Cloud-Edge Collaborative Federated GAN Based Data Processing for IoT-Empowered Multi-Flow Integrated Energy Aggregation Dispatch

The convergence of Internet of Things(IoT),5G,and cloud collaboration offers tailored solutions to the rigorous demands of multi-flow integrated energy aggregation dispatch data processing.While generative adversarial networks(GANs)are instrumental in resource scheduling,their application in this domain is impeded by challenges such as convergence speed,inferior optimality searching capability,and the inability to learn from failed decision making feedbacks.Therefore,a cloud-edge collaborative federated GAN-based communication and computing resource scheduling algorithm with long-term constraint violation sensitiveness is proposed to address these challenges.The proposed algorithm facilitates real-time,energy-efficient data processing by optimizing transmission power control,data migration,and computing resource allocation.It employs federated learning for global parameter aggregation to enhance GAN parameter updating and dynamically adjusts GAN learning rates and global aggregation weights based on energy consumption constraint violations.Simulation results indicate that the proposed algorithm effectively reduces data processing latency,energy consumption,and convergence time.

Monte Carlo simulation of ramified aggregates on hetero-substrates

We have studied the aggregation of particles on a hetero-substrate consisting of two different substrates A and B with finite surface barriers EAB and EBA between the AB and BA boundaries, respectively. With the diffusion energy limited aggregation （DELA） model, we find that the number of clusters and the mean radius of gyration of the clusters are dependent on the surface barriers EAB and EBA. For the case with a constant of EBA, a series of minima are summarized as EAB ： （E0 - kBAEBA）/kAB with kAB and kBA being two integers, for main minima （kBA = kAB = 1） and two local minima （kBA = kAB and kBA = kAB ＋ 1） between two neighbouring main minima.

Luminescent properties of thermally activated delayed fluorescence molecule with intramolecular π-π interaction between donor and acceptor

Influence of intramolecular π-π interaction on the luminescent properties of thermally activated delayed fluorescence（TADF） molecule（3, 5-bis（3,6-di-tert-butyl-9 H-carbazol-9-yl）-phenyl）（pyridin-4-yl） methanone（DTCBPY） is theoretically studied by using the density functional theory（DFT） and time-dependent density functional theory（TD-DFT）.Four conformations（named as A, B, C, and D） of the DTCBPY can be found by relax scanning, and the configuration C corresponds to the luminescent molecule detected experimentally. Besides, we calculate the proportion of each conformation by Boltzmann distribution, high configuration ratios（44% and 52%） can be found for C and D. Moreover, C and D are found to exist with an intramolecular π-π interaction between one donor and the acceptor; the intramolecular interaction brings a smaller Huang-Rhys factor and reduced reorganization energy. Our work presents a rational explanation for the experimental results and demonstrates the importance of the intramolecular π-π interaction to the photophysical properties of TADF molecules.

Deep reinforcement learning for the optimized operation of large amounts of distributed renewable energy assets

This study utilizes machine learning and,more specifically,reinforcement learning(RL)to allow for an optimized,real-time operation of large numbers of decentral flexible assets on private household scale in the electricity domain.The potential and current obstacles of RL are demonstrated and a guide for interested practitioners is provided on how to tackle similar tasks without advanced skills in neural network programming.For the application in the energy domain it is demonstrated that state-of-the-art RL algorithms can be trained to control potentially millions of small-scale assets in private households.In detail,the applied RL algorithm outperforms common heuristic algorithms and only falls slightly short of the results provided by linear optimization,but at less than a thousandth of the simulation time.Thus,RL paves the way for aggregators of flexible energy assets to optimize profit over multiple use cases in a smart energy grid and thus also provide valuable grid services and a more sustainable operation of private energy assets.

Synthesis,characterization,photophysical and electrochemical properties of oxygen bridged twisted heptatomic biphenyl substituted perylene diimides

The perylene diimide derivatives（s-THBPDI and d-THBPDI） bearing oxygen bridged twisty heptatomic biphenyl in the bay positions of the perylene core through acetylene bond were designed and synthesized.The photophysical properties of the functionalized dyes were investigated in solution and solid state by UV-vis and photoluminescence（PL） spectra.Their UV-vis and PL spectrum both exhibited the different concentration-dependent behaviors due to the difference of chemical structure.Moreover,cyclic voltammetry results indicated that the introduction of oxygen bridged twisted heptatomic biphenyl could decrease the LUMO energy level of the perylene diimide effectively and made it promising material in photoelectric devices.