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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 FROM AUTHOR]

Franchising has played a pivotal role in driving China's economic growth in recent years. With the prominent problem of franchise asymmetry, the protection of franchisees has emerged as a significant concern. This paper conducts a comparative study of franchisee protection practices under franchising regulation in China and certain jurisdictions such as the United States, Australia, and Malaysia. This paper aims to analyze three aspects of franchisee protection, including information disclosure, registration, liability, and relief for violations. To this end, the researchers adopted library data collection methods. Franchisingrelated laws, regulations, rules, and cases are the primary data sources, while journals, books, industry reports, and network resources are the secondary data sources. The results of this study show that although China's franchisee protection is generally in line with international practice, there is still room for further improvement. The authors proposed recommendations such as strictly implementing registration requirements, introducing a disclosure exemption system, specifying the exact duration of the cooling-off period, and diversifying dispute resolution. The contribution of this paper is to learn from the advanced experience of foreign franchisee protection and improve the current franchisee protection system in China. [ABSTRACT FROM AUTHOR]

A gray market emerges when some distributors divert products to unauthorized distributors/retailers to make sneaky profits from the manufacturers' differential channel incentives, such as quantity discounts. Traditionally, manufacturers rely heavily on internal audits to periodically investigate the flows of products and funds so as to deter the gray market; however, this is too costly given the large number of distributors and their huge volumes of orders. Owing to the advances in data analytics techniques, the ordering quantities of a distributor over time, which form multivariate time series, can help reveal suspicious product diversion behaviors and narrow the audit scope drastically. To that end, in this paper, we build on the recent advancement of representation learning for time series and adopt a sequence autoencoder to automatically characterize the overall demand patterns. To cope with the underlying entangled factors and interfering information in the multivariate time series of ordering quantities, we develop a disentangled learning scheme to construct more effective sequence representations. An interdistributor correlation regularization is also proposed to ensure more reliable representations. Finally, given the highly scarce anomaly labels for the detection task, an unsupervised deep generative model based on the learned representations of the distributors is developed to estimate the densities of distributions, which enables the anomaly scores generated through end-to-end learning. Extensive experiments on a real-world distribution channel data set and a larger simulated data set empirically validate our model's superior and robust performances compared with several state-of-the-art baselines. Additionally, our illustrative economic analysis demonstrates that the manufacturers can launch more targeted and cost-effective audits toward the suspected distributors recommended by our model so as to deter the gray market. History: Accepted by Ram Ramesh, Area Editor for Data Science & Machine Learning. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72031001, 72301017, 72371011, and 72242101]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0155) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2022.0155). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

Rank aggregation combines the preference rankings of multiple alternatives from different voters into a single consensus ranking, providing a useful model for a variety of practical applications but posing a computationally challenging problem. In this paper, we provide an effective hybrid evolutionary ranking algorithm to solve the rank aggregation problem with both complete and partial rankings. The algorithm features a semantic crossover based on concordant pairs and an enhanced late acceptance local search method reinforced by a relaxed acceptance and replacement strategy and a fast incremental evaluation mechanism. Experiments are conducted to assess the algorithm, indicating a highly competitive performance on both synthetic and real-world benchmark instances compared with state-of-the-art algorithms. To demonstrate its practical usefulness, the algorithm is applied to label ranking, a well-established machine learning task. We additionally analyze several key algorithmic components to gain insight into their operation. History: Accepted by Erwin Pesch, Area Editor for Heuristic Search & Approximation Algorithms. Funding: This work was supported by the National Natural Science Foundation of China [Grant 72371157] and Shanghai Pujiang Programme [Grant 23PJC069]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0019) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2022.0019). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

With the development of society, the protection of historical buildings has been paid more and more attention. In recent years, China has increased the surveying and mapping of historical buildings buildings. This paper takes the historical building protection project in Zhejiang Province, China as an example. This paper introduces the method of combining UAV aerial photography, 3D laser scanning, close close-range photogrammetry and traditional surveying and mapping techniques. Mapping and information collection of historical buildings. And on this basis, the content and methods of quality inspection of historical architectural achievements are introduced. Guarantee the reliability of historical building archives and data quality quality. This will play a supporting role in the future repair and maintenance of historical buildings buildings. [ABSTRACT FROM AUTHOR]

