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2023:A year of accomplishments for the 13 Science Citation Index Expanded-and Emerging Sources Citation Index-indexed baishideng journals

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World Journal of Gastroenterology 2024年第1期30卷 9-16页

作者： Jin-Lei Wang Jia-Ping Yan Jia-Ru Fan Xiang Li Xu Guo Jia-Wei Li Yun-Xiaojian Wu Jing-Jie Wang Yu-Lu Chen Li Li Cong Lin Xin-Liang Qu Ji-Hong Liu Yan-Liang Zhang Ying-Yi Yuan Hua-Ge Yu Yu-Xi Chen Yi-Xuan Cai Xiang-Di Zhang Si Zhao Zi-Hang Xu Li Ma Na Ma Diao-Mei Guo Lian-Sheng Ma baishideng Publishing Group inc PleasantonCA 94566United States

In 2023,baishideng Publishing Group(baishideng)routinely published 47 openaccess journals,including 46 English-language journals and 1 Chinese-language journal.Our successes were accomplished through the collective dedicated efforts of baishideng staffs,Editorial board Members,and Peer Reviewers.Among these 47 baishideng journals,7 are included in the Science Citation Index Expanded(SCIE)and 6 in the Emerging Sources Citation Index(ESCI).With the support of baishideng authors,company staffs,Editorial board Members,and Peer Reviewers,the publication work of 2023 is about to be successfully completed.This editorial summarizes the 2023 activities and accomplishments of the 13 SCIEand ESCI-indexed baishideng journals,outlines the baishideng publishing policy changes and additions made this year,and highlights the unique advantages of baishideng journals.

关键词： baishideng Publishing Group Journal Impact Factor Open access Policy changes Policy additions Unique advantages Anxiety disorders Colorectal cancer Inflammatory bowel disease Hepatocellular carcinoma Gut microbes Atrial fibrillation

SMEC:Scene Mining for E-Commerce

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Journal of Computer Science & Technology 2024年第1期39卷 192-210页

作者：王罡李翔郭子义殷大伟马帅 State Key Laboratory of Software Development Environment Beihang UniversityBeijing 100191China School of Data Science and Engineering East China Normal UniversityShanghai 200062China *** Inc.Beijing 100176China baidu Inc.Beijing 100085China

Scene-based recommendation has proven its usefulness in E-commerce,by recommending commodities based on a given scene.However,scenes are typically unknown in advance,which necessitates scene discovery for E-commerce.In this article,we study scene discovery for E-commerce systems.We first formalize a scene as a set of commodity cate-gories that occur simultaneously and frequently in real-world situations,and model an E-commerce platform as a heteroge-neous information network(HIN),whose nodes and links represent different types of objects and different types of rela-tionships between objects,respectively.We then formulate the scene mining problem for E-commerce as an unsupervised learning problem that finds the overlapping clusters of commodity categories in the HIN.To solve the problem,we pro-pose a non-negative matrix factorization based method SMEC(Scene Mining for E-Commerce),and theoretically prove its convergence.Using six real-world E-commerce datasets,we finally conduct an extensive experimental study to evaluate SMEC against 13 other methods,and show that SMEC consistently outperforms its competitors with regard to various evaluation measures.

关键词： graph clustering E-commerce heterogeneous information network(HIN) scene mining

Origin of the Dashuigou independent tellurium deposit at Qinghai–Xizang Plateau: constraints from the light stable isotopes C, O, and H

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Acta Geochimica 2024年第2期43卷 385-398页

