Aboveground-belowground interactions: roles of soil biotic and abiotic factors on switchgrass\u27s (panicum virgatum) growth, defense against herbivory and cell wall chemistry.

Plants constantly interact with their biotic and abiotic soil environments. Most terrestrial plants form beneficial associations with soil microbes such as arbuscular mycorrhizal (AM) fungi, which are widely known for their ability to transfer soil phosphorus and nitrogen to the host plants. They help plants tolerate drought stress and improve plant defense against herbivores such as plant-parasitic nematodes (PPNs). This dissertation investigates the role of AM fungi on switchgrass’s (Panicum virgatum) growth, cell wall chemistry, and defense against PPNs using a combination of growth chamber and field studies. Switchgrass is a native warm-season species which is gaining traction as candidate lignocellulosic biofuel crop. It forms a tight association with AM fungi, and PPNs like Pratylenchus penetrans can become a potential threat when the plant is grown in monocultures. My first study was a growth chamber experiment where I manipulated the absence and presence of an AM fungal species to examine its effects on switchgrass’s growth and defense against Pratylenchus penetrans under drought conditions. I found that AM fungi increased root biomass under drought conditions and reduced the abundance of the PPN in plant roots by about 66%. I followed this study with a field experiment where I manipulated soil fungi and nematodes by applying biocides. I found that the application of biocides resulted in an altered monomeric composition of lignin in switchgrass. My final study was a growth chamber study where I looked at the effects of AM fungi on switchgrass along a drought intensity gradient. Overall, I found that AM fungi provided maximum benefit to the plants in extreme drought and the benefit declined with increasing moisture indicating that the functioning of AM fungi can span a mutualism-parasitism continuum. Mycorrhizal plants also had increased root biomass, cellulose, and hemicellulose regardless of drought treatment. These studies provide evidence that AM fungi can have wide ranging effects on switchgrass growth and physiology and these effects can sometimes be altered by the soil abiotic environments. Overall, my results suggest that plant-AM fungal symbiosis can become ever more important in the face of climate change

Pandey\u27s Method of Cube Root Extraction: Is it Better than Aryabhata’s Method?

We compare two methods of cube root extraction: one proposed by the Nepali mathematician Gopal Pandey in the 19th century, which uses proportionality, and another one provided by the Indian mathematician and astronomer Aryabhata

Engaging Students with High-stakes Problems

Engaging students in meaningful mathematics problem-solving is the intention of many education stakeholders around the world. Research suggests that the implementation of high-stakes problems in mathematics teaching is one way to strengthen students’ conceptual understanding. Many carefully crafted open-ended problems constitute high-stakes problems, and proper use of such problems in teaching and learning not only encourages learners’ flexible thinking but also helps detect their misconceptions. However, what is less practiced and understood is: how exactly one should aim to implement such problems in a classroom setting. Teaching pre-service middle school teachers for a few years using high-stakes (mostly open-ended problems) has given me insights that may be useful to teachers around the world. In this paper, we share my experience of teaching with high-stakes problems. We will demonstrate how user-friendly interactive graphing tools can be used in the creative process of problem-solving

Living with landslides: Integrating knowledge for landslide risk reduction in rural Nepal

