usually have enough knowledge to understand how much money must be allocated in each loan. So, it is important to help investors to assess the loans. According to the needs of investors, generally, a bi-objective optimization model is considered that maximizes return and minimizes risk. In current work, internal rate of return as return and probability of default as risk variable is considered. First, an artificial neural network is used to evaluate the return. Then, we apply logistic regression to predict the probability of loan default. After providing the model, due to its complexity, one of the essential issues is solving it. In this study, the model is solved by metaheuristic algorithms. This is done from different points of view, including non-dominated sorting genetic algorithm II, multi-objective particle swarm optimization, and Pareto envelope-based selection algorithm II. Finally, from the perspective of an investor, the best algorithm is introduced. [ABSTRACT FROM AUTHOR]

In the dynamic smart grid landscape, accurate probabilistic forecasting of electric load is critical. This paper presents a novel 24-hour-ahead probabilistic load forecasting model by integrating quantile regression with a parallel convolutional neural network (CNN) and bidirectional gated recurrent unit (BiGRU) architecture. Carefully tuning hyperparameters can enhance model performance and generalization capability. Consequently, we propose an improved whale optimization algorithm for automatic hyperparameter tuning of the forecasting model. Case studies demonstrate the proposed method's superior performance over benchmark models in terms of average interval score and pinball loss. In addition, it exhibits valid coverage and tight interval bandwidths. The model provides precise short-term load forecasts to support robust smart grid planning and operations. [ABSTRACT FROM AUTHOR]

The prediction of land use and land cover (LULC) patterns is essential for land use planning, natural resources management, and climate change mitigation. In this study, the artificial neural network (ANN) technique and QGIS software have been proposed to project 5- and 10-year future LULC for the Mahanadi River basin in Chhattisgarh, India. The results reveal that slope, aspect, and hillshade maps have significant effects on LULC. The projection of the future LULC for the years 2027 and 2032 reveals that the grassland and cropland areas would significantly decline and increase, respectively, in the study region. The results from trend analysis show an increasing trend for forests, permanent wetlands, cropland, urban built-up land, waterbodies, and cropland/natural vegetation, while decreasing trends for the remaining classes. The overall finding from this study suggests that remote sensing enabled with the ANN technique has the potential to project the future LULC with high accuracy. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to develop a scenario analysis on the experts' perceptions of benefits and barriers related to adopting digital technologies for the life cycle assessment (LCA) to catalyse a circular economy transition in the agri-food system. Methods: A literature review was performed to identify LCA's digital technologies that can be implemented within the agri-food system. Furthermore, an in-depth interview with a panel of senior researchers was conducted to establish a set of items and assess the perceived benefits and barriers associated with an "empowered LCA", i.e. a future-oriented LCA based on digital technologies. To this end, a two-stage exploratory factor analysis relying on the principal component analysis technique was carried out to refine the set of items. Finally, a covariance-based structural equation model was performed, built on a confirmatory factor analysis, to test the measurement model. Results and discussion: The study's findings provide five constructs to explore the potential benefits and barriers related to adopting a digital technologies-based LCA (empowered LCA) for a circular economy transition in the agri-food system. More specifically, the benefits can be assessed using the following constructs: "benefits for the data collection and analysis", "benefits for the LCA analysts", "benefits for the management" and "benefits for traceability". In addition, the barriers have been evaluated using a single construct labelled "general barriers". Conclusions: The study highlights the relevance of digital technologies for a circular economy transition to develop a more reliable LCA, enhancing legislative compliance and supporting the traceability processes in the agri-food system. The associated implications for LCA experts, agri-food managers and policymakers are presented. Furthermore, limitations and future research directions are also discussed. [ABSTRACT FROM AUTHOR]

The supply chain for prefabricated buildings (PB) currently grapples with pressing challenges. In order to ensure the safe and stable development of the prefabricated building supply chains (PBSC), this study aims to identify the key factors and internal mechanisms affecting the PBSC, and propose a supply chain resilience enhancement mechanism, so as to promote the sustainable development of the PB industry. The study combined a literature review and survey data to identify key resilience factors in PBSC. A Structural Equation Model (SEM) was used to explore the relationships between these factors. System dynamics were applied to create a simulation model, assessing the resilience impact level and conducting sensitivity analysis. The results show that the transportation and procurement processes are the most significant factors influencing supply chain resilience. The external environmental factors wielded a more pronounced impact on the overall evaluation of supply chain resilience than the delivery and use processes, but delivery and use processes are more sensitive. The study uses the Pressure-State-Response (PSR) model to suggest strategies for enhancing supply chain resilience. This study contributes to more sustainable and efficient construction practices by offering an innovative theoretical framework to analyze the factors influencing PBSC resilience and proposing enhancement strategies. [ABSTRACT FROM AUTHOR]

