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Factors affecting the seismic behavior of segmental precast bridge columns

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Frontiers of structural and Civil Engineering 2014年第4期8卷 388-398页

作者： Haitham DAWOOD Mohamed ELGAWADY joshua HEWEs Virginia Polytechnic Institute and state University Blacksburg VA 24061-0002 USA Department of Civil Architectural and Environmental Engineering Missouri University of Science and Technology Rolla MO65409 USA Department of Civil and Environmental Engineering Northern Arizona University Flagstaff AZ 86011 USA

This manuscript discusses the design parameters that potentially affect the lateral seismic response of segmental precast post-tensioned bridge piers. The piers consist ofprecast circular cross section segments stacked one on top of the other with concentric tendons passing through ducts made in the segments during casting. The bottommost segments of the piers were encased in steel tubes to enhance ductility and minimize damage. An FE model was used to investigate different design parameters and how they influence the lateral force - displacement response of the piers. Design parameters investigated included the initial post-tensioning stress as a percentage of the tendon yield stress, the applied axial stresses on concrete due to post-tensioning, pier aspect ratios, construction details, steel tube thicknesses, and internal mild steel rebar added as energy dissipaters. Based on the data presented, an initial tendon stress in the range of 40%-60% of its yield stress and initial axial stress on concrete of approximately 20% of the concrete＇s characteristic strength is appropriate for most typical designs. These design values will prevent tendon yielding until lateral drift angle reaches approximately 4.5%. Changing the steel tube thickness, height, or a combination of both proved to be an effective parameter that may be used to reach a target performance level at a specific seismic zone.

关键词： finite element analysis concrete precast units bridges

A perspective on environmental sustainability in the cement industry

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Waste Disposal and sustainable Energy 2020年第3期2卷 161-164页

作者： joshua O.Ighalo Adewale George Adeniyi Department of Chemical Engineering Faculty of Engineering and TechnologyUniversity of IlorinIlorin P.M.B.1515Nigeria

Cement production is a major contributor to anthropogenic CO_(2) emissions and solid waste *** paper discusses some of the key issues as regards the slow progress in achieving environmental sustainability in the cement ***-niques such as carbon capture and storage(CCs),material substitution,alternative fuels and energy efficient technologies have been proposed as means of mitigating the negative environmental impact of cement *** was observed that much progress has been made in the area of eco-friendly research *** innovations(considered as disruptive by the mega co-operations)have had issues at the entrepreneurial *** so-called disruptive technologies have strug-gled to meet some performance requirements and satisfy funding evaluators thus are still difficult to fully *** systematisation of the environmental sustainability problem was discussed as a possible way forward both in the environ-mental,economic and social *** into the future,a greater emphasis on systematised decision-based approach could help breach the gap between on-paper plans and practical implementations to achieve environmental sustainability.

关键词： Cement Environment Waste Global warming Pollution sustainability

Density estimation-based method to determine sample size for random sample partition of big data

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Frontiers of Computer science 2024年第5期18卷 57-70页

作者： Yulin HE Jiaqi CHEN Jiaxing sHEN Philippe FOURNIER-VIGER joshua Zhexue HUANG Guangdong Laboratory of Artificial Intelligence and Digital Economy(sZ) Shenzhen 518107China College of Computer science and software Engineering Shenzhen UniversityShenzhen 518060China Department of Computing and Decision sciences Lingnan UniversityHong Kong 999077China

Random sample partition(RsP)is a newly developed big data representation and management model to deal with big data approximate computation *** research and practical applications have confirmed that RsP is an efficient solution for big data processing and ***,a challenge for implementing RsP is determining an appropriate sample size for RsP data *** a large sample size increases the burden of big data computation,a small size will lead to insufficient distribution information for RsP data *** address this problem,this paper presents a novel density estimation-based method(DEM)to determine the optimal sample size for RsP data ***,a theoretical sample size is calculated based on the multivariate Dvoretzky-Kiefer-Wolfowitz(DKW)inequality by using the fixed-point iteration(FPI)***,a practical sample size is determined by minimizing the validation error of a kernel density estimator(KDE)constructed on RsP data blocks for an increasing sample ***,a series of persuasive experiments are conducted to validate the feasibility,rationality,and effectiveness of *** results show that(1)the iteration function of the FPI method is convergent for calculating the theoretical sample size from the multivariate DKW inequality;(2)the KDE constructed on RsP data blocks with sample size determined by DEM can yield a good approximation of the probability density function(p.d.f);and(3)DEM provides more accurate sample sizes than the existing sample size determination methods from the perspective of *** demonstrates that DEM is a viable approach to deal with the sample size determination problem for big data RsP implementation.

