Mass transfer in heterogeneous biofilms: Key issues in biofilm reactors and AI-driven performance prediction

Biofilm reactors,known for utilizing biofilm formation for cell immobilization,offer enhanced biomass concentration and operational stability over traditional planktonic systems.However,the dense nature of biofilms poses challenges for substrate accessibility to cells and the efficient release of products,making mass transfer efficiency a critical issue in these systems.Recent advancements have unveiled the intricate,heterogeneous architecture of biofilms,contradicting the earlier view of them as uniform,porous structures with consistent mass transfer properties.In this review,we explore six biofilm reactor configurations and their potential combinations,emphasizing how the spatial arrangement of biofilms within reactors influences mass transfer efficiency and overall reactor performance.Furthermore,we discuss how to apply artificial intelligence in processing biofilm measurement data and predicting reactor performance.This review highlights the role of biofilm reactors in environmental and energy sectors,paving the way for future innovations in biofilm-based technologies and their broader applications.

Transferable adversarial slow feature extraction network for few-shot quality prediction in coal-to-ethylene glycol process

In the coal-to-ethylene glycol(CTEG)process,precisely estimating quality variables is crucial for process monitoring,optimization,and control.A significant challenge in this regard is relying on offline laboratory analysis to obtain these variables,which often incurs substantial monetary costs and significant time delays.The resulting few-shot learning scenarios present a hurdle to the efficient development of predictive models.To address this issue,our study introduces the transferable adversarial slow feature extraction network(TASF-Net),an innovative approach designed specifically for few-shot quality prediction in the CTEG process.TASF-Net uniquely integrates the slowness principle with a deep Bayesian framework,effectively capturing the nonlinear and inertial characteristics of the CTEG process.Additionally,the model employs a variable attention mechanism to identify quality-related input variables adaptively at each time step.A key strength of TASF-Net lies in its ability to navigate the complex measurement noise,outliers,and system interference typical in CTEG data.Adversarial learning strategy using a min-max game is adopted to improve its robustness and ability to model irregular industrial data accurately and significantly.Furthermore,an incremental refining transfer learning framework is designed to further improve few-shot prediction performance achieved by transferring knowledge from the pretrained model on the source domain to the target domain.The effectiveness and superiority of TASF-Net have been empirically validated using a real-world CTEG dataset.Compared with some state-of-the-art methods,TASF-Net demonstrates exceptional capability in addressing the intricate challenges for few-shot quality prediction in the CTEG process.

An active learning workflow for predicting hydrogen atom adsorption energies on binary oxides based on local electronic transfer features

Machine learning combined with density functional theory(DFT)enables rapid exploration of catalyst descriptors space such as adsorption energy,facilitating rapid and effective catalyst screening.However,there is still a lack of models for predicting adsorption energies on oxides,due to the complexity of elemental species and the ambiguous coordination environment.This work proposes an active learning workflow(LeNN)founded on local electronic transfer features(e)and the principle of coordinate rotation invariance.By accurately characterizing the electron transfer to adsorption site atoms and their surrounding geometric structures,LeNN mitigates abrupt feature changes due to different element types and clarifies coordination environments.As a result,it enables the prediction of^(*)H adsorption energy on binary oxide surfaces with a mean absolute error(MAE)below 0.18 eV.Moreover,we incorporate local coverage(θ_(l))and leverage neutral network ensemble to establish an active learning workflow,attaining a prediction MAE below 0.2 eV for 5419 multi-^(*)H adsorption structures.These findings validate the universality and capability of the proposed features in predicting^(*)H adsorption energy on binary oxide surfaces.

Predictive modeling of critical temperatures in magnesium compounds using transfer learning

This study presents a transfer learning approach for discovering potential Mg-based superconductors utilizing a comprehensive target dataset.Initially,a large source dataset(Bandgap dataset)comprising approximately∼75k compounds is utilized for pretraining,followed by fine-tuning with a smaller Critical Temperature(T_(c))dataset containing∼300 compounds.Comparatively,there is a significant improvement in the performance of the transfer learning model over the traditional deep learning(DL)model in predicting Tc.Subsequently,the transfer learning model is applied to predict the properties of approximately 150k compounds.Predictions are validated computationally using density functional theory(DFT)calculations based on lattice dynamics-related theory.Moreover,to demonstrate the extended predictive capability of the transfer learning model for new materials,a pool of virtual compounds derived from prototype crystal structures from the Materials Project(MP)database is generated.T_(c) predictions are obtained for∼3600 virtual compounds,which underwent screening for electroneutrality and thermodynamic stability.An Extra Trees-based model is trained to utilize E_(hull)values to obtain thermodynamically stable materials,employing a dataset containing Ehull values for approximately 150k materials for training.Materials with Ehull values exceeding 5 meV/atom were filtered out,resulting in a refined list of potential Mg-based superconductors.This study showcases the effectiveness of transfer learning in predicting superconducting properties and highlights its potential for accelerating the discovery of Mg-based materials in the field of superconductivity.

