274 research outputs found
Non-Abelian currents bootstrap
No full textWe initiate the study of correlation functions of non-Abelian spin-1 conserved current in three-dimensional conformal field theories using numerical conformal bootstrap. We discuss the general framework and apply it to the particular cases of SU(N) and O(N) global symmetry. In both cases, we obtain general bounds on operator dimensions. In the large-N limit our bounds show features in correspondence of the expected position of fermionic QED3 in three dimensions, as well as other interesting theories. By imposing gaps inspired by the spectrum of QED3 at large-N, we manage to restrict the plane of certain operator dimensions to a small island, where QED3 must live
PARAMETRIC LIFE CYCLE ASSESSMENT OF COMBINED COOLING, HEATING, AND POWER INTEGRATED WITH RENEWABLE ENERGY AND ENERGY STORAGE
No full textBuildings use about 40% of global energy supply, mainly from natural gas and electric grids powered by fossil fuel-based centralized power plants. This study examines a more sustainable energy generation system --- the distributed combined cooling, heating, and power integrated with renewable energy and energy storage system (CCHP-RE-ESS). A parametric hybrid life cycle assessment framework approach is used to evaluate the environmental, economic, and social impacts of the proposed distributed energy generation system. The rationale for a parametric LCA approach is that it extends conventional LCA, which is cases-specific and shows how impacts change with different input factors such as ambient temperature, climate, and operation strategies. The impact results integrate with a multi-objective optimization method, Pareto front, to find the optimal environmental and economic impact trade-offs for different building energy demand scenarios. The parametric framework includes six commercially available trigeneration technologies: two for prime movers (microturbine and fuel cells), two for renewables (solar power and small wind turbine), and two for energy storage (lithium-ion battery and compressed air energy storage). The model is able to find the best combination of technologies and their corresponding sizes for different building demand profiles. After billions of simulations, the Microturbine-Solar PVs-Lithium ion Battery and Fuel Cells-Solar PVs-Lithium-ion Battery are two optimal distributed energy solutions. The simulation impact result shows that the system can primarily reduce the environmental impact as compared to the conventional energy system. However, the life cycle cost of CCHP-RE-ESS is higher than the traditional energy generation, especially for fuel cell-based system.Finally, the model evaluates the social cost and the current U.S. clean energy policy incentives impacts on the distributed CCHP-RE-ESS system. The model uses the Air Pollution Emission Experiments and Policy model to evaluate the marginal damages emissions on a dollar per ton basis. Results show that the social cost of conventional energy is significantly higher than the distributed energy generation. Based on the simulation result, it is estimated that the installation of the distributed CCHP-RE-ESS can help avoid more than 50 billion dollars of social cost per year for commercial buildings in U.S. Besides, the model study the cost-saving potential of current U.S. clean energy policy incentives, including federal tax credit, low-interest loan, and Modified Accelerated Cost Recovery System (MACRS). The tax credit and MACRS can primarily reduce the cost of distributed energy by average 50%, while low-interest loan increases the cost by average 30%. In some scenarios, the after-policy life cycle cost of distributed energy generation is competitive compared to conventional power, but for most situations, the life cycle cost is still higher as compared to conventional power.Ph.D
Who Learns More? Cultural Differences in Implicit Sequence Learning
No full textBackground: It is well documented that East Asians differ from Westerners in conscious perception and attention. However, few studies have explored cultural differences in unconscious processes such as implicit learning. Methodology/Principal Findings: The global-local Navon letters were adopted in the serial reaction time (SRT) task, during which Chinese and British participants were instructed to respond to global or local letters, to investigate whether culture influences what people acquire in implicit sequence learning. Our results showed that from the beginning British expressed a greater local bias in perception than Chinese, confirming a cultural difference in perception. Further, over extended exposure, the Chinese learned the target regularity better than the British when the targets were global, indicating a global advantage for Chinese in implicit learning. Moreover, Chinese participants acquired greater unconscious knowledge of an irrelevant regularity than British participants, indicating that the Chinese were more sensitive to contextual regularities than the British. Conclusions/Significance: The results suggest that cultural biases can profoundly influence both what people consciously perceive and unconsciously learn
Cognitive deterioration in predicting relapse of psychosis
No full textA large proportion of patients with psychosis experience the obstacles imposed by relapse, incurring huge economic and psychological burden. Therefore, relapse prevention is an important target in long-term management of psychosis, and accurate identification of individuals who are at increased risks will largely facilitate this process. Only few predictors have been confirmed to predict relapse, such as medication non-adherence and stressful life events.
