Offlattice Model in the Prediction of Protein 3D Structure

3-dimension HPNX offiattice model is developed from the 2-dimension HP offiattice model. In the HP model, 20 types of amino acid monomers are divided into two classes, H （non-polar monomer） and P （polar monomer）. In the HPNX model, polar monomers are split into positively charged （P）, negatively charged （N） and neutral （X） monomers. A new evolutionary algorithm is applied to study long chains of the HPNX offiattice protein model. This method successfully predict the structures of several proteins in the 3-dimension space that are similar to the structures gotten by X-Ray Crystallography and NMR and published in the PDB（Protein Data Bank）.

Polar-coordinate line-projection light-curing continuous 3D printing for tubular structures

3D printing techniques offer an effective method in fabricating complex radially multi-material structures.However,it is challenging for complex and delicate radially multi-material model geometries without supporting structures,such as tissue vessels and tubular graft,among others.In this work,we tackle these challenges by developing a polar digital light processing technique which uses a rod as the printing platform.The 3D model fabrication is accomplished through line projection.The rotation and translation of the rod are synchronized to project and illuminate the photosensitive material volume.By controlling the distance between the rod and the printing window,we achieved the printing of tubular structures with a minimum wall thickness as thin as 50 micrometers.By controlling the width of fine slits at the printing window,we achieved the printing of structures with a minimum feature size of 10 micrometers.Our process accomplished the fabrication of thin-walled tubular graft structure with a thickness of only 100 micrometers and lengths of several centimeters within a timeframe of just 100 s.Additionally,it enables the printing of axial multi-material structures,thereby achieving adjustable mechanical strength.This method is conducive to rapid customization of tubular grafts and the manufacturing of tubular components in fields such as dentistry,aerospace,and more.

Biomimetic 3D printing of composite structures with decreased cracking

The development of tissue engineering and regeneration research has created new platforms for bone transplantation.However,the preparation of scaffolds with good fiber integrity is challenging,because scaffolds prepared by traditional printing methods are prone to fiber cracking during solvent evaporation.Human skin has an excellent natural heat-management system,which helps to maintain a constant body temperature through perspiration or blood-vessel constriction.In this work,an electrohydrodynamic-jet 3D-printing method inspired by the thermal-management system of skin was developed.In this system,the evaporation of solvent in the printed fibers can be adjusted using the temperature-change rate of the substrate to prepare 3D structures with good structural integrity.To investigate the solvent evaporation and the interlayer bonding of the fibers,finite-element analysis simulations of a three-layer microscale structure were carried out.The results show that the solvent-evaporation path is from bottom to top,and the strain in the printed structure becomes smaller with a smaller temperaturechange rate.Experimental results verified the accuracy of these simulation results,and a variety of complex 3D structures with high aspect ratios were printed.Microscale cracks were reduced to the nanoscale by adjusting the temperature-change rate from 2.5 to 0.5℃s-1.Optimized process parameters were selected to prepare a tissue engineering scaffold with high integrity.It was confirmed that this printed scaffold had good biocompatibility and could be used for bone-tissue regeneration.This simple and flexible 3D-printing method can also help with the preparation of a wide range of micro-and nanostructured sensors and actuators.

3D Road Network Modeling and Road Structure Recognition in Internet of Vehicles

Internet of Vehicles (IoV) is a new system that enables individual vehicles to connect with nearby vehicles,people, transportation infrastructure, and networks, thereby realizing amore intelligent and efficient transportationsystem. The movement of vehicles and the three-dimensional (3D) nature of the road network cause the topologicalstructure of IoV to have the high space and time complexity.Network modeling and structure recognition for 3Droads can benefit the description of topological changes for IoV. This paper proposes a 3Dgeneral roadmodel basedon discrete points of roads obtained from GIS. First, the constraints imposed by 3D roads on moving vehicles areanalyzed. Then the effects of road curvature radius (Ra), longitudinal slope (Slo), and length (Len) on speed andacceleration are studied. Finally, a general 3D road network model based on road section features is established.This paper also presents intersection and road section recognition methods based on the structural features ofthe 3D road network model and the road features. Real GIS data from a specific region of Beijing is adopted tocreate the simulation scenario, and the simulation results validate the general 3D road network model and therecognitionmethod. Therefore, thiswork makes contributions to the field of intelligent transportation by providinga comprehensive approach tomodeling the 3Droad network and its topological changes in achieving efficient trafficflowand improved road safety.

