Improving efficiency of industrial maintenance with context aware adaptive authoring in augmented reality

Efficiency of industrial maintenance operation is significantly dependent on the skill and practice of the technicians involved. This paper demonstrates a novel approach to improve the maintenance efficiency through adaptive operational support using a context aware Augmented Reality (AR) technique that adapts with available data and the skill level of the technicians and without the need for prior working knowledge of AR. The AR system can be dynamically adapted by non-programmer maintenance technicians to improve the efficiency further

Quantifying Wetting Dynamics with Triboelectrification

Wetting is often perceived as an intrinsic surface property of materials, but determining its evolution is complicated by its complex dependence on roughness across the scales. The Wenzel state, where liquids have intimate contact with the rough substrate, and the Cassie-Baxter state, where liquids sit onto air pockets formed between asperities, are only two states among the plethora of wetting behaviors. Furthermore, transitions from the Cassie-Baxter to the Wenzel state dictate completely different surface performance, such as anti-contamination, anti-icing, drag reduction etc.; however, little is known about how transition occurs during time between the several wetting modes. In this paper, we show that wetting dynamics can be accurately quantified and tracked using solid-liquid triboelectrification. Theoretical underpinning reveals how surface micro-/nano-geometries regulate stability/infiltration, also demonstrating the generality of our theoretical approach in understanding wetting transitions.Comment: Both Main and SI uploaded in a single fil

Digitally-enhanced lubricant evaluation scheme for hot stamping applications

Digitally-enhanced technologies are set to transform every aspect of manufacturing. Networks of sensors that compute at the edge (streamlining information flow from devices and providing real-time local data analysis), and emerging Cloud Finite Element Analysis technologies yield data at unprecedented scales, both in terms of volume and precision, providing information on complex processes and systems that had previously been impractical. Cloud Finite Element Analysis technologies enable proactive data collection in a supply chain of, for example the metal forming industry, throughout the life cycle of a product or process, which presents revolutionary opportunities for the development and evaluation of digitally-enhanced lubricants, which requires a coherent research agenda involving the merging of tribological knowledge, manufacturing and data science. In the present study, data obtained from a vast number of experimentally verified finite element simulation results is used for a metal forming process to develop a digitally-enhanced lubricant evaluation approach, by precisely representing the tribological boundary conditions at the workpiece/tooling interface, i.e., complex loading conditions of contact pressures, sliding speeds and temperatures. The presented approach combines the implementation of digital characteristics of the target forming process, data-guided lubricant testing and mechanism-based accurate theoretical modelling, enabling the development of data-centric lubricant limit diagrams and intuitive and quantitative evaluation of the lubricant performance

Self-Compensating Liquid-Repellent Surfaces with Stratified Morphology.

Artificial liquid-repellent surfaces have recently attracted vast scientific attention; however, achieving mechanical robustness remains a formidable challenge before industrialization can be realized. To this end, inspired by plateaus in geological landscapes, a self-compensating strategy is developed to pave the way for the synthesis of durable repellent surfaces. This self-compensating surface comprises tall hydrophobic structural elements, which can repel liquid droplets. When these elements are damaged, they expose shorter structural elements that also suspend the droplets and thus preserve interfacial repellency. An example of this plateau-inspired stratified surface was created by three-dimensional (3D) direct laser lithography micro-nano fabrication. Even after being subjected to serious frictional damage, it maintained static repellency to water with a contact angle above 147° and was simultaneously able to endure high pressures arising from droplet impacts. Extending the scope of nature-inspired functional surfaces from conventional biomimetics to geological landscapes, this work demonstrates that the plateau-inspired self-compensating strategy can provide an unprecedented level of robustness in terms of sustained liquid repellency

Stock price forecasting over adaptive timescale using supervised learning and receptive fields

Pattern recognition in financial time series is not a trivial task, due to level of noise, volatile context, lack of formal definitions and high number of pattern variants. A current research trend involves machine learning techniques and online computing. However, medium-term trading is still based on human centric heuristics, and the integration with machine learning support remains relatively unexplored. The purpose of this study is to investigate potential and perspectives of a novel architectural topology providing modularity, scalability and personalization capabilities. The proposed architecture is based on the concept of Receptive Fields (RF), i.e., sub-modules focusing on specific patterns, that can be connected to further levels of processing to analyze the price dynamics on different granularities and different abstraction levels. Both Multilayer Perceptrons (MLP) and Support Vector Machines (SVM) have been experimented as a RF. Early experiments have been carried out over the FTSEMIB index

The Biological Impact of Concurrent Exposure to Metallic Nanoparticles and a Static Magnetic Field

