Viscosity and structure relationship with equimolar substitution of CaO with MgO in the CaO–MgO–Al_(2)O_(3)–SiO_(2)slag melts

Currently,the Al_(2)O_(3)content in the high-alumina slag systems within blast furnaces is generally limited to 16wt%–18.5wt%,making it challenging to overcome this limitation.Unlike most studies that concentrated on managing the MgO/Al_(2)O_(3)ratio or basicity,this paper explored the effect of equimolar substitution of MgO for CaO on the viscosity and structure of a high-alumina CaO-MgO-Al_(2)O_(3)-SiO_(2)slag system,providing theoretical guidance and data to facilitate the application of high-alumina ores.The results revealed that the viscosity first decreased and then increased with higher MgO substitution,reaching a minimum at 15mol%MgO concentration.Fourier transform infrared spectroscopy(FTIR)results found that the depths of the troughs representing[SiO_(4)]tetrahedra,[AlO_(4)]tetrahedra,and Si-O-Al bending became progressively deeper with increased MgO substitution.Deconvolution of the Raman spectra showed that the average number of bridging oxygens per Si atom and the X_(Q^(3))/X_(Q^(2))(X_(Q^(i))is the molar fraction of Q^(i) unit,and i is the number of bridging oxygens in a[SiO_(4)]tetrahedral unit)ratio increased from 2.30 and 1.02 to 2.52 and 2.14,respectively,indicating a progressive polymerization of the silicate structure.X-ray photoelectron spectroscopy(XPS)results highlighted that non-bridging oxygen content decreased from 77.97mol% to 63.41mol% with increasing MgO concentration,whereas bridging oxygen and free oxygen contents increased.Structural analysis demonstrated a gradual increase in the polymerization degree of the tetrahedral structure with the increase in MgO substitution.However,bond strength is another important factor affecting the slag viscosity.The occurrence of a viscosity minimum can be attributed to the complex evolution of bond strengths of non-bridging oxygens generated during depolymerization of the[SiO_(4)]and[AlO_(4)]tetrahedral structures by CaO and MgO.

Advanced Functional Electromagnetic Shielding Materials:A Review Based on Micro‑Nano Structure Interface Control of Biomass Cell Walls

Research efforts on electromagnetic interference(EMI)shielding materials have begun to converge on green and sustainable biomass materials.These materials offer numerous advantages such as being lightweight,porous,and hierarchical.Due to their porous nature,interfacial compatibility,and electrical conductivity,biomass materials hold significant potential as EMI shielding materials.Despite concerted efforts on the EMI shielding of biomass materials have been reported,this research area is still relatively new compared to traditional EMI shielding materials.In particular,a more comprehensive study and summary of the factors influencing biomass EMI shielding materials including the pore structure adjustment,preparation process,and micro-control would be valuable.The preparation methods and characteristics of wood,bamboo,cellulose and lignin in EMI shielding field are critically discussed in this paper,and similar biomass EMI materials are summarized and analyzed.The composite methods and fillers of various biomass materials were reviewed.this paper also highlights the mechanism of EMI shielding as well as existing prospects and challenges for development trends in this field.

Molecular Structure Tailoring of Organic Spacers for High‑Performance Ruddlesden–Popper Perovskite Solar Cells

Layer-structured Ruddlesden–Popper(RP)perovskites(RPPs)with decent stability have captured the imagination of the photovoltaic research community and bring hope for boosting the development of perovskite solar cell(PSC)technology.However,two-dimensional(2D)or quasi-2D RP PSCs are encountered with some challenges of the large exciton binding energy,blocked charge transport and poor film quality,which restrict their photovoltaic performance.Fortunately,these issues can be readily resolved by rationally designing spacer cations of RPPs.This review mainly focuses on how to design the molecular structures of organic spacers and aims to endow RPPs with outstanding photovoltaic applications.We firstly elucidated the important roles of organic spacers in impacting crystallization kinetics,charge transporting ability and stability of RPPs.Then we brought three aspects to attention for designing organic spacers.Finally,we presented the specific molecular structure design strategies for organic spacers of RPPs aiming to improve photovoltaic performance of RP PSCs.These proposed strategies in this review will provide new avenues to develop novel organic spacers for RPPs and advance the development of RPP photovoltaic technology for future applications.

