Search

Background: Prognostic models combine several prognostic factors to provide an estimate of the likelihood (or risk) of future events in individual patients, conditional on their prognostic factor values. A fundamental part of evaluating prognostic models is undertaking studies to determine whether their predictive performance, such as calibration and discrimination, is reproduced across settings. Systematic reviews and meta-analyses of studies evaluating prognostic models' performance are a necessary step for selection of models for clinical practice and for testing the underlying assumption that their use will improve outcomes, including patient's reassurance and optimal future planning.Methods: In this paper, we highlight key concepts in evaluating the certainty of evidence regarding the calibration of prognostic models.Results and Conclusion: Four concepts are key to evaluating the certainty of evidence on prognostic models' performance regarding calibration. The first concept is that the inference regarding calibration may take one of two forms: deciding whether one is rating certainty that a model's performance is satisfactory or, instead, unsatisfactory, in either case defining the threshold for satisfactory (or unsatisfactory) model performance. Second, inconsistency is the critical GRADE domain to deciding whether we are rating certainty in the model performance being satisfactory or unsatisfactory. Third, depending on whether one is rating certainty in satisfactory or unsatisfactory performance, different patterns of inconsistency of results across studies will inform ratings of certainty of evidence. Fourth, exploring the distribution of point estimates of observed to expected ratio across individual studies, and its determinants, will bear on the need for and direction of future research.(c) 2021 Elsevier Inc. All rights reserved.

Background: A living systematic review (LSR) is an emerging review type that makes use of continual updating. In the COVID-19 pandemic, we were confronted with a shifting epidemiological landscape, clinical uncertainties and evolving evidence. These unexpected challenges compelled us to amend standard LSR methodology. Objective and outline: Our primary objective is to discuss some challenges faced when conducting LSRs in the context of the COVID-19 pandemic, and to provide methodological guidance for others doing similar work. Based on our experience and lessons learned from two Cochrane LSRs and challenges identified in several non-Cochrane LSRs, we highlight methodological considerations, particularly with regards to the study design, interventions and comparators, changes in outcome measure, and the search strategy. We discuss when to update, or rather when not to update the review, and the importance of transparency when reporting changes. Lessons learned and conclusion: We learned that a LSR is a very suitable review type for the pandemic context, even in the face of new methodological and clinical challenges. Our experience showed that the decision for updating a LSR depends not only on the evolving disease or emerging evidence, but also on the individual review question and the review teams' resources. (c) 2021 Elsevier Inc. All rights reserved.

Objectives: The aim of this study is to propose an approach for developing trustworthy recommendations as part of urgent responses (1-2 week) in the clinical, public health, and health systems fields. Study Design and Setting: We conducted a review of the literature, outlined a draft approach, refined the concept through iterative discussions, a workshop by the Grading of Recommendations Assessment, Development and Evaluation Rapid Guidelines project group, and obtained feedback from the larger Grading of Recommendations Assessment, Development and Evaluation working group. Results: A request for developing recommendations within 2 week is the usual trigger for an urgent response. Although the approach builds on the general principles of trustworthy guideline development, we highlight the following steps: (1) assess the level of urgency; (2) assess feasibility; (3) set up the organizational logistics; (4) specify the question(s); (5) collect the information needed; (6) assess the adequacy of identified information; (7) develop the recommendations using one of the 4 potential approaches: adopt existing recommendations, adapt existing recommendations, develop new recommendations using existing adequate systematic review, or develop new recommendations using expert panel input; and (8) consider an updating plan. Conclusion: An urgent response for developing recommendations requires building a cohesive, skilled, and highly motivated multidisciplinary team with the necessary clinical, scientific, and methodological expertise; adapting to shifting needs; complying with the principles of transparency; and properly managing conflicts of interest. (C) 2020 The Author(s). Published by Elsevier Inc.

