Assessing the Remanufacturability of Office Furiniture: A Multi-Criteria Decision Making Approach

While the average life cycle of consumer goods is continuously decreasing, the amount of used product at their end-of-life (EOL) is accumulating fast at and at the same pace. Most EOL products end up in landfills, and many of which are not biodegradable. These two challenges have necessitated renewed global interest in product EOL management strategies by manufacturers, third party companies, consumers and governments. Remanufacturing is one of the EOL strategies which is highly environmental-friendly. Additionally, remanufacturing is seen as one of the highly profitable re-use business strategies. The selling price of remanufactured products is usually about 50—80% of a new one, making remanufacturing a win—win solution, saving both money and preserving the environment as well as raising the bottom-line of enterprises. Through the literature review of remanufacturing, we realize many researchers in this area have focused on a few product categories such as automotive, electrical and electronic equipment as well as ink cartridge, thus accelerating innovations for the remanufacture of these product categories. There is therefore, a need to explore the remanufaturability of other products, especially the ones with high market potential growth as well as profit margin. Furniture industry is the one that fits the description and is the focus of this thesis. The goal of this exploratory research is to present the first framework of its kind that aims at assessing the remanufacturability of office furniture. The proposed evaluation model considers three aspects of the assessment problem: economic, social and environmental to obtain a holistic view of remanufacturability of office furniture. We apply the fuzzy TOPSIS methodology to deal with incomplete and often subjective information during the evaluation. Furthermore, we validate our evaluation model using published research data for a multi-criteria allocation decision making (MCDM) problem. Through the model validation, we show that the proposed evaluation model has the capability to solve MCDM problems. Lastly, a case study which involves three pieces of office furniture is used to illustrate the function of the proposed model

Productivity Change in Taiwan's Farmers' Credit Unions: A Nonparametric Risk-Adjusted Malmquist Approach

This article proposes an extended three-stage DEA methodology similar to Fried et al. (2002) to improve the measurement of productivity growth then the assumption of free disposability of undesirable outpu t does not apply. A directional distance function is used to construct adjusted Malmquist-Luenberger productivity indexes which simultaneously account for the impacts of undesirable outputs, environmental variables, and statistical noise. Panel data for 264 farmers' credit unions (FCUs) in Taiwan covering the 1998-2000 period are employed to illustrate the advantages of this method. On average, the productivity of Taiwan's FCUs is found to have deteriorated over the 1998-2000 period. Although an improvement in efficiency has been observed, the major reason for the deterioration is found to be due to the regression of techno logy.Malmquist-Luenberger productivity index, three-stage DEA, undesirable outputs, directional distance function, Agricultural Finance, Productivity Analysis,

EPG2S: Speech Generation and Speech Enhancement based on Electropalatography and Audio Signals using Multimodal Learning

Speech generation and enhancement based on articulatory movements facilitate communication when the scope of verbal communication is absent, e.g., in patients who have lost the ability to speak. Although various techniques have been proposed to this end, electropalatography (EPG), which is a monitoring technique that records contact between the tongue and hard palate during speech, has not been adequately explored. Herein, we propose a novel multimodal EPG-to-speech (EPG2S) system that utilizes EPG and speech signals for speech generation and enhancement. Different fusion strategies based on multiple combinations of EPG and noisy speech signals are examined, and the viability of the proposed method is investigated. Experimental results indicate that EPG2S achieves desirable speech generation outcomes based solely on EPG signals. Further, the addition of noisy speech signals is observed to improve quality and intelligibility. Additionally, EPG2S is observed to achieve high-quality speech enhancement based solely on audio signals, with the addition of EPG signals further improving the performance. The late fusion strategy is deemed to be the most effective approach for simultaneous speech generation and enhancement.Comment: Accepted By IEEE Signal Processing Lette

Machine learning ensures rapid and precise selection of gold sea-urchin-like nanoparticles for desired light-to-plasmon resonance

Sustainable energy strategies, particularly solar-to-hydrogen production, are anticipated to overcome the global reliance on fossil fuels. Thereby, materials enabling the production of green hydrogen from water and sunlight are continuously designed,; e.g.; , ZnO nanostructures coated by gold sea-urchin-like nanoparticles, which employ the light-to-plasmon resonance to realize photoelectrochemical water splitting. But such light-to-plasmon resonance is strongly impacted by the size, the species, and the concentration of the metal nanoparticles coating on the ZnO nanoflower surfaces. Therefore, a precise prediction of the surface plasmon resonance is crucial to achieving an optimized nanoparticle fabrication of the desired light-to-plasmon resonance. To this end, we synthesized a substantial amount of metal (gold) nanoparticles of different sizes and species, which are further coated on ZnO nanoflowers. Subsequently, we utilized a genetic algorithm neural network (GANN) to obtain the synergistically trained model by considering the light-to-plasmon conversion efficiencies and fabrication parameters, such as multiple metal species, precursor concentrations, surfactant concentrations, linker concentrations, and coating times. In addition, we integrated into the model's training the data of nanoparticles due to their inherent complexity, which manifests the light-to-plasmon conversion efficiency far from the coupling state. Therefore, the trained model can guide us to obtain a rapid and automatic selection of fabrication parameters of the nanoparticles with the anticipated light-to-plasmon resonance, which is more efficient than an empirical selection. The capability of the method achieved in this work furthermore demonstrates a successful projection of the light-to-plasmon conversion efficiency and contributes to an efficient selection of the fabrication parameters leading to the anticipated properties

Set voltage distribution stabilized by constructing an oxygen reservoir in resistive random access memory

In this letter, the instability mechanism of RRAM was investigated, and a technique was developed to stabilize the distribution of high resistance state (HRS) and better concentrate the SET voltage. In previous research, we found that an interface-type switching characteristic was observed on the I-V curve beneath the filament-type switching behavior, owing to the oxygen accumulation effect. In this letter, this interface-type switching characteristic is used to fit the natural distribution of HRS for an analysis of the instability mechanism. According to the results, the reason for the HRS distribution is the accumulation of extra oxygen ions which are left over from a lower degree of oxygen and oxygen vacancy recombination during the reset process. We propose a solution which creates an extra oxygen reservoir by changing the surface topography of the electrode to store the surplus oxygen ions from the reset process, eliminating the accumulation effect, and indeed improving stability. Please click Additional Files below to see the full abstract

Mechanism of thermal field and electric field in resistive random access memory using the high/low-k side wall structure

In the Internet of things (IoT) era, low power consumption memory will be a critical issue for further device development. Among many kinds of next-generation memories, resistive random access memory (RRAM) is considered as having the most potential due to its high performance. To prevent unrecoverable hard break-down of a RRAM device, the RRAM should be collocated with a transistor for external current compliance. With decreasing device cell size, however, the operating voltage of the transistor will become smaller and smaller. Previous study has determined that the forming voltage of RRAM increases when device cell size is reduced, which is a very crucial issue especially when the device is scaled down. We have proposed a high-k sidewall spacer structure in RRAM to solve the dilemma of increasing forming voltages for device cell scaling down. Based on the COMSOL-simulated electrical field distributions in the high-k RRAM. In addition, thermal conductivity of sidewall spacer influenced resistive switching behavior. Suitable thermal conductivity of sidewall materials can enhance resistive switching behavior. Please click Additional Files below to see the full abstract