Acoustical properties of a 3D printed honeycomb structure filled with nanofillers:Experimental analysis and optimization for emerging applications

The novelty of this research lies in the successful fabrication of a 3D-printed honeycomb structure filled with nanofillers for acoustic properties,utilizing an impedance tube setup in accordance with ASTM standard E 1050-12.The Creality Ender-3,a 3D printer,was used for printing the honeycomb structures,and polylactic acid(PLA)material was employed for their construction.The organic,inorganic,and polymeric compounds within the composites were identified using fourier transformation infrared(FTIR)spectroscopy.The structure and homogeneity of the samples were examined using a field emission scanning electron microscope(FESEM).To determine the sound absorption coefficient of the 3D printed honeycomb structure,numerous samples were systematically developed using central composite design(CCD)and analysed using response surface methodology(RSM).The RSM mathematical model was established to predict the optimum values of each factor and noise reduction coefficient(NRC).The optimum values for an NRC of 0.377 were found to be 1.116 wt% carbon black,1.025 wt% aluminium powder,and 3.151 mm distance between parallel edges.Overall,the results demonstrate that a 3Dprinted honeycomb structure filled with nanofillers is an excellent material that can be utilized in various fields,including defence and aviation,where lightweight and acoustic properties are of great importance.

Elimination of cracks in stainless steel casings via 3D printed sand molds with an internal topology structure

The important supporting component in a gas turbine is the casing,which has the characteristics of large size,complex structure,and thin wall.In the context of existing 3DP sand casting processes,casting crack defects are prone to occur.This leads to an increase in the scrap rate of casings,causing significant resource wastage.Additionally,the presence of cracks poses a significant safety hazard after the casings are put into service.The generation of different types of crack defects in stainless steel casings is closely related to casting stress and the high-temperature concession of the sand mold.Therefore,the types and causes of cracks in stainless steel casing products,based on their structural characteristics,were systematically analyzed.Various sand molds with different internal topology designs were printed using the 3DP technology to investigate the impact of sand mold structures on high-temperature concession.The optimal sand mold structure was used to cast casings,and the crack suppression effect was verified by analyzing its eddy current testing results.The experimental results indicate that the skeleton structure has an excellent effect on suppressing cracks in the casing.This research holds important theoretical and engineering significance in improving the quality of casing castings and reducing production costs.

RNA structure prediction:Progress and perspective

Many recent exciting discoveries have revealed the versatility of RNAs and their importance in a variety of cellular functions which are strongly coupled to RNA structures. To understand the functions of RNAs, some structure prediction models have been developed in recent years. In this review, the progress in computational models for RNA structure prediction is introduced and the distinguishing features of many outstanding algorithms are discussed, emphasizing three- dimensional （3D） structure prediction. A promising coarse-grained model for predicting RNA 3D structure, stability and salt effect is also introduced briefly. Finally, we discuss the major challenges in the RNA 3D structure modeling.

3D characterization and analysis of pore structure of packed ore particle beds based on computed tomography images

Methods and procedures of three-dimensional （3D） characterization of the pore structure features in the packed ore particle bed are focused. X-ray computed tomography was applied to deriving the cross-sectional images of specimens with single particle size of 1-2, 2-3, 3-4, 4-5, 5-6, 6-7, 7-8, 8-9, 9-10 ram. Based on the in-house developed 3D image analysis programs using Matlab, the volume porosity, pore size distribution and degree of connectivity were calculated and analyzed in detail. The results indicate that the volume porosity, the mean diameter of pores and the effective pore size （d50） increase with the increasing of particle size. Lognormal distribution or Gauss distribution is mostly suitable to model the pore size distribution. The degree of connectivity investigated on the basis of cluster-labeling algorithm also increases with increasing the particle size approximately.

