Iron complexes of [2+2] and [6+6] Schiff-base macrocycles derived from 2,2′-oxydianiline and their applications

Reaction of [2+2] and [6+6] Schiff-base macrocycles with FeBr2 are reported, together with preliminary studies of the applications of the iron-containing products. In particular, we have investigated peroxidase-like activity and determination of H2O2, as well as their ability to act as catalysts for ring opening polymerization of cyclic esters

Corrigendum to “Iron complexes of [2 + 2] and [6 + 6] Schiff-base macrocycles derived from 2,2′-oxydianiline and their applications” [Inorg. Chem. Commun. 139 (2022) 109376] (Inorganic Chemistry Communications (2022) 139, (S1387700322001848), (10.1016/j.inoche.2022.109376))

The correct formula of 3 is [Fe2(L2H4)][FeBr3OFeBr3]2‧8MeCN.The revised CIF has been deposited with the CSD An updated version of the supplementary information file is provided

An exploration of improving collaborative recommender systems via user-item subgroups

Collaborative filtering (CF) is one of the most successful recommendation approaches. It typically associates a user with a group of like-minded users based on their preferences over all the items, and recommends to the user those items enjoyed by others in the group. However we find that two users with similar tastes on one item subset may have to-tally different tastes on another set. In other words, there exist many user-item subgroups each consisting of a subset of items and a group of like-minded users on these items. It is more natural to make preference predictions for a user via the correlated subgroups than the entire user-item ma-trix. In this paper, to find meaningful subgroups, we for-mulate the Multiclass Co-Clustering (MCoC) problem and propose an effective solution to it. Then we propose an unified framework to extend the traditional CF algorithms by utilizing the subgroups information for improving their top-N recommendation performance. Our approach can be seen as an extension of traditional clustering CF models. Systematic experiments on three real world data sets have demonstrated the effectiveness of our proposed approach

Block Hankel Tensor ARIMA for Multiple Short Time Series Forecasting

This work proposes a novel approach for multiple time series forecasting. At first, multi-way delay embedding transform (MDT) is employed to represent time series as low-rank block Hankel tensors (BHT). Then, the higher-order tensors are projected to compressed core tensors by applying Tucker decomposition. At the same time, the generalized tensor Autoregressive Integrated Moving Average (ARIMA) is explicitly used on consecutive core tensors to predict future samples. In this manner, the proposed approach tactically incorporates the unique advantages of MDT tensorization (to exploit mutual correlations) and tensor ARIMA coupled with low-rank Tucker decomposition into a unified framework. This framework exploits the low-rank structure of block Hankel tensors in the embedded space and captures the intrinsic correlations among multiple TS, which thus can improve the forecasting results, especially for multiple short time series. Experiments conducted on three public datasets and two industrial datasets verify that the proposed BHT-ARIMA effectively improves forecasting accuracy and reduces computational cost compared with the state-of-the-art methods.Comment: Accepted by AAAI 202

The molecular clouds in a section of the third Galactic quadrant: observational properties and chemical abundance ratio between CO and its isotopologues

We compare the observational properties between $^{12}$CO, $^{13}$CO, and C$^{18}$O and summarize the observational parameters based on 7069 clouds sample from the Milky Way Imaging Scroll Painting (MWISP) CO survey in a section of the third Galactic quadrant. We find that the $^{13}$CO angular area ($A_{\rm ^{13}CO}$) generally increases with that of $^{12}$CO ($A_{\rm ^{12}CO}$), and the ratio of $A_{\rm ^{13}CO}$ to $A_{\rm ^{12}CO}$ is 0.38 by linear fitting. We find that the $^{12}$CO and $^{13}$CO flux are tightly correlated as $F_{\rm ^{13}CO}~=~0.17~ F_{\rm ^{12}CO}$ with both fluxes calculated within the $^{13}$CO-bright region. This indicates that the abundance $X_{\rm ^{13}CO}$ is a constant to be 6.5$^{+0.1}_{-0.5}$ $\times 10^{-7}$ for all samples under assumption of local thermodynamic equilibrium (LTE). Additionally, we observed that the X-factor is approximately constant in large sample molecular clouds. Similarly, we find $F_{\rm C^{18}O}~=~0.11~F_{\rm ^{13}CO}$ with both fluxes calculated within C$^{18}$O-bright region, which indicates that the abundance ratios ${X_{\rm ^{13}CO}/X_{\rm C^{18}O}}$ stays the same value 9.7$^{+0.6}_{-0.8}$ across the molecular clouds under LTE assumption. The linear relationships of $F_{\rm ^{12}CO}$ vs. $F_{\rm ^{13}CO}$ and $F_{\rm ^{13}CO}$ vs. $F_{\rm C^{18}O}$ hold not only for the $^{13}$CO-bright region or C$^{18}$O-bright region, but also for the entire molecular cloud scale with lower flux ratio. The abundance ratio ${X_{\rm ^{13}CO}/X_{\rm C^{18}O}}$ inside clouds shows a strong correlation with column density and temperature. This indicates that the ${X_{\rm ^{13}CO}/X_{\rm C^{18}O}}$ is dominated by a combination of chemical fractionation, selectively dissociation, and self-shielding effect inside clouds.Comment: 11 pages, 16 figures, 1 table, accepted by A

Document recommendation in social tagging services

Social tagging services allow users to annotate various on-line resources with freely chosen keywords (tags). They not only facilitate the users in finding and organizing online re-sources, but also provide meaningful collaborative semantic data which can potentially be exploited by recommender systems. Traditional studies on recommender systems fo-cused on user rating data, while recently social tagging data is becoming more and more prevalent. How to perform re-source recommendation based on tagging data is an emerg-ing research topic. In this paper we consider the problem of document (e.g. Web pages, research papers) recommen-dation using purely tagging data. That is, we only have data containing users, tags, documents and the relation-ships among them. We propose a novel graph-based rep-resentation learning algorithm for this purpose. The users, tags and documents are represented in the same semantic space in which two related objects are close to each other. For a given user, we recommend those documents that are sufficiently close to him/her. Experimental results on two data sets crawled from Del.icio.us and CiteULike show that our algorithm can generate promising recommendations and outperforms traditional recommendation algorithms

Remediation of cadmium and lead polluted soil using thiol-modified biochar

Thiol-modified rice straw biochar (RS) was prepared by an esterification reaction with β-mercaptoethanol and used for the remediation of Cd and Pb polluted soils. Modified biochar was characterized through elemental analysis, BET analysis, FE-SEM, FT-IR and XPS. These analytical results revealed that thiol groups were successfully grafted onto the surface of the biochar and were involved in metal ion complexation. Batch sorption experiments indicated that Cd2+ and Pb2+ sorption onto RS described well by a pseudo second order kinetic model and a Langmuir isotherm. The maximum adsorption capacities for Cd2+ and Pb2+, in the single-metal systems, were 45.1 and 61.4 mg g−1, respectively. In the binary-metal systems, RS selectively adsorbed Cd2+ over Pb2+. Cd2+ and Pb2+ were removed mainly through surface complexation. In the soil incubation experiments (28 days), RS reduced the available Cd by 34.8–39.2 %; while, RS reduced the available Pb by 8.6 %–11.1 %. This research demonstrates RS as a potentially effective amendment for the remediation of heavy metal polluted soils