Numerical Predictions of Laminar Forced Convection Heat Transfer with and without Buoyancy Effects from an Isothermal Horizontal Flat Plate to Supercritical Nitrogen

Numerical predictions are made for Laminar Forced convection heat transfer with and without buoyancy effects for Supercritical Nitrogen flowing over an isothermal horizontal flat plate with a heated surface facing downwards.Computations are performed by varying the value ofΔT from5 to 30 K and P_(∞)/P_(cr)ratio from1.1 to 1.5.Variation of all the thermophysical properties of supercritical Nitrogen is considered.The wall temperatures are chosen in such a way that two values of Tw are less than T∗(T*is the temperature at which the fluid has a maximum value of Cp for the given pressure),one value equal to T∗and two values greater than T∗.Three different values of U∞are used to obtain Re∞range of 3.6×10_(4)to 4.74×10^(5)for forced convection without buoyancy effects and Gr_(∞)/Re^(2)_(∞)range of 0.011 to 3.107 for the case where buoyancy effects are predominant.Six different forms of correlations are proposed based on numerical predictions and are compared with actual numerical predictions.It has been found that in all six forms of correlations,the maximum deviations are found to occur in those cases where the pseudocritical temperature TT∗lies between the wall temperature and bulk fluid temperature.

Slope displacement prediction based on multisource domain transfer learning for insufficient sample data

Accurate displacement prediction is critical for the early warning of landslides.The complexity of the coupling relationship between multiple influencing factors and displacement makes the accurate prediction of displacement difficult.Moreover,in engineering practice,insufficient monitoring data limit the performance of prediction models.To alleviate this problem,a displacement prediction method based on multisource domain transfer learning,which helps accurately predict data in the target domain through the knowledge of one or more source domains,is proposed.First,an optimized variational mode decomposition model based on the minimum sample entropy is used to decompose the cumulative displacement into the trend,periodic,and stochastic components.The trend component is predicted by an autoregressive model,and the periodic component is predicted by the long short-term memory.For the stochastic component,because it is affected by uncertainties,it is predicted by a combination of a Wasserstein generative adversarial network and multisource domain transfer learning for improved prediction accuracy.Considering a real mine slope as a case study,the proposed prediction method was validated.Therefore,this study provides new insights that can be applied to scenarios lacking sample data.

Short-term displacement prediction for newly established monitoring slopes based on transfer learning

This study makes a significant progress in addressing the challenges of short-term slope displacement prediction in the Universal Landslide Monitoring Program,an unprecedented disaster mitigation program in China,where lots of newly established monitoring slopes lack sufficient historical deformation data,making it difficult to extract deformation patterns and provide effective predictions which plays a crucial role in the early warning and forecasting of landslide hazards.A slope displacement prediction method based on transfer learning is therefore proposed.Initially,the method transfers the deformation patterns learned from slopes with relatively rich deformation data by a pre-trained model based on a multi-slope integrated dataset to newly established monitoring slopes with limited or even no useful data,thus enabling rapid and efficient predictions for these slopes.Subsequently,as time goes on and monitoring data accumulates,fine-tuning of the pre-trained model for individual slopes can further improve prediction accuracy,enabling continuous optimization of prediction results.A case study indicates that,after being trained on a multi-slope integrated dataset,the TCN-Transformer model can efficiently serve as a pretrained model for displacement prediction at newly established monitoring slopes.The three-day average RMSE is significantly reduced by 34.6%compared to models trained only on individual slope data,and it also successfully predicts the majority of deformation peaks.The fine-tuned model based on accumulated data on the target newly established monitoring slope further reduced the three-day RMSE by 37.2%,demonstrating a considerable predictive accuracy.In conclusion,taking advantage of transfer learning,the proposed slope displacement prediction method effectively utilizes the available data,which enables the rapid deployment and continual refinement of displacement predictions on newly established monitoring slopes.

