BeeMind AI: Development of an AI-Based System to Assess Honeybee Health, Behavior, and Nutrient Effects on Learning and Memory
Due to their pollination services, honeybees are one of the most ecologically vital animals, being singlehandedly responsible for nearly 80% of global agricultural pollination [1]. However, in recent years, they have experienced large declines in populations, and as a survey reported roughly 50% of beekeepers in the US lost their honeybee colonies [2]. These losses are experienced globally due to a combination of many factors, including but not limited to habitat loss, pesticides, climate change, and other invasive species [3, 4]. One of the biggest factors attributed to the decline of honeybee colonies is the usage of pesticides, specifically neonicotinoids [3-6]. Neonicotinoid compounds have been used globally since their introduction in the early 1990s [4]. Studies have shown that neonicotinoids can have both sublethal and lethal effects on honeybees, depending on the dosages that they are exposed to, as neonicotinoids bind to nervous system receptors of honeybees [7]. These effects can range from behavior changes to altered motor functions [7-9]. Among the reported effects, one of the more significant ones is the effect of neonicotinoids on honeybee learning and memory [10, 11]. Additionally, there is a lack of availability for methods of monitoring of honeybee hives, essentially meaning that the only methods to track honeybee health are through obtrusive physical methods of inspection. This paper aims to develop a novel AI-based honeybee health assessment system, able to monitor beehives using the following functions: continuous temperature and humidity monitoring both inside and outside the hive, as well as video and audio recording to assess honeybee health as well as population. In addition, this system can be used for honeybee-related studies such as nutrition effects and evaluation on health, learning, and memory. To do this, four types of nutrition have been studied and their effects have been analyzed by a deep learning approach.
A Humanoid Robot on the Basis of Modules Controlled Through a Serial Half-Duplex UART Bus
This thesis presents the design and construction of a small-scale humanoid robot, covering all aspects from 3D modeling to electronics design and programming. The robot is built entirely from custom 3D-printed components, with a new servomotor developed specifically to meet the project’s requirements. During the robot’s development, custom electronics were also designed, leading to a modular platform that enables easy interaction with diverse modules like servomotors and inertial measurement unit (IMU) modules. This modular approach allows these components to be programmed and controlled with minimal adjustments, as well as making development of potential future modules straightforward. The robot is operated via a computer application that includes a graphical user interface for displaying real-time data from the robot.
Equation of Ellipse over Fp and Pairs of Quadratic Residues/Nonresidues Related to Catalan Numbers
The equation of an ellipse and quadratic residues are well-known concepts in elementary geometry and number theory, respectively. While the properties of ellipse equations in Euclidean space have been extensively studied, many characteristics of quadratic residues, such as consecutive quadratic residues, have also been explored in past research. In this study, we discovered the characteristic polynomial of the equation of an ellipse over finite fields Fp, a single-variable polynomial that shares the same roots as the ellipse. Furthermore, by examining the parallels between the equation of an ellipse and the pairs of residues and nonresidues, we derived a characteristic polynomial for this concept and demonstrated its connection to the Catalan number, a significant sequence in combinatorics. This research was conducted through the following steps. First, the power sums of the roots of the ellipse in Fp were calculated using the Legendre symbol and Euler’s criterion. Next, the characteristic polynomial of the ellipse was determined using Newton’s identity, generating functions, and Vieta’s theorem. Finally, leveraging the equivalence between the equation of the ellipse and the pairs of residues and nonresidues, we established the main results connecting these two concepts with Catalan numbers.
Revolutionizing Metabolic Health: The Therapeutic Potential of Next-Generation Probiotic Akkermansia Strains (Z62, IR119) for Metabolic Syndromes
The human gut microbiome is integral to digestion, overall health, and metabolic disorder imbalances. Recent advancements in fecal microbiota transplantation (FMT) have highlighted the therapeutic promise of restoring healthy gut microbiota in populations with high incidences of diseases. Focusing on fecal DNA samples from healthy Asian individuals, this study examines the potential of novel Akkermansia strains, specifically Akkermansia muciniphila (Z62) and Akkermansia massiliensis (IR119), as next-generation probiotics for mitigating metabolic syndrome. A key aspect of the study is the investigation of short-chain fatty acids (SCFAs), which are produced and play a crucial role in regulating metabolic processes. SCFAs such as butyrate, acetate, and propionate are essential for energy provision to colon cells and exerting anti-inflammatory effects. The methodology involves selecting two Akkermansia strains, analyzing them through 16S rRNA and WGS, evaluating their growth and survival rates under acidic and bile-salt conditions, alongside their cell adhesion capabilities. The study focuses on the production of key short-chain fatty acids (SCFAs) and tryptophan derivatives by bacteria in regulating metabolic processes, as well as their anti-inflammatory effects on colon cells. Through in vitro assays, both strains exhibited survival in acidic/bile-rich conditions, though Z62 demonstrated superior adhesion to Caco-2 cells, suggesting a higher colonization potential. Metabolomic analysis revealed both strains produce SCFAs, including propionic and acetic acids, and indole metabolites, such as indole-3-propionic acid and indole-3-acetic acid, which are known to influence lipid metabolism and insulin sensitivity. In adipocyte cell models, IR119 significantly reduced lipid accumulation, while Z62 increased lipid presence. Furthermore, IR119 reduced pro-inflammatory cytokine levels, including IL-6 and TNF-α, suggesting potential for inflammation mitigation. The future potential of IR119 as a therapeutic probiotic is extraordinary in addressing complex metabolic and inflammatory diseases, which open new avenues for managing chronic inflammatory conditions like type 2 diabetes and cardiovascular disease. Future clinical trials could refine IR119’s efficacy, positioning it as a leading probiotic in preventive and therapeutic contexts.