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Estimating the demand for imported green coffee in Saudi Arabia using the Almost Ideal Demand System
No full textIntroduction: Understanding the demand for imported green coffee in Saudi Arabia is crucial for stakeholders in the coffee trade. This study examines how demand varies by country of origin, focusing on Ethiopia, Brazil, India, and other exporting nations such as Colombia, Vietnam, and Kenya. Given the growing coffee consumption in Saudi Arabia, analyzing consumer preferences and market dynamics can provide insights for importers, exporters, and policymakers. Methods: This study employs the Almost Ideal Demand System (AIDS) model to estimate demand elasticities for green coffee imports. The analysis considers income and price elasticities to determine the classification of coffee imports as necessities or luxury goods, as well as cross-price elasticities to assess substitution and complementarity relationships between different origins. Results: Findings indicate that Ethiopian coffee dominates the Saudi market and is considered a luxury good with high income elasticity. In contrast, Brazilian and other coffees exhibit characteristics of necessities, with relatively stable demand. Indian coffee is highly price-elastic but maintains a smaller market share. Cross-price elasticity estimates reveal that Ethiopian and Indian coffees act as substitutes, whereas Ethiopian coffee complements imports from other sources. Discussion and conclusion: Projected trends suggest continued growth in Ethiopian, Brazilian, and Indian coffee imports, while imports from other origins may decline. These findings have practical implications for importers and marketers, who can refine sourcing and promotion strategies, and for exporters, particularly from Ethiopia and Brazil, who can strengthen their market positioning. Policymakers may leverage these insights to ensure stable coffee import flows through targeted trade policies. This study contributes to the coffee demand literature by emphasizing the role of origin differentiation in shaping market dynamics in Saudi Arabia's green coffee sector
Investigating Helical Hypersurfaces Within 7-Dimensional Euclidean Space
No full textDifferential geometry of a kind of helical hypersurface family that depends on six parameters within the seven-dimensional Euclidean space is explored. The curvatures of these hypersurfaces are determined, and their minimality is examined, along with illustrative examples being provided. Lastly, the Laplace–Beltrami operator for that kind of hypersurfaces is calculated
Characterization and Modeling of Nanocrystalline Magnetic Cores for Pulsed Power Applications
No full textMagnetic cores are critical components in pulsed power systems, including pulse compression circuits, pulse transformers, and linear transformer drivers. This work investigates the response of magnetic cores subject to pulsed voltages. The results show that higher magnetization rates lead to a widening of the hysteresis curve, with the magnetic cores' physical and magnetic properties driving this effect. Modeling the response of magnetic cores is challenging, with traditional lumped circuit methods producing inadequate results. To address the shortcomings of conventional methods, a behavioral model was developed in LTspice using an equivalent magnetic circuit approach. This model accurately captures core behavior with only a few empirical parameters, some correlated with fundamental magnetic properties, allowing for potential scalability to new materials. The model was validated against experimental data and applied to a pulse transformer, demonstrating strong agreement with measured results. This modeling approach offers a fast and practical solution for simulating magnetic core behavior in pulsed power systems, enabling many design iterations with minimal computational effort
CDRILS Flight Demonstration Unit: 2025 Update
No full textMeghan Pipitone, Honeywell Aerospace Technologies Defense & Space, United StatesSean Skomurski, Honeywell Aerospace Technologies Defense & Space, United StatesZachery Clark, Honeywell Aerospace Technologies Defense & Space, United StatesEric Pope, Honeywell Aerospace Technologies, Advanced & Applied Technology, United StatesRebecca Kamire, Honeywell Aerospace Technologies Advanced & Applied Technology, United StatesMark Triezenberg, Honeywell Aerospace Technologies Advanced & Applied Technology, United StatesDavid Gray, Honeywell Aerospace Technologies Advanced & Applied Technology, United StatesStephen F. Yates, Honeywell Aerospace Technologies Advanced & Applied Technology, United StatesChristian Junaedi, Precision Combustion Inc., United StatesKyle Hawley, Precision Combustion Inc., United StatesICES302: Physico-Chemical Life Support- Air Revitalization
