The security of a cyber-physical system depends on the safety of the handled data, software, and underlying hardware. Securing the hardware is not a simple task because of the globalization of integrated circuits’ manufacturing flow. One hardware attack to be considered is the modification of the design to insert a “backdoor” which maliciously alters the behavior of the original system. Such a malicious and intentional insertion is called a Hardware Trojan Horse (HTH). In this thesis, an HTH detection technique was proposed and implemented. The detection technique made use of side-channel power analysis along with machine learning to detect the presence of an HTH. Power traces from a golden implementation (HTH-free) of the AES encryption algorithm on an FPGA were used to train a logistic regression model. The obtained model was then tested on new power traces collected from the golden implementation and was able to make correct predictions with 95% accuracy. Next, an HTH, of a few gates, was implemented in the AES circuit to carry out a denial-of-service attack along with a breach of plaintext secrecy. The power data from the HTH-infected circuit were collected and tested on the trained logistic regression model. An amount of 81% of the HTH-infected data was detected as flawed by the logistic regression model allowing the detection of the HTH even when it was not triggered. In fact, even when an HTH was dormant, the HTH would constantly be checking its triggering condition and hence consumed power.

Autonomous agents have seen tremendous advancements in solving sequential decision-making problems in recent years, primarily driven by Reinforcement Learning (RL), and more recently, by Large Generative Models. The capability of these autonomous agents depends crucially on the quality and diversity of the learning environments they are trained in. This thesis presents research on designing frameworks and algorithms to formulate and systematically generate environments that improve the generalization capabilities of autonomous agents in solving sequential decision-making tasks. First, we explore the benefits of human-guided programmatic environment generation for training, testing, and debugging autonomous agents in complex real-time strategic (RTS) environments. We present a novel framework that, for the first time, demonstrates the benefits of using scenario specification languages (e.g., SCENIC) for systematic modeling and generation of realistic and diverse RTS RL environments (e.g., Soccer). Next, we discuss a class of algorithms called adaptive teacher Unsupervised Environment Design (UED), which automatically generates training tasks with an RL teacher agent. UED shows promising zero-shot generalization by simultaneously learning a task distribution (i.e., curriculum) and agent policies on the generated tasks. This is a non-stationary process where the task distribution evolves along with agent policies, creating instability over time. While prior works demonstrated the potential of such approaches, training the teacher remained a practical challenge. To this end, we introduce Curriculum Learning via Unsupervised Task Representation Learning (CLUTR): a novel unsupervised curriculum learning algorithm that decouples task representation and curriculum learning into a two-stage optimization to solve the training instability by pretraining a latent task manifold. Following that, we present MultiModal Reasoning and Critique for web navigation (MMRC), which introduces augmented environments with multimodal critic agents to enhance the performance of Large Foundational Multimodal Language agents on autonomous web navigation tasks. Together, these approaches portray the importance and usefulness of environment formulation and generation encompassing traditional RL-based and contemporary LLM-based agents.

Two field experiments were carried out at the Agricultural Experimental Station of the National Research Center at Shalakan, Qalubeia Governorate, Egypt, during the two successive winter seasons of 2005/2006 and 2006/2007 to study the response of some canola varieties (Topas and Pactol cvs.) to nitrogen fertilizer levels (20, 40 and 60 kg N/faddan, (faddan = 4200 m2)) and number of zinc foliar application, one zinc foliar application (rosette stage) or two zinc foliar applications (rosette and bud stages). Concerning varitial differences, the obtained results illustrate that no significant differences between both varieties on the most studied characters, except for plant height and seed yield/faddan where, Pactol superior in plant height, 1000-seed weight, seed yield /plant, seed yield /faddan and oil yield /faddan. Increasing nitrogen fertilizer from 20 to 60 kg N/faddan significantly increased plant height, number of siliqua/plant, seed yield /plant, seed yield /faddan and oil yield/faddan. Most of studied characters were significantly increased by increasing number of zinc foliar application where, two zinc foliar application significantly affected in plant height, number of siliqua /plant, seed yield/ plant, seed yield /faddan and oil yield/faddan. The interactions between different factors show significant differences in the most studied characters.

The introduction of Senate Bill (SB 375) in 2008 stimulated more research linking travel behavior to the built environment. Smart growth tools mandated by this bill aim to reduce vehicle miles traveled (VMT), greenhouse gas (GhG) emissions and promote alternative modes to motorized travel. These tools encompass an array of land use improvements that are expected to influence active travel. Potential changes in the built environment may impact the frequency, amount and even the selection of routes for walking.

Data used in this dissertation was obtained from Phase I of the Expo Study, a three-phase travel survey of residents living near the Expo Light Rail Line in Los Angeles, CA. Respondents carried GPS devices and accelerometers to track locations and activity levels; and completed seven-day trip logs. Phase I of the survey was administered in Fall 2011, prior to the introduction of the Expo Line in April 2012.

This dissertation is comprised of three research topics. The first topic uses a "place-oriented" approach to examine where active travel occurs in neighborhoods adjacent to the Expo Light Rail Line. This chapter is based on the Behavioral Model of Environments, which emphasizes the influence of the physical environment on individuals' travel behavior and route choices. Results indicate that the routes selected by pedestrians have higher densities of commercial and retail centers and better access to more transit stations.

The second research topic uses an ecological modeling approach. Multilevel analysis of the effects of the built environment on active transport was performed in three geographic levels of aggregation near respondents' homes. Examination of land uses at the half-mile extent yield the least number of significant results. In contrast, land uses examined at the segment-level and quarter-mile distance from homes emphasize the importance of street connectivity and green space on increasing transport-related physical activity (TPA). This suggests the importance of analyzing the data at finer geographic levels.

The third research topic proposes a practical methodology of pedestrian route analysis in which observed GPS-tracked routes were examined and compared to GIS-simulated shortest paths. The two route types were compared over deviations in trip-level travel indices, respondents' socio-demographic traits, time of day variations and differences in objectively measured built environment features along both sets of routes. Results suggest that observed routes diverged more from shortest paths with increasing distance and were more circuitous beyond the 2.4-mile threshold. Most walks were completed after the AM Off Peak time. With the exception of the Evening time, observed routes were found to be much longer in all time periods especially in the AM Peak time. Moreover, higher densities of commercial centers, local businesses and green spaces were observed more for GPS-tracked routes than for shortest paths. These routes also had more street intersections and transit stops. Overall, results imply that pedestrians selected routes that were longer than the respective shortest paths and that may have been due to greater access to amenities and activity centers.