企业主导的现代产业学院是如何建设的?——以华为ICT学院为例

现代产业学院是促进产教融合的有效平台,是多主体联合区域产业发展所需复合型高技术技能人才的重要载体。华为ICT学院是现代产业学院建设的典型模式,主要从五个方面进行了创新性建设:制定适应ICT人才特点的人才培养方案;打造以实验实训平台为核心的ICT基础设施;组建校企混编的专业课教学团队;构建以实用理论与实际操作类为主的课程体系;实施对华为ICT学院人才培养质量的多元监控。华为ICT学院建设案例对现代产业学院建设的启示为:要重视企业利益,激发企业主体活力;要明细校企双方权责,规范工作流程;要注重合作的全面性和系统性,协同推进各项工作。

中美ICT产品贸易演变研究——基于空间模式可视化的分析方法

在全球进入数字技术驱动和引领的创新时代,信息通信技术(ICT)已经成为衡量一个国家数字贸易实力的重要标志。以2003—2022年中美ICT产品双边贸易数据为样本,基于空间模式可视化的分析方法,从微观和宏观角度分析了中美ICT产品贸易的特征和地位。首先,通过绘制贸易空间结构分析了中国向美国出口的ICT产品种类。其次,通过应用头尾断裂法和绘制希尔伯特曲线,对ICT产品分类的重要性进行了空间模式可视化呈现。结果表明:中美ICT产品双边贸易结构稳定,产品贸易变化程度均匀,存在贸易增长的空间和发展潜力。从产品看,“重量≤10kg的便携自动数据处理设备”在中美ICT产品贸易中表现出极其重要和稳定的特征。通过位序-规模法发现,中美ICT产品贸易呈现典型的重尾分布特征,78%的尾部产品对于保持和巩固ICT产品贸易结构的稳定性至关重要,22%的头部产品在中美贸易中扮演着重要的角色;从空间网格分布看,中美ICT产品贸易呈现出显著的正向空间溢出效应。

企业投入数字化的污染减排效应——基于ICT产品进口的视角

ICT产品进口作为企业投入数字化的重要表现形式,可以为企业污染减排提供重要支撑。文章基于2002—2012年中国海关贸易数据库、中国工业企业污染数据库和中国工业企业数据库的合并数据,利用双重差分法识别ICT产品进口对企业污染减排的影响。结果显示,ICT产品进口能够显著降低企业污染物排放强度,且经过一系列稳健性检验后该结论仍然成立。机制检验表明:一方面,ICT产品进口通过提升全要素生产率,促进企业产出规模增长,进而降低企业污染物排放强度;另一方面,ICT产品进口通过优化能源结构、提高能源效率,降低企业污染物排放规模,进而降低企业污染物排放强度。此外,ICT产品进口对大规模企业、技术密集型行业企业和数字经济发展水平较高地区企业的污染减排效应更为明显。基于研究结论,未来可通过鼓励和扩大ICT产品进口、推动ICT产品核心技术研发创新、拓展ICT产品应用场景等途径,实现企业数字化转型与污染减排协同发展。

数字贸易规则对“一带一路”国家ICT产品出口的影响研究

数字贸易规则是驱动“一带一路”国家ICT产品出口的关键力量。本文利用2000~2023年间123个国家的双边数据,结合社会网络分析法,揭示了数字贸易规则网络结构的变化,并构建了衡量其广度和深度的指标。研究发现,在考虑内生性问题并进行稳健性检验后,数字贸易规则广度和深度的提升显著促进了“一带一路”国家ICT产品出口,主要通过缩小境内监管质量差异、降低境外贸易成本等渠道改善数字贸易环境。异质性分析表明,在规则特征层面,贸易方式数字化规则效应更为显著;在贸易关系层面,签署数字贸易规则对贸易关系疏远国家尤为有利;在网络节点结构权力层面,缔约国度数中心度强化了数字贸易规则的积极效应。进一步研究还发现,数字贸易规则的影响呈现出非线性特征,随着缔约国数字基础设施差异扩大,其效应呈现出“强促进-弱促进-牵制”的轨迹。本文丰富了“把规则标准‘软联通’作为共建‘一带一路’重要支撑”思想的理论研究,对发展中国家如何开展数字贸易规则谈判、加快ICT产品发展提供了实证参考和政策启示。

产教融合背景下的高校ICT类专业实训基地建设研究

产教融合是当前高等教育领域的热门话题,如何开展校企深度合作,打造一流的产教融合实训基地,改革创新校企共同育人的教学方法和教学模式,培养学生综合实践技能以及创新创业综合素质,提升人才培养质量是很多高等院校一直在思考的问题.通过对ICT类专业产教融合实训基地建设进行深入研究,提出了一些方法与途径,以促进高等院校与企业在教学、生产和创新创业工作方面的相互配合、相互支持,有效推进校企合作共赢.