3D Printing of Tough Hydrogel Scaffolds with Functional Surface Structures for Tissue Regeneration

Hydrogel scaffolds have numerous potential applications in the tissue engineering field.However,tough hydrogel scaffolds implanted in vivo are seldom reported because it is difficult to balance biocompatibility and high mechanical properties.Inspired by Chinese ramen,we propose a universal fabricating method(printing-P,training-T,cross-linking-C,PTC&PCT)for tough hydrogel scaffolds to fill this gap.First,3D printing fabricates a hydrogel scaffold with desired structures(P).Then,the scaffold could have extraordinarily high mechanical properties and functional surface structure by cycle mechanical training with salting-out assistance(T).Finally,the training results are fixed by photo-cross-linking processing(C).The tough gelatin hydrogel scaffolds exhibit excellent tensile strength of 6.66 MPa(622-fold untreated)and have excellent biocompatibility.Furthermore,this scaffold possesses functional surface structures from nanometer to micron to millimeter,which can efficiently induce directional cell growth.Interestingly,this strategy can produce bionic human tissue with mechanical properties of 10 kPa-10 MPa by changing the type of salt,and many hydrogels,such as gelatin and silk,could be improved with PTC or PCT strategies.Animal experiments show that this scaffold can effectively promote the new generation of muscle fibers,blood vessels,and nerves within 4 weeks,prompting the rapid regeneration of large-volume muscle loss injuries.

Graphene Aerogel Composites with Self‑Organized Nanowires‑Packed Honeycomb Structure for Highly Efficient Electromagnetic Wave Absorption

With vigorous developments in nanotechnology,the elaborate regulation of microstructure shows attractive potential in the design of electromagnetic wave absorbers.Herein,a hierarchical porous structure and composite heterogeneous interface are constructed successfully to optimize the electromagnetic loss capacity.The macro–micro-synergistic graphene aerogel formed by the ice template‑assisted 3D printing strategy is cut by silicon carbide nanowires(SiC_(nws))grown in situ,while boron nitride(BN)interfacial structure is introduced on graphene nanoplates.The unique composite structure forces multiple scattering of incident EMWs,ensuring the combined effects of interfacial polarization,conduction networks,and magnetic-dielectric synergy.Therefore,the as-prepared composites present a minimum reflection loss value of−37.8 dB and a wide effective absorption bandwidth(EAB)of 9.2 GHz(from 8.8 to 18.0 GHz)at 2.5 mm.Besides,relying on the intrinsic high-temperature resistance of SiC_(nws) and BN,the EAB also remains above 5.0 GHz after annealing in air environment at 600℃ for 10 h.

Emerging structures and dynamic mechanisms ofγ-secretase for Alzheimer’s disease

γ-Secretase,called“the proteasome of the membrane,”is a membrane-embedded protease complex that cleaves 150+peptide substrates with central roles in biology and medicine,including amyloid precursor protein and the Notch family of cell-surface receptors.Mutations inγ-secretase and amyloid precursor protein lead to early-onset familial Alzheimer’s disease.γ-Secretase has thus served as a critical drug target for treating familial Alzheimer’s disease and the more common late-onset Alzheimer’s disease as well.However,critical gaps remain in understanding the mechanisms of processive proteolysis of substrates,the effects of familial Alzheimer’s disease mutations,and allosteric modulation of substrate cleavage byγ-secretase.In this review,we focus on recent studies of structural dynamic mechanisms ofγ-secretase.Different mechanisms,including the“Fit-Stay-Trim,”“Sliding-Unwinding,”and“Tilting-Unwinding,”have been proposed for substrate proteolysis of amyloid precursor protein byγ-secretase based on all-atom molecular dynamics simulations.While an incorrect registry of the Notch1 substrate was identified in the cryo-electron microscopy structure of Notch1-boundγ-secretase,molecular dynamics simulations on a resolved model of Notch1-boundγ-secretase that was reconstructed using the amyloid precursor protein-boundγ-secretase as a template successfully capturedγ-secretase activation for proper cleavages of both wildtype and mutant Notch,being consistent with biochemical experimental findings.The approach could be potentially applied to decipher the processing mechanisms of various substrates byγ-secretase.In addition,controversy over the effects of familial Alzheimer’s disease mutations,particularly the issue of whether they stabilize or destabilizeγ-secretase-substrate complexes,is discussed.Finally,an outlook is provided for future studies ofγ-secretase,including pathways of substrate binding and product release,effects of modulators on familial Alzheimer’s disease mutations of theγ-secretase-substrate complexes.Comprehensive understanding of the functional mechanisms ofγ-secretase will greatly facilitate the rational design of effective drug molecules for treating familial Alzheimer’s disease and perhaps Alzheimer’s disease in general.

