Recent publications
The rapid expansion of devices on the Internet of Things (IoTs) has led to a significant rise in IoT botnet attacks, creating an urgent need for advanced detection and classification methods. This study aims to evaluate the effectiveness of Kolmogorov-Arnold Networks (KANs) and their architectural variations in classifying IoT botnet attacks, comparing their performance with traditional machine learning and deep learning models. We conducted a comparative analysis of five KAN architectures, including Original-KAN, Fast-KAN, Jacobi-KAN, Deep-KAN, and Chebyshev-KAN, against models like Multi-Layer Perceptron (MLP), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and Gated Recurrent Units (GRU). The evaluation was performed on three IoT botnet datasets: N-BaIoT, IoT23, and IoT-BotNet, using metrics such as accuracy, precision, recall, F1-score, training time, and model complexity. KAN variants consistently demonstrated robust performance, often exceeding traditional ML and DL models in accuracy and stability across all datasets. The Original-KAN variant, in particular, excelled in capturing complex, non-linear patterns inherent in IoT botnet traffic, achieving higher accuracy and faster convergence rates. Variations such as Fast-KAN and Deep-KAN offered favorable trade-offs between computational efficiency and modeling capacity, making them suitable for real-time and resource-constrained IoT environments. Kolmogorov-Arnold Networks prove to be highly effective for IoT botnet classification, outperforming conventional models and offering significant advantages in adaptability and accuracy. The integration of KAN-based models into existing cybersecurity frameworks can enhance the detection and mitigation of sophisticated botnet threats, thus contributing to more resilient and secure IoT ecosystems.
This study examines the impact of culture on consumer behavior in two Caribbean countries namely Trinidad &Tobago, and Jamaica. A set of hypotheses are developed to understand the impact of culture on consumerbehavior. Data is collected by surveying consumers from both countries. The findings of the research suggestthere is a relationship between family structure, language (local dialect), values and beliefs on consumerbehavior in both Trinidad & Tobago and Jamaica.
Globally, aquatic ecosystems are one of the largest but most uncertain sources of methane, a potent greenhouse gas. It is unclear how climate change will affect methane emissions, but recent work suggests that glacial systems, which are melting faster with climate change, may be an important source of methane to the atmosphere. Currently, studies quantifying glacial emissions are limited in number, and the role of methanotrophy, or microbial methane oxidizers, in reducing atmospheric emissions from source and receiving waters is not well known. Here we discuss three potential sites for methane oxidation that could mitigate emissions from glaciers into the atmosphere: under ice oxidation, oxidation within proglacial lakes, and oxidation within melt rivers. The research presented here increases the number of glacial sites with methane concentration data and is one of only a few studies to quantify the net microbial activity of methane production and oxidation in two types of land-terminating glacial runoff (lake and river). We find that oxidation in a glacial river may reduce atmospheric methane emissions from glacial melt by as much as 53%. Incorporating methane oxidation in estimates of glacial methane emissions may significantly reduce the estimated magnitude of this source in budgeting exercises.
Background
Inclusive teaching practices have become essential to support student success in our diverse college classrooms. Given the variations in how inclusivity is translated into teaching behavior, it is important for us to examine how our students define and perceive inclusivity.
Objective
The main goal of this qualitative study is to define and characterize inclusive classroom communities and inclusive pedagogy practices through the voices of current college students.
Method
Participants included 365 undergraduate students from a public and a private university. Students responded to open-ended questions about inclusive teaching practices, characteristics of an inclusive classroom, inclusive teaching behaviors, and inclusive behaviors of classmates.
Results
Participants highlighted that inclusive teaching impacts all students while noting that teaching practice may differentially impact specific student populations, including students with disabilities and students of color. Students also identified specific instructor and classmate characteristics and behaviors that contribute to an inclusive (or non-inclusive) classroom community.
Conclusion
This study highlights students’ views on what characterizes inclusive teaching. Participants underscored the importance of respectful and welcoming environments both from faculty and peers.
Teaching Implications
Findings offer practical implications for instructors as they create and support an inclusive classroom, particularly as it pertains to teacher and student characteristics and behaviors.
In this article, we present findings from our first iterative design study for Project ENHANCE to share our findings as well as provide an exemplar for others engaged in design inquiry. In particular, we explain how we used a data-informed design process with district partners to determine content and features of three foundational professional learning modules to support implementation of integrated tiered systems of support. We report findings from three groups of individuals: advisory board and expert panel members (Phase 0), Ci3T Leadership Team members (Phase 1), and role-specific user groups from school faculty and staff (Phase 2). Results indicated acceptability and overall usability of content from multiple perspectives, with priority placed on smaller units of professional learning and flexible resources (e.g., videos infographics). Results also raised questions about how to use resources without overwhelming teachers as they manage multiple responsibilities within a finite amount of time. We discuss limitations and future directions.
Standards and practices for preparing teachers of mathematics emphasize that preservice teachers (PSTs) must possess robust knowledge of mathematical concepts of what they teach to support student learning. Additionally, two standards of mathematical practices, use and connect mathematical representations and mathematical modeling (MM), play a pivotal role in developing students’ mathematical thinking. Thus, this study investigated 31 PSTs’ performance, representations used, and sense making in a four-part MM task centered on perimeter and area concepts. Participants were recruited from two, four-year university mathematics methods courses located in the United States. The findings showed that the PSTs’ performance on each part of the MM task was weak and indicated limited conceptual understanding of the relationships between perimeter and area. In terms of representations, most PSTs used two representations in their solution process; however, no tabular and graphical representations were present. Furthermore, no strong relationship existed between the PSTs’ success on the task and their multiple representations utilized. The directions for future research and implications for researchers and teacher education programs to support PSTs’ development are discussed.
