MicroGrid Designer:user-friendly design,operation and control assist tools for resilient microgrid and autonomous community

During this decade,many countries have experienced natural and accidental disasters,such as typhoons,floods,earthquakes,and nuclear plant accidents,causing catastrophic damage to infrastructures.Since the end of 2019,all countries of the world are struggling with the COVID-19 and pursuing countermeasures,including inoculation of vaccine,and changes in our lifestyle and social structures.All these experiences have made the residents in the affected regions keenly aware of the need for new infrastructures that are resilient and autonomous,so that vital lifelines are secured during calamities.A paradigm shift has been taking place toward reorganizing the energy social service management in many countries,including Japan,by effective use of sustainable energy and new supply schemes.However,such new power sources and supply schemes would affect the power grid through intermittency of power output and the deterioration of power quality and service.Therefore,new social infrastructures and novel management systems to supply energy and social service will be required.In this paper,user-friendly design,operation and control assist tools for resilient microgrids and autonomous communities are proposed and applied to the standard microgrid to verify its effectiveness and performance.

Neuronal degeneration in the hippocampus and dorsolateral prefrontal cortex in depressive disorder Correlation between ~1H-MRS and Minnesota Multiphasic Personality Inventory

Previous studies using magnetic resonance imaging（MRI）and functional MRI to study depression have primarily focused on proton magnetic resonance spectroscopy（1H-MRS）appearance in various areas of the brain and volume measurements in the limbic system.However,results have not been consistent.To the best of our knowledge,very little is known about the relationship between 1H-MRS appearance and depression inventory.In the present study,the relationship between 1H-MRS appearance in depressive patients and Minnesota Multiphasic Personality Inventory-2 scale was analyzed.MRI and 1H-MRS exhibited widened sulci and cisterns,as well as an absence of abnormal signals in depressive patients.In addition,N-acetyl aspartate/total creatine ratios in bilateral hippocampi and dorsolateral prefrontal cortex were significantly less in depressive patients than in control subjects（P 〈 0.01）.In contrast,choline-containing compounds/total creatine ratios in the dorsolateral prefrontal cortex were significantly greater in depressive patients than in control subjects（P 〈 0.01）.These ratios significantly and positively correlated with patient total depression scores as assessed using the Minnesota Multiphasic Personality Inventory-2 scale（r=0.934 7,0.878 7,P 〈 0.01）.These results suggested that 1H-MRS could be used to reveal a reduced number of neurons in the hippocampus and dorsolateral prefrontal cortex,as well as altered membrane phospholipid metabolism in the dorsolateral prefrontal cortex,in patients with depressive disorder.Abnormal mechanisms partially reflected severity of depressive disorder.

Effective ensemble learning approach for SST field prediction using attention-based PredRNN

Accurate prediction of sea surface temperature (SST) is extremely important for forecasting oceanic environmental events and for ocean studies. However, the existing SST prediction methods do not consider the seasonal periodicity and abnormal fluctuation characteristics of SST or the importance of historical SST data from different times;thus, these methods suffer from low prediction accuracy. To solve this problem, we comprehensively consider the effects of seasonal periodicity and abnormal fluctuation characteristics of SST data, as well as the influence of historical data in different periods, on prediction accuracy. We propose a novel ensemble learning approach that combines the Predictive Recurrent Neural Network(PredRNN) network and an attention mechanism for effective SST field prediction. In this approach, the XGBoost model is used to learn the long-period fluctuation law of SST and to extract seasonal periodic features from SST data. The exponential smoothing method is used to mitigate the impact of severely abnormal SST fluctuations and extract the a priori features of SST data. The outputs of the two aforementioned models and the original SST data are stacked and used as inputs for the next model, the PredRNN network. PredRNN is the most recently developed spatiotemporal deep learning network, which simulates both spatial and temporal representations and is capable of transferring memory across layers and time steps. Therefore, we used it to extract the spatiotemporal correlations of SST data and predict future SSTs. Finally, an attention mechanism is added to capture the importance of different historical SST data, weigh the output of each step of the PredRNN network, and improve the prediction accuracy. The experimental results on two ocean datasets confirm that the proposed approach achieves higher training efficiency and prediction accuracy than the existing SST field prediction approaches do.