作者： Jianzhao Yin Yuhong Chao Haoyu Yin Hongyun Shi Shoupu Xiang Wuhan Institute of Technology Wuhan 430074China College of Earth Sciences Jilin UniversityChangchun 130061China Ganzhongnan Institute of Geology and Mineral Exploration Nanchang 330002China Guinea Westfi eld Mining Company-Sarlu Jinzhong 031300China Orient Resources Ltd. RichmondB.C.V7E 1M8Canada Silvercorp Metals inc. Beijing 100027China

by studying the light isotopic compositions of carbon,oxygen,and hydrogen,combined with previous research results on the ore-forming source of the deposit,the authors try to uncover its metallogenic origin.The δ^(18)O and δ^(13)C isotope signatures of dolomite samples vary between 10.2 and 13.0‰,and between−7.2 and−5.2‰,respectively,implying that the carbon derives from the upper mantle.δD and δ^(18) O of quartz,biotite,and muscovite from diff erent ore veins of the deposit vary between−82 and−59‰,and between 11.6 and 12.4‰,respectively,implying that the metallogenic solutions are mainly magmatic.According to the relevant research results of many isotope geologists,the fractionation degree of hydrogen isotopes increases as the depth to the Earth’s core increases,and the more diff erentiated the hydrogen isotopes are,the lower their values will be.In other words,mantle-derived solutions can have extremely low hydrogen isotope values.This means that the δD‰ value−134 of the pyrrhotite sample numbered SD-34 in this article may indicate mantle-derived oreforming fl uid of the deposit.The formation of the Dashuigou tellurium deposit occurred between 91.71 and 80.19 Ma.

关键词： Carbon oxygen and hydrogen isotopes Origin Independent tellurium deposit The Qinghai–Xizang Plateau

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Changes in microorganisms and metabolites during long-term fermentation of commercially manufactured anchovy sauce

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Food Science and Human Wellness 2024年

作者： Daehyeon Kim Seong-Eun Park Eun-Ju Kim Seung-Ho Seo Kwang-Moon Cho Sun Jae Kwon Hong-Seok Son Department of biotechnology College of Life Sciences and biotechnology Korea University Sonlab inc. 3. AccuGene inc.

This study was performed to investigate the changes in microbial communities and metabolites during the long-term fermentation of commercially manufactured anchovy sauce. Samples of commercial anchovy sauce were collected from large-scale fermentation tanks with fermentation periods of up to 7.87 years. The complex bacterial community was simplified to two genera， Tetragenococcus and Halanaerobium， after approximately 0.55 years of fermentation. Although genera， such as Saccharomyces， Cladosporium， Candida， and Aspergillus， were relatively dominant， no clear pattern was identified in fungal community analysis. The longitudinal metabolite profile demonstrated that approximately half (55.8%) of the metabolites present in anchovy sauce were produced within a year of fermentation due to rapid fermentation. Despite the static microbial community， the contents of several metabolites including amino acids and biogenic amines changed continuously during the long-term fermentation of anchovy sauce. This study provides novel insights into the changes in microbiota and metabolites in fish sauce produced without any starter inoculation.

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Modeling displacement flow inside a full-length casing string for well cementing

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Petroleum Research 2024年第1期9卷 1-16页

作者： Hu Dai Ali Eslami Jason Schneider Gefei Liu Fred Schwering Pegasus Vertex Inc.USA Laval University Canada Sanjel Energy Services Canada Athabasca Oil Corporation Canada

While computer modeling of annular displacement efficiency is widely applied in cementing engineering,modeling the displacement flow inside a casing or drill string for cementing operations has received less attention.Although predicting displacement efficiency inside a full-length pipe is desired by cementing engineers,the attempt of developing a model with both efficiency and accuracy faces challenges.Access to computer simulators for this purpose is limited.Compared with annular flow,the displacement flow inside pipe,although within a simpler geometry and without eccentricity effect,is not simpler in physics,modelling strategy and predictability,because a variety of flow patterns and flow instabilities can develop to create complicated fluid interfaces.In this paper,we present an integrated numerical model developed to simulate displacement flows inside a full-length pipe,which connects an existing annulus model to enable complete displacement simulations of cementing jobs.The model uses three-dimensional grid to solve fluid concentrations with degrees of mixing,and incorporates flow instability detection and flow regime determination.Applied in cementing,the model accounts for effects of pumping rate,well inclination,pipe rotation,fluid densities,rheological parameters and more.This computationally efficient model does not rely on high-resolution mesh as often required by conventional Computational Fluid Dynamics models,thus it is suitable to be implemented in a cementing software for daily use by well cementing engineers.The methodology of the model is discussed in detail in this paper.To validate the model,we examine simulation results against experimental results obtained in our laboratory tests and CFD simulations;acceptable agreement is found under different testing conditions.We also presented two case studies of real cementing jobs with cement evaluation logs compared to simulation results,showing that the model can predict consistent displacement efficiency results.

