Search results for: James Irvine

Authors: Nazrina Aziz, Dong Q. Wang

Abstract:

There have been various methods created based on the regression ideas to resolve the problem of data set containing censored observations, i.e. the Buckley-James method, Miller-s method, Cox method, and Koul-Susarla-Van Ryzin estimators. Even though comparison studies show the Buckley-James method performs better than some other methods, it is still rarely used by researchers mainly because of the limited diagnostics analysis developed for the Buckley-James method thus far. Therefore, a diagnostic tool for the Buckley-James method is proposed in this paper. It is called the renovated Cook-s Distance, (RD* i ) and has been developed based on the Cook-s idea. The renovated Cook-s Distance (RD* i ) has advantages (depending on the analyst demand) over (i) the change in the fitted value for a single case, DFIT* i as it measures the influence of case i on all n fitted values Yˆ∗ (not just the fitted value for case i as DFIT* i) (ii) the change in the estimate of the coefficient when the ith case is deleted, DBETA* i since DBETA* i corresponds to the number of variables p so it is usually easier to look at a diagnostic measure such as RD* i since information from p variables can be considered simultaneously. Finally, an example using Stanford Heart Transplant data is provided to illustrate the proposed diagnostic tool.

Keywords: Buckley-James estimators, censored regression, censored data, diagnostic analysis, product-limit estimator, renovated Cook's Distance.

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Authors: Darshana Thomas, Edward Wilkie, James Irvine

Abstract:

The Internet of things (IoT) is currently a highly researched topic, especially within the context of the smart home. These are small sensors that are capable of gathering data and transmitting it to a server. The majority of smart home products use protocols such as ZigBee or Bluetooth Low Energy (BLE). As these small sensors are increasing in number, the need to implement these with much more capable and ubiquitous transmission technology is necessary. The high power consumption is the reason that holds these small sensors back from using other protocols such as the most ubiquitous form of communication, WiFi. Comparing the power consumption of existing transmission technologies to one with WiFi inbuilt, would provide a better understanding for choosing between these technologies. We have developed a small IoT device with WiFi capability and proven that it is much more efficient than the first protocol, 433 MHz. We extend our work in this paper and compare WiFi power consumption with the other most widely used protocol BLE. The experimental results in this paper would conclude whether the developed prototype is capable in terms of power consumption to replace the existing protocol BLE with WiFi.

Keywords: Bluetooth, internet of things, power consumption, WiFi.

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Authors: Maria E. de Boyrie, Darlene Nelson, James A. Nelson

Abstract:

The history of money is described in relationship to the history of computing. With the transformation and acceptance of money as information, major challenges to the security of money have involved engineering, computer science, and management. Research opportunities and challenges are described as money continues its transformation into information.

Keywords: Electronic, information, money, risk.

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Authors: Kasthurirangan Gopalakrishnan, Siddhartha Khaitan, Anshu Manik

Abstract:

Cluster analysis is the name given to a diverse collection of techniques that can be used to classify objects (e.g. individuals, quadrats, species etc). While Kohonen's Self-Organizing Feature Map (SOFM) or Self-Organizing Map (SOM) networks have been successfully applied as a classification tool to various problem domains, including speech recognition, image data compression, image or character recognition, robot control and medical diagnosis, its potential as a robust substitute for clustering analysis remains relatively unresearched. SOM networks combine competitive learning with dimensionality reduction by smoothing the clusters with respect to an a priori grid and provide a powerful tool for data visualization. In this paper, SOM is used for creating a toroidal mapping of two-dimensional lattice to perform cluster analysis on results of a chemical analysis of wines produced in the same region in Italy but derived from three different cultivators, referred to as the “wine recognition data" located in the University of California-Irvine database. The results are encouraging and it is believed that SOM would make an appealing and powerful decision-support system tool for clustering tasks and for data visualization.

Keywords: Artificial neural networks, cluster analysis, Kohonen maps, wine recognition.

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Authors: James E. Ponder

Abstract:

The requirement for consistency in physics can sometimes offer a common ground between disciplines such that their fundamental equations share a common parameter set and mathematical method for equation extraction. The parameter set shared by Relativity and Quantum Wave Mechanics enables an analysis which will be seen to be very straightforward, primarily classical in nature using linear algebra concepts, yet deriving a theoretical estimate of the value of the Gravitational Constant along with dependencies never before known.

