Pomeranchuk cooling of the SU(2N2N) ultra-cold fermions in optical lattices

We investigate the thermodynamic properties of a half-filled SU(2N) Fermi-Hubbard model in the two-dimensional square lattice using the determinantal quantum Monte Carlo simulation, which is free of the fermion "sign problem". The large number of hyperfine-spin components enhances spin fluctuations, which facilitates the Pomeranchuk cooling to temperatures comparable to the superexchange energy scale at the case of SU$(6)$. Various quantities including entropy, charge fluctuation, and spin correlations have been calculated.Comment: 7 page

Bis(2,6-dihy­droxy­benzoato-κ2 O 1,O 1′)(nitrato-κ2 O,O′)bis­(1,10-phenanthroline-κ2 N,N′)dysprosium(III)

In the mononuclear title complex, [Dy(C7H5O4)2(NO3)(C12H8N2)2], the DyIII atom is in a distorted bicapped square-anti­prismatic geometry formed by four N atoms from two chelating 1,10-phenanthroline (phen) ligands, four O atoms from two 2,6-dihy­droxy­benzoate (DHB) ligands and two O atoms from a nitrate anion. Inter­molecular π–π stacking inter­actions between the phen and DHB ligands [centroid–centroid distances = 3.542 (4) and 3.879 (4) Å] and between the pyridine and benzene rings of adjacent phen ligands [centroid–centroid distance = 3.751 (4) Å] stabilize the crystal structure. Intra­molecular O–H⋯O hydrogen bonds are observed in the DHB ligands

Diaqua­(2,6-dihy­droxy­benzoato-κ2 O 1 ,O 1′)bis­(2,6-dihy­droxy­benzoato-κO 1)bis­(1,10-phenanthroline-κ2 N,N′)lanthanum(III)–1,10-phenanthroline (1/1)

In the title compound, [La(C7H5O4)3(C12H8N2)3(H2O)2]·C12H8N2, the LaIII atom is coordinated by four N atoms from two chelating 1,10-phenanthroline (phen) ligands, four O atoms from three 2,6-dihy­droxy­benzoate (DHB) anions (one monodentate, the other bidentate) and two water O atoms, completing a distorted LaN4O6 bicapped square-anti­prismatic geometry. Within the mononuclear complex mol­ecule, intra­molecular π–π stacking inter­actions are observed, the first between a coordinated phen mol­ecule and a DHB ligand [centroid–centroid distance = 3.7291 (16) Å], and the second between a coordinated phen mol­ecule and an uncoordinated phen ligand [centroid–centroid distance = 3.933 (2) Å]. Inter­molecular π–π stacking is observed between adjacent complexes [inter­planar distance = 3.461 (3) Å]. Intra- and inter­molecular O—H⋯O hydrogen bonds are observed in the DHB ligands and between a water mol­ecule and DHB ligands, respectively. O—H⋯N hydrogen bonds are also observed in the DHB ligands and between uncoordinated phen mol­ecules and aqua ligands

4,4′-(Ethene-1,2-diyl)dipyridinium bis­[4-(2-carboxy­benzo­yl)benzoate]

In the crystal structure of the title compound, C12H12N2 2+·2C15H9O5 −, the cation has site symmetry with the mid-point of C=C bond located on an inversion center. The two benzene rings of the anion are oriented at a dihedral angle 85.87 (6)°. In the crystal, inter­molecular O—H⋯O and N—H⋯O hydrogen bonds link the cations and anions into supra­molecular double chains, which are further connected into a three-dimensional network through inter­molecular C—H⋯O and π–π stacking between parallel pyridine rings [centroid–centroid distance = 3.4413 (12)Å] and between parallel benzene rings [centroid–centroid distance = 3.6116 (14)Å]

Workspace Analysis of a Novel Parallel Robot Named 3-R2H2S with Three Freedoms

Abstract: In order to meet the sorting and packing needs of the drug and food industries, a novel parallel robot mechanism named 3-R2H2S is proposed in this study, the kinematics equation of the robot was deduced and the inverse kinematics was calculated. The workspace model of the robot is analyzed by the boundary search method through the MATLAB and ADAMS kinematics software. The analysis results show that the robot has a large effective workspace with smooth boundary and can be widely applied in the field of industrial robots, the kinematics of micro robots and 3D coordinate measurements and the workspace of the robot can meet the needs of drug and food automation production line