Paleoclimate reconstructions from the Holocene are important for defining baseline conditions in order to interpret and contextualize the effects of modern climate change. Such records are particularly lacking for Siberia, a region that represents ~ 50% of the Arctic. In addition, the majority of proxy-based paleoclimate reconstructions for the Holocene represent mean annual conditions, and few quantify winter temperature, which is particularly important for predicting the effects of global warming in Arctic environments. Here we provide the first quantitative proxy reconstruction of precipitation and temperature for both summer and winter for 3000 years ago via novel high-resolution intra-annual carbon and oxygen isotope measurements across annual growth rings of fossil wood mummified within the permafrost of far northeastern Siberia. We found that the site experienced greater precipitation year-round (~ 10% increase in summer and ~ 30% increase in winter), cooler summer temperatures, and warmer winter temperatures, compared with today. Our findings indicate that warmer winter temperatures (+ 3.0 °C above early twentieth century values) in the Arctic 3000 years ago drove higher mean annual temperature by up to 1 °C, despite the existence of cooler summers, a similar phenomenon to what is observed within today's Arctic environments, and past intervals of extreme global warmth. [ABSTRACT FROM AUTHOR]

Friction is not an inherent attribute in natural science. Indeed, there are various friction phenomena present in social systems. In this study, the researchers developed the general concept of social friction and distinguished the two-dimensional structure of explicit-implicit social friction phenomena. Specifically, explicit friction includes institutional friction, economic friction, behavioral friction, and migration friction. Implicit friction includes cultural friction, cognitive friction, interpersonal friction, technological friction, and information friction. Physical theories such as friction force, classical mechanics, and functional principles have been introduced to construct a social friction force model. This paper describes the emergence and evolution of social friction phenomena from the mechanics perspective and clarifies the various characteristics of social friction force in different areas. This study also provides a new theoretical perspective for examining social friction phenomena and adds innovative content to intersection studies in physics and the social sciences. [ABSTRACT FROM AUTHOR]

The present study proposes a new fuzzy finite element method for dynamic multibody interaction with consideration for structural damage. Here, fuzzy parameters are equivalently transformed into stochastic parameters using information entropy, and the fuzzy response of the structure is obtained by fuzzy calculation combined with the new point estimation method. Numerical examples are used to illustrate the accuracy and efficiency of the presented methods, and scanning method simulations are implemented to validate the computational results. Considering that the damage degree of the pier is uncertain, namely fuzzy uncertainty, stiffness reduction is used to simulate the damage of the pier. The fuzzy dynamic response of the train–bridge system is investigated when the pier structure and the mass of the train are fuzzy parameters. The response of the train–bridge interaction considering damage far exceeds that obtained from conventional deterministic parameter calculations. To ensure running safety, studying the response of the vehicle-system coupled vibration with fuzzy parameters is of great significance. [ABSTRACT FROM AUTHOR]

One of the current research gaps in multi-scale data analysis is studying information systems characterized by attributes with interval sets as attribute values and multiple scales. To address this gap, we first introduce the concepts of a multi-scale interval-set information system (MISIS) and a multi-scale interval-set decision table (MISDT). We then define the similarity relation between objects in an MISIS and the corresponding rough approximations. We further propose the positive region optimal scale, the modified conditional entropy optimal scale, and the positive complementary conditional entropy optimal scale in an MISDT. We examine the relationships among these optimal scales in consistent and inconsistent MISDTs and show that the positive region optimal scale and the modified conditional entropy optimal scale are equivalent in a consistent MISDT, while in an inconsistent MISDT, the positive region optimal scale is the same as the positive complementary conditional entropy optimal scale, and the modified conditional entropy optimal scale is not greater than the positive complementary conditional entropy optimal scale. Based on the optimal scale, we also develop attribute reduction approaches in MISDTs. Finally, through experimental analysis of data on the UCI dataset, we verify the effectiveness and reasonableness of our proposed methods. [ABSTRACT FROM AUTHOR]