Objectives: In this first of a series of five articles, we provide an overview of how and why healthcare-related tests and diagnostic strategies are currently applied. We also describe how our findings can be integrated with existing frameworks for making decisions that guide the use of healthcare-related tests and diagnostic strategies. Study Design and Setting: We searched MEDLINE, references of identified articles, chapters in relevant textbooks, and identified articles citing classic literature on this topic. Results: We provide updated frameworks for the potential roles and applications of tests with suggested definitions and practical examples. We also discuss study designs that are commonly used to assess tests' performance and the effects of tests on people's health. These designs include diagnostic randomized controlled trials and retrospective validation. We describe the utility of these and other currently suggested designs, which questions they can answer and which ones they cannot. In addition, we summarize the challenges unique to decision-making resulting from the use of tests. Conclusion: This overview highlights current challenges in the application of tests in decision-making in healthcare, provides clarifications, and informs the proposed solutions. (C) 2017 Elsevier Inc. All rights reserved.

Simplicial map neural networks (SMNNs) are topology-based neural networks with interesting properties such as universal approximation ability and robustness to adversarial examples under appropriate conditions. However, SMNNs present some bottlenecks for their possible application in high-dimensional datasets. First, SMNNs have precomputed fixed weight and no SMNN training process has been defined so far, so they lack generalization ability. Second, SMNNs require the construction of a convex polytope surrounding the input dataset. In this paper, we overcome these issues by proposing an SMNN training procedure based on a support subset of the given dataset and replacing the construction of the convex polytope by a method based on projections to a hypersphere. In addition, the explainability capacity of SMNNs and effective implementation are also newly introduced in this paper.

This is the fourth in a series of papers that provides an historical record in this journal of contributions made by the most influential figures in the field of stuttering. Ann Packman is an Australian researcher who will retire shortly. This paper reflects on her long and productive career, and her contributions to the field. With a background in literature, linguistics, philosophy, psychology, and the brain and language, she became well equipped to contribute to understanding stuttering causality. That work, and an accompanying collection of basic and applied clinical research, was constantly grounded with the thoughts and feelings of those who stutter in the community.

Value-based selling (VBS) has become an increasingly important selling practice in contemporary business-tobusiness (B2B) markets, but currently most VBS studies are limited to exploratory and descriptive case studies. Therefore, the purpose of this paper is to develop a theoretical toolbox that provides a foundation for more theory-driven research on VBS and thereby advances both the theoretical and managerial understanding of VBS. Specifically, we consider equity theory, framing theory, mental accounting, social exchange theory, signaling theory, agency theory, game theory, and institutional theory in terms of their implications for VBS. We provide a brief overview of each theory and its applications, highlight the key implications and potential research questions for VBS, and summarize our insights into a research agenda. This paper contributes to the contemporary VBS literature by developing a theory-driven research agenda. It also provides managerial insights on how different theories can inform decision-making for the implementation and management of VBS in B2B markets., Funding Agencies|Jan Wallander and Tom Hedelius Foundation [W19-0018]

This paper explores the digitalization of business-to-business (B2B) customer journeys, which is recognized as a top research priority, but one that has not yet received substantial academic attention. Due to their complexity, customer journeys are particularly relevant in B2B settings, and this is further emphasized through the increasing use of digital technologies. This paper identifies three key dimensions of B2B customer journeys that are influenced by digitalization. These are the digitalization of touchpoints (i.e., adding digital touchpoints and transforming or facilitating touchpoints), the change of roles in digitalized journeys (i.e., introducing new roles, activating customers, and emphasizing collectivity), and the digitalization of the overall process (i.e., extending, enhancing, and supporting the process). The research is based on an in-depth case study of a B2B firm and four customers. Insights from the research add to the customer journey literature through the exploration of digita-lized B2B customer journeys, providing guidance for academics and practitioners.

