Knowledge discovery with recommenders for big data management in science and engineering communities

Recent science and engineering research tasks are increasingly becoming dataintensive and use workflows to automate integration and analysis of voluminous data to test hypotheses. Particularly, bold scientific advances in areas of neuroscience and bioinformatics necessitate access to multiple data archives, heterogeneous software and computing resources, and multi-site interdisciplinary expertise. Datasets are evolving, and new tools are continuously invented for achieving new state-of-the-art performance. Principled cyber and software automation approaches to data-intensive analytics using systematic integration of cyberinfrastructure (CI) technologies and knowledge discovery driven algorithms will significantly enhance research and interdisciplinary collaborations in science and engineering. In this thesis, we demonstrate a novel recommender approach to discover latent knowledge patterns from both the infrastructure perspective (i.e., measurement recommender) and the applications perspective (i.e., topic recommender and scholar recommender). In the infrastructure perspective, we identify and diagnose network-wide anomaly events to address performance bottleneck by proposing a novel measurement recommender scheme. In cases where there is a lack of ground truth in networking performance monitoring (e.g., perfSONAR deployments), it is hard to pinpoint the root-cause analysis in a multi-domain context. To solve this problem, we define a "social plane" concept that relies on recommendation schemes to share diagnosis knowledge or work collaboratively. Our solution makes it easier for network operators and application users to quickly and effectively troubleshoot performance bottlenecks on wide-area network backbones. To evaluate our "measurement recommender", we use both real and synthetic datasets. The results show our measurement recommender scheme has high performance in terms of precision, recall, and accuracy, as well as efficiency in terms of the time taken for large volume measurement trace analysis. In the application perspective, our goal is to shorten time to knowledge discovery and adapt prior domain knowledge for computational and data-intensive communities. To achieve this goal, we design a novel topic recommender that leverages a domain-specific topic model (DSTM) algorithm to help scientists find the relevant tools or datasets for their applications. The DSTM is a probabilistic graphical model that extends the Latent Dirichlet Allocation (LDA) and uses the Markov chain Monte Carlo (MCMC) algorithm to infer latent patterns within a specific domain in an unsupervised manner. We evaluate our scheme based on large collections of the dataset (i.e., publications, tools, datasets) from bioinformatics and neuroscience domains. Our experiments result using the perplexity metric show that our model has better generalization performance within a domain for discovering highly-specific latent topics. Lastly, to enhance the collaborations among scholars to generate new knowledge, it is necessary to identify scholars with their specific research interests or cross-domain expertise. We propose a "ScholarFinder" model to quantify expert knowledge based on publications and funding records using a deep generative model. Our model embeds scholars' knowledge in order to recommend suitable scholars to perform multi-disciplinary tasks. We evaluate our model with state-of-the-art baseline models (e.g., XGBoost, DNN), and experiment results show that our ScholarFinder model outperforms state-ofthe-art models in terms of precision, recall, F1-score, and accuracy.Includes bibliographical references (pages 113-124)

Battery-type column for caesium ions separation using electroactive film of copper hexacyanoferrate nanoparticles

We reported a novel and compact battery-type column using nanoparticles film of copper hexacyanoferrate (CuHCF NPs film) for sequential removal of Cs from wastewater. Different from the electrochemical deposition, chemical spray or chemical bath deposition, our film was prepared by coating water dispersed CuHCF NPs ink on the electrode surface through a simple wet process similar to ink printing. The battery-type column indicated Cs adsorption and desorption can be achieved by electrochemical redox of CuHCF NPs film, through switching the potentials between two sandwiched electrodes. Kinetic studies revealed both the static attraction and electrochemical oxidation-reduction of Fe (II/III) contributed to Cs separation. Insignificant change in the current after 100\ua0cycles of durability test indicated the CuHCF NPs film is relatively stable, suggesting the battery-type column has a long service life for Cs removal from wastewater

JointNet: Extending Text-to-Image Diffusion for Dense Distribution Modeling

We introduce JointNet, a novel neural network architecture for modeling the joint distribution of images and an additional dense modality (e.g., depth maps). JointNet is extended from a pre-trained text-to-image diffusion model, where a copy of the original network is created for the new dense modality branch and is densely connected with the RGB branch. The RGB branch is locked during network fine-tuning, which enables efficient learning of the new modality distribution while maintaining the strong generalization ability of the large-scale pre-trained diffusion model. We demonstrate the effectiveness of JointNet by using RGBD diffusion as an example and through extensive experiments, showcasing its applicability in a variety of applications, including joint RGBD generation, dense depth prediction, depth-conditioned image generation, and coherent tile-based 3D panorama generation

Designing nano-aluminum laden fuel pump for aviation applications

In view of the fact that traditional liquid propellants cannot meet the design requirements of large-thrust flight vehicle, it has become a new trend to add nano-metal powder to liquid propellants to greatly increase density and specific impulse. In order to achieve the variable flow-rate and variable-proportion transportation of aviation fuel with nano-aluminum, a new type of solid-liquid mixing pumping system is designed, including powder conveying device, stirring device, pump and corresponding drive and transmission system. For the purpose of avoiding the frictional contact between the rotors, which will bring potential hazard to nano-aluminum powder, a non-contact twin-screw pump with synchronous gears is designed. Among them, based on the considerations of flow pulsation, volumetric efficiency and manufacturing difficulty, cycloid profile is adopted for screw rotors. After completing the functional design, geometric parameter design, structural design, 3D modeling, prototype manufacturing and preliminary performance estimation of the mixing pumping system, the performance of the screw feeder, agitator, screw pump was tested through experiments to meet the expected design requirements. The designed agitator can achieve sufficient mixing at 500 rpm in less than 10 s. Even though 50-micron clearances are designed with a relatively small rotor diameter, the volumetric efficiency of screw pump can reach above 50% when the discharge pressure is below 450 kPa and the flow rate is set as 10 L/min, the power of the screw pump is less than 700 W. This design facilitates the rapid real-time preparation of metallized propellants and provides a reference for further improving the design and control methods of nanoparticle two-phase flow pumping