The sense of agency – the subjective feeling of being in control of our own actions – is one central aspect of the phenomenology of action. Computational models provided important contributions toward unveiling the mechanisms underlying the sense of agency in individual action. In particular, the sense of agency is believed to be related to the match between the actual and predicted consequences of our own actions (comparator model). In the study of joint action, models are even more necessary to understand the mechanisms underlying the development of coordination strategies and how the subjective experiences of control emerge during the interaction. In a joint action, we not only need to predict the consequences of our own actions; we also need to predict the actions and intentions of our partner, and to integrate these predictions to infer their joint consequences. Understanding our partner and developing mutually satisfactory coordination strategies are key components of joint action and in the development of the sense of joint agency. Here we discuss a computational architecture which addresses the sense of agency during intentional, real-time joint action. We first reformulate previous accounts of the sense of agency in probabilistic terms, as the combination of prior beliefs about the action goals and constraints, and the likelihood of the predicted movement outcomes. To look at the sense of joint agency, we extend classical computational motor control concepts - optimal estimation and optimal control. Regarding estimation, we argue that in joint action the players not only need to predict the consequences of their own actions, but also need to predict partner's actions and intentions (a 'partner model') and to integrate these predictions to infer their joint consequences. As regards action selection, we use differential game theory – in which actions develop in continuous space and time - to formulate the problem of establishing a stable form of coordination and as a natural extension of optimal control to joint action. The resulting model posits two concurrent observer-controller loops, accounting for 'joint' and 'self' action control. The two observers quantify the likelihoods of being in control alone or jointly. Combined with prior beliefs, they provide weighing signals which are used to modulate the 'joint' and 'self' motor commands. We argue that these signals can be interpreted as the subjective sense of joint and self agency. We demonstrate the model predictions by simulating a sensorimotor interactive task where two players are mechanically coupled and are instructed to perform planar movements to reach a shared final target by crossing two differently located intermediate targets. In particular, we explore the relation between self and joint agency and the information available to each player about their partner. The proposed model provides a coherent picture of the inter-relation of prediction, control, and the sense of agency in a broader range of joint actions. [ABSTRACT FROM AUTHOR]

Localization measurements for an autonomous underwater vehicle (AUV) are often difficult to obtain. In many cases, localization measurements are only available sporadically after the AUV comes to the sea surface. Since the motion of AUVs is often affected by unknown underwater flow fields, the sporadic localization measurements carry information of the underwater flow field. Motion tomography (MT) algorithms have been developed to compute a underwater flow map based on the sporadic localization measurements. This paper extends MT by introducing Laplacian regularization in to the problem formulation and the MT algorithm. Laplacian regularization enforces smoothness in the spatial distribution of the underwater flow field. The resulted Laplacian regularized motion tomography (RMT) algorithm converges to achieve a finite error bounded. The performance of the RMT and other variants of MT are compared through the method of data resolution analysis. The improved performance of RMT is confirmed by experimental data collected from underwater glider ocean sensing experiments. [ABSTRACT FROM AUTHOR]

Fog computing has emerged as the most popular technology for processing delay-sensitive tasks in the Internet of Things platform. However, offloading tasks to suitable fog nodes (FNs) for their processing is a very challenging problem which is known to be NP-hard. In this paper, we propose a metaheuristic-based task offloading scheme that jointly optimizes the offloading delay and resource utilization. We first formulate the task offloading as a multi-objective optimization problem and then propose an algorithm based on the Discrete Jaya Algorithm (DJA) to solve it. The DJA is presented with efficient candidate representation and mutation operation. We also propose a task migration algorithm to transfer the partially completed task to another FN before failure. Through extensive simulations, we demonstrate the efficacy of the proposed work by comparing the state-of-the-art algorithms and validating it through the Friedman test. [ABSTRACT FROM AUTHOR]

