Subjects: Psychology >> Social Psychology submitted time 2023-03-28 Cooperative journals: 《心理科学进展》
Abstract： Compulsivity is a core neuropsychological element connected to perseverations and persistent behaviors that are continued in the face of adverse consequences. It has been assumed that breakdown of the regulatory mechanism for compulsive behaviors in the brain might be a fundamental cause of drug addiction. Although addiction studies in recent decades have manifested a key role of the reward system (i.e., the meso-cortico-limbic circuitry) in addictive behaviors, little is known about the role of compulsivity per se and the neural substrates of the prefrontal cortex-anti-reward system circuitry implicated in the development of drug addiction. Particularly, there was lack of a systematic investigation of compulsivity linked to drug addiction, and family studies and convergent evidence from non-stimulants drugs (e.g., opiates) were rare. This project thus aimed to investigate the compulsivity profiles of heroin addicts and their healthy drug-free first-degree relatives (i.e., siblings), compared to healthy controls, from a family-risk perspective of addictive behaviors. A series of neurocognitive tasks, together with event-related potentials (ERPs) and neuroimaging techniques (e.g., functional magnetic resonance imaging, fMRI), would be employed to assess compulsivity facets and the potential neurophysiological correlates among these participants, aiming to probe into the hereditary feasibility and neural substrates of compulsivity in drug addiction. Expectantly, this project might be conducive to future discernment of potential medical or non-medical intervention targets for addictive disorders. This project would be mainly consisted of three human studies: (1) The purpose of the Study 1 was to compare the neurocognitive profiles of compulsive characteristics between heroin addicts, their healthy drug-free first-degree relatives (i.e., siblings), and irrelevant healthy controls, using the Padua Inventory of Obsessive Compulsive Disorder Symptoms (PI-WSUR), the Stop-Signal Task, the Stroop Task, the Intradimensional/Extradimensional set-shifting task (IDED), and the Probabilistic Reversal Learning Task (PRLT); (2) From the neurophysiological level, the Study 2 would test the associations of cognitive event-related potentials (ERPs) correlates (e.g., N200, P300) with compulsivity measured by several neurocognitive tasks, including the Go/No Go Task and the Probabilistic Reversal Learning Task (PRLT), among the heroin addicts, their healthy drug-free first-degree relatives (i.e., siblings), and irrelevant healthy controls; and (3) The Study 3, combining neurocognitive tasks (i.e., the Go/No Go Task, the Probabilistic Reversal Learning Task, and the Analogic Social Exclusion Task) with structural and functional Magnetic Resonance Imaging (fMRI), was targeted at exploring the neural substrates of the prefrontal and anti-reward systems involved in heroin addiction, such as the loops of the lateral orbitofrontal cortex-dorsal striatum and the medial orbitofrontal cortex-ventral striatum/central nucleus of the amygdala, between heroin addicts, their healthy drug-free first-degree relatives (i.e., siblings), and irrelevant healthy controls. Based on these studies, this project expected to improve the understandings of the susceptibility of compulsive traits and neural substrates of the prefrontal and anti-reward systems implicated in drug addiction. Generally, it was hypothesized that main neurocognitive deficits associated with compulsivity might be potential markers for heroin use disorder, that is, as the possible familial risk factors of compulsivity, these neurocognitive deficits would be increased not only in the heroin addicts, but also in their healthy drug-free first-degree relatives (i.e., siblings), compared with the irrelevant healthy controls. Furthermore, on the neurological bases, the abnormalities of certain neurophysiological components (e.g., N200、P300) tested by the event-related potentials (ERPs), would be expected to be highlighted both in the heroin addicts and their healthy drug-free first-degree relatives (i.e., siblings). Most importantly, the structural and functional neural abnormalities in the prefrontal and anti-reward systems, including the loops of the dorsolateral prefrontal cortex, the dorsal anterior cingulate cortex, the right inferior frontal gyrus to the dorsal striatum as well as the ventral prefrontal cortex and the orbitofrontal cortex to the central nucleus of the amygdala, which are mostly linked to cognitive control, stress responses and emotional control functions, might be involved in the poor performance of these compulsivity-related neurocognitive tasks (i.e., the Go/No Go Task, the Probabilistic Reversal Learning Task, and the Analogic Social Exclusion Task). Thus, this project would be of great help for detecting potential biological markers and intervention targets for addictive disorders.