Keywords: Gravitational Constant, Physical Consistency, Quantum Mechanics, Relativity.

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Authors: Cuneyt Yucelbas, Seral Ozsen, Sule Yucelbas, Gulay Tezel

Abstract:

Due to the fact that there exist only a small number of complex systems in artificial immune system (AIS) that work out nonlinear problems, nonlinear AIS approaches, among the well-known solution techniques, need to be developed. Gaussian function is usually used as similarity estimation in classification problems and pattern recognition. In this study, diagnosis of breast cancer, the second type of the most widespread cancer in women, was performed with different distance calculation functions that euclidean, gaussian and gaussian-euclidean hybrid function in the clonal selection model of classical AIS on Wisconsin Breast Cancer Dataset (WBCD), which was taken from the University of California, Irvine Machine-Learning Repository. We used 3-fold cross validation method to train and test the dataset. According to the results, the maximum test classification accuracy was reported as 97.35% by using of gaussian-euclidean hybrid function for fold-3. Also, mean of test classification accuracies for all of functions were obtained as 94.78%, 94.45% and 95.31% with use of euclidean, gaussian and gaussian-euclidean, respectively. With these results, gaussian-euclidean hybrid function seems to be a potential distance calculation method, and it may be considered as an alternative distance calculation method for hard nonlinear classification problems.

Keywords: Artificial Immune System, Breast Cancer Diagnosis, Euclidean Function, Gaussian Function.

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Authors: James A. Nelson

Abstract:

The history of technology and banking is examined as it relates to risk and technological determinism. It is proposed that the services that banks offer are determined by technology and that banks must adopt new technologies to be competitive. The adoption of technologies paradoxically forces the adoption of other new technologies to protect the bank from the increased risk of technology. This cycle will lead to bank examiners and regulators to focus on human behavior, not on the ever changing technology.

Keywords: Banking, information security, risk, technologicaldeterminism.

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Authors: Ching-Shoei Chiang, Sheng-Hsin Tsai, James Chen

Abstract:

The PH curve can be constructed by given parameters, but the shape of the curve is not so easy to image from the value of the parameters. On the contract, Bézier curve can be constructed by the control polygon, and from the control polygon, we can image the figure of the curve. In this paper, we want to use the hodograph of Bézier curve to construct PH curve by selecting part of the control vectors, and produce other control vectors, so the property of PH curve exists.

Keywords: PH curve, hodograph, Bézier curve.

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Authors: Mohsen Zahraei

Abstract:

In this paper, the notion of rank−k numerical range of rectangular complex matrix polynomials are introduced. Some algebraic and geometrical properties are investigated. Moreover, for Є > 0, the notion of Birkhoff-James approximate orthogonality sets for Є−higher rank numerical ranges of rectangular matrix polynomials is also introduced and studied. The proposed definitions yield a natural generalization of the standard higher rank numerical ranges.

Keywords: Rank−k numerical range, isometry, numerical range, rectangular matrix polynomials.

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Authors: Assad Maalouf, Lunjin Lu, James Lynott

Abstract:

We design and implement a precise model of string operations using finite state machine transformers and state transformers to approximate the values string variables can take throughout the execution of the program.We use our model to analyze Android program string variables. Our experimental results show that our string analysis is very efficient at detecting the contextual effect of string operations on the string variables. Our model proved to be very useful when it came to verifying statements about the string variables of the program.

Keywords: Abstract interpretation, android, static analysis, string analysis.

