In monolayer transition metal dichalcogenide semiconductors,valley coherence degrades rapidly due to a combination of fast scattering and inter-valley exchange *** leads to a sub-picosecond valley coherence time,makin...
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In monolayer transition metal dichalcogenide semiconductors,valley coherence degrades rapidly due to a combination of fast scattering and inter-valley exchange *** leads to a sub-picosecond valley coherence time,making coherent manipulation of exciton a highly challenging task Using monolayer MoS_(2) sandwiched between top and bottom graphene,here we demonstrate fully valley-coherent excitons by observing~100%degree of linear polarization in steady state *** is achieved in this unique design through a combined effect of(a)suppression in exchange interaction due to enhanced dielectric screening,(b)reduction in exciton lifetime due to a fast inter-layer transfer to graphene,and(c)operating in the motional narrowing *** disentangle the role of the key parameters affecting valley coherence by using a combination of calculation(solutions of Bethe-Salpeter and Maiallle-Silva-Sham equations)and a careful choice of design of experiments using four different stacks with systematic variation of screening and exciton *** the best of our knowledge,this is the first report in which the excitons are found to be valley coherent in the entire lifetime in monolayer semiconductors,allowing optical readout of valley coherence possible.
— In recent years, significant efforts have been dedicated to detect human emotions. This interest primarily stems from the fact that emotions influence individuals' reactions and behaviors. Understanding these i...
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Over the past two decades, the rise in video streaming has been driven by internet accessibility and the demand for high-quality video. To meet this demand across varying network speeds and devices, transcoding is ess...
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Amidst rising distributed generation and its potential role in grid management, this article presents a new realistic approach to determine the operational space and flexibility potential of an unbalanced active distr...
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A theoretical framework for analyzing the mechanical exfoliation of 2D materials at finite temperature is introduced. As an example, this framework, based on transition-state theory and calculation of the reaction pat...
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A theoretical framework for analyzing the mechanical exfoliation of 2D materials at finite temperature is introduced. As an example, this framework, based on transition-state theory and calculation of the reaction pathway, is applied to the mechanical exfoliation of multilayer MoS2. The theory predicts that altering the bonding of the top layer to the bulk through straining or twisting and slowly exfoliating at low temperatures will enhance the yield of the monolayer material.
Vehicular edge computing (VEC) allows vehicles to process part of the tasks locally at the network edge while offloading the rest of the tasks to a centralized cloud server for processing. A massive volume of tasks ge...
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The dynamic pricing environment offers flexibility to the consumers to reschedule their switching *** the dynamic pricing environment results in several benefits to the utilities and consumers,it also poses some *** c...
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The dynamic pricing environment offers flexibility to the consumers to reschedule their switching *** the dynamic pricing environment results in several benefits to the utilities and consumers,it also poses some *** crowding among residential customers is one of such *** scheduling of loads at low-cost intervals causes crowding among residential customers,which leads to a fall in voltage of the distribution system below its prescribed *** order to prevent crowding phenomena,this paper proposes a priority-based demand response program for local energy *** the program,past contributions made by residential houses and demand are considered as essential parameters while calculating the priority *** non-linear programming(NLP)model proposed in this study seeks to reschedule loads at low-cost intervals to alleviate crowding *** the NLP model does not guarantee global optima due to its non-convex nature,a second-order cone programming model is proposed,which captures power flow characteristics and guarantees global *** proposed formulation is solved using General Algebraic Modeling System(GAMS)software and is tested on a 12.66 kV IEEE 33-bus distribution system,which demonstrates its applicability and efficacy.
Now that the population is growing, the expenditure on basic needs of life is also increasing due to a lack of or less availability of resources. The economy consumed electricity is reaching peaks as its main fuel, co...
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作者：
Zjavka, LadislavDepartment of Computer Science
Faculty of Electrical Engineering and Computer Science VŠB-Technical University of Ostrava 17. Listopadu 15/2172 Ostrava Czech Republic
Photovoltaic (PV) power is generated by two common types of solar components that are primarily affected by fluctuations and development in cloud structures as a result of uncertain and chaotic processes. Local PV for...
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Photovoltaic (PV) power is generated by two common types of solar components that are primarily affected by fluctuations and development in cloud structures as a result of uncertain and chaotic processes. Local PV forecasting is unavoidable in supply and load planning necessary in integration of smart systems into electrical grids. Intra- or day-ahead modelling of weather patterns based on Artificial Intelligence (AI) allows one to refine available 24 h. cloudiness forecast or predict PV production at a particular plant location during the day. AI usually gets an adequate prediction quality in shorter-level horizons, using the historical meteo- and PV record series as compared to Numerical Weather Prediction (NWP) systems. NWP models are produced every 6 h to simulate grid motion of local cloudiness, which is additionally delayed and usually scaled in a rough less operational applicability. Differential Neural Network (DNN) is based on a newly developed neurocomputing strategy that allows the representation of complex weather patterns analogous to NWP. DNN parses the n-variable linear Partial Differential Equation (PDE), which describes the ground-level patterns, into sub-PDE modules of a determined order at each node. Their derivatives are substituted by the Laplace transforms and solved using adapted inverse operations of Operation Calculus (OC). DNN fuses OC mathematics with neural computing in evolution 2-input node structures to form sum modules of selected PDEs added step-by-step to the expanded composite model. The AI multi- 1…9-h and one-stage 24-h models were evolved using spatio-temporal data in the preidentified daily learning sequences according to the applied input–output data delay to predict the Clear Sky Index (CSI). The prediction results of both statistical schemes were evaluated to assess the performance of the AI models. Intraday models obtain slightly better prediction accuracy in average errors compared to those applied in the second-day-ahead
Unmanned aerial vehicles(UAVs) have gained significant attention in practical applications, especially the low-altitude aerial(LAA) object detection imposes stringent requirements on recognition accuracy and computati...
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Unmanned aerial vehicles(UAVs) have gained significant attention in practical applications, especially the low-altitude aerial(LAA) object detection imposes stringent requirements on recognition accuracy and computational resources. In this paper, the LAA images-oriented tensor decomposition and knowledge distillation-based network(TDKD-Net) is proposed,where the TT-format TD(tensor decomposition) and equalweighted response-based KD(knowledge distillation) methods are designed to minimize redundant parameters while ensuring comparable performance. Moreover, some robust network structures are developed, including the small object detection head and the dual-domain attention mechanism, which enable the model to leverage the learned knowledge from small-scale targets and selectively focus on salient features. Considering the imbalance of bounding box regression samples and the inaccuracy of regression geometric factors, the focal and efficient IoU(intersection of union) loss with optimal transport assignment(F-EIoU-OTA)mechanism is proposed to improve the detection accuracy. The proposed TDKD-Net is comprehensively evaluated through extensive experiments, and the results have demonstrated the effectiveness and superiority of the developed methods in comparison to other advanced detection algorithms, which also present high generalization and strong robustness. As a resource-efficient precise network, the complex detection of small and occluded LAA objects is also well addressed by TDKD-Net, which provides useful insights on handling imbalanced issues and realizing domain adaptation.
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