Optimal design of screw and flow field analysis for twin-screw pump

A new solving transcendental equation method combining graphics and analyzing approach—Matlab Anonymous Function Method (MAFM) was developed to solve the screw contact line equation. The influence of contact line on screw sealing property was revealed and the effect of involute meshing angle on the screw contact line properties of type A twin-screw pump was investigated. The tooth profiles, spiral surface equations and contact line equations of type A twin-screw pump were obtained based on the optimal involute meshing angle. The three dimensional model and flow field digital model with modified involute meshing angle for screw rotors was developed to analyze the pressure field, velocity field and other screw pump’s characteristics by applying finite volume method, and the volumetric efficiency of flow channel for twin-screw pump study has been gained. The study provides the theoretical basis for the further screw pump development and property evaluation

Effect of PVA fibers on durability of nano-SiO2-reinforced cement-based composites subjected to wet-thermal and chloride salt-coupled environment

Marine engineering structures are often faced with complex environmental factors. It is the focus of current research to modify cement-based composites (CBCs) to achieve their high durability in complex environments such as seawater. In this study, the effect of polyvinyl alcohol (PVA) fibers on durability of nano-SiO2 (NS)-reinforced cement-based composites was investigated by simulating seawater environment and taking PVA fiber content as variable. In addition, based on the Weibull probability distribution model, the damage degree of NS and PVA fiber-reinforced cement-based composites (NFRCCs) subjected to wet-thermal and chloride salt-coupled environment (WTCSE) after 300 freeze–thawing cycles (FTCs) was predicted. The test results demonstrated that the NFRCC exhibited the most excellent durability subjected to WTCSE when the content of PVA fibers was 1.2%. Compared with the reference group only doped with NS subjected to WTCSE, its impermeability pressure increased by 150%, the chloride ion electric flux decreased by 31.71%, the compressive strength loss rate decreased by 19.00% after 125 FTC, and the compressive strength corrosion resistance coefficient of chloride salt erosion increased by 9.15% after 25 wetting–drying cycles. The predicted results of the Weibull probability distribution model indicated that the damage degree of NFRCC subjected to WTCSE after 300 FTC would not exceed 0.35. The microscopic test analysis showed that the incorporation of PVA fibers reduced the proportion of large pores and the overall porosity of NFRCC subjected to WTCSE. PVA fibers bridged microcracks while adsorbing NS and its hydration products, thus enhancing the adhesion of the substrate. This study provides a reference for the research of high-performance CBC in complex environment

Investigation of mechanical properties of PVA fiber-reinforced cementitious composites under the coupling effect of wet-thermal and chloride salt environment

In this study, the mechanical properties of polyvinyl alcohol fiber-reinforced cementitious composites (PVA-FRCC) under the coupling effect of wet thermal and chloride salt environment were investigated through a series of experiments, including compressive strength, flexural performance, elastic modulus, three-point bending fracture, and scanning electron microscope (SEM) tests. An environmental simulation test chamber was used to simulate the wet-thermal and chloride salt environment, in which the parameters of temperature, relative humidity (RH), mass fraction of the NaCl solution, and action time were determined to be 50 °C, 100%, 5%, and 30 d, respectively. The volume contents of the PVA fibers incorporated in the cementitious composites were 0, 0.3%, 0.6%, 0.9%, 1.2%, and 1.5%. The results indicated that the mechanical properties of the cementitious composites decreased after being subjected to the coupling effect of the wet-thermal and chloride salt environment. The incorporation of the PVA fibers improved the mechanical properties of the cementitious composites under the coupling effect of the wet-thermal and chloride salt environment. When the addition content of PVA fiber was approximately 0.6–0.9%, the mechanical performance of PVA-FRCC was the best. Compared with the cementitious composite without fibers, the maximum growth rates of the cube, axial and residual compressive strength, elastic modulus, and flexural strength of the PVA-FRCC under the coupling effect of the wet-thermal and chloride salt environment owing to the addition of PVA fiber reached 29.96%, 46.92%, 29.71%, 46.15%, and 67.06%, respectively. In particular, the 1.5% PVA fiber dosage increased the initiation and unstable fracture toughness, and fracture energy by 145.57%, 333.01%, and 2656.38%, respectively

Simulation of Hybrid WAAM and Rotation Compression Forming Process

Wire arc additive manufacturing (WAAM) has been studied and widely applied due to its high forming efficiency and low production cost. In the process of WAAM, there are problems of cracking, deformation, large residual stress, insufficient properties, and instability caused by repeated rapid heating and chilling. Welding with rotation compression can control the performance and shape synchronously in the semi-solidified state of the weld pool. In this study, a new solution of hybrid WAAM and rotation compression is presented using follow-up rotating device to form the weld bead layer by layer. Base on the finite element analysis result of the hybrid process, the force energy curve of the rotation forming metal flowing process is obtained by arranging history monitory points and paths on the bead, and the relationship between the shape and forming load is revealed. The simulation model was verified by forming tests on middle carbon steel.Mechanical Engineerin