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Authors: Jimeng Shi, Mahek Jain, Giri Narasimhan

Abstract:

Time Series Forecasting (TSF) is used to predict the target variables at a future time point based on the learning from previous time points. To keep the problem tractable, learning methods use data from a fixed length window in the past as an explicit input. In this paper, we study how the performance of predictive models change as a function of different look-back window sizes and different amounts of time to predict into the future. We also consider the performance of the recent attention-based transformer models, which had good success in the image processing and natural language processing domains. In all, we compare four different deep learning methods (Recurrent Neural Network (RNN), Long Short-term Memory (LSTM), Gated Recurrent Units (GRU), and Transformer) along with a baseline method. The dataset (hourly) we used is the Beijing Air Quality Dataset from the website of University of California, Irvine (UCI), which includes a multivariate time series of many factors measured on an hourly basis for a period of 5 years (2010-14). For each model, we also report on the relationship between the performance and the look-back window sizes and the number of predicted time points into the future. Our experiments suggest that Transformer models have the best performance with the lowest Mean   Absolute Errors (MAE = 14.599, 23.273) and Root Mean Square Errors (RSME = 23.573, 38.131) for most of our single-step and multi-steps predictions. The best size for the look-back window to predict 1 hour into the future appears to be one day, while 2 or 4 days perform the best to predict 3 hours into the future.

Keywords: Air quality prediction, deep learning algorithms, time series forecasting, look-back window.

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Authors: James Meernik, Joseph Ignagni, Rebecca Deen

Abstract:

This paper analyzes the extent to which the justices of the U.S. Supreme Court cast votes that support the positions of the president, or more generally the Executive Branch. Can presidents count on such deference from those justices they nominate or those whom are nominated by other presidents of the same party? Or, do the justices demonstrate judicial independence and impartiality such that they are not so predisposed to vote in favor of arguments of their nominating president-s party? The results suggest that while in general the justices do not exhibit any marked tendency to partisan support of presidents, more recent and conservative Supreme Court justices are significantly more likely to support Republican presidents.

Keywords: Separation of Powers, Solicitor General, U.S. President

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Authors: Ya Hong Dong, Conrad T.C. Wong, S. Thomas Ng, James M.W. Wong

Abstract:

Precast concrete has been widely adopted in public housing construction of Hong Kong since the mid-1980s. While pre-casting is considered an environmental friendly solution, there is lack of study to investigate the life cycle performance of precast concrete units. This study aims to bridge the knowledge gap by providing a comprehensive life cycle assessment (LCA) study for two precast elements namely façade and bathroom. The results show that raw material is the most significant contributor of environmental impact accounting for about 90% to the total impact. Furthermore, human health is more affected by the production of precast concrete than the ecosystems.

Keywords: Environment, green, LCA, LCIA, precast.

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Authors: Abeer Aljohani

Abstract:

The COVID-19 virus spread has been one of the extreme pandemics across the globe. It is also referred as corona virus which is a contagious disease that continuously mutates into numerous variants. Currently, the B.1.1.529 variant labeled as Omicron is detected in South Africa. The huge spread of COVID-19 disease has affected several lives and has surged exceptional pressure on the healthcare systems worldwide. Also, everyday life and the global economy have been at stake. Numerous COVID-19 cases have produced a huge burden on hospitals as well as health workers. To reduce this burden, this paper predicts COVID-19 disease based on the symptoms and medical history of the patient. As machine learning is a widely accepted area and gives promising results for healthcare, this research presents an architecture for COVID-19 detection using ML techniques integrated with feature dimensionality reduction. This paper uses a standard University of California Irvine (UCI) dataset for predicting COVID-19 disease. This dataset comprises symptoms of 5434 patients. This paper also compares several supervised ML techniques on the presented architecture. The architecture has also utilized 10-fold cross validation process for generalization and Principal Component Analysis (PCA) technique for feature reduction. Standard parameters are used to evaluate the proposed architecture including F1-Score, precision, accuracy, recall, Receiver Operating Characteristic (ROC) and Area under Curve (AUC). The results depict that Decision tree, Random Forest and neural networks outperform all other state-of-the-art ML techniques. This result can be used to effectively identify COVID-19 infection cases.

Keywords: Supervised machine learning, COVID-19 prediction, healthcare analytics, Random Forest, Neural Network.

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Authors: Assad Maalouf, Lunjin Lu, James Lynott

Abstract:

We present a taint analysis that can automatically detect when string operations result in a string that is free of taints, where all the tainted patterns have been removed. This is an improvement on the conservative behavior of previous taint analyzers, where a string operation on a tainted string always leads to a tainted string unless the operation is manually marked as a sanitizer. The taint analysis is built on top of a string analysis that uses finite state automata to approximate the sets of values that string variables can take during the execution of a program. The proposed approach has been implemented as an extension of FlowDroid and experimental results show that the resulting taint analyzer is much more precise than the original FlowDroid.