This paper develops a cost-effective sequential route recommender system to provide real-time routing recommendations for vacant taxis searching for the next passenger. We propose a prediction-and-optimization framework to recommend the searching route that maximizes the expected profit of the next successful passenger pickup based on the dynamic taxi demand-supply distribution. Specifically, this system features a deep learning-based predictor that dynamically predicts the passenger pickup probability on a road segment and a recursive searching algorithm that recommends the optimal searching route. The predictor integrates a graph convolution network (GCN) to capture the spatial distribution and a long short-term memory (LSTM) to capture the temporal dynamics of taxi demand and supply. The GCN-LSTM model can accurately predict the pickup probability on a road segment with the consideration of potential taxi oversupply. Then, the dynamic distribution of pickup probability is fed into the route optimization algorithm to recommend the optimal searching routes sequentially as route inquiries emerge in the system. The recursion tree-based route optimization algorithm can significantly reduce the computational time and provide the optimal routes within seconds for real-time implementation. Finally, extensive experiments using Beijing Taxi GPS data demonstrate the effectiveness and efficiency of the proposed recommender system. History: Accepted by Ram Ramesh, Area Editor for Data Science and Machine Learning. Funding: This work was partially supported by the Hong Kong Research Grants Council [Grants CityU 21500220, CityU 11504322] and the National Natural Science Foundation of China [Grant 72201222]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2021.0112) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2021.0112). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

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Online reviews published on the e-commerce platform provide a new source of information for designers to develop new products. Past research on new product development (NPD) using user-generated textual data commonly focused solely on extracting and identifying product features to be improved. However, the competitive analysis of product features and more specific improvement strategies have not been explored deeply. This study fully uses the rich semantic attributes of online review texts and proposes a novel online review–driven modeling framework. This new approach can extract fine-grained product features; calculate their importance, performance, and competitiveness; and build a competitiveness network for each feature. As a result, decision making is assisted, and specific product improvement strategies are developed for NPD beyond existing modeling approaches in this domain. Specifically, online reviews are first classified into redesign- and innovation-related themes using a multiple embedding model, and the redesign and innovation product features can be extracted accordingly using a mutual information multilevel feature extraction method. Moreover, the importance and performance of features are calculated, and the competitiveness and competitiveness network of features are obtained through a personalized unidirectional bipartite graph algorithm. Finally, the importance performance competitiveness analysis plot is constructed, and the product improvement strategy is developed via a multistage combined search algorithm. Case studies and comparative experiments show the effectiveness of the proposed method and provide novel business insights for stakeholders, such as product providers, managers, and designers. History: Accepted by Ram Ramesh, Area Editor for Data Science and Machine Learning. Funding: This work was supported by the National Natural Science Foundation of China [Project 72071151] and the Natural Science Foundation of Hubei Province, China [Grant 2023CFB712]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0333) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2022.0333). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

This study considers a well-known critical node detection problem that aims to minimize a pairwise connectivity measure of an undirected graph via the removal of a subset of nodes (referred to as critical nodes) subject to a cardinality constraint. Potential applications include epidemic control, emergency response, vulnerability assessment, carbon emission monitoring, network security, and drug design. To solve the problem, we present a "reduce-solve-combine" memetic search approach that integrates a problem reduction mechanism into the popular population-based memetic algorithm framework. At each generation, a common pattern mined from two parent solutions is first used to reduce the given problem instance, then the reduced instance is solved by a component-based hybrid neighborhood search that effectively combines an articulation point impact strategy and a node weighting strategy, and finally an offspring solution is produced by combining the mined common pattern and the solution of the reduced instance. Extensive evaluations on 42 real-world and synthetic benchmark instances show the efficacy of the proposed method, which discovers nine new upper bounds and significantly outperforms the current state-of-the-art algorithms. Investigation of key algorithmic modules additionally discloses the importance of the proposed ideas and strategies. Finally, we demonstrate the generality of the proposed method via its adaptation to solve the node-weighted critical node problem. History: Accepted by Erwin Pesch, Area Editor for Heuristic Search & Approximation Algorithms. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72371157, 61903144, 72031007]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0130) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2022.0130). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

Federated learning is a new distributed machine learning framework, where numerous heterogeneous clients collaboratively train a model without sharing training data. In this work, we consider a practical and ubiquitous issue when deploying federated learning in mobile environments: intermittent client availability, where the set of eligible clients may change during the training process. Such intermittent client availability would seriously deteriorate the performance of the classical federated averaging algorithm (FedAvg). Thus, we propose a simple distributed nonconvex optimization algorithm, called federated latest averaging (FedLaAvg), which leverages the latest gradients of all clients, even when the clients are not available, to jointly update the global model in each iteration. Our theoretical analysis shows that FedLaAvg achieves guaranteed convergence and a sublinear speedup with respect to the total number of clients. We implement FedLaAvg along with several baselines and evaluate them over the benchmarking MNIST and Sentiment140 data sets. The evaluation results demonstrate that FedLaAvg achieves more stable training than FedAvg in both convex and nonconvex settings and reaches a sublinear speedup. Source code and online supplement are available at the IJOC GitHub site (http://dx.doi.org/10.1287/ijoc.2022.0057.cd, https://github.com/INFORMSJoC/2022.0057). History: Accepted by Ram Ramesh, Area Editor for Data Science & Machine Leaning. Funding: This work was supported by the National Key R&D Program of China [Grant 2022ZD0119100], the National Natural Science Foundation of China (NSFC) [Grants 61972252, 61972254, 62072303, 62025204, 62132018, 62202296, and 62202297], the Alibaba Innovation Research (AIR) Program, and the Tencent Rhino Bird Key Research Project. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0057) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2022.0057). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