© 2010 Elsevier Inc. The authors thank the NASA TOMS Science Team for the satellite data used in this paper. Manuel Anton thanks the Ministerio de Ciencia e Innovacion and Fondo Social Europeo for the award of a postdoctoral grant (Juan de la Cierva). Author (Pavan S Kulkarni) is thankful to the Geophysics Centre of the University of Evora (CGE-UE) for the fellowship in the project 'SPATRAM-MIGE Polar Project', funded by the Portuguese Science Foundation - FCT. This work was partially supported by Ministerio de Ciencia e Innovacion under project CGL2008-05939-C03-02/CLI and by Fundacao para a Ciencia e a Tecnologia though projects PROPOLAR and PTDC/CTE-ATM/102142/2008., This paper focuses on the spatial-temporal structure of total ozone column (TOC) over Portugal. This relevant region of southwestern Europe has not been evaluated yet in detail due to the lack of continuous and well-covered ground-based TOC measurements. The data used in this study are derived from the NASA's Total Ozone Mapping Spectrometer (TOMS) for the period 1978-2005. The TOC spatial behavior shows no significant longitudinal variability (smaller than 3%). In contrast, the variation in latitude changes between 3.5% and 6% depending on the calendar month. The TOC in the northern Portugal is, on average, higher than that recorded in the South. The temporal variability was analyzed for three scales: long-term, seasonal and short-term. The long-term TOC changes are analyzed between 1978 and 1999 by means of linear least squares fits. The results show an annual TOC trend of (2.65 +/- 0.70)%/decade which is statistically significant at the 95% confidence level. This TOC decrease is smaller than the trends obtained in other midlatitudes regions which could be partially explained by the compensation due to the observed increase in the tropospheric ozone over the Iberian Peninsula. A trend analysis performed for each individual month shows a statistically significant TOC decline between March and October, with a maximum linear trend value of (-7.30 +/- 45)%/decade in May. The amplitude of the seasonal TOC cycle over Portugal shows a slight dependence in latitude, varying from 28.6 DU (37.5 degrees N) to 33.6 DU (41.5 degrees N). Finally, the short-term variability showed a notable seasonal behavior, with maximum day-to-day TOC changes in winter (similar to 6%) and minimum in summer (similar to 3%). In addition, the persistence (characterized by the autocorrelation coefficients) strongly decreases after a few days (except in summer months)., Ministerio de Ciencia e Innovación, Fondo Social Europeo, Geophysics Centre of the University of Evora (CGE-UE), Portuguese Science Foundation - FCT, Fundaçao para a Ciencia e a Tecnologia, Depto. de Física de la Tierra y Astrofísica, Fac. de Ciencias Físicas, TRUE, pub

© 2008 Elsevier Inc., This research study focuses on the modeling and simulation of a gas distribution pipeline network with a special emphasis on gas ducts. Gas ducts are the most important components of such kind of systems since they define the major dynamic characteristics. Isothermal, unidirectional flow is usually assumed when modeling the gas flow through a gas duct. This paper presents two simplified models derived from the set of partial differential equations governing the dynamics of the process. These models include the inclination term, neglected in most related papers. Moreover, two numerical schemes are presented for the integration of such models. Also, it is shown how the pressure drop along the pipe has a strong dependency with the inclination term. To solve the system dynamics through the proposed numerical schemes a based MATLAB-Simulink library was built. With this library it is possible to simulate the behavior of a gas distribution network from the individual simulation of each component. Finally, the library is tested through three application examples, and results are compared with the existing ones in the literature., Sección Deptal. de Arquitectura de Computadores y Automática (Físicas), Fac. de Ciencias Físicas, TRUE, pub

The authors thank the referee for his comments on an earlier version of this paper. This research is supported by the DGINV through the project BFM2002-02189., This paper analyses adiscrete-timeGeo/G/1retrialqueue with batch arrivals in which individual arriving customers have acontrol of admission. We study the underlying Markov chain at the epochs immediately after the slot boundaries making emphasis on the computation of its steady-state distribution. To this end we employ numerical inversion and maximum entropy techniques. We also establish a stochastic decomposition property and prove that the continuous-timeM/G/1retrialqueue with batch arrivals and control of admission can be approximated by our discrete-time system. The outcomes agree with known results for special cases., DGINV, Depto. de Estadística e Investigación Operativa, Fac. de Ciencias Matemáticas, TRUE, pub