关键词： random sample partition big data sample size Dvoretzky-Kiefer-Wolfowitz inequality kerneldensity estimator probability density function

Mercantilism in the Trump Era

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Mercantilism in the Trump Era

作者： joshua Gilmore 山东大学

学位级别：硕士

United states President Donald Trump’s fiery rhetoric has increased the salience behind trade imbalances,pushing the United states towards newly-founded trade *** unilateral policy measures have been employed at unprecedented levels to ensure the competitiveness of struggling United states ***,this imposition of trade barriers flies in the face of traditional neo-liberal theories,and contrasting methods for employing said policy measures from previous instances suggests a systemic shift in economic philosophy for the United states going *** thesis seeks an answer to the forces behind this reorganization into a newly mercantilist sentiment,and suggests that this movement has its roots from before Trump’s *** systemic realization also promotes the idea that these trade measures are the new normal in American politics as the relative position of the United states is threatened by up and coming ***,the United states will seek to reorganize into a more regional structure,while reducing vulnerabilities to economic competitors brought about by an increased proliferation of interdependence.

关键词： Mercantilism Multilateral Institutions Interdependence China United states Trade War Tariffs Trump

Electrospun bundled carbon nanofibers for skin-inspired tactile sensing,proprioception and gesture tracking applications

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npj Flexible Electronics 2021年第1期5卷 242-255页

作者： Debarun sengupta joshua Romano Ajay Giri Prakash Kottapalli Department of Advanced Production Engineering(APE) Engineering and Technology Institute Groningen(ENTEG)University of GroningenGroningen 9747 AGThe Netherlands MIT sea Grant College Program Massachusetts Institute of Technology(MIT)77 Massachusetts AvenueNW98-151CambridgeMA 02139USA

In this work,we report a class of wearable,stitchable,and sensitive carbon nanofiber(CNF)-polydimethylsiloxane(PDMs)composite-based piezoresistive sensors realized by carbonizing electrospun polyacrylonitrile(PAN)nanofibers and subsequently embedding in PDMs elastomeric thin ***-mechanical tactile sensing characterization of the resulting piezoresistive strain sensors revealed a linear response with an average force sensitivity of~1.82 kN−1 for normal forces up to 20 *** real-time functionality of the CNF-PDMs composite sensors in wearable body sensor networks and advanced bionic skin applications was demonstrated through human motion and gesture monitoring experiments.A skin-inspired artificial soft sensor capable of demonstrating proprioceptive and tactile sensory perception utilizing CNF bundles has been ***,a 16-point pressure-sensitive flexible sensor array mimicking slow adapting low threshold mechanoreceptors of glabrous skin was *** devices in tandem with neuromorphic circuits can potentially recreate the sense of touch in robotic arms and restore somatosensory perception in amputees.

关键词： prop gesture artificial

Application of omics technologies for a deeper insight into quali-quantitative production traits in broiler chickens:A review

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Journal of Animal science and Biotechnology 2018年第4期9卷 751-768页

作者： Marco Zampiga joshua Flees Adele Meluzzi sami Dridi Federico sirri Department of Agricultural and Food sciences Alma Mater Studiorum-University of Bologna Center of Excellence for Poultry science University of Arkansas

The poultry industry is continuously facing substantial and different challenges such as the increasing cost of feed ingredients, the European Union’s ban of antibiotic as growth promoters, the antimicrobial resistance and the high incidence of muscle myopathies and breast meat abnormalities. In the last decade, there has been an extraordinary development of many genomic techniques able to describe global variation of genes, proteins and metabolites expression level. Proper application of these cutting-edge omics technologies(mainly transcriptomics, proteomics and metabolomics) paves the possibility to understand much useful information about the biological processes and pathways behind different complex traits of chickens. The current review aimed to highlight some important knowledge achieved through the application of omics technologies and proteo-genomics data in the field of feed efficiency, nutrition, meat quality and disease resistance in broiler chickens.

