An agent-based model of jaguar movement through conservation corridors

Wildlife corridors mitigate against habitat fragmentation by connecting otherwise isolated regions, bringing well established benefits to conservation both in principle and practice. Populations of large mammals in particular may depend on habitat connectivity, yet conservation managers struggle to optimise corridor designs with the rudimentary information generally available on movement behaviours. We present an agent-based model of jaguars (Panthera onca), scaled for fragmented habitat in Belize where proposals already exist for creating a jaguar corridor. We use a leastcost approach to simulate movement paths through alternative possible landscapes. Six different types of corridor and three control conditions differ substantially in their effectiveness at mixing agents across the environment despite relatively little difference in individual welfare. Our best estimates of jaguar movement behaviours suggest that a set of five narrow corridors may out-perform one wide corridor of the same overall area. We discuss the utility of ALife modelling for conservation management

Integrating Career-Connected Learning and Academics in K-12: Starting the Conversation

This improvement science inquiry uses a Plan, Do, Study, Act cycle (PDSA) to study the knowledge, attitudes, and behavioral change of academic leaders regarding integrating career connected learning (Langley, 2014). This model utilizes continuous improvement as a process to implement small changes with the goal of making long-term improvement (Shakman, et. al, 2017). This study includes educational leaders from nine school districts that are part of a consortium of schools whose students attend the career and technical school. The literature indicates that integrated career and academic curricula for workforce and post-secondary education can better prepare students to compete in a 21st century economy (Gentry, Peters, Rizza, 2008). Students, however, often exhibit a lack of technical skills needed for future careers (Capelli, 2015). Currently, modern educational models strongly emphasize traditional disciplines instead of connecting skills and academics to modern careers (Gammil, 2015). A professional development meeting held at the career and technical school in early October 2019 gathered educational leaders from the nine sending school districts. The participants included principals, assistant principals, directors of special populations, and school counselors. Participants of the professional development session completed an “entry ticket” survey several weeks prior to attending the meeting. The responses from the survey helped develop a portion of the professional development meeting. Participants then completed an “exit ticket” survey at the conclusion of the meeting. These two surveys were compared to the interviews conducted later in January to analyze the knowledge, attitudes and behaviors of the participants. The professional development meeting provided the opportunity to address the knowledge and attitudes of the participants and for participants to identify actions they were willing to take to integrate career-connected learning at their school or district. The PDSA cycle helped to build greater capacity in the knowledge, attitudes, and behaviors about career-connected learning among the districts and career center in this study (Langley, 2014). After the surveys and interviews were matched and analyzed, it revealed growth among the participants in knowledge, attitudes, and behavior. These participants were then able to increase capacity of knowledge, attitudes and behavior in their schools and districts

Natural History of the Marbled Salamander, Ambystoma opacum (Gravenhorst), in West Virginia, with Special Notes on Reproduction and Larval Development

This study observed a population of Ambystoma opacum in Kanawha State Forest, Kanawha County, five miles south of Charleston, West Virginia. Data were collected from September 1995 to November 1997. Reproductive biology and salamander development were examined. Predation of A. opacum and potential predators were also noted. Like other species in the genus Ambystoma, adult A. opacum are mostly fossorial and rarely seen above ground outside of the breeding season. During the breeding season, adults make their way to the breeding pools. Males ventured to the breeding pool first in early September and ranged in size from 49 to 75mm snout-vent length (SVL). Females arrive a few days later and ranged in size from 59 to 77mm SVL. In this study, two females were found over 160 meters from the breeding pool. During courtship a male deposits a spermatophore on the substrate and the female clips the sperm packet off the top with the lips of her cloaca. Two spermatophores were collected and described. Successful courtship can occur at the breeding pond, or during the migration to the pond. Ambystoma opacum differs from other West Virginia ambystomatids in three ways: 1) breeding occurs in the fall, 2) the female lays eggs in a terrestrial habitat and 3) females generally brood the eggs for some time, but will abandon the nest if disturbed. This habit of brooding may increase viability of the eggs by deterring potential predators and decreasing the chance of fungal growth on the eggs. A clutch of eggs is comprised of eggs laid singly in a crudely excavated nest found mostly under logs in this study. Clutch size ranged from 61 to 113 eggs. Eggs were laid directly on the soil in most nests. Ambystoma opacum eggs are well adapted to a terrestrial habitat and readily dehydrate/hydrate while the embryo develops inside. This was witnessed on numerous occasions. Eggs hatch when the nest is inundated with water and will do so while the embryo is at various stages of development; from early stages with a great deal of the yolk sac remaining to well-developed, late-stage larvae. In this population, newly hatched larvae possessed well-developed forelimbs and were approximately 17.0mm total length. Once hatched, larvae are mostly nocturnal, but could be observed on cloudy, overcast days. Stratification of larvae was not witnessed. Larvae attained total lengths of over 45mm by mid-May but were not observed to transform. Literature reports numerous predators of A. opacum eggs and larvae, many of which were observed at the study site. Predation was observed by rusty blackbirds. Larvae at this time were approximately 30mm TL. Although no oophagy was witnessed, a red-spotted newt was observed on an unbrooded nest

An Economic Risk Analysis of No-Till Rice Management from the Landlord’s Perspective

Rice production generally involves intensive cultivation. The profitability of no-till rice has been investigated but solely from the producer’s perspective. Most farmed cropland is owned by someone else. This study evaluates the risk efficiency of no-till rice from the landlord’s perspective using stochastic efficiency with respect to a function (SERF).Crop Production/Industries,

