Mesa Community College

This dissertation explores the convergence of artificial intelligence (AI) and Internet of Things (IoT) technologies within sustainable agriculture. The goal is to automate crop monitoring via drone-based imaging and computer vision specifically, for keeping tabs on disease, soil health, and irrigation needs. There’s a particular focus here on the worldwide issue of refining crop monitoring techniques to boost agricultural output and reduce agriculture's negative impacts on the environment. Evidence suggests that pairing high-resolution (5 cm) aerial images with both soil moisture data and disease incidence records can really improve crop monitoring results. Indeed, AI image processing algorithms, as the research demonstrates, lead to roughly a 30% improvement in identifying diseases. Furthermore, they facilitate up to a 25% cut in agricultural water use through precisely timed irrigation. Generally speaking, these findings should influence agricultural practices; what's more, they have the potential to make food systems more sustainable. Benefits could include increased yields, less wasted resources, and, ultimately, better public health thanks to enhanced food security and nutritional quality. This dissertation offers some innovative and practical solutions to current agricultural problems. Plus, it showcases the importance of interdisciplinary strategies when tackling complex, global challenges related to both agriculture and environmental sustainability.

Abstract Although culture contact is a well-studied area of archaeological inquiry, complex ancient cross-cultural interactions can be challenging to discern. As zones of innovation in which boundaries are obscure, ancient frontiers offer ideal contexts to analyze the nuances of such interactions. To address the challenges of interpreting a multicultural frontier in the Moquegua Valley, southern Peru, we apply a practice-based approach using foodways to elucidate the complexity of culture contact between Wari-affiliated and Indigenous Huaracane communities during the Middle Horizon (AD 600–1000). Our findings indicate that after Wari colonization of the Moquegua frontier, the Huaracane community at Yahuay Alta began brewing chicha de molle, an alcoholic beverage associated with and central to Wari political and religious social structures. They did not, however, adopt the practice in a completely Wari fashion. Instead, we see Huaracane leaders brewed and served chicha de molle in ways that aligned with their own cultural practices. The material remains of chicha de molle production and consumption at Yahuay Alta should not be seen as a simple adoption of a nonlocal cultural practice by an Indigenous group, but instead an active manipulation of practice as part of frontier cultural negotiations and entanglements.

Hyperparameter optimization is an essential component in many data science pipelines and typically entails exhaustive time and resource-consuming computations in order to explore the combinatorial search space. Similar to this problem, other key operations in data science pipelines exhibit the exact same properties. Important examples are: neural architecture search, where the goal is to identify the best design choices for a neural network, and query cardinality estimation, where given different predicate values for a SQL query the goal is to estimate the size of the output. In this paper, we abstract away those essential components of data science pipelines and we model them as instances of tensor completion, where each variable of the search space corresponds to one mode of the tensor, and the goal is to identify all missing entries of the tensor, corresponding to all combinations of variable values, starting from a very small sample of observed entries. In order to do so, we first conduct a thorough experimental evaluation of existing state-of-the-art tensor completion techniques and introduce domain-inspired adaptations (such as smoothness across the discretized variable space) and an ensemble technique which is able to achieve state-of-the-art performance. We extensively evaluate existing and proposed methods in a number of datasets generated corresponding to (a) hyperparameter optimization for nonneural network models, (b) neural architecture search, and (c) variants of query cardinality estimation, demonstrating the effectiveness of tensor completion as a tool for automating data science pipelines. Furthermore, we release our generated datasets and code in order to provide benchmarks for future work on this topic.

Virtual learning environments (VLEs) in physical geography education offer significant potential to aid students in acquiring the essential skills for the environmental interpretation of glacial and periglacial environments for geoscience careers. Simulated real-world field experiences aim to help the student evaluate landscapes for natural hazards, assess their intensity, and translate and communicate this information to various stakeholders in human systems. The TREE-PG framework and VRUI model provide a philosophical and practical foundation for VLE architects, aiming to cultivate students’ knowledge, skills, and identity as geoscientists, specifically as physical geographers and geomorphologists. These frameworks emphasize the importance of translating scientific knowledge from physical features into engaging, accessible online lessons, exemplified by landscapes like those in Glacier National Park, Montana. Open-source software and open educational resources (OERs) can broaden access and incorporate diverse perspectives in these experiences, which are necessary to address the impacts of vulnerable communities to global deglaciation. Designing and creating virtual proxies of field-based education may help address issues associated with inclusion and belonging within geoscience disciplines to connect all students with dynamic physical environments beyond the classroom. Ethical AI approaches and discipline-specific repositories are needed to ensure high-quality, contextually accurate VLEs. AI’s tendency to produce output necessitates using domain-specific guardrails to maintain relevance and precision in virtual educational content.

Sentiment analysis is the understanding of opinions, feelings, or attitudes conveyed in texts, such as tweets, reviews, or other forms. The film “Taylor Swift: The Eras Tour” is trending among teenager, narrating Taylor Swift’s journey in the “Eras Tour” concert across various countries, encapsulated in a music-filled film. This has prompted research on sentiment analysis of netizens’ tweets about this film, considering the possibility of negative reviews. Three algorithms, Naïve Bayes, Decision Tree, and Random Forest. were used with an 80:20 data ratio and the SMOTE oversampling method, which is a unique in this research to ensure data sizes for the three sentiments: positive, negative, and neutral. The final result of this research is a word cloud for each sentiment towards the film, with the Decision Tree algorithm achieving the highest accuracy at 91%. The hope for future research is to implement and focus on the emotional aspect in conducting sentiment analysis.

with a B.S. and Stanford with a Ph.D. in engineering.He worked in the semiconductor industry for many years on a wide variety of projects such as ion-implantation, oxidation, diffusion, metalsemiconductor contacts, device physics, CCDs, LEDs, electronic noise, high voltage devices, radiation-hardened devices, thermal management, statistical analysis of yield, statistical process control, plasma process, IC materials adhesion and fracture, software, etc.He turned to community college teaching in 2005 and has worked as an instructor in engineering, physics, and math until the present time.Jim's interests are improving the engagement of engineering students and bringing more experiential learning to the students to better prepare them for real-world engineering work.

The article presents the clinical case of a young woman with the dirofilariosis of the nose slope on the right side. This disease is quite rare, and therefore there is a difficulty in setting the correct one. This case is a professional interest both for young doctors and for experienced specialists. Specialists,will be able, if necessary, to correctly diagnose and effectively treat this disease analyzing the given clinical example.

Many school-based physical activity statutes and regulations have been enacted, with the expectation that schools will comply. However, policy alone does not equate to implementation, and many policies fail for a variety of reasons. The purpose of the study was to determine whether the strength of reported state, district, and school-level physical activity policies were associated with reported recess, physical education, and other school-based physical activity practices at elementary schools in Arizona. A modified Comprehensive School Physical Activity Program(CSPAP) Questionnaire was administered to staff at elementary schools across Arizona (N = 171). Summative indices of the number of school physical activity policies and best practices at the state, district, and school levels were created. Relationships between policy strength and best practices were examined using linear regression analyses stratified by recess, physical education, and other school-based physical activity practices. Stronger physical activity-related policies were associated with a greater number of recess (F1,142 = 9.87, P < .05), physical education (F4,148 = 4.58, P < .05, Adj. R2 = .09), and other school-based physical activity (F4,148 = 4.04, P < .05, Adj. R2 = .07) best practices at all levels while controlling for school-level demographic factors. The strength of policies may improve opportunities for comprehensive physical activity for children in schools. Strengthening policy language (eg,specifying duration and frequency) may contribute to better physical activity practices in schools, improving children's health at the population level.