Application of Neural-Like P Systems With State Values for Power Coordination of Photovoltaic/Battery Microgrids

The power coordination control of a photovoltaic/battery microgrid is performed with a novel bio-computing model within the framework of membrane computing. First, a neural-like P system with state values (SVNPS) is proposed for describing complex logical relationships between different modes of Photovoltaic (PV) units and energy storage units. After comparing the objects in the neurons with the thresholds, state values will be obtained to determine the con guration of the SVNPS. Considering the characteristics of PV/battery microgrids, an operation control strategy based on bus voltages of the point of common coupling and charging/discharging statuses of batteries is proposed. At rst, the SVNPS is used to construct the complicated unit working modes; each unit of the microgrid can adjust the operation modes automatically. After that, the output power of each unit is reasonably coordinated to ensure the operation stability of the microgrid. Finally, a PV/battery microgrid, including two PV units, one storage unit, and some loads are taken into consideration, and experimental results show the feasibility and effectiveness of the proposed control strategy and the SVNPS-based power coordination control models

Bis[2,6-bis­(4,5-dihydro-1H-imidazol-2-yl)pyridine]manganese(II) bis­(per­chlorate) acetonitrile solvate

In the cation of the title compound, [Mn(C11H13N5)2](ClO4)2·CH3CN, the metal atom is located on a twofold rotation axis and is six-coordinated by six N atoms from two different 2,6-bis­(4,5-dihydro-1H-imidazol-2-yl)pyridine (bip) ligands in a distorted octahedral geometry. The O atoms of the perchlorate anions are disordered with occupancies in the ratio 0.593 (10):0.407 (10). In the crystal, mol­ecules are stabilized by two N—H⋯O hydrogen bonds, forming zigzag chains along the a axis, which are further inter­connected by N—H⋯O hydrogen bonds and π–π inter­actions [centroid–centroid distance = 3.50 (1) Å] into a three-dimensional network

1,4-Bis(4,5-dihydro-1H-imidazol-2-yl)benzene–terephthalic acid–water (1/1/4)

The asymmetric unit of the title compound, C12H14N4·C8H6O4·4H2O, consists of one half of the 1,4-bis­(4,5-dihydro-1H-imidazol-2-yl)benzene (bib) mol­ecule, one half of the terephthalic acid (TA) mol­ecule and two water mol­ecules. Both the bib and the TA mol­ecules reside on crystallographic inversion centers, which coincide with the centroids of the respective benzene rings. The bib and the TA, together with the water mol­ecules, are linked through inter­molecular O—H⋯O, O—H⋯N and N—H⋯O hydrogen bonds, forming a three-dimensional network of stacked layers. Weak inter­molecular C—H⋯O contacts support the stability of the crystal structure

1,4-Bis(4,5-dihydro-1H-imidazol-2-yl)benzene–4-amino­benzene­sulfonic acid–water (1/2/2)

The asymmetric unit of the title compound, C12H14N4·2C6H7NO3S·2H2O, contains one half of a centrosymmetric 1,4-bis­(4,5-dihydro-1H-imidazol-2-yl)benzene (bib) molecule, one 4-amino­benzene­sulfonic acid molecule and one water mol­ecule. In the bib molecule, the imidazole ring adopts an envelope conformation. The benzene rings of bib and 4-aminobenzenesulfonic acid are oriented at a dihedral angle of 21.89 (4)°. In the crystal structure, inter­molecular N—H⋯O, O—H⋯N and O—H⋯O inter­actions link the mol­ecules into a three-dimensional network. Weak π–π contacts between the benzene and imidazole rings and between the benzene rings [centroid–centroid distances = 3.895 (1) and 3.833 (1) Å, respectively] may further stabilize the structure

Few-Shot Learning with a Strong Teacher

Few-shot learning (FSL) aims to train a strong classifier using limited labeled examples. Many existing works take the meta-learning approach, sampling few-shot tasks in turn and optimizing the few-shot learner's performance on classifying the query examples. In this paper, we point out two potential weaknesses of this approach. First, the sampled query examples may not provide sufficient supervision for the few-shot learner. Second, the effectiveness of meta-learning diminishes sharply with increasing shots (i.e., the number of training examples per class). To resolve these issues, we propose a novel objective to directly train the few-shot learner to perform like a strong classifier. Concretely, we associate each sampled few-shot task with a strong classifier, which is learned with ample labeled examples. The strong classifier has a better generalization ability and we use it to supervise the few-shot learner. We present an efficient way to construct the strong classifier, making our proposed objective an easily plug-and-play term to existing meta-learning based FSL methods. We validate our approach in combinations with many representative meta-learning methods. On several benchmark datasets including miniImageNet and tiredImageNet, our approach leads to a notable improvement across a variety of tasks. More importantly, with our approach, meta-learning based FSL methods can consistently outperform non-meta-learning based ones, even in a many-shot setting, greatly strengthening their applicability

Effects of Astragalus

This paper studied the chronic fatigue induced by excessive exercise and the restoration effects of Astragalus polysaccharides (APS) on mitochondria. In vivo, we found that excessive exercise could cause oxidative stress statue which led to morphological and functional changes of mitochondria. The changes, including imbalance between mitochondria fusion-fission processes, activation of mitophagy, and decrease of PGC-1α expression, could be restored by APS. We further confirmed in vitro, and what is more, we found that APS may ameliorate mitochondrial dysfunction through Sirt1 pathway. Based on the results, we may figure out part of the molecular mechanism of mitochondrial amelioration by APS

2,3,5,6-Tetra­fluoro-1,4-bis­(2-pyridylmethyl­eneamino­meth­yl)benzene

The title compound, C20H14F4N4, is a flexible bis-pyridine-type ligand with an extended fluorinated spacer group between the two pyridyl functions. The centroid of the central aromatic ring is situated on a crystallographic center of inversion. The dihedral angle between the pyridine ring and the central benzene ring is 63.85 (9)°. The crystal structure exhibits inter­molecular C—H⋯F hydrogen-bonding inter­actions