Nepal is a country highly exposed to the impact of climate change, a situation that is set against the ongoing experience of year-on-year losses from environmental disasters. Of these, landslides result in the highest number of fatalities, a situation that is attributed to a combination of factors: the monsoon climate, the steep unstable topography and a large and often highly vulnerable rural population. Unfortunately, Nepal’s experience in this respect is also of global significance: between 2004 and 2016 the country accounted for approximately 10% of all rainfall-triggered landslide fatalities, despite occupying less than 0.1% of the Earth’s land area. The majority of these incidents are experienced as a result of relatively small-scale localised landslides, the perennial nature of which means they are often seen as part of life, despite their significant chronic impact on people’s livelihoods. This chronic background hazard was then overprinted by the Mw7.8 2015 Gorkha earthquake, which resulted in a significant number of fatalities and had a further devastating impact on people’s livelihoods. It also caused additional landslides across Central and Western Nepal. Six years later, these impacts are still being felt. As a result, there is a real need to build greater resilience to landslide risks in rural Nepal; however, efforts to do this lack innovation, and are relatively limited in number and success. To tackle the problem, this research presents a study of a valley badly hit by the 2015 earthquake where the residents have to live alongside active landslides. The research starts with a household survey to explore the depth of understandings of landslides, the risks they pose and how these features in day-to-day lives. A participatory mapping exercise follows; this seeks to explore in more detail the geographical dimensions of local risk awareness, highlighting several knowledge gaps with regard to why, where and when landslides may occur. Finally, the research presents the development of a novel live demonstration system, which models an actively failing slope to allow participants to gain more insight into the mechanisms of the landslides around them and the risks they pose. Critically, the demonstrator provides a way of visualising and evaluating potential forms of landslide mitigation, such as monitoring or small-scale engineering interventions, that could help to reduce these risks in future. The thesis concludes by considering how this approach might be developed further as a means of reducing landslide risks in rural Nepal

Tikaram and Chandrakala Dhananjaya: A collaborative couple in mathematics from Nepal

Within the history of mathematics and mathematics education in Nepal, Tikaram and Chandrakala Dhananjaya are relatively well-known figures for their two books Śiśubodha Taraṅgiṇī and Līlāvatī. This is despite there being almost no archival or manuscript materials offering a window into their lives: we have no letters, notebooks, diaries, or school records. Rather than focusing on either individual in isolation, in this article we present an argument for considering the Dhananjayas as an analytically indivisible collaborative couple in mathematics. Of the two aforementioned books, one is attributed to Chandrakala and the other to Tikaram; but in fact, both are translations of the same Sanskrit source text, Līlāvatī, into Nepali. By comparing the mathematical contents of these two works, which were published within a few years of each other, we explore what it means to be an author or translator of a mathematical text and propose different models of spousal collaboration which could plausibly have been adopted by the Dhananjayas. In the absence of documentary evidence, the impossibility of delineating each individual’s contributions removes the temptation to focus exclusively on apportioning credit. Instead, we offer the alternative perspective of considering what labour must have been undertaken to bring their books to publication. This article was published Open Access through the CCU Libraries Open Access Publishing Fund. The article was first published in Endeavour: https://doi.org/10.1016/j.endeavour.2023.10089

Comparison of Land Building by Mississippi River Diversion Using One and Two Dimensional Numerical Models

River sediment diversions have been identified as one strategy for creating new land and offsetting Mississippi River delta plain land loss. Numerical modeling is one tool for estimating the amount of land, geomorphic features and ecological benefits from diversions. There are a number of models proposed to estimate sediment diversion land building, ranging from simplistic approaches that provide bulk characteristics and use little computational resources to process-based models that require a large amount of input parameters and computing power. This thesis aims to compare and contrast two approaches to simulating the land building processes in a simplified receiving basin: a 1D spatially averaged model; and a horizontal 2D, process-based Delft3D model. Four scenarios were run: three with varying amounts of non-cohesive sediment; and one with a mixture of non-cohesive and cohesive sediment. A number of simplifying assumptions were made for more direct comparisons of the bulk and detailed delta properties and the computational resources. These included the bulking of cohesive and non-cohesive sediments on deposition are assumed equal; erosion below the pre-delta strata is not allowed; and the river sediment diversion operates continuously at a given flow and sediment concentration. Note that this last assumption was made for easier model comparisons and not how any proposed diversions would be operated. Distributary channel network information, missing in the 1D model but important for ecohydrological processes, is extracted from the 2D model. The 1D model took less than one minute to simulate the same scenario that required over 20 hours on 32 processors using the 2D model. Results showed the 1D model delta radii and areas were always larger, but relatively close, to those simulated by the 2D model, particularly for non-cohesive sediments. The deltas formed from solely non-cohesive sediments had numerous short, but wide, channels and were roughly fan shaped, thus justifying the radial symmetry assumption of the 1D model. The ratios of the 2D to 1D model delta areas were 70% and 55% for non-cohesive and mixed scenarios, respectively. The 2D model results showed that presence of cohesive sediment promoted narrower and weakly sinuous channels that affect delta growth dynamics and result in increased vertical aggradation, thus limiting the area of land built

