Ultrasonic Assisted Nano-structures of Novel Organotin Supramolecular Coordination Polymers as Potent Antitumor Agents

Journal of Inorganic and Organometallic Polymers and Materials - The reaction of potassium tetracyanocupprate(I) with triethyltin bromide in presence of phenanthroline (Phen) and quinoxaline (Qox)...     

Simulation and optimization of tool-life in manufacturing centers

This paper presents the development of an optimization and a computer-simulation model to evaluate the process plans of a manufacturing center by analyzing the effect of tool failure on system performance. The GPSS/PC simulation program that is used in this study has been built with variables, functions and matrices so that many experiments could be conducted with the model. Sensitivity analysis is presented and the developed model has proven to be useful in determining optimum sequencing of parts for various operating policies.     

Effects of Luminous Environment on Worker Productivity in Building Spaces

This paper is based in part, on the results of a survey undertaken by the writer for an industrial client. The purpose was to collect and systematize information on quantitative relations between the indoor environment and worker productivity. Office workers strongly believe that lighting conditions are an extremely important aspect of their workspace environment. Unfavorable conditions may hamper productivity. Daylighting is of particular importance. It is further shown that lighting conditions have a strong impact on worker performance in industrial facilities. Certain lighting strategies can contribute to an enhancement in worker productivity while cutting down on energy consumption. The influence on worker productivity of different characteristics of illumination are described. These include spectral distribution, color rendition, glare, daylight versus artificial light, and others. The presence or absence of windows on worker comfort and perception is also discussed. It is shown that improving lighting conditions is a highly cost-effective method of increasing worker productivity in office spaces as well as in manufacturing facilities.     

D-side: A facility and workforce planning group multi-criteria decision support system for Johnson Space Center

“To understand and protect our home planet, to explore the universe and search for life, and to inspire the next generation of explorers” is NASA's mission. The Systems Management Office at Johnson Space Center (JSC) is searching for methods to effectively manage the Center's resources to meet NASA's mission. D-Side is a group multi-criteria decision support system (GMDSS) developed to support facility decisions at JSC. D-Side uses a series of sequential and structured processes to plot facilities in a three-dimensional (3-D) graph on the basis of each facility's alignment with NASA's mission and goals, the extent to which other facilities are dependent on the facility, and the dollar value of capital investments that have been postponed at the facility relative to the facility's replacement value. A similarity factor rank orders facilities based on their Euclidean distance from Ideal and Nadir points. These similarity factors are then used to allocate capital improvement resources across facilities. We also present a parallel model that can be used to support decisions concerning allocation of human resources investments across workforce units. Finally, we present results from a pilot study where 12 experienced facility managers from NASA used D-Side and the organization's current approach to rank order and allocate funds for capital improvement across 20 facilities. Users evaluated D-Side favorably in terms of ease of use, the quality of the decision-making process, decision quality, and overall value-added. Their evaluations of D-Side were significantly more favorable than their evaluations of the current approach.     

A large vocabulary system for Arabic online handwriting recognition

The success of using Hidden Markov Models (HMMs) for speech recognition application has motivated the adoption of these models for handwriting recognition especially the online handwriting that has large similarity with the speech signal as a sequential process. Some languages such as Arabic, Farsi and Urdo include large number of delayed strokes that are written above or below most letters and usually written delayed in time. These delayed strokes represent a modeling challenge for the conventional left-right HMM that is commonly used for Automatic Speech Recognition (ASR) systems. In this paper, we introduce a new approach for handling delayed strokes in Arabic online handwriting recognition using HMMs. We also show that several modeling approaches such as context based tri-grapheme models, speaker adaptive training and discriminative training that are currently used in most state-of-the-art ASR systems can provide similar performance improvement for Hand Writing Recognition (HWR) systems. Finally, we show that using a multi-pass decoder that use the computationally less expensive models in the early passes can provide an Arabic large vocabulary HWR system with practical decoding time. We evaluated the performance of our proposed Arabic HWR system using two databases of small and large lexicons. For the small lexicon data set, our system achieved competing results compared to the best reported state-of-the-art Arabic HWR systems. For the large lexicon, our system achieved promising results (accuracy and time) for a vocabulary size of 64k words with the possibility of adapting the models for specific writers to get even better results.     

A new algorithm for modeling online search behavior and studying ranking reliability variations

We design an information retrieval algorithm that mimics the stochastic behavior of decision-makers (DMs) when evaluating the alternatives displayed by an online search engine. The algorithm consists of a decision tree that incorporates all the 1024 decision nodes that may arise from the information retrieval process of DMs. We calibrate the behavior of the algorithm to the one observed from online users and run several sets of 1,000,000 queries. Each query lets DMs decide which subset of the ten alternatives composing the initial page of results to click, allowing us to evaluate their behavior as ranking reliability is assumed to decrease when DMs decide not to click on an alternative. We compare the click-through rates (CTRs) obtained when modifying the degree of ranking reliability derived from the alternatives displayed on the first page of search results. We illustrate how the stability of the CTR prevails among the top-ranked alternatives within relatively reliable scenarios while it drops when imposing large initial decrements in reliability. The resulting consequences regarding the importance of relative ranking positions are analyzed, the top three alternatives exhibiting a generally contained decrease in their CTRs that contrasts with the cumulative pattern arising from the fourth position onwards.

     

A robust optimization approach for imprecise data envelopment analysis

Crisp input and output data are fundamentally indispensable in traditional data envelopment analysis (DEA). However, the input and output data in real-world problems are often imprecise or ambiguous. Some researchers have proposed interval DEA (IDEA) and fuzzy DEA (FDEA) to deal with imprecise and ambiguous data in DEA. Nevertheless, many real-life problems use linguistic data that cannot be used as interval data and a large number of input variables in fuzzy logic could result in a significant number of rules that are needed to specify a dynamic model. In this paper, we propose an adaptation of the standard DEA under conditions of uncertainty. The proposed approach is based on a robust optimization model in which the input and output parameters are constrained to be within an uncertainty set with additional constraints based on the worst case solution with respect to the uncertainty set. Our robust DEA (RDEA) model seeks to maximize efficiency (similar to standard DEA) but under the assumption of a worst case efficiency defied by the uncertainty set and it’s supporting constraint. A Monte-Carlo simulation is used to compute the conformity of the rankings in the RDEA model. The contribution of this paper is fourfold: (1) we consider ambiguous, uncertain and imprecise input and output data in DEA; (2) we address the gap in the imprecise DEA literature for problems not suitable or difficult to model with interval or fuzzy representations; (3) we propose a robust optimization model in which the input and output parameters are constrained to be within an uncertainty set with additional constraints based on the worst case solution with respect to the uncertainty set; and (4) we use Monte-Carlo simulation to specify a range of Gamma in which the rankings of the DMUs occur with high probability.     

A novel facile synthesis and characterization of molybdenum nanowires

We describe a straightforward technique to synthesize pure Mo nanowires (NWs) from Mo₆S_yI_z (8,2 <y + z ≤ 10) NWs as precursor templates. The structural transformations occur when Mo₆S_yI_z NWs are annealed in Ar/H₂ mixture leading to the formation of pure Mo NWs with similar structures as initial morphologies. Detailed microscopic characterizations show that large diameters (>15 nm) Mo NWs are highly porous, while small diameters (<7 nm) are made of solid nanocrystalline grains. We find NW of diameter 4 nm can carry up to 30 μA current without suffering structural degradation. Moreover, NWs can be elastically deformed over several cycles without signs of plastic deformation.