This research investigates the application of the ordered ranked set sampling (ORSSA) procedure in constant-stress partially accelerated life-testing (CSPALTE). The study adopts the assumption that the lifespan of a specific item under operational stress follows a half-logistic probability distribution. Through Bayesian estimation methods, it concentrates on estimating the parameters, utilizing both asymmetric loss function and symmetric loss function. Estimations are conducted using ORSSAs and simple random samples, incorporating hybrid censoring of type-I. Real-world data sets are utilized to offer practical context and validate the theoretical discoveries, providing concrete insights into the research findings. Furthermore, a rigorous simulation study, supported by precise numerical calculations, is meticulously conducted to gauge the Bayesian estimation performance across the two distinct sampling methodologies. This research ultimately sheds light on the efficacy of Bayesian estimation techniques under varying sampling strategies, contributing to the broader understanding of reliability analysis in CSPALTE scenarios. [ABSTRACT FROM AUTHOR]

The practice of credit scoring is ubiquitous in today's economy. Three-digit credit scores or their underlying data are applied well beyond the lending decisions for which they were originally designed and are routinely used in the contexts of employment, housing, and more. Drawing on carceral logics and abolitionist politics, we develop a framework to critically interpret the practice of credit scoring. We theorize credit scoring as a carceral practice and technology in the afterlife of slavery that expands anti-black discipline and punishment. We suggest that credit scoring is incapable of objectively assessing risk and that claims of objectivity legitimize an exploitative system of evaluation that mediates people's access to the means of survival. Moreover, credit scoring expands the scope of how people are conscripted into consumerism and disciplined and punished under racial capitalism. We review research literature on credit scoring as a step toward applying this framework and demonstrate how research provides an alibi for anti-black racism embedded in contemporary credit scores. We conclude with a call to abolish the practice of credit scoring and imagine new, abolitionist alternatives for people to live safely and with dignity. [ABSTRACT FROM AUTHOR]

Every positive multilinear map between C ∗ -algebras is separately weak ∗ -continuous. We show that the joint weak ∗ -continuity is equivalent to the joint weak ∗ -continuity of the multiplications of the C ∗ -algebras under consideration. We study the behavior of general tracial positive maps on properly infinite von Neumann algebras and by applying the Aron–Berner extension of multilinear maps, we establish that under some mild conditions every tracial positive multilinear map between general C ∗ -algebras enjoys a decomposition Φ = φ 2 ∘ φ 1 , in which φ 1 is a tracial positive linear map with the commutative range and φ 2 is a tracial completely positive map with the commutative domain. As an immediate consequence, tracial positive multilinear maps are completely positive. Furthermore, we prove that if the domain of a general tracial completely positive map Φ between C ∗ -algebra is a von Neumann algebra, then Φ has a similar decomposition. As an application, we investigate the generalized variance and covariance in quantum mechanics for arbitrary positive maps. Among others, an uncertainty relation inequality for commuting observables in a composite physical system is presented. [ABSTRACT FROM AUTHOR]

To achieve better surface quality and close dimensional tolerance, finishing operations, like surface and cylindrical grinding are widely employed in many of the manufacturing industries. For determining the optimal values of various grinding parameters, like wheel speed, feed rate, depth of cut, width of cut, wheel material etc., multi-criteria decision making (MCDM) methods have already proven to be an effective way to simultaneously deal with multiple input parameters, affecting attainment of the required surface finish. In this paper, three MCDM tools, i.e. multi-attributive border approximation area comparison (MABAC), compromise ranking of alternatives from distance to ideal solution (CRADIS) and evaluation based on distance from average solution (EDAS) are adopted in intuitionistic fuzzy (IF) environment to demonstrate their effectiveness in solving parametric optimization problems of a surface grinding process and a cylindrical grinding process, while considering varying opinions of multiple stakeholders with respect to relative importance assigned to the responses. In the surface grinding process, all the three IF-MCDM tools identify grinding wheel speed = 90 m/s, infeed speed = 0.5 m/min and grinding depth = 0.1 mm as the optimal parametric combination. In the cylindrical grinding process, an intermix of different input parameters as work speed = 36 m/min, feed rate = 25 mm/min and depth of cut = 0.02 mm is singled out as the best by IF-MABAC and IF-CRADIS, and ranked second-best by IF-EDAS method. Thus, the derived results validate applicability of the considered IF-MCDM methods in effectively optimizing different grinding processes in an uncertain group decision making environment. [ABSTRACT FROM AUTHOR]