Text Summarization Using Large Language Models: A Comparative Study of MPT-7b-instruct, Falcon-7b-instruct, and OpenAI Chat-GPT Models

Text summarization is a critical Natural Language Processing (NLP) task with applications ranging from information retrieval to content generation. Leveraging Large Language Models (LLMs) has shown remarkable promise in enhancing summarization techniques. This paper embarks on an exploration of text summarization with a diverse set of LLMs, including MPT-7b-instruct, falcon-7b-instruct, and OpenAI ChatGPT text-davinci-003 models. The experiment was performed with different hyperparameters and evaluated the generated summaries using widely accepted metrics such as the Bilingual Evaluation Understudy (BLEU) Score, Recall-Oriented Understudy for Gisting Evaluation (ROUGE) Score, and Bidirectional Encoder Representations from Transformers (BERT) Score. According to the experiment, text-davinci-003 outperformed the others. This investigation involved two distinct datasets: CNN Daily Mail and XSum. Its primary objective was to provide a comprehensive understanding of the performance of Large Language Models (LLMs) when applied to different datasets. The assessment of these models' effectiveness contributes valuable insights to researchers and practitioners within the NLP domain. This work serves as a resource for those interested in harnessing the potential of LLMs for text summarization and lays the foundation for the development of advanced Generative AI applications aimed at addressing a wide spectrum of business challenges.Comment: 4 pages, 2 table

Mathematics Textbook: Motivation, Experiences, and Didactical Aspect from Authors’ Perspectives

Textbooks play a vital role in the Nepali education system since they are one of the main resources for teaching and learning mathematics. Because of poor physical infrastructure and inadequate educational resources, both teachers and students heavily rely on textbooks. In this regard, this study investigated the mathematics textbook authors\u27 experiences and motivation, and what types of didactical knowledge were utilised while writing textbooks. A convenient yet purposeful sampling method was utilised to select four participants. The data analysis unveiled that each textbook author had different types of motivation and experiences, and only one participant was aware of the ideas of didactical knowledge and utilised them to some extent in textbooks. The writing process was more influenced by the examination, mathematical content, and classroom experiences. As a result, textbooks seemed to be content-heavy and examination-oriented. Additional professional development programmes likely would help authors to produce more effective textbooks in Nepal. This article was published Open Access through the CCU Libraries Open Access Publishing Fund. The article was first published in Research in Mathematics Education: https://doi.org/10.1080/14794802.2022.208660

Histopathologic Cancer Detection

Early diagnosis of the cancer cells is necessary for making an effective treatment plan and for the health and safety of a patient. Nowadays, doctors usually use a histological grade that pathologists determine by performing a semi-quantitative analysis of the histopathological and cytological features of hematoxylin-eosin (HE) stained histopathological images. This research contributes a potential classification model for cancer prognosis to efficiently utilize the valuable information underlying the HE-stained histopathological images. This work uses the PatchCamelyon benchmark datasets and trains them in a multi-layer perceptron and convolution model to observe the model's performance in terms of precision, Recall, F1 Score, Accuracy, and AUC Score. The evaluation result shows that the baseline convolution model outperforms the baseline MLP model. Also, this paper introduced ResNet50 and InceptionNet models with data augmentation, where ResNet50 is able to beat the state-of-the-art model. Furthermore, the majority vote and concatenation ensemble were evaluated and provided the future direction of using transfer learning and segmentation to understand the specific features.Comment: 5 pages, 5 figures, 2 table