© 2005 Elsevier B.V. The authors wish to thank the INM for providing the CG lightning data used in the development of the paper. Our thanks also to the two anonymous referees of this paper for their interesting suggestions and comments., In this paper cloud-to-ground (CG) lightning flashes of 33 Mesoscale Convective Systems (MCSs) over the Western Mediterranean area are analyzed. Mean values of 22% for the positive CG ratio, 1.1 (2.4) for positive (negative) CG multiplicity, 17 kA (22.6 kA) of peak current for the positive (negative) CG flashes and a mean flash rate of around 13 min(-1) are obtained. The percentage of positive CGs and the multiplicity of negative CG are higher, while the mean peak currents are lower than in previous studies on MCSs. A more detailed case analysis reveals that there are great differences among the CG characteristics of MCSs. The life cycle of CG lightning associated with MCSs is also analyzed. The growing stages of MCSs are characterised by high CG lightning activity. The positive CG flash rate generally reaches a maximum before the negative CG flash rate does. In both cases the peak is recorded before or when MCSs show the largest area. Maximum flash rate and densities coincide with the area where the MCS shows the minimum cloud-top temperatures and therefore shows highest vertical development., Depto. de Física de la Tierra y Astrofísica, Fac. de Ciencias Físicas, TRUE, pub

One of the main problems in practice is the difficulty in dealing with membership functions. Many decision makers ask for a graphical representation to help them to visualize results. In this paper, we point out that some useful tools for fuzzy classification can be derived from fuzzy coloring procedures. In particular, we bring here a crisp grey coloring algorithm based upon a sequential application of a basic black and white binary coloring procedure, already introduced in a previous paper [D. Gomez, J. Montero, J. Yañez, C. Poidomani, A graph coloring algorithm approach for image segmentation, Omega, in press]. In this article, the image is conceived as a fuzzy graph defined on the set of pixels where fuzzy edges represent the distance between pixels. In this way,we can obtain a more flexible hierarchical structure of colors, which in turn should give useful hints about those classes with unclear boundaries., Depto. de Estadística e Investigación Operativa, Fac. de Ciencias Matemáticas, TRUE, pub

This paper considers the dynamic features of housing prices in metropolises that are characterised by a high degree of internationalisation. Using the wavelet coherency procedure, the degree of comovement and causality between housing, stock markets, and macroeconomic uncertainty are investigated. In addition, the existence of volatility spillover across housing markets is assessed in the time-frequency domain using a novel procedure that involves combining the wavelet decomposition with a time varying parameter vector autoregression model. The results highlight that the clustering of global business in a limited number of metropolises that act as "global hubs" leaves the local housing markets exposed to international shocks and volatility spillover. The empirical analysis suggests that the correlations between real estate and stock markets from one side, and real estates and uncertainty on the other side, intensify during the turmoil periods. Causality and co-movement relationships appear predominately in the medium and long run periods. The evidence presented in this paper suggests that policymakers cannot ignore the possibility that international shocks to housing markets may affect the domestic markets. In this respect, macroprudential policy tools may target tapering off the unintended effects of housing market globalisation such as house price shock synchronisation, especially when these shocks take place in cities that are also major financial centres.

Given that the internal states of a system, such as position, velocity, acceleration, and other important factors, naturally obey the integral processes of a physical system in kinematics, this paper presents an adaptive noise filtering system that can reconstruct these system states at the kinematic level. This is done without using any prior knowledge of the statistical properties of measurement noises. In the proposed filtering system here, each noise-contaminated estimated state is filtered by an average filter to compensate for phase delay and amplitude distortion. Unlike existing model-based estimation methods, the dynamic equation is not explicitly used in the proposed method, and the uncertainties in the nonlinear dynamic equation can be isolated. Furthermore, this application is much more straightforward as there are no gains to be processed. To verify our proposed adaptive filtering system, it has been applied to a variable speed path-following control task for unmanned surface vehicles (USVs), where accurate system states must be known. In particular, this paper also proposes a state-constrained finite-time control framework to realize the path-following control objectives. The proposed controller here mainly consists of two parts, i.e., an online state-constrained polynomial planning function and an execution of an algebraic control law. Simulations and experiments have been conducted to validate the effectiveness and reliability of the proposed filtering system and the finite-time controller. The results show that the proposed filtering system considerably outperformed several of conventional observers such as the extended Kalman filter (EKF), the passive observer, as well as the high-order differentiator.