This paper argues that firms in certain life cycle stages may be more subjectively valued by individual investors, leading to an optimistic bias in stock prices that is subsequently corrected upon the release of earnings news. Using a cash flow-based life cycle stage classification, introduction and decline stage companies exhibit three-day cumulative abnormal returns (CARs) around earnings announcements that are at least 112 bps lower than firms in growth, maturity, and shake-out stages. Specifically, introduction and decline stage stocks exhibit less positive reactions to positive earnings surprises and more negative reactions to negative earnings surprises relative to companies in other life cycle stages. Lottery stocks' excess returns around earnings announcements (Liu et al. in Journal of Financial Economics 138: 789–817, 2020) also vary based on firm life cycle stage. Our findings suggest that individual investors' optimistic expectations for introduction and decline stage stocks are met with disappointment when value-relevant earnings news is released. This study demonstrates that firm life cycle stage has real implications for stock price reactions to earnings announcements. [ABSTRACT FROM AUTHOR]

Prior research suggests that quarterly reports released concurrently with earnings depress trading due to information overload. In this study, we predict that concurrent reports help investors trade when they face uncertainty about how to interpret earnings news. We rely on the implementation of ASC 606 as a quasi-exogenous increase in uncertainty about how to trade in response to earnings news. Specifically, we find that when uncertainty is high in the first quarter of ASC 606 implementation, 10-Qs released concurrently with earnings are associated with increased trading around the earnings announcement. We find the relation is more pronounced when investors face greater uncertainty about earnings and when firms increase disclosure in their revenue recognition footnote. We also find some evidence that our results hold in a broader sample of accounting standard changes and generalize to other proxies for investor uncertainty. Our results suggest that concurrent quarterly reports are informative to investors when uncertainty about earnings is especially high. [ABSTRACT FROM AUTHOR]

The Review of Accounting Studies (RAST) recently completed 25 years of publication. We provide a descriptive analysis of RAST's characteristics that should interest the stakeholders who have been part of the journal's evolution. We benchmark RAST's characteristics against the other top accounting journals: Journal of Accounting and Economics, Journal of Accounting Research, the Accounting Review, and Contemporary Accounting Research. We provide information on RAST's most prominent subject matter themes, methodologies used by RAST authors, the journals citing RAST, RAST's most highly cited papers for different periods, and affiliated institutions and countries of RAST's most prolific and cited authors. Results show that RAST has developed unique expertise in valuation and financial statement analysis, arguably becoming the market leader despite being the youngest journal among its peer group. It now publishes the highest number of articles among non-association journals. RAST continues to make inroads into Asian countries emerging as new research centers. [ABSTRACT FROM AUTHOR]

The disclosure of information is a necessary tool for the management of severe environmental problems, revealing how resources are used and pollution managed. This article examines the connections between corporate executives' political connections and the quality of the environmental information their enterprises disclose. It also explores the regulatory role of the assessments of officials' environmental performance. Using panel data of 236 state-owned enterprises in heavy-polluting industries listed on the Shanghai and Shenzhen stock exchanges from 2012 to 2016, the results show that the political connection has a significant positive effect on improving the quality of the environmental information disclosure at the 5% confidence level, and every one level increase in the political connection will improve the level of corporate environmental information disclosure by 17.8%. And also, taking environmental performance as an evaluation category for promotion purposes had a significant positive adjustment effect on the relationship between the political connections of the leader of a state-owned enterprise and the quality of environmental information that enterprise disclosed at the 10% confidence level. As our findings suggest, the evaluation of the environmental information disclosed by enterprises in emerging economies such as China requires caution. The leaders of these companies are affected by economic incentives, but are also focused on the political incentives brought about by mandatory regulations. The research provides an empirical basis for the effectiveness of environmental regulation on environmental information disclosure and also helps to understand the impact of official assessment mechanism in China's environmental development. [ABSTRACT FROM AUTHOR]

This research aimed to describe the use and functionality that migrant women provide to social networks and the role they play in the social integration of migrant women. Using a qualitative methodology design, a virtual ethnography approach, and with the support of ATLAS, this research analyses 863 discursive threads of a WhatsApp group comprised of 123 migrant women belonging to an association related to the social and labour integration of the migrant population. The results indicate that the WhatsApp group is a reliable source of information as well as spaces of solidarity and emotional support among women who experience similar situations during the migration process. The originality of these results is that they provide insight into the uses of social networks by migrant women, on which there is little research, through observations in a WhatsApp group. Organisations working with migrant women should rethink the functionalities of these social networks and the importance of these tools in improving the social and labour integration of migrant women. [ABSTRACT FROM AUTHOR]