Whole Farm Economic Evaluation of No-Till Rice Production in Arkansas

Rice in Arkansas is typically produced using intensive tillage. No-till rice has been studied, but the research focus has been limited to impacts on yields and per acre net returns. This analysis evaluates the profitability of no-till rice at the whole-farm level using both enterprise budget analysis and linear programming.Crop Production/Industries,

Efficient Retrieval of Images with Irregular Patterns using Morphological Image Analysis: Applications to Industrial and Healthcare datasets

Image retrieval is the process of searching and retrieving images from a database based on their visual content and features. Recently, much attention has been directed towards the retrieval of irregular patterns within industrial or medical images by extracting features from the images, such as deep features, colour-based features, shape-based features and local features. This has applications across a spectrum of industries, including fault inspection, disease diagnosis, and maintenance prediction. This paper proposes an image retrieval framework to search for images containing similar irregular patterns by extracting a set of morphological features (DefChars) from images; the datasets employed in this paper contain wind turbine blade images with defects, chest computerised tomography scans with COVID-19 infection, heatsink images with defects, and lake ice images. The proposed framework was evaluated with different feature extraction methods (DefChars, resized raw image, local binary pattern, and scale-invariant feature transforms) and distance metrics to determine the most efficient parameters in terms of retrieval performance across datasets. The retrieval results show that the proposed framework using the DefChars and the Manhattan distance metric achieves a mean average precision of 80% and a low standard deviation of 0.09 across classes of irregular patterns, outperforming alternative feature-metric combinations across all datasets. Furthermore, the low standard deviation between each class highlights DefChars' capability for a reliable image retrieval task, even in the presence of class imbalances or small-sized datasets.Comment: 35 pages, 5 figures, 19 tables (17 tables in appendix), submitted to Special Issue: Advances and Challenges in Multimodal Machine Learning 2nd Edition, Journal of Imaging, MDP

ForestMonkey: Toolkit for Reasoning with AI-based Defect Detection and Classification Models

Artificial intelligence (AI) reasoning and explainable AI (XAI) tasks have gained popularity recently, enabling users to explain the predictions or decision processes of AI models. This paper introduces Forest Monkey (FM), a toolkit designed to reason the outputs of any AI-based defect detection and/or classification model with data explainability. Implemented as a Python package, FM takes input in the form of dataset folder paths (including original images, ground truth labels, and predicted labels) and provides a set of charts and a text file to illustrate the reasoning results and suggest possible improvements. The FM toolkit consists of processes such as feature extraction from predictions to reasoning targets, feature extraction from images to defect characteristics, and a decision tree-based AI-Reasoner. Additionally, this paper investigates the time performance of the FM toolkit when applied to four AI models with different datasets. Lastly, a tutorial is provided to guide users in performing reasoning tasks using the FM toolkit.Comment: 6 pages, 5 figures, accepted in 2023 IEEE symposium series on computational intelligence (SSCI

The Benefits of Strategic Entrepreneurship: A case study of Habitual Entrepreneurs

This management project aims to address the benefits of applying the concept of Strategic Entrepreneurship from the experience of habitual entrepreneurs. Of special concern will be the connection between possessing an entrepreneurial mindset and providing entrepreneurial leadership and culture in the search for new opportunities and the creation of continuous innovation. The opportunity-seeking behaviour of both portfolio and serial entrepreneurs will be examined closely and any learning curve effects deriving from previous business-ownership will be assessed. Of special interest will be the question how habitual entrepreneurs strike the balance between exploiting their current business(es) and exploring for new opportunities. We also want to discover whether the benefits of Strategic Entrepreneurship can ease the transition from being novice entrepreneur to becoming an habitual entrepreneur. In the course of this management project, data will be collected through in-depth interviews with entrepreneurs of roughly the same age group who have shown a track record of previous business experience and can be categorised as either serial or portfolio entrepreneurs. Through these interviews, the entrepreneurs will be given the chance to analyse their thinking and behaviour in terms of searching for opportunities and pursuing opportunities, which they deem to be worth it. The findings show that although some entrepreneurs do not actively search for new opportunities, their opportunity-seizing behaviour changes over time with business experience and with growing confidence they usually find it easier the second or third time around to start a new business. The wealth created through previous business(es) does indeed help to facilitate the transition from novice to habitual entrepreneur. As for the research limitations, it has to be mentioned that the scope of this study is limited and therefore generalisations for entrepreneurial behaviour cannot easily be drawn. An important implication to note is that, in the current economic climate, all entrepreneurs face the pressing need of making the most out of their resources in order to stay ahead of the competition

Morphological Image Analysis and Feature Extraction for Reasoning with AI-based Defect Detection and Classification Models

As the use of artificial intelligent (AI) models becomes more prevalent in industries such as engineering and manufacturing, it is essential that these models provide transparent reasoning behind their predictions. This paper proposes the AI-Reasoner, which extracts the morphological characteristics of defects (DefChars) from images and utilises decision trees to reason with the DefChar values. Thereafter, the AI-Reasoner exports visualisations (i.e. charts) and textual explanations to provide insights into outputs made by masked-based defect detection and classification models. It also provides effective mitigation strategies to enhance data pre-processing and overall model performance. The AI-Reasoner was tested on explaining the outputs of an IE Mask R-CNN model using a set of 366 images containing defects. The results demonstrated its effectiveness in explaining the IE Mask R-CNN model's predictions. Overall, the proposed AI-Reasoner provides a solution for improving the performance of AI models in industrial applications that require defect analysis.Comment: 8 pages, 3 figures, 5 tables; submitted to 2023 IEEE symposium series on computational intelligence (SSCI