Keywords: Android, static analysis, string analysis, taint analysis.

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Authors: James D. Clark, Bernard H. Stark

Abstract:

The paper presents a modelling methodology for small scale multi-source renewable energy systems. Using historical site-specific weather data, the relationships of cost, availability and energy form are visualised as a function of the sizing of photovoltaic arrays, wind turbines, and battery capacity. The specific dependency of each site on its own particular weather patterns show that unique solutions exist for each site. It is shown that in certain cases the capital component cost can be halved if the desired theoretical demand availability is reduced from 100% to 99%.

Keywords: Energy Analysis, Forecasting, Distributed powergeneration.

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Authors: James A Foster, Guglielmo S Aglietti

Abstract:

Multifunctional structures are a potentially disruptive technology that allows for significant mass savings on spacecraft. The specific concept addressed herein is that of a multifunctional power structure. In this paper, a parametric optimisation of the design of such a structure that uses commercially available battery cells is presented. Using numerical modelling, it was found that there exists several trade-offs aboutthe conflict between the capacity of the panel and its mechanical properties. It was found that there is no universal optimal location for the cells. Placing them close to the mechanical interfaces increases loading in the mechanically weak cells whereas placing them at the centre of the panel increases the stress inthe panel and reduces the stiffness of the structure.

Keywords: Design Optimization, Multifunctional Structures, Power Storage.

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Authors: Adam Sobey, James Blake, Ajit Shenoi

Abstract:

The leisure boatbuilding industry has tight profit margins that demand that boats are created to a high quality but with low cost. This requirement means reduced design times combined with increased use of design for production can lead to large benefits. The evolutionary nature of the boatbuilding industry can lead to a large usage of previous vessels in new designs. With the increase in automated tools for concurrent engineering within structural design it is important that these tools can reuse this information while subsequently feeding this to designers. The ability to accurately gather this materials and parts data is also a key component to these tools. This paper therefore aims to develop an architecture made up of neural networks and databases to feed information effectively to the designers based on previous design experience.

Keywords:

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Authors: James K. Ho

Abstract:

We present a prototype interactive (hyper) map of strategic, tactical, and logistic options for Supply Chain Management. The map comprises an anthology of options, broadly classified within the strategic spectrum of efficiency versus responsiveness, and according to logistic and cross-functional drivers. They are exemplified by cases in diverse industries. We seek to get all these information and ideas organized to help supply chain managers identify effective choices for specific business environments. The key and innovative linkage we introduce is the configuration of competitive forces. Instead of going through seemingly endless and isolated cases and wondering how one can borrow from them, we aim to provide a guide by force comparisons. The premise is that best practices in a different industry facing similar forces may be a most productive resource in supply chain design and planning. A prototype template is demonstrated.

Keywords: Competitive forces, strategic innovation, supplychain management.

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Authors: Toshihiko Matsuka, James E. Corter

Abstract:

Most neural network (NN) models of human category learning use a gradient-based learning method, which assumes that locally-optimal changes are made to model parameters on each learning trial. This method tends to under predict variability in individual-level cognitive processes. In addition many recent models of human category learning have been criticized for not being able to replicate rapid changes in categorization accuracy and attention processes observed in empirical studies. In this paper we introduce stochastic learning algorithms for NN models of human category learning and show that use of the algorithms can result in (a) rapid changes in accuracy and attention allocation, and (b) different learning trajectories and more realistic variability at the individual-level.

Keywords: category learning, cognitive modeling, radial basis function, stochastic optimization.

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Authors: Dongkoo Yun, Melissa James-MacEachern

Abstract:

This study analyzes Americans’ views of four Atlantic Canadian provinces as travel destinations regarding specific destination attributes for a pleasure trip, awareness (heard) of the destinations, past visit to the destinations during the prior two years, and intention to visit in the next two years. Results indicate that American travellers perceived the four Atlantic Canadian provinces as separate and distinct when rating best-fit destination attributes to each destination. The results suggest that travel destinations, specifically the four selected destinations, must be prepared to differentiate their destination’s image and the range of experiences and services to appeal and attract more American travellers.