This article raises the question of whether the European Insider Trading and Market Manipulation Regulation (Market Abuse Regulation—MAR) affected the behaviour of investors around the date of the final financial report announcement. The MAR is a relatively recent regulation in the European Union and provides stricter rules for information disclosure than the previous Market Abuse Directive. One can expect that this regulation should improve the information flow from companies to investors, lowering the volatility of stock returns and abnormal rate of return around the date of the final report. To verify this hypothesis, the panel regressions and ANOVA analysis were applied to the stock prices of 60 medium and large-size companies listed on the Warsaw Stock Exchange in the years 2013–2018. The analysis demonstrates that implementing the regulation has increased the average volatility of stock prices, whereas prices in the period before and after the publication period of annual financial reports remained unaffected. It indicates that the information provided was of lower quality, despite the increase in the information available to the public. Therefore, the anti-abusive regulation requires a modification of rules applied to small stock markets as they do not meet their objectives. [ABSTRACT FROM AUTHOR]

This paper focuses on the maximum speed at which biological evolution can occur. I derive inequalities that limit the rate of evolutionary processes driven by natural selection, mutations, or genetic drift. These rate limits link the variability in a population to evolutionary rates. In particular, high variances in the fitness of a population and of a quantitative trait allow for fast changes in the trait's average. In contrast, low variability makes a trait less susceptible to random changes due to genetic drift. The results in this article generalize Fisher's fundamental theorem of natural selection to dynamics that allow for mutations and genetic drift, via trade-off relations that constrain the evolutionary rates of arbitrary traits. The rate limits can be used to probe questions in various evolutionary biology and ecology settings. They apply, for instance, to trait dynamics within or across species or to the evolution of bacteria strains. They apply to any quantitative trait, e.g., from species' weights to the lengths of DNA strands. [ABSTRACT FROM AUTHOR]

Due to the diversity and complexity of the actual data distribution, the traditional neighborhood classifier (NEC) is weak in adapting to the global data and has low utilization of local information, which leads to the degradation of the classifier's effectiveness. To adapt NEC to the differences of different dimensions in data distribution, this paper defines attribute sensitivity and improves the purity of neighborhood information granules by weighted distance. To improve the resolution of local information, this paper constructs an intuitionistic fuzzy neighborhood classifier (IFNEC) by combining NEC with the intuitionistic fuzzy set (IFS) and defines the membership degree and non-membership degree of the object in the neighborhood to depict characteristics of local data. In IFNEC, the multi-attribute decision matrix is used in the decision-making process, which is constructed by a support function and intuitionistic fuzzy aggregation operator to filter the information with large uncertainty. Finally, taking seven data sets from UCI, and using accuracy and F1-score as evaluation indicators, we conduct a comparative experiment between NEC and IFNEC. The experimental results show that IFNEC has better performance than NEC. [ABSTRACT FROM AUTHOR]

The construction and building industry (CBI) is considered one of the major carbon emission contributors to the environment and faces various social and technical challenges in implementing decarbonized and circular economy (DCE) practices. Resource optimization can be achieved using the CBI product circularity and carbon offset mechanisms for greater business sustainability. In past studies, DCE practices have been loosely considered for improving the sustainability performance of emerging economies’ CBI. To fill this gap, this study uniquely focuses on CBI practices on DCE in consideration of socio-technical system (STS) availability because STS plays a vital role in adopting DCE practices. The proposed research framework is based on STS theory to measure the inter-influence of socio-technical challenges in achieving decarbonized and circular CBI. The STS-based framework consists of six dimensions namely: goals of circular construction (GCC), people barriers (PB), cultural barriers (CB), infrastructural barriers (IB), technological barriers (TB), and construction process and procedural barriers (CPB). The main STS dimensions are further divided into 26 sub-dimensions to assess interinfluencing mapping. The proposed framework is analyzed using integrated grey information uncertainty theory with decision-making trial and evaluation laboratory (DEMATEL), and interpretive structural modelling (ISM), i.e., grey-DISM. The grey-DISM method measures the driving power and dependence of the interdependencies of the decarbonized circularity barriers of the Indian CBI. Fifteen industry experts participated in the data survey, and their participation consistency was more than 95%. Barriers IB and CPB were found to be the major and minor challenges in implementing DCE in CBI. The findings of this research reported that lack of decarbonized and CE-based infrastructure hinders the adoption of DCE practices. This paper’s results also highlight the construction design dependency on infrastructure and machinery and low DCE practice perceptions for improving circularity in CBI. The entire decision model is divided into three levels of hierarchical mapping, which helps construction practitioners and policymakers develop strategic planning for improving DCE practices in CBI. The paper also presented managerial and policy implications for CBI practitioners and policymakers. [ABSTRACT FROM AUTHOR]