Subjects: Mechanics >> Applied Mechanics submitted time 2023-03-20 Cooperative journals: 《应用力学学报》
Abstract： In order to improve the energy harvesting efficiency of the energy harvesting system at low windspeed range , a nonlinear dynamic magnet is introduced into the galloping energy harvesting system.A pairof magnets with opposite magnetic poles are installed at the end of the cantilever beam and the fixture,respectively.A dynamic magnet is connected to the base through a spring and it can move vertically withthe change of the magnetic repulsion force. Firstly , based on the energy method , the multi-field coupledvibration governing equation of dynamic magnetically coupled galloping energy harvesting system ( DM-GEH) is established. Next , the voltage outputs of the DM-GEH and the fixed magnetically coupled gallo-ping energy harvesting system ( FM-GEH ) under low wind speed are compared and analyzed by theRunge-Kutta method.Compared to the that of FM-CEH, the cut-in wind speed of the DM-GEH system is reduced by 81.82% , and the energy harvesting efficiency is increased by 124.22% in the wind speedrange of 1 m/s-5 m/s.Finally , the parameter of the spring support stiffness is optimized to improve theenergy harvesting eficiency at low wind speed. By changing the stiffness of elastic support , the vibrationfrequency will be varied and the cut-in wind speed of the system will be reduced.Compared to that of theDM-GEH with the spring stiffness of 1 000 N/m, the cut-in wind speed of the DM-CEH with the springstiffness of 500 N/m will be reduced by 54.55% , and the energy harvesting efficiency will be increased by15.35%.
Subjects: Psychology >> Medical Psychology submitted time 2021-03-21
Abstract： "
Peer Review Status:
Awaiting Review
Subjects: Computer Science >> Integration Theory of Computer Science submitted time 2018-06-19 Cooperative journals: 《计算机应用研究》
Abstract： The quality of the fuzzing test case is an important factor to affect the effectiveness of fuzzing. At present, the conventional generation method is random variation and artificial protocol analysis, which have the problems of low blind efficiency and high complexity of construction. In view of the above problems, this paper proposed the use of deep learning technology to assist test case generation. Using the advantage of recurrent neural network to deal with character text sequences, it learned training structure features through sample data, and predicted new data that conforms to structural features, and constructed an automatic generation model combined with random mutation algorithm. By using LSTM and GRU algorithm model to generate and evaluate the input type test case of PDF files, the test cases generated ware better than conventional methods with better pass rate and coverage rate. With the help of recurrent neural network, the method achieved the advantages of fast and efficient construction and low difficulty of construction, and achieves the balance of generating effect and cost.
Subjects: Civil Engineering and Building Construction >> Civil Construction Engineering submitted time 2017-12-21 Cooperative journals: 《土木建筑工程信息技术》
Abstract： There are many drawbacks in the present modeling of the grid structure in Revit. Due to the excesive number of components，the conventional modeling is heavy in workload and difficult in to check the errors and to modify them. Applying the volume modeling can be in speed , but the overall model is not accurate enough，and the component parameters cannot be set separately. In order to improve the design efficiency and the modeling accuracy，this paper uses Microsoft’s visual studio 2015 development platform for a secondary development of Revit to prepare a grid automation modeling program. This program can generate the Revit model of the welded spherical joints grid quickly，easily and accurately，through reading the LOG text to load the welded hollow sphere joint fami1lly and the member family. This program is suitable for the non-curved standard grid of welded spherical joints. The generated grid structure can be modified by the size of the corresponding components according to the LOG text. The overall size of the grid structure is in accordance with the actual situation，which greatly reduces the workload of the design staff.
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