A bioadhesive delivery system as an alternative to infiltration anesthesia

OBJECTIVE: The aim of this study was to determine the feasibility of a novel saliva-activated bioadhesive drug delivery system of lidocaine hydrochloride as a viable alternative to infiltration anesthesia in dentistry. STUDY DESIGN: The study was carried out in three stages. First, the drug delivery system (DDS) was subjectively evaluated for adherence to the gingival mucosa and peak effect of anesthesia. In the second stage, a comparative subjective and objective evaluation of the DDS with a marketed topical gel preparation was carried out. Finally an open label, nonblinded clinical trial was carried out using the exodontia model. A total of 49 extractions were attempted in 41 patients. The effect of the following variables was investigated in the study: (1) jaw (maxillary and mandibular), (2) overall mobility, (3) position-notation of tooth (1, 2, 3, 4 ...). The positive extractions were statistically analyzed by the t test comparison of means of two independent variables. RESULTS: Subjective evaluation revealed that the DDS adheres to the gingiva within a minute and produces peak effect in 15 minutes. Comparative study revealed that the DDS produces greater depth of anesthesia than the marketed topical gel. Of 49 extractions attempted with the DDS, 40 were successful, giving an efficacy of 81.63%. CONCLUSION: The novel saliva-activated bioadhesive drug delivery system of lidocaine hydrochloride exhibits potential as a feasible alternative to infiltration anesthesia in dentistry.     

Dynamically Driven Emergence in a Nanomagnetic System

Emergent behaviors occur when simple interactions between a system's constituent elements produce properties that the individual elements do not exhibit in isolation. This article reports tunable emergent behaviors observed in domain wall (DW) populations of arrays of interconnected magnetic ring-shaped nanowires under an applied rotating magnetic field. DWs interact stochastically at ring junctions to create mechanisms of DW population loss and gain. These combine to give a dynamic, field-dependent equilibrium DW population that is a robust and emergent property of the array, despite highly varied local magnetic configurations. The magnetic ring arrays’ properties (e.g., non-linear behavior, “fading memory” to changes in field, fabrication repeatability, and scalability) suggest they are an interesting candidate system for realizing reservoir computing (RC), a form of neuromorphic computing, in hardware. By way of example, simulations of ring arrays performing RC approaches 100% success in classifying spoken digits for single speakers.     

I/O Acceleration via Multi-Tiered Data Buffering and Prefetching

Modern High-Performance Computing(HPC)systems are adding extra layers to the memory and storage hierarchy,named deep memory and storage hierarchy(DMSH),to increase I/O performance.New hardware technologies,such as NVMe and SSD,have been introduced in burst buffer installations to reduce the pressure for external storage and boost the burstiness of modern I/O systems.The DMSH has demonstrated its strength and potential in practice.However,each layer of DMSH is an independent heterogeneous system and data movement among more layers is significantly more complex even without considering heterogeneity.How to efficiently utilize the DMSH is a subject of research facing the HPC community.Further,accessing data with a high-throughput and low-latency is more imperative than ever.Data prefetching is a well-known technique for hiding read latency by requesting data before it is needed to move it from a high-latency medium(e.g.,disk)to a low-latency one(e.g.,main memory).However,existing solutions do not consider the new deep memory and storage hierarchy and also suffer from under-utilization of prefetching resources and unnecessary evictions.Additionally,existing approaches implement a client-pull model where understanding the application's I/O behavior drives prefetching decisions.Moving towards exascale,where machines run multiple applications concurrently by accessing files in a workflow,a more data-centric approach resolves challenges such as cache pollution and redundancy.In this paper,we present the design and implementation of Hermes:a new,heterogeneous-aware,multi-tiered,dynamic,and distributed I/O buffering system.Hermes enables,manages,supervises,and,in some sense,extends I/O buffering to fully integrate into the DMSH.We introduce three novel data placement policies to efficiently utilize all layers and we present three novel techniques to perform memory,metadata,and communication management in hierarchical buffering systems.Additionally,we demonstrate the benefits of a truly hierarchical data prefetcher that adopts a server-push approach to data prefetching.Our evaluation shows that,in addition to automatic data movement through the hierarchy,Hermes can significantly accelerate I/O and outperforms by more than 2x state-of-the-art buffering platforms.Lastly,results show 10%-35%performance gains over existing prefetchers and over 50%when compared to systems with no prefetching.     

