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Abstract Source‐to‐sink analysis examines the effects of source areas and basin (sink) dynamics on the generation, transport, composition, distribution and deposition of sediment in modern and ancient sedimentary systems. Detrital zircon, as one of the most stable detrital minerals, its U–Pb geochronology and geochemistry is pivotal for sedimentary provenance analysis and reconstructing palaeogeography. The South China Sea (SCS), as the largest marginal sea in the Southeast Asia, is closely related to the uplift of the Tibetan Plateau and the evolution of Chinese rivers. In this study, we mainly collect published detrital zircon of Cenozoic sediments in the SCS (include Hainan and Taiwan Islands). These detrital zircon were concentrated on Cenozoic multiple episodic rift basins in the northern part of the SCS. A total of 21,760 detrital zircon U–Pb data from 293 samples and 995 detrital zircon Lu‐Hf isotope data from 22 samples were collected in this dataset, with the main study epochs being the Palaeogene and Neogene. Best ages of these grains are range from 4,691 to 10 Ma and more than half of them within age less than 500 Ma. The 176Hf/177Hf ratios of the SCS samples ranging from 0.280509 to 0.28306 and the εHf(t) values from −63.8 to 24.6. The main age group of the SCS Cenozoic sediments were at 130–95 Ma, 175–130 Ma and 265–230 Ma. The detrital zircon U–Pb age and Lu–Hf isotope data contained in this dataset is an important geological record of the sedimentary and tectonic evolution of the SCS and the evolution of rivers such as the ancient Pearl River and the ancient Red River, which can provide a basis and important clues or exploring the source of sediments in the SCS, the dynamical processes of basin evolution, the evolution of the coastal drainage system and the tectonic uplift of the Tibetan Plateau.

Abstract Complications of detrital zircons databases provide a means for statistically analysing a variety of significant geological problems. In this work, we tried to collect a database about the South China Phanerozoic detrital zircon geochronology data. The data statistics of this paper rely on the OneSediment Working Group of The Deep‐time Digital Earth program (DDE). By November 2022, the database contains a total of 699 samples with 55,532 U–Pb ages and 3,770 effective Hf isotope data, from 130 papers. Abundant information including reference title, sample ID, locality, rock type, research institution, GPS coordinates, U–Pb ages, εHf(t) values, etc., have been involved in our database, and all data can be downloaded from DDE Data Publish & Repository website, https://repository.deep‐time.org/. Through the integrated data, we can improve the previous studies and avoid the waste of resources caused by a large number of repeated studies.

Abstract Stream data mining aims to handle the continuous and ongoing generation of data flows (e.g. weather, stock and traffic data), which often encounters concept drift as time progresses. Traditional offline algorithms struggle with learning from real-time data, making online algorithms more fitting for mining the stream data with dynamic concepts. Among families of the online learning algorithms, single pass stands out for its efficiency in processing one sample point at a time, and inspecting it only once at most. Currently, there exist online algorithms tailored for single pass over the stream data by converting the problems of classification into minimum enclosing ball. However, these methods mainly focus on expanding the ball to enclose the new data. An excessively large ball might overwrite data of the new concept, creating difficulty in triggering the model updating process. This paper proposes a new online single pass framework for stream data mining, namely Scalable Concept Drift Adaptation (SCDA), and presents three distinct online methods (SCDA-I, SCDA-II and SCDA-III) based on that framework. These methods dynamically adjust the ball by expanding or contracting when new sample points arrive, thereby effectively avoiding the issue of excessively large balls. To evaluate their performance, we conduct the experiments on 7 synthetic and 5 real-world benchmark datasets and compete with the state-of-the-arts. The experiments demonstrate the applicability and flexibility of the SCDA methods in stream data mining by comparing three aspects: predictive performance, memory usage and scalability of the ball. Among them, the SCDA-III method performs best in all these aspects.

