iMobile EE – An Enterprise Mobile Service Platform

iMobile is an enterprise mobile service platform that allows resource-limited mobile devices to communicate with each other and to securely access corporate contents and services. The original iMobile architecture consists of devlets that provide protocol interfaces to different mobile devices and infolets that access and transcode information based on device profiles. iMobile Enterprise Edition (iMobile EE) is a redesign of the original iMobile architecture to address the security, scalability, and availability requirements of a large enterprise such as AT&T. iMobile EE incorporates gateways that interact with corporate authentication services, replicated iMobile servers with backend connections to corporate services, a reliable message queue that connects iMobile gateways and servers, and a comprehensive service profile database that governs operations of the mobile service platform. The iMobile EE architecture was also extended to provide personalized multimedia services, allowing mobile users to remotely control, record, and request video contents. iMobile EE aims to provide a scalable, secure, and modular software platform that makes enterprise services easily accessible to a growing list of mobile devices roaming among various wireless networks.     

Groundwater contributions to surface water in the Assiniboine Delta Aquifer (ADA): A water quantity and quality perspective

In the Lake Winnipeg Basin (LWB), at both basin and regional scales, there are currently gaps regarding the significance of groundwater as a mediator of nutrient and contaminant fluxes to Lake Winnipeg. During 2018, surface water and groundwater samples were collected from various locations across the largest sand and gravel aquifer (Assiniboine Delta Aquifer [ADA]; 3800 km²) in the LWB as well as from surface water courses flowing above this aquifer. The samples collected during spring and fall were analyzed for anions and cations as well as for a series of isotopic and geochemical tracers (e.g. water isotopes, carbon 14, artificial sweeteners, pesticides, etc.). The results reveal that groundwater and the small watercourses flowing above the ADA have a similar chemical composition, which is in contrast with the chemical composition of the main watercourse flowing above the aquifer (Assiniboine River [AR]). When corroborated with stream discharge measurements this indicates that groundwater plays a significant role at local scale in controlling both the flow and the chemical composition of the AR tributaries. Nitrate showed low to non-detectable concentrations in both groundwater and surface water. With respect to groundwater, this could be related to the reducing conditions in the deeper aquifer in conjunction with relatively large groundwater travel times of up to 2400 years.     

Identification and Identifiability of Unknown Parameters in Multibody Dynamic Systems

Assessing design changes in mechanical systems from simulationresults requires both accurate dynamic models and accurate values forparameters in the models. Model parameters are often unavailable ordifficult to measure. This study details an identification procedure fordetermining optimal values for unknown or estimated model parametersfrom experimental test data. The resulting optimization problem issolved by Levenberg–Marquardt methods. Partial derivative matricesneeded for the optimization are computed through sensitivity analysis.The sensitivity equations to be solved are generated analytically.Unfortunately, not all parameters can be uniquely determined using anidentification procedure. An issue of parameter identifiability remains.Since a global identifiability test is impractical for even the simplestmodels, a local identifiability test is developed. Two examples areprovided. The first example highlights the test for parameteridentifiability, while the second shows the usefulness of parameteridentification by determining vehicle suspension parameters fromexperimentally measured data.     

Inhibitive effects of structurally modified azasteroids and related nitrogen containing steroids on insect growth and development

A number of azasteroids and other nitrogen containing steroids with a modified nucleus or side chain were prepared and tested for their inhibitory effects on the growth and development of several species of insects. Structure-activity studies showed that compounds with a structurally related steroid nucleus and side chain were approximately equal in inhibitory activity for a particular species. The replacement of the tertiary amino group in the side chain of the 5beta-steroid with other nitrogen substituents, such as nitro, cyano, acetylamino, or a quaternary ammonium salt, resulted in a considerable loss of inhibitive activity in the tobacco hornworm or the yellowfever mosquito. However, certain modifications of the azasteroid nucleus resulted in compounds that still retained high biological activity. As a result, a compound was synthesized that lacked the A and B rings of the steroid nucleus and that inhibited insect growth, molting, and metamorphosis and the delta24-sterol reductase enzyme system of the tobacco hornworm.     

A software defined radio modeling framework for enabling cognitive applications

The rapid advances in software defined radio (SDR) technologies over the past decade encouraged their transition from the lab to field deployment, and shifted the usage from disparate communication devices to the creation of large resilient wireless networks capable of adapting to highly variable environment conditions in a cognitive manner. Simulation models capable of accurately representing the behavior and properties of SDR devices operating in a networked environment thus become a prerequisite for both the evaluation of such networks as well as for providing a development platform for creating new cognitive capabilities. This paper describes our work in creating a network simulation model framework for software defined radios that takes into account some of the unique behaviors and requirements of SDRs not previously seen in purely hardware devices. Factors such as large and variable communication delays between software modules, contention to shared resources, such as CPU or memory, as well as continuous tuning and environment awareness functionality essential to SDR demand a different modeling approach as well as novel techniques that enable accurate scale testing.     