Systems -Technology and Process DevelopmentThe 54th International Conference on Environmental Systems was held in Prague, Czechia, on 13 July 2025 through 17 July 2025.Honeywell Aerospace Technologies is currently developing and fabricating a Carbon Dioxide Removal by Ionic Liquid System (CDRILS) for a 3-year flight demonstration. CDRILS utilizes a continuously recirculated ionic liquid sorbent and hollow fiber membrane contactors for carbon dioxide removal from air. The CDRILS flight demonstration unit (FDU) design incorporates an integrated Sabatier reactor which saves overall system power and recovers water which can then be returned to the Oxygen Generation Assembly (OGA). The cost benefits of an integrated Sabatier reactor are described. Precision Combustion, Inc. (PCI) fabricated, performed a Factory Acceptance Test (FAT), and delivered a Sabatier Reactor Engineering Unit to Honeywell Aerospace Technologies for integration with CDRILS. CDRILS dormancy stability will be quantified by operating breadboard and prototype scale CDRILS test stands after over one year of dormancy. The path for a CDRILS flight demonstration on the ISS is outlined
The Ethics of Representation and Nonviolence in Cristina Rivera Garza's El invencible verano de Liliana
No full textThis thesis explores the ethical challenges in representing gendered violence by examining the ethical representation of femicide in Cristina Rivera Garza’s El invencible verano de Liliana (2021). Drawing on Karen Barad's concept of intra-action, which emphasises the mutual constitution of entangled agencies, in contrast to 'interaction', which assumes the existence of separate individual agencies, I analyze how the materials, fragmented form and embodied language in the book challenge dominant victim narratives and conventional methods of representing gendered violence. The book's ethicality is underscored by the formal, material and linguistic intra-actions that resist a simplistic narrative of victims of femicide and offer a nuanced understanding of the forces that engage and contribute to the phenomena of femicide in the Mexican context. I explore how these textual intra-actions work to recognise Liliana’s life and humanity, aligning with Judith Butler’s concept ‘nonviolence’ from Frames of War (2010), which is an ethical and political position that is characterized by a recognition of social bonds in the face of violence and subsequent refusal to reify violence in response. My approach illuminates how the book challenges dominant representation, shifting away from reductive narratives of victims of femicide towards a more nuanced understanding, highlighting the urgent need for new ways of addressing femicide in relation to its ongoing crisis in Mexico
Evaluating Food Safety Interventions: Salmonella serovar variation in response to antimicrobial treatments and FSMA Capacity Building for Latin American Companies
No full textThe increase in global population directly impacts the demand for food, leading to a rise in international food trade to attain these necessities. This results in more complex food supply chains and increases their vulnerability to threats such as contamination or adulteration of food products. To effectively protect public health, more than one single approach to solve these problems is necessary, including the establishment of standardized regulatory standards and the implementation of targeted strategies to mitigate foodborne pathogens. This thesis explores two approaches to ensure food safety and protect public health, from the perspectives of microbial control of Salmonella and improving regulatory compliance in international stakeholders interested in exporting to the U.S.
The first project investigates the difference in response to two chemical antimicrobial interventions (sodium hypochlorite and peracetic acid) of seven Salmonella serovars, both in biofilm and planktonic stages. Salmonella remains one of the leading causes of foodborne outbreaks worldwide, this could be related to the genetic differences that exist between serovars, including different mechanisms that would allow for some of them to be more tolerant to antimicrobial treatments. Additionally, it has been reported that bacterial biofilms are harder to remove from food contact surfaces than their planktonic counterparts. The findings of this study support this statement and evidence the need for the industry to re-evaluate their cleaning and disinfection practices, as treatment effectiveness varied between serovars.