Because cognitive deficits have been found to precede initial onset, they could also be relevant to predict subsequent psychotic episodes. Given its potential to be objectively measured and prospectively traced over time, and further echoing the recent trend in mobile health technologies that could support timely and remote assessments, cognitive deficits and/or deterioration could contribute useful insights to relapse monitoring. Nevertheless, its role as an early warning sign in relapse prediction remains largely unexplored.
In this study, cognitive deterioration was explored as a main predictor of relapse, alongside some other clinical and psychosocial factors, in a naturalistic observational study with a one-year prospective follow-up design. A total of 120 patients with psychosis who were in full clinical remission for at least six months were included and followed up for one year, or until relapse, whichever earlier. Following baseline assessment, follow-up assessments were scheduled on a monthly basis, where cognitive performance was measured at each assessment with a verbal (Letter Number Sequencing) and a visual (Visual Patterns Test) working memory task, via a mobile App. Cognitive deterioration was defined as worsened performance over a two-month period prior to relapse, or study termination.
At one year, 18 of 110 (16.36%) patients relapsed. Potential factors related to relapse were first identified through univariate between-group comparisons, which were then entered into a binary multiple logistic regression. The final model explained around 60% of variance in relapse, and revealed the independent and statistically significant contributions of three factors, i.e., cognitive deterioration on the verbal working memory task (Letter Number Sequencing), low resilience, as well as medication non-adherence.
This is one of the first studies to establish the predictive role of cognitive deterioration in psychotic relapse. It not only supports the promising direction for further research into cognitive predictors, but also uncovers important clinical significance of cognitive deterioration in relapse monitoring, early relapse identification, as well as suggests the therapeutic potentials of cognition-based training programs that could further translate into favorable clinical outcomes.published_or_final_versionPsychiatryMasterMaster of Philosoph
China's oil reserve forecast and analysis based on peak oil models
No full textIn order to forecast future oil production it is necessary to know the size of the reserves and use models. In this article, we use the typical Peak Oil models, the Hu-Chen-Zhang model usually called HCZ model and the Hubbert model, which have been used commonly for forecasting in China and the world, to forecast China's oil Ultimate Recovery (URR). The former appears to give more realistic results based on an URR for China of 15.64 billion tons. The study leads to some suggestions for new policies to meet the unfolding energy situation.Ultimate Recovery Peak oil models Energy policy
SnO2-based R134a gas sensor: Sensing materials preparation, gas response and sensing mechanism
No full textPhase-shift, targeted nanoparticles for ultrasound molecular imaging by low intensity focused ultrasound irradiation
No full textMaoping Li,1,2 Hua Luo,3 Weiyang Zhang,1 Kunyan He,4 Yong Chen,3 Jianxin Liu,2 Junchen Chen,5 Dong Wang,1 Lan Hao,2 Haitao Ran,2 Yuanyi Zheng,2 Zhigang Wang,2 Pan Li2 1Department of Ultrasound, The First Affiliated Hospital, Chongqing Medical University, Chongqing 400016, China; 2Institute of Ultrasound Imaging, Chongqing Medical University, Chongqing 400010, China; 3Chongqing Protein way Biotechnology Co., Ltd., Chongqing 400039, China; 4The Fifth Affiliated Hospital of Sun Yat-sen University, Guangzhou, 519000, China; 5Hunan Key Laboratory of Skin Cancer and Psoriasis, Changsha, 410008, China Purpose: Ultrasound (US) molecular imaging provides a non-invasive way to visualize tumor tissues at molecular and cell levels and could improve diagnosis. One problem of using US molecular imaging is microbubbles challenges, including instability, short circulation time, and poor loading capacity and penetrability. It is urgent to design new acoustic contrast agents and new imaging methods to facilitate tumor-targeted imaging. In this study, phase-shift poly lactic-co-glycolic acid (PLGA) nanoparticles modified with folate as an efficient US molecular probe were designed and the long–term targeted imaging was achieved by low-intensity focused US (LIFU) irradiation. Methods: A new 5-step method and purification procedure was carried out to obtain uniform folic acid polyethylene glycol PLGA (PLGA-PEG-FA), the structure of which was confirmed by 1H nuclear magnetic resonance spectroscopy and thin-layer chromatography. Perflenapent (PFP) was wrapped in PLGA-PEG-FA by a double emulsion solvent evaporation method to obtain PFP/PLGA-PEG-FA nanoparticles. The targeted ability of the resulting nanoparticles