新时代背景下全球ICT融合应用研究述评

通过采集近3年来开放存取平台上所收录的全球范围内与信息通信技术(InformationandCommunication Technology,ICT)融合相关的学术文献,文章基于文献计量学的词频、主题分析等方法进行了统计分析和整合聚类,筛选了与社会公共工作和经济发展层面关联性较大的5个主题领域,对其中不同国家及地区的部分重点文献进行了逐一述评,展示了各国在相关领域开展的工作及研究,能够为我国ICT在各领域的应用提供数据参考,从而应对新型管理模式与数据治理结构等方面的变革。

ICT服务贸易与制造业全球价值链地位提升

ICT服务贸易作为当前中国数字服务贸易中发展最快的贸易类型,已经成为提升制造业在全球价值链地位的新“动能”。利用2007-2021年57个国家14个产业的面板数据,从理论和经验角度分析了ICT服务贸易对制造业全球价值链地位的影响。结果显示:ICT服务贸易的出口和进口都有助于提升一国制造业在全球价值链的地位,使用替换被解释变量和工具变量的方法检验后,结果依旧稳健。引入中介变量后发现,ICT服务贸易也可通过降低贸易成本和提升制造业投入信息化水平,间接地推动本国制造业在全球价值链地位的提升。进一步研究表明,ICT服务贸易对资本密集型产业在全球价值链地位的影响最强,劳动密集型次之,技术密集型最弱,而以技术水平为产业分类标准,ICT服务贸易在高技术产业对全球价值链地位的正向作用最强,低技术产业次之,中技术产业最弱。以上结论为提升中国ICT服务贸易水平和制造业全球价值链地位提供参考依据。

信息技术服务智慧城市建设的理论与方法——评《智慧城市建设——大数据分析、信息技术(ICT)与设计思维》

城市化是社会发展的必然趋势。现代社会,城市的发展主要依托信息技术,人们将信息技术与诸多产业相结合,可实现城市信息共享,为城市居民提供更完善的民生服务,从而构建新型智慧城市。本文参考由卡罗尔·斯蒂梅尔(Carol L.Stimmel)著,李晓峰译,机械工业出版社出版的《智慧城市建设——大数据分析、信息技术(ICT)与设计思维》一书,该书阐述了作者20余年来对智慧城市和新兴技术的思考,分析了当前人类对城市发展的真实需求,提出了利用信息技术建设现代化智慧城市的方法,为现代城市发展提供了参考。

基于华为ICT大赛为载体“课-证-赛”融通的ICT产业人才培养模式的改革研究

高技能人才已经成为国家的重要战略资源,培养高技能人才是应用型本科教育实现高端发展优化人才结构的必然选择。应用型本科教育急需创新体现类型教育特征的育人模式,桂林电子科技大学北海校区(以下简称桂电北海校区)多年协办华为ICT大赛区赛,在推广运营华为ICT大赛的过程中,建设了“课-证-赛”融通的ICT产业人才培养体系,全面将华为职业认证和华为ICT大赛比赛内容融入ICT产业人才培养课程体系中。 。

Toxicity and horizontal transfer of bifenthrin and dimefluthrin against the red imported fire ant, Solenopsis invicta Buren(Hymenoptera: Formicidae), and the efficacy of their dust applications in the field