关键词： Inside pipe displacement Displacement efficiency Numerical model Primary cementing Displacement flow

Optimal SOC Headroom of Pump Storage Hydropower for Maximizing Joint Revenue from Day-ahead and Real-time Markets Under Regional Transmission Organization Dispatch

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Journal of Modern Power Systems and Clean Energy 2024年第1期12卷 238-250页

作者： Yikui Liu bing Huang Yang Lin Yonghong Chen Lei Wu Electrical and Computer Engineering Department Stevens Institute of TechnologyHobokenNJ 07030USA Midcontinent Independent System Operator Inc.CarmelIN 46032USA

In response to the increasing penetration of volatile and uncertain renewable energy,the regional transmission organizations(RTOs)have been recently focusing on enhancing the models of pump storage hydropower(PSH)plants,which are one of the key flexibility assets in the day-ahead(DA)and real-time(RT)markets,to further boost their flexibility provision potentials.Inspired by the recent research works that explored the potential benefits of excluding PSHs’cost-related terms from the objective functions of the DA market clearing model,this paper completes a rolling RT market scheme that is compatible with the DA market.Then,with the vision that PSHs could be permitted to submit state-of-charge(SOC)headrooms in the DA market and to release them in the RT market,this paper uncovers that PSHs could increase the total revenues from the two markets by optimizing their SOC headrooms,assisted by the proposed tri-level optimal SOC headroom model.Specifically,in the proposed tri-level model,the middle and lower levels respectively mimic the DA and RT scheduling processes of PSHs,and the upper level determines the optimal headrooms to be submitted to the RTO for maximizing the total revenue from the two markets.Numerical case studies quantify the profitability of the optimal SOC headroom submissions as well as the associated financial risks.

关键词： Pump storage hydropower energy market state-of-charge(SOC) headroom market revenue tri-level problem

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A reinforcement learning approach for thermostat setpoint preference learning

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building Simulation 2024年第1期17卷 131-146页

作者： Hussein Elehwany Mohamed Ouf burak Gunay Nunzio Cotrufo Jean-Simon Venne Department of Environmental Engineering Carleton UniversityOttawaCanada Departmentof building Civiland Environmental EngineeringConcordia UniversityMontrealCanada brainbox Al inc. MontrealCanada

Occupant-centric controls(OcC)is an indoor climate control approach whereby occupant feedback is used in the sequence of operation of building energy systems.While OcC has been used in a wide range of building applications,an OcC category that has received considerable research interest is learning occupants'thermal preferences through their thermostat interactions and adapting temperature setpoints accordingly.Many recent studies used reinforcement learning(RL)as an agent for OcC to optimize energy use and occupant comfort.These studies depended on predicted mean vote(PMV)models or constant comfort ranges to represent comfort,while only few of them used thermostat interactions.This paper addresses this gap by introducing a new off-policy reinforcement learning(RL)algorithm that imitates the occupant behaviour by utilizing unsolicited occupant thermostat overrides.The algorithm is tested with a number of synthetically generated occupant behaviour models implemented via the Python APl of EnergyPlus.The simulation results indicate that the RL algorithm could rapidly learn preferences for all tested occupant behaviour scenarios with minimal exploration events.While substantial energy savings were observed with most occupant scenarios,the impact on the energy savings varied depending on occupants'preferences and thermostat use behaviour stochasticity.