Ensemble Machine Learning to Enhance Q8 Protein Secondary Structure Prediction

Protein structure prediction is one of the most essential objectives practiced by theoretical chemistry and bioinformatics as it is of a vital importance in medicine,biotechnology and more.Protein secondary structure prediction(PSSP)has a significant role in the prediction of protein tertiary structure,as it bridges the gap between the protein primary sequences and tertiary structure prediction.Protein secondary structures are classified into two categories:3-state category and 8-state category.Predicting the 3 states and the 8 states of secondary structures from protein sequences are called the Q3 prediction and the Q8 prediction problems,respectively.The 8 classes of secondary structures reveal more precise structural information for a variety of applications than the 3 classes of secondary structures,however,Q8 prediction has been found to be very challenging,that is why all previous work done in PSSP have focused on Q3 prediction.In this paper,we develop an ensemble Machine Learning(ML)approach for Q8 PSSP to explore the performance of ensemble learning algorithms compared to that of individual ML algorithms in Q8 PSSP.The ensemble members considered for constructing the ensemble models are well known classifiers,namely SVM(Support Vector Machines),KNN(K-Nearest Neighbor),DT(Decision Tree),RF(Random Forest),and NB(Naïve Bayes),with two feature extraction techniques,namely LDA(Linear Discriminate Analysis)and PCA(Principal Component Analysis).Experiments have been conducted for evaluating the performance of single models and ensemble models,with PCA and LDA,in Q8 PSSP.The novelty of this paper lies in the introduction of ensemble learning in Q8 PSSP problem.The experimental results confirmed that ensemble ML models are more accurate than individual ML models.They also indicated that features extracted by LDA are more effective than those extracted by PCA.

Earthquake relocation and 3-dimensional crustal structure of P-wave velocity in cen-tral-western China

采用中国中西部地区(2l°～36°N，98°～112°E)193个地震台在1992～1999年间记录到的9988次地震的Pg和Sg震相走时的读数资料，用Roecker的SPHYPIT90程序，反演了该地区三维地壳P波速度结构，并用SPHREL3D90程序进行了地震的重新定位．反演结果揭示了中国中西部地区地震P波速度结构明显的横向不均匀性，这些不同深度上波速的横向变化多以该地区的活动断裂为分界线．可以看出活动断裂两侧存在明显的速度反差．通过重新定位，得到了6459次地震的震源参数，这些精确定位的地震震中明显沿该区活动断裂呈现条带状分布，其范围和尺度清晰地表示了这一地区地震活动与活动断裂的紧密关系．其中，82％重新精确定位的事件的震源深度在20km以内．这一结果与笔者用双差地震定位法得到的重新定位的震源深度分布相一致．

3-D velocity structure in the central-eastern part of Qilianshan

The 3-D velocity tomography image of the central-eastern part of Qilianshan is obtained by the joint inversion of 3-D velocity structure and focal parameters based on the S-P data of micro-earthquakes recorded by the digital seismic network set up for a Sino-French cooperation program since 1996. The inversed velocity structure does primarily reflect some important features of the deep structure in the region and provide the scientific background for the further study of active tectonic structure and the calculation of earthquake parameters.

The 3D magnetic structure beneath the continental margin of the northeastern South China Sea

Understanding the continental margin of the Northeastern South China Sea is critical to the study of deep structures, tectonic evolution, and dynamics of the region. One set of important data for this endeavor is the total-field magnetic data. Given the challenges associated with the magnetic data at low latitudes and with remanent magnetism in this area, we combine the equivalent-source technique and magnetic amplitude inversion to recover 3D subsurface magnetic structures. The inversion results show that this area is characterized by a north-south block division and east-west zonation. Magnetic regions strike in EW, NE and NW direction and are consistent with major tectonic trends in the region. The highly magnetic zone recovered from inversion in the continental margin differs visibly from that of the magnetically quiet zones to the south. The magnetic anomaly zone strikes in NE direction, covering an area of about 500 km × 60 km, and extending downward to a depth of 25 km or more. In combination with other geophysical data, we suggest that this strongly magnetic zone was produced by deep underplating of magma associated with plate subduction in Mesozoic period. The magnetically quiet zone in the south is an EW trending unit underlain by broad and gentle magnetic layers of lower crust. Its magnetic structure bears a clear resemblance to oceanic crust, assumed to be related to the presence of ancient oceanic crust there.

Advanced 3D ordered electrodes for PEMFC applications: From structural features and fabrication methods to the controllable design of catalyst layers

