Hyperelastic, shape‐memorable, and ultra‐cell‐adhesive degradable polycaprolactone‐polyurethane copolymer for tissue regeneration

Novel polycaprolactone-based polyurethane (PCL-PU) copolymers with hyperelasticity, shape-memory, and ultra-cell-adhesion properties are reported as clinically applicable tissue-regenerative biomaterials. New isosorbide derivatives (propoxylated or ethoxylated ones) were developed to improve mechanical properties by enhanced reactivity in copolymer synthesis compared to the original isosorbide. Optimized PCL-PU with propoxylated isosorbide exhibited notable mechanical performance (50 MPa tensile strength and 1150% elongation with hyperelasticity under cyclic load). The shape-memory effect was also revealed in different forms (film, thread, and 3D scaffold) with 40%–80% recovery in tension or compression mode after plastic deformation. The ultra-cell-adhesive property was proven in various cell types which were reasoned to involve the heat shock protein-mediated integrin (α5 and αV) activation, as analyzed by RNA sequencing and inhibition tests. After the tissue regenerative potential (muscle and bone) was confirmed by the myogenic and osteogenic responses in vitro, biodegradability, compatible in vivo tissue response, and healing capacity were investigated with in vivo shape-memorable behavior. The currently exploited PCL-PU, with its multifunctional (hyperelastic, shape-memorable, ultra-celladhesive, and degradable) nature and biocompatibility, is considered a potential tissue- regenerative biomaterial, especially for minimally invasive surgery that requires small incisions to approach large defects with excellent regeneration capacity

Atomic Layer Deposition of Inorganic Thin Films on 3D Polymer Nanonetworks

Atomic layer deposition (ALD) is a unique tool for conformally depositing inorganic thin films with precisely controlled thickness at nanoscale. Recently, ALD has been used in the manufacture of inorganic thin films using a three-dimensional (3D) nanonetwork structure made of polymer as a template, which is pre-formed by advanced 3D nanofabrication techniques such as electrospinning, block-copolymer (BCP) lithography, direct laser writing (DLW), multibeam interference lithography (MBIL), and phase-mask interference lithography (PMIL). The key technical requirement of this polymer template-assisted ALD is to perform the deposition process at a lower temperature, preserving the nanostructure of the polymer template during the deposition process. This review focuses on the successful cases of conformal deposition of inorganic thin films on 3D polymer nanonetworks using thermal ALD or plasma-enhanced ALD at temperatures below 200 °C. Recent applications and prospects of nanostructured polymer–inorganic composites or hollow inorganic materials are also discussed

Sustainability in Higher Education: Perceptions of Social Responsibility among University Students

Many construction-related training and education programs in the United States have now embraced the concept of sustainability, offering sustainable construction courses that highlight sustainable design and construction practices. These courses have mainly focused on green building strategies for the design and construction of built environments and indoor environmental quality necessary for students’ knowledge enhancement and career development. This study examined the effect of sustainability course on students’ knowledge as well as their perceptions of social responsibility and sustainable behaviors. Data were collected by conducting a survey from construction related programs in U.S. universities. Students were categorized based on their experience of taking such course(s), and results were compared in terms of their environmental concerns, objective and subjective knowledge, and sustainable consumer behaviors by conducting independent two-sample t-tests. The purpose of this study was to examine sustainable behaviors and social responsibility perceptions among U.S. university students enrolled in construction-related courses. The results indicated that environmental concern and sustainable consumer behavior scores were significantly lower among students who had taken the course than those who had not. Both objective and subjective knowledge scores were relatively low. There was no difference between the two groups in objective knowledge scores and unexpectedly, subjective knowledge was significantly lower among students who had taken the course than those who had not. The findings of this study provide implications for sustainability curriculum development that can enhance students’ learning experience

A human error detection system in nuclear power plant operations

The most significant factor in nuclear power plant operations is safety. A lot of people in the nuclear industry have continued their unremitting efforts. After Three Miles Island accident, human factors came out into the open that it greatly contributes to the course of the accident of nuclear power plants. Thus, a lot of efforts have been made to reduce the human factor error. As nuclear power plant design developed, a new type of digitalized main control rooms has appeared, the conventional paper-based procedures have been left behind as backup. In advanced main control rooms (MCRs), computerized procedure system (CPS) is used to support human operators. Applying computer-based procedures in the main control room allows to reduce mental workload, enhance situation awareness, and produce lower errors of omission than paper-based procedure. However, current CPS does not yet utilize artificial intelligence technology. In order to reduce human errors, the framework which detects unsafe acts of human operators is suggested. The unsafe acts (UAs) detecting system implements Coloured Petri Nets, and deep neural networks to determine if an operating action is an error. The system uses two steps of filters to discover the effect of an operating action on the plant integrity

