The level effect and volatility effect of uncertainty shocks in China

Previous studies have assumed that the volatility of exogenous shocks is constant, which can only measure the level effects of uncertain shocks. This article introduces the time-varying volatility model into a Dynamic Stochastic General Equilibrium (D.S.G.E.) model and uses the third-order perturbation method to identify and decompose the level and volatility effects of uncertainty shocks. Based on the results of empirical research in China, the effect of volatility shocks is different from that of level shocks: the effect of level shocks is direct and positive, and its impact is larger, while the effect of volatility shocks is indirect and negative, and its impact is smaller. This article also finds that the impact of uncertainty shocks will lead to economic stagnation, inflation, and the stagflation effect

Neural Multi-Objective Combinatorial Optimization with Diversity Enhancement

Most of existing neural methods for multi-objective combinatorial optimization (MOCO) problems solely rely on decomposition, which often leads to repetitive solutions for the respective subproblems, thus a limited Pareto set. Beyond decomposition, we propose a novel neural heuristic with diversity enhancement (NHDE) to produce more Pareto solutions from two perspectives. On the one hand, to hinder duplicated solutions for different subproblems, we propose an indicator-enhanced deep reinforcement learning method to guide the model, and design a heterogeneous graph attention mechanism to capture the relations between the instance graph and the Pareto front graph. On the other hand, to excavate more solutions in the neighborhood of each subproblem, we present a multiple Pareto optima strategy to sample and preserve desirable solutions. Experimental results on classic MOCO problems show that our NHDE is able to generate a Pareto front with higher diversity, thereby achieving superior overall performance. Moreover, our NHDE is generic and can be applied to different neural methods for MOCO.Comment: Accepted at NeurIPS 202

Involvement of 5-HT1A receptors of the thalamic descending pathway in the analgesic effect of intramuscular heating-needle stimulation in a rat model of lumbar disc herniation

BackgroundIntramuscular (IM) heating-needle therapy, a non-painful thermal therapy, has been found to exert an analgesic effect via the thalamic ventromedial (VM) nucleus, solely by reducing the triggering threshold for descending inhibition; this could be modulated by intracephalic 5-hydroxytryptamine-1A (5-HT1A) receptors, rather than via the regular analgesia pathway. In this study, the effect and the potential serotonergic mechanism of IM heating-needle stimulation at 43°C were explored in the case of the pathological state of lumbar disc herniation (LDH).MethodsA modified classic rat model of LDH, induced via autologous nucleus pulposus implantation, was utilized. IM inner heating-needles were applied at the attachment point of skeletal muscle on both sides of the L4 and L5 spinous processes. WAY-100635 and 8-OH-DAPT, 5-HT1A receptor antagonist and agonist, were separately injected into the bilateral thalamic mediodorsal (MD) and VM nucleus via an intrathalamic catheter. Nociception was assessed by bilateral paw withdrawal reflexes elicited by noxious mechanical and heat stimulation.ResultsIM heating-needle stimulation at a temperature of 43°C for 30 or 45 min significantly relieved both mechanical and heat hyperalgesia in the rat model of LDH (P < 0.05). Heat hyperalgesia was found to be significantly enhanced by administration of WAY-100635 into the thalamic VM nucleus, blocking the effect of heating-needle stimulation in a dose-dependent manner (P < 0.05), while no effects were detected after injection into the thalamic MD nucleus (P > 0.05). Injection of 8-OH-DAPT into the thalamic MD nucleus exerted no modulating effects on either mechanical or heat hyperalgesia (P > 0.05).ConclusionIM heating-needle stimulation at 43°C for 30 min may activate 5-HT1A mechanisms, via the thalamic VM nucleus, to attenuate hyperalgesia in a rat model of LDH. This innocuous form of thermal stimulation is speculated to selectively activate the descending inhibition mediated by the thalamic VM nucleus, exerting an analgesic effect, without the involvement of descending facilitation of the thalamic MD nucleus

Research progress of chilled meat freshness detection based on nanozyme sensing systems

peer reviewedIt is important to develop rapid, accurate, and portable technologies for detecting the freshness of chilled meat to meet the current demands of meat industry. This report introduces freshness indicators for monitoring the freshness changes of chilled meat, and systematically analyzes the current status of existing detection technologies which focus on the feasibility of using nanozyme for meat freshness sensing detection. Furthermore, it examines the limitations and foresees the future development trends of utilizing current nanozyme sensing systems in evaluating chilled meat freshness. Harmful chemicals are produced by food spoilage degradation, including biogenic amines, volatile amines, hydrogen sulfide, and xanthine, which have become new freshness indicators to evaluate the freshness of chilled meat. The recognition mechanisms are clarified based on the special chemical reaction with nanozyme or directly inducting the enzyme-like catalytic activity of nanozyme

Could social robots facilitate children with autism spectrum disorders in learning distrust and deception?

