Facile preparation and strong adhesive strength of honeycomb polyurethane films with small pore diameter

With the continuous development of bionics, such as, geckos and virginia creeper with both superhydrophobic and super-adhesive, the surface wetting and super-adhesive properties of various porous materials have attracted extensive attention of the scientific and medical communities. Here, the honeycomb polyurethane (PU) porous films with strong adhesion were successfully prepared by microphase separation method and the effects of growth parameters on their microstructure and adhesive strength to ice were investigated. It was found that a high relative humidity (e.g., 100%) and a low solution concentration (e.g., 2%) facilitated the formation of ordered honeycomb PU porous films, and as-prepared PU pores with average pore diameter as small as 5 μm are better ordered and more uniform than these in related documents. Although the contact angle of water droplets on the surface of PU porous films increased from the premodification value of 85–130° to more than 160° after surface modification with polydopamine (PDA), the corresponding rolling angle remained approximately constant (180°), indicating that the surface of PU porous films has strong adhesion similar to geckos and virginia creeper. Furthermore, at lower temperature, the PU porous films exhibited the high adhesive strength of 142.13 kPa on ice, which was strongly dependent on the porous microstructures and surface compositions. The improved adhesive behavior to ice of honeycomb PU porous films modified with PDA provides new strategies for surface modification of materials and potential applications in medical domain.     

A Case Report of Germline Compound Heterozygous Mutations in the BRCA1 Gene of an Ovarian and Breast Cancer Patient

The germline carrier of the BRCA1 pathogenic mutation has been well proven to confer an increased risk of breast and ovarian cancer. Despite BRCA1 biallelic pathogenic mutations being extremely rare, they have been reported to be embryonically lethal or to cause Fanconi anemia (FA). Here we describe a patient who was a 48-year-old female identified with biallelic pathogenic mutations of the BRCA1 gene, with no or very subtle FA-features. She was diagnosed with ovarian cancer and breast cancer at the ages of 43 and 44 and had a strong family history of breast and gynecological cancers.     

Multimodal sensing and active continuous closed‐loop feedback for achieving reliable manipulation in the outdoor physical world

The use of field robots can greatly decrease the amount of time, effort, and associated risk compared to if human workers were to carryout certain tasks such as disaster response. However, transportability and reliability remain two main issues for most current robot systems. To address the issue of transportability, we have developed a lightweight modularizable platform named AeroArm. To address the issue of reliability, we utilize a multimodal sensing approach, combining the use of multiple sensors and sensor types, and the use of different detection algorithms, as well as active continuous closed‐loop feedback to accurately estimate the state of the robot with respect to the environment. We used Challenge 2 of the 2017 Mohammed Bin Zayed International Robotics Competition as an example outdoor manipulation task, demonstrating the capabilities of our robot system and approach in achieving reliable performance in the fields, and ranked fifth place internationally in the competition.     

Knowledge-Based Virtual Modeling and Simulation of Manufacturing Processes for Industry 4.0

Abstract

Industry 4.0 aims at providing a digital representation of a production landscape, but the challenges in building, maintaining, optimizing, and evolving digital models in inter-organizational production chains have not been identified yet in a systematic manner. In this paper, various Industry 4.0 research and technical challenges are addressed, and their present scenario is discussed. Moreover, in this article, the novel concept of developing experience-based virtual models of engineering entities, process, and the factory is presented. These models of production units, processes, and procedures are accomplished by virtual engineering object (VEO), virtual engineering process (VEP), and virtual engineering factory (VEF), using the knowledge representation technique of Decisional DNA. This blend of the virtual and physical domains permits monitoring of systems and analysis of data to foresee problems before they occur, develop new opportunities, prevent downtime, and even plan for the future by using simulations. Furthermore, the proposed virtual model concept not only has the capability of Query Processing and Data Integration for Industrial Data but also real-time visualization of data stream processing.     

Deployment of a cloud pipeline for real-time visual inspection using fast streaming high-definition images

We investigate the challenges of building an end-to-end cloud pipeline for real-time intelligent visual inspection system for use in automotive manufacturing. Current methods of visual detection in automotive assembly are highly labor intensive, and thus prone to errors. An automated process is sought that can operate within the real-time constraints of the assembly line and can reduce errors. Components of the cloud pipeline include capture of a large set of high-definition images from a camera setup at the assembly location, transfer and storage of the images as needed, execution of object detection, and notification to a human operator when a fault is detected. The end-to-end execution must complete within a fixed time frame before the next car arrives in the assembly line. In this article, we report the design, development, and experimental evaluation of the tradeoffs of performance, accuracy, and scalability for a cloud system.     

Multi-objective particle swarm optimisation based integrated production inventory routing planning for efficient perishable food logistics operations

Sustainable and efficient food supply chain has become an essential component of one’s life. The model proposed in this paper is deeply linked to people's quality of life as a result of which there is a large incentive to fulfil customer demands through it. This proposed model can enhance food quality by making the best possible food quality accessible to customers, construct a sustainable logistics system considering its environmental impact and ensure the customer demand to be fulfilled as fast as possible. In this paper, an extended model is examined that builds a unified planning problem for efficient food logistics operations where four important objectives are viewed: minimising the total expense of the system, maximising the average food quality along with the minimisation of the amount of CO₂ emissions in transportation along with production and total weighted delivery lead time minimisation. A four objective mixed integer linear programming model for intelligent food logistics system is developed in the paper. The optimisation of the formulated mathematical model is proposed using a modified multi-objective particle swarm optimisation algorithm with multiple social structures: MO-GLNPSO (Multi-Objective Global Local Near-Neighbour Particle Swarm Optimisation). Computational results of a case study on a given dataset as well as on multiple small, medium and large-scale datasets followed by sensitivity analysis show the potency and effectiveness of the introduced method. Lastly, there has been a scope for future study displayed which would lead to the further progress of these types of models.     

Estimates of pollutant temporal and spatial variability in commercial buildings from the joint urban 2003 field experiments

In this paper, we report on the indoor concentrations from a suite of full-scale outdoor tracer-gas point releases conducted in the downtown area of Oklahoma City in 2003. A point release experiment consisted of releases of sulfur hexafluoride (SF₆) in multiple buildings and from different outdoor locations. From the measurements, we are able to estimate the concentration variations indoors for a building operating under “typical” operating conditions. The mean indoor spatial coefficients of variation are 30% to 45% from a daytime outdoor release are around 80% during an outdoor evening release. Having estimates of the spatial coefficient of variation provides stakeholders, including first responders, with the likely range of concentrations in the building when little is known about the building characteristics and operating behavior, such as developing urban-scale hazard and consequence analyses. We show differences in indoor measurements at different distances to the release points, floors of the building, and heating, ventilation, and air conditioning system (HVAC) operation. We also show estimates at different time resolutions. The statistics show that in the studied medium to large commercial buildings, spatial differences would result in peak indoor concentrations in certain parts of the buildings that may be substantially higher than the building average. To our knowledge, very few tracer gas measurements have been conducted in buildings of this scope, particularly with measurements on multiple floors and within a floor. The resulting estimates of spatial variability provide a unique opportunity for hazard assessment, and comparison to multi-zone models.