Comparative performance evaluation of carbon dot-based paper immunoassay on Whatman filter paper and nitrocellulose paper in the detection of HIV infection

Carbon dots synthesized from citric acid and ethylene diamine by a one-step hydrothermal technique were used to develop a carbon dot-based paper immunoassay (CDPIA) for rapid detection of HIV-1 p24 antigen. In the present study, the 96-well template was hand patterned using a wax pencil, as a prototype method, on two types of paper, (1) Whatman filter paper and (2) nitrocellulose paper. The sandwich immunoassay was performed on both paper microplates for detection of HIV-1 p24 antigen which is an early marker of HIV infection. The detection range was from 10 μg/mL to 1 ng/mL for the Whatman filter paper while the nitrocellulose paper exhibited a higher range from 10 μg/mL to 250 pg/mL. CDPIA on the nitrocellulose paper (CDNIA) exhibited a fourfold increase in sensitivity and reduced the assay time by threefold compared with CDPIA on Whatman paper (CDWIA). HIV-negative and HIV-positive plasma samples were tested using CDNIA for the presence of HIV-1 p24 antigen. This immunoassay exhibited no false-positive and false-negative results with the clinical samples tested. This simple and sensitive paper-based HIV-1 p24 antigen assay may be useful in preventing HIV transmission by blood transfusion in resource-limited settings by reducing the antibody negative, infectious window period in blood donors and for early diagnosis of HIV infected individuals where nucleic acid-based testing is not practical or feasible.     

Minimum pickup velocity: The transition between nano‐scale and micro‐scale

The transport of nano‐scale particles has become increasingly important, but the knowledge base available is limited. This study aims to bridge the knowledge gap between the nano‐ and micro‐scales for pneumatic conveying. A key parameter is the minimum pickup velocity (U_pu), which is the minimum fluid velocity required to initiate motion in a particle originally at rest. The U_pu values of nine alumina particles with particle diameters (d_p) ranging from 5 to 110,000 nm were determined using the weight loss method, then compared against the established pickup Zones (analogous to the Geldart Groups). Results indicated that: (1) U_pu varied non‐monotonically with increasing d_p, thus revealing the missing link between the nano‐ and micro‐scales; (2) the intermediate particle diameters surprisingly did not agree with any pickup Zone; (3) Zone III (analogous to Geldart Group C) is inadequate for all the nano‐scale particles, so new boundaries and a new Zone are proposed. © 2016 American Institute of Chemical Engineers AIChE J, 63: 1512–1519, 2017     

Emerging Applications for Cyber Transportation Systems

Recent advances in connected vehicles and autonomous driving are going to change the face of ground trans- portation as we know it. This paper describes the design and evaluation of several emerging applications for such a cyber transportation system （CTS）. These applications have been designed using holistic approaches, which consider the unique roles played by the human drivers, the transportation system, and the communication network. They can improve driver safety and provide on-road infotainment. They can also improve transportation operations and efficiency, thereby benefiting travelers and attracting investment from both government agencies and private businesses to deploy infrastructures and bootstrap the evolutionary process of CTS.     

Primal explicit max margin feature selection for nonlinear support vector machines

Embedding feature selection in nonlinear support vector machines (SVMs) leads to a challenging non-convex minimization problem, which can be prone to suboptimal solutions. This paper develops an effective algorithm to directly solve the embedded feature selection primal problem. We use a trust-region method, which is better suited for non-convex optimization compared to line-search methods, and guarantees convergence to a minimizer. We devise an alternating optimization approach to tackle the problem efficiently, breaking it down into a convex subproblem, corresponding to standard SVM optimization, and a non-convex subproblem for feature selection. Importantly, we show that a straightforward alternating optimization approach can be susceptible to saddle point solutions. We propose a novel technique, which shares an explicit margin variable to overcome saddle point convergence and improve solution quality. Experiment results show our method outperforms the state-of-the-art embedded SVM feature selection method, as well as other leading filter and wrapper approaches.     