Keywords: Atlantic Canadian provinces (travel destinations), American perceptions, competitiveness, positioning analysis.

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Authors: Prabu D., Andrew Aaron James, Vanamala V., Vineeth Simon, Sanjeeb Kumar Deka, Sridharan R., Prahlada Rao B.B., Mohanram N.

Abstract:

In this paper we propose a novel Run Time Interface (RTI) technique to provide an efficient environment for MPI jobs on the heterogeneous architecture of PARAM Padma. It suggests an innovative, unified framework for the job management interface system in parallel and distributed computing. This approach employs proxy scheme. The implementation shows that the proposed RTI is highly scalable and stable. Moreover RTI provides the storage access for the MPI jobs in various operating system platforms and improve the data access performance through high performance C-DAC Parallel File System (C-PFS). The performance of the RTI is evaluated by using the standard HPC benchmark suites and the simulation results show that the proposed RTI gives good performance on large scale supercomputing system.

Keywords: RTI, C-MPI, C-PFS, Scheduler Interface.

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Authors: P. Sankar, P. James Daniel Paul, Siddhant Sahu

Abstract:

A brief review of the empirical studies on the methodology of the stock market decision support would indicate that they are at a threshold of validating the accuracy of the traditional and the fuzzy, artificial neural network and the decision trees. Many researchers have been attempting to compare these models using various data sets worldwide. However, the research community is on the way to the conclusive confidence in the emerged models. This paper attempts to use the automotive sector stock prices from National Stock Exchange (NSE), India and analyze them for the intra-sectorial support for stock market decisions. The study identifies the significant variables and their lags which affect the price of the stocks using OLS analysis and decision tree classifiers.

Keywords: Indian Automotive Sector, Stock Market Decisions, Equity Portfolio Analysis, Decision Tree Classifiers, Statistical Data Analysis.

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Authors: James Kuria, John Kihiu

Abstract:

A mathematical model for determining the overall efficiency of a multistage tractor gearbox including all gear, lubricant, surface finish related parameters and operating conditions is presented. Sliding friction, rolling friction and windage losses were considered as the main sources of power loss in the gearing system. A computer code in FORTRAN was developed to simulate the model. Sliding friction contributes about 98% of the total power loss for gear trains operating at relatively low speeds (less than 2000 rpm input speed). Rolling frictional losses decrease with increased load while windage losses are only significant for gears running at very high speeds (greater than 3000 rpm). The results also showed that the overall efficiency varies over the path of contact of the gear meshes ranging between 94% to 99.5%.

Keywords: Efficiency, multistage gear train, rolling friction, slidingfriction, windage losses.

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Authors: A. A. James, A. O. Adesanya, M. R. Odekunle, D. G. Yakubu

Abstract:

This paper examines the development of one step, five hybrid point method for the solution of first order initial value problems. We adopted the method of collocation and interpolation of power series approximate solution to generate a continuous linear multistep method. The continuous linear multistep method was evaluated at selected grid points to give the discrete linear multistep method. The method was implemented using a constant order predictor of order seven over an overlapping interval. The basic properties of the derived corrector was investigated and found to be zero stable, consistent and convergent. The region of absolute stability was also investigated. The method was tested on some numerical experiments and found to compete favorably with the existing methods.

Keywords: Interpolation, Approximate Solution, Collocation, Differential system, Half step, Converges, Block method, Efficiency.

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Authors: Yingjeng James Li, Lung-Yu Sung, Andrew S. Lin, Huan-Jyun Ciou

Abstract:

Durability of Membrane Electrode Assembly for Proton Exchange Membrane Fuel Cells was evaluated in both steady state and accelerated decay modes. Steady state mode was carried out at constant current of 800mA/cm2 for 2500 hours using air as cathode feed and pure hydrogen as anode feed. The degradation of the cell voltage was 0.015V after such 2500 hrs operation. The degradation rate was therefore calculated to be 6uV/hr. Continuously Vigorous fluctuation of the cell voltage, which was switched between OCV and 0.2V, was employed for the accelerated decay mode. No obvious change in performance of the MEA was observed after 10000 cycles of such operation.

Keywords: Durability, lifetime, membrane electrode assembly, proton exchange membrane fuel cells.

