On the Role of Contact Resistance and Electrode Modification in Organic Electrochemical Transistors

Contact resistance is renowned for its unfavorable impact on transistor performance. Despite its notoriety, the nature of contact resistance in organic electrochemical transistors (OECTs) remains unclear. Here, by investigating the role of contact resistance in n‐type OECTs, the first demonstration of source/drain‐electrode surface modification for achieving state‐of‐the‐art n‐type OECTs is reported. Specifically, thiol‐based self‐assembled monolayers (SAMs), 4‐methylbenzenethiol (MBT) and pentafluorobenzenethiol (PFBT), are used to investigate contact resistance in n‐type accumulation‐mode OECTs made from the hydrophilic copolymer P‐90, where the deliberate functionalization of the gold source/drain electrodes decreases and increases the energetic mismatch at the electrode/semiconductor interface, respectively. Although MBT treatment is found to increase the transconductance three‐fold, contact resistance is not found to be the dominant factor governing OECT performance. Additional morphology and surface energy investigations show that increased performance comes from SAM‐enhanced source/drain electrode surface energy, which improves wetting, semiconductor/metal interface quality, and semiconductor morphology at the electrode and channel. Overall, contact resistance in n‐type OECTs is investigated, whilst identifying source/drain electrode treatment as a useful device engineering strategy for achieving state of the art n‐type OECTs.     

Thermomechanical Framework for the Constitutive Modeling of Asphalt Concrete

This study is concerned with the constitutive modeling of asphalt concrete. Unlike most constitutive models for asphalt concrete that do not take into account the evolution of the microstructure of the material, this study incorporates the evolution of the microstructure by using a framework that recognizes that a body’s natural configurations can evolve as the microstructure changes. The general framework, on which this study is based, is cast within a full thermomechanical setting. In this paper, we develop models within the context of a mechanical framework that stems from the general framework for models based on the full thermodynamic framework and the resulting equations represent a nonlinear rate type viscoelastic model. The creep and stress relaxation experiments of Monismith and Secor are used for validating the efficacy of the model, and it is found that the predictions of the theory agree very well with the available experimental results. The advantages of using such a framework are many, especially when one wants to model the diverse mechanical and thermodynamic response characteristics of asphalt and asphalt concrete.     

X-ray and ion emission characteristics of plasmas ablated from solid materials using a high power Nd:Glass laser

X-ray and ion emissions from high temperature plasmas from solid targets with different atomic numbers have been studied. Plasma is generated using a high power Nd:Glass laser generating focused intensity in the range of 10¹² to 10¹³ Watts/cm² on targets. Plasma temperature is typically between 50 to 100 eV. X-ray emission scaling as a function of laser intensity as well as ion velocity has been measured in these targets. Non-uniform plasma expansion and generation of fast ions are observed for targets with higher atomic numbers.     

H-Infinity Static Output-feedback Control for Rotorcraft

The problem of stabilization of an autonomous rotorcraft platform in a hover configuration subject to external disturbances is addressed. Necessary and sufficient conditions are presented for static output-feedback control of linear time-invariant systems using the H-Infinity approach. Simplified conditions are given which only require the solution of two coupled matrix design equations. This paper also proposes a numerically efficient solution algorithm for the coupled design equations to determine the output-feedback gain. A major contribution is that an initial stabilizing gain is not needed. The efficacy of the control law and the disturbance accommodation properties are shown on a rotorcraft design example. The helicopter dynamics do not decouple as in the fixed-wing aircraft case, so that the design of helicopter flight controllers with a desirable intuitive structure is not straightforward. In this paper an output feedback approach is given that allows one to selectively close prescribed multivariable feedback loops using a reduced set of the states. Shaping filters are added that improve performance and yield guaranteed robustness and speed of response. This gives direct control over the design procedure and performance. Accurate identification of the System parameters is a challenging task for rotorcraft control, addition of loop shaping facilitates implementation engineers to counteract unmodeled high frequency dynamics. The net result yields control structures that have been historically accepted in the flight control community.     

