On the performance of US fiscal forecasts : government vs. private information

This paper contributes to shed light on the quality and performance of US fiscal forecasts. The first part inspects the causes of official (CBO) fiscal forecasts revisions between 1984 and 2016 that are due to technical, economic or policy reasons. Both individual and cumulative means of forecast errors are relatively close to zero, particularly in the case of expenditures. CBO averages indicate net average downward revenue and expenditure revisions and net average upward deficit revisions. Focusing on the causes of the technical component, we uncover that its revisions are quite unpredictable which casts doubts on inferences about fiscal policy sustainability that rely on point estimates. Comparing official with private-sector (Consensus) forecasts, despite the informational advantages CBO might have, one cannot unequivocally say that one or the other is more accurate. Evidence also seems to suggest that CBO forecasts are consistently heavily biased towards optimism while this is less the case for Consensus forecasts. Not only is the extent of information rigidity is more prevalent in CBO forecasts, but evidence also seems to indicate that Consensus forecasts dominate CBO’s in terms of information content.info:eu-repo/semantics/publishedVersio

Pinning control of fractional-order weighted complex networks

In this paper, we consider the pinning control problem of fractional-order weighted complex dynamical networks. The well-studied integer-order complex networks are the special cases of the fractional-order ones. The network model considered can represent both directed and undirected weighted networks. First, based on the eigenvalue analysis and fractional-order stability theory, some local stability properties of such pinned fractional-order networks are derived and the valid stability regions are estimated. A surprising finding is that the fractional-order complex networks can stabilize itself by reducing the fractional-order q without pinning any node. Second, numerical algorithms for fractional-order complex networks are introduced in detail. Finally, numerical simulations in scale-free complex networks are provided to show that the smaller fractional-order q, the larger control gain matrix D, the larger tunable weight parameter , the larger overall coupling strength c, the more capacity that the pinning scheme may possess to enhance the control performance of fractional-order complex networks

Multiobjective synchronization of coupled systems

Copyright @ 2011 American Institute of PhysicsSynchronization of coupled chaotic systems has been a subject of great interest and importance, in theory but also various fields of application, such as secure communication and neuroscience. Recently, based on stability theory, synchronization of coupled chaotic systems by designing appropriate coupling has been widely investigated. However, almost all the available results have been focusing on ensuring the synchronization of coupled chaotic systems with as small coupling strengths as possible. In this contribution, we study multiobjective synchronization of coupled chaotic systems by considering two objectives in parallel, i. e., minimizing optimization of coupling strength and convergence speed. The coupling form and coupling strength are optimized by an improved multiobjective evolutionary approach. The constraints on the coupling form are also investigated by formulating the problem into a multiobjective constraint problem. We find that the proposed evolutionary method can outperform conventional adaptive strategy in several respects. The results presented in this paper can be extended into nonlinear time-series analysis, synchronization of complex networks and have various applications

Feedback learning particle swarm optimization

This is the author’s version of a work that was accepted for publication in Applied Soft Computing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published and is available at the link below - Copyright @ Elsevier 2011In this paper, a feedback learning particle swarm optimization algorithm with quadratic inertia weight (FLPSO-QIW) is developed to solve optimization problems. The proposed FLPSO-QIW consists of four steps. Firstly, the inertia weight is calculated by a designed quadratic function instead of conventional linearly decreasing function. Secondly, acceleration coefficients are determined not only by the generation number but also by the search environment described by each particle’s history best fitness information. Thirdly, the feedback fitness information of each particle is used to automatically design the learning probabilities. Fourthly, an elite stochastic learning (ELS) method is used to refine the solution. The FLPSO-QIW has been comprehensively evaluated on 18 unimodal, multimodal and composite benchmark functions with or without rotation. Compared with various state-of-the-art PSO algorithms, the performance of FLPSO-QIW is promising and competitive. The effects of parameter adaptation, parameter sensitivity and proposed mechanism are discussed in detail.This research was partially supported by the National Natural Science Foundation of PR China (Grant No 60874113), the Research Fund for the Doctoral Program of Higher Education (Grant No 200802550007), the Key Creative Project of Shanghai Education Community (Grant No 09ZZ66), the Key Foundation Project of Shanghai(Grant No 09JC1400700), the International Science and Technology Cooperation Project of China under Grant 2009DFA32050, and the Alexander von Humboldt Foundation of Germany