On the other hand, the second project focuses on evaluating the effect of food safety technical assistance on measurable food safety outcomes. Three food processing facilities from Latin America were selected to participate. During this study, we evaluated their performance by applying a scoring system to grade food safety regulatory compliance. In addition, environmental samples were collected, which served as an indication of training effectiveness. The results of this study evidence the importance of personalized in-person assistance in achieving successful results in capacity-building efforts
Limits of Learning Dynamical Systems
No full textA dynamical system is a transformation of a phase space, and the transformation law is the primary means of defining as well as identifying the dynamical system and is the object of focus of many learning techniques. However, there are many secondary aspects of dynamical systems—invariant sets, the Koopman operator, and Markov approximations—that provide alternative objectives for learning techniques. Crucially, while many learning methods are focused on the transformation law, we find that forecast performance can depend on how well these other aspects of the dynamics are approximated. These different facets of a dynamical system correspond to objects in completely different spaces—namely, interpolation spaces, compact Hausdorff sets, unitary operators, and Markov operators, respectively. Thus, learning techniques targeting any of these four facets perform different kinds of approximations. We examine whether an approximation of any one of these aspects of the dynamics could lead to an approximation of another facet. Many connections and obstructions are brought to light in this analysis. Special focus is placed on methods of learning the primary feature—the dynamics law itself. The main question considered is the connection between learning this law and reconstructing the Koopman operator and the invariant set. The answers are tied to the ergodic and topological properties of the dynamics, and they reveal how these properties determine the limits of forecasting techniques
Exploring the Experiences of Neurodivergent Educators in Schools
No full textNeurodivergent educators are an under-researched and under-recognized population in our schools. This qualitative study explored the experiences of 14 educators in K-12 classrooms in the U.S. who are diagnosed or self-identify as neurodivergent. This study was framed using existing research on teachers with disabilities combined with theoretical approaches in disability studies, neurodiversity, and self-determination theory. Using the six-phase approach of reflexive thematic analysis (Braun & Clarke, 2006), this study explored themes in self-understanding, acceptance, advocacy, and disclosure; teaching and learning with empathy, engagement, and differentiation; professional relationships and support systems; and the impact of neurodivergence on professional life. This study also explored the accommodations and supports emerging from the participants’ experiences, explicitly noting the need for physical accommodations along with suggested supports in communication, professional development, and professional expectations. This research provides further guidance and insights for schools to support neurodivergent educators through policies and initiatives focused on teacher inclusion
Advanced Brain Tumor Segmentation Using SAM2-UNet
No full textImage segmentation is one of the key factors in diagnosing glioma patients with brain tumors. It helps doctors identify the types of tumor that a patient is carrying and will lead to a prognosis that will help save the lives of patients. The analysis of medical images is a specialized domain in computer vision and image processing. This process extracts meaningful information from medical images that helps in treatment planning and monitoring the condition of patients. Deep learning models like CNN have shown promising results in image segmentation by identifying complex patterns in the image data. These methods have also shown great results in tumor segmentation and the identification of anomalies, which assist health care professionals in treatment planning. Despite advancements made in the domain of deep learning for medical image segmentation, the precise segmentation of tumors remains challenging because of the complex structures of tumors across patients. Existing models, such as traditional U-Net- and SAM-based architectures, either lack efficiency in handling class-specific segmentation or require extensive computational resources. This study aims to bridge this gap by proposing Segment Anything Model 2-UNetwork, a hybrid model that leverages the strengths of both architectures to improve segmentation accuracy and consumes less computational resources by maintaining efficiency. The proposed model possesses the ability to perform explicitly well on scarce data, and we trained this model on the Brain Tumor Segmentation Challenge 2020 (BraTS) dataset. This architecture is inspired by U-Networks that are based on the encoder and decoder architecture. The Hiera pre-trained model is set as a backbone to this architecture to capture multi-scale features. Adapters are embedded into the encoder to achieve parameter-efficient fine-tuning. The dataset contains four channels of MRI scans of 369 glioma patients as T1, T1ce, T2, and T2-flair and a segmentation mask for each patient consisting of non-tumor (NT), necrotic and non-enhancing tumor (NCR/NET), and peritumoral edema or GD-enhancing tumor (ET) as the ground-truth value. These experiments yielded good segmentation performance and achieved balanced performance based on the metrics discussed next in this paragraph for each tumor region. Our experiments yielded the following results with minimal hardware resources, i.e., 16 GB RAM with 30 epochs: a mean Dice score (mDice) of 0.771, a mean Intersection over Union (mIoU) of 0.569, an (Formula presented.) score of 0.692, a weighted F-beta score ( (Formula presented.) ) of 0.267, a F-beta score ( (Formula presented.) ) of 0.261, an (Formula presented.) score of 0.857, and a Mean Absolute Error (MAE) of 0.04 on the BraTS 2020 dataset