Diabetes mellitus is known to be a serious chronic disease that requires great attention because it is the cancer that never dies and affects many people worldwide; its later complications are often numerous and frightening, and some of them may even affect their children as hereditary diseases. The relationship between lipid metabolism and insulin sensitivity in relation to diabetes is therefore even more important in this context. Insulin plays an important role as a key metabolic hormone in regulating glucose intake and maintaining lipid metabolic homeostasis. Insulin resistance interferes with the homeostasis of lipid metabolism while triggering related metabolic diseases such as type 2 diabetes. This review focuses on the relationship as well as interactions between lipid metabolism as well as insulin sensitivity and the specific factors affecting lipid metabolism and insulin sensitivity in terms of excess lipid accumulation, lipid deficiency, altered lipid composition, and specific lipid species. Finally, interventions based on the factors affecting lipid metabolism and insulin are proposed to improve metabolic health and reduce the risk of metabolic diseases such as diabetes.
Recently published professional learning outcomes require future engineers to think of the impacts that engineering decisions have on society. History shows that construction and civil engineering projects can exacerbate inequality by ignoring community concerns and failing to consider the impacts on marginalized and vulnerable stakeholders, among other factors. How might construction engineering professors help students meet these standards, and how do construction and civil engineering students respond when construction engineering is framed as inextricably linked to these obligations? We designed and evaluated curriculum modules aimed at helping develop a critical consciousness with construction and civil engineering students (𝑁=177) in three construction and civil engineering courses at two universities in the midwestern region between 2020 and 2021. The curriculum builds on a three-phase framework aimed at encouraging students to see social inequities and their impacts, finding social inequities unjust, and enhancing reflective self-awareness. Post-implementation responses to the case study designed to make clear the need for a critical consciousness in engineering found that students responded positively to both the instructional approach of a case study and the content connected to critical consciousness. We discuss implications for the development of further curriculum and the implementation of such an approach.
The management of alopecia areata (AA) in pediatric patients poses unique challenges, particularly regarding treatment discussions and decision making involving both patients and their families. This commentary presents findings from unpublished research on treatment‐discontinuation discussions between AA patients and their treating providers, shedding light on the hopes, expectations, and disappointments of individuals with severe AA. The study explored patient and guardian satisfaction with these discussions, emphasizing the importance of addressing psychosocial concerns, facilitating contact with support groups, and demonstrating empathy. The role of dermatologists in conversations about treatment, prognosis, and quality of life is examined, emphasizing the need for honesty, empathy, and realistic expectations. The authors propose a patient‐centered approach to initiating and guiding discussions, focusing on understanding the impact of AA on patients and their families and collaboratively deciding on treatment options. The mantra: ‘I need to understand how this is affecting all of you, so we can decide together what to do next’ is central to this proposed approach. Special considerations for different scenarios are discussed, highlighting the importance of individualized care and effective communication. Overall, the commentary emphasizes the significance of actively listening, acknowledging emotions, and prioritizing patient and family goals to optimize care for pediatric AA patients.
The COVID-19 Stressors Scale measures individuals’ appraisals of stressors related to the pandemic. Measurement of perceptions of stressors is necessary to understand the socioemotional impacts of not only the COVID-19 pandemic, but other disasters. The study examined the factor structure of the scale among adults in the U.S. over six time points. A shortened version was used, and the fit was examined over time. The results of the study show contextual appraisals change over time and offer important implications for the measurement of stressfulness of disasters, a critical step in designing and assessing impacts of social programs aimed to reduce the deleterious effects of disasters.
BACKGROUND
Food allergies are a growing concern worldwide, with soy proteins being important allergens that are widely used in various food products. This study investigated the potential of transglutaminase (TGase) and lactic acid bacteria (LAB) treatments to modify the allergenicity and structural properties of soy protein isolate (SPI), aiming to develop safer soy‐based food products.
RESULTS
Treatment with TGase, LAB or their combination significantly reduced the antibody reactivity of β‐conglycinin and the immunoglobulin E (IgE) binding capacity of soy protein, indicating a decrease in allergenicity. TGase treatment led to the formation of high‐molecular‐weight aggregates, suggesting protein crosslinking, while LAB treatment resulted in partial protein hydrolysis. These structural changes were confirmed by Fourier transform infrared spectroscopy, which showed a decrease in β‐sheet content and an increase in random coil and β‐turn contents. In addition, changes in intrinsic fluorescence and ultraviolet spectroscopy were also observed. The alterations in protein interaction and the reduction in free sulfhydryl groups highlighted the extensive structural modifications induced by these treatments.
CONCLUSION
The synergistic application of TGase and LAB treatments effectively reduced the allergenicity of SPI through significant structural modifications. This approach not only diminished antibody reactivity of β‐conglycinin and IgE binding capacity of soy protein but also altered the protein's primary, secondary and tertiary structures, suggesting a comprehensive alteration of SPI's allergenic potential. These findings provide a promising strategy for mitigating food allergy concerns and lay the foundation for future research on food‐processing techniques aimed at allergen reduction. © 2024 Society of Chemical Industry.
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