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A techno-economic analysis was conducted to validate the commercial viability of commercial-scale lignocellulosic bio-butanol production by pretreatment and hydrolysis using concentrated sulfuric acid, and a continuous fermentation. The process was simulated using Aspen Plus®, and the data was based on the results of laboratory and pilot plant operation, and demonstration plant design. The production cost and minimum butanol selling price (MBSP) for a base case of 40,000 t/y plant capacity and 60 $/t bone dry based feedstock price are determined. In addition, sensitivity analysis on minimum butanol selling price (MBSP) is conducted by changing single parameter of discount cash flow rate of return (DCFROR) and by changing multiple parameters conjoint feedstock price (from 10 to 60 $/t in increments of 10 $/t), plant capacity (from 40,000 to 80,000 $/t in increments of 20,000 t/y), and equity rate (from 20 to 100% in increments of 20%). The MBSP was determined to be 2668 $/t at the base case, and 2139 $/t when capacity is doubled (from 40,000 t/y to 80,000 t/y). Production cost should be reduced approximately 55% in oder to secure commercial viability. This study suggests R&D and business strategies to enhance the commercial viability of lignocellulosic bio-butanol technology.

When using internal building materials, hazardous substances that affect indoor air quality are emitted from not only the material itself, but also from subsidiary materials such as construction adhesives. For wallpaper and flooring material, emissions from adhesives exceed the emissions from the material itself. Recently, functional building materials with absorption properties have been widely used in board products, inorganic paints and wall-papers. These materials remove chemical substances from indoor air through physical sorption or chemical reaction and control humidity through capillary condensation. These materials have been used as countermeasures to sick building syndrome. This study was conducted on inorganic paint, which is a functional building material that received New Excellent Technology certification for its moisture absorption and desorption performance. This moisture property affects the humidity in the indoor air and adsorbs formaldehyde and volatile organic chemicals. In this study, a construction convergence technology that can improve performance by at least 30% is proposed based on the principles of moisture absorption and desorption. The findings of this study confirmed the importance of convergence technology that organically interconnects manufacturing and construction technologies to improve and maintain the performance of functional building materials.

Ranking of efficient decision-making units (DMUs) using data envelopment analysis (DEA) results is very important for various purposes. We propose a new comprehensive ranking method using network analysis for efficient DMUs to improve the discriminating power of DEA. This ranking method uses a measure, namely dominance value, which is a network centrality-based indicator. Thus far, existing methods exploiting DMU&rsquo, s positional features use either the superiority, which considers the efficient DMUs&rsquo, relative position on the frontier compared to other DMUs, or the influence, which captures the importance of the DMUs&rsquo, role as benchmarking targets for inefficient DMUs. However, in this research, the dominance value is the compounded measure of both core positional features of DMUs. Moreover, a network representation technique has been used to ensure the performance of the dominance value compared to the superiority and influence. To demonstrate the proposed ranking method, we present two examples, research and development (R&amp, D) efficiency of small and medium-sized enterprises (SMEs) and technical efficiency of plug-in hybrid electric vehicles (HEVs). Through these two examples, we can see how the known weaknesses and the unobserved points in the existing method differ in this new method. Hence, it is expected that the proposed method provides another new meaningful ranking result that can show different implications.

Falls are the leading cause of fatal injuries in the elderly such as fractures, and secondary damage from falls can lead to death. As such, fall detection is a crucial topic. However, due to the trade-off relationship between privacy preservation, user convenience, and fall detection performance, it is generally difficult to develop a fall detection system that simultaneously satisfies all conditions. The main goal of this study is to build a practical fall detection framework that can effectively classify the various behavior types into &ldquo, Fall&rdquo, and &ldquo, Activities of daily living (ADL)&rdquo, while securing privacy preservation and user convenience. For this purpose, signal data containing the motion information of objects was collected using a non-contact, unobtrusive, and non-restraint impulse-radio ultra wideband (IR-UWB) radar. These data were then applied to a convolutional neural network (CNN) algorithm to create an object behavior type classifier that can classify the behavior types of objects into &ldquo, ADL.&rdquo, The data were collected by actually performing various activities of daily living, including falling. The performance of the classifier yielded satisfactory results. By combining an IR-UWB and CNN algorithm, this study demonstrates the feasibility of building a practical fall detection system that exceeds a certain level of detection accuracy while also ensuring privacy preservation and user convenience.

As quality of life has improved, the need for high-performance building materials that meet specific technological requirements has increased. Residential environments have also changed owing to climate change. A technology roadmap could define and systematically reflect a timeline for the development of future core technologies. The purpose of this research is to build a technology roadmap that could be utilized for the development of technology in the eco-friendly building material industry. This research is composed of multiple analysis processes—patent analysis, Delphi, and analytic hierarchy process analysis—that minimize the uncertainty caused by the lack of information in the eco-friendly construction industry by securing objective future forecast data. Subsequently, the quality function deployment test is implemented to verify the feasibility of the technology roadmap that is constructed. The design of various types of functional, low-carbon building materials could reduce carbon emissions and save energy by ensuring a hazardous-material-free market in the future. This design development roadmap is required to complement this technology roadmap.

Industrial revolution which is represented by specialization, standardization, and simplification significantly improves productivity, however it makes tasks in production line more simple and repetitive. This monotonous work environment affects most factory workers to be suffered from lack of motivation and boredom, so consequently makes workers to perceive their job unsatisfied and meaningless. We believe that gamification approach can make this tedious workplace more playful and motivating. In this context, a case study was conducted for a bolt-tightening task in the automotive assembly line. Especially, we explored our five-step design framework which can be useful as a basic procedure for the manufacturing gamification: (1) target system analysis; (2) goal and constraints identification; (3) concept generation; (4) concept evaluation; and (5) scenario development. Based on this design framework, a gamified interface for a bolt-tightening task was developed. The effectiveness of gamified interface was evaluated by lab-based experiment with semi-structured interview, and lessons learnt and related design suggestions are also dealt with.