The catalyst layers(CLs) electrode is the key component of the membrane electrode assembly(MEA) in proton exchange membrane fuel cells(PEMFCs). Conventional electrodes for PEMFCs are composed of carbon-supported, ionomer, and Pt nanoparticles, all immersed together and sprayed with a micron-level thickness of CLs. They have a performance trade-off where increasing the Pt loading leads to higher performance of abundant triple-phase boundary areas but increases the electrode cost. Major challenges must be overcome before realizing its wide commercialization. Literature research revealed that it is impossible to achieve performance and durability targets with only high-performance catalysts, so the controllable design of CLs architecture in MEAs for PEMFCs must now be the top priority to meet industry goals. From this perspective, a 3D ordered electrode circumvents this issue with a support-free architecture and ultrathin thickness while reducing noble metal Pt loadings. Herein, we discuss the motivation in-depth and summarize the necessary CLs structural features for designing ultralow Pt loading electrodes. Critical issues that remain in progress for 3D ordered CLs must be studied and characterized. Furthermore, approaches for 3D ordered CLs architecture electrode development, involving material design, structure optimization, preparation technology, and characterization techniques, are summarized and are expected to be next-generation CLs for PEMFCs. Finally, the review concludes with perspectives on possible research directions of CL architecture to address the significant challenges in the future.

Interpenetrated Structures for Enhancing Ion Diffusion Kinetics in Electrochemical Energy Storage Devices

The architectural design of electrodes offers new opportunities for next-generation electrochemical energy storage devices(EESDs)by increasing surface area,thickness,and active materials mass loading while maintaining good ion diffusion through optimized electrode tortuosity.However,conventional thick electrodes increase ion diffusion length and cause larger ion concentration gradients,limiting reaction kinetics.We demonstrate a strategy for building interpenetrated structures that shortens ion diffusion length and reduces ion concentration inhomogeneity.This free-standing device structure also avoids short-circuiting without needing a separator.The feature size and number of interpenetrated units can be adjusted during printing to balance surface area and ion diffusion.Starting with a 3D-printed interpenetrated polymer substrate,we metallize it to make it conductive.This substrate has two individually addressable electrodes,allowing selective electrodeposition of energy storage materials.Using a Zn//MnO_(2) battery as a model system,the interpenetrated device outperforms conventional separate electrode configurations,improving volumetric energy density by 221%and exhibiting a higher capacity retention rate of 49%compared to 35%at temperatures from 20 to 0℃.Our study introduces a new EESD architecture applicable to Li-ion,Na-ion batteries,supercapacitors,etc.

A three-dimensional co-continuous network structure polymer electrolyte with efficient ion transport channels enabling ultralong-life all solid-state lithium metal batteries

Solid polymer electrolytes(SPEs)have emerged as one of the most promising candidates for the construction of solid-state lithium batteries due to their excellent flexibility,scalability,and interface compatibility with electrodes.Herein,a novel all-solid polymer electrolyte(PPLCE)was fabricated by the copolymer network of liquid crystalline monomers and poly(ethylene glycol)dimethacrylate(PEGDMA)acts as a structural frame,combined with poly(ethylene glycol)diglycidyl ether short chain interspersed serving as mobile ion transport entities.The preparaed PPLCEs exhibit excellent mechanical property and out-standing electrochemical performances,which is attributed to their unique three-dimensional cocontinuous structure,characterized by a cross-linked semi-interpenetrating network and an ionic liquid phase,resulting in a distinctive nanostructure with short-range order and long-range disorder.Remarkably,the addition of PEGDMA is proved to be critical to the comprehensive performance of the PPLCEs,which effectively modulates the microscopic morphology of polymer networks and improves the mechanical properties as well as cycling stability of the solid electrolyte.When used in a lithiumion symmetrical battery configuration,the 6 wt%-PPLCE exhibites super stability,sustaining operation for over 2000 h at 30 C,with minimal and consistent overpotential of 50 mV.The resulting Li|PPLCE|LFP solid-state battery demonstrates high discharge specific capacities of 160.9 and 120.1 mA h g^(-1)at current densities of 0.2 and 1 C,respectively.Even after more than 300 cycles at a current density of 0.2 C,it retaines an impressive 73.5%capacity.Moreover,it displayes stable cycling for over 180 cycles at a high current density of 0.5C.The super cycle stability may promote the application for ultralong-life all solid-state lithium metal batteries.

Runout prediction of potential landslides based on the multi-source data collaboration analysis on historical cases

Long runout landslides involve a massive amount of energy and can be extremely hazardous owing to their long movement distance,high mobility and strong destructive power.Numerical methods have been widely used to predict the landslide runout but a fundamental problem remained is how to determine the reliable numerical parameters.This study proposes a framework to predict the runout of potential landslides through multi-source data collaboration and numerical analysis of historical landslide events.Specifically,for the historical landslide cases,the landslide-induced seismic signal,geophysical surveys,and possible in-situ drone/phone videos(multi-source data collaboration)can validate the numerical results in terms of landslide dynamics and deposit features and help calibrate the numerical(rheological)parameters.Subsequently,the calibrated numerical parameters can be used to numerically predict the runout of potential landslides in the region with a similar geological setting to the recorded events.Application of the runout prediction approach to the 2020 Jiashanying landslide in Guizhou,China gives reasonable results in comparison to the field observations.The numerical parameters are determined from the multi-source data collaboration analysis of a historical case in the region(2019 Shuicheng landslide).The proposed framework for landslide runout prediction can be of great utility for landslide risk assessment and disaster reduction in mountainous regions worldwide.

