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My Two Bodies
Get PDFMy Two Bodies examines the intersection of visibility, memory, and gendered absence through photographic and material strategies. Engaging with censorship and instability of the image, it investigates how realities are shaped by what is seen, obscured, or erased. Referencing the gendered absence of women in Iranian visual culture, the project considers how censorship intensifies rather than eliminates visibility. Drawing from Jacques Derrida’s concept of hauntology, absence is approached not as a void but as an active presence that disrupts the surface of the image. Through processes such as collage, projection, and fabric-based image making, photographs are treated as tactile, mutable objects. Material in this project functions as a conduit for physical connection with images, challenging the separation between vision and touch, reconsidering how absence is materially and sensorially experienced
Beyond Barcodes
Get PDFBeyond Barcodes is a conceptual zine that investigates the narratives encoded within barcodes and reinterprets receipts as tools to examine the emotional layers of consumer behavior. Often dismissed as transactional byproducts, receipts in this project are reframed as carriers of memory, identity and self-worth—reflecting personal values shaped by frugality, guilt, and inherited beliefs about money. The zine unfolds through a series of illustrated vignettes, each centered around a category of spending: groceries, essentials, dining, vanity, and experiences. This arc traces a journey from survival to indulgence, ending on a bittersweet note—critiquing how consumer culture quietly shapes how we spend, what we feel, and what those choices signify. Each vignette pairs symbolic black-and-white illustrations with short narrative reflections, inviting both self-inquiry and cultural critique. The project encourages viewers to reconsider the emotional and cultural systems behind even the smallest purchases. It invites reflection on the commodification of everyday life and the silent weight of receipts—documents that often outlast the purchases they record. This project asks, quietly but insistently: would you like your receipt
Evaluating faults detection and their impact on Photovoltaic (PV) Modules
Get PDFPhotovoltaic (PV) modules are critical to the transition toward renewable energy, offering a sustainable solution for global power generation. However, faults in PV modules significantly reduce energy output, increase maintenance costs, and compromise system reliability. Traditional fault detection methods, while useful, often lack the precision and efficiency required for real-time applications, leading to prolonged downtime and revenue losses. Machine learning (ML) offers a promising approach for detecting and classifying faults in PV modules with greater accuracy and speed. This study evaluates fault detection techniques in PV modules and their impact on operational efficiency, focusing on machine learning-based classification models. A dataset comprising 15,300 records from PV installations, including electrical parameters such as current, voltage, and efficiency, was analyzed using statistical and ML-based methods. The study employed four classification algorithms, Logistic Regression, Linear Support Vector Machine (LSVM), Random Trees, and Neural Networks, to detect and categorize faults, including voltage loss, current degradation, and series resistance issues. Data preprocessing involved exploratory data analysis (EDA), outlier detection, and dimensionality reduction using Principal Component Analysis (PCA). Feature selection techniques were applied to optimize model performance. To address class imbalance, random under sampling was utilized, ensuring a more balanced representation of normal and faulty modules. The models were evaluated using accuracy, recall, precision, F1-score, and the Area Under the Curve (AUC) metric. Given the criticality of minimizing false negatives, recall was prioritized to ensure accurate fault detection. Two fault classes were analyzed: Normal and Curr (representing current loss due to homogeneous delamination) and Normal and FFVoltCurr (representing fill factor, voltage, and current loss due to micro-cracks, contacts degradation, and Potential Induced Degradation (PID). For the Normal and Curr case, Neural Network achieved the highest performance across multiple evaluation metrics, making it the most effective model for fault detection, followed by Logistic regression ,LSVM, and The Random Trees model underperformed in fault detection. For the Normal and FFVoltCurr case, NN outperformed other models with a recall of 93.9%, closely followed by LSVM and Logistic Regression (92.8%). Random Trees demonstrated lower effectiveness, highlighting the importance of selecting ML models that prioritize recall for fault detection in PV modules. The research highlights the benefits of integrating ML-based models into PV system