Designing Electronic Structures of Multiscale Helical Converters for Tailored Ultrabroad Electromagnetic Absorption

Atomic-scale doping strategies and structure design play pivotal roles in tailoring the electronic structure and physicochemical property of electromagnetic wave absorption(EMWA)materials.However,the relationship between configuration and electromagnetic(EM)loss mechanism has remained elusive.Herein,drawing inspiration from the DNA transcription process,we report the successful synthesis of novel in situ Mn/N co-doped helical carbon nanotubes with ultrabroad EMWA capability.Theoretical calculation and EM simulation confirm that the orbital coupling and spin polarization of the Mn–N4–C configuration,along with cross polarization generated by the helical structure,endow the helical converters with enhanced EM loss.As a result,HMC-8 demonstrates outstanding EMWA performance,achieving a minimum reflection loss of−63.13 dB at an ultralow thickness of 1.29 mm.Through precise tuning of the graphite domain size,HMC-7 achieves an effective absorption bandwidth(EAB)of 6.08 GHz at 2.02 mm thickness.Furthermore,constructing macroscale gradient metamaterials enables an ultrabroadband EAB of 12.16 GHz at a thickness of only 5.00 mm,with the maximum radar cross section reduction value reaching 36.4 dB m2.This innovative approach not only advances the understanding of metal–nonmetal co-doping but also realizes broadband EMWA,thus contributing to the development of EMWA mechanisms and applications.

Big Data Access Control Mechanism Based on Two-Layer Permission Decision Structure

Big data resources are characterized by large scale, wide sources, and strong dynamics. Existing access controlmechanisms based on manual policy formulation by security experts suffer from drawbacks such as low policymanagement efficiency and difficulty in accurately describing the access control policy. To overcome theseproblems, this paper proposes a big data access control mechanism based on a two-layer permission decisionstructure. This mechanism extends the attribute-based access control (ABAC) model. Business attributes areintroduced in the ABAC model as business constraints between entities. The proposed mechanism implementsa two-layer permission decision structure composed of the inherent attributes of access control entities and thebusiness attributes, which constitute the general permission decision algorithm based on logical calculation andthe business permission decision algorithm based on a bi-directional long short-term memory (BiLSTM) neuralnetwork, respectively. The general permission decision algorithm is used to implement accurate policy decisions,while the business permission decision algorithm implements fuzzy decisions based on the business constraints.The BiLSTM neural network is used to calculate the similarity of the business attributes to realize intelligent,adaptive, and efficient access control permission decisions. Through the two-layer permission decision structure,the complex and diverse big data access control management requirements can be satisfied by considering thesecurity and availability of resources. Experimental results show that the proposed mechanism is effective andreliable. In summary, it can efficiently support the secure sharing of big data resources.

Influence of topography on the fine structures of stratospheric gravity waves:An analysis using COSMIC-2 temperature data