Low-carbon industrial upgrading is a central means to achieve long-term climate change mitigation and sustainable industrial development. Yet the empirical evidence on the effects of environmental policies on low-carbon industrial upgrading remains relatively rare, and the analysis of channels is unexamined. Based on the panel data of 30 provinces in China from 2000 to 2016, this paper has contributed to the literature by testing the impacts of four distinct environmental policies on low-carbon upgrading and their possible channels. We found that energy-saving goals (ESGs) and new energy subsidies (NESs) promoted low-carbon upgrading, while pollution discharge fees (PDFs) restrained low-carbon upgrading and emission trading schemes (ETSs) had no impact. These conclusions were still tenable after a series of robustness tests. Channel analysis reveals that ESGs realized both low-carbon innovation and high-carbon industry restructuring (especially reflected in manufacturing), NESs were effective, but only in directing innovation. PDFs did not improve low-carbon upgrading, possibly due to their negative effects in low-carbon fields (including two sub-fields: energy efficiency and renewable energy). The restructuring and innovative effects of ETSs were insignificant. Finally, this paper put forward several policy implementations, including the promotion of market mechanisms, more strict law enforcement, and low-carbon industry development.

This research paper introduces a novel approach for forecasting the severity of damage in column elements within a complex 3D concrete structure. The paper presents two innovative methods that have not been previously proposed. Firstly, a new equation is suggested to enhance the sensitivity of the dataset used for training and learning in machine learning techniques. This equation incorporates two crucial dynamic characteristics, namely frequencies and mode shapes. The second method involves a new technique for optimizing the architecture of a deep neural network (DNN) using K-means optimizer (KO), referred to KODNN. By employing KO, the optimal values for the DNN architecture and learning rate are determined. To assess the effectiveness of the proposed method, KODNN is employed to develop a model for predicting the compressive strength of concrete using benchmark datasets. A comparison is made between the obtained results from this example and those obtained using the original DNN model to demonstrate the performance improvement achieved. Finally, KODNN is utilized to predict the severity of damage in elements of a 3D concrete building under various temperature conditions, considering two specific damage cases. The results indicate that the proposed method achieves a high level of accuracy and reliability.

Introduction: In 2020, a working group of 13 renal pathologists published consensus definitions for 47 individual glomerular lesions found on light microscopy (LM) and 47 glomerular lesions and 9 normal structures found on electron microscopy (EM). Methods: To test the impact of these definitions on identification of these lesions and structures, 2 surveys were circulated to all members of the Renal Pathology Society (RPS), each having 32 images (19 LM, 13 EM) and accompanying questions with 5 multiple-choice answers, one being the consensus choice of the working group. The first survey (survey 1 [S1]), answered by 297 RPS members, was sent in September 2020, before publication of the consensus definitions. The second (survey 2 (S2]), with images of the same lesions and structures (but not the same images) and the same questions and multiple choices in different order, was sent in April 2020, 5 months after the publication of the definitions. Results: S2 was taken by 181 RPS members; 64% also took 51 and 61% reported having read the definitions paper (def. paper). Mean agreement with the consensus answers increased modestly between the 2 surveys (65.2% vs. 72.0%, P = 0.097); the increase was greater and significant when only respondents to S2 who read the def. paper were considered (65.2% vs. 74.8%, P = 0.026). Furthermore, in S2 agreement with consensus answers was greater among respondents who read this paper versus those who did not (66.9% vs. 74.8%, P < 0.0001). Conclusions: Publication of the consensus definitions modestly improved interobserver agreement in identification of glomerular lesions.