maintenance strategies. By leveraging real-world operational data, this study contributes to the advancement of predictive maintenance in PV plants, reducing operational costs and extending module lifespan. The proposed approach is particularly relevant for large-scale solar farms, such as the Mohammed Bin Rashid (MBR) Solar Park, supporting Dubai’s strategic clean energy goals. Future research can enhance the model by incorporating additional environmental variables, expanding the dataset to cover a broader range of PV technologies, and exploring hybrid ML techniques for improved fault classification. This study demonstrates the potential of data-driven fault detection in PV modules, paving the way for more resilient and efficient solar energy systems
Deleting Values May Either Increase or Decrease Variance
Get PDFThe impact upon variance when a value is deleted is addressed. It is shown that the cutoff for the deleted value yielding an increase or a decrease in variance is approximately one standard deviation from the mean for a univariate random variable with equally distributed probability on a finite set of elements and for a univariate set of observations. The influence of truncation of the domain for such a discrete random variable and for observations is considered
Enhancement of Ultrasonic Welding of Bulk Carbon Nanotube-Metallic and Graphene-Metallic Conductors
Get PDFNanostructured carbon sheets comprising carbon nanotube (CNT) or graphene are appealing for electrode and antenna applications. Physical connection to metal conductors requires enhanced mechanical strength, electrical performance, and thermal capability. Ultrasonic welding is a viable technique to fabricate stable and robust bonds but requires methods to further reduce electrical resistance and improve mechanical strength. The present dissertation has advanced ultrasonic welding between bulk CNT electrodes and Cu foil by utilizing chemically doped junctions at the bond interface. Specifically, a selective doping strategy using KAuBr4 at the bonding regions between bulk CNT electrodes and Cu lowers the electrical contact resistance measured utilizing novel Transfer Length Method (TLM) structures. This reduced electrical contact resistance at the CNT-metal weld interface also leads to a lower surface temperature measured by thermal imaging under high-applied current. A CNT interlayer was utilized during ultrasonic welding of graphene sheet to itself and Cu to improve adhesion. Optimized weld conditions were attained by varying the amplitude for a constant ultrasonic energy, altering the interface thickness between graphene and graphene/Cu. Mechanical analysis at optimal conditions demonstrated near-equivalent breaking forces to intrinsic graphene sheet. Welding of a chemically doped CNT adhesion layer reduced specific contact resistivity by 2X while retaining mechanical strength. Optical microscopy, coupled with elemental analysis from scanning electron microscopy (SEM), demonstrates failure in the graphene sheet layer limits the strength of the welded structures, while the CNT interlayer remains bonded to the graphene and Cu layers. Overall, the work has established the fundamental relationship for optimal bond properties in ultrasonically welded graphene-CNT-graphene and graphene-CNT-Cu structures
Steering through change: How aging eyes and stroke-induced vision loss affect driver control
Get PDFImaging system engineers could greatly benefit from understanding how biological systems have evolved to solve difficult imaging problems. In this thesis, we study how the human visual system has evolved to address the complex task of visually-guided steering, particularly as a person ages and experiences a prevalent type of vision loss from stroke called cortical blindness (CB) that results in the loss of one quarter to one half of the visual field. In a series of three studies, we test the hypothesis that the effects of CB on vision and steering extend beyond those that accompany healthy aging, and we predict that the effects cannot be accounted for by the common characterization of CB as an occlusion of visual information. Our approach involves a custom-made virtual reality steering task that facilitates the systematic exploration of how manipulating visual information affects both steering and gaze behavior. Studies 1 and 2 highlight the remarkable robustness of steering in the presence of aging visual systems and large visual impairments from CB, but they also characterize the widely variable behavior across CB individuals. In study 3, visually-healthy drivers were subjected to gaze-contingent masks and did not demonstrate the same biases as CB drivers. We concluded that the influence of CB on steering is more complicated than can be explained by a simple occlusion of visual information. Ultimately, we learned that the visual-motor strategies that facilitate steering are adaptable and resilient to the slow changes related to aging as well as the abrupt changes due to stroke. Our findings contribute knowledge to the scientific community about the role of vision for steering, and they also have the potential to inform future CB vision rehabilitation initiatives
