10,741 research outputs found

    Predicting construction productivity with machine learning approaches

    Get PDF
    Machine learning (ML) is a purpose technology already starting to transform the global economy and has the potential to transform the construction industry with the use of data-driven solutions to improve the way projects are delivered. Unrealistic productivity predictions cause increased delivery cost and time. This study shows the application of supervised ML algorithms on a database including 1,977 productivity measures that were used to train, test, and validate the approach. Deep neural network (DNN), k-nearest neighbours (KNN), support vector machine (SVM), logistic regression, and Bayesian networks are used for predicting productivity by using a subjective measure (compatibility of personality), together with external and site conditions and other workforce characteristics. A case study of a masonry project is discussed to analyse and predict task productivity

    Traffic agents for improving QoS in mixed infrastructure and ad hoc modes wireless LAN

    Get PDF
    As an important complement to infrastructured wireless networks, mobile ad hoc networks (MANET) are more flexible in providing wireless access services, but more difficult in meeting different quality of service (QoS) requirements for mobile customers. Both infrastructure and ad hoc network structures are supported in wireless local area networks (WLAN), which can offer high data-rate wireless multimedia services to the mobile stations (MSs) in a limited geographical area. For those out-of-coverage MSs, how to effectively connect them to the access point (AP) and provide QoS support is a challenging issue. By mixing the infrastructure and the ad hoc modes in WLAN, we propose in this paper a new coverage improvement scheme that can identify suitable idle MSs in good service zones as traffic agents (TAs) to relay traffic from those out-of-coverage MSs to the AP. The service coverage area of WLAN is then expanded. The QoS requirements (e.g., bandwidth) of those MSs are considered in the selection process of corresponding TAs. Mathematical analysis, verified by computer simulations, shows that the proposed TA scheme can effectively reduce blocking probability when traffic load is light

    Biological potential of polyethylene glycol (Peg)-functionalized graphene quantum dots in in vitro neural stem/progenitor cells

    Get PDF
    Stem cell therapy is one of the novel and prospective fields. The ability of stem cells to differentiate into different lineages makes them attractive candidates for several therapies. It is essential to understand the cell fate, distribution, and function of transplanted cells in the local microenvironment before their applications. Therefore, it is necessary to develop an accurate and reliable labeling method of stem cells for imaging techniques to track their translocation after transplantation. The graphitic quantum dots (GQDs) are selected among various stem cell labeling and tracking strategies which have high photoluminescence ability, photostability, relatively low cytotoxicity, tunable surface functional groups, and delivering capacity. Since GQDs interact easily with the cell and interfere with cell behavior through surface functional groups, an appropriate surface modification needs to be considered to get close to the ideal labeling nanoprobes. In this study, polyethylene glycol (PEG) is used to improve biocompatibility while simultaneously maintaining the photoluminescent potentials of GQDs. The biochemically inert PEG successfully covered the surface of GQDs. The PEG-GQDs composites show adequate bioimaging capabilities when internalized into neural stem/progenitor cells (NSPCs). Furthermore, the bio-inertness of the PEG-GQDs is confirmed. Herein, we introduce the PEG-GQDs as a valuable tool for stem cell labeling and tracking for biomedical therapies in the field of neural regeneration

    Mixed Matrix Carbon Molecular Sieve and Alumina (CMS-Al₂O₃) Membranes

    Get PDF
    This work shows mixed matrix inorganic membranes prepared by the vacuum-assisted impregnation method, where phenolic resin precursors filled the pore of a-alumina substrates. Upon carbonisation, the phenolic resin decomposed into several fragments derived from the backbone of the resin matrix. The final stages of decomposition (>650 degrees C) led to a formation of carbon molecular sieve (CMS) structures, reaching the lowest average pore sizes of similar to 5 angstrom at carbonisation temperatures of 700 degrees C. The combination of vacuum-assisted impregnation and carbonisation led to the formation of mixed matrix of CMS and a-alumina particles (CMS-Al2O3) in a single membrane. These membranes were tested for pervaporative desalination and gave very high water fluxes of up to 25 kg m(-2) h(-1) for seawater (NaCl 3.5 wt%) at 75 degrees C. Salt rejection was also very high varying between 93-99% depending on temperature and feed salt concentration. Interestingly, the water fluxes remained almost constant and were not affected as feed salt concentration increased from 0.3, 1 and 3.5 wt%