Reasoning about uncertainty is one of the main cornerstones of Knowledge Representation. More recently, combining logic with probability has been of major interest. Rough set methods have been proposed for modeling incompleteness and imprecision based on indiscernibility and its generalizations and there is a large body of work in this direction. More recently, the classical theory has been generalized to include probabilistic rough set methods of which there are also a great variety of proposals. Pragmatic, easily accessible, and easy to use tools for specification and reasoning with this wide variety of methods is lacking. It is the purpose of this paper to fill in that gap where the focus will be on probabilistic rough set methods. A landscape of (probabilistic) rough set reasoning methods and the variety of choices involved in specifying them is surveyed first. While doing this, an abstract generalization of all the considered approaches is derived which subsumes each of the methods. One then shows how, via this generalization, one can specify and reason about any of these methods using PROBLOG, a popular and widely used probabilistic logic programming language based on PROBLOG. The paper also considers new techniques in this context such as the use of probabilistic target sets when defining rough sets and the use of partially specified base relations that are also probabilistic. Additionally, probabilistic approaches using tolerance spaces are proposed. The paper includes a rich set of examples and provides a framework based on a library of generic PROBLOG relations that make specification of any of these methods, straightforward, efficient and compact. Complete, ready to run PROBLOG code is included in the Appendix for all examples considered., Funding Agencies|ELLIIT Network Organization for Information and Communication Technology, Sweden; Swedish Foundation for Strategic Research SSF [RIT15-0097]; Guangdong Department of Science and Technology, China [2020A1313030098]; National Science Centre Poland [2017/27/B/ST6/02018]

This paper presents a picture of the risk spillover relationship of green & black bonds in Asia. In normal situations and the long-term horizon, green bonds and black bonds have similar impacts with each other, with a slight predominance of black bonds. Based on the dynamic connectedness results, we classify the sample period into three stages. The first stage is a period with equal role of green and black bonds from Jan 2018 to Feb 2020, which is regarded as a normal situation of the bond market. The second stage is an unbalanced period with a pivotal point of the Covid-19, demonstrating an increased gap of the connectedness exported by green and black bonds. The third stage is the recovery period after Oct 2020, where we see a correction with the role of green and black bonds recovering gradually to the equal status. In addition, the green-to-black connectedness in longer term witnesses faster and stronger recovery, which suggests that the long-term influences of green bonds are relatively stable than the short-term influences. Moreover, the paper tests the effects of the same issuer. Our analysis shows that there are strong connections among bonds in the Philippines that are issued by the same institution. However, the same issuer is not a sufficient condition for a strong connectedness. Furthermore, our analysis in China Mainland, reveals that the green policy will firstly cause the change of green bonds price and then spillover the impact to conventional markets. Through the study of the drivers of connectedness dynamics in four directions (green-to-green, black-to-black, green-to-black, black-to-green), we present empirical findings that are crucial for investors and policymakers in risk management, hedging strategy, and green investment acceleration.

Regional economic disparity and environmental sustainability are two prioritized areas of societal concern. By focusing on how societal support and company ambitions influence firms decisions, this paper reveals the tension between environmental sustainability and attempts to create a balance between regions. The paper describes the consequences of allocation of environmental effects for and of regional economic disparity and thereby highlights a dualistic causality. A case study illustrates the point by focusing on transportation and location decisions. The paper concludes that the delicate dilemma of allocating environmental effects is a matter of balancing various interests, hence indicating a paradox. The paradox extends between regional economic disparity and environmental sustainability but also between societal concerns and firms decisions. The paper contributes to the debate on sustainability by indicating that allocation of environmental effects is not as straightforward as corporate social responsibility suggests. The paper also contributes to research on regional economic disparities by indicating that focusing attention on environmental effects may have regional consequences and thereby potentially threaten other goals set by society.