Hybrid Location-based Recommender System for Mobility and Travel Planning

In recent times, the modern developments of internet technologies and social networks have attracted global researchers to explore the recommender systems for generating personalized location-based services. Recommender Systems (RSs) as proven decision support tools have gained immense popularity to solve information overloading problem among various real-time applications of e-commerce, travel and tourism, movies and e-learning. RSs emerge as a popular and reliable information filtering approach that is capable of suggesting relevant items, movies, and locations to the active target user based on dynamic preferences and interests. Beyond the development of many feature-rich recommendation algorithms, the need for a better full-fledged RS to produce precise and highly relevant recommendations based on ratings and preferences provided by the target user is very high. With the specific focus to the travel domain, the global research community has been involved in the development of a complete travel recommender system that is immune to the sparsity and cold start problems. In this paper, we present a new Hybrid Location-based Travel Recommender System (HLTRS) through exploiting ensemble based co-training method with swarm intelligence algorithms to enhance the personalized travel recommendations. The proposed HLTRS is experimentally validated on the real-world large-scale dataset, and we have made an extensive user study to determine the ability of developed RS to produce user satisfiable recommendations in real-time scenarios. The obtained results and analyses demonstrate the improved performance of the proposed Hybrid Location-based Travel Recommender System over existing baselines of recommender systems research.

     

Multiparty gaze preservation through perspective switching for interactive elearning environments

Multimedia Tools and Applications - Existing live tele-teaching systems enable eye-contact between interacting participants, however, they are often incomplete as they neglect finer levels of...     

A signed directed graph-based systematic framework for steady-state malfunction diagnosis inside control loops

Although signed directed graphs (SDG) have been widely used for modeling control loops, due to lack of adequate understanding of SDG-based steady-state process modeling, special and cumbersome methods are used to analyze control loops. In this paper, we discuss a unified SDG model for control loops, in which both disturbances (sensor bias, etc.) as well as structural faults (sensor failure, controller failure, etc.) can be easily modeled under steady-state conditions. Various fault scenarios such as external disturbances, sensor bias, controller failure, etc. have been thoroughly analyzed. A new algorithm for steady-state fault diagnosis using the SDG model for the steady-state system, that uses a combination of forward- and backward-reasoning, is proposed. Three case studies are presented to show the utility of the steady-state SDG model for fault diagnosis. A tank-level control system is used as the first case study. The second case study deals with fault diagnosis of a multi-stream-controlled CSTR. The third case study deals with fault/failure diagnosis in a process flowsheet containing a CSTR with one control loop and a flash vaporizer with three control loops.     

A MODEL OF THE VISCOUS WALL REGION OF TURBULENT SHEAR FLOWS

The viscous wall region of a fully developed turbulent pipe flow is investigated using a nonlinear, time-dependent, three-dimensional model. In the model, the velocity field is assumed to satisfy periodic boundary conditions in the longitudinal and spanwise directions, the velocity vanishes at the pipe wall, the velocity fluctuations are assumed to vanish at large distances from the wall, and a law of the wall profile is imposed on the longitudinal and spanwise average of the longitudinal component of velocity outside the viscous wall region. The model equations are solved using pseudospectral methods and the computed mean velocity profile, fluctuation intensities, and turbulence production rate are found to be in good agreement with experiment in the viscous wall region. It is found that the bulk of turbulence production is generated by length scales larger than 40 in the spanwise direction and 200 in the longitudinal direction.     

Synthesis and high performance of ceria supported nickel catalysts for hydrodechlorination reaction

Catalysts containing 10 wt% Ni supported on CeO₂ were prepared by two ways, namely, co-precipitation method using nickel nitrate precursor and impregnation method using nickel nitrate and nickel acetylacetonate as two separate precursors. The catalysts were characterized by pulse chemisorption of H₂, X-ray diffraction, and temperature programmed reduction (TPR) techniques and evaluated for the gas phase hydrodechlorination (HDC) of chlorobenzene to benzene in a fixed-bed down-flow glass reactor at 573 K under normal atmospheric pressure. The hydrogen uptake values were used to determine the catalyst properties of Ni/CeO₂ like dispersion, metal area, and particle size. Among the two preparatory routes, co-precipitation method gave better catalytic performance in terms of hydrogenation activity, benzene selectivity, and coking resistivity than impregnated Ni/CeO₂ catalysts. This may be attributed to high dispersion of smaller NiO crystallites and the appearance of the second reduction peak at a higher temperature (578 K) in TPR profile with co-precipitated Ni/CeO₂ catalyst. This indicates that a strong interaction may take place between the NiO crystallites and CeO₂ on the surface of co-precipitated Ni/CeO₂ catalyst. Contrary to general expectation that the large Ni particles are preferable for HDC reaction, it is observed that smaller metal particles with high dispersion, as in the case of co-precipitated Ni/CeO₂ catalyst, promotes better catalyst with longer life.     

Power Plant Intake Entrainment Analysis

Power plant condenser cooling water intake entrainment of fish eggs and larvae is becoming an issue in evaluating environmental impacts around the plants. Methods are required to evaluate intake entrainment on different types of water bodies. Presented in this paper is a derivation of the basic relationships for evaluating entrainment from the standing crop of fish eggs and larvae for different regions of a water body, and evaluating the rate of entrainment from the standing crop. These relationships are coupled with a 3D hydrodynamic and transport model that provides the currents and flows required to complete the entrainment evaluation. Case examples are presented for a simple river system, and for the more complex Delaware River Estuary with multiple intakes. Example evaluations are made for individual intakes, and for the cumulative impacts of multiple intakes.