    Drop Traffic in Microfluidic Ladder Networks with Fore-Aft Structural Asymmetry

    Full text link
    We investigate the dynamics of pairs of drops in microfluidic ladder networks with slanted bypasses, which break the fore-aft structural symmetry. Our analytical results indicate that unlike symmetric ladder networks, structural asymmetry introduced by a single slanted bypass can be used to modulate the relative drop spacing, enabling them to contract, synchronize, expand, or even flip at the ladder exit. Our experiments confirm all these behaviors predicted by theory. Numerical analysis further shows that while ladder networks containing several identical bypasses are limited to nearly linear transformation of input delay between drops, mixed combination of bypasses can cause significant non-linear transformation enabling coding and decoding of input delays.Comment: 4 pages, 5 figure

    Tuning ultrafast electron thermalization pathways in a van der Waals heterostructure

    Get PDF
    Ultrafast electron thermalization - the process leading to Auger recombination, carrier multiplication via impact ionization and hot carrier luminescence - occurs when optically excited electrons in a material undergo rapid electron-electron scattering to redistribute excess energy and reach electronic thermal equilibrium. Due to extremely short time and length scales, the measurement and manipulation of electron thermalization in nanoscale devices remains challenging even with the most advanced ultrafast laser techniques. Here, we overcome this challenge by leveraging the atomic thinness of two-dimensional van der Waals (vdW) materials in order to introduce a highly tunable electron transfer pathway that directly competes with electron thermalization. We realize this scheme in a graphene-boron nitride-graphene (G-BN-G) vdW heterostructure, through which optically excited carriers are transported from one graphene layer to the other. By applying an interlayer bias voltage or varying the excitation photon energy, interlayer carrier transport can be controlled to occur faster or slower than the intralayer scattering events, thus effectively tuning the electron thermalization pathways in graphene. Our findings, which demonstrate a novel means to probe and directly modulate electron energy transport in nanoscale materials, represent an important step toward designing and implementing novel optoelectronic and energy-harvesting devices with tailored microscopic properties.Comment: Accepted to Nature Physic

    Detecting topological currents in graphene superlattices

    Get PDF
    This is the author accepted manuscript. The final version is available from AAAS via the DOI in this record.Topological materials may exhibit Hall-like currents flowing transversely to the applied electric field even in the absence of a magnetic field. In graphene superlattices, which have broken inversion symmetry, topological currents originating from graphene's two valleys are predicted to flow in opposite directions and combine to produce long-range charge neutral flow. We observed this effect as a nonlocal voltage at zero magnetic field in a narrow energy range near Dirac points at distances as large as several micrometers away from the nominal current path. Locally, topological currents are comparable in strength with the applied current, indicating large valley-Hall angles. The long-range character of topological currents and their transistor-like control by means of gate voltage can be exploited for information processing based on valley degrees of freedom.This work was supported by the European Research Council, the Royal Society, the National Science Foundation (STC Center for Integrated Quantum Materials, grant DMR‐1231319), Engineering & Physical Research Council (UK), the Office of Naval Research and the Air Force Office of Scientific Research

    Heat dissipation in atomic-scale junctions

    Full text link
    Atomic and single-molecule junctions represent the ultimate limit to the miniaturization of electrical circuits. They are also ideal platforms to test quantum transport theories that are required to describe charge and energy transfer in novel functional nanodevices. Recent work has successfully probed electric and thermoelectric phenomena in atomic-scale junctions. However, heat dissipation and transport in atomic-scale devices remain poorly characterized due to experimental challenges. Here, using custom-fabricated scanning probes with integrated nanoscale thermocouples, we show that heat dissipation in the electrodes of molecular junctions, whose transmission characteristics are strongly dependent on energy, is asymmetric, i.e. unequal and dependent on both the bias polarity and the identity of majority charge carriers (electrons vs. holes). In contrast, atomic junctions whose transmission characteristics show weak energy dependence do not exhibit appreciable asymmetry. Our results unambiguously relate the electronic transmission characteristics of atomic-scale junctions to their heat dissipation properties establishing a framework for understanding heat dissipation in a range of mesoscopic systems where transport is elastic. We anticipate that the techniques established here will enable the study of Peltier effects at the atomic scale, a field that has been barely explored experimentally despite interesting theoretical predictions. Furthermore, the experimental advances described here are also expected to enable the study of heat transport in atomic and molecular junctions, which is an important and challenging scientific and technological goal that has remained elusive.Comment: supporting information available in the journal web site or upon reques
    corecore