Mapping of groundwater potential zones in the Musi basin using remote sensing data and GIS

The objective of this study is to explore the groundwater availability for agriculture in the Musi basin. Remote sensing data and geographic information system were used to locate potential zones for groundwater in the Musi basin. Various maps (i.e., base, hydrogeomorphological, geological, structural, drainage, slope, land use/land cover and groundwater prospect zones) were prepared using the remote sensing data along with the existing maps. The groundwater availability of the basin is qualitatively classified into different classes (i.e., very good, good, moderate, poor and nil) based on its hydrogeomorphological conditions. The land use/land cover map was prepared for the Kharif season using a digital classification technique with the limited ground truth for mapping irrigated areas in the Musi basin. The alluvial plain in filled valley, flood plain and deeply buried pediplain were successfully delineated and shown as the prospective zones of groundwater.     

Sequential Monte Carlo in reachability heuristics for probabilistic planning

Some of the current best conformant probabilistic planners focus on finding a fixed length plan with maximal probability. While these approaches can find optimal solutions, they often do not scale for large problems or plan lengths. As has been shown in classical planning, heuristic search outperforms bounded length search (especially when an appropriate plan length is not given a priori). The problem with applying heuristic search in probabilistic planning is that effective heuristics are as yet lacking.In this work, we apply heuristic search to conformant probabilistic planning by adapting planning graph heuristics developed for non-deterministic planning. We evaluate a straight-forward application of these planning graph techniques, which amounts to exactly computing a distribution over many relaxed planning graphs (one planning graph for each joint outcome of uncertain actions at each time step). Computing this distribution is costly, so we apply Sequential Monte Carlo (SMC) to approximate it. One important issue that we explore in this work is how to automatically determine the number of samples required for effective heuristic computation. We empirically demonstrate on several domains how our efficient, but sometimes suboptimal, approach enables our planner to solve much larger problems than an existing optimal bounded length probabilistic planner and still find reasonable quality solutions.     

Mining gene–sample–time microarray data: a coherent gene cluster discovery approach

Extensive studies have shown that mining microarray data sets is important in bioinformatics research and biomedical applications. In this paper, we explore a novel type of gene–sample–time microarray data sets that records the expression levels of various genes under a set of samples during a series of time points. In particular, we propose the mining of coherent gene clusters from such data sets. Each cluster contains a subset of genes and a subset of samples such that the genes are coherent on the samples along the time series. The coherent gene clusters may identify the samples corresponding to some phenotypes (e.g., diseases), and suggest the candidate genes correlated to the phenotypes. We present two efficient algorithms, namely the Sample-Gene Search and the Gene–Sample Search, to mine the complete set of coherent gene clusters. We empirically evaluate the performance of our approaches on both a real microarray data set and synthetic data sets. The test results have shown that our approaches are both efficient and effective to find meaningful coherent gene clusters. Daxin Jiang received the Ph.D. degree in computer science and engineering from the State University of New York at Buffalo in 2005. He received the B.S. degree in computer science from the University of Science and Technology of China. From 1998 to 2000, he was a M.S. student in Software Institute, Chinese Academy of Sciences. He is currently an assistant professor at the School of Computer Engineering, Nanyang Technology University, Singapore. His research interests include data mining, bioinformatics, machine learning, and information retrieval. Jian Pei received the Ph.D. degree in computing science from Simon Fraser University, Canada, in 2002, under Dr. Jiawei Han's supervision. He also received the B.Eng. and the M.Eng. degrees from Shanghai Jiao Tong University, China, in 1991 and 1993, respectively, both in Computer Science. He is currently an assistant professor of computing science at Simon Fraser University. His research interests include developing effective and efficient data analysis techniques for novel data intensive applications. He is currently interested in various techniques of data mining, data warehousing, online analytical processing, and database systems, as well as their applications in bioinformatics. His current research is supported in part by the Natural Sciences and Engineering Research Council of Canada (NSERC) and the National Science Foundation (NSF) of the United States. Since 2000, he has published over 70 research papers in refereed journals, conferences, and workshops, has served in the organization committees and the program committees of over 60 international conferences and workshops, and has been a reviewer for some leading academic journals. He is a member of the ACM, the ACM SIGMOD, and the ACM SIGKDD. Murali Ramanathan is an associate professor of pharmaceutical sciences and neurology. He received the B.Tech. (Honors) in chemical engineering from the Indian Institute of Technology, India, in 1983. After a 4-year stint in the chemical industry, he obtained the M.S. degree in chemical engineering from Iowa State University, Ames, IA, in 1987, and the Ph.D. degree in bioengineering from the University of California-San Francisco and University of California-Berkeley Joint Program in Bioengineering in 1994. Dr. Ramanathan research interests are primarily focused on the treatment of multiple sclerosis (MS), an inflammatory-demyelinating disease of the central nervous system that affects over 1 million patients worldwide. MS is a complex, variable disease that causes physical and cognitive disability and nearly 50% of patients diagnosed with MS are unable to walk after 15 years. The etiology and pathogenesis of MS remains poorly understood. Dr. Ramanathan's research interests include stochastic modeling of pharmaceutical systems and novel approaches to analyzing and using genetic and genomic data for improving patient care and optimizing therapy. Chuan Lin is currently a Ph.D. student in the Department of Computer Science and Engineering, State University of New York at Buffalo. She received the B.E. and the M.S. degrees in computer science and technology from Tsinghua University in China. Her research interests include bioinformatics, data mining, and machine learning. Chun Tang received the B.S. and M.S. degrees from Peking University, China, in 1996 and 1999, respectively, and the Ph.D. degree from State University of New York at Buffalo, USA, in 2005, all in computer science. Currently, she is a postdoctoral associate of Center for Medical Informatics, Yale University. Her research interests include bioinformatics, data mining, machine learning, database, and information retrieval. Aidong Zhang received the Ph.D. degree in computer science from Purdue University, West Lafayette, Indiana, in 1994. She was an assistant professor from 1994 to 1999, an associate professor from 1999 to 2002, and has been a professor since 2002 in the Department of Computer Science and Engineering at State University of New York at Buffalo. Her research interests include multimedia systems, content-based image retrieval, bioinformatics, and data mining. She is an author of over 140 research publications in these areas. Dr. Zhang's research has been funded by NSF, NIH, NIMA, and Xerox. Zhang serves on the editorial boards of International Journal of Bioinformatics Research and Applications (IJBRA), ACM Multimedia Systems, International Journal of Multimedia Tools and Applications, and International Journal of Distributed and Parallel Databases. She was the editor for ACM SIGMOD DiSC (Digital Symposium Collection) from 2001 to 2003. She was co-chair of the technical program committee for ACM Multimedia in 2001. She has also served on various conference program committees. Dr. Zhang is a recipient of the National Science Foundation CAREER award and SUNY Chancellor's Research Recognition award.     