The ℓ 0 trend filtering ( ℓ 0 -TF) is a new effective tool for nonparametric regression with the power of automatic knot detection in function values or derivatives. It overcomes the drawback of ℓ 1 -TF that is known to have bias issues. To solve the ℓ 0 -TF problem, we propose an alternating minimization induced active set (AMIAS) search method based on the necessary optimality conditions derived from an augmented Lagrangian framework. The proposed method takes full advantage of the primal and dual variables with complementary supports, and decouples the high-dimensional problem into two subsystems on the active and inactive sets, respectively. A sequential AMIAS algorithm with warm start initialization is developed for efficient determination of the cardinality parameter, along with the output of solution paths. Theoretically, the oracle estimator of ℓ 0 -TF is justified to behave like regression splines under the continuous time setting with mild conditions. Our numerical experiments include simulation studies for comparing ℓ 0 -TF to ℓ 1 -TF and free-knot splines on several synthetic examples, and a real data application of time series segmentation on Hong Kong PM2.5 indexes. History: Accepted by Antonio Frangioni, Area Editor for Design & Analysis of Algorithms – Continuous. Funding: This work was supported in part by Hong Kong General Research Fund [No. 17306519]. C. Wen's research is partially supported by National Science Foundation of China [12171449] and Fundamental Research Funds for the Central Universities [WK3470000027, YD2040002019]. X. Wang's research is partially supported by National Natural Science Foundation of China [Grants 72171216, 12231017, 71921001, and 71991474], and the National Key R&D Program of China [No. 2022YFA1003803]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/ijoc.2021.0313. The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2021.0313) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2021.0313). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

We consider selecting the top-m alternatives from a finite number of alternatives via Monte Carlo simulation. Under a Bayesian framework, we formulate the sampling decision as a stochastic dynamic programming problem and develop a sequential sampling policy that maximizes a value function approximation one-step look ahead. To show the asymptotic optimality of the proposed procedure, the asymptotically optimal sampling ratios that optimize the large deviations rate of the probability of false selection for selecting the top-m alternatives have been rigorously defined. The proposed sampling policy is not only proved to be consistent but also achieve the asymptotically optimal sampling ratios. Numerical experiments demonstrate superiority of the proposed allocation procedure over existing ones. History: Accepted by Bruno Tuffin, Area Editor for Simulation. Funding: This work was supported by the National Natural Science Foundation of China [Grants 72250065, 72293582, 72022001, and 71901003], and the National Science Foundation [Grant DMS-2053489], the major project of the National Natural Science Foundation of China [Grant 72293582], and the China Scholarship Council [Grant CSC202206010152]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2021.0333) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2021.0333). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

We extend the notion of globalized robustness to consider distributional information beyond the support of the ambiguous probability distribution. We propose the globalized distributionally robust counterpart that disallows any (respectively, allows limited) constraint violation for distributions residing (respectively, not residing) in the ambiguity set. By varying its inputs, our proposal recovers several existing perceptions of parameter uncertainty. Focusing on the type 1 Wasserstein distance, we show that the globalized distributionally robust counterpart has an insightful interpretation in terms of shadow price of globalized robustness, and it can be seamlessly integrated with many popular optimization models under uncertainty without incurring any extra computational cost. Such computational attractiveness also holds for other ambiguity sets, including the ones based on probability metric, optimal transport, ϕ-divergences, or moment conditions, as well as the event-wise ambiguity set. Numerical studies on an adaptive network lot-sizing problem demonstrate the modeling flexibility of our proposal and its emphases on globalized robustness to constraint violation. History: Antonio Frangioni, Area Editor for Design & Analysis of Algorithms—Continuous. Funding: Z. Chen was supported by [General Research Fund Grant 9043424, NSFC/RGC Joint Research Scheme N_CityU105/21] from the Hong Kong Research Grants Council. S. Wang was supported by the National Natural Science Foundation of China [Grants 71922020, 72171221, and 71988101, entitled "Econometric Modeling and Economic Policy Studies"] and the Fundamental Research Funds for the Central Universities [Grant UCAS-E2ET0808X2]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0274) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2022.0274). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