Enhanced excitability of small dorsal root ganglion neurons in rats with bone cancer pain

<p>Abstract</p> <p>Background</p> <p>Primary and metastatic cancers that affect bone are frequently associated with severe and intractable pain. The mechanisms underlying the development of bone cancer pain are largely unknown. The aim of this study was to determine whether enhanced excitability of primary sensory neurons contributed to peripheral sensitization and tumor-induced hyperalgesia during cancer condition. In this study, using techniques of whole-cell patch-clamp recording associated with immunofluorescent staining, single-cell reverse-transcriptase PCR and behavioral test, we investigated whether the intrinsic membrane properties and the excitability of small-sized dorsal root ganglion (DRG) neurons altered in a rat model of bone cancer pain, and whether suppression of DRG neurons activity inhibited the bone cancer-induced pain.</p> <p>Results</p> <p>Our present study showed that implantation of MRMT-1 tumor cells into the tibial canal in rats produced significant mechanical and thermal hyperalgesia in the ipsilateral hind paw. Moreover, implantation of tumor cells provoked spontaneous discharges and tonic excitatory discharges evoked by a depolarizing current pulse in small-sized DRG neurons. In line with these findings, alterations in intrinsic membrane properties that reflect the enhanced neuronal excitability were observed in small DRG neurons in bone cancer rats, of which including: 1) depolarized resting membrane potential (RMP); 2) decreased input resistance (R_in); 3) a marked reduction in current threshold (CT) and voltage threshold (TP) of action potential (AP); 4) a dramatic decrease in amplitude, overshot, and duration of evoked action potentials as well as in amplitude and duration of afterhyperpolarization (AHP); and 5) a significant increase in the firing frequency of evoked action potentials. Here, the decreased AP threshold and increased firing frequency of evoked action potentials implicate the occurrence of hyperexcitability in small-sized DRG neurons in bone cancer rats. In addiotion, immunofluorescent staining and single-cell reverse-transcriptase PCR revealed that in isolated small DRG neurons, most neurons were IB4-positive, or expressed TRPV1 or CGRP, indicating that most recorded small DRG neurons were nociceptive neurons. Finally, using in vivo behavioral test, we found that blockade of DRG neurons activity by TTX inhibited the tumor-evoked mechanical allodynia and thermal hyperalgesia in bone cancer rats, implicating that the enhanced excitability of primary sensory neurons underlied the development of bone cancer pain.</p> <p>Conclusions</p> <p>Our present results suggest that implantation of tumor cells into the tibial canal in rats induces an enhanced excitability of small-sized DRG neurons that is probably as results of alterations in intrinsic electrogenic properties of these neurons. Therefore, alterations in intrinsic membrane properties associated with the hyperexcitability of primary sensory neurons likely contribute to the peripheral sensitization and tumor-induced hyperalgesia under cancer condition.</p

State Regularized Policy Optimization on Data with Dynamics Shift

In many real-world scenarios, Reinforcement Learning (RL) algorithms are trained on data with dynamics shift, i.e., with different underlying environment dynamics. A majority of current methods address such issue by training context encoders to identify environment parameters. Data with dynamics shift are separated according to their environment parameters to train the corresponding policy. However, these methods can be sample inefficient as data are used \textit{ad hoc}, and policies trained for one dynamics cannot benefit from data collected in all other environments with different dynamics. In this paper, we find that in many environments with similar structures and different dynamics, optimal policies have similar stationary state distributions. We exploit such property and learn the stationary state distribution from data with dynamics shift for efficient data reuse. Such distribution is used to regularize the policy trained in a new environment, leading to the SRPO (\textbf{S}tate \textbf{R}egularized \textbf{P}olicy \textbf{O}ptimization) algorithm. To conduct theoretical analyses, the intuition of similar environment structures is characterized by the notion of homomorphous MDPs. We then demonstrate a lower-bound performance guarantee on policies regularized by the stationary state distribution. In practice, SRPO can be an add-on module to context-based algorithms in both online and offline RL settings. Experimental results show that SRPO can make several context-based algorithms far more data efficient and significantly improve their overall performance.Comment: Preprint. Under Revie