Silver carboxylate nanostructure nucleation and growth on AgBr crystals

The initial stage of the reaction between sodium stearate (NaSt) and AgNO(3) produces silver stearate (AgSt) micelles, [(C(18)H(35)O(2))(x)(Na(x-y))(Ag(y))(H(2)O)(z)], and aggregations of these AgSt micelles in the form of cubic pre-AgSt crystals. When cubic grains of 50?nm AgBr are added to the NaSt dispersion prior to the AgNO(3), the reaction proceeds to form the silver stearate micelles, but not the aggregation of those micelles. Instead, the {111} silver ion planes of the cubic AgBr crystal corners provide nucleation sites for silver stearate micelle deposition and crystal growth. After nucleation, the AgSt micelles evolve into nanostructured bud-like formations via an epitaxial interface on one or several corners of each AgBr cubic crystal. Over time, additional AgSt micelle deposition enables the buds to grow longer into strand-like structures, which then connect to form the beginnings of the ultimate silver stearate crystal plates.     

Productivity ratings of psychology programs based on publications in clinical journals.

Sought to identify the relative standing of clinical psychology programs by evaluating their frequency of publication in the major clinical journals. In addition, the relative contribution of university programs, hospital programs, medical centers, and other institutions to the clinical literature was investigated. It was found that although American Psychological Association (APA)-approved university clinical programs are responsible for the largest number of articles in the sample examined, (14 journals for the years 1975, 1976, and 1977), 6 of every 10 articles were published either by non-APA-approved academic programs or by individuals in other institutional settings. The general reputation of universities did not reflect their productivity in the clinical journals. The article includes an analysis of the major sources of publication in the clinical literature and an evaluation of the meaning of these findings for clinical psychology. (3 ref) (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Training opportunities in neuropsychology at APA-approved internship settings.

Survey data from 98 American Psychological Association (APA)-approved or conditionally APA-approved internship programs indicated that 86 offered training in neuropsychology. Of these, 30 indicated that they admitted students who wished to specialize in clinical neuropsychology. Information on the type of neuropsychological training involved, the length of training, type of tests given, and the number of cases seen by the student are reported. (4 ref) (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Algorithm design for a class of base station location problems in sensor networks

Base station placement has significant impact on sensor network performance. Despite its significance, results on this problem remain limited, particularly theoretical results that can provide performance guarantee. This paper proposes a set of procedure to design (1− ε) approximation algorithms for base station placement problems under any desired small error bound ε > 0. It offers a general framework to transform infinite search space to a finite-element search space with performance guarantee. We apply this procedure to solve two practical problems. In the first problem where the objective is to maximize network lifetime, an approximation algorithm designed through this procedure offers 1/ε² complexity reduction when compared to a state-of-the-art algorithm. This represents the best known result to this problem. In the second problem, we apply the design procedure to address base station placement problem when the optimization objective is to maximize network capacity. Our (1− ε) approximation algorithm is the first theoretical result on this problem. Yi Shi received his B.S. degree from University of Science and Technology of China, Hefei, China, in 1998, a M.S. degree from Institute of Software, Chinese Academy of Science, Beijing, China, in 2001, and a second M.S. degree from Virginia Tech, Blacksburg, VA, in 2003, all in computer science. He is currently working toward his Ph.D. degree in electrical and computer engineering at Virginia Tech. While in undergraduate, he was a recipient of Meritorious Award in International Mathematical Contest in Modeling and 1997 and 1998, respectively. His current research focuses on algorithms and optimizations for wireless sensor networks, wireless ad hoc networks, UWB-based networks, and SDR-based networks. His work has appeared in journals and highly selective international conferences (ACM Mobicom, ACM Mobihoc, and IEEE Infocom). Y. Thomas Hou received the B.E. degree from the City College of New York in 1991, the M.S. degree from Columbia University in 1993, and the Ph.D. degree from Polytechnic University, Brooklyn, New York, in 1998, all in Electrical Engineering. Since Fall 2002, he has been an Assistant Professor at Virginia Tech, the Bradley Department of Electrical and Computer Engineering, Blacksburg, VA. His current research interests are radio resource (spectrum) management and networking for software-defined radio wireless networks, optimization and algorithm design for wireless ad hoc and sensor networks, and video communications over dynamic ad hoc networks. From 1997 to 2002, Dr. Hou was a Researcher at Fujitsu Laboratories of America, Sunnyvale, CA, where he worked on scalable architectures, protocols, and implementations for differentiated services Internet, service overlay networking, video streaming, and network bandwidth allocation policies and distributed flow control algorithms. Prof. Hou is a recipient of an Office of Naval Research (ONR) Young Investigator Award (2003) and a National Science Foundation (NSF) CAREER Award (2004). He is a Co-Chair of Technical Program Committee of the Second International Conference on Cognitive Radio Oriented Wireless Networks and Communications (CROWNCOM 2007), Orlando, FL, August 1–3, 2007. He also was the Chair of the First IEEE Workshop on Networking Technologies for Software Defined Radio Networks, September 25, 2006, Reston, VA. Prof. Hou holds two U.S. patents and has three more pending. Alon Efrat earned his Bachelor in Applied Mathematics from the Technion (Israel’s Institute of Technology) in 1991, his Master in Computer Science from the Technion in 1993, and his Ph.D in Computer Science from Tel-Aviv University in 1998. During 1998–2000 he was a Post Doctorate Research Associate at the Computer Science Department of Stanford University, and at IBM Almaden Research Center. Since 2000, he is an assistant professor at the Computer Science Department of the University of Arizona. His main research areas are Computational Geometry, and its applications to sensor networks and medical imaging.     

Completeness in Two-Party Secure Computation: A Computational View

A Secure Function Evaluation (SFE) of a two-variable function f(·,·) is a protocol that allows two parties with inputs x and y to evaluate f(x,y) in a manner where neither party learns "more than is necessary". A rich body of work deals with the study of completeness for secure two-party computation. A function f is complete for SFE if a protocol for securely evaluating f allows the secure evaluation of all (efficiently computable) functions. The questions investigated are which functions are complete for SFE, which functions have SFE protocols unconditionally and whether there are functions that are neither complete nor have efficient SFE protocols. The previous study of these questions was mainly conducted from an information theoretic point of view and provided strong answers in the form of combinatorial properties. However, we show that there are major differences between the information theoretic and computational settings. In particular, we show functions that are considered as having SFE unconditionally by the combinatorial criteria but are actually complete in the computational setting. We initiate the fully computational study of these fundamental questions. Somewhat surprisingly, we manage to provide an almost full characterization of the complete functions in this model as well. More precisely, we present a computational criterion (called computational row non-transitivity) for a function f to be complete for the asymmetric case. Furthermore, we show a matching criterion called computational row transitivity for f to have a simple SFE (based on no additional assumptions). This criterion is close to the negation of the computational row non-transitivity and thus we essentially characterize all "nice" functions as either complete or having SFE unconditionally.     

Cost optimization of cross-laminated timber panels in one-way bending

European Journal of Wood and Wood Products - For the purposes of structural design, cross-laminated timber (CLT) panels are typically chosen from tables detailing standardized layups. While this is...