A multidepot capacitated vehicle routing problem aims to serve customers' demands using a fleet of capacitated vehicles located in multiple depots, such that the total travel cost of the vehicles is minimized. We study a variant of this problem, the k-depot split delivery vehicle routing problem (or k-DSDVRP in short), for the situation where each customer's demand can be served by more than one vehicle, and the total number of depots, denoted by k ≥ 2 , is a fixed constant. This is a challenging problem with broad applications in the logistics industry, for which no constant ratio approximation algorithm is known. We develop a new approximation algorithm for the k-DSDVRP, ensuring an approximation ratio of (6 − 4 / k) and a polynomial running time for any fixed constant k ≥ 2. To achieve this, we propose a novel solution framework based on a new relaxation of the problem, a cycle splitting procedure, and a vehicle assignment procedure. To further enhance its efficiency for practical usage, we adapt the newly developed approximation algorithm to a heuristic, which runs in polynomial time even when k is arbitrarily large. Experimental results show that this heuristic outperforms a commercial optimization solver and a standard vehicle routing heuristic. Moreover, our newly proposed solution framework can be applied to developing new constant ratio approximation algorithms for several other variants of the k-DSDVRP with k ≥ 2 being a fixed constant. History: Accepted by Erwin Pesch, Area Editor for Heuristic Search & Approximation Algorithms. Funding: This work was supported in part by the National Natural Science Foundation of China [Grants 71971177, 71725001, U1811462], Research Grants Council of Hong Kong SAR, China [Grant 15221619], and Guangdong Basic and Applied Basic Research Foundation [Grant 2023A1515030260]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/ijoc.2021.0193. The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2021.0193) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2021.0193). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

We study adjustable distributionally robust optimization problems, where their ambiguity sets can potentially encompass an infinite number of expectation constraints. Although such ambiguity sets have great modeling flexibility in characterizing uncertain probability distributions, the corresponding adjustable problems remain computationally intractable and challenging. To overcome this issue, we propose a greedy improvement procedure that consists of solving, via the (extended) linear decision rule approximation, a sequence of tractable subproblems—each of which considers a relaxed and finitely constrained ambiguity set that can be iteratively tightened to the infinitely constrained one. Through three numerical studies of adjustable distributionally robust optimization models, we show that our approach can yield improved solutions in a systematic way for both two-stage and multistage problems. History: Accepted by Pascal Van Hentenryck, Area Editor for Computational Modeling: Methods & Analysis. Funding: Financial support by the Early Career Scheme from the Hong Kong Research Grants Council [Project No. CityU 21502820], the CityU Start-Up Grant [Project No. 9610481], the CityU Strategic Research Grant [Project No. 7005688], the National Natural Science Foundation of China [Project No. 72032005], and Chow Sang Sang Group Research Fund sponsored by Chow Sang Sang Holdings International Limited [Project No. 9229076] is gratefully acknowledged. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2021.0181), as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2021.0181). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

Water data is a crucial asset for sustainable water resource management. However, the availability of China's water datasets lags far behind modern expectations for open geoscientific data. This dataset is a part of the China Water Data Archive (CWDA), an upcoming national collection of water-related data covering all aspects of water data for boosting data sharing in China. The CWDA aims at providing free, clean, non-sensitive, coherent, and reliable water data within China for global researchers to support the national and global water resources management and the United Nations-Water Integrated Monitoring Initiative for Sustainable Development Goals 6 and 14. In this paper, we used Python and R language to collect, tidy, reorganize, and curate the publicly available inland and coastal/ocean surface water quality data in China, following a series of data quality dimensions (integrity, completeness, consistency, and accuracy). As the most comprehensive, publicly available, handy, and clean water quality dataset in China so far, it included water quality data for daily, weekly, and monthly in the period of 1980-2022, with 17 indicators for over 330,000 observations at 2384 sites from inland to coastal/ocean areas. This dataset will greatly support works relevant to the assessment, modelling, and projection of water quality, ocean biomass, and biodiversity in China. [ABSTRACT FROM AUTHOR]

Purpose The purpose of this paper is to explore the professional opinion of LIS with reference to open source software (OSS) adoption, status, problems and future measures in research and academic libraries of Beijing, China.Design/methodology/approach Data were collected using a structured questionnaire, and semi-structured in-depth interviews were conducted among 20 academic and 20 research libraries in Beijing, China. The convenient sampling technique was used to collect data. The data collected were interpreted using descriptive statistics and independent t-test.Findings The results reveal that Chinese research and academics libraries depend mostly upon commercial software and place a great deal of trust on locally produced free software. This situation may be caused due to the lack of professional knowledge in OSS and lack of appropriate technical expertise. Although there were a sufficient number of librarians who have shown interest in OSS, they did not enthusiastically agree to implement it in their libraries. Furthermore, the interviews highlighted the professionals’ opinion that most librarians are reluctant to adopt OSS due to the risk factor, lack of professional expertise, insufficient interest of the Chinese Government and lack of professional training.Originality/value The findings are useful for the Chinese LIS community, software developers, technology administrators and library administrators. In particular, it is beneficial for research and academic libraries of China to adapt OSS for library management and provide better library services and sources to their library users. [ABSTRACT FROM AUTHOR]