Spatiotemporally mode-locked soliton fiber laser at 2.8µm

Spatiotemporal mode-locking creates great opportunity for pulse energy scaling and nonlinear optics research in fiber.Until now,spatiotemporal mode-locking has only been realized in normal-dispersion dissipative soliton and similariton fiber lasers.In this paper,we demonstrated the first experimental realization of a spatiotemporally mode-locked soliton laser in mid-infrared fluoride fiber with anomalous dispersion.The mode-locked fluoride fiber oscillator directly generated a record pulse energy of 16.1 nJ and peak power of 74.6 kW at 2.8µm wavelength.This work extends the spatiotemporal mode-locking to soliton fiber lasers and should have a wide interest for the laser community.

Miniature integrated spectrometers towards high-performance and cost-effective

The conjugated mode of bound states in a continuum is integrated as a narrowband wavelength extraction unit.A low-cost and easy-to-prepare strategy,using solution-processable semiconductors,has been demonstrated to form a new platform for on-chip spectral analysis.

Catalyst-free,direct electrochemical trifluoromethylation/cyclization of N-arylacrylamides using TfNHNHBoc as a CF_(3) source

A new electrochemical strategy for trifluoromethylation/cyclization using TfNHNHBoc as a CF_(3)source was established.This approach was realized by the direct electrolysis of Tf NHNHBoc under external oxidantfree and catalyst-free conditions,and afforded various trifluoromethylated oxindoles with good functional group compatibility and broad substrate scope.Preliminary mechanistic studies show that the reaction proceeds by a radical process.

Quantum interference enhanced thermopower in single-molecule thiophene junctions

Quantum interference(QI)effects,which offer unique opportunities to widely manipulate the charge transport properties in the molecular junctions,will have the potential for achieving high thermopower.Here we developed a scanning tunneling microscope break junction technique to investigate the thermopower through single-molecule thiophene junctions.We observed that the thermopower of 2,4-TPSAc with destructive quantum interference(DQI)was nearly twice of 2,5-TP-SAc without DQI,while the conductance of the 2,4-TP-SAc was two orders of magnitude lower than that of 2,5-TP-SAc.Furthermore,we found the thermopower was almost the same by altering the anchoring group or thiophene core in the control experiments,suggesting that the QI effect is responsible for the increase of thermopower.The density functional theory(DFT)calculations are in quantitative agreement with the experimental data.Our results reveal that QI effects can provide a promising platform to enhance the thermopower of molecular junctions.

Iridium(I)-catalyzed deoxgenation of fluoroalkylsulfoxides with dimethyl diazomalonate to access fluoroalkylthioethers

A new method for the preparation of fluoroalkylthioethers including trifluoromethylthioether and difluoromethylthioether by iridium(Ⅰ)-catalyzed deoxgenation of fluoroalkylsulfoxides with dimethyl diazomalonate was developed.In the reaction system,dimethyl diazomalonate was used as reducing reagent and the corresponding fluoroalkylthioethers were produced through oxygen atom transfer from fluoroalkylsulfoxides to diazomalonate.The protocol featuring effective oxygen atom transfer,mild reaction conditions and good functional groups tolerance offers an alternative strategy for the synthesis of fluoroalkylthioethers.

Synthesis of S-monofluoromethyl phosphorothioates from P^(Ⅴ)-H compounds and PhSO_(2)SCH_(2)F

S-Monofluoromethyl phosphorothioates represent an important class of organofluorine compounds and are re ported here for the first time.A series of S-monofluoromethyl phosphorothioates are conveniently synthesized from different P^(Ⅴ)-H compounds and PhSO_(2)SCH_(2)F under mild conditions.The method is compatible with common functional groups and provides potential opportunities to synthesize new bioactive molecules for medicinal chemistry.

Grating-free 2.8μm Er:ZBLAN fiber chirped pulse amplifier

We report on a grating-free fiber chirped pulse amplifier(CPA)at 2.8μm for the first time.The CPA system adopted Er:ZBLAN fiber with large anomalous dispersion as the stretcher and germanium(Ge)rods as the compressor with a compact structure.High-energy picosecond pulses of 2.07μJ were generated at the repetition rate of 100 kHz.Using highly dispersive Ge rods,the amplified pulses were compressed to 408 fs with a pulse energy of 0.57μJ,resulting in a peak power of approximately 1.4 MW.A spectral broadening phenomenon in the main amplifier was observed,which was caused by the special gain shape of the Er:ZBLAN fiber amplifier in operation and confirmed by our numerical simulation.This compact fiber CPA system at 2.8μm will be practical and meaningful for application fields.