1-Carboxymethyl-1′-carboxylatomethyl-3,3′-[p-phenylenebis(oxymethylene)]dipyridinium bromide dihydrate

In the crystal structure of the title salt, C22H21N2O6 +·Br−·2H2O, pairs of betaine mol­ecules are bridged by protons (the bridging proton is disordered), forming strong and symmetrical O—H⋯O hydrogen bonds, leading to an infinite chain along the b axis. The water mol­ecules are linked to the betaine mol­ecule and the bromide ion through O—H⋯O and O—H⋯Br inter­actions. The central ring, located on an inversion centre, makes dihedral angles of 1.2 (2)° with the outer rings. One of the carboxylic acid groups is deprotonated

catena-Poly[[[diaqua­copper(II)]-μ-2,2′-{[p-phenyl­enebis(oxymethyl­ene)]bis­(pyridinium-3,1-di­yl)}diacetate] dibromide]

The title centrosymmetric coordination polymer, {[Cu(C22H20N2O6)(H2O)2]Br2}n, formed by the reaction of the flexible double betaine ligand 2,2′-{[p-phenyl­enebis(oxymethyl­ene)]bis­(pyridine-3,1-di­yl)}diacetic acid with CuBr2, contains a Cu(II) atom ( symmetry) which is surrounded by two water molecules and bridged by two anions in a square-planar coordination. In the crystal, polymeric zigzag chains are linked via O—H⋯Br inter­actions, forming a two-dimensional network extending parallel to (011)

catena-Poly[[[tetra­aqua­cadmium(II)]-μ-3,3′-[p-phenyl­enebis(oxymethyl­ene)]bis­(1-pyridinioacetate)] dinitrate hemihydrate]

In the title polymeric coordination complex, {[Cd(C22H20N2O6)(H2O)4](NO3)2·0.5H2O}n, obtained from the self-assembly of the flexible double betaine 3,3′-[p-phenyl­enebis(oxymethyl­ene)]bis­(1-pyridinioacetate) with cadmium nitrate, both the octa­hedrally coordinated CdII cation and the substituted betaine ligand lie on inversion centres. The chains constructed through the trans-related acetate groups of the ligand are inter-connected via O—H⋯O hydrogen bonds involving coordinated aqua ligands, the nitrate anions and the partial-occupancy (0.25) water mol­ecule of solvation, forming a three-dimensional structure

AT4CTR: Auxiliary Match Tasks for Enhancing Click-Through Rate Prediction

Click-through rate (CTR) prediction is a vital task in industrial recommendation systems. Most existing methods focus on the network architecture design of the CTR model for better accuracy and suffer from the data sparsity problem. Especially in industrial recommendation systems, the widely applied negative sample down-sampling technique due to resource limitation worsens the problem, resulting in a decline in performance. In this paper, we propose \textbf{A}uxiliary Match \textbf{T}asks for enhancing \textbf{C}lick-\textbf{T}hrough \textbf{R}ate prediction accuracy (AT4CTR) by alleviating the data sparsity problem. Specifically, we design two match tasks inspired by collaborative filtering to enhance the relevance modeling between user and item. As the "click" action is a strong signal which indicates the user's preference towards the item directly, we make the first match task aim at pulling closer the representation between the user and the item regarding the positive samples. Since the user's past click behaviors can also be treated as the user him/herself, we apply the next item prediction as the second match task. For both the match tasks, we choose the InfoNCE as their loss function. The two match tasks can provide meaningful training signals to speed up the model's convergence and alleviate the data sparsity. We conduct extensive experiments on one public dataset and one large-scale industrial recommendation dataset. The result demonstrates the effectiveness of the proposed auxiliary match tasks. AT4CTR has been deployed in the real industrial advertising system and has gained remarkable revenue

Deep Group Interest Modeling of Full Lifelong User Behaviors for CTR Prediction

Extracting users' interests from their lifelong behavior sequence is crucial for predicting Click-Through Rate (CTR). Most current methods employ a two-stage process for efficiency: they first select historical behaviors related to the candidate item and then deduce the user's interest from this narrowed-down behavior sub-sequence. This two-stage paradigm, though effective, leads to information loss. Solely using users' lifelong click behaviors doesn't provide a complete picture of their interests, leading to suboptimal performance. In our research, we introduce the Deep Group Interest Network (DGIN), an end-to-end method to model the user's entire behavior history. This includes all post-registration actions, such as clicks, cart additions, purchases, and more, providing a nuanced user understanding. We start by grouping the full range of behaviors using a relevant key (like item_id) to enhance efficiency. This process reduces the behavior length significantly, from O(10^4) to O(10^2). To mitigate the potential loss of information due to grouping, we incorporate two categories of group attributes. Within each group, we calculate statistical information on various heterogeneous behaviors (like behavior counts) and employ self-attention mechanisms to highlight unique behavior characteristics (like behavior type). Based on this reorganized behavior data, the user's interests are derived using the Transformer technique. Additionally, we identify a subset of behaviors that share the same item_id with the candidate item from the lifelong behavior sequence. The insights from this subset reveal the user's decision-making process related to the candidate item, improving prediction accuracy. Our comprehensive evaluation, both on industrial and public datasets, validates DGIN's efficacy and efficiency