Parameters identification of unknown delayed genetic regulatory networks by a switching particle swarm optimization algorithm

The official published version can be found at the link below.This paper presents a novel particle swarm optimization (PSO) algorithm based on Markov chains and competitive penalized method. Such an algorithm is developed to solve global optimization problems with applications in identifying unknown parameters of a class of genetic regulatory networks (GRNs). By using an evolutionary factor, a new switching PSO (SPSO) algorithm is first proposed and analyzed, where the velocity updating equation jumps from one mode to another according to a Markov chain, and acceleration coefficients are dependent on mode switching. Furthermore, a leader competitive penalized multi-learning approach (LCPMLA) is introduced to improve the global search ability and refine the convergent solutions. The LCPMLA can automatically choose search strategy using a learning and penalizing mechanism. The presented SPSO algorithm is compared with some well-known PSO algorithms in the experiments. It is shown that the SPSO algorithm has faster local convergence speed, higher accuracy and algorithm reliability, resulting in better balance between the global and local searching of the algorithm, and thus generating good performance. Finally, we utilize the presented SPSO algorithm to identify not only the unknown parameters but also the coupling topology and time-delay of a class of GRNs.This research was partially supported by the National Natural Science Foundation of PR China (Grant No. 60874113), the Research Fund for the Doctoral Program of Higher Education (Grant No. 200802550007), the Key Creative Project of Shanghai Education Community (Grant No. 09ZZ66), the Key Foundation Project of Shanghai (Grant No. 09JC1400700), the Engineering and Physical Sciences Research Council EPSRC of the UK under Grant No. GR/S27658/01, the International Science and Technology Cooperation Project of China under Grant No. 2009DFA32050, an International Joint Project sponsored by the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany

Controller design for synchronization of an array of delayed neural networks using a controllable

This is the post-print version of the Article - Copyright @ 2011 ElsevierIn this paper, a controllable probabilistic particle swarm optimization (CPPSO) algorithm is introduced based on Bernoulli stochastic variables and a competitive penalized method. The CPPSO algorithm is proposed to solve optimization problems and is then applied to design the memoryless feedback controller, which is used in the synchronization of an array of delayed neural networks (DNNs). The learning strategies occur in a random way governed by Bernoulli stochastic variables. The expectations of Bernoulli stochastic variables are automatically updated by the search environment. The proposed method not only keeps the diversity of the swarm, but also maintains the rapid convergence of the CPPSO algorithm according to the competitive penalized mechanism. In addition, the convergence rate is improved because the inertia weight of each particle is automatically computed according to the feedback of fitness value. The efficiency of the proposed CPPSO algorithm is demonstrated by comparing it with some well-known PSO algorithms on benchmark test functions with and without rotations. In the end, the proposed CPPSO algorithm is used to design the controller for the synchronization of an array of continuous-time delayed neural networks.This research was partially supported by the National Natural Science Foundation of PR China (Grant No 60874113), the Research Fund for the Doctoral Program of Higher Education (Grant No 200802550007), the Key Creative Project of Shanghai Education Community (Grant No 09ZZ66), the Key Foundation Project of Shanghai(Grant No 09JC1400700), the Engineering and Physical Sciences Research Council EPSRC of the U.K. under Grant No. GR/S27658/01, an International Joint Project sponsored by the Royal Society of the U.K., and the Alexander von Humboldt Foundation of Germany

Shenxian-Shengmai Oral Liquid Reduces Myocardial Oxidative Stress and Protects Myocardium from Ischemia-Reperfusion Injury