The red imported fire ant,Solenopsis invicta Buren,poses a significant threat to biodiversity,agriculture,and public health in its introduced ranges.While chemicals such as toxic baits and dust are the main methods for S.invicta control,toxic baits are slow,requiring approximately one or two weeks,but dust can eliminate the colony of fire ants rapidly in just three to five days.To explore more active ingredients for fire ant control using dusts,the toxicity of bifenthrin and dimefluthrin,the horizontal transfer of bifenthrin and dimefluthrin dust and their efficacy in the field were tested.The results showed that the LD50(lethal dose) values of bifenthrin and dimefluthrin were 3.40 and 1.57 ng/ant,respectively.The KT50(median knockdown time) and KT95(95%knockdown time) values of a 20μg mL^(–1)bifenthrin dose were 7.179and 16.611 min,respectively.The KT50and KT95of a 5μg mL^(–1)dimefluthrin dose were 1.538 and 2.825 min,respectively.The horizontal transfers of bifenthrin and dimefluthrin among workers were effective.The mortality of recipients (secondary mortality) and secondary recipients (tertiary mortality) were both over 80%at 48 h after 0.25,0.50 and 1.00%bifenthrin dust treatments.The secondary mortality of recipients was over 99%at 48 h after 0.25,0.50 and 1.00% dimefluthrin dust treatments,but the tertiary mortality was below 20%.The field trial results showed that both bifenthrin and dimefluthrin exhibited excellent fire ant control effects,and the comprehensive control effects of 1.00%bifenthrin and dimefluthrin dusts at 14 d post-treatment were 95.87 and 85.70%,respectively.

ICT资本对旅游绿色发展效率的影响机理研究

ICT资本赋能旅游业绿色转型,驱动旅游业走向高质量发展道路。文章创新性地核算了中国30个省(区、市)的ICT资本和旅游绿色发展效率,采用SYSGMM和空间杜宾模型验证了ICT资本对旅游绿色发展效率的影响机制及其空间溢出效应。研究结果表明:(1)ICT资本对旅游绿色发展效率具有持续的积极影响。(2)ICT资本主要通过激发旅游技术创新、优化旅游产业结构、提升旅游人力资本来提高旅游绿色发展效率。(3)ICT资本对旅游绿色发展效率具有正向的空间溢出效应,且空间间接效应强于直接效应。在地理矩阵下,ICT资本对东部和西部省(区、市)的旅游绿色发展效率具有显著的空间溢出效应;而在经济矩阵下,东部和中部省(区、市)旅游绿色发展效率受ICT资本影响较显著。这为中国充分释放ICT资本红利,塑造中国旅游绿色发展格局,全面提升旅游绿色发展效率提供了价值指导。

全球价值链参与度对ICT制造业产品出口的影响研究

数字经济时代,资本、技术密集型产业的代表——ICT制造业的全球价值链(GVC)分工最为深入。基于2000—2018年61个国家和地区的数据,深入考察了ICT产业GVC参与度对ICT制造业产品出口的影响。研究发现:ICT制造业GVC参与度对ICT制造业产品出口规模影响显著为正;分维度看,ICT制造业GVC前向参与度对ICT制造业产品出口额的影响显著为负,GVC后向参与度对ICT制造业产品出口额的影响显著为正;异质性检验结果表明,发达国家和地区ICT制造业GVC参与度对ICT制造业产品出口额的影响显著为正,且GVC后向参与度比GVC前向参与度更有利于出口规模的扩大,然而深化GVC后向参与度固然有助于其贸易规模扩大,但从盈利能力角度看却并非最优;发展中国家和地区ICT制造业GVC参与度对ICT制造业产品出口额的影响显著为负,且GVC后向参与度对出口规模的负向影响显然更深,加深GVC前向参与度有助于减轻其对出口规模的负向效应。

Transductive Transfer Dictionary Learning Algorithm for Remote Sensing Image Classification

To create a green and healthy living environment,people have put forward higher requirements for the refined management of ecological resources.A variety of technologies,including satellite remote sensing,Internet of Things,artificial intelligence,and big data,can build a smart environmental monitoring system.Remote sensing image classification is an important research content in ecological environmental monitoring.Remote sensing images contain rich spatial information andmulti-temporal information,but also bring challenges such as difficulty in obtaining classification labels and low classification accuracy.To solve this problem,this study develops a transductive transfer dictionary learning(TTDL)algorithm.In the TTDL,the source and target domains are transformed fromthe original sample space to a common subspace.TTDL trains a shared discriminative dictionary in this subspace,establishes associations between domains,and also obtains sparse representations of source and target domain data.To obtain an effective shared discriminative dictionary,triple-induced ordinal locality preserving term,Fisher discriminant term,and graph Laplacian regularization termare introduced into the TTDL.The triplet-induced ordinal locality preserving term on sub-space projection preserves the local structure of data in low-dimensional subspaces.The Fisher discriminant term on dictionary improves differences among different sub-dictionaries through intra-class and inter-class scatters.The graph Laplacian regularization term on sparse representation maintains the manifold structure using a semi-supervised weight graphmatrix,which can indirectly improve the discriminative performance of the dictionary.The TTDL is tested on several remote sensing image datasets and has strong discrimination classification performance.