Purpose The purpose of this paper is to report the results of a qualitative study exploring the conditions associated with the creation, preservation and transmission of Shuishu archives in China, and the crises today in their preservation and transmission and the reasons behind them. It also proposes activation mechanisms to shift Shuishu archives from jeopardized collective memory to preservable cultural memory.Design/methodology/approach Semi-structured interviews and ethnographic fieldwork were conducted over the course of a month in 2015.Findings The creation, preservation and transmission of Shuishu archives in the community of the Shui rely upon the community’s closed system. But this system has been broken as a result of modernization and wide use of new media in China. To preserve and transmit Shuishu archives to future generations, there needs to be mutual trust and equitable cooperation between government archives and the Shuishushi. The “cultural consciousness” of the Shui needs to be stimulated, and more members of the Shui and the whole of society need to participate in the preservation and transmission of this distinctive memory.Practical implications The study can provide a provocative example for education in preservation and LIS about community culture and archiving, and the preservation of social memory, identity and culture. The activation mechanisms seek to aid in the preservation and transmission of Shuishu archives and other similar community memory.Originality/value The study uses semi-structured interviews and ethnographic methodology to develop a rich understanding of the history and the status quo of the preservation and transmission of Shuishu archives. It redefines Shuishu archives and sheds light on the roles government archives should play in the preservation and transmission of Shuishu archives. [ABSTRACT FROM AUTHOR]

• Collected and consolidated a comprehensive set of supply and demand data from multiple data repositories, government agencies, freight carriers, research papers and reports, and text and oral communications with industry professionals for China-Europe containerized freight flows; • Constructed a multimodal freight transportation supernetwork for China-Europe containerized freight flows, including a liner shipping network run by all major liner shipping alliances, a freight rail network exclusively managed by the China Railway Express, and a domestic highway network in China and a multinational highway network in Europe; • Proposed and analyzed a multimodal multicommodity freight transportation network flow model with bottleneck congestion functions and service capacity constraints, in which the individual mode-route choice behavior is regulated by stochastic user equilibrium; • Developed, tested and analyzed a Lagrangian relaxation framework embedding a disaggregate simplicial decomposition algorithm for the developed multimodal multicommodity freight transportation network problem. As a traditional way for transporting containerized cargoes, international liner shipping has long played a dominant role in the China-Europe freight transportation market. In recent years, China Railway Express has offered a new and competitive freight transportation option, contributing to a significant increase in the trade volume by rail between China and Europe. With the goal of providing a holistic computational tool that can predict and analyze the spatial distribution of containerized freight flows and suggest strategic transportation infrastructure and service expansion options, this paper develops and implements a multimodal multicommodity freight transportation network equilibrium model, based on a newly constructed supernetwork integrating international liner shipping lines, international freight rail services, and international and domestic highway networks in relevant countries. The combined transportation mode-route choice behavior of individual shippers is characterized by the widely used multinomial logit model, where the values of time of different commodity categories are included in its utility functions. The congestion effects arising from bottleneck facilities along liner shipping and freight rail lines (i.e., seaports, water channels, and break-of-gauge stations) are first captured by queueing models and then approximated by polynomial delay functions, while the accommodation capacity of individual service lines is explicitly embedded into the model as hard constraints. This supernetwork equilibrium model is tackled by a subgradient algorithm in the Lagrangian relaxation framework embedding a disaggregate simplicial decomposition algorithm. Computational experiments are conducted with a multi-layer, hetero-structure set of supply and demand data collected from multiple data repositories, government agencies, freight carriers, and research papers and reports. The preliminary results exhibit the computational performance of the solution procedure and reveal the variation of congestion levels of bottleneck facilities, capacity usage profiles of service lines, and competitiveness conditions between liner shipping and freight rail services under different monthly demand patterns. [ABSTRACT FROM AUTHOR]