Argentometric titration of procaine hydrochloride with zero current bipotentiometric indication using pretreated silver electrodes

A new procedure for the quantitative estimation of procaine hydrochloride is given based on the argentometric titration of its hydrochloride content, and using a zero current bipotentiometric indication. For this purpose two differently prepared silver electrodes are used. Quantitative data concerning precision are also given.     

Clustering Random Curves Under Spatial Interdependence With Application to Service Accessibility

Service accessibility is defined as the access of a community to the nearby site locations in a service network consisting of multiple geographically distributed service sites. Leveraging new statistical methods, this article estimates and classifies service accessibility patterns varying over a large geographic area (Georgia) and over a period of 16 years. The focus of this study is on financial services but it generally applies to any other service operation. To this end, we introduce a model-based method for clustering random time-varying functions that are spatially interdependent. The underlying clustering model is nonparametric with spatially correlated errors. We also assume that the clustering membership is a realization from a Markov random field. Under these model assumptions, we borrow information across functions corresponding to nearby spatial locations resulting in enhanced estimation accuracy of the cluster effects and of the cluster membership as shown in a simulation study. Supplementary materials including the estimation algorithm, additional maps of the data, and the C++ computer programs for analyzing the data in our case study are available online.     

Exergetic Comparative Analysis of Ammonia and Carbon Dioxide Two-Stage Cycles for Simultaneous Cooling and Heating

The paper deals with the comparative analysis of the performance of cooling and heating systems operating with NH3 （ammonia） or CO2 （carbon dioxide）, both natural refrigerants. The study is based on the exergetic analysis that points out the location and the magnitude of a system malfunction. Both systems, with NH3 or CO2 operate in two stages. The exergetic analysis gives the direction of the structural optimization. The exergetic analysis has shown that the best structural schematic is not the same for the two agents. The exergetic analysis points out that the largest exergy destruction in the CO2 cycle is due to the throttling process and offers solutions to diminish it.     

Artificial neural networks whispering to the brain: nonlinear system attractors induce familiarity with never seen items

Attractors of nonlinear neural systems are at the core of the memory self-refreshing mechanism of human memory models that suppose memories are dynamically maintained in a distributed network [Ans, B., and Rousset, S. (1997), ‘Avoiding Catastrophic Forgetting by Coupling Two Reverberating Neural Networks’ Comptes Rendus de l'Académie des Sciences Paris, Life Sciences, 320, 989–997; Ans, B., and Rousset, S. (2000), ‘Neural Networks with a Self-Refreshing Memory: Knowledge Transfer in Sequential Learning Tasks Without Catastrophic Forgetting’, Connection Science, 12, 1–19; Ans, B., Rousset, S., French, R.M., and Musca, S.C. (2002), ‘Preventing Catastrophic Interference in Multiple-Sequence Learning Using Coupled Reverberating Elman Networks’, in Proceedings of the 24th Annual Meeting of the Cognitive Science Society, eds. W.D. Gray and C.D. Schunn, Mahwah, NJ: Lawrence Erlbaum Associates, pp. 71–76; Ans, B., Rousset, S., French, R.M., and Musca, S.C. (2004), ‘Self-Refreshing Memory in Artificial Neural Networks: Learning Temporal Sequences Without Catastrophic Forgetting’, Connection Science, 16, 71–99; Ans, B. (2004), ‘Sequential Learning in Distributed Neural Networks Without Catastrophic Forgetting: A Single and Realistic Self-Refreshing Memory can do it’, Neural Information Processing-Letters and Reviews, 4, 27–32]. Are humans able to learn never seen items from attractor patterns generated by a highly distributed artificial neural network? First, an opposition method was implemented to ensure that the attractors are not the items used to train the network, the source items: attractors were selected to be more similar (both at the exemplar and the centroïd level) to some control items than to the source items. In spite of this very severe selection, blank networks trained only on selected attractors performed better at test on the never seen source items than on the never seen control items. The results of two behavioural experiments using the opposition method show that humans exhibit more familiarity with the never seen source items than with the never seen control items, just as networks do. Thus, humans are sensitive to the particular type of information that allows distributed artificial neural networks to dynamically maintain their memory, and this information does not amount to the exemplars used to train the network that produced the attractors.     

Liver Transplantation for Colorectal Liver Metastases: Current Management and Future Perspectives

Patients with nonresectable liver metastases from colorectal cancer have few therapeutic options and a dismal prognosis. Although liver transplantation for this indication has historically a poor reputation, recent advances in the field of chemotherapy and immunosuppression have paved the way to revisit the concept. New data have shown promising results that need to be validated in several ongoing clinical trials. Since liver grafts represent a scarce resource, several new tools are being explored to expand the donor pool for this indication. The purpose of this review is to present all current available data and perspectives about liver transplantation for nonresectable liver metastases from colorectal cancer.