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The enantiomeric contents of 2-pentanol of Baijiu were analyzed by liquid-liquid extraction (LLE) coupled with gas chromatography-mass spectrometry (GC-MS) using β-cyclodextrin as a chiral stationary phase. In this study, the average enantiomeric ratios R:S were 72:28, 64:36, and 94:6 in soy sauce aroma-type Baijiu (SSB), strong aroma-type Baijiu (STB), and light aroma-type Baijiu (LTB), respectively, and only (R)- configuration was found in rice aroma-type Baijiu (RTB). The highest enantiomeric concentration of 2-pentanol was found in STB. (R)-2-pentanol dominated in 48 Baijiu studied, and the concentration of (R)-2-pentanol was higher than that of the (S)-configuration. The results showed that the enantiomers of 2-pentanol were discrepant in different aroma types of Baijiu, and it may be the result of differences in raw materials, environment, and production processes. The 2-pentanol enantiomers had different odor characteristics, with different olfactory thresholds in pure water and 46% ethanol solutions by sensory analysis. (R)-2-pentanol was described as paint, rubber, grease, while the (S)-form had mint, plastic, and pungent notes. The olfactory thresholds of (R)- and (S)-form were 163.30 mg/L and 78.58 mg/L in 46% ethanol and 12.62 mg/L and 3.03 mg/L in pure water, respectively. The different enantiomeric distribution and aroma characteristics of the 2-pentanol enantiomers in Baijiu could be a potential marker for determining adulteration.

In this paper, we focus on training a support vector machine (SVM) online with a single pass over streaming data.Traditional batch-mode SVMs require previously prepared training data; these models may be unsuitable for streaming data circumstances. Online SVMs are effective tools for solving this problem by receiving data streams consistently and updating model weights accordingly. However, most online SVMs require multiple data passes before the updated weights converge to stable solutions, and may be unable to address high-rate data streams. This paper presents OSVM_SP, a new online SVM with a single pass over streaming data, and three budgeted versions to bound the space requirement with support vector removal principles. The experimental results obtained with five public datasets show that OSVM_SP outperforms most state-of-the-art single-pass online algorithms in terms of accuracy and is comparable to batch-mode SVMs. Furthermore, the proposed budgeted algorithms achieve comparable predictive performance with only 1/3 of the space requirement.

Task offloading and resource allocation are the major elements of edge computing. A reasonable task offloading strategy and resource allocation scheme can reduce task processing time and save system energy consumption. Most of the current studies on the task migration of edge computing only consider the resource allocation between terminals and edge servers, ignoring the huge computing resources in the cloud center. In order to sufficiently utilize the cloud and edge server resources, we propose a coarse-grained task offloading strategy and intelligent resource matching scheme under Cloud-Edge collaboration. We consider the heterogeneity of mobile devices and inter-channel interference, and we establish the task offloading decision of multiple end-users as a game-theory-based task migration model with the objective of maximizing system utility. In addition, we propose an improved game-theory-based particle swarm optimization algorithm to obtain task offloading strategies. Experimental results show that the proposed scheme outperforms other schemes with respect to latency and energy consumption, and it scales well with increases in the number of mobile devices.

In this study, the complete mitochondrial genome of Modiolus comptus was determined and annotated for the first time. The 15,591 bp circular genome has a base composition of 24.3% A, 38.6% T, 12.5% C, and 24.5% G, demonstrating a bias of higher AT content (63.0%) than GC content (27.0%). The mitochondrial genome contains 12 protein-coding genes (PCGs), 20 transfer RNA genes (tRNA), 2 ribosomal RNA genes (12S rRNA and 16S rRNA), and one control region. All genes of M. comptus were encoded on the heavy strand, except trnT(ugu) gene. The whole mitochondrial genome of M. comptus and 21 mitogenomes of other Mytilidae species were used for phylogenetic analysis. The result indicated the newly sequenced species had the closest relationship with Modiolus nipponicus (MK721547) and was clustered within the clade of genus Modiolus.