The seismological observation system in China achieved rapid development during the last five years. The Data Management Center (DMC) of the China Earthquake Network Center (CENC) of the China Earthquake Administration (CEA) now receives and archives waveform data from more than 1000 permanent seismic stations around China in real time. For operational, backup, and data security considerations, the DMC at the Institute of Geophysics (IGP), the CEA was established at the end of 2007. The IGP DMC is capable of receiving and processing continuous waveform data in real time from more than 1000 permanent seismic stations around China. Currently, the data processing and management mainly include data quality control, data format conversion, event data extraction at user's request, and data download service via Internet. By now, the IGP DMC has supplied about 150 terabytes waveform data to over 120 researches of more than 30 academic institutions. More than 20 papers have been published in professional journals. After the great Wenchuan earthquake, the real-time waveform data from 56 portable stations deployed in the aftershock area were added to IGP DMC. All these data make the IGP DMC a critical platform for supporting relevant seismological research. This paper gives a detailed description of the permanent seismic stations of the CEA's seismological observation system, the technical system construction of the IGP DMC, establishment of the near-real-time automatic event-extraction system for large earthquakes, as well as the prompt data support to Wenchuan earthquake-related researches. [ABSTRACT FROM AUTHOR]

Purpose: Balancing exploration and exploitation is a strategic challenge for technology-based companies striving to successfully implement ambidexterity in rapidly changing markets. This study aims to look at the extent in which corporate universities can be instrumental in the cross-functional deployment of the resources, capabilities and experience needed to achieve organizational ambidexterity. Design/methodology/approach: This study is the result of a single case study of ZTE University in China. Data from archives, direct observations, and semi-open interviews have been triangulated and analyzed with pattern matching technique. Findings: This study analyzed the development of capabilities allowing the strategic combinations of exploration and exploitation, and to clearly witness how the corporate university was dynamically linked with those development. Originality/value: The empirical results offer new insights on the most relevant capabilities for technology-based companies and notably those that are more likely to be exploited through a corporate university. [ABSTRACT FROM AUTHOR]

The largest earthquake (Ms 8½) in eastern China occurred near Tancheng in 1668. Six M ≥ 6 earthquakes occurred in this region in historical time. We re-examine the primary historical archives from this region, adding independent constraints such as maps of active faults, local topography, intensity attenuation laws for China, and relevant administrative practices in archiving data, to reassess source parameters (epicenter, magnitude, or maximum intensity). We use the seismic density index—a measure of the clustering of seismicity weighted by source magnitude—to identify candidate zones of present-day clustering of seismicity than may represent a long-term memory of past historical events in an intraplate setting. We update at least one of the source parameters for four of the seven events. The main shock and two aftershocks of the 1668–1672 Ms 8½ Tancheng earthquake sequence locate along the strike of the Tan-Lu fault zone, each associated with significant and contiguous seismic density anomalies whose elliptical contours are elongated along its strike. A third major aftershock (Ms ≈ 6) in this sequence locates some 200 km away from the fault zone. Six of the seven historical events have seismic density anomalies that cannot be explained by aftershocks of modern seismicity (ML ≤ 5.3), and hence are candidate zones for long-lived aftershock sequences. In contrast, there are no significant seismic density anomalies along the section of Tan-Lu fault south of Suqian, despite palaeo-seismic evidence for a Ms 8½ earthquake around 6280 B.C., providing an upper bound to long-term memory of great earthquakes in this region. [ABSTRACT FROM AUTHOR]

National Materials Data Management and Service platform (NMDMS) is a materials data repository for the publication and sharing of heterogeneous materials scientific data and follows the FAIR principles: Findable, Accessible, Interoperable, and Reusable. To ensure data are 'Interoperable, NMDMS uses a user-friendly semi-structured scientific data model, named dynamic container', to define, exchange, and store heterogeneous scientific data. Then, a personalized yet standardized data submission subsystem, a rigorous project data review and publication subsystem, and a multi-granularity data query and retrieval subsystem collaboratively make data 'Reusable', 'Findable', and 'Accessible'. Finally, China's "National Key R&D Program: Material Genetic Engineering Key Special Project" has adopted NMDMS to publish and share its project data. There are 12,251,040 pieces of data published in NMDMS since 2018, under 87 categories and 1,912 user-defined schemas from 45 projects. The platform has been accessed 908875 times, and 2403,208 pieces of data have been downloaded. In short, NMDMS effectively accelerates the publication and sharing of material project data in China. [ABSTRACT FROM AUTHOR]

