Measuring Learning Outcomes in Library Instruction

The author uses clicker technology to incorporate polling and multiple choice question techniques into library instruction classes. Clickers can be used to give a keener understanding of how many students grasp the concepts presented in a specific class session. Typically, a student that aces a definition-type question will fail to answer an application-type question correctly. Immediate, electronic feedback helps to calibrate teaching approaches and gather data about learning outcomes. This presentation will analyze learning outcomes specific to scientific disciplines, and demonstrate the usefulness of clickers to engage and sustain student learning.

Construction, extension and evaluation of the improved smoking kiln (Banda) in Kainji Lake area

Fish smoking, as a traditional occupation of fishermen and women in Kainji Lake Area (Nigeria) is done using simple traditional ovens called 'Banda', the fuel for the smoking being almost hundred percent dependent on wood. A simple modification was made to the traditional 'Banda' oven using a damper to prevent burning of the fish. A comparison of the improved and the traditional 'Banda' was made. The results indicate that fuel wood consumption was reduced 52 percent by using the improved 'Banda', which implied that 50 percent of fish processor's income could be saved through the adoption of this technology. The most important advantage of the improved kiln, fuel wood conservation, represents for fishers a problem of an economic importance. Whilst they are aware that it is becoming much more difficult to get the needed fuel wood, the children can still conveniently collect enough wood for both home use and processing activities. The cost of the components of the improved kiln, when compared with the traditional version may be considered quite significant, and hence the reluctance of the fish processors in constructing similar ones. Selected blacksmiths were trained to continue the fabrication of the kiln component. The training was carried out to assure that the improved kiln will be constructed even after the project will end to support the fabrication

The role of extension in fisheries development among rural communities

It is a common knowledge that there exists a wide gap between domestic fish production and demand in Nigeria. Government recognizes this situation and has in recent years encouraged fish production through fishing inputs subsidies, DFRRI assisted fingerlings production among others. Despite these efforts, impact at the grassroots has been low. One of the major reasons for the failure could be attributable to inadequate involvement of rural communities in fish production. This missing link appears to be ignorance of local communities in harnessing this potential to stimulate fish production. There is therefore the need to educate the rural dwellers through effective extension services. Strategies to achieve the required awareness have been discussed

I. Singular perturbation problems involving singular points and turning points. II. On the averaged Lagrangian technique for nonlinear dispersive waves

In Part I a class of linear boundary value problems is considered which is a simple model of boundary layer theory. The effect of zeros and singularities of the coefficients of the equations at the point where the boundary layer occurs is considered. The usual boundary layer techniques are still applicable in some cases and are used to derive uniform asymptotic expansions. In other cases it is shown that the inner and outer expansions do not overlap due to the presence of a turning point outside the boundary layer. The region near the turning point is described by a two-variable expansion. In these cases a related initial value problem is solved and then used to show formally that for the boundary value problem either a solution exists, except for a discrete set of eigenvalues, whose asymptotic behaviour is found, or the solution is non-unique. A proof is given of the validity of the two-variable expansion; in a special case this proof also demonstrates the validity of the inner and outer expansions.

Nonlinear dispersive wave equations which are governed by variational principles are considered in Part II. It is shown that the averaged Lagrangian variational principle is in fact exact. This result is used to construct perturbation schemes to enable higher order terms in the equations for the slowly varying quantities to be calculated. A simple scheme applicable to linear or near-linear equations is first derived. The specific form of the first order correction terms is derived for several examples. The stability of constant solutions to these equations is considered and it is shown that the correction terms lead to the instability cut-off found by Benjamin. A general stability criterion is given which explicitly demonstrates the conditions under which this cut-off occurs. The corrected set of equations are nonlinear dispersive equations and their stationary solutions are investigated. A more sophisticated scheme is developed for fully nonlinear equations by using an extension of the Hamiltonian formalism recently introduced by Whitham. Finally the averaged Lagrangian technique is extended to treat slowly varying multiply-periodic solutions. The adiabatic invariants for a separable mechanical system are derived by this method.

Psychobiophysics of transcranial magnetic stimulation

Transcranial magnetic stimulation (TMS) is a technique that stimulates the brain using a magnetic coil placed on the scalp. Since it is applicable to humans non-invasively, directly interfering with neural electrical activity, it is potentially a good tool to study the direct relationship between perceptual experience and neural activity. However, it has been difficult to produce a clear perceptible phenomenon with TMS of sensory areas, especially using a single magnetic pulse. Also, the biophysical mechanisms of magnetic stimulation of single neurons have been poorly understood.

