On Application of Carbon-Containing Electrode Materials in Technology of Electrospark Alloying: Part 1. Peculiarities of Coating Formation Using Electrospark Treatment of Titanium Alloy OT4-1

Electrospark treatment of OT4-1 titanium alloy was performed sequentially with a STIM-20N hard-alloy electrode (TiC–20% Ni) and carbon-containing material (graphite and carbon-based composite materials). Kinetics of the mass transfer of the hard-alloy electrode was studied. The cathode mass loss during the first minute of the treatment was established. The kinetics results were processed using the methods of mathematical statistics. The erosion resistance of the applied carbon-containing materials was determined. Phase composition and relief of the coatings formed were analyzed. It was found that the application of the carbon-containing material increases the content of refractory phases in the coatings. Increase in the time of the treatment using the carbon-containing materials decreases the roughness of the coatings.     

Medicine Meets Thermal Spray Technology: A Review of Patents

The patent literature concerning thermal spraying for biomedical applications is reviewed in this contribution. The patents were compiled from multiple databases search spanning the 2005-2018 period. For clarity and ease of reading, the results have been grouped into sections according to four individual material groups (apatites, titanium, oxide ceramics, other), with the secondary sorting criterion being related to the specific bioapplication areas (maxillofacial, orthopedic, methods). Lastly, the patents are grouped according to the selected thermal spray method within the individual subsections. In the paper, recent R&D trends in this field are further identified and briefly commented.     

Assessment of Dam Overtopping Reliability using SUFI Based Overtopping Threshold Curve

Estimation of earth-fill dam height and dimension of spillway are strongly depend on accurate evaluation of dam overtopping reliability. Ideally, whole random and effective variables on overtopping should be considered for dam overtopping reliability assessment. However, taking into account all affecting random variables as well as overtopping reliability assessment using algorithms such as Monte Carlo (MC) could be excessively time consuming and impossible in some cases. In this study, new approach has been introduced as conditional reliability method and it has been tried to reduce the simulations time significantly by using combination of rainfall threshold theory, SUFI (Sequential Uncertainty Fitting) and MC methods. According two defined indexes, the obtain results shown, the relative error of new approach in computation of Jamishan dam overtopping reliability is less than 0.23%. Also, time saving in new method against to regular MC method is more than 87%. So, the new introduced method has more efficiency with acceptable accuracy for assessment of dam overtopping reliability. In addition, among hydrological and hydraulic uncertainties, hydrological uncertainties have more effect on overtopping reliability.     

Reoptimization framework and policy analysis for maritime inventory routing under uncertainty

We study a maritime inventory routing problem, in which shipments between production and consumption nodes are carried out by a fleet of vessels. The vessels have specific capacities and can be chartered under different agreements. The inventory levels of all consumption nodes and some production nodes should be maintained within specified bounds; for the remaining production nodes, orders should be picked up within pre-defined time windows. We propose a discrete-time mixed-integer programming model. In the face of new information and uncertainty, this optimization model has to be re-solved, as the horizon is rolled forward. We discuss how to account for different sources of uncertainty. We present a rolling-horizon reoptimization framework that allows us to study different policies that impact the quality of the implemented solution, so we can identify the optimal set of policies.     

Food recognition using neural network classifier and multiple hypotheses image segmentation

ABSTRACT

This paper proposes the multiple-hypotheses image segmentation and feed-forward neural network classifier for food recognition to improve the performance. Initially, the food or meal image is given as input. Then, the segmentation is applied to identify the regions, where a particular food item is located using salient region detection, multi-scale segmentation, and fast rejection. Then, the features of every food item are extracted by the global feature and local feature extraction. After the features are obtained, the classification is performed for each segmented region using a feed-forward neural network model. Finally, the calorie value is computed with the aid of (i) food volume and (ii) calorie and nutrition measure based on mass value. The experimental results and performance evaluation are validated. The outcome of the proposed method attains 0.947 for Macro Average Accuracy (MAA) and 0.959 for Standard Accuracy (SA), which provides better classification performance.     

Quantum Sensing in a Physiological‐Like Cell Niche Using Fluorescent Nanodiamonds Embedded in Electrospun Polymer Nanofibers

Fluorescent nanodiamonds (fNDs) containing nitrogen vacancy (NV) centers are promising candidates for quantum sensing in biological environments. This work describes the fabrication and implementation of electrospun poly lactic‐co‐glycolic acid (PLGA) nanofibers embedded with fNDs for optical quantum sensing in an environment, which recapitulates the nanoscale architecture and topography of the cell niche. A protocol that produces uniformly dispersed fNDs within electrospun nanofibers is demonstrated and the resulting fibers are characterized using fluorescent microscopy and scanning electron microscopy (SEM). Optically detected magnetic resonance (ODMR) and longitudinal spin relaxometry results for fNDs and embedded fNDs are compared. A new approach for fast detection of time varying magnetic fields external to the fND embedded nanofibers is demonstrated. ODMR spectra are successfully acquired from a culture of live differentiated neural stem cells functioning as a connected neural network grown on fND embedded nanofibers. This work advances the current state of the art in quantum sensing by providing a versatile sensing platform that can be tailored to produce physiological‐like cell niches to replicate biologically relevant growth environments and fast measurement protocols for the detection of co‐ordinated endogenous signals from clinically relevant populations of electrically active neuronal circuits.     

Owner’s Risk Allocation and Contractor’s Role Behavior in a Project: A Parallel-mediation Model

Risk allocation decisions are of critical importance in project management. The present study proposes an explanation for how risk allocation in a contract motivates a contractor to cooperate with a project owner. Theories of risk allocation and trust were used to motivate the research. Using a survey methodology, we collected data concerning 124 construction projects in China. We found that risk allocation influenced the contractor’s role behavior through the contractor’s feeling of being trusted but not the contractor’s trust in the owner. Feeling of being trusted partially mediated the effect of risk allocation on the contractor’s in-role (i.e., contractual) behavior and fully mediated the effect on extra-role behavior. The study introduces a social and psychological view of the impacts of risk allocation to the project and engineering management literature. We contribute to theory by arguing and demonstrating the mediating effect of trust on the relationship between risk allocation and contractor behavior. From a practical standpoint, we conclude that contractual risk allocation has a significant impact on building a trusting relationship between owners and contractors and that contractors who feel trusted perform both contractually mandated actions and actions external to the contract more diligently, resulting in the likelihood of improved outcomes for both parties.     

Demonstration of a tool for automatic learning and re-use of knowledge in the activated sludge process.

Wastewater treatment plant operators encounter complex operational problems related to the activated sludge process and usually respond to these by applying their own intuition and by taking advantage of what they have learnt from past experiences of similar problems. However, previous process experiences are not easy to integrate in numerical control, and new tools must be developed to enable re-use of plant operating experience. The aim of this paper is to investigate the usefulness of a case-based reasoning (CBR) approach to apply learning and re-use of knowledge gained during past incidents to confront actual complex problems through the IWA/COST Benchmark protocol. A case study shows that the proposed CBR system achieves a significant improvement of the benchmark plant performance when facing a high-flow event disturbance.