China ranks in the highest position for nitrogen (N) fertilizer consumption in the world. Although N fertilizer use has greatly contributed to the China's food production, this has also caused an unprecedented alteration in the biogeochemical cycles and endangered terrestrial and aquatic ecosystems. Existing use of N fertilizers in China, as shown by digital maps, is usually coarse in resolution and intermittently covered with a biased gridded dataset. Here, we have reconstructed a historical, annual N fertilizer use dataset in China and resampled it to 5km×5km resolution, covering the period from 1952 to 2018 by integrating improved cropland maps. Results showed that most of the N input was directly applied as N-only fertilizer, while the contribution from compound fertilizers has ranged between 16 % and 24 % since 1980. The national total N fertilizer input increased from 0.06 Tg N yr -1 (0.05 g N m -2 yr -1) in 1952 to 31.15 Tg N yr -1 (18.83 g N m -2 yr -1) in 2014 and then decreased to 28.31 Tg N yr -1 (17.06 g N m -2 yr -1) in 2018. Despite the total N input decreasing by 9.1 % (2.84 Tg N yr -1) from 2014 to 2018, the N input from compound fertilizers has increased by 6 % (0.43 Tg N yr -1) during the corresponding period. The previous Food and Agriculture Organization (FAO) data-based N fertilizer products in China overestimated N use in low cropland coverage areas but underestimated N use in high cropland coverage areas. However, our newly reconstructed data have not only corrected the existing biases and improved the spatial distribution but have also shown that vegetable and other crops (e.g., orchards), but not grain crops, are the most intensively fertilized crops in China, implying the importance of quantifying greenhouse gas (GHG) emissions from these croplands. We argue that the reconstructed, spatially explicit N fertilizer use data in this study are expected to contribute to better understanding of biogeochemical cycles, including the simulations of GHG emissions and food production in China. The spatially explicit N fertilizer use and the crop-specific N fertilizer use datasets are available via an open data repository (10.6084/m9.figshare.21371469.v1; Yu, 2022). [ABSTRACT FROM AUTHOR]

• A depletion chain simplification method was proposed based on contribution functions. • Fission rate-weighted-yield was used to consider the energy correlation of fission yield. • The method considers the convenience and feasibility of the simplification process. Recently, an advanced neutronics lattice code called Kylin-2 has been developed by Nuclear Power Institute of China. In order to develop multi-group library for Kylin-2, a fission product depletion chain simplification method was proposed in this paper to generate burnup data for the multi-group library. This method constructed two quantitative contribution functions based on the thought of transmutation trajectory analysis (TTA), according to these contribution functions, a code was developed to identify important fission product nuclides and then a simplification depletion chain was reconstructed using these important nuclides. Verification and validation showed that this method successfully identifies important fission product nuclides and improve the depletion accuracy. [ABSTRACT FROM AUTHOR]

Abstract: As top enterprise in China, the power supply companies should be excellent in the information tide wave, and they will realize the transformation from data processing to data mining. It is an attracting goal, but the fact is a little bit depressing, for there is a severe defect, called information isolated island, in the information system of the power supply companies. So an compatible and advanced information platform is an urgent affairs..Through the analysis of existing technology, starting from the system architecture the data center with Similar to the EXCEL is proposed, and the function implementation of the system is elaborated in this paper. [Copyright &y& Elsevier]

Sharing of scientific data can help scientific research to flourish and facilitate more widespread use of scientific data for the benefit of society. The Environmental and Ecological Science Data Center for West China (WestDC), sponsored by the National Natural Science Foundation of China (NSFC), aims to collect, manage, integrate, and disseminate environmental and ecological data from western China. It also aims to provide a long-term data service for multidisciplinary research within NSFC's 'Environment and Ecology of West China Research Plan' (NSFC West Plan). An integrated platform has been developed by the WestDC, and this has the function of data sharing, acting as a knowledge repository. Major data sets developed by the WestDC include basic geographic data, the regionalization of global data set for China, scientific data for cold and arid regions in China, scientific data for the cryosphere in countries that neighbor China, data relating to the inland river basins in northwestern China, and data submitted by the NSFC West Plan projects. In compliance with the 'full and open' data sharing policy, most data in the WestDC can be accessed online. Highlights include detailed data documentation, the integration of data with bibliographic knowledge, data publishing, and data reference. [ABSTRACT FROM AUTHOR]

In this study, the northern Asian summer onset (NASO) is investigated in terms of its key features, thermodynamic driving factors, and its associations with the rainfall pattern in eastern China on the basis of the U.S. National Centers for Environmental Prediction and the U.S. National Center for Atmospheric Research reanalysis data, the snow cover extent data archived by the National Oceanic and Atmospheric Administration's National Centers for Environmental Information, and the precipitation data of observational stations in China. We define the NASO as the critical time when seasonal surface warming in northern Asia accelerates prior to this time and then stabilizes after this time. The climatological NASO occurs in the second pentad of June, across which systematic changes occur in the near surface warming in northern Asia. The heat budget analysis demonstrates from the climatic perspective that the snowmelt process in northern Asia plays a crucial role in the NASO. The positive feedback between surface warming and snowmelt facilitates an intense surface warming associated with the NASO. It is revealed that large‐scale wave patterns in the sea‐level pressure field contribute to early and late advents of NASOs. In association with the wave patterns, the horizontal linear advection term and adiabatic term are the primary thermodynamic drivers to the early and late NASOs. From late May to early July, the circulation anomalies from northern Asia to the subtropical western Pacific are significantly different between the early and late NASO years. Accordingly, in the rainy period, the seasonal evolution of rainfall pattern in eastern China also shows a considerable difference in these two NASO years. [ABSTRACT FROM AUTHOR]

