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The adaptive mechanical properties of soft and fibrous biological materials are relevant to their functionality. The emergence of the macroscopic response of these materials to external stress and intrinsic cell traction from local deformations of their structural components is not well understood. Here, we investigate the nonlinear elastic behavior of blood clots by combining microscopy, rheology, and an elastic network model that incorporates the stretching, bending, and buckling of constituent fibrin fibers. By inhibiting fibrin cross-linking in blood clots, we observe an anomalous softening regime in the macroscopic shear response as well as a reduction in platelet-induced clot contractility. Our model explains these observations from two independent macroscopic measurements in a unified manner, through a single mechanical parameter, the bending stiffness of individual fibers. Supported by experimental evidence, our mechanics-based model provides a framework for predicting and comprehending the nonlinear elastic behavior of blood clots and other active biopolymer networks in general.

With the growing interest in student-centered approaches to learning, it becomes important to look at student learning experiences in the self-directed learning environment as students may feel uncomfortable and unprepared for the demands associated with student-centered learning environments (SCLEs). This qualitative study explored student beliefs about teaching and learning and described how students reacted in a technology-intensive SCLE. The results showed that students' learning experiences in the SCLE resulted in different types of reactions in relation to their beliefs about teaching and learning: match, conflict, confirmation, appreciation, withdrawal, and transformation. This study suggests that teachers and educational practitioners acknowledge individual differences, provide personalized support and guidance, and encourage students to become self-directed learners.

In response to the COVID-19 pandemic, K-12 schools in the United States closed and teachers transitioned to distance learning. The purpose of this survey research study is to determine technology resources and strategies K-12 teachers have used in this transition. Additionally, this study examines the difficulties teachers experienced, along with support they wish they had during the transition. Findings indicate that a wide variety of websites and applications were used to provide academic continuity, the majority of which were familiar to teachers. In the transition process, teachers were faced with various challenges, including difficulty engaging students and parents, a lack of school/district guidelines, and student Internet and computer access issues. Recommendations to prepare for future emergencies include making clear plans for emergencies and incorporating online components and training within current face-to-face classes and professional development.

With the advancements in artificial intelligence (AI) technology, attackers are increasingly using sophisticated techniques, including ChatGPT. Endpoint Detection & Response (EDR) is a system that detects and responds to strange activities or security threats occurring on computers or endpoint devices within an organization. Unlike traditional antivirus software, EDR is more about responding to a threat after it has already occurred than blocking it. This study aims to overcome challenges in security control, such as increased log size, emerging security threats, and technical demands faced by control staff. Previous studies have focused on AI detection models, emphasizing detection rates and model performance. However, the underlying reasons behind the detection results were often insufficiently understood, leading to varying outcomes based on the learning model. Additionally, the presence of both structured or unstructured logs, the growth in new security threats, and increasing technical disparities among control staff members pose further challenges for effective security control. This study proposed to improve the problems of the existing EDR system and overcome the limitations of security control. This study analyzed data during the preprocessing stage to identify potential threat factors that influence the detection process and its outcomes. Additionally, eleven commonly-used machine learning (ML) models for malware detection in XAI were tested, with the five models showing the highest performance selected for further analysis. Explainable AI (XAI) techniques are employed to assess the impact of preprocessing on the learning process outcomes. To ensure objectivity and versatility in the analysis, five widely recognized datasets were used. Additionally, eleven commonly-used machine learning models for malware detection in XAI were tested with the five models showing the highest performance selected for further analysis. The results indicate that eXtreme Gradient Boosting (XGBoost) model outperformed others. Moreover, the study conducts an in-depth analysis of the preprocessing phase, tracing backward from the detection result to infer potential threats and classify the primary variables influencing the model's prediction. This analysis includes the application of SHapley Additive exPlanations (SHAP), an XAI result, which provides insight into the influence of specific features on detection outcomes, and suggests potential breaches by identifying common parameters in malware through file backtracking and providing weights. This study also proposed a counter-detection analysis process to overcome the limitations of existing Deep Learning outcomes, understand the decision-making process of AI, and enhance reliability. These contributions are expected to significantly enhance EDR systems and address existing limitations in security control. [ABSTRACT FROM AUTHOR]