Effective forecasting of energy consumption structure is vital for China to reach its "dual carbon" objective. However, little attention has been paid to existing studies on the holistic nature and internal properties of energy consumption structure. Therefore, this paper incorporates the theory of compositional data into the study of energy consumption structure, which not only takes into account the specificity of the internal features of the structure, but also digs deeper into the relative information. Meanwhile, based on the minimization theory of squares of the Aitchison distance in the compositional data, a combined model based on the three single models, namely the metabolism grey model (MGM), back-propagation neural network (BPNN) model, and autoregressive integrated moving average (ARIMA) model, is structured in this paper. The forecast results of the energy consumption structure in 2023–2040 indicate that the future energy consumption structure of China will evolve towards a more diversified pattern, but the proportion of natural gas and non-fossil energy has yet to meet the policy goals set by the government. This paper not only suggests that compositional data from joint prediction models have a high applicability value in the energy sector, but also has some theoretical significance for adapting and improving the energy consumption structure in China. [ABSTRACT FROM AUTHOR]

The implication of machine repair problems in the continued functioning of real-time machining models keeps growing with the advent of technology for socioeconomic progression, mobility, security, and safety. The uninterrupted functioning of critical appliances, monitoring controllers, next-generation devices, workstations, and data exchange systems is expected whenever needed prompt. When active units fail, the results may be catastrophic, injury, or loss, leading to critical reliability challenges that must be resolved. This article aims to provide a comprehensive, state-of-the-art study for failure/repair/operation uncertainties and impreciseness in optimistic and pessimistic conditions. We consider the fault-tolerant machining system consisting of two-active units, a single-warm standby unit, and a single-repair facility in a fuzzy environment governing the involved imperfectness, vagueness, uncertainty. Switching the standby unit to the failed active unit is also subject to failure. The notion of imperfect repair makes the proposed model more insightful. A membership grade function of the reliability characteristics: mean time-to-failure and system availability are constructed to study uncertainties in-depth for the fault-tolerant redundant repairable system with switching failure and imperfect repair for well to poor design. The nonlinear parametric program technique converts the studied problem into a set of conventional problems. It is employed to compute the upper and lower bounds of the reliability characteristic based on the γ -cut approach and Zadeh's extension principle for extreme design constrained limits. Extensive numerical simulations are also performed for the different sets of governing parameters ranging from well-conditioned to ill-conditioned. The concluding remarks and future scopes are also included. [ABSTRACT FROM AUTHOR]

Extracting knowledge from hybrid data, comprising both categorical and numerical data, poses significant challenges due to the inherent difficulty in preserving information and practical meanings during the conversion process. To address this challenge, hybrid data processing methods, combining complementary rough sets, have emerged as a promising approach for handling uncertainty. However, selecting an appropriate model and effectively utilizing it in data mining requires a thorough qualitative and quantitative comparison of existing hybrid data processing models. This research aims to contribute to the analysis of hybrid data processing models based on neighborhood rough sets by investigating the inherent relationships among these models. We propose a generic neighborhood rough set-based hybrid model specifically designed for processing hybrid data, thereby enhancing the efficacy of the data mining process without resorting to discretization and avoiding information loss or practical meaning degradation in datasets. The proposed scheme dynamically adapts the threshold value for the neighborhood approximation space according to the characteristics of the given datasets, ensuring optimal performance without sacrificing accuracy. To evaluate the effectiveness of the proposed scheme, we develop a testbed tailored for Parkinson's patients, a domain where hybrid data processing is particularly relevant. The experimental results demonstrate that the proposed scheme consistently outperforms existing schemes in adaptively handling both numerical and categorical data, achieving an impressive accuracy of 95% on the Parkinson's dataset. Overall, this research contributes to advancing hybrid data processing techniques by providing a robust and adaptive solution that addresses the challenges associated with handling hybrid data, particularly in the context of Parkinson's disease analysis. [ABSTRACT FROM AUTHOR]

