Replicating sucess: a model for conservation and development projects

"This study investigates how successful development and conservation projects can be replicated without investing inordinate amounts of money and manpower in the planning process. It offers examples and ideas from 12 different development fields, details key requirements, outlines the strengths and weaknesses of these components and shows how best practices can be emulated elsewhere." [author's abstract

Enhancing Student Learning through Proactive Feedback Based Adaptive Teaching for Engineering Courses

This paper presents a proactive anonymous feedback based adaptive teaching for enhancing student learning for engineering courses. In conventional university teaching, typically, students come to the class and instructors lecture the material, assign home assignments, take exams, etc. After grading assignments or exams, the instructor provides feedback to students. Most of the time, students are reluctant to ask questions or ask instructor to revisit the topic which was already covered. However, there is no immediate anonymous feedback mechanism for each topic or class to notify the instructor about topics which are not clear to students. There are advantages that enhance students’ learning experience by using a proactive anonymous feedback approach in teaching, learning and assessment. In this paper, we present the immediate impacts of proactive anonymous feedback based adaptive teaching on student learning and assessment. Furthermore, anonymous online based feedback mechanism provides faster feedback than conventional mechanism (where students wait until the first exam or so). Immediate feedback for each topic discussed in the class streamlines the process of reporting and the provision of active studying. The results show that students get better grade and instructors get better student evaluation score since the anonymous feedback provides a mechanism for students to ask questions anonymously and the instructors get an opportunity to answer the questions or concerns in a timely manner. We implemented the proactive anonymous feedback approach in many courses in different semesters and observed similar results. However, as an example, we present one course and instructor to illustrate the effectiveness of the proposed approach

Reforms, Incentives and Banking Sector Productivity: A Case of Nepal

We model banks as profit-cum-utility maximizing firms and study, inter alia, bankers' incentives (optimal effort) and incentive driven productivity following deregulations. Our model puts to test a panel of Nepalese commercial banks which went through deep financial reforms in the recent past. We find that (i) bankers' efforts and productivity have notably improved in Nepal, (ii) bankers' efforts significantly explain the banking sector's productivity, (iii) the proportion of non-performing loans has considerably declined, and (iv) banking services have become costly, although the bank spread has moderately declined. Our approach is different from the widely used data envelopment analysis (DEA) of bank productivity, hence complements the literature. It also informs the current policy debate in Nepal where the Central Bank is seen to be geared towards regulating the financial system and micro-managing the banking institutions

Self-Supervised Learning of Terrain Traversability from Proprioceptive Sensors

Robust and reliable autonomous navigation in unstructured, off-road terrain is a critical element in making unmanned ground vehicles a reality. Existing approaches tend to rely on evaluating the traversability of terrain based on fixed parameters obtained via testing in specific environments. This results in a system that handles the terrain well that it trained in, but is unable to process terrain outside its test parameters. An adaptive system does not take the place of training, but supplements it. Whereas training imprints certain environments, an adaptive system would imprint terrain elements and the interactions amongst them, and allow the vehicle to build a map of local elements using proprioceptive sensors. Such sensors can include velocity, wheel slippage, bumper hits, and accelerometers. Data obtained by the sensors can be compared to observations from ranging sensors such as cameras and LADAR (laser detection and ranging) in order to adapt to any kind of terrain. In this way, it could sample its surroundings not only to create a map of clear space, but also of what kind of space it is and its composition. By having a set of building blocks consisting of terrain features, a vehicle can adapt to terrain that it has never seen before, and thus be robust to a changing environment. New observations could be added to its library, enabling it to infer terrain types that it wasn't trained on. This would be very useful in alien environments, where many of the physical features are known, but some are not. For example, a seemingly flat, hard plain could actually be soft sand, and the vehicle would sense the sand and avoid it automatically

Equipment Using a Predictive Health Model

Abstract—In this paper, a model-predictive control based framework is proposed for modeling and optimization of the health state of power system equipment. In the framework, a predictive health model is proposed that predicts the health state of the equipment based on its usage and maintenance actions. Based on the health state, the failure rate of the equipment can be estimated. We propose to use this predictive health model to predict the effects of different maintenance actions. The effects of maintenance actions over a future time window are evaluated by a cost function. The maintenance actions are optimized using this cost function. The proposed framework is applied in the optimization of the loading of transformers based on the thermal degradation of the paper insulation

Vehicle Detection for RCTA/ANS (Autonomous Navigation System)

Using a stereo camera pair, imagery is acquired and processed through the JPLV stereo processing pipeline. From this stereo data, large 3D blobs are found. These blobs are then described and classified by their shape to determine which are vehicles and which are not. Prior vehicle detection algorithms are either targeted to specific domains, such as following lead cars, or are intensity- based methods that involve learning typical vehicle appearances from a large corpus of training data. In order to detect vehicles, the JPL Vehicle Detection (JVD) algorithm goes through the following steps: 1. Take as input a left disparity image and left rectified image from JPLV stereo. 2. Project the disparity data onto a two-dimensional Cartesian map. 3. Perform some post-processing of the map built in the previous step in order to clean it up. 4. Take the processed map and find peaks. For each peak, grow it out into a map blob. These map blobs represent large, roughly vehicle-sized objects in the scene. 5. Take these map blobs and reject those that do not meet certain criteria. Build descriptors for the ones that remain. Pass these descriptors onto a classifier, which determines if the blob is a vehicle or not. The probability of detection is the probability that if a vehicle is present in the image, is visible, and un-occluded, then it will be detected by the JVD algorithm. In order to estimate this probability, eight sequences were ground-truthed from the RCTA (Robotics Collaborative Technology Alliances) program, totaling over 4,000 frames with 15 unique vehicles. Since these vehicles were observed at varying ranges, one is able to find the probability of detection as a function of range. At the time of this reporting, the JVD algorithm was tuned to perform best at cars seen from the front, rear, or either side, and perform poorly on vehicles seen from oblique angles

Development of a 1 kW Gravitational Water Vortex Hydropower Plant Prototype

A pilot testing of a Gravitational Water Vortex Hydropower Plant (GWVHP) has been done to evaluate the applicability in a real-world scenario and validate the results from the lab-scale model. A scaled-up model of a capacity of 1 kW was constructed for the evaluation purpose. The test provided data in good agreement with a lab-scale model and a proper visualization to install Gravitational Water Vortex in real-world scenarios. The project lasted for nearly four months and thus provided important information on the problems that might arise in scaling up the lab model to a micro-hydro system. The pilot testing shows an overall plant efficiency of 49%, validating the lab-based studies conducted beforehand. The information obtained from this pilot study shall be implemented in a micro-hydro project on a larger scale