The rapid advancement of technology has led to an exponential increase of both nanomaterial and magnetic field utilization in applications spanning a variety of sectors. While extensive work has focused on the impact of these two variables on biological systems independently, the existence of any synergistic effects following concurrent exposure has yet to be investigated. This study sought to ascertain the induced alterations to the stress and proliferation responses of the human adult low calcium, high temperature keratinocyte (HaCaT) cell line by the application of a static magnetic field (approximately 0.5 or 30 mT) in conjunction with either gold or iron oxide nanoparticles for a duration of 24 h. By evaluating targets at a cellular, protein, and genetic level a complete assessment of the HaCaT response was generated. A magnetic field-dependent proliferative effect was found (∼15%), which correlated with a decrease in reactive oxygen species and a simultaneous increase in ki67 expression, all occurring independently of nanoparticle presence. Furthermore, the application of a static magnetic field was able to counteract the cellular stress response induced by nanoparticle exposure through a combination of decreased reactive oxygen species production and modification of gene regulation. Therefore, we conclude that while these variables each introduce the potential to uniquely influence physiological events, no negative synergistic reactions were identified

Smart energy management and conversion

This chapter introduced power management circuits and energy storage unit designs for sub‐1 mW low power energy harvesting technologies, including indoor light energy harvesting, thermoelectric energy harvesting and vibration energy harvesting. The solutions address several of the problems associated with energy harvesting, power management and storage issues including low voltage operation, self‐start, efficiency (conversion efficiency as well as impact of power consumption of the power management circuit itself), energy density and leakage current levels. Additionally, efforts to miniaturize and integrate magnetic parts as well as integrate discrete circuits onto silicon are outlined to offer improvements in cost, size and efficiency. Finally initial results from efforts to improve energy density of storage devices using nanomaterials are introduced

Anomalous boundary behavior in non-newtonian fluids at amphiphobic surfaces

In this work, the effect of amphiphobic surfaces on the rheological behavior and boundary slip of the shear thickening fluids (STFs) was investigated. The experimental results suggested the viscosities were diminished, shear thickening was delayed and weakened, and an ultrahigh drag reduction was obtained. Furthermore, slip length was observed to vary with shear rate. Dissipative particle dynamics (DPD) simulations were adopted to further investigate these specific rheology and slip behavior. The simulation results conformed with experiments and established a linear relationship between the slip length and viscosity. We consider this study could be a conducive practical reference for the investigation of boundary slip in complex fluids and possibly a crucial protocol for analyzing STFs’ manipulation

Measurement of the Ratio of the Vector to Pseudoscalar Charm Semileptonic Decay Rate \Gamma(D+ > ANTI-K*0 mu+ nu)/\Gamma(D+ > ANTI-K0 mu+ nu)

Using a high statistics sample of photo-produced charm particles from the FOCUS experiment at Fermilab, we report on the measurement of the ratio of semileptonic rates \Gamma(D+ > ANTI-K pi mu+ nu)/\Gamma(D+ > ANTI-K0 mu+ nu)= 0.625 +/- 0.045 +/- 0.034. Allowing for the K pi S-wave interference measured previously by FOCUS, we extract the vector to pseudoscalar ratio \Gamma(D+ > ANTI-K*0 mu+ nu)/\Gamma(D+ > ANTI-K0 mu+ nu)= 0.594 +/- 0.043 +/- 0.033 and the ratio \Gamma(D+ > ANTI-K0 mu+ nu)/\Gamma(D+ > K- pi+ pi+)= 1.019 +/- 0.076 +/- 0.065. Our results show a lower ratio for \Gamma(D > K* \ell nu})/\Gamma(D > K \ell nu) than has been reported recently and indicate the current world average branching fractions for the decays D+ >ANTI-K0(mu+, e+) nu are low. Using the PDG world average for B(D+ > K- pi+ pi+) we extract B(D+ > ANIT-K0 mu+ nu)=(9.27 +/- 0.69 +/- 0.59 +/- 0.61)%.Comment: 15 pages, 1 figur

Search for Λc+→pK+π−\Lambda_c^+ \to p K^+ \pi^- and Ds+→K+K+π−D_s^+ \to K^+ K^+ \pi^- Using Genetic Programming Event Selection

We apply a genetic programming technique to search for the double Cabibbo suppressed decays $\Lambda_c^+ \to p K^+ \pi^-$ and $D_s^+ \to K^+ K^+ \pi^-$. We normalize these decays to their Cabibbo favored partners and find $\text{BR}($\Lambda_c^+ \to p K^+ \pi^-$)/\text{BR}($\Lambda_c^+ \to p K^- \pi^+$) = (0.05 \pm 0.26 \pm 0.02)$ and $\text{BR}($D_s^+ \to K^+ K^+ \pi^-$)/\text{BR}($D_s^+ \to K^+ K^- \pi^+$) = (0.52\pm 0.17\pm 0.11)$ where the first errors are statistical and the second are systematic. Expressed as 90% confidence levels (CL), we find $< 0.46$ and $< 0.78$ respectively. This is the first successful use of genetic programming in a high energy physics data analysis.Comment: 10 page