Effects of system-bath entanglement on the performance of light-harvesting systems: A quantum heat engine perspective

We explore energy transfer in a generic three-level system, which is coupled to three non-equilibrium baths. Built on the concept of quantum heat engine, our three-level model describes non-equilibrium quantum processes including light-harvesting energy transfer, nano-scale heat transfer, photo-induced isomerization, and photovoltaics in double quantum-dots. In the context of light-harvesting, the excitation energy is first pumped up by sunlight, then is transferred via two excited states which are coupled to a phonon bath, and finally decays to the ground state. The efficiency of this process is evaluated by steady state analysis via a polaron-transformed master equation; thus a wide range of the system-phonon coupling strength can be covered. We show that the coupling with the phonon bath not only modifies the steady state, resulting in population inversion, but also introduces a finite steady state coherence which optimizes the energy transfer flux and efficiency. In the strong coupling limit, the steady state coherence disappears and the efficiency approaches the heat engine limit given by Scovil and Schultz-Dubois in Phys. Rew. Lett. 2, 262 (1959).Comment: 10 pages, 8 figures, all comments are welcom

Examining Student Learning and Perceptions in Social Annotation-Based Translation Activities

Limited research has been conducted on how to incorporate computer-supported collaborative learning into translation instruction despite the potential benefits. A study was conducted with a group of college English majors in China to examine the effects of using a social annotation tool to encourage student interaction during translation activities. The results showed that students made greater improvement when they completed the translation assignments with the support of a social annotation tool than when they completed the assignments in the traditional way. In addition, students had a positive attitude toward the use of the social annotation tool

Forecasting of global horizontal irradiance by exponential smoothing, using decompositions

Time series methods are frequently used in solar irradiance forecasting when two dimensional cloud information provided by satellite or sky camera is unavailable. ETS (exponential smoothing) has received extensive attention in the recent years since the invention of its state space formulation. In this work, we combine these models with knowledge based heuristic time series decomposition methods to improve the forecasting accuracy and computational efficiency.<p></p> In particular, three decomposition methods are proposed. The first method implements an additive seasonal-trend decomposition as a preprocessing technique prior to ETS. This can reduce the state space thus improve the computational efficiency. The second method decomposes the GHI (global horizontal irradiance) time series into a direct component and a diffuse component. These two components are used as forecasting model inputs separately; and their corresponding results are recombined via the closure equation to obtain the GHI forecasts. In the third method, the time series of the cloud cover index is considered. ETS is applied to the cloud cover time series to obtain the cloud cover forecast thus the forecast GHI through polynomial regressions. The results show that the third method performs the best among three methods and all proposed methods outperform the persistence models.<p></p&gt

SaaFormer: Spectral-spatial Axial Aggregation Transformer for Hyperspectral Image Classification

Hyperspectral images (HSI) captured from earth observing satellites and aircraft is becoming increasingly important for applications in agriculture, environmental monitoring, mining, etc. Due to the limited available hyperspectral datasets, the pixel-wise random sampling is the most commonly used training-test dataset partition approach, which has significant overlap between samples in training and test datasets. Furthermore, our experimental observations indicates that regions with larger overlap often exhibit higher classification accuracy. Consequently, the pixel-wise random sampling approach poses a risk of data leakage. Thus, we propose a block-wise sampling method to minimize the potential for data leakage. Our experimental findings also confirm the presence of data leakage in models such as 2DCNN. Further, We propose a spectral-spatial axial aggregation transformer model, namely SaaFormer, to address the challenges associated with hyperspectral image classifier that considers HSI as long sequential three-dimensional images. The model comprises two primary components: axial aggregation attention and multi-level spectral-spatial extraction. The axial aggregation attention mechanism effectively exploits the continuity and correlation among spectral bands at each pixel position in hyperspectral images, while aggregating spatial dimension features. This enables SaaFormer to maintain high precision even under block-wise sampling. The multi-level spectral-spatial extraction structure is designed to capture the sensitivity of different material components to specific spectral bands, allowing the model to focus on a broader range of spectral details. The results on six publicly available datasets demonstrate that our model exhibits comparable performance when using random sampling, while significantly outperforming other methods when employing block-wise sampling partition.Comment: arXiv admin note: text overlap with arXiv:2107.02988 by other author

Fatigue Behavior of an Al-12.7Si-0.7Mg Alloy Processed by Extrusion and Heat Treatment

The fatigue life of a hot extruded Al-12.7Si-0.7Mg alloy under T1, T4, and T6 conditions was studied. The microstructure and tensile properties of the alloy were investigated in order to analyze the fatigue behavior. The results of the fatigue test showed that an extruded Al-12.7Si-0.7Mg alloy provided greater fatigue life compared to a cast Al-Si alloy, which was explained by the refined microstructure characterized by fine Si particles uniformly distributed in the Al matrix of fine equiaxed grains promoted by hot extrusion. The fatigue property of the alloy in T6 treatment was higher than that in the T4 and T1 conditions due to strengthening precipitation