Predicting 3D Radiotherapy Dose-Volume Based on Deep Learning

Cancer is one of the most dangerous diseaseswith highmortality.One of the principal treatments is radiotherapy by using radiation beams to destroy cancer cells and this workflow requires a lot of experience and skill from doctors and technicians.In our study,we focused on the 3D dose prediction problem in radiotherapy by applying the deeplearning approach to computed tomography(CT)images of cancer patients.Medical image data has more complex characteristics than normal image data,and this research aims to explore the effectiveness of data preprocessing and augmentation in the context of the 3D dose prediction problem.We proposed four strategies to clarify our hypothesis in different aspects of applying data preprocessing and augmentation.In strategies,we trained our custom convolutional neural network model which has a structure inspired by the U-net,and residual blocks were also applied to the architecture.The output of the network is added with a rectified linear unit(Re-Lu)function for each pixel to ensure there are no negative values,which are absurd with radiation doses.Our experiments were conducted on the dataset of the Open Knowledge-Based Planning Challenge which was collected from head and neck cancer patients treatedwith radiation therapy.The results of four strategies showthat our hypothesis is rational by evaluating metrics in terms of the Dose-score and the Dose-volume histogram score(DVH-score).In the best training cases,the Dose-score is 3.08 and the DVH-score is 1.78.In addition,we also conducted a comparison with the results of another study in the same context of using the loss function.

3D Positional and Quantitative Prediction of the Xiaoqinling Gold Ore Belt in Tongguan, Shaanxi, China

Quantitative prediction of deep orebody based on 3D visualization technology is of great significance in mineral exploration. Based on the 2D traditional quantitative predicting method, the geoanomaly theory and the mineral exploration model idea, we constructed 3D models of the topography, strata, structure, magmatite and prospecting engineering of the study area using the commercial 3D modeling software Micromine, delineated eight prospective areas and estimated the gold resources amount with methods of Abundance Estimation and Volume Estimation. Then, we compared and counted the known ore blocks and the predicted blocks, which quantitatively explains this prediction＇s validity. The results show that Xiaoqinling gold belt in Tongguan has convincing potential for gold development and utilization and the prediction method based on 3D visualization technology proves to be effective.

Synthesis,Bioactivity and Crystal Structure Analysis of Novel Benzo[d]isothiazol-3(2H)-ones

Two compounds,3-oxo-N-o-tolylbenzo[d]isothiazole-2（3H）-carboxamide （1） and N-（2-methoxyphenyl）-3-oxobenzo[d]isothiazole-2（3H）-carboxamide （2）,were synthesized from the initial compound benzo[d]isothiazol-3（2H）-one （BIT） and characterized by 1 H NMR,IR and elemental analysis,respectively.The single crystals of compounds 1 and 2 were obtained and determined by X-ray diffraction analysis.The preliminary results of biological activity experiment show that some of the title compounds exhibited a favorable antimicrobial activity.

3D printing biomimeticmaterials and structures for biomedical applications

Over millions of years of evolution,nature has created organisms with overwhelming performances due to their unique materials and structures,providing us with valuable inspirations for the development of next-generation biomedical devices.As a promising new technology,3D printing enables the fabrication of multiscale,multi-material,and multi-functional threedimensional(3D)biomimetic materials and structures with high precision and great flexibility.The manufacturing challenges of biomedical devices with advanced biomimetic materials and structures for various applications were overcome with the flourishing development of 3D printing technologies.In this paper,the state-of-the-art additive manufacturing of biomimetic materials and structures in the field of biomedical engineering were overviewed.Various kinds of biomedical applications,including implants,lab-on-chip,medicine,microvascular network,and artificial organs and tissues,were respectively discussed.The technical challenges and limitations of biomimetic additive manufacturing in biomedical applications were further investigated,and the potential solutions and intriguing future technological developments of biomimetic 3D printing of biomedical devices were highlighted.