GenAI Literacy Framework for Library Instruction
Get PDFA generative artificial intelligence (genai) framework for library instruction. This short framework is designed to incorporate into existing library instruction across many subject areas. The basic elements of the framework are: know & understand genai, use & evaluate genai, and library research & discovery with genai
Advancing Institutional Sustainability Through Green Roofs: Performance-Based Policy for RIT
No full textUtilization of green roof systems continues growth as a sustainable architectural strategy, offering enhancements to key building performance metrics while contributing to overall community resilience. The ability of green roofs to deliver a wide range of performance benefits, even in environments with harsh climatic conditions, positions them as an asset for advancing sustainable development. Within this context, university campuses represent an underutilized, yet important, setting for green roof adoption. Functioning much like small cities, campuses contain diverse building types and extensive infrastructure networks, which draw significant energy demands, positioning them to lead in sustainable building practices. Yet, most lack rigorous, campus-wide policies to guide the integration of green roofs into both new and existing facilities. This thesis responds to that gap by focusing on the institutional scale. It develops a performance-based policy framework for universities in plant hardiness zone 6 to implement green roofs on both new construction and existing facilities. The research examines existing green roof regulations from cities such as Toronto (6b) and Chicago (6a) to identify key transferable strategies and best practices in policy design and enforcement types. Key performance factors including vegetation selection, thermal insulation, freeze–thaw resilience, and drainage efficiency were analyzed for their influence on green roof success in cold climates. Comparative analysis of municipal policies highlighted gaps and opportunities for adaptation into the higher education setting. The Golisano Institute of Sustainability at the Rochester Institute of Technology served as the primary case study, offering modeled insights into how a cold-climate green roof performs at the building scale. The results confirmed meaningful reductions in runoff and improvements to seasonal energy performance, though these gains were relatively modest when compared to the campus’s overall demand. More importantly, the study showed that long-term value lies not only in technical performance, but also in the policy and operational frameworks that support it, since cost, maintenance, and cold-weather durability remain significant factors shaping feasibility. By integrating lessons from leading municipal policies with building science and institution-specific sustainability goals, this study offers a comprehensive green roof policy model tailored to higher education. Framing universities as both living laboratories and replicable models for urban sustainability, the proposed framework addresses the environmental performance gap in campus infrastructure while demonstrating how institutional policies can scale outward to support sustainable city development
A Place to Pause: Designing Ritual Space in the Domestic Realm
No full textModern society in Western developed countries is focused on the rush and go, often leaving people feeling discombobulated from constant running around, task-oriented days and switching of roles one must play in different areas of their life, such as at home, work, socializing, parenting, etc. How can ritual bring a sense of calmness and order to lives of those on the go? Ritual studies are often only categorized under psychology, sociology, or religious studies. However, as industrial design encompasses human interaction with objects, often with an emotional quality, ritual can be studied more wholesomely with the added lens of industrial design. This thesis examines how designing a space for ritual affects the performance of that ritual. Does having a permanent ritualistic space set up and encourage its use for ritualistic purpose? Focusing on spatial context created by visual enclosure and the ways designating space for ritual practice effects our performance of such rituals, this thesis addresses the making and use of a hanging shoji-inspired screen as a room divider and to define a ritual space combined with the making of a tea set for performing ritual. The hanging screen is modular and can be installed anywhere – without any nails or drilling – making it perfect for changing spaces and those who move frequently. It echoes the design of a ritualistic tea set to create and define a comprehensive space, designed as an inclusive ritualistic experience