The sixth-generation (6G) wireless communication system is envisioned be cable of providing highly dependable services by integrating with native reliable and trustworthy functionalities. Zero-trust vehicular networks is one of the typical scenarios for 6G dependable services. Under the technical framework of vehicle-and-roadside collaboration, more and more on-board devices and roadside infrastructures will communicate for information exchange. The reliability and security of the vehicle-and-roadside collaboration will directly affect the transportation safety. Considering a zero-trust vehicular environment, to prevent malicious vehicles from uploading false or invalid information, we propose a malicious vehicle identity disclosure approach based on the Shamir secret sharing scheme. Meanwhile, a two-layer consortium blockchain architecture and smart contracts are designed to protect the identity and privacy of benign vehicles as well as the security of their private data. After that, in order to improve the efficiency of vehicle identity disclosure, we present an inspection policy based on zero-sum game theory and a roadside unit incentive mechanism jointly using contract theory and subjective logic model. We verify the performance of the entire zero-trust solution through extensive simulation experiments. On the premise of protecting the vehicle privacy, our solution is demonstrated to significantly improve the reliability and security of 6G vehicular networks. [ABSTRACT FROM AUTHOR]

Innovation is an important driver for organizational development. Stimulating innovative behavior among employees becomes an important way to improve the competitive advantage of a company. The main purpose of this study is to explore the mechanisms of influence of superior-subordinate guanxi (SSG) on employees' innovative behavior and to test the mediating role of psychological ownership and the moderating role of emotional uncertainty. We collected data from 88 China Mobile's service centers in Shandong Province. The research participants comprised 88 supervisors and 321 subordinates. The results indicated that SSG enhanced employees' psychological ownership and innovative behavior. Moreover, the mediation effect of psychological ownership on the relationship between SSG and subordinates' innovative behavior was significant only for employees with high emotional uncertainty and not for employees with low emotional uncertainty. [ABSTRACT FROM AUTHOR]

In quantum information theory, one needs to consider systems with incomplete information. To estimate a quantum system as an information resource, one uses various characteristics of non-classical correlations. Currently, much attention is paid to coherence quantifiers averaged over a set of specially selected states. In particular, mutually unbiased bases, symmetric informationally complete measurements, and some of their generalizations are of importance in this regard. The aim of the present study is to derive uncertainty relations for coherence quantifiers based on divergences of the Tsallis type. The obtained inequalities concern quantifiers averaged over a set of mutually unbiased bases and a set of states that form an equiangular tight frame. [ABSTRACT FROM AUTHOR]

There is no lack of studies dealing with the consistency of evaluations performed by pairwise comparison in the decision-making literature. Mostly, these studies offer algorithms for reducing the inconsistency of evaluations and indices to measure the evaluation's consistency degree. The focus on these two research fronts does not cover all the gaps associated with the inconsistent evaluation problem. The existing algorithms are difficult to implement and do not preserve the original evaluations since the original evaluation matrix is replaced with a new matrix. Furthermore, the inconsistency of pairwise comparison has been associated with the specialist's bounded rationality only at the theoretical-conceptual level. This research investigates the relationship between the lack of specialist knowledge and the inconsistency of evaluations, as well as introduces an approach that ensures the evaluation's consistency by reducing the specialist's cognitive stress when comparing a high number of alternatives. The results reveal that the specialist's limited knowledge about the topic does not impact the degree of consistency of the evaluations as expected. The evaluation's consistency degree is 59% lower when the specialist does have no knowledge about the decision topic but has theoretical knowledge and experience in evaluating alternatives by pairwise comparison. This is a remarkable contribution with a high degree of universality and applicability because instructing decision-makers on the inconsistency problem is a cheaper, easier way to increase the evaluation's consistency degree without altering the original information. Furthermore, the introduced approach reduces the number of evaluations and evaluation time by 8.0 and 7.8 times, respectively. [ABSTRACT FROM AUTHOR]