关键词： reinforcement learning preference learning occupant-centric controls smart thermostats off-policy learning

A novel alternative for pyrogen detection based on a transgenic cell line

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Signal Transduction and Targeted Therapy 2024年第3期9卷 1221-1229页

作者： Qing He Chuan-Fei Yu Gang Wu Kai-Qin Wang Yong-bo Ni Xiao Guo Zhi-Hao Fu Lan Wang De-Jiang Tan Hua Gao Can Wang Gang Chen Xu-Hong Chen bo Chen Jun-Zhi Wang National Institutes for Food and Drug Control BeijingChina School of Life Science and biopharmaceutics Shenyang Pharmaceutical UniversityShenyangChina Shanghai Institute for Food and Drug Control ShanghaiChina KeyMed biosciences inc. ChengduChina

Pyrogen,often as a contaminant,is a key indicator affecting the safety of almost all parenteral drugs(including biologicals,chemicals,traditional Chinese medicines and medical devices).It has become a goal to completely replace the in vivo rabbit pyrogen test by using the in vitro pyrogen test based on the promoted‘reduction,replacement and refinement’principle,which has been highly considered by regulatory agencies from different countries.We used NF-κb,a central signalling molecule mediating inflammatory responses,as a pyrogenic marker and the monocyte line THP-1 transfected with a luciferase reporter gene regulated by NF-κb as an in vitro model to detect pyrogens by measuring the intensity of a fluorescence signal.Here,we show that this test can quantitatively and sensitively detect endotoxin(lipopolysaccharide from different strains)and nonendotoxin(lipoteichoic acid,zymosan,peptidoglycan,lectin and glucan),has good stability in terms of NF-κb activity and cell phenotypes at 39 cell passages and can be applied to detect pyrogens in biologicals(group A&C meningococcal polysaccharide vaccine;basiliximab;rabies vaccine(Vero cells)for human use,freeze-dried;Japanese encephalitis vaccine(Vero cells),inactivated;insulin aspart injection;human albumin;recombinant human erythropoietin injection(CHO Cell)).The within-laboratory reproducibility of the test in three independent laboratories was 85%,80%and 80%and the interlaboratory reproducibility among laboratories was 83.3%,95.6%and 86.7%.The sensitivity(true positive rate)and specificity(true negative rate)of the test were 89.9%and 90.9%,respectively.In summary,the test provides a novel alternative for pyrogen detection.

关键词： pyrogen rabbit replace

Semantic segmentation via pixel-to-center similarity calculation

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CAAI Transactions on Intelligence Technology 2024年第1期9卷 87-100页

作者： Dongyue Wu Zilin Guo Aoyan Li Changqian Yu Nong Sang Changxin Gao National Key Laboratory of Science and Technology on Multispectral Information Processing School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhanChina Meituan inc. BeijingChina

Since the fully convolutional network has achieved great success in semantic segmentation,lots of works have been proposed to extract discriminative pixel representations.However,the authors observe that existing methods still suffer from two typical challenges:(i)The intra-class feature variation between different scenes may be large,leading to the difficulty in maintaining the consistency between same-class pixels from different scenes;(ii)The inter-class feature distinction in the same scene could be small,resulting in the limited performance to distinguish different classes in each scene.The authors first rethink se-mantic segmentation from a perspective of similarity between pixels and class centers.Each weight vector of the segmentation head represents its corresponding semantic class in the whole dataset,which can be regarded as the embedding of the class center.Thus,the pixel-wise classification amounts to computing similarity in the final feature space between pixels and the class centers.Under this novel view,the authors propose a Class Center Similarity(CCS)layer to address the above-mentioned challenges by generating adaptive class centers conditioned on each scenes and supervising the similarities between class centers.The CCS layer utilises the Adaptive Class Center Module to generate class centers conditioned on each scene,which adapt the large intra-class variation between different scenes.Specially designed Class Distance Loss(CD Loss)is introduced to control both inter-class and intra-class distances based on the predicted center-to-center and pixel-to-center similarity.Finally,the CCS layer outputs the processed pixel-to-center similarity as the segmentation prediction.Extensive experiments demonstrate that our model performs favourably against the state-of-the-art methods.

关键词： computer vision deep neural networks image segmentation scene understanding