We derive the potential energy of gravity waves(GWs)in the upper troposphere and stratosphere at 45°S-45°N from December 2019 to November 2022 by using temperature profiles retrieved from the Constellation Observing System for Meteorology,Ionosphere,and Climate-2(COSMIC-2)satellite.Owing to the dense sampling of COSMIC-2,in addition to the strong peaks of gravity wave potential energy(GWPE)above the Andes and Tibetan Plateau,we found weak peaks above the Rocky,Atlas,Caucasus,and Tianshan Mountains.The land-sea contrast is responsible for the longitudinal variations of the GWPE in the lower and upper stratosphere.At 40°N/S,the peaks were mainly above the topographic regions during the winter.At 20°N/S,the peaks were a slight distance away from the topographic regions and might be the combined effect of nontopographic GWs and mountain waves.Near the Equator,the peaks were mainly above the regions with the lowest sea level altitude and may have resulted from convection.Our results indicate that even above the local regions with lower sea level altitudes compared with the Andes and Tibetan Plateau,the GWPE also exhibits fine structures in geographic distributions.We found that dissipation layers above the tropopause jet provide the body force to generate secondary waves in the upper stratosphere,especially during the winter months of each hemisphere and at latitudes of greater than 20°N/S.

Analysis of Urban Agglomeration Network Structure Based on Baidu Migration Data: A Case Study of the Guangdong-Hong Kong-Macao Greater Bay Urban Agglomeration

The inter-city linkage heat data provided by Baidu Migration is employed as a characterization of inter-city linkages in order to facilitate the study of the network linkage characteristics and hierarchical structure of urban agglomeration in the Greater Bay Area through the use of social network analysis method.This is the inaugural application of big data based on location services in the study of urban agglomeration network structure,which represents a novel research perspective on this topic.The study reveals that the density of network linkages in the Greater Bay Area urban agglomeration has reached 100%,indicating a mature network-like spatial structure.This structure has given rise to three distinct communities:Shenzhen-Dongguan-Huizhou,Guangzhou-Foshan-Zhaoqing,and Zhuhai-Zhongshan-Jiangmen.Additionally,cities within the Greater Bay Area urban agglomeration play different roles,suggesting that varying development strategies may be necessary to achieve staggered development.The study demonstrates that large datasets represented by LBS can offer novel insights and methodologies for the examination of urban agglomeration network structures,contingent on the appropriate mining and processing of the data.

Glycomol: A pervasive tool for structure predication of natural saponin products basing on MS data

Due to the multitudinous structural types of glycosylated components,accurate identification of glycosylation modifications and secondary metabolite structures in herbs remains a challenge for natural drug analysis and new drug discovery[1].To solve this problem,we developed an auxiliary spectral analysis strategy based on Glyco-GNN machine learning techniques(Fig.1).It is named as“Glycomol”,an auxiliary tool for the identification of glycoside molecules.In Glycomol,the structure of glycosylated components was analyzed in terms of parent nucleus,glycan modification sites,and glycan types.Saponins were taken as representative glycosylated components due to their high structural complexity.To estimate the likelihood of results from this modularization strategy,first we extracted the liquid phase and mass spectrum information of 96 structure-known saponins from multi-stage mass spectrometry data.Then,24 standard saponins were applied for analysis and a 95.8%hit rate was achieved.

New roles of research data infrastructure in research paradigm evolution

Research data infrastructures form the cornerstone in both cyber and physical spaces,driving the progression of the data-intensive scientific research paradigm.This opinion paper presents an overview of global research data infrastructure,drawing insights from national roadmaps and strategic documents related to research data infrastructure.It emphasizes the pivotal role of research data infrastructures by delineating four new missions aimed at positioning them at the core of the current scientific research and communication ecosystem.The four new missions of research data infrastructures are:(1)as a pioneer,to transcend the disciplinary border and address complex,cutting-edge scientific and social challenges with problem-and data-oriented insights;(2)as an architect,to establish a digital,intelligent,flexible research and knowledge services environment;(3)as a platform,to foster the high-end academic communication;(4)as a coordinator,to balance scientific openness with ethics needs.

Teaching Reform and Practice of Data Structure Course Based on Smart Teaching Platform

With the rapid development of information technology,smart teaching platforms have become important tools for higher education teaching reform.As a core course of computer science and technology-related majors in higher education,the data structure course lays a solid foundation for students’professional learning and plays an important role in promoting their future success in technology,research,and industry.This study conducts an in-depth analysis of the pain points faced by the data structure course,and explores a teaching reform and practice of integration of theory and practice based on the system application of a smart teaching platform before class,during class,and after class.The reform practice shows that this teaching mode improves students’learning initiative,learning motivation,and practical skills.Students not only achieved better results in knowledge mastery but also significantly improved in problem analysis and solution.

