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Lachish V: Regional and Site-Level Implications Revealed through the Past Decade of Research
No full textThe last decade of research has seen a dramatic increase in the archaeological knowledge surrounding Lachish Level V. Many of the publications have been especially focused on Lachish V’s city wall; meanwhile, other aspects of the city, speaking both regionally and at the site-level, have fallen to the wayside. After a summary of the recent discourse surrounding the city wall, the site-level implications of these other aspects are explored. Results of this discussion show that Lachish V is larger than previously thought and further support both the suggestion that the city was not violently destroyed and that Podium and Palace A may have been part of the Level V city. Afterwards, a preliminary least cost path analysis between Lachish and two other cities was carried out to explore Lachish V in its regional setting. Results show that the city’s importance can not be understood based on its geographic location, but propose a separate possibility in which Lachish V’s importance can be understood as a result of it simply having been formed earlier than its main competitor in the analysis: Beth Shemesh
The Delineation of the Fibrinogen/Triggering Receptor in Myeloid Cells (TREM) Like Transcript - (TLT) -1 Molecular Interaction
No full textTriggering Receptor Expressed in Myeloid Cells -1 (TLT- 1) is a platelet specific receptor that facilitates platelet aggregation and plays a role in immunohemostasis. Previous studies identified fibrinogen as a ligand for TLT-1 but little has been reported on the molecular interaction or the role this TLT-1/Fibrinogen interaction has on pathophysiological progression of disease. This dissertation research presents basic research, with potential for translational application for this interaction, highlighting the versatile functionalization and application of TLT-1 as a contributor to clot formation, as a biomarker for disease progression and, extends to understand its regulation of fibrinogen deposition in pathophysiological models of disease
Uncertain Interval Systems with Application to Separately Excited DC Motor and DC-DC Converters
No full textElectric drive systems in automobiles, aircraft, and maritime crafts have significantly advanced due to changes in hardware and software applications. Drive systems consisting of multidisciplinary subsystems often post non-trivial control problems that must be overcome. For example, redundant input systems related to a separately excited DC motor (SEDCM) or highly varying gains related to DC-DC converters. This thesis introduces a novel approach utilizing an optimization scheme to transform the redundant input process into an uncertain interval system. An armature voltage minimization scheme was developed for the SEDCM to address these redundancies and uncertainties. The comprehensive analysis and feedback control design for an uncertain interval optimal redundant input system is a significant departure from traditional methods, such as Root Locus. The Kharitonov stability criterion is used to analyze the interval systems and determine the parameter boundaries for the controller gains. At the same time, the Root-Locus method is employed to visualize the stable regions of the controller parameters. Next, the Lyapunov stability criterion-based adaptive controller is designed to guarantee stability and tracking for the optimal redundant input systems. For illustration, a PI-controlled separately excited DC motor (SEDCM) will be used. The proposed uncertain interval technique is extended to analyze and design a robust PI controller for DC-DC power electronic converters. The results unify the control design for the buck, boost, and buck-boost converter
Resilience Mitigates the Effects of Rumination by Reducing Symptoms of Depression, Anxiety, and Stress
No full textExploring the Experience of Using the Madrasati Platform to Deliver Instruction
No full textThis research explored the impact of the Madrasati platform on the performanceand role of high school teachers in Saudi Arabia, with a focus on the Jazan region. Utilizing a mixed-methods approach, the study addressed the key research questions, including the extent of the Madrasati platform contribution to teacher support and its impact on content delivery, communication, evaluation, motivation, autonomy, and knowledge sharing. The research design of this study incorporates both qualitative and quantitative methods. The purpose of using mixed methods was to provide a comprehensive understanding of teacher experiences. The study setting in the Jazan region is characterized by diverse terrain, a growing population, and significant developments in educational infrastructure. The choice of this region allows an in-depth exploration of how the Madrasati Platform aligns with the cultural and educational context. Participants of this study were 333 high school teachers from various disciplines, ensuring a diverse sample for robust statistical analysis. Data collection involved a structured survey with multiple-choice and open-ended questions, addressing demographic information, platform experiences, and suggestions for improvement. The researcher has followed ethical guidelines, secured approvals from the Institutional Review Board and the Ministry, and emphasized participants’ confidentiality. The data analysis was done by using statistical software and qualitative methods to derive meaningful insights into the platform impact. This research contributed to the broader understanding of educational technology in Saudi Arabia, particularly in the context of government initiatives to enhance teaching practices. The findings of this study aimed to inform educational policies and improve the Madrasati Platform, ultimately benefiting high school teachers and students in the Jazan region and in other parts of Saudi Arabia
Detecting and Classifying Malware in Electrical Power Grids Via Cyberdeception
No full textArtificial intelligence (AI) has become an essential instrument for enterprises aiming to protect their digital assets within a progressively aggressive cyber landscape. As the dependence on digital technologies increases among companies and individuals, the risks associated with cyberattacks are also advancing in terms of complexity and magnitude. AI and the proliferation of technology has led to a significant concern over security, mostly due to the escalating prevalence of malware on industrial computers. This has resulted in potential physical harm to computer systems and the individuals involved. Malware is a collection of malicious programming code that aims to inflict harm against computer systems, programs, or online apps. These applications lack the ability to differentiate between legitimate system calls and those that are intended to cause harm. Therefore, it is imperative to ensure that computer systems and online applications are constructed in a manner that enables the identification and differentiation of malicious activities from legitimate application activities. The utilization of AI in the realm of cybersecurity is revolutionizing the domain of digital protection. There are various techniques that can be used to identify malicious activity, leveraging innovative concepts such as AI, machine learning, and deep learning. The present study presents a proposal for utilizing AI approaches to identify and mitigate malware activity in computer memory, with the aim of safeguarding against unauthorized access to and manipulation of physical data within the system. This research aims to combine the traditional K-means algorithm with other methods and functionalities to perform data aggregation tasks on a physical dataset. The primary objective is to identify anomalies in the dataset using clustering techniques. These anomalies will serve as triggers for creating a replica of the main process as a decoy thread. The decoy thread will be equipped with decoy sensors and actuators. The analysis will be conducted on the decoy thread rather than the main process, allowing for intrusive observation. The same host environment will be provided to memory-resident malware, enabling it to continue operating within the main operating system process. The analysis process involves utilizing a replicated instance of malware that resides within a deceptive thread
Table-to-Floor-to-Table-Robot
No full textCreating autonomous robots in order to complete tasks has received increasing attention in recent years due to efforts to increase productivity and efficiency. The objective of this project is to create an autonomous table-to-floor-to-table robot. The robot will start inside a square on a table, travel to the floor, trace a square without hitting any cones, travel back to the table, and end up in the same square. The table-to-floor-to-table robot will be autonomously flown as a drone and then driven with a set of treads, wheels, and axles once on the ground. Through testing, the robot was successful in completing the ground course of the challenge, tracing the square in forty seconds, which is ideal given that the time limit for the entire course is five minutes. The total cost of all of the components that are used on the robot add up to 300. These two factors display that the robot meets the criteria of the challenge