关键词： Broiler chicken Feed efficiency Meat quality Nutrition Omics technologies

screening outstanding mechanical properties and low lattice thermal conductivity using global attention graph neural network

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Energy and AI 2023年第4期14卷 360-370页

作者： joshua Ojih Alejandro Rodriguez Jianjun Hu Ming Hu Department of Mechanical Engineering University of South CarolinaSC 29208United States of America Department of Computer science and Engineering University of South CarolinaSC 29208United States of America

Mechanical and thermal properties of materials are extremely important for various engineering and scientific fields such as energy conversion and energy ***,the characterization of these properties via high throughput screening at the quantum level,although highly accurate,is inefficient and very time-and *** contrast,prediction at the classical level is highly efficient but less *** deploy scalable global attention graph neural network for accurate prediction of mechanical properties which bridge the gap between the accuracy at the quantum level and efficiency at the classical *** 10,158 elastic constants as training data,we trained the models on 5 mechanical properties,namely bulk modulus,shear modulus,Young’s modulus,Poisson’s ratio,and *** the trained model,we predicted 775,947 data in search of materials with ultrahigh *** further verify the recommended ultrahigh hardness materials by high precision first principles calculations,and we finally identify 20 structures with extreme hardness close to diamond,the hardest material in *** those,two super hard materials are completely new and have not been reported in literature so *** further recommend potential materials from bulk modulus prediction to search low lattice thermal conductivity,and we verify the thermal conductivity of 338 structures with first *** results demonstrate that one can find materials with extreme mechanical properties recommended by graph neural network and low thermal conductivity material from bulk modulus prediction with minimal first principles calculations of the structures(only 0.04%)in the large-scale materials pool.

关键词： Graph neural network Machine learning Mechanical properties Ultrahigh hardness Lattice thermal conductivity DFT calculations Novel material discovery

Artificial neural networks based on memristive devices

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science China(Information sciences) 2018年第6期61卷 145-158页

作者： Vignesh RAVICHANDRAN Can LI Ali BANAGOZAR J.joshua YANG Qiangfei XIA Department of Electrical and Computer Engineering University of Massachusetts

The advent of memristive devices and the continuing research and development in the field of neuromorphic computing show great potential as an alternative to traditional von Neumann computing and bring us ever closer to realizing a true "thinking machine". Novel neural network architectures and algorithms inspired by the brain are becoming more and more attractive to keep up with computing needs,relying on intrinsic parallelism and reduced power consumption to outperform more conventional computing methods. This article provides an overview of various neural networks with an emphasis on networks based on memristive emerging devices, with the advantages of memristor neural networks compared with pure complementary metal oxide semiconductor(CMOs) implementations. A general description of neural networks is presented, followed by a survey of prominent CMOs networks, and finally networks implemented using emerging memristive devices are discussed, along with the motivation for developing memristor based networks and the computational potential these networks possess.

关键词： neuromorphic computing memristors neural networks

Efficiently searching extreme mechanical properties via boundless objective-free exploration and minimal first-principles calculations

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npj Computational Materials 2022年第1期8卷 1336-1347页

作者： joshua Ojih Mohammed Al-Fahdi Alejandro David Rodriguez Kamal Choudhary Ming Hu Department of Mechanical Engineering University of South CarolinaColumbiaSC29208USA Materials science and Engineering Division National Institute of Standards and TechnologyGaithersburgMD20899USA

Despite the machine learning(ML)methods have been largely used recently,the predicted materials properties usually cannot exceed the range of original training *** deployed a boundless objective-free exploration approach to combine traditional ML and density functional theory(DFT)in searching extreme material *** combination not only improves the efficiency for screening large-scale materials with minimal DFT inquiry,but also yields properties beyond original training *** use stein novelty to recommend outliers and then verify using *** data are then added into the training dataset for next round *** test the loop of training-recommendation-validation in mechanical property *** screening 85,707 crystal structures,we identify 21 ultrahigh hardness structures and 11 negative Poisson’s ratio *** algorithm is very promising for future materials discovery that can push materials properties to the limit with minimal DFT calculations on only~1%of the structures in the screening pool.

关键词： extreme properties minimal