GIS Approach of Delineation and Risk Assessment of Areas Affected by Arsenic Pollution in Drinking Water

The objective of this study is to analyze health effects of arsenic pollution of drinking water using a geographical information system (GIS). The paper reports the regional impact of arsenic contamination in six administrative blocks of the central part of the Murshidabad district, West Bengal, India. In this area about 1,248,580 people are exposed to arsenic pollution out of whom 388,316 people are exposed to arsenic concentrations above 0.05?mg/L, the WHO maximum permissible level of arsenic in drinking water. The study estimates that 65% of the total area of the six blocks has arsenic concentrations below 0.05?mg/L, 26.12% of the area has arsenic concentrations above 0.05?mg/L, and for the rest of the area no arsenic distribution data available. The total number of expected death cases has been estimated considering the percent of risk involved in a concentration range and corresponding total population using such water for drinking purpose. The analysis forecasts that 11,890 people may risk death due to arsenic pollution in the whole life span. The maximum number of death cases is expected in Domkal and Beldanga 1 blocks and the minimum number of death cases is expected in Block Bhagabangola 2. This study also reports a comparison between the theoretical expectation of death cases and actual reported arsenicosis cases for the Domkal block. The areas of theoretical expectation and the areas of actually reported cases match fairly well except in a few cases. The present study helps planning and implementing of priority-based arsenic mitigation options.     

In-Place Stabilization of Pond Ash Deposits by Hydrated Lime Columns

Abandoned coal ash ponds cover up vast stretches of precious land and cause environmental problems. Application of suitable in situ stabilization methods may bring about improvement in the geotechnical properties of the ash deposit as a whole, converting it to a usable site. In this study, a technique of in-place stabilization by hydrated lime columns was applied to large-scale laboratory models of ash ponds. Samples collected from different radial distances and different depths of the ash deposit were tested to study the improvements in the water content, dry density, particle size distribution, unconfined compressive strength, pH, hydraulic conductivity, and leachate characteristics over a period of one year. The in-place stabilization by lime column technique has been found effective in increasing the unconfined compressive strength and reducing hydraulic conductivity of pond ash deposits in addition to modifying other geotechnical parameters. The method has also proved to be useful in reducing the contamination potential of the ash leachates, thus mitigating the adverse environmental effects of ash deposits.