Comparative Study of Genetic Structure and Genetic Diversity betweenWild and Cultivated Populations of Taxus cuspidata,Northeast China

Taxus cuspidata is a rare plant with important medicinal and ornamental value.Aiming at the obvious differences between wild and cultivated populations of T.cuspidata from Northeast China,a total of 61 samples,that is,33 wild yews and 28 cultivated yews were used to analyze the differences and correlations of the kinship,genetic diversity,and genetic structure between them by specific length amplified fragment sequencing(SLAF-seq).Finally,470725 polymorphic SLAF tags and 58622 valid SNP markers were obtained.Phylogenetic analysis showed that 61 samples were classified into 2 clusters:wild populations and cultivated populations,and some wild yews were categorized into the cultivated populations;the genetic diversity analysis showed that the Nei diversity index of wild populations(0.4068)was smaller than that of cultivated populations(0.4414),and the polymorphic information content(PIC)of wild populations(0.2861)was smaller than that of cultivated populations(0.3309).The genetic differentiation analysis showed that the total populations of gene diversity(H_(t))of cultivated and wild populations were respectively 0.8159 and 0.5685,the coefficient of gene differentiation(G_(st))of cultivated and wild populations was respectively 0.3021 and 0.1068,and the gene flow(N_(m))(2.4967)of wild populations was larger than cultivated populations(0.8199).The molecular variance(AMOVA)revealed that inter-population variation accounted for 29.57%of the total genetic variation,while intra-population variation accounted for 70.42% of the total genetic variation(p<0.001),this suggested that the genetic variation in the T.cuspidata is mainly attributed to within-population factors.In conclusion,the genetic distance between geographical ecological groups of wild populations was generally smaller than that of cultivated populations,and the degree of genetic diversity and genetic differentiation was smaller than that of cultivated populations.As evident,the utilization of SLAF-seq technology enables efficient and accurate development of SNP markers suitable for genetic analysis of T.cuspidata species.These developed SNP markers can provide a molecular foundation for T.cuspidata breeding,construction of genetic maps,variety identification,and association analysis of agronomic traits.

Three-dimensional thermohaline structure estimation derived from HY-2 satellite data over the Maritime Silk Road and its applications

Estimated ocean subsurface fields derived from satellite observations provide potential data sources for operational marine environmental monitoring and prediction systems.This study employs a statistic regression reconstruction method,in combination with domestic autonomous sea surface height and sea surface temperature observations from the Haiyang-2(HY-2)satellite fusion data,to establish an operational quasi-realtime three-dimensional(3D)temperature and salinity products over the Maritime Silk Road.These products feature a daily temporal resolution and a spatial resolution of 0.25°×0.25°and exhibit stability and continuity.We have demonstrated the accuracy of the reconstructed thermohaline fields in capturing the 3D thermohaline variations through comprehensive statistical evaluations,after comparing them against Argo observations and ocean analysis data from 2022.The results illustrate that the reconstructed fields effectively represent seasonal variations in oceanic subsurface structures,along with structural changes resulting from mesoscale processes,and the upper ocean’s responses to tropical cyclones.Furthermore,the incorporation of HY-2 satellite observations notably enhances the accuracy of temperature and salinity reconstructions in the Northwest Pacific Ocean and marginally improves salinity reconstruction accuracy in the North Indian Ocean when compared to the World Ocean Atlas 2018 monthly climatology thermohaline fields.As a result,the reconstructed product holds promise for providing quasi-real-time 3D temperature and salinity field information to facilitate fast decisionmaking during emergencies,and also offers foundational thermohaline fields for operational ocean reanalysis and forecasting systems.These contributions enhance the safety and stability of ocean subsurface activities and navigation.