Social robots have been increasingly involved in our daily lives and provide a new environment for children\u27s growth. The current study aimed to examine how children with and without Autism Spectrum Disorders (ASD)learned complex social rules from a social robot through distrust and deception games. Twenty children with ASD between the ages of 5–8 and 20 typically-developing (TD)peers whose age and IQ were matched participated in distrust and deception tasks along with an interview about their perception of the human-likeness of the robot. The results demonstrated that: 1)children with ASD were slower to learn to and less likely to distrust and deceive a social robot than TD children and 2)children with ASD who perceived the robot to appear more human-like had more difficulty in learning to distrust the robot. Besides, by comparing to a previous study the results showed that children with ASD appeared to have more difficulty in learning to distrust a human compared to a robot, particularly in the early phase of learning. Overall, our study verified that social robots could facilitate children with ASD\u27s learning of some social rules and showed that children\u27s perception of the robot plays an important role in their social learning, which provides insights on robot design and its clinical applications in ASD intervention

Modeling Bedload Transport Trajectories along a Sine-generated Channel

This study explores the influences of flow discharge and particle size on bedload transport trajectory by applying a depth-averaged two-dimensional model to a 110° sine-generated laboratory flume with wide-and-shallow sections. Calculated results exhibit two erosion regions in a bend: Zone-1 ‒ foreside of the point bar near the convex bank and Zone-2 ‒ near the apex of the concave bank. Sediments eroded from Zone-1 are mainly transported along the same-side convex bank rather than crossing the channel centerline, indicating the crucial role of longitudinal flow in shaping point bars. Most particles from Zone-2, however, behave more complicated by changing their trajectories with the developing bar-pool topography. Besides, sensitivity analyses indicate that, the shifting of bedload trajectory in the curved channel is not susceptible to particle size while considerably varies with flow discharge. Moving particles in a meandering channel are ultimately constrained within the belt of “concave bank‒crossing bar‒concave bank” after the bend topography is fully developed and the bed deformation reaches a dynamic equilibrium

Method of Eliminating Helicopter Vibration Interference Magnetic Field with a Pair of Magnetometers

The low-frequency electromagnetic fields and magnetic anomalies generated by ships and other underwater platforms are widely recognized as important features for ocean target detection. Low-frequency magnetic fields and anomalies are typically measured by optically pumped magnetometers installed on aircraft. However, the interference that is generated by the aircraft platform may significantly affect the detection performance. The traditional aeromagnetic compensation model has a good effect on eliminating the interference magnetic field that is caused by the carrier attitude variation. Usually, the magnetometer is fixed at the top of a long probe on the aircraft to avoid the influence from the main body in the aircraft. However, the probe is sensitive to external vibrations, and vibration-induced magnetic interference can occur in the measurements. The magnetometer is especially easily affected by the interference magnetic field, including the vibration frequency and harmonic frequency of the probe, in a moving platform, such as a helicopter. These interference fields usually have independent frequency characteristics that can be eliminated by compensation methods. In this paper, we propose a method based on the improved coherent noise suppression method with a pair of magnetometers to eliminate the effects from these magnetic field disturbances and improve the detection performance of the measurement system. The results of the flight experiment show that the method can effectively eliminate low-frequency vibration interference and improve the detection ability of weak signals from targets

Electroplating sludge-derived magnetic copper-containing catalysts for selective hydrogenation of bio-based furfural

A series of inexpensive electroplating sludge-derived magnetic copper-containing catalysts were developed for the selective hydrogenation of biomass-based furfural (FFR) into furfuryl alcohol (FA) and 2-methylfuran (MF). The specific structural characteristics of as-prepared magnetic copper-containing samples were clearly identified through various techniques, including XRD, XPS, N-2 adsorption-desorption, NH3-TPD, SEM and so on. The characterizations revealed that the hydrogen pre-activated magnetic catalysts supplied metallic Cu species, medium acidity and porosity for the catalytic upgrading of FFR in hydrogen atmosphere. As for FFR-to-FA as well as FFR-to-MF transformations, the magnetic copper-containing catalyst with calcination temperature of 800 degrees C exhibited excellent performance towards the formation of FA and MF, wherein desirable FA yield of 98.5 mol% and MF yield of 71.3 mol% were achieved at reaction temperatures of 160 degrees C and 240 degrees C, respectively. The reuse experiments indicated that the recycled catalysts still maintained excellent activity and stability even after four-time recycling. The present study thus highlights a new approach for the resource utilization of electroplating sludge, which also supplies low-cost and efficient catalytic materials for the selective upgrading of various biomass-derived platform molecules

Learning large neighborhood search for vehicle routing in airport ground handling

Dispatching vehicle fleets to serve flights is a key task in airport ground handling (AGH). Due to the notable growth of flights, it is challenging to simultaneously schedule multiple types of operations (services) for a large number of flights, where each type of operation is performed by one specific vehicle fleet. To tackle this issue, we first represent the operation scheduling as a complex vehicle routing problem and formulate it as a mixed integer linear programming (MILP) model. Then given the graph representation of the MILP model, we propose a learning assisted large neighborhood search (LNS) method using data generated based on real scenarios, where we integrate imitation learning and graph convolutional network (GCN) to learn a destroy operator to automatically select variables, and employ an off-the-shelf solver as the repair operator to reoptimize the selected variables. Experimental results based on a real airport show that the proposed method allows for handling up to 200 flights with 10 types of operations simultaneously, and outperforms state-of-the-art methods. Moreover, the learned method performs consistently accompanying different solvers, and generalizes well on larger instances, verifying the versatility and scalability of our method.</p