An inventory of invasive alien species in China

Invasive alien species (IAS) are a major global challenge requiring urgent action, and the Strategic Plan for Biodiversity (2011–2020) of the Convention on Biological Diversity (CBD) includes a target on the issue. Meeting the target requires an understanding of invasion patterns. However, national or regional analyses of invasions are limited to developed countries. We identified 488 IAS in China’s terrestrial habitats, inland waters and marine ecosystems based on available literature and field work, including 171 animals, 265 plants, 26 fungi, 3 protists, 11 procaryots, and 12 viruses. Terrestrial plants account for 51.6% of the total number of IAS, and terrestrial invertebrates (104 species) for 21.3%. Of the total numbers, 67.9% of plant IAS and 34.8% of animal IAS were introduced intentionally. All other taxa were introduced unintentionally despite very few animal and plant species that invaded naturally. In terms of habitats, 64.3% of IAS occur on farmlands, 13.9% in forests, 8.4% in marine ecosystems, 7.3% in inland waters, and 6.1% in residential areas. Half of all IAS (51.1%) originate from North and South America, 18.3% from Europe, 17.3% from Asia not including China, 7.2% from Africa, 1.8% from Oceania, and the origin of the remaining 4.3% IAS is unknown. The distribution of IAS can be divided into three zones. Most IAS are distributed in coastal provinces and the Yunnan province; provinces in Middle China have fewer IAS, and most provinces in West China have the least number of IAS. Sites where IAS were first detected are mainly distributed in the coastal region, the Yunnan Province and the Xinjiang Uyghur Autonomous Region. The number of newly emerged IAS has been increasing since 1850. The cumulative number of firstly detected IAS grew exponentially

Low-frequency micro/nano-vibration generator using a piezoelectric actuator

Low-frequency vibration must be detected because of its harmful effects on micro/nano measuring machines. Thus, the authors developed a low-cost and high-precision detector for low-frequency micro-vibration. A high-precision vibration generator is required to calibrate the vibration detector because of the high cost and complex structure of existing vibration generators. A new vibration generator that can produce low-cost and high-precision lowfrequency vibration was also developed. A piezoelectric actuator is used as a vibration exciter, which is driven by a high-precision signal generator and a high-voltage amplifier. A beryllium bronze-based leaf spring was used as an elastic component, which is optimally designed and verified by the ANSYS software. The proper size and natural frequency of the leaf spring were obtained. The leaf spring was fixed horizontally on a four-point cylinder-shaped pedestal and driven by the actuator vertically. The worktable on the top surface of the leaf spring only had an up-and-down direction. A high-precision eddy current sensor was used to test the performance of the developed vibration generator. Experimental results show that the vibration generator can produce simple harmonic vibrations with a frequency and amplitude ranges of 10–50 Hz and 0.90–19.87 μm, respectively, and the repeatability of the open-looped vibration amplitude is less than 90 nm (K=2). The developed vibration generator can be used when a micro/nano-vibration detector is calibrated

Cultivation of novel Atribacterota from oil well provides new insight into their diversity, ecology, and evolution in anoxic, carbon-rich environments

BackgroundThe Atribacterota are widely distributed in the subsurface biosphere. Recently, the first Atribacterota isolate was described and the number of Atribacterota genome sequences retrieved from environmental samples has increased significantly; however, their diversity, physiology, ecology, and evolution remain poorly understood.ResultsWe report the isolation of the second member of Atribacterota, Thermatribacter velox gen. nov., sp. nov., within a new family Thermatribacteraceae fam. nov., and the short-term laboratory cultivation of a member of the JS1 lineage, Phoenicimicrobium oleiphilum HX-OS.bin.34TS, both from a terrestrial oil reservoir. Physiological and metatranscriptomics analyses showed that Thermatribacter velox B11T and Phoenicimicrobium oleiphilum HX-OS.bin.34TS ferment sugars and n-alkanes, respectively, producing H2, CO2, and acetate as common products. Comparative genomics showed that all members of the Atribacterota lack a complete Wood-Ljungdahl Pathway (WLP), but that the Reductive Glycine Pathway (RGP) is widespread, indicating that the RGP, rather than WLP, is a central hub in Atribacterota metabolism. Ancestral character state reconstructions and phylogenetic analyses showed that key genes encoding the RGP (fdhA, fhs, folD, glyA, gcvT, gcvPAB, pdhD) and other central functions were gained independently in the two classes, Atribacteria (OP9) and Phoenicimicrobiia (JS1), after which they were inherited vertically; these genes included fumarate-adding enzymes (faeA; Phoenicimicrobiia only), the CODH/ACS complex (acsABCDE), and diverse hydrogenases (NiFe group 3b, 4b and FeFe group A3, C). Finally, we present genome-resolved community metabolic models showing the central roles of Atribacteria (OP9) and Phoenicimicrobiia (JS1) in acetate- and hydrocarbon-rich environments.ConclusionOur findings expand the knowledge of the diversity, physiology, ecology, and evolution of the phylum Atribacterota. This study is a starting point for promoting more incisive studies of their syntrophic biology and may guide the rational design of strategies to cultivate them in the laboratory. Video Abstract