Differential expression profiling between the relative normal and dystrophic muscle tissues from the same LGMD patient

BACKGROUND: Limb-girdle muscular dystrophy (LGMD) is a group of heterogeneous muscular disorders with autosomal dominant and recessive inheritance, in which the pelvic or shoulder girdle musculature is predominantly or primarily involved. Although analysis of the defective proteins has shed some light onto their functions implicated in the etiology of LGMD, our understanding of the molecular mechanisms underlying muscular dystrophy remains incomplete. METHODS: To give insight into the molecular mechanisms of AR-LGMD, we have examined the differentially expressed gene profiling between the relative normal and pathological skeletal muscles from the same AR-LGMD patient with the differential display RT-PCR approach. The research subjects came from a Chinese AR-LGMD family with three affected sisters. RESULTS: In this report, we have identified 31 known genes and 12 unknown ESTs, which were differentially expressed between the relative normal and dystrophic muscle from the same LGMD patient. The expression of many genes encoding structural proteins of skeletal muscle fibers (such as titin, myosin heavy and light chains, and nebulin) were dramatically down-regulated in dystrophic muscles compared to the relative normal muscles. The genes, reticulocalbin 1, kinectin 1, fatty acid desaturase 1, insulin-like growth factor binding protein 5 (IGFBP5), Nedd4 family interacting protein 1 (NDFIP1), SMARCA2 (SWI/SNF related, matrix associated, actin dependent regulator of chromatin, subfamily a, member 2), encoding the proteins involved in signal transduction and gene expression regulation were up-regulated in the dystrophic muscles. CONCLUSION: The functional analysis of these expression-altered genes in the pathogenesis of LGMD could provide additional information for understanding possible molecular mechanisms of LGMD development

Brain anomaly networks uncover heterogeneous functional reorganization patterns after stroke

Stroke has a large physical, psychological, and financial burden on patients, their families, and society. Based on functional networks (FNs) constructed from resting state fMRI data, network connectivity after stroke is commonly conjectured to be more randomly reconfigured. We find that this hypothesis depends on the severity of stroke. Head movement-corrected, resting-state fMRI data were acquired from 32 patients after stroke, and 37 healthy volunteers. We constructed anomaly FNs, which combine time series information of a patient with the healthy control group. We propose data-driven techniques to automatically identify regions of interest that are stroke relevant. Graph analysis based on anomaly FNs suggests consistently that strong connections in healthy controls are broken down specifically and characteristically for brain areas that are related to sensorimotor functions and frontoparietal control systems, but new links in stroke patients are rebuilt randomly from all possible areas. Entropic measures of complexity are proposed for characterizing the functional connectivity reorganization patterns, which are correlated with hand and wrist function assessments of stroke patients and show high potential for clinical use

Brain Map of Intrinsic Functional Flexibility in Anesthetized Monkeys and Awake Humans

Emerging neuroimaging studies emphasize the dynamic organization of spontaneous brain activity in both human and non-human primates, even under anesthesia. In a recent study, we were able to characterize the heterogeneous architecture of intrinsic functional flexibility in the awake, resting human brain using time-resolved analysis and a probabilistic model. However, it is unknown whether this organizational principle is preserved in the anesthetized monkey brain, and how anesthesia affects dynamic and static measurements of spontaneous brain activity. To investigate these issues, we collected resting-state functional magnetic resonance imaging (fMRI) datasets from 178 awake humans and 11 anesthetized monkeys (all healthy). Our recently established method, a complexity measurement (i.e., Shannon entropy) of dynamic functional connectivity patterns of each brain region, was used to map the intrinsic functional flexibility across the cerebral cortex. To further explore the potential effects of anesthesia, we performed time series analysis and correlation analysis between dynamic and static measurements within awake human and anesthetized monkey brains, respectively. We observed a heterogeneous profile of intrinsic functional flexibility in the anesthetized monkey brain, which showed some similarities to that of awake humans (r = 0.30, p = 0.007). However, we found that brain activity in anesthetized monkeys generally shifted toward random fluctuations. Moreover, there is a negative correlation between nodal entropy for the distribution of dynamic functional connectivity patterns and static functional connectivity strength in anesthetized monkeys, but not in awake humans. Our findings indicate that the heterogeneous architecture of intrinsic functional flexibility across cortex probably reflects an evolutionarily conserved aspect of functional brain organization, which persists across levels of cognitive processing (states of consciousness). The coupling between nodal entropy for the distribution of dynamic functional connectivity patterns and static functional connectivity strength may serve as a potential signature of anesthesia. This study not only offers fresh insight into the evolution of brain functional architecture, but also advances our understanding of the dynamics of spontaneous brain activity