Technology Agnostic Analysis and Design for Improved Performance, Variability, and Reliability in Thin Film Photovoltaics

Thin film photovoltaics (TFPV) offer low cost alternatives to conventional crystalline Silicon (c-Si) PV, and can enable novel applications of PV technology. Their large scale adoption however, requires significant improvements in process yield, and operational reliability. In order to address these challenges, comprehensive understanding of factors affecting panel yield, and predictive models of performance reliability are needed. This has proved to be especially challenging for TFPV for two reasons in particular. First, TFPV technologies encompass a wide variety of materials, processes, and structures, which fragments the research effort. Moreover, the monolithic manufacturing of TFPV modules differs significantly from that of c-Si technology, and requires new integrated approaches to analysis and design for these technologies. In this thesis, we identify a number of features affecting the variability and reliability of TFPV technologies in general, and propose technology agnostic design solutions for improved performance, yield, and lifetime of TFPV modules. We first discuss the universal features of current conduction in TFPV cells, for both intrinsic dark and light currents, and parasitic (shunt) leakage. We establish the universal physics of space-charge-limited shunt conduction in TFPV technologies, and develop physics based compact model for TFPV cells. We examine the statistics of parasitic shunting, and demonstrate its universal log-normal distribution across different technologies. We also evaluate the degradation behavior of cells under reverse bias stress, and identify different degradation mechanisms for intrinsic and parasitic components. We then embed the physics and statistics of cell operation and degradation, in a circuit simulation framework to analyze module performance and reliability. With this integrated circuit-device simulation, we establish log-normal shunt statistics as a major cause of module efficiency loss in TFPV, and develop a in-line technique for module efficiency and yield enhancement. Finally, we study the features of TFPV module reliability under partial shading using this circuit simulation, and propose a geometrical design solution for shade tolerant TFPV modules. The most important theme of this thesis is to establish that TFPV technologies share many universal performance, variability, and reliability challenges. And, by using a technology agnostic approach for studying these problems, we can achieve fruitful cross coupling of ideas and enable broadly applicable solutions for important technological challenges in TFPV

Emotion Recognition from Speech using GMM and VQ

In this paper, there is a tendency to study the effectiveness of anchor models applied to the multiclass drawback of Emotion recognition from speech. Within the anchor models system, Associate in nursing emotion category is characterized by its line of similarity relative to different emotion categories. Generative models like Gaussian Mixture Models (GMMs) are typically used as front-end systems to get feature vectors wont to train complicated back-end systems like support vector machines (SVMs) or a multilayer perceptron (MLP) to enhance the classification performance. There is a tendency to show that within the context of extremely unbalanced knowledge categories, these back-end systems will improve the performance achieved by GMMs as long as Associate in nursing acceptable sampling or importance coefficient technique is applied. The experiments conducted on audio sample of speech; show that anchor models improve considerably the performance of GMMs by half dozen.2 % relative. There is a tendency to be employing a hybrid approach for recognizing emotion from speech that may be a combination of Vector quantization (VQ) and mathematician Mixture Models (GMM). A quick review of labor applied within the space of recognition victimization VQ-GMM hybrid approach is mentioned here. DOI: 10.17762/ijritcc2321-8169.15082

We are what we remember: unravelling memories

Do you remember your first day at school? Try to visualize what your classroom looked like. What colour was your uniform? Who accompanied you to school? Were you crying? What did your teacher look like? Some of us have such a vivid memory of our first day at school that it can make us nostalgic even much later in life. If you cannot remember your first day of school, just relax! There is a biological reason behind this lapse in memory that I will explain a little later. Try another example. Think of the best birthday you’ve ever had. How old did you turn that year? Did you cut a cake? What flavour was it? Do you remember the people who were part of your birthday celebrations? What were you wearing? Reminiscences of the first day at school or a fun birthday are memories of events or specific episodes in one’s life, and are thus called episodic memories. While significant episodes from our lives are remembered long after, like the ones highlighted in the examples above; insignificant ones, like what you ate for breakfast a month ago, are soon forgotten. Episodic memories are one kind of memory. Are there other types of memory

Integration of microvascular, interstitial, and lymphatic function to determine the effect of their interaction on interstitial fluid volume

