Planck Constraints on Holographic Dark Energy

We perform a detailed investigation on the cosmological constraints on the holographic dark energy (HDE) model by using the Planck data. HDE can provide a good fit to Planck high-l (l>40) temperature power spectrum, while the discrepancy at l=20-40 found in LCDM remains unsolved in HDE. The Planck data alone can lead to strong and reliable constraint on the HDE parameter c. At 68% CL, we get c=0.508+-0.207 with Planck+WP+lensing, favoring the present phantom HDE at > 2sigma CL. Comparably, by using WMAP9 alone we cannot get interesting constraint on c. By combining Planck+WP with the BAO measurements from 6dFGS+SDSS DR7(R)+BOSS DR9, the H0 measurement from HST, the SNLS3 and Union2.1 SNIa data sets, we get 68% CL constraints c=0.484+-0.070, 0.474+-0.049, 0.594+-0.051 and 0.642+-0.066. Constraints can be improved by 2%-15% if we further add the Planck lensing data. Compared with the WMAP9 results, the Planck results reduce the error by 30%-60%, and prefer a phantom-like HDE at higher CL. We find no evident tension between Planck and BAO/HST. Especially, the strong correlation between Omegam h^3 and dark energy parameters is helpful in relieving the tension between Planck and HST. The residual chi^2_{Planck+WP+HST}-chi^2_{Planck+WP} is 7.8 in LCDM, and is reduced to 1.0 or 0.3 if we switch dark energy to the w model or the holographic model. We find SNLS3 is in tension with all other data sets; for Planck+WP, WMAP9 and BAO+HST, the corresponding Delta chi^2 is 6.4, 3.5 and 4.1, respectively. Comparably, Union2.1 is consistent with these data sets, but the combination Union2.1+BAO+HST is in tension with Planck+WP+lensing, corresponding to a Delta chi^2 8.6 (1.4% probability). Thus, it is not reasonable to perform an all-combined (CMB+SNIa+BAO+HST) analysis for HDE when using the Planck data. Our tightest self-consistent constraint is c=0.495+-0.039 obtained from Planck+WP+BAO+HST+lensing.Comment: 29 pages, 11 figures, 3 tables; version accepted for publication in JCA

(E)-4-Chloro­benzyl 3-(3-nitro­benzyl­idene)dithio­carbazate

In the title compound, C15H12ClN3O2S2, the dihedral angle between the aromatic rings is 89.71 (10)°. In the crystal, inversion dimers linked by pairs of N—H⋯S hydrogen bonds occur

3-(4-Acetoxy­phen­yl)-4-oxo-4H-1-benzopyran-5,7-diyl diacetate

In the title mol­ecule, C21H16O8, the dihedral angle between the ring systems is 58.5 (1)°. In the crystal, C—H⋯O inter­actions help to establish the packing

Performance Evaluation of Semi-supervised Learning Frameworks for Multi-Class Weed Detection

Effective weed control plays a crucial role in optimizing crop yield and enhancing agricultural product quality. However, the reliance on herbicide application not only poses a critical threat to the environment but also promotes the emergence of resistant weeds. Fortunately, recent advances in precision weed management enabled by ML and DL provide a sustainable alternative. Despite great progress, existing algorithms are mainly developed based on supervised learning approaches, which typically demand large-scale datasets with manual-labeled annotations, which is time-consuming and labor-intensive. As such, label-efficient learning methods, especially semi-supervised learning, have gained increased attention in the broader domain of computer vision and have demonstrated promising performance. These methods aim to utilize a small number of labeled data samples along with a great number of unlabeled samples to develop high-performing models comparable to the supervised learning counterpart trained on a large amount of labeled data samples. In this study, we assess the effectiveness of a semi-supervised learning framework for multi-class weed detection, employing two well-known object detection frameworks, namely FCOS and Faster-RCNN. Specifically, we evaluate a generalized student-teacher framework with an improved pseudo-label generation module to produce reliable pseudo-labels for the unlabeled data. To enhance generalization, an ensemble student network is employed to facilitate the training process. Experimental results show that the proposed approach is able to achieve approximately 76\% and 96\% detection accuracy as the supervised methods with only 10\% of labeled data in CottenWeedDet3 and CottonWeedDet12, respectively. We offer access to the source code, contributing a valuable resource for ongoing semi-supervised learning research in weed detection and beyond.Comment: 11 pages, 7 figure

6,6′-Dieth­oxy-2,2′-[propane-1,3-diyl­dioxy­bis(nitrilo­methyl­idyne)]diphenol

The complete mol­ecule of the title compound, C21H26N2O6, is generated by a crystallographic twofold axis and adopts a trans configuration with respect to the azomethine group. The two benzene rings are almost perpendicular to one another, making a dihedral angle of 89.53 (3)°. In the mol­ecular structure, pairs of intra­molecular O—H⋯N hydrogen bonds generate two six-membered rings. The crystal structure is further stabilized by inter­molecular C—H⋯O hydrogen bonds, which link four adjacent mol­ecules into a network structure

2,2′-[1,1′-(Propane-1,3-diyldioxy­dinitrilo)diethyl­idyne]di-1-naphthol

The mol­ecule of the title compound, C27H26N2O4, lies across a crystallographic inversion centre and adopts an l-shaped configuration. Within the mol­ecule, the two naphthalene units are approximately perpendicular, making a dihedral angle of 80.24 (5)°. The two intramolecular O—H⋯N hydrogen bonds, generate S(6) ring motifs. In the crystal structure, every mol­ecule links five other mol­ecules into an infinite cross-linked layered supra­molecular structure via inter­molecular C—H⋯O hydrogen bonds, C—H⋯π inter­actions and π–π stacking inter­actions [centroid–centroid distance = 3.956 (4) Å]

μ-2-Amino­terephthalato-κ2 O 1:O 4-bis­[triphenyl­tin(IV)]

The title compound, [Sn2(C6H5)6(C8H5NO4)], contains two triphenyl­tin groups bridged by a 2-amino­terephthalate ligand. The two SnIV centers have similar distorted tetra­hedral coordination geometries. Each SnIV atom is bonded to three phenyl C atoms and one O atom from a carboxyl­ate group. The other O atom of the carboxyl­ate group has a weak inter­action with the Sn atom. The amino group is disordered over two sites, with site-occupancy factors of 0.779 (11) and 0.221 (11). Intra­molecular N—H⋯O hydrogen bonds are observed