Background/Aims: Shenxian-shengmai (SXSM) oral liquid, a Chinese patent compound medicine, has been used to treat sinus bradyarrhythmias induced by mild sick sinus syndrome in clinical practice. Myocardial ischemia, in particular in serious or right coronary-related heart diseases, can cause bradyarrhythmias and cardiac dysfunction. Moreover, reperfusion of ischemic myocardium is associated with additional myocardial damage known as myocardial ischemia-reperfusion (I/R) injury. This study was designed to evaluate the effects of SXSM on bradyarrhythmias and cardiac dysfunction induced by myocardial I/R injury, and to explore the underlying mechanisms. Methods: Administration of SXSM to adult male Sprague Dawley (SD) rats was achieved orally by gavage and control rats were given equivalent deionized water every day for 14 days. After the last administration, the heart was connected with the Langendorff perfusion apparatus and both groups were subjected to ischemia for 20 min followed by reperfusion for 40 min to induce myocardial I/R injury. Heart rate (HR), left ventricular developed pressure (LVDP), the maximal increase rate of left ventricular pressure (+dp/dtmax) and the maximal decrease rate of left ventricular pressure (-dp/dtmax) were recorded by a physiological signal acquisition system. The heart treated with ischemic preconditioning (IPC) for 3 times at a range of 5 min/time before ischemia served as a positive control group. The hearts without I/R injury served as control group. After reperfusion, superoxide dismutase (SOD), glutathione (GSH) and glutathione peroxidase (GSH-Px) activities in the myocardium were determined by appropriate assay kits. Myocardial SOD1 and glutamate cysteine ligase catalytic subunit (GCLC) expression were assessed by western blot analysis. For the in vitro study, SXSM serum was prepared according to the serum pharmacological method and neonatal rat cardiomyocytes were isolated from the heart of new born SD rats. Neonatal rat cardiomyocytes were pretreated with SXSM serum and subjected to H2O2 or anoxia/ reoxygenation (A/R) treatment to induce oxidative damage. Cell viability was evaluated using a Cell Counting Kit-8 (CCK8) assay. Levels of reactive oxygen species (ROS), SOD, GSH and GSH-Px in cardiomyocytes were determined by appropriate assay kits. SOD1 and GCLC expression were assessed by western blot analysis. Buthionine-[S, R]-sulfoximine (BSO), a GCLC inhibitor, and SOD1 siRNA were also used for identifying the cardiac protective targets of SXSM. Results: SXSM and ischemic preconditioning (IPC) significantly increased heart rate during myocardial reperfusion and protected cardiac function against myocardial I/R injury, including an increase in left ventricular diastolic pressure (LVDP), the maximal increase rate of left ventricular pressure (+dp/dtmax) and the maximal decrease rate of left ventricular pressure (-dp/dtmax). We also found that SXSM and IPC improved the expansion of myocardial interstitium, the structural abnormality and morphological changes of cardiomyocytes induced by I/R injury. Meanwhile, SXSM protected cardiomyocytes against the oxidative damage induced by H2O2 and A/R injury through reducing intracellular ROS levels. Moreover, SXSM increased SOD activity through enhancing SOD1 expression and increased GSH content through promoting GCLC expression as well as GSH-Px activity. BSO and SOD1 siRNA counteracted anti-arrhythmic and cardiac protective effect of SXSM, suggesting that the therapeutic targets of SXSM might be SOD1 and GCLC. Conclusion: SXSM is effective in protecting the myocardium from I/R injury, with myocardial SOD1 and GCLC being the potential therapeutic targets

On the Substitution and Complementarity between Robots and Labor: Evidence from Advanced and Emerging Economies

This paper aims to empirically study the short-term relationship between robot adoption and the labor market across a diverse set of advanced and emerging economies. Additionally, it seeks to analyze the impact of macroeconomic and institutional factors on this relationship. This study reveals robot adoption promotes employment growth in advanced economies, while it has a negative effect on employment in emerging economies. This heterogeneity can be attributed to both direct and indirect linkages between robots and labor in production. Directly, robots can either substitute or complement human labor. Indirectly, robot adoption stimulates output growth, leading to increased labor demand. We also show that the robot–labor relationship is influenced by macroeconomic variables such as development stage, unemployment rate, and education level, as well as institutional variables such as business regulation and structural reforms. These findings suggest the need for a more inclusive and sustainable approach to the advancement of robot adoption and automation