Although the physics of interstitial fluid balance is relatively well understood, clinical options for the treatment of edema, the accumulation of fluid in the interstitium, are limited. Two related reasons for this failure can be identified. First, the processes involved in the transfer of fluid and proteins into the interstitium from the microvasculature, and their transfer out of the interstitium via the lymphatic system, are governed by complex equations that are not amenable to manipulation by physiologists. Second, the fundamental processes involved include complex anatomical structures that are not amenable to characterization by engineers. The dual tools of the batwing model and simplified mathematical modeling can be used to address the main objective: to integrate microvascular, interstitial, and lymphatic function to determine the effect of their interaction on interstitial fluid volume. In order to address this objective and the limitations of the current state of knowledge of the field, three specific aims were achieved. 1) Develop a simple, transparent, and general algebraic approach that predicts interstitial fluid pressure, volume and protein concentration resulting from the interaction of microvascular, interstitial and lymphatic function. These algebraic solutions provide a novel characterization of interstitial fluid pressure as a balance point between the two processes that determine interstitial inflow and outflow. 2) Develop a simple, algebraic formulation of Edemagenic Gain (the change in interstitial fluid volume resulting from changes in effective microvascular driving pressure) in terms of microvascular, interstitial and lymphatic structural parameters. By separating the structural parameters from functional variables, this novel approach indicates how these critical parameters interact to determine the tendency to form edema. 3) To expand the list of known interactions of microvascular, interstitial, and lymphatic functions to include the direct interaction of venular and lymphatic function. Venomotion was found not only to extrinsically pump lymph but also to mechanically trigger intrinsic lymphatic contractions. These three advances together represent a new direction in the field of interstitial fluid balance, and could only be possible by taking an interdisciplinary approach integrating physiology and engineering

Towards Explainable and Safe Conversational Agents for Mental Health: A Survey

Virtual Mental Health Assistants (VMHAs) are seeing continual advancements to support the overburdened global healthcare system that gets 60 million primary care visits, and 6 million Emergency Room (ER) visits annually. These systems are built by clinical psychologists, psychiatrists, and Artificial Intelligence (AI) researchers for Cognitive Behavioral Therapy (CBT). At present, the role of VMHAs is to provide emotional support through information, focusing less on developing a reflective conversation with the patient. A more comprehensive, safe and explainable approach is required to build responsible VMHAs to ask follow-up questions or provide a well-informed response. This survey offers a systematic critical review of the existing conversational agents in mental health, followed by new insights into the improvements of VMHAs with contextual knowledge, datasets, and their emerging role in clinical decision support. We also provide new directions toward enriching the user experience of VMHAs with explainability, safety, and wholesome trustworthiness. Finally, we provide evaluation metrics and practical considerations for VMHAs beyond the current literature to build trust between VMHAs and patients in active communications.Comment: 10 pages, 3 figures, 2 table

Bilayer Interdiffused Heterojunction Organic Photodiodes Fabricated by Double Transfer Stamping

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/136265/1/adom201600784-sup-0001-S1.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/136265/2/adom201600784_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/136265/3/adom201600784.pd

Forensic interviewing of mentally disordered suspects: The impact of interview style on investigation outcomes.

The investigative interviewing of a vulnerable suspect is a complex and difficult task. Current best practice advocates for the use of open questions in order to elicit a free recall. However, those with mental health conditions have limited cognitive abilities that relate to free recall and episodic memory, and there is emerging evidence that suggests open questions may not always be most suitable for the vulnerable interviewee. As such, the present study examined the impact of two different interview models (best practice v modified interview) on the amount and accuracy of investigation relevant information obtained within an experimental vulnerable ‘suspect’ sample. Participants engaged in two tasks; a minor transgression and a matched non-transgression. Each participant was then subject to either a best practice (containing largely open questions) or a modified interview (containing largely closed questions). Vulnerable participants provided a significantly higher and more accurate amount of investigation relevant information during the modified interview rather than the best practice interview. In addition, participants that have mental health conditions sought more clarifications during the best practice interviews. The type of interview did not impact upon the level of vulnerability displayed. Our findings challenge current best practice in that vulnerable participants performed worse in interviews containing more open questions than closed questions. These findings add to the emerging evidence base that vulnerable individuals may require an alternative method of questioning, including the use of closed questions as ‘scaffolding’ during an investigative interview

T2-weighted cardiovascular magnetic resonance in acute cardiac disease

Cardiovascular magnetic resonance (CMR) using T2-weighted sequences can visualize myocardial edema. When compared to previous protocols, newer pulse sequences with substantially improved image quality have increased its clinical utility. The assessment of myocardial edema provides useful incremental diagnostic and prognostic information in a variety of clinical settings associated with acute myocardial injury. In patients with acute chest pain, T2-weighted CMR is able to identify acute or recent myocardial ischemic injury and has been employed to distinguish acute coronary syndrome (ACS) from non-ACS as well as acute from chronic myocardial infarction