was tested in vivo and in vitro. LIFU irradiation can irritate nanoparticle phase-shift to enhance tumor imaging both in vivo and in vitro. Results: PLGA-PEG-FA was a light yellow powder with a final purity of at least 98%, the structure of which was confirmed by 1H nuclear magnetic resonance spectroscopy and thin-layer chromatography. Highly dispersed PFP/PLGA-PEG-FA nanoparticles with spherical morphology have an average diameter of 280.9±33.5 nm, PFP load efficiency of 59.4%±7.1%, and shells, thickness of 28±8.63 nm. The nanoparticles can specifically bind to cells expressing high folate receptor both in vivo and in vitro. Ultrasonic imaging was significantly enhanced in vitro and in vivo by LIFU irradiation. The retention time was significantly prolonged in vivo. Conclusion: Phase-shift PFP/PLGA-PEG-FA nanoparticles induced by LIFU can significantly enhance ultrasonic imaging, specifically targeting tumors expressing folate receptor. As a potential targeting acoustic molecular probe, PFP/PLGA-PEG-FA nanoparticles can be used to achieve targeted localization imaging. Keywords: folic acid, targeted, phase-shift, nanoparticles, acoustic contrast agen
Negative Affect Reduces Performance in Implicit Sequence Learning
No full textBackground: It is well documented that positive rather than negative moods encourage integrative processing of conscious information. However, the extent to which implicit or unconscious learning can be influenced by affective states remains unclear. Methodology/Principal Findings: A Serial Reaction Time (SRT) task with sequence structures requiring integration over past trials was adopted to examine the effect of affective states on implicit learning. Music was used to induce and maintain positive and negative affective states. The present study showed that participants in negative rather than positive states learned less of the regularity. Moreover, the knowledge was shown by a Bayesian analysis to be largely unconscious as participants were poor at recognizing the regularity. Conclusions/Significance: The results demonstrated that negative rather than positive affect inhibited implicit learning of complex structures. Our findings help to understand the effects of affective states on unconscious or implicit processing
An Improved Frequency Domain Guided Thermal Imager Strips Removal Algorithm Based on LRSID
No full textThe thermal imaging image of the Sustainable Development Science Satellite (SDGSAT-1) is mainly used for high-resolution observations of the ground width, due to the influence of blind elements and non-uniformity of the detector, and the system is a pendulum sweep imaging mode, resulting in fringed noise in the image. In this paper, a Fringing algorithm based on LRSID (low-rank-based single-image decomposition) algorithm is proposed, which can effectively remove the lateral and vertical fringe noise of the thermal imager and maintain the detail and clarity of the image. First, pretreatment of the obvious light and dark stripes then, based on LLSID algorithm, the vertical direction pinstripes and horizontal stripes are processed; finally, the fringed frequency band of the original image is replaced in the frequency domain with the image frequency domain processed by the LRSID algorithm, and then the Fourier inverse transformation is performed to obtain the final image. Using the method proposed in this paper, the simulated and actual SDGSAT-1 thermal imaging camera remote sensing stripes images are removed, and the visual and quantitative indicators are compared with the processing results of other algorithms, and the results show that the proposed algorithm has the best performance to remove the stripes, which can effectively remove horizontal and vertical fringes at the same time, and retain the detail and clarity of the image
Conformal bootstrap bounds for the Dirac spin liquid and Stiefel liquid
No full textWe apply the conformal bootstrap technique to study the Dirac spin
liquid (i.e. QED) and the newly proposed Stiefel liquid (i.e.
a conjectured 3d non-Lagrangian CFT without supersymmetry). For the
QED, we focus on the monopole operator and ( adjoint) fermion
bilinear operator. We bootstrap their single correlators as well as the mixed
correlators between them. We first discuss the bootstrap kinks from single
correlators. Some exponents of these bootstrap kinks are close to the expected
values of QED, but we provide clear evidence that they should not be
identified as the QED. By requiring the critical phase to be stable on the
triangular and the kagome lattice, we obtain rigorous numerical bounds for the
Dirac spin liquid and the Stiefel liquid. For the triangular and kagome
Dirac spin liquid, the rigorous lower bounds of the monopole operator's scaling
dimension are and , respectively. These bounds are consistent
with the latest Monte Carlo results.Comment: 14 pages plus refs, 3 figures. Close to the published versio
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