The environmental crisis induced by global warming has generated an increasingly heightened recognition of the imperative to embark on a journey toward sustainability. In this paper, we focus on the price correlations between two potential emission mitigation instruments: green bond (GB) and the carbon trading market. The CrossQuantilogram (CQ) method is employed to identify these relationships within Chinese markets spanning from July 16, 2021, to March 31, 2023. Our findings reveal a diverse array of dependence structures, which are most pronounced at the first lag and tend to diminish with subsequent lags. Further analysis through the Partial CrossQuantilogram (PCQ) approach reveals that the dependence structure is reshaped by both micro-market volatility and climate policy uncertainty, but the latter exerts influence at more extended lags. Likewise, these correlations are subject to temporal shifts, with coefficients displaying heightened magnitude and increased volatility in extreme scenarios compared to normal states. Therefore, investors and industries seeking to participate in the carbon trading market should closely monitor the performance of green bonds, particularly during tumultuous periods.

Based upon generalized spillover index method under Markov switching vector auto-regression (MS-VAR) framework, this paper aims at a regime dependent measure of the volatility spillover asymmetry across Shanghai, Hong Kong and the U.S. stock markets. For that, we apply a regime weighted measure of spillover and its asymmetry. The empirical results show that there existed evident negative asymmetric effect of spillover across the three markets, and it was regime dependent and time-varying: it got intensified under high volatility regime and turned out stronger after the Program of Shanghai-Hong Kong stock connect. The regime weighted measure of spillover asymmetry is found to be reliable; it could be explained appropriately with the economic policy uncertainty and investors fear in the U.S., extreme shocks like crash of Shanghai stock market and COVID-19 as well as the capital flow through the Program. From in-sample evidence and out-of-sample forecast of Shanghai stock market, we infer that traditional forecast models could be improved if regime dependent spillover and its asymmetry are considered as additional factors., Funding Agencies|National Social Science Foundation of China [18BJY240]; Overseas Visiting Fellow Program of Shanghai University & the Ministry of Sci- ence and Technology, China [G2023013030]; Overseas Visiting Fellow Program of Shanghai University; Ministry of Science and Technology, China [G2023013030]

At least three probabilistic definitions of hidden Markov models (HMMs) have been used frequently in the literature. Unfortunately, one of these definitions shows fatal flaws, however nowadays a lot of literature still uses this definition. The aim of this paper is on one hand to specifically point out one such fatal flaw (in terms of deriving the well-known forward-backward algorithm), and on the other hand to list key properties of HMMs under the other two plausible (and equivalent) probabilistic definitions for further developments. As applications, we rigorously layout forward-backward algorithms for inhomogeneous HMMs and hidden reciprocal models, and fully present connections between HMMs and undirected graphical models which are not mentioned anywhere in the literature, to the best of our knowledge., Funding Agencies|Swedish International Development Cooperation Agency (SIDA)

This paper investigates the role of cognitive ability and the Big Five factors in predicting individuals' probability of moving from rural to urban areas in China. Our estimation results suggest that a one standard deviation increase in cognitive ability is associated with a 0.7 percentage points increase in a person's migration probability during 2010-2020; while a one standard deviation increase in the personality trait openness to new experience correlates to a 0.8 percentage points increase in the probability of migration. Our estimates are substantial compared to unconditional migration probabilities, and are robust to issues such as reverse causality and measurement errors. We also provide suggestive evidence that cognitive ability may affect an individual's migration decision through the endowment channel, and personality traits may exert effects on one's migration probability through the preference channel.

In this paper, we investigate the association between net operating losses (NOLs) and Chapter 11 filings by firms that decided to restructure their business ("restructuring firms"). Studying a sample of public firms in the US over a 30-year period, we show that after controlling for the expected industry post-reorganization tax rates, higher NOLs are associated with the decision to file under Chapter 11. Further investigation shows that fresh-start firms benefit from lower tax rates after emerging from the Chapter 11 procedure. We contribute to the tax literature on NOLs and important firm decisions and on taxation in fresh-start firms, as well as to the literature on Chapter 11. We inform policymakers and practitioners regarding the use of tax benefits in restructuring frameworks.