RAFT: A router architecture with frequency tuning for on-chip networks

With increasing number of cores being integrated on a single die, Network-on-Chips (NoCs) have become the de-facto standard in providing scalable communication backbones for these multi-core chips. NoCs have a significant impact on the system’s performance, power and reliability. However, NoCs can be plagued by higher power consumption and degraded throughput if the network and router are not designed properly. Towards this end, this paper proposes a novel router architecture, where we tune the frequency of a router in response to network load to manage both performance and power. We propose three dynamic frequency tuning techniques, FreqBoost, FreqThrtl and FreqTune, targeted at congestion and power management in NoCs. We also propose and evaluate a novel fine-grained frequency tuning scheme where we vary the number of virtual-channels in a router dynamically. As a further optimization to these schemes, we propose a frequency tuning scheme where we tune the frequency of the four ports of a mesh router separately from the local port. As enablers for these techniques, we exploit Dynamic Voltage and Frequency Scaling (DVFS) and the imbalance in a generic router pipeline through time stealing. We also evaluate and analyze the proposed schemes from the point of view of reliability against soft error vulnerability and provide guidelines in choosing the appropriate scheme when reliability is the prime design constraint.Experiments using synthetic workloads on an 8 × 8 wormhole-switched mesh interconnect show that FreqBoost is a better choice for reducing average latency (maximum 40%) while, FreqThrtl provides the maximum benefits in terms of power saving and energy delay product (EDP). The FreqTune scheme is a better candidate for optimizing both performance and power, achieving on an average 36% reduction in latency, 13% savings in power (up to 24% at high load), and 40% savings (up to 70% at high load) in EDP. With application benchmarks, we observe IPC improvement up to 23% using our design. Our analysis shows FreqBoost to be the most robust scheme amongst the three schemes when reliability is a concern.     

Beam search algorithms for multilabel learning

Multilabel learning is a machine learning task that is important for applications, but challenging. A recent method for multilabel learning called probabilistic classifier chains (PCCs) has several appealing properties. However, PCCs suffer from the computational issue that inference (i.e., predicting the label of an example) requires time exponential in the number of tags. Also, PCC accuracy is sensitive to the ordering of the tags while training. In this paper, we show how to use the classical technique of beam search to solve both these problems. Specifically, we show how to apply beam search to make inference tractable, and how to integrate beam search with training to determine a suitable tag ordering. Experimental results on a range of datasets show that the proposed improvements yield a state-of-the-art method for multilabel learning.     

Personalized ranking in web databases: establishing and utilizing an appropriate workload

The emergence of the deep Web has given a new connotation to the concept of ranking database query results. Earlier approaches for ranking either resorted to analyzing frequencies of database values and query logs or establishing user profiles. In contrast, an integrated approach, based on the notion of a similarity model, for holistically supporting user- and query-dependent ranking has been recently proposed (Telang et al. in IEEE Transactions on Knowledge and Data Engineering (TKDE), 2011). An important component of this framework is a workload consisting of ranking functions, wherein each function represents an individual user’s preferences towards the results of a specific query. At the time of answering a query for which no prior ranking function exists, the similarity model is employed, and is expected to ensure a good quality of ranking as long as a ranking function for a very similar user-query pair exists in this workload. In this paper, we address the problem of determining an appropriate set of user-query pairs to form a workload of ranking functions to support user- and query-dependent ranking for Web databases. We propose a novel metric, termed workload goodness, that quantifies the notion of a “good” workload into an absolute value. The process of finding such a workload of optimal goodness is a combinatorially explosive problem; therefore, we propose a heuristic solution, and advance three approaches for determining an acceptable workload, in a static as well as a dynamic environment. We discuss the effectiveness of our proposal analytically as well as experimentally over two Web databases.     

Multi-resolution texture analysis for fingerprint based age-group estimation

Multimedia Tools and Applications - In this paper the possibility of using digital fingerprints to estimate age-groups of human being, particularly children is investigated. To our knowledge,...