In the psychophysical part of this thesis, perceptual phenomena induced by TMS of the human visual cortex are demonstrated as results of the interactions with visual inputs. We first introduce a method to create a hole, or a scotoma, in a flashed, large-field visual pattern using single-pulse TMS. Spatial aspects of the interactions are explored using the distortion effect of the scotoma depending on the visual pattern, which can be luminance-defined or illusory. Its similarity to the distortion of afterimages is also discussed. Temporal interactions are demonstrated in the filling-in of the scotoma with temporally adjacent visual features, as well as in the effective suppression of transient visual features. Also, paired-pulse TMS is shown to lead to different brightness modulations in transient and sustained visual stimuli.

In the biophysical part, we first develop a biophysical theory to simulate the effect of magnetic stimulation on arbitrary neuronal structure. Computer simulations are performed on cortical neuron models with realistic structure and channels, combined with the current injection that simulates magnetic stimulation. The simulation results account for general and basic characteristics of the macroscopic effects of TMS including our psychophysical findings, such as a long inhibitory effect, dependence on the background activity, and dependence on the direction of the induced electric field.

The perceptual effects and the cortical neuron model presented here provide foundations for the study of the relationship between perception and neural activity. Further insights would be obtained from extension of our model to neuronal networks and psychophysical studies based on predictions of the biophysical model.

Noninterferometric measurement of lead zirconate titanate inverse piezoelectric extension

A noncontacting and noninterferometric depth discrimination technique, which is based on differential confocal microscopy, was used to measure the inverse piezoelectric extension of a piezoelectric ceramic lead zirconate titanate actuator. The response characteristics of the actuator with respect to the applied voltage, including displacement, linearity, and hysteresis, were obtained with nanometer measurement accuracy. Errors of the measurement have been analyzed. (C) 2001 Society of Photo-Optical Instrumentation Engineers.

A mathematical study of finite-amplitude rock-folding

The problem of the finite-amplitude folding of an isolated, linearly viscous layer under compression and imbedded in a medium of lower viscosity is treated theoretically by using a variational method to derive finite difference equations which are solved on a digital computer. The problem depends on a single physical parameter, the ratio of the fold wavelength, L, to the "dominant wavelength" of the infinitesimal-amplitude treatment, L_d. Therefore, the natural range of physical parameters is covered by the computation of three folds, with L/L_d = 0, 1, and 4.6, up to a maximum dip of 90°.

Significant differences in fold shape are found among the three folds; folds with higher L/L_d have sharper crests. Folds with L/L_d = 0 and L/L_d = 1 become fan folds at high amplitude. A description of the shape in terms of a harmonic analysis of inclination as a function of arc length shows this systematic variation with L/L_d and is relatively insensitive to the initial shape of the layer. This method of shape description is proposed as a convenient way of measuring the shape of natural folds.

The infinitesimal-amplitude treatment does not predict fold-shape development satisfactorily beyond a limb-dip of 5°. A proposed extension of the treatment continues the wavelength-selection mechanism of the infinitesimal treatment up to a limb-dip of 15°; after this stage the wavelength-selection mechanism no longer operates and fold shape is mainly determined by L/L_d and limb-dip.

Strain-rates and finite strains in the medium are calculated f or all stages of the L/L_d = 1 and L/L_d = 4.6 folds. At limb-dips greater than 45° the planes of maximum flattening and maximum flattening rat e show the characteristic orientation and fanning of axial-plane cleavage.

Robot navigation in dense crowds : statistical models and experimental studies of human robot cooperation

This thesis explores the problem of mobile robot navigation in dense human crowds. We begin by considering a fundamental impediment to classical motion planning algorithms called the freezing robot problem: once the environment surpasses a certain level of complexity, the planner decides that all forward paths are unsafe, and the robot freezes in place (or performs unnecessary maneuvers) to avoid collisions. Since a feasible path typically exists, this behavior is suboptimal. Existing approaches have focused on reducing predictive uncertainty by employing higher fidelity individual dynamics models or heuristically limiting the individual predictive covariance to prevent overcautious navigation. We demonstrate that both the individual prediction and the individual predictive uncertainty have little to do with this undesirable navigation behavior. Additionally, we provide evidence that dynamic agents are able to navigate in dense crowds by engaging in joint collision avoidance, cooperatively making room to create feasible trajectories. We accordingly develop interacting Gaussian processes, a prediction density that captures cooperative collision avoidance, and a "multiple goal" extension that models the goal driven nature of human decision making. Navigation naturally emerges as a statistic of this distribution.

Most importantly, we empirically validate our models in the Chandler dining hall at Caltech during peak hours, and in the process, carry out the first extensive quantitative study of robot navigation in dense human crowds (collecting data on 488 runs). The multiple goal interacting Gaussian processes algorithm performs comparably with human teleoperators in crowd densities nearing 1 person/m², while a state of the art noncooperative planner exhibits unsafe behavior more than 3 times as often as the multiple goal extension, and twice as often as the basic interacting Gaussian process approach. Furthermore, a reactive planner based on the widely used dynamic window approach proves insufficient for crowd densities above 0.55 people/m². We also show that our noncooperative planner or our reactive planner capture the salient characteristics of nearly any dynamic navigation algorithm. For inclusive validation purposes, we show that either our non-interacting planner or our reactive planner captures the salient characteristics of nearly any existing dynamic navigation algorithm. Based on these experimental results and theoretical observations, we conclude that a cooperation model is critical for safe and efficient robot navigation in dense human crowds.