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Authors: Faaza A, Almarsoomi, James D, O'Shea, Zuhair A, Bandar, Keeley A, Crockett

Abstract:

This paper is concerned with the production of an Arabic word semantic similarity benchmark dataset. It is the first of its kind for Arabic which was particularly developed to assess the accuracy of word semantic similarity measurements. Semantic similarity is an essential component to numerous applications in fields such as natural language processing, artificial intelligence, linguistics, and psychology. Most of the reported work has been done for English. To the best of our knowledge, there is no word similarity measure developed specifically for Arabic. In this paper, an Arabic benchmark dataset of 70 word pairs is presented. New methods and best possible available techniques have been used in this study to produce the Arabic dataset. This includes selecting and creating materials, collecting human ratings from a representative sample of participants, and calculating the overall ratings. This dataset will make a substantial contribution to future work in the field of Arabic WSS and hopefully it will be considered as a reference basis from which to evaluate and compare different methodologies in the field.

Keywords: Arabic categories, benchmark dataset, semantic similarity, word pair, stimulus Arabic words

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Authors: Satya P. Dubey, Hrushikesh A. Abhyankar, Veronica Marchante, James L. Brighton, Björn Bergmann

Abstract:

PLA emerged as a promising polymer because of its property as a compostable, biodegradable thermoplastic made from renewable sources. PLA can be polymerized from monomers (Lactide or Lactic acid) obtained by fermentation processes from renewable sources such as corn starch or sugarcane. For PLA synthesis, ring opening polymerization (ROP) of Lactide monomer is one of the preferred methods. In the literature, the technique mainly developed for ROP of PLA is based on metal/bimetallic catalyst (Sn, Zn and Al) or other organic catalysts in suitable solvent. However, the PLA synthesized using such catalysts may contain trace elements of the catalyst which may cause toxicity. This work estimated the usefulness and drawbacks of using different catalysts as well as effect of alternative energies and future aspects for PLA production.

Keywords: Alternative energy, bio-degradable, metal catalyst, poly lactic acid (PLA), ring opening polymerization (ROP).

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Authors: Richard K. F. Lee, James M. Hill

Abstract:

In this paper, we summarize recent work of the authors on nanocomputing memory devices. We investigate two memory devices, each comprising a charged metallofullerene and carbon nanotubes. The first device involves two open nanotubes of the same radius that are joined by a centrally located nanotube of a smaller radius. A metallofullerene is then enclosed inside the structure. The second device also involves a etallofullerene that is located inside a closed carbon nanotube. Assuming the Lennard-Jones interaction energy and the continuum approximation, for both devices, the metallofullerene has two symmetrically placed equal minimum energy positions. On one side the metallofullerene represents the zero information state and by applying an external electrical field, it can overcome the energy barrier, and pass from one end of the tube to the other, where the metallofullerene then represents the one information state.

Keywords: Carbon nanotube, continuous approach, energy barrier, Lennard-Jones potential, metallofullerene, nanomemory device.

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Authors: Chun-Yuan Huang, Chiao-Yang Cheng, Chun-Yem Huang, Yan-Kuin Su, James Chin-Lung Fang

Abstract:

ZnO nanostructures including nanowires, nanorods, and nanoneedles were successfully deposited on GaAs substrates, respectively, by simple two-step chemical method for the first time. A ZnO seed layer was firstly pre-coated on the O2-plasma treated substrate by sol-gel process, followed by the nucleation of ZnO nanostructures through hydrothermal synthesis. Nanostructures with different average diameter (15-250 nm), length (0.9-1.8 μm), density (0.9-16×109 cm-2) were obtained via adjusting the growth time and concentration of precursors. From the reflectivity spectra, we concluded ordered and taper nanostructures were preferential for photovoltaic applications. ZnO nanoneedles with an average diameter of 106 nm, a moderate length of 2.4 μm, and the density of 7.2×109 cm-2 could be synthesized in the concentration of 0.04 M for 18 h. Integrated with the nanoneedle array, the power conversion efficiency of single junction solar cell was increased from 7.3 to 12.2%, corresponding to a 67% improvement.

Keywords: Anti-reflection, Chemical synthesis, Solar cells, ZnO nanostructures.

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