Fractional Differential Equations

Object-oriented analysis

Harmonic Analysis and the Theory of Probability. By S. Bochner pp. 176. 35s. 1955. (California University Press and Cambridge University Press)

Advective flows rapidly transport water, solutes, and particles into and out of permeable sand beds and significantly affects the biogeochemistry of coastal environments. In this paper, we reviewed the drivers of porewater and groundwater advection in permeable shelf sediments in an attempt to bridge gaps among different disciplines studying similar problems. We identified the following driving forces: (1) terrestrial hydraulic gradients, (2) seasonal changes in the aquifer level on land moving the location of the subterranean estuary, (3) wave setup and tidal pumping, (4) water level differences across permeable barriers, (5) flow- and topography-induced pressure gradients, (6) wave pumping; (7) ripple and other bed form migration, (8) fluid shear, (9) density-driven convection, (10) bioirrigation and bioturbation, (11) gas bubble upwelling, and (12) sediment compaction. While these drivers occur over spatial scales ranging from mm to km, and temporal scales ranging from seconds to years, their ultimate biogeochemical implications are very similar (i.e., they are often a source of new or recycled nutrients to seawater and transform organic carbon into inorganic carbon). Drivers 2–12 result in no net water input into the ocean. Taking all these mechanisms into account, we conservatively estimate that a volume equivalent to that of the entire ocean is filtered by permeable sediments at time scales of about 3000 years. Quantifying the relative contribution of these drivers is essential to understand the contribution of sediments to the global cycles of matter.

The driving forces of porewater and groundwater flow in permeable coastal sediments: a review

Glutamine utilization by the small intestine.

Horizontal coefficient of consolidation ch is a key parameter in the design of vertical drains and the following consolidation analysis of a soil layer. There are graphical and non-graphical methods available to estimate ch from laboratory radial consolidation tests with a central drain. Currently, the consolidation tests with peripheral drains have to be analysed through a curve fitting method for determining ch. In this technical note, a non-graphical inflection point method is proposed for determining ch for an oedometer test with peripheral drainage, based on the characteristic feature observed when the gradient of the theoretical Ur –log Tr relationship was plotted against Tr. The proposed method is validated through a series of consolidation tests on two reconstituted dredged clay specimens, tested in an oedometer subjected to radial drainage with peripheral drains. The consolidation settlements predicted from the proposed method, for the two different clays, were in excellent agreement with those measured in the oedometer. The proposed method will be a very valuable tool in the analysis of radial consolidation data when the drains are peripheral.

Inflection Point Method to Estimate ch From Radial Consolidation Tests with Peripheral Drain

Handwritten signatures are considered as the most natural method of authenticating a person’s identity (compared to other biometric and cryptographic forms of authentication). The learning process inherent in Neural Networks (NN) can be applied to the process of verifying handwritten signatures that are electronically captured via a stylus. This paper presents a method for verifying handwritten signatures by using a NN architecture. Various static (e.g., height, slant, etc.) and dynamic (e.g., velocity, pen tip pressure, etc.) signature features are extracted and used to train the NN. Several Network topologies are tested and their accuracy is compared. The resulting system performs reasonably well with an overall error rate of 3:3% being reported for the best case.

/pdf/neural-network-based-handwritten-signature-verification-3j6z94adtz.pdf

Neural Network-based Handwritten Signature Verification

Shill bidding is where spurious bids are introduced into an auction to drive up the final price for the seller, thereby defrauding legitimate bidders. While shilling is recognized as a problem, presently there is little or no established means of defense against shills. This chapter presents an algorithm to detect the presence of shill bidding in online auctions. It observes bidding patterns over a series of auctions, providing each bidder a score indicating the likelihood of his/her potential involvement in shill behavior. The algorithm has been tested on data obtained from a series of realistic simulated auctions, and commercial online auctions. The algorithm is able to prune the search space required to detect which bidders are likely to be shills. This has significant practical and legal implications for commercial online auctions where shilling is considered a major threat. This chapter presents a framework for a feasible solution, which acts as a detection mechanism and a deterrent.

Detecting shill bidding in online English auctions

The initial rate of incorporation of [15N]alanine into the 6-amino group of the adenine nucleotides in rat hepatocytes was about one-eighteenth of the rate of incorporation into urea Thus the purine nucleotide cycle cannot provide most of the ammonia needed in urea synthesis for the carbamoyl phosphate synthase reaction (EC 2725) On the other hand, contrary to the view expressed by McGivan & Chappell [(1975) FEBS Lett 52, 1–7], the experiments support the view that hepatic glutamate dehydrogenase can supply the required ammonia

Wayne Read

Papers

Inflection Point Method to Estimate ch From Radial Consolidation Tests with Peripheral Drain

Neural Network-based Handwritten Signature Verification

Detecting shill bidding in online English auctions

Sources of ammonia for mammalian urea synthesis

Neural Network-based Hwritten Signature Verification.