Finally, we produce a large database of ground truth pedestrian crowd data. We make this ground truth database publicly available for further scientific study of crowd prediction models, learning from demonstration algorithms, and human robot interaction models in general.

Using climate extension to assist coastal decision-makers with climate adaptation

Coastal managers need accessible, trusted, tailored resources to help them interpret climate information, identify vulnerabilities, and apply climate information to decisions about adaptation on regional and local levels. For decades, climate scientists have studied the impacts that short term natural climate variability and long term climate change will have on coastal systems. For example, recent estimates based on Intergovernmental Panel on Climate Change (IPCC) warming scenarios suggest that global sea levels may rise 0.5 to 1.4 meters above 1990 levels by 2100 (Rahmstorf 2007; Grinsted, Moore, and Jevrejeva 2009). Many low-lying coastal ecosystems and communities will experience more frequent salt water intrusion events, more frequent coastal flooding, and accelerated erosion rates before they experience significant inundation. These changes will affect the ways coastal managers make decisions, such as timing surface and groundwater withdrawals, replacing infrastructure, and planning for changing land use on local and regional levels. Despite the advantages, managers’ use of scientific information about climate variability and change remains limited in environmental decision-making (Dow and Carbone 2007). Traditional methods scientists use to disseminate climate information, like peer-reviewed journal articles and presentations at conferences, are inappropriate to fill decision-makers’ needs for applying accessible, relevant climate information to decision-making. General guides that help managers scope out vulnerabilities and risks are becoming more common; for example, Snover et al. (2007) outlines a basic process for local and state governments to assess climate change vulnerability and preparedness. However, there are few tools available to support more specific decision-making needs. A recent survey of coastal managers in California suggests that boundary institutions can help to fill the gaps between climate science and coastal decision-making community (Tribbia and Moser 2008). The National Sea Grant College Program, the National Oceanic and Atmospheric Administration's (NOAA) university-based program for supporting research and outreach on coastal resource use and conservation, is one such institution working to bridge these gaps through outreach. Over 80% of Sea Grant’s 32 programs are addressing climate issues, and over 60% of programs increased their climate outreach programming between 2006 and 2008 (National Sea Grant Office 2008). One way that Sea Grant is working to assist coastal decision-makers with using climate information is by developing effective methods for coastal climate extension. The purpose of this paper is to discuss climate extension methodologies on regional scales, using the Carolinas Coastal Climate Outreach Initiative (CCCOI) as an example of Sea Grant’s growing capacities for climate outreach and extension. (PDF contains 3 pages)

Improving aquaculture through increased fisheries extension research

The present study was conducted, as an attempt to disabuse minds of practicing fish farmers and also encourage prospective farmers who are of the opinion that fish culture is not as profitable as it is widely reported. The study was carried out in an abandoned concrete water fountain tank (20m super(2)) made primarily for recreational purposes. The tank was stocked initially with 125 post fingerlings (Heterobtranchus bidorsalis) bought at rate of N30 each. Cost of feeding was N3, 149.85. Gross and net profits for the passive 2- year culture stood at N27, 149.85 and N20, 000.00 respectively. The longest fish (L sub(max)) was 64.0 com TL while the smallest 41.5cm TL (64.8%L sub(max)) and weight 1.9kg (W sub(max)) and 0.5kg 26.3%W sub(max)). Weight and length data generated from the study were examined

Convex analysis for minimizing and learning submodular set functions

The connections between convexity and submodularity are explored, for purposes of minimizing and learning submodular set functions.

First, we develop a novel method for minimizing a particular class of submodular functions, which can be expressed as a sum of concave functions composed with modular functions. The basic algorithm uses an accelerated first order method applied to a smoothed version of its convex extension. The smoothing algorithm is particularly novel as it allows us to treat general concave potentials without needing to construct a piecewise linear approximation as with graph-based techniques.

Second, we derive the general conditions under which it is possible to find a minimizer of a submodular function via a convex problem. This provides a framework for developing submodular minimization algorithms. The framework is then used to develop several algorithms that can be run in a distributed fashion. This is particularly useful for applications where the submodular objective function consists of a sum of many terms, each term dependent on a small part of a large data set.

Lastly, we approach the problem of learning set functions from an unorthodox perspective---sparse reconstruction. We demonstrate an explicit connection between the problem of learning set functions from random evaluations and that of sparse signals. Based on the observation that the Fourier transform for set functions satisfies exactly the conditions needed for sparse reconstruction algorithms to work, we examine some different function classes under which uniform reconstruction is possible.