Structural Health Monitoring by Accelerometric Data of a Continuously Monitored Structure with Induced Damages

The possibility of determining the integrity of a real structure subjected to non-invasive and non-destructive monitoring,such as that carried out by a series of accelerometers placed on the structure,is certainly a goal of extreme and current interest.In the present work,the results obtained from the processing of experimental data of a real structure are shown.The analyzed structure is a lattice structure approximately 9 m high,monitored with 18 uniaxial accelerometers positioned in pairs on 9 different levels.The data used refer to continuous monitoring that lasted for a total of 1 year,during which minor damage was caused to the structure by alternatively removing some bracings and repositioning them in the structure.Two methodologies detecting damage based on decomposition techniques of the acquired data were used and tested,as well as a methodology combining the two techniques.The results obtained are extremely interesting,as all the minor damage caused to the structure was identified by the processing methods used,based solely on the monitored data and without any knowledge of the real structure being analyzed.The results use 15 acquisitions in environmental conditions lasting 10 min each,a reasonable amount of time to get immediate feedback on possible damage to the structure.

Data for critical infrastructure network modelling of natural hazard impacts:Needs and influence on model characteristics

Natural hazards impact interdependent infrastructure networks that keep modern society functional.While a va-riety of modelling approaches are available to represent critical infrastructure networks(CINs)on different scales and analyse the impacts of natural hazards,a recurring challenge for all modelling approaches is the availability and accessibility of sufficiently high-quality input and validation data.The resulting data gaps often require mod-ellers to assume specific technical parameters,functional relationships,and system behaviours.In other cases,expert knowledge from one sector is extrapolated to other sectoral structures or even cross-sectorally applied to fill data gaps.The uncertainties introduced by these assumptions and extrapolations and their influence on the quality of modelling outcomes are often poorly understood and difficult to capture,thereby eroding the reliability of these models to guide resilience enhancements.Additionally,ways of overcoming the data avail-ability challenges in CIN modelling,with respect to each modelling purpose,remain an open question.To address these challenges,a generic modelling workflow is derived from existing modelling approaches to examine model definition and validations,as well as the six CIN modelling stages,including mapping of infrastructure assets,quantification of dependencies,assessment of natural hazard impacts,response&recovery,quantification of CI services,and adaptation measures.The data requirements of each stage were systematically defined,and the literature on potential sources was reviewed to enhance data collection and raise awareness of potential pitfalls.The application of the derived workflow funnels into a framework to assess data availability challenges.This is shown through three case studies,taking into account their different modelling purposes:hazard hotspot assess-ments,hazard risk management,and sectoral adaptation.Based on the three model purpose types provided,a framework is suggested to explore the implications of data scarcity for certain data types,as well as their reasons and consequences for CIN model reliability.Finally,a discussion on overcoming the challenges of data scarcity is presented.

Aeromagnetic data and geological structure of continental China:A review

We review the latest aeromagnetic geological data of continental China. We discuss the latest achievements in geological mapping and the newly detected features based on aeromagnetic data. Using aeromagnetic data collected for more than 50 years, a series of 1：5000000 and 1：1000000 aeromagnetic maps of continental China were compiled using state-of-the-art digital technology, and data processing and transformation. Guided by plate tectonics and continental dynamics, rock physical properties, and magnetic anomalies, we compiled maps of the depth of the magnetic basement of continental China and the major geotectonic units, and presented newly detected geological structures based on the aeromagnefic data.

Multiresolution analysis over triangle meshes: method and data structure

A robust and efficient algorithm is presented to build multiresolution models (MRMs) of arbitrary meshes without requirement of subdivision connectivity. To overcome the sampling difficulty of arbitrary meshes, edge contraction and vertex expansion are used as downsampling and upsampling methods. Our MRMs of a mesh are composed of a base mesh and a series of edge split operations, which are organized as a directed graph. Each split operation encodes two parts of information. One is the modification to the mesh, and the other is the dependency relation among splits. Such organization ensures the efficiency and robustness of our MRM algorithm. Examples demonstrate the functionality of our method.

Lossless Mapping from Semi-Structured Data to Structured Data

Most semi-structured data are of certain structure regularity. Having beenstored as structured data in relational database (RDB), they can be effectively managed by databasemanagement system (DBMS). Some semi-structured data are difficult to transform due to theirirregular structures. We design an efficient algorithm and data structure for ensuring losslesstransformation. We bring forward an approach of schema extraction through data mining, in whichdifferent kinds of elements are transformed respectively and lossless mapping from semi-structureddata to structured data can be achieved.