Comparison of Multilayer Perceptron and Long Short-Term Memory for Plant Parameter Trend Prediction

Human operators always have the possibility to commit human errors, and in safety-critical infrastructures such as a nuclear power plant, human error could cause serious consequences. Since nuclear plant operations involve highly complex and mentally taxing activities, especially in emergency situations, it is important to detect human errors to maintain plant safety. This work proposes a method to predict the future trends of important plant parameters to determine whether a performed action is an error or not. To achieve this prediction, a recursive strategy is adopted that employs an artificial neural network as its prediction model. Two artificial neural networks were selected and compared: multilayer perceptron and long short-term memory (LSTM). Model training was accomplished using emergency operation data from a nuclear power plant simulator. From the comparison results, it was observed that the future trends of plant parameters were quite accurately predicted through the LSTM model. It is expected that the plant parameter prediction function proposed in this work can give useful information for detecting and recovering human errors

Onshore Oil and Gas Design Schedule Management Process Through Time-Impact Simulations Analyses

Korean oil and gas contractors have recently incurred significant losses due to improper engineering performance on EPC (engineering procurement and construction) projects in overseas markets. Several previous studies have verified the significant impact engineering has on EPC construction cost and project lifecycle. However, no literature has studied the time impact engineering has on EPC projects, representing a gap in the existing body of knowledge. To fill this gap, a Monte Carlo simulation was performed with the Pertmaster, Primavera risk analysis software for three sample onshore oil and gas projects. From said simulation of all major EPC critical activities, the authors found that the engineering phase is up to 10 times as impactful as the procurement and construction phases on the overall schedule duration. In assessing the engineering activities, the authors found the piping design activities to have the greatest impact on the overall schedule performance. Using these findings, the authors present a design schedule management process which minimizes the delays of project completion in EPC projects. Said process includes the following six steps: (1) Milestone management, (2) drawing status management, (3) productivity management of engineering, (4) interface management, (5) management of major vendor documents, and (6) work front management. The findings of this paper add to the body of knowledge by confirming the design phase to be the most impactful on the overall project schedule success. Furthermore, the presented design schedule management will aid industry with successfully executing the design phase in a timely manner, including examples from case study projects for a greater understanding

A construction quality index for highway construction

Changes in policy regarding the use of contractor-conducted testing in quality assurance decisions have hit state highway agencies (SHAs) during a time of continuing reduction in agency personnel. These changes have increased the need for quality-driven contractors. This, coupled with more agencies adopting specifications tied to performance, places more requirements on contractors to emphasize quality management in their operations. There is a need for rational, comprehensive methods to evaluate a contractor's end product from a quality perspective; thus, there is a need for new techniques and approaches for examining and rating the quality of performance. Researchers and practitioners alike should benefit from this description of the construction quality index (CQI) developed through a grant from the Florida Department of Transportation. The CQI is a rating of the quality of materials and workmanship on highway projects that, unlike current quality rating models used by SHAs, is completely objective. Under limited validation testing, the model proved able to assign quality index values consistent with the owner's level of satisfaction with the overall project.Highways, pavement, analytical hierarchy process, quality,

A Framework for Guaranteed Maximum Price and Contingency Development for Integrated Delivery of Transportation Projects

This paper discusses the components of a guaranteed maximum price (GMP) and proposes a framework for the development of GMPs as contract payment provisions for construction manager-at-risk (CMR) and design-build (DB) contracts for transportation projects. The framework is the synthesis of a comprehensive literature review, a content analysis of CMR and DB solicitation documents and contracts, and case study project output from twelve projects in nine states worth $3.1 billion. The research also discusses the development of three common types of contingencies that are often utilized in projects with GMPs. The study concludes that owners should specify the structure of the GMP and its components to enhance clarity and understanding of the GMP’s composition. It recommends that this structure be included in the CMR and DB solicitation documents so that pricing proposals can be formulated in a manner that is consistent with the contract payment provisions that will be useful to practitioners that need to implement GMP-based contracts.This article is from Journal of Construction Engineering and Project Management 1 (2011): 1–10, doi:10.6106/JCEPM.2011.1.1.001. Posted with permission.</p