THE TECHNOLOGY FORECASTING OF NEW MATERIALS: THE EXAMPLE OF NANOSIZED CERAMIC POWDERS

New materials have been recognized as significant drivers for corporate growth and profitability in today’s fast changing environments. The nanosized ceramic powders played important parts in new materials field nowadays. However, little has been done in discussing the technology forecasting for the new materials development. Accordingly, this study applied the growth curve method to investigate the technology performances of nanosized ceramic powders. We adopted the bibliometric analysis through EI database and trademark office (USPTO) database to gain the useful data for this work. The effort resulted in nanosized ceramic powders were all in the initial growth periods of technological life cycles. The technology performances of nanosized ceramic powders through the EI and USPTO databases were similar and verified by each other. And there were parts of substitutions between traditional and nanosized ceramic powders. The bibliometric analysis was proposed as the simple and efficient tools to link the science and technology activities, and to obtain quantitative and historical data for helping researchers in technology forecasting, especially in rare historical data available fields, such as the new materials fields.new materials, bibliometric analysis, technology forecasting.

Electricity demand-side management for an energy efficient future in China : technology options and policy priorities

Thesis (Ph. D.)--Massachusetts Institute of Technology, Engineering Systems Division, 2005.Includes bibliographical references (p. 278-289).The main objective of this research is to identify robust technology and policy options which achieve substantial reductions in electricity demand in China's Shandong Province. This research utilizes a scenario-based approach to identify sensible and feasible energy efficiency and load reduction strategies. The research consists of technical analyses through the development of an hourly load simulation model to study the time and temperature sensitive impacts on electricity demand growth by different demand-side management (DSM) scenarios and a policy analysis to formulate policy priorities based on the socio-economic and environmental realities in China. This bottom-up comprehensive study helps inform decision-making given the technological, consumption and socio-economic conditions in large-scale electricity grid systems of Shandong and China, thus preferred DSM strategies are identified, and sensible policy recommendations are made with respect to Shandong province and China as a whole. This study developed a computer-based modeling tool for peak-load based electric demand analysis and long-term projections.(cont.) The model simulates disaggregated hourly electric loads by end-user types with temperature-sensitive load simulation capability, which takes into account time use patterns, life-style and behavioral factors, distributed consumption behaviors of electricity users, appliances and equipment utilization patterns, environmental factors, and industrial structural and operational parameters. The simulation and scenario based research methodology provides a comparative basis, and dynamic insights to electricity demand in areas when limited generation and consumption information is available, which is especially appropriate for electricity sector studies in developing countries. The research showed that demand side management strategies could result in significant reduction in the peak loads as well as the total electricity consumption in Shandong.(cont.) The results of the technical analysis concluded that (1) temperature sensitive load makes up the fastest growing demand within the entire consumption profile; (2) implementation of building energy efficiency strategies demonstrates the largest energy saving potential; (3) implementation of appliances standards, has limited effects on energy saving; (4) load management strategies to induce changes in consumption behaviors also shows great potential, however, they are difficult to estimate; and (5) urbanization policies also have a strong impact on electricity consumption. The recommended DSM policy priorities are based on the energy-saving potentials of the DSM strategies, which are listed in priority order: (1) improvement of building technology, (2) management of new installation first (3) management of temperature sensitive loads, (4) implementation of behavioral and load management strategies, (5) better management of urbanization policies (6) promotion of aggressive industrial motor substitution measures & industrial structural changes, and (6) improvement of appliance efficiency.(cont.) This research also formulated integrated DSM policy recommendations to the Chinese government that are centered by the development of coordinated DSM policy framework, and that are based upon the current technological, managerial and institutional capacities of Chinese industry and governmental agencies. The details include moving away from the traditional utility centered IRP/DSM framework, developing a robust energy efficiency services industry, setting correct DSM priorities and implementing them, developing and upgrading the domestic energy efficiency product industry, and engaging end-user participation. The thesis recognized the barriers and difficulties in the policy implementation and stressed the importance of continuous adaptation and institutional learning in the implementation process.by Chia-Chin Cheng.Ph.D

Root Coverage Procedure With Connective Tissue Graft Harvested From a Distal Wedge: A Case Report

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/141955/1/cap0134.pd

Distributed Training Large-Scale Deep Architectures

Scale of data and scale of computation infrastructures together enable the current deep learning renaissance. However, training large-scale deep architectures demands both algorithmic improvement and careful system configuration. In this paper, we focus on employing the system approach to speed up large-scale training. Via lessons learned from our routine benchmarking effort, we first identify bottlenecks and overheads that hinter data parallelism. We then devise guidelines that help practitioners to configure an effective system and fine-tune parameters to achieve desired speedup. Specifically, we develop a procedure for setting minibatch size and choosing computation algorithms. We also derive lemmas for determining the quantity of key components such as the number of GPUs and parameter servers. Experiments and examples show that these guidelines help effectively speed up large-scale deep learning training

Reliable Power Delivery System Design for Three-dimensional Integrated Circuits (3D ICs)

Three-dimensional integrated circuits (3D ICs) have drawn groundswell of interest in both academia and industry in recent years. However, the power integrity of 3D ICs is threatened by the increased current density brought by vertical integration. to enhance reliability, the locations of power/ground through-silicon-vias (P/G TSVs), which are used to deliver power/ground signals to different layers, must be carefully placed to minimize IR-drop. However, the currents in 3D ICs are not deterministic and exhibit both spatial and temporal correlations. in view of this, we propose a correlation based heuristic algorithm for P/G TSV placement. Unlike most existing works, the proposed algorithm does not need iterations of full-grid simulations. Thus, it is especially attractive for large designs with millions of nodes. Experimental results on TSMC 90nm industrial designs indicate that the proposed method can achieve up to 70% reduction in IR-drop compared with the current industry practice, which uniformly distributes P/G TSVs. © 2012 IEEE