The recently released CENDL-3.2 nuclear data library is deemed as an important achievement in the field of nuclear data research in China. To verify the applicability of the library to the shielding calculation of PWR and analyze the influence of multigroup cross-section parameters on the shielding calculation, ARES-MACXS module is used to process the MATXS format multigroup library based on CENDL-3.2 to generate multigroup working cross sections for PWR shielding calculation. VENUS-3 experimental facility has a clear and complete geometry. It is often used to test the ability of the advanced transport calculation method of calculating RPV fast neutron flux and to evaluate the accuracy of cross-section library. Different cross-section parameters are chosen for ARES to calculate VENUS-3 benchmark, and equivalent neutron flux of 58Ni(n,p)58Co, 115In(n,n′)115mIn and 27Al(n,α)24Na detectors is calculated according to the data provided by the benchmark report. The numerical results demonstrate that almost all the relative deviations between the calculated results and the experimental results are within 20%, which satisfies the requirement of shielding calculation. CENDL-3.2 is suitable for PWR shielding calculation. The comparison of various cross-section parameters results indicates that multigroup cross-section parameters have large effects on the transport calculation results. [ABSTRACT FROM AUTHOR]

• The seasonal cycles of simulated runoff show good agreement with the UNH/GRDC composite runoff. • VIC-CN05.1 generally reproduces the monthly time series of latent heat fluxes well at flux tower sites in China. • The monthly modeled 0–10 cm soil moisture anomalies are highly correlated to in situ measurements. • The spatial patterns of simulated snow variables are generally similar to observations in northeastern and northwestern China. • Except for the northwest basin, the simulated monthly TWSC match well with that from the GRACE. This paper describes a daily 0.25° × 0.25° terrestrial hydrological dataset (VIC-CN05.1) for China during 1961–2017. The dataset is simulated by the latest variable infiltration capacity (VIC) model and driven by pure station-based atmospheric forcings and high-resolution soil parameters based on field surveys. Multiple observational (in situ and remote sensing) products and five statistical metrics, including biases, relative error (RE), root mean square error (RMSE), Nash-Sutcliffe efficiency coefficient (NSE), and correlation coefficient (R), are used to validate the model outputs. The results indicate that the seasonal cycles of simulated total runoff and composite runoff from the University of New Hampshire and Global Runoff Data Centre (UNH/GRDC) show good agreement in the Yangtze, southeast, and Pearl River basins, with positive REs ≤ 7% and NSEs ≥ 0.87. At the Changbaishan, Haibei Shrubland, and Qianyanzhou flux towers, VIC-CN05.1 reproduces the monthly latent heat fluxes well during 2003–2005. The monthly modeled 0–10 cm soil moisture anomalies are highly correlated to in situ measurements (438 stations) during 2003–2016, with mean R = 0.80. The spatial patterns of simulated and observed snow depth, snow water equivalent (SWE), and snow cover in winter are generally consistent in the northeastern and northwestern China. Except for the northwest basin, the simulated monthly terrestrial water storage changes in most river basins (particularly the Yangtze, Huai, and Yellow River basins) match well with observations based on the Gravity Recovery and Climate Experiment (GRACE) during 2003–2016, with positive NSEs and mean R = 0.41. This terrestrial hydrology dataset is valuable for hydroclimatic research in China, such as model evaluations and long-term trend analyses. [ABSTRACT FROM AUTHOR]

The Hong Kong (HKEx) and Singapore (SGX) exchanges remain conflicted about high frequency trading (HFT), reflecting the environment of private and public sector actors in which the HKEx and SGX operate. Neither exchange has resolved these conflicts, leaving the HFT controversy simmering and limiting the amounts of such trading occurring on their exchanges. Competitor exchanges in Asia, however, are more supportive of HFT. With the aid of technology providers which enable HFT, the HKEx and SGX significantly improved their trading infrastructures. At the same time, these providers developed data centres at other exchanges and built fibre-optic connections which permit low-latency trading across Asia. Traders in Hong Kong and Singapore